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Evaluation of design, environmental, and sustainability attributes affecting urban fisheries restoration… Slogan, James Randolph 2015

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Evaluation of Design, Environmental, and Sustainability Attributes Affecting Urban Fisheries Restoration Habitat in Vancouver, British Columbia, Canada by James Randolph Slogan  B.Sc., University of Manitoba, 1994 M.Sc., University of Manitoba, 1997 B.Tech., British Columbia Institute of Technology, 2006  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2015 © James Randolph Slogan, 2015    ii Abstract This thesis examines how structural complexity and environmental variability affects biodiversity and species assemblages on an engineered intertidal habitat named the Habitat Skirt, and develops a rapid assessment tool (RESTORE) to evaluate the long-term sustainability of restoration areas based on ecological, social and economic indicators to inform adaptive management. First, I tested how species diversity and assemblages on the Habitat Skirt compares with riprap; and if differences can be explained by environmental factors. Diversity at the Habitat Skirt and rip-rap were similar; however, species assemblages were not (P = 0.021). Species assemblages on the Habitat Skirt were dominated by Mytilus trossulus, while riprap had greater diversity of macroalgae. Both light intensity and water motion accounted for significant variation among species assemblages (P = 0.008; P = 0.019). Light intensity was positively correlated with macroalgal cover (P = 0.014); and both were lower at the Habitat Skirt (P = 0.001) then riprap. When constructing shoreline infrastructure with limited buffers to reduce shading, habitat managers need to determine whether potential differences in species composition meet regional coastal management objectives. Second, I examine intertidal assemblages and diversity among four microhabitats on the Habitat Skirt. Species richness was greatest within tidepools from two to four metres above chart datum (m CD) and vertical habitats at zero and one m CD. Richness decreased with increasing tidal elevation, except in tidepools where richness remained similar irrespective of tidal elevation. Species composition of many microhabitats varied with tidal  iii height, these did not change significantly during the last two years. These results show that the relative effectiveness of microhabitats vary with tidal height. Lastly, I develop a tool, RESTORE, for assessing the sustainability of restoration areas using 25 ecological, social and economic attributes. I use RESTORE in a case study to examine differences in sustainability among marine, estuarine and freshwater ecosystems. Sustainability was significantly higher in marine than estuarine restoration areas (P = 0.029) with no significant relation of area on sustainability after controlling for ecosystem. RESTORE was successful in comparing a 25 attributes to assess the long-term sustainability of restoration areas to inform future adaptive management.    iv Preface Currently, the work in this thesis has not been submitted for publication to peer-reviewed journals, although this will occur in the upcoming months.  The initial sample design for the monitoring program of the Vancouver Convention Centre West, in which Chapters 2 and 3 evolved from, was created by EBA Engineering Consultants Ltd. (EBA), specifically Richard Sims, Rick Hoos and Tim Abercrombie. I modified this design prior to starting the study with approval from EBA and Fisheries and Oceans Canada (DFO). Dr. Chris Harley proposed the idea of sampling each microhabitat individually during the first year of my program and this forms the basis for the sample design for Chapter 3. I collected all data in the thesis with some help in the upper intertidal from Cam Kulak. Diving support for data collection at the lower Habitat Skirt included Martyn Bayne, Geoff Grognet and Tim Abercrombie over the course of the last four years. I conducted all data analysis for each chapter and wrote all aspects of the thesis with peer review from my committee members Drs. Tony Pitcher, Richard Sims, Chris Harley, William Cheung and Gary Bradfield. The RESTORE tool used in Chapter 4 has been modified from the ‘Rapfish’ method created by Tony Pitcher. Rapfish is a rapid appraisal technique that estimates the status of fisheries.  All new attributes, data collection and writing were my own contribution. The new attributes are based upon the attributes of restored ecosystems established by the Society for Ecological Restoration. Dr. Divya Varkey conducted modifications to the Rapfish software.    ii  Table of contents Abstract .................................................................................................................................... ii Preface ..................................................................................................................................... iv Table of contents ..................................................................................................................... ii List of tables ........................................................................................................................... vi List of figures ........................................................................................................................ viii List of symbols and abbreviations ........................................................................................ xi Glossary ................................................................................................................................. xii Acknowledgements ............................................................................................................... xv Dedication ............................................................................................................................. xvi  Introduction ......................................................................................................... 1 1.1 Overview, purpose and background ...................................................................................... 2 1.1.1 Overview .......................................................................................................................... 2 1.1.2 Purpose ............................................................................................................................. 3 1.1.3 Thesis structure ................................................................................................................. 4 1.2 Review of restoration ecology .............................................................................................. 7 1.2.1 What is restoration ecology? ............................................................................................ 7 1.2.2 Why conduct restoration? ................................................................................................. 9 1.2.3 Marine and estuarine restoration .................................................................................... 11 1.2.4 Risks to successful restoration projects .......................................................................... 13 1.2.5 Intertidal restoration ecology .......................................................................................... 15  Methods and materials ...................................................................................... 23 2.1 Study areas .......................................................................................................................... 24 2.1.1 Vancouver harbour ......................................................................................................... 24 2.1.2 Estuarine and freshwater restoration areas ..................................................................... 29 2.2 Methods............................................................................................................................... 30 2.2.1 Sample design ................................................................................................................. 31 2.2.2 Sampling method ............................................................................................................ 32 2.2.3 Data analysis ................................................................................................................... 36  iii  2.3 Rapid assessment tools and Rapfish ................................................................................... 42 2.3.1 Evaluation fields ............................................................................................................. 45 2.3.2 Scoring system ................................................................................................................ 49 2.3.3 Scoring the case study and sources of information......................................................... 52 2.3.4 Method of analysis and outputs ...................................................................................... 54 2.3.5 Supplemental data analysis ............................................................................................. 55  Effectiveness of an engineered intertidal shoreline to replace riprap as offsetting habitat and the effect of water movement and light exposure on species richness and assemblages .................................................................................................................... 85 Summary .......................................................................................................................................... 86 3.1 Introduction ......................................................................................................................... 88 3.2 Study area and methods ...................................................................................................... 93 3.3 Results ................................................................................................................................. 94 3.3.1 Species richness and diversity ........................................................................................ 94 3.3.2 Comparison of intertidal assemblages ............................................................................ 95 3.3.3 Environmental data ......................................................................................................... 95 3.3.4 Indicator groups and species analysis ............................................................................. 97 3.4 Discussion ........................................................................................................................... 98 3.5 Conclusions ....................................................................................................................... 104  Intertidal species diversity and assembly vary with engineered microhabitat design and tidal height ........................................................................................................ 118 4.1 Summary ........................................................................................................................... 119 4.2 Introduction ....................................................................................................................... 120 4.3 Study area and methods .................................................................................................... 124 4.4 Results ............................................................................................................................... 124 4.4.1 Species diversity ........................................................................................................... 124 4.4.2 Species composition among habitats ............................................................................ 126 4.4.3 Species composition within habitats ............................................................................. 128 4.4.4 Species composition among time periods ..................................................................... 129 4.4.5 Functional group composition with time ...................................................................... 131 4.5 Discussion ......................................................................................................................... 133 4.6 Conclusion ........................................................................................................................ 139  iv   RESTORE - a new rapid assessment tool for evaluating the long-term sustainability of restoration areas using ecological, social and economic attributes .... 154 Summary ........................................................................................................................................ 155 5.1 Introduction ....................................................................................................................... 157 5.2 Study area and methods .................................................................................................... 160 5.3 Results ............................................................................................................................... 161 5.3.1 Overall scores ............................................................................................................... 161 5.3.2 Evaluation fields ........................................................................................................... 163 5.3.3 Area .............................................................................................................................. 165 5.4 Discussion ......................................................................................................................... 166 5.5 Conclusion ........................................................................................................................ 171  Conclusion ........................................................................................................ 181 6.1 Overall conclusions ........................................................................................................... 182 6.2 Limitations of the thesis research ...................................................................................... 185 6.3 Future research based upon the work this thesis ............................................................... 187 6.4 Significant findings ........................................................................................................... 188 References ............................................................................................................................ 190 Appendices ........................................................................................................................... 225 Appendix A Referencing Chapter 3 ............................................................................................... 226 A.1 Starting position of sample transects at the Habitat Skirt, Harbour Green and New Brighton Park. ............................................................................................................................ 227 A.2 Estimates of percent cover estimates of mobile species observed on the Habitat Skirt between 2009 and 2011. ............................................................................................................ 228 A.3 Species codes defined for multivariate analyses. .......................................................... 230 A.4 Similarity percentage (SIMPER) for comparison of species on riprap and the Habitat Skirt in 2010 and 2011. .............................................................................................................. 231 A.5 Correlation matrix of environmental variables used in the DISTLM. .......................... 237 Appendix B Referencing Chapter 4 ............................................................................................... 238 B.1 PerMANOVA pairwise test results for habitat within the habitat x year x height interaction term .......................................................................................................................... 239  v  B.2 Box and whisker plots showing percent cover of functional group at four locations by habitat (inner = horizontal lines, tidepool = dotted, outer = clear, vertical = grey) and year (2009, 2010 and 2011) at five intertidal heights (0, 1, 2, 3, 4 metres above CD). ................................ 251 B.3 Bivariate correlation matrix for functional groups in 2009, 2010, and 2011. .............. 256 Appendix C Referencing Chapter 5 ............................................................................................... 259 C.1 Attributes for determining success of restored ecosystems (SER 2004). ..................... 260 C.2 Example of scoring the RESTORE tool Table 2.3 for the Vancouver Convention Centre West (VC). ................................................................................................................................. 261 C.3 Guide for rating pedigree of data for RESTORE pilot study. ...................................... 305 C.4 Information sources contributing the scoring of Chapter 4. ......................................... 306 C.5 Photos of restoration areas evaluated in Chapter 4. ...................................................... 309 C.6 Raw scores for all five fields including lower and upper scores. ................................. 318 C.7 Monte Carlo simulation results for all restoration areas in each of five evaluation fields for the RESTORE model ........................................................................................................... 321 C.8 Raw pedigree ratings for all 25 attributes within each of the 11 restoration areas. ...... 326 C.9 One-way and pairwise ANOSIM results testing for differences among marine, estuarine and freshwater restoration areas using five RESTORE evaluation field scores. ....................... 327 C.10 Attribute leverage analysis results from the RESTORE model for scores on the x-axis between the best and worst performance of each attribute. ....................................................... 328 C.11 Estimated value of ecosystem services for various ecosystems. .................................. 329     vi  List of tablesTable 2.1 Summary of the restoration structures at the Vancouver Convention Centre West. ................................................................................................................................................ 57 Table 2.2 Summary of the 11 restoration areas assessed in the RESTORE pilot study. ....... 58 Table 2.3 RESTORE score sheet describing each field, attribute, measurement and scoring details. ..................................................................................................................................... 59 Table 3.1 Species richness and Simpson’s Diversity by substrate, site and year in Vancouver Harbour in 2010 and 2011 (n = 6). ....................................................................................... 106 Table 3.2 Permutational MANOVA results for the comparison of species assemblages from locations on riprap (New Brighton Park and Harbour Green) and the Habitat Skirt (n = 18) within Vancouver Harbour in 2010 and 2011. Analysis is based upon 4999 permutations (P). Su = substrate, Yr = year, Lo (Su) = location nested in substrate. ....................................... 107 Table 3.3 Summary of average species abundance at the Habitat Skirt (HS) and riprap (RR) locations in Vancouver Harbour in 2010 and 2012. δ = dissimilarity and SD = standard deviation. ............................................................................................................................... 108 Table 3.4 Summary of environmental data collected at the Habitat Skirt, New Brighton Park, and Harbour Green in Vancouver Harbour. .......................................................................... 109 Table 3.5 Marginal tests results from a distance-based linear model (DISTLM) for five environmental variables and biota in 2011 at New Brighton Park, Harbour Green and the Habitat Skirt in Vancouver Harbour. .................................................................................... 110 Table 4.1 Total number of taxa observed within and among the four microhabitats of the Habitat Skirt over a three year period (2009, 2010 and 2011). Totals represent the number of different taxa within each microhabitat or year across all microhabitats. ............................ 140 Table 4.2 PerMANOVA main test results for differences in intertidal assemblages among habitats (Ha), years (Ye) and heights (He) at the Habitat Skirt in Vancouver Harbour. *** P ≤ 0.001; ** P ≤ 0.01; * P ≤ 0.05. ...................................................................................... 141 Table 5.1 RESTORE scores for the overall mean and totals for each of the five evaluation fields. ..................................................................................................................................... 172 Table 5.2 Overall mean pedigree rating and standard error of data for each of the five evaluation fields and 11 restoration areas. ............................................................................ 173 Table 5.3 SIMPER results indicating evaluation attributes that contribute the greatest to  vii  within ecosystem scoring variability. ................................................................................... 174 Table 5.4 SIMPER results indicating evaluation attributes that contribute the greatest to variability of among ecosystem scoring. .............................................................................. 175     viii  List of figures  Figure 2.1 The 12 study sites of Chapters 3, 4 and 5 including five marine sites (M1-M4, NB), four estuarine sites (E1-E4) and three freshwater sites (F1-F3). ................................... 80 Figure 2.2 Photo of the Habitat Skirt during installation in April 2008 at a 0.2 m above chart datum low tide. ....................................................................................................................... 81 Figure 2.3 Photo of the design features of an individual habitat bench during installation in April 2008. In the photo, the inner edge faces right, while the outer edge faces left, the tidepool is central, and the outer vertical face is similar to the vertical face shown. ............. 81 Figure 2.4 Photo of Harbour Green Park intertidal and shallow subtidal bench during a -0.1 m below chart datum low tide in May of 2009. ............................................................ 82 Figure 2.6 One-metre squared quadrat used to sample percent cover of macroalgae and sessile invertebrates. ............................................................................................................... 83 Figure 2.7 Environmental sampling unit consisting of a plaster block (blue circle) and light sensor (orange circle) attached to a piece and vinyl siding and then a paving stone at New Brighton Park, July 2012. ....................................................................................................... 83 Figure 2.8 Overview of the structure of the RESTORE tool illustrating the five main evaluation fields and the scoring attributes within each field (RA = restoration area). ......... 84 Figure 3.1 Study areas for Chapter 3. .................................................................................. 112 Figure 3.2 Principle coordinates analysis illustrating Bray-Curtis similarities among intertidal species assemblages on riprap (black) and the Habitat Skirt (grey) in 2010 (no fill) and 2011 (solid). Sites are symbolized as: a) Habitat Skirt (circles), b) New Brighton Park (up triangles), and c) Harbour Green (down triangles). The black contour represents 50 percent similarity among locations within contour. .............................................................. 113 Figure 3.3 Bar plots of mean daily light exposure (lux) in July 2012 and mean relative water motion in July 2011 and 2012 ± 1 SE at locations on the Habitat Skirt (HS, clear circle) and riprap (RR, filled circle) within Vancouver Harbour. Relative water motion was measure as a percent loss of starting mass. ................................................................................................ 114 Figure 3.4 Distance-based redundancy analysis (dbRDA) ordination plot of distance-based linear model (DISTLM) results showing environmental variables, presented in a vector overlay (grey vectors), with intertidal species assemblages from New Brighton Park (up triangle), Harbour Green (down triangle), and the Habitat Skirt (circle) in Vancouver  ix  Harbour. Triangles are filled to designate they have a riprap substrate. The circle indicates unit length in three dimensions projected onto two dimensions. .......................................... 115 Figure 3.5 Regression analyses showing: (top) abundance of macroalgae with mean daily light intensity (lux), and (bottom) abundance of sessile invertebrates with relative water motion; each at nine locations within Vancouver Harbour. Black circles indicate locations on riprap substrate and clear circles indicate locations on the Habitat Skirt. ............................ 116 Figure 3.6 Bar plots of mean macroalgal abundance (top) and mean sessile invertebrate abundance (bottom) ± 1 SE at locations on the Habitat Skirt (HS, clear circle) and riprap (RR, filled circle) within Vancouver Harbour. ..................................................................... 117 Figure 4.1 Scatterplots illustrating regression analyses of species richness by tidal height (m above CD) for: a) inner habitat, b) tidepool habitat, c) outer habitat, and d) vertical face in 2009, 2010, and 2011 (n = 15). Note: some values may appear to be missing due to overlap. .............................................................................................................................................. 146 Figure 4.2 Bar plots illustrating  mean species richness ± 1 SE for 2009, 2010, and 2011 (n = 12) by microhabitat at: a) 0 m, b) 1 m, c) 2 m, d) 3 m, and e) 4 m CD. ............................... 147 Figure 4.3 Summary of the number of significant (P < 0.05) post-hoc pairwise differences between microhabitats by tidal level when tested against the habitat - year interaction term. .............................................................................................................................................. 148 Figure 4.4 Principal coordinate ordination plots illustrating temporal trajectories of the centroid position of species assemblages sampled within each of four microhabitats (inner = black triangle, tidepool = inverted blue triangle, outer = green square, and vertical = red diamond) at five intertidal heights (4, 3, 2, 1 and 0 metres above CD) over three years (2009=9, 2010=10 and 2011=11). ........................................................................................ 149 Figure 5.1 Radar diagram illustrating differences among marine, estuarine and freshwater ecosystem mean scores for five fields of evaluation: ecological, environmental, stress, social and economic. Zero indicates the worst score (centre), five indicates a passing score and ten indicates the best score (perimeter)....................................................................................... 176 Figure 5.2 RESTORE performance scores for marine (black circle), estuary (grey circle), and freshwater (white circle) for each of five evaluation fields: ecological, environmental, stress, social and economic evaluation fields. Error bars represent 50% interquartile range for each field. .............................................................................................................................. 177  x  Figure 5.3 Linear regression analysis comparing RESTORE overall scores with area of restoration area (r = 0.518, P = 0.103). ................................................................................. 180     xi  List of symbols and abbreviations ANOSIM: analysis of similarities ANOVA: analysis of variance BC: British Columbia CD: chart datum CI: confidence interval DFO: Fisheries and Oceans Canada EBA: EBA Engineering Consultants Ltd.  – A Tetra Tech Company EBM: coastal or ecosystem based management HG: Harbour Green HS: Habitat Skirt m: metre(s) MDS: non-metric multidimensional scaling  NB: New Brighton Park PERMANOVA: a multivariate software package produced by M. J. Anderson, R. N. Gorley and K.R. Clarke perMANOVA: permutation based multivariate analysis of variance RA: Restoration area RESTORE: a new rapid assessment tool for evaluating the long-term sustainability of restoration areas using ecological, social and economic attributes developed in Chapter 4. SD: standard deviation SIMPER: similarity percentage analysis VC: Vancouver Convention Centre West   xii  Glossary Benthos: the organisms living on the benthic substrate. Chart datum: represents the lowest low water level, large tide for the year. Tidal heights below chart datum are indicated by a “- #. # m CD”   Colonization: ability for a species to enter and spread in an area. Condition factor: length: 100*(weight*/length3);*may be multiplied by a unifying coefficient. Conditional test: a test of the relationship between Y versus X2 given X1 in the model. Culturally significant: a species or service of historic value to a human group. Diversity, alpha: within habitat diversity. Diversity, beta: between habitat diversity. Diversity, gamma: landscape level diversity. Ecological restoration: the process of assisting the recovery of an ecosystem that has been degraded, damaged or destroyed. For the purpose of this thesis, I interpret “assisting the recovery” to include reconstructing a degraded ecosystem, managing recovery through maintenance and monitoring, providing legal or physical protection, educating and engaging the public as a form of social assistance, and finally, any economic assistance that aids the recovery of an ecosystem. I also extend this definition such that “recovery” is any desired or targeted self-sustaining state. Ecosystem: the biota, their interactions and the environment that sustains them. Ecosystem function: includes the nutrient cycling, carbon capture, reproduction and growth of organisms within an ecosystem; and the ability of a species assembly to perform valued processes such as filter water and air, stabilize substrate, and structure habitat for individual species and provide microclimate.  xiii  Ecosystem structure: refers to the way in which species assemble with respect to abundance, size, age class, function, and trophic level. Habitat: the place where an organism occurs (SER 2004). Habitat banking: the process of creating, enhancing or restoring habitat for the purpose of compensation prior to its requirement to offset project development.  Habitat restoration: is used in this thesis as a broad term which encompasses habitat mitigation, habitat creation, habitat enhancement and habitat restoration. In Canada, these area all forms of habitat offsetting for projects causing serious harm under the Fisheries Act. Indicator species: is a species which occurs 95% of the time or greater in a species assemblage. Alternatively, an indicator species may also be sensitive to certain biotic or abiotic conditions that can signal an unhealthy or incomplete state. Leverage analysis: leverage analysis shows the effects of the removal of one variable at a time, in this case attributes from RESTORE, on the overall tool. Marginal test: a test of the relationship between Y versus X2 alone. Marginal value: the change in value at a given time from the initial value. Niche, fundamental: the range of physical and biological factors within which a species can exist. Niche, realized: the portion of the fundamental niche that the species actually occupies or uses within a habitat. Normalize: a method to standardize values that subtracts the mean and divides by the standard deviation. Recruitment: the process of colonization at a site and the initial growth of the juvenile life history stage.  xiv  Residual: the estimate of the error obtained as the deviation of each actual observation from its fitted value.  Resilience: ability of an ecosystem to return to its common structure and function after stress and disturbance. Resistance: ability of an ecosystem to maintain its common structure and function during stress and disturbance. Self-sustaining: ability of a restoration area to maintain its common structure and function over the long-term, typically a minimum of 25 years. Semidiurnal tidal: a tide with two high and two low periods during a lunar day. Species assemblage: a group of species that are commonly observed together in nature. Species richness: total number of species per a given sample area Standardize: divide the total for each variable by the total abundance in the sample, which converts all counts into relative percentages. Sustainability: see Self-sustaining.     xv  Acknowledgements First, I would like to thank EBA Engineering Consultants Ltd., in particular Dr. Richard Sims, for awarding of funds that allowed me to pursue this goal. Second, I thank Dr. Tony Pitcher who agreed to supervise my program. Finally, special thanks goes to BC Pavilion Corporation / the Vancouver Convention Centre West who fully supported my program, always allowing me access to the site, and demonstrating how corporate Canada can support high-level research while achieving their own end goals.  I thank Drs. Chris Harley, Gary Bradfield and William Cheung for taking the time to be a part of my committee, making themselves available to answer questions, teach courses that I attended, review my work and mentor me through this process. I am grateful for all the funding I received. Again, I thank EBA for the Al MacDonald Life-long Learning Award; the Natural Sciences and Engineering Research Council of Canada for their Industrial Post-Secondary II Scholarship (NSERC IPS II); the University of British Columbia - Faculty of Science for awarding the Entrance and Graduate Awards over the last four years; and the Association of Professional Biologists for awarding me their graduate scholarship in 2011. This program did not go without the support and advice from many colleagues including Martyn Bayne, Rick Hoos, Jeff Matheson, Cam Kulak, Tim Abercrombie, David Morantz, Lucas Hennecker, Morgan Zondervan, Geoff Grognet and the boat captains of Champion Barge, Norm and Dagfin.   Finally, I thank my wife Zobeida, who has shown unwavering support even through the birth of our two beautiful children, Kiana and Skylar. There is nothing I can write here to communicate the effort and sacrifice they have given over the last six years.  xvi   Dedication This is dedicated to my Dad who showed me anything is possible and has supported me throughout my life, regardless of his own opinion, allowing me to pursue all my dreams, which is more than I could have ever asked for. This is also for my two children, Kiana and Skylar, and my wife Zobeida; I hope this improves their world in some small way and shows them they can always pursue their dreams, regardless of the judgment of others, for in the end the only opinion we live with is our own.  1    Introduction   2  1.1 Overview, purpose and background  1.1.1 Overview Urban expansion within North America’s coastal regions is leading to a greater need for ecological restoration, often legally required as compensation for development within naturally productive areas (DFO 1986, Minns et al. 2011). Although forms of ecological restoration and management have been practiced for centuries (Pitcher 2001, Timbrook 2006), the study of the ecological processes behind their application is relatively new and evolving (Young et al. 2005). This is particularly true in urban centres, which present complex problems for habitat engineers and managers including the presence of habitat fragmentation (Fahrig 2003), substrate homogenization (Thrush et al. 2006), altered environmental gradients (Duraiappah et al. 2005, Thrush et al. 2008) and invasive species (Galil 2007). In addition, a change from managing for individual valued species to a regional or ecosystem based approach is gaining acceptance through incorporating ecological, social and economic values (Cicin-Sain and Belfiore 2005, DFO 2013b). The fiscal costs of restoring ecosystems require justification through efficient monitoring and documentation of their benefits to gain support from the public (Simenstad et al. 2005). The importance of rapid monitoring or evaluation programs (Quigley and Harper 2006), partnered with strong scientific methodology (Chapman and Underwood 2010), will lead to a higher level of ecological success with increasing cost efficiency, while meeting the social values desired by surrounding populations. This thesis will: (1) examine how structural and environmental variability associated with a new and innovative intertidal engineered habitat structure referred to as the Habitat Skirt affects biodiversity and species assemblages associated with urban restoration areas in Metro Vancouver, and (2) develop a rapid assessment tool (RESTORE) that can inform resource  3  managers of the long-term sustainability of marine, estuarine and freshwater restoration areas.  In Canada, prior to recent changes in the Fisheries Act (November 2013) when this thesis began, projects that took place in and around water that had the potential to harm or alter fish habitat were subject to regulation by Fisheries and Oceans Canada (DFO) under the Fisheries Act Sections 34, 35 and 36. In order to receive a fisheries authorization under the Fisheries Act, a proponent may have to compensate for habitat altered or harmed by the project such that it results in an overall “no net loss of productive capacity” of fish habitat (DFO 1986; DFO 1998; DFO 2002). Locally, it has been estimated that forty-five percent of all coastline in Burrard Inlet, British Columbia and eighty percent of Vancouver Inner Harbour (Vancouver Harbour) has been impacted by urban development (Haggarty 2001). An example of a project that recently worked through this process is the development of the Vancouver Convention Centre West (VC) in Vancouver Harbour, which completed construction of fish compensation habitat in 2008 and monitoring of the compensation features between 2008 and 2013. The fish habitat compensation features associated with the VC, primarily an engineered shoreline referred to as the Habitat Skirt (HS), a neighboring site at Harbour Green Park (HG) and a third site at New Brighton Park (NB), will be used to examine issues associated with how design of intertidal restoration habitat can be used to affect species assemblages and diversity.  1.1.2 Purpose  The goals of this thesis are to: examine how variability in environmental parameters and the incorporation of varying microhabitats impact the effectiveness of an engineered  intertidal restoration area within Vancouver Harbour to better inform future offsetting, and to develop a rapid assessment tool (RESTORE) for monitoring the sustainability of these restoration areas (RAs). The main overarching questions proposed for this study are:  4   (1) Does varying levels of environmental parameters including water motion, temperature and light intensity at the site level (100s of metres) affect intertidal species assemblages and diversity in an urban centre and what are the implications for ecological restoration?; and (2) How does structural complexity through the addition of microhabitats (less than one metre), increase ecological complexity measured by greater species diversity and variation among species assemblages in an urban environment? In addition to answering the above questions, I develop a new management tool, that incorporates ecological, social and economic attributes, to assess the long-term sustainability of restoration areas where herein sustainablility is defined as the ability to maintain desired structure and function. In order to test the functionality of the evaluation tool, I apply RESTORE to 11 restoration areas throughout Metro Vancouver in a pilot study and use the results to try to answer the questions: (3) Are there differences in the sustainability of marine, estuarine and freshwater ecosystems and what are the implications for regional ecosystem based management of restoration areas?; and  (4) Does size of a restoration area affect its sustainability measured through scoring in RESTORE?  1.1.3 Thesis structure The thesis consists of six chapters including an introduction, methods, three main study chapters, and a final discussion. Chapters 1 and 2 provide the background for the thesis, an overview of the study questions to be examined, a review of the relevant literature behind the questions, and the methods used to conduct the studies. The review includes an examination  5  of current practices of ecological restoration (Hobbs and Norton 1996, Suding 2011) and methods to evaluate these works. I discuss some of the fundamental issues in ecological community dynamics relating to biotic and environmental interactions that are critical to understanding how natural and man-made intertidal ecosystems work. Finally, Chapter 2 presents a description of the regional and local environemnt and the methods used test the hypotheses presented in the main chapters of the thesis. Chapter 3 assesses how species richness, diversity and assemblages observed on a new and innovative engineered intertidal shoreline, referred to as the Habitat Skirt, compares with local riprap restoration areas; and if differences can be explained by environmental factors including water movement, relative light exposure and temperature. It is hypothesized that the Habitat Skirt will have greater species richness and diversity than neighbouring riprap due to the increased structural complexity engineered into its design, unless environmental conditions are significantly different among sites. Lower light levels due to shading from urban infrastructure and higher relative water motion from being positioned over deeper water with a flow-through design are predicted to result in lower macroalgae and higher sessile invertebrates on the Habitat Skirt. The study is replicated at three sites within Vancouver Harbour over a three-year period. Results of the study are related to potential urban impacts of constructing marine restoration areas within urban ports. Chapter 4 examines how intertidal species richness, diversity and assemblages recruit and mature through the first three years of development on the Habitat Skirt and test how four engineered microhabitats vary among each other and with tidal height. Specifically, I hypothesize that: (1) species richness within microhabitats will increase at lower tidal elevations due to reduced overall environmental stress; except tidepools, which are expected  6  to maintain a more constant environment; (2) species richness among the four microhabitats at the mid to high tidal heights will be greatest in the tidepools as they provide a more constant or low stress environment, while at low tidal heights, where environmental stress is assumed to be lower, species richness among microhabitats of the same tidal height will be equal; (3) species assemblages among the four microhabitats will be significantly different due to fine-scale differences in light exposure and water retention; and (4) changes in species diversity and variation among sample locations by years are expected to decrease due to longer time to recruit to the structure, growth of established perennial macroalgae and the resulting reduction in cover by annual species. The four microhabitats include inward (shaded) and outward (exposed) positioned horizontal habitats, a central tidepool, and an outer vertical face. Microhabitats were sampled at four locations and at five tidal heights to examine how each develops over time with different levels of emersion or overall environmental stress. The results are related to the value of adding microhabitat complexity for the purposes of increasing species and assemblage diversity.  In Chapter 5, I develop a rapid assessment tool, RESTORE, for evaluating ecological, social and economic sustainability of restoration areas. Twenty-five attributes were developed based upon the Society of Ecological Restoration’s attributes of restored ecosystems (SER 2004) and incorporated into a modified Rapfish - the Rapid Appraisal for Fisheries approach (Pitcher and Preikshot 2001, Pitcher et al. 2013). To test how RESTORE works, I evaluate 11 restoration areas based on their overall scores and uncertainty produced in a Monte Carlo simulation. In addition, RESTORE conducts a leverage analysis (Pitcher and Preikshot 2001) on each of the 25 attributes to determine how they contribute to the overall result. I supplemented the RESTORE tool with a similarity percentage analysis (SIMPER) to identify  7  how attributes contribute to overall scoring and sustainability of the restoration areas, an analysis of similarities (ANOSIM) to test for significant differences in the relative sustainability among marine, estuarine and freshwater restoration areas, and a regression analysis to examine if increasing size of a restoration area increases the sustainability scores produced by RESTORE.  Finally, Chapter 6 reviews the overall body of work developed in Chapters 3, 4 and 5 to relate the significance of these findings to current theory in restoration ecology. I discuss limitations of interpreting the results of my studies due to experimental design and inability to manipulate variables. Finally, I relate my findings to future design and management of restorations areas and regional planning to improve the effectiveness of restoration areas and suggest studies to further the work undertaken herein.  