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Determining the impacts of hydrological drought on endangered Nooksack dace (Rhinichthys cataractae)… Avery-Gomm, Stephanie 2013

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Determining The Impacts Of Hydrological Drought On Endangered Nooksack Dace (Rhinichthys cateractae) At The Population And Individual Level: Implications For Minimum Environmental Flow Requirements by Stephanie Avery-Gomm B.Sc., University of Victoria, 2009  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Zoology)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2013 © Stephanie Avery-Gomm, 2013  Abstract Understanding the impacts of hydrological drought is crucial to the conservation of freshwater fishes. In British Columbia, Nooksack dace (Cyprinidae: Rhinichthys cataractae) are an endangered riffle specialist and are threatened by extremely low summer flows. The purpose of this thesis was to explore the impacts of drought on Nooksack dace, whether pool habitats may act as refugia to mitigate these impacts, and to define minimum environmental flow requirements. The first two objectives were addressed using a combination of field survey and experimental manipulations. A reduction in Nooksack dace population size with declining summer flow in Bertrand Creek, and a marked decrease in growth at low discharge in experimental riffles, indicated that low discharge has negative impacts on dace at both population and individual levels. Pool habitats were found to play a minor role in mitigating the negative impacts of hydrological drought (e.g., decreased growth rate), save as a refuge from stranding when riffles dewater. The third objective was addressed using the Instream Flow Incremental Methodology (IFIM). Because this study involved an endangered species an emphasis was placed on evaluating two fundamental assumptions of the methodology. Experimental results for Nooksack dace growth at different depths and velocities provided support for the first assumption, that density-based Habitat Suitability Curves (HSCs) accurately reflect habitat quality, but only for the lower limits of the HSCs. Next, a significant positive relationship between Weighted Usable Area (WUA) and dace biomass was found, supporting the assumption that such a relationship exists. However, this relationship was weak indicating a high degree of uncertainty in how Nooksack dace biomass will respond at high discharges. The IFIM model predicted that habitat availability for Nooksack dace begins to decline most rapidly at discharges of 0.12 m3·s-1. As there is low confidence in upper ranges of the HSCs this low flow threshold may underestimate declines with discharge, and therefore protection of at least 0.12 m3·s-1 is considered necessary for the persistence of Nooksack dace individuals and populations. Compared to conventional instream flow criteria, 0.12 m3·s-1 represents ~10% mean annual discharge which is the threshold for severely degraded habitat.  ii  Table Of Contents Abstract.................................................................................................................................... ii	
   Table Of Contents .................................................................................................................. iii	
   List Of Tables .......................................................................................................................... v	
   List Of Figures........................................................................................................................ vi	
   Acknowledgements .............................................................................................................. viii	
   Dedication ................................................................................................................................ x	
   Chapter 1: General Introduction ......................................................................................... 1	
   1.1	
   Background .................................................................................................................. 1	
   1.2	
   Nooksack Dace Conservation And Management ........................................................ 4	
   1.2.1	
   Conservation Context............................................................................................ 4	
   1.2.2	
   Habitat Requirements And Threats ....................................................................... 5	
   1.3	
   Thesis Objectives And Structure ................................................................................. 6	
   Chapter 2: Drought And The Role Of Refugia In An Endangered Riffle-Dwelling Freshwater Fish....................................................................................................................... 9	
   2.1	
   Introduction .................................................................................................................. 9	
   2.2	
   Methods...................................................................................................................... 11	
   2.2.1	
   Field Survey: Flow Effects On Nooksack Dace Populations And Habitat Use . 12	
   2.2.1.1	
   Site Selection ............................................................................................... 12	
   2.2.1.2	
   Sampling Methods ....................................................................................... 13	
   2.2.1.3	
   Data Analysis ............................................................................................... 13	
   2.2.2	
   Experimental Manipulation: Flow Effects On Nooksack Dace Growth Rate .... 14	
   2.2.2.1	
   Experimental Design .................................................................................... 14	
   2.2.2.2	
   Sampling Methods ....................................................................................... 15	
   2.2.2.3	
   Data Analysis ............................................................................................... 16	
   2.3	
   Results ........................................................................................................................ 17	
   2.3.1	
   Field Survey: Flow Effects On Nooksack Dace Populations And Habitat Use . 17	
   2.3.2	
   Experimental Manipulation: Flow Effects On Nooksack Dace Growth Rate .... 18	
   2.4	
   Discussion .................................................................................................................. 19	
   2.5	
   Conclusion ................................................................................................................. 24	
   iii  Chapter 3: Modelling Minimum Flow Requirements For Nooksack Dace .................... 37	
   3.1	
   Introduction ................................................................................................................ 37	
   3.2	
   Methods...................................................................................................................... 41	
   3.2.1	
   Field Data Collection .......................................................................................... 41	
   3.2.2	
   Hydraulic-Habitat Modelling .............................................................................. 42	
   3.2.3	
   Evaluating Assumptions ..................................................................................... 43	
   3.2.3.1	
   Do Density-Based Habitat Suitability Curves Reflect Habitat Quality? ..... 43	
   3.2.3.2	
   Is There A Positive Relationship Between WUA And Biomass? ............... 44	
   3.2.4	
   Estimation Of Minimum Environmental Flow Threshold .................................. 45	
   3.3	
   Results ........................................................................................................................ 46	
   3.3.1	
   Evaluating Assumptions ..................................................................................... 46	
   3.3.1.1	
   Do Density-Based Habitat Suitability Curves Reflect Habitat Quality? ..... 46	
   3.3.1.2	
   Is There A Positive Relationship Between WUA And Biomass? ............... 46	
   3.3.2	
   Estimation Of Minimum Environmental Flow Threshold .................................. 47	
   3.4	
   Discussion .................................................................................................................. 47	
   3.5	
   Conclusion ................................................................................................................. 52	
   Chapter 4: General Conclusions......................................................................................... 64	
   References .............................................................................................................................. 69	
   Appendices ............................................................................................................................. 80	
   Appendix A Assessing Efficiency Of Single-Pass Electrofishing ...................................... 80	
    iv  List Of Tables Table 2.1 Characteristics of the four focal reaches where Nooksack dace were sampled in 2010. These reaches were previously identified as high-quality habitat (i.e., >10 % riffle habitat by length), and are designated as critical habitat under the Species At Risk Act (Pearson et al., 2008). ............................................................................................. 25	
   Table 2.2 Within each focal reach, up to four pool and riffle habitat units were randomly selected for repeat sampling. The number of habitat units identified and sampled is shown. The number of habitat units included in the RM-ANCOVA analysis is shown in parentheses and justification for excluding some habitat units is provided. ................. 26	
   Table 2.3 Benthic invertebrates were identified to Order (or Family in the case of Hydropsychidae) and assigned a classification as palatable, unpalatable or inaccessible. Palatability was determined based on diet literature and observations. ........................ 27	
   Table 2.4 Summary of Nooksack dace densities in pool and riffle habitat units, for each sampling period, and at each focal reach. Data are presented as means + standard errors. ............................................................................................................................. 28	
   Table 3.1 Studies that have tested for a relationship, or correlation, between Weighted Usable Area and stream fish abundance or biomass, and the corresponding strengths of those relationships. ........................................................................................................ 54	
   Table 4.1 Capture efficiency data collected between June 22nd 2011 and August 19th 2011, using two-stage method as described by Bonamis (2011). Data are presented as means + standard error……………………………………………………...…………83	
    v  List Of Figures Figure 1.1 Canadian and global Nooksack dace distribution. In southwestern British Columbia, Canada, Nooksack dace are found in the 1) Brunette River, 2) Bertrand Creek, 3) Pepin Creek and 4) Fishtrap Creek. In addition, Nooksack dace are found throughout northwestern Washington, USA. Figure credit: Pearson et al., 2008. .......... 8	
   Figure 2.1 Map of Bertrand Creek. Dark blue lines define Nooksack dace critical habitat reaches and red circles identify focal reaches where Nooksack dace field surveys were carried out between May and August 2010. Figure credit: Pearson et al., 2008........... 29	
   Figure 2.2 Pool and riffle habitat units in Bertrand Creek were stop-netted using a Fyke stopnet, placed across the downstream end of both habitat types for consistency. This Fyke stop-net was particularly suited to single-pass electrofishing for Nooksack dace, as described in Appendix A. .............................................................................................. 30	
   Figure 2.3 In-ground experimental channels featured 16 habitat units (four channels with isolated, alternating pools and riffles). Discharge in each channel was manipulated so that the four treatments represented a gradient of habitat quality. ................................ 31	
   Figure 2.4 Observed changes in (A) Nooksack dace abundance and (B) Nooksack dace density (fish·m-2) in pool and riffle habitats units in Bertrand Creek, which were sampled over range of discharges (0.113 m3·s-1 to 0.007 m3·s-1). .................................. 32	
   Figure 2.5 Effect of discharge (L·s-1) and habitat type (riffle, pool) on Nooksack dace Specific Growth Rate (SGRMASS; %·day-1). .................................................................. 33	
   Figure 2.6 Availability of benthic prey (g·m-2), which included Ephemeroptera, Elmidae, Simuliidae, Tipulidae, Plecoptera and Hydropsychidae, was quantified for each of the 16 experimental units. Data are shown as means + standard errors (N=2). .................. 34	
   Figure 2.7 Experimental channel (A) mean and (B) maximum daily water temperatures were monitored in the upstream riffle and the downstream pool for each discharge treatment (L·s-1). ............................................................................................................ 35	
   Figure 2.8 Relationship between Nooksack dace Specific Growth Rate (%·day-1) and prey availability (g·m-2; r2 = 0.33) for pool and riffle experimental units. ............................ 36	
    vi  Figure 3.1 Examples of discharge in a Bertrand Creek riffle habitat where Nooksack dace abundance was sampled and hydraulic data were collected. Clockwise from top: 1.58 m3·s-1, 0.09 m3·s-1, 0.05 m3·s-1, and 0.02 m3·s-1. ............................................................ 56	
   Figure 3.2 Density-based Habitat Suitability Curves were developed for Nooksack dace in Bertrand Creek by Inglis et al., (1994). ......................................................................... 57	
   Figure 3.3 The relationship between WUA (Weighted Usable Area; units of microhabitat area per 1000 m stream length) and Discharge for the three focal reaches was averaged to yield a composite WUA-Discharge relationship for Bertrand Creek. ...................... 58	
   Figure 3.4 Mean Specific Growth Rate (SGRMASS; %·day-1) for Nooksack dace in experimental pools (blue) and riffles (red) was positively correlated with corresponding suitability factors for depths and velocities from Inglis et al., (1994) Habitat Suitability Curves across the limited range of (A) depths and (B) velocities present in the experimental pool and riffle experimental units. ........................................................... 59	
   Figure 3.5 (A) Significant positive relationship between Weighted Usable Area (WUA) and Nooksack dace biomass (g) for each habitat unit sampled. (B) A lack of a significant relationship between Combined Habitat Suitability and Nooksack dace density (g·m-2), indicates that the relationship in (A) is not driven by the suitability of sampled habitat units. .............................................................................................................................. 60	
   Figure 3.6 Relationship between Weighted Usable Area (WUA; units of microhabitat area per 1000 m stream length) and discharge for each focal reaches across a range of simulated discharges (i.e., 0.01 to 0.30 m3·s-1). ............................................................. 61	
   Figure 3.7 Daily mean discharges for Bertrand Creek (USGS, 2012) and the percent of Mean Annual Discharge that these discharges represent (i.e., percent of daily mean discharge averaged over a period of years; Ptolemy, 2009). ......................................................... 62	
   Figure 3.8 The composite WUA-Discharge relationship for Bertrand Creek was converted to a relationship between Nooksack dace biomass (g) and discharge relationship using the linear relationship between biomass and WUA (Biomass = 9.393(WUA)+33.96 + 95% confidence intervals). .................................................................................................... 63	
