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Influence of physical and biological habitat variables on juvenile salmonid and invertebrate drift abundance.. Nicol, Sandra Diane 2007-12-31

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INFLUENCE OF PHYSICAL AND BIOLOGICAL HABITAT VARIABLES ON JUVENILE SALMONID AND INVERTEBRATE DRIFT ABUNDANCE IN SOUTHWEST BRITISH COLUMBIA STREAMSbySANDRA DIANE NICOLB.Sc., The University of British Columbia, 2003A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFMASTER OF SCIENCEinTHE FACULTY OF GRADUATE STUDIES(Zoology)THE UNIVERSITY OF BRITISH COLUMBIADecember 2007?  Sandra Diane Nicol, 2007iiAbstractDetermining the physical and biological habitat variables that influence the abundance of juvenile salmonids in British Columbia streams will improve management practices.  Habitat models are tools that provide insight into organisms habitat needs and provide a more efficient mechanism for estimating population abundance than direct measurement.  Models have been developed for salmonids in other jurisdictions, but very few have included invertebrate drift (a primary food source for juvenile salmonids) as a predictive variable.  This is because temporal and spatial variation of drift abundance are widely assumed to be so high that drift cannot be reliably estimated without unreasonable ef fort.  This thesis investigates the temporal and spatial variability of invertebrate drift and the impact of its inclusion in habitat models for juvenile salmonid abundance in two chapters.   The first objective of the first chapter was to evaluate the temporal variability of invertebrate drift by comparing the seasonal and day-to-day variation in drift abundance to spatial variation within and between sites.  The second objective was to develop predictive models for invertebrate drift abundance. Aquatic, terrestrial and total invertebrate drift abundances varied primarily between sites and very little between days or months at the same site, indicating that a single day of sampling is sufficient to assess drift abundance for comparison among sites.   The abundance of invertebrate drift was related to productivity- and flow-related habitat variables.   The objectives of the second chapter were to develop predictive models for juvenile salmonid abundance in southwestern BC using physical and biological habitat variables, to determine whether habitat variables differ between the Coast and Interior regions of BC, to determine the contribution of invertebrate drift to the relative predictive ability of the models, and to determine cost:benefit ratios for the predictive models and their component variables.  The final models for predicting abundance of all young-of-year salmonids combined, and  rainbow trout (Oncorhynchus mykiss) and coho salmon (O. kisutch) individually , included variables related to stream structure and productivity, and the models for rainbow and coho showed regional differences.  Invertebrate drift did not improve model fit.iiiTable of ContentsAbstract  ................................................................ iiTable of Contents ........................................................ iiiList of Tables ........................................................... ivList of Figures  ........................................................... vAcknowledgements  ...................................................... viDedication ............................................................. viiCo-Authorship Statement ................................................. viiiChapter 1: Introduction  .................................................... 1References ......................................................... 4Chapter 2: Spatial and temporal variability of invertebrate drift in southwest British Columbia streams ......................................................... 6Introduction  ........................................................ 6Methods ........................................................... 8Results  ........................................................... 1 4Discussion ......................................................... 2 6References ........................................................ 32Chapter 3: Influence of physical and biological habitat factors on juvenile salmonid abundance  ............................................................. 36Introduction  ....................................................... 36Methods .......................................................... 38Results  ........................................................... 45Discussion ......................................................... 54References ........................................................ 61Conclusion ............................................................. 65References ........................................................ 68Appendix 1: Chapter 1 Habitat Measurements  ................................. 69Appendix 2: Chapter 2 Habitat Measurements  ................................. 73Appendix 3: UBC Research Ethics Board Certificate of Approval .................. 79ivList of TablesTable 2.1 ............................................................... 1 8Table 2.2 ............................................................... 1 9Table 2.3 ............................................................... 2 2Table 2.4 ............................................................... 2 3Table 3.1 ............................................................... 42Table 3.2 ............................................................... 46Table 3.3 ............................................................... 47Table 3.4 ............................................................... 55Table 3.5 ............................................................... 56vList of FiguresFigure 2.1 .............................................................. 1 1Figure 2.2 .............................................................. 1 5Figure 2.3 .............................................................. 1 6Figure 2.4 .............................................................. 1 7Figure 2.5 .............................................................. 1 9Figure 2.6 .............................................................. 2 4Figure 2.7 .............................................................. 2 4Figure 2.8 .............................................................. 2 5Figure 3.1 .............................................................. 39Figure 3.2 .............................................................. 51Figure 3.3 .............................................................. 52Figure 3.4 .............................................................. 53viAcknowledgementsFunding for this study was provided by the Forest Science Program of British Columbia.Thanks to Richard Bailey of Fisheries and Oceans Canada, Allen Hanson of the British Columbia Conservation Foundation, David Coutlee of the Nicola Watershed Stewardship and Fisheries Authority, Mike Wallis of the Salmon River Watershed Roundtable, Peter Tschaplinski of BC Ministry of Forests, Ron Ptolemly of BC Ministry of Environment, Scott Decker and Dennis Morgan for assistance selecting sites and/or for sharing their electrofishing data.Many thanks to the multitude of landowners who allowed us to work on their property .Field assistance was provided by Tessa Richardson, Tom Hicks and Brock Ramshaw.Laboratory assistance was provided by Geoff Cheung, Mikaela Davis and Patrick Carrier.  Statistical advice was provided by Lorne Rothman.Thanks to the Shurin, Srivastava and Harley labs for support throughout the project.Many thanks to those who reviewed the manuscript: John Richardson, Jackie Ngai, Christian Veenstra, and the Shurin, Srivastava and Harley labs.viiFor ScottviiiCo-Authorship StatementSandra Nicol was the primary researcher responsible for identifying and designing this research program, performing the research, analyzing the data and preparing the manuscript.  However, assistance with research program identification and design, data analysis, and manuscript preparation was provided by Jordan Rosenfeld and Jonathan Shurin.1Chapter 1: IntroductionManagement and conservation of species requires accurate and efficient estimation of populations sizes.  Habitat models are one way of obtaining these estimates.  Models that predict population size using habitat measurements have many applications for population management and conservation.  These predictive models can improve understanding of species? habitat requirements, including how these requirements interact, and they can require fewer resources to apply than direct field measurements of organism abundance.  Habitat models are used to identify high quality habitat for protection, plan habitat restoration projects and monitor resource extraction activities (e.g. Binns and Eiserman 1979, Clark et al. 1993).  Freshwater fish such a salmonids (Family Salmonidae) are well suited to habitat modeling due o their restricted habitat (lakes and streams, rather than patches of grassland or ocean floor) and due to the  potential utility of predictive models based on simple habitat metrics relative to labour-intensive fish sampling.Salmonid habitat is often categorized into physical features ? such as the area of pools and the number of pieces of large woody debris ? and biological variables that influence food abundance such as the amount of detritus .  Most studies of juvenile salmonid habitat have emphasized physical variables like channel width, depth, and structure, and reach characteristics like gradient and sediment size (see Coulombe-Pontbriand and LaPointe 2004, Davies 1989, Rosenfeld et al. 2000, Sharma and Hilborn 2001) which are all related to salmonid habitat quantity or quality.  The most direct measurement of food availability is invertebrate drift. Salmonids primarily feed on drifting invertebrates (W ipfli 1997) that are delivered by two mechanisms: in situ invertebrate production based on algal- or detrital-based food chains (Vannote et al. 1980), or input of terrestrial invertebrates from the riparian zone (Cada et al. 1987).  Canopy cover and composition, organic sediment, temperature, and water conductivity (see Binns and Eiserman 1979, Johansen et al. 2005, Scarnecchia and Bergersen 1987) are additional variables that may influence the abundance of invertebrate prey.Although invertebrate drift is the preferred food of many juvenile salmonids, it is 2conspicuously absent from most habitat models and is rarely used to assess habitat quality (Fausch et al. 1988).   Drifting invertebrates, both aquatic and terrestrial, are a primary food source for juvenile salmonids (Wipfli 1997), and elevated drift contributes to increased juvenile salmonid growth (Imre et al. 2004, Nielsen 1992, Rosenfeld et al. 2005, Slaney 1972), density (Slaney 1972) and survival (Imre et al. 2004, Nislow et al. 1998).  Food supply is often estimated by proxy through measurements of canopy cover or other variables (Johansen et al. 2005).  However, including a direct measurement of invertebrate drift abundance as a candidate variable in habitat models may improve the predictive ability of the models compared to indirect estimates, and may help determine the degree to which food availability limits salmonid abundance.Invertebrate drift is perceived as difficult to quantify (Binns and Eiserman 1979, Brittain and Eikeland 1988) due to high temporal and spatial variability.  This variability has led some authors to suggest that multiple samples (4-7) need to be collected on multiple days to accurately estimate invertebrate drift abundance (Allan and Russek 1985, Matthaei et al. 1998, Shearer et al. 2002).  Unfortunately, such resource-intensive sampling protocols discourage investigators from using drift to assess habitat quality for juvenile salmonids.  However, samples collected on a single day may be sufficient depending on the degree of temporal variation between days relative to spatial variation between sites.   As one of the reasons to use habitat models is their improved efficiency, the sampling protocol for invertebrate drift will impact its inclusion in models; if drift sampling requires multiple visits to a site it will not be viewed by fisheries managers as a practical index of habitat quality .Habitat models are often a more time-efficient way to estimate salmonid abundance than direct measurements. The predictive variables that biologists choose to include in regression analysis are usually based on expert opinion as to which variables are most ef fective.  However, variables are rarely selected in an explicit cost-benefit framework.  Assessing the cost-effectiveness of habitat models and their terms (the habitat variables) should allow fisheries scientists to choose which habitat models and component variables maximize predictive power.  A cost-benefit analysis should quantify the advantages and disadvantages of various habitat variables and whole models, allowing investigators to choose to use 3direct measurements of fish abundance, an existing model, or a new combination of habitat variables to estimate habitat quality.This thesis is organized into two related studies.  Chapter 2 examined the temporal and spatial variability of invertebrate drift to determine the level of sampling intensity (single visit or multiple visit) necessary to compare drift abundance between sites.  If multi-day sampling is required then it is unrealistic to expect fisheries managers to use invertebrate drift as an index of habitat quality.  The objectives of this study were : 1) to determine whether accurately measuring drift requires sampling on multiple days by comparing the temporal variation in drift abundance on a seasonal and day-to-day basis to spatial variation between sites, 2) to determine the range of drift between sites in Coast and Interior regions of southwestern British Columbia, 3) to develop predictive models between invertebrate drift abundance and a suite of physical and biological habitat variables, and 4) to determine the variables associated with drift abundance at a landscape scale.Chapter 3 involved developing habitat models for young-of-the-year (YOY) rainbow trout (Oncorhynchus mykiss), coho salmon ( O. kisutch), and all YOY combined using physical and biological habitat variables, including invertebrate drift.  The objectives of this study were 1) to develop predictive models for juvenile salmonid abundance in southwestern BC using physical and biological habitat factors, 2) to determine whether habitat variables dif fer between the Coast and Interior regions, 3) to determine the contribution of invertebrate drift to the relative predictive ability of models, and 4) to determine benefit:cost ratios for predictive models and their component variables.  4ReferencesBinns, N.A., and Eiserman, F.M. 1979. Quantification of fluvial trout habitat in Wyoming. Transactions of the American Fisheries Society  108: 215-228.Cada, G.F., Loar, J.M., and Cox, D.K. 1987. Food and feeding preferences of rainbow and brown trout in southern Appalachian streams. The American Midland Naturalist  117: 374-385.Clark, J.D., Dunn, J.E., and Smith, K.G. 1993. A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management 57(3): 519-526.Coulombe-Pontbriand, M., and LaPointe, M. 2004. Landscape controls on boulder -rich, winter habitat availability and their effects on Atlantic salmon ( Salmo salar) parr abundance in two fifth-order mountain streams. Canadian Journal of Fisheries and Aquatic Sciences  61(4): 648-658.Davies, P.E. 1989. Relationships between habitat characteristics and population abundance for brown trout,  Salmo trutta L., and blackfish,  Gadopsis marmoratus Rich., in Tasmanian streams. Australian Journal of Marine and Freshwater Research  40: 341-359.Deno?l, M., and Lehmann, A. 2006. Multi-scale effect of landscape processes and habitat quality on newt abundance: Implications for conservation. Biological Conservation 130: 495-504.Dorgel?h, W.G. 2006. Habitat suitability for tsessebe  Damaliscus lunatus lunatus. Journal of African Ecology  44: 329-336.Fausch, K.D., Hawkes, C.L., and Parsons, M.G. 1988. Models that predict standing crop of stream fish from habitat variables: 1950-1985. Gen Tech Rep, U.S. Department of Agriculture Forest Service Pacific Northwest Research Station, Portland, Oregon.Gavashelishvili, A., McGrady, M.J., and Javakhishvili, Z. 2006. Planning the conservation of the breeding population of cinereous vultures Aegypius monachus in the Republic of Georgia. Oryx  40(1): 1-8.Imre, I., Grant, J.W.A., and Keeley, E.R. 2004. The effect of food abundance on territory size and population density of juvenile steelhead trout ( Oncorhynchus mykiss). Oecologia 138: 371-378.Johansen, M., Elliott, J.M., and Klemetsen, A. 2005. A comparative study of juvenile salmon density in 20 streams throughout a very large river system in northern Norway. Ecology of Freshwater Fish  14: 96-110.McAlpine, C.A., Rhodes, J.R., Callaghan, J.G., Bowen, M.E., Lunney , D., Mitchell, D.L., Pullar, D.V., and Possingham, H.P. 2006. The importance of forest area and configuration relative to local habitat factors for conserving forest mammals:  A case study of koalas in Queensland Australia. Biological Conservation  132: 153-165.Nielsen, J.L. 1992. Microhabitat-specific foraging behavior, diet, and growth of juvenile coho salmon. Transactions of the American Fisheries Society  121: 617-634.5Nislow, K.H., Folt, C., and Seandel, M. 1998. Food and foraging behavior in relation to microhabitat use and survival of age-0 Atlantic salmon. Canadian Journal of Fisheries and Aquatic Sciences  55: 116-127.Rosenfeld, J., Porter, M., and Parkinson, E. 2000. Habitat factors affecting the abundance and distribution of juvenile cuttrhoat trout ( Oncorhynchus clarki) and coho salmon (Onchorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences  57: 766-774.Rosenfeld, J.S., Leiter, T., Lindner, G., and Rothman, L. 2005. Food abundance and fish density alters habitat selection, growth, and habitat suitability curves for juvenile coho salmon (Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences 62: 1691-1701.Scarnecchia, D.L., and Bergersen, E.P. 1987. Trout production and standing crop in Colorado?s small streams, as related to environmental features. North American Journal of Fisheries Management  7: 315-330.Sharma, R., and Hilborn, R. 2001. Empirical relationships between watershed characteristics and coho salmon (Oncorhynchus kisutch) smolt abundance in 14 western Washington streams. Canadian Journal of Fisheries and Aquatic Sciences  58: 1453-1463.Slaney, P.A. 1972. Effects of prey abundance on distribution, density and territorial behaviour of young rainbow trout in streams, University of British Columbia.Vannote, R.L., Minshall, G.W., Cummins Kenneth, W., Sedell, J.R., and Cushing, C.E. 