1.2 Review of restoration ecology  1.2.1 What is restoration ecology?  In 1987 the Society of Ecological Restoration (SER) was formed to provide a platform to present research and theory on restoration ecology. This includes providing a common language through a series of thorough definitions. This thesis adopts many of the definitions provided by the SER, including those for restoration ecology and ecological restoration. Simply, “Ecological restoration is the process of assisting the recovery of an ecosystem that has been degraded, damaged or destroyed (SER 2004)”. Ecological restoration is the practical application of ecological theory, which together forms the science of restoration ecology (Palmer et al. 1997). For the purpose of this thesis, I interpret “assisting the recovery” to include such processes as reconstructing a degraded ecosystem, managing recovery through  8  maintenance and monitoring, providing legal or physical protection, educating and engaging the public as a form of socio-ecological assistance, and finally, any economic assistance that aids the recovery of an ecosystem. I also extend this definition such that “recovery” is any targeted self-sustaining state.  Restoring nature is an art and science (Van Diggelen et al. 2001) requiring detailed knowledge of local environmental conditions and how species assemble through time.  In order to gain a comprehensive and reliable understanding of how to restore nature, habitat managers require specific practical examples of successes, failures and unexpected outcomes (Suding 2011) in a natural setting. These projects then become experiments of a much larger scale that we can use to adaptively manage our environment (Walters 1997, Walters 2007). This thesis will base its results on observations in a landscape setting, and due to limitations in the ability to manipulate the environment, draw on literature and other studies of a similar nature to make inferences about the underlying ecological processes. Globally, there has been a wide range of research in marine intertidal (Vaselli et al. 2008, Chapman and Underwood 2011, Firth et al. 2013a) and subtidal restoration (Elliott et al. 2007, Seaman 2007, Brumbaugh and Coen 2009); however, detailed accounts of restoration projects and techniques across Canada have mainly focused on freshwater and estuarine systems (Harper and Quigley 2005, Carter et al. 2013). Within North America’s Pacific Northwest, many projects focus on restoring salmonid habitat (Simenstad and Cordell 2000, Adams and Williams 2004, Levings 2004b, Pearson et al. 2005, Simenstad et al. 2005), with fewer examples of marine intertidal and subtidal restoration projects (Buckley 1997, Naito 2001, Pearson et al. 2005, Smiley 2006, Brumbaugh and Coen 2009, Aronson et al. 2010, Hemmera 2014). Detailed examination of new and innovative restoration areas, such as the  9  intertidal Habitat Skirt in Vancouver Harbour, may help answer unknowns associated with the design of urban ports. Some of the complex urban design constraints include building directly over subtidal areas (eight to 15 metres below chart datum), trying to mimic natural substrates, shoreline connectivity, and providing protection for infrastructure from trespassers and against floating debris such as logs.  1.2.2 Why conduct restoration? The main drivers for undertaking restoration, other than ecological, include ethical, economic, social, and legal. It can be argued that proponents and resource managers have a moral obligation to future generations to try to mitigate all that we damage or destroy and ensure equal opportunity and access for future generations regardless of costs, values or cultural boundaries. A measure of ethical behavior has been incorporated into models based upon the Rapfish approach (Lam and Pitcher 2012, Pitcher et al. 2013) to account for equity in use among generations and mitigation of damage; however, this driver is infrequently accounted for elsewhere. Societal necessities and valued services such as clean water, flood protection, harvestable fish populations, aesthetics and recreation are another reason to restore damaged ecosystems (Zedler and Kercher 2005, Egoh et al. 2007). The economic pressure to maintain healthy natural resources that support valuable ecosystem services has recently been brought to the forefront of public concern as we observe mass die-offs of bees affecting the pollination of fruit crops (Watanabe 2008), closures of commercial fisheries due to overharvesting (Tegner 1993) and environmental degradation, or flooding of coastal cities due impart to the destruction of natural buffer zones (Temmerman et al. 2013). In Canada, the main driver for habitat restoration is a legal responsibility to ensure the long-term productivity of fish habitat as governed through the Fisheries Act (Quigley and Harper 2006). Similar  10  legislation exists in the United States for the protection of fish and fish habitat in the form of the Magnusson-Stevens Fishery Conservation and Management Act and the Clean Water Act (Marsh et al. 1996).  1.2.2.1 Regulatory framework in Canada In Canada, restoration or compensation for habitat loss is governed through the Fisheries Act section 35(1) which states “No person shall carry on any work or undertaking that results in the harmful alteration, disruption or destruction of fish habitat”, or a HADD. When DFO determines a project proposal submission will likely result in a HADD, and cannot reasonably be avoided, then the project proceeds through the authorization process, which includes a detailed project review by DFO. According to habitat conservation and protection guidelines (DFO 1998), the hierarchy of favored options for a project review is: (1) relocation of the project to a site so that will not result in a HADD; (2) redesign the project to minimize the HADD; (3) develop mitigation to minimize any impacts of project activities; and finally, (4) habitat compensation, when residual impacts cannot be mitigated. Habitat compensation is the least preferred method for proceeding with an authorization. In 1986, DFO created the Policy for the management of fish habitat (DFO 1986) to clarify the objectives, goals and strategies for the compensation of altered fish habitat. The policy objective states its guiding principle of “no net loss of the productive capacity of habitats”. It is this principle of no net loss, which guides the process of fisheries authorizations, restoration work and ultimately the success of any compensation project. It is the measure of “productive capacity” that is used to assess whether or not no net loss of fish habitat has been achieved. Unfortunately, productive capacity came with little guidance until many years later  11  (Pearson et al. 2005), before which numerous sources identified problems with how to determine productive capacity (Minns 1995, Jones et al. 1996, Packman et al. 2006, Quigley and Harper 2006). Various proposals for measuring productive capacity have been introduced since 1986, mainly on a project specific basis (Packman et al. 2006). A review by DFO (Quigley et al. 2006) indicates numerous other issues with methods and monitoring programs of no net loss of productive capacity including: required compensation areas are often too small and do not account for the loss in productive capacity due to time lag (Minns 2006); design of monitoring programs with insufficient frequency, duration and statistical power; and some habitats being compensated for may not be viable ecosystems and their function may be difficult to replicate in some instances. In 2012, the federal Omnibus Bill C-38 made changes to the wording of the Fisheries Act, which came into effect on November 25, 2013. Fundamental differences centre around removing the protection of all fish and fish habitat and instead focusing on development that causes serious harm to the ongoing productivity of commercial, recreational or aboriginal fisheries (CRA). In all, the government expects the changes to the Fisheries Act will focus protection “…on real and significant threats to the fisheries and the habitat that supports them…” (Miller and Thomson 2012). However, some scientists see this as a step back by removing protection from many fish species that lack proven economic or cultural significance to-date. This change also does not consider all fish habitat that further supports a more holistic or ecosystem based approach (Taylor 2012).  1.2.3 Marine and estuarine restoration Ecological restoration within marine and estuarine ecosystems of North America take three main forms (Hawkins et al. 1999, Chapman and Underwood 2011): (1) adding hard  12  substrate as a source of attachment for target species including macroalgae and sessile invertebrates, while potentially mitigating against erosional processes (Harris 2009, EBA 2013); (2) adding or shaping soft sediments to improve shoreline spawning and rearing habitats for forage fish and shorebirds, while serving also as recreational beach areas (Hanson et al. 2002); and (3) transplanting valued or habitat forming species such as native eelgrass (Zostera marina), tidal marsh vegetation, or sessile marine invertebrates including bivalves or sea pens (Adams 2002, Precision 2002). In addition to ecological value, the choice of restoration approach is highly dependent on other goals or values including or enhancing culturally significant species (Brennan and Culverwell 2005).  A common form of intertidal and subtidal restoration in Metro Vancouver, and other areas along the west coast of North America, is introducing a hard substrate in the environment as a substitute for rocky shorelines and reefs (Haggarty 2001, Deysher et al. 2002, Pister 2009). The hard substrate may take many forms (Seaman 2007) including intertidal and subtidal sloped riprap (Deysher et al. 2002, Reynolds et al. 2007, Elwany et al. 2011), formations of concrete culverts (EBA 2013), Reefballs ® (Smiley and Burd 2004, Harris 2009), chains of tires (Naito 2001) ecologically engineered seawalls or artificial habitats (Goff 2010, Browne and Chapman 2011, EBA 2013) and the sinking of retired ships (Naito 2001, Smiley 2006, Seaman 2008). Chapters 2 and 3 will focus on an engineered intertidal system known as the Habitat Skirt that incorporates tidepools and surficial complexity (EBA 2013).  Introducing riprap along an intertidal shoreline is a common design used by engineers as a method of shoreline protection that has also been shown to provide an effective substrate for the attachment of important structure forming macroalgae such as Fucus distichus, N. Luetkeana, S. latissima (Mumford Jr 2007) and sessile invertebrates including Mytilus  13  trossulus and Metridium farcimen. If design includes appropriately sized spacing among boulders, then shelter may also be created for mobile invertebrates such as ochre star (Pisaster ochraceus) (Pister 2009), limpets (Martins et al. 2010) and periwinkles (Littorina species) (personal observation). Although riprap is an effective substrate for intertidal species in the Pacific Northwest, this technique has likely become overused resulting in many homogeneous shorelines in urban centres, ultimately reducing overall habitat and species diversity (Quigley and Harper 2004). Building shorelines with a common 2.5:1 sloped, hard surface reduces the overall structural complexity of the shoreline (Adams 2002) that would naturally have a mosaic of different slopes and substrates. Total intertidal area is also generally reduced since natural shorelines typically have gentler slopes (4H:1V or greater based upon a substrates angle of repose) than a riprap design, resulting in different species assemblages (Vaselli et al. 2008). Although more prominent along riverbanks, long areas of riprap typically increase the velocity of water movement along the shore due to the regular hard surface and the straightening of shorelines and riverbanks through engineered processes (Quigley and Harper 2004).  1.2.4 Risks to successful restoration projects Risks to the long-term sustainability of restoration areas (RAs) are numerous and include catastrophic events such as flood, contaminant spills and hurricanes (Thompson et al. 2002), invasion by non-native species (Williams 2007), disturbance by wildlife (Gayton 2001, Rivers and Short 2007), and physical deterioration of the site (Asmus et al. 2009). Although catastrophic events such as extreme floods and hurricanes are rare, they can result in extensive, potentially permanent, damage to the structure and function of an ecosystem. Ecosystems are also constantly being altered by natural and anthropogenic, low magnitude disturbances (Sousa 1984b, Platt and Connell 2003) making understanding the causes and temporal cycles critical  14  to the success of a restoration project (Hobbs et al. 2007b). Natural disturbance regimes typically direct or alter successional trajectories and affect species assembly patterns and turnover (Hobbs et al. 2007a). Finally, barriers such as dikes or jetties may require engineering to restore or maintain natural ecological processes of nutrient cycling and hydraulics to achieve desired ecosystem structure (Temmerman et al. 2013).  In terms of establishing new restoration areas, biotic disturbances can result in failure to sustain a desired species assemblage or environmental state, resulting in an alternate state that presents complicated restoration strategies (Zavaleta et al. 2001). Common negative biotic disturbances include grazing by waterfowl on eelgrass or marsh plantings (Levings 2004b, Rivers and Short 2007), sea urchins on kelp beds (Jackson et al. 2001), and ungulates on riparian plantings (Gayton 2001). Burrowing animals also cause multiple stressors by altering soil and site stability through physically damaging vegetation and their root systems.  Finally, the presence of non-native species is a primary stressor in many environments (Sala et al. 2000). Invasive species specific to southern British Columbia include Himalayan blackberry (Rubus armeniacus) in riparian areas (Caplan and Yeakley 2006), English cordgrass (Spartina angelica) in estuaries (Taylor and Hastings 2004) and wireweed (Sargassum muticum) (Williams 2007) in marine habitats. Because many non-native invasive plant species are rapid colonizers (Sakai et al. 2001), competition with native and/or transplanted species can occur early on in these projects. One method of returning an area to its natural species composition is to provide a bare substrate for colonization or transplant dominant species to initiate growth of a desired state (Sheley et al. 2006). In these situations, monitoring and maintenance of non-native species is crucial to achieving the targeted assemblages (Zavaleta et al. 2001). The continual removal of any undesirable species is  15  necessary until the targeted species assemblage can establish to a threshold that may be considered resilient to disturbance or competition (Suding and Hobbs 2009).  1.2.5 Intertidal restoration ecology  Designing marine restoration areas requires a diverse knowledge of ecological and environmental processes that take place over the lifetime of a project (Bergen et al. 2001, Firth et al. 2013a). These processes include how species will recruit to an area (Wehkamp 2012), interactions that take place to drive long-term species composition (Vaselli et al. 2008, Bertocci et al. 2010), and how environmental parameters affect these interactions (Crain and Bertness 2006). Intertidal communities exhibit the greatest variation across a vertical distribution (Benedetti-Cecchi 2001, Somero 2002) which relates to stress generated through environmental and biotic interactions (Paine 1969, Grime 1977, Menge and Sutherland 1987, Bruno et al. 2003). Generally, the lower limit of rocky intertidal species is regulated by biotic or competitive factors including herbivory, predation, and competition for space (Paine 1966, Connell 1972, Lubchenco 19978, Menge et al. 1994, Yamada and Boulding 1996, Rochette and Dill 2000, Livore and Connell 2012), while, environmental or stress-tolerant factors are believed to control the upper limit of intertidal species ranges through their tolerance of desiccation, thermal stress (Somero 2002, Harley 2003), ability to uptake nutrients and photosynthesize (Dring 1982, Gómez et al. 2004, Williams and Dethier 2005). However, these effects are not straightforward and are altered through complex interactions including the timing of tidal cycles (i.e., low tides), extreme daily temperatures (Harley 2008, Mislan et al. 2009) and nutrient loading in relation to levels of herbivory (Burkepile and Hay 2006).   In addition to environmental and biotic interactions, disturbance can act as a form of stress (Sousa 1984, Menge and Sutherland 1987). In ecology, patterns of diversity have been  16  related to levels of disturbance and primarily the intermediate disturbance hypothesis (Connell 1978), which states that “the highest diversity is maintained at intermediate scales of disturbance”. In intertidal ecology, reasons for low levels of diversity can be attributed to few species being able to tolerate high levels of environmental stress at upper tidal elevations (Menge and Sutherland 1987), and low or infrequent disturbance, stress, or predation resulting in competitive exclusion or a few dominant species (Paine 1966, Grime 1977, Menge and Sutherland 1987), generally observed at lower tidal elevations. At intermediate levels of disturbance, a mixture of species competitive abilities and their tolerance to disturbance allow for co-existence resulting in the greatest amount of species present (Connell 1978). More recent studies have developed theories on disturbance and stress to include facilitation by dominant space holders at low levels of stress creating greater species richness of secondary and mobile species (Bruno et al. 2003, Scrosati and Heaven 2007). The increase in these rarer species leads to greater total species richness.  1.2.5.1 Species assembly Restoration methods often create large areas of bare substrate that initially follow models of primary succession or the initial colonization of a site. The order and rate of species assembly depends on patch size, seasonality, presence of grazers, environmental conditions and neighbouring species (Benedetti-Cecchi 2000, Miller and Etter 2008, O'Connor et al. 2011). Recruitment in large areas is the result of colonization from the water column and migration from neighbouring areas (Langhamer 2005). Algae and sessile invertebrates tend to recruit from the water column, which may require weeks to months (Bram et al. 2005). Small patches are generally colonized by neighbouring species of macroalgae through vegetative  17  growth (Kim and DeWreede 1996); however, meiofauna (Mirto and Danovaro 2004), mobile invertebrates (e.g., crabs) (Page et al. 1999), and fish can migrate rapidly (i.e., within hours to days) into large and small spaces to use structures such man-made reefs for shelter. A common sequence of early colonization along the west coast of North America begins with the formation of a biofilm consisting of diatoms and bacteria. This is followed by recruitment of green algae including Ulva species and/or barnacles (Balanus glandula) that add rugosity and surface complexity, and finally mussels (e.g., Mytilus trossulus) and various macroalgae species such as Pelvetiopsis limitata, Endocladia muricata or Fucus distichus (Sousa 1984a, Farrell 1991, Nybakken and Bertness 2005).  Environmental variables including light exposure (Miller and Etter 2008), wave exposure (Nishihara and Terada 2010), and temperature (Somero 2002) are critical in directing community assembly, particularly in Vancouver Harbour where tidal exchanges reach five metres exposing a high range of tidal elevations. In the northern hemisphere, sun exposure is correlated with aspect with the greatest sunlight reaching south and southwest facing slopes. The rate of photosynthesis and primary production is related to the amount of light available to macro- and microalgae (Graham et al. 2009, Kavanaugh et al. 2009). Shading due to urban infrastructure and light filtration through the water column (Kavanaugh et al. 2009) reduce the ability of algal species to recruit (Blockley and Chapman 2006), determine vertical zonation of algae due to their photosynthetic pigments (Bischof et al. 2007, Graham et al. 2009, Kavanaugh et al. 2009) and may result in a competitive advantage for sessile invertebrates (Glasby 1999, Miller and Etter 2008). Settlement and fusion of algal and invertebrate propagules can also be directed by photoreception (Haxo and Clendenning 1953, Cronin 1986, Miyamura et al. 2003) to ensure settlement under proper light conditions. Shading and shadows  18  also affect fish movement, including juvenile salmonid migration, for short periods of time while the rod cells in their eyes adjust to varying light levels (Weitkamp personal communication).  Wave exposure and water motion have long been shown to be a key determinant of how species assemble in the intertidal (Leigh et al. 1987, Harley and Helmuth 2003, Blockley and Chapman 2008, Burrows et al. 2008). The force water exerts on the shoreline is controlled by water depth, slope of the shoreline, fetch, benthic substrate angle of incidence, wind and shoreline rugosity (Seaman 2000, Denny and Gaylord 2002, Neelamani and Sandhya 2005, Burrows et al. 2008). Most of these factors can be manipulated during restoration design to create desired hydraulic conditions for a target species assemblage; however, the effects may vary among phyla (Nishihara and Terada 2010) making local examples important for reference. Habitat design in areas of greater wave exposure also needs to account for physical disturbance due to associated debris including logs, which can damage the physical integrity of structures and transplanted vegetation (Williams 1993, Adams and Williams 2004). Exposure to high water movement shapes intertidal communities through limiting species presence and form due to strong hydraulic forces. Specific impacts include regulating the presence, mobility and feeding rates of predators and grazers, favoring invertebrates that are more streamlined (Boulding et al. 1999). This may also affect the size and quantity of their prey (Richardson and Brown 1990). Waves can lead to direct mortality of algal species through drag and breaking force (Haring et al. 2002, Pratt and Johnson 2002). Individual shape and size in algae (Denny and Gaylord 2002, Koehl et al. 2008) and invertebrates (Boulding et al. 1999) have been correlated to wave exposure with larger macroalgae ecotypes and invertebrates where there are low to moderate hydraulic forces. Macroalgae and filter feeders  19  require some minimal amount of water movement to deliver adequate nutrients and maintain oxygen levels (Mass et al. 2010) that can lead to preferred establishment in areas of greater water movement (Leigh et al. 1987, Menge 1992, Hurd 2001). Wave exposure is also important in creating bare substrate in a space-limiting environment, allowing for new larval settlement or recruitment by adjacent and opportunistic individuals (Sousa 1984a, Guichard et al. 2003). Where high wave exposure exists, water movement may reduce the chance of settlement by propagules, limiting species establishment to those that can recruit during calm periods, withstand the hydrodynamic forces (Vadas et al. 1992) or result in bare substrate.  Surface orientation is a simple design feature and can help control multiple environmental factors such as temperature, potential for desiccation, and photosynthesis due to exposure to sunlight. Excessive heat generated from direct sunlight results in thermal stress and increased desiccation for algal and invertebrate species reducing the likelihood of survival (Helmuth and Hoffman 2001; Haring et al. 2002; Harley 2003).  Thermal stress has been shown to be higher on sun exposed shorelines (Helmuth and Hofmann 2001, Harley 2003), resulting in local mortalities of limpets and mussels (Somero 2002, Harley 2008), as extreme heat exposure can damage cellular structures. Extreme heat stress, through increased sunlight exposure, also initiates thermoregulation in some intertidal invertebrates indirectly increasing rates of desiccation through behaviour to avoid overheating. Marine algal species that undergo extended periods of exposure to air, particularly during peak sunlight exposure, are subject to increased rates of desiccation resulting in higher susceptibility to breakage from wave exposure (Haring et al. 2002). Cooler temperatures due to shading may reduce heat stress and desiccation and allow for greater competition among species including macroalgae and invertebrates (Connell 1972). Desiccation also affects the rate of photosynthetic activity (Dring 1982,  20  Davison 1991), as observed in Fucus that increased photosynthesis during exposure to the air for short periods of time until desiccation led to reduced overall activity (Williams and Dethier 2005).   1.2.5.2 Urban settings and ecological restoration Although, the Pacific Northwest has an extensive history of intertidal research dating back to classic studies on intertidal community dynamics (Paine 1969, Connell 1972, Connell and Slatyer 1977, Sousa 1984b, Farrell 1991), considerably less research has been done in urban restoration areas (Quigley and Harper 2006, Aronson et al. 2010) making inferences on the effects of using man-made structures uncertain (Schroder 2009).  Much of the peer-reviewed literature has been conducted outside of Canada (Connell and Glasby 1999, Seaman 2008, Vaselli et al. 2008, Chapman and Underwood 2011, Zanuttigh 2011) and presents mixed conclusions. Studies comparing marine assemblages on riprap and natural substrates conclude they differ significantly (Bulleri 2005, Moschella et al. 2005, Bulleri and Chapman 2010), do not differ significantly (Chapman 2006, Pister 2009), or results depend on timing of installation, tidal height, wave exposure and presence of non-native species (Chapman 2003, Bulleri and Airoldi 2005, Bulleri et al. 2005). References to urban work on marine compensation habitat in British Columbia is also lacking in quantity and quality due to cost and time (Naito 2001, Harper and Quigley 2005, Hemmera 2014b). Few studies have been written documenting the methods, successes, and failures of restoring tidal marshes (Adams and Williams 2004, Levings 2004b), eelgrass transplants (Precision 2002, Wright 2002, Kelly and Volpe 2007), and artificial reefs in British Columbia (Naito 2001) until recently (Hemmera 2014b). This is of particular concern considering the potential for creating alternate stable  21  species assemblages (Suding et al. 2004) or novel ecosystems (Hobbs et al. 2006), and the cost associated with fixing ineffective designs.  Urban settings add complexity to predicting biotic interactions by altering connectivity, trophic interactions and competition for space (Belgrano 2005). Changes in biotic interactions may result in novel species assemblages (Hobbs et al. 2006), cause trophic cascades (Lindberg et al. 1998, Sala and Sugihara 2005, Österblom et al. 2007) or lead to stable alternating stable states (Scheffer et al. 2003, Petraitis and Dudgeon 2004); unfortunately, there are few detailed examples within urban settings in the Pacific Northwest to work from. Altering vertical connectivity to the sea floor may remove top-down trophic pressure from predatory mobile species and favour bottom-up processes leading to algal or mussel dominated systems. Urban environments may also exclude higher trophic level predators such as marine mammals that may not tolerate human disturbance and underwater noise (Tyack 2008), releasing predatory pressures on lower trophic consumers (Estes et al. 1978) resulting in a cascading effect of low standing crop primary production (Schmitz et al. 2010).  In an urban setting, predicting environmental gradients is also complex and species assemblages may be altered by infrastructure that increases shading (Glasby 1999, Miller and Etter 2008) or removes coastal vegetation increasing intertidal sun exposure and substrate temperatures. Waves and currents are equally difficult to predict as docks, piers and breakwaters lower or alter wave and current exposure (Vaselli et al. 2008, O'Connor 2010), whereas boat wake increases wave frequency and magnitude (Williams 1993, Demes et al. 2012).  The “straightening” and hardening of shorelines through armouring by riprap for erosion control and maintaining boating channels tends to increase water velocity (Quigley and Harper 2004). Urban water bodies also have other many confounding effects on  22  environmental gradients including impacts from the disposal of municipal and industrial effluent (Díez et al. 2012) and the introduction of non-native species in ballast water (Suchanek 1994). Again, detailed local studies that examine how urban restoration areas develop in this complex environment will aid future design.     23   Methods and materials      24  2.1 Study areas The study areas for this thesis comprise 12 sites; 11 distributed across the lower mainland of southwestern British Columbia, and one marine RA approximately 5.5 kilometres east of Nanaimo, British Columbia (Figure 2.1). Chapters 3 and 4 focus on three marine intertidal sites within Vancouver Harbour: the Vancouver Convention Centre West (M1 or VC), Harbour Green Park (HG or M3, Harbour Green Park makes up a portion to the full M3 site) and New Brighton Park (NB) that are described in Section 2.1.1. The VC has multiple constructed habitat restoration features that were selectively chosen for study based upon design. A description of each restoration feature at the VC is summarized in Table 2.1. Chapter 4 focuses specifically on engineered intertidal shoreline around the VC referred to as the Habitat Skirt (see Section 2.1.1.1). Finally, Chapter 5 conducts an overall assessment of sustainability at 11 RAs including four marine, four estuarine three and freshwater ecosystems. Descriptions of these restoration areas are presented in Table 2.2 and Appendix C.4.  2.1.1 Vancouver harbour Vancouver Harbour is approximately 10 km2 in area and is defined by First Narrows to the west and Second Narrows to the east (Figure 2.1). First Narrows connects to English Bay, the Strait of Georgia and the Pacific Ocean; while Second Narrows connects inland to Indian Arm and Port Moody Arm. Mean monthly water temperature ranges annually from 16.7oC in August to 6.5oC in January (DFO unpublished); while surface salinity (top five metres) ranges between 18 and 24 practical salinity units (psu) in July (Levings and Samis 2001). Salinity at five metres depth and lower is typically 27 psu or higher. The main natural forces impacting water quality are inputs of freshwater runoff during the winter rainy season and the influence of the Fraser River that deposits large quantities of fresh and silt-rich water  25  into Vancouver Harbour from May through July (Burd et al. 2008). Water flow within Vancouver Harbour is maintained by a semi-diurnal tidal pattern with maximum tidal ranges of 5.0 m (DFO 2006) and an estimated velocity of 0.05 m/s in the vicinity of VC (EAO unpublished). Wave exposure is typically low, reaching maximum heights of less than one metre due mainly to winter storms and wake resulting from boat and seaplane traffic (EAO unpublished). The marine intertidal assemblages of Burrard Inlet and Vancouver Harbour have been classified based upon dominant substrate (Morris 2001). All substrates used for marine compensation habitats in my thesis were hard surfaces including riprap (boulders > 0.5 m in diameter) and concrete, making them comparable to the bedrock-boulder category used by Morris (2001). Intertidal assemblages were defined by relative tidal height and wave exposure based upon modified fetch (Howes et al. 1999). Typical upper intertidal communities in semi-exposed, semi-protected, and protected environments include those dominated by acorn barnacle (Balanus glandula) and rockweed (Fucus distichus). Mid-tidal levels were classified by Pacific blue mussel (Mytilus trossulus), sea lettuce (Ulva/ Ulvaria) species or various red algae, with Pacific oyster (Crassostrea gigas) and ochre star (Pisaster ochraceus) at mid to low intertidal heights. Low intertidal heights included filamentous red algae (Microcladia species) and the marine invasive alga wireweed (Sargassum muticum). Bull kelp (Nereocystis luetkeana), sea urchin (Strongylocentrotus species) and eelgrass (Zostera marina) were three indicator species associated with subtidal areas.  2.1.1.1 Vancouver Convention Centre West (VC) The VC consists of six restoration features; a description of each of these structures is  26  presented in Table 2.1.  Chapters 3 and 4 examine the Habitat Skirt, while Chapter 5 evaluates all features together. The Habitat Skirt (HS) was installed around the marine perimeter of the VC between February and May 2008. Components of the HS were constructed by SureSpan Structures Ltd. in Duncan, BC and consist of an engineered series of stepped, pre-cast, concrete benches attached to, and extending out from, the perimeter of the VC (Figure 2.2 and 2.3).  The structure was installed around the west, north, and east aspects of the VC to provide connectivity to the existing shoreline for migrating juvenile salmon (Oncorhynchus species) and extends through the entire five-metre intertidal zone.  Secondary features of the HS were to provide a barrier to logs from getting under the building and damaging infrastructure, while adding a security feature by removing access to below the building that housed the media for the 2010 Olympic and Paralympic games.   The linear length of the HS is 477 metres with a total surface area of 6,122 m2, of which 3,756 m2 is in a horizontal orientation. Each individual bench was designed to maximize surface area, assist in retaining moisture during low tide conditions and constructed of concrete that incorporated fly-ash and designed to resist saltwater corrosion. The top surface of each bench of the HS has an undulating pattern of exposed aggregate designed to increase the variability of the surface texture and microelevations. In addition, a central tidepool was incorporated into each bench to retain water and add habitat complexity. The HS consists of a linear series of concrete benches at each of five tidal heights: zero (0.0-0.5), one (1-1.5), two (2.0-2.5), three (3.0-3.5) and four (4.0-4.5) m above chart datum (CD). Each individual bench is approximately 6.0 m long and 1.2 m wide with a corrugated horizontal surface covered by a pebbled aggregate to improve propagule settlement (Figures 2.2 and 2.3). To increase habitat complexity, a central rectangular tidepool 0.25 m across at the surface, 0.13 m deep and closed  27  at both ends, was designed into each bench. Adjacent to each tidepool is a horizontal outer and inner area approximately 0.45 m wide. Finally, a vertical face, approximately 0.50 m in height, sloping slightly inward to allow for light penetration and water flow underneath the building makes up the outer face of each bench.  The inward positioned horizontal surface on the four lower tidal levels (0.5 through 3.5 m CD) can be considered a shaded environment and the least exposed to tidal and wave generated hydraulic forces. The central tidepool located in each bench receives sunlight on the east and west aspects and maintains water constantly throughout tidal cycles. The outer horizontal surface has the greatest sun exposure, particularly on the east and west aspects; however, the majority of the northeast location is shaded by the building throughout the year. The outer horizontal surface is also expected to have greater exposure to wave forces than the inner surface. Finally, the vertical face of each habitat bench is sloped slightly inward from top to bottom and retains little moisture when emersed during lower water levels. The vertical portion of the habitat bench faces outward and likely receives the highest degree of wave and tidal current exposure.  The four metre bench is a slight anomaly sitting at 4.0 to 4.5 m CD, where it is well above the mean water level and both inner and outer positioned horizontal surfaces are exposed to approximately the same degree of sunlight as there is no bench constructed above it. In addition, a flat vertical seawall constructed immediately behind the upper bench may reflect waves creating a downward force on the four metre bench’s upper surface (Sheng 2000) during the highest tides of the year and winter storm events. Wave height in Vancouver Harbour is generally low, less than one metre high, with the main sources of waves being wake generated from seaplanes and large container ships, and storms during the windier winter season (EAO  28  unpublished). Variable currents generated from semi-diurnal tidal exchanges in the harbour are a regular force acting on the outer bench and vertical face.   2.1.1.2 Harbour Green Park (HG) Harbour Green Park (HG) was chosen primarily as a reference area for the restoration features at VC. Harbour Green Park consists of 90,000 m2 of intertidal and subtidal habitat. The intertidal habitat (Figure 2.4) is riprap (~0.75 m diameter) engineered with a slope of approximately 2.5:1, beginning at 3.5 m CD and extending to 0 m CD. Below the intertidal, a large, shallow, nearly flat subtidal bench was created of riprap with a few large (greater 1.0 m diameter) erratic boulders placed throughout for complexity. At approximately -5 m CD, the bench reverts back to a 2.5:1 riprap slope until it joins with a silty seafloor at approximately -10 m CD.   2.1.1.3 New Brighton Park (NB) The New Brighton Park site (NB) was chosen due to its similar size and shoreline orientations as the VC site. In the early 1980’s, an expanse of intertidal shoreline was raised with fill and armoured with riprap and concrete demolition waste to construct the current configuration of NB (Figure 2.5). The eastern end of the site was re-worked in 1997, with new riprap to armour the shore and a series of beaches to the east. This configuration is very similar in orientation with north, east and west facing slopes to the intertidal of the HS site. The park and adjacent Port Metro Vancouver property have approximately 900 m of intertidal shoreline consisting of a mix of sand beaches and rocky habitat. The area used in the study is approximately 350 m of intertidal rocky shoreline in the western portion of these properties.  29   2.1.2 Estuarine and freshwater restoration areas The tidal marshes within estuarine ecosystems (E1-E4) evaluated in Chapter 4 were  limited to the moist maritime Coastal Douglas Fir subzone (CDFmm) and are typically brackish with relatively low salinities averaging less than 15 psu (Williams et al. 2009); rather than those that typify more saline areas (≥30 psu). Tidal fluctuations are approximately 4.5 m, slightly less than the coastal marine areas with marsh vegetation being restricted to approximately 1.7 m to 4.5 m above chart datum (CD). Vegetation associations are specific to tidal elevation, salinity, water flow and substrate (Levings and Nishimura 1997, Adams and Williams 2004, Williams et al. 2009). Intertidal marsh in the lower Fraser River is generally dominated by pickle weed (Sarcocornia pacifica) and salt grass (Distichlis spicata) where salinity is high. Where salinity is lower, Lyngbye’s sedge (Carex lyngbyei ssp. cryptocarpa), with three-square bulrush (Schoenoplectus americanus) and creeping spike-rush (Eleocharis palustris) are common at the lower tidal levels, and soft-stem rush (Schoenoplectus tabernaemontani) and cattail (Typha species) at the higher tidal levels (Levings 2004b). The fish spawning and rearing areas (F1-F3) of the freshwater ecosystems studied in Chapter 5 are located in the dry maritime (CWHdm) and very dry maritime Coastal Western Hemlock (CWHxm) biogeoclimatic subzones of the Chilliwack Forest Region (MOF 2003). The CWH occurs at low elevations on the mainland from Hardwicke Island in the north to the Chilliwack River in the southeast. Elevation limits are from sea level to 650 metres above sea level (asl) (Green and Klinka 1994). The area has relatively dry summers and moist, mild winters with limited snowfall. The mean annual air temperature is +9.8°C, ranging from monthly averages of between 1.9°C to 17.6°C. Mean annual rainfall is 1827 mm (Pojar et al. 1991). Riparian areas in the CWH typically include western red cedar (Thuja plicata), Sitka  30  spruce (Picea sitchensis), black cottonwood (Populus trichocarpa), red alder (Alnus rubra), willow (Salix species) and shrubs including snowberry (Symphoricarpos albus), salmonberry (Rubus spectabilis), salal (Gaultheria shallon), and red huckleberry (Vaccinium parvifolium).   2.2 Methods  Currently, there are a number of methods and criteria to assess restoration areas including effects assessment, similarity analysis, trajectory analysis, attribute analysis and ecosystem simulation models (DFO 2013b). Variations of before-after-control-impact (BACI) methods (Stewart-Oaten et al. 1986; Underwood 1994) are common in environmental impact assessment and allow determination and quantification of an effect where there is no replication of the main area (Osenberg et al. 2006). Similarity analyses can be conducted on the abundance of target species and/ or between restoration and control areas using univariate (ANOVA, GLM) and multivariate techniques (ANOSIM, perMANOVA) (White and Walker 1997, Pearson et al. 2005, EC 2012b), as employed in Chapters 3 and 4. Comparing the trajectories of species assemblages over time to monitor if they are developing in the desired manner (Zedler and Callaway 1999, Evans and Short 2005, Suding and Gross 2006, Langman et al. 2012) is another suggested method and is also used in Chapter 4. Attribute analysis, which is the focus of Chapter 5, specifies a series of ideal qualities or criteria to evaluate restoration outcomes. Attribute analysis includes developing multimetric indices (Langman et al. 2012) and assessment tools such as Rapfish- the Rapid Appraisal for Fisheries technique (Pitcher et al. 2013). Finally, there are ecosystem-based models including Ecopath (Christensen and Walters 2004) and Atlantis (Fulton et al. 2011) that model restoration outcomes prior to project implementation and can be used with real data to predict changes in productivity over time. Unfortunately, these models are both data and time intensive (Fulton et al. 2011), which  31  translates into expensive baseline programs, likely limiting their use to projects with large budgets or regions with previously developed models, such as British Columbia. The use of any method needs to weigh the data requirements, timeframe to complete work, budget, goals and accuracy of the analysis.  2.2.1 Sample design The sample design for Chapter 3 consisted of three sites (HS, NB and HG, see Figure 2.1 or 3.1), each with three randomly selected sample locations per site and five randomly selected sub-samples per sample location. Within site differences in environmental variability were systematically accounted for by randomly placing one sample location on each aspect within a site (i.e., west, north, east) or at HG, within locations of predicted differences in water motion due to the presence of local infrastructure (i.e., Harbour Green public dock). Sample locations for Chapter 3 consisted of ten vertical transects distributed over a 20 metre distance with a minimum of two metres between the midpoints of each adjacent vertical transect. The starting point of the ten vertical transects was selected randomly prior to the first season of sampling. Vertical transects included three-1.0 m2 quadrats (Figure 2.6) with the midpoint placed at each of three intertidal heights: approximately 0.5, 2.5, and 3.5 m CD. These heights were chosen to sample different species assemblages consistent with marine community zonation patterns observed during reconnaissance in 2008 (EBA 2009).  Specific techniques were taken during data analysis to address assumptions associated with statistical tests. To address repeated sampling measures and pseudoreplication, five transects were randomly selected within each location and each year of analysis. The multivariate centroid of all vertical transects selected within a location were used when  32  comparing sites, and year was used as a replicate. Year can be treated as either fixed or random based upon the hypotheses being tested (Anderson 2008). Because there were large differences in species composition among years (2008 and 2009) at the Habitat Skirt likely due to early colonization and successional patterns, only data from the final two years (2010 and 2011) were used in the analysis to allow years to be treated as a random factor and increase replication.  In Chapter 4, sampling took place at four randomly selected locations, one on each of four aspects (east, north, northeast and west) and five intertidal heights (zero, one, two, three and four m CD) within the HS. Locations were systematically divided to account for potential difference in environmental exposure to sun and waves. Sample locations for Chapter 4 were 20 metres long and consisted of 10 quadrats spaced with their midpoints two metres apart to avoid sampling the same individuals within separate quadrats. For data analysis, five of the ten quadrats sampled were randomly chosen to remove repeated measures between sampling periods.   2.2.2 Sampling method Biota was sampled at HS, NB and HG between late-May and mid-July in each of 2009, 2010, and 2011 during periods of low tide. Spacing between locations within sites was approximately 50 metres (Robinson et al. 1996). Sampling was limited to a 20 m wide section of each aspect to allow for efficient sampling during brief periods of low tide and to facilitate access using SCUBA; required at HS for the lowest tidal height. Most aspects were relatively short in length (~60 m or less) and during reconnaissance there appeared to be little variation; therefore, 20 m sections were assumed to be representative of the whole aspect. Sample  33  locations were randomly chosen and numbered in a west-to-east direction with the location marked using a GPS with an accuracy of approximately four metres and referenced to a physical structure (Appendix A.1).   