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Species invasion in the marine fouling communities of British Columbia : factors that influence invasion.. Nelson, Jocelyn Christine 2014-12-31

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Species invasion in the marine foulingcommunities of British Columbia:factors that influence invasiondynamics and how they may affectBotrylloides violaceusbyJocelyn Christine NelsonB.Sc., The University of British Columbia 2009A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinThe Faculty of Graduate and Postdoctoral Studies(Zoology)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)April 2014c© Jocelyn Christine Nelson 2014AbstractSpecies invasion has been recognized as a major threat to biodiversity. Knowledge of thefactors that limit the establishment and spread of non-indigenous species (NIS), such asbiotic resistance and unfavourable environmental conditions, are important to their effec-tive management. To test the biotic resistance and environmental favourability hypothesesin the fouling communities of British Columbia (BC), 22 locations were compared usingsettlement tiles in a large-scale survey. Biotic resistance is believed to be stronger inmore diverse communities, therefore NIS richness and abundance were compared to nativespecies richness and environmental conditions to investigate their importance using gener-alized and linear mixed models. Invader taxonomic group may influence biotic resistance,and environmental tolerances vary by species, therefore factors that affected Botrylloidesviolaceus presence and abundance were investigated as a case study. The biotic resistancehypothesis was not supported for NIS richness or NIS abundance, but could not be fullydiscounted due to a trend toward a negative slope between native species richness andB. violaceus presence and abundance, and the absence of predator data. Environmentalvariables affected NIS: salinity had a positive influence on NIS richness, NIS abundance,and B. violaceus presence, and temperature had a positive effect on B. violaceus presenceand abundance. Salinity had a positive impact on native species richness as well, support-ing the environmental favourability hypothesis. This suggests that knowledge of relevantenvironmental conditions is more important for the management of invasive species thaniithe species richness of vulnerable communities.Environmental conditions are not static, so species invasion must be considered in thecontext of climate change. To understand how climate change may influence species in-vasion, B. violaceus presence and abundance in BC were compared to a range of abioticconditions. This comparison informed a GAMLSS model that used linear trends fromhistorical shore station data to project potential abundance in BC forward 50 years. Over-all, the abundance of B. violaceus in BC was projected to increase. A larger increase inB. violaceus abundance was forecast for locations where conditions increased into the rangefavourable for growth. If temperature and salinity become more favourable for B. violaceus,as projected, climate change could intensify the invasion.iiiPrefaceDrs. Christopher D. G. Harley and Thomas W. Therriault guided the research. The surveywas designed by Jocelyn C. Nelson, Dr. Christopher D. G. Harley, Dr. Thomas W. Ther-riault and Dr. Claudio DiBacco, with help from Dr. Cathryn Clarke Murray. The fieldwork in British Columbia was conducted by Jocelyn C. Nelson. The field work in Califor-nia was conducted by Jocelyn C. Nelson, Dr. Christopher D. G. Harley, and Theraesa A.Coyle. Equipment for field work was provided by Dr. Thomas W. Therriault and Fisheriesand Oceans Canada. Specimens were transferred by Francis Choi and Garth Covernton.Tile analysis was performed by Jocelyn C. Nelson and Jake A. Hupman. Loggers werecalibrated by Michael MacGillivary, Andrea Moore and Dr. Claudio DiBacco. Jocelyn C.Nelson wrote the first version of the thesis, and Drs. Christopher D. G. Harley and ThomasW. Therriault edited the thesis. Comments were provided by Drs. Mary O’Connor, KaiChan and Jill Jankowski.For Chapter 2, the data were analyzed by Jocelyn C. Nelson with guidance and R codefrom Dr. Mary O’Connor.For Chapter 3, Jocelyn C. Nelson built the model in collaboration with Laura Tremblay-Boyer.This work was funded by the Second Canadian Aquatic Invasive Species Network(CAISN II), Fisheries and Oceans Canada, a UBC Biodiversity Research Integrative Teach-ing and Education (BRITE) internship grant, a McLean Fraser Memorial Scholarship, andivtwo Faculty of Science Graduate Awards. Research performed in California for Chapter 3was authorized by the California Department of Fish and Game (permit number SC-11710),Sonoma County Regional Parks (permit number 407), Moss Landing Harbor District, andthe county of San Mateo Parks and Recreation division.An animal care certificate was not required in the completion of this work.vTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiAcknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiii1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Species invasion in the northeast Pacific . . . . . . . . . . . . . . . . . . . . 11.2 Where will species invade? . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.3 Structure of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Patterns of invasion in British Columbia marine fouling communities:Biotic resistance or environmental favourability? . . . . . . . . . . . . . 62.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.1.1 Determinants of invasion success:Biotic resistance or environmental favourability? . . . . . . . . . . . 6vi2.1.2 B. violaceus and the fouling community: A case study . . . . . . . . 92.1.3 Research questions . . . . . . . . . . . . . . . . . . . . . . . . . . . 102.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.1 Field survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112.2.2 Modelling approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 132.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3.1 Field survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172.3.2 Factors that influenced NIS richness . . . . . . . . . . . . . . . . . . 192.3.3 Factors that influenced NIS abundance . . . . . . . . . . . . . . . . 212.3.4 Factors that influenced B. violaceus presence . . . . . . . . . . . . . 242.3.5 Factors that influenced B. violaceus abundance . . . . . . . . . . . . 282.3.6 Factors that influenced native species richness . . . . . . . . . . . . 322.3.7 Factors that influenced native species abundance . . . . . . . . . . . 352.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382.4.1 NIS and native species distribution patterns in the fouling commu-nity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 382.4.2 B. violaceus distribution pattern in the fouling community . . . . . 412.4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443 Climate change and species invasion: using spatial variation in temper-ature and salinity to forecast potential changes in Botrylloides violaceusabundance in British Columbia . . . . . . . . . . . . . . . . . . . . . . . . . 453.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.1.1 The role of climate change in species invasion . . . . . . . . . . . . 453.1.2 Abiotic conditions in British Columbia and implications for B. vio-laceus invasions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47vii3.1.3 Research question . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.2.1 Field survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.2.2 Generalized additive model for location, scale and shape . . . . . . 553.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.3.1 Field Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583.3.2 Generalized additive model for location, scale and shape . . . . . . 593.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653.4.1 Potential change in B. violaceus abundance . . . . . . . . . . . . . . 653.4.2 Implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 683.4.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.1 Summary of the results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 714.2 Limitations of the research . . . . . . . . . . . . . . . . . . . . . . . . . . . 754.3 Future directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 764.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79AppendicesA Sampling site GPS locations . . . . . . . . . . . . . . . . . . . . . . . . . . . 91B Species list for BC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93C Abiotic conditions per site . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96viiiD Primary model results for sets with a dropped variable . . . . . . . . . 99E Correlations of fixed effects . . . . . . . . . . . . . . . . . . . . . . . . . . . 101E.1 NIS correlation tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101E.2 B. violaceus correlation tables . . . . . . . . . . . . . . . . . . . . . . . . . 103E.3 Native species correlation tables . . . . . . . . . . . . . . . . . . . . . . . . 105F Variation in shore station trends from 1967–2011 . . . . . . . . . . . . . 107ixList of Tables2.1 Main set of models used to evaluate the alternative hypotheses . . . . . . . 152.2 Species richness per type for each location . . . . . . . . . . . . . . . . . . . 182.3 Equivalent AICc models for NIS richness . . . . . . . . . . . . . . . . . . . . 202.4 Variable estimates for NIS richness . . . . . . . . . . . . . . . . . . . . . . . 212.5 Equivalent AICc models for NIS abundance . . . . . . . . . . . . . . . . . . 232.6 Variable estimates for NIS abundance . . . . . . . . . . . . . . . . . . . . . 242.7 Equivalent AICc models for B. violaceus presence . . . . . . . . . . . . . . . 262.8 Variable estimates for B. violaceus presence . . . . . . . . . . . . . . . . . . 272.9 Equivalent AICc models for B. violaceus abundance . . . . . . . . . . . . . 302.10 Variable estimates for B. violaceus abundance . . . . . . . . . . . . . . . . . 322.11 Equivalent AICc models for native species richness . . . . . . . . . . . . . . 332.12 Variable estimates for native species richness . . . . . . . . . . . . . . . . . 342.13 Equivalent AICc models for native species abundance . . . . . . . . . . . . 362.14 Variable estimates for native species abundance . . . . . . . . . . . . . . . . 373.1 B. violaceus thermal tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . 483.2 B. violaceus salinity tolerance . . . . . . . . . . . . . . . . . . . . . . . . . . 503.3 Equivalent AICc models for µ . . . . . . . . . . . . . . . . . . . . . . . . . . 60A.1 GPS locations for the BC sites . . . . . . . . . . . . . . . . . . . . . . . . . 91xA.2 GPS locations for the California sites . . . . . . . . . . . . . . . . . . . . . . 92B.1 Species list for British Columbia . . . . . . . . . . . . . . . . . . . . . . . . 93C.1 Salinity for the summer of 2011 in BC . . . . . . . . . . . . . . . . . . . . . 97C.2 Salinity for the summer of 2011 in California . . . . . . . . . . . . . . . . . 97C.3 Temperature for the summer of 2011 in BC . . . . . . . . . . . . . . . . . . 98C.4 Temperature for the summer of 2011 in California . . . . . . . . . . . . . . 98D.1 Equivalent AICc models for the primary set of models for NIS richness . . . 99D.2 Equivalent AICc models for the primary set of models for NIS abundance . 99D.3 Equivalent AICc models for the primary set of models for native speciesrichness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100E.1 Correlation of fixed effects for NIS richness . . . . . . . . . . . . . . . . . . 101E.2 Correlation of fixed effects for NIS abundance . . . . . . . . . . . . . . . . . 102E.3 Correlation of fixed effects for B. violaceus presence . . . . . . . . . . . . . . 103E.4 Correlation of fixed effects for B. violaceus abundance . . . . . . . . . . . . 104E.5 Correlation of fixed effects for native species richness . . . . . . . . . . . . . 105E.6 Correlation of fixed effects for native species abundance . . . . . . . . . . . 106F.1 Linear trends in minimum temperature between shore stations used for theprojections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107F.2 Linear trends in minimum salinity between shore stations used for the pro-jections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108xiList of Figures2.1 Map of field survey locations in BC . . . . . . . . . . . . . . . . . . . . . . . 122.2 Distribution of B. violaceus presence by minimum temperature and mini-mum salinity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283.1 Map of field survey locations in BC and California . . . . . . . . . . . . . . 543.2 Distribution of B. violaceus in BC in 2011 . . . . . . . . . . . . . . . . . . . 613.3 Difference between model of current B. violaceus distribution and observeddata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613.4 Field data compared to model current and future . . . . . . . . . . . . . . . 643.5 Projected increase of B. violaceus in BC . . . . . . . . . . . . . . . . . . . . 64xiiAcknowledgementsFirst, I am grateful to my supervisors, Chris Harley and Tom Therriault, for the guidance,support, and feedback that made this research possible. Thanks for encouraging anddeveloping my ideas, while making sure to keep them grounded. Your astute questionsbroadened my thinking and kept things interesting throughout this journey. Thank youfor putting up with the bombardment of emails and questions, punctuated by ominoussilences as I wandered down tangential lines of inquiry.Thank you to Cathryn Clarke Murray and my committee members, Mary O’Connor andKai Chan, for their insight and guidance with this project and for highlighting importantalternate perspectives.I wish to express my appreciation of Laura Tremblay-Boyer and Mary O’Connor forteaching me how to model my data, and their patience with my constant questions.The Second Canadian Aquatic Invasive Species Network and Fisheries and OceansCanada have my gratitude for funding this project and providing equipment.Many people helped this project come together. I would like to thank Jake Hupmanfor sorting and identifying samples, and for providing support and patience with my lovefor “sea junk”. Kyle Demes, Norah Brown, Manon Picard and Francis Choi, thanks foryour ideas, support and expertise. Laura Tremblay-Boyer, Mary O’Connor and Ed Gregr,thank you for taking the time to teach me about modelling. Thank you to Theraesa Coyleand Chris Harley for support in California, Garth Covernton for the lab assistance, andxiiiTed Hart and Carling Gerlinsky for the R code. Andrea Moore, Michael MacGillivary,and Claudio DiBacco, thank you for calibrating the HOBO loggers. Thank you HeidiGartner for providing your data to assist with my site selection. Greg Fergusen, thanks forrescuing my Digby Island site. Kevin Kinast, Chris Patton, and Craig A. Barney, thankyou for your help organizing and shipping the California samples. Haley Kenyon, thanksfor the motivation with the positive peer pressure. To my lab mates, Megan Vaughn,Kat Anderson, Becca Kordas, Jenn Jorve, and Becca Gooding, thanks for your help andsupport. I would like to give a special thanks to Gretchen Lambert and Jim Carlton foryour assistance and guidance with specimen identification.Thank you to all of the marinas and their staff for the use of their facilities. Including,but not limited to, Keri Weick and Rick Hill of Prince Rupert, the Digby Island Ferry crew,Steve Collinson of Queen Charlotte City, Al Ward of Masset, Heather Nelson-Smith of PortClements, Kathy of Sandspit Harbour, Daisy Hanson of Fair Harbour, Arlene Coburn ofZeballos, Tia Russell and the CAO of Tahsis, Larry Plourde of Gold River, Dave Riddell ofBamfield, Vince Payette of Tofino, Steve Bird of Ucluelet, Bruce Chappell, Gil and Pat ofToquart Bay, Phyllis Titus of Campbell River, Brad Jenkins and Trudy Atwood of ComoxBay, Julie Blood of French Creek, Tom Therriault for PBS, David and Cathy of MapleBay, Wayne Pullen of Sidney, Nancy, Collen and Lindsay for Eagle Harbour, Mark at theRVYC, Steve Scheiblauer of Monterey, Linda McIntyre of Moss Landing, Lisa Ekers, ChuckIzenstark and Kurt Fouts of Santa Cruz, John Draper of Pillar Point Harbor, Ed Hallett ofCoyote Point Marina, James Walter of South Beach Harbor, Pat Lopez, Ken Coykendall,and Betsy Oller of Loch Lomond, and Jill Meuchel of Bodega Bay.Last, but not least, thank you to all of my friends and family for the emotional support,their patience, and for reminding me that I am also a person, not just an extension of mycomputer.xivChapter 1Introduction1.1 Species invasion in the northeast PacificSpecies invasion has been recognized as one of the main threats to global biodiversity,ranked among land-use change and climate change (Sala et al., 2000). Species invasion con-tributes to the homogenization of previously distinct ecosystems (Simberloff et al., 2013),and can alter habitat, food webs, and resource availability (Bock et al., 2011; Carver et al.,2006; Crooks, 2002; Epelbaum et al., 2009a; Wonham & Carlton, 2005). The availability ofvectors for species introduction has increased due to the globalization of human societies,making it possible for species to invade locations that would not have been accessible bynatural dispersal (Crooks, 2002; Ruiz et al., 1997; Wonham & Carlton, 2005). The modernera of human-mediated marine introductions to the northeast Pacific began with the influxof Europeans to North America in the 1500s (Wonham & Carlton, 2005), though ma-rine introduced species were largely unnoticed until the late 1900s (Ruiz et al., 1997).In their new ranges, introduced species are referred to as non-indigenous, non-native,alien, or exotic (Mack et al., 2000). Introductions of marine species can occur throughmany vectors, such as intentional and accidental imports for aquaculture and fisheries,release of pets, and connections of water bodies via canals, but a substantial fraction ofintroductions appear to be mediated by shipping, either via ballast (water or sediment) orhull fouling, including sea chests (Coutts & Taylor, 2004; Minchin et al., 2009; Ruiz et al.,11997; Wonham & Carlton, 2005).Shellfish aquaculture, one of the main historical routes of transportation for non-indigenous species (NIS) to the northeastern Pacific, began with imports from the Atlanticcoast in the late 1800s and from the western Pacific in the early 1900s (Quayle, 1988; Won-ham & Carlton, 2005). Some NIS introductions were intentional for food cultivation, buthitchhikers associated with the target species were also unknowingly transported (Quayle,1988; Wonham & Carlton, 2005). In the northeastern Pacific, aquaculture imports areresponsible for 20 % of the introduced marine NIS while ballast water transport and hullfouling have contributed 13 % and 8 % respectively; these three routes are the most com-mon pathways for marine introductions (Wonham & Carlton, 2005). More recently, hullfouling of recreational boats has been identified as an important vector (Clarke Murrayet al., 2011; Davidson et al., 2010).While there is consensus on the vectors of invasion, the number of NIS present is stilluncertain. Wonham & Carlton (2005) documented 123 NIS have established in marineand estuarine waters of the Northeast Pacific (Cape Mendocino, California, USA, to HaidaGwaii , British Columbia, Canada), including 99 invertebrate species. However, a broad-scale survey using traps and settlement plates identified only 31 NIS on the west coast ofthe USA from San Diego, CA, to Kachemak Bay, AK (de Rivera et al., 2005). There isa great deal of difficulty in the detection of new species and identification of origin (Ruizet al., 1997; Wonham & Carlton, 2005). Even if a new species can be identified, the lackof historical taxonomic information can prevent tracing its origin since study of marineinvasions only began in earnest in the late 1970s, though new molecular techniques mayhelp clarify historic patterns (Grosholz, 2002).NIS can cause impacts through the introduction of novel parasites or pathogens toa region, increased competition for space or other resources with native species, direct2consumption of native species, genetic effects through hybridization or change in geneflow, and homogenization of ecosystems (Crooks, 2002). NIS may also be able to changefood webs and disturbance regimes (Crooks, 2002). In marine ecosystems, an influx ofnon-indigenous filter-feeding species may also change the rate of water filtration, whichcould lead to an altered distribution of biomass and energy in marine food webs (Byrnes& Stachowicz, 2009). The physical structure of the habitat can also be altered by NIS(Crooks, 2002; Wonham & Carlton, 2005), with the potential to reduce available spacefor recruitment of native species (Bock et al., 2011; Carver et al., 2006; Epelbaum et al.,2009a).In addition to ecological impacts, there are also financial consequences of many inva-sions. For example, the non-indigenous tunicates Styela clava and Ciona intestinalis havecaused dramatic economic losses to mussel aquaculture in Prince Edward Island (Leblancet al., 2007; LeGresley & Martin, 2008). Economic impacts associated with aquatic andterrestrial invasion in Canada are projected to be between $13.3 to 34.5 billion/year (Co-lautti et al., 2006). The study of species invasions has the capability to help mitigateimpacts by highlighting the areas of greatest potential risk through an understanding ofthe mechanisms influencing invasion patterns (Jeschke et al., 2012).1.2 Where will species invade?The likelihood of success and rate of species invasion are influenced by both biotic andabiotic factors such as competition, predation, resource availability, propagule pressure, andenvironmental conditions (Alpert et al., 2000; Simberloff, 2009). One hypothesis suggeststhat increased native diversity will reduce the establishment of NIS (Elton, 1958), calledthe biotic resistance hypothesis. Communities with greater diversity of native species mayuse resources with greater complementarity, or decrease the likelihood that the community3will be naive or susceptible to an invader (Kimbro et al., 2013). However, only six of11 marine empirical studies supported the biotic resistance hypothesis (Jeschke et al.,2012). Others have found that invasion risk increased with native species diversity (e.g.Davies et al., 2007; Dunstan & Johnson, 2004; Levine, 2000). This could be because thefavourable conditions found in a location could benefit both NIS and native species; thishypothesis was termed the environmental favourability hypothesis (Davies et al., 2007;Levine, 2000). In the context of marine ecosystems, environmental conditions such astemperature and salinity often have a large influence on species distribution and abundancepatterns, whether native or NIS (Epelbaum et al., 2009a; Rahel & Olden, 2008; Reusser& Lee II, 2008). Past studies have investigated whether biotic resistance can be detectedin the invasion of marine fouling communities, but results have been mixed (Dunstan &Johnson, 2004; Grey, 2009; Stachowicz et al., 2002a, 1999). Thus, the first goal of thisthesis was to investigate whether there was evidence in the pattern of species invasionin the marine fouling communities of British Columbia (BC) to support either the bioticresistance or the environmental favourability hypothesis.While the current status of invasion in fouling communities is important, the abioticconditions in which these communities exist will also be changing over time (Rosenzweiget al., 2007). This could change the distribution and abundance of NIS (Coˆte´ & Green,2012; Dukes & Mooney, 1999; Hellmann et al., 2008; Hoegh-Guldberg & Bruno, 2010;Lambert & Lambert, 2003; Rahel & Olden, 2008; Sorte et al., 2013, 2010a,b; Stachowiczet al., 2002b; Walther et al., 2009; Zerebecki & Sorte, 2011). Climate change is expectedto lead to warmer sea surface temperatures (Rosenzweig et al., 2007), and change pat-terns of precipitation, river discharge (Knowles & Cayan, 2004; Morrison et al., 2002), andevaporation rates (Scavia et al., 2002), which will alter the salinity of coastal BC waters.As temperature and salinity are important factors for the survival of marine species, it is4important to understand species invasion in the context of climate change. While alteredabiotic conditions may limit NIS spread if temperature and salinity become less favourable,it is more likely that increasing temperature and salinity would relax natural abiotic bar-riers to NIS survival and proliferation (Cockrell & Sorte, 2013; Dukes & Mooney, 1999;Hellmann et al., 2008; Hoegh-Guldberg & Bruno, 2010; Rahel & Olden, 2008; Sorte et al.,2013, 2010b; Stachowicz et al., 2002b; Walther et al., 2009; Zerebecki & Sorte, 2011). Us-ing Botrylloides violaceus Oka 1927 as a case study, the second goal of this thesis was tounderstand how climate change may influence the future distribution and abundance ofNIS.1.3 Structure of this thesisTo investigate the influence of native species richness and abiotic conditions on speciesinvasion in BC, I focused on the fouling communities found in marinas and harbours. InChapter 2, the biotic resistance and environmental favourability hypotheses were testedon non-indigenous species richness and abundance, with a specific investigation of thesehypotheses in the invasion of B. violaceus. In Chapter 3, the abundance of B. violaceuswas modelled in response to abiotic conditions. Then, using conditions expected as a resultof climate change, the potential change in B. violaceus abundance was projected forward50 years. Chapter 4 summarizes the research presented in the data chapters and puts it inthe context of current knowledge, with a discussion of its limitations and future directions.5Chapter 2Patterns of invasion in BritishColumbia marine foulingcommunities: Biotic resistance orenvironmental favourability?2.1 Introduction2.1.1 Determinants of invasion success:Biotic resistance or environmental favourability?Scientists have investigated and debated which factors influence where invasions will oc-cur, and which species will successfully invade, for decades. One of the early hypothesesdeveloped in invasion ecology was that systems with a more diverse set of native specieswould be able to resist the invasion of new species better than a system with fewer species,known as the biotic resistance hypothesis (Elton, 1958). Biotic resistance, exemplified bya negative relationship between native species diversity and non-indigenous species (NIS)diversity or abundance, was believed to be due to competitive exclusion at smaller spatialscales (Davies et al., 2007). Thus, sites with lower species diversity should be less resistant6to invasion, meaning that more NIS would be able to establish and spread, with evidenceto support this view derived from multiple systems (Jeschke et al., 2012; Stachowicz et al.,2002a, 1999).The relationship between native species diversity and NIS diversity is not always neg-ative. A recent meta-analysis found that only 55 % of marine empirical studies supportedthe biotic resistance hypothesis (6 out of 11) (Jeschke et al., 2012). To explain the positiverelationship that has been found between native species diversity and NIS diversity, analternative hypothesis was developed based on the idea that conditions that are favourableto native species should also be favourable for NIS, termed the environmental favourabilityhypothesis (Davies et al., 2007; Levine, 2000). In the decades since the biotic resistance hy-pothesis was developed, debate has followed about whether native communities with highernative species diversity are more able to resist invasion or whether hospitable environmentsfavour the establishment of both native and non-indigenous species. Low environmentalstress and abundant resources should favour both native species and NIS (Davies et al.,2007). Thus, the mechanisms that increase native species diversity should also apply to NISdiversity. A positive relationship between NIS and native species diversity would supportthe environmental favourability hypothesis.Environmental conditions that influence species richness, such as temperature and salin-ity, are often factors that have a large influence on species success or failure in marineenvironments (Epelbaum et al., 2009a; Rahel & Olden, 2008; Reusser & Lee II, 2008).Temperature and salinity can constrain the survival, reproduction and population growthof a species (Epelbaum et al., 2009a), and even affect recruitment timing of some NIS(Stachowicz et al., 2002a).Survival and establishment of species can also be affected by the number of propagulesthat reach an area, also known as propagule pressure. A steady supply of propagules can7increase the chance that a whole introduced population could persist through a rescue effect(Simberloff, 2009). In addition, it could increase genetic variability and thus enhance thelikelihood of survival in the conditions found in the introduced area (Simberloff, 2009). Assuch, high propagule pressure increases the chance of the successful establishment of a NIS(Clark & Johnston, 2009; von Holle & Simberloff, 2005). Vectors of propagules for aquaticspecies include hull-fouling, ballast water transfer, accidental imports for aquaculture orfisheries, release of pets, and connections of water bodies via canals (Minchin et al., 2009;Ruiz et al., 1997; Wonham & Carlton, 2005). Ports and harbours are often the first pointof introduction for ship-mediated vectors of NIS (Dafforn et al., 2009) and can be a sourceof secondary spread of propagules to the surrounding area via hull fouling, especially ofrecreational boats (Clarke Murray et al., 2011; Davidson et al., 2010).Biotic resistance and environmental favourability are not mutually exclusive (Cheng& Hovel, 2010; Fridley et al., 2007; Levine, 2000). The seemingly paradoxical finding ofsupport for both hypotheses has been suggested to be scale dependent, likely due to differentprocesses controlling the relationship at different spatial scales. Smaller scales are morelikely to be influenced by biotic resistance due to competition, while at larger scales highnative and non-indigenous diversity often correlate positively (Byers & Noonburg, 2003;Dunstan & Johnson, 2004; Shea & Chesson, 2002). This discrepancy at different scaleshas been found within multiple systems (Davies et al., 2005; Levine, 2000), but findingsare not always consistent (e.g. Davies et al., 2007; Dunstan & Johnson, 2004; Grey, 2009;Stachowicz et al., 2002a). The relative importance of the two mechanisms may also vary inspace, with one mechanism supplanting the other along a stress gradient (Cheng & Hovel,2010).82.1.2 B. violaceus and the fouling community: A case studyTaxonomic group of the invading species could affect the strength of biotic resistance fromthe native community (Kimbro et al., 2013). In addition, the importance of environmentalfactors vary by species (Ojaveer et al., 2011). To investigate whether the native foulingcommunities were able to resist the invasion of a representative non-indigenous species inBritish Columbia (BC), Botrylloides violaceus was selected. The factors that affect thisspecies were also compared to the factors that effect NIS as a group. On the east coast ofthe USA, Stachowicz et al. (1999) found that in fouling communities with higher speciesrichness, there was decreased survival of Botrylloides violaceus recruits. Stachowicz et al.(2002a) subsequently noted that increased native diversity reduced open space and thushindered NIS cover by more fully utilizing a limiting resource. On the west coast of theUSA, Grey (2009) also found biotic resistance when she examined NIS cover relative tonative species richness at local and regional scales in areas that included B. violaceus,though she did not find this trend at the community level.Two environmental variables, temperature and salinity, have been found to be impor-tant factors for B. violaceus survival, growth and reproduction, thereby contributing to itsspread (Dijkstra et al., 2008; Epelbaum et al., 2009a; Sorte et al., 2011). B. violaceus hasbroad temperature and salinity tolerances, so while there are no large-scale areas in BritishColumbia (BC) that are unfavourable to survival (Epelbaum et al., 2009a), some localizedsites may be uninhabitable due to low salinity or temperature, or out of the range neces-sary for growth or reproduction. In addition, Stachowicz et al. (2002b) found that warmerwinter temperatures led to earlier and greater recruitment of B. violaceus the followingsummer, though native species were not found to change recruitment timing with win-ter temperatures. In support of the environmental favourability hypothesis, Grey (2011)found that temperature and salinity were more influential in the success of B. violaceus9than species interactions.Propagule pressure is also an important factor for B. violaceus introduction and spreadas it is a common hull-fouling species. Adult colonies living on hulls may release tadpolelarvae that would be able to colonize nearby dock surfaces (Clarke Murray et al., 2011). Inaddition, due to a low dislodgement velocity, whole or partial colonies may detach from thehull along boats’ routes of travel (Clarke Murray et al., 2012). These colony fragments areable to reattach to new substrates as an efficient dispersal strategy (Bullard et al., 2007;Clarke Murray et al., 2012).2.1.3 Research questionsMany attributes of the environment and ecology of BC marine waters may influence thediversity and distribution of NIS, but the relative importance of abiotic and biotic factorsto invasion dynamics is not yet clear. Accordingly, the goal of this study was to answerthe following questions:1. Did biotic resistance or environmental favourability influence the patterns of invasionin the marine fouling communities of BC?2. Was there evidence of biotic resistance against the invasion of B. violaceus?To test whether the biotic resistance or environmental favourability hypothesis had greaterinfluence in marine fouling communities, species richness and abundance were measuredon controlled substrates across sites varying in environmental conditions. A set of mixed-effects models was constructed on the field data, where each model represented a hypothesisregarding the drivers of the observed pattern. These models were used to investigate therelative importance of biotic resistance and environmental favourability for NIS richnessand NIS abundance, and also if the result for NIS was applicable to a specific species, where10B. violaceus was used as a case study. The drivers of native species richness and abundancewere compared with the those of NIS patterns, to clarify whether marine species respondedsimilarly to ecological pressures or if the origin of a species was a partitioning factor.2.2 Methods2.2.1 Field surveyThis field survey utilized natural spatial variation in temperature, salinity and invasionlevel to explore the relationship between environmental conditions, community diversityand invasion success. Studies have shown that local diversity patterns of marine epifaunalcommunities are largely driven by regional patterns (Kimbro et al., 2013; Witman et al.,2004). To account for this, I surveyed across three ecoregions in one biogeographic realmin British Columbia (Spalding et al., 2007). Species richness was not manipulated becausethe disrupted mortality rate could alter the mechanisms that influence invasion patterns(Dunstan & Johnson, 2004) .Site descriptionsTwenty four sites were selected in three BC ecoregions: the north coast, the Salish Sea, andthe west coast of Vancouver Island (Figure 2.1, GPS locations in Appendix A, Table A).Regions were selected to have a 3 ◦C difference in mean summer temperatures among them.Specific sites were selected to represent a range of salinities within regions. In situ loggershung at one meter below sea level measured temperature and salinity every two hours forthe duration of deployment to quantify the conditions experienced at each site. These datawere checked against manual field measurements and inaccurate data were removed.Two sites, Tofino and Gold River, had to be eliminated on the west coast of Vancouver110 100 200 kmNorthCoastSalishSeaWest coast ofVancouver IslandFigure 2.1: Map of field survey locations on the coast of BritishColumbia. Sites that were eliminated have been crossed out.Island. Gold River had a maximum salinity of 0.76 h and so was not saline enough tobe suitable for a study on marine species. The temperature and salinity logger was lost atTofino and a data substitution could not be found.Sampling techniqueAt each site, ten 14.5 cm by 14.5 cm roughened PVC tiles were deployed face down at onemeter below sea surface on a floating dock, spaced at least three meters apart. A brick wasattached to the back of the