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences  37: 130-137.Wipfli, M. 1997. Terrestrial invertebrates as salmonid prey and nitrogen sources in streams: contrasting old-growth and young-growth riparian forests in southeastern  Alaska USA. Canadian Journal of Fisheries and Aquatic Sciences  54: 1259-1269.Zharikov, Y., Lank, D.B., Huettmann, F., Bradley, R.W., Parker, N., Yen, P.P.W., McFarlane-Tranquilla, L.A., and Cooke, F. 2006. Habitat selection and breeding success in a forest-nesting Alcid, the marbled murrelet, in two landscapes with different degrees of forest fragmentation. Landscape Ecology 21: 107-120.6Chapter 2: Spatial and temporal variability of invertebrate drift in southwest British Columbia streams1IntroductionDrifting invertebrates of aquatic and terrestrial origin are a primary food source for juvenile salmonids (Wipfli 1997), and elevated drift contributes to increased juvenile salmonid growth (Imre et al. 2004, Nielsen 1992, Rosenfeld et al. 2005, Slaney 1972) , density (Slaney 1972) and survival (Imre et al. 2004, Nislow et al. 1998).  Invertebrates are a critical component of stream food webs, yet drift is seldom used as a measure of habitat quality in favour of aspects of habitat structure like woody debris (Fausch et al. 1988).   This is largely because drift is perceived as difficult to quantify (Binns and Eiserman 1979, Brittain and Eikeland 1988), with high temporal and spatial variability.  This variability has led some authors to suggest that multiple samples (4-7) need to be collected on dif ferent days to accurately estimate invertebrate drift abundance (Allan and Russek 1985, Matthaei et al. 1998, Shearer et al. 2002).  Such resource-intensive sampling protocols (laboratory time required to sort and quantify a single drift sample can vary from 3 to 10 hours) have likely contributed to the limited use of drift to assess habitat quality for juvenile salmonids.  However , fewer samples collected on a single day may be sufficient depending on the degree of temporal variation between days relative to spatial variation between sites.  Understanding how invertebrate drift biomass varies in time and space will allow investigators to better evaluate the number of samples required to accurately characterize drift biomass, and to evaluate the potential of drift as a predictor of salmonid abundance.The scale and magnitude of temporal variation in drift abundance will af fect the sampling effort required to accurately assess prey abundance, and therefore prey availability for drift-feeding fish.  Temporal variation in drift occurs at a hierarchy of scales, ranging from well-documented diel variation to variation between days and longer seasonal trends (Resh and Rosenberg 1989). Aquatic invertebrates are vulnerable to visual predators and preferentially drift at night (Flecker 1992), so that drift has relatively uniform low daytime abundance and 1  A version of this chapter will be submitted for publication:Nicol, SD, JS Rosenfeld and JB Shurin.  Spatial and temporal variability of inverte-brate drift in southwest British Columbia streams.7peaks shortly after dark (Waters 1962), whereas terrestrial invertebrates fall into streams more frequently during the day (Rinc?n and Lob?n-Cervi? 1997).   Variation between days has also been studied but reports of the magnitude of day-to-day variation dif fer (Shearer et al. 2002, Williams 1980).  Williams (1980) found that between-day variation in drift abundance varied up to 6 times, suggesting that investigators mush sample a site on more than one day in order to estimate drift abundance.  Other studies suggest that day-to-day variation is no greater than variation between replicates (Shearer et al. 2002).Day-to-day variability in drifting aquatic invertebrates is affected by nocturnal light levels (aquatic drift is suppressed on bright nights that expose invertebrates to visual predators; Holt and Waters 1967), temporal lags that cause compensatory drifting (high night time aquatic drift after a night of suppressed drifting), life history events such as mass emer gence of adults (Williams 1980) and variable discharge.  Day-to-day variability of terrestrial invertebrates is generally higher than for aquatic invertebrates (Nakano et al. 1999), indicating higher variance in the factors (eg. wind) that deliver terrestrial prey from riparian zones.   Total drift abundance also shows longer seasonal trends.  Invertebrate drift biomass (per m3) in temperate regions tends to be higher in the summer than the winter (Brittain and Eikeland 1988, Kawaguchi and Nakano 2001, Shearer et al. 2002).  Allan et al. (2003) found that drift abundance peaked in July, and others (eg. Bacon et al. 2005, Rinc?n and Lob?n-Cervi? 1997, Slaney 1972) have observed spring peaks in drift abundance followed by summer declines.  Understanding the drivers of temporal variation in drift allows improved sampling protocols for examining the mechanisms that influence prey abundance for drift-feeding fish.Differences in average drift abundance between streams leads to spatial variation in drift at regional scales, which could be a major driver of differences in habitat capacity for juvenile salmonids.  Aquatic drift abundance should be influenced by variables from one or both of two categories: those related to biotic productivity factors and those related to invertebrate entrainment in the water column.  In principle, aquatic invertebrate drift should be higher in streams with greater productivity, in which case aquatic drift biomass may be associated with habitat variables that affect benthic productivity, such as temperature, stream nutrients (Richardson 1993), algae (chlorophyll-a) concentrations (Schell 1999, Shearer et al. 2003), 8detritus density or quality (Chadwick and Huryn 2007), or factors such as riparian canopy cover that influence benthic primary production in light-limited streams (W allace et al. 1997, Wipfli 1997, Wipfli and Musselwhite 2004).  Flow-related physical factors that affect entrainment of benthic invertebrates into the drift, such as water velocity (Harvey et al. 2006), channel gradient, the availability of riffle habitat (Rader 1997), or flow regime (Lancaster 1999) may be associated with drift.  Biomass of drifting terrestrial invertebrates is affected by the chance occurrence of an invertebrate falling into a stream; terrestrial invertebrates may be influenced by factors that affect their activity level such as temperature (Edwards and Huryn 1995), or their overall abundance in the riparian area such as canopy cover and composition.  However, evidence for the importance of canopy cover composition on terrestrial invertebrate abundance is mixed (Johansen et al. 2005, Mason and MacDonald 1982, Wipfli 1997, Wipfli and Musselwhite 2004).  Identifying the factors that influence drift abundance will improve understanding of how habitat influences salmonid food supply , and consequently could lead to improved management of fish habitat.The primary objectives of this study were: 1) to determine whether accurately measuring drift requires sampling on multiple days by comparing the temporal variation in drift abundance on a seasonal and day-to-day basis to spatial variation between sites at a subset of 4 intensively sampled streams; 2) to determine the range of drift between sites in Coast and Interior regions of southwestern British Columbia, 3) to develop predictive models between invertebrate drift abundance and a suite of physical and biological habitat variables, and 4) to determine the variables associated with drift abundance at a landscape scale.MethodsTemporal variation: magnitude of day-to-day and seasonal variation relative to site effectsFour sites were chosen for repeated sampling to determine the magnitude of day-to-day variation in drift biomass: one small and one large stream from the west coast of British Columbia (Coast region, Husdon and Chapman Creeks, respectively), and one small and 9one large stream from the southern interior of BC (Interior region, Senn and Yard Creeks, respectively).  Sites were chosen that differed in size and geographic area so that any conclusions are not limited to a specific type of stream, such as small streams in the Coast region.  Sites were chosen from the pool of sties recommended by local fisheries biologists because they were representative of streams in their size classes in their regions.  Each site was sampled twice per month over four months (June to September 2005), with within-month samples 1-3 days apart to minimize effects of seasonal trends on daily variation and to allow us to examine variation at two temporal scales (days and months).  No sampling was conducted during or soon after precipitation to standardize the ef fect of weather on drift abundance. Two replicate drift samples were collected on each sampling date, yielding 16 samples per stream except for Yard Creek, where a second visit in June was cancelled due to weather.Invertebrate Drift SamplingInvertebrate drift samples were collected at the upstream end of pools immediately below riffles to standardize habitat effects on drift and to measure the quantity of drift that a dominant fish would experience at the head of a pool.  The nets were placed in separate pools, and as much as possible nets were positioned so that they did not filter the same water .  At sites with no suitable pools nets were placed in riffles.  Samples were collected with a 1 m long, 250 ?m mesh net with an opening 0.2 m wide, for 180-300 minutes, depending on water velocity.  The depth and velocity of the water in the net opening were measured in order to calculate the volume of water sampled, and thereby the biomass of drifting invertebrates per m3.  We conducted daytime sampling rather than 24-hour sampling because daytime drift abundance is more uniform (Waters, 1962), and most juvenile salmonids are visual predators that benefit from day time drift abundance.  Arguably, daytime drift samples more accurately represent prey abundance for juvenile salmonids than 24 hour drift samples where biomass is dominated by nocturnal peaks.  Drift samples were collected at least two hours after sunrise and two hours before sunset to reduce daily variability in light intensity , and were not collected during or after rain events.  Samples were preserved in 5% formalin and returned to the lab for sorting.10Invertebrates were sorted from detritus at 10X magnification in the laboratory and preserved in 70% ethanol for later identification and measurement using a digitizer (Rof f and Hopcroft 1986).  Invertebrates were identified to order with some exceptions; non-arthropods such as nematodes were identified to phylum, and common arthropods such as chironomids were identified to sub-family.  Each taxon was categorized as aquatic or terrestrial in origin.  Adult insects with aquatic larvae, such as Nematocera (suborder of Diptera), were counted as aquatic.  The dry mass (mg) of invertebrates in each sample was calculated using published taxa-specific length-weight regressions (Benke et al. 1999, McCauley 1984, M.  Wipfli unpubl., Meyer 1989, Sabo et al. 2002, Sample et al. 1993, Smock 1980).Temporal Variation Statistical AnalysisThe magnitude of day-to-day, monthly, and between- and within-site variance was compared with mixed model analysis of variance using the lme4 package (Bates 2007) in R 2.4.1 (www.r-project.org).  The models were fit using restricted maximum likelihood (REML).  Candidate models with the four combinations of fixed factors were compared to determine which best explained the variability in the drift density (mg dry mass per m 3 water).  The full model included two fixed factors (region (Coast vs. Interior) and month (June-September)) and two random grouping factors (Julian day and site).  Month was analyzed as an ordered factor to allow polynomial analyses (by default R examines the polynomial terms of an ordered factor).  Site was considered to be a random factor because the four sites were chosen to be representative of the population of available sites, not for specific interest in those particular sites.  The region model included region, Julian day, and site.  The month model included month, Julian day and site.  The random factors model included only the random factors Julian day and site.  The analysis was not performed as a nested ANOVA due to complications arising from different dates; using Julian day addresses daily variation. Drift densities were log transformed so that model residuals would meet assumptions of normality .  The models were compared using Akaike weights based on the Second-Order Information Criterion (AIC c, for small samples).  The weights were used to calculate evidence ratios to select the best fit model (Burnham and Anderson 2002). Variance components were 11calculated for the random factors to assess their relative contribution to total variance in drift abundance, with the residual variance representing variance among replicate samples collected on a single day.  This analysis was applied to total invertebrate biomass, aquatic invertebrates and terrestrial invertebrates separately.Spatial variation: factors influencing drift abundance across multiple streams at a landscape scaleThirty sites (including the four sites used for the temporal study) in the Coast and Interior regions of British Columbia were sampled for drift abundance and habitat features using the protocols described above in the summer of 2005 or 2006 (Figure 2.1).   At the four temporal variation sites a third sample was collected on the first August sampling date, and the three samples from that date were used for the spatial analysis.  Three drift samples were taken at all other sites, and average biomasses of replicate samples were used in analysis.   The 30 Figure 2.1: Locations of study streams in south western British Columbia, Canada.  Some sites are close enough together that they share a point.  Number labels refer to row labels in Appendix 1.  Universal Transverse Mercator coordinates are also available in Appendix 1.12sites were chosen to cover a wide range of productivity levels and stream sizes (1.7-33.1 m channel width; Appendix 1).  Measurements of habitat features were made on the same day as samples were collected when possible.Habitat MeasurementsSpatial variation in drift, between and within streams, can also be considerable and is influenced by measurable habitat factors.  However, the measurement of habitat effects is complicated by the impact of upstream invertebrate density, which can be detected long distances downstream (Waters 1965, Wipfli and Gregovich 2002).  Habitat factors must therefore be consistent for a great enough distance that upstream invertebrate production levels do not mask their effects.  That is, the impact of a short stretch of stream with no canopy cover probably cannot be detected downstream or even within that stretch due to mixing with invertebrate drift originating upstream.  For this reason habitat surveys were conducted over 80-250 m and average values were calculated for the surveyed area.  Invertebrate drift samples were collected within the survey area.Habitat surveys were conducted at all 30 sites (see Moore et al. 1997) to collect information on reach-level factors that may influence invertebrate drift abundance.   Average summer temperature was estimated for each site using ClimateBC V.3.2 (Wang et al. 2006) and UTM coordinates and elevation measurements for each site.  Conductivity was measured once at each site.  The intermediate dimension (median length of the x, y, and z axes) of the five largest water-moved particles (Hogan 1996), and the percent canopy cover and the canopy composition (percent cover of conifer, alder, and other deciduous trees) were measured at 3-5 locations spaced throughout each site.  Note that the sum of the conifer , alder and other deciduous canopy cover was often greater than the total canopy cover estimate when the different types of canopies overlapped.  