A non-destructive sampling technique, approved by Fisheries and Oceans Canada (DFO) prior to beginning the study, consisted of estimating percent cover of macroalgae and sessile invertebrates. At NB, HG in all years and the HS in 2008, macroalgal and sessile invertebrate percent cover was estimated for each species using a 1.0 x 1.0 m quadrat (1 m2, Figure 2.6). To accommodate sampling requirements to address questions about differences among microhabitats on the HS, in 2009, 2010, and 2011, four 0.33 m2 quadrats were placed adjacent to each other to sample the different microhabitats. The vertical face was sampled approximately 0.10 m below the top of the bench. To calculate abundance values based on similar sampling area for the HS as with HG and NB in Chapter 3, only the three non-vertical 0.33 m2 quadrats (i.e., inner, tidepool and outer) were summed and used for comparison. Mobile invertebrate densities were also sampled with a 0.5 x 0.5 m quadrat (0.25 m2) randomly placed within one corner of each 1.0 m2 quadrat for NB, HG in all years and the HS in 2008 and multiplied by four to get an estimated value for 1 m2. For the HS in 2009, 2010 and 2011, mobile species densities, mainly invertebrates and a few fish, were counted in one randomly selected 0.33 m2 quadrat within each of the ten quadrats and adjusted by multiplying by three to get an estimated equivalent for 1 m2. All species were recorded and densities of mobile invertebrates were converted to percent cover per square metre (Appendix A.2) based upon the observed size of each species and the local field guides (Lamb and Hanby 2005). Species observed in dense numbers, such as periwinkles (Littorina spp.), were recorded on a scale of 0, 1-10, 25, 50, 75, 100, 150, 200, 250, and 500 based upon the closest number of  34  observed individuals based on prescribed methods form DFO and to allow for more efficient sampling during restricted tidal access.  2.2.2.1 Environmental factors Environmental factors were successfully measured in 2011 and 2012 to quantify light intensity, relative water motion and temperature exposure among sites and locations. The first attempt to measure temperature in 2011 was unsuccessful due to failure of the data logging devices. Specific parameters measured for the analysis include relative water motion at 3.5 m and 1.5 m CD, modified fetch calculated according the Resources Inventory Committee of British Columbia (Howes et al. 1999), maximum and mean temperature (oC), and maximum and mean daytime light intensity (lux). In addition to wave, light, and temperature, slope was measured.  2.2.2.2 Water motion Relative water motion was estimated using circular plaster blocks (Figure 2.7) placed at 3.5 m CD and 1.5 m CD in 2011, and 3.5 m CD only in 2012. It is assumed that waves and wake-generated forces will be better represented at 3.5 m CD, which is slightly above the mean water level (MWL) for Vancouver Harbour of 3.1 m CD; while currents and tidal generated flow should be the main forces at 1.5 m CD. Plaster blocks were set at HS, HG, and NB over a 72 hour period between July 23 to 26, 2011 and at HS, HG, NB and NE for 48 hour period between July 4 and 6, 2012. In 2011, two plaster blocks were placed at each location (n = 9) and tidal height (n = 2) approximately 10 m apart (total n = 36), within each 20 m section where epibiota was sampled. In 2011, only one block was placed at each location (n = 12) with  35  the purpose of quantifying relative wave exposure at NE. To average values from the two time periods for the analyses in Chapter 3, values for 2012 were adjusted for exposure time by 1.5. In addition, three plaster blocks where submerged in sea water collected in Burrard Inlet with no water motion to estimate the decomposition rate of the blocks in sea water. Plaster blocks were not tested experimentally against constant flow rates of differing velocities as we were only interested in relative exposure to water motion in Vancouver Harbour, which is constantly changing due to diurnal tidal cycles and seasonal patterns.  Plaster blocks were made from approximately 0.24 L (1 cup) of Plaster of Paris, molded in 240 mL paper cups with a hole created through the centre to allow the block to be secured to a piece of vinyl siding (approximately 0.10 x 0.15 m) by a zap-strap. The plaster block and vinyl were attached to the HS benches with bungee cords, allowing the blocks to sit at approximately the same height as the substrate on the horizontal plane. At NB and HG, blocks were attached to vinyl siding and then to a paving stone using a bungee cord and placed amongst the rip-rap to allow the plaster block to rest immobile at approximately the same height as the surface of substrate and again in a horizontal plane (Figure 2.7). Dry mass was measured using an electronic scale (± 0.1 g) after blocks were allowed to dry for two weeks prior to, and after, the sampling period. Initial mass of the blocks varied from 78.8g to 114.7g. The percentage change in mass was calculated to give a relative measure of water motion with a greater percent difference indicating greater water movement (Carrington Bell and Denny 1994, Thompson and Glenn 1994).   2.2.2.3 Light and temperature Light intensity and temperature were measured simultaneously using Hobo® light and  36  temperature data loggers (Figure 2.7) set out at 3.5 m CD during two separate sampling periods: 12:00 pm July 4 to 12:00 pm July 6, 2012 and 12:00 pm July 30 to 12:00 pm Aug 2, 2012. Data loggers were programed to sample every 15 minutes. Data loggers were attached to vinyl siding with zap-straps to ensure sensors were mounted horizontally. Light was measured in lux (0 to 320,000) with a range of 150 to 1200 nanometres (nm). These data loggers do not differentiate among wavelengths; rather they are designed to record relative light levels. Daily average light intensity was calculated for each 24 hour period by averaging all non-zero readings, which represent approximately dawn to dusk. This resulted in two daily maximum and mean light intensities for July 4 to July 6, and three for July 30 to August 2. The average for all time periods were used in the analyses for Chapter 3. Temperature was measured in degrees Celsius (0 to 50 ± 0.5 oC). Average daily and maximum temperatures were determined for each 24 hour period, again resulting in two maximum and mean daily temperatures for July 4 to July 6, and three for July 30 to August 2. At NB west during the first sampling period, the paving stone moved after the first 24 hour period, likely due to wave action, so only the first 24 hours of data were used in average light and temperature calculations. Weather during both periods were free of precipitation (EC 2012a). The mean value for all time periods were used in the analyses for Chapter 3.   2.2.3 Data analysis  2.2.3.1 Diversity The analysis of species diversity was based upon two measures:  (1) species richness (S) = the total number of species per sampling unit, and  (2) Simpson’s index of diversity (1-D), where D = S∑i pi2   37  p = proportion of individuals belonging to species i  S = number of species  D = the probability that two individuals randomly selected from a sample will belong to the same species. Species richness is commonly used in describing species diversity, although in different forms including sample (per sample, Ss), alpha (within habitat, Sα), beta (between habitats or time periods, Sβ), and gamma (landscape, Sγ) diversity to gain insight into how species may change with scale (Gray 2000).  In Chapter 3, to compare species richness among structures, riprap (RR) and engineered concrete at the HS, total species richness per site was used for each of 2010 and 2011. Richness was compared in an asymmetrical design (n = 6: HS =2 and RR = 4). Only 2010 and 2011 were used because it was predicted that total richness would be low at HS during earlier sample periods (2008 and 2009) as a result of differences in time for species to recruit to the HS, when compared to more mature sites at HG and NB. To determine if significant differences (α = 0.05) in species richness were observed between HS and riprap, an independent t-test was run. Levene’s test was used to determine if there was a significant difference in variance among groups. In addition, to gain an estimate of the total species richness for the harbour, the gamma diversity, all species sampled at all sites, years and locations were used, including those at Northeast Point (NE) at the VC sampled during the same time period (EBA 2013). In Chapter 4, species richness among microhabitats were compared across tidal heights by year. Species richness values were based upon the total number of species observed at each tidal height by year within the HS (n=15).  A general linear model (GLM) was used in SPSS version 17 to test if trends in species richness deceased with increasing tidal height.   38   2.2.3.2 Multivariate analyses  Biotic data were standardized to percent cover during sampling with no other transformations prior to calculating similarity matrices in order to preserve the structure of the data. Mobile species were originally sampled as counts; however, in order to include these values in the multivariate analysis, individual densities were converted to area based upon field observation and literature values for the region (Appendix A.2). Percent cover of bare substrate was sampled in the field and included as the equivalent of a single species to account for potential differences in relative abundance. Bray-Curtis dissimilarity measures were calculated on final cover data.  2.2.3.3 Comparison of intertidal species assemblages  To test for differences among intertidal species assemblages, a permutational MANOVA (perMANOVA) was conducted using the software package PERMANOVA (Anderson 2001, Andersen et al. 2008). PerMANOVA allows for partitioning of variation and multivariate factorial experiments by calculating the sum of squared interpoint distances to determine within group variation, which addresses many common violations of analyzing ecological data. Significance (P) is determined by permutation tests used to generate a distribution of F under the null hypothesis of no relationship, termed pseudo-F.   2.2.3.4 Habitat Skirt versus riprap  To test for significant differences between riprap shorelines and the Habitat Skirt, a  39  three-factor perMANOVA with substrate, location and year was run with an asymmetrical design; which is commonly used in environmental impact studies with a single impact site and multiple controls (Anderson et al. 2008). In this study there were: two substrates (Su: HS versus RR; fixed); nine locations nested within substrate, (Lo(Su), three in the HS and six in RR; random,), and two years (Yr: 2010 and 2011; random). If significant interaction terms were observed with year, then pairwise tests were run for the main effect of interest against the significant interaction term; nested terms cannot interact. To visualize the results of the perMANOVA analysis, principle coordinates analysis (PCoA) was used to generate low-dimensional ordination diagrams. Principle coordinates analysis was used because it preserves pairwise distances among points, rather than ranks, as done by multidimensional scaling (MDS). Since perMANOVA is a semiparametric method that preserves distance, PCoA is more appropriate in this instance than MDS, although either could be used. Microhabitats In Chapter 4, to test hypotheses regarding differences among species assemblages of the four microhabitats within each tidal height, and year, a three-factor perMANOVA design was used with four habitats (Ha: in, pool, out, and vertical; fixed), three years (Yr: 2009, 2010 and 2011; fixed) and five heights (He: 0, 1, 2, 3, 4 m CD; fixed). Year was treated as fixed to account for differences among years that may be due to changes in species composition that occur during early succession and test if the mean species composition of each microhabitat changed by year. Year can be either fixed or random depending upon the hypotheses being tested (Anderson et al. 2008). Location was treated as random as I did not test for differences by location, but used the multivariate centroid position of the five samples at each location as replicates within each tidal height. The multivariate centroid position was used to avoid  40  pseudoreplication within locations. In Chapter 4, in addition to testing for differences in species composition, taxa were grouped mainly by phylogeny or ecosystem function in order to identify major relationships among sample locations. These main groups included bare space; algae (Chlorophyta, Phaeophyceae, Rhodophyta and Colonial diatoms); sessile invertebrates (Arthropoda and Bivalvia); and grazers and predators (Gastropoda and Echinodermata). An ANOVA was run for each year and taxon with habitat and height as fixed factors. Bivariate correlations among taxa were generated using SPSS version 17 to identify significant trends in taxonomic abundance among habitats within similar height and year. The groupings of Bryozoa, Chiton, Cnidaria, other Mollusca, Porifera, Tubeworms, Tunicata, Worms, and Fish (Vertebrata) were included in the analysis, but excluded from the discussion due to their low abundance, making them poor indicators for changes in species composition.   2.2.3.5 Environmental data A distance-based linear model procedure (DISTLM) in PERMANOVA was used to identify environmental indicators explaining variation among biota of sample locations. DISTLM is a multivariate multiple regression or distance-based redundancy analysis (dbRDA) technique (McArdle and Anderson 2001) that can use non-metric distance measures, such as Bray-Curtis, and fits a linear cloud of environmental variables to the biotic variables. DISTLM partitions the total multivariate variability, like perMANOVA, so the variability attributable to each environmental variable or subset(s) of environmental variables can be quantified, as well as their overlap. Marginal tests were conducted to quantify the relationship of each environmental variable alone, while conditional tests quantify the relationship of each  41  environmental variable given the relationship of those previously selected in the model.  The BEST routine within DISTLM was used to identify environmental factors that exhibit the greatest correlation with response variables using forward selection and backward elimination sequential methods, and Akaike information criterion (AIC) and Bayesian information criterion (BIC) model selection criteria. A pseudo-F test statistic was generated using 4999 permutations to allow for a P-value of 0.0002 (Andersen et al. 2008). Illustration of the results was achieved using a dbRDA ordination method with a vector overlay showing the direction and strength of environmental variables with the biotic data summarized by sample locations. The selection criteria R2 was used to explain the proportion of variation in the multivariate space for each of the environmental variables (X) or the SSregression divided by the SStotal.  2.2.3.6 Distances among species assemblages In Chapter 4, analysis of species contribution to the similarity among habitats of the same tidal height and within habitats of differing tidal heights was conducted using SIMPER (similarity percentage analysis) within PRIMER ver.6 (Clarke and Gorley 2006). SIMPER analysis identifies the amount each taxon contributes to Bray-Curtis similarity within habitats and dissimilarity among habitats by listing each group in order of contribution. To illustrate relationships among habitats by year, multivariate centroid positions were calculated based on all quadrats per habitat - tidal height - year. Similarity among habitats was presented in ordinations based on a principal coordinates analysis to preserve pairwise distances between points. Vectors representing successional trajectories were drawn between centroids to show the amount of change in direction and distance between years. An overlay of species  42  similarities to samples was added to indicate the species composition of samples.  2.2.3.7 Indicator species for environmental light and water motion To examine the hypothesis that both low light and high water motion would result in lower relative macroalgal abundance and higher relative sessile abundance of invertebrates, regression analyses were run between light intensity and macroalgae cover and relative water motion and sessile invertebrate cover. In addition, a similarity percentage analysis (SIMPER) was used to identify key indicator species by comparing species cover and exposure level to either high or low water motion or light within Vancouver Harbour. Four locations with the highest light exposure and water motion were tested against the four locations with the lowest light exposure and water motion to assess whether any environmental variables may explain variation among species. SIMPER identifies the amount each taxon-species pair contributes to the Bray-Curtis similarity within a group, and dissimilarity (δ) between groups. In order to gain a sense of how consistently a species contributes, the dissimilarity value was divided by the standard deviation (SD) with values greater than approximately 1.4 indicating a strong indicator species (Clarke and Warwick 2001). Dissimilarity was also presented as a percent of total dissimilarity to show relative contribution of each species.   2.3 Rapid assessment tools and Rapfish Post-construction monitoring and adaptive management have been identified as a key component to the long-term sustainability of restoration areas (Pearson et al. 2005). Rapid assessments provide critical information in a time efficient and cost-effective manner (Djojhlaf and Bridgewater 2006) and have been used to assess biological inventories (McKenna et al.  43  2002), marine protected areas (Alder et al. 2002), fisheries management (Pitcher and Preikshot 2001, Mora et al. 2009, Pitcher et al. 2013), and ecosystem based management (Pitcher et al. 2009b). Post-construction effectiveness monitoring needs to identify the performance of key indicators quickly (Rowe et al. 2009) in order to implement adaptive management for invasive species control, wildlife management, erosion control or address other unforeseen issues (Langman et al. 2012). Evaluating economic and social value has immediate implications for resource managers that oversee and approve similar projects (Cicin-Sain and Belfiore 2005, Djojhlaf and Bridgewater 2006). The use of rapid appraisal techniques need to be weighed against objectives, time, money, resources, and expertise available to conduct an assessment (Djojhlaf and Bridgewater 2006). In particular, rapid appraisal methods may not be appropriate for looking at seasonal and periodic changes or trends, unless repeat surveys can be applied. The main analysis method of the RESTORE tool presented in Chapter 5 is based on Rapfish (Pitcher and Preikshot 2001, Pitcher et al. 2013) with the evaluation fields and attributes modified for evaluating restoration areas. Rapfish uses six evaluation fields: ecological, technological, economic, social, ethical, and institutional with between six and 12 attributes scored within each field (Pitcher et al. 2013). One of the reasons for applying the Rapfish method lies in its flexibility for modification as exemplified by its application to evaluating management of marine protected areas (Alder et al. 2002), ecosystem based management of fisheries (Pitcher et al. 2009b), and the UN Code of Conduct for Responsible Fisheries (Pitcher et al. 2009a). Furthermore, the design of Rapfish permits the evaluation of ecological, social, and economic values, which are scored using quantitative and categorical measures. Finally, the scoring system used by Rapfish allows for robust use in both data rich and data poor environments, particularly with the addition of uncertainty.   44  The RESTORE tool evaluates three disciplines of relevance to the restoration process: ecological, social and economic value. Based upon the Rapfish approach (Pitcher and Preikshot 2001, Pitcher et al. 2013), these disciplines are broken down into five fields that contribute to the long-term sustainability of a restoration area (SER 2004), each with five attributes that are scored by the user to give an overall value for each field (Figure 2.8; Table 2.3). The five fields are: (1) ecological function and structure; (2) environment, landscape and connectivity; (3) stress; (4) social values; and (5) economics and are described below (Section 2.3.1). The basis for determining sustainability of RAs in the RESTORE is the nine attributes of restored ecosystems determined by the Society of Ecological Restoration and three others suggested as social attributes (Appendix C.1, SER 2004). The main assumption of the RESTORE tool is that if these attributes score highly, then the RA will be sustainable over the long-term. These attributes have been supplemented with guidelines for restoration monitoring and conducting ecological baseline studies in Canada (Pearson et al. 2005, EC 2012b). An economics evaluation field has been included to emphasize the overall importance of managing costs, relevance of funding, and the economic value of nature in an RA (Robbins and Daniels 2012, Nielsen-Pincus and Moseley 2013).  Both Rapfish and RESTORE are tools that can benefit decision makers that need to evaluate high risk problems with potentially highly uncertain data of variable quality. These situations occur when: 1) dealing with environmental monitoring for development projects; or a regulator having determine the sustainability of restoration projects where timelines may not be ecological sufficient, data quality is inconsistent, and evaluation requires the use of value-based inputs from multiple stakeholders. Approaches of this nature have been described as post-normal science (Funtowicz and Ravetz 1993) and are commonly used in the field of  45  ecological economics (Baumgartner et al. 2008). Key criteria to aid in the validation of these approaches are: 1) accounting for uncertainty; 2) identifying variability in the quality of the data; and, 3) ensuring a comprehensive peer review process. Uncertainty in the data can be informed by applying a statistical method, such as a Monte Carlo simulation, that generates confidence intervals around a mean or median determined from a range of input values (McCune and Grace 2002). Quality of the data can be informed by adding a “pedigree” to the data (Christensen et al. 1995; Duan et al. 2009). Pedigree sets out to rank or quantify the reliability of inputs based upon a user-defined system that can be also be incorporated into a Monte Carlo simulation to inform the range of values from which the simulations draws from (i.e., the lower the pedigree and quality of the data, the greater the range of values from which the Monte Carlo selects, resulting in a wider the confidence values around the mean). Finally, a comprehensive peer review process should be in place with reviewers from each major discipline to ensure data quality and reduce the risk of interpretation in value-based assessments.    2.3.1 Evaluation fields The first field in the RESTORE tool evaluates ecological structure and function to incorporate SER attributes 1, 2, 3, and 5 (SER 2004; Appendix C.1) including appropriate community structure, the presence of indigenous species and functional groups, and that the RA functions normally or as expected including absence of dysfunction. The attributes chosen include: the presence of indicator species (e.g., Nereocystis luetkeana in a subtidal reef), using local genotypes through transplant, seed collection or natural colonization; whether age class is properly represented (e.g., spawning adult salmon in a spawning channel); if key functional groups are present (e.g., primary producers, structure forming groups, grazers, primary and  46  secondary carnivores (Steneck and Dethier 1994, Britton‐Simmons 2006)); and, if the RA as a whole appears to be functioning as expected. The abundance of indicator or culturally significant species assures the identity of desired species assemblages and habitats where predetermined assemblages have been recognized (Green and Klinka 1994, Morris 2001, MacKenzie and Moran 2004, Rombouts et al. 2013), while using native species from local gene pools through transplanting is a measure of preserving local genetic diversity and ecotypes that are adapted to local conditions (Adams and Williams 2004, SER 2004). The presence of the expected functional groups and number of trophic levels indicates that the expected structure of the ecosystem is present. An overall assessment for dysfunction of the RA targets ecological processes (e.g., hydrodynamics, nutrient cycling, presence of barriers to process) and whether the ecosystem is appears to be able to function as a whole (Cordell et al. 2011) including interactions among functional groups and abiotic conditions (Holt et al. 1999, Levings 2004a, Nybakken and Bertness 2005, Naeem 2006). Finally, attributes that examine age-class structure help determine if age specific mortality or exclusion is occurring (Short et al. 2000, Peterson et al. 2003), including targeted life stages such as rearing juvenile coho salmon (Oncorhynchus kisutch). The scores in this field ultimately assess whether the desired components of an ecosystem are present and if these components are functioning as a natural self-sustaining system or if the ecosystem is dysfunctional in some way.   Even in an urban environment, RAs are part of a larger framework of nature. The second field of RESTORE evaluates how a RA interacts with its landscape and environment, evaluating SER attributes 4 and 6 (SER 2004; Appendix C.1) that inform how the physical environment is capable of sustaining the RA and whether the RA is integrated into the larger ecological landscape (SER 2004). Attributes assess the health of the physical environment  47  based on the quality of water, air, soil, and sediments on and off-site (EC 2012b, Langman et al. 2012). Biotic and abiotic connectivity examines the ability of RAs to interact with the surrounding landscape (Thrush et al. 2008), since ultimately most restoration areas are too small to provide all the needs to their inhabitants throughout the full life cycle (RISC 2006, Cowen et al. 2007, Walters et al. 2007, Gaydos et al. 2008). In addition, how a RA is shaped in terms of scale and fragmentation affects its ability to be self-sustaining (RISC 2006). Finally, evaluation of the physical or structural integrity of the substrate, including soil and sediment stability, and any engineered features, including supporting or erosion control structures and culverts, are performed to rate long-term physical sustainability of the site (Kemp and O'Hanley 2010, Toft et al. 2013). Once a restoration area has been constructed, the ability for it to remain self-sustaining is the measure of ultimate success (SER 2004). The third field of RESTORE assesses stress to the RA, or the risks to the RA’s long-term sustainability in its predicted state and its level of resilience as suggested by SER attributes 7, 8 and 9 (SER 2004; Appendix C.1). The stress field estimates the risk of outside forces such as catastrophic events (Allison et al. 2003, Platt and Connell 2003), invasive species (Zavaleta et al. 2001, Sheley et al. 2006, Williams and Grosholz 2008, Sheehy and Vik 2010), and human development (Elliott et al. 2007). It also estimates the RA’s resilience or ability to recover from moderate to small magnitude natural disturbance events (Peterson et al. 1998, Langman et al. 2012). Finally, RESTORE credits any short- and long-term monitoring programs that are in place. Monitoring for the presence of stressors is a key component for ultimate success and the implementation of adaptive management (Roegner et al. 2008, Langman et al. 2012, Jacobson et al. 2014). Further to this, monitoring that does not inform maintenance programs likely leaves the RA susceptible to  48  converting to an alternate or failed state (Levings 2004b). Therefore, the adequacy of human resources and funds to conduct monitoring and maintenance programs are included each as their own category, therefore giving each a high weight in evaluating the long-term sustainability of RAs. Incorporation of a social field into RESTORE ensures how society values each RA is deliberated by environmental managers and addresses the SER’s additional attributes including restoring aesthetics and social activity such as recreational use (SER 2004; Appendix C.1). Social attributes chosen for RESTORE evaluate whether the restoration area is valued by local stakeholders (Granek et al. 2008, Petursdottir et al. 2013) and if they protect these areas (Granek and Brown 2005). This includes whether stakeholders have access to the RA or want to keep access limited for the RA’s protection. Including the public in the consultation, design and even the construction process are critical steps to engaging people (Davis 2005), which inevitably increase the area’s value by introducing a form of ownership and responsibility (Reyes 2011, Naylor et al. 2012). Unfortunately, people are generally not aware of the benefits of restoring natural areas, so public education on the local and regional benefits and simply making people aware of the RA are likely to increase its overall value and long-term success (Davis 2005, McCann 2011, Whitney 2011). The final field of RESTORE focuses on evaluating the economics of processes at various scales associated with RAs. Restoring nature is no small feat and can range in cost from a few hundred dollars for community projects with volunteers, to tens and hundreds of thousands of dollars for more complex ecosystems, to millions of dollars for governments to maintain large tracts of land (Hemmera 2014a, NOAA 2014). Measuring project management with respect to budget control assesses if ecological requirements overwhelm economic  49  capacity (Christie and White 2007), potentially impacting funding for future projects. Restoration projects may take many years through a full project life cycle (i.e., from concept to completion of monitoring), during which time government regulations and funding sources may change posing a risk to the sustainability of each RA, particularly in developing countries (White et al. 2002). An estimate of the general funding environment and the reliability of funding for a given project through its life cycle are helpful in judging its overall likelihood of success and commitment to monitoring and maintenance (Christie and White 2007). Natural environments provide large economic benefits (Farber et al. 2002, Boyd and Banzhaf 2007, Egoh et al. 2007, Suzuki Foundation 2015) through recreation (Enmark 2002), harvest, aesthetics (van Marwijk et al. 2012), natural resources and maintaining environmental health that are generally undervalued (Aronson et al. 2010). Restoring production of goods and services to improve social capital is another attribute suggested to be restored by the SER (2004). The value of direct and indirect benefits created by the spending associated with developing a project while in the area gives a measure of the total economic output a RA has on the surrounding community (Nielsen-Pincus and Moseley 2013). Finally, future markets are being created within the environmental field including habitat banking and carbon offsetting. Although these markets are still in their infancy, they directly relate to RAs and the economic value generated from them. Additionally, these values tend to be recurring annually for decades potentially covering the costs for the whole project over time (Galatowitsch 2009, Hansen 2009).  2.3.2 Scoring system Building a cross-disciplinary evaluation tool for restoration areas is complicated. Problems lie in comparing restoration areas of different size, ecosystem type, location,  50  environmental heterogeneity and age (Alder et al. 2002). Essentially, no two areas are exactly the same and there is inherent variation among sites (White and Walker 1997). For this reason, robust indicators or broad measures that address critical features common throughout all areas are important for repeatability, comparability and accountability. Another issue, particularly with measuring social values, is results can be subjective to the opinions of the user and the measurements are typically categorical or nominal, meaning they may have no inherent continuous scale of valuation unless one is developed relative to a predefined state (McCune and Grace 2002). The evaluation tool then becomes reliant on the method of scoring, the measures used to evaluate opinion, and the tool accounts for or explains variation in results. For these reasons, scorers using RESTORE require: (1) interdisciplinary knowledge, (2) familiarity with the study areas, and (3) expert opinion (Rowe et al. 2009). Whenever possible, multiple scorers should be used to remove single user bias and either provide provide a range of uncertainty in scoring, or gain consensus. This may be conducted by generating a mean score and range for a Monte Carlo simulation, or for scorers to work together until they agree on a final score for entry into the tool, similar to a Delphi technique (Rowe and Wright 1999).  The “scoring details” in Table 2.3 outline, as specifically as possible, how to score each attribute; however, scoring is highly dependent upon the region and ecosystem and before scoring Table 2.3, attributes may need to be adjusted to local criteria. Scoring should be used in conjunction with a reference site or conceptual model. For this reason, scoring details outlined herein only guide the user to determine what defines each major category (i.e., best, pass, fail and worst) before they conduct a site visit. Since this tool is a rapid evaluation method, it is expected that there may be cases where not enough information is available to sample for or calculate scores; in these cases, choosing the median value of the appropriate  51  category based on professional judgment with a wider range of uncertainty input to the Monte Carlo simulation will need to be relied upon. This method is commonly used in other prominent ecosystem modelling tools such as Ecopath with Ecosim (Christensen et al. 2005).  In order to rate the quality of the data and the overall RESTORE assessment, a rating system or pedigree was developed based on the source of information, the qualifications of the scorers, and the number of scorers (Funtowicz and Ravetz 1993, Christensen et al. 2005; ESSA-UBC 2015). A table for determining the pedigree is outlined Appendix C.2 with separate rating systems for single and multiple scorers. Only a single incremental increase was assigned for adding a second scorer to account for an increase in precision through replication in scores, greater breadth of expertise and an opportunity for scorers to question values. If the user of RESTORE determines greater precision is created by adding three or more scorers, the pedigree rating system could be adjusted accordingly. I set a value of 0.5, or a minimal passing score, for a single scorer who is a qualified professional, has conducted a literature review, and performed at least a single reconnaissance level field visit to investigate uncertainty and gain intimate knowledge of the RA. The pedigree then adjusts the rating based on this central criteria.  Scoring each attribute is done by either using the prescribed methods within each attribute to develop a numerical score, or by assigning one of four general categories: best (10, 9, 8), pass (7, 6, 5), fail (4, 3, 2), and worst (1, 0) based upon qualitative criteria and assessment. Pass scores are given to a RA that meets all minimum requirements outlined in the attribute to allow it to be self-sustaining, while achieving specific attribute traits. Best scores are awarded to RAs exemplifying ideal forms of the attribute that are estimated to be typical of a self-sustaining habitat. Fail scores mean the RA has not met some of the minimum requirements to  52  be self-sustaining as described in the attribute and either requires further monitoring to determine if it will achieve these attributes with time or is determined to require remediation. Worst scores are given when an attribute is unlikely to be self-sustaining, even with further monitoring over time or remediation. Situations that are unable to be remediated are likely instances where the location of the RA was chosen incorrectly and will not establish due to larger-scale negative forces such as inadequate hydrodynamics or an irreversible change in the surrounding environment due to urban development.   2.3.3 Scoring the case study and sources of information  Information required for an assessment of each RA used in the case study includes ecological, social and economic assessments based on a review of: 1) project reports produced documenting the ecology, social interactions and economics of the project; 2) local community databases; 3) peer-reviewed papers; 4) a site visit; 5) interviews or surveys; and, 6) where no information could be collected, professional judgement. Scoring was attempted prior to going into the field and revised in the field to allow for maximum efficiency; this sequence of scoring is especially important where tidal windows are required to observe the restoration area.  Scoring generally requires a reference RA or a simple conceptual model of the ecosystem including information on indicator species, general trophic structure, key abiotic processes and expected uses by the public. In Canada, most monitoring programs require a control or reference area so monitoring reports typically have this comparison already made or the biotic information available. A list of sources of information used in this study is included in Appendix C.3. Information on economic and social attributes was collected from the project managers, people associated with the project, public sources, and where information was not available, scores were estimated with a higher degree of uncertainty by observations in the  53  field. For restoration areas F1, F2, F3, E1, M1 and M3, I conducted baseline or effectiveness-monitoring studies, had been involved in all aspects of the project, and had access to detailed information from project reports and experience. For all RAs I conducted a minimum of a one-day field visit after reviewing project reports, online data sources that track species presence or abundance and habitat compensation sites. Social attributes were further informed by observations onsite.  All field assessments were conducted in person at low tide where applicable (Appendix C.4), except M4 that which was done using SCUBA, over the course of the last four years with each RA being visited at least once since January 2012 (thesis was originally submitted in May of 2013). During each site assessment Table 2.3 was completed for each of the 25 attributes, with a high and low estimate for each score added to inform the Monte Carlo simulation. The range of uncertainty was determined by limitations of the data available reviewed prior to the site visit, timing of the field assessment with respect to expectation of species presence, and time of day/ week for recreational use. It should be noted that the scoring details in Table 2.3 initially evolved through a series of iterations. Most of these were done before attempting to score the RAs, however, M1 and M2 were visited twice, after some attributes proved difficult to score based upon the initial criteria (i.e., scoring details). Also, the initial scale was designed to be symmetric to assure even weighting for pass or fail scores: best (9, 8, 7), pass (6, 5), fail (4, 3), and worst (2, 1, 0). Unfortunately, this made it difficult to incorporate into the Rapfish algorithm, so it was adjusted to be weighted with an extra value on the pass side to allow for greater discrimination among passing scores.  Scores within and among fields were weighted evenly giving each attribute equal value,  54  this helps to identify an attribute that contributes the most to scoring in the leverage analysis performed by the Rapfish algorithm. If it is pre-determined that specific attributes are more important within a specific assessment, then these scores could be given a different weighting method. Common methods of weighting include: stairstep, steep-stairstep, extreme-stairstep, even, tertiary or low-weight, and primary of greatest importance (RISC 2006). An even weighting was also applied to the fields as RESTORE was designed to incorporate a single field to address each primary area contributing to the sustainability of RAs; however, this could be adjusted if one field is identified as more important to the objectives of an assessment.  2.3.4 Method of analysis and outputs Once scores are determined, they are entered into the Rapfish algorithm, where they are normalized to Z-values to ensure a common scale and then converted to a squared Euclidean distance matrix. Rapfish inputs the distance matrix into the non-parametric statistical technique of multidimensional scaling (MDS) to produce ordination diagrams for each of the individual fields. MDS ranks all pairwise distances between points and then maps them out in multivariate space ultimately producing a two-dimensional ordination plot. Ordinations are constrained such that the best possible and worst possible scores for each attribute are included as “anchor points”. The raw ordination is then rotated and scaled so that these extremes denote an axis from zero to 100% success in restoration, based on idealized metrics, and each restoration site can then be allocated a percentage value representing its restoration status. This is repeated for each of the five evaluation fields. The highest and lowest scores thought to occur for each attribute in the case of a single scorer, or the range of opinions with multiple scorers, determines the uncertainty for the Monte Carlo simulation. The Monte Carlo simulations were adopted by Rapfish to address scoring uncertainty within the model  55  and estimate a ‘true’ value for the statistic. For the RESTORE tool, normal error (Gaussian) option was chosen to calculate mean, median, 95% confidence intervals and interquartile range based upon 100 simulations. The RESTORE analysis produces basic summary statistics for each field, quantifies uncertainty (95% confidence intervals), performs a leverage analysis of each attribute, and creates an overall comparative display in a radar diagram. Basic statistics include the mean, median, upper and lower quartiles, and the two-tailed 95% confidence limit. An attribute leverage analysis (jackknife method) is conducted to evaluate which attribute has the greatest influence on the results. This is done by conducting m+1 (m=number of attributes) runs; a run with all attributes and a run each with a different attribute removed. Finally, a single comparison among fields is achieved through display in a radar or kite diagram for a simple, visual method for relating scores. Within the radar diagram, performance levels of interest including pass and fail levels are shown through colour coding or fixed markers.   2.3.5 Supplemental data analysis The Rapfish analysis method applied in the RESTORE tool was supplemented by an analysis of similarities (ANOSIM) and a similarity percentage analysis (SIMPER) to test similarities among groups and attributes. Values were considered transformed during the scoring to a common scale and then normalized prior to analysis. Pairwise squared Euclidean distances were calculated in a triangular resemblance matrix. A one-way test of similarities among groups (ecosystems) was conducted using ANOSIM, which ranked pairwise distances to calculate a test statistic as follows:  R = (rb – rw)/ ((n (n-1)/2)*1/2) where n = number of samples;  56  rb = average within group rank; and rw = the average between group. Significance levels were calculated using a permutation test with 4999 permutations. Contributions of variables to similarities and species discriminating two groups were computed using the SIMPER routine in Primer v.6 (Clarke and Gorley 2006). Primer v.6 allows for the use of Euclidean distance measures in SIMPER rather than just Bray-Curtis values as in previous versions. To address the question that RAs of larger area are more sustainable than those of smaller area, a regression analysis on the overall scores from RESTORE was initially run using a general linear model in Systat ver. 17 with results illustrated in a scatterplot diagram. Since there is a potential for ecosystem type to confound the effect of size, an ANCOVA was also run to partial-out the effect of ecosystem. Levene’s test was run to test for equality of variance and to test the assumption of homogeneity of regression slopes. The ANCOVA was also run a second time with an interaction term of the main effects (i.e., area x ecosystem). If the interaction term is significant, than the assumption of homogeneity of regression slopes is violated (Field 2009).  57  Table 2.1 Summary of the restoration structures at the Vancouver Convention Centre West. Restoration Feature Date Completed Tidal Height (chart datum) Area Description Habitat Skirt (HS) May 2008 +4.5 m to 0 m 0.61 ha Five tier engineered concrete benches with central tide pool, corrugated exposed aggregate surface. Northeast Point  July 2006 +3.5 m to -6 m  0.14 ha An intertidal and subtidal sloping shoreline consisting of rock/boulder substrate.  