tiles to keep them at the correct depth and orientation. Tileswere deployed between May 22 2011 and June 15 2011, and collected between September 232011 and October 10 2011. Only organisms that settled onto the downward-facing surfaceof the tile were evaluated, following the methods of Lindeyer & Gittenberger (2011). Tileswere preserved in 3 % formaldehyde and 0.5mm filtered seawater for transport back to the12lab for analysis.Tile analysisTiles were transferred from 3 % formaldehyde into 40 % ethanol prior to analysis. Each tilewas visually analyzed for percent cover using a 5 x 5 grid to aid with estimation followingDethier et al. (1993), then converted to square centimetres by multiplying by tile area.Percent cover was assessed in layers to ensure that species that are able to foul others werecounted along with the ones upon which they grew. Only individuals over 0.5 mm werecounted. Carlton (2007) and Lamb & Hanby (2005) were used to identify samples to thelowest taxonomic level possible. Species status was assigned as non-indigenous, native orcryptogenic according to literature consensus. A selection of difficult species identities andstatuses were verified by Dr. James T. Carlton (pers. comm.). Some individuals could notbe identified to a low enough taxonomic level for status assignment and so were excludedfrom further analyses.2.2.2 Modelling approachAn information-theoretic approach was utilized to test the strength of evidence for a set ofalternative hypotheses, each expressed as a model (Anderson et al., 2000). The responsevariables, NIS richness, NIS abundance, B. violaceus presence and B. violaceus abundance,were analyzed using a common set of models (Table 2.1). Temperature and salinity wereselected to represent environmental favourability because they are two important driversof the distribution of marine organisms (Epelbaum et al., 2009a; Rahel & Olden, 2008;Reusser & Lee II, 2008). Minimum temperature and salinity were given as the lowest 10thpercentile rather than the absolute minimum to avoid over-emphasizing short, transientevents.13Biotic resistance was evaluated through the slope of the relationship between the re-sponse variable (NIS richness or abundance, or B. violaceus presence or abundance) andnative species richness. A negative slope between the response variable and native speciesrichness would provide support for the biotic resistance hypothesis, while a positive slopewould indicate environmental favourability. To test whether support of the biotic resis-tance and environmental favourability hypotheses could change between sites, models thatallowed the slope for the richness term to vary by location were included. In the event thatnative species richness did influence the response variable, but the slope of the relationshipchanged between sites, the models where the slope was allowed to vary would be supported.The distance in kilometres from the sampling location to the closest neighbouring dock-ing facility, a measure of docking facility abundance in an area, served as a proxy forpropagule pressure. For this study, it was assumed that higher boat traffic would occurwhere independent docking facilities were located in close proximity, as a greater numberof boats would be required for multiple independent docking facilities to be financiallyfeasible. As 65.7 % of boats surveyed in BC had fouled hulls, 25.7 % with NIS (ClarkeMurray et al., 2011), docking facilities with more traffic and more boats would likely re-sult in greater propagule pressure than areas with fewer boats. This proxy for propagulepressure will be referred to as “dock distance.” While propagule pressure is not technicallya part of either the biotic resistance or the environmental favourability hypotheses, it wasincluded to account for sites where the presence or abundance of species may be moreheavily influenced by the availability of vectors to facilitate spread than the factors thataffect establishment once the species is present.The model set for native species was the same, but with native species richness re-placed by NIS richness as an explanatory variable. If the same variables influenced nativespecies and NIS, it would show additional support for the environmental favourability hy-14Table 2.1: Main set of models used to evaluate the evidence for the alternativehypotheses.Model Fixed effects Hypothesesint Intercept only Patterns explained by random effects andoverall meanTS Temperature and salinity Patterns explained by environmentalfactorsTSD Temperature, salinityand dock distancePatterns explained by environmentalfactors and propagule pressureSr Native species richness Patterns explained by biotic factorsSrVar Native species richness Patterns explained by biotic factors, butslope is allowed to vary by locationDSr Dock distance andnative species richnessPatterns explained by propagule pressureand biotic factorsDSrVar Dock distance andnative species richnessPatterns explained by propagule pressureand biotic factors, but slope of bioticinfluence is allowed to vary by locationTSDSr Temperature, salinity,dock distance andnative species richnessAll variables are needed to explain thepattern foundTSDSrVar Temperature, salinity,dock distance andnative species richnessAll variables are needed to explainthe pattern found, and slope of bioticinfluence is allowed to vary by locationpothesis, as they responded as marine species rather than separately as native or NIS. Allmodels included location nested within region as random effects to account for the spatialdistribution of the sites in the observational survey.The type of model employed varied with the response variable of interest. Linear mixed-models were fit on cube-root transformed abundances, while generalized linear mixed-models (GLMMs) were used to analyze the Poisson-distributed richness variables andbinomial-distributed presence of B. violaceus. In order to meet model assumptions, B. vi-olaceus was split into two model sets: a binomial GLMM for presence and absence, and alinear mixed-model on cube-root transformed abundance when B. violaceus was present.15All models were fit using maximum likelihood estimates (Bolker et al., 2009) and consideredequivalent when within approximately two units of the lowest AICc (Burnham & Anderson,2002). Each model in the top-ranked set was evaluated for spatial autocorrelation. Afterthe top-ranked models were identified, the explanatory variable estimates and the amountof variability explained by the random effects were calculated by the modelling package.A normal approximation was used to calculate 95 % confidence intervals for the variableestimates. When the model with the lowest AICc value included a variable whose 95 %confidence interval overlapped zero, a subset of models was created without that variableand all of the models were compared with AICc to test the importance of that variable(Pinheiro & Bates, 2000). When the result of the AICc selection did not support one “best”model, multimodel inference was employed with the AICc equivalent models. Averagingthe variable estimates over all of the candidate models may shrink the estimates to wherethey become unhelpful (Symonds & Moussalli, 2011); because the aim of this study wasto relate the explanatory variables to the response variable, the variable estimates wereinstead preserved.All analyses were conducted in R version 3.0.2 (www.R-project.org). The lme4 pack-age (http://lme4.r-forge.r-project.org/) was used to fit the models, with AICc cal-culated according to Anderson et al. (2000), Symonds & Moussalli (2011), and R codeadapted from (http://glmm.wikidot.com/faq). Akaike weights were calculated utiliz-ing the qpcR package (http://cran.r-project.org/web/packages/qpcR/index.html).Following instructions in Fultz (2012), Moran’s I was calculated with the Ape package(http://ape-package.ird.fr/ to test for spatial autocorrelation.162.3 Results2.3.1 Field surveyA total of 51 sessile species were found in the fouling communities of BC, including 10NIS, 16 native species, 24 uncategorized species, and one cryptogenic species (AppendixB, Table B). Richness varied across sites (Table 2.2).17Table 2.2: Species richness per type for each location. “Other” species richness includes cryptogenic species andthose with uncertain identification. Average B. violaceus cover for each site is reported with standard error.Region Location NativeNon-indigenousOtherB. violaceus(cm2 ± SE)Numberof tilesNorth CoastDigby Island 3 3 6 0 9Fairview 6 2 4 0 9Masset 1 2 4 6.17 ± 1.81 10Port Clements 4 2 3 0 10Port Edward 3 2 3 0 8Queen Charlotte 4 2 9 18.34 ± 7.67 9Rushbrook 7 3 2 0 10Sandspit 1 2 3 18.71 ± 9.72 10Bamfield 5 3 3 143.18 ± 17.01 10Fair Harbour 1 1 2 0 9Gold River 1 0 1 0 10West coast of Tahsis 1 0 3 0 10Vancouver Island Tofino 5 4 3 12.77 ± 5.14 10Toquart Bay 3 3 1 82.63 ± 32.70 10Ucluelet 6 4 7 37.00 ± 11.56 10Zeballos 1 2 2 0 10Salish SeaCampbell River 5 3 3 0.07 ± 0.05 10Comox Bay 2 2 1 0 10Eagle Harbour Yacht Club 3 0 1 0 10French Creek 3 4 5 0.42 ± 0.42 10Maple Bay 5 4 5 14.72 ± 5.67 10PBS 2 1 1 0 10Port Sidney 4 2 4 18.28 ± 4.17 9Royal Vancouver Yacht Club 3 0 1 0 618Averaged across site-level data loggers for the duration of tile deployment, the SalishSea had a mean salinity (± standard error) of 20.71 ± 2.80 h, the west coast of Van-couver Island had a mean of 23.19 ± 1.56 h, and the north coast had a mean salinity of25.92 ± 1.28 h. The Salish Sea had a mean temperature of 15.80 ± 0.69 ◦C, the westcoast of Vancouver Island averaged 15.92 ± 0.53 ◦C, and the north coast had a meantemperature 12.96 ± 0.44 ◦C. However, salinity (Appendix C, Table C.1) and temperature(Appendix C, Table C.3) also varied from site to site within regions.2.3.2 Factors that influenced NIS richnessThere was substantial evidence that minimum salinity explained the variation in NIS rich-ness across all sites (Table 2.3). There was some evidence for the models that includedeither temperature or dock distance, however these models had much lower weights, whichsuggested that the weight of the evidence was for minimum salinity alone (Burnham &Anderson, 2002). Minimum salinity was present in each of the top-ranked models, whichemphasized its important role in the pattern of NIS richness in BC fouling communities.The 95 % confidence interval (CI) for the temperature variable estimate overlapped zeroin the lowest AICc model, so the model comparison was re-run with the addition of modelsthat lacked the temperature term (for the top-ranked models of the primary set, see Ap-pendix D, Table D.1). Native species richness was not present in any of the models in thetop-ranked set. None of the models had significant spatial autocorrelation, verified usingMoran’s I.19Table 2.3: Models for Poisson distribution of NIS richness within approximately two units of the lowest AICc value. The“weight” column refers to the Akaike weight and “log-likelihood” refers to the natural log of the likelihood for the set ofparameter values.Model Explanatory variables Random effects AICc Weight Log-likelihood Moran’s I (p value)NISrichS Minimum salinity Region, location 534.96 0.457 -262.334 0.017 (0.062)NISrichTS Minimum temperature,minimum salinityRegion, location 536.44 0.218 -262.014 0.007 (0.285)NISrichSD Minimum salinity,dock distanceRegion, location 537.08 0.159 -262.333 0.016 (0.064)20Minimum salinity had a positive influence on NIS richness, meaning that more NIS werefound in areas with a higher minimum salinity 2.4). There was minimal support for the roleof propagule pressure, measured through dock distance, as demonstrated through the lowweight for the model that contained dock distance (Table 2.3) and that its confidence inter-val overlapped zero where present. There was additional variation in NIS richness amonglocations that was not captured by the fixed effects, but there was little of the unexplainedvariability accounted for by region (intercept random effects were much greater for loca-tion than for region). The explanatory variables were not strongly correlated (AppendixE, Table E.1).Table 2.4: Variable estimates for fixed effects with the 95 % confidence intervals and randomeffects variance for each of the top-ranked Poisson generalized linear mixed models of NISrichness.Model Fixed effects estimate Random effects varianceVariable Estimate (95 % CI) Location RegionNISrichSIntercept -1.414 (-2.282, -0.546) 0.316 2.120E-12Minimum salinity 0.077 (0.038, 0.116)NISrichTSIntercept -0.451 (-2.907, 2.005) 0.304 8.876E-10Minimum temperature -0.069 (-0.237, 0.099)Minimum salinity 0.073 (0.034, 0.113)NISrichSDIntercept -1.409 (-2.291, -0.527) 0.316 2.441E-10Minimum salinity 0.077 (0.038, 0.116)Dock distance -0.002 (-0.080, 0.076)2.3.3 Factors that influenced NIS abundanceAs with NIS richness, there was strong evidence that minimum salinity was the primarypredictor of NIS abundance (Table 2.5). Native species richness was not present in the setof top-ranked models. The temperature term confidence interval overlapped zero in thelowest AICc model of the primary set, so a second model comparison was performed with21the inclusion of models where temperature was absent (for the top-ranked models of theprimary set, see Appendix D, Table D.2). The models for NIS abundance were also free ofsignificant spatial autocorrelation.22Table 2.5: Models for the cube root of NIS abundance within approximately two units of the lowest AICc value. The “weight”column refers to the Akaike weight and “log-likelihood” refers to the natural log of the likelihood for the set of parametervalues.Model Explanatory variables Random effects AICc Weight Log-likelihood Moran’s I (p value)NISabS Minimum salinity Region, location 662.28 0.466 -325.994 2.125E-04 (0.657)NISabSD Minimum salinity,dock distanceRegion, location 664.28 0.172 -325.932 1.676E-04 (0.660)NISabTS Minimum temperature,minimum salinityRegion, location 664.37 0.164 -325.978 3.071E-04 (0.651)23The explanatory variables found to influence NIS abundance were very similar to thosefor NIS richness. Minimum salinity had a positive effect on NIS abundance, with NIS moreabundant where the minimum salinity was more saline (Table 2.6). Location explainedsome of the variability for all three models, which indicates that there was between-sitevariability in NIS abundance that was not explained by the fixed effects. However, theregion in which sites were located accounted for little to no variation in the models. Ex-planatory variables were not strongly correlated (Appendix E, Table E.2).The residuals from the models for NIS abundance diverged from normality in the tails,but the analysis returned the same results with these outliers removed, so the result wasconsidered robust.Table 2.6: Variable estimates for fixed effects with the 95 % confidence intervals and random effectsvariance for each of the top-ranked linear mixed models of cube root transformed NIS abundance.Model Fixed effects estimate Random effects varianceVariable Estimate (95 % CI) Location Region ResidualNISabSIntercept 0.2839 (-1.293, 1.861) 1.843 3.558E-10 0.973Minimum salinity 0.122 (0.047, 0.197)NISabSDIntercept 0.340 (-1.264, 1.943) 1.832 0.000 0.973Minimum salinity 0.123 (0.048, 0.198)Dock distance -0.031 (-0.204, 0.142)NISabTSIntercept -0.173 (-5.513, 5.167) 1.840 0.000 0.973Minimum temperature 0.033 (-0.334, 0.400)Minimum salinity 0.124 (0.046, 0.201)2.3.4 Factors that influenced B. violaceus presenceB. violaceus was present (defined as at least 0.1 cm2 cover) on 26 out of 75 (34.7 %) tileson the north coast, while it was present on 26 out of 59 (44.1 %) tiles on the west coast ofVancouver Island and 20 out of 75 (26.7 %) in the Salish Sea.24The evidence supported minimum salinity and minimum temperature as drivers of thevariation in B. violaceus presence. The highest weighted model was comprised of minimumsalinity and minimum temperature, which were also present in each model of the top-ranked set (Table 2.7). However, there was some evidence for dock distance and nativespecies richness. None of the models had significant spatial autocorrelation.25Table 2.7: Models for binomial distribution of B. violaceus presence within approximately two units of the lowest AICc value.The “weight” column refers to the Akaike weight and “log-likelihood” refers to the natural log of the likelihood for the set ofparameter values.Model Explanatory variables Random effects AICc Weight Log-likelihood Moran’s I (p value)aBvTS Minimum temperature,minimum salinityRegion, location 106.74 0.495 -47.164 0.008 (0.258)aBvTSDSr Minimum temperature,minimum salinity,dock distance,native species richnessRegion, location 107.60 0.322 -45.442 0.008 (0.248)aBvTSD Minimum temperature,minimum salinity,dock distanceRegion, location 108.80 0.177 -47.122 0.008 (0.261)26There was strong evidence for minimum salinity and minimum temperature as predic-tors of B. violaceus presence. Minimum salinity and minimum temperature had positiveeffects on B. violaceus presence in each of the models, which indicated that higher minimacorresponded with a higher likelihood of presence (Table 2.8). While there was some sup-port for dock distance, the confidence interval for the parameter estimate overlapped zeroin both models. Native species richness was present in the mid-weighted model, where ithad a negative effect on B. violaceus presence, however the confidence interval overlappedzero. For each of the models, there was a fair amount of between-site variability that wasnot explained by the fixed effects.As with NIS richness and abundance, salinity was important to the presence of B. vio-laceus. However, temperature impacted B. violaceus presence more than it did NIS richnessor abundance.Table 2.8: Variable estimates for fixed effects with the 95 % confidence intervals and randomeffects variance for each of the top-ranked binomial generalized linear mixed models ofB. violaceus presence.Model Fixed effects estimate Random effects varianceVariable Estimate (95 % CI) Location RegionaBvTSIntercept -40.496 (-62.088, -18.903) 5.449 0.000Minimum temperature 1.375 (0.366, 2.384)Minimum salinity 0.924 (0.415, 1.433)aBvTSDSrIntercept -37.351 (-56.720, -17.982) 3.903 2.182E-10Minimum temperature 1.248 (0.354, 2.143)Minimum salinity 0.947 (0.463, 1.431)Dock distance -0.059 (-0.411, 0.294)Native species richness -0.821 (-1.709, 0.068)aBvTSDIntercept -40.780 (-63.134, -18.427) 5.699 7.161E-09Minimum temperature 1.358 (0.314, 2.403)Minimum salinity 0.937 (0.404, 