The length and width of each channel unit (riffle, pool, glide, run, and cascade) were measured, including channel units in secondary and backwater channels.  These measurements were used to calculate mean bankfull width and the percent area of each site by channel unit type.  Substrate composition (fines, gravel, cobbles, boulders, bedrock, percent particulate organic matter (POM)), gradient, and depth 13of each channel unit were weighted by channel unit area to calculate average values for each site.Spatial Variation Statistical AnalysisRegression models to predict total, aquatic, and terrestrial drift abundance were fit using linear models in R 2.4.1.  Models were fit using ordinary least squares on transformed, centred data.  In order to apply these models to new data sets the new data must be centred using the same constants as this study (see Appendix 1 to calculate variable means).  Data were log transformed for normality (Appendix 1) and centred to reduce colinearity between main effects and interaction effects (Quinn and Keough 2002).  After examining correlation between the measured habitat variables, explanatory variables with no correlation coefficients greater than 0.7 were selected as a starting point for model selection (if two variables were strongly correlated then the model selection process was started twice, once with each variable).  These variables were average summer temperature (temp), conductivity (cond), largest particle (lp), percent alder cover, gradient, percent cover of gravel sediment (grav), and percent cover of organic sediment (org).  Site elevation (elev) was related to region (region), and percent canopy cover (can) was correlated with bankful width (wb).  One of elev and region (strongly related), one of can and wb, and the other seven tar get variables were included in four starting models for each invertebrate biomass value (total, aquatic, terrestrial).  Interaction terms were added to the starting models based on improved  AIC values.  Terms were then removed from the starting models in a stepwise fashion until removing terms no longer improved AIC values.  The models with the lowest AIC values were selected for comparison. The models were compared using Akaike weights based on the Second-Order Information Criterion (AICc for small samples).   The weights were used to calculate evidence ratios to select the best fit model (Burnham and  Anderson 2002).  Tolerance, Cook?s Distance, and normality of the residuals were checked for all selected models.  Interactions included in final models were investigated using simple slopes (Quinn and Keough 2002).  The simple slope of a variable is its slope when the variable that it interacts with is held constant.  Generally the simple slopes are calculated at the mean of the other variable, and one standard deviation above and below the mean (low , average and high 14values of the interacting variable).  Simple slope calculations allow greater understanding of trade-offs and other interactions.  Habitat measurements from the two regions were compared using two-sample Student?s t-tests to examine differences between the regions.ResultsTemporal VariationTotal invertebrate drift biomass in a sample varied from 1.59 mg/m 3 in Husdon Creek (the small Coast stream) to 180.60 mg/m 3 in Yard Creek (the large Interior stream; Figure 2.2).  The four sites, Chapman, Husdon, Yard and Senn had averages (over all samples) of 26 ? 19 (standard deviation) mg/m 3, 19 ? 24 mg/m 3, 92 ? 44 mg/m 3, and 44 ? 27 mg/m 3, respectively.  Average drift biomass in the Interior region was three times the biomass in the Coast region (66 ? 43  vs. 22 ? 21 mg/m 3, respectively).Aquatic invertebrates made up 78% of the total invertebrate biomass averaged across all samples (Figure 2.3); this proportion was consistent between sites.  Terrestrial invertebrates made up the remaining 22% (Figure 2.4).  Terrestrial and aquatic invertebrates also showed the same relative drift densities between sites as total invertebrates;  Yard Creek had the highest drift density followed by Senn, Chapman, and Husdon.  The average terrestrial and aquatic invertebrate drift densities in the Interior region greater than in the Coast region (aquatic: 51 ? 41 mg/m 3 and 17 ? 18 mg/m 3, respectively; terrestrial: 15 ? 16 mg/m 3 and 5 ? 10 mg/m 3, respectively).The best fit model for total, aquatic, and terrestrial invertebrates based on  AICc Akaike weights ( ? i) was the region model (including region fixed , site random, and Julian day random; Table 2.1).  The region model had an Akaike weight 2.9 times higher than the random factors model for total invertebrate density, 1.9 times higher than the random factors model for aquatic invertebrate density, and 2.2 times higher than the full model for terrestrial invertebrate density, indicating that the region model was approximately twice as likely as the next best model for all invertebrate categories.  These results suggest that there was no consistent 15Figure 2.2: Total invertebrate drift biomass of samples collected at four sites (A Senn Creek, B Yard Creek, C Husdon Creek, D Chapman Creek) over four months.  Open and filled circles indicate the first and second visits to the site each month.  No consistent between day variation is apparent; variation between within the sites appear to be the primary sources of variation.050100150200A B050100150200Jun Jul Aug SepCJun Jul Aug SepDtotal drift biomass (mgm3)16Figure 2.3: Aquatic invertebrate drift biomass of samples collected at four sites (A Senn Creek, B Yard Creek, C Husdon Creek, D Chapman Creek) over four months.  Open and filled circles indicate the first and second visits to the site each month. No consistent between day variation is apparent; variation between within the sites appear to be the primary sources of variation.050100150200A B050100150200Jun Jul Aug SepCJun Jul Aug SepDaquatic drift biomass (mgm3)17Figure 2.4: Terrestrial invertebrate drift biomass of samples collected at four sites (A Senn Creek, B Yard Creek, C Husdon Creek, D Chapman Creek) over four months.  Open and filled circles indicate the first and second visits to the site each month. No consistent between day variation is apparent; variation between within the sites appear to be the primary sources of variation.050100150200A B050100150200Jun Jul Aug SepCJun Jul Aug SepDterrestrial drift biomass (mgm3)18Model AIC c D i lik ? i ? max  /  ? iTotalregion + month + Julian day + site 170.77 6.57 0.04 0.03 26.7region + Julian day + site 164.2 0 1 0.71 1month + Julian day + site 172.63 8.43 0.01 0.01 67.5Julian day + site 166.31 2.11 0.34 0.25 2.9Aquaticregion + month + Julian day + site 168.07 6.57 0.04 0.02 26.7region + Julian day + site 161.5 0 1 0.63 1month + Julian day + site 169.13 7.63 0.02 0.01 45.3Julian day + site 162.81 1.31 0.51 0.32 1.9Terrestrialregion + month + Julian day + site 237.11 1.6 0.45 0.29 2.2region + Julian day + site 235.51 0 1 0.65 1month + Julian day + site 242.66 0.03 0.02 0.02 35.5Julian day + site 241.12 0.06 0.06 0.04 16.5Table 2.1: Likelihood ratio calculations for the temporal candidate models for total, aquatic and terrestrial drift.  AIC c is the Second-Order Information Criterion (used for small sam -ples), ? i is the difference between the AIC c and the minimum AIC c, lik is the likelihood of the model given the data (e^(- ? * ? i)),  ? i is the likelihood of the model divided by the sum of the likelihoods for the competing models (see Burnham and Anderson 2002 ch 2).  The  ? max  /  ? i ratio indicates how much more likely the best model ( ? max ) is compared to the candidate model (? i).seasonal effect on any of the invertebrate groups, but that drift biomass was higher in the Interior.  There was strong support for the inclusion of region in the final model; the region-only model had a likelihood weight up to 16 times higher (was 16 times more likely;  Table 2.1) than the random factors model that rejected region.The coefficients for region for total, aquatic, and terrestrial invertebrates confirm that invertebrate drift is higher in the Interior than the Coast region (T able 2.2).  Variance components analysis indicated that the random variation contributed by Julian day was negligible for total and aquatic invertebrate drift and low (8%) for terrestrial invertebrates (Figure 2.5).  Between-site variation is negligible for terrestrial invertebrates but contributes 28% of the random variation for total invertebrate drift and 47% for aquatic drift.   Within-19Invertebrates Intercept (SE) Region (Interior) coefficient (SE)Total 2.68 (0.39) 1.29 (0.55)Aquatic 2.39 (0.54) 1.24 (0.76)Terrestrial 0.46 (0.31) 1.58 (0.44)Table 2.2: Model coefficients and standard errors for the intercept and fixed factor (region) for total, aquatic and terrestrial invertebrates temporal models.Figure 2.5: Cumulative variance components of site, day, and residual (within-site) variation.  The variance components show the proportion of random variation that can be attributed to site, day, and residual variation for total, aquatic and terrestrial invertebrate drift.Total Aquatic TerrestrialProportion of Variance0.00.20.40.60.81.0Julian DaySiteResidual20site variation (residual variation due to spatial replication (n = 2 samples) within a site) contributed the majority of the random variation for total, aquatic and terrestrial invertebrates (72%, 51% and 92%, respectively).  The high residual variation of terrestrial invertebrates indicates greater variation between replicate samples than between days, possibly due to the small number of replicates (2) collected on each day, the effects of single large terrestrial invertebrates that contribute disproportionately to biomass (e.g. a single lar ge beetle can skew a dry weight measurement) or greater accumulations of leaves bearing terrestrial invertebrates in one drift net than another.Spatial VariationInvertebrate biomass at the 30 sites varied over an order of magnitude from 7.9-283 mg/m3, except for one site with very high biomass (High Falls with 1062 mg/m3; Appendix 1).  Average invertebrate drift biomass was 97 ? 191 mg/m 3 for total invertebrates, 52 ? 46 mg/m 3 for aquatic invertebrates, and 45 ? 169 mg/m 3 for terrestrial invertebrates.  Similar to seasonal samples, aquatic invertebrates made up 76% ? 20% of total invertebrate drift biomass; aquatic invertebrate mass was greater than terrestrial invertebrate mass at all sites except for one Coast stream (High Falls) and one in the Interior (Criss) due to the presence of several large terrestrial invertebrates (caterpillars).  Total invertebrate biomass was higher in the Coast than the Interior region (100 mg/m 3 and 92 mg/m 3), but this is largely due to a single site (High Falls) with particularly high biomass due to very lar ge terrestrial inputs (several caterpillars fell into the stream).  When this site was identified as an outlier (using a box plot) and excluded the average total invertebrate drift biomass in the Coast region fell to 44 ? 34 mg/m 3.  Aquatic invertebrate drift biomass was 41 ? 38 mg/m 3 in the Coast region and 70 ? 52 mg/m 3 in the Interior, while terrestrial invertebrate drift densities in the Coast and Interior were 8 ? 9 mg/m 3 and 23 ? 34 mg/m 3, respectively, with the High Falls Coast site excluded as an outlier (60 ? 218 mg/m 3  with High Falls site included).  The High Falls site was excluded from multiple regression analysis if its Cook?s Distance exceeded 1 indicating high influence (Quinn and Keough, 2002), otherwise it was included in analysis.Total canopy cover was significantly higher in the Coast region (39% ? 29% versus 17% ? 2120%, Welch two-sample t-test on untransformed data: t = 2.48, p = 0.02).  The proportion of total canopy in each category (alder, other deciduous, and canopy) did not differ between regions.  Alder constituted 52% ? 38% of total canopy cover at Coast sites, and 50% ? 34% at Interior sites.  Other deciduous trees were 25% ? 24% and 35% ? 49%, and coniferous trees were 23% ? 27% and 28% ? 37% in the Coast and Interior, respectively.  Elevation (199 m and 517 m, Welch two-sample t-test: t = -6.74, p = 1.13 x 10 -6) and conductivity (65.7 ?S ? 41.6 ?S and 130.0 ?S ? 91.2 ?S, Welch two-sample t-test: t = -2.3, p = 0.04) were lower at Coast sites.The final models predicting total, aquatic, and terrestrial invertebrate densities using habitat measurements were selected using the same evidence ratio method as for temporal variation (Table 2.3).  All final models had acceptable Cook?s Distance and tolerance values and normally distributed residuals.  The best models for total, aquatic, and terrestrial invertebrate densities used different variables (Table 2.4).  The best model for total invertebrates had an r2  of 0.28 (F 4,24  = 2.38, p = 0.08), and included variables related primarily to aquatic and riparian productivity: conductivity, alder cover, canopy cover, and the interaction between conductivity and alder cover (Figure 2.6).  Simple slopes analysis (Quinn and Keough 2002) of the interaction between conductivity and percent alder shows that the slope of the alder coefficient was positive (2.64) when conductivity was low (one standard deviation below the centred average of 0, log(conductivity) = 0.898). The alder coefficient was positive but lower (0.79) at average conductivity, and negative at high conductivity (-1.06 at one standard deviation above average conductivity).  The same effect occurred for the slope of conductivity when alder was held at low, average and high values; the slope for conductivity was 0.65 at low alder cover (SD asin?alder = 0.216), 0.20 at average alder cover and -0.24 at one standard deviation above average.  The best model for aquatic invertebrate drift had greater predictive power (r 2  = 0.44; F 2,25  = 4.94, p = 0.004), and included region and flow-related habitat variables: gradient, bankfull width, and the interaction between region and gradient (Figure 2.7).  Simple slopes analysis showed a coefficient for gradient of 0.62 in the Coast region  and -0.34 in the Interior, indicating a positive effect of gradient on drift in the Coast but not in the Interior.  The High Falls site did not have high influence on these two models and was included throughout the analysis.  However, it had high influence for 22Model AICc ? j lik ? i ? max  /  ? iTotalgrad + wb + cond + ald + cond*ald 9.08 4.91 0.09 0.04 11.63wb + cond + ald + cond*ald 7.05 2.88 0.24 0.11 4.23cond + ald + cond*ald 6.56 2.39 0.3 0.14 3.31reg+ reg*can + can + cond + ald + cond*ald 7.75 3.58 0.17 0.08 6reg+ can + cond + ald + cond*ald 7.06 2.89 0.24 0.11 4.24can + cond + ald + cond*ald 4.17 0 1 0.46 1org + can + cond + ald + cond*ald 7.79 3.62 0.16 0.07 6.11Aquaticcond + wb + reg + grad + reg*grad -3.49 4.48 0.11 0.04 9.4wb + reg + grad + reg*grad -7.97 0 1 0.34 1reg + grad + reg*grad -7.7 0.27 0.87 0.3 1.15elev + grad + elev*grad -6.86 1.11 0.57 0.2 1.74wb + elev + grad + elev*grad -5.93 2.04 0.36 0.12 2.77Terrestrialwb + grad + lp + org + ald + grav + cond + cond*ald + lp*wb31 15.15 0 0 1949.16org + grav + elev + cond + elev*cond 20.08 4.22 0.12 0.07 8.27ald + grav + elev + cond + elev*cond 19.89 4.03 0.13 0.07 7.51grav + elev + cond + elev*cond 17.03 1.18 0.56 0.31 1.8elev + cond + elev*cond 15.85 0 1 0.55 1elev + grad + elev*grad -6.86 1.11 0.57 0.2 1.74wb + elev + grad + elev*grad -5.93 2.04 0.36 0.12 2.77Table 2.3: Likelihood ratio calculations (see Table 2.1) for the spatial candidate models for total, aquatic and terrestrial drift.  Abbreviations are: grad (gradient), wb (bankfull width), cond (conductivity), ald (alder cover), reg (region), can (canopy cover), or g (organic sedi -ment), elev (elevation), lp (largest particles).23log(total drift) = 3.8 + 0.2*log(cond) ? 1.6*asin(?can) + 0.8*asin(?ald) ? 2.1*log(cond)* asin(?ald)r2 = 0.28SS df F p Partial r 2conductivity 0.374 1 0.414 0.526 0.017canopy 3.547 1 3.927 0.059 0.141alder 1.705 1 1.888 0.182 0.073conductivity*alder 3.891 1 4.307 0.049 0.152residuals 21.678 24log(aquatic drift) = 3.4 + 0.6*reg(Coast) + 0.3*grad + 0.3*wb ? 1.0*reg(Coast)*gradr2 = 0.44SS df F p Partial r 2region 1.947 1 3.365 0.08 0.118gradient 0.102 1 0.175 0.68 0.069bankfull width 1.765 1 3.026 0.094 0.108region*gradient 6.660 1 11.412 0.002 0.313residuals 14.591 25log(terrestrial drift) = 1.9 + 0.0005*elevation + 0.2*log(cond) + 0.0022*elev*log(cond)r2 = 0.31SS df F p Partial r 2elevation 1.699 1 1.762 0.197 0.068conductivity 3.022 1 3.135 0.089 0.116elevation*conductivity 4.837 1 5.017 0.035 0.173residuals 23.139 24Table 2.4: Model coefficients and partial r 2  values for total, aquatic and terrestrial spatial models.  Abbreviations are same as for Table 2.3.24Figure 2.6: Scatter plots of the habitat factors included in the total drift model ? conductivity , alder canopy cover and total canopy cover ? against total drift.   The lines indicate the model slopes, not the simple regression slopes.  The solid lines indicate the model slope without any interaction effect (i.e. when all other values are held at their means).  The dashed and dotted lines on the alder and conductivity graphs indicate the slope when the interacting value is low and high, respectively (i.e. the dashed line on the alder graph represents the slope of alder when conductivity is low).  Analyses were performed on transformed data.  Open symbols indicate Interior region sites, filled symbols indicate Coast sites.Figure 2.7: Scatter plots of the habitat factors included in the aquatic drift model ? bankfull width and gradient ?  versus total drift.  