The eastern portion of the subtidal zone is also enhanced with nested 6.1 m long concrete pipes retained with steel H-beams. Southwest Corner Reef  July 2006 0 m to -5 m 0.03 ha Intertidal and subtidal habitat with rock/boulder substrate and 10-6.1 m long concrete pipes. Northwest Deep Shoal  Dec 2005 -12 m to -15 m 0.14 ha A subtidal oval shaped shoal consisting of a rock/boulder substrate with numerous 6.1 m long concrete pipes. Southeast Corner Intertidal Habitat Sept 2005 +3.5 m to -1 m 0.06 ha Intertidal and subtidal sloping shoreline consisting of a rock/boulder substrate.  Below Deck Dec 2005 +3 m to -1 m Upper Intertidal: ~1.4 ha Sloping Intertidal: ~0.2 ha Intertidal habitat consisting of a sloping shoreline with upper flats interspersed with water channeling swales.        58  Table 2.2 Summary of the 11 restoration areas assessed in the RESTORE pilot study. Name/ Code Restoration Area Description Age  Area (m2) Status1 F1 - Katzie Slough Freshwater slough for rearing salmonids (coho), culturally significant plants (Wapato), and a riparian area. 2  11,100 Monitoring / remediation F2 - Salwein Creek  Freshwater slough with rearing ponds for coho and riparian area. 5 25,353 Monitoring/ remediation F3 - Ed Leon Slough Freshwater rearing ponds for coho with spawning channels for chum 5 13,500 Complete E1 - Iona Wastewater Estuarine intertidal mudflat, rip rap shoreline with riparian upland. 5 1,100 Remediation E2 - No.2 Road Bridge Estuarine tidal marsh with barrier island and riparian area for rearing salmonids. 22 4,500 Complete E3 - Garry Point Park Estuarine tidal marsh benches and intertidal and shallow subtidal beaches.   24 1,640 Complete E4 - Lulu Island Outfall Estuarine tidal marsh bench with rip rap shoreline protection. 19 1,300 Complete M1 - Vancouver Convention Centre Marine intertidal and shallow subtidal rip rap and engineered shoreline for a previously contaminated site. 5 25,800 Complete M2 - Jericho Beach Marine backshore, intertidal and shallow subtidal reclamation of a site with creosote piles. 1 40,000 Monitoring M3 - Marathon Marine intertidal slope and shallow subtidal bench composed of rip rap. 13 24,000 Complete M4 - HMCS Saskatchewan Subtidal marine reef-a decommissioned naval warship sunk with structure between -30 m and -12 m CD. 16 6,100 Complete 1 Regulatory status based on personal knowledge of the site or information acquired from compensation habitat database (http://www.cmnmaps.ca/FREMP/) in 2013. Monitoring =RA built, but success to be determined at end of prescribed monitoring period; Complete = monitoring complete or not required and RA successful; Remediation required = RA has failed to meet success criteria and requires remediation. 59  Table 2.3 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Ecosystem structure – indicator species Main indicator species are present; may be species expected in an early successional assemblages that may differ from the mature or desired assemblage.  Trophic structure is as expected.  (SER attribute 1) Indicator species presence: compare species presence with reference or model ecosystem. Sample for abundance if possible. Score out of 5. Trophic structure: compare trophic structure with reference or model ecosystem; ensure number of trophic levels are the same. Score out of 5.  Score = Indicator species score + Trophic Structure score  Best - all indicator species present and established 4-5 points; all trophic levels present 4, with redundancy of  key groups 5 points Pass - main indicator species present 4-5 points; not all trophic levels present 1-3 points Fail – Some, but not all of the main indicator species present and not expected to be form target assemblage over the long-term 2-3 points; and, trophic structure incomplete (e.g., Zostera marina sparse (≤5%); trophic levels include eelgrass, crab – no fish or grazers) 1-2 points. Worst - indicator species absent 0-1 points, undesirable species or environmental conditions present that is preventing long-term sustainability; trophic structure incomplete 0-1 points. Ecosystem structure – local genotypes Native and indigenous species are used and present including local genotypes in Species presence: proportion of individuals from local sources and gene pools, including transplanting from similar local donor populations. Success of (re)establishment of culturally significant species with Best - all species present are native and/or culturally desired, established from local genotypes and are reproducing successfully with recruitment observed 8-10 points. Pass - all species present are native and/or culturally desired, but successful establishment may still not be determined 7 points; not all species established from local genotypes (i.e., nursery stock) with reproduction success still not confirmed 5-6 points.  60  Table 2.3 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details an abundance sufficient to maintain a reproducing population.  Any culturally significant plants targeted have established successfully.  (SER attribute 2)   minimal impact on main indicator species. Score= most suitable detail.  Fail - reproductive success is low and likely not self-sustaining, maintenance is required; species not from local genotypes; non-native species present (>5%) 2-4 points;  Worst - native species failed to survive (<25%), reproduce, or recruit to RA. RA does not appear to self-sustaining in desired state 0-1 points. Ecosystem Structure - age class All expected age classes are represented in the restoration area where applicable; or if the habitat provides value to a specific life history stage (salmon spawning, rearing) it is represented.  Age class presence: presence of all targeted age classes for indicator species. Sample for estimated survivorship of age class and compare age-frequency with predicted value or an appropriate reference population, if possible.  Score= most suitable detail. Best - all targeted age classes present and use RA as intended 8 points; attributes observed for more than one life cycle 9-10 points. Pass - all targeted age classes present or are expected to establish with time 5-7 points. Fail - targeted life stage not present, but could establish in future 4 points, and/or not using RA 3-4 points. Worst - targeted age class not present 1 point; age specific mortality to targeted life stage 0 points.  61  Table 2.3 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Ecosystem structure – functional groups All functional groups expected from reference ecosystems are present or have the potential to colonize and recruit with time. (SER attribute 3)   Functional group presence: compare presence of functional groups with reference or model ecosystem. (e.g. algae, filter feeders, grazers, 1o carnivores, 2o carnivores, decomposers).  Can compare number of connections in the food web with reference of model system for more quantitative and detailed method, if possible. Score= most suitable detail.  Best - all functional groups present with redundancy where expected (e.g., tidal marsh - rush and Carex species, major macrofaunal families, Cyprinids and juvenile salmonids (O. tshawytscha), various water fowl) 8-10 points. Pass - all functional groups present (e.g., tidal marsh – C. lyngbyei, major macrofaunal families, Cyprinids, waterfowl) 5-7 points. Fail - Some functional groups not present or may be represented by invasive species (e.g., tidal marsh – primary producers by Spartina alternifolia instead of C. lyngbyei) 4-5 points. Worst – Indicator functional groups not present 1 point and/or barriers exist to their establishment 0 points (e.g., accreted tidal channel – no juvenile salmonids, or trapped after freshet; extensive Phalaris arundinacea). Are bio-physical structure and interactions among organisms and the Best – Within ecosystem interactions are occurring as expected; RA structure is stable or improving for the long-term (e.g., spawning fish present in new channel and returning annually) 8-10 points.  62  Table 2.3 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Ecosystem function - overall Restoration habitat functions as intended, signs of dysfunction are absent.  (SER attribute 5)   environment occurring as expected or as in reference or model ecosystems? This attribute makes an overall assessment of the functionality of the RA that may not be assessed though its individual components.  Score= most suitable detail.  Pass –Ecosystem performs functions as intended, although some functions may not yet be fully developed, but appears to be stable or improving with time 5-7 points (e.g., spawning fish present  in new channel). Fail – Ecosystem does not perform functions as intended but may with maintenance 3-4 points; some function may not yet be fully developed and appears to be declining 3 points (e.g., spawning fish present in new channel, few due to partial siltation, low water flow). Worst - Ecosystem does not function as intended, is in an alternate or deteriorating state and may not re-establish desired level of function with maintenance 0-1 points (e.g., spawning fish present in new channel, no fry due to siltation, low water flow).    63  Table 2.3 (continued) Environment, landscape and connectivity: Score each attribute based upon it positive or negative contribution to the physical environment, landscape and connectivity. The physical environment includes the quality of water, soil, sediment, and air; and how they affect the species present in the RA. Landscape quality and connectivity relates to the ability of species to use its surrounding environment to acquire resources that it cannot access in the RA. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Health of environment Water, soil, air quality are healthy and do not negatively impact reproduction or vigor of individuals within the community and its associated food chain.  (SER attribute 4) Water, air, sediment, and/ or soil quality: use local accepted environmental standards (i.e, Canadian Environmental Quality Guidelines for reference  (http://ceqg-rcqe.ccme.ca/); or,  Are environmental health issues observed on individuals in the RA including: stressed vegetation, visible abnormalities on species (lesions), absence of sensitive species in RA.   Score= most suitable detail.  Best – No observed development in environment and no contamination suspected 8 points; environmental samples meet all local quality guidelines 9-10 points; reproduction and health of species within natural range where possible to detect (e.g., remote coastline without development) 8-10 points. Pass - minimal develop in environment or history of development in environment, but RA improves physical environment from previous state (e.g., sediment contaminant remediation of harbour and replacement with clean substrate) 5-7 points; although contaminants may be detected, not harmful to species long-term survival 6-7 points.  Fail -contaminants detected/ likely, with exceedences to acceptable guidelines and poses health/reproduction risk to resident populations 3-4 points; abnormal level of lesions or physical abnormalities detected 3-4 points; reduction in reproduction success expected or determined (e.g., fish downstream of outfall show multiple age class; however, have lesions and discoloration) 3 points.   64  Table 2.3 (continued) Environment, landscape and connectivity: Score each attribute based upon it positive or negative contribution to the physical environment, landscape and connectivity. The physical environment includes the quality of water, soil, sediment, and air; and how they affect the species present in the RA. Landscape quality and connectivity relates to the ability of species to use its surrounding environment to acquire resources that it cannot access in the RA. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Worst - multiple contaminants determined at levels posing threats to the health and reproductive success of populations 0-1 point; target species unlikely to survive long-term 0-1 point; visual evidence of mortality due to local environment and remediation may not improve local conditions long-term due to landscape level contamination (e.g., fish downstream of outfall show few young of the year, fish kill present) 0 points. Connectivity – abiotic The restoration area connects with neighbouring natural or restored areas such that it allows and contributes to the larger scale cycling of abiotic resources. (SER attribute 6) Presence of barriers: do barriers exist to the movement of abiotic resources in and out of the RA? Connectivity limited by season: are abiotic resources limited seasonally due to Best – unimpeded flow of abiotic resources in and out of RA 8 points; and RA contributes to landscape nutrient cycling (i.e., detritus) 9 points; and RA improves landscape abiotic connectivity 10 points. Pass - flow of abiotic resources in and out of RA, however some resources in low supply 7 points; RA may or may not contribute to landscape nutrient cycles 6 points; and RA may or may not improve landscape abiotic connectivity 5 points.   Fail – supply and cycling abiotic resources limiting and connectivity is limited 3-4 points, potentially seasonal connectivity due to naturally reoccurring barriers 4 points (e.g., vegetation blocks channel flow during low water levels).   65  Table 2.3 (continued) Environment, landscape and connectivity: Score each attribute based upon it positive or negative contribution to the physical environment, landscape and connectivity. The physical environment includes the quality of water, soil, sediment, and air; and how they affect the species present in the RA. Landscape quality and connectivity relates to the ability of species to use its surrounding environment to acquire resources that it cannot access in the RA. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details low water, vegetation barriers, and seasonal weather patterns. Consider surface water, ground water, air movement, soil and sediment.  Score= most suitable detail.  Worst - supply and cycling abiotic resources limiting, long-term failure of RA likely 1 point; connectivity limited to urban developed areas 1 point; RA impedes landscape abiotic resource cycling or acts as a sink, negatively affecting neighbouring areas (e.g., presence of beaver dam alters water levels and flow). May have been sited incorrectly 0 points.  Connectivity – biotic The restoration area connects with neighbouring natural or restored habitats such that it allows for the migration of species, and dispersal of propagules. (SER attribute 6) Presence of barriers: do barriers exist to the movement of species or larvae/ propagules in and out of RA? Connectivity limited by season: is biota movement limited seasonally due to low water, vegetation barriers, and seasonal weather patterns. Consider indicator, target and functional species.  Score= most suitable detail. Best – high connectivity with neighbouring habitats 8-9 points; and RA contributes to landscape connectivity for multiple species 10 points. Pass – moderate connectivity with neighbouring and RA may have limited contribution to overall landscape connectivity 5-7 points. Fail – low connectivity with neighbouring habitats. (e.g., vegetation blocks channel flow during low water levels) 3-4 points; and  RA does not contribute to overall landscape connectivity 3 points. Worst - low or no connectivity to neighbouring habitats 0-1 points; and reproduction to become limiting with time without intervention 0 points.  66  Table 2.3 (continued) Environment, landscape and connectivity: Score each attribute based upon it positive or negative contribution to the physical environment, landscape and connectivity. The physical environment includes the quality of water, soil, sediment, and air; and how they affect the species present in the RA. Landscape quality and connectivity relates to the ability of species to use its surrounding environment to acquire resources that it cannot access in the RA. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Scale and fragmentation The RA and surrounding natural landscape is of sufficient size and pattern to provide connectivity on local or regional scale.  The scale for habitat is of sufficient size and design to allow for long-term sustainability and habitat redundancy.  Area:  B = >50 ha, 50-10 ha, P  = 1-10 ha, F = 1.0 – 0.1 ha, W  <0.1 ha Fragmentation: B = 0-5%,  P = 6-25%, F = 26-50%, W >50%, (RISC 2006)  Score = Area score + Fragmentation score  Best - Area is large enough to support or contribute to species from all trophic levels and functional groups 4-5 points; continuous corridors exist within RA and with  neighbouring areas of similar composition; (<5% fragmentation) 4-5 points. Pass - Area is large enough to support or contribute to indicator species and functional groups 2-4 points; continuous or near continuous RA (<25% fragmentation) with corridors within RA and neighbouring areas of similar composition 3-4 points.    Fail - Area is small for the expected indicator species and not large enough to support or contribute to indicator species from most trophic levels and functional groups long-term 1-2 points; connectivity is limited (26-50% fragmentation) creating large edge effects within the RA and neighbouring areas of similar composition 1-2 points.    Worst - Area is small for the predicted species assemblage and not large enough to support indicator species from most trophic levels and functional groups long-term 0-1 points; RA is isolated or with little or no connectivity (>50% fragmentation) within RA or barriers may exist 0-1 points.    67  Table 2.3 (continued) Environment, landscape and connectivity: Score each attribute based upon it positive or negative contribution to the physical environment, landscape and connectivity. The physical environment includes the quality of water, soil, sediment, and air; and how they affect the species present in the RA. Landscape quality and connectivity relates to the ability of species to use its surrounding environment to acquire resources that it cannot access in the RA. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Physical integrity The physical or engineered structure supporting the restoration area is stable.  Natural substrate is stable.  Physical stability: substrate mediums stable and resilient to small disturbance. Some metrics include: are all engineered structures stable (for what time period/ storm event)?  Have any substrates or engineered structures changed position such that it influences species behavior? Do the restoration features integrate in the natural environmental? Consider substrate, slopes, and engineered structures.  Score= most suitable detail. Best - physical and engineered substrate is stable and integrating with the natural environment. Expected to maintain structure for 25+ years or small storm events (1in 25 years) 8-10 points.  Pass - physical substrate is stable with minor, non-significant failures; integrating with natural environment. Expected to maintain structure for 10 to 25 years or small storm events 5-7 points. Fail - physical substrate is unstable with significant failures that are repairable; not completely integrating with the natural environment; will likely fail in mid-term (< 10 years) 2-4 points. Worst - physical substrate is unstable with significant failures that are not easily repairable; not integrating with the natural environment; will degenerate in short term (< 5years) 0-1 points.     68  Table 2.3 (continued) Field: Stress. Score each attribute based upon its potential threat to the short-term and long-term sustainability of a restoration area. Stress is a measure of the risk of ultimate failure of a restoration area due to natural or anthropogenic physical, biotic or abiotic stressors. The stress field will evaluate a restoration area’s risk to stress, its response to stress, and if monitoring and maintenance are in place to manage stress. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Risk of catastrophic event The risk of a natural (hurricane, flood, fire) or anthropogenic (development, change in legislation) catastrophic event. (SER attribute 7 and 8)  Frequency of a catastrophic event.  Score= most suitable detail.  Best - low risk of catastrophic event; no major events recorded in area in last 100 years 8-10 points. Pass - catastrophic events do occur infrequently in the area; typically between 25 and 100 years 5-7 points. Fail –a single type or annual event with catastrophic force recorded in the area; return cycles typically less than 25 years (e.g., spring flood) 2-4 points. Worst – multiple types or a high frequency of catastrophic events occur within the location of the RA making establishment of the main species assemblages unlikely; return cycles of less than 25 years (e.g., hurricane and spring flood) 0-1 points. Invasive species The risk of an undesirable species entering the RA and becoming self-sustaining and dominant. (SER attribute 7) Abundance of non-native or invasive species: presence and abundance of invasive species in, or adjacent to, the RA.  Score= most suitable detail. Best –non-native or invasive species absent from the RA and absent or limited in adjacent areas 0% presence in RA and 0-1% in adjacent areas 8-10 points  Pass –non-native or invasive species present in RA and/or in adjacent areas (<5% presence in RA and/or 2-5% presence adjacent to RA) 5-7 points.  69  Table 2.3 (continued) Field: Stress. Score each attribute based upon its potential threat to the short-term and long-term sustainability of a restoration area. Stress is a measure of the risk of ultimate failure of a restoration area due to natural or anthropogenic physical, biotic or abiotic stressors. The stress field will evaluate a restoration area’s risk to stress, its response to stress, and if monitoring and maintenance are in place to manage stress. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Fail – non-native or invasive species present in the RA and in adjacent areas; not dominant but may be competing with indicator or target species (5 to 25% presence in RA and/or 5 to 25% presence adjacent to RA) 2-4 points. Worst - non-native or invasive species present in the RA and in adjacent areas; species dominant and in competition with indicator or target species (> 25% presence in RA and/or > 25% presence adjacent to RA). Native planting or species unlikely to survive short-term without maintenance (<5 years) 0-1 points. Resistance and Resilience  Ecological attributes of the RA that allow it to return to expected structure and function following disturbance. (SER attribute 8)  Species composition with time: Length of time for indicator species of the assemblage to become dominant, or for the species assemblage to become similar to reference assemblage (≥55% similarity to reference assemblage) after a disturbance or from time of initial construction.  Score= most suitable detail.  Best - regular moderate to small magnitude, non-catastrophic disturbances do not change the structure of the RA and may help top maintain ecosystem health (e.g., fire reducing shrub cover in grassland); re-establishment of indicator species, target species assemblage (≥60% similarity to reference assemblage) or known early successional takes one year or less 8-10 points. Pass - regular moderate to small magnitude, non-catastrophic disturbances do not change the structure of the RA; re-establishment of indicator species, target species assemblage (≥50% similarity to reference assemblage) or known early successional takes less than five years 5-7 points.  70  Table 2.3 (continued) Field: Stress. Score each attribute based upon its potential threat to the short-term and long-term sustainability of a restoration area. Stress is a measure of the risk of ultimate failure of a restoration area due to natural or anthropogenic physical, biotic or abiotic stressors. The stress field will evaluate a restoration area’s risk to stress, its response to stress, and if monitoring and maintenance are in place to manage stress. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Fail - regular moderate to small magnitude, non-catastrophic disturbances change the structure of the RA (<50% similarity to reference assemblage) and restoration of expected attributes was not observed within 10 years. The RA may require more time for recruitment or succession, or may require maintenance 2-4 points.    Worst - regular moderate to small magnitude, non-catastrophic disturbances change the overall RA (<50% similarity to reference assemblage) and restoration to original habitat is not likely long-term maintenance. Site does not appear suitable for targeted species assemblage(s) 0-1 points. Monitoring program Is there a monitoring program in place to ensure the initial success of the RA and identify adaptive management/ maintenance issues?  Monitoring program in place with documentation to inform adaptive management.  Score= most suitable detail. Best - monitoring program for first three to five years or three to five inspections over a series of 10 years 8 points; and evaluation of RA with adaptive management measures and documented ecological conditions 9 points, and auditing of RA effectiveness every 10 years or less 10 points Pass - monitoring program first year and at the end of a prescribed period (five to 10 years); RA evaluation or summary report issued 5-7 points. Fail – one or more monitoring inspections with no formal reporting or process to advise adaptive management 2-4 points.  71  Table 2.3 (continued) Field: Stress. Score each attribute based upon its potential threat to the short-term and long-term sustainability of a restoration area. Stress is a measure of the risk of ultimate failure of a restoration area due to natural or anthropogenic physical, biotic or abiotic stressors. The stress field will evaluate a restoration area’s risk to stress, its response to stress, and if monitoring and maintenance are in place to manage stress. Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measure Scoring details Worst - no monitoring or documentation of the RA incase follow-up is desired at a later period 0-1 points. Maintenance Maintenance of ecological, physical, and other valued attributes until RA becomes self-sustaining.  (SER attribute 9) Maintenance program in place with documentation to inform adaptive management.  Score= most suitable detail.  Best - long-term (≥ 5 years or 5 events) maintenance program secured 8 points; and funded until RA is self-sustaining 9 points; adaptive maintenance to be integrated into program 10 points Pass - short-term (1 to 2 years/ events) maintenance program secured and funded or volunteer group/ users maintain RA 5-7 points. Fail - no maintenance program in place, but may be available if required; limited or no guarantee of replacement for poor survivorship or engineering 2-4 points. Worst - no maintenance program secured; no contractor guarantee of replacement for poor survivorship or engineering 0-1 points.   72  Table 2.3 (continued) Field: Social. Score each attribute based upon its positive or negative contribution to social values. Social values are defined as benefits from the RA to human stakeholders or the contribution from human stakeholders to the sustainability of the RA.  Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measurement Scoring details Social value Does the surrounding social network value the RA? Assess public use or opinion of the value of the RA. Measures can include observation or records of public use; public opinion in the media or open houses.  Score = use score + cultural features score Best – the public actively uses the RA for social purposes (recreational, spiritual or cultural use) 4-5 points; local cultural knowledge or features have been integrated into the RA 4-5 points. Pass –– the public passively uses the RA for social purposes (walking) 3-4 points; local cultural knowledge were reviewed or are not applicable 2-3 points. Fail - the public does not use the RA for social purposes and it may have a negative impact on social use 1-2 points (e.g., urban marsh will bring more mosquitoes to public recreational area); local cultural knowledge was not reviewed 1-2 points. Worst - the public does not use the RA for social purposes 0-1 points; and indicate it  negatively impacts their cultural values (e.g., alter its current use) 0 points.  Social protection Does the surrounding social network protect the RA? Best – local government actively protects the RA long-term (i.e., legislation) 5-6 points; and a social stigma exists for those that damage the RA including a form of punishment 3-4 points. Pass – local government actively protects the RA until it is declared successful, then it is treated as a natural area and no longer protected 4-5 points; local stakeholders value or are neutral to the RA, but no social pressure exists to protect it 1-2 points.  73  Table 2.3 (continued) Field: Social. Score each attribute based upon its positive or negative contribution to social values. Social values are defined as benefits from the RA to human stakeholders or the contribution from human stakeholders to the sustainability of the RA.  Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measurement Scoring details Is there a form of social protection in place for the RA?  Is there legislation to protect the RA? Is there social pressure to ensure the integrity/ sustainability of the RA?  Score = regulatory score + community score Fail - local government does not protect actively protect the RA 1-2 points; Stakeholders do not value or are neutral to protect the 1-2 points. Worst - local government does not protect actively protect the RA 1-2 points; there is a negative perception to the RA and a risk that some stakeholders may act to negatively impact it (e.g., fishing, harvesting) 0 points.  Social  involvement and engagement The ability of local stakeholders to participate in and add local knowledge to the process of developing and maintaining a restoration area. Did local stakeholders consult on the throughout the planning of the RA, or participate in the construction, monitoring and/or maintenance of the RA?  Score = consultation score + participation score Best – local stakeholders were consulted throughout the planning process and culturally significant features and values were integrated 4-5 points; local stakeholders actively participated to construct and maintain the RA long-term and may harvest culturally significant features sustainably 4-5 points.  Pass – limited open consultation and planning with local stakeholders throughout the process 3-4 points; limited local stakeholders’ participation in the construction and monitoring process 2-3 points.  Fail – local stakeholders were not invited to consult 1-2 points or participate throughout the process 1-2 points.   74  Table 2.3 (continued) Field: Social. Score each attribute based upon its positive or negative contribution to social values. Social values are defined as benefits from the RA to human stakeholders or the contribution from human stakeholders to the sustainability of the RA.  Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measurement Scoring details Worst - local stakeholder’s knowledge was ignored throughout the process; and/or local stakeholder’s were excluded from long-term monitoring and maintenance of RAs even though the opportunity for inclusion existed 0-1 points. Access Are the social benefits accessible to local stakeholders? Can local stakeholders access the RA?  Score = most suitable detail. Best - culturally significant attributes were integrated into the RA and stakeholders have access; if access is not granted it is agreed upon by the local stakeholders for reasons to protect of the RA (e.g., covering an archaeological site to protect it from looting) 8-10 points. Pass - culturally significant attributes are accessible on a limited or seasonal basis 5-7 points. Fail – there is no stakeholder access to the RA 2-4 points. Worst - access by local stakeholders to the RA is blocked without agreement even though culturally significant benefits exist 0-1 points. Education Does the public gain from a form of awareness or education Is there a form of public education regarding the RA?  Possible forms of education may be: signs, tours, presentations, internet-based information, training of local stakeholders Best - public awareness and education was conducted in multiple forms and legacy programs are in place 8 points; and training or mentoring was provided to local stakeholders allowing them to participate in future RAs or initiate their own 9-10 points (e.g., professionals mentored local volunteers, schools on technical aspects of program).   75  Table 2.3 (continued) Field: Social. Score each attribute based upon its positive or negative contribution to social values. Social values are defined as benefits from the RA to human stakeholders or the contribution from human stakeholders to the sustainability of the RA.  Best (10, 9, 8), Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0). Attribute description Measurement Scoring details on the needs or benefits of the RA?  during the process of constructing, monitoring or maintaining the RA, and was research integrated into process to benefit future restoration projects?  Score = most suitable detail. Pass - minimal or single media education was provided 5 points (e.g., educational signage; internet-based information); and public was included in process with education about the RA 6-7 points (i.e., collect monitoring data).    Fail - public notice of the RA was given with no educational information about benefits 2-4 points (e.g., simple add in newspaper notifying the public of the project). Worst – public was not informed about the RA or its benefits; local groups were denied active inclusion to the process (e.g., private or public entity did not engage stakeholders outside of hired project team) 0-1 points.   76  Table 2.3 (continued) Field: Economic: Score each attribute based upon the positive or negative contribution to the overall economics of the RA. Economics is defined as the direct and indirect monetary value society derives from the RA and how economics contributes to the sustainability of the RA. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) unless otherwise specified. Attribute description Measurement Scoring details Budget How did the process of building and monitoring the RA perform with respect to budget?  Cost of RA process relative to budget.  Score = most suitable detail. Best - RA was completed on or under budget 8-10 points.  Pass - RA was completed on budget or within contingency 5-7 points.    Fail - RA was not completed on budget or within contingency 2-4 points.      Worst – RA was not completed on budget or within contingency; debt owing on the RA negatively impacts local stakeholders affecting future opportunities 0-1 points.      Ecosystem services Estimated marginal value of the ecosystem services provided by the RA minus any value lost due to the construction of the RA (marginal value/value lost) = net value.  a) Identify the ecosystem services provided and determine any marginal change in their value, plus any new services created, minus any lost services. Rank value high, medium or low; positive or negative; or b) Use estimated values in Appendix C.10 (Costanza et al. 1997), multiplied by area and assume a 25 year life of the RA, unless otherwise determined. Multiply values by 1.02 per year between 1997 and the present day to adjust for inflation or add a 2% discount rate on the 25 years or Best – ecosystem services valued prior to planning receive the greatest net gain as RA implemented; RA enhances benefits of multiple services; benefits are or predicted to be self-sustaining for greater than 25 years; ecosystem services net value calculation is positive 8-10 points. Pass – ecosystem services valued maintained with a net gain; benefits are or predicted to be self-sustaining for 10-25 years.; or not sure of value, but believed to be positive in short-term without mitigation; Ecosystem services net value calculation is estimated to be  + or – 5% 5-7 points.  Fail – ecosystem services evaluation not integrated into RA planning; or not sure of overall value, but it is estimated to be negative short-term without mitigation; ecosystem services net value calculation is estimated at between -5 and -25 % 2-4 points.  77  Table 2.3 (continued) Field: Economic: Score each attribute based upon the positive or negative contribution to the overall economics of the RA. Economics is defined as the direct and indirect monetary value society derives from the RA and how economics contributes to the sustainability of the RA. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) unless otherwise specified. Attribute description Measurement Scoring details estimated life expectancy of the RA (or multiply by 3.4); or c) Conduct a habitat equivalency analysis (HEA, see NOAA 2000). Worst - overall negative impact on surrounding ecosystem services long-term without mitigation; mitigation or compensation is required to offset losses; ecosystem services net value calculation greater than -25 % 0-1 points. Economic benefit to local and regional communities Economic value of the restoration area including employment, spin-off benefits from construction and future recreational value. Economic value to local community: dollar value of employment, food, lodging, travel, and recreational spending spent in the local communities. Best - economic benefit can be determined to be positive, and reoccurring for stakeholders from pre RA conditions 8-10 points ( examples of  long-term sustainable economics created through seasonal harvesting or costs associated with annual wildlife viewing) Pass - economic benefit is estimated to be positive to some local stakeholders 5-6 points, and long-term recurring economic benefits 7 points.  Fail – the program is subsidized at a cost to local stakeholders with majority of employment imported and economic spin-offs created outside the local community 2-3 points; may be small reoccurring economic gains that are estimated to not result in a net positive gain over the long-term 4 points. Worst – no employment of the local stakeholders, all benefits created outside the local community, and local community left with legacy cost or reduced overall ecosystem services 0-1 points.  78  Table 2.3 (continued) Field: Economic: Score each attribute based upon the positive or negative contribution to the overall economics of the RA. Economics is defined as the direct and indirect monetary value society derives from the RA and how economics contributes to the sustainability of the RA. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) unless otherwise specified. Attribute description Measurement Scoring details Funding security Economic supply fundamentals  Government policy. Level of government, corporate and NGO support available.  Score = most suitable detail. Best – government policy, corporate partnership, private and/ or non-governmental organizational sources of funding available long-term and increasing or estimated to increase (e.g., government policy requires habitat restoration is stable; history of active, non-legislated restoration) 8-10 points. Pass – Current and short-term funding sources secure with neutral outlook for growth (e.g., government policy requires habitat restoration and is stable, but may come under review, or general economic outlook poor, which may affect non-legislated restoration activity) 5-7 points. Fail – a negative change in governance or corporate support for funding limits future RAs (e.g., change in government policy) and may impact support for monitoring and maintenance funding of some current or future RAs 2-4 points. Worst – major funding sources for RAs have been dissolved; governance has been reduced or removed 0-1 points.  79  Table 2.3 (continued) Field: Economic: Score each attribute based upon the positive or negative contribution to the overall economics of the RA. Economics is defined as the direct and indirect monetary value society derives from the RA and how economics contributes to the sustainability of the RA. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) unless otherwise specified. Attribute description Measurement Scoring details Offsetting credit value - carbon  credit and habitat banking net economic gain derived from the RA with respect to managing carbon or the value of the habitat for mitigation/ habitat banking. value = size + C + Term  Value Size (ha) Carbon mt/ha/yr Term (years) 1 <1,  < 1 Short 1-10 2 1-10 1-4 Med 10-25 3 11-50, >50 >4  Long  >25  or banking value : low 0-10, med 10 - 25, high >25 ($/m2)  Score = most suitable detail. Best (value 8-9) offsetting credit value exceeds cost of developing RA history of development in environment, but RA improves physical environment from previous state (e.g., sediment contaminant remediation of harbour and replacement with clean substrate) 5-7 points; although contaminants may be detected, not harmful to species long-term survival, and RA audited every 5 years to confirm ongoing benefit 10 points. Pass (value 6-7) No calculation of carbon performed, but based on change in land type is estimated to be neutral or positive 5-6 points; or offsetting credit value is neutral over the long-term 6 points, and is monitored at least once to confirm value 7 points.  Fail (value 4-5) offsetting credit value is neutral to negative over the long-term. No calculation of carbon offset performed, but based on change in land type and success of RA, is estimated to be negative 2-4 points.  Worst (value 3) RA offsetting credit value is neutral to negative over the long-term.; legacy cost left due to failure of RA acts as net carbon generator 0-1 points.   80   M4 F2 M3M1 M2 E1 E2 E3 E4 F1 F3 Strait of  Georgia Vancouver Island United States Canada Fraser River Figure 2.1 The 12 study sites of Chapters 3, 4 and 5 including five marine sites (M1-M4, NB), four estuarine sites (E1-E4) and three freshwater sites (F1-F3). N 0 12.5 25 km Vancouver Harbour NB  81                     Figure 2.2 Photo of the Habitat Skirt during installation in April 2008 at a 0.2 m above chart datum low tide.   Figure 2.3 Photo of the design features of an individual habitat bench during installation in April 2008. In the photo, the inner edge faces right, while the outer edge faces left, the tidepool is central, and the outer vertical face is similar to the vertical face shown.      82   Figure 2.4 Photo of Harbour Green Park intertidal and shallow subtidal bench during a -0.1 m below chart datum low tide in May of 2009.    Figure 2.5 Photo of New Brighton Park north-facing intertidal aspect June 2009.   83   Figure 2.6 One-metre squared quadrat used to sample percent cover of macroalgae and sessile invertebrates.    Figure 2.7 Environmental sampling unit consisting of a plaster block (blue circle) and light sensor (orange circle) attached to a piece and vinyl siding and then a paving stone at New Brighton Park, July 2012.    84   Figure 2.8 Overview of the structure of the RESTORE tool illustrating the five main evaluation fields and the scoring attributes within each field (RA = restoration area). 85    Effectiveness of an engineered intertidal shoreline to replace riprap as offsetting habitat and the effect of water movement and light exposure on species richness and assemblages   86  Summary Urban infrastructure is expanding over water, altering natural light exposure and water motion. This study assesses how species diversity and assemblages on an engineered intertidal shoreline, the Habitat Skirt, compares with riprap; and if differences can be explained by environmental factors. It is hypothesized that the Habitat Skirt will have greater species richness than neighbouring riprap due to increased engineered complexity, unless environmental conditions are significantly different. Lower light exposure due to shading from infrastructure and higher relative water motion from being positioned over deeper water may result in lower macroalgae and greater sessile invertebrate abundance on the Habitat Skirt. Species richness and Simpson’s Diversity at riprap sites versus the Habitat Skirt were not significantly different; however, species assemblages were (P = 0.021), with high variation among locations within each substrate (P = 0.022). Species assemblages on the Habitat Skirt were dominated by Mytilus trossulus, while riprap had a more even distribution of macroalgae including Fucus distichus and Mastocarpus papillatus. Data on light intensity, water motion and temperature were collected to relate environmental factors to variation in biotic assemblages. Mean light intensity was lower at the Habitat Skirt (P = 0.001); however, mean relative water motion was not significantly higher as hypothesized. A multivariate multiple regression analysis indicates that both average daily light intensity and relative water motion accounted for a significant proportion of variation among species assemblages in the Harbour (P = 0.008; P = 0.019, respectively). Regression analyses exhibit a significant positive relationship of macroalgal cover with mean light intensity (P = 0.014) and a non-significant relationship of relative water motion with sessile  87  invertebrate cover. Macroalgal cover was lower (P < 0.001) and sessile invertebrate cover higher (P < 0.001) on the Habitat Skirt versus riprap.  These results indicate that the Habitat Skirt was effective in achieving similar species richness and diversity to neighbouring riprap structures, but species assemblages were different due to shading from urban infrastructure. When constructing shoreline structures that extend out over water with limited buffer for shading, habitat managers need to determine whether potential differences in species composition meet regional coastal management objectives.     