   Figure 4.1 Relationship between capture efficiency of single-pass electrofishing for Nooksack dace in riffle and glide habitats (A) tended to be higher in habitats with higher velocities (m·s-1), but (B) had no relationship to depth (m)................................ 84 vii  Acknowledgements I was fortunate to have not one, but two excellent supervisors, John Richardson and Jordan Rosenfeld. Thank you, John, for your enthusiasm, for allowing me independence, and for your excellent advice in all things. Jordan, thank you for your encouragement, support (both in the field and in the office!), and for challenging me question convention. I appreciate the opportunity to have worked with you both. I’d also like to acknowledge the contributions of my committee members: Rick Taylor, for his invaluable input and innate interest in all things dace, and Dan Moore, for lending me his hydraulic expertise. Gratitude is also due to Thomas Payne, who generously guided me towards mastering the intricacies of RHABSIM. Both John and Jordan have assembled excellent lab groups. I consider myself lucky to have shared the last couple of years with the following talented individuals: Dave Allen, Mike Champion, Sean Naman, Jill Miner, Tonya Ramey, Ana Marcela Chara, Jose RuizEsquide, Felipe Rossetti de Paula, Kirsten Campbell, Takuya Sato, Pauliina Louhi, Trisha Atwood, Rachael Dundaniec, Amanda Klemmer, and Hamish Greig and Gerrit Velema. I thoroughly enjoyed the two seasons of fieldwork spent wading through the rural streams of Aldergrove, but it wouldn’t have been possible to accomplish all that I did without a great number of people. Thanks to Mike Pearson for helping me to get started, and lending me his expertise along the way. To Hélène Boulanger, Eric Balke, Aija White, and Tristan Slade- thank you for all of your hard work, it was a pleasure working with you. Sincere thanks go to George Feddis and family, for generously allowing us to camp on their land, helping us to build a stellar field camp, and for the many shared stories and laughs. I am also indebted to the many volunteers, friends and family who kindly assisted with construction of the experimental channels and data collection: Mikaela Davis, Jill Miners, John Hill, Oliver Podyzcki, Lindsay Boliszczuk, Jason McGregor, Dave Scott, Jenn Burt, Noel Swain, Sebastian Pardo, Brittney Keeling, Dave Allen, Kathleen Avery-Wilson, Laura AveryWilson and William Avery-Wilson. I do not believe there is a better department for graduate studies than the Zoology Department at UBC. I’d like to thank all of the fixtures and fellow grad students without  viii  whom I would not have had such an epic time, in particular Georgina Cox, Carla Crossman, Roya Eshragh, Kat Anderson, and Milica Mandic. This project was made possible by generous financial support from the Fisheries and Oceans Canada, the British Columbia Ministry of Environment, the Habitat Trust Conservation Fund, and the Canadian Wildlife Foundation. It is challenging to express how grateful I am to my amazing family. To my parents, Kathleen Avery-Wilson, who has always encouraged me to do what I am most passionate about, and to Ian Gomm who always lends me his perspective on what matters most, I will be forever thankful. For their love, support and encouragement I thank my extended family, Peter and Beryl Gomm, Bill and Marilyn Wilson, Eileen Avery and David Budinski, Al Juzak Kevin Wilson, Nancy Gomm, Emma Avery, Colleen Avery, Sue and Tim Flynn. To my siblings, Laura Avery-Wilson and William Avery-Wilson, thank you for being such excellent people and for allowing me to subject you to my enthusiasm for ecology. Finally, a special thank you is due to my partner Ben Speers-Roesch, who often lent me the benefit of his experience and provided me with comic relief when it was needed most. Having you by my side made an exceptional experience even better.  ix  Dedication  This thesis is dedicated to my Grandparents.  x  Chapter 1: General Introduction 1.1 Background North America is reported to have the highest temperate freshwater biodiversity in the world (Abell et al., 2000). Unfortunately, extinction rates for species in freshwater are exceedingly high (Ricciardi and Rasmussen, 1999). An estimated 40% of freshwater fish species in continental North America are currently extinct or in serious decline (700 species; Jelks et al., 2008), which is a 92% increase over the number of imperiled freshwater fish reported in 1989 (364 species; Williams et al., 1989). A variety of stressors are responsible for this trend, including invasive species, overexploitation, physical habitat degradation, and flow modification (Dudgeon et al., 2006). Arguably the most pervasive threat is flow modification, as alteration of the natural flow regime (i.e., the magnitude, duration, frequency, timing and flashiness of flows; Poff et al., 1997) contributes greatly to physical habitat degradation (Bunn and Arthington, 2002; Arthington et al., 2010). Declining stream flows resulting from human activities such as dam construction, inter-basin diversions, and water withdrawals are a global issue (Naiman et al., 2002). A large percentage of the world’s large rivers have reduced discharges now compared to 50 years ago, including rivers ranging from the Congo in Africa to the Mississippi in North America (Dai et al., 2009). Even in Canada, a country commonly believed to have abundant freshwater, there has been a significant decrease in mean monthly stream discharge over the past 30-50 years, with the greatest decreases occurring near the end of the summer low flow period (August to September) and in the southern part of the country (Zhang et al., 2001). The current trend of declining stream flows has led to an increase in the frequency and intensity of droughts that, combined with climate-induced changes in precipitation patterns and an exponentially increasing human population, are anticipated to worsen (Jackson et al., 2001; IPCC, 2007; Kundzewicz et al., 2008; Dai, 2012). There are four categories of drought, which are defined on the basis of their causes and consequences: meteorological, agricultural, hydrological and socio-economic (Bond et al., 2008). In this thesis I refer to drought in the hydrological sense, which can be defined as deficiencies in water supplies, measured by stream flow, lake or reservoir level, or elevation of the ground water surface and may last for weeks or years (Smakhtin, 2001). Whether or 1  not hydrological drought is natural or anthropogenic, it results in reduced total habitat area, lateral connectivity between mainstem and floodplain habitat, and decreased water quality (Magoulick and Kobza, 2003). In rivers and streams, drought affects habitat types differently and can severely alter relative habitat availability (Hakala and Hartman, 2004). In fast flowing, turbulent riffles habitats area, depth and velocity decrease and in extreme cases riffle habitats may dry completely. Pool habitats also shrink, but retain water for longer after cessation of flow. During drought both pool and riffle habitats may be characterized by increases in physicochemical extremes of temperature, dissolved oxygen, pH and nutrient loading (reviewed in Lake, 2003). As streams dry up and water quality degrades, deteriorating conditions may exceed tolerance thresholds causing fish to seek refuge (Grossman and Ratajczak, 1998; Hodges and Magoulick, 2011), which can be defined spatially as locations where the negative effects of disturbance are lower than in the surrounding area (Lancaster and Beleya, 1997). For example, as discharge decreases riffle specialists may abandon riffles to avoid stranding and seek refuge in pools (Gelwick, 1990). The role that pool refugia play in mitigating the negative effects of drought in terms of population persistence and individual performance in terms of growth and survival is not well understood for riffle dwelling species (Magoulick and Kobza, 2003). Unlike many other types of disturbance, drought typically increases densitydependent interactions such as predation and competition (Power et al., 1985; Harvey and Stewart, 1991). These biotic interactions can combine with degraded water quality to amplify the negative effects of drought, which may impact ecosystem function, community structure, population dynamics, and in extreme cases may cause local or global extinction (Kushlan, 1976; Matthews and Manass, 1979; Xenopoulos et al., 2005). A recent meta-analysis of the effects of drought on fish found that the most frequently demonstrated effects of drought were population declines, habitat loss, altered community structure, negative effects from changes in water quality, movement within catchments, and crowding of fish in reduced microhabitats (Matthews and Marsh-Matthews, 2003). The management of freshwater resources has evolved a great deal over the past 50 years (reviewed in Jowett, 1997). In the 1970s and 1980s management focused on the protection of minimum instream flow, which was often defined as a percentage of historical 2  annual flow (e.g., Tennent, 1976) or wetted width (e.g., Bartschi, 1976). These fixed minimum flow approaches often had no apparent linkage to a specific aquatic habitat benefit in any particular stream, and thus ran the risk of failing to protect sufficient water to maintain optimal habitat conditions and ecosystem function (Stalnaker et al., 1995). Following this, management moved towards an incremental method, which still aimed to protect minimum instream flows, but on the basis of preferred depth and velocities for a species of commercial or recreational interest (Bovee, 1982). Today it is well known that protecting only minimum flows is insufficient, and that maintenance of stream channel morphology and ecological function requires preservation of the natural variability in the flow regime (Poff et al., 1997). As a result there is growing interest in protecting environmental flows in a way that both benefits riverine ecosystems and society, where environmental flow “describes the quantity, timing and quality of water flows required to sustain freshwater and estuarine ecosystems and the human livelihoods and wellbeing that depend upon these ecosystems” (Brisbane Declaration, 2007). However, in the case of managing stream flows during hydrological drought, the most important consideration remains identifying minimum environmental flows (i.e., the discharges over which the availability of habitat steeply declines) and understanding how fish populations will respond to prolonged periods of extremely low flow (Bunn and Arthington, 2002). The most widely applied tool for predicting the biological effects of low flow is the Physical Habitat Simulations (PHABSIM) component of the Instream Flow Incremental Methodology (IFIM; Bovee, 1982; Bovee et al., 1998). The PHABSIM model combines hydraulic information about a given stream with information about depth and velocity preferences of a target species, to model how the availability and quality of habitat changes as a result of incrementally changing discharge (Milhous et al., 1984). The modelled relationship between availability of suitable habitat and discharge can be used to identify a threshold discharge at which the availability of habitat begins to decline rapidly with decreasing flow. Ensuring that these models generate accurate predictions of minimum environmental flows is critical in regions that are vulnerable to hydrological drought. If minimum environmental flows are set higher than necessary, there may be unnecessary socio-economic costs (i.e., by reducing available water for agriculture), while setting minimum 3  environmental flows too low may undermine their purpose of protecting target species, freshwater ecosystems and human livelihoods (Rosenfeld and Ptolemy, 2012). Therefore, it is particularly important to have a comprehensive understanding of the impacts of low flow on the species of interest, and to correctly identify minimum environmental flows in situations where human activities have the potential to exacerbate drought.  1.2 Nooksack Dace Conservation And Management 1.2.1  Conservation Context One species that may be significantly impacted by the increasing frequency and  duration of hydrological droughts is Nooksack dace (Cyprinidae: Rhinichthys cataractae sp.), a fish of federal and provincial conservation concern in Canada. Nooksack dace are endemic to southwestern British Columbia and northwestern Washington. This species is of scientific interest because they diverged postglacially from Columbian populations of the longnose dace (Rhinichthys cataractae) after they became isolated in the ice-free Chehalis River Valley throughout all four of the major Pleistocene glaciations (McPhail, 1967; McPhail and Lindsey, 1986; McPhail and Taylor, 1999). As the ice sheets withdrew after the Vashon glaciation approximately twelve thousand years ago, Nooksack dace dispersed and are thought to have been among the first species to recolonize the post-glacial streams of the lower Fraser River Valley of southwestern BC (McPhail and Carveth, 1993). Their geographic distribution includes the Chehalis River in Washington, some rivers on the west side of the Olympic Peninsula and on the east side of Puget Sound, as well as three tributaries of the Nooksack River (Bertrand Creek, Fishtrap Creek, Pepin Creek), and the Brunette River north of the USA-Canada border (Figure 1.1). Recent analysis of mtDNA and morphological characteristics indicate that populations of Nooksack dace are also found in Kanaka Creek, the Coquitlam River and the Alouette River (Taylor, 2013, pers. comm.) Nooksack dace populations in Washington appear to be stable; however, in Canada Nooksack dace have been extirpated from two tributaries of the Nooksack River watershed, Howes Creek and Cave Creek (McPhail, 1997; Pearson, 2010, pers. comm.), and have declined throughout the rest of their Canadian distribution since the 1960s. As a result of limited distribution, small and declining populations, and the low likelihood of re-  4  colonization from the south (should populations become extirpated), this species was federally listed as an Endangered species under the Species At Risk Act in 2002 1.2.2  Habitat Requirements And Threats Adult Nooksack dace are adapted to living on the stream bottom amongst loose  gravel, cobble or boulder substrates, and are found at highest densities in riffle habitats (McPhail, 1997; Pearson, 1999). Inglis et al., (1994) found Nooksack dace at highest densities in riffles where velocities ranged from 0.25-0.30 m·s-1 and depths were between 0.10 and 0.19 m. Like many riffle-dwelling species, adult Nooksack dace use a mobile foraging search mode to locate aquatic insect larvae such as mayflies, beetle larvae, stoneflies and chironomids (Huey, 1987). Adults typically spawn at the upstream end of riffles, and egg number (200-2000) is a function of female size (McPhail, 1997). Young-ofthe-year Nooksack dace are associated with slow water near the downstream end and margins of pools where they forage primarily on chironomid pupae and ostracods (McPhail, 1997). Throughout their Canadian distribution Nooksack dace coexist with various coastal species including coastal cutthroat trout (Oncorhynchus clarkii clarkii), rainbow trout (Oncorhynchus mykiss), steelhead trout (O. mykiss), coho salmon (Oncorhynchus kisutch), pacific lamprey (Entosphenus tridentatus), three-spined stickleback (Gasterosteus aculeatus), coastrange sculpins (Cottus aleuticus), longnose sucker (Catostomus catostomus), salish sucker (C. catostomus) and American signal crayfish (Pacifastacus leniusculus). The Nooksack Dace Recovery Strategy, published by Fisheries and Oceans Canada, lists the degradation of riffle habitat as the primary factor believed to have caused the observed declines of Nooksack dace populations (Pearson et al., 2008). Much of the physical impacts to riffle habitat in the Fraser River Valley, such as dredging, channelization, and clearing of riparian vegetation, has occurred as a direct consequence of agriculture and rapid urbanization. In order to ensure the long-term viability of current Nooksack dace populations reaches (i.e., segments) of stream that consist of more than 10% riffle by length have been identified as ‘critical habitat’ under the Species At Risk Act (Pearson et al., 2008). This designation protects (in principle if not in practice) all aquatic habitats and riparian reserves on both banks for the entire critical habitat reach (Richardson et al., 2010); however, it does not protect the amount of water within the critical habitat reaches. Summer low flows in these critical habitat reaches are maintained by groundwater inputs (Berg and Allen, 2007) but 5  increasing surface and groundwater withdrawals for irrigation and residential use exacerbate the late summer low flow period, contributing to hydrological drought (e.g., Winter et al., 1998; Adelsman, 2003; Golder 2005; Pruneda et al., 2010). This drought directly impacts the quality and quantity of riffle habitats used by Nooksack dace at the most favourable time of the year for Nooksack dace growth (McPhail, 1997), and may be contributing to population declines. Therefore, seasonal lack of water has been identified as a major concern limiting Nooksack dace recovery (Pearson et al., 2008).  1.3 Thesis Objectives And Structure The purpose of my thesis is to identify the significance of impacts of low flow (i.e., hydrological drought) on Nooksack dace, determine the degree to which pool refugia may mitigate these impacts, and identify environmental flow requirements for this species. This information will contribute to the technical basis for decisions related to environmental flow management and species recovery. This thesis has been organized into four chapters. Following this Introduction, the next two chapters focus on specific objectives using a combination of field methods and experimental manipulations (Chapter 2) and modelling (Chapter 3). Chapter 2 describes two complementary studies designed to quantify the impacts of hydrological drought on Nooksack dace. The key research questions addressed in this chapter are: How does extremely low flow affect Nooksack dace at the population and individual level? And, do pool habitats provide low flow refugia that mitigate the impacts of low flow? To answer these questions I first use a field survey of Nooksack dace population size and habitat use across a range of flows. Second, I used an experimental manipulation of flow in artificial stream channels to assess impacts of low flow on individual performance (i.e., growth). In this chapter I also discuss possible mechanisms linking flow to individual performance. Chapter 3 describes the Instream Flow Incremental Methodology (IFIM) which was used predict how habitat availability changed with decreasing discharge in representative reaches of Bertrand Creek. The key research question addressed in this chapter is: At what discharge does habitat availability for Nooksack dace begin to rapidly decline? And, what level of biological support is there for the two fundamental assumptions that underpin an effective 6  application of the hydraulic-habitat model, PHABSIM? This chapter incorporates experimental data and field data from Chapter 2 to ensure that recommendations for minimum instream flow are biologically relevant and provides the basis for recommendations about minimum environmental flows. Finally, a summary of these studies is presented in Chapter 4 with implications of the results for species recovery, recommendations for flow management and the incorporation of environmental flow under the description of critical habitat for lotic Species At Risk.  7  Figure 1.1 Canadian and global Nooksack dace distribution. In southwestern British Columbia, Canada, Nooksack dace are found in the 1) Brunette River, 2) Bertrand Creek, 3) Pepin Creek and 4) Fishtrap Creek. In addition, Nooksack dace are found throughout northwestern Washington, USA. Figure credit: Pearson et al., 2008.  