The lines indicate the model slopes, not the simple regression slopes.  The solid line on the bankfull width graph indicates the model slope.  The dashed line on the gradient graph is the slope in the Coast region.   The dotted line is the slope in the Interior.  Analyses were performed on transformed data. Open symbols indicate Inte -rior region sites, filled symbols indicate Coast sites.5 10 15 20 25 30102050100bankfull width (m)0 1 2 3 4 5102050100gradient (%)aquatic drift biomass (mgm3)50 100 150 200 2501050200500conductivity( ? S)0 10 20 30 40 501050200500alder canopy cover (%)0 20 40 60 801050200500total canopy cover (%)total drift biomass (mgm3)25Figure 2.8: Scatter plots of the habitat factors included in the terrestrial drift model ? con -ductivity and elevation ? against total drift.  The lines indicate the model slopes, not the simple regression slopes. The solid lines indicate the model slope without any interaction effect (i.e. when all other values are held at their means).  The dashed and dotted lines on the conductivity graph indicate the slopes when elevation is low and high, respectively .  Analy -ses were performed on transformed data.  Open symbols indicate Interior region sites, filled symbols indicate Coast sites.  Data from High Falls, the site with high drift biomass, was ex -cluded from the final model due to high influence and is therefore not shown on these graphs.50 100 150 200 250conductivity( ? S)1520100200 400 600 800elevation (m)1520100terrestrial drift biomass (mgm3)26terrestrial invertebrates and was therefore excluded.  The best model for terrestrial drift included elevation, conductivity, and the interaction between elevation and conductivity (Figure 2.8) (r 2  of 0.31, F 3,24  = 3.62, p = 0.03).  Simple slopes analysis of the interaction between conductivity and elevation shows that conductivity has a neutral or negative ef fect on terrestrial drift at low elevation and a positive effect at high elevation (coefficient of -0.39 at elevation = -249, 0.16 at average elevation and 0.71 at elevation = 249).   These final models all had tolerance values above 0.1, Cook?s Distance values below 1, and normally distributed residualsDiscussionDay-to-day variation of daytime samples is much less than spatial variation between and within sites (that is, the difference in the average drift biomass between days is less than the difference between samples taken on one day), suggesting that sampling effort should be focused on collecting more samples from each site, rather than more visits to each site.  This result addresses our first objective: to determine whether drift must be measured on multiple days.  Shearer et al. (2002) found that day-to-day and within site variation were similar for 24 hour samples, whereas this study found greater within site variation than day-to-day variation.  However, Shearer et al. (2002) came to the same conclusion that sampling effort should be concentrated on one day when the objective is to compare between sites.  The daytime-only sampling used in this study probably resulted in lower variability than in Shearer et al. (2002) as only the daytime drift was captured, rather than the more variable night peaks.  These results contrast with Williams (1980) and Gibson and Galbraith (Gibson and Galbraith 1975), who found high day-to-day variability, although the relative magnitudes of day-to-day, between and within site variability were not measured. Williams (1980) used 24 h samples and Gibson and Galbraith (Gibson and Galbraith 1975) used 48 h samples, capturing two successive nights of peaks, potentially increasing between sample variation.  None of these studies reported controlling for rain events as we did in this study , possibly contributing to high day-to-day variation.27The majority of invertebrate drift in this study was of aquatic origin, and aquatic invertebrate drift had lower day-to-day variability than terrestrial drift (1% and 8% respectively , Figure 2.5).  Other studies have found that terrestrial invertebrates constitute the majority of invertebrate drift (eg. Nakano et al. 1999), but also found greater variability in terrestrial invertebrates (Kawaguchi and Nakano 2001, Nakano et al. 1999).   The greater day-to-day variability of terrestrial invertebrates may account for the higher temporal variability found in other studies compared to this study. In streams where terrestrial invertebrates contribute more to drift samples, the relatively stable levels of aquatic invertebrates may be masked by variation in terrestrial inputs.  The higher temporal variability of terrestrial invertebrates may be a sampling artefact due to the lower number and larger individual biomass of terrestrial prey ? a single large beetle or caterpillar is much larger than the majority of aquatic invertebrates caught in drift nets ? or temporal invertebrates may be influenced by random events such as high wind.  Our samples collected on windy days captured many leaves, and these leaves could have been vectors for the large numbers of Hemipterans (aphids and leaf hoppers) found in these samples.The absence of a strong seasonal effect in this study may be caused by sampling protocol, but this result was well supported by the data.  Other studies found greatest invertebrate density in midsummer (Elliott 2002, Shearer et al. 2002) and in late spring (Hansen and Closs 2007, Rinc?n and Lob?n-Cervi? 1997).  This study used small sample sizes and had large residual variation; these effects may have masked a seasonal effect.  However, the Akaike weights for the model with month effect but without region effect indicate that it has low likelihood.  Adding month to the random factors only model decreased the likelihood of the random factors model by 25, 32 and 2 times for the total, aquatic and terrestrial invertebrates respectively (Table 2.1), indicating strong support for dropping month from the model.  The absence of a seasonal effect indicates that daytime samples collected throughout the summer months can be compared to each other, lengthening the sampling window.Total drift biomass was most influenced by habitat factors that control food availability.  Lower invertebrate densities were found in areas with more canopy cover . It is also possible that the negative influence of canopy cover was related to bankfull width; wider streams 28(with less canopy cover) tended to have greater densities of drifting invertebrates, probably due to higher relative discharge.  However, the models that included bankfull width instead of canopy cover were less likely according to their AIC c values (Table 2.3), supporting the interpretation that direct shading effects are a more probable cause of the negative relationship with invertebrate densities. Alder cover has a positive influence on invertebrate drift density, indicating that detritus is a major source of food for aquatic invertebrates as found by Wipfli and Musselwhite (2004) and Rosenfeld (2000), or a direct source for terrestrial invertebrates (Mason and MacDonald 1982), or both.  Conductivity also has a positive influence on total invertebrate density, suggesting that the periphyton is a major food source for aquatic invertebrates, or that conductivity and terrestrial invertebrate drift biomass are related.  Shearer et al. (2003) found a density dependent relationship between chlorophyll a concentration and invertebrate drift, indicating that greater periphyton abundance can support more invertebrates and therefore higher baseline drift levels as found in this study .The significant negative interaction between conductivity and alder density is most likely a result of the accelerating impact of conductivity on decomposition of detrital food sources (Greenwood et al. 2007).  Both alder and conductivity have positive coefficients when the other is low (Table 2.4); when stream nutrient levels are low there is food available from the detritus-based food chain and when canopy levels are low there is abundant light for the periphyton-based chain.  However, when alder densities are high there is less light available for periphyton.  Some periphyton species, such as diatoms, are able to compensate for low light levels and produce nearly the same biomass as in full light (Rier et al. 2006), while others, such as chlorophytes, require more light (Richardson et al. 1983).   Therefore when light is restricted there can be a drop in primary productivity, depending on what species of algae dominate the periphyton.  In these streams the detritus-based food chain becomes more important.  A large proportion of the detritus in streams is deposited in the fall, and consumed throughout the year; when nutrient levels (conductivity) are high decomposition rates increase (Greenwood et al. 2007), due to the nutrient needs of decomposers such as bacteria, and the detritus may not last through the year.  Invertebrates dependent on detritus will not be able to sustain high densities if the detritus supply is too low due to decomposition.  For these reasons the negative interaction between conductivity and alder density is not surprising.29Aquatic invertebrate biomass was most correlated with physical habitat variables, not the productivity variables that impacted total invertebrates.  It is surprising that productivity variables were not included in the aquatic model; the physical variables may simply overwhelm the productivity variables when only aquatic invertebrates are considered.  However, conductivity was included in a candidate model (Table 2.3); its exclusion may be primarily related to low sample size.  Region was included in the final model as a factor variable and explained 12% of the total variation of aquatic invertebrates (T able 2.4).  Invertebrate density was consistently higher in the Interior than the Coast.   The inclusion of region in the final model indicates that the habitat factor(s) that caused greater density in the Interior were not measured in this study.  Bankfull width had a positive but non-significant influence on aquatic invertebrate drift density, as did gradient averaged over all of the sites.  Water velocity rises with gradient and bankfull width; greater water velocity results in longer invertebrate suspension distances, and greater drift densities because the time that each invertebrate spends in the drift increases (Elliott 2002).   The negative interaction between region and gradient shows that the slope for gradient is significantly higher in the Coast region than in the Interior, where it is actually slightly negative (Figure 2.7).  These results contrast with other examinations of invertebrate biomass that have found that food availability has the greatest impact (Richardson 1993, Rosenfeld 2000,  Wipfli and Musselwhite 2004).  However, flow has been shown to influence invertebrate drift biomass as well (Elliott 2002, Harvey et al. 2006).  Further research into the relationship between benthic invertebrate biomass, water velocity, and drift invertebrate biomass could clarify the sources of variation of aquatic invertebrate biomass.Surprisingly, terrestrial invertebrate drift biomass was not influenced by canopy cover or composition, but rather by elevation and conductivity.  The relationship between canopy cover and terrestrial invertebrate input to streams is well studied but inconsistent (positive effect: Mason and MacDonald 1982, no effect: Wipfli , Wipfli and Musselwhite 2004).  The relationship between conductivity and terrestrial invertebrate density is probably indirect. Riparian vegetation composition may influence conductivity as well as the input of terrestrial invertebrates to the stream.  Alternatively, water conductivity and terrestrial 30invertebrates may both be influenced by a factor that was not measured in this study such as underlying geology.  Site elevation was probably included in the final model because of the significant interaction effect with conductivity.  At low elevations conductivity has a negative coefficient, but at average and high elevations the coefficient is positive.   The temporal variation component of this study showed that terrestrial invertebrates had more day to day variability than aquatic invertebrates; this variability makes it more difficult to determine what spatial factors influence terrestrial invertebrate density, as there is more unexplained variation than for total and aquatic invertebrates.  Physical habitat features that are not usually measured for stream ecology studies such as average wind speed may influence terrestrial invertebrate drift density.The coefficients of determination for the three final models were relatively low , but within the typical range for habitat capacity models (r2  for the total, aquatic, and terrestrial drift were 0.28, 0.44, and 0.31, respectively).  Multiple regression habitat capacity models often yield similar coefficients of determination for other species (e.g. Davies 1989, Scarnecchia and Bergersen 1987).  The model for total drift includes taxonomically diverse invertebrates with different habitat preferences, so the low explanatory power is not surprising.  The taxa were divided into aquatic and terrestrial types, each of which is less taxonomically diverse than all taxa combined.  This decreased diversity may have increased the predictive power for aquatic invertebrates but not for terrestrial.  Aquatic and terrestrial biomass were affected by different habitat variables as well, and showed different levels of day-to-day variability.  The terrestrial model did not have increased predictive ability like that of the aquatic model, possibly because of the greater day-to-day variability found in the temporal study .  Nevertheless, these models provide some insight into the processes that affect drift biomass and suggest specific habitat factors that merit further research.Measurable habitat factors that influence productivity and water velocity affect the abundance of juvenile salmonid prey.  As prey abundance is positively correlated with growth and survival (Imre et al. 2004, Nielsen 1992, Nislow et al. 1998, Rosenfeld et al. 2005) it is an important consideration for juvenile salmonid habitat managers and fisheries scientists.  Many of the habitat factors measured in this study are also used in salmonid habitat capacity 31models (eg. Davies 1989, Scarnecchia and Bergersen 1987) and habitat assessment protocols (Moore et al. 1997).  These habitat factors impact juvenile salmonids directly and indirectly their impact on prey abundance.  As this study shows, the impact on prey abundance is complex, involving interactions between habitat factors.  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Hydrobiologia 520: 153-163.36Chapter 3: Influence of physical and biological habitat factors on juvenile salmonid abundance1IntroductionAccurate population size estimation is fundamental to species management, from conservation of endangered species? habitat to harvest of exploited species.  Models that predict abundance based on habitat characteristics serve two primary purposes in population management: they improve understanding of species? habitat requirements, and they can be a more cost-effective way to estimate abundance than direct field measurement.  Habitat models are often applied to identify high quality habitat for protection, a primary objective of many conservation projects (e.g. Clark et al. 1993). They are also useful indicators of potential habitat capacity when fish abundance is depleted due to over -harvest.  Designing habitat restoration projects and monitoring the effects of resource extraction on habitat quality can be addressed using predictive models as well (Binns and Eiserman 1979).  Such models have been developed for a variety of taxa including mammals (Dor gel?h 2006, McAlpine et al. 2006), amphibians (Deno?l and Lehmann 2006), birds (Gavashelishvili et al. 2006, Zharikov et al. 2006), and freshwater fish (see Fausch et al. 1988).Freshwater fish such as salmonids (Family Salmonidae) are well suited to habitat modeling due to their restricted habitat in lakes and streams and the difficulty of obtaining direct population estimates.  However, they are often regionally specific (Bowlby and Roff 1986) ? a model developed in one location is rarely a good predictor in another ? although similar variables are included in most models. Variables used in regression analysis can be divided into those that are related to the physical structure of available habitat, and those that are correlated with the abundance or productivity of prey. For example, physical habitat variables like channel width, percent pool habitat, and LWD abundance are commonly used as correlates of habitat structure.  Stream gradient and sediment size (see Coulombe-Pontbriand and LaPointe 2004, Davies 1989, Rosenfeld et al. 2000, Sharma and Hilborn 1  A version of this chapter will be submitted for publication:Nicol, SD, JS Rosenfeld and JB Shurin.  Influence of physical and biological habitat factors on juvenile salmonid abundance.372001) are additional variables that correlate with channel size and velocity , which can be expected to influence habitat availability for fish.  Invertebrate drift is the most direct index of prey abundance for drift-feeding fishes. Invertebrate prey are delivered to fish by two primary mechanisms: in situ invertebrate production based on algal- or detrital-based food chains (Vannote et al. 1980), or input of terrestrial invertebrates from the riparian zone (Cada et al. 1987).  