88  3.1 Introduction The ecological importance of marine restoration habitat is increasing as it becomes fundamental in offsetting the loss of naturally productive areas damaged or destroyed through urban development (Perkol-Finkel et al. 2012). It is essential to the success of restoration habitats that during the design phase, managers are able to predict how species recruit and assemble at restoration structures as a function of varying environmental conditions (Deysher et al. 2002, Elwany et al. 2011). As urban infrastructure expands further offshore and tall buildings reach over the shoreline, natural levels of exposure to light, temperature (Glasby and Connell 2001, Harley 2008) and water motion (Heaven and Scrosati 2008, Nishihara and Terada 2010, Tam and Scrosati 2013) are altered and become less predictable. These environmental factors deliver nutrients, oxygen (Sheng 2000, Mass et al. 2010) and contribute to physical stress that is critical in determining species recruitment and diversity (Blockley and Chapman 2006, Blockley and Chapman 2008). Currently, few examples document how overwater structures alter the intertidal environment and how it affects species assembly and diversity, even though shading is a primary concern of regulators overseeing habitat development (DFO 2012b). This study will assess how species richness and assemblages on a new and innovative engineered intertidal shoreline, referred to as the Habitat Skirt, compares with riprap, and if differences can be explained by relative exposure to environmental factors including water movement, relative light exposure and temperature.  Currently in Canada, marine habitat managers and engineers design and construct restoration areas (RAs) to mitigate and offset urban development of productive coastal areas. A few well-documented examples exhibit success (Harper and Quigley 2005), but many RAs that have been reviewed appear to fail in achieving “no net loss” of productive capacity (Carter et al. 2013). Methods of restoration can take many forms that generally fall under two broad  89  categories including: (1) restoring landscape-level processes, or (2) designing and engineering site-specific habitats around infrastructure (Elliott et al. 2007). Ideally, resource managers will restore natural processes such as hydraulic conditions (MacBroom and Schiff 2012, Temmerman et al. 2013), migration corridors (Jordán 2000) and nutrient cycling (Anisfeld 2012), as this approach is likely to support historic conditions and long-term sustainability. However, due to constraints with either budget, methodology or the surrounding urban environment, many resource managers must work at a site-specific level, designing and engineering habitats that incorporate ecological structure and process into man-made shorelines, breakwaters, piers, seawalls and shipping terminals (Chapman and Underwood 2011, Adams et al. 2012, Firth et al. 2013a). Historically, engineering designs have been primarily concerned with ensuring the long-term physical integrity of coastal structures with secondary consideration for ecological features; however, changes in government policies (Naylor et al. 2012) are leading to more cooperation between engineers and ecologists resulting in habitat designs that make structures more favourable to ecologically valuable species (Firth et al. 2013a). Some of the main design variables include substrate (i.e., soft or hard), tidal elevation, and adding microhabitats such as water retaining features (Deysher et al. 2002, Goff 2010, Chapman and Underwood 2011, Firth et al. 2013a, Firth et al. 2013b). One such structure is the Habitat Skirt at the Vancouver Convention Centre West in Vancouver Harbour that incorporates: multiple orientations (vertical and horizontal), water-retaining feature in the form of tidal pools throughout the structure, and an outward steeping design with gaps between intertidal benches to allow water and light to flow and penetrate through the structure and underneath the building (see Section 2.1.1.1). Concrete seawalls and riprap armouring of shorelines are becoming the dominant  90  intertidal habitat in many urban centres (Suchanek 1994, Haggarty 2001, Simenstad et al. 2011). Numerous studies have been undertaken to determine how biodiversity and species assemblages on seawalls and riprap compare to natural habitats with mixed results (Bulleri et al. 2005, Lam et al. 2009, Pister 2009, Ravinesh and Bijukumar 2013).  Many of these man-made structures do not perform as well ecologically as the natural habitats they try to restore becoming vectors for invasive species (Vaselli et al. 2008) or producing less diverse habitats (Pister 2009, Ravinesh and Bijukumar 2013). To improve the ecological effectiveness of engineered shorelines, more recent designs have incorporated tidepools to retain water during low tide (Chapman and Blockley 2009, Firth et al. 2013b), crevices and pits for refuge for mobile invertebrates (Moreira et al. 2007, Martins et al. 2010), and variation in substrate size and surface roughness to enhance colonization and recruitment (Chapman and Blockley 2009, Firth et al. 2013a, Perkol-Finkel and Sella 2014). These features have been shown to be similar to or promote species diversity and abundance relative to non-enhanced seawalls and riprap shorelines (Chapman and Blockley 2009, Pister 2009, Browne and Chapman 2011, Firth et al. 2013a). These results support a hypothesis that the Habitat Skirt will have equal or greater overall species diversity than neighbouring riprap areas. Species richness is a common metric used in assessing the effectiveness of RAs due to its importance in maintaining resiliency of species assemblages to disturbance and ease of use in application (Peterson et al. 1998, Gray 2000). Species richness within a habitat (e.g., rocky intertidal) is well-documented to increase with area (Lomolino 2000, Neigel 2003), although this rate slows with saturation of space, long-term utilization of resources and time since disturbance (Mouquet et al. 2003). Species richness also increases with structural and environmental complexity and the number of types of habitats within an area (Neigel 2003,  91  Roberts et al. 2003), both of which are typically reduced through urban development (Crooks 2002, Airoldi et al. 2008). Differences in abiotic conditions such as substrate, water motion, light, temperature, osmotic stress and nutrient availability can lead to variation among species assemblages. In the intertidal zone, the greatest variation in species diversity and assemblages occurs across a very short vertical scale (i.e., metres) driven by diurnal tidal cycles and the resulting change in environmental conditions (Benedetti-Cecchi 2001, Heaven and Scrosati 2008). Horizontal heterogeneity in species assembly tends to occur across larger scales (100s to 1000s m) due to coastal hydrodynamics and light exposure patterns (Archambault and Bourget 1996, Benedetti-Cecchi 2001). This study will examine if differences in water movement and light conditions within a single harbour can contribute to differences in species diversity and assemblages. Differences in light exposure due to aspect have long been used as a method to predict species assemblages in terrestrial and wetland ecology (RIC 1999) and is typically included with tidal elevations in data collection for assessing estuarine and marine shorelines (Mason and Booth 2004). Lowering light exposure should reduce photosynthetically active radiation (PAR), temperature, and potential for desiccation (Hanelt 1996, Bischof et al. 2007, Harley 2008). PAR is required by algae for photosynthesis with productivity and species distributions impacted by shading (Fresh et al. 2006, Blockley 2007, Miller and Etter 2008, Kavanaugh et al. 2009). Excessive heat generated from the sun can result in thermal stress and increased desiccation and even mortality for algal and invertebrate epibiotic species (Helmuth and Hofmann 2001, Haring et al. 2002, Somero 2002, Harley and Helmuth 2003, Harley 2008); although these relationships may be mediated by the timing of tidal cycles and wave exposure (Harley 2008, Mislan et al. 2009). Marine algal species that undergo extended periods of  92  emersion, particularly during peak sunlight exposure, are subject to higher rates of desiccation resulting in greater susceptibility to breakage from wave exposure (Haring et al. 2002) and lower rates of photosynthesis (Dring 1982). Inversely, cooler temperatures may reduce heat stress and desiccation, allowing for higher rates of photosynthesis (Dring 1982, Davison 1991, Williams and Dethier 2005) and greater competition among macroalgae and invertebrates (Connell 1972, Tomanek and Helmuth 2002). Overall, the interactions of environmental variables in determining species assemblages are complex; however lower light exposure due to shading from overhead structures, should negatively impact macroalgae and alter species composition to those that can tolerate low light conditions; these include favouring red algal species over green algal species, and functional groups such sessile invertebrates, including bivalves (Mytilus trossulus) and barnacles (Balanus glandula) that rely less on light for successful recruitment, over macroalgae. Constructing further overwater and away from historic shorelines places offsetting habitats in deeper water, potentially with greater relative water motion within RAs and around habitat structures (Chu et al. 2000, O'Connor 2010). High water movement shapes intertidal communities through restricting the presence and mobility of predators and grazers (Menge and Sutherland 1987; Boulding et al. 1999) and altering the size and quantity of their prey (Richardson and Brown 1990). High water movement can also lead to direct mortality of algal species through excessive drag and breaking forces (Haring et al. 2002, Pratt and Johnson 2002), while low water movement may allow for larger within species ecotypes to persist (Denny and Gaylord 2002, Koehl et al. 2008). Water motion is also vital for providing adequate nutrients, sufficient oxygen levels (Mass et al. 2010) and removal of waste products for photosynthesis (Raven and Hurd 2012) and productivity (Menge 1992, Hurd 2001).  93  Constructing habitats further away from natural shorelines and over deeper water should lead to greater water motion impacting intertidal habitats and favor functional groups such as sessile invertebrates and algal species that can tolerate greater water movement. The purpose of this study is to examine if an engineered intertidal shoreline can be used to offset development as effectively as riprap in an urban harbour, and if environmental variables of light, temperature and water motion can account for differences among species assemblages. Specifically: (1) Hypothesis A - species diversity and richness on the Habitat Skirt is expected to be equal to, or greater than, neighbouring riprap shorelines due to the small-scale heterogeneity designed into the structure; if the Habitat Skirt exhibits conditions of high shading and water motion as predicted, then lower overall diversity and richness may result due to reduced presence of macroalgae species and greater dominance by sessile invertebrates, particularly M. trossulus; and (2) Hypothesis B - species composition on the Habitat Skirt will be similar to riprap and representative of intertidal assemblages on hard substrates within Vancouver Harbour; if sample locations on Habitat Skirt are exposed to higher shading and water motion than riprap locations, differences in species assemblages between the Habitat Skirt and riprap will be correlated with environmental variables of water motion and light exposure, resulting in a higher abundance of sessile invertebrates and lower abundance of macroalgae.   3.2 Study area and methods The study was conducted at three study sites within Vancouver Harbour (Figure 3.1) including: the Habitat Skirt (HS) at the Vancouver Convention Centre West (VC); New  94  Brighton Park (NB) located approximately three kilometres east of VC; and Harbour Green Park (HG) located immediately west of VC. For detailed descriptions of each of these sites see Section 2.2.1. The overall sample design is described in Section 2.2.1 with detailed descriptions of sampling methods for biota and environmental variables in Section 2.2.2 Finally, methods for data analysis are described in Section 2.2.3.  3.3 Results  3.3.1 Species richness and diversity A total of eighty-five taxa, the gamma diversity, were observed across all sites and sampling locations (including NE) during three years of sampling within Vancouver Harbour. All diversity values were conservative as some taxa were recorded to genus to accommodate identification constraints due to sampling time, sampling techniques and costs associated with working in the inter- and subtidal environments. When comparing sites with differing substrate (Table 3.1), mean richness (± 1 SE) of the HS was lower than riprap (RR = 42.8 ± 1.0; HS = 38.0 ± 1.4), but not significantly (F1,4 = 7.184, P = 0.055). Simpson’s Diversity, which incorporates species richness and evenness, was also used to examine differences in diversity between riprap sites and the HS. Mean Simpson’s Diversity was also lower at the HS than riprap sites (RR = 0.868 ± 0.008; HS = 0.665 ± 0.011), but not significantly (F1,4 = 220, P = 0.055). When examining species richness and Simpson’s Diversity at the site level, the HS and riprap sites (NB and HG) did not have a different mean and total species and diversity between 2010 and 2011 (Table 3.1).    95  3.3.2 Comparison of intertidal assemblages  Intertidal species assemblages on the HS and riprap were compared to test if the HS and riprap had similar species assemblages (Table 3.2; Figure 3.2). Higher order interactions of substrate with year (Su x Yr) and locations nested within substrates with year (Lo (Su) x Yr) were both non-significant. When higher order interactions are non-significant the next step is to examine the test for each factor. The test of no difference between species assemblages of differing substrates (Su) and species assemblages among locations nested within substrate (Lo(Su)) were both significant (F = 12.636, P = 0.021; F = 2.836, P = 0.022). However, significant differences were not observed between years supporting the use of years as a replicate. Species assemblages between riprap and HS were significantly different with a high variation among locations within each substrate. A SIMPER analysis was conducted between locations at the HS and riprap to identify the most abundant species for each substrate and indicator species that best distinguish the two (Table 3.3; Appendix A.4). Species composition among locations on riprap had a similarity of 55% with B. glandula, F. distichus, M. papillatus, Ulva species and S. latissima most abundant (Appendix A.4). The HS had 81% similarity among locations and was dominated by M. trossulus, B. glandula and colonial diatoms, F. distichus, and S. droebachiensis (Appendix A.4). The most important indicator species distinguishing the substrates were M. trossulus (riprap) and F. distichus and M. papillatus (HS) (Table 3.3). Similarity in species composition between riprap and the HS was only 31%. These results indicate strong differences in species composition between the HS and riprap locations.  3.3.3 Environmental data  Data were collected on light intensity, water motion, temperature, fetch, and slope at  96  three locations within each of the three sites to relate variation in environmental factors to biotic assemblages (Table 3.4). Overall, mean daily light intensity was significantly greater at riprap locations than the HS (F1,7 = 32.167, P = 0.001; Figure 3.3); however, mean relative water motion at 3.5m CD was not (F1,7 = 5.246, P = 0.056; Figure 3.3). Comparing environmental variables by site (mean ± 1SE), light and temperature were higher at NB (42,865 ± 7,443 lux, 40.0 ± 2.0oC) and HG (46,754 ± 4,756 lux, 38.4 ± 1.0oC) than HS (21,396 ± 7,463 lux, 32.9 ± 1.8 oC. Water motion at 3.5 m CD was greatest at HS (60.4 ± 6.4 % loss of mass), while at 1.5 m CD NB (49.7 ± 0.7 % loss of mass) had the greatest water motion. The slope of the riprap shorelines at HG and NB is 2.5:1, which is equal to 25 degrees or a 40% grade. The concrete tiers at HS have a slope of approximately 60 degrees or a 150% grade, which is much steeper than the other two sites. HS also received less sunlight that may also be a result of the steeper slope of the intertidal combined with the proximity of tall urban infrastructure acting as a shade. Overall, light was observed to be the only variable that was significantly different (lower) at locations on the HS versus riprap.  To examine which environmental variables explain the greatest amount of variation among biotic assemblages, a multivariate distance-based linear model (DISTLM) was run using species abundance data from 2011 and environmental data 2011 and 2012 (Table 3.5; Figure 3.4). Maximum daily light exposure daily (Lmax) and maximum daily temperature (Tmax) were not included in the final analysis as they were highly correlated with other variables (see Appendix A.5):  Lmax with average daily light exposure (Lave) (r = 0.811), and Tmax with both average daily temperature (Tave) (r = 0.920) and Lave (r = 0.902).  Results indicate that both Lave and water motion at 3.5 m CD accounted for a significant proportion of variation among species assemblages (43.7%, P = 0.008; 34.5%, P = 0.019, respectively), while Tave  97  and water motion at 1.5 m CD were non-significant (30.5%, P = 0.056; 27.1%, P = 0.078 respectively).  Average light intensity was correlated with the first axis (0.598) and accounted for 48.8% of the total variation and 60.4% of the fitted variation. The first axis was also negatively correlated with the three locations at the Habitat Skirt; with the north location having the greatest negative correlation and the lowest light intensity. Relative water motion at 3.5 m CD and 1.5 m CD were both highly negatively correlated with second axis 2, which appears to relate to the spread among the riprap locations (Figure 3.4). Axis 2 accounted for a high amount of variation (22.2% total and 27.4% fitted) and was negatively correlated with north location at HG and positively correlated with the north location at NB; locations among the lowest and highest in relative water motion. These results support literature that both light and wave exposure are important in species assembly patterns.    3.3.4 Indicator groups and species analysis   The two environmental variables identified during the DISTLM analysis that significantly accounted for variation among species assemblages were Lave and relative water motion at 3.5 CD. To test the hypothesis that low light results in lower macroalgae, while higher water motion may result in greater sessile invertebrates, I ran regression analyses of these environmental factors against the two major ecological functional groups. Among the nine locations within Vancouver Harbour, regression analyses indicate a significant positive relationship of Lave with macroalgal cover (r = 0.776, P = 0.014; Figure 3.5) and a non-significant positive relationship of water motion at 3.5 m CD with sessile invertebrate cover (r = 0.531, P = 0.141; Figures 3.5). Since we established earlier significantly lower light conditions and non-significantly higher water motion at HS compared to other riprap locations within Vancouver Harbour, I tested whether HS had lower macroalgae and higher sessile  98  invertebrate cover. Macroalgal cover was significantly higher at the HS than riprap locations (F1,7 = 27.107, P = <0.001; Figure 3.6) and sessile invertebrate cover was significantly higher at locations on the HS than riprap (F1,7 = 42.541, P = <0.001; Figure 3.6). These results support the hypothesis that areas of low light and relatively high water motion would be higher in sessile invertebrates and areas of higher light and lower water motion would support a cover greater macroalgae assemblage. A SIMPER analysis was conducted on the species assemblages with the four highest (NB west, NB east, HG north, HG west) and four lowest (HS west, HS north, HS east and HG east) average daily light exposure and the four highest (NB west, HS west, HS north, HS east) and four lowest (NB east, HG west, HG north, HG east) relative water motion values to identify indicator species based upon light and water motion directly. Results show that mean abundance of macroalgal species: M. papillatus, F. distichus, U. lactuca, S. latissima and U. intestinalis were greater at locations with high light exposure than locations with low light;  while M. trossulus and colonial diatoms were greater in low light conditions than high (Table 3.6).  Locations with high water motion were dominated by M. trossulus, B glandula, and colonial diatoms. Most macroalgal species were more abundant in locations with relatively lower water motion than high. These results support the hypotheses of greater sessile invertebrates, specifically, M. trossulus, in areas of low light and higher relative water motion, and greater macroalgal cover in areas of high average light intensity.  3.4 Discussion  The goal of this study was to assess if the recently installed Habitat Skirt (May 2008), with its engineered complexity, was as effective as local riprap locations in providing a hard  99  substrate habitat for local intertidal marine. Species richness and diversity were predicted to be greatest at the HS, where habitat heterogeneity had been designed in to the structure through the incorporation of tidepools and multiple orientations (i.e., horizontal and vertical). Both species richness and Simpson’s diversity at the HS were similar to riprap habitats located in Vancouver Harbour after only three years from installation. This indicates that the Habitat Skirt is able to support a rich and diverse assemblage of species, but the added complexity engineered into the structure may not contribute to greater species diversity as predicted. Engineered habitats in other urban harbours have performed well and were observed to have similar or greater species diversity when compared to seawalls, riprap and natural habitats (Chapman and Blockley 2009, Pister 2009, Thompson et al. 2010). In studies where enhanced heterogeneity resulted in lower species richness, it was believed to be the result of high wave exposure limiting species presence (Pister 2009) or the lack of habitat heterogeneity relative to natural habitats (Firth et al. 2013). Since habitat heterogeneity is not the only factor contributing to species richness and diversity and environmental gradients affect levels of stress and species richness (Heaven and Scrosati 2008), the inability of the Habitat Skirt to achieve greater species richness and diversity may be related to differences in local environmental conditions including low light availability and greater wave exposure that favour conditions of a less diverse species assemblage.  Although species diversity and richness did not differ significantly between riprap and the HS, species assemblages were observed to be differ among locations on riprap and the HS. The HS was dominated by M. trossulus, B. glandula and colonial diatoms, all species predicted to be representative of locations with low light exposure and moderate water motion (Tam et al. 2014). Additionally, S. droebachiensis was observed in relatively high quantities on the HS,  100  given it would have only been observed in the lowest tidal quadrats, which may have also contributed to further reduction in the presence of large kelp abundance. Riprap had a more diverse and evenly distributed abundance of species including: B. glandula, F. distichus, M. papillatus, M. borealis, Ulva spp., Dermasterias imbricata, Pisaster ochraceus and Evasterias troschelii. Dermasterias imbricata was the only predator not observed on the HS; since it is known to prey on S. droebachiensis, its absence may contribute to higher abundance of S. droebachiensis and indirectly a lower abundance of N. leutkeana and S. latissima. Both large kelps were observed at the riprap locations, but rarely at the HS. These results indicate that it may be a combination of environmental effects and biotic interactions leading to differences between the two substrates depending on tidal height.   Environmental conditions across all locations within Vancouver Harbour were sampled to identify if differences within environmental variables could explain variation among observed biota. At the outset of this study I expected that both water motion and light would lead to variation in species assemblages (Nybakken and Bertness 2005, Nishihara and Terada 2010, Tam and Scrosati 2013); however, my predictions were based upon results from studies that examined either light or wave exposure separately. Both average daily light exposure and relative water motion at 3.5 m CD accounted for significant variation within the biotic data and correlated with differences between the Habitat Skirt and riprap. When examining the effects of two variables together, average light intensity and water motion at 1.5 m CD explained the greatest amount of variation in the data indicating the importance of each variable and how they act relatively independently. Macroalgal cover increased with average light intensity within Vancouver Harbour; while sessile invertebrate cover exhibited a non-significant change in abundance with greater relative water motion. These results are supported  101  by other studies in urban centres that have identified increased dominance of mussels on shaded rocky shorelines (Miller et al. 2008) with low to moderate water motion (Tam and Scrosati 2014). Shading as a result of phytoplankton blooms and docks have also been shown to reduce kelp abundance and decrease the viability of eelgrass populations (Kavanaugh et al. 2009).   With environmental trends identified in Vancouver Harbour, differences between the Habitat Skirt and riprap habitats were examined with the hypotheses that the close proximity of the overhead structure to the Habitat Skirt would lead to relatively higher shading, while the position of the VC extending out into the harbour would lead to higher water motion. Lower light conditions were observed at the HS, but not significantly greater water motion relative to other shoreline locations. Differences in species composition were as predicted based on these conditions with significantly lower macroalgae and significantly higher sessile invertebrate species on the HS. Low average light intensity and high relative water motion appear to have limited the presence of macroalgae in favor of sessile and mobile invertebrates; M. trossulus, B. glandula, S. droebachiensis and E. troschelii; reducing overall species richness of macroalgae at high and mid-tidal levels in favor of M. trossulus, similar to other urban studies (Glasby 1999, Miller and Etter 2008). In conditions of stress it is suggested that organisms acting as ecosystem engineers, such as mussel beds, ameliorate stress and promote species richness (Crain and Bertness 2006). However, at HS, where physical conditions, such as wave exposure, are moderated by urban infrastructure, and predation appears to be limited at mid-tidal levels by access, abundant mussel beds appear to have formed that exclude macroalgae and lower total species richness (Paine 1966, Dayton 1971, Menge and Sutherland 1987, Bruno et al. 2003, Scrosati and Heaven 2007). At lower tidal levels, grazing by S. droebachiensis on  102  macroalgae and predation of E. troschelii on mussels may be reducing the presence of S. latissima, N. luetkeana, and M. trossulus.  Low light intensity at HS may limit growth of macroalgae and promote invertebrates, whereas high light conditions and low water movement at HG appears to promote the growth of macroalgae rather than sessile invertebrates. Although species richness is similar between these two sites, HG is dominated by macroalgal species including F. distichus in the high intertidal, U. lactuca - M. papillatus in the mid-tide ranges, and S. latissima and other large brown and red algae near 0 m CD. The Habitat Skirt is mainly dominated by B. glandula – Littorina in the high intertidal, M. trossulus in the mid-tide ranges, and S. droebachiensis - E. troschelii - M. trossulus near 0 m CD. These findings support the predictions that high light and low water motion will result in high macroalgal cover, and low light and high water motion supports sessile invertebrate dominated species assemblages.  Vancouver Harbour is a sheltered area as shown by the results of the modified fetch rankings for each site and location (see Table 3.4). Docks and piers shelter many of the sample locations including one at HS and two at HG, allowing only small waves, less than one metre high, even during winter storms. However, the frequency of wake from seaplanes and boats that use the docking facilities at HS potentially contributed to unnatural forces that urban intertidal assemblages must withstand. These forces likely explain the higher relative water motion at 3.5 m CD observed on the north and east HS, which is adjacent to one of the busiest seaplane terminals in North America with over 150 seaplane movements per day (EBA 2012b). The other sites had greater water motion at 1.5 m CD where we would expect currents to have a larger effect than waves in a protected harbour with large diurnal tidal patterns in excess of 4.5 metres. Elsewhere, similar current movement has been shown to carry the same community  103  structuring force that comes from larger waves resulting in higher cover of barnacles and mussels (Leonard et al. 1998).  Average light intensity was greatest at the NB and HG. I expected that HG and the north aspects of NB and HS would have relatively low light exposure due to shading effects from vegetation and buildings on shore. Average daily light intensity recorded at HS was considerably lower than other sample locations and appears to be not intense enough to allow many macroalgal species to recruit. Any effect increased habitat heterogeneity may have had in contributing to differences in species assemblages due to a light gradient could have been negated by the presence of shading from large urban structures altering sunlight patterns. These results also indicate that minimal shading takes place on sloped riprap on north facing aspects in Vancouver Harbour where building and tall vegetation are not present. This highlights the importance of a buffer equal to the length a buildings shadow to be maintained between the intertidal zone and urban infrastructure to allow sufficient light exposure for marine macroalgae.   This study presents results that support the HS as a rich and diverse habitat feature, but differences in area specific environmental conditions and a lack of replication of the HS may be confounding the ability to determine the effectiveness of the structure relative to neighbouring riprap substrates. Difficulties in drawing conclusions from this study lie in that there is only one Habitat Skirt and any effects may be attributed to differences in the area and the environmental conditions associated with that area; other than the ones sampled. For this reason riprap locations were chosen within the same harbour and as close as possible to limit these area differences. Replication was gained through sampling over two years with the assumption that differences due to early recruitment would not likely be significant; which was  104  later confirmed in the perMANOVA test. Studies that lack replication are common in impact studies and sample designs have been developed to address the weaknesses in their application (Stewart-Oaten et al. 1986; Underwood 1994; Anderson 2007). To further test the effects of shading by overhead structures on intertidal habitats, replication in other urban harbours where infrastructure has resulted in extensive shading compared to neighbouring shoreline needs to be conducted. In addition, controlled experiments further exploring the interactions of waves, currents, light and temperature with species assembly patterns in urban environments are needed to better inform future design of intertidal restoration areas. Although generalizations about relative differences in environmental factors and species assemblages can be reached, greater replication of sample locations of biotic and environmental data through time would also improve reliability of these results. In addition, although species composition on the HS was shown to be stable, further changes could happen with greater time to recruit to the structure. The riprap sites were between five and ten years older than the HS and may be exhibiting species composition of a more mature species assemblage. Although many restoration habitats, including rocky intertidal habitats in the Pacific Northwest, have been shown to reach stable states after three years, resampling the sites in 5 years would help to support this assumption.  3.5 Conclusions  With increasing expansion of urban centres along shorelines and limitations on space, innovative methods such as engineering intertidal shorelines around the perimeter of buildings extending overwater are required. Engineers need to work with biologists to inform their design to not only include ecologically beneficial designs, but consider how local environmental conditions may affect species assemblages. The Habitat Skirt was effective in  105  achieving similar species richness and diversity as neighbouring riprap locations; however, significantly lower light levels at the HS likely contributed to reduced abundance of common macroalgae species, allowing M. trossulus to dominate the structure. The ability to discriminate between local area effects and the structure could not be made due to low replication, suggesting repeating this study in other urban harbours could help inform regulators to the effects of shading from infrastructure constructed along shorelines and whether adequate buffers for shading are required. Where buffers for shading are not able to be incorporated in project design, habitat managers need to determine whether potential differences in species composition meet regional coastal management objectives.     106  Table 3.1 Species richness and Simpson’s Diversity by substrate, site and year in Vancouver Harbour in 2010 and 2011 (n = 6). Substrate       Habitat Skirt                     Riprap Site HS  HG  NB   Mean SE 2010 2011 Mean SE 2010 2011 2010 2011 Species Richness 38.0 1.0 39 37 42.8 1.4 45 40 42 44 Simpson’s Diversity 0.665 0.008 0.646 0.684 0.868 0.011 0.872 0.864 0.879 0.857    107  Table 3.2 Permutational MANOVA results for the comparison of species assemblages from locations on riprap (New Brighton Park and Harbour Green) and the Habitat Skirt (n = 18) within Vancouver Harbour in 2010 and 2011. Analysis is based upon 4999 permutations (P). Su = substrate, Yr = year, Lo (Su) = location nested in substrate. Source of variation df MS Pseudo-F P Su 1 14,490 2.84          0.018* Yr 1     727 2.01          0.131 Lo (Su) 4     563 2.99          0.022* Su x Yr 1     659 1.82          0.141 Lo(Su) x Yr 4     198 0.16          0.999 Residual 6    1260     Total 17 26,903   * P  ≤  0.05, **P ≤ 0.01, *** P ≤ 0.001    108  Table 3.3 Summary of average species abundance at the Habitat Skirt (HS) and riprap (RR) locations in Vancouver Harbour in 2010 and 2012. δ = dissimilarity and SD = standard deviation. Species RR HS Mean δ δ/SD δ% Cumulative% Myti_tro 1.22 65.642 25.80 5.66 37.53 37.53 Fucu_dis 18.62 2.02 6.58 1.42 9.58 47.10 Mast_pap 13.61 0.01 5.32 1.56 7.74 54.85 Bala_gla 26.01 14.95 4.61 1.16 6.71 61.55 Bare 20.66 21.20 4.30 1.69 6.26 67.81 Colo_dia 3.62 9.58 3.37 1.40 4.90 72.71 Micr_bor 6.46 0.03 2.59 0.76 3.77 76.49 Ulva_lac 6.03 0.97 2.03 1.04 2.96 79.44 Bang_spp 4.56 0.00 1.78 0.88 2.58 82.03 Sacc_lat 4.60 0.56 1.72 1.02 2.50 84.53 Sarg_mut 2.81 0.00 1.07 0.69 1.55 86.08 Pisa_och 2.68 0.00 1.04 0.74 1.51 87.59 Ulva_int 2.58 0.45 0.94 0.76 1.37 88.96 Onch_ket 0.00 1.67 0.65 0.70 0.95 89.91 Mast_pet 1.59 0.00 0.64 1.94 0.93 90.84 Stro_dro 0.00 1.48 0.60 1.43 0.87 91.70 Br_string 1.34 0.23 0.59 0.61 0.86 92.56 Litt_spp 1.42 0.03 0.57 0.56 0.83 93.39 Bala_cre 1.38 0.85 0.56 0.86 0.82 94.21 Poly_hol 1.72 0.55 0.53 1.09 0.78 94.99 Evas_tro 0.60 0.40 0.34 0.59 0.49 95.48   See Appendix A.3 for full species names.  2 Bold indicates higher cover.      109  Table 3.4 Summary of environmental data collected at the Habitat Skirt, New Brighton Park, and Harbour Green in Vancouver Harbour.   Habitat Skirt New Brighton Park Harbour Green  West North East West North East West North East Light (maximum)   lux 152,475 96,215 150,179 172,683 203,912 180,031 172,223 178,194 141,453 Light (mean daily 3.5 m)   lux 25,473 12,589 36,991 51,022 54,888 55,702 59,057 57,984 48,603 Temperature (maximum 24 hour) oC 26.9 33.1 39.2 35.4 38.1 28.7 39.2 40.4 39.2 Temperature (mean daily 3.5 m)   oC 17.8 16.3 18.0 19.8 17.1 19.6 20.2 20.9 20.5 Relative water motion (3.5 m) %1 49.0 63.4 48.4 51.0 46.2 37.5 39.6 25.2 37.1 Relative water motion (1.5 m) %1 41.8 49.3 44.6 49.7 50.3 49.0 42.3 33.0 34.9 Modified fetch2  0.29 2.18 1.57 0.42 1.08 1.20 1.44 0.15 0.05 Exposure Rank2  Protected Semi-protected Semi-protected Protected Semi-protected Semi-protected Semi-protected Semi-protected Semi-protected                                                  1 Percent of loss of mass after 72 hours. 2 Based upon Howes et al. 1999  110  Table 3.5 Marginal tests results from a distance-based linear model (DISTLM) for five environmental variables and biota in 2011 at New Brighton Park, Harbour Green and the Habitat Skirt in Vancouver Harbour.   DISTLM Marginal Tests  2011 Biotic Data  SS pseudo-F P Proportion Lightaverage 5250 5.427 0.008** 0.437 Water motion 3.5 m 4150 3.690       0.019*  0.345 Temperatureaverage 3669 3.074       0.056 0.305 Water motion 1.5 m 3256 2.599       0.078  0.271 Modified Fetch    977 0.619       0.622 0.081  Residual df = 7 *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001   111  Table 3.6 SIMPER results identifying species contribution to the dissimilarity between locations in Vancouver Harbour with the highest and lowest light exposure and water motion at 3.5 m CD. δ = dissimilarity and SD = standard deviation. (a) Mean daily light intensity Species High Light Low Light Mean δ δ/SD δ% Mean Cover Mean Cover Myti_tro3       0.19     49.234   19.80       1.63    31.00 Mast_pap      15.30      1.35    5.64       1.39     8.83 Bala_gla      27.73     17.75    5.49       1.20     8.59 Bare      23.23     22.44    4.77       1.53     7.46 Fucu_dis      14.03      5.97    4.68       1.18     7.33 Colo_dia       3.37      8.33    3.57       1.33     5.60 Micr_bor       5.71      0.15    2.32       0.84     3.63 Ulva_lac       5.08      3.39    2.25       0.97     3.52 Sacc_lat       4.78      2.31    2.01       1.02     3.14 Bang_spp       4.64      1.39    1.99       0.88     3.12 Pisa_och       3.47      0.01    1.28       0.44     2.01 Ulva_int       3.13      1.08    1.26       0.72     1.97 Bala_cre       1.56      1.01    0.81       0.83     1.26 Br_string       1.05      1.13    0.76       0.63     1.19 Litt_spp       1.57      0.53    0.73       0.50     1.15 (b) Relative water motion (3.5 m CD)  Species High wave Low wave Mean δ δ/SD δ % Mean Cover Mean Cover Myti_tro      49.40      0.03   19.93       1.65    31.39 Bala_gla      16.84     28.64    5.73       1.25     9.03 Bare      20.24     25.43    4.89       1.48     7.70 Mast_pap       5.62     11.03    4.68       1.24     7.36 Fucu_dis       8.85     11.16    4.63       1.39     7.30 Colo_dia       7.18      4.52    3.23       1.14     5.08 Sacc_lat       1.10      5.99    2.26       1.08     3.56 Ulva_lac       2.09      6.38    2.25       0.87     3.54 Micr_bor       0.69      5.17    2.16       0.80     3.40 Bang_spp       1.33      4.70    2.00       0.88     3.14 Ulva_int       0.64      3.57    1.36       0.76     2.13 Pisa_och       0.27      3.21    1.24       0.43     1.95 Br_string       0.17      2.01    0.85       0.66     1.34 Bala_cre       0.64      1.93    0.83       0.83     1.31 Litt_spp       0.07      2.03    0.81       0.54     1.27 Mast_pet       0.28      1.84    0.72       1.33     1.14                                                  3 See Appendix A.3 for full species names.  4 Bold indicates higher cover.   112     Figure 3.1 Study areas for Chapter 3.  113      Figure 3.2 Principle coordinates analysis illustrating Bray-Curtis similarities among intertidal species assemblages on riprap (black) and the Habitat Skirt (grey) in 2010 (no fill) and 2011 (solid). Sites are symbolized as: a) Habitat Skirt (circles), b) New Brighton Park (up triangles), and c) Harbour Green (down triangles). The black contour represents 50 percent similarity among locations within contour.        -60 -40 -20 0 20 40PCO1 (57.3% of total variation)-40-2002040PCO2 (18% of total variation) 114    Figure 3.3 Bar plots of mean daily light exposure (lux) in July 2012 and mean relative water motion (percent loss of mass) in July 2011 and 2012 ± 1 SE at locations on the Habitat Skirt (HS, clear circle) and riprap (RR, filled circle) within Vancouver Harbour. Relative water motion was measure as a percent loss of starting mass.   115     Figure 3.4 Distance-based redundancy analysis (dbRDA) ordination plot of distance-based linear model (DISTLM) results showing environmental variables, presented in a vector overlay (grey vectors), with intertidal species assemblages from New Brighton Park (up triangle), Harbour Green (down triangle), and the Habitat Skirt (circle) in Vancouver Harbour. Triangles are filled to designate they have a riprap substrate. The circle indicates unit length in three dimensions projected onto two dimensions. -60 -40 -20 0 20 40dbRDA1 (60.4% of fitted, 48.8% of total variation)-40-2002040dbRDA2 (27.4% of fitted, 22.2% of total variation)Light aveTemp aveFetch modRel Hydro 3.5 mRel Hydro 1.5 m 116      Figure 3.5 Regression analyses showing: (top) abundance of macroalgae with mean daily light intensity (lux), and (bottom) abundance of sessile invertebrates with relative water motion (percent loss of mass); each at nine locations within Vancouver Harbour. Black circles indicate locations on riprap substrate and clear circles indicate locations on the Habitat Skirt.    117      Figure 3.6 Bar plots of mean macroalgal abundance (top) and mean sessile invertebrate abundance (bottom) ± 1 SE at locations on the Habitat Skirt (HS, clear circle) and riprap (RR, filled circle) within Vancouver Harbour.      118   Intertidal species diversity and assembly vary with engineered microhabitat design and tidal height            119  4.1 Summary Innovative methods for habitat restoration are required to maintain shoreline connectivity, while maintaining native species assemblages. This study examines how marine intertidal assemblages and diversity developed over a three-year period among four engineered microhabitats incorporated into an intertidal fish habitat compensation structure referred to as the “Habitat Skirt”. The differing microhabitats include an inner horizontal surface, a central tidepool, an outer horizontal surface, and a vertical face incorporated into ‘benches’ over a range of five intertidal levels.  Among microhabitats, species richness was greatest within tidepools from 2 to 4 metres above chart datum (m CD) and vertical habitats at 0 and 1 m CD. Species richness decreased with increasing tidal elevation, except in tidepools where richness remained similar irrespective of tidal elevation, as predicted. The uppermost benches were mainly bare, except the tide pool that supported Ulva intestinalis and various limpet species, while Mytilus trossulus dominated the mid-intertidal heights. The lowest tidal level was initially colonized by Nereocystis luetkeana and Saccharina latissima; however, once Strongylocentrotus droebachiensis was able to recruit in year two, Phaeophyta species mainly disappeared from the lower tide levels.  Variation in species composition was expected to decrease with time. Although species composition of many microhabitats varied with tidal height, these did not change significantly between the last two years; however, total variation among locations within the last year was greatest, likely due to disturbance at two of the sampling locations. These changes support the value of incorporating tidepools at upper intertidal elevations to increase species richness and variation, and the importance of importance of multi-year studies to document species assembly patterns.     120  4.2 Introduction Increasing waterfront development in urban centres, such as Vancouver, British Columbia and Seattle, Washington, is reducing the amount of naturally productive marine intertidal habitat in favor of shorelines armoured with riprap and seawalls (Suchanek 1994, Haggarty 2001, Simenstad et al. 2011).  Concrete seawalls, breakwaters, floating piers and building piles may not replenish historical levels of species diversity, ecosystem function and ecosystem services without design enhancement (Connell and Glasby 1999, Bulleri and Chapman 2010, Chapman and Underwood 2011). New methods for restoring intertidal habitats that incorporate engineered features such as tidepools are needed so that productive native marine assemblages can return post-development (Bergen et al. 2001, Gaydos et al. 2008, Chapman and Underwood 2011). This study examines the development of intertidal marine species diversity and assemblages among four different microhabitats designed into an enhanced engineered shoreline, collectively referred to as the Habitat Skirt (HS), over its first three years of establishment. Additionally, I examine the relative effectiveness of each microhabitat across five tidal heights to inform future design of fisheries offsetting habitat on hard intertidal substrates along Canada’s west coast.  