8  Chapter 2: Drought And The Role Of Refugia In An Endangered Riffle-Dwelling Freshwater Fish 2.1  Introduction Declining stream flows are a global concern, affecting rivers from the Niger River in  Africa to the Colorado River in the continental U.S.A. (Dai et al., 2009). In Canada mean annual stream flow has generally decreased over the past 30-50 years, with significant decreases in minimum daily stream flow across the southern parts of the country, and in British Columbia in particular (Zhang et al., 2001; Schindler and Donahue, 2006). This global trend of decreasing stream flow is driven both by climate-induced changes in precipitation patterns and a rapidly growing human population that is impacting water availability through dams, diversions and water withdrawals (Vörösmarty et al., 2000, 2010). Together these impacts are contributing to an increase in the frequency and intensity of hydrological droughts (Postel and Richter, 2003), which are characterized by deficiencies in water supplies as measured by stream flows, lake or reservoir levels, or groundwater levels (Smakhtin, 2001). Hydrological drought is a disturbance that is best defined in terms of its physical properties, so that comparisons between drought events are possible (Lake, 2000). Drought may last for days or years, and may involve lower than average stream flow or complete stream drying (Humphries and Baldwin, 2003). Whether or not a given drought is natural (i.e., seasonal drought in intermittent streams) or anthropogenically driven, drought results in reduced total habitat area, reduced lateral connectivity between mainstem and floodplain habitat, and decreased water quality (Magoulick and Kobza, 2003). Drought affects habitat types differently, severely altering relative habitat availability. In shallow, fast-flowing riffle habitats, velocity, depth, and habitat area decrease and in extreme cases riffle habitats may dry completely. Pools also shrink in size, but retain water for longer after cessation of surface flow (Hakala and Hartman, 2004). During drought both riffles and pool habitats may be characterized by increases in physio-chemical extremes of temperature, dissolved oxygen, pH and nutrient loading (reviewed in Lake, 2003). Finally, unlike other types of disturbance, drought also increases density-dependent biotic interactions such as predation and  9  competition (Power, 1984; Harvey and Stewart, 1991), which can combine with high temperatures and low dissolved oxygen concentrations to amplify the negative effects of drought. Most stream fish have a range of preferred abiotic habitat characteristics such as depth, velocity, temperature, and dissolved oxygen (i.e., the ecological niche; Hutchinson, 1957). As streams dry up, deteriorating water quality conditions may exceed tolerance thresholds forcing fish to seek refuges (Grossman and Ratajczak, 1998; Hodges and Magoulick, 2011), which can be defined spatially as locations where the negative effects of disturbance are lower than in the surrounding area (Lancaster and Beleya, 1997). For example, as discharge decreases riffle specialists may abandon riffles to avoid stranding and seek refuge in pools (Gelwick, 1990). The role that pool refugia play in mitigating the negative effects of drought is not currently well understood for riffle dwelling species (Magoulick and Kobza, 2003). Drought has been shown to impact ecosystem function, community structure, population dynamics, and in extreme cases may cause local or global extinction (Kushlan, 1976; Xenopoulos et al., 2005). Population declines are driven by a species inability to tolerate the abiotic and biotic changes that accompany drought (Lake, 2003). At the individual level, deteriorating water quality and increasing biotic interactions may directly influence growth and survival (Matthews and Maness, 1979; Matthews and Marsh-Matthews 2003; Harvey et al., 2006). As a result, flow modification- including drought- has been implicated as a factor contributing to the degradation of freshwater ecosystems and the growing proportion of freshwater fish species that are extinct or in serious decline (Moyle and Leidy, 1992; Dudgeon, 2000; Jelks et al., 2008). Understanding the impacts of drought and the role of refugia in mitigating these impacts is crucial to the conservation of freshwater fish, particularly in areas where demand for consumptive water use is high and likely to rise. Nooksack dace (Cyprinidae: Rhinichthys cataractae sp.) are riffle-specialists endemic to seven streams in southwestern British Columbia and are federally listed as Endangered under the Species At Risk Act, due to a limited distribution and small populations (Pearson et al., 2008) that are threatened by extremely low summer flows (COSEWIC, 2007; Pearson et al., 2008). In this seasonally dry coastal natural low flows are exacerbated by surface and groundwater withdrawals for residential and agricultural uses (Golder, 2005). In this chapter, 10  I assessed the generalized effects of hydrological drought in a non-arid system, while determining the impact of low flows on the Nooksack dace. My specific study objectives were to identify impacts of drought on Nooksack dace at the population and individual level, and to determine whether pool habitats provide refugia that act to mitigate these impacts. I monitored seasonal changes in Nooksack dace population size, as well as habitat-specific abundance and density of Nooksack dace, as a function of decreasing stream discharge, in order to test the hypotheses that as discharge decreased Nooksack dace would leave riffles and seek refuge in pool habitats, and that overall Nooksack dace population size would be negatively affected by drought. One challenge in determining the impacts of drought from field surveys within streams is that changes in Nooksack dace abundance and densities may be confounded by seasonal changes in predation risk or behaviour (i.e., reproduction, recruitment or migration), making it difficult to identify the costs and benefits of using different habitat types. To examine the direct effect of drought and the role of refugia at the individual level I examined growth rates of Nooksack dace in experimental pool and riffle habitats across a manipulated discharge gradient. I tested the hypothesis that, as a riffle specialist, Nooksack dace growth rates would be highest in riffle habitats and negatively affected by low discharge. To assess the role of pools as low-flow refugia I tested the hypothesis that, in the absence of predation, pool habitats would provide a refuge where the negative impacts of low discharge were less than in the normally preferred riffle habitat. If so, then I expect growth rates in pools to be higher than growth rates in riffles at very low flow. Finally, many studies have demonstrated that benthic invertebrate abundance is flow sensitive and may influence growth rates of fish (e.g., Minshall and Wiger, 1968; Cowx et al., 1984). Consequently, I tested the hypotheses that flow- and habitat- related differences in Nooksack dace growth rate were related to prey availability.  2.2 Methods Research took place in the Canadian portions of Bertrand and Pepin creeks, two transboundary lowland watercourses in southwestern British Columbia, Canada, and adjacent Washington State, USA. The coastal climate in this temperate region is characterized by warm, dry summers and mild, wet winters with high annual precipitation (1573 mm·year-1; 11  Environment Canada, 2002). The majority of precipitation falls from October to May, and from June to September baseflow is largely maintained by groundwater contributions from the Abbotsford-Sumas Aquifer (Berg and Allen, 2007). These two perennial streams have low mean August discharges of 0.063 m3·s-1 (~5% Mean Annual Discharge) in Bertrand Creek and 0.170 m3·s-1 in Pepin Creek (August 1984-2008; Environment Canada, 2012; USGS, 2012). Both streams are increasingly susceptible to hydrological drought due to an increase in surface and groundwater withdrawals, which have reduced present-day baseflows 20% below historical volumes (Golder, 2005). These streams host additional species including coastal cutthroat trout (Oncorhynchus clarkii clarkii), rainbow trout (Oncorhynchus mykiss), steelhead trout (O. mykiss), coho salmon (Oncorhynchus kisutch), pacific lamprey (Entosphenus tridentatus), three-spined stickleback (Gasterosteus aculeatus), longnose sucker (Catostomus catostomus), salish sucker (C. catostomus) and American signal crayfish (Pacifastacus leniusculus). 2.2.1 2.2.1.1  Field Survey: Flow Effects On Nooksack Dace Populations And Habitat Use Site Selection To determine the effects of hydrological drought on Nooksack dace population size  and habitat use, a field study was conducted in Bertrand Creek from May to August 2010. Bertrand Creek drains a 46.6 km2, low-elevation watershed dominated by agriculture before flowing south into the United States where it enters the Nooksack River (Environment Canada, 2012). Four reaches of Bertrand Creek (Table 2.1, Figure 2.1), previously identified as having high quality Nooksack dace habitat (i.e., >10 % riffle habitat by length and as critical habitat under the Species At Risk Act; Pearson et al., 2008), were selected as focal reaches. Within each focal reach representative habitat units were identified as riffles or pools using criteria from Johnston and Slaney (1996), and three of each were randomly selected (Table 2.2; Johnston and Slaney, 1996). During the 2010 sampling, late summer discharge in Bertrand Creek was exceptionally low, with cessation of surface flow in one reach.  12  2.2.1.2  Sampling Methods Each habitat was stop-netted and sampled three times (May 23-July 7, July 13-23,  August 16-20) over a range of declining discharges (i.e., between 0.113 m3·s-1 and 0.007 m3·s-1) to quantify how declining flow affected Nooksack dace populations, and habitat specific abundance and density. Pool habitats were seined (triple-pass) and riffle habitats were sampled using single-pass electrofishing upstream of a modified Fyke stop-net, which was highly effective for catching benthic, riffle-dwelling fish which were swept downstream into the bag of the net (Pearson, 2009; Figure 2.2). All captured fish were identified and counted, and Nooksack dace greater than 40 mm fork-length (+1 age class; Inglis et al., 1994) were measured to the nearest mm and weighed to the nearest 0.01 g before being returned to the habitat where they were captured. In pools Nooksack dace abundance was estimated using the Schnute (1983) maximum likelihood procedure adapted to a three-pass depletion seining. In riffles, Nooksack dace abundance was estimated by adjusting the number of Nooksack dace captured using single pass electrofishing by a capture efficiency, calculated from eight two-stage, mark recapture experiments in representative riffles across a range of discharges (Appendix A; Bonamis, 2011). Population size (referred to as the “population”) for the focal reaches was calculated as the total abundance of Nooksack dace within pool and riffle habitats. Density in each sampled habitat unit was calculated as the estimated population divided by habitat area. Although the area of each sampled habitat unit declined with flow, the length of each habitat unit was constant across samplings. Flow effects on Nooksack dace at the habitat scale (i.e., pool or riffle) were therefore characterized both in terms of density (fish·m-2) as well as the change in abundance (fish·habitat unit-1), to control for flow-related changes in habitat area. Stream discharge was estimated in each habitat by collecting depth and velocity data along transects using a Marsh–McBirney Flo-Mate 2000 velocity metre (Hach Company, Loveland, California). These data were entered into the Riverine Habitat Simulation Model software (RHABSIM) to generate discharge estimates (Payne, 1994; Scruton et al., 1998). 2.2.1.3  Data Analysis In one of the focal reaches (Otter Park), the population of Nooksack dace was very  small (< 5 fish collected in total) despite high sampling effort and an abundance of suitable riffle habitat with cobble and gravel substrate during the first two sampling events. By 13  August riffles in this reach were dry, which is consistent with past reports that this reach is particularly vulnerable to drying (Pearson, 2010, pers. comm.). As the purpose of this study was to quantify how flow affects Nooksack dace density and abundance among habitats, I deemed this focal reach to have an insufficient population for meaningful statistical analysis and so excluded data collected here from further analysis. For the remaining focal reaches repeated measures mixed-effects analysis of covariance (RM-ANCOVA) was used to evaluate the effects of focal reach, habitat type (pool or riffle; fixed) and discharge (m3·s-1; covariate) on Nooksack dace abundance at the habitat scale (fish·habitat unit-1) and density (fish·m2) using the nlme package in R version 2.8.1 statistical software (Pinhero et al., 2012; R Development Core Team, Vienna, Austria). Nooksack dace abundance was log10 transformed to meet the assumption of homogeneity of variance, and covariance structure was modelled as Continuous AR(1) to account for autocorrelation of repeated samplings of the same habitat at different discharges. 2.2.2 2.2.2.1  Experimental Manipulation: Flow Effects On Nooksack Dace Growth Rate Experimental Design An experimental manipulation in semi-natural in-ground stream channels was used to  investigate the effects of low discharge on growth of Nooksack dace in pool and riffle habitats. The in-ground channels used for the experiment were constructed on the Pepin Creek floodplain in 2009. Water from Pepin Creek was diverted into four parallel experimental channels, each of which included two serial riffle-pool sequences (Figure 2.3). Discharge in each channel was regulated using upstream weirs and monitored twice daily using discharge-calibrated staff gauges. Discharge treatments of 3 L·s-1, 8 L·s-1, 15 L·s-1, and 27 L·s-1 were randomly assigned to each channel. These discharges were chosen because they represent a gradient from low to high habitat quality, based on published Habitat Suitability Curves that describe the relationship between depth and velocity and Nooksack dace density (Inglis et al., 1994). Prior to the start of the experiment all fish (i.e., potential predators and competitors) were removed from the stream channels by electroshocking and seining, and 6 mm mesh galvanized steel fences were installed to ensure that Nooksack dace could not move between the sixteen experimental pool and riffle units. The channels were left fallow  14  for one month to allow colonization by benthic invertebrates before the addition of experimental Nooksack dace. 2.2.2.2  Sampling Methods The relatively small size of the Nooksack dace population in Pepin Creek was  deemed insufficient to allow their use in the growth experiment. Therefore, 109 Nooksack dace (length 72 mm + 1 SE; mass 3.86 g + 0.14 SE) were captured in Bertrand Creek instead. These fish were transported to Pepin Creek, where they were allowed to acclimatize overnight. Fish were then weighed, measured and individually marked using visual implant elastomer (VIE, Northwest Marine Technologies, Inc.) before being stocked in experimental units at natural densities (based on field collections) of 0.75 fish·m-2 on June 27, 2011. Mesh fences were cleaned daily and any blooms of filamentous algae were removed to prevent obstruction of flow. After 33 days, Nooksack dace were recaptured, and measured for length and mass. To estimate how flow and habitat affected the availability of Nooksack dace prey, three benthic invertebrate samples were collected along the thalweg (i.e., deepest part of the stream channel) at 20%, 50% and 80% of the length of each experimental unit, using a Hess sampler in riffles and a Surber sampler adapted for use in pools, prior to fish removal. Invertebrates were preserved in 5% formalin, and later sorted and identified to order (or family in the case of Hydropsychidae). Benthic invertebrate samples were then dried in an oven at 60˚C for 24 hours, weighed, and ash-free dry mass (g) was calculated after the sample dried sample was combusted in a muffle furnace at 500˚C for one hour. Biomass (g) was standardized to an area of 1 m2. Invertebrate orders and families were classified as palatable or otherwise, such that taxa not reported as being consumed by Nooksack dace, or the closely related longnose dace, were classified as unpalatable and invertebrates which were inaccessible due to high association with sediments or filamentous algae were classified as unavailable (Table 2.3; Copes, 1982; Culp, 1989; Beers and Culp, 1990; McPhail, 1997). For example, the Surber sampler used to collect benthic invertebrates in the pool habitats collected sediments to a depth of 3 cm, thus including chironomids that were deeply buried in the soft sediments at a depth believed to be inaccessible to foraging Nooksack dace. Thus, although chironomids are prey items for Nooksack dace, their presence in both surface and subsurface sediments made it unclear to what extent they were available to Nooksack dace in 15  pool sediments, and they were dropped from the analysis over concerns that differences in availability between riffle gravel and pool sediments would confound the analysis. Palatable benthic invertebrate biomass per unit area (g·m-2) was calculated as the mean for the three samples collected in each experimental unit. HOBO temperature loggers (Onset Computer Corp., Bourne, Mass.) were used to record water temperature at half hour intervals in the upstream riffle and downstream pool of each discharge treatment for the duration of the experiment. Dissolved oxygen was not measured during the experiment, although a 2010 pilot study showed that concentrations in the stream pools did not differ across discharges and remained above 5 mg·L-1, which is the federal guideline for the protection of aquatic life (CCREM, 1987). Consequently, dissolved oxygen was not considered a factor limiting Nooksack dace growth. 2.2.2.3  Data Analysis Specific Growth Rate of each fish within experimental units was calculated as  SGRMASS=  !" !"##!"#$% !!" !"##!"#$%" !"#$  ×100 and average growth was calculated for each  experimental pool and riffle habitat. To test the hypothesis that growth rates would be higher in riffles than in pools, and that they would be negatively impacted by low flow, I modelled mean SGRMASS as a function of discharge, habitat type (two levels - pool and riffle) and a habitat x discharge interaction using ANCOVA, with discharge as the covariate. Mean SGRMASS was log10 transformed to meet assumptions of normality and alpha was set to 0.05. In order to evaluate whether prey availability or water temperature were flow sensitive or differ between habitats I used ANCOVA, with habitat as a fixed effect and discharge as a covariate. Then, to determine if flow or habitat related differences in Nooksack dace growth rate (SGRMASS), I regressed SGRMASS on prey availability and water temperature. Depth and velocity were included as continuous covariate alternatives to discharge and habitat (which could not be used because it is a categorical variable), to aid in identification of collinearity. A step-wise regression was used to identify models which best predicts Nooksack dace growth rate. Mean and maximum daily water temperature were log10 transformed to normalize the distribution of the right-skewed residuals. Analyses were conducted using SPSS Version 20.0 (IBM Corp., 1989, 2011).  