Canopy cover and composition, organic sediment, temperature, and water conductivity (see Binns and Eiserman 1979, Johansen et al. 2005, Scarnecchia and Ber gersen 1987) affect the abundance of invertebrate prey delivered from terrestrial and aquatic sources.Invertebrate drift is conspicuously absent from the majority of juvenile salmonid habitat models (Fausch et al. 1988) even though drifting aquatic and terrestrial invertebrates are a primary food source for juvenile salmonids (Wipfli 1997), and elevated drift contributes to increased juvenile salmonid growth (Imre et al. 2004, Nielsen 1992, Rosenfeld et al. 2005, Slaney 1972), density (Slaney 1972) and survival (Imre et al. 2004, Nislow et al. 1998).  Benthic chlorophyll-a, riparian canopy cover, water column nutrients (or surrogates like conductivity) are often used as expected correlates of the abundance of prey for drift-feeding fish. Benthic invertebrate abundance is sometimes used in models as a more direct measure of food availability (eg. Bowlby and Roff 1986, Jowett 1992).  Invertebrate drift should be a more direct measurement of food availability, as benthic density and drift biomass are not strongly correlated (Shearer et al. 2003).  Including invertebrate drift biomass as a candidate variable in habitat models may therefore improve the predictive ability of habitat capacity models and help determine the degree to which food availability limits salmonid abundance.The predictive models or variables that biologists include in regression analysis are usually based on expert opinion and professional judgement as to which are most ef fective and practical to collect.  However, models are rarely selected in an explicit cost-benefit framework, where the benefit is the proportion of variance in fish abundance explained by a model (or variable) and the cost is the time and effort required to collect the associated data in the field or laboratory  Assessing the cost-effectiveness of habitat models and their terms (the habitat variables) should allow fisheries scientists to choose which habitat models and component variables maximize predictive power.  This improved efficiency is important for 38fisheries scientists with limited resources (i.e. personnel, funding), and should allow scientists to make more informed decisions about the optimal variables to include in habitat models. For example, a variable with high predictive ability that takes more time to measure than fish abundance is clearly less efficient than a direct measurement. The objectives of this study were 1) to develop reach-scale predictive models for juvenile salmonid abundance in southwestern BC using physical and biological habitat factors, 2) to determine whether habitat variables differ between the Coast and Interior regions, 3) to determine the contribution of invertebrate drift to the relative predictive ability of models, and 4) to do a cost-benefit analysis for predictive models and their component variables.  MethodsStudy SitesFifty stream sites in the Coast and Interior regions of British Columbia were sampled for drift abundance and habitat features in the summers of 2005 or 2006 (Figure 3.1).  Sites were chosen to span a wide range of productivities and stream sizes (1.3 ? 33.1 m channel width; Appendix 2).  Every effort was made to find sites with reliable accounts of spawning adults to ensure that sites would be saturated with juveniles and rearing habitat would be near carrying capacity.  Sample reach lengths were 20-30 bankfull widths (80-250 m) with no major tributaries or changes in valley form, vegetation, or land use (as in Moore et al. 1997).Fish densityData on salmonid density were either collected in the field (n = 19) or from existing multi-pass removal data sets (Hagen 2005, Hanson 2005, Morgan 2001, Rosenfeld et al. 2000, Tschaplinski 2006).  Regression analysis showed that the year fish were collected had no significant impact on fish density (fish/m 2 , F 1,48  = 1.9, p = 0.17) or biomass (g wet mass/m 2 , F1,45  = 0.9, p = 0.35). For streams without existing fish abundance data, a subset of channel units were triple- or double-pass depletion electrofished in August of 2006 or 2007.  Removal 39data were used to calculate an estimate of fish abundance as described by Schnute (1983).  The wetted area electrofished was measured for each site to determine the density of fish (fish per m2  wetted area).Biomass (g wet mass per m 2  wetted area) was calculated by multiplying the average wet mass of fish at a site by the estimated number of fish present.  When only fish lengths were available biomass was estimated using the following power functions generated from the data set of fish lengths and masses collected at all sites:YOY: log(mass) = -11.73 + 3.07*log(length)  ......... (F1,3380  = 1.1 x 10 5, p = 2.2 x 10 -16 )rainbow: log(mass) = -11.54 + 3.04*log(length) ....... (F1,1065  = 4.1 x 10 4, p = 2.2 x 10 -16 )coho: log(mass) = -12.05 + 3.14*log(length)  ......... (F1,1311  = 2.2 x 10 4, p = 2.2 x 10 -16 )Figure 3.1: Locations of study streams in south western British Columbia, Canada.  Some sites are close enough together that they share a point.  Number labels refer to row labels in Appendix 2.  Universal Transverse Mercator coordinates are also available in Appendix 2.40To control for seasonal effects on fish size associated with different collection dates (July 12- September 22), we tested for a relationship between calendar day and average fish biomass.  There was no relationship for YOY or coho, so biomass on the day of sampling was used in regression analysis.  However, there was a significant day effect for rainbow trout (F 1,26  = 5.7, p = 0.02).  Rainbow trout data were corrected to September 1st biomass by multiplying the number of days between the fishing date and September 1st by 0.013 (the slope of the mass-day relationship) and adding this correction to the measured mass of juvenile rainbow trout.  This method maintained the existing variation in the data while correcting for the day ef fect.Invertebrate Drift SamplingInvertebrate drift samples were collected at the upstream end of pools immediately below riffles to standardize habitat effects and to measure the quantity of drift that a dominant fish would experience at the head of a pool.  At sites with no suitable pools, nets were placed in riffles (approximately one third of the sites).  Samples were collected with a 1 m long, 250 mm mesh net with an opening 0.2 m square for a duration of 180-300 minutes.   The depth and velocity of the water in the mouth of the net were measured in order to calculate the volume of water sampled, and thereby the biomass of drifting invertebrates per m 3.  We sampled drift in the daytime rather than overnight because daytime drift abundance is more uniform (Waters 1962), and most juvenile salmonids are visual predators that feed during daylight.  Arguably, daytime drift samples more accurately represent prey abundance for juvenile salmonids than 24 hour drift samples where biomass is dominated by nocturnal peaks (Waters 1962).  Drift samples were collected at least two hours after sunrise and two hours before sunset to reduce daily variability in light intensity , and were not collected during or after rain events until flow had returned to pre-flood levels.  Samples were preserved in 5% formalin and returned to the laboratory for sorting.Invertebrates were sorted from detritus at 10X magnification in the laboratory and preserved in 70% ethanol for later identification and measurement using a digitizer (Rof f and Hopcroft 1986).  Invertebrates were identified to Order with some exceptions: non-arthropods such as nematodes were identified to Phylum, and common arthropods such as chironomids were 41identified to sub-Family.  Each taxon was categorized as aquatic or terrestrial in origin.  Adult insects with aquatic larvae, such as Nematocera (Diptera), were counted as aquatic in origin.  The dry mass (mg) of invertebrates in each sample was calculated using published taxa-specific length-mass regressions (Benke et al. 1999, McCauley 1984 ; M.  Wipfli lab pers. comm., Meyer 1989, Sabo et al. 2002, Sample et al. 1993, Smock 1980).Habitat MeasurementsHabitat surveys were conducted at all 50 sites (see Moore et al. 1997) to collect information on reach-level factors that may influence juvenile salmonid density .  Average summer temperature was estimated using ClimateBC V.3.2 (Wang et al. 2006) and UTM coordinates and elevation measurements for each site.  Conductivity was measured once at each site.   The intermediate dimension (middle length of the x, y, and z axes) of the five largest particles likely to have been moved by the peak flow (Hogan 1996), percent canopy cover , and canopy composition (percent cover of conifer, alder [Alnus rubra], and other deciduous trees) were measured at 3 to 5 locations spaced throughout each site.  The length and width of each channel unit (riffle, pool, glide, run, and cascade) were measured, including channel units in secondary and backwater channels.  These measurements were used to calculate site mean bankfull width and the percent area of each site by channel unit type.   The percent substrate composition ? fines, gravel, cobbles, boulders, bedrock, and particulate or ganic matter (POM) ? was estimated for each channel unit, as were gradient and the percent of the channel unit with protective cover.  Measurements made in each channel unit were weighted by channel unit area to calculate average values for each site.   All large woody debris (LWD; greater than 10cm diameter or 1m length) at each site were counted and identified as pool-forming or non-pool forming.Statistical Analysis: Habitat Capacity ModelingHabitat capacity models were developed using ordinary least squares multiple regression with R 2.6.0. Models were fit for young of the year salmonids (all taxa combined), and for 42YOY rainbow trout ( Oncorhynchus mykiss) and YOY coho salmon ( O. kisutch) seperately.  Models were fit using log-transformed density (number/m 2  wetted area) and log-transformed biomass (grams wet mass/m 2  wetted area) as response variables. All variables were centred to control for colinearity between model main effects and interaction effects (Quinn and Keough 2002).  In order to apply these models to new data sets the new data must be centered using the same constants as this study (i.e. a constant must be added or subtracted to the data so that the mean equals zero; see Appendix 1 for variable means).  Correlation between the habitat variables was examined to ensure that the subset selected for regression were not correlated (r < 0.7), as strongly correlated variables make it difficult or impossible to perform the matrix inversion required to calculate regression coefficients (Zar 1999).  Fourteen non-correlated Category VariableProductivity organic substrate (proportion)  orgconductivity ( m S)  condaverage summer temperature ( ? C)  tempalder cover (proportion)  aldercanopy cover (proportion)  canopy *invertebrate drift (dry mg m-3)  drift !Physical Habitatlargest particle (cm)  lppool area (proportion)  poolriffle (proportion)  rifflegravel substrate (proportion)  gravgradient (%)  gradbankfull width (m)  wb *percent cover for fish  coverLWD pieces (m -1  of stream)  lwdpool forming LWD pieces (m -1 )  lwd_pfRegional Effectsregion ?  Interior or Coast regionelevation (m)  elev! drift was not included in the starting models* only one of can and wb was included in a starting modelTable 3.1: Variables included in the starting models, with units, and abbreviations in bold. Variables marked with (*) are correlated with each other so only one at a time was used in any starting model.  The variable marked with (!), drift, was not included in the starting mod -els for the full model selection, but was included in the starting models for the subset model selection.43variables were chosen to begin model selection (Table 2.1). Canopy cover and bankfull width were correlated with each other (r = 0.7), so two starting models were used: one with the 14 variables and canopy cover, and one with the 14 variables and bankfull width for a total of 15 variables.Two-way interaction terms were added to the starting models based on improved Akaike Information Criterion (AIC) values.  Terms were then removed from the starting models in a stepwise fashion until removing terms no longer improved AIC values.  The models with the lowest AIC values were selected for comparison. The models were compared using Akaike weights based on the Second-Order Information Criterion (AIC c for small samples).  Weights were used to calculate evidence ratios to select the best fit model (Burnham and  Anderson 2002).  Tolerance, Cook?s Distance, and normality of the residuals were checked for all selected models.  Interactions included in final models were investigated using simple slopes (Quinn and Keough 2002). The simple slope of a variable is its slope when the variable that it interacts with is held constant.  Simple slopes were calculated at the mean of the other variable, and one standard deviation above and below the mean (low , average and high values of the interacting variable).  Simple slope calculations allow greater understanding of trade-offs and other interactions by revealing changes in the sign or magnitude of the target variable coefficient, at different levels of the interacting variable.A best model was determined for each fish species ? young-of-the-year of all species combined (YOY), of rainbow only (rainbow) and of coho only (coho) ? for density and biomass separately all of the available sites (the full habitat model); not all sites had both coho and rainbow, so the sample size was lower for these models.  Because invertebrate drift was collected at only 30 of 50 sites, best models were then run using the subset of sites that also had invertebrate drift measurements (the  habitat subset model).  To determine the benefit of including invertebrate drift in the best model, invertebrate drift was then added to the subset habitat model (the  habitat + drift subset model) and the improvement in model fit assessed using evidence ratios.  Finally, models were developed using only the subset of drift sites and including drift in the starting models (the  reduced habitat + drift model).  The main function of this last analysis was to determine if the drift term was dropped during 44model selection or included in the final model.  If drift was dropped then the resulting habitat model was considered to be inferior to the full habitat model (based on all available data), because the reduced + drift model was based on a subset of the data.  In this case only the results of the full habitat models were presented.Region (Coast or Interior), was included in the starting models to determine if there was a region effect that was not captured by the habitat variables.  Habitat measurements from the two regions were compared using two-sample Student?s t-tests to assess regional differences in average habitat characteristics.Statistical Analysis: Cost-Benefit AssessmentTo determine which variables explained the most variance in fish abundance for the least effort, a benefit:cost ratio was calculated for each variable included in the full habitat model, for drift from the habitat + drift subset model, and for the full habitat models themselves.  Model benefit was the full or partial r 2  of the model or variable, and cost was the sum of the time (in minutes) required to assess all of the variables in the model.   The partial r 2 , a measure of a variable?s individual contribution to the predictive ability of the model, was calculated for each variable included in a final model.Field effort measurements were made at 18 of the 50 sites.  We used time (effort) as the metric of cost since labour is the primary cost for all of the variables measured.  The cost for each variable was the total time at the site required to measure a variable (including any subsequent laboratory processing if needed).  Field activities were timed each summer after a training period, so that all estimates of field time are based on experienced field staf f.  All timed field work was done with teams of two people; if the two field staf f worked together to make a measurement (e.g. channel unit length) then the number of minutes for that activity were doubled.  We did not include travel time to the site because it made an equal contribution to all variables, and because it would be necessary to travel to the site in order to estimate fish abundance directly.  However, at more remote sites with greater travel time, the relative cost of time spent on site is lower, and therefore the benefit of reduced field time 45is lower than at sites with low travel time; this trade-off was not addressed in this cost-benefit analysis.ResultsStudy SitesInvertebrate drift and conductivity (a correlate of nutrient levels) were higher in the Interior than the Coast whereas total canopy cover and alder cover were higher in the Coast (T able 3.2),  suggesting higher prey availability at Interior sites compared to the more heavily shaded, nutrient-poor Coast.  Habitat in the Coast was generally more structured with greater pool area, gravel substrate, and large woody debris.  Despite the greater number of LWD pieces in the Coast region, there was no difference in the amount of pool-forming LWD between regions (0.007 pieces/m in the Coast, 0.004 pieces/m in the Interior; t = 1.0, p = 0.3), indicating that a higher proportion of Interior LWD pieces function in pool-formation.  