Understanding how each microhabitat contributes to species diversity and assembly at a small scale (i.e., less than one metre) may help habitat engineers to maximize future intertidal productivity and target focal species. Small-scale heterogeneity has been shown to contribute to species diversity and variation among species assemblages to a greater extent than at larger scales (100s of metres) within the same habitat type (i.e., rocky shores) (Archambault and Bourget 1996, Benedetti-Cecchi 2001, Fraschetti et al. 2005). Typical microhabitats within intertidal rocky shores include crevices, pits (Moreira et al. 2007, Martins et al. 2010), tidepools (Chapman and Blockley 2009, Firth et al. 2013b) and varied angle of surface  121  exposure (Goff 2010). These microhabitats create refuge from heat (Beck 2000), waves, currents and predation (Martins et al. 2010, Borsje et al. 2011, Firth et al. 2013a). Vertical substrates have been shown to relieve temperature-related stress in mussels by providing shade (Helmuth and Hofmann 2001). Tidepools provide refuge from heat and desiccation, which can increase in the vertical range of some species through the intertidal (Metaxas and Scheibling 1993, Chapman and Blockley 2009), house unique species assemblages, act as a place of congregation for individuals during low tides (Murray et al. 2006), and concentrate nutrient inputs from seabirds (Methratta 2004). Overall, tidepools and other microhabitats can provide a relatively constant environment, while increasing local heterogeneity and the potential for greater diversity of species (Chapman and Blockley 2009, Firth et al. 2013b).  Tidal elevation is a critical factor in how species assemble in the intertidal with vertical environmental heterogeneity significantly affecting species diversity and assembly (Benedetti-Cecchi 2001, Chapman and Blockley 2009, Wulff et al. 2009). Habitat engineers need to understand how each microhabitat (e.g., tidepool, horizontal surface and vertical surface) functions at varying tidal heights in order to determine where their inclusion may be most ecologically and cost effective. In the rocky intertidal of the North America’s Pacific Northwest, the diurnal tidal cycle changes up to five metres in a day and exposes intertidal organisms to a high degree of variation in environmental conditions, particularly higher up on the shoreline. Stress on individuals is created by having to cope to these constantly changing environmental conditions and exposure to extreme levels of salinity (Held and Harley 2009, Moffett et al. 2010), temperature (Somero 2002, Harley 2008), light (Bischof et al. 2007, Lee et al. 2007), nutrients (Lee et al. 2007, Mass et al. 2010), water motion (Blockley and Chapman 2008, Tam and Scrosati 2013) and predation (Yamada and Boulding 1996). Levels of  122  environmental stress are generally greater at higher tidal heights due to longer emersion time than lower in the subtidal where conditions are more constant (Thompson et al. 2002, Scrosati and Heaven 2007).  Predicting species diversity based upon tidal height alone is complex as elevation affects environmental stress, recruitment, competition and predation (Paine and Vadas 1969, Menge and Sutherland 1987, Bruno et al. 2003, Scrosati and Heaven 2007). Historically, the intermediate disturbance theory (Grime 1973) and environmental stress models (Menge and Sutherland 1987) have predicted a unimodal distribution of species with low richness where stress (a type of disturbance) is high or low. In intertidal ecology, this is represented by high environmental stress driving low species diversity in the upper intertidal, and competitive exclusion by a dominant species or heavy grazing reducing basal cover in the low intertidal. More recent interpretations of this theoretical model (Bruno et al. 2003, Scrosati and Heaven 2007) incorporate positive interactions, while also considering less dominant and mobile species (other than just basal or sessile species) resulting in predictions of low species diversity in the most stressful environments (i.e., high intertidal), and high diversity in low stress environments (i.e., low intertidal and subtidal). It is important to verify this model in Vancouver Harbour because if a more constant environment at lower tidal heights leads to reduced variation in species assemblages among microhabitats, then their function as a refuge from stressful conditions may be reduced. If specialized microhabitats do not contribute to species diversity or create habitat required for target species, then habitat engineers may limit microhabitat design to the higher tidal levels where they serve a greater ecological function in reducing environmental stress.  This study examined intertidal species richness, diversity and assemblage recruitment  123  through the first three years of development and tested how four engineered microhabitats vary in their ecological effectiveness among each other and with tidal height. Based upon a review of the literature, I hypothesize that:   (1) Species richness within microhabitats will increase at lower tidal elevations due to reduced overall environmental stress from exposure during low tide; except tidepools, which are expected to maintain a more constant environment across tidal heights and should have similar species richness across all tidal elevations; (2) Species richness among the four microhabitats of the same tidal height will be: a) greatest in the tidepools at the mid- to high-tidal heights (above the mean water level of 3.0 m CD) as they provide a more constant or low stress environment during periods of low tidal levels; at low tidal heights (below the mean water level of 3.0 m CD), as environmental stress related to exposure is reduced, species richness among microhabitats of the same tidal height will be equal; and b) the outer microhabitats will have greater richness than the inner microhabitats resulting from higher sun exposure and a more diverse macroalgal cover; (3) Species assemblages among the four microhabitats will be significantly different due to fine-scale differences in light exposure and water retention; variation in species assemblages among microhabitats will be greatest at mid to high tidal levels where emersion times and environmental stress related to desiccation, water motion and differences in light exposure are assumed to be greater and adding microhabitats such as tidepools should have the most effect on reducing stress; and (4) Changes in species diversity and variation are expected to decrease with greater time to recruit to the structure and as perennial species become established, increase in size and reduce opportunity for annual species to colonize.   124  4.3 Study area and methods As described in Chapter 2, the study was conducted at the Vancouver Convention Centre West in Vancouver Harbour, British Columbia between 2008 and 2011 (Figure 3.1). The main structure was the engineered intertidal habitat compensation structure referred to as the “Habitat Skirt” (HS) that forms the marine perimeter of the Vancouver Convention Centre (VC). Detailed descriptions of Vancouver Harbour and the Habitat Skirt are presented in Sections 2.1.1.and 2.1.1.1. The sample design used in this study is described in Section 2.2.1, while simple and multivariate statistical methods used in the analyses are presented in detail in Sections 2.2.3.  4.4 Results  4.4.1 Species diversity Total species richness at the Habitat Skirt including all microhabitats was 67 taxa (Table 4.1). Thirty-three taxa were observed in 2009, one year after installation, and this increased with time to 45 and 46 in 2010 and 2011, respectively. Among the four microhabitats at the HS, the vertical face had the greatest species richness with 58 different taxa observed across all years and an annual peak of 38 in 2011 (Table 4.1). The tidepools had the second most taxa observed (42), while the inner and outer horizontal habitats had least. Total species richness within all habitats increased between 2009 and 2011, indicating recruitment of new species to the Habitat Skirt with time. The rate of new recruitment decreased from 33 new taxa in the first year, to an additional 12 taxa after the second year and only one new taxa in the third year.  Species richness was predicted to increase with decreasing tidal height (i.e., from 4 m  125  CD to 0 m CD) across all microhabitats, except the tidepools. Regression analyses show a consistent and significant overall trend of increasing species richness from 3.7 ± 3.9 (95% CI) at 4 m CD to 11.0 ± 3.9 (95% CI) at 0 m CD (F1,58 = 34.004, P < 0.001). Within each microhabitat, significantly higher species richness were observed at lower tidal elevations for the inner, outer (F1,13 = 10.324, P = 0.007 for both), and vertical habitats (F1,13 = 35.392, P < 0.001), but not tidepools (F1,13 = 10.324, P = 0.265), which exhibited no change (Figure 4.1). These results support the hypothesis that species richness would be similar across tidal heights within the relatively constant environment of the tidepools; while negatively correlated to increasing tidal height and increasing environmental stress in adjacent microhabitats. Two hypotheses were proposed for differences among microhabitats. The first was that tidepools, with more favorable environmental conditions, would have greater species richness than the other microhabitats at mid- to upper intertidal elevations where environmental stress is assumed to be high. Significantly higher species richness among habitats were only observed in tidepools at 4 m CD (F = 4.757, P = 0.035) (Figure 4.2.e) with a mean of 9.3 ± 3.1 species versus 3.7 ± 3.1 for inner habitats, 3.0 ± 3.1 for outer habitats and 4.3 ± 3.1 for the vertical face. I hypothesized the outer habitat would have greater species richness than the inner habitat due to more sun exposure resulting in a greater diversity of macroalgae (see Section 4.2); however, this pattern was not observed at any of the tidal heights (Figure 4.2 a-e). However, Figure 4.2.a does show that the vertical habitat to have higher species richness than the other microhabitats at 0 m CD, the lowest tidal level. Results of the main ANOVA test found no significant differences among all microhabitats (F = 3.239, P = 0.082), although pairwise differences based on LSD were significant for all three comparisons of the vertical  126  face with the other microhabitats (mean species ± 95% CI: vertical = 19.3 ± 5.0, inner = 11.0 ± 5.0, outer = 12.0 ± 5.0 and tidepool = 11.7 ± 5.0). Overall, results partially support my hypothesis that tidepools would have the greatest species richness among microhabitats near the mean water level (3.1 m CD) and higher, but the outer and inner habitats were not observed to have significantly different species richness based upon assumed differences in sun exposure.   4.4.2 Species composition among habitats  Species composition among the four microhabitats was hypothesized to differ at each tidal height, with overall variation decreasing with tidal height and assumed levels of environmental stress.  A three-factor perMANOVA tested for significant differences among habitat (Ha), years (Ye) and tidal heights (He) and for any significant interactions among these terms. Bray-Curtis similarities of species percent cover within intertidal assemblages showed significant higher order interactions among habitats, tidal heights and years (Ha x Ye x He) (Table 4.2). When significant multivariate interactions are identified, pairwise comparisons are suggested to identify relationships between factors (Anderson 2010). Pairwise tests for habitat were analysed within the Ha x Ye x He interaction (P < 0.001) at each of the five tidal heights in each of the three years (Appendix B.1).  Heterogeneity in species assemblages among habitats at varying tidal heights was measured by the number of significantly different pairs of habitats (P ≤ 0.05) (Appendix B.1) and the Bray-Curtis distance between and within habitats (Table 4.3; Appendix B.1). The number of significantly different post-hoc test results increased with tidal height from 0 m CD  127  through 1 and 2 m CD, and was highest at 3 and 4 m CD (Figure 4.3). At 0 m CD, only 22% of post-hoc tests were significant, with all differences among habitats in 2010 and with the vertical face. At 1 m CD, 44% of post-hoc tests were significant including the inner and vertical habitats in 2009 and the vertical face with each other habitat in later years. At 2 m CD, 39% of post-hoc tests were significant mainly due to the vertical habitat. The greatest number of pairwise differences occurred at 3 m CD (78%) due to differences with the vertical face in 2009 and 2010 and tidepools in 2011. Finally, at 4 m CD, 72% of post-hoc tests were significant with tidepools differentiating from all other habitats. These results indicate increasing variation among microhabitats with higher tidal elevation.  Trends in similarity among microhabitats by tidal height were further examined using the distance between the multivariate centroid positions (Appendix B.1) for all samples at each location-tidal height-year combination (n = 240). Bray-Curtis distances among microhabitats exhibited changes over both tidal height and time (Table 4.3; Appendix B.1). In 2009, the mean distance among microhabitats was greatest at 4 m CD (44.0) and decreased with each tidal height to 0 m CD (24.6). This pattern reversed by 2011 when the mean distance among microhabitats was greatest at 0 m CD (48.6) and decreased to 4 m CD (29.5).  Increases in variation appear consistent between 2009 and 20111 at 0 and 1 m CD with the greatest variation involving pairwise comparisons with the vertical face. At 2 m CD and higher, mean among habitat variation was lower in 2011 than in 2009 with the greatest between habitat BC distances including tidepools and vertical face. Earlier results support the hypothesis of increasing variation at higher tidal heights, but not by 2011.  To better understand how biota contributed to variation among microhabitats, species were grouped into ecological functional groups based on phylogeny and similarity of life- 128  history traits. Main differences in the presence of functional groups among horizontal habitats (inner, tidepool, and outer) in 2009 and 2010 were Bivalvia and Chlorophyta (Tables 4.4; Appendices B.2 and B.3). By 2011, Bivalvia remained the most abundant species, but decreased from between 55 and 59% cover to between 43 and 46% cover; while diatoms became the second most abundant functional group. Over all tidal heights, greater macroalgae abundance on the outer habitat versus the inner habitat was not observed as hypothesized; however, differences were significant at 3 m CD, where F. distichus was expected to grow in 2010 (P = 0.028) and marginally non-significant in 2011 (P = 0.059). Dissimilarities among the vertical face and all horizontal habitats were distinguished mainly by a high abundance of Bivalvia and Arthropoda (B. glandula) on the vertical face. Between 2009 and 2011, Chlorophyta remained abundant in tidepools, while Porifera became more abundant on the vertical face. Invertebrate groupings Echinodermata, Porifera and Tubeworms (Order Sabellida) appear in 2010 and more so in 2011. In most cases, an increasing number of phyla contributed to the overall discrimination process among habitats from 2009 to 2011.  4.4.3 Species composition within habitats  Within microhabitat Bray-Curtis distances generated from pairwise tests were examined to gain insight into the degree that species assemblages varied by tidal height, habitat and year (Table 4.3; Appendix B.1). In general, Bray-Curtis distances within microhabitats increased with time (between 2009 and 2011), and decreased with higher tidal elevations indicating lower variation. Functional group specific contributions to within-habitat similarity across all tidal heights, years and locations showed that Bivalvia was dominant (Table 4.5), although this decreased by 2011. Variation in within-habitat similarity may be attributed to  129  sub-dominant taxa, as the presence of Bivalvia remained relatively constant with time. Changes in sub-dominant taxa within the inner habitat included an increase in Diatoms and Arthropoda. The tidepools had a lower percent of Bivalvia than the inner habitats, but Chlorophyta again made a large contribution along with Diatoms. The only sub-dominant groupings on the outer habitat in 2009 were Arthropoda and Phaeophyta, as Diatoms, Chlorophyta, Gastropods and Echinodermata recruited gradually over the next two years. Finally, Bivalvia and Arthropoda dominated the vertical face with Chlorophyta in 2009 and Echinodermata recruiting between 2009 and 2012.  4.4.4 Species composition among time periods Differences in species composition of the microhabitats among years were tested between time periods by examining perMANOVA pairwise differences of year within the habitat-year-height interaction term (Appendix B.1). It was hypothesized (see Section 4.2) that differences within species composition and diversity would differ by year, but that these differences would decrease with time. Species diversity was already observed to increase by year; however, at a slower rate each year, with only one new taxa added in 2011 (Table 4.1).  Early community assembly patterns at the 4 m level (Figure 4.4 a) indicate that all microhabitats changed significantly between 2009 and 2011, but only tidepools were different between 2010 and 2011 (Appendix B.1). Tidepools and vertical habitats both support different assemblages from those on the inner and outer habitats (Figure 4.4 a; Table 4.3). The inner and outer habitats were dominated by bare space with very little change over the three-year period, while the vertical habitats exhibited typical high-intertidal assemblage composition dominated by Balanus species and Littorina species.  The tidepools were distinct from the  130  other habitats due to dominance of U. intestinalis in all years and an increasing abundance of limpet species in 2011.  The 3 and 2 m CD habitats had considerably different community dynamics than the lower intertidal benches (Figures 4.4 b, c). The three metre habitats varied significantly among all years (Appendix B.3); except the outer habitats, which maintained composition of either M. trossulus or F. distichus. The tidepool and inner habitats changed in species composition between 2009 and 2011 from a pioneering assemblage of Ulva spp. to either M. trossulus or a mixture of both. At the two metre tidal height, species composition did not change significantly among years (Appendix B.3) and remained dominated by M. trossulus throughout the full study period.  Intertidal assemblages at 1 m CD showed different patterns from those at 0 m CD (Figures 4.4 d, e), potentially due to a lower abundance and predation by Echinodermata (Appendix B.2). Within tidal heights, only the inner habitat at 1 m CD changed significantly in the last year (Appendix B.1). The vertical habitat in 2009, already distinct from the other habitats, changed very little over the next two years remaining a Mytilus-Balanus dominated assemblage (Figure 4.4 d, e). The largest transition in species composition occurred between 2010 and 2011 within the horizontal habitats that gained in Chlorophyta (Tables 4.3 and 4.4). The 1 m CD benches maintained a 65% similarity among all habitats and years, while the 0 m CD benches exhibited two distinct states displayed by the 65% similarity contours, indicating a transition from a kelp dominated assemblage to an invertebrate dominated between 2009 to 2010 (Figure 4.4 e).    131  4.4.5 Functional group composition with time Analyses of algal cover indicated Chlorophyta had only a minor presence other than U. intestinalis, which dominated the tidepools at 4 m CD (Appendix B.2). In 2011, the outer habitats and tidepools of the 1, 2 and 3 m CD benches all increased in U. lactuca, likely due to its ability to rapidly colonize disturbed patches exposed to high levels of sunlight within the mussel dominated assemblages. Phaeophyta contained a mix of annual (N. luetkeana) and perennial (F. distichus var. evanescens, S. latissima) species that exhibited large changes in abundance between 2009 and 2011 (Appendix B.2). Phaeophyta decreased from a peak of 23% cover in 2009 to being almost absent across the lower tidal elevations in 2011. Conversely, Phaeophyta increased from five to 25 percent on the three metre benches due to F. distichus var. evanescens. Rhodophyta increased in most habitats between 2009 and 2010, slowly establishing on the two and three metre benches (Appendix B.2). Sporadic outbreaks of greater than 10 percent cover of filamentous species of Hollenbergia species, Polysiphonia species, and Microcladia borealis were observed on the vertical, inner and outer habitats. Colonial diatoms dominated where there was little water movement and otherwise uncolonized substrate in the horizontal and tidepool habitats (Appendix B.3).  Sessile invertebrates mainly consisted of Arthropoda and Bivalvia. Arthropoda was dominated by barnacles (B. glandula) throughout the tidal range, with Chthamalus dalli also co-occurring on the upper benches and Balanus crenatus on the lower benches. Spatial cover of Arthropoda was relatively sparse on the low intertidal benches (Appendix B.2), with the highest cover on the vertical habitat of the 2 and 3 m CD benches in 2010 (39.6) and 2011 (26.5). By 2011, the inner and outer habitats of the 2 and 3 m CD benches also had significant increases in cover of M. trossulus. Bivalvia were almost exclusively M. trossulus that  132  dominated most habitats of the 0 through 3 m CD benches (Appendix B.2). By 2011, abundance of M. trossulus on the horizontal habitats of the 0 m CD benches appeared to decrease by nearly half; which negatively correlates with the colonization and increase in the echinoderms Strongylocentrotus droebachiensis, Evasterias troschelii, and Pisaster ochraceus (Appendix B.3). Generally, M. trossulus was found growing over a layer of Balanus species that often made estimating percent cover difficult due to the non-destructive nature of the sampling method. The two main groups of mobile invertebrates were Gastropoda and Echinodermata.  Gastropoda consisted mainly of the grazers Lottia species, Tectura species, and Littorina species, while S. droebachiensis and E. troschelii were the main grazing and omnivorous echinoderms observed. Echinodermata were near absent in 2009 (Appendix B.2), increasing in abundance by 2011 on the 0 m CD benches, particularly within the vertical and outer habitats. Pisaster ochraceus was generally not detected within sample quadrats, but was observed within crevices between benches of the zero metre tide level, likely to avoid desiccation through exposure to the sun and predation by birds. Gastropoda were observed on the two, three and four metre benches in all years, and the one metre bench in 2011 (Appendix B.2). The greatest increase in Gastropoda was on the 4 m CD bench by 2011 across all habitats and most prominent on the cooler northern aspect. Gastropoda were also positively correlated with bare space, likely due to the presence of microalgae that grow rapidly, in moist, open locations and form a main component of their diet.  In addition to species abundances, a major contributor to the differences in species assemblages and richness was bare space (Appendix B.2), which increased from 25.6 percent cover in 2009 to 34.6 in 2011. Bare space had significant positive correlations with increasing  133  tidal height in 2009, 2010, and 2011 (Appendix B.3) and Gastropoda (2010); this supports the hypothesis of decreasing richness with tidal height. Negative correlations with bare space were observed with Bivalvia (2009, 2010, 2011), Arthropoda (2010) and Phaeophyta (2011). The greatest amount of bare space occurred on the four metre bench.   4.5 Discussion Species richness was consistently greatest at the 0 m CD level and decreased with elevation for all four microhabitats as predicted based on the species assemblage being driven by variation in environmental stress across the intertidal zone (Scrosati and Heaven 2007). Low species richness was predicted at high tide levels due to the extended exposure times to the air and higher environmental stress related to increased desiccation. This effect was considerably greater at the 4 m CD benches than at all other tidal heights, except in the tidepools which appeared to reduce, but not eliminate the magnitude of this effect. Richness at mid-tidal elevations was predicted, and observed, to have relatively high species richness similar to, but slightly lower than, the lowest benches. This is contrary to other studies in urban centres (Chapman and Blockley 2009, Lam et al. 2009) in Australia and China. Low tidal elevations consistently were observed to have high species richness. Although my predictions held true, by 2011 the abundance of M. trossulus on the lowest benches appeared to be reduced by predation (by P. ochraceus and E. troschelii) and disturbance (sloughing of aggregations due to wave action or log damage), while remaining relatively high at 1 and 2 m CD. Although I did not directly test for this, the presence of M. trossulus could be excluding species such as macroalgae at mid-tidal sites from the opportunity to recruit, where predation by P. ochraceus and E. troschelii appears to be low due to poor vertical migration corridors on the HS. Any future studies at the Habitat Skirt may try to mimic predation by excluding or removing M.  134  trossulus from mid-tidal areas intermittently to further examine this. Of the four microhabitats engineered into the HS, the vertical face had the greatest species richness, followed by the engineered tidepools. High species richness within the tidepools across all tidal heights is likely due to their ability to act as a refuge from desiccation and high temperature allowing macroalgae and grazing gastropods to extend their upward range and increase species richness relative to neighbouring horizontal and vertical habitats. In contrast to tidepools, one of the unique features of vertical structures is that they retain little water or sediment. At lower tide levels, where immersion time per tidal cycle is much longer, environmental stress due to desiccation and temperature is likely reduced allowing macroalgae, and a variety of both sessile and mobile invertebrates to persist or remain for longer periods of time. The transition in stress appears to occur at between the 3 and 2 m CD tidal elevations. This study does not suggest seawalls are a beneficial marine habitat on their own; however, results indicate that vertical surfaces are important for species diversity and abundance, particularly at the intertidal-subtidal transition zone. Vertical surfaces can act as a thermal refuge (Helmuth and Hofmann 2001), while supporting species such as sponges, tubeworms (Serpula spp.), sea urchins (S. droebachiensis), mussels (M. trossulus) and barnacles (Balanus spp.) able to tolerate a more energetic surface. These results indicate that a mixture of vertical and sloping habitats (Goff 2010) can create a diverse array of rockweed (F. distichus) and sessile invertebrate dominated assemblages. Tidepools in rocky shorelines have also been shown to act as a unique environment in marine intertidal ecosystems often supporting diverse species assemblages determined by tidal height, initial colonization (Dethier 1984), nutrient enrichment and predation by seabirds (Methratta 2004). At 4 m CD, tidepools were the only habitat to support macroalgae and  135  invertebrate species such as U. intestinalis, F. distichus, Tectura spp., Lottia spp., Caprella alaskana and M. trossulus in low abundance. Heat stress and wave reflection may explain why the horizontal surfaces remained mainly bare (Sheng 2000, Helmuth and Hofmann 2001). Tidepools in the mid-tidal heights allowed larger brown kelps, S. latissima and N. luetkeana, to extend their range above tidal levels that supported their consumers and higher than other sites including New Brighton Park and Harbour Green Park. Tidepools also had the greatest within-habitat variation. This supports other work that shows each tidepool can be relatively unique and structured by local processes such as recruitment and nutrient inputs (Metaxas and Scheibling 1993, Firth et al. 2013b). The engineered tidepools were sampled at low tide so mobile species were likely restricted from moving during that tidal cycle further increasing each tidepools uniqueness. Incorporating tidepools into marine intertidal habitats has varying results within each tide level; however, an increase of species diversity, particularly at higher tide levels is a beneficial result of their inclusion into enhanced structures.   A mix of sheltered and exposed horizontal surfaces was found to add overall structural complexity leading to a diversity of marine assemblages. Outer surfaces were typically covered with macroalgae including: F. distichus on the 3 m CD benches, U. lactuca and Porphyra species on the 2 m CD benches, and various red algae and laminarian kelps (N. luetkeana and S. latissima) on the lower benches. The inner habitats of the low tidal levels did not support any Chlorophyta, but did have a low diversity consisting of mainly barnacles, filamentous red algae, and colonial diatoms. Lack of Chlorophyta combined with the presence of red algae is consistent with predictions of low light availability due differences in photosynthetic pigments contained by each algal group. The lower horizontal habitats also had a high cover of bare space potentially indicating removal of species due to predation by E. troschelii, P. ochraceus,  136  S. droebachiensis and various birds including gulls (Larus glaucescens) and ducks (Bucephala islandica) (personal observation).  This result was not expected because bare space was predicted to decrease with increasing time for recruitment and growth in size. Overall, the inner and outer microhabitats did not differ in species richness, but did in species composition supporting the need for both richness and composition studies together in evaluating restoration areas. The initial planning for the HS incorporated vertical connectivity by using heavy gauge chains to join each tidal height with the seafloor allowing some mobile species to access the structure. One unexpected and important finding was the relationship among Phaeophyta (N. luetkeana and S. latissima), Echinodermata (S. droebachiensis, E. troschelii, P. ochraceus and P. helianthus), and Arthropoda (Metacarcinus magister and Cancer productus). In the early sampling periods prior to 2010, the 0 and 1 m CD benches were colonized with N. luetkeana and S. latissima, but declined to half the cover by the summer of 2010, when S. droebachiensis and E. troschelii were observed to have recruited to the 0 m CD benches with increasing densities. Successful colonization by N. luetkeana in the first year may be due to a delay in the recruitment of S. droebachiensis or possibly a preference for juvenile sea urchins to graze on microalgae versus larger kelps. There currently does not appear to be enough predation on S. droebachiensis to control the population on HS, as observed in adjacent fish compensation areas composed of riprap (EBA 2012a) or on the edges of the HS adjacent to the NE point (personal observations), which are both connected directly to the seafloor and were observed to support C. productus.  At the mid-tidal levels, the HS was dominated by M. trossulus, with Ulva-diatom assemblages where disturbed or bare patches appeared to have been created. This differs from  137  the riprap sites at HG and NB where Ulva-Mastocarpus or F. distichus assemblages were common (Chapter 3; EBA 2013). The difference in species assemblages may be the result of low predation in the mid-tidal heights on M. trossulus by E. troschelii, reduced surface salinity during the summer months leading avoidance behaviour by the echinoderms, greater water motion and low light levels at the HS when compared to HG and NB (Chapter 3), and high predation on the sea stars by gulls (personal observation). Although connectivity between tide levels is relatively frequent, with two vertical support structures connecting tide levels every six metres, adult P. ochraceus or E. troschelii were rarely observed at these tidal heights and adult crabs (C. productus or M. magister) would likely be unable to maneuver these narrow vertical structure at high tides. A salinity level of 20 psu, characteristic of the upper water column during the summer months in Vancouver Harbour, may be forcing P. ochraceus to remain at lower tide levels or migrate off the HS during seasons of low salinity; however, specimens from Vancouver Harbour have been shown to acclimatize to low salinity conditions and still feed normally (Held and Harley 2009). The high abundance of M. trossulus also may be the result of HS being exposed to relatively higher hydraulic forces due to its steep slope, flow-through design, and placement further out in the harbour (Chapter 3). These conditions likely favor M. trossulus over macroalgae (Leonard et al. 1998) when combined with the high degree of shading due to urban infrastructure that reduces the competitive ability of macroalgae, especially on the north aspect. Finally, observed predation by birds, specifically gulls, may be also be limiting E. troschelii and P. ochraceus at the mid- and upper tidal levels.  Temporal development among habitats relative to tidal height was examined with the ecological structure of each microhabitat changing with tidal height and time. Species richness increased from a simple biofilm in May 2008 (EBA 2009) to mixture of Ulva spp., juvenile M.  138  trossulus and Balanus spp in 2009, to a diverse species assemblage in 2011. The rate of change in species richness did slow and appears as a relatively stable community in this respect. The abundance of species does vary in the final year of the study, with locations three and four separating in the PCO ordination due to greater bare space and pioneering species such diatoms and U. lactuca. These clearings are likely the result of disturbance from log debris scraping mussels from the structure. Being able to monitor assembly patterns in clearings created in different years concurrently with undisturbed areas would provide stronger evidence for the repeatability of assembly patterns that may be unique to urban environments, rather than monitoring from a single installation time as was done in this study.  Although this study was necessarily limited to one engineered compensation site due to the cost and availability of replicating and installing the custom-made concrete intertidal benches, the importance of adding heterogeneity to the various tidal heights was demonstrated through the complex distribution of species among habitats, across tidal heights and through time. Some limitations of the research were the result of the restriction on experimental manipulation to the structure or destructive sampling, such as clearing or caging experiments, due to compliance with the regulatory process. Clearing of areas of the HS would have allowed for repeated observation of species assembly patterns. Exclusion cages for consumers such as E. troschelii and S. droebachiensis on the lowest tidal levels would have allowed observations on the effect of these species on N. luetkeana, S. latissima, and M. trossulus. Given that this was a new and untested design in coastal restoration, a control structure separate to, or integrated with, the HS with variation on the design, such as benches without tidepools, would have allowed us to better quantify the contribution of each microhabitat to the overall diversity and variation among species assemblages. Also, this study only analyzed the first three years  139  of biotic growth after the installation of the HS; and although there are indications that species assembly patterns and diversity stabilized, repeated sampling in the future would allow for greater more precise conclusions regarding the stability of the biotic assemblages over time and potentially identify recruitment time of species that not yet observed.   4.6 Conclusion This study shows how engineering a variety of intertidal microhabitats into a false shoreline can contribute to the restoration of a species rich and complex area. Among microhabitats on the Habitat Skirt, tidepools had the greatest species richness from 2 to 4 m CD where they can provide refugia from environmental stress during periods of low tide. Vertical habitats also act as a diverse environment, particularly at 0 and 1 m CD. Increasing environmental stress associated with longer emersion times at higher tidal elevations was negatively associated with species richness and variation among species assemblages of differing microhabitats on the HS; except tidepools which appear to act as refugia from stress in the upper intertidal zone.  Outer habitats were typically higher in abundance and diversity of macroalgae than the shaded inner habitats in later years when perennial macroalgae had time to recruit increase cover. On the lowest tide level of the Habitat Skirt, Phaeophyta assemblages were eliminated after the first year, likely due to overgrazing by S. droebachiensis, while the perennial F. distichus increased on the 3 m CD bench from year one through three. These changes in species composition over the three-year study emphasize the importance of multi-year studies to document early species assembly patterns and the time it takes for intertidal systems to reach an equilibrium or mature state. The ecological success of the Habitat Skirt further supports other studies that show the importance of enhancing human-made structures to improve ecological success in urban marine environments.   140  Table 4.1 Total number of taxa observed within and among the four microhabitats of the Habitat Skirt over a three year period (2009, 2010 and 2011). Totals represent the number of different taxa within each microhabitat or year across all microhabitats. Year Inner Tidepool Outer Vertical Total 2009 15 16 16 27 33 2010 21 30 22 33 45 2011 21 26 22 38 46 Total 36 42 34 58 67    141  Table 4.2 PerMANOVA main test results for differences in intertidal assemblages among habitats (Ha), years (Ye) and heights (He) at the Habitat Skirt in Vancouver Harbour. *** P ≤ 0.001; ** P ≤ 0.01; * P ≤ 0.05.  Source df SS MS Pseudo-F P(perm) Ha 3 26,855 8,952 27.6 < 0.001*** Ye 2 8,031 4,015 12.4 < 0.001*** He 4 238,480 59,620 183.9 < 0.001*** Ha x Ye 6 4,194 699 2.2 0.003** Ha x He 12 34,798 2,900 8.9 < 0.001*** Ye x He 8 22,143 2,768 8.5 < 0.001*** Ha x Ye x He 24 21,148 881 2.7 < 0.001*** Residual 180 58,344 324   Total 239 413,990          142   Table 4.3 Summary of Bray-Curtis similarity distances within and between habitats from pairwise perMANOVA post-hoc tests for habitat within the interaction term of tidal height, habitat, and year. Lower values indicate a greater similarity between habitats.  2009 2010 2011 0 m I P O V I P O V I P O V in 12.81 - - - 24.73 - - - 53.13 - - - pool 21.42 25.02 - - 22.40 15.82 - - 46.12 58.59 - - out 16.58 26.74 21.20 - 22.56 31.62 14.97 - 39.47 42.46 41.24 - vertical 22.91 34.55 25.60 27.71 60.29 66.60 54.20 27.44 55.04 54.87 53.42 40.40 1 m I P O V I P O V I P O V in 16.31 - - - 14.92 - - - 37.23 - - - pool 19.78 19.13 - - 13.42 13.13 - - 37.62 30.42 - - out 15.74 13.88 11.31 - 15.05 14.80 15.16 - 35.04 34.42 40.42 - vertical 30.95 23.11 22.05 7.07 27.33 30.14 28.33 15.37 39.46 49.73 45.84 18.70 2 m I P O V I P O V I P O V in 25.31 - - - 9.93 - - - 32.73 - - - pool 29.75 28.92 - - 93.78 8.59 - - 30.43 39.37 - - out 26.01 28.56 24.56 - 13.14 11.03 13.85 - 27.65 28.97 24.45 - vertical 49.93 54.21 44.55 3.99 26.15 26.62 24.66 24.67 25.30 30.72 24.89 14.73 3 m I P O V I P O V I P O V in 14.00 - - - 1.96 - - - 19.97 - - - pool 24.02 16.31 - - 9.97 10.21 - - 24.66 25.19 - - out 26.99 34.41 18.70 - 26.96 29.32 19.78 - 28.01 35.75 24.67 - vertical 61.42 67.75 41.30 17.68 40.45 42.34 25.96 4.36 39.89 46.92 30.33 21.10 4 m I P O V I P O V I P O V in 7.13 - - - 11.58 - - - 14.08 - - - pool 66.21 18.96 - - 45.17 20.35 - - 41.15 42.14 - - out 5.20 71.35 0.20 - 6.99 47.31 1.38 - 10.26 42.33 10.47 - vertical 25.57 70.28 25.19 21.95 9.49 46.71 4.48 3.87 20.10 45.93 17.15 16.97     143  Table 4.4 Average percent cover, average dissimilarity and percent contribution to dissimilarity of major functional groups among four microhabitats (i=inner horizontal; p=tidepool; o=outer horizontal; v=vertical face) across all tidal heights for 2009, 2010, and 2011.   2009     2010     2011   Species % Coverave δave % Species % Coverave δave % Species % Coverave δave % Groups i  &  p iave pave δavei,p = 48.1  iave pave δavei,p = 51.6  iave pave δavei,p = 56.38 Bivalvia* 59.7 55.3 18.1 37.6 Bivalvia* 54.57 52.2 20.4 39.5 Bivalvia 45.9 43.3 19.5 34.6 Bare* 30.5 23.2 14.2 29.5 Bare* 36.36 34.5 17.5 33.9 Bare 36.4 34.4 17.5 31.0 Chlorophyta 1.0 13.8 7.0 14.5 Chlorophyta 1.73 9.4 5.1 9.8 Diatoms 13.8 14.7 8.3 14.8 Diatoms 4.1 6.6 4.4 9.1 Diatoms 6.35 5.9 4.4 8.5 Chlorophyta 1.7 8.7 4.4 7.7 Phaeophyceae 3.1 2.5 2.4 4.9 Arthropoda 2.51 1.8 1.7 3.2 Arthropoda 8.4 5.2 4.2 7.4 Arthropoda 3.2 0.9 1.8 3.8 Rhodophyta 1.66 0.9 1.1 2.1 Phaeophyceae 0.2 3.0 1.3 2.3      Phaeophyceae 0.25 2.0 1.0 1.9 Gastropoda 0.3 0.5 0.6 1.1 Groups o  &  p pave oave δaveo,p = 40.9  pave oave δaveo,p = 51.7  pave oave δaveo,p = 57.2 Bivalvia* 55.3 59.2 17.1 35.0 Bivalvia* 52.15 55.3 19.2 36.2 Bivalvia 43.3 44.9 19.2 33.6 Bare 23.2 29.6 14.2 29.0 Bare* 34.47 35.2 17.2 32.3 Bare 34.4 35.4 17.3 30.3 Chlorophyta 13.8 1.5 7.0 14.2 Chlorophyta 9.36 2.2 5.0 9.4 Diatoms 14.7 10.2 7.7 13.4 Arthropoda 0.9 7.7 3.9 7.9 Arthropoda 1.77 9.6 4.3 8.0 Chlorophyta 8.7 5.3 5.1 8.8 Diatoms 6.6 2.1 3.7 7.5 Diatoms 5.9 5.4 4.0 7.6 Arthropoda 5.2 7.7 3.9 6.7 Phaeophyceae 2.5 3.8 2.5 5.1 Phaeophyceae 1.96 4.1 2.3 4.3 Phaeophyceae 3.0 3.2 2.3 3.9 Rhodophyta 0.4 0.9 0.6 1.1 Rhodophyta 0.91 0.8 0.7 1.4 Rhodophyta 0.8 1.4 1.0 1.7 Groups i  &  o  iave oave δavei,o = 46.7   iave oave δavei,o = 53.1   iave oave δavei,o = 56.9 Bivalvia* 59.7 59.2 17.7 38.0 Bivalvia* 54.57 55.3 19.4 37.6 Bivalvia* 45.9 44.9 19.6 34.4 Bare 30.5 29.6 17.5 37.3 Bare* 36.36 35.2 18.5 35.8 Bare* 36.4 35.4 19.0 33.4 Arthropoda 3.2 7.7 4.4 9.5 Arthropoda 2.51 9.6 4.4 8.6 Diatoms 13.8 10.2 7.6 13.4 Phaeophyceae 3.1 3.8 2.7 5.9 Diatoms 6.35 5.4 4.4 8.5 Arthropoda 8.4 7.7 5.0 8.8 Diatoms 4.1 2.1 2.7 5.9 Phaeophyceae 0.25 4.1 1.8 3.5 Chlorophyta 1.7 5.3 2.8 4.9 Chlorophyta 1.0 1.5 1.1 2.4 Chlorophyta 1.73 2.2 1.7 3.3 Phaeophyceae 0.2 3.2 1.4 2.4 Rhodophyta 0.1 0.9 0.5 1.0 Rhodophyta 1.66 0.8 1.0 2.0 Rhodophyta 0.6 1.4 0.9 1.6   144  Table 4.4 (continued).   2009     2010     2011   Species % Coverave δave % Species % Coverave δave % Species % Coverave δave % Groups i  &  v iave vave δave = 57.1  iave vave δave = 56.6  iave vave δave = 59.0 Bivalvia* 59.7 58.8 18.5 32.4 Bivalvia* 54.6 65.4 18.1 31.9 Bivalvia* 45.9 66.7 21.3 36.1 Bare 30.5 19.1 16.2 28.4 Bare* 36.4 24.9 17.4 30.8 Bare* 36.4 21.3 17.4 29.4 Arthropoda 3.2 38.1 15.4 27.0 Arthropoda* 2.5 39.6 14.1 25.0 Arthropoda* 8.4 26.5 10.0 17.0 Phaeophyta 3.1 2.4 2.2 3.8 Diatoms 6.4 0.1 2.7 4.7 Diatoms 13.8 0.6 5.9 10.0 Diatoms 4.1 0.1 1.9 3.3 Rhodophyta 1.7 1.6 1.2 2.0 Chlorophyta 1.7 0.9 1.0 1.7 Chlorophyta 1.0 2.8 1.8 3.1 Chlorophyta 1.7 0.4 0.8 1.4 Phaeophyta 0.2 2.1 0.8 1.4 Rhodophyta 0.1 2.0 0.9 1.6 Tunicata 0.1 1.2 0.5 0.9 Porifera 0.0 1.3 0.6 1.1      Phaeophyta 0.3 1.3 0.5 0.9 Echinodermata 0.1 1.1 0.6 1.0           Rhodophyta 0.6 0.8 0.6 0.9 Groups p  &  v pave vave δave = 60.2  pave vave δave = 59.0  pave vave δave = 61.5 Bivalvia* 55.3 58.8 18.1 30.1 Bivalvia* 52.2 65.4 18.0 30.5 Bivalvia* 43.3 66.7 21.5 34.9 Arthropoda 0.9 38.1 15.5 25.7 Bare* 34.5 24.9 16.5 27.9 Bare* 34.4 21.3 15.8 25.7 Bare* 23.2 19.1 13.5 22.5 Arthropoda* 1.8 39.6 14.2 24.0 Arthropoda* 5.2 26.5 9.8 15.9 Chlorophyta 13.8 2.8 6.9 11.5 Chlorophyta 9.4 0.4 4.1 6.9 Diatoms 14.7 0.6 6.2 10.0 Diatoms 6.6 0.1 2.9 4.9 Diatoms 5.9 0.1 2.5 4.2 Chlorophyta 8.7 0.9 3.8 6.2 Phaeophyta 2.5 2.4 1.9 3.2 Phaeophyta 2.0 1.3 1.1 1.9 Phaeophyceae 3.0 2.1 1.7 2.8 Rhodophyta 0.4 2.0 1.0 1.7 Rhodophyta 0.9 1.6 0.9 1.5 Echinodermata 0.4 1.1 0.6 1.0      Tunicata 0.3 1.2 0.6 1.0 Porifera 0.0 1.3 0.6 1.0 Groups o  &  v oave vave δave = 55.9  oave vave δave = 55.3  oave vave δave = 60.1 Bivalvia* 59.2 58.8 17.9 32.0 Bivalvia* 55.3 65.4 17.0 30.8 Bivalvia* 44.9 66.7 21.3 35.5 Bare 29.6 19.1 15.9 28.5 Bare 35.2 24.9 16.9 30.5 Bare* 35.4 21.3 17.1 28.4 Arthropoda 7.7 38.1 15.6 27.8 Arthropoda* 9.6 39.6 14.0 25.2 Arthropoda* 7.7 26.5 10.1 16.9 Phaeophyta 3.8 2.4 2.3 4.1 Diatoms 5.4 0.1 2.2 3.9 Diatoms 10.2 0.6 4.4 7.3 Chlorophyta 1.5 2.8 1.9 3.4 Phaeophyta 4.1 1.3 1.8 3.3 Chlorophyta 5.3 0.9 2.3 3.8 Rhodophyta 0.9 2.0 1.2 2.1 Chlorophyta 2.2 0.4 1.0 1.8 Phaeophyta 3.2 2.1 1.8 3.0 Diatoms 2.1 0.1 0.9 1.7 Rhodophyta 0.8 1.6 0.9 1.5 Rhodophyta 1.4 0.8 0.9 1.5      Tunicata 0.0 1.2 0.5 0.9 Echinodermata 0.7 1.1 0.8 1.3           Porifera 0.0 1.3 0.6 1.0 *= δave/SD > 1 which may indicate a candidate discriminating species (Clarke & Warwick 2001)  145  Table 4.5 Average percent cover (Cover), average Bray-Curtis similarity (BCave) and percent contribution (%) of major functional groups to within habitat similarity for 2009, 2010 and 2011. 2009 2010 2011 Species Cover BCave % Species Cover BCave % Species Cover BCave %     Inner Habitat     Bivalvia* 59.7 39.9 74.3 Bivalvia* 54.6 31.9 63.9 Bivalvia 45.9 22.1 49.6 Bare 30.5 12.4 23.1 Bare 36.4 16.1 32.2 Bare 36.4 15.9 35.8 Arthropoda 3.2 0.5 0.9 Diatoms 6.4 1.2 2.5 Diatoms 13.8 4.3 9.5 Diatoms 4.1 0.4 0.8 Arthropoda 2.5 0.5 1.0 Arthropoda 8.4 2.1 4.6 Phaeophyta 3.1 0.4 0.8             Tidepool Habitat     Bivalvia* 55.3 36.8 67.3 Bivalvia 52.15 28.5 59.1 Bivalvia* 43.3 19.8 44.5 Bare 23.2 13.4 24.5 Bare 34.5 16.4 33.9 Bare 34.4 16.6 37.2 Chlorophyta 13.8 23.5 4.6 Diatoms 5.90 1.6 2.7 Diatoms 14.7 4.7 10.6 Diatoms 6.6 1.7 3.1 Chlorophyta 9.36 1.3 0.5 Chlorophyta 8.7 1.6 3.5         Arthropoda 5.2 1.5 3.3     Outer Habitat      Bivalvia* 59.2 39.6 74.3 Bivalvia* 55.3 30.5 64.1 Bivalvia 44.9 21.4 50.5 Bare 29.6 11.4 21.4 Bare 35.2 14.2 29.8 Bare 35.4 15.2 35.8 Arthropoda 7.7 1.4 2.6 Arthropoda 9.6 1.5 3.1 Diatoms 10.2 2.6 6.0 Phaeophyta 3.8 0.5 1.0 Diatoms 5.4 0.7 1.5 Arthropoda 7.7 1.7 3.9         Chlorophyta 5.3 1.2 2.8         Phaeophyta 3.2 0.3 0.6     Vertical Habitat     Bivalvia 58.8 29.5 65.3 Bivalvia* 65.4 32.4 63.3 Bivalvia* 66.7 35.4 68.2 Arthropoda 38.1 11.2 24.7 Arthropoda 39.6 11.9 23.2 Arthropoda* 26.5 10.3 19.8 Bare 19.1 4.0 8.9 Bare 24.9 6.4 12.6 Bare 21.3 5.5 10.5 Chlorophyta 2.8 0.2 0.5     Echinodermata 1.1 1.0 1.4 *= Save /SD > 1 indicates a candidate indicator taxon (Clarke & Warwick 2001)  146    Figure 4.1 Scatterplots illustrating regression analyses of species richness by tidal height (m above CD) for: a) inner habitat, b) tidepool habitat, c) outer habitat, and d) vertical face in 2009, 2010, and 2011 (n = 15). Note: some values may appear to be missing due to overlap.   