16  2.3 Results 2.3.1  Field Survey: Flow Effects On Nooksack Dace Populations And Habitat Use A total of 458 Nooksack dace were captured across all four focal reaches and  sampling dates, representing an estimated 1,470 fish. The captured fish had a mean length of 60.7 mm + 3.7 SE and a mean mass of 2.8 g + 0.1 SE (length-mass regression: Mass (g) = 2·10-5 x Length (mm)2.806; r2 = 0.91). Across all sampling events, Nooksack dace densities were three times higher in riffles than in pools (2.04 fish·m-2 and 0.68 fish·m-2 respectively; RM-ANCOVA, F1,11 = 7.46, p = 0.02) and densities were significantly higher in the 248th and Farm focal reaches than in the 0 Avenue focal reach (Table 2.4; RM-ANCOVA, F1,11 = 4.50, p = 0.04). There was one notable outlier, a pool habitat in the 248th focal reach. Within this pool Nooksack dace densities ranged from 10.0 fish·m-2 to 6.2 fish·m-2 to 3.2 fish·m-2 over the three sampling periods. These densities were significantly higher that the average riffle densities reported for Nooksack dace in earlier studies (0.75- 1.9 fish·m-2; Inglis et al., 1994; Pearson, 1999) and were more than two standard deviations greater than the average density of Nooksack dace in this reach (i.e., Average density of Nooksack dace ranged from 2.30 + 1.64 fish·m-2 to 2.17 + 4.37 fish·m-2 to 1.21 + 2.61 over the three sampling periods). As a result this habitat unit was excluded from the RM-ANCOVA analysis because it was such a strong statistical and ecological outlier. Ecological reasons for this anomalous pool density are considered in the discussion. As flows declined from 0.113 m3·s-1 to 0.007 m3·s-1 over the course of the summer low-flow period, total habitat area decreased by 12.5%. This decline was not uniform, with riffle area decreasing by 18% + 5 SE, while pool area decreased by only 4 % + 2 SE. Depth and velocity in riffles also decreased more rapidly than in pools. For example, velocities in riffles decreasing from 0.24 m·s-1 + 0.03 SE to 0.08 m·s-1 + 0.02 SE, while velocities in pools decreased from 0.04 m·s-1 + 0.01 SE to less than 0.01 m·s-1. Between the first and third sampling periods, the estimated Nooksack dace total population across all focal reaches decreased from 696 to 332 fish, or by 52%, which is consistent with the significant observed decrease in Nooksack dace abundance within the repeatedly sampled pool and riffle habitats (Figure 2.4a; RM-ANCOVA, F1,14 = 11.05, p < 0.01).  17  I hypothesized that if Nooksack dace use pool habitats as refugia during low flow, then Nooksack dace would leave riffle habitats and move into pools as discharge declined. This would appear as a decrease in Nooksack dace abundance in riffles, and an increase in both the abundance and density of Nooksack dace in pools (due to crowding) as discharges decrease. Contrary to expectations, the observed decrease in Nooksack dace abundance in riffles was not marked by a concomitant increase in Nooksack dace abundance (RMANCOVA, F1,14 = 1.46, p > 0.05) or density in pools (RM-ANCOVA, F1,14 = 0.319, p > 0.05). Rather, there was a non-significant decrease in Nooksack dace density in both pools and riffles (Figure 2.4b; RM-ANCOVA, F1,11 = 1.93, p > 0.05). This apparent paradox of a significant decline in fish abundance but not density, is partly due to the fact that both fish abundance and surface area of habitat declined with flows, thereby tending to maintain densities. 2.3.2  Experimental Manipulation: Flow Effects On Nooksack Dace Growth Rate Specific Growth Rates (SGR%) for Nooksack dace in experimental habitats ranged  from -0.02%·day-1 to 0.99%·day-1. There was no significant main effect of discharge or habitat type on SGRMASS; however, there was a significant habitat x discharge interaction (Figure 2.5; ANCOVA on log10-transformed data, F1,12 = 5.99, p = 0.03). The highest growth rates for Nooksack dace were achieved in the high flow riffle habitats (0.84%·day-1 + 0.11 SE), and growth rates were negatively influenced by low flow (0.39%·day-1 + 5 SE). In contrast, growth rates in pools were about half of this (0.36%·day-1 + 7 SE) and did not vary significantly with discharge (0.40%·day-1 + 16 SE at low flow versus 0.25%·day-1 + 27 SE at high flow). Palatable benthic invertebrate biomass (g·m-2), or prey availability, included Ephemeroptera, Elmidae, Simuliidae, Tipulidae, Plecoptera and Hydropsychidae. Prey availability was significantly higher in riffles than in pools (Figure 2.6, ANCOVA on squareroot transformed prey availability, F1,12 = 7.22, p = 0.02), although it did not vary significantly with discharge (F1,12 = 3.13, p > 0.05), and there was no significant habitat by discharge interaction (F1,12 = 0.30, p > 0.05). Water temperatures ranged from 11.9˚C to 19.5˚C, and averaged 13.9˚C. Maximum daily temperatures tended to be higher in the lowest discharge treatment (17.14˚C in 3 L·s-1 riffle versus 14.90˚C + 0.09 SE for other riffles 18  Figure 2.7a); however, there was no significant difference in maximum daily water temperature between discharge treatments (F1,4 =3.36, p > 0.05) or habitats (F1,4 =0.038, p > 0.05), and there was no significant habitat x discharge interaction (F1,4 =0.080, p > 0.05). The same was true for mean water temperature (Figure 2.7b). Nooksack dace SGRMASS was not significantly related to mean or maximum water temperature in the global model, which also included prey availability, depth and velocity as predictors. The lack of significant results may have been due to low power, because temperature loggers were only placed in eight of the sixteen of experimental units halving the potential sample size. In order to increase the sample size to 16, temperature was removed from the list of factors. The subsequent analysis yielded two valid models. The simplest model included only velocity (r2 = 0.30, F1,14 = 6.09, p = 0.03), but did not explain as much of the variation in Nooksack dace growth rate as the second model which included prey availability and velocity as factors (r2 = 0.56, F2,13 = 8.17, p < 0.01). Condition indices and eigenvalues showed that collinearity between prey availability and velocity was not significant.  2.4 Discussion A reduction in Nooksack dace population with declining summer low flow in Bertrand Creek, and a marked decrease in Nooksack dace growth at low discharge in experimental riffles, indicate that low discharge has negative impacts on Nooksack dace at both population and individual levels. Collectively, these negative impacts of low flow suggest that drought may be a serious conservation concern for endemic riffle fish with small populations. Insofar as Nooksack dace are ecologically typical of small riffle-dwelling lotic invertivore, this study shows that riffle specialists in small streams are especially vulnerable to the negative impacts of hydrological drought. Nooksack dace were found at their highest densities in riffle habitats, which is consistent with previous studies of Nooksack dace habitat use (Inglis et al., 1994; Pearson, 1999) and the closely related but more widely distributed longnose dace (Gee and Northcote, 1963; Mullen and Burton, 1995). The one exception was the anomalous high-density pool in the 248th Avenue reach of Bertrand Creek. I speculate that this apparent anomaly may be related to reach characteristics and predator avoidance behaviour. This reach of Bertrand 19  Creek has a bankfull width (i.e., maximum width) of ~30 m and frequently changes course during winter flows, resulting in a channel that is braided and unstable with substrate dominated by cobble that is relatively free of fine sediment compared to the other focal reaches, where the channel is more stable and confined (i.e. embedded). Relative to the other reaches, this likely provided a much greater amount of suitable riffle habitat at higher flows which may have supported a larger population of Nooksack dace in this reach prior to the field season (i.e., in spring). Consequently, a greater seasonal abundance, and then loss, of suitable habitat within the 248th focal reach may have resulted in greater movement of fish into pool habitats. It is unclear, however, why Nooksack dace aggregated at such high densities within a single pool. It may be related to the particular attributes of this pool, which was the deepest within the 248th focal reach, and had abundant unembeded cobble, large boulder rip-rap, and the potential for upwelling hyporheic flow. Alternatively, it is possible that Nooksack dace aggregated to minimize predation risk, as schooling fish are less vulnerable to predators. This latter speculation is supported by observations of schooling behaviour within the pool with highest densities, which also hosted large coastal cutthroat trout (>15 cm). Unfortunately, the year-to-year consistency with which Nooksack dace used this particular pool could not be evaluated, as winter scour had shifted flows to the other side of the channel the following year. Over the course of the summer low flow period the Nooksack dace population in Bertrand Creek decreased, suggesting a progressive degradation in abiotic or biotic conditions associated with drought, causing mortality or emigration from the sampled reaches. Because the sampled reaches included some of the highest quality riffle habitat available in Bertrand Creek, and were separated by long reaches of stream dominated by pool habitat, it seems unlikely that the observed 48% population decline was due entirely to movement of Nooksack dace out of sampled stream reaches. This is particularly unlikely as the majority of Nooksack dace individuals are thought to have limited home ranges (50-100 m; Pearson, 1999). A more parsimonious explanation may be that the observed population decrease was a result of seasonal, drought-related, mortality. Although a portion of this apparent mortality may be flow-independent (i.e., constant background rates), population declines due to mortality are the most commonly reported consequence of hydrological drought (Matthew and Marsh-Matthews, 2003). 20  Mortality during drought is usually associated with degradation in water quality, such as increasing temperatures and low nocturnal dissolved oxygen, or with increases in biotic interactions, such as competition for diminishing food resources and vulnerability to predation in a reduced foraging arena (Marsh and Maness, 1979; Power, 1984). In many cases, mortality is due not to one factor alone but rather due to a combination of these factors (Tramer, 1977). In Bertrand Creek, water temperature did not exceed 21.5˚C, which is well below the suspected critical thermal maxima of Nooksack dace, based on the tolerances of the closely related longnose dace (i.e., 31.4˚C; Brazo et al., 1978; Wismer and Christie, 1987). Because dead Nooksack dace were not observed during sampling, it seems likely that degraded water quality was not the primary cause of the population decline. One likely cause of mortality, and thus population decline, is increased vulnerability of Nooksack dace to aquatic and terrestrial predators. Coastal cutthroat trout were captured in sampled pools on numerous occasions, and past studies have shown that decreases in water level can reduce the ability of fish to escape predation (Kushlan, 1976; Power et al., 1985). In addition, herons, kingfishers, and small mammal tracks (i.e., mink and raccoon) were observed along the banks of Bertrand Creek. Therefore, in conjunction with movement of some fish out of the sampled reaches, increased vulnerability of Nooksack dace to aquatic and terrestrial predators seems the most likely cause of apparent mortality, leading to the observed declines in abundance in the sampled reaches. My hypothesis that Nooksack dace would emigrate out of riffles and into pools as the summer low-flow period progressed was not supported, although it has been reported for other riffle specialists (slender madtom (Noturus exilis); fantail darter (Etheostoma flabellare); banded sculpin (Cottus carolinae; Gelwick, 1990). Rather, Nooksack dace densities in riffles remained constant, and there was no measurable increase in the abundance or density of Nooksack dace in pools, despite an overall decline in habitat area. Although I was unable to directly track movement of individuals between habitats, these results suggest that the significance of pools as a refuge habitat did not increase over time, perhaps due to increased predation. Using mesocoms, Schaefer (2001) demonstrated that movement of a riffle-dwelling herbivore into pools during low flow was delayed by the presence of caged predators. Nooksack dace may avoid seeking refuge in pools over the sampled range of discharges for similar reasons. Although Nooksack dace remaining in shrinking riffles may 21  have been exposed to terrestrial predators (Power, 1987), if survival of dace in pools was lower this may have prevented any increase in observed abundance within pools. The discharge at which pool refugia confer a net benefit for Nooksack dace likely depends on the abundance of predators and the availability of prey in pools, relative to the rate of decline in per capita resource availability in riffles. My results suggest that pools did not serve as a critical refuge over the range of measured discharge, although pools undoubtedly provide refuge from stranding of Nooksack dace as riffles dewater, irrespective of predation risk. Experimental manipulations are complementary to observational field survey data because they can provide support for the inference that that observed population declines are primarily related to hydrological drought, and not to seasonal changes in predation risk or behaviour, and may help inform the mechanisms whereby low flow affects individual performance. Growth rates are a useful measure of individual performance, as growth (or increased size-at-age) is associated with increased survivorship, maturation and fecundity (Sogard, 1997). Within experimental riffles, drought-like conditions had a negative effect on Nooksack dace growth rate, with fish in low discharge treatments growing at half the rate of fish in high discharges treatments (0.39%·day-1 versus 0.84%·day-1). In contrast, individual performance in pools was low irrespective of flow. Even in the absence of aquatic predators, this growth experiment demonstrates that riffles are intrinsically better habitat for Nooksack dace than pools, but the elevated habitat quality in riffles is strongly flow-dependent. Therefore, invoking higher predation risk in pools is not necessary to explain higher riffle quality, although if predators (e.g., coastal cutthroat trout) were present in pools it is likely that growth and survival of Nooksack dace may have been even lower. If refugia are defined as habitats where the negative effects of a disturbance are reduced relative to the surrounding habitat matrix (Lancaster and Belyea, 1997), then it is not until the very lowest discharge that pool habitats may confer a benefit in terms of individual performance, relative to riffles. Low growth rates in pools (and the likelihood of higher predation risk in their natural habitat) may provide some explanation for why Nooksack dace did not appear to be immigrating into pools in Bertrand Creek, and supports the inference that pool habitats provide poor quality refuge from all but the most negative effects of drought (i.e., stranding when riffles dewater).  22  Drought-related decreases in water quality, and increases in biotic interactions can also impact populations and individuals (Harvey et al., 2006; Falke et al., 2010). For example, temperature plays an important role in the metabolism of fishes, and high water temperatures have been associated with reduced growth, particularly where temperature tolerances are exceeded or per capita resource availability is limited (Brett et al., 1969). Although prey availability and water temperature were not manipulated in the experimental channels, they were quantified in order to test the hypothesis that these two factors contribute to habitat quality (growth rate) in this benthic invertivore. Availability of Nooksack dace prey (i.e., stoneflies, mayflies, blackfly larvae) was significantly higher in riffles than in pools, and tended to decrease with discharge, while water temperatures in Pepin Creek tended to be higher at low flow, but only varied trivially between habitats. These findings, while not all significant, are consistent with documented responses of macroinvertebrates and temperature to drought (Minshall and Wiger, 1968; Matthews and Mannes, 1979). My analysis indicates that growth rate was more closely related to prey availability and water velocity than temperature, which may be partly because temperatures in Pepin Creek were relatively stable, and never exceeded temperatures likely to negatively impact Nooksack dace (Brazo et al., 1978; Wismer and Christie, 1987). In contrast, if temperatures in Bertrand Creek typically approach Nooksack dace temperature tolerances during summer low flow, Nooksack dace growth and survival will likely be affected (Matthews and Mannes, 1979). One way to conceptualize the impact of hydrological drought on Nooksack dace is to extrapolate differences in short term growth in high and low flow riffles over the course of a summer growing season. Summer is the most productive time of the year for Nooksack dace, which likely do much of their growing between mid-June and mid-September, when water temperatures are between 14.5 ˚C and 20.5˚C (McPhail, 1997). In low-flow riffles growth rates averaged 0.39%·day-1whereas in high flow riffles growth rates average 0.78%·day-1. For a 70 mm fish over a 120-day growing season the cumulative difference in growth would result in a deficit of 13 mm or 3.07 g. The fitness consequences of such a growth deficit could include delayed maturity (Hutchings, 1993), decreased reproductive success (e.g., from reduced fecundity of smaller females; Falke et al., 2010) or reduced overwinter survival (Quinn and Peterson, 1996; Boss and Richardson, 2002), as well as increased vulnerability to aquatic predators like coastal cutthroat trout (Scharf et al., 2000). When scaled up, it is clear 23  that the low growth rates of Nooksack dace in pools and at low discharge may have negative impacts at the population level.  