1.471)Dock distance 0.058 (-0.332, 0.449)27Temperature and salinity constrained where B. violaceus was able to survive. In areaswhere either temperature or salinity were higher, the colonies were able to survive lowerlevels of the other factor. All regions in British Columbia had sites with and withoutB. violaceus (Figure 2.2).llllllllllllllllllllll10 11 12 13 14 15 1651015202530Minimum temperature (°C)Minimum salinity (ppt)llPresenceAbsenceNorth CoastWCVISalish SeaFigure 2.2: Presence and absence of B. violaceus by abioticconditions, coloured by the three ecoregions.2.3.5 Factors that influenced B. violaceus abundanceB. violaceus was present (at least 0.1 cm2 cover) on only 72 out of the 209 tiles. Coverranged from 0 to 100.92 cm2 on the north coast, from 0 to 273.33 cm2 on the west coast ofVancouver Island, and in the Salish Sea cover ranged from 0 to 52.56 cm2. Assessed onlyon tiles where B. violaceus was present, the north coast had 15.92 ± 4.53 cm2 B. violaceus28cover on average, while the west coast of Vancouver Island had 101.08 ± 15.47 cm2 cover,and the Salish Sea had 15.83 ± 3.37 cm2 cover.Estimated only on tiles on which B. violaceus was present, two models were nearly tiedfor lowest AICc and highest weight for B. violaceus abundance (Table 2.9). The modelwith the lowest AICc did not contain any explanatory variables, and so was based on theoverall mean and the random effects of region and location. Minimum temperature andsalinity were present in the model that had a slightly lower weight, which was evidence thatthey may have influenced B. violaceus abundance. Native species richness was present intwo models with lower weights, so there was some support for an effect. Moran’s I verifiedthat none of the models had significant spatial autocorrelation.29Table 2.9: Models for the cube root of B. violaceus abundance (only when present) within approximately two units of thelowest AICc value. The “weight” column refers to the Akaike weight and “log-likelihood” refers to the natural log of thelikelihood for the set of parameter values.Model Explanatory variables Random effects AICc Weight Log-likelihood Moran’s I (p value)pBvint Intercept Region, location 241.96 0.272 -116.680 -0.006 (0.778)pBvTS Minimum temperature,minimum salinityRegion, location 242.30 0.229 -114.506 -0.012 (0.942)pBvSr Native species richness Region, location 242.61 0.196 -115.850 -0.007 (0.811)pBvTSD Minimum temperature,minimum salinity,dock distanceRegion, location 243.17 0.149 -113.709 -0.015 (0.963)pBvDSr Dock distance,native species richnessRegion, location 244.30 0.084 -115.502 -0.006 (0.794)30There was evidence that B. violaceus abundance varied per site, but it was not clearlydriven by any of the hypothesized factors, as indicated by the model based only on theoverall mean and the random effects of region and location receiving the highest weight.Region accounted for approximately 2.3 times more of the unexplained variability thanlocation in this model (Table 2.10), with a greater abundance of B. violaceus on the westcoast of Vancouver Island (effect estimate of 1.160) than on the north coast or in the SalishSea (effect estimates of -0.561 and -0.599, respectively).The model that contained minimum salinity and temperature was weighted similarlyto the intercept-only model, so there was evidence for their influence on B. violaceus abun-dance. Minimum salinity and temperature were present in two of the models, where tem-perature had a positive effect, but confidence intervals for salinity overlapped zero. Therewas some support for native species richness and dock distance as predictors of B. vio-laceus abundance, but the confidence intervals for the estimates of each overlapped zero.Region did not account for any of the variability when minimum salinity and temperaturewere present, but a fair amount when they were not. Though minimum temperature andminimum salinity were positively correlated, the rest of the explanatory variables were notstrongly correlated (Appendix E, Table E.4).31Table 2.10: Variable estimates for fixed effects with the 95 % confidence intervals and randomeffects variance for each of the top-ranked linear mixed models of cube root transformedB. violaceus abundance.Model Fixed effects estimate Random effects varianceVariable Estimate (95 % CI) Location Region ResidualpBvint Intercept 2.758 (1.639, 3.877) 0.350 0.811 1.197pBvTSIntercept -3.839 (-13.855, 6.177) 0.411 0.000 1.196Minimum temperature 0.549 (0.140, 0.958)Minimum salinity -0.026 (-0.250, 0.197)pBvSrIntercept 3.211 (1.875, 4.547) 0.316 0.883 1.172Native species richness -0.211 (-0.528, 0.106)pBvTSDIntercept -4.126 (-13.646, 5.394) 0.350 0.000 1.186Minimum temperature 0.570 (0.181, 0.960)Minimum salinity -0.017 (-0.229, 0.195)Dock distance -0.076 (-0.191, 0.040)pBvDSrIntercept 3.483 (2.043, 4.922) 0.301 0.803 1.167Dock distance -0.264 (-0.602, 0.075)Native species richness -0.053 (-0.175, 0.070)2.3.6 Factors that influenced native species richnessThere was strong evidence that minimum salinity and dock distance explained the dis-tribution of native species richness in the fouling communities of BC (Table 2.11). NISrichness was present in a model with a low weight, which suggested that there may be someevidence of influence from biotic factors. The presence of dock distance in a mid-weightmodel provided evidence that propagule pressure may have affected native species richness.32Table 2.11: Models for Poisson distribution of native species richness within approximately two units of the lowest AICc value.The “weight” column refers to the Akaike weight and “log-likelihood” refers to the natural log of the likelihood for the set ofparameter values.Model Explanatory variables Random effects AICc Weight Log-likelihood Moran’s I (p value)natrichSD Minimum salinity,dock distanceRegion, location 606.58 0.452 -297.080 -0.019 (0.217)natrichTSD Minimum temperature,minimum salinity,dock distanceRegion, location 608.18 0.203 -296.809 -0.020 (0.172)natrichSDSr Minimum salinity,dock distance,NIS richnessRegion, location 608.34 0.187 -296.890 -0.019 (0.210)33Minimum salinity had a positive influence in each of the models for native speciesrichness (Table 2.12), as it did for NIS richness. Both NIS and native species richnessincreased with more saline minimum salinity. Dock distance had a negative influence onnative species richness. The closer the nearest neighbouring dock was to the samplingsite, the higher the native species richness was at the sampling site. Though there wassome evidence for the models that contained minimum temperature and NIS richness,the confidence intervals for each parameter estimate overlapped zero. The explanatoryvariables were not strongly correlated (Appendix E, Table E.5).The models that were identified for native species richness were different from those thatdescribed NIS richness. The highest weighted model for native species richness includeddock distance in addition to minimum salinity, whereas NIS richness was best determinedby minimum salinity alone.Table 2.12: Variable estimates for fixed effects with the 95 % confidence intervals and randomeffects variance for the top-ranked Poisson generalized linear mixed models of native speciesrichness.Model Fixed effects estimate Random effects varianceVariable Estimate (95 % CI) Location RegionnatrichSDIntercept 0.143 (-0.306, 0.591) 0.068 0.000Minimum salinity 0.034 (0.014, 0.054)Dock distance -0.071 (-0.118, -0.023)natrichTSDIntercept 0.624 (-0.720, 1.969) 0.063 1.192E-07Minimum temperature -0.035 (-0.128, 0.058)Minimum salinity 0.032 (0.012, 0.053)Dock distance -0.067 (-0.115, -0.019)natrichSDSrIntercept 0.131 (-0.313, 0.575) 0.064 3.063E-10Minimum salinity 0.032 (0.010, 0.053)Dock distance -0.070 (-0.117, -0.023)NIS richness 0.043 (-0.091, 0.176)342.3.7 Factors that influenced native species abundanceThe candidate model set for native species abundance differed from native species richnessand NIS abundance (Table 2.13). The highest weighted model was comprised of onlythe overall mean and the random effect of region and location, which suggested that thedrivers of the pattern of native species abundance were not among the hypothesized factors.There was some support for a role of NIS richness, both with and without allowing the slopebetween native species abundance and NIS richness to vary by location. In contrast, NISabundance was mainly influenced by minimum salinity. None of the models had significantspatial autocorrelation, verified using Moran’s I.35Table 2.13: Models for the cube root of native species abundance within approximately two units of the lowest AICc value. The“weight” column refers to the Akaike weight and “log-likelihood” refers to the natural log of the likelihood for the set ofparameter values.Model Explanatory variables Random effects AICc Weight Log-likelihood Moran’s I (p value)natabint Intercept Region, location 519.11 0.394 -255.459 1.109E-04 (0.666)natabSr NIS richness Region, location 519.98 0.256 -254.841 7.540E-05 (0.668)natabSrVar NIS richness Region, location,slope of NIS richnessvarying by location521.53 0.118 -251.312 -4.806E-04 (0.704)36Native species abundance varied among sites, but the variation was not clearly linkedto any of the hypothesized drivers. For the model that contained only the overall meanand the random effects, the effect of location accounted for approximately 30 times moreof the unexplained variability than region (Table 2.14). Though the effect of region waslow, there was a slightly lower abundance of native species found on the west coast ofVancouver Island than average (effects estimate -0.147), about average found on the northcoast (0.027), and a slightly higher abundance of native species than average in the SalishSea (0.120).There was some support for the role of NIS richness in the pattern of native speciesabundance, but the confidence interval for the parameter estimate in each model overlappedzero. Allowing the slope between native species abundance and NIS richness to vary withlocation explained only a slight amount of the variability in the model. The explanatoryvariables were not strongly correlated (Appendix E, Table E.6).The residuals from the models for native species abundance diverged from normalityin the tails, but the analysis returned the same results with these outliers removed, so theresult was considered robust.Table 2.14: Variable estimates for fixed effects with the 95 % confidence intervals and random effectsvariance for the top-ranked linear mixed models of cube root transformed native species abundance.Model Fixed effects estimate Random effects varianceVariable Estimate (95 % CI)Location(intercept)Location (NISrichness slope)Region Residualnatabint Intercept 3.244 (2.602, 3.887) 1.848 0.063 0.457natabSrIntercept 3.376 (2.697, 4.056) 1.765 0.070 0.456NIS richness -0.095 (-0.261, 0.071)natabSrVarIntercept 3.244 (2.602, 3.887) 2.698 0.081 0.000 0.442NIS richness -0.127 (-0.325, 0.071)372.4 Discussion2.4.1 NIS and native species distribution patterns in the foulingcommunityContrary to the biotic resistance hypothesis, native species richness did not have an effecton the species richness or abundance of NIS in this study. Instead, support was found for theenvironmental favourability hypothesis. As would be expected of marine species runningup against physiological constraints, more species, both native and NIS, were found in areasof higher minimum salinity. NIS richness and abundance were both influenced mainly byminimum salinity.One aspect of biotic resistance could not be investigated in this study: predation. Manyof the predators in marine fouling communities are mobile species (Epelbaum et al., 2009b;Nydam & Stachowicz, 2007; Osman & Whitlatch, 2004; Simkanin et al., 2013), which thissurvey was not able to quantify. Predation by mobile species is capable of reducing thepopulations of some NIS (Epelbaum et al., 2009b; Nydam & Stachowicz, 2007; Osman &Whitlatch, 2004; Simkanin et al., 2013; but see Grey, 2010). While there was no evidence ofbiotic resistance from the native sessile communities in this study, the possibility of bioticresistance cannot be ruled out completely without examining the role of predation.Other studies in marine fouling communities have found evidence of biotic resistance(e.g. Grey, 2009; Stachowicz et al., 2002a, 1999), though not all (Dunstan & Johnson,2004). Aspects of methodology such as observational vs. manipulative studies, habitattype, the material used for settlement, and geographic location could have led to differentresults among studies. Artificial substrates may favour non-indigenous tunicates (Tyrrell& Byers, 2007), which could be one reason that the two methods in Grey (2009) produceddifferent results. Biotic resistance was found in the manipulated fouling communities of38the Stachowicz et al. (1999) and Stachowicz et al. (2002a) studies. The communities inthe Stachowicz et al. (2002a) field survey, Dunstan & Johnson (2004), Grey (2009), andthis survey were not manipulated, but evidence of biotic resistance was mixed amongthese studies. Consistent with the findings of Levine (2000), methodology does appearto influence the probability of detecting biotic resistance, though the variability betweenstudies is not fully explained by methodological differences. Another difference betweenthese studies are their location. Freestone et al. (2013) and Kimbro et al. (2013) eachfound an influence of latitude on biotic resistance: higher latitude communities were lessresistant to invasion than lower latitude communities. This pattern could be the result ofincreased predation pressure (Freestone et al., 2013) or greater species richness (Kimbroet al., 2013) at lower latitudes. The studies described above that found biotic resistanceall occurred at lower latitudes than this study.Each of the aforementioned studies (i.e. Dunstan & Johnson, 2004; Grey, 2009; Sta-chowicz et al., 2002a, 1999) were conducted on established marine fouling communities,unlike the present study. The use of bare plates makes this study a conservative test ofbiotic resistance, because the native species competed to populate the new substrate atthe same time as the NIS. However, harbours and marinas experience high levels of distur-bance (Piola et al., 2009) and get cleaned, therefore free space can be present. As dockingfacilities are often first point of contact for ship-related invasions (Dafforn et al., 2009),bare plates can provide insight into invasion dynamics.Contrary to what was found for NIS richness, native species richness was affected bythe sampling location’s proximity to other docking facilities (dock distance). This wasunexpected as the assumption was that the source populations for native species would bethe local area rather than fouling habitat. However, native species are also known to foulthe hulls of recreational boats: 65.7 % of boats surveyed in BC had fouled hulls, while only3925.7 % had one or more NIS (Clarke Murray et al., 2011). Native species may be movedto new areas via hull fouling, which could increase the native species richness of the area.Potential habitat surrounding the sampling site was not surveyed in this study, so it is notknown whether the species found on the docks were also found on the natural substrata.Even if the native species were present in the sampling areas prior to dock construction,the flow of new individuals from fouled hulls could provide benefits to these populationsof native species that were similar to those that NIS may experience. Increased propagulepressure, which may be supplied by hull fouling, could increase genetic variability andthus the likelihood that beneficial traits would be present in the population (Simberloff,2009). Steady propagule supply could also reduce the likelihood that a population couldbe eliminated due to stochastic events (Simberloff, 2009). Marinas and harbours havebeen documented to experience greater disturbance and changed environmental conditionsrelative to areas just outside of the facility (Rivero et al., 2013), which may alter theenvironment enough that it is just as novel to native species as it is to NIS. Thus, theadaptive advantage for native species could be lost (Byers, 2002), and the supply of newindividuals to populations of native species could promote their persistence as it can forNIS.None of the explanatory variables used in this study adequately described the pattern ofnative species abundance, as indicated by the model without explanatory variables receiv-ing the highest weight. It is possible that the abiotic conditions were not limiting factorsbecause the native species that were able to persist have had time to adapt to the localenvironments (Byers, 2002). The introduction of new species is very recent on the evolu-tionary time scale and native species may not have the ability to resist the novel sourceof competition, especially if disturbance has altered the habitat (Byers, 2002). However,rapid adaptation to new stressors cannot be completely ruled out (Strauss et al., 2006).40NIS richness may have had a minor effect on native species richness and abundance, demon-strated by the trend toward a positive slope for richness and a negative one for abundance.The weak evidence for a positive slope between native species and NIS richness emphasizedthat environmental favourability was more important than biotic interactions for speciespresence, which has been found for at least one NIS (B. violaceus) (Grey, 2011). Theweak evidence for a negative slope between NIS richness and native species abundancecould indicate that increased NIS richness reduced the availability of limiting resources fornative species and thereby reduced their abundance, or that areas of high native speciesabundance were characterized by lower NIS richness. It would require experimentationto properly elucidate the mechanism behind this pattern. This study demonstrated thatspecies origin was a factor in which variables affected species richness and abundance, asthe explanatory variables that influenced native and non-indigenous species were not thesame.2.4.2 B. violaceus distribution pattern in the fouling communityTaxonomic group of the NIS may impact biotic resistance (Kimbro et al., 2013) and theimportance of environmental factors vary by species (Ojaveer et al., 2011). Therefore, thisstudy compared the factors that influence NIS in general to one of an introduced speciesin BC marine fouling