a)c)b)d) 147   Figure 4.2 Bar plots illustrating  mean species richness ± 1 SE for 2009, 2010, and 2011 (n = 12) by microhabitat at: a) 0 m, b) 1 m, c) 2 m, d) 3 m, and e) 4 m CD.   a) 0 m b) 1 mc) 2 m d) 3 me) 4 m 148   72% 4 m - inner and outer habitats similar, differences with tidepool  78% 3 m - all habitats different in 2010, 5 of 6 in 2009,    39% 2 m - all differences with vertical habitat   inner and outer habitats similar  44% 1 m – all horizontal habitats similar,  all differences with vertical habitat.  22% 0 m - all horizontal habitats similar,   all differences in 2010              Heterogeneity in Species Assemblages Figure 4.3 Summary of the number of significant (P < 0.05) post-hoc pairwise differences between microhabitats by tidal level when tested against the habitat - year interaction term.  149  Figure 4.4 Principal coordinate ordination plots illustrating temporal trajectories of the centroid position of species assemblages sampled within each of four microhabitats (inner = black triangle, tidepool = inverted blue triangle, outer = green square, and vertical = red diamond) at five intertidal heights (4, 3, 2, 1 and 0 metres above CD) over three years (2009=9, 2010=10 and 2011=11).  a)  150  Figure 4.4 (continued).  b)  151   Figure 4.4 (continued).  c)  152     Figure 4.4 (continued).  d)  153   Figure 4.4 (continued).   e)  154   RESTORE - a new rapid assessment tool for evaluating the long-term sustainability of restoration areas using ecological, social and economic attributes  155  Summary A defining goal of ecological restoration is the long-term sustainability of biodiversity within restored areas. Deficiencies identified with many restoration projects include insufficient monitoring to inform adaptive management; also monitoring programs typically only include ecological attributes, ignoring the socio-economics benefits.  This study develops a rapid assessment tool, RESTORE, for assessing the long-term sustainability of restoration areas using ecological, social and economic attributes and applies various analyses to identify: (1) how attributes affect sustainability; (2) whether ecosystem type contributes to sustainability; and (3) if larger restoration areas have higher sustainability than smaller.  RESTORE modifies the ‘Rapfish’ tool (Pitcher and Preikshot 2001) by developing 25 new attributes within five evaluation fields. I use RESTORE in a pilot study to assess 11 restoration areas within marine, estuarine and freshwater ecosystems in Vancouver, British Columbia. Scores from RESTORE are analyzed using a similarity percentage analysis to quantify how attributes contribute to sustainability, analysis of similarities (ANOSIM) to compare sustainability among ecosystems, and regression analysis to evaluate if sustainability of restoration areas improves with increasing area. Results from RESTORE produce comparable findings to the regulatory process in Canada and another Rapfish study. Attributes that contributed most to sustainability were: protection of restoration areas, project management, and presence of functional groups for marine ecosystems; habitat and carbon banking, using local species and genotypes, and age class distribution for estuarine areas; and monitoring, maintenance, and the presence of indicator species for freshwater areas. Although replication was low in this case study, ecosystems scored significantly different (ANOSIM: R = 0.24, P = 0.039) from each other with marine restoration areas scoring higher for overall sustainability than estuarine  156  ecosystems (ANOSIM: R = 0.32, P = 0.029). An ANCOVA showed no significant relation of area to ecosystem (F2, 10 = 1.951, P = 0.212), or area on overall score after controlling for ecosystem (F1, 10 = 0.047, P = 0.835). The RESTORE tool was successful in scoring and comparing a broad range of ecological, social and economic attributes to assess the long-term sustainability of restoration areas across ecosystems to inform future adaptive management and resource management.    157  5.1 Introduction Coastal area development has led to a large increase in ecological restoration activity due to legislated requirements for compensation of altered habits or due to concerned stakeholders wanting to enhance their natural resources (Harper and Quigley 2005, Quigley and Harper 2006, DFO 2012a). A defining goal of ecological restoration is the long-term sustainability of the natural or desired state of an ecosystem (SER 2004, DFO 2013a). One shortfall identified with many restoration projects includes not enough practical application of monitoring for the purposes of informing adaptive management (UNEP 2011). Reasons identified for inadequate monitoring include: lack of initial long-term planning, not securing long-term funding, infrequent monitoring, expensive or slow assessments and delayed or absent incorporation of adaptive management (Lundquist and Granek 2005, Quigley and Harper 2006, UNEP 2011). One solution is the incorporation of assessments that are performed in a rapid (i.e., days to weeks) and cost-effective manner (Djojhlaf and Bridgewater 2006). Rapid assessments have been used to provide critical information in biological assessments (McKenna et al. 2002), marine protected areas (Alder et al. 2002), and fisheries management (Pitcher and Preikshot 2001, Pitcher et al. 2013).  Further to the deficiencies identified in monitoring programs, many effectiveness assessments of restoration areas (RAs) typically focus only on quantitative ecology and exclude social attributes that may be value-based rather than numerical (Aronson et al. 2010). Social attributes identify how stakeholders value their local environment and builds consensus and long-term support for projects; all of which can result in greater protection, monitoring and more effective management (Davis 2005, Lundquist and Granek 2005). Social values have been incorporated into evaluation tools for fisheries (Pitcher and Preikshot 2001) and marine parks (Heck and Dearden 2012); frameworks for sustainability of socio-ecological systems  158  (Ostrom 2009); environmental assessments in Canada (CEAA 2014); and comprehensive reviews for restoration ecology (Egan et al. 2011). As developers continue to encroach on natural environments, restoration areas are sited within urban centres where social values become more intertwined with restoration attributes, including how they are assessed. Ensuring there is a tool to allow a process for ecological, social and economic evaluation can help to enhance the understanding of broader benefits of restoring nature (Schultz et al. 2012). The benefits of incorporating economic attributes in restoration assessments include maximizing regional ecological value and sustainability through managing the costs and benefits of projects (McClanahan 1999) and gaining support from the public by incorporating a common metric for valuing nature that the public understands (Farber et al. 2002). Unfortunately, habitat managers currently tend to give little weight to attributes other than ecological (Costanza et al. 1997, DFO 2012a). Successfully restoring nature is a complex multidisciplinary process that involves not only biologists, but also hydrogeologists, civil engineers, construction crews and volunteers, public approval and funding sources (MWLAP 2002, Roman and Burdick 2012). Generally, nature can be restored in most situations, but at what cost and effort? Regulating restoration at any cost is not likely the best approach and may eventually lead to public outcry against high costs and seemingly inefficient use of available finances. Conversely, restoration has many economic and social benefits to local communities that go unaccounted, including short- and long-term employment, direct and indirect spending on equipment, food and lodging, and post-construction enjoyment for recreational purposes (Nielsen-Pincus and Moseley 2013). Tools that assess economic attributes associated with RA projects should improve future restoration works by identifying risks to the financial and managerial aspects of RA construction and maintenance, while potentially building greater  159  acceptance by the public by educating them on the economic benefits of these projects.  Habitat compensation in Canada (prior to recent changes in the Fisheries Act, November 25, 2013) dictates that habitat restoration primarily takes place near the development using similar habitat (i.e., marsh destroyed must be restored with marsh). In many instances, these habitat alterations are small in scale (less 5,000 m2, Hemmera 2014b), restored in areas where current and future development will lead to greater fragmentation (i.e., urban centres) and may not be able to support target species as desired. Assessing how scale affects restoration or conservation value has been examined for decades with a consensus that bigger is better (Neigel 2003, Roberts et al. 2003, Nyström et al. 2012) and there are minimum area requirements in marine ecosystems based upon local habitats and dispersal (Botsford et al. 2003, Davis 2005, Fernandes et al. 2009). Although the total area of most RAs are typically dictated by the magnitude of the loss of original habitat productivity, value or productive capacity (DFO 1986, 2013a, 2014; NOAA 2000), if larger restoration areas are more effective it may inform managers to pool compensation into larger projects rather than continue to build smaller, fragmented RAs as has typically been the policy to date. One application of the RESTORE tool will be to test for differences in scores based upon area with the hypothesis that the larger the restoration area, the greater the overall score or sustainability. In addition to questions about the how the area of an RA may affect its sustainability, on the west coast of Canada valued ecosystem components such as salmonids, crabs, flatfish and forage fish use multiple ecosystems throughout differing critical stages of their life-history including freshwater streams for spawning, estuaries for rearing and the marine environment for maturation (Beamish et al. 2003, Levings 2004a). Identifying habitats that may be more difficult to restore over the long-term may better inform future planning, funding and  160  monitoring requirements (Simenstad and Cordell 2000, Kemp and O'Hanley 2010). Currently, there is no indication that either marine, estuarine or freshwater areas may be more sustainable than another.  I will use the RESTORE tool described in Chapter 2 with supplementary statistical methods to test if restoration areas of one ecosystem may be more sustainable than the others. Developing a comprehensive, rapid assessment tool to examine the ecological, social and economic sustainability of restoration areas and their individual attributes may increase: the ease of evaluating restorations areas; the frequency that they are monitored; and allow cross-ecosystem comparisons to identify key attributes contributing the success and failure of these projects. Ultimately, improving monitoring and evaluation methods should increase sustainability, uphold values of the public, and increase the economic viability and benefits of the restoration process.  The purpose of this chapter is to: 1) develop a rapid assessment tool to evaluate the sustainability of restoration projects through a multidisciplinary approach focusing on ecological, social and economic performance indicators; 2) use RESTORE in a case pilot study to test its functionality; and 3) use output from RESTORE with known statistical tests to examine a series of questions including: i) the RESTORE tool differentiate the success of RAs to try to identify ways of improving ecological restoration;  ii) Are there differences in the success of marine, estuarine and freshwater restoration areas; and iii) Are larger RAs more sustainable than smaller RAs?   5.2 Study area and methods The study area for Chapter 5 includes 11 restoration areas (RAs) within southwestern British Columbia (Figure 2.1). The 11 RAs include four in marine ecosystems (M1-M4), four  161  in estuarine ecosystems (E1-E4) and three in freshwater ecosystems (F1-F3), with a list of their approximate size and regulatory status are included in Table 2.2 and a general description of the sites and environment in Section 2.1. Photos for nine of the 11 RAs are presented in Appendix C.5. A detailed description of the methods and the RESTORE tool are presented in Section 2.3, Table 2.3 and a working example of the scoring system in Appendix C.2.  5.3 Results  5.3.1 Overall scores Overall evaluation scores were high across restoration areas ranging from 55.9 to 81.6 (Table 5.1). Raw evaluation scores, with upper and lower estimates, are included in Appendix C.6 with Monte Carlo simulation results presented in Appendix C.7. Based upon the scoring categories created for RESTORE, all RAs resulted in a pass with one (M3) categorized as best. The three highest overall scores were all marine ecosystems, while three of the four lowest scoring RAs were estuarine. Marine RAs consistently scored the highest in all categories, estuarine RAs scored the lowest and freshwater RAs exhibited intermediary scores. A radar diagram (Figure 5.1) illustrates the differences in results for each of the five evaluation fields with the overall average.  The overall quality of the data used to input into the RESTORE pilot study was estimated at 0.61 (Table 5.2; Appendix C.8); this is equivalent to a single qualified professional doing the assessment based on a literature review, field assessment with low precision, single-year data; or multiple qualified professionals doing the survey, each having performed a literature review and only a reconnaissance level field assessment.    162   To examine if the sustainability scores of RAs differed significantly among ecosystems, a one-way ANOSIM was performed. ANOSIM results (Appendix C.9) show significant differences (α = 0.05) among ecosystems (R = 0.24, P = 0.039) with pairwise tests indicating a significant difference between scores of marine and estuarine RAs (R = 0.32, P = 0.029). Each pairwise test result was based on only 35 available permutations limiting the power of the test and the significant result was not corrected for familywise error (i.e., multiple pairwise tests).  With a simple Bonferroni correction, the difference in marine and estuarine ecosystems would not be considered significant (α = 0.16).  To further confirm differences among ecosystems and identify the key attributes, a SIMPER analysis was performed. SIMPER results confirm ANOSIM findings with the largest squared Euclidean distance between marine and estuarine areas, followed by estuarine and freshwater, and finally marine and freshwater. The greatest within-group variability, measured as the mean squared Euclidean distance between RAs, was observed in the marine ecosystem, followed by the estuarine and finally the freshwater (Table 5.3). Important attributes contributing to within-ecosystem scores were: protection of RAs, project management, and presence of functional groups for marine ecosystems; habitat and carbon banking, ecosystem structure - using local species and genotypes, and ecosystem structure - age class distribution for estuarine areas; and monitoring, maintenance and ensuring indicator species and trophic levels were present for freshwater areas.   The greatest amount of variation among ecosystems was observed between marine and estuarine RAs (Table 5.4). The estuarine RAs used in this study tended to be small in area, which may have affected scores as ecosystem services and direct economic benefit are area and cost dependent attributes. Six attributes each contributed to greater than five percent of the  163  variability including: physical stability, public involvement, direct economic benefits, ecosystem services, public education and biological connectivity.  Large differences between ecosystem scores occurred where marine RAs scored high for physical stability, public involvement and direct benefits from relatively large projects, whereas estuarine areas received failing scores for ecosystem services and public education. Attributes from the stress field contributed the greatest to differences between marine versus freshwater RAs and estuary versus freshwater RAs, although differences were not significant. Attributes from the ecology field did not contribute greatly to variation between ecosystems and may be the result of a high regulatory focus on ecology by the government, which should be equal across RAs.  5.3.2 Evaluation fields  The ecological evaluation field of RESTORE scores the RAs ecological function and structure relative to expected or required outcomes. An ordination plot is used to illustrate the relative difference among RAs in Figure 5.2a. Scores for the ecology field ranged from 92.4 at M4 to a failing score of 41.4 at E1 (Table 5.1), with the ordination points appearing to group by ecosystem. Interestingly, a failing grade at E1 is consistent with an evaluation performed for the regulator (DFO) resulting in the RA requiring further remediation (FREMP 2013). Mean scores for ecological attributes were in a very narrow range with total function scoring the lowest (7.2) and ecosystem structure - using local genotypes and native species scoring the highest (7.9). Leverage values for the five attributes were all less than 5.0, indicating that removal of a single attribute had little effect on the overall tool (Appendix C.10). Leveraging indicates that ecosystem structure - native and local genotypes had the largest impact (3.7).  This effect was also observed within the estuarine SIMPER analysis in comparisons of estuarine with marine and freshwater ecosystems.   164  The second field scores the contribution of environment, landscape and connectivity to the sustainability of the restoration area. Four separate clusters of restoration areas appear to score closely by attribute, rather than ecosystem (Figure 5.2b). The scores for the environment field ranged from a high of 92.4 at M4 to a low and passing score of 51.8 at E4 (Table 5.1). The results indicate that all 11 RAs scored a pass, although three had total scores in the 50s due to low scores in scale and fragmentation. A second cluster of RAs all had similar scores within the pass-to-best threshold for the field, whereas another three RAs scored best for physical stability and pass to best for biological connectivity. Results of leverage analysis were less than 4.2 indicating no one attribute overly dominated the results from the field.  The stress field estimates the susceptibility of an area to various stressors, its ability to survive stress, and a manager’s effort to detect and manage the effects of stress. All points tend to cluster close together with overall values ranging from 79.1 at M1 to 53.5 and 53.7 at F2 and F3 respectively (Table 5.1).  All RAs scored within in the pass category, although F2 and F3 were close to failing (Figure 5.2c). F2 had a high amount of invasive species (Phalaris arundinacea), while F3 did not follow through with planned monitoring and maintenance. Because these scores indicate susceptibility to stress and the ability to recover from disturbance, low passing scores may imply that stressors are likely to impact the RAs resulting in future failing grades. Leverage analysis results show invasive species (6.9) and monitoring program (6.1) to have slightly elevated influence on the results over other attributes for the field. The social field incorporates societal values into the RESTORE tool. The ordination (Figure 5.2d) illustrates a relatively large variation among the total scores of RAs (x-axis), from a high of 98.6 at M2 to a low of 59.0 at E1 (Table 5.1), and high within-field variation  165  among attributes (y-axis).  One outlier on the y-axis (M4) scored best (10) for both public involvement and public education. Low scores for some RAs may be due to being restored for reason of public good, such as sewer outflows, and although subject to public notification as a result of legislation, it is likely minimal effort and minimal public access were required at these areas resulting in low, but passing scores.  Finally, results from the economic field measuring monetary value and cost efficiency indicates total overall scores cluster in a relatively narrow range, similar to the stress field, with a high of 83.4 at M3 and a low of 52.6 at M4 (Table 5.1). Marine RAs in general were positioned away from the other RAs (Figure 5.2e), scoring positive on the y-axis, while all other RAs were negative on y-axis.  This may be attributed to how anchor scores in that quadrant of the ordination space are weighted. Marine RAs scoring high for ecosystem services and low for carbon and habitat banking are being pulled into the positive Y-axis quadrant. Finally, leverage analysis showed that funding security, ecosystem services, and direct benefits to society contributed to greater than five percent variation exhibited upon their individual removal from the field, but each value is less than the 10%, which indicates that they had an undue influence on the results.  5.3.3 Area  Overall scores were compared with area to determine if the larger RAs may exhibit higher sustainability. The mean overall score and area were 72.4 and 14,036 m2, respectively. Although, overall scores from the RESTORE tool were shown to increase slightly with area indicated by a positive sloping trend line in Figure 5.3, results from the regression analysis indicate this was not statistically significant (R = 0.518, P = 0.103). An ANCOVA run to partition out the effect of ecosystem resulted in no significant relation of area to ecosystem (F2,  166  10 = 1.951, P = 0.212), or area on overall score after controlling for ecosystem (F1, 10 = 0.047, P = 0.835). Levene’s test was not significant for either analysis, nor was the interaction term used to test for homogeneity of regression lines.  5.4 Discussion Using the RESTORE tool I was able to effectively categorize the overall sustainability of each RA based on attributes determined by the Society of Ecological Restoration (SER), examine the performance of individual attributes and use supplementary tools such as ANOSIM, SIMPER and regression analysis to answer specific questions about how the RAs perform. Application of these supplementary analyses are the first time this has been attempted with the Rapfish tool or any modification thereof. The results of RESTORE effectively distinguish between RAs from different ecosystems even though relatively few replicates were used in this pilot work. Using a permutation technique such as ANOSIM appears to work well to supplement the Rapfish method and confirm findings, although increasing replicates to five or greater in each group will improve power in significance testing of pairwise tests. Five RAs were initially planned for each ecosystem; however, finding and accessing additional RAs from each ecosystem, particularly marine, to produce a balanced design was difficult. In addition to ANOSIM, the use of SIMPER was highly effective in identifying key attributes that contributed disproportionately to the results and again confirm findings from the ordination diagrams. Finally, overall scores were able to be analyzed further through regression analysis to show that area of the RA does not significantly improve its long-term sustainability. All RAs scored with in a relatively small range for RESTORE as a whole, with all but one receiving a passing grade and one scoring in the ‘best’ category. These results match the RA status determined by the regulatory process in Canada in cases where the monitoring period  167  for mainly ecological attributes was complete (Table 2.2). Restoration area E1, which received the lowest overall score by RESTORE, was determined to still require remedial action before being considered complete by the regulatory body. The high performance of RAs within Canada has also been demonstrated in ecosystem-based management using a Rapfish-based model (Pitcher et al. 2009b), where the overall score was at or near 70 percent, comparable to the mean overall scores of the RESTORE tool (72.4). The similarity within scoring can be expected as some attributes build on requirements commonly included in regulatory authorizations in Canada. These common requirements include: (1) similarities to a reference or model ecosystem; (2) the use of native species; (3) assessing for physical integrity of the RA; (4) assessing for invasive species; and (5) ensuring indicator species and age classes are present. Many regulatory processes applying to RAs in Canada also incorporate public consultation prior to siting of the RA, and provide public and regulatory approval of ecological design, monitoring and maintenance if deemed unsuccessful at the end of an authorized period.  Low variation in scores may also be the result of a single person scoring all sites. Incorporating multiple scorers would have produced a truer variation than my estimates of high and low values, although the Monte Carlo situation does attempt to account for this. Again, scoring was originally planned with two members from DFO, but recent government changes to the department rendered them unavailable during times of scoring. Finally, to further test the robustness of RESTORE, cross-jurisdictional comparisons among countries may highlight how regulatory or social values lead to differential success or failure in restoring RAs, better identifying key traits to their sustainability, than having all RAs in one region as with this study. A major finding of RESTORE was that marine areas consistently scored highest across  168  all evaluation fields. Reasons for the higher overall scores may be attributed to the spatial, physical and biological features of the marine RAs used in the pilot study. Three of the four marine RAs were greater than 24,000 m2, which is relatively large in area for pilot study, increasing scoring of area-dependent attributes such as scale and fragmentation, values of banking habitat and carbon credits, the values of ecosystem services, and, if infrastructure is required for the foreshore, direct economic benefits to the community. If assessments need to focus on effectiveness per area, then standardizing results by the size of the largest RA can be performed. (McCune and Grace 2002).  With respect to stressors, rocky intertidal systems in the Pacific Northwest are subject to competition from relatively few invasive species compared to estuarine and freshwater (Taylor and Hastings 2004, Caplan and Yeakley 2006, Matthews and Spyreas 2010). Marine RAs used in this study are also typically created as a bare substrate, colonized directly by local genotypes versus nursery stock used in estuarine and freshwater riparian habitats, and have a rapid recruitment rate of macroalgae and sessile invertebrates increasing resilience (Thompson et al. 2002). Marine ecosystems in urban centres are also highly valued and used recreationally by the public for recreation, such that access and information are more likely to be incorporated to a higher level within these RAs.  Finally, the marine RAs chosen for the pilot study were designed with hard substrate including riprap (M1, M2, and M3) and a decommissioned naval ship (M4), all of which are extremely stable compared to other marine habitats such as sand and gravel beaches, eelgrass beds or salt marsh, which were not included. Future work could focus on the difference in sustainability between: 1) naturally hard and soft substrate habitats overall; 2) naturally hard and soft substrate habitats within the same ecosystem (gravel-sand beaches versus intertidal  169  rocky shoreline); and 3) within habitat performance where rip rap or another hard substrate has been for erosion control in a soft substrate habitat. These are timely questions given recent trends in reducing and reversing the use of hard substrates in naturally soft-sediment environments (http://www.stewardshipcentrebc.ca/tag/green-shores/). All freshwater and estuarine RAs in the region scored well in RESTORE, even with many being relatively small in area. Most of these RAs are the result of small infrastructure projects and appear as patches in the urban environment along a major waterway used for shipping. The estuaries are highly valued for ecosystem services (Costanza et al. 1997; NOAA 2000, Suzuki Foundation 2015), and local planting methods typically incorporate transplanting individuals from local donor sites (Adams and Williams 2004), which scores highest in RESTORE. Unfortunately, freshwater and estuarine RAs are: (1) highly susceptible to invasive species including Phalaris arundinacea, Rubus armeniacus, and Spartina anglica; (2) relatively slow-developing ecosystems with freshwater riparian areas including shrubs and trees taking greater than five years to mature; and (3) upland and intertidal portions of the RAs that tend to be planted with nursery plant stock rather than local transplants. Plant material can also be susceptible to low survivorship due to invasive grasses and lack of maintenance early in the post-development phase reducing site sustainability.  RESTORE indicates that small estuarine areas do not act well as RAs. Although, none of the RAs failed, four of the five RAs that scored the lowest were all estuarine RAs under 5,000 m2. Although a non-significant positive correlation was observed between area and sustainability, this could be related to ecosystem type rather than area. An expansion of this study with more equal distribution of size of RAs within each ecosystem type may help to better support these findings. If area were to be found to significantly contribute to  170  sustainability as initially postulated, a change in compensation policy may be necessary favoring relatively large restoration areas for future development. A proponent needing to account for smaller restoration areas may contribute monetary compensation into a habitat bank based on a market value basis for the ecosystem type requiring compensation until a larger area can be constructed. Another option is to purchase already created habitat from a large habitat bank.  As previously mentioned, one criterion not considered in this tool was adjusting spatially dependent scoring by ecosystem type or the largest RA.  For this tool, area categories were relatively small and mainly based on a terrestrial model (Resources Inventory Committee 2006). Evaluation scales for marine areas are suggested to be much larger to account for greater home ranges of many species (Alder et al. 2002, Davis 2005), although this may not be applicable to urban, estuarine or freshwater restoration (Cowen et al. 2007). Area ultimately contributes to edge effects, species connectivity and the ability to maintain the presence of higher trophic level species due to overcoming constraints of density dependent behavior including predation or grazing (Botsford et al. 2003, Halpern 2003). Scoring was mainly based on a simple linear response scale, although recent evidence indicates some ecosystem services such as coastal protection due to wave attenuation are non-linear (Barbier et al. 2008). Refining the weighting of area could improve future versions of this tool. Finally, RESTORE was initially implemented as a tool to assess or predict the long-term sustainability of restoration areas post-construction to help inform resource managers and stakeholders. However, to best assess any RA, RESTORE would be used throughout the life-cycle of each RA to monitor changes in scoring over time and determine if the predicted sustainability was occurring in nature and society. This includes using RESTORE at the  171  planning stage or pre-construction. Basing the design of RAs on the criteria in RESTORE and monitoring if predicted sustainability actually occurred over the long-term could help inform if the attributes used in RESTORE, and by the SER, truly predict self-sustaining restoration areas.   5.5 Conclusion   The first attempt to employ RESTORE was successful in differentiating the long-term sustainability among the case study ecosystems analyzed, with the overall assessment consistent with independent evaluations by regulators within Canada. Marine restoration areas in Metro Vancouver were determined to be the most sustainable, while estuarine areas the least due differences in physical stability, public involvement, and ecosystem services. These findings highlight the importance of including or improving erosion control measures in estuarine ecosystems and more actively including socio-economic attributes. The size of the restoration area does improve the sustainability within the case study; however, more restoration areas across a larger range of sizes may be needed to prove a significant correlation. The RESTORE tool shows how supplemental statistical methods can be applied to rapid assessment tools such as Rapfish to test hypotheses supporting ecosystem-based management decisions that include ecological, social and economic attributes. The ease of use of the tool should allow for frequent and rapid monitoring to inform and improve adaptive management of future restoration areas.    172  Table 5.1 RESTORE scores for the overall mean and totals for each of the five evaluation fields. Site Code Mean Ecology Environment Stress Social Economics M3 81.6 91.6 85.2 72.4 75.5 83.4 M4 78.4 92.4 92.4 76.4 78.1 52.6 M1 76.9 71.6 73.1 79.1 83.6 77.2 F1 76.8 79.2 76.7 76.1 86.0 66.2 F2 75.0 70.0 88.6 53.5 86.3 76.8 M2 74.2 67.0 71.9 63.7 97.2 71.0 E2 73.7 75.3 73.3 68.4 82.0 69.7 E3 70.8 80.2 54.7 74.1 80.2 64.7 F3 69.2 77.5 72.4 53.7 86.4 56.0 E4 64.3 73.3 51.8 75.6 65.4 55.4 E1 55.9 41.4 63.0 57.7 58.9 58.8     173  Table 5.2 Overall mean pedigree rating and standard error of data for each of the five evaluation fields and 11 restoration areas. Field Restoration Area    M1 M2 M3 M4 E1 E2 E3 E4 F1 F2 F3 Mean SE Ecology 0.90 0.50 0.80 0.62 0.80 0.50 0.50 0.50 0.60 0.60 0.60 0.63 0.04 Environment 0.78 0.60 0.68 0.62 0.66 0.56 0.56 0.56 0.64 0.62 0.64 0.63 0.02 Stress 0.80 0.50 0.74 0.66 0.74 0.54 0.54 0.54 0.62 0.58 0.62 0.63 0.03 Social 0.68 0.52 0.52 0.70 0.54 0.50 0.50 0.50 0.56 0.50 0.68 0.56 0.02 Economic 0.66 0.52 0.52 0.66 0.64 0.48 0.48 0.48 0.64 0.64 0.64 0.58 0.02 Mean 0.76 0.53 0.65 0.65 0.68 0.52 0.52 0.52 0.61 0.59 0.64   Overall Rating            0.61 0.01           174  Table 5.3 SIMPER results indicating evaluation attributes that contribute the greatest to within ecosystem scoring variability. Field Attribute Contribution1 (%) Marine  (total distance = 25.922) Social Social protection 9.24 Economics Budget 6.69 Stress Functional groups 6.31 Economics Funding security 6.25 Stress Risk of catastrophe 6.09 Social Public education 5.41 Stress Resistance resilience 5.04 Estuary  (total distance = 22.102) Economics Banking and credits 11.96 Ecology Structure II - native and local genotypes 11.21 Ecology Structure -  age class 7.93 Social Public access 7.15 Stress Invasive species 6.41 Ecology Total function 5.66 Environment Abiotic connectivity 5.66 Freshwater  (total distance = 18.802) Stress Monitoring 14.78 Ecology Structure I - indicators and trophic levels 8.84 Stress Maintenance 8.30 Stress Invasive species 8.28 Economics Funding security 8.01 Economics Project management 6.39 Environment Abiotic connectivity 5.32 1 Attributes are expected to contribute four percent if all assume an equal weight. 2 Squared Euclidean distance.    175  Table 5.4 SIMPER results indicating evaluation attributes that contribute the greatest to variability of among ecosystem scoring. Field Attribute Mean Distance2 Distance2/ SD3 Contribution1 (%) Marine and Estuary (total distance = 62.02) Environment Physical stability 3.9 1.3 6.2 Social Public involvement 3.5 1.1 5.6 Economics Direct economic benefit 3.4 1.0 5.5 Economics Ecosystem services 3.3 1.0 5.4 Social Public education 3.2 1.2 5.2 Environment Biological connectivity 3.2 1.1 5.2 Estuary and Freshwater (total distance =56.02) Social Social values 3.5 1.4 6.2 Social Public involvement 3.4 1.1 6.1 Environment Biological connectivity 3.3 1.4 5.9 Environment Quality 2.8 0.8 5.0 Stress Maintenance 2.8 0.8 4.9 Social Public education 2.6 1.4 4.7 Marine and Freshwater (total distance = 43.62) Economics Ecosystem services 3.7 0.9 8.5 Stress Maintenance  3.6 0.9 8.2 Stress Resilience & resistance 3.3 1.1 7.5 Stress Monitoring 3.0 0.7 6.8 Social Protection 2.4 0.8 5.5 Ecology Structure -indicator species 2.3 0.8 5.2 1 Attributes are expected to contribute four percent if all assume an equal weight. 2 Squared Euclidean distance. 3 Standard deviation   176  .  Figure 5.1 Radar diagram illustrating differences among marine, estuarine and freshwater ecosystem mean scores for five fields of evaluation: ecological, environmental, stress, social and economic. Zero indicates the worst score (centre), five indicates a passing score and ten indicates the best score (perimeter).    0510EcologyEnvironmentStressSocialEconomicsmarine estuary fresh overall 177    Figure 5.2 RESTORE performance scores for marine (black circle), estuary (grey circle), and freshwater (white circle) for each of five evaluation fields: ecological, environmental, stress, social and economic evaluation fields. Error bars represent 50% interquartile range for each field.  -30-20-100102030400 20 40 60 80 100Ecological scores (%)-20-15-10-50510150 20 40 60 80 100Environment scores (%)a b  178     Figure 5.2 (continued).   -60-50-40-30-20-1001020304050600 20 40 60 80 100Stress score (%)-50-40-30-20-1001020300 20 40 60 80 100Social scores (%) c d  179   Figure 5.2 (continued).    -25-20-15-10-505101520250 20 40 60 80 100Economic scores (%)e  180   Figure 5.3 Linear regression analysis comparing RESTORE overall scores with area of restoration area (r = 0.518, P = 0.103).     R² = 0.2681505560657075808590951000 10,000 20,000 30,000 40,000RESTORE overall scoreSize of restoration area (m2) 181   Conclusion    182  6.1 Overall conclusions The goals of this thesis were to: (1) examine how structural and environmental heterogeneity affects biodiversity and species assemblages associated with an engineered intertidal habitat referred to as the Habitat Skirt, and (2) develop a rapid assessment tool (RESTORE) that can inform resource managers of the long-term sustainability of marine, estuarine and freshwater restoration areas. The Habitat Skirt had similar diversity to neighbouring intertidal riprap, but species composition was different including greater cover of sessile invertebrates (M. trossulus) and lower cover of macroalgae (F. distichus and M. papillatus). Examination of environmental parameters in this urban environment show us that water motion, light exposure and temperature all affect species assembly, with shading reducing macroalgal cover. Adding complexity in the form of various engineered habitats, particularly tidepools and vertical faces, added diversity and variability within a site; however, these outcomes differed with tidal elevation. Finally, a new rapid assessment tool, RESTORE, was created to improve assessment of the sustainability of restoration areas and was shown to function well in a case study assessing 11 restoration areas in Metro Vancouver. Supplemental analyses of results indicate marine restoration areas were more sustainable than estuarine ecosystems, but increasing the area of the RA may not contribute to overall sustainability. The Habitat Skirt was designed to mimic the surrounding intertidal shoreline, even though it is positioned over the deep water (i.e., 10 to 15 metres) marine perimeter of the building. Microhabitats including tidepools, vertical and horizontal surfaces, each appear to contribute to the overall biodiversity and variability among species assemblages resulting in similar species richness on the Habitat Skirt when compared to neighbouring riprap sites. It was expected that the Habitat Skirt would have greater species richness due to the number of different microhabitats, each potentially supporting a different assemblage of species;  183  however, shading from the surrounding infrastructure significantly reduced light exposure, and likely resulted in limited macroalgal cover. Ideally, another Habitat Skirt would exist with greater and varying intensities of light exposure, similar to the neighbouring riprap, to further support for this conclusion; however, neighbouring riprap shorelines with both higher light levels also exhibited greater macroalgae cover. The performance among microhabitats by tidal height has implications for future habitat design. The greatest species richness was observed within tidepools from 2 to 4 m CD and vertical habitats at 0 and 1 m CD. Previous studies in Australia and Europe with similar design also exhibited greater species richness of mobile invertebrates at mid-tidal levels (Chapman and Blockley 2009). These results suggest that effective incorporation of tidepools and vertical faces have specific ranges and could be limited in their placement to the tidal heights where they are most effective promoting species diversity and reduce overall costs of these engineered structures; while, maximizing ecological benefit.  The long-term sustainability of restoration areas depends upon many attributes including ecological functionality (Naeem 2006, Beane et al. 2008, Cordell et al. 2011), social acceptance, public participation (Reyes 2011), economic management and funding (Nielsen-Pincus and Moseley 2013). The RESTORE model uses 25 attributes divided equally among five fields to assess which attributes appear to be critical to the sustainability of restoration areas. Key indicators of the success among ecosystems included: biological connectivity, public education, local values, physical stability, and public involvement in the process. These results have implications for directing effort in future planning and research associated with restoration areas including placement of new RAs to allow migration for target species, while promoting funding for monitoring and maintenance programs that include local stakeholders.   184  Based on a case study using RESTORE, overall sustainability of RAs increased slightly from small areas to larger ones, but not significantly. This is what not as expected based on literature of ecological indicators such as species richness (Neigel 2003); where generally the larger the reserve or RA, the greater the species diversity (Carr et al. 2003). Although not definitively determined in the case study, the literature indicates that scale is an important factor when considering ecological restoration (Botsford et al. 2003, Davis 2005). Many project related disturbances are relatively small with RAs becoming isolated “islands” in a sea of urban development. Although species and their function do not contribute equally to the overall structure of an ecosystem, greater species abundance and diversity promote higher functional diversity, redundancy and ecological resilience (Peterson et al. 1998). As surrounding urban development increases, the RAs generally lose abiotic and biotic connectivity to other restoration areas and the natural landscape, ultimately reducing the overall health of the ecosystem (Lundquist and Granek 2005). Promoting larger restoration areas may have implications for developing habitat banks that take funds set aside for numerous small projects, and apply them to fewer larger areas. These larger habitat banks may also be better incorporated into regional planning, rather than offsetting numerous small areas adjacent to the development. It would be beneficial to re-examine this question with a greater number of RAs, specifically with greater replication by size class (i.e., <1000 m2, 1000 to 10,000 m2, and > 10,000 m2) within each of the three ecosystem types. The economic analysis of restoration areas indicates that some industries are willing to accept long duration return for the natural benefits of RAs such as access to recreational diving (M4) (Enmark 2002), sustainable wildlife (fish) and plant harvest (F3 and F1). The markets for habitat banking and carbon offsetting still require development to realize pricing that can  185  substantially or fully fund restoration projects. Government commitment and regulation could provide initial support for these markets to grow into a self-sustaining business (Galatowitsch 2009, Hansen 2009); this is particulrly important given the ability of kelp beds, tidal marshes and native eelgrass beds to sequester carbon (McLeod et al. 2011). The cost of ecological restoration is relatively high to fully fund from start to finish with all project components including monitoring and maintenance (exceeding $100 per m2 for some marine projects). It may be that large industry or government needs to take a “regional” view and fund habitat banking projects themselves with the greatest ecological and social benefit; while establishing a price for the restoration habitats. Proponents can then purchase the habitat without having to financially manage or worry about overseeing their successful development, resulting in savings in project management. This would focus and advance expertise in the field of ecological restoration, while developing projects determined to be the most effective at a larger landscape level. Forms of habitat banking do exist in the United States, and to a lesser extent in Canada where it is currently not a common form of compensation.  