2.5 Conclusion The frequency and duration of hydrological droughts is expected to increase as climates change and human demands for freshwater increase, and understanding the impacts of hydrological drought is crucial to the conservation of freshwater fishes. My study demonstrates the sensitivity of riffle-dwelling species such as Nooksack dace to flow reductions, with low discharge being associated with population decline and decreased growth rates for fish in riffles. The role that pool habitats play as refugia, in terms of mitigating the negative effects of hydrological droughts on populations and individuals, is not well understood for riffle dwelling species. These studies demonstrate that pools play a relatively minor role, save as a refuge from stranding at extreme low flows where riffles dewater which may be very important for short periods. This highlights the importance of protecting existing riffle habitats, and how essential sufficient instream flow is for maintaining habitat quality for riffle dwelling species. Further research is needed to identify minimum environmental flows, and protecting these flows for Nooksack dace, and other imperiled riffle-dwelling species, should be prioritized.  24  Table 2.1 Characteristics of the four focal reaches where Nooksack dace were sampled in 2010. These reaches were previously identified as high-quality habitat (i.e., >10 % riffle habitat by length), and are designated as critical habitat under the Species At Risk Act (Pearson et al., 2008). Focal Reach Designation Distance upstream from USA-Canada Border  248th  0 Avenue  Otter Park  6.3 km  Farm  0 km  5.6 km  GPS  49° 00’ 010" N 122° 31' 20" W  49˚ 02’ 010”N 122˚ 32’ 231”W  Reach Length  150 m  90 m  200 m  110 m  Estimated Riparian Cover  100%  50-70%  80-100%  0%  Substrate  Pools and riffles dominated by gravel and cobble.  Pools and riffles dominated by unembedded cobble.  Pools and Riffles dominated by embedded cobble and gravel.  Riffles dominated by unembedded cobble. Pools dominated by sand and clay.  Relationship with Underlying Aquifer (Starzky 2012)  Losing water to underlying aquifer (i.e., Recharging)  Gaining water from the underlying aquifer (i.e., discharging)  Losing water to underlying aquifer (i.e., Recharging)  Losing water to underlying aquifer (i.e., Recharging)  49˚ 02’ 257” N 122˚ 31’ 992” W  10.6 km 49˚ 02’ 182” N 122˚ 29’ 347” W  In many years, including 2010, flow in this reach ceases all together (Pearson, 2012, pers. comm.).  Comments  N/A  During the 2010 summer the main channel of Bertrand Creek at the 248th focal reach was divided into a dominant and subdominant channel by a large gravel bar, and the dominant channel was sampled.  An upstream movement barrier at the downstream end of the Otter Park focal reach, where 248th street bridges Bertrand Creek, likely limits colonization of Nooksack dace from downstream. During low flows the bridge forms a hanging culvert above a deep plunge pool that is a barrier to upmigrating Nooksack dace, but not salmonids.  Reach was restored in 2009 after the mainstem of Bertrand Creek avulsed out of a channelized section adjacent to a blueberry field.  25  Table 2.2 Within each focal reach, up to four pool and riffle habitat units were randomly selected for repeat sampling. The number of habitat units identified and sampled is shown. The number of habitat units included in the RM-ANCOVA analysis is shown in parentheses and justification for excluding some habitat units is provided.  Reach Name  Farm  Number of Habitat Units Sampled  7(3)  Riffles Sampled  3(1)  Pools Samp led  Note  4(2)  A beaver dam, built in early August, flooded four of seven habitat units, which prevented sampling of ‘low flow’ conditions in this focal reach. These four habitats were excluded from the analysis to balance the RM-ANOVA.  Otter  7 (0)  4(0)  3(0)  This focal reach was excluded from analysis because the Nooksack dace population in this reach was exceedingly small, and insufficient for my purposes (i.e., detecting decrease in abundance and shift in habitat use).  248th  7 (6)  3(3)  4(3)  Habitat Unit 5 (Pool) was excluded as a statistical and biological outlier.  0 Avenue  6(6)  3(3)  3(3)  All sampled pool and riffle habitat units were used in the analysis.  Total N:  27(15)  13(7)  14(8)  26  Table 2.3 Benthic invertebrates were identified to Order (or Family in the case of Hydropsychidae) and assigned a classification as palatable, unpalatable or inaccessible. Palatability was determined based on diet literature and observations. Benthic Invertebrate  Classification  Amphipoda  Inaccessible  Coleoptera- Elmidae  Palatable  Decapoda- Astacidae  Unpalatable  Diptera- Chironomidae  Inaccessible  Diptera- Simuliidae  Palatable  Diptera- Tipulidae  Palatable  Ephemeroptera  Palatable  Harpacticoida  Inaccessible  Hemiptera- Belostomatidae  Unpalatable  Hirudinea  Unpalatable  Isopoda- Asellidae  Inaccessible  Lymnaeoidea- Lymnaeidea  Inaccessible  Megaloptera- Sialidae  Unpalatable  Oligochaeta  Inaccessible  Pelecypoda  Unknown  Planorboidea- Ancylidae  Unpalatable  Planorboidea- Planorbidae  Unpalatable  Plecoptera  Palatable  Trichoptera  Unpalatable  Trichoptera- Hydropsychidae  Palatable  27  Table 2.4 Summary of Nooksack dace densities in pool and riffle habitat units, for each sampling period, and at each focal reach. Data are presented as means + standard errors.  Habitat  Flow  Discharge (m3·s-1)  Number of Replicate Habitat Units  H M L H M L  0.073 0.032 0.017 0.073 0.032 0.017  2 2 2 1 1 1  2.32 0.45 1.62 0.95 6.13 0.72  + 2.21 + 0.34 + 1.58 . . .  H M L H M L  0.113 - 0.106 0.019 0.007 0.113 - 0.106 0.019 0.007  3 3 3 3 3 3  0.54 2.32 0.10 2.50 2.09 3.80  + 0.37 + 0.43 + 0.10 + 0.18 + 0.70 + 1.08  H M L H M L  0.095 0.055 0.024 0.095 0.055 0.024  3 3 3 3 3 3  0.33 0.52 0.52 0.74 0.34 0.37  + 0.24 + 0.30 + 0.51 + 0.53 + 0.30 + 0.23  Mean Density (fish·m-2) + SE  Farm Pool  Riffle  248th Pool  Riffle  0 Avenue Pool  Riffle  28  Figure 2.1 Map of Bertrand Creek. Dark blue lines define Nooksack dace critical habitat reaches and red circles identify focal reaches where Nooksack dace field surveys were carried out between May and August 2010, over a range of decreasing stream discharge (0.113 m3·s-1 to 0.007 m3·s-1). Figure credit: Pearson et al., 2008.  29  Figure 2.2 Pool and riffle habitat units in Bertrand Creek were stop-netted using a Fyke stopnet, placed across the downstream end of both habitat types for consistency. This Fyke stopnet was particularly suited to single-pass electrofishing for Nooksack dace, as described in Appendix A.  30  Figure 2.3 In-ground experimental channels featured 16 habitat units (four channels with isolated, alternating pools and riffles). Discharge in each channel was manipulated so that the four treatments represented a gradient of habitat quality, with 27 L·s-1 representing good quality habitat and 3 L·s-1 representing poor habitat quality.  31  A	
    B	
    Figure 2.4 Observed changes in (A) Nooksack dace abundance and (B) Nooksack dace density (fish·m-2) in pool and riffle habitats units in Bertrand Creek, which were sampled over range of discharges (0.113 m3·s-1 to 0.007 m3·s-1).  32  Figure 2.5 Effect of discharge (L·s-1) and habitat type (riffle, pool) on Nooksack dace Specific Growth Rate (SGRMASS; %·day-1). Lines are logarithmic and are consistent with the analysis that was performed on log10 transformed data.  33  Figure 2.6 Availability of benthic prey (g·m-2), which included Ephemeroptera, Elmidae, Simuliidae, Tipulidae, Plecoptera and Hydropsychidae, was quantified for each of the 16 experimental units. Data are shown as means + standard errors (N=2). No benthic prey was found in either of the pool habitat units in the 3 L·s-1 discharge treatment.  34  A	
    B	
    Figure 2.7 Experimental channel (A) mean and (B) maximum daily water temperatures were monitored in the upstream riffle and the downstream pool for each discharge treatment (L·s-1). Water temperatures ranged from 11.91˚C to 19.47˚C, and averaged 13.91˚C + 0.01 SE. Data are shown as means + standard errors (N= 19 days). 35  Figure 2.8 Relationship between Nooksack dace Specific Growth Rate (%·day-1) and prey availability for pool and riffle experimental units was significant when velocity was included in the model (r2 = 0.56, F2,13 = 8.17, p < 0.01).  36  Chapter 3: Modelling Minimum Flow Requirements For Nooksack Dace 3.1 Introduction Declining stream discharge is a global issue, resulting in part from flow modification due to human activities such as dam construction, inter-basin diversions, and water withdrawals (Naiman et al., 2002). These activities, combined with climate-induced changes in precipitation patterns and increased rates of evapotranspiration, are leading to an increase in the frequency and duration of hydrological droughts, which in turn threaten the biodiversity of riverine ecosystems and their ability to provide important ecosystem goods and services (Jackson et al., 2001; Kundzewicz et al., 2008; Vörösmarty et al., 2010). As a result there is a growing interest in protecting environmental flows which are defined as “Those components of the flow regime required to sustain freshwater and estuarine ecosystems and the human livelihoods and well-being that depend upon these ecosystems” (Brisbane Declaration, 2007). In the case of managing environmental flows during drought, the most important consideration remains identifying the minimum environmental flows (i.e., minimum instream flows) over which habitat availability declines and fish are negatively impacted (Bunn and Arthington, 2002). The most commonly used method of determining minimum environmental flows is the Physical Habitat Simulation (PHABSIM) model of the Instream Flow Incremental Methodology (IFIM). This mechanistic, predictive model has a series of logically linked functions that build on one another to predict how the availability of suitable habitat for a target fish species changes, with incremental changes in discharge (Bovee, 1982). This is accomplished by combining a hydraulic component, which predicts how depth and velocity in a given stream vary with discharge, with a biological component, the Habitat Suitability Curves (HSCs), which describes preferences for depth, velocity and other stream attributes such as substrate. There are several different categories of HSCs; the most commonly used ones being ‘Category II’ HSCs, which are developed from density-based habitat (or microhabitat) associations. In short, depth, velocity and substrate are measured at microhabitat locations used by the target species, and frequency distributions, corrected for  37  habitat availability, are developed (Bovee, 1986). These HSCs are known as utilization or habitat use functions because it is assumed that fish are actively selecting the habitats where they are found at highest density, and that the most highly selected habitats confer the greatest benefit in terms of fitness (growth, survival, or future reproductive potential). Model output is an estimate of weighted usable area (WUA), in units of usable habitat area per 1000 linear metres of stream channel, across a range of simulated discharges (Milhous et al., 1984). This WUA-Discharge relationship, including the discharge threshold at which habitat availability begins to decline most rapidly, is interpreted based on the shape of the curve. Despite being the most widely applied method for managing instream flow (e.g., Reiser et al., 1989; Hatfield and Bruce, 2000) the IFIM has been subject to a variety of criticisms (Mathur et al., 1985; Gore and Nestler, 1988; Castleberry et al., 1996; Railsback, 1999; Dunbar et al., 2011). According to Maughan and Barrett (1992) at least some of these criticisms arise from a failure to recognize the limitations of the methodology. Other concerns relate to uncertainties that arise from measurement errors, model errors, or sampling errors (Kondolf et al., 2000), and which are rarely represented in the final WUA-Discharge relationship. Four key assumptions underpin an effective application of the IFIM (Waddle, 2001); the first two pertain to the biological component of the model, while the latter two relate to the model itself. First, depth, velocity and substrate are assumed to be the most important physical habitat attributes affecting the distribution and abundances of fishes, and are assumed to independently influence habitat selection by fishes. Second, it is assumed that behavioural preference of a life stage of a species for each physical variable can be established from instantaneous fish observations in the field and that the resulting HSCs accurately reflect habitat quality. Third, the calculation of WUA within the model assumes that preference factors for depth, velocity, and substrate, can be combined through multiplication to create a combined index of Suitability that also accurately represents habitat quality. Finally, the most significant underlying assumption of the IFIM is that a linear relationship exists between WUA and biomass of fishes (Bovee, 1978), and thus that protection of a discharge threshold that maximizes WUA will benefit fish populations (Mathur et al., 1985). This final assumption is dependent on habitat for the life history stage in question being the primary factor limiting populations of the target species. Over the past 40 years numerous studies have set out to evaluate sensitivity of model 38  predictions to assumption violations, and in the process many limitations of the IFIM have been revealed (e.g., Orth, 1987; Reiser et al., 1989; Kondolf et al., 2000). Despite these, the IFIM is generally considered a valuable planning tool that can provide the basis of negotiations for maintenance of water flows, although the degree of confidence in the model output must be contingent on careful consideration of the key assumptions and limitations (Moyle et al., 2011). In general, a successful application of the IFIM, and identification of an accurate Discharge-WUA relationship, necessitates consideration of the two most significant and often violated assumptions. These are 1) that the HSCs derived from habitat-use observations accurately represent habitat quality, and thus that the modelled WUA will be of direct use to the target species, and 2) that there is a significant positive relationship between WUA and fish biomass (i.e., habitat is the primary limiting factor). The foundation of the first assumption - that habitat use indicates habitat quality in terms of growth and fitness - is rarely assessed (Garshelis, 2000; Rosenfeld, 2003). For example, in fish with dominance hierarchies such as salmonids, the highest densities of fish may be found in marginal habitats populated by subordinate individuals, while a single dominant fish monopolizes the highest quality habitat (Beecher et al., 2010). Consequently, HSCs may not necessarily accurately reflect the availability of higher-quality habitat, which could undermine the value of the generated model output (i.e., WUA vs. Discharge relationship). The second assumption is undoubtedly the most important to verify. If there is no significant positive relationship between WUA and fish biomass then there can be little confidence that protection of a discharge, and thus WUA, will have the desired positive effect on fish populations (Mathur et al., 1985; Orth, 1987; Gore and Nestler, 1988). Testing for a significant positive relationship between WUA and fish biomass is recommended as a method of verifying that this key assumption is met (Waddle. 2001), and in fact Conder and Annear (1987) suggest that recommendations based on the PHABSIM WUA-Discharge relationship should not be made before a significant positive relationships is shown. In some cases, this relationship is also interpreted as validation of the Habitat Suitability Curves (Waddle, 2001). Unfortunately, few of the thousands of IFIM studies test for this relationship as a matter of course because the necessary collection of fish data is onerous. Of those that do, many fail to report a significant positive relationship between biomass and WUA, casting 39  doubt on the likelihood that modelling will accurately predict flow effects on the target species (Orth and Maughan, 1981, 1982; Shirvell and Morantz, 1983; Irvine et al., 1987; Jowett, 1992; Zorn and Seelbach, 1995; Gallagher and Gard, 1999; Nuhfer and Baker, 2004; Willis et al., 2006; Beecher et al., 2010). Fewer still consider the strength of this relationship and associated uncertainty in the biological response of biomass to changes in discharge. Careful consideration of PHABSIM assumptions and limitations, and associated uncertainty is necessary to establish confidence in the Discharge-WUA relationship, particularly in situations where protection of minimum environmental flows is essential for the conservation of endangered species, but there is a high demand for out-of-stream water use. If minimum environmental flows are set higher than necessary, there may be unnecessary socio-economic costs to limiting water use, while setting flows too low undermines their purpose in sustaining freshwater and estuarine ecosystems. Nooksack dace (Cyprinidae: Rhinichthys cataractae) are a riffle-specialist endemic to seven streams in southwestern British Columbia, which were federally listed as Endangered under the Species At Risk Act in 2003 due to small population size and limited distribution (Pearson et al., 2008). Primary threats include degradation of riffle habitat and extremely low summer flows (i.e., hydrological drought), both of which have been shown to negatively impact this species at both the population and individual level (Chapter 2). Extremely low flows are a problem in Bertrand Creek in particular, as water withdrawals for agricultural and residential use have reduced present-day baseflows by 20% below historical volumes (Golder, 2005; Figure 3.1). Given future projections of urbanization and agricultural intensification, this trend is expected to continue highlighting the need to identify and protect minimum environmental flows for Nooksack dace. Because flow recommendations may have socio-economic consequences for residential and agricultural use, the assumptions underlying PHABSIM were subjected to additional scrutiny during model development. In this study I used the hydraulic-habitat modelling component of the IFIM to assess the relationship between discharge and habitat availability for Nooksack dace in Bertrand Creek, in support of establishing minimum environmental flow needs. In order to identify sources of uncertainty and establish confidence in flow recommendations, I also evaluated two of the key IFIM assumptions using field and experimental data from Chapter 2. Specifically, I test the assumption that 1) density-based Habitat Suitability Curves accurately 40  reflect habitat quality in terms of growth, and 2) that there is a significant positive relationship between the estimated availability of suitable habitat (in terms of WUA) and Nooksack dace biomass. The strength of this relationship is determined, and its influence on the reliability of the modelled output is discussed.  