communities, B. violaceus. While there was no evidence of bioticresistance against NIS in general, there may have been a slight impact of native speciesrichness on the presence and abundance of B. violaceus. The confidence interval for thenative species richness estimate overlapped zero, but it trended toward a negative rela-tionship, which could be evidence of either resistance of native communities or an impactof B. violaceus on native species. Reduced survivorship of B. violaceus recruits has beenfound in areas of higher native species richness previously, through manipulative experi-41ments in which the direction of the effect (i.e. whether native species affected the successof B. violaceus or B. violaceus impacted native species) could be determined (Simkaninet al., 2013; Stachowicz et al., 2002a, 1999). In light of these previous studies that foundbiotic resistance, it is possible that some of the communities in this observational studywere able to resist B. violaceus invasion. The impact of native species richness was greateron the presence rather than the abundance of B. violaceus, which was contrary to otherstudies that found native species diversity having more influence on invasion success thaninvader establishment (Kimbro et al., 2013; Levine et al., 2004). The inconsistent relation-ship between B. violaceus and native species richness could also be due to the interactionbetween biotic resistance and abiotic factors, because the strength of biotic resistance candepend on environmental conditions (Cheng & Hovel, 2010).There is evidence that B. violaceus has been able to outcompete native species for space(Dijkstra et al., 2007; Rajbanshi & Pederson, 2007; Stachowicz et al., 2002b), which is themain limiting resource in marine fouling communities (Sellheim et al., 2010; Stachowiczet al., 2002a; Teo & Ryland, 1995). The studies that found competitive dominance ofB. violaceus were conducted with different native species than were found in BC, so whilethese studies suggest that the role of competition in biotic resistance may be minor, itmay not be the case in BC. However, predation may influence B. violaceus abundancein British Columbia. Osman & Whitlatch (2004) found that B. violaceus colonies overone week old were able to escape predation and dominate the community, though coloniescould be significantly reduced or absent if exposed to predation before reaching that criticalage. Survival of B. violaceus colonies, both adult and juvenile, was found to be higherin caged treatments that protected them from predators (Simkanin et al., 2013). Themossy chiton Mopalia muscosa was an effective predator of colonial tunicates, and reducedB. violaceus cover by 43 % (Nydam & Stachowicz, 2007). Epelbaum et al. (2009b) found42that red (Strongylocentrotus franciscanus) and green (S. droebachiensis) urchins, leatherseastars (Dermasterias imbricata), and opalescent sea slugs (Hermissenda crassicornis) ateB. violaceus as well. However, these predators selected their regular prey over B. violaceuswhen given a choice (Epelbaum et al., 2009b) and Grey (2010) found that large predatorexclusion did not affect the recruitment or abundance of B. violaceus in Washington, USA.Small numbers of Hermissenda crassicornis and juvenile seastars were found in this survey,but the sampling protocol was not optimized to quantify mobile species and so it was notpossible to investigate predator interactions in this study.Previous research found that abiotic conditions have more influence on the populationgrowth of B. violaceus than direct species interactions (Grey, 2011), which supports the en-vironmental favourability hypothesis. This may be why evidence of a relationship betweenB. violaceus (presence and abundance) and native species richness was not strong, whiletemperature and salinity acted positively on presence, and temperature acted positively onabundance. The impact of temperature and salinity on B. violaceus survival is consistentwith past studies (Dijkstra et al., 2008; Epelbaum et al., 2009a; Sorte et al., 2011). Salinityand temperature may have had less of an effect on abundance than they did on presencebecause the abundance models were run strictly on tiles where B. violaceus was present.The analysis of B. violaceus abundance only where it was present, and the necessary filterof conditions that would represent, could be why the highest weighted model for abun-dance was based on the overall mean and the random effects of region and location alone,though temperature had a positive effect in lower ranked models. Increased temperatureand salinity have been demonstrated to increase colony growth, but few of the sites in thissurvey had means in the ideal range of conditions for growth (above 26 h and 15 ◦C)(Epelbaum et al., 2009a).432.4.3 ConclusionThe biotic resistance hypothesis was not supported in terms of NIS richness or NIS abun-dance, but it cannot be fully ruled out for one key NIS due to the weak evidence for anegative slopes between native species richness and B. violaceus presence and abundance.However, because there was not strong evidence to support those negative relationshipsand because native species richness was not a factor for either NIS richness or abundance,there was no compelling evidence to support the biotic resistance hypothesis.NIS richness and abundance were more affected by environmental variables. Salinityhad a positive influence on NIS richness, NIS abundance, and B. violaceus presence, whiletemperature had a positive effect on B. violaceus presence and abundance. Salinity also hada positive influence on native species, which provided evidence to support the environmentalfavourability hypothesis. However, salinity and temperature are expected to be altered byclimate change (Rosenzweig et al., 2007). Sea surface temperatures are expected to warm(Rosenzweig et al., 2007) and as patterns of precipitation, river discharge (Knowles &Cayan, 2004; Morrison et al., 2002), evaporation rates (Scavia et al., 2002) and currentsare altered, salinity off of BC’s coast will change. These changes will affect the speciesliving in nearshore areas. It is possible that the altered abiotic conditions could resultin the natural control of NIS spread if temperature and salinity become less favourableover time, but it is more likely that increasing temperature and salinity would relax thenatural abiotic barriers to NIS survival and proliferation (Cockrell & Sorte, 2013; Dukes& Mooney, 1999; Hellmann et al., 2008; Hoegh-Guldberg & Bruno, 2010; Rahel & Olden,2008; Sorte et al., 2013, 2010b; Stachowicz et al., 2002b; Walther et al., 2009; Zerebecki &Sorte, 2011). Climate change is not expected to slow anytime soon, so it is important toconsider species invasion in this dynamic context (Ch. 3).44Chapter 3Climate change and speciesinvasion: using spatial variation intemperature and salinity toforecast potential changes inBotrylloides violaceus abundancein British Columbia3.1 Introduction3.1.1 The role of climate change in species invasionClimate change is expected to alter temperature, precipitation, sea level, and frequency ofextreme events (Harley et al., 2006; Rosenzweig et al., 2007). As a result, many impor-tant ecological properties, including temperature regimes, species’ ranges and abundances,nutrient availability and the salinity profile of the ocean, are predicted to change (Harleyet al., 2006; Hoegh-Guldberg & Bruno, 2010; Scavia et al., 2002). These changing condi-45tions could influence another important threat to ecosystems: species invasion (Cockrell& Sorte, 2013; Hellmann et al., 2008; Stachowicz et al., 2002a, 1999). Climate change isexpected to alter vectors of species introduction and the effectiveness of control strategies,in addition to allowing new species to establish, and altering the distributions and impactsof already present NIS (Hellmann et al., 2008).Two abiotic variables that substantially influence invader success or failure, and indeedthe performance of all marine organisms, are temperature and salinity (Epelbaum et al.,2009a; Rahel & Olden, 2008; Reusser & Lee II, 2008). Climate change is expected toimpact both temperature and salinity (Rosenzweig et al., 2007), therefore it is importantto consider species invasions in the context of climate change because the relationshipbetween them could have many consequences. It is possible that climate change couldalter abiotic conditions from what is optimal for presently invading species, resulting innatural control of their spread. However, in light of past studies it is more likely thatwarming water temperature and altered salinity would release the natural abiotic barriersto non-indigenous species’ survival and proliferation, and shift competitive interactions tofavour invaders that are better adapted to the changing and new conditions (Coˆte´ & Green,2012; Dukes & Mooney, 1999; Hellmann et al., 2008; Hoegh-Guldberg & Bruno, 2010; Rahel& Olden, 2008; Sorte et al., 2013, 2010a,b; Stachowicz et al., 2002b; Walther et al., 2009;Zerebecki & Sorte, 2011). Currently benign NIS may also begin to have negative impactsif conditions become more favourable in the future (Smith et al., 2012).One of the most common non-indigenous tunicate species in the northeastern Pacific isBotrylloides violaceus. B. violaceus is a species of interest for both economic and ecologicalreasons. It is noted as a fouling concern for various shellfish and finfish growers whoseaquaculture gear includes netting (Carver et al., 2006). Despite logistical issues created bythis non-indigenous tunicate, there has not yet been evidence of impact on yield or survival46of industrially-grown mussels on the east coast of Canada (Carver et al., 2006; Cordell et al.,2012; Paetzold et al., 2012). The ecological concern is that B. violaceus has the ability tosubstantially alter hard substrata as it fouls structures as thin mats or irregular lobes(Bock et al., 2011; Carver et al., 2006; Epelbaum et al., 2009a), changing the texture ofthe substratum and reducing the amount of available space for recruitment. In additionto pre-emptive competition (Dijkstra et al., 2007; Stachowicz et al., 2002b), B. violaceusmay monopolize space through interference competition by overgrowing species alreadypresent (Rajbanshi & Pederson, 2007). B. violaceus is expected to increase in invasivenessin many areas as water temperatures warm to a more favourable range for the species(Cockrell & Sorte, 2013; Stachowicz et al., 2002b). The potential for increased impactsas environmental conditions become more favourable makes it important to understandwhere this species is likely to proliferate.3.1.2 Abiotic conditions in British Columbia and implications forB. violaceus invasionsEnvironmental conditions in British Columbia (BC) affect the distribution and abundanceof B. violaceus (Epelbaum et al., 2009a). Based on the thermal tolerance of B. violaceus(Table 3.1), there are no large-scale regions that have uninhabitable temperatures year-round at present though conditions are below the optimal range in many locations (Epel-baum et al., 2009a). However, ocean surface temperatures could increase by 1.1 to 6.4 ◦Cby the end of the century (Rosenzweig et al., 2007). The projected warming has the po-tential to elevate the temperature in more locations into the optimal growth range forB. violaceus, which may result in higher abundance in BC.47Table 3.1: Thermal tolerance for adult colonies of B. violaceus.StudyThermaltoleranceTemperaturestestedSampling locationConditions wheresamples collectedEpelbaum et al.,2009aNo survival below0 ◦C (unpubl. data)Survivable range:≤ 5 to ≥ 25 ◦CGrowth range:15 to ≥ 25 ◦COptimal range:20 to ≥ 25 ◦C5, 10, 15, 20, 25 ◦C British ColumbiaRange at time ofcollection: 13 – 14 ◦C.Sorte et al., 2011(NorthwestAtlantic)LT5027.4 ◦CTrials began at 17 ◦Cand were elevated to21, 25, 29, or 34 ◦Cfor a 24 hour periodMassachusettsMean summersea surface temperature2.4 ◦C higherthan west coast siteAnnual range:24.9 ◦CJune - August,2006 - 2010Sorte et al., 2011(Northeast Pacific)LT5025.3 ◦CTrials began at 17 ◦Cand were elevated to21, 25, 29, or 34 ◦Cfor a 24 hour periodCaliforniaAnnual range:12.4 ◦CJune - August,2006 - 201048Changing patterns of precipitation, river discharge (Knowles & Cayan, 2004; Morrisonet al., 2002), evaporation rates (Scavia et al., 2002) and currents will affect nearshoresalinity in BC and the species living in these areas. B. violaceus cannot survive below 8 h(Epelbaum et al., 2009a), so salinity can drop below the habitable range at sites wherefreshwater inputs are high relative to mixing and flushing times. Runoff from the FraserRiver can lower the salinity in the summer to 8 h in some areas of the Salish Sea, whilethe north coast is usually around 31h, though will also vary locally with river runoff (Coˆte´et al., 2012; Held & Harley, 2009). Low salinities at some areas in the Salish Sea havereduced the likelihood of invasion, while low temperatures in some areas of the north coastlikely have limited invasion in spite of their higher (favourable) salinity Epelbaum et al.(2009a). This is consistent with the findings of Ch. 2. The wide range of temperatureand salinity tolerances (Table 3.2), including the ability to survive short term exposureto salinities as low as 10 h (Dijkstra et al., 2008), has enabled B. violaceus to survive inmany introduced locations.49Table 3.2: Salinity tolerance for adult colonies of B. violaceus.Study Salinity tolerance Salinity tested Sampling locationConditions wheresamples collectedDijkstraet al., 2008Survivable range:15 to ≥ 30 psu5, 10, 15, 20, 25, 30 psu Gulf of MaineSalinity duringcollection not given,but maintained at30psu prior toexperiments.Epelbaumet al., 2009aSurvivable range:20 to ≥ 38 hGrowth range:26 to ≥ 38 hOptimal range:26 to ≥ 38 h14, 20, 26, 32, 38 h Strait of Georgia, BCSalinity duringcollection not given.Unpublished datashowed no survivalbelow 8 h50Though the Strait of Georgia currently experiences reduced salinity in the summer(Harley et al., 2013) making it less suitable for B. violaceus, peak Fraser River outflowis expected to decrease with climate change (Morrison et al., 2002) with implications forthe salinity regime in the Strait. This potential increase in minimum salinity will likelymake the Strait of Georgia more invasible for species that are currently prevented fromestablishing due to hypo-osmotic stress. Furthermore, temperatures in the northeasternPacific, while currently below optimum for B. violaceus, are expected to rise (Rosenzweiget al., 2007). Studies have shown that warmer temperatures (20 ◦C and warmer) favourB. violaceus, as it can grow and reach reproductive condition more quickly in warmer water(Epelbaum et al., 2009a). For this species, this could mean multiple reproductive events persummer (Epelbaum et al., 2009a), which, combined with earlier recruitment than nativespecies (Stachowicz et al., 2002b), could result in this species quickly dominating availablespace.3.1.3 Research questionTwo methods for forecasting temporal processes that are otherwise unobservable includespace-for-time substitutions and time-for-time predictions (Blois et al., 2013). Space-for-time substitutions for climate-driven changes utilize observed spatial relationships betweenabiotic conditions and a desired biotic metric to infer a future state of the ecological sys-tem(Blois et al., 2013). Time-for-time predictions use observed changes over time in onelocation to project future change for that same location (Blois et al., 2013). Time-for-timepredictions may be more optimal as the dynamics unique to the location are accountedfor, however space-for-time substitutions have been demonstrated to give reasonable pre-dictions in models of community responses to climate change and do not require long-termmonitoring data (Blois et al., 2013).51Docking facilities are recognized as focal points for invasion because they are often thefirst area of contact for ship-related vectors of potential invaders, therefore these areascan provide key information about how abiotic conditions affect species invasion (Daffornet al., 2009). Temperature and salinity have demonstrated their ability to predict thedistributions of both native and non-indigenous species, with increased predictive abilitywhen geographic variables are added (Reusser & Lee II, 2008). Grey (2011) found thatabiotic variables (specifically temperature and salinity) play a larger role in determiningthe success of B. violaceus invasions than species interactions, which was also found inCh. 2. Thus, as biotic interactions are less relevant for B. violaceus, temperature andsalinity should be able to approximate the future distribution, and potential abundance,of this species. This large-scale field study in harbours and marinas took advantage of thenatural variation in salinity and temperature within BC, and between BC and California,to construct a space-for-time substitution model to answer the question: How might futurechanges in salinity and temperature due to climate change influence the abundance ofB. violaceus in British Columbia?3.2 Methods3.2.1 Field surveySite descriptionsIn addition to the 24 sites in BC used for Ch. 2, eight sites in central California were selectedto fit the model over a wider range of abiotic conditions (Figure 3.1, GPS locations inAppendix A, Table A.2 for California). Regions were selected to have a 4 ◦C range in meansummer temperatures among them from north to south. Sites were selected to represent arange of salinities within regions. In situ loggers measured temperature and salinity every52two hours for the duration of deployment for use in the model. These data were checkedagainst manual field measurements and inaccurate data were removed. Missing salinitydata for Loch Lomond and Moss Landing (California, USA) were substituted with buoydata measured near the sampling locations from the The Central and Northern CaliforniaOcean Observing System (http://www.cencoos.org/).Four sites had to be eliminated, two on the west coast of Vancouver Island and twoin California. Both California sites (Coyote Point and Pillar Point) were missing salinitylogger data and one BC site (Tofino) did not have temperature or salinity logger dataand substitutions could not be found. The second BC site (Gold River) had a maximumsalinity of 0.76 h and so was deemed unsuitable to include.Sampling techniqueFor the sampling technique, please refer