Interior sites also had significantly higher elevation (616 m compared to 102 m in the Coast; t = -10.2, p = 5.2 x 10 -12 ).  Density of young-of-the-year (YOY) salmonids averaged 1.0 ? 0.93 (standard deviation) fish per m 2  of wetted habitat.  Six salmonid species were collected: coho salmon, rainbow trout, cutthroat trout ( O. clarki), chinook salmon ( O. tshawytscha), bull trout ( Salvelinus confluentus ) and mountain whitefish ( Prosopium williamsoni).  Rainbow trout were present at 31 sites (Appendix 1) at an average density of 0.47 ? 0.39 fish/m 2 .  Coho salmon were present at 39 sites at an average density of 0.59 ? 0.73 fish/m 2 .  Fish biomass (g/m 2 ) showed the same pattern; YOY had the greatest biomass (1.88 ? 2.01 g/m 2 ), followed by coho (1.57 ? 2.05 g/m 2 ), and rainbow (0.56 ? 0.49 g/m 2 ).  Mean fish densities and biomasses did not differ between regions (Table 3.2).Habitat Capacity ModelsAll models included terms relating to both stream productivity and physical structure, and 46Coast mean Interior mean Welch?s t pFish AbundanceYOY (fish/m 3)   1.10   (0.90)     0.84    (0.97)  0.92   0.36YOY (g/m 3)   1.95   (1.38)     1.76    (2.95)  0.24   0.81rainbow (fish/m 3)   0.47   (0.42)     0.47    (0.39) -0.05   0.96rainbow (g/m 3)   0.51   (0.48)     0.60    (0.51) -0.5   0.62coho (fish/m 3)   0.60   (0.54)     0.57    (1.04)  0.1   0.92coho (g/m 3)   1.44   (1.01)     1.87    (3.53) -0.4   0.7Productivityorg   0.12   (0.13)     0.07    (0.14)  1.11   0.27cond 66.2     (37.8) 121.1      (83.7) -2.69   0.01temp 15.5     (0.93)   15.9      (0.95) -1.44   0.16alder   0.23   (0.24)     0.09    (0.12)  2.64   0.01canopy   0.44   (0.26)     0.19    (0.20)  3.78 <0.01drift 43.7     (34.2)   92.4      (74.5) -2.11   0.05Physical Structurelp 23.9     (26.9)   37.4      (27.9) -1.63   0.11riffle   0.32   (0.23)     0.29    (0.26)  0.41   0.69grav   0.34   (0.23)     0.23    (0.15)  2.02   0.05grad   1.70   (1.60)     2.09    (1.04) -1.03   0.31wb   7.68   (5.69)   10.5      (7.54) -1.38   0.18pool   0.20   (0.18)     0.07    (0.11)  3.1 <0.01cover   0.11   (0.06)     0.09    (0.05)  1.47   0.15lwd   0.08   (0.08)     0.02    (0.03)  3.76 < 0.01lwd_pf   0.01   (0.01)     0.004  (0.01)  1.04   0.3Regional Effectselev 102 606 -9.85 <0.01Table 3.2: Salmonid abundance and habitat variable means (SD) in the Coast and Interior regions and the results of Welch?s t-test comparing the means.  Significant p-values are in bold, and show several significant differences among the habitat variables, but none for the salmonid abundances.  Abbreviations are listed in Table 3.1.47log(YOY density) = 0.3 + 0.01(lp) + 2.6(pool) + 0.4(temp) ? 0.003(cond) + 0.0003(elev) ? 3.1(cover) + 1.6(grav) + 2.9(org) + 0.03(grad) + 36.6(lwd_pf) + 0.01(pool)(elev) ? 0.0007(elev)(grad) + 54.0(cover)(org) + 1.52(grav)(grad)r2 = 0.75Coefficients SS df F P partial r2lp   0.01 2.02 1   6.65    0.01 0.17pool   2.56 2.55 1   8.42    0.01 0.21temp   0.44 3.61 1 11.9 < 0.01 0.27cond  -0.003 0.88 1   2.90    0.10 0.08elev <0.01 0.00 1   0.01    0.94 0cover  -3.13 0.49 1   1.60    0.21 0.05grav   1.55 0.2 1   0.67    0.42 0.02org   2.86 3.46 1 11.42 < 0.01 0.26grad   0.03 1.00 1   3.30    0.08 0.09lwd_pf 36.56 4.35 1 14.34 < 0.01 0.31pool:elev   0.01 2.6 1   8.59    0.01 0.21elev:grad  -0.001 1.83 1   6.03    0.02 0.16cover:org 53.97 4.24 1 13.97 < 0.01 0.3grav:grad   1.52 2.26 1   7.44    0.01 0.19residuals 9.71 32log(YOY biomass) = 0.2 ? 0.003(lp) + 0.3(temp) ? 0.005(cond) ? 0.001(elev) ? 0.1(grad) ? 0.02(wb) ? 0.01(lp)(grad) + 0.001(cond)(wb)r2 = 0.65coefficients SS df F p partial r2lp  -0.003 0.34 1   0.76    0.39 0.02temp   0.27 1.95 1   4.34    0.04 0.11cond  -0.005 5.04 1 11.22 < 0.01 0.24elev  -0.001 3.28 1   7.31    0.01 0.17grad  -0.13 0.82 1   1.82    0.19 0.05wb  -0.02 6.31 1 14.04 < 0.01 0.29lp:grad  -0.01 4.00 1   8.89    0.01 0.2cond:wb   0.001 2.68 1   5.96    0.02 0.15residuals 15.73 35Table 3.3: Model coefficients, sums of squares (SS), degrees of freedom (df), F-, p-, and partial r2 values for YOY, rainbow and coho full models.  Significant p-values are in bold.  Model r2 values are listed following the equations.  Abbreviations are listed in Table 1.  Type II ANOVA results are presented.48log(rainbow density) = 0.7 + 0.03(lp) + 6.3(alder) + 0.01(cond) ? 17.1(cover) + 41.2(lwd) + 0.2(region) ? 5.2(canopy) ? 0.04(lp)(region)r2 = 0.83Coefficients SS df F P partial r2lp    0.03   1.73 1   5.7    0.03 0.23alder    6.3   8.11 1 26.71 < 0.01 0.58cond    0.01   7.16 1 23.58 < 0.01 0.55cover -17.09 10.3 1 33.92 < 0.01 0.64lwd  41.23 13.0 1 42.93 < 0.01 0.69region    0.16   0.26 1   0.85    0.37 0.04canopy   -5.22   9.17 1 30.19 < 0.01 0.61lp:region   -0.04   6.42 1 21.13 < 0.01 0.53residuals   5.77 19log(rainbow biomass) = 0.3 + 0.03(lp) + 6.2(alder) + 0.01(cond) ? 15(cover) + 33(lwd) + 0.7(region I) ? 4(can) ? 0.03(lp)(region I)r2 = 0.76coefficients SS df F p partial r2lp    0.03 2.92 1   7.41    0.01 0.3alder    6.17 8.03 1 20.39 < 0.01 0.55cond    0.01 1.8 1   4.56    0.05 0.21cover  -15.2 7.47 1 18.95 < 0.01 0.53lwd   32.65 7.18 1 18.23 < 0.01 0.52region    0.73 0.37 1   0.93    0.35 0.05can   -3.99 5.14 1 13.04 < 0.01 0.43lp:region   -0.03 4.19 1 10.62 < 0.01 0.38residuals 6.7 17Table 3.3: continued49log(coho density) = 0.3 ? 0.8(alder) ? 1.7(pool) ? 0.9(riffle) + 0.07(temp) ? 0.01(cond) + 0.001(elev) + 2.3(grav) + 1.0(org) + 0.04(grad) + 2.0(lwd) + 0.4(region) ? 0.002(elev)(grad) + 23.2(org)(region) ? 6.3(tavsm)(lwd)r2 = 0.92Coefficients SS df F P partial r2alder   -0.83   0.51 1   1.68    0.21 0.07pool   -1.68   1.25 1   4.13    0.05 0.15riffle   -0.92   1.18 1   3.89    0.06 0.14temp    0.07   0.67 1   2.22    0.15 0.09cond   -0.01   2.19 1   7.23    0.01 0.24elev    0.001   0.21 1   0.69    0.41 0.03grav    2.31   5.57 1 18.42 < 0.01 0.44org    1.04   1.52 1   5.03    0.03 0.18grad    0.04   1.56 1   5.15    0.03 0.18lwd    2.0   0.53 1   1.76    0.2 0.07region    0.39   0.12 1   0.4    0.54 0.02elev:grad  -0.002   4.33 1 14.33 < 0.01 0.38org:region 23.24 11.23 1 37.14 < 0.01 0.62tavsm:lwd  -6.33   4.57 1 15.11 < 0.01 0.4residuals   6.96 23log(coho biomass) = ? 0.1 ? 1.4(riffle) ? 0.003(elev) + 2.2(grav) ? 0.7(or g) ? 0.1(grad) + 1.4(region I) ? 0.002(elev)(grad) + 26(org)(region I)r2 = 0.81coefficients SS df F p partial r2riffle  -1.39   2.64 1   5.25    0.03 0.16elev  -0.003   1.67 1   3.33    0.08 0.11grav   2.23   5.9 1 11.74 < 0.01 0.3org  -0.66   0.17 1   0.33    0.57 0.01grad  -0.1   2.02 1   4.02    0.06 0.13region   1.38   2.15 1   4.28    0.05 0.14elev:grad  -0.002   2.92 1   5.82    0.02 0.18org:region 26.2 17.22 1 34.3 < 0.01 0.56residuals 13.56 27Table 3.3: continued.50passed Cook?s Distance, tolerance and normality tests.  The full habitat model for YOY density explained a large proportion of the variance in fish abundance (Table 3.3; r 2  = 0.75, F8,39  = 7.1, p < 0.0001).  The biomass full habitat model had a poorer fit (r 2  = 0.57) and is not considered in detail since it shared most terms with the density model; it is not discussed further here.  The interaction terms in the density model suggested that intermediate gradients are associated with the greatest salmonid abundances (gradient and gravel for the density model).  Simple slopes analysis shows that the slope of gradient was positive (0.6) when there was a lot of gravel sediment (one standard deviation above average, gravel SD = 0.2) and negative when there was very little gravel (-0.004).   The model also suggested that more complex habitats are associated with greater salmonid density (coefficients for pool-forming LWD, largest particles and pool area were all positive [Figure 3.2]).  Two interactions indicated regional effects: the pool-elevation interaction suggested that impact of pools was greater in the Interior (coefficient = 4.3) than the Coast (0.8), possibly because pool area was lower in the Interior (Table 3.2); the gravel-elevation interaction showed that the higher gradients (within the range of the data) were more favourable at low elevations in the Coast (elevation SD = 297) and negative at high elevations in the Interior (0.2 and -0.2, respectively).The density full habitat model for rainbow trout included productivity and physical habitat related variables and a regional effect (Table 3.3, Figure 3.3; r 2  = 0.83, F 8,19  = 11.5, p < 0.01).  The biomass model had the same terms as the density model, with the same signs and similar magnitudes, so only the density model is discussed here.  The rainbow trout density model included both alder and total canopy cover; while the alder coefficient was positive, the total canopy cover coefficient was negative (6.3 and -5.2).  The coefficient of conductivity was also positive (0.01), in contrast with the YOY model.  The model showed that rainbow abundance was greater in streams with more woody debris, but lower in streams with more cover from predators, indicating a mixed response to habitat complexity .  The impact of largest particle size was slightly negative in the Interior (-0.01), but positive in the Coast (0.03).The density full habitat model for coho salmon included more physical structure than 51Figure 3.2: Scatter plots of the key habitat factors included in the YOY density full model (average summer temperature, organic sediment, pool-forming LWD and pool area) against YOY density.  YOY density was log transformed for the analyses and is displayed on a log scale.  The solid lines indicate the full model regression best fit lines, not the simple regres -sion best fit lines.  Dashed and dotted lines indicate interaction ef fects: in the organic sedi -ment graph the dashed line is the slope of organic sediment when cover is high and the dotted line is the slope when cover is low.  The dashed and dotted lines in the pool area graph repre -sent the slopes at high and low elevations, respectively.  Filled symbols indicate Coast region sites, the open symbols indicate Interior region sites.14.0 15.0 16.0 17.00.10.20.51.02.0summer temperature ( ? C)0.0 0.1 0.2 0.3 0.4 0.50.10.20.51.02.0organic sediment0.00 0.01 0.02 0.03 0.040.10.20.51.02.0pool?forming LWD  m20.0 0.1 0.2 0.3 0.4 0.5 0.6 0.70.10.20.51.02.0stream pool areayoung?of?year salmonid density (fishm2)productivity variables, and both variable types showed regional dif ferences (Table 3.3, Figure 3.4; r 2  = 0.91, F 14,23  = 18.4, p < 0.01).  The biomass model only included a subset of the terms that were present in the density model so it is not discussed here.   The coho models shared many similarities with the YOY models, such as the negative gradient-elevation interaction and conductivity coefficients, and the positive average summer temperature coefficient.   The impact of stream structure (pool and riffle area, sediment size) on the density of coho salmon was complex and somewhat contradictory (riffle and pool both had negative coefficients but gravel had a positive coefficient).  There was a regional difference in the impact of organic sediment (Figure 3.5): the coefficient of organic sediment was much larger in the Interior (24.3) than the Coast (1.0), and there was also an interaction between average summer temperature and large woody debris.  The impact of an increase in temperature in areas with 52Figure 3.3: Scatter plots of the key habitat factors included in the rainbow density full model (alder cover, canopy cover, conductivity, largest particle size, LWD, and cover) against rain -bow density.  Rainbow density was log transformed for the analyses and is displayed on a log scale below.  The solid lines indicate the full model slopes, not the simple regression slopes.  The dashed and dotted lines in the largest particles graph indicate the slopes in the Coast and Interior regions, respectively.  The biomass model distributions are very similar to these and are therefore not presented.  Filled symbols indicate Coast region sites, the open symbols indicate Interior region sites.0.0 0.2 0.4 0.6 0.80.020.050.200.50canopy cover0.0 0.2 0.4 0.6 0.80.020.050.200.50alder canopy cover50 100 150 200 2500.020.050.200.50conductivity( ? S)20 40 60 800.020.050.200.50largest particle size (cm)0.00 0.02 0.04 0.06 0.08 0.10 0.120.020.050.200.50LWD  m20.00 0.05 0.10 0.15 0.200.020.050.200.50proportion of stream with coveryoung?of?year rainbow density (fishm2)abundant woody debris (LWD SD = 0.07) was negative, but was positive in areas with low LWD (-0.38 and 0.53). Comparisons of the habitat subset and habitat + drift subset models for the three species showed that adding drift to the full models did not significantly improve their predictive ability (Table 3.4).  Evidence ratios ranged from 3.8 for the YOY biomass model to 3.9 x 10 14  for the coho density model.  That is, the full habitat model run on the subset of sites with drift data ranged from 3.8 to 3.9 x 10 14  times more likely than the habitat + drift subset model with drift added.  The r 2  values of the habitat + drift subset models were not greatly improved compared to the full-subset models, despite the addition of an extra term (T able 3.4).  None of the models developed using only the subset of sites that included drift data (the reduced 53Figure 3.4: Scatter plots of the key habitat factors included in the coho density full model (organic sediment, gravel sediment, and riffle area) against coho density.  On the organic sedi -ment graph the filled symbols indicate Coast region sites, the open symbols indicate Interior region sites.  Coho density was log transformed for the analyses and is displayed on a log scale below.  The solid lines indicate the full model slopes, not the simple regression slopes.  The dashed and dotted lines in the organic sediment graph indicate the slopes in the Coast and Interior regions, respectively.  The dashed and dotted lines in the gradient graph indicate the slopes of gradient at high and low elevations.  The biomass model distributions are very similar to these and are therefore not presented.  Filled symbols indicate Coast region sites, the open symbols indicate Interior region sites.habitat + drift models) included drift as a term in the final model.   Thererfore, drift was never retained when it was present as a starting variable.  The subset models are not presented here.The cost-benefit analysis revealed that the full habitat models are more efficient (have a higher benefit:cost ratio [r 2 /minute]) than a typical triple-pass depletion estimate of salmonid density.  Typical triple pass depletion electrofishing takes approximately four person-hours (240 minutes) depending on stream size.  The benefit:cost ratio for a direct measurement of fish abundance is therefore 1/240 minutes, or 0.004 r 2 /min, which is lower than the full models and many of the individual variables in this study (Table 3.5).  The full habitat model ratios were all near 0.007 (Table 3.5).  The total cost in person-minutes was approximately 110 minutes.  Conductivity had the greatest individual term ratio at 0.64 for the rainbow density model, and drift the lowest (2.7 x 10 -5 for YOY density).  The ratio for drift was based on partial r2 values from the habitat + drift subset models, and was low (T able 3.5) because the cost was high for both field and laboratory time.  Setting and collecting three drift nets took approximately 45 person-minutes (this time does not include the time that the nets were filtering the water), and laboratory processing of a single sample could take more than 0.0 0.1 0.2 0.3 0.4 0.50.010.050.201.00organic sediment0.2 0.4 0.6 0.80.010.050.201.00gravel sediment0 1 2 3 40.010.050.201.00gradient (%)young?of?year coho density (fishm2)5410 hours.  The minimum time for laboratory processing was approximately 1 hour for one sample.  The minimum cost for one site was therefore 225 minutes (3 samples), much higher than the other individual variables (1-73 minutes).DiscussionOne of the more robust inferences that could be made from the model results is that juvenile salmonids are generally positively associated with habitat that is more structurally complex; LWD or pool-forming LWD had significantly positive relationships with all species.  More complex habitat may support more fish because increased visual isolation reduces predation and aggression (Dolinsek et al. 2007).  As the visual isolation of the fish increases, their territory size decreases (less competition for foraging space) and more fish can be supported in a habitat.  Several other inferences and conclusions can be drawn from these habitat models.  However, although our habitat models explained a high proportion of the variance in both density and biomass of juvenile salmonids, some of the regression coefficients and interactions were difficult to interpret in terms of underlying mechanisms.The negative influence of conductivity in the YOY and coho models was difficult to interpret.  Other studies have found positive relationships between conductivity and salmonid abundance (e.g. Coughlan et al. 2007, Scarnecchia and Bergersen 1987).  Conductivity was expected to be positively related to salmonid abundance due to the positive impact of conductivity on stream productivity (e.g. Ch?telat et al. 1999).  However , conductivity may also influence the timing of food availability by affecting decomposition rate.  