3.2 Methods To implement the IFIM, and model changes in habitat availability with discharge, I used the Riverine Habitat Simulation (RHABSIM) model. RHABSIM is functionally identical to the Physical Habitat Simulation (PHABSIM) model, and was developed by members of the PHABSIM development team but is preferable because of its user-friendly interface and the fact that it is still supported (Scruton et al., 1998). 3.2.1  Field Data Collection This study was carried out in Bertrand Creek, where summer low flows and limited  availability of riffle habitat has been identified as a major threat to Nooksack dace recovery. During the summer months Bertrand Creek base flow is maintained by groundwater inputs, and has mean August minimum discharges of 0.063 m3·s-1 (1985-2010; Environment Canada, 2012; USGS, 2012) and long-term mean annual discharge of 1.15 m3·s-1. In 2010, the year this study was conducted, flows decreased below the 25-year mean August minimum discharge for 56 days. Flows in Bertrand Creek were recorded as low as 0.01 m3·s-1 and in some reaches flow ceased entirely. The study area for this IFIM assessment of minimum environmental flow requirements encompasses the mainstem of Bertrand Creek from the Canada-USA border to 10.6 km upstream (Figure 2.1). Within the study area, four reaches of stream previously identified as having a relatively high proportion of riffle habitat (i.e., designated as critical habitat under the Species At Risk Act; Pearson et al., 2008) were selected as focal reaches (Table 2.1). Within each focal reach representative habitat units were identified as riffles (1%-2% gradient, shallow, high velocity, turbulent flow), glides (0-1% gradient, slow current velocity, laminar flow) or pools (0% gradient, low current velocity, deep) and up to four of each were randomly selected (Table 2.2; Johnston and Slaney, 1996). Within each habitat unit hydrological data were collected along three cross-sectional transects located at 20, 50 41  and 80% of the length of the habitat unit to fully characterize variation in each habitat type. Headpins were driven into the left and right bank at each cross section so that collection of data along transects could be repeated at multiple discharges between May 23rd and August 20th 2010. Depth and velocity were measured at intervals ranging from 5 cm to 20 cm along each transect such that at least 15 measurements were taken within the wetted width of the channel. Velocity measurements were made at 60% of total depth from the water surface using a Marsh–McBirney Flo-Mate 2000 velocity metre (Hach Company, Loveland, California). Depth and velocity were measured three times over a range of declining summer discharges (0.01 m3·s-1 to 0.11 m3·s-1). Substrate for each cross section was characterized once as the dominant substrate according to the Wentworth Scale (Bovee and Cochnauer, 1977). In total, hydraulic data were repeatedly collected for a total of 108 transects in 36 habitats across four focal reaches (550 m). 3.2.2  Hydraulic-Habitat Modelling The RHABSIM model which simulates how incremental changes in discharge affect  changes in depth and velocity, and how those in turn effect the availability of Nooksack dace habitat, was run on hydraulic data for all four focal reaches separately using previously published depth, velocity and substrate HSC (Inglis et al., 1994). Each transect was weighted to reflect the relative availability of different habitat types in Bertrand Creek (i.e., 71% pool, 20% glide, 9% riffle) using information from an earlier habitat mapping survey (Pearson unpublished data). For example, a focal reach with 30 transects evenly distributed among three habitat types would have ten transects within glides, ten transects within riffles and ten transects within pools. Each of those ten cross sections would be weighted based on their relative availability in Bertrand Creek, so that each glide cross section would be weighted to 2%, each pool to 7.1% and each riffle to 0.9%. Water surface elevation at different flows was estimated as the difference between the streambed and the water surface measured to the nearest cm with a Marsh–McBirney Flo-Mate 2000 velocity metre wading rod. Density-based HSCs for Nooksack dace in Bertrand Creek were developed in the early 1990s by Inglis et al., (1994; Figure 3.2) by quantifying depth, velocity and substrate at locations where Nooksack dace were found during two-pass removal electrofishing. According to that study, the average density (fish·m-2) of Nooksack dace was estimated for 42  various ranges of depth, velocity and substrate type and weighting factors, or suitability factors, were calculated from these density estimates by dividing the relative density within a given interval by the relative density within the optimal range. These suitability factors identify optimal (1) and least suitable (0) ranges of depth, velocity and substrate size, and form the basis of the published HSCs. Transect depth and velocity data were entered into RHABSIM for each of the three calibration discharges (high (0.06-0.11 m3·s-1), medium (0.02-0.06 m3·s-1), and low (0.01– 0.02 m3·s-1). Changes in habitat depth and velocity were simulated over a range of discharges from 0.01 to 0.30 m3·s-1, by increments of 0.01 m3·s-1. Although this is a relatively narrow range of discharges, it typifies Bertrand Creek during the summer months when Nooksack dace are thought to be most habitat limited by extremely low flows. Combined Suitability (CSi) was calculated for each measurement along a given transect (i.e., the cell) as the product of the Suitability Factors (SF) corresponding to the modelled depth, velocity and substrate (Figure 3.2) for each incremental discharge. CS! =    Depth  SF! ∗ Velocity  SF! ∗ Substrate  SF! RHABSIM multiplies this Combined Suitability for each cell (CSi) by the area of the cell (Ai) to generate incremental estimates of WUA, which are summed for each cross section. !"#$$  Weighted  Usable  Area =  CS! A! !!!  The standard output of RHABSIM, as with PHABSIM, is a sum of the total WUA for each cross section within a focal reach, for each simulated discharge, which yields a curve of WUA versus Discharge. 3.2.3 3.2.3.1  Evaluating Assumptions Do Density-Based Habitat Suitability Curves Reflect Habitat Quality? Application of RHABSIM assumes that density-based Habitat Suitability  Curves published by Inglis et al. (1994) accurately reflect habitat quality for Nooksack dace in terms of growth or other correlates of fitness, so that protection of WUA as defined by these HSCs will reflect the amount of limiting habitat available at a given flow. In Chapter 2, Nooksack dace were stocked for one month in experimental pool and riffle habitats in four parallel in-ground channels (Figure 2.3). A range of discharge treatments was established in 43  each channel represented a range of habitat qualities, based on HSCs for depth and velocity. At the end of the experiment Nooksack dace were recaptured and Specific Growth Rate of each fish was calculated as SGRMASS=  !" !"##!"#$% !!" !"##!"#$%" !"#$  ×100. To test the  assumption that density-based HSCs accurately represent habitat quality, mean depth and velocity were calculated for each of the 16 experimental pool and riffle habitats and Pearson’s partial correlation analysis was used to evaluate the relationship between the mean growth rates of Nooksack dace for each experimental habitat and the corresponding suitability factor for depth and velocity from the published HSC (Inglis et al., 1994). I tested the hypothesis that if density-based HSCs accurately reflect describing habitat quality then there will be a strong, significant, and positive correlation between average SGR% (scaled to a maximum of 1) and suitability factor (also scaled to a maximum of 1). 3.2.3.2  Is There A Positive Relationship Between WUA And Biomass? To test the assumption of a significant positive linear relationship between modelled  WUA and Nooksack dace biomass, Nooksack dace abundance was estimated for each of the habitat units where hydraulic data were collected at each of the three discharges (see Chapter 2). Pool habitats were seined (triple-pass) while glide and riffle habitats were sampled using single-pass electrofishing upstream of a modified Fyke stop-net (Figure 2.2; Pearson, 2009). Populations in each pool were estimated using the Schnute (1983) maximum likelihood procedure adapted to a three-pass depletion, and Nooksack dace abundance in each glide and riffle was adjusted for capture efficiency, which was calculated from eight two-stage markrecapture experiments in representative riffles across a range of discharges (Appendix A; Bonamis, 2011). Biomass for each habitat was calculated as the estimated abundance of Nooksack dace within that habitat multiplied by the mean mass of all captured Nooksack dace (i.e., 2.8 g). Fewer than five Nooksack dace were captured at one of the focal reaches (Otter Park) throughout the summer despite an abundance of habitat at high and medium flow, indicating that something other than habitat availability at the time of sampling was limiting productivity. For this reason (habitat limitation being a key assumption of PHABSIM) data collected in the Otter Park focal reach was excluded from the WUA-Biomass analysis. Extremely low Nooksack dace abundance in this reach, even in early summer when wetted  44  habitat was abundant, is most likely related to the fact that surface flow often ceases here by late summer (zero discharge), so that water is only present in standing pools. Mean WUA was calculated for each of the riffle, glide and pool habitat units as an average of the modelled WUA for each of the three transects within each habitat (i.e., at 20, 50 and 80% of the length of each habitat). A linear regression was used to test for a positive relationship between total Nooksack dace biomass and WUA for each habitat. I also regressed Nooksack dace density (g·m-2) on Combined Suitability (i.e., product of suitability values for depth, velocity and substrate) as a more direct test of the validity of Habitat Suitability Curves. Linear regression coefficients were judged significant for p < 0.05 and the r2 was examined to determine the strength of the relationship (i.e., the proportion of the variance in Nooksack dace biomass or density that could be attributed to differences in WUA or Combined Suitability). Analyses were conducted using SPSS Version 20.0 (IBM Corp., 1989, 2011). 3.2.4  Estimation Of Minimum Environmental Flow Threshold Following evaluation of the two key model assumptions, the relationship between  estimated Weighted Usable Area (scaled to units of microhabitat area per 1000 m stream length) and discharge was determined for each focal reach. A composite curve was determined based on mean values for three of the four focal reaches (Otter Park, 0 Avenue, Farm). The 248th focal reach was excluded from this analysis as it is a braided reach and only one of the two side channels was sampled for hydraulic data. As a result, the WUADischarge relationship cannot be calculated for the entire width of the mainstem at 248th as was done for the other reaches. Otter Park was included despite the low densities of Nooksack dace because the channel structure of the reach is representative of Bertrand Creek, and changes in habitat availability with flow should therefore also be broadly representative. The composite WUA-Discharge relationship was interpreted based on the shape of the curve. The general location of a minimum environmental flow threshold may be identified by drawing a tangent to the low flow section of the curve and extending a line tangent to the maximum WUA (Figure 3.3; Jowett and Briggs, 2006). Where these two lines depart from the curve indicates the flow range over which habitat availability decreases most 45  rapidly with flow. For management purposes, the uppermost discharge within that range (i.e., the discharge at which habitat availability begins to decrease most rapidly) was identified as the minimum environmental flow threshold.  3.3 Results 3.3.1 3.3.1.1  Evaluating Assumptions Do Density-Based Habitat Suitability Curves Reflect Habitat Quality? Average depths for the experimental habitats ranged from 26 cm to 44 cm in pools  and 4 cm to 8 cm in riffles. Average velocities ranged from 0.0 cm·s-1 to 2.5 cm·s-1 in pools and 5.7 cm·s-1 to 24.0 cm·s-1 in riffles. As discussed in Chapter 2, Specific Growth Rates (SGR%) ranged from -0.02%·day-1 to 0.99%·day-1, and were highest in deeper faster riffles, and lowest in pools and shallower, slower riffles. Visual comparison of SGR% and the HSCs for depth and velocity indicates a moderate degree of correspondence (Figure 3.4) and there was a significant positive correlation between Specific Growth Rate (%·day-1) and Habitat Suitability for both depth (r = 0.43, df = 15, p = 0.04) and velocity (r = 0.57, df = 15, p = 0.01) across the somewhat limited range of depths and velocities present in the experimental channels. It should be noted that in our study Nooksack dace were collected at depths and velocities that exceeded those predicted to have a suitability of zero (i.e., fish were caught in habitats with velocities greater than 40 cm·s-1 and depths greater than 80 cm), suggesting that the published Habitat Suitability Curves (Inglis et al., 1994) may underestimate the quality of habitats with higher depths and velocities. 3.3.1.2  Is There A Positive Relationship Between WUA And Biomass? A total of 921 Nooksack dace were captured across all four reaches and sampling  dates, distributed among 36 independent habitat units. These dace had a mean length of 60.7 mm + 3.7 SE and a mean mass of 2.8 g + 0.1 SE. I found a significant positive linear relationship between Nooksack dace biomass per habitat unit (g·habitat unit-1) and the modelled WUA for that habitat unit; however, the strength of the relationship was low (r2 = 0.28, F1,62= 24.6, p < 0.01; Figure 3.5a). Conversely, there was no significant relationship between Nooksack dace biomass per m2 (g·m2) and Combined Suitability for the same sampled flow (r2 = 0.04, F1,62= 2.7, p > 0.05; Figure 3.5b).  46  3.3.2  Estimation Of Minimum Environmental Flow Threshold Over the range of simulated discharges (i.e., 0.01 to 0.30 m3·s-1), the discharge that  corresponds to the maximum availability of suitable habitat (WUA) was identified for each representative reach: 0.14 m3·s-1 for the Farm, 0.30 m3·s-1 for Otter Park, and 0.30 m3·s-1 for 0 Avenue. Maximum WUA for Otter Park and 0 Avenue over the range of flow simulation occurred at the highest simulated discharge of 0.30 m3·s-1, indicating that the discharge at which the maximum amount of habitat availability is achieved likely occurs at discharges greater than those simulated (Figure 3.6). This may be related to the channel geomorphology of these two reaches, which was less confined. Thus as discharge increases and water levels rise in the Otter Park and 0 Avenue focal reaches, new shallow fast habitat is inundated creating new habitat for Nooksack dace. For the composite WUA-Discharge relationship the maximum estimated WUA occurred at 0.30 m3·s-1, which again suggests that maximum WUA may occur above the maximum simulated discharge. The composite WUA-Discharge relationship did not exhibit an inflection point at which the slope changes rapidly; rather, habitat decreased across a range of discharges ~0.12 m3·s-1 to 0.06 m3·s-1 (Figure 3.6). In 2010 discharge was below a 0.12 m3·s-1 threshold for 79 days (June 24th - September 10th; Figure 3.7). The low r2 for the relationship between WUA and Biomass indicates that there was large variation in biomass that could not be accounted for by WUA generated using the published HSCs (Inglis et al., 1994), and thus confidence in the biological response of Nooksack dace biomass to changes in discharge is low. To represent this uncertainty in the WUA-Discharge relationship, the WUA-Discharge relationship was converted to a biomass– discharge relationship using the equation from the linear relationship between biomass and WUA (Biomass = 9.4(WUA)+34.0 + 95% confidence intervals; Figure 3.8).  