to Ch. 2, Section 2.2.1.Tile analysisTiles were transferred from 3 % formaldehyde into 40 % ethanol prior to analysis. Each tilewas visually analyzed for percent cover using a 5 x 5 grid to aid with estimation followingDethier et al. (1993). Percent cover was estimated in layers to ensure that species that areable to foul others are counted along with the ones upon which they grow. Only individualsover 0.5 mm were counted. Carlton (2007) was used to identify samples. B. violaceus coverwas calculated as the sum of B. violaceus cover in all layers, divided by the total amount ofcover for that tile in order to obtain the proportion of total cover occupied by B. violaceus.The proportion of cover that consisted of B. violaceus, averaged per site, was the responsevariable for the model.530 100 200 kmNorthCoastSalishSeaWest coast ofVancouver IslandCaliforniaFigure 3.1: Map of field survey locations on the coasts of BC andCalifornia with eliminated sites crossed out. The red triangles markapproximate locations of the shore stations used to gathertemperature and salinity trends.543.2.2 Generalized additive model for location, scale and shapeModel selectionA zero-inflated beta distribution generalized additive model for location, scale and shape(GAMLSS) was created in an all-subset approach (Symonds & Moussalli, 2011) to describethe observed spatial relationships between abiotic conditions and B. violaceus abundance.Temperature and salinity affect the survival and growth of B. violaceus (Epelbaum et al.,2009a; Rahel & Olden, 2008; Reusser & Lee II, 2008), and therefore were included aspossible explanatory variables in the models. Models were run with either the minimum ormean temperatures and salinities, as maximum values for temperature and salinity in BCare unlikely to be stressful for B. violaceus, even in the future. For the minima, the lowest10th percentile was used rather than the absolute minimum to avoid over-emphasizing brief,transient events. The distance in kilometres from the sampling location to the closestneighbouring docking facility, a measure of docking facility abundance in an area, servedas a proxy for propagule pressure. It was assumed for this study that higher boat trafficwould occur where independent docking facilities were located in close proximity, as agreater number of boats would be required for multiple independent docking facilities tobe financially feasible. Thus, as 25.7 % of boats surveyed in BC had hulls fouled with NIS(Clarke Murray et al., 2011), docking facilities with more traffic and more boats wouldlikely result in greater propagule pressure than areas with fewer boats. Studies have shownthat local diversity patterns in marine epifaunal communities are largely driven by regionalpatterns (Kimbro et al., 2013; Witman et al., 2004). There were two options to accountfor spatial variation in the models: region, a categorical variable grouping locations bythe ecoregion in which they occurred, and latitude. Only one spatial variable was used inparameters in which they were present to avoid over-fitting. Each continuous variable wasrun as a singular and quadratic term in separate models to account for possible quadratic55relationships in the data. AICc was used to select which terms were included in the finalmodel.Models were fit using the GAMLSS package (www.GAMLSS.org) in R version 3.0.2 (www.R-project.org). This method models both the mean of the statistical distribution thatgenerates the observed values, as well as other parameters describing a user-defined shapefor this distribution. In this case we chose a zero-inflated beta distribution. It is a unimodaldistribution which resembles the normal under certain parameter values, but is only definedbetween 0 and 1, making it especially suitable to model percent cover data. An additionalparameter, “ν,” describes the probability of obtaining zero percent cover. In accordancewith the GAMLSS method, the sections of the model were fit sequentially with respectto the parameter hierarchy. First the mean percent cover per site when the species waspresent (µ) was fit using AICc for model comparison. The selected model from the µ fit wasused to create the set of models for the parameter describing the probability of obtaining avalue of zero (ν), also using the all-subset method to determine the variables to include inthe ν parameter. Then, with the model selected from the ν fit as the base (which includedthe whole model: both µ and ν parameters), the parameter for scale (σ) was determinedusing the same method as the ν parameter. The model used as the base for each stepwas included with the all-subset model comparison to test whether the added parameterimproved the overall fit. If the model with the highest Akaike weight was that used asthe base (i.e. the model that did not include the parameter being fit in that step), thenthe parameter being fit was not included. The model with the highest weight was selectedwhen multiple models were identified by the AICc comparison.The probability of obtaining a zero was calculated as ν/(1+ν). B. violaceus was con-sidered absent at sites where the probability of obtaining a zero was estimated at 95 % orhigher.56AICc was calculated according to Anderson et al. (2000) and Symonds & Moussalli(2011), with code adapted from (http://glmm.wikidot.com/faq), which was then up-dated to include the ν and σ parameters. Akaike weights were calculated using the qpcRpackage (http://cran.r-project.org/web/packages/qpcR/index.html). Spatial auto-correlation was calculated on the model residuals using Moran’s I with the Ape package(http://ape-package.ird.fr/ following instructions in (Fultz, 2012).Temperature and salinity projectionsTemporal trends for minimum and mean temperature and salinity during the warmestsummer months (July to September, when tunicate growth and reproduction were maxi-mal) from 1967 – 2011 were determined with data from the British Columbia Shore StationOceanographic Program (BCSOP; http://www.pac.dfo-mpo.gc.ca/science/oceans/data-donnees/lighthouses-phares/index-eng.html) . The shore stations used per regionwere Bonilla Island and Langara Point for the north coast, Amphitrite Point and KainsIsland for the west coast of Vancouver Island, Chrome Island, Departure Bay, EntranceIsland and Active Pass for the Salish Sea, and Farallon Islands for California (Figure 3.1).Regional trends for BC were calculated on the shore stations within 100 km of anysite within the region (without crossing land) with data from 1967 – 2011 and fewer thanfive years missing. Years with the summer months missing were not used to calculatethe trend. Currently, sea surface temperatures in California are warmer than in BC.The relationships between the warm sea surface temperatures, salinity and B. violaceusabundance in California were used to inform the future values for B. violaceus in BCbecause sea surface temperatures are expected to increase with climate change (Rosenzweiget al., 2007). However, projections for the California sites were not calculated.Temperature and salinity were projected 50 years into the future using the linear trend57of annual summer mean and minimum values, with the value used based on the variablesselected in the model comparison. This trend was calculated for each shore station andaveraged per region. The change from 2011 to 2061 was estimated per region and addedto each site’s 2011 value from the logger data. Admittedly, the change is unlikely to belinear due to the non-linear increase in atmospheric CO2 (Rosenzweig et al., 2007), decadalvariability in sea surface temperature (Palmer et al., 2011), and the El Nin˜o SouthernOscillation (Chung et al., 2013). However, a suitable model that was able to generateprojections at a regional scale could not be found. Levitus et al. (2009) found that thelinear trend accounted for 68 % of the variance in Pacific ocean heat content from 1969 –2008, so while there is variation in the BCSOP data, projecting the observed trends using alinear regression should approximate the direction and magnitude of future environmentalchange.3.3 Results3.3.1 Field SurveyNearshore temperature and salinity data were recorded by the in situ loggers to demon-strate the conditions the tiles had experienced at each site. Averaged across sites for theduration of tile deployment, the California region (averaged across all sites with standarderror) had a mean salinity of 27.41 ± 2.05 h. The Salish Sea had a mean salinity of20.71 ± 2.80 h, the west coast of Vancouver Island had a mean of 23.19 ± 1.56 h, andthe north coast had a mean salinity of 25.92 ± 1.28 h. California had a mean tempera-ture of 16.16 ± 0.82 ◦C, the Salish Sea had a mean of 15.80 ± 0.69 ◦C, the west coast ofVancouver Island averaged 15.92 ± 0.53 ◦C, and the north coast had a mean temperature12.96 ± 0.44 ◦C. Salinity (Appendix C, Table C.1 for BC and Table C.2 for California)58and temperature (Table C.3 for BC and Table C.4 for California) also varied per site.B. violaceus was found at three out of eight sites on the north coast, four of eight siteson the west coast of Vancouver Island (three of the six sites included in the analyses),four of eight sites in the Salish Sea, and seven of eight sites in California. The amount ofB. violaceus cover ranged from 0 to 82.8 percent cover. The west coast of Vancouver Islandhad the greatest range within any of the regions, and had the two sites with overall highestcover (82.8 and 77.4 percent cover, Figure 3.2). The remaining site with B. violaceuspresent on the west coast of Vancouver Island had 26.7 percent cover. The three sites onthe north coast with B. violaceus had 21.9, 8.5, and 3.2 percent cover. The four sites inthe Salish Sea with B. violaceus had 15.9, 11.0, 2.6 and 0.3 percent cover. The seven sitesin California that had B. violaceus ranged from 37.9 percent cover to 8.4 percent cover.3.3.2 Generalized additive model for location, scale and shapeModel SelectionFourteen models were within approximately two units of the lowest AICc value for the meanof the distribution for non-zero values (Table 3.3), and only one of the models displayedspatial autocorrelation. The model that included minimum temperature, dock distanceand latitude (bolded) had the highest weight, and therefore was selected to move forwardin the model fit.The AICc comparisons for the ν and σ parameters each only identified single models.The ν parameter had the probability of obtaining a zero set by minimum salinity and regionand was not spatially autocorrelated (Moran’s I = -0.183, p = 0.150). The σ parameterwas also not spatially autocorrelated (Moran’s I = -0.115, p = 0.443), and was describedby minimum temperature, minimum salinity and latitude.59Table 3.3: Models for the µ parameter of the GAMLSS model within approximately twounits of the lowest AICc value.Explanatory variables AICc Weight Moran’s I (p value)Minimum temperature, dock distance,latitude15.290 0.063 0.158 (0.056)Mean temperature, dock distance, region 15.387 0.060 0.020 (0.568)Minimum temperature, minimum salinity,dock distance, region15.537 0.056 0.170 (0.032)Minimum temperature, region 15.799 0.049 0.078 (0.256)Minimum temperature, mean salinity, region 15.886 0.047 0.147 (0.069)Minimum temperature, minimum salinity,region16.122 0.042 0.149 (0.069)Mean temperature, minimum salinity,dock distance, region16.164 0.041 0.131 (0.092)Minimum temperature, dock distance, region 16.370 0.037 0.069 (0.296)Minimum temperature, mean salinity,dock distance, region16.552 0.034 0.213 (0.012)Mean temperature, region 16.598 0.033 0.108 (0.156)Minimum temperature, minimum salinity(squared), dock distance, region16.764 0.030 0.141 (0.074)Mean temperature, mean salinity,dock distance, region16.983 0.027 0.113 (0.136)Mean temperature, mean salinity, region 17.451 0.021 0.129 (0.101)Minimum temperature,dock distance (squared), latitude17.705 0.019 0.104 (0.165)60Figure 3.2: Distribution of B. violaceus in BC in 2011.Figure 3.3: Map of the difference between model of currentB. violaceus percent cover and observed data in BC.61In summary, the final model had the proportion of cover when the species is presentbased on minimum temperature, dock distance and latitude, the probability of obtaininga zero based on minimum salinity and region, and the scale of the data was based onminimum temperature, minimum salinity and latitude.Model projectionBased on the trends of the summer BCSOP data, the north coast and the west coast ofVancouver Island were expected to warm by 0.25 ◦C and 0.41 ◦C in the next 50 years,respectively, while the Salish Sea was predicted to warm 2.09 ◦C. Salinity was predictedto decrease on the north coast by 0.28 h, but the west coast of Vancouver Island isprojected to increase by 0.60 h, and the Salish Sea was projected to increase by 2.32 h.The projections varied between shore stations in each region and the range between thehighest and lowest minima experienced per station between 1967–2011 was greater thanthe magnitude of the projected increase (Appendix ??app:LH, Table F.1 for temperatureand Table F.2 for salinity).Most of the model estimates for 2011 were near to the field value for B. violaceusabundance (Figure 3.3). Seventeen sites were modelled within five percent cover of thefield value, with another two within eight percent cover. The site with the second greatestabundance of B. violaceus in the survey, Bamfield, was underestimated by 24.5 percentcover. The remaining two sites were overestimated by over 20 percent cover and were inthe Salish Sea, Maple Bay (20.2) and Comox Bay (34.2). These two sites had habitableminimum temperatures, and Maple Bay had a minimum salinity in the survivable rangewhile Comox Bay was just outside of it, and both were located within 1.3 km of otherdocking facilities. However B. violaceus was absent in Comox Bay and had a moderateabundance in Maple Bay (15.9 percent cover in the field). In California, five of six sites62were modelled within 10 percent cover of the field data. However, one California site whereB. violaceus had high percent cover (37.9) was underestimated by 23.6 percent cover.Increased B. violaceus cover was projected for BC over the next 50 years, in eachregion, though the amount varied between and within regions (Figure 3.4). The increasein B. violaceus on the north coast averaged 1.2 ± 0.6 percent cover and on the west coastof Vancouver Island the increase averaged 4.4 ± 2.1 percent cover. The greatest changewas forecast for the Salish Sea, which averaged an increase of 32.2 ± 9.7 percent cover.The projected increase of B. violaceus cover varied within regions (Figure 3.5), es-pecially in the Salish Sea. Two sites were heavily dominated by the Fraser River outflow(specifically in the outer region of Burrard Inlet), which reduced the amount of B. violaceuscover. These two sites were accurately modelled as absent sites, and were not projectedto have B. violaceus within the next 50 years. One other site was accurately modelled forcurrent absence, however this site was expected to increase to 79.6 percent cover by 2061.As B. violaceus has previously been found at this site (Clarke Murray, 2012), presence ofB. violaceus in the future would not be unexpected though the amount of increase for 2061was surprisingly high. The final site where B. violaceus was not detected in the field wasmodelled at 34.2 percent cover with a large projected increase by 2061 (to 83.0 percentcover). The four sites where B. violaceus was detected were modelled at 3.0, 3.1, 10.4 and36.1 percent cover for 2011, which increased to 22.4, 23.1, 52.2, and 84.1 percent cover,respectively.The north coast did not vary as much and experienced relatively little increase. Foursites where B. violaceus was not detected were modelled accurately and were not expectedto have B. violaceus by 2061. The remaining site where B. violaceus was not found hada slightly warmer temperature than the other sites without B. violaceus, though a lowersalinity, and was estimated at 1.9 percent cover with a projected increase to 2.4. B. vio-63020406080100B. violaceus cover (%)North coast                WCVI                Salish SeaData 2011Model 2011Model 2061Figure 3.4: Field data for B. violaceus percent cover perregion compared to model estimates for present andprojected conditions.Figure 3.5: Projected increase of B. violaceus cover in BC.Warmer colours indicate a larger increase in percent cover.64laceus was found with low to moderate cover at three sites in the north coast, which weremodelled at 8.0, 8.6 and 21.9 percent cover to 10.1, 10.9 and 26.7 percent cover.The west coast of Vancouver Island had the two sites with the highest cover in the fieldsurvey, which were modelled at 52.9 and 81.1 percent cover for 2011, and these sites wereprojected to increase to 63.6 and 87.0 percent cover. The remaining site where B. violaceuswas present in the field with 26.7 percent cover increased from a modelled 27.2 to 36.7percent cover. B. violaceus was not detected in the field at three sites, possibly due to lowminimum salinities, and were modelled with absence both for 2011 and in the projection.3.4 Discussion3.4.1 Potential change in B. violaceus abundancePresence and abundance of B. violaceus was variable along the coast of BC, influenced atleast in part by temperature and salinity. It follows that the expected increase in abun-dance over the next 50 years varied from site to site depending on current and projectedvalues. On the north coast, where the increase in temperature was not sufficient to cre-ate conditions optimal for reproduction and growth of B. violaceus (see Tables 3.1 and3.2 for survivable, growth, and optimal ranges of temperature and salinity), the currentabundance and expected increase were low. However, all sites on the north coast with B. vi-olaceus currently present were expected to see a slight increase in B. violaceus abundanceas temperatures become more favourable for this species. While not all sites become morefavourable, no sites become less favourable. On the west coast of Vancouver Island, thethree sites that have conditions favourable for B. violaceus presently have high cover andare very close to optimal conditions. As the conditions at these sites were already highlyfavourable, B. violaceus has been able to exploit the habitat, therefore a low increase was65projected over the next 50 years. The three sites on the west coast of Vancouver Islandwhere B. violaceus was not detected