Greenwood et al. (2007) found that decomposition rate was positively associated with conductivity , and suggested that in streams with high conductivity detritus decompose earlier in the year , reducing the food supply for detritivorous invertebrates, resulting in lower food availability by the end of the summer growing season.  The negative impact of conductivity may also have been due to unseen correlations with other variables.Habitat variables related to stream discharge suggest that the relationships between flow and density differ between combined YOY salmonids, rainbow and coho.  The YOY model 55Model AIC c ? j lik ? i ? max  /  ? i r2YOY fish/m 2full-subset 40.1 0 1 1 1 0.82full-subset-drift 58 17.9 1.30 x 10 -4 1.30 x 10 -4 7692 0.82YOY g/m 2full-subset 6.6 0 1 0.79 1 0.69full-subset-drift 9.29 2.69 0.26 0.21 3.76 0.74rainbow fish/m 2full-subset 25.4 0 1 1 1 0.85full-subset-drift 49.3 23.9 6.49 x 10 -6 6.49 x 10 -6 1.54 x 10 5 0.85rainbow g/m 2full-subset 46.15 0 1 1 1 0.77full-subset-drift 80.2 34.04 4.05 x 10 -8 4.05 x 10 -8 2.47 x 10 7 0.79coho fish/m 2full-subset 116 0 1 1 1 0.96full-subset-drift 183 67.21 2.54 x 10 -15 2.54 x 10 -15 3.94 x 10 14 0.97coho g/m 2full-subset 29.92 0 1 0.81 1 0.68full-subset-drift 32.86 2.94 0.23 0.19 4.26 0.76Table 3.4: Likelihood ratio calculations for the full-subset (best model constructed using the full data set, run on the subset of sites with invertebrate drift data) and full-subset-drift (same model with drift added) models for YOY, rainbow and coho.  AIC c is the Second-Order Infor -mation Criterion (used for small samples), ? i is the difference between the AIC c and the mini-mum AIC c, lik is the likelihood of the model given the data (e^(- ? * ? i)),  ? i  is the likelihood of the model divided by the sum of the likelihoods for the competing models (see Burnham and Anderson 2002 ch 2).  The  ? max  /  ? i ratio indicates how much more likely the best model (? max ) is compared to the candidate model ( ? i).56YOYYOYrainbowrainbowcohocohodensitybiomassdensitybiomasdensitybiomassCost  (min)benefit  (partial  or full r2)benefit  / cost     (r2/min)benefit (partial or  full r2)benefit  / cost    (r2/min)benefit  (partial  or full r2)benefit  / cost    (r2/min)benefit  (partial  or full r2)benefit /  cost (r2/min)benefit (partial  or full r2)benefit  / cost    (r2/min)benefit  (partial  or full r2)benefit  / cost     (r2/min)lp50.170.030.020.0040.230.050.3canopy50.610.120.430.09alder50.580.120.550.110.070.01cond10.080.080.240.240.640.640.210.210.240.24temp30.270.090.110.090.03elev3000.170.060.030.010.110.04grad9 + 640.090.0010.050.0010.180.0020.130.002cover9 + 640.050.0010.640.010.530.01org9 + 640.260.0040.180.0020.010grav9 + 640.020.0000.440.0060.30.004wb640.290.005riffle640.140.0020.160.003pool640.210.0030.150.002lwd290.690.230.520.020.070.002lwd_pf290.310.01full model1110.750.0070.650.0060.830.0080.760.0070.920.0080.810.008108 106drift2250.0062.7x 10-50.146.2x 10-40.0073.1x 10-50.062.7x 10-40.1245.5x 10-40.26 1.2x 10-3Table 3.5: Costs (person-minutes per site) and benefits (partial r2) values for the terms included in the full density and biomass models for YOY, rainbow and coho.  Abbreviations are listed in Table 3.1.  The partial r2 values for all of the variables except for drift come from the full models, whereas the partial r2 values for drift come from the full-subset-drift model run on the subset of sites with inver-tebrate drift data.57includes an interaction effect (gravel-gradient, Table 3.3) which suggests that intermediate water velocities are most beneficial for YOY abundance.  The rainbow trout models include only one variable related to flow: largest particle size.  The impact of largest particle size was positive in the Coast region but negative in the Interior region, probably due to dif ferences in the habitat preferences of Oncorhynchus mykiss in the Coast and Interior.  Steelhead trout, the anadromous variety of rainbow trout, are abundant in the Coast region, where the positive largest particle size slope indicated that rainbow (mostly juvenile steelhead) were associated with larger, more powerful streams with larger particle sizes like cobbles (as in Hill et al. 2006).The effect of discharge on coho density was unclear.  Pool area had a significant negative coefficient, contrasting with other studies (e.g. Nickelson et al. 1992, Roni and Quinn 2001).  The negative pool coefficient may be an anomaly specific to this data set.  Most research has examined coastal coho populations; coho may respond dif ferently to discharge in the Interior than the Coast for reasons unexamined in this study.  Further research on Interior coho populations may help determine whether this result was unique to this data set, or if there are regional differences in coho abundance patterns.The models for rainbow and coho showed regional interactions, indicating that habitat effects observed in one region cannot be generalized to others.  Sites sampled in the Interior and Coast regions differed in several key attributes ? notably, conductivity and drift were higher in the Interior, but alder cover, canopy cover, percent gravel substrate, percent pool, and LWD were higher in the Coast.  Salmonid abundance did not differ between regions, but region was involved in significant interactions with largest particle size in the rainbow model, and organic sediment in the coho model.  As discussed above, the regional interaction in the rainbow model may be related to a difference in habitat use between steelhead and rainbow juveniles.  The regional interaction in the coho model was unexpected and difficult to explain.  The effect may be a Type I Error, or may be related to unexplored differences in the food webs between regions (organic sediment is related to invertebrate production).  The presence of regional interactions in the models confirms observations by earlier authors (e.g. Fausch et al. 1988) that regional differences affect the predictive ability of habitat models.  Invertebrate drift abundance did not significantly improve the fit of the habitat + drift 58subset models using the Second-Order Information Criterion (AIC c) for small sample sizes (Burnham and Anderson 2002).  However, assessing model improvement following drift inclusion using the standard Akaike Information Criterion indicated that drift significantly improved the YOY and coho biomass models.  This suggests that drift may have a positive effect on salmonid biomass, but the effect was neither large nor consistent enough to be significant in our study.Invertebrate drift positively impacts juvenile salmonid growth (Imre et al. 2004, Nielsen 1992, Rosenfeld et al. 2005, Slaney 1972), survival (Imre et al. 2004, Nislow et al. 1998) and density (Imre et al. 2004, Slaney 1972), so it seems unlikely that prey abundance has no real effect on fish abundance.  The absence of invertebrate drift from the models could be an artifact of imprecise characterization of prey abundance (drift) at the dif ferent sites.  We characterized prey abundance using only three replicate invertebrate drift samples, collected on a single day from each site between June and September .  Other studies suggest a May peak in drift abundance (Slaney 1972), and that salmonid growth peaks in early spring (Bacon et al. 2005), before our drift sampling started, so we may not have accurately characterized differences in prey availability between sites.  Within-site variability of invertebrate drift is also high; other studies suggest collecting four or more samples (e.g. Allan and Russek 1985, Shearer et al. 2002), although drift sampling (Chapter 2) suggests that two replicate samples are sufficient to detect major differences between sites.  The final potential sampling issue that could have masked a relationship between drift and fish abundance was that salmonid populations may have been below carrying capacity at some sites due to low spawner abundance or high predator density.  However, the models showed strong relationships with between juvenile abundance and habitat variables other than drift, suggesting that there was generally sufficient recruitment for salmonid populations to be habitat limited.  Finally, drift may not be sufficiently variable among our sites to emerge as a significant factor constraining salmonid populations.Our initial expectation of a positive relationship between drift and juvenile salmon implicitly assumes bottom-up control of fish abundance, and minimal top-down ef fects of fish predation.  However, it is possible that fish deplete drift to varying degrees depending on site 59characteristics, thereby obscuring any relationship between drift biomass and fish abundance across sites.  A more realistic expectation would be a positive relationship between fish abundance and invertebrate production, rather than standing crop.  Depletion of drift biomass (standing crop) through predation may cause drift concentration to poorly represent drift production.  Salmonid populations have been shown to deplete stream invertebrate populations (Huryn 1998), so it is likely that some degree of top-down control occurred at some of our sites.  Dependence on prey sources other than drift would also weaken the relationship with fish abundance.  Although drift is the primary food source of stream salmonids, they can switch to benthic invertebrates when drift is unavailable (Nislow et al. 1998) or if forced through competition (Nielsen 1992).  Finally, the range of variation in prey abundance may be small relative to other drivers of habitat quality in our data set, so that it correlates weakly with fish abundance. The final objective of this study was to examine the cost effectiveness of different models and variables for assessing or monitoring habitat quality for juvenile salmonids.  Predictive models of habitat capacity in this sense are most useful when stream habitats are not saturated (e.g. because of habitat-independent factors like over -harvesting of adults), and fish density cannot be used as a direct index of habitat quality.  For streams that are heavily under-recruited, habitat-based estimates of capacity are more reliable indices of capacity than measured fish abundance (as well as potentially more cost-effective).  Deviation of observed fish abundance from that predicted by habitat capacity models is also a useful way of identifying site-specific management issues (e.g. low spawner abundance from over harvest, or poor spawner access).The cost-benefit analysis in this study showed that habitat models with strong predictive ability (r2  = 0.65 to 0.92) can be developed and the associated field data collected after less than two hours of field work, less time than typical triple-pass electrofishing.  Other fish collection methods, such as single-pass electrofishing (Jones and Stockwell 1995), require less field time than triple-pass electrofishing and may therefore have higher benefit:cost ratios than the full habitat models presented here.  Most of the habitat variables used in this study took small enough amounts of time to measure that they had high benefit:cost (r 2 /min) ratios.  60The exception was invertebrate drift, which took more field time and much more laboratory time than any of the other variables (Table 3.5).  While the benefit:cost ratios for individual variables calculated in this study are good indicators of their general predictive ability , their exact efficiency (r 2 /min) could be different when combined with other variables in regression.  That is, the partial r 2  values of model terms depend in part on what other terms are included in the model.  Therefore, while they provide a useful starting point for evaluating the cost-effectiveness of different predictive variables, they should be considered context dependent.This study suggests that physical habitat variables appear to be more robust predictors of fish abundance than direct measures of invertebrate drift.  Future research on stream food webs, particularly on the impact of salmonid density on benthic and drifting invertebrate abundance, and on the degree to which drift accurately represents prey availability for drift feeding fish, should improve understanding of the factors that limit the abundance of stream salmonids.  Further investigation of juvenile salmonid foraging behaviour (drift versus benthic feeding), especially in relation to the abundance of drift and benthic prey , could lead to more precise capacity models and improve the ability of habitat managers to consider the role of food availability during planning, monitoring, and restoration activities.  61ReferencesAllan, J.D., and Russek, E. 1985. The quantification of stream drift. Canadian Journal of Fisheries and Aquatic Science 42: 210-215.Bacon, P.J., Gurney, W.S.C., Jones, W., McLaren, I.S., and Youngson, A.F. 2005. Seasonal growth patterns of wild juvenile fish: partitioning variation among explanatory variables based on individual growth trajectories of Atlantic salmon ( Salmo salar) parr. Journal of Animal Ecology 74: 1-11.Benke, A.C., Huryn, A.D., Smock, L.A., and Wallace, J.B. 1999. Length-mass relationships for freshwater macroinvertebrates in North America with particular reference to the southeastern United States. Journal of the North American Benthological Society 18(3): 308-343.Binns, N.A., and Eiserman, F.M. 1979. Quantification of fluvial trout habitat in Wyoming. Transactions of the American Fisheries Society 108: 215-228.Bowlby, J.N., and Roff, J.C. 1986. Trout biomass and habitat relationships in southern Ontario streams. Transactions of the American Fisheries Society 115: 503-514.Burnham, K.P., and Anderson, D.R. 2002. Model Selection and Multimodel Inference: A practical information-theoretic approach. Springer, New York.Cada, G.F., Loar, J.M., and Cox, D.K. 1987. Food and feeding preferences of rainbow and brown trout in southern Appalachian streams. The American Midland Naturalist 117: 374-385.Ch?telat, J., Pick, F.R., Morin, A., and Hamilton, P.B. 1999. Periphyton biomass and community composition in rivers of different nutrient status. Canadian Journal of Fisheries and Aquatic Science 56: 560-569.Clark, J.D., Dunn, J.E., and Smith, K.G. 1993. A multivariate model of female black bear habitat use for a geographic information system. Journal of Wildlife Management 57(3): 519-526.Coughlan, S.M.C.J., Connerton, M.J., Ringler, N.H., Stewart, D.J., and Mead, J.V. 2007. Survival and growth responses of juvenile salmonines stocked in eastern Lake Ontario tributaries. Transaction of the American Fisheries Society 136: 56-71.Coulombe-Pontbriand, M., and LaPointe, M. 2004. Landscape controls on boulder -rich, winter habitat availability and their effects on Atlantic salmon ( Salmo salar) parr abundance in two fifth-order mountain streams. Canadian Journal of Fisheries and Aquatic Sciences 61(4): 648-658.Davies, P.E. 1989. Relationships between habitat characteristics and population abundance for brown trout,  Salmo trutta L., and blackfish,  Gadopsis marmoratus Rich., in Tasmanian streams. Australian Journal of Marine and Freshwater Research 40: 341-359.Deno?l, M., and Lehmann, A. 2006. Multi-scale effect of landscape processes and habitat quality on newt abundance: Implications for conservation. Biological Conservation 130: 495-504.62Dolinsek, I.J., Biron, P.M., and Grant, J.W.A. 2007. Assessing the effect of visual isolation on the population density of Atlantic salmon ( Salmo salar) using GIS. River Research and Applications 23: 763-774.Dorgel?h, W.G. 2006. Habitat suitability for tsessebe  Damaliscus lunatus lunatus. Journal of African Ecology 44: 329-336.Fausch, K.D., Hawkes, C.L., and Parsons, M.G. 1988. Models that predict standing crop of stream fish from habitat variables: 1950-1985. Gen Tech Rep, U.S. Department of Agriculture Forest Service Pacific Northwest Research Station, Portland, Oregon.Gavashelishvili, A., McGrady, M.J., and Javakhishvili, Z. 2006. Planning the conservation of the breeding population of cinereous vultures Aegypius monachus in the Republic of Georgia. Oryx 40(1): 1-8.Greenwood, J.L., Rosemond, A.D., Wallace, J.B., Cross, W.F., and Weyers, H.S. 2007. Nutrients stimulate leaf breakdown rates and detritivore biomass: bottom-up ef fects via heterotrophic pathways. Oecologia 151: 637-649.Hagen, J., Decker, A.S., and Bison, R.G.  2005.  Calibrated rapid assessment methods for estimating total steelhead parr abundance in the Thompson River system, British Columbia - 2004 interim report.  Report Prepared for BC Ministry of  Water, Land, and Air Protection, Kamloops.Hanson, A.E. 2005. Juvenile density survey summaries. British Columbia Conservation Foundation, Surrey, BC. Unpublished Report.Hill, M.S., Zydlewski, G.B., and Gale, W.L. 2006. Comparisons between hatchery and wild steelhead trout ( Oncorhynchus mykiss) smolts: physiology and habitat use. Canadian Journal of Fisheries and Aquatic Science 63: 1627-1638.Hogan, D. 1996. Channel Assessment Procedure Field Guide Book.Huryn, A.D. 1998. Ecosystem-level evidence for top-down and bottom-up control of production in a grassland stream system. Oecologia 115: 173-183.Imre, I., Grant, J.W.A., and Keeley, E.R. 2004. The effect of food abundance on territory size and population density of juvenile steelhead trout ( Oncorhynchus mykiss). Oecologia 138: 371-378.Johansen, M., Elliott, J.M., and Klemetsen, A. 2005. A comparative study of juvenile salmon density in 20 streams throughout a very large river system in northern Norway. Ecology of Freshwater Fish 14: 96-110.Jowett, I.G. 1992. Models of the abundance of large brown trout in New Zealand rivers. North American Journal of Fisheries Management 12: 417-432.McAlpine, C.A., Rhodes, J.R., Callaghan, J.G., Bowen, M.E., Lunney , D., Mitchell, D.L., Pullar, D.V., and Possingham, H.P. 2006. The importance of forest area and configuration relative to local habitat factors for conserving forest mammals:  A case study of koalas in Queensland Australia. Biological Conservation 132: 153-165.McCauley, E. 1984. The estimation of the abundance and biomass of zooplankton in samples. In A manual on methods for the assessment of secondary productivity in freshwaters. 