3.4 Discussion The PHABSIM component of the Instream Flow Incremental Methodology (IFIM) is considered a useful tool for modelling flow-related changes in habitat availability and for instream flow management, though its reliability is subject to a set of fairly restrictive conditions (Maughan and Barrett, 1992). Much of the criticism of the IFIM concerns potential violations of the underlying assumptions of the methodology (e.g., Mathur, 1985), 47  or from failure of PHABSIM applications to adequately represent uncertainties associated with sampling (e.g., Kondolf et al., 2000; Williams 2010). The goals of this study were to evaluate the degree to which two fundamental, but rarely assessed, mechanistic assumptions of PHABSIM are met for Nooksack dace: first, that HSCs accurately reflect fitness consequences of habitat use, and second, that a significant positive linear relationship exists between modelled WUA and fish biomass. Although I found this application of the IFIM to have general support for both underlying assumptions, there remains a large degree of uncertainty around the relationship between Nooksack dace biomass and the estimated availability of suitable habitat (i.e., WUA). A direct comparison between habitat suitability factors and measured growth at different depths and velocities showed that the published density-based HSCs for Nooksack dace were broadly consistent with growth data for Nooksack dace in experimental pools and riffles. This indicates that the published HSCs broadly reflect habitat quality, at least over the range of depths and velocities tested; however, the experimental habitats represented a truncated subset of depths and velocities available to Nooksack dace. Depths over 44 cm and velocities above 0.24 cm·s-1 were not represented in the experimental pools or riffles, although the published HSCs include depths up to 100 m and velocities up to 50 cm·s-1 (Inglis et al., 1994). Further, effects of mean depths and velocities on growth were not assessed independently; deep habitats tended to be slow pools, and shallow habitats tended to be fast-flowing riffles. Lack of independence and the limited range of depths and velocities over which growth was measured means that this comparison cannot be a definitive test of the HSCs. Rather, my analysis is an example of opportunistically using multiple lines of evidence to inform the appropriate confidence in model assumptions. Future studies aiming to validate that density-based Habitat Suitability Curves accurately reflect habitat quality in terms of growth would benefit from assessing growth over the full range of depth and velocity combinations that are available in the species’ natural habitat. The assumption of a positive relationship between biomass and WUA is fundamental to the IFIM. Condor and Annear (1987) have gone as far as to suggested that recommendations based on the WUA relationship should not be made unless a significant positive relationship is demonstrated. In this study I found a significant positive relationship between estimated WUA and Nooksack dace biomass, which supports the assumption that 48  factors limiting biomass are related to habitat availability (Figure 3.5a). Surprisingly, the lack of a significant relationship between Nooksack dace biomass and the suitability of the habitat units (i.e., Combined Suitability) where they were captured (Figure 3.5b) indicates that the relationship between Nooksack dace biomass and WUA relationship is driven not by the suitability (i.e., quality) of the habitat units, but by their area (availability). Of the studies that evaluate whether a relationship exists between fish biomass and WUA, many document a negative relationship or no relationship at all. For example, a negative or non-significant positive relationship has been described for smallmouth bass (Micropterus dolomieu; Orth and Maughan, 1982), brook trout (Salvelinus fontinalis; Nuhfer and Baker, 2004), rainbow trout (Oncorhynchus mykiss; Irvine et al., 1987), coho salmon (Oncorhynchus kisutch; Beecher et al., 2010) and even benthic macroinvertebrates (Willis et al., 2006), precipitating a low degree of confidence in the PHABSIM predictions. In these cases, the lack of a positive relationship between WUA and fish abundance has been attributed to inaccuracy of the HSCs (i.e., Beecher et al., 2010) or limitation of biomass by other factors (i.e., recruitment, food, predation, temperature or habitat complexity; Shirvell and Morantz, 1983; Condor and Annear, 1987; Nuhfer and Baker, 2004). Among studies that do demonstrate a significant positive relationship between WUA and biomass, this is often interpreted as a blanket validation of recommendations based on the WUA-Discharge curve. I propose that consideration should be given to what the strength of the relationship implies for confidence in predictions (i.e., Jowett, 1992; Gallagher and Gard, 1999). For example, although a significant positive relationship between WUA and Nooksack dace biomass was found, only 28% of the variation in biomass was explained by changes in WUA. When this relationship was used to estimate how Nooksack dace biomass is predicted to change with incremental changes in discharge (Figure 3.8), there were large confidence intervals around the mean, indicating a high degree of uncertainty (i.e., low degree of confidence in the slope of the curve or location of any peak WUA). This level of uncertainty between WUA and biomass for Nooksack dace in Bertrand Creek is not atypical for studies with reasonable sample sizes that report r2 values for significant positive relationships between WUA and biomass (See Table 3.1). For example, Gallagher and Gard (1999) found a positive significant relationship between WUA and Chinook salmon (Oncorhynchus tshawytscha) in the Merced and American rivers, California, 49  with r2 values of 0.17 and 0.40, respectively. In this study, the low proportion of variation in Nooksack dace biomass that was explained by WUA is likely related to inaccurate HSCs, that habitat availability is not the only factor limiting Nooksack dace populations, or a combination of the two factors. Observations of Nooksack dace utilizing habitats at depths and velocities well in excess of those predicted by the published HSCs (Inglis et al., 1994) suggest that the published HSCs are biased towards low depths and velocities, possibly because they were generated by sampling during low stream flow over a narrow range of depths and velocities. Substrate size for this study was also characterized at the scale of the cross-section rather than the cell, which may have weakened the biomass-WUA relationship. The IFIM uses only three physical factors to describe habitat (velocity, depth and substrate), but it is possible that other variables such as temperature or dissolved oxygen may have affected the abundance of Nooksack dace. Additionally, biological factors such as predation and competition may limit Nooksack dace biomass. Given the significant relationship between prey availability and growth rate of Nooksack dace (see Chapter 2), it is possible that incorporating additional limiting factors such as food availability into the model might account for a greater proportion of the variability in biomass. This was found for brown trout (Salmo trutta) in New Zealand where the relationship between WUA and trout biomass was strengthened by including additional factors, such as invertebrate biomass (r2 = 0.64; Jowett, 1992). The weak relationship between WUA and biomass is not the only source of uncertainty inherent in PHABSIM models. Uncertainty may also arise from sampling errors, measurement errors or modelling errors (Bartz, 1990; Jackson, 1992; Kondolf et al., 2000). For example, Williams (1996) demonstrated that significant uncertainty in the estimated WUA-Discharge relationship can arise from an inadequate number of transects, which would reduce the degree to which sampled transects accurately represent reach-scale habitat availability (i.e., few transects inflates sampling error). In addition, measurement errors associated with the collection of hydraulic data can introduce additional uncertainty (Kondolf et al., 2000), as can errors associated with the biological component of PHABSIM (i.e., the Habitat Suitability Curves; Jackson, 1992), which generally identify a single suitability value for each depth and velocity without confidence intervals. Although bootstrapping was not used in this study to generate confidence intervals on WUA associated with variation among 50  transects as recommended by Williams (2010), 108 cross sections representing 36 discrete habitats across four unique reaches were selected and measured at multiple discharges, which is thought to be largely sufficient to generate a representative estimate of physical habitat (Gard, 2005). Like most that are used, the published density-based HSCs that were used for this study, did not include confidence intervals. Therefore, uncertainty due to the HSCs could not be represented in WUA output although biological uncertainty was partly represented by the plot of Nooksack dace density versus predicted WUA. Although the weak WUA-Biomass relationship (r2 = 0.28) for Nooksack dace generates considerable uncertainty in the exact response of Nooksack dace populations to protection of discharge, it does indicate that protecting minimum flows will be beneficial for Nooksack dace populations. The modelled relationship between weighted usable area and discharges indicates that habitat availability is maximized at 0.30 m3·s-1, although is likely that habitat availability would continue to increase to an unspecified optima beyond the range of simulated discharges (0.01 m3·s-1 to 0.30 m3·s-1). Identification of low-flow thresholds may be difficult, particularly when decline in available habitat with flow is linear; non-linearity helps identify threshold based on a rapid increase in the projected loss of WUA with incrementally declining flow. The non-linear shape of the WUA-Discharge curve for Nooksack dace indicates that habitat availability begins to decrease most rapidly over a range of discharges beginning at ~0.12 m3·s-1. However, because Nooksack dace were captured at velocities well in excess of those predicted by the HSCs (i.e., greater than 40 cm·s-1), the reach-average WUA-Discharge curve may underestimate the availability of suitable habitat at higher discharges. This would bias peak WUA towards lower flows, and potentially displace the inflection point to lower flows as well. These concerns over low discharge bias are supported by the observation that HSCs for longnose dace, a closely-related form of R. cataractae, are reported to have habitat suitability values ranging from 0.5 to 0.8 at velocities as high as 1.0 m·s-1 (Edwards et al., 1983; Glozier et al., 1997). A low-flow threshold of ~0.12 m3·s-1 based on the Inglis et al., (1994) curves should therefore be viewed as erring on the side of underestimating declines in WUA with discharge. Although the considerable uncertainty in WUA predictions has been emphasized, my confidence that availability of Nooksack dace habitat approaches a minimum at low discharge is high, and the inference of a strong population level effect at zero discharge is 51  supported by the observation of negligible Nooksack dace abundance at Otter Park- a reach that lacks surface flow during the late summer months in most years, despite abundant suitable habitat during the rest of the year when higher flows are available. Finally, 0.12 m3·s-1 represents approximately 10% mean annual discharge (Ptolemy, 2009), which is considered the threshold for severely degraded habitat in terms of more conventional instream flow criteria (e.g., Tennant, 1976), supporting the assessment that protection of this minimum environmental flow threshold is critical.  3.5 Conclusion Although this study is in many ways a straightforward application of the IFIM, because it involved an endangered species I placed special emphasis on evaluating two fundamental assumptions of the methodology: first, that density-based HSCs accurately reflect habitat quality in terms of growth or survival; and second, that there is a positive relationship between modelled WUA and fish biomass. My goal was to better inform the degree of uncertainty in model output, so as to provide a more robust basis for interpreting model predictions. Independent data on Nooksack dace growth rate at different depths and velocities provided support for the lower limits of the HSCs, but could not reduce uncertainty in habitat quality at higher depths and velocities. Although there was a positive relationship between WUA and biomass it was weak, reducing confidence in the WUA-Discharge relationship. Modelling suggests that protecting minimum environmental flows above 0.12 m3·s-1 will minimize habitat impacts to Nooksack dace populations in Bertrand Creek. The standard output from PHABSIM is a single point estimate of WUA at each modelled flow, and the methodology has been rightly criticized for not representing uncertainty around these estimates (i.e., confidence intervals). Although the importance of representing uncertainty in site selection has been emphasized (i.e., how well selected crosssections represent reach-scale habitat availability; Williams, 1996; Gard, 2005), biological uncertainty has not received the same attention. In the absence of a quantitative representation of uncertainty in HSCs (i.e., confidence intervals), independent data on fish growth or survival at different depths and velocities, or the strength of the relationship between Combined Suitability and fish Biomass may inform confidence in Habitat Suitability Curves. Alternatively, converting WUA to estimated fish biomass and plotting it 52  against modelled discharge (using confidence intervals from the WUA-Biomass regression) is another way to explore and explicitly represent the biological component of uncertainty associated with model predictions. These approaches will help inform the level of confidence in potential management outcomes, although they are not a substitute for development of more direct methods to include confidence intervals on HSCs and to represent this uncertainty in model (WUA) predictions.  53  Table 3.1 Studies that have tested for a relationship, or correlation, between Weighted Usable Area (WUA) and stream fish abundance or biomass, and the corresponding strengths of those relationships. Reference  Species  N  Model Type  Significance  r2 or r  Adult; Nooksack dace (Rhinichthys cataractae) Spawning Habitat; Chinook salmon (O. tshawytscha); Lower American River Spawning Habitat; Chinook salmon (O. tshawytscha); Merced River Adult; Brown trout (Salmo trutta) Adult; Cutthroat trout (Oncorhynchus clarkii) Adult; Steelhead trout (Oncorhynchus mykiss)  63  LR  < 0.001  r2 = 0.28  50  LR  < 0.001  r2 = 0.40  48  LR  < 0.001  r2 = 0.38  59  LR  < 0.05  r2 = 0.44  29  LR  <0.05  r2 = 0.27  23  LR  <0.05  r2 = 0.52  Juvenile; Atlantic salmon (Salmo salar) Adult; Brown trout (Salmo trutta) Adult: Rainbow trout (Oncorhynchus mykiss) Trout  19  LR  unk  C  <0.05 for 5 of 16 sites NS  r2 = 0.180.95 r = 0.24  unk  C  NS  r = 0.25  6  LR  < 0.001  r2 = 0.90  Fry; Rainbow trout (S. gairdnerii Richardson); Stream 1. 1982 Fry; Rainbow trout (S. gairdnerii Richardson); Stream 1.1982 Fry; Rainbow trout (S. gairdnerii Richardson); Stream 1. 1982 Fry; Rainbow trout (S. gairdnerii Richardson); Stream 1. 1981 Adult (> 13 cm); Brown trout (Salmo trutta) Yearling And Older; Brown trout (Salmo trutta) Yearling And Older; Rainbow trout (Oncorhynchus mykiss)  7  C  NS  r = 0.03  7  C  NS  r = -0.14  6  C  NS  r = 0.55  6  C  NS  r = 0.18  7  C  < 0.05  r = 0.76  5  C  nr  r = 0.96  5  C  nr  r = 0.92  Models with N > 20 Avery-Gomm (present study) Gallagher and Gard (1999)  Jowett (1992) Nickelson et al. (1979)  Models with N < 20 Bourgeois et al. (1996) Condor and Annear (1983) Gowan (1984) Irvine et al. (1987)  Nehring (1979) Nehring and Anderson (1983, 1984)  54  Reference  Species  N  Model Type  Significance  r2 or r  Yearling And Older (YOA); Brook trout (Salvelinus fontinalis) Young Of Year (YOY); Brook trout (Salvelinus fontinalis) Adult; Red Shiners (Notropis lutrensis) Adult; Orangebelly darter (Etheostoma radiosum) Adult; Freckled madtom (Noturus nocturnus) Adult; Central stoneroller (Campostoma anomalum) Adult; Smallmouth bass (Micropterus dolomieu) Juvenile; Smallmouth bass (Micropterus dolomieu) Adult; Brown trout (Salmo trutta)  15  LR  < 0.05  r2 = 0.30  14  LR  NS  r2 = 0.12  2  C  nr  nr  9  C  < 0.01  r = 0.71  8  C  < 0.001  r = 0.86  8  C  < 0.01  r = 0.71  8  C  NS  r = 0.42  8  C  NS  r = -0.29  19  LR  < 0.05  r2 = 0.81  Adult; Brown trout (Salmo trutta) Baetidae  9  LR  <0.01  r2 = 0.82  15  LR  < 0.05  r2 = 0.25  Ceratopogonidae  15  LR  NS  r2 = 0.09  Elmidae adult  15  LR  NS  r2 = 0.22  Empididae  15  LR  < 0.05  r2 = 0.26  Ephemerellidae  15  LR  NS  r2 = 0.21  Glossosomatidae  15  LR  NS  r2 = 0.22  Heptageniidae  15  LR  < 0.005  r2 = 0.51  Hydropsychidae  15  LR  NS  r2 = 0.22  Nemouridae  15  LR  NS  r2 = 0.19  Perlodidae  15  LR  NS  r2 = 0.00  Rhyacophilidae  15  LR  NS  r2 = 0.07  Simuliidae  15  LR  NS  r2 = 0.14  Tipulidae  15  LR  NS  r2 = 0.01  Models with N > 20 Nuhfer and Baker (2004)  Orth and Maughan (1981) Orth and Maughan (1982)  Stalnaker 1979 (data from Wesche 1976) Wesche (1980) Willis et al. (2006)  55  Figure 3.1 Examples of discharge in a Bertrand Creek riffle when Nooksack dace abundance was sampled and hydraulic data were collected. Clockwise from top: 1.58 m3·s-1, 0.09 m3·s-1, 0.05 m3·s-1, and 0.02 m3·s-1.  56  Figure 3.2 Density-based Habitat Suitability Curves were developed for Nooksack dace in Bertrand Creek by Inglis et al., (1994). According to Inglis et al., (1994), average Nooksack dace densities were estimated for various ranges of depth, velocity and substrate size (bars). Weighting Factors, or Suitability Factors, were calculated from these density estimates by dividing the relative density within a given interval by the relative density within the optimal range. These suitability factors identify optimal (1) and least suitable (0) ranges of depth, velocity and substrate size, and form the basis of the suitability curves.  57  Figure 3.3 The relationship between WUA (Weighted Usable Area; units of microhabitat area per 1000 m stream length) and Discharge for the three focal reaches was averaged to yield a composite WUA-Discharge relationship for Bertrand Creek. The maximum estimated WUA occurred at 0.30 m3·s-1, however it is likely that habitat availability would continue to increase up to an unspecified optima beyond the simulated range of discharges (0 m3·s-1 to 0.30 m3·s-1). Dashed lines, drawn tangent to the low flow section of the curve, and extended through the maximum WUA, indicate that habitat availability begins to decrease most rapidly at 0.12 m3·s-1.  58  A	
    B	
    Figure 3.4 Mean Specific Growth Rate (SGRMASS; %·day-1) for Nooksack dace in experimental habitats was positively correlated with corresponding suitability factors for depths and velocities from Inglis et al., (1994) Habitat Suitability Curves across the limited range of (A) depths and (B) velocities present in the pool and riffle experimental units.  59  A	
    B	