6.2 Limitations of the thesis research Research associated with environmental assessments and restoration projects are commonly limited due to the lack of replication of the impact or restoration area, resulting in low replication and/or asymmetric sample designs (Stewart-Oaten et al. 1986; Underwood 1994, Osenberg et al. 2006). This research encounters some of these problems including: 1) lack of replication of the Habitat Skirt or control designs for the engineered microhabitats; 2) limitation of the study to a single urban harbour; 3) the inability to manipulate conditions in the field other than through sample design; and 4) not working in a controlled environment, allowing for manipulation of all environmental variables. In addition, as the Habitat Skirt was  186  only installed in 2008, limiting the research to the first few years since installation. The study was based on an active fish habitat compensation project that was under review by a regulating agency for effectiveness (EBA 2013). Projects of this kind typically are expensive to replicate and do not allow for destructive sampling or require regulatory approval for manipulation during the process; which was not an option in this instance. In the future, partnerships with research agencies to create separate structures that could be set aside as long-term controls, or manipulated with varying levels of light, water motion or enclosure cages to study species interactions would allow for greater determination of effect size and cause. Proponents may also want to include these controls in their design to remove subjectivity in the interpretation of project effectiveness when presenting to regulators. Within the Habitat Skirt, a few unmodified or non-engineered benches would have allowed for quantification of the effect size of the microhabitats. Currently only comparisons with other compensation substrates such as riprap can be made. Although, the restoration areas were established to meet compensation requirements set-forth by a regulator with a simple monitoring design; future situations that present new and innovative designs could allow research to partner with industry earlier on in the regulatory process to incorporate experimental procedure for more comprehensive results while allowing the proponent leniency with the outcome due to the long-term benefits of the research. The RESTORE tool developed in Chapter 5 is in its infancy and requires further testing to improve its functionality and applicability. RESTORE was created for rapid assessment based upon a single field visit, using prior reporting or established environmental data, background research on observed social values or a questionnaire, and an analysis of the economic data surrounding a project. Evaluation and scoring fields within the tool provide a  187  good first approximation of how to score them with attributes within each field based on SER (2004) guidance for the sustainability of restoration areas, federal regulatory guidelines and my experience. To better fully develop the RESTORE tool, applying it to a broader range of users and jurisdictions would greatly add to its robustness. Scoring was limited to one person; ideally, multiple professionals would score the sites either separately and average answers, or jointly and agree on a single score and potential range in scores. Including a detailed survey and interviews to better evaluate social issues would also greatly increase the relevance of the social field. As RESTORE is used in greater detail, it is likely that attributes may require further redefinition and the scoring methods more detailed.   6.3 Future research based upon the work this thesis This thesis examines how to improve design and assessment of restoration areas in urban environments; however, during the study many other ideas for future work were identified. These include:  (1) Quantifying the edge effect observed on the Habitat Skirt by determining the distance from vertical connectivity to a change in species assemblage, while also identifying key species contributing to those differences; (2) Repeat these studies in five to ten years to continue to monitor long-term change in species assemblages in an urban setting; (3) Identify more restoration areas that would increase replication and sample points within environmental gradients to better understand the contribution of single and complex interactions of environmental parameters on species diversity and assembly; (4) Further testing of the RESTORE model across jurisdictions, particularly regions that have less regulatory guidance than Canada, and where the tool may identify greater  188  discrepancies among fields and identify weaknesses in restoration area planning and implementation;  (5) Work with an agency such as DFO, who has hundreds of reports regarding ecological, social, and economic information on restoration areas to apply RESTORE across a larger sample size and engage the regulator to add multiple scorers to review the functionality of the tool; and (6) Rather than base social values on observations during site visits and reports from public stakeholder consultation, include detailed surveys of local stakeholders.  6.4 Significant findings Finally, this thesis set out to: (1) examine how structural and environmental heterogeneity affects biodiversity and species assemblages associated with the Habitat Skirt, and (2) develop a rapid assessment tool (RESTORE) that can inform resource managers of the long-term sustainability of marine, estuarine and freshwater restoration areas. I believe this thesis addresses each of these goals and successfully produces findings that can further the field of ecological restoration including:    (1) The Habitat Skirt was effective in achieving similar species diversity as neighbouring riprap locations even with its position in a shaded environment and over deep water, not directly connected to the seafloor; (2) Water motion, light, temperature and their interaction are critical to species assembly on intertidal habitats in urban centres; requiring habitat engineers and managers to design restoration areas on site specific conditions,  which may be particularly complex in an urban environment;  (3) Microhabitats are not equally effective across all tidal heights, with tidepools  189  supporting the greatest species richness from 2 to 4 m CD and vertical habitats from 0 and 1 m CD, this has implications for design of engineered intertidal shorelines in the future; (4) Marine restoration areas in Metro Vancouver were determined to be the most sustainable, while estuarine areas the least due differences in physical stability, public involvement, and ecosystem services;  (5) Increasing the area of a restoration areas may not improve its overall long-term sustainability; and  (6) Ecological, social and economic attributes can be used together in monitoring the long-term sustainability of restoration projects to more comprehensively inform managers of project success.      190  References Adams, M., B. 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Resour. 30:39-74.     225  Appendices     226  Appendix A  Referencing Chapter 3    227  A.1 Starting position of sample transects at the Habitat Skirt, Harbour Green and New Brighton Park. Site Location Start position Comment Habitat Skirt HS1 491395.0 E 5459718.0 N Sixth bench south from SW corner on west aspect Habitat Skirt HS2 491626.0 E 5459656.4 N Seventh bench west from NE corner on north aspect  Habitat Skirt HS3 491688.3 E 5459607.4 N Sixth bench south from NE corner on east aspect Harbour Green Park HG1 491096.0 E 5459803.5 N West of Harbour Green dock Harbour Green Park HG2 491151.9 E 5459758.4 N Within shelter area of Harbour Green dock-western area  Harbour Green Park HG3 491292.5 E 5459695.7 N East of Harbour Green Dock and VC New Brighton Park NB1 496824.7 E 5459936.1 N West aspect New Brighton Park NB2 496992.6 E 5459944.8 N Just east of path through bush on north aspect New Brighton Park NB3 497113.7 E 5459885.6 N East aspect     228  A.2 Estimates of percent cover estimates of mobile species observed on the Habitat Skirt between 2009 and 2011. Common Name Scientific Name % Cover Hudson's dorid Acanthodoris hudsoni 0.04 White ribbon worm Amphiporus formidbalis 0.01 Scalyhead sculpin Artedius harringtoni 0.16 Smoothhead sculpin Artedius lateralis 0.20 Tubesnout Aulorhynchus flavidus 0.15 Moon jelly Aurelia labiata 2.25 Kelp perch Brachyistius frenatus 2.50 Dungeness crab Cancer magister(Metacarcinus magister) 2.50 Dungeness crab (juv) Cancer magister (Metacarcinus magister) 0.70 Red rock crab Cancer productus 1.80 Skeleton shrimp Caprella alaskana 0.04 Chiton chiton 0.32 pink scallop Chlamys spp 0.40 Leather star Dermasterias imbricata 0.45 Rough keyhole limpet Diodora aspera 0.03 White-lined dirona Dirona albolineata 0.03 Monterey sea-lemon Doris montereyensis 0.50 White nudibranch Doris odneri 0.50 Nudibranch egg rings Nudibranch egg rings 0.09 Striped perch Embiotoca lateralis 5.25 Green ribbon worm Emplectonema purpuratum 0.05 Eualid Eualus spp 0.06 Mottled star Evasterias troschelii 12.00 Red flabellina Flabellina verrucosa 0.02 Northern clingfish Gobiesox maeandricus 1.13 Fifteen scale worm Harmothoe imbricata 0.07 Green shore crab Hemigrapsus oregonensis 0.15 Blood star Henricia leviuscula 0.45 Fat blood star Henricia sanguinolenta 6.00 Opalescent nudibranch Hermissenda crassicornis 0.04 Northern sculpin Icelinus borealis 0.20 Isopod Idotea spp 0.04 Black katy chiton Katherina tunicata 0.72 Shield Limpet Lottia pelta 0.05 Limpet juvenile Limpet juvenile 0.01 Checkered periwinkle Littorina scutulata 0.02 Littorine snail Littorina spp. 0.01 Giant plumose anemone Metridium giganteum 2.25 Swan's mopalia Mopalia swani 0.50 Mysids Mysids 0.01 Banner nymph Nereis vexillosa 0.15 Fenestrate limpet Tectura fenestrata 0.02 Mask limpet Tectura persona 0.05 Tidepool sculpin Oligocottus maculosus 0.20    229  A.2 (continued) Common Name Scientific Name % Cover Lingcod Ophiodon elongatus 24.00 Daisy brittle star Ophiopholis aculeata 0.75 Decorator crab Oregonia gracilis 0.15 Tube-dwelling anemone Pachycerianthus fimbriatus 0.49 Spiny pink shrimp Pandalus borealis 0.08 Coonstripe shrimp Pandalus danae 0.20 Giant pink star Pisaster brevispinus 18.00 Ochre star Pisaster ochraceus 8.00 Rock sole Pleuronectes bilineatus 2.00 Northern helmet crab Pugettia producta 0.45 Sunflower star Pycnopodia helianthoides 32.00 Calcareous tube worm Serpula columbiana 0.02 Calcareous tube worm Serpula vermicularis 0.02 Green sea urchin Strongylocentrotus droebachiensis 0.48 Black turban Tegula funebralis 0.04 Lined red chiton Tonicella lineata 0.32 Horse clam Tresus capax 0.12 Clown nudibranch Triopha catalinae 0.30 Unknown Sculpin Unknown sculpin 0.16   230  A.3 Species codes defined for multivariate analyses. Species code Species name Bala_cre Balanus crenatus Bala_gla Balanus glandula Bang_spp Bangia species Br_string Brown filamentous algae Chon_exp Chondracanthus exasperatus Colo_dia Colonial diatoms Derm_imb Dermasterias imbricata Evas_tro Evasterias troschelii Fucu_dis Fucus distichus ssp. evanescens Hemi_ore Hemigrapsus oregonensis Mast_pap Mastocarpus papillatus Mast_pet Mastocarpus crust phase Mazz_par Mazzaella parksii Mazz_spl Mazzaella splendens Micr_bor Microcladia borealis Myti_tro Mytilus trossulus Nere_leu Nereocystis luetkeana Odon_spp Odonthalia floccosa Pisa_och Pisaster ochraceus Poly_hol Red filamentous algae Sacc_lat Saccharina latissima Sarg_mut Sargassum muticum Ulva_int Ulva intestinalis Ulva_lac Ulva lactuca     231  A.4 Similarity percentage (SIMPER) for comparison of species on riprap and the Habitat Skirt in 2010 and 2011. SIMPER Similarity Percentages - species contributions  One-Way Analysis  Data worksheet Name: yr.si.lo Data type: Abundance Sample selection: All Variable selection: All  Parameters Resemblance: S17 Bray Curtis similarity Cut off for low contributions: 100.00%  Factor Groups Sample Substrate 11NB1 Riprap 11NB2 Riprap 11NB3 Riprap 10NB1 Riprap 10NB2 Riprap 10NB3 Riprap 11MR1 Riprap 11MR2 Riprap 11MR4 Riprap 10MR1 Riprap 10MR2 Riprap 10MR4 Riprap 11HS1 Engineered 11HS3 Engineered 11HS4 Engineered 10HS1 Engineered 10HS3 Engineered  232  A.4 (continued) 10HS4 Engineered  Group Riprap Average similarity: 55.24  Species Av.Abund Av.Sim  Sim/SD Contrib%  Cum.% Bala_gla    26.01  15.82    4.44    28.63  28.63 Bare    20.66  10.89    1.38    19.72  48.35 Fucu_dis    18.62   9.11    1.79    16.49  64.84 Mast_pap    13.61   6.25    1.58    11.31  76.16 Ulva_lac     6.03   2.58    1.56     4.66  80.82 Sacc_lat     4.60   1.85    1.00     3.35  84.17 Micr_bor     6.46   1.55    0.58     2.80  86.97 Bang_spp     4.56   1.18    0.47     2.14  89.11 Mast_pet     1.59   0.85    1.72     1.55  90.66 Ulva_int     2.58   0.83    0.81     1.50  92.16 Poly_hol     1.72   0.76    1.59     1.37  93.54 Colo_dia     3.62   0.54    0.36     0.98  94.52 Pisa_och     2.68   0.53    0.45     0.97  95.48 Sarg_mut     2.81   0.49    0.47     0.89  96.38 Litt_spp     1.42   0.31    0.86     0.56  96.93 Bala_cre     1.38   0.28    0.53     0.51  97.44 Mazz_spl     0.77   0.25    0.84     0.45  97.89 Clad_spp     0.69   0.20    0.58     0.36  98.25 Br_string     1.34   0.16    0.25     0.30  98.54 Derm_imb     0.71   0.15    0.43     0.28  98.82 Chon_exp     0.38   0.11    0.77     0.20  99.03 Acro_coa     0.51   0.09    0.44     0.17  99.19 Odon_spp     0.39   0.05    0.26     0.09  99.29 Hemi_ore     0.45   0.05    0.33     0.09  99.38 Tect_scu     0.19   0.04    0.46     0.07  99.45 Ulva_lin     0.16   0.03    0.38     0.06  99.51 Porp_spp     0.10   0.03    0.52     0.05  99.56 Noto_per     0.12   0.03    0.48     0.05  99.61 Grat_dor     0.17   0.03    0.34     0.05  99.66 Evas_tro     0.60   0.03    0.12     0.05  99.71  233  A.4 (continued) Limp_juv     0.16   0.03    0.33     0.05  99.75 Kath_tun     0.21   0.02    0.34     0.04  99.80 Cost_cos     0.13   0.02    0.38     0.04  99.84 Myti_tro     1.22   0.02    0.18     0.04  99.88 Nere_leu     0.70   0.01    0.21     0.02  99.90 Poly_lat     0.08   0.01    0.30     0.02  99.92 Chiton     0.06   0.01    0.39     0.02  99.94 Serp_ver     0.06   0.01    0.27     0.01  99.95 Enc_bryo     0.04   0.01    0.21     0.01  99.96 Mela_int     0.04   0.01    0.21     0.01  99.97 Spar_per     0.08   0.01    0.12     0.01  99.98 Cryp_spp     0.06   0.01    0.30     0.01  99.99 Desm_vir     0.14   0.00    0.12     0.01 100.00 Alar_mar     0.02   0.00    0.12     0.00 100.00 Lott_dig     0.01   0.00    0.21     0.00 100.00 Tegu_fun     0.01   0.00    0.12     0.00 100.00  Group Engineered Average similarity: 81.26  Species Av.Abund Av.Sim  Sim/SD Contrib%  Cum.% Myti_tro    65.64  48.00    6.81    59.07  59.07 Bare    21.20  14.95    6.64    18.39  77.46 Bala_gla    14.95  10.02    5.17    12.34  89.80 Colo_dia     9.58   5.03    2.07     6.19  95.99 Fucu_dis     2.02   0.75    0.89     0.93  96.92 Stro_dro     1.48   0.75    3.06     0.92  97.84 Bala_cre     0.85   0.34    1.07     0.42  98.26 Ulva_lac     0.97   0.33    1.00     0.40  98.66 Onch_ket     1.67   0.26    0.26     0.32  98.98 Sacc_lat     0.56   0.19    0.71     0.24  99.22 Ulva_int     0.45   0.17    1.06     0.21  99.43 Poly_hol     0.55   0.16    0.68     0.19  99.62 Unk_Spon     0.30   0.08    0.62     0.10  99.73 Whit_ane     0.13   0.04    0.61     0.05  99.78 Core_spp     0.24   0.04    0.42     0.05  99.83  234  A.4 (continued) Porp_spp     0.10   0.03    0.73     0.04  99.87 Serp_ver     0.12   0.03    0.43     0.03  99.90 Metri_sen     0.06   0.02    1.05     0.02  99.92 Cnem_fin     0.03   0.01    0.78     0.01  99.94 Clad_spp     0.07   0.01    0.38     0.01  99.95 Canc_mag     0.14   0.01    0.45     0.01  99.96 Limp_juv     0.04   0.01    0.53     0.01  99.97 Br_string     0.23   0.01    0.26     0.01  99.98 Acro_coa     0.03   0.00    0.26     0.01  99.99 Micr_bor     0.03   0.00    0.26     0.01  99.99 Litt_spp     0.03   0.00    0.27     0.00 100.00 Eudi_van     0.03   0.00    0.26     0.00 100.00 Mazz_spl     0.01   0.00    0.26     0.00 100.00 Herm_cra     0.00   0.00    0.44     0.00 100.00 Sculpin     0.00   0.00    0.26     0.00 100.00 Kath_tun     0.00   0.00    0.26     0.00 100.00  Groups Riprap & Engineered Average dissimilarity = 68.74   Group Riprap Group Engineered                                 Species     Av.Abund         Av.Abund Av.Diss Diss/SD Contrib%  Cum.% Myti_tro         1.22            65.64   25.80    5.66    37.53  37.53 Fucu_dis        18.62             2.02    6.58    1.42     9.58  47.10 Mast_pap        13.61             0.01    5.32    1.56     7.74  54.85 Bala_gla        26.01            14.95    4.61    1.16     6.71  61.55 Bare        20.66            21.20    4.30    1.69     6.26  67.81 Colo_dia         3.62             9.58    3.37    1.40     4.90  72.71 Micr_bor         6.46             0.03    2.59    0.76     3.77  76.49 Ulva_lac         6.03             0.97    2.03    1.04     2.96  79.44 Bang_spp         4.56             0.00    1.78    0.88     2.58  82.03 Sacc_lat         4.60             0.56    1.72    1.02     2.50  84.53 Sarg_mut         2.81             0.00    1.07    0.69     1.55  86.08 Pisa_och         2.68             0.00    1.04    0.74     1.51  87.59 Ulva_int         2.58             0.45    0.94    0.76     1.37  88.96 Onch_ket         0.00             1.67    0.65    0.70     0.95  89.91  235  A.4 (continued) Mast_pet         1.59             0.00    0.64    1.94     0.93  90.84 Stro_dro         0.00             1.48    0.60    1.43     0.87  91.70 Br_string         1.34             0.23    0.59    0.61     0.86  92.56 Litt_spp         1.42             0.03    0.57    0.56     0.83  93.39 Bala_cre         1.38             0.85    0.56    0.86     0.82  94.21 Poly_hol         1.72             0.55    0.53    1.09     0.78  94.99 Evas_tro         0.60             0.40    0.34    0.59     0.49  95.48 Mazz_spl         0.77             0.01    0.30    0.95     0.44  95.91 Nere_leu         0.70             0.00    0.29    0.33     0.43  96.34 Derm_imb         0.71             0.00    0.27    0.79     0.40  96.74 Clad_spp         0.69             0.07    0.27    0.94     0.39  97.13 Acro_coa         0.51             0.03    0.21    0.65     0.30  97.43 Hemi_ore         0.45             0.00    0.18    0.52     0.26  97.69 Odon_spp         0.39             0.00    0.16    0.59     0.23  97.92 Chon_exp         0.38             0.00    0.15    0.93     0.22  98.13 Unk_Spon         0.00             0.30    0.12    0.99     0.17  98.31 Core_spp         0.00             0.24    0.10    0.76     0.14  98.45 Kath_tun         0.21             0.00    0.09    0.50     0.13  98.57 Tect_scu         0.19             0.00    0.08    0.69     0.11  98.69 Desm_vir         0.14             0.04    0.07    0.45     0.10  98.79 Grat_dor         0.17             0.00    0.07    0.65     0.10  98.88 Limp_juv         0.16             0.04    0.07    0.67     0.10  98.98 Ulva_lin         0.16             0.00    0.06    0.75     0.09  99.07 Canc_mag         0.00             0.14    0.06    0.54     0.08  99.16 Cost_cos         0.13             0.00    0.05    0.65     0.08  99.23 Whit_ane         0.00             0.13    0.05    1.09     0.08  99.31 Serp_ver         0.06             0.12    0.05    1.03     0.08  99.39 Noto_per         0.12             0.00    0.05    0.72     0.07  99.46 Porp_spp         0.10             0.10    0.04    1.24     0.07  99.52 Spar_per         0.08             0.00    0.03    0.44     0.04  99.57 Poly_lat         0.08             0.00    0.03    0.60     0.04  99.61 Lott_pel         0.06             0.00    0.03    0.30     0.04  99.65 Chiton         0.06             0.01    0.02    0.63     0.04  99.69 Metri_sen         0.00             0.06    0.02    0.83     0.04  99.72 Embi_lat         0.00             0.06    0.02    0.44     0.03  99.75 Cryp_spp         0.06             0.00    0.02    0.51     0.03  99.78  236  A.4 (continued) Mela_int         0.04             0.01    0.02    0.67     0.03  99.81 Enc_bryo         0.04             0.00    0.02    0.56     0.03  99.84 Desm_mun         0.00             0.04    0.02    0.44     0.02  99.87 Cnem_fin         0.00             0.03    0.01    1.25     0.02  99.88 Ophi_spp         0.03             0.00    0.01    0.30     0.02  99.90 Eudi_van         0.00             0.03    0.01    0.50     0.02  99.92 Unk_clam         0.03             0.00    0.01    0.30     0.02  99.93 Herm_cra         0.02             0.00    0.01    0.34     0.01  99.95 Abie_spp         0.02             0.00    0.01    0.30     0.01  99.96 Alar_mar         0.02             0.00    0.01    0.44     0.01  99.97 Para_cal         0.00             0.01    0.00    0.44     0.01  99.98 Tegu_fun         0.01             0.00    0.00    0.33     0.01  99.98 Lott_dig         0.01             0.00    0.00    0.54     0.00  99.99 Tect_fen         0.01             0.00    0.00    0.33     0.00  99.99 Sculpin         0.00             0.00    0.00    0.67     0.00  99.99 Peta_fas         0.00             0.00    0.00    0.44     0.00 100.00 Onch_bil         0.00             0.00    0.00    0.44     0.00 100.00 Olig_mac         0.00             0.00    0.00    0.44     0.00 100.00 Worm_red         0.00             0.00    0.00    0.37     0.00 100.00 Idot_wos         0.00             0.00    0.00    0.30     0.00 100.00 Crangon         0.00             0.00    0.00    0.44     0.00 100.00 Paga_spp         0.00             0.00    0.00    0.30     0.00 100.00      237   A.5 Correlation matrix of environmental variables used in the DISTLM.   Light max Light ave T max Tave Modified Fetch Water Motion 3.5 m Water Motion 1.5 m Light ave 0.811        Tmax 0.727 0.902       Tave 0.453 0.791 0.920      Fetch mod -0.176 -0.353 -0.433 -0.502     Water Motion 3.5 m -0.754 -0.695 -0.517 -0.316 0.365   Water Motion 1.5 m 0.096 0.433 0.261 0.412 -0.278 -0.343  Slope    -0.493 -0.552 -0.620 -0.487 0.258 0.337 0.345    238  Appendix B  Referencing Chapter 4     239  B.1 PerMANOVA pairwise test results for habitat within the habitat x year x height interaction term Permutational MANOVA  Resemblance worksheet Name: Centroid ha.yr.he.lo Data type: Distance Selection: All Resemblance: S17 Bray Curtis similarity  Sums of squares type: Type III (partial) Fixed effects sum to zero for mixed terms Permutation method: Permutation of residuals under a reduced model Number of permutations: 4999  Factors Name Abbrev. Type Levels habitat ha Fixed      4 year ye Fixed      3 height he Fixed      5  i=in, p=tidepool, o=outer, v=vertical habitat  PAIR-WISE TESTS  Term 'haxyexhe' for pairs of levels of factor 'habitat'  Within level '11' of factor 'year' Within level '0' of factor 'height'                  Unique Groups       t P(perm)  perms i, p 0.33878  0.8924     35 i, o 0.26459       1     35 i, v  1.6317  0.0862     35 p, o 0.41644  0.9708     35 p, v  1.3273   0.164     35 o, v  1.8357  0.0574     35  240  B.1 (continued) Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 53.137                      p 46.124 58.585               o 39.474 42.462 41.242        v 55.042 54.865 53.421 40.404  Within level '11' of factor 'year' Within level '1' of factor 'height'                  Unique Groups       t P(perm)  perms i, p  1.1736  0.2654     35 i, o  0.7247  0.5148     35 i, v  1.9879  0.0284     35 p, o 0.87352   0.567     35 p, v  3.5166  0.0294     35 o, v  2.3566  0.0274     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6   241  B.1 (continued) Average Distance between/within groups       i      p      o      v i 37.231                      p  37.62 30.417               o 35.036 34.416  40.42        v 39.463 49.731 45.843 18.698  Within level '11' of factor 'year' Within level '2' of factor 'height'                  Unique Groups       t P(perm)  perms i, p 0.42996  0.7636     35 i, o 0.92254   0.495     35 i, v   1.379  0.1766     35 p, o 0.61899  0.6436     35 p, v  1.4481  0.2106     35 o, v  1.7502  0.0798     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 32.732                      p 30.43 39.371               o 27.646 28.968  24.45        v 25.298 30.718 24.893 14.732  Within level '11' of factor 'year' Within level '3' of factor 'height'  242  B.1 (continued)                 Unique Groups      t P(perm)  perms i, p 1.3816   0.142     35 i, o 1.7148  0.0596     35 i, v 3.2032  0.0282     35 p, o 2.2815  0.0324     35 p, v  3.519   0.031     35 o, v 1.8704   0.059     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 19.966                      p 24.665 25.189               o 28.005 35.745 24.672        v 39.887 46.924 30.326 21.095  Within level '11' of factor 'year' Within level '4' of factor 'height'                  Unique Groups       t P(perm)  perms i, p  1.9289  0.0904     35 i, o 0.40833  0.7062     35 i, v  1.7861  0.0518     35 p, o  2.1366  0.0278     35 p, v  2.1509  0.0302     35 o, v    1.81   0.112     35   243  B.1 (continued) Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 14.082                      p 41.149 42.138               o 10.256 42.333 10.473        v 20.103 45.932 17.145 16.973  Within level '10' of factor 'year' Within level '0' of factor 'height'                 Unique Groups      t P(perm)  perms i, p 1.3845   0.148     35 i, o 1.4256  0.0854     35 i, v 4.0568  0.0286     35 p, o 3.4522  0.0262     35 p, v 5.2245  0.0278     35 o, v 4.0811   0.025     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6   244  B.1 (continued) Average Distance between/within groups       i      p      o      v i 24.728                      p 22.404  15.82               o 22.562 31.623 14.972        v 60.293 66.597 54.195 27.437  Within level '10' of factor 'year' Within level '1' of factor 'height'                  Unique Groups       t P(perm)  perms i, p 0.82584  0.6392     35 i, o 0.94489  0.4618     35 i, v    3.05  0.0264     35 p, o  1.1198  0.3226     35 p, v  3.6544  0.0264     35 o, v  3.1112  0.0298     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 14.922                      p  13.42  13.13               o 15.049 14.799 15.164        v 27.329  30.14  28.33 15.368  Within level '10' of factor 'year' Within level '2' of factor 'height'  245  B.1 (continued)                  Unique Groups       t P(perm)  perms i, p  1.4195  0.2656     35 i, o  1.2964  0.1892     35 i, v  2.2175  0.0274     35 p, o 0.75614  0.6044     35 p, v  2.3628  0.0274     35 o, v  1.8714  0.0268     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o     v i 9.9313                     p 9.7768 8.5936              o 13.141 11.032 13.849       v 26.154  26.62 24.658 24.67  Within level '10' of factor 'year' Within level '3' of factor 'height'                 Unique Groups      t P(perm)  perms i, p 2.1298   0.032     35 i, o 3.4972  0.0282     35 i, v 22.315  0.0258     35 p, o 3.2916  0.0252     35 p, v 10.321  0.0304     35 o, v 3.2711  0.0272     35   246  B.1 (continued) Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 1.9649                      p 9.9707 10.207               o 26.958 29.319 19.783        v 40.448 42.338 25.956 4.3567  Within level '10' of factor 'year' Within level '4' of factor 'height'                 Unique Groups      t P(perm)  perms i, p 4.5243  0.0286     35 i, o 1.1935  0.2304     25 i, v 1.5297  0.0274     35 p, o 5.5094  0.0272     25 p, v 5.3797   0.027     35 o, v 2.4886   0.028     25  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6   247  B.1 (continued) Average Distance between/within groups       i      p      o      v i 11.581                      p 45.173 20.346               o 6.9906 47.311 1.3848        v 9.4954 46.708 4.4757 3.8735  Within level '9' of factor 'year' Within level '0' of factor 'height'                  Unique Groups       t P(perm)  perms i, p  1.3322  0.2288     35 i, o 0.89492  0.5648     35 i, v  1.0702   0.286     35 p, o  1.4672  0.1726     35 p, v  1.8875  0.0606     35 o, v  1.0406  0.3912     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i  12.81                      p 21.419 25.017               o 16.581 26.738 21.199        v 22.905 34.552 25.598 27.708  Within level '9' of factor 'year' Within level '1' of factor 'height'  248  B.1 (continued)                  Unique Groups       t P(perm)  perms i, p  1.4291   0.116     35 i, o  1.4276  0.1806     35 i, v  4.3711  0.0292     35 p, o 0.49879  0.8298     35 p, v  2.7396  0.0596     35 o, v   3.977  0.0276     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 16.309                      p 19.778 19.133               o 15.736 13.877 11.311        v 30.953 23.111 22.045 7.0729  Within level '9' of factor 'year' Within level '2' of factor 'height'                 Unique Groups      t P(perm)  perms i, p  1.187  0.2292     35 i, o 1.0838  0.3816     35 i, v 4.5041  0.0278     35 p, o 1.2572  0.2516     35 p, v 4.8002  0.0302     35 o, v 4.1534  0.0296     35   249  B.1 (continued) Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o     v i 25.305                     p 29.751 28.923              o 26.009 28.558 24.556       v 49.933 54.208 44.551 3.989  Within level '9' of factor 'year' Within level '3' of factor 'height'                 Unique Groups      t P(perm)  perms i, p 2.5566  0.0298     35 i, o 2.7707  0.0604     35 i, v 7.0845  0.0246     35 p, o 3.4265  0.0286     35 p, v 7.5908  0.0262     35 o, v 4.1868  0.0308     35  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6   250  B.1 (continued) Average Distance between/within groups       i      p      o      v i 14.003                      p 24.022 16.306               o 26.996 34.409 18.695        v 61.422 67.751 41.297 17.677  Within level '9' of factor 'year' Within level '4' of factor 'height'                 Unique Groups      t P(perm)  perms i, p 8.2519  0.0302     35 i, o 1.7346  0.0352     15 i, v 2.6525  0.0282     35 p, o 9.6232  0.0292     15 p, v 6.0073  0.0308     35 o, v  2.881  0.0306     14  Denominators Groups Denominator Den.df i, p 1*Res      6 i, o 1*Res      6 i, v 1*Res      6 p, o 1*Res      6 p, v 1*Res      6 o, v 1*Res      6  Average Distance between/within groups       i      p      o      v i 7.1333                      p 66.206 18.957               o    5.2 71.347    0.2        v 25.569 70.279 25.187 21.952  251          B.2 Box and whisker plots showing percent cover of functional group at four locations by habitat (inner = horizontal lines, tidepool = dotted, outer = clear, vertical = grey) and year (2009, 2010 and 2011) at five intertidal heights (0, 1, 2, 3, 4 metres above CD).   252         Appendix B.2 (continued).  253        Appendix B.2 (continued).  254        Appendix B.2 (continued).   255     Appendix B.2 (continued).       256  B.3 Bivariate correlation matrix for functional groups in 2009, 2010, and 2011. Correlations 2009 Bare Chlorophyta Phaeophyta Rhodophyta Diatoms Arthropoda Bivalvia Echinodermata Gastropoda Height Habitat Location Bare  Pearson Correlation 1            Sig. (1-tailed)             Chlorophyta Pearson Correlation .137 1           Sig. (1-tailed) .113            Phaeophyta Pearson Correlation -.192* -.103 1          Sig. (1-tailed) .044 .181           Rhodophyta Pearson Correlation -.010 -.005 .362** 1         Sig. (1-tailed) .465 .481 .000          Diatoms Pearson Correlation -.089 .166 -.103 -.086 1        Sig. (1-tailed) .215 .071 .182 .224         Arthropoda Pearson Correlation -.361** -.148 -.210* -.070 -.168 1       Sig. (1-tailed) .001 .095 .030 .269 .068        Bivalvia Pearson Correlation -.807** -.512** .224* -.005 -.117 .058 1      Sig. (1-tailed) .000 .000 .023 .482 .151 .305       Echinodermata Pearson Correlation .a .a .a .a .a .a .a .a     Sig. (1-tailed) . . . . . . .      Gastropoda Pearson Correlation -.098 -.053 -.153 -.066 -.092 .467** -.058 .a 1    Sig. (1-tailed) .194 .321 .087 .282 .208 .000 .306 .     *. Correlation is significant at the 0.05 level (1-tailed). **. Correlation is significant at the 0.01 level (1-tailed). a. Cannot be computed because at least one of the variables is constant.    257   Appendix B.3 (continued). Correlations 2010 Bare Chlorophyta Phaeophyta Rhodophyta Diatoms Arthropoda Bivalvia Echinodermata Gastropoda Bare Pearson Correlation 1         Sig. (1-tailed)          Chlorophyta Pearson Correlation .200* 1        Sig. (1-tailed) .038         Phaeophyta Pearson Correlation -.128 -.122 1       Sig. (1-tailed) .128 .140        Rhodophyta Pearson Correlation -.054 -.161 .156 1      Sig. (1-tailed) .316 .077 .083       Diatoms Pearson Correlation .092 -.185 .080 .239* 1     Sig. (1-tailed) .208 .050 .241 .016      Arthropoda Pearson Correlation -.403** -.122 .159 -.006 -.272** 1    Sig. (1-tailed) .000 .140 .079 .479 .007     Bivalvia Pearson Correlation -.943** -.405** .066 -.005 -.164 .326** 1   Sig. (1-tailed) .000 .000 .282 .483 .073 .002    Echinodermata Pearson Correlation -.057 -.118 .042 .148 .064 .004 -.003 1  Sig. (1-tailed) .308 .148 .355 .095 .285 .485 .489   Gastropoda Pearson Correlation .331** -.023 -.035 -.168 -.196* .065 -.298** -.112 1 Sig. (1-tailed) .001 .419 .379 .068 .041 .282 .004 .162         258    Appendix B.3 (continued).  Correlations 2011 Bare Chlorophyta Phaeophyta Rhodophyta Diatoms Arthropoda Bivalvia Echinodermata Gastropoda Bare Pearson Correlation 1         Sig. (1-tailed)          Chlorophyta Pearson Correlation .022 1        Sig. (1-tailed) .424         Phaeophyta Pearson Correlation -.220* -.019 1       Sig. (1-tailed) .025 .434        Rhodophyta Pearson Correlation -.095 .094 -.072 1      Sig. (1-tailed) .202 .203 .262       Diatoms Pearson Correlation -.201* .194* -.005 .304** 1     Sig. (1-tailed) .037 .043 .482 .003      Arthropoda Pearson Correlation -.444** -.147 .363** -.094 -.178 1    Sig. (1-tailed) .000 .097 .000 .204 .057     Bivalvia Pearson Correlation -.884** -.260** .211* -.106 -.105 .419** 1   Sig. (1-tailed) .000 .010 .030 .174 .177 .000    Echinodermata Pearson Correlation .031 -.158 -.143 -.071 -.119 -.102 -.067 1  Sig. (1-tailed) .393 .080 .103 .266 .147 .183 .276   Gastropoda Pearson Correlation .366** .087 -.045 .016 -.168 -.131 -.332** -.118 1 Sig. (1-tailed) .000 .222 .345 .444 .069 .123 .001 .149     259   Appendix C  Referencing Chapter 5      260  C.1 Attributes for determining success of restored ecosystems (SER 2004). 1 The restored ecosystem contains a characteristic assemblage of the species that occur in the reference ecosystem and that provide appropriate community structure. 2 The restored ecosystem consists of indigenous species to the greatest practicable extent. In restored cultural ecosystems, allowances can be made for exotic domesticated species and for non-invasive ruderal and segetal species that presumably co-evolved with them. Ruderals are plants that colonize disturbed sites, whereas segetals typically grow intermixed with crop species. 3 All functional groups necessary for the continued development and/or stability of the restored ecosystem are represented or, if they are not, the missing groups have the potential to colonize by natural means. 4 The physical environment of the restored ecosystem is capable of sustaining reproducing populations of the species necessary for its continued stability or development along the desired trajectory. 5 The restored ecosystem apparently functions normally for its ecological stage of development, and signs of dysfunction are absent. 6 The restored ecosystem is suitably integrated into a larger ecological matrix or landscape, with which it interacts through abiotic and biotic flows and exchanges. 7 Potential threats to the health and integrity of the restored ecosystem from the surrounding landscape have been eliminated or reduced as much as possible. 8 The restored ecosystem is sufficiently resilient to endure the normal periodic stress events in the local environment that serve to maintain the integrity of the ecosystem. 9 The restored ecosystem is self-sustaining to the same degree as its reference ecosystem, and has the potential to persist indefinitely under existing environmental conditions. 10 Goods and services to improve the social good 11 Habitat for rare species or act as a gene pool 12 Aesthetics or accommodation of social activity   261  C.2 Example of scoring the RESTORE tool Table 2.3 for the Vancouver Convention Centre West (VC).  262  Appendix C.2 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Scoring and rationale Ecosystem structure – indicator species Main indicator species are present; there may be species expected in an early successional assemblages that differ from the mature or desired assemblage.  Trophic structure is as expected.  (SER attribute 1) Indicator species presence: compare species presence with reference or model ecosystem. Sample for abundance if possible. Score out of 5. Trophic structure: compare trophic structure with reference or model ecosystem; ensure number of trophic levels are the same. Score out of 5.  Score = Indicator species score + Trophic Structure score  Best - all indicator species present and established 4-5 points; all trophic levels present 4, with redundancy of  key groups 5 points Score = 3 + 3 = 6 Rationale:  Main indicator species present (3); all trophic levels present, but not stable throughout year (3). Red, green and brown algae all present (less than expected); sessile invertebrates present (dominant); various crab species and sea stars present, but distribution appears limited; salmonids, perch and small demersal fish present; gulls and diving ducks present and feeding on structure. References:   Chapters 3 and 4, EBA 2013. Pass - main indicator species present 3-4 points; all trophic levels present, but not stable throughout year 1-3 points Fail – Some, but not all of the main indicator species present and not expected to be form target assemblage over the long-term 2-3 points; and, trophic structure incomplete (e.g., Zostera marina sparse (≤5%); trophic levels include eelgrass, crab – no fish or grazers) 1-2 points. Worst - indicator species absent 0-1 points, undesirable species or environmental conditions present that is preventing long-term sustainability; trophic structure incomplete 0-1 points.  263  Appendix C.2 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Scoring and rationale Ecosystem structure – local genotypes Native and indigenous species are used and present including local genotypes in an abundance sufficient to maintain a reproducing population.  Any culturally significant plants targeted have established successfully.  (SER attribute 2)   Species presence: proportion of individuals from local sources and gene pools, including transplanting from similar local donor populations. Success of (re)establishment of culturally significant species with minimal impact on main indicator species. Score= most suitable detail.  Best - all species present are native and/or culturally desired, established from local genotypes and are reproducing successfully with recruitment observed 8-10 points. Score = 9 Rationale:  All species present are native and/or culturally desired, established from local genotypes and are reproducing successfully with recruitment observed. All species present recruit from the local water column and were non-invasive. References:   Chapters 3 and 4, EBA 2013. Pass - all species present are native and/or culturally desired, but successful establishment may still not be determined 7 points; not all species established from local genotypes (i.e., nursery stock) with reproduction success still not confirmed 5-6 points. Fail - reproductive success is low and likely not self-sustaining, maintenance is required; species not from local genotypes; non-native species present (>5%) 2-4 points;   264  Appendix C.2 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Scoring and rationale Worst - native species failed to survive (<25%), reproduce, or recruit to RA. RA does not appear to self-sustaining in desired state 0-1 points. Ecosystem Structure - age class All expected age classes are represented in the restoration area where applicable; or if the habitat provides value to a specific life history stage (salmon spawning, rearing) it is represented.  Age class presence: presence of all targeted age classes for indicator species. Sample for estimated survivorship of age class and compare age-frequency with predicted value or an appropriate reference population, if possible.  Score= most suitable detail. Best - all targeted age classes present and use RA as intended 8 points; attributes observed for more than one life cycle 9-10 points. Score = 5 Rationale:  All targeted age classes present or are expected to establish with time.  Mature kelp on the low bench did not establish as expected, but is present; Rockweed is also limited in the north side, likely due to shading. Limited predation at mid-tidal level by sea stars – present but may have limited access. References:   Chapters 3 and 4, EBA 2013. Pass - all targeted age classes present or are expected to establish with time 5-7 points. Fail - targeted life stage not present, but could establish in future 4 points, and/or not using RA 3-4 points. Worst - targeted age class not present 1 point; age specific mortality to targeted life stage 0 points.  265  Appendix C.2 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Scoring and rationale Ecosystem structure – functional groups All functional groups expected from reference ecosystems are present or have the potential to colonize and recruit with time. (SER attribute 3)   Functional group presence: compare presence of functional groups with reference or model ecosystem. (e.g. algae, filter feeders, grazers, 1o carnivores, 2o carnivores, decomposers).  Can compare number of connections in the food web with reference of model system for more quantitative and Best - all functional groups present with redundancy where expected (e.g., tidal marsh - rush and Carex species, major macrofaunal families, Cyprinids and juvenile salmonids (O. tshawytscha), various water fowl) 8-10 points. Score = 9 Rationale: All functional groups present, little redundancy in primary producers and presence may be limited throughout year at some tidal heights. (Note: this score was given a range to 7, as all functional groups are present, but the lack of kelp at the low tidal level cold lead to a lower, but passing score) References:   Chapters 3 and 4, EBA 2013. Pass - all functional groups present (e.g., tidal marsh – C. lyngbyei, major macrofaunal families, Cyprinids, waterfowl) 5-7 points. Fail - Some functional groups not present or may be represented by invasive species (e.g., tidal marsh – primary producers by Spartina alternifolia instead of C. lyngbyei) 4-5 points.  266  Appendix C.2 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Scoring and rationale detailed method, if possible. Score= most suitable detail.  Worst – Indicator functional groups not present 1 point and/or barriers exist to their establishment 0 points (e.g., accreted tidal channel – no juvenile salmonids, or trapped after freshet; extensive Phalaris arundinacea). Ecosystem function - overall Restoration habitat functions as intended, signs of dysfunction are absent.  (SER attribute 5)   Are bio-physical structure and interactions among organisms and the environment occurring as expected or as in reference or model ecosystems? This attribute makes an overall assessment of the functionality of the RA that may not be Best – Within ecosystem interactions are occurring as expected; RA structure is stable or improving for the long-term (e.g., spawning fish present in new channel and returning annually) 8-10 points. Score = 6 Rationale: Ecosystem performs functions as intended, although some functions may not yet be fully developed, but appears to be stable or improving with time. The RA performs all ecosystem functions as planned including: primarily a migration corridor for juvenile Pass –Ecosystem performs functions as intended, although some functions may not yet be fully developed, but appears to be stable or improving with time 5-7 points (e.g., spawning fish present  in new channel).  267  Appendix C.2 RESTORE score sheet describing each field, attribute, measurement and scoring details. Field: Ecological function and structure. Score each attribute based upon its positive or negative contribution to the overall sustainability of ecological values for each restoration area (RA). Ecological structure and function is defined as the way species assemble with respect to abundance, size, age class, function, and trophic level. Ecological structure and function includes the presence and dynamic interactions among an assemblage of organisms that are expected to occur within a restoration community, and the valued processes these species assemblages provide to the restoration community and its environment. Best (10, 9, 8); Pass (7, 6, 5), Fail (4, 3, 2) Worst (1, 0) (SER 2004). Attribute description Measure Scoring details Scoring and rationale assessed though its individual components.  Score= most suitable detail.  Fail – Ecosystem does not perform functions as intended but may with maintenance 3-4 points; some function may not yet be fully developed and appears to be declining 3 points (e.g., spawning fish present in new channel, few due to partial siltation, low water flow). salmonids, shoreline protection, support for primary producers, and structure and food for fish. References:   Chapters 3 and 4, EBA 2013. Worst - Ecosystem does not function as intended, is in an alternate or deteriorating state and may not re-establish desired level of function with maintenance 0-1 points (e.g., spawning fish present in new channel, no fry due to siltation, low water flow).   268  Appendix C.2  (continued)  Environment, landscape and connectivity: Score each attribute based upon it positive or negative contribution to the physical environment, landscape and connectivity. The physical environment includes the quality of water, soil, sediment, and air; and how they affect the species present in the RA. Landscape quality and connectivity relates to the abi