in the field or modelled for current conditions did nothave B. violaceus in the projection as conditions did not improve enough to support sur-vival. In the Salish Sea, some sites were expected to experience temperature and salinityincreases that create conditions closer to the optimal range within the next 50 years. Atthese sites, there was a larger projected increase in abundance. The Salish Sea includedthe only site where B. violaceus was absent both in the field and in the model for currentconditions, yet had B. violaceus present in the projection as conditions became favourable.The overall increase in B. violaceus was consistent with other studies, which projectedincreased abundance of this species with climate change (Sorte & White, 2013; Stachowiczet al., 2002b; but see Cockrell & Sorte, 2013).All sites had at least some potential for B. violaceus introduction, since this work wasconducted at active saltwater marinas and B. violaceus is a hull-fouling species, howeverB. violaceus was not detected at all of the sites. While this pattern did vary with abioticconditions, the observed pattern of B. violaceus presence and abundance was not fullyexplained by temperature and salinity in the model. Proximity of the nearest neighbouringdocking facility to the sampling location, which was used as a proxy for propagule pressure,explained some of the variability in the pattern. The more docking facilities in an area,indicated by shorter distances between them, the more boats that can be accommodatedat one time. Docking facilities located in close proximity was likely only necessary in areasof higher boat traffic. Due to the increased boat traffic, there could be higher potentialpropagule pressure at those locations and a higher potential for B. violaceus establishment.Not all of the sources of variability could be deciphered, but some of it was likely due tothe amount of time that boats resided in each of the marinas (Clarke Murray et al., 2011),time since population establishment, or food supply, the increase of any of which would66increase B. violaceus abundance. The locations from which the boats arrived would alsoaffect the amount of B. violaceus, because not all marinas are invaded (Clarke Murray et al.,2011). Another source of variability could be due to the frequency of dock cleaning, whichcould vary per site, and more frequent cleaning would likely reduce B. violaceus abundance.Other anthropogenic and environmental changes, such as acidification, increased human-mediated transport mechanisms, and development of the coast could change the rate ofB. violaceus spread (Epelbaum et al., 2009a), and climate change is expected to modifyhuman activity in ways that may increase the risk of invasion (Walther et al., 2009). Bioticinteractions could also have been a source of variability. B. violaceus colonies under oneweek old were vulnerable to predation, where predation during that critical period couldreduce or eliminate colonies (Osman & Whitlatch, 2004). Simkanin et al. (2013) found thatboth adult and juvenile colonies were more likely to survive when protected from predators.However, many predators selected their regular prey over B. violaceus when given a choice(Epelbaum et al., 2009b) and Grey (2010) found that large predator exclusion did notaffect the recruitment or abundance of B. violaceus. B. violaceus has been found to bea dominant competitor for space in marine fouling communities (Dijkstra et al., 2007;Rajbanshi & Pederson, 2007; Stachowicz et al., 2002b), which is the main limiting resource(Sellheim et al., 2010; Stachowicz et al., 2002a; Teo & Ryland, 1995), so competition maynot have a strong effect on B. violaceus. This was also found in Ch. 2.As only temperature and salinity were allowed to vary in the model, if other factorschange, the change in the abundance of B. violaceus could differ from the projection. Fac-tors that could cause the future distribution and abundance of B. violaceus include com-petition, predation, resource availability, or vector-related factors such as boating traffic,marina size and number, or regulations against hull fouling. Nonetheless, previous studieshave shown that temperature and salinity perform well in predicting species distributions67(Grey, 2011; Reusser & Lee II, 2008; though see Therriault & Herborg, 2008), lending somecredence to the model projection of increased B. violaceus cover in BC.3.4.2 ImplicationsThe invasion and secondary spread of B. violaceus has been facilitated in part throughits broad temperature and salinity tolerances (Epelbaum et al., 2009a), but what are theimplications of this? Clarke Murray et al. (2011) found that 25.7 % of recreational boatsin BC were fouled with NIS. In addition, B. violaceus was the third most commonly foundNIS on boats and was the most consistently found NIS at surveyed marinas(Clarke Murrayet al., 2011). BC has an estimated 400,000 boats with more visiting from the USA (ClarkeMurray et al., 2011), providing ample opportunities for B. violaceus to spread. Whileports and marinas are often the first location that NIS establish, NIS are able to spread toother areas through many human-mediated vectors, including hull fouling (Carlton, 1996;Floerl et al., 2009; Minchin et al., 2006; Wasson et al., 2001). Pristine areas and protectedmarine parks are popular spots to stop for recreational boaters, thus B. violaceus and otherNIS could spread to undeveloped locations along the coast (Clarke Murray et al., 2011;Simkanin et al., 2012).A greater distribution and increased abundance of B. violaceus could have consequencesfor both natural ecosystems and industry. Native communities could experience increasedcompetition where B. violaceus is present. Pre-emptive competition may increase as adultascidians such as B. violaceus can reduce the amount of space available for larval settlementof other species (Carver et al., 2006; Osman & Whitlatch, 1995; Zajac et al., 1989), whichis an important limiting resource in marine fouling communities (Sellheim et al., 2010;Stachowicz et al., 2002a; Teo & Ryland, 1995). Interference competition may also increasebecause B. violaceus has been known to overgrow native species and can become competi-68tively dominant in subtidal benthic communities (Berman et al., 1992; Bock et al., 2011).Fouling organisms and algae were very vulnerable to B. violaceus overgrowth (Carver et al.,2006; Pederson et al., 2005), especially in terms of competition for space (Dijkstra et al.,2007; Rajbanshi & Pederson, 2007; Stachowicz et al., 2002b). The ability for B. violaceusto overgrow other species also makes them a concern for aquaculture (Bock et al., 2011;Carver et al., 2006; Epelbaum et al., 2009a). There is a risk that it could smother targetspecies, reduce food availability, and make harvest difficult as tunicates coat aquacultureequipment (Bock et al., 2011; Carver et al., 2006). The fouling of aquaculture facilitiesmay also increase local B. violaceus populations, as fragmented colonies created by high-pressure washing of contaminated equipment are viable if they resettle on suitable habitat(Bock et al., 2011; Paetzold & Davidson, 2010).Climate change could exacerbate the competitive imbalance between B. violaceus andother species. As salinity and temperature along most of the coast of BC increase to rangescloser to what is optimal for B. violaceus, increased B. violaceus dominance, and the result-ing changes to the substrate and available space, could result in reduced native diversityin the fouling community (Sellheim et al., 2010). Stachowicz et al. (2002b) found thatwarmer winter temperatures led to earlier and more abundant recruitment of B. violaceus,which means that climate change could favour its proliferation. B. violaceus would beable to establish before native species could arrive, thus dominating the limited availablespace. Further, native tunicate recruitment decreased with warmer winter temperatures(Stachowicz et al., 2002b). While the Stachowicz et al. (2002b) study was conducted in adifferent ocean basin and so does not directly apply to BC, it does highlight that there isa possibility that species may respond similarly here. That would mean that in additionto increased competition with B. violaceus as waters warm, climate change could directlyaffect the native assemblage of species in fouling communities, resulting in reduced native69species survival overall. Reduced native diversity could increase the likelihood of estab-lishment of new non-indigenous species, leading to an invasion meltdown, adding furtherstress to native communities (Simberloff & Holle, 1999; Stachowicz et al., 2002b). Whilefew of the surveyed sites in BC were currently dominated by B. violaceus, climate changecould lead to an increase of sites with high B. violaceus cover.3.4.3 ConclusionThis study provides predictions on province-wide trends in B. violaceus invasion, predictedon a small scale, and so sheds light on where it would be most important for managersto focus efforts to prevent introduction as conditions become more favourable. Increasedtemperature and salinity resulted in a projected increase of B. violaceus cover in BC overthe next 50 years. Sites expected to undergo a large increase in temperature, or a salinityincrease into a range in which B. violaceus can grow more quickly, were the most likely toexperience increased abundance. If temperature and salinity become more favourable forB. violaceus, as the temporal trends in temperature and salinity project, climate changecould make the invasion more widespread and severe.70Chapter 4Conclusion4.1 Summary of the resultsSpecies invasion in coastal marine ecosystems can be harmful both ecologically and eco-nomically. Ecologically, non-indigenous species (NIS) can increase competition for limitingresources and alter habitat (Crooks, 2002; Dijkstra et al., 2007; Rajbanshi & Pederson,2007; Stachowicz et al., 2002b). Economically, NIS can harm industry, such as the impactthat invasive tunicates have had on mussel aquaculture in Prince Edward Island (Leblancet al., 2007; LeGresley & Martin, 2008). The economic impacts associated with aquaticand terrestrial invasions are estimated to be between $13.3 to 34.5 billion/year in Canadadue to control costs, reduced yield, reduced land value, trade bans on exported goods,compensation paid to farmers, health care costs, and reduced tourism and tourism-relatedrevenues (Colautti et al., 2006). Knowledge of the factors that influence invasion successare important to the effective management of NIS, and the study of species invasions canhighlight areas of potential risk as the pattern of NIS distribution and abundance are betterunderstood (Grey, 2011; Jeschke et al., 2012). Conditions will not remain constant, so itis necessary to consider species invasion in the context of climate change as the frequencyof invasions is likely to increase (Hellmann et al., 2008). Accordingly, this thesis aimedto elucidate which factors had the greatest influence on NIS richness and abundance, andhow the distribution and abundance of NIS may be altered by climate change.71In Ch. 2, the biotic resistance hypothesis was not supported in the fouling communitiesof British Columbia (BC), against NIS richness or abundance, which brings the proportionof marine studies that found evidence of biotic resistance (Jeschke et al., 2012) down to50 % (6 of 12). The results are mixed even when focused specifically on marine foulingcommunities, and differences do not align between observational and manipulative studies(Dunstan & Johnson, 2004; Grey, 2009; Stachowicz et al., 2002a, 1999). However, latitudehas been a factor in whether biotic resistance was found (Freestone et al., 2013; Kimbroet al., 2013), possibly due to increased predation pressure (Freestone et al., 2013) or greaterspecies richness (Kimbro et al., 2013) at lower latitudes. Lower latitude communities maybe more resistant to invasion than higher latitude communities, and each of the studiesthat found biotic resistance in marine fouling communities occurred at a lower latitudethan this study. Stachowicz et al. (1999) and Stachowicz et al. (2002a) conducted theirstudies at approximately 42.3 N, both finding resistance, while Grey (2009) studied atbetween 47.8 N and 48.6 N with mixed results, and Dunstan & Johnson (2004) did notfind biotic resistance in their study at 42 S. Sites in this study were between 48.7 N and54.3 N, which is higher than the studies included in the reviews of Freestone et al. (2013)and Kimbro et al. (2013).Observed patterns of NIS richness and abundance were both affected primarily byminimum salinity. Salinity often has a large impact on whether species can survive, growand reproduce in marine environments (Epelbaum et al., 2009a; Rahel & Olden, 2008;Reusser & Lee II, 2008), so this was not an unexpected result.Minimum salinity was a factor in native species richness, as it was with NIS richness,which supports the environmental favourability hypothesis. However, contrary to whatwas found for NIS, native species richness was affected by both minimum salinity andproximity of the sampling location to other docking facilities. The main drivers of native72species abundance were not among the hypothesized factors, demonstrated by the supportfor the model based on the overall mean and the random effects of region and location, withlocation accounting for about 30 times more of the unexplained variability than region. Itis possible that the abiotic conditions did not have a strong influence on native speciesabundance because the native species that were able to persist have had time to adaptto the local environments (Byers, 2002). As there were differences in the explanatoryvariables that influenced NIS and native species in terms of richness and abundance, thisstudy demonstrated that species origin could be a factor in how marine species respond toecological pressures.The presence of B. violaceus was strongly influenced by minimum salinity and minimumtemperature. However, there was weak evidence for reduced B. violaceus presence withhigher native species richness, which may be evidence of a minor effect of biotic resistanceagainst the presence of B. violaceus.The model with the highest support for B. violaceus abundance was based on theoverall mean and the random effects of region and location, with region explaining ap-proximately 2.3 times more of the variability than location. However, there was evidencethat temperature had a positive influence on B. violaceus abundance. Native species rich-ness had a negative slope with B. violaceus abundance in separate models from minimumtemperature, however the confidence intervals for the estimates overlapped zero so therewas not strong evidence for the effect. The greater trend toward biotic resistance on thepresence rather than the abundance of B. violaceus (more negative estimate with less over-lap of positive values in the 95 % CI) was in contrast to previous studies that found thatnative species diversity had more influence on invasion success (population growth) thanestablishment (Kimbro et al., 2013; Levine et al., 2004). The nature of the relationshipbetween B. violaceus and native species richness could also be due to the interaction be-73tween biotic resistance and abiotic factors, as the strength of biotic resistance can dependon environmental conditions (Cheng & Hovel, 2010).In Ch. 3, minimum temperature was projected to increase in all three regions of BCin the next 50 years, with the greatest increase in the Salish Sea. Minimum salinity wasprojected to decrease on the North coast, but expected to increase on the west coast ofVancouver Island and in the Salish Sea. Accordingly, B. violaceus abundance was projectedto increase with the largest increases expected where future environmental conditions werecloser to the optimal range for growth than they are now. All but one location whereB. violaceus was not detected in 2011 were not projected to have presence in 2061. Thisis consistent with what would be expected based on the results for B. violaceus from Ch.2, which found increased presence of B. violaceus with higher minimum temperature andminimum salinity, and greater B. violaceus abundance with higher minimum temperature.An expected increase of B. violaceus with climate change was also found in past studies ofthis species (Sorte & White, 2013; Stachowicz et al., 2002b; though see Cockrell & Sorte,2013). Presence and abundance of B. violaceus was found to be variable along the coast ofBC in the field survey. While the variability in abundance was conserved in the projection,the overall trend for the province was toward increased B. violaceus abundance by 2061.The results of the model can be used to highlight which areas are most vulnerable toincreased abundance of B. violaceus, and thus identify high risk areas for targeted NISmanagement (Grey, 2009). In addition, in light of the overlap between areas of invasionand aquaculture tenures (Epelbaum et al., 2009a) and the potential risks that B. violaceusposes to aquaculture (Bock et al., 2011; Carver et al., 2006; Epelbaum et al., 2009a), thisinformation could also be useful to those who manage sea-based aquaculture ventures.744.2 Limitations of the researchCh. 