    Figure 3.5 (A) Significant positive relationship between Weighted Usable Area (WUA) and Nooksack dace biomass (g) for each habitat unit sampled. (B) A lack of a significant relationship between Combined Habitat Suitability and Nooksack dace density (g·m-2), indicates that the relationship in (A) is not driven by the suitability of sampled habitat units.  60  Figure 3.6 Relationship between Weighted Usable Area and discharge for each focal reaches across a range of simulated discharges (i.e., 0.01 to 0.30 m3·s-1). The range of discharges over which WUA could be modelled for the 248th focal reach is constrained due to the braided channel morphology.  61  Figure 3.7 Mean Daily discharges for Bertrand Creek (USGS, 2012) and the percent of Mean Annual Discharge that these discharges represent (i.e., percent of daily mean discharge averaged over a period of years; Ptolemy, 2009). The black line represents 100% MAD (1.15 m3·s-1) and the red line represents the discharge at which habitat availability begins to decrease (0.12 m3·s-1). In terms of conventional instream flow criteria, 0.12 m3·s-1 represents approximately 10% MAD, which is considered the threshold for severely degraded habitat (Tennant 1976). Discharges were below this threshold for 79 days in 2010.  62  Figure 3.8 The composite WUA-Discharge relationship for Bertrand Creek was converted to a relationship between Nooksack dace biomass (g) and discharge relationship using the linear relationship between biomass and WUA (Biomass = 9.4(WUA) + 34.0 + 95% confidence intervals). This visually represents the moderate and asymmetrical uncertainty around the response of Nooksack biomass to incremental changes in discharge that resulted from the weak positive significant relationship between WUA and biomass (r2 = 0.28).  63  Chapter 4: General Conclusions Nooksack dace are federally listed as Endangered under Canada’s Species At Risk Act, and the motivation for this thesis was to understand the significance of hydrological drought as a factor threatening the persistence of individuals and populations. Once the impacts of low flow on Nooksack dace were assessed, the relationship between discharge and Nooksack dace habitat availability was modelled, and the range of discharges over which habitat for Nooksack dace becomes most limiting was identified. This information may be used to aid in the management and conservation of Nooksack dace and, insofar as Nooksack dace are ecologically typical of small riffle-dwelling lotic insectivores, this study shows that riffle specialists in small streams are especially vulnerable to the negative impacts of hydrological drought. Given the projected global increase in the frequency and duration of hydrological droughts and water scarcity in general (Vörösmarty et al., 2000, 2010), the need for water conservation and proactive management of stream flows is more important now than ever. In Chapter 2, I explored the impacts of hydrological drought on Nooksack dace, and whether or not pool habitats may act as refugia that mitigate these impacts. I demonstrated that decreasing stream discharge negatively impacts Nooksack dace population size and individual performance, and confirmed that seasonal lack of water is indeed one of the major concerns limiting recovery of Nooksack dace populations (Pearson et al., 2008). Nooksack dace population declines over the course of the summer were suggested to be due primarily to increased vulnerability of Nooksack dace to aquatic and terrestrial predators, but may also have been due to movement of Nooksack dace out of the sampled reaches (i.e., segments of stream). The experimental manipulation showed that individual performance of Nooksack dace was highest in high flow riffles, and that in low flow riffles and in pools growth rate was reduced by 50%, even in the absence of aquatic predators. The reduced growth rate in pools and low flow riffles was most likely related to low prey availability. Refugia can be defined as places where the negative effects of a disturbance are less than in the surrounding areas (Lancaster and Belyea, 1997). According to this definition, the field and experimental findings in this thesis suggest that pools do not mitigate the negative impacts of low flow, although they no doubt provide refugia for Nooksack dace during periods of riffle drying. It is possible that pools provide similarly poor quality refugia during low flow events for other 64  riffle-dwelling species, which may have implications for how riffle-dwelling species in other regions are managed (i.e., highlighting the necessity of protecting riffle habitats and maintaining sufficient flow within them). Required habitats are those that are necessary for the survival and persistence of individuals and populations (Rosenfeld, 2003; Rosenfeld and Hatfield, 2006). This thesis provides strong support for the conclusion that Nooksack dace require riffle habitats, which is supported by previous research (Inglis et al., 1994; McPhail, 1997; Pearson et al., 2008). As an endangered species, critical habitat for Nooksack dace has been identified and, in theory, protected. Unfortunately the current the definition of critical habitat, which protects the streambed and riparian vegetation, does not protect the quantity of water available within the streambed. My thesis provides good evidence that sufficient instream flow, which is needed to maintain required riffle habitats and riffle habitat quality, is necessary for the persistence of individuals and populations, thus providing a strong biological justification for the incorporation of minimum environmental flows in the definition of critical habitat for lotic species in Canada. Establishing or maintaining adequate flow in all habitats with high potential is one of the eight broad strategies necessary in order to achieve Nooksack dace recovery (Pearson et al., 2008). To do so ‘adequate flows’, or minimum environmental flows must first be identified. This was the objective of Chapter 3, which utilized the most widely applied method for predicting the biological effects of low flow: the Instream Flow Incremental Methodology (IFIM). In the process of applying the IFIM, experimental and field data collected in Chapter 2 were used to evaluate the degree to which two fundamental mechanistic assumptions of the methodology are met for Nooksack dace. These assumptions are rarely assessed before recommendations are adopted, however, because this study involved an endangered species I placed special emphasis on identifying the degree of uncertainty in model output, so as to provide a more robust basis for interpreting model predictions. Experimental data on Nooksack dace growth at different depths and velocities provided support for the hypothesis that density-based HSCs accurately reflect habitat quality in terms of growth or survival, but only the lower limits of the HSCs were evaluated. Observation of Nooksack dace using habitats in Bertrand Creek with depths and velocities well in excess of those predicted as suitable by the published HSCs (Inglis et al., 1994) 65  suggests that the HSCs are likely biased towards low depths and velocities. Field data were then used to evaluate the relationship between WUA and fish biomass, to test the key underlying assumption that there is a significant, positive relationship exists, and therefore that protection of habitat (i.e., WUA) will yield an increase in biomass. Although I found a significant positive relationship, only 28% of the variation in biomass was driven by changes in WUA. I converted WUA to estimated fish biomass and plotted it against modelled discharge (using confidence intervals from the WUA-Biomass regression) in order to explicitly represent the biological component of uncertainty associated with model predictions of WUA. A summary of past studies which have quantified the strength of this relationship indicate that only 27- 52% of the variation in fish biomass may be attributed to changes in WUA (Table 3.1), suggesting that this level of biological uncertainty may be inherent to IFIM studies although it has not previously been discussed or explored. The IFIM has been rightly criticized for not representing uncertainty (i.e., confidence intervals) around the standard model output, which is a single point estimate of WUA at each simulated discharge. Evaluating the degree to which IFIM studies meet the underlying assumptions of the methodology, and especially quantifying the existence of a significant positive WUABiomass relationship, and the strength of that relationship, is recommended to inform the level of confidence in management outcomes. According to the modelled WUA-Discharge relationship, results indicate that habitat is maximized at or above 0.30 m3·s-1 and that protecting flows above 0.12 m3·s-1 will minimize habitat limitation for Nooksack dace populations in Bertrand Creek. On average, summer flows are lower than this threshold for several weeks to months each year (USGS, 2012) and this thesis provides strong evidence to support the inference that extended periods of low flow will have negative impacts on Nooksack dace habitat availability, growth rate and population size. Future projections of urbanization and agricultural intensification indicate that the frequency and duration of low flow is expected to continue (Golder, 2005), necessitating consideration of how to maintain and restore stream flow to Bertrand Creek. In general there are three avenues for restoring stream flow once the finite nature of water as a renewable resource is recognized: water conservation, water efficiency, and innovation in water use practices that minimize environmental impacts without necessarily 66  reducing water demand (i.e., Pruneda et al., 2010). Conservation initiatives that could restore stream flow in Nooksack dace watersheds might include initiating a campaign to educate residents, developers and farmers about the water footprint concept (Hoekstra, 2003, 2009; Hoekstra et al., 2009), which measures both water use and the impacts of use and how to reduce this impact. Following this, tax incentives could be provided to encourage residents to retrofit their homes with low volume plumbing fixtures and devices, or to developers to follow best management practices for improving water efficiency in new building projects (i.e., USDOE, 2011; Colorado WaterWise and Aquacraft Inc., 2010). Water withdrawals for irrigation of agriculture within the Bertrand Creek watershed represent a large percentage of water use during the summer months when stream discharge is naturally at its lowest. Farmers can reduce their water footprint by improving water management and irrigation efficiency, for example by adjusting irrigation schedules, identifying and localizing water leakages, or upgrading on-farm water delivery systems (Texas Water Conservation Advisory Council, 2004). One innovative way to minimize instream environmental impacts of low flow that requires neither conservation nor increased water efficiency relates to groundwater and surface-water interactions, which are prominent in the Bertrand Creek and Fishtrap Creek watersheds. Pruneda et al., (2010) found that replacing surface-water use with groundwater pumping wells was a viable option for reducing the impact of water withdrawals on summer stream flow, if pumping wells were placed within zones of a low response ratio. They developed a visual analysis tool (i.e., STELLA) to support this innovative alternative, which can be used by stakeholders to evaluate the impact associated with moving their water rights. Restoring stream flow and river ecosystem health becomes increasingly challenging and expensive the longer the situation is allowed to deteriorate before it is addressed. In parts of the world where hydrological droughts have become a serious problem (i.e., Australia), multi-billion dollar programs have been implemented in order to restore stream flows and riverine ecosystem health (Australia Department of Sustainability, Environment, Water, Population and Communities, 2013). In Canada, water is often considered a plentiful resource, however this thesis and other studies (i.e., Zhang et al., 2001; Rosenau and Angelo, 2003) have demonstrated that low summer flows are increasingly becoming an issue of concern. At present, Canadians are in a unique position to begin proactively managing water 67  resources (i.e., Apedaile et al., 2007), and in the Fraser Valley, to use the verifiable impacts of low flow on Nooksack dace as justification for implementing water conservation measures in Bertrand Creek and similar streams in the area.  68  References Abell, R. Α., Olsen, D. M., Dinerstein, E., Hurley, P. T., Diggs, J. 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North American Journal of Fisheries Management 15: 773-783.  79  Appendices Appendix A Assessing Efficiency Of Single-Pass Electrofishing Best management practices for sampling Nooksack dace populations recommend single-pass electrofishing in order to minimize negative impacts on endangered populations (Pearson, 2009); however, single pass captures can only serve as an index of absolute population size if capture efficiency is known and the single-pass estimate can be scaled up to the full population. Unfortunately, capture efficiencies for single-pass electrofishing are generally low and variable, in the range of 25-30% (Flèvet et al., 1999; Rosenberger and Dunham, 2005; Temple and Pearsons, 2006; Carrier et al., 2009). Finally, population estimates based on single-pass electrofishing typically assume that capture efficiency, once calculated, is constant. A previous study by Bonamis (2011) describes a two-stage sampling process which combines single-pass electrofishing with a two-day, mark-recapture method to generate an average capture efficiency of 28% + 3% for Nooksack dace in Bertrand Creek. This information is valuable, but cannot be directly applied to my study because I collected Nooksack dace under different sampling conditions and using different sampling methods, and I suspect these may affect capture efficiency. For example, Bonamis (2011) sampled Nooksack dace populations using only a pole seine across a portion of the downstream channel width and a single sweeping upstream pass of the electrofisher. This method did not isolate the sampled habitat, and relied on sightings of the stunned fish and subsequent capture with a dip net, although it had the advantage of being less time consuming. In contrast, I used an upstream stop-net and a modified Fyke stop-net and with a cod-end bucket at the downstream end (Figure 2.2) to isolate the sampled habitat. Instead of moving upstream, I used a single sweeping downstream pass of the habitat towards the modified Fyke stop-net, which collected Nooksack dace that were swimming downstream to escape the electrical field of the electrofisher, and also functioned to collect stunned fish that were swept downstream by fast-flowing currents. Finally Bonamis (2011) focused on riffle habitats with an average depth of 0.16 + 0.19 m and average velocities of 0.40 + 0.19 m·s-1, while the objective of my study was to identify the impact of drought on Nooksack dace by sampling across a range of moderate to low discharges (0.113 m3·s-1 to 0.007 m3·s-1). As a result, the 80  mean depths (0.15 + 0.013 m) and velocities (0.11 + 0.01 m·s-1) of the habitats I sampled are much lower than those sampled by Bonamis (2011). I suspect that the method I used worked better to catch Nooksack dace at high discharges, which assisted in carrying fish into the downstream Fyke stop-net cod-end bucket. So, I hypothesize that capture efficiency for my single-pass electrofishing method may be influenced by water velocity and water depth. If this is true, then applying the averaged 28% capture efficiency from Bonamis (2011), which was developed for higher velocities, may underestimate population sizes in habitats with lower velocities (i.e., low discharge riffles), compromising my ability to accurately assess the impacts of drought on Nooksack dace population size and density. On three occasions during the summer low flow period in 2011, I conducted a twostage sampling process that combines single-pass electrofishing with a two-day, markrecapture method to estimate capture efficiency, as described by Bonamis (2011). Due to logistical constraints, this was only repeated seven times in different habitat units, and average depth, velocity and discharge were measured each time as covariates in predicting capture efficiency (Table A.1). I used a multiple regression to relate capture efficiency to mean depth (m) and mean velocity (m·s-1), and to test for collinearity. I found that the model explained 58% of the variation in capture efficiency, but was not significant (r2 = 0.58, p > 0.05. N = 7). Depth was not significant (p > 0.05) and velocity was also non-significant, although less so (p > 0.05). Collinearity was not significant so I dropped depth from the model, and regressed capture efficiency against mean water velocity (m·s-1) to test for a positive relationship, and generate an equation which could be used to predict capture efficiency and thus population size, of Nooksack dace in Bertrand Creek. The model was nearly significant (r2 = 0.52, p = 0.07, N = 7), and velocity had a positive relationship with capture efficiency. This indicates that capture efficiency tended to be higher in habitats that had higher velocities and that velocity alone explains a large proportion of the variation in capture efficiency. Lack of significance may be due to low statistical power or may indicate that there is no actual relationship between capture efficiently and velocity. I assumed the former, based on visual interpretation of the relationship in Figure A.1, and used the equation for the linear relationship between capture efficiency and velocity (Capture Efficiency [%] = 0.61(m3·s-1) + 0.14). This equation was used to calculate capture efficiencies for each of the habitats I sampled in the Chapter 2 field 81  survey in order to calculate population size and density. Capture efficiencies for sampled habitats in Bertrand Creek ranged from 36.27% to 13.90%, and on average was 20.70% + 0.68%. This is lower than the average capture efficiency reported by Bonamis (0.28% + 0.03% for riffle habitats averaging 0.40 + 0.19 m·s-1; 2011), and may be due to the lower average velocities of habitats sampled here (0.11 + 0.01 m·s-1). The benefit of using this equation to calculate capture efficiency for my field study, despite its marginal significance, is that I have ensured that the rest of my analysis, looking for an effect of low flows on abundance, is conservative. For example, adjusting capture efficiency to the velocities of the sampled habitats will tend to increase my population estimates at low flows or velocities, so that if I do detect significantly lower density or abundance at low flow, I can be confident that it is not an artifact of sampling, due to underestimating abundance at low flow because of low capture efficiency. This study demonstrates that the efficiency of single-pass electrofishing can be influenced by velocity, and highlights the importance of considering velocity (i.e., discharge) and its effects on capture efficiency when estimating population size. With benthic species like Nooksack dace, substrate size likely also affects capture efficiency, and future population studies would benefit from determining the effect of additional habitat characteristics such as substrate on capture efficiency.  82  Table 4.1 Capture efficiency data collected between June 22nd 2011 and August 19th 2011, using two-stage method as described by Bonamis (2011). Data are presented as means + standard error. % Capture Efficiency (R/M)  Habitat Type  Discharge (m3·s-1)  Depth  Velocity  Marked  (m)  (m·s-1)  (Recapture)  Glide  0.091  0.208 + 0.04  0.150 + 0.03  30  (6)  20.00  Glide  0.091  0.158 + 0.03  0.150 + 0.06  32  (9)  28.13  Riffle  0.091  0.070 + 0.01  0.250 + 0.04  31  (8)  25.81  Riffle  0.026  0.047 + 0.02  0.046 + 0.02  18  (2)  11.11  Glide  0.026  0.171 + 0.04  0.049 + 0.02  21  (3)  14.29  Riffle  0.024  0.054 + 0.01  0.206 + 0.05  31  (9)  29.03  Glide  0.024  0.188 + 0.03  0.054 + 0.02  29  (7)  24.14  83  A	
    B	
    Figure 4.1 Relationship between capture efficiency of single-pass electrofishing for Nooksack dace in riffle and glide habitats (A) tended to be higher in habitats with higher velocities (m·s-1), but (B) had no relationship to depth (m).  84  

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