2: Patterns of invasion in BC fouling communitiesTo determine which explanatory variables are responsible for the observed patterns foreach of the response variables, experimental manipulations will be required. Such experi-ments will help determine the degree to which the relationships that were found based onobservational evidence were, in fact, causal in the ways that were hypothesized.Predators were not quantified in this study, as the sampling method was optimized onlyfor sessile invertebrates. However, many marine fouling community predators are mobilespecies (Epelbaum et al., 2009b; Nydam & Stachowicz, 2007; Osman & Whitlatch, 2004;Simkanin et al., 2013), and past studies have shown that predation is capable of reducingthe populations of some NIS (Epelbaum et al., 2009b; Nydam & Stachowicz, 2007; Osman &Whitlatch, 2004; Simkanin et al., 2013; but see Grey, 2010). Predation pressure cannot beassumed constant between locations as species richness and abundance varied per location(Table 2.2). Accordingly, biotic resistance cannot be dismissed as a possibility withoutexamining the role of predation, though there was not evidence of resistance from thenative sessile communities in this study.Ch. 3: Climate change and species invasionIt is possible that the salinity and temperature from prior seasons were responsible forthe presence and abundance of B. violaceus measured during the survey. For example,warmer winter temperatures may lead to earlier and greater recruitment of B. violaceus thefollowing summer (Stachowicz et al., 2002b). Further, if the salinity remained below 10pptfor longer than two days, colonies could be eliminated (Dijkstra et al., 2008), which wouldthen not be present in future seasons unless more propagules were introduced. Yet, thedata collected for the abiotic conditions in the model were only from a single summer. This75model could be improved by monitoring the abiotic conditions and B. violaceus coloniesover a longer time scale to include conditions for past growth and over-wintering seasonsas this may affect the current distribution of B. violaceus.An additional improvement would involve the method for projecting future climateconditions. The projections of temperature and salinity used in this thesis were basedon linear trends from historical BCSOP data. Ideally, this would be done using climatemodels as climate is not likely to change linearly. However, a suitable model with regional,nearshore data on temperature and salinity is not yet available.4.3 Future directionsTo improve the model of present and future B. violaceus distribution and abundance, moreaspects of environmental suitability and propagule pressure could be investigated. Highamounts of suspended sediment can smother tunicates (Cohen et al., 1998) and watermovement has a large influence on tunicate distribution (Lambert & Lambert, 2003), butthese were not measured in this study. A more accurate measure of propagule pressuremay increase the accuracy of the model. While boats did travel to the sampling sites, theymight not have carried B. violaceus. Clarke Murray et al. (2011) detected B. violaceuson only 9.8 % of boats, so the proxy may have over-estimated the propagule pressure.Alternatively, there could be reproductive colonies of B. violaceus living on the dockingfacilities themselves, which would provide a steady supply of propagules to the tiles, butnot be detected with the dock distance proxy and lead to an underestimate of propagulepressure. A more detailed understanding of the factors that contribute to the presence andabundance of B. violaceus at present could increase the predictive power of future models.Unexpectedly, the distance between docking facilities was found to influence nativespecies richness. To investigate whether this was a true result or simply an artefact of the76data, the natural areas proximal to the docks could be surveyed to compare the communitiesto those on the docks. The known range, geographically and in terms of abiotic tolerances,of the native species found on the docks should also be evaluated to test whether thesespecies are expanding their range or simply taking advantage of a novel method of dispersal.Though not discussed, some species observed during this study were able to surviveclose association with B. violaceus while other species did not. Research into why somespecies are more easily overgrown and smothered than others, and which species survive,could provide information on the ecological dynamics of the new communities that will beformed as B. violaceus invade BC. As most of the competition experiments with B. vio-laceus were conducted with different native species than are found in BC (Dijkstra et al.,2007; Rajbanshi & Pederson, 2007; Stachowicz et al., 2002b), it is worth repeating suchexperiments in BC to see if the results are consistent. This is especially important as ourstudy and others (e.g. Sorte & White, 2013; Stachowicz et al., 2002b) have found thatB. violaceus could become increasingly dominant with climate change.4.4 ConclusionIn conclusion, the biotic resistance hypothesis was not supported for NIS richness or NISabundance, but it cannot be fully ruled out due to the weak evidence for a negative slopebetween native species richness and B. violaceus presence and abundance, and the absenceof predator data. However, because the confidence intervals for the native species richnessparameter estimates for both B. violaceus presence and abundance overlapped zero, andbecause native species richness was not a factor for either NIS richness or abundance, therewas no compelling evidence to support biotic resistance by native sessile species in thefouling communities of BC.Environmental variables did have an effect on NIS. Salinity had a positive influence on77NIS richness, NIS abundance, and B. violaceus presence, as it did on native species rich-ness, supporting the environmental favourability hypothesis. Temperature had a positiveeffect on B. violaceus presence and abundance. Some of the explanatory variables differedbetween NIS and native species in terms of richness or abundance, which suggested thatspecies origin does affect how marine species respond to ecological pressures.Salinity and temperature were projected to increase in BC over the next 50 years, andaccordingly the cover of B. violaceus was also projected to increase. Projections occurredon a small scale, thus sites where temperature and salinity increased into a range in whichB. violaceus could grow more quickly were the most likely to experience increased abun-dance. 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PloS one, 6(4), e14806.90Appendix ASampling site GPS locationsTable A.1: GPS locations for the BC sites in decimal degrees, with eliminatedsites (Gold River and Tofino) included.Region Site Latitude LongitudeNorth CoastDigby Island 54.3131 -130.4037Fairview 54.2936 -130.3541Masset 54.0077 -132.1413Port Clements 53.6901 -132.1823Port Edward 54.2281 -130.2976Queen Charlotte 53.2535 -132.0729Rushbrook 54.3248 -130.3056Sandspit 53.2383 -131.8616Bamfield 48.8335 -125.1367Fair Harbour 50.0612 -127.1172Gold River 49.6791 -126.1169West coast of Tahsis 49.9115 -126.6617Vancouver Island Tofino 49.1539 -125.9007Toquart Bay 49.0205 -125.3577Ucluelet 48.9452 -125.5526Zeballos 49.9785 -126.8439Continued on next page91Table A.1 – continued from previous pageRegion Site Latitude LongitudeSalish SeaCampbell River 50.0235 -125.2382Comox Bay 49.6711 -124.9298Eagle Harbour Yacht Club 49.3531 -123.2705French Creek 49.3502 -124.3563Maple Bay 48.7977 -123.6013PBS 49.2101 -123.9569Port Sidney 48.6538 -123.3947Royal Vancouver Yacht Club 49.2753 -123.1882Table A.2: GPS locations for the California sites in decimal degrees, witheliminated sites (Coyote Point and Pillar Point) included.Region Site Latitude LongitudeCaliforniaBodega Bay 38.3300 -123.0577Coyote Point 37.5897 -122.3159Loch Lomond 37.9719 -122.4833Monterey 36.6043 -121.8909Moss Landing 36.8128 -121.7875Pillar Point 37.5024 -122.4822Santa Cruz 36.9632 -122.0018South Beach Harbor 37.7816 -122.385592Appendix BSpecies list for BCTable B.1: Species list per region, with status is given for BC. When only one member of ataxon was present on a tile, but could not be identified further, it was designated “sp.”When more than one member of a taxon was present on a tile, and also could not beidentified further, they were designated as “sp. A” and “sp. B” to differentiate betweenthem. When an unidentified species was found repeatedly, it was given a code to keep itconsistent among tiles, e.g. “Hydroid sp. 2,” “Porifera1V13,” and “stems.”Taxon name North Coast Salish Sea WCVI StatusPhylum AnnelidaSerpulidae x x x NativeSerpulidae sp. A x NativeSerpulidae sp. B x x NativePhylum ArthropodaBalanus crenatus x x x NativeCirripedia x x x NativePhylum BryozoaAnascina x x UncertainAscophora x UncertainBowerbankia sp. x x UncertainBryozoan (encrusting hydroid) x UncertainBugula neritina x Non-indigenousBugula sp. x x x UncertainBugula sp. A x x UncertainBugula sp. B x x UncertainCryptosula pallasiana x x Non-indigenousCyclostomatida x UncertainContinued on next page93Table B.1 – continued from previous pageSpecies name North Coast Salish Sea WCVI StatusDendrobeania lichenoides x x NativeEncrusting bryozoan x UncertainMembranipora sp. x x x NativeSchizoporella japonica x x x Non-indigenousSchizoporella pseudoerrata x Non-indigenousSchizoporella sp. x Non-indigenousTegella sp. x NativePhylum ChordataAplidium sp. x NativeBotrylloides violaceus x x x Non-indigenousBotryllus schlosseri x x x Non-indigenousCorella sp. x x x NativeCorella willmeriana x x x NativeDistaplia sp. x NativeMetandrocarpa sp. x UncertainMetandrocarpa taylori x NativeTunicate x UncertainPhylum CiliophoraFolliculinids x NativePhylum CnidariaAnemone x x x UncertainCalycella syringa x NativeEctopleura sp. x UncertainHydroid x UncertainHydroid sp. 2 x UncertainObelia dichotoma x x x Non-indigenousObelia longissima x x Non-indigenousObelia sp. x x Non-indigenousSegmented hydroid x UncertainPhylum MolluscaContinued on next page94Table B.1 – continued from previous pageSpecies name North Coast Salish Sea WCVI StatusAnomiidae x UncertainHiatella arctica x x NativeMytilus sp. x x x CryptogenicPectinidae x UncertainPhylum PoriferaPorifera x x x UncertainPorifera1v13 x UncertainPorifera1v3 x UncertainPorifera2V3 x UncertainUnknown phylumFuzz x UncertainStems x Uncertain95Appendix CAbiotic conditions per siteMinima reported here are the lower 10th percentile, and the maxima used in the range arethe upper 10th percentile, rather than the absolute values to avoid over-emphasizing brief,transient events. Means for each site are reported with standard errors.96Table C.1: Salinity (h) for the summer of 2011 in BC.Region Site Minimum Mean (± SE) RangeNorth CoastDigby Island 21.46 24.24 (0.06) 5.66Fairview 22.12 24.62 (0.06) 4.37Masset 26.44 27.24 (0.004) 1.64Port Clements 18.43 19.35 (0.03) 1.70Port Edward 24.24 25.61 (0.04) 2.30Queen Charlotte 29.90 30.95 (0.02) 1.92Rushbrook 22.07 25.36 (0.06) 5.84Sandspit 28.21 29.96 (0.07) 5.65Bamfield 21.95 25.60 (0.02) 5.96Fair Harbour 6.61 20.31 (0.21) 21.72West coast of Tahsis 5.80 20.67 (0.24) 22.87Vancouver Island Toquart Bay 22.63 24.11 (0.04) 2.97Ucluelet 28.09 29.18 (0.04) 2.30Zeballos 6.89 19.25 (0.18) 18.77Salish SeaCampbell River 26.79 27.43 (0.02) 1.27Comox Bay 19.35 22.18 (0.07) 6.03Eagle HarbourYacht Club6.68 8.91 (0.07) 5.10French Creek 21.46 24.73 (0.08) 7.05Maple Bay 22.96 24.13 (0.03) 2.98PBS 17.23 23.04 (0.12) 11.55Port Sidney 26.21 27.69 (0.04) 3.55Royal VancouverYacht Club5.86 7.61 (0.07) 3.65Table C.2: Salinity (h) for the summer of 2011 in California.Region Site Minimum Mean (± SE) RangeCaliforniaBodega Bay 22.00 27.89 (0.07) 10.21Loch Lomond 13.70 19.62 (0.04) 10.70Monterey 26.74 30.21 (0.04) 6.34Moss Landing 24.30 28.92 (0.03) 9.80Santa Cruz 33.43 33.88 (0.06) 1.53South Beach Harbor 19.07 24.00 (0.05) 8.2397Table C.3: Temperature (◦C) for the summer of 2011 in BC.Region Site Minimum Mean (± SE) RangeNorth CoastDigby Island 10.76 12.08 (0.03) 2.38Fairview 10.37 11.76 (0.03) 2.41Masset 11.58 13.06 (0.01) 2.75Port Clements 12.64 14.51 (0.04) 3.37Port Edward 10.19 11.61 (0.02) 2.37Queen Charlotte 12.79 14.36 (0.03) 2.80Rushbrook 10.58 12.01 (0.03) 2.47Sandspit 12.80 14.28 (0.03) 2.64Bamfield 14.60 16.33 (0.04) 3.50Fair Harbour 13.29 16.03 (0.05) 5.26West coast of Tahsis 11.39 14.71 (0.06) 5.97Vancouver Island Toquart Bay 16.25 18.23 (0.04) 4.10Ucluelet 13.58 14.88 (0.03) 2.56Zeballos 12.71 15.35 (0.05) 4.92Salish SeaCampbell River 11.17 12.56 (0.03) 2.92Comox Bay 13.87 16.70 (0.05) 5.42Eagle HarbourYacht Club14.82 17.00 (0.05) 4.41French Creek 13.68 16.79 (0.06) 5.64Maple Bay 14.00 16.45 (0.05) 4.78PBS 13.86 16.93 (0.06) 5.52Port Sidney 11.50 12.78 (0.02) 2.48Royal VancouverYacht Club15.15 17.16 (0.04) 4.12Table C.4: Temperature (◦C) for the summer of 2011 in California.Region Site Minimum Mean (± SE) RangeCaliforniaBodega Bay 12.94 14.44 (0.02) 2.85Loch Lomond 17.50 19.96 (0.04) 4.50Monterey 13.45 14.76 (0.02) 2.28Moss Landing 14.50 15.92 (0.03) 3.00Santa Cruz 13.88 15.41 (0.02) 2.52South Beach Harbor 14.49 16.49 (0.02) 3.1398Appendix DPrimary model results for setswith a dropped variableTable D.1: Models for Poisson distribution of NIS richness within approximately twounits of the lowest AICc value. “Log-likelihood” refers to the natural log of the likelihoodfor the set of parameter values.Explanatory variables Random effects AICc Log-likelihoodMoran’s I(p value)Minimum temperature,minimum salinityRegion, location 536.44 -262.0140.007(0.285)Minimum temperature,minimum salinity,dock distanceRegion, location 538.56 -262.000.007(0.283)Table D.2: Models for the cube root of NIS abundance within approximately two units ofthe lowest AICc value. “Log-likelihood” refers to the natural log of the likelihood for theset of parameter values.Explanatory variables Random effects AICc Log-likelihoodMoran’s I(p value)Minimum temperature,minimum salinityRegion, location 664.37 -325.9783.071E-04(0.651)Minimum temperature,minimum salinity,dock distanceRegion, location 666.35 -325.8963.076E-04(0.651)99Table D.3: The primary set of models for Poisson distribution of native species richnesswithin approximately two units of the lowest AICc value. “Log-likelihood” refers to thenatural log of the likelihood for the set of parameter values.Explanatory variables Random effects AICc Log-likelihoodMoran’s I(p value)Minimum temperature,minimum salinity,dock distanceRegion, location 608.18 -296.809-0.020(0.172)Minimum temperature,minimum salinity,dock distance,NIS richnessRegion, location 609.99 -296.636-0.020(0.197)100Appendix ECorrelations of fixed effectsE.1 NIS correlation tablesCorrelations between fixed effects for the top-ranked models for NIS richness are found inTable E.1.Table E.1: Correlation of fixed effects for NIS richness, in theorder in which they were presented in Table 2.4.Model Fixed effectsNISrichSInterceptMinimum salinity -0.950NISrichTSInterceptMinimumsalinityMinimum salinity -0.514Minimum temperature -0.937 0.202NISrichSDInterceptMinimumsalinityMinimum salinity -0.924Dock distance -0.182 -0.046101Correlations between fixed effects for the top-ranked models for NIS abundance arefound in Table E.2.Table E.2: Correlation of fixed effects for NIS abundance, in theorder in which they were presented in Table 2.6.Model Fixed effectsNISabSInterceptMinimum salinity -0.929NISabSDInterceptMinimumsalinityMinimum salinity -0.892Dock distance -0.195 0.082NISabTSInterceptMinimumsalinityMinimum salinity -0.518Minimum temperature -0.955 0.265102E.2 B. violaceus correlation tablesCorrelations between fixed effects for the top-ranked models for B. violaceus presence arefound in Table E.3.Table E.3: Correlation of fixed effects for B. violaceus presence, in the order in which they werepresented in Table 2.8.Model Fixed effectsaBvTSInterceptMinimumsalinityMinimum salinity -0.826Minimum temperature -0.842 0.399aBvTSDSrInterceptMinimumsalinityMinimumtemperatureNativespeciesrichnessMinimum salinity -0.824Minimum temperature -0.828 0.393Native species richness 0.135 -0.320 -0.092Dock distance -0.029 0.065 -0.147 0.261aBvTSDInterceptMinimumsalinityMinimumtemperatureMinimum salinity -0.828Minimum temperature -0.826 0.376Dock distance -0.057 0.151 -0.135103Correlations between fixed effects for the top-ranked models for B. violaceus abundanceare found in Table E.4.Table E.4: Correlation of fixed effects for B. violaceus abundance, in the orderin which they were presented in Table 2.10.Model Fixed effectspBvint Not applicablepBvTSInterceptMinimumsalinityMinimum salinity -0.902Minimum temperature -0.890 0.611pBvSrInterceptNative species richness -0.506pBvTSDInterceptMinimumsalinityMinimumtemperatureMinimum salinity -0.903Minimum temperature -0.891 0.616Dock distance 0.079 -0.100 -0.108pBvDSrInterceptNativespeciesrichnessNative species richness -0.596Dock distance -0.441 0.372104E.3 Native species correlation tablesCorrelations between fixed effects for the top-ranked models for native species richness arefound in Table E.5.Table E.5: Correlation of fixed effects for native species richness, in the order in whichthey were presented in Table 2.12.Model Fixed effectsnatrichSDInterceptMinimumsalinityMinimum salinity -0.916Dock distance -0.188 -0.048natrichTSDInterceptMinimumsalinityMinimumtemperatureMinimum salinity -0.540Minimum temperature -0.945 0.265Dock distance 0.152 -0.104 -0.224natrichSDSrInterceptMinimumsalinityNIS richnessMinimum salinity -0.809Dock distance -0.089 -0.385NIS richness -0.194 -0.074 0.082105Correlations between fixed effects for the top-ranked models for native species abun-dance are found in Table E.6.Table E.6: Correlation of fixed effects for native species abundance,in the order in which they were presented in Table 2.14.Model Fixed effectsnatabint Not applicablenatabSrInterceptNIS richness -0.344natabSrVarInterceptNIS richness -0.794106Appendix FVariation in shore station trendsfrom 1967–2011Trends varied between shore stations used for the linear projections in Ch. 3 for bothminimum temperature (Table F.1) and minimum salinity (Table F.2).Table F.1: Linear trends in minimum temperature (◦C) between shore stations usedfor the projections from 2011 to 2061. The lowest and highest minima that occurredbetween 1967–2011 is reported with the year in which it occurred.Region Shore stationLinearprojectionLowestminimum(year)Highestminimum(year)North CoastLangara Point 0.34 9.40 (1972) 12.11 (1981)Bonilla Island 0.16 9.70 (1985) 13.00 (1997)West coast of Kains Island 0.56 10.61 (1970) 13.92 (1997)Vancouver Island Amphitrite Point 0.27 11.08 (2001) 13.00 (1997)Salish SeaChrome Island 2.49 10.83 (1972) 15.40 (1998)Departure Bay 0.78 11.13 (1972) 15.70 (1990)Entrance Island 2.27 11.21 (1972) 16.10 (1990)Active Pass 2.84 10.42 (1972) 15.21 (1990)107Table F.2: Linear trends in minimum salinity (h) between shore stations used for theprojections from 2011 to 2061. The lowest and highest minima that occurred between1967–2011 is reported with the year in which it occurred.Region Shore stationLinearprojectionLowestminimum(year)Highestminimum(year)North CoastLangara Point -0.97 30.21 (2007) 32.00 (19.71)Bonilla Island 0.42 29.80 (2011) 31.60 (1989)West coast of Kains Island -0.04 29.40 (2003) 31.81 (1970)Vancouver Island Amphitrite Point 1.24 27.72 (1976) 30.90 (1998)Salish SeaChrome Island 1.47 21.80 (1977) 27.31 (1995)Departure Bay 0.82 15.91 (2011) 24.60 (2009)Entrance Island 3.00 15.34 (1976)25.1(1987 & 2009)Active Pass 3.99 12.08 (1974) 23.33 (1987)108109

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