63Edited by J.A. Downing and F.H. Rigler. Blackwell Scientific Publications, Oxford. pp. 228-265.Meyer, E. 1989. The relationship between body length parameters and dry mass in running water invertebrates. Arch. Hydrobiol. 117(2): 191-203.Moore, K., Jones, K., and Dambacher, J. 1997. Methods for Stream Habitat Surveys: Aquatic Inventory Project. Oregon Department of Fish and Wildlife Natural Production Program, Corvallis, OR, 97330.Morgan, D. 2001. Coho surveys. Unpublished.Nickelson, T.E., Rodgers, J.D., Johnson, S.L., and Solazzi, M.F. 1992. Seasonal changes in habitat use by juvenile coho salmon ( Oncorhynchus kisutch) in Oregon coastal streams. Canadian Journal of Fisheries and Aquatic Sciences 49: 783-789.Nielsen, J.L. 1992. Microhabitat-specific foraging behavior, diet, and growth of juvenile coho salmon. Transactions of the American Fisheries Society 121: 617-634.Nislow, K.H., Folt, C., and Seandel, M. 1998. Food and foraging behavior in relation to microhabitat use and survival of age-0 Atlantic salmon. Canadian Journal of Fisheries and Aquatic Sciences 55: 116-127.Quinn, G.P., and Keough, M.J. 2002. Experimental design and data analysis for biologists. Cambridge University Press.Roff, J.C., and Hopcroft, R.R. 1986. High precision microcomputer based measuring system for ecological research. Canadian Journal of Fisheries and Aquatic Sciences 43: 2044-2048.Roni, P., and Quinn, T.P. 2001. Density and size of juvenile salmonids in response to placement of large woody debris in western Oregon and Washington streams. Canadian Journal of Fisheries and Aquatic Sciences 58: 282-292.Rosenfeld, J., Porter, M., and Parkinson, E. 2000. Habitat factors affecting the abundance and distribution of juvenile cutthroat trout ( Oncorhynchus clarki) and coho salmon (Onchorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences 57: 766-774.Rosenfeld, J.S., Leiter, T., Lindner, G., and Rothman, L. 2005. Food abundance and fish density alters habitat selection, growth, and habitat suitability curves for juvenile coho salmon (Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences 62: 1691-1701.Sabo, J.L., Bastow, J.L., and Power Mary, E. 2002. Length-mass relationships for adult aquatic and terrestrial invertebrates in a California watershed. Journal of the North American Benthological Society 21(2): 336-343.Sample, B.E., Cooper, R.J., Greer, R.D., and Whitmore, R.C. 1993. Estimation of insect biomass by length and width. American Midland Naturalist 129(2): 234-240.Scarnecchia, D.L., and Bergersen, E.P. 1987. Trout production and standing crop in Colorado's small streams, as related to environmental features. North  American Journal of Fisheries Management 7: 315-330.64Schnute, J. 1983. A new approach to estimating populations by the removal method. Canadian Journal of Fisheries and Aquatic Sciences 40: 2153-2169.Sharma, R., and Hilborn, R. 2001. Empirical relationships between watershed characteristics and coho salmon (Oncorhynchus kisutch) smolt abundance in 14 western Washington streams. Canadian Journal of Fisheries and Aquatic Sciences 58: 1453-1463.Shearer, K.A., Hayes, J.W., and Stark, J.D. 2002. Temporal and spatial quantification of aquatic invertebrate drift in the Maruia River, South Island, New Zealand. New Zealand Journal of Marine and Freshwater Research 36: 529-536.Shearer, K.A., Stark, J.D., Hayes, J.W., and Young, R.G. 2003. Relationships between drifting and benthic invertebrates in three New Zealand rivers: implications for drift-feeding fish. New Zealand Journal of Marine and Freshwater Research 37: 809-820.Slaney, P.A. 1972. Effects of prey abundance on distribution, density and territorial behaviour of young rainbow trout in streams, University of British Columbia.Smock, L.A. 1980. Relationships between body size and biomass of aquatic insects. Freshwater Biology 10: 375-383.Tschaplinski, P. 2006. Carnation Creek electrofishing August 2006. Unpublished.Vannote, R.L., Minshall, G.W., Cummins Kenneth, W., Sedell, J.R., and Cushing, C.E. 1980. The river continuum concept. Canadian Journal of Fisheries and Aquatic Sciences 37: 130-137.Wang, T., Hamann, A., Spittlehouse, D., and Aitken, S.N. 2006. Development of scale-free climate data for western Canada for use in resource management. International Journal of Climatology 26(3): 383-397.Waters, T.F. 1962. Diurnal periodicity in the drift of stream invertebrates. Ecology 43(2): 316-320.Wipfli, M. 1997. Terrestrial invertebrates as salmonid prey and nitrogen sources in streams: contrasting old-growth and young-growth riparian forests in southeastern  Alaska USA. Canadian Journal of Fisheries and Aquatic Sciences 54: 1259-1269.Zar, J.H. 1999. Biostatistical Analysis. Simon & Schuster, New Jersey.Zharikov, Y., Lank, D.B., Huettmann, F., Bradley, R.W., Parker, N., Yen, P.P.W., McFarlane-Tranquilla, L.A., and Cooke, F. 2006. Habitat selection and breeding success in a forest-nesting Alcid, the marbled murrelet, in two landscapes with different degrees of forest fragmentation. Landscape Ecology 21: 107-120.65ConclusionThe underlying rationale for this study was to determine 1) the degree to which prey (invertebrate drift) for drift-feeding salmonids varies across time and space, and 2) whether drift abundance can be used as a significant predictor of fish abundance.Temporal variation of invertebrate drift at the day-to-day scale is lower than spatial variation within or between sites (Figure 2.5).  Therefore it is unnecessary to resample a site on successive days in order to calculate a precise estimate of invertebrate drift abundance.  However, variation within sites (residual variation) was high: up to 90% of the random variation.  High within site variation made estimates of drift abundance less accurate, and probably contributed to the low predictive ability of the habitat models for drift in Chapter 2.   The reduced precision of these estimates may also have contributed to the rejection of drift from the salmonid habitat models in the salmonid chapter.  Therefore multiple samples are necessary to estimate invertebrate drift abundance, but there appears to be no need to repeat that sampling effort on additional visits to the site.  Invertebrate drift measurements could be used in habitat models for juvenile salmonids in areas with lower within site variation, or by collecting more than three samples.Future research to characterize the variation of invertebrate drift would allow further refinement of sampling protocols, and a greater understanding of the timing of food availability for juvenile salmonids.  It would be useful to determine the relationship between daytime drift samples, samples collected at dusk, and samples collected in the first hours after dark (the first night time peak, Waters 1962), in order to determine how representative daytime samples are of food availability for juvenile salmonids throughout the day and night.   Examination of the environmental conditions that influence terrestrial invertebrate input (e.g. wind, rain) would also improve sampling protocols, and could explain some of the day-to-day variation of terrestrial drift (Figure 2.5).Physical and biological habitat variables were both included in the final models predicting the abundance of young-of-the-year coho salmon, rainbow trout, and all salmonids combined, 66but invertebrate drift was not included.  In general, habitats with more structure were better habitat for salmonids, but there were mixed responses to all of the candidate variables.  Models for individual species had higher coefficients of variation than the  YOY-combined model, suggesting real differences in habitat requirements or preferences between the species.  The absence of invertebrate drift from the final models may have been related to sampling problems (see Chapter 3 Discussion), but other considerations, such as complex trophic interactions, are also possible explanations.Further research on complex trophic interactions such as the effect of habitat on foraging behaviour, prey-switching, and thresholds of food abundance that prompt changes in feeding behaviour would help explain how food availability affects salmonid abundance in a natural setting.  Much of the previous research on the impacts of invertebrate drift abundance on juvenile salmonids has been conducted in artificial channels (e.g. Imre et al. 2004, Rosenfeld et al. 2005, Slaney 1972).  In real stream channels, where benthic food is more abundant and other behaviour-influencing factors such as competition are present, the positive impact of invertebrate drift on salmonid growth, abundance and survival may be overwhelmed by other factors and by behaviour.The predictive models for juvenile salmonids developed in this study are directly applicable to fisheries management projects such as habitat assessment, monitoring, and restoration.  The models could be used as presented, or selected variables could be applied based on available resources; the cost:benefit analysis in Chapter 3 can provide fisheries scientists with a good starting point for management decisions related to the power of potential inventory variables.  The regional differences in the rainbow and coho models indicate that there is a need for increased regional specificity in habitat assessment protocols ? paradigms of fisheries management developed in the Coast region are less applicable in other regions of British Columbia than has previously been assumed.  Further research on regional habitat differences and their impacts on salmonid abundance would improve the management of these species.As do all Master of Science theses, this one had weaknesses and strengths.   The scale of the 67project caused weaknesses regarding sample size and data collection: it was not feasible to sort drift samples for all of the sites, or all three of the samples collected on each visit for the temporal study.  Had all of the samples been sorted, invertebrate drift may have had a greater impact in the habitat models.  Due to the scale of the project, data collected by other people were used extensively; the quality of the data is not being questioned, but there may have been slight differences in electrofishing method that resulted in differences between those data and my own.  However, the scale of the project was also its greatest strength, and allowed this project to stand apart from many habitat modeling exercises.  In an extensive review, Fausch (1988) found that the majority of habitat models were based on less than 20 sites, as were the majority of models that had coefficients of determination greater than 0.75.  In contrast, the salmonid models in this study included up to 50 sites and five of the six coefficients of determination were 0.75 or more.  The model with the greatest r 2 , for coho density, had an r 2  = 0.92 and was based on 38 sites.  Fausch (1988) also found that many of the reviewed studies did not place enough emphasis on the statistical procedures used to develop models, in particular regarding poor methods for choosing the best models.  In this study a great amount of emphasis was placed on researching and applying the most objective and statistically sound model selection method available, resulting in greater confidence that the most appropriate models were chosen.68ReferencesFausch, K.D., Hawkes, C.L., and Parsons, M.G. 1988. Models that predict standing crop of stream fish from habitat variables: 1950-1985. Gen Tech Rep, U.S. Department of Agriculture Forest Service Pacific Northwest Research Station, Portland, Oregon.Imre, I., Grant, J.W.A., and Keeley, E.R. 2004. The effect of food abundance on territory size and population density of juvenile steelhead trout ( Oncorhynchus mykiss). Oecologia 138: 371-378.Rosenfeld, J.S., Leiter, T., Lindner, G., and Rothman, L. 2005. Food abundance and fish density alters habitat selection, growth, and habitat suitability curves for juvenile coho salmon (Oncorhynchus kisutch). Canadian Journal of Fisheries and Aquatic Sciences 62: 1691-1701.Slaney, P.A. 1972. Effects of prey abundance on distribution, density and territorial behaviour of young rainbow trout in streams, University of British Columbia.Waters, T.F. 1962. Diurnal periodicity in the drift of stream invertebrates. Ecology 43(2): 316-320.69Drift Invertebrate Density (mg/m 3)site code region total aquatic terrestrial1 Brohm brom C 71.2 63.9 7.22 Carnation carn C 15.8 12.0 3.83 Chapman chap C 14.7 8.7 6.04 Coho South cohs C 36.1 34.5 1.75 Colvin colv C 16.8 16.0 0.96 Cook cook C 38.8 32.6 6.27 Elk elkk C 91.5 54.6 37.08 Finish Early fini C 9.3 9.2 0.19 High Falls higa C 1062.0 130.8 931.210 Husdon husd C 10.8 6.6 4.211 Lost Shoe lost C 51.7 41.5 10.212 Rousseau rous C 51.1 33.1 18.113 Sandhill sand C 22.8 16.3 6.514 Shovelnose shov C 57.2 38.9 18.315 Silver Hope silh C 57.3 47.9 9.416 Slesse sles C 137.0 134.2 2.917 Wakefield wake C 52.2 45.2 7.018 Yola yola C 7.9 6.7 1.219 Crazy craz I 46.9 39.6 7.420 Criss cris I 84.2 34.6 49.721 Lemieux Main lemm I 61.2 48.0 13.222 Lemieux Side lems I 161.7 147.4 14.323 Lindquist lind I 77.9 61.1 16.824 Moore moor I 135.2 120.9 14.325 Senn senn I 34.8 15.5 19.326 Silver B silb I 12.6 9.9 2.627 Six Mile sixm I 57.9 52.3 5.628 Spius spiu I 283.6 161.9 121.729 Vermelin verm I 33.6 31.6 1.930 Yard yard I 119.2 114.4 4.9average 97.1 52.3 44.8Appendix 1: Habitat measurements from Chapter 2 sites.70Appendix 1: continued.UTM coordinatessite easting northing datum zone elev (m)1 Brohm 491286 5517692 WGS84 10 2602 Carnation 353871 5420162 WGS84 10 203 Chapman 447858 5478537 WGS84 10 604 Coho South 430330 5498771 WGS84 10 805 Colvin 437426 5481030 WGS84 10 206 Cook 444195 5481677 WGS84 10 207 Elk 585091 5444106 WGS84 10 408 Finish Early 307631 5448910 NAD27 10 409 High Falls 478552 5531752 WGS84 10 6010 Husdon 448842 5478484 WGS84 10 8011 Lost Shoe 311047 5431297 NAD27 10 4012 Rousseau 349310 5409882 WGS84 10 4013 Sandhill 311047 5431297 WGS84 10 4014 Shovelnose 476144 5545906 WGS84 10 16015 Silver Hope 617821 5454219 WGS84 10 44016 Slesse 594561 5436690 WGS84 10 24017 Wakefield 441671 5479843 WGS84 10 2018 Yola 617879 5453010 WGS84 10 48019 Crazy 384614 5650320 WGS84 11 40020 Criss 643436 5639327 WGS84 10 54021 Lemieux Main 693929 5701713 WGS84 10 40022 Lemieux Side 693929 5701713 WGS84 10 40023 Lindquist 696491 5688907 WGS84 10 40024 Moore 681607 5569893 WGS84 10 64025 Senn 374055 5645513 WGS84 11 38026 Silver B 332562 5607099 WGS84 11 40027 Six Mile 314538 5603249 WGS84 11 74028 Spius 636528 5534836 WGS84 10 86029 Vermelin 302950 5691650 WGS84 11 66030 Yard 372188 5640897 WGS84 11 380average 27871Appendix 1: continued.site average summer temperature (?C)conductivity (m S)largest particle (cm)canopy cover (proportion)alder cover (proportion)1 Brohm 16.0 17.2 60.8 0.00 0.592 Carnation 15.1 40.5 5.3 0.10 0.093 Chapman 16.3 20.5 84.0 0.14 0.104 Coho South 16.1 63.4 7.4 0.71 0.175 Colvin 17.0 116.5 2.6 0.59 0.096 Cook 16.6 129.2 4.2 0.81 0.117 Elk 17.1 119.7 24.4 0.58 0.088 Finish Early 15.3 NA NA 0.88 0.009 High Falls 15.9 10.0 45.0 0.19 0.2410 Husdon 16.2 46.0 2.8 0.57 0.1111 Lost Shoe 14.1 76.1 10.4 0.30 0.2912 Rousseau 15.2 42.5 13.0 0.08 0.0513 Sandhill 14.1 144.7 2.8 0.56 0.3814 Shovelnose 15.2 28.9 43.1 0.24 0.2415 Silver Hope 15.2 72.1 37.8 0.03 0.0016 Slesse 15.2 53.9 78.7 0.04 0.0217 Wakefield 16.9 92.9 36.8 0.62 0.2118 Yola 14.7 42.8 85.4 0.03 0.0119 Crazy 16.0 24.4 21.5 0.02 0.0020 Criss 16.4 274.0 66.4 0.15 0.0121 Lemieux Main 17.3 201.0 NA 0.10 0.0822 Lemieux Side 17.3 201.0 NA 0.10 0.0823 Lindquist 17.1 66.0 24.6 0.13 0.0824 Moore 15.9 220.0 2.1 0.14 0.1225 Senn 16.7 54.7 5.0 0.31 0.3126 Silver B 17.0 210.0 18.0 0.73 0.1327 Six Mile 15.2 100.5 2.0 0.00 0.0028 Spius 14.6 163.3 82.3 0.04 0.0129 Vermelin 15.0 13.0 12.9 0.34 0.1530 Yard 16.3 31.8 20.3 0.00 0.00average 15.9 92.3 29.6 0.28 0.1272Appendix 1: continued.site bankfull width (m)pool area (proportion)riffle area (proportion)gravel sediment (proportion)organic sediment (proportion)gradient (%)1 Brohm 5.0 0.1 0.6 0.84 0.00 5.12 Carnation 9.1 0.3 0.2 0.10 0.05 1.03 Chapman 20.1 0.0 0.3 0.68 0.00 1.34 Coho South 2.7 0.2 0.3 0.53 0.12 1.85 Colvin 2.8 0.1 0.2 0.54 0.30 0.56 Cook 2.4 0.2 0.5 0.14 0.14 0.77 Elk 4.2 0.0 0.2 0.00 0.00 3.48 Finish Early 1.7 0.7 0.2 0.19 0.00 0.09 High Falls 10.5 0.0 0.8 0.40 0.00 1.610 Husdon 2.6 0.2 0.1 0.26 0.14 0.411 Lost Shoe 7.8 0.3 0.0 0.60 0.37 1.312 Rousseau 10.1 0.3 0.5 0.21 0.10 0.813 Sandhill 4.5 0.2 0.0 0.30 0.13 0.014 Shovelnose 13.6 0.0 0.5 0.09 0.00 2.615 Silver Hope 17.4 0.0 0.4 0.20 0.06 1.616 Slesse 20.8 0.0 0.6 0.20 0.01 1.817 Wakefield 6.3 0.1 0.0 0.08 0.00 1.918 Yola 15.9 0.1 0.4 0.45 0.01 2.019 Crazy 17.5 0.0 0.3 0.08 0.01 2.320 Criss 8.0 0.1 0.6 0.27 0.01 3.321 Lemieux Main 21.1 0.0 0.5 0.30 0.00 1.522 Lemieux Side 6.7 0.1 0.3 0.34 0.36 1.023 Lindquist 6.9 0.0 0.4 0.57 0.13 0.924 Moore 4.2 0.2 0.0 0.08 0.01 0.525 Senn 3.3 0.1 0.2 0.11 0.17 1.726 Silver B 5.7 0.0 0.3 0.10 0.04 3.627 Six Mile 3.1 0.0 0.0 0.03 0.50 1.028 Spius 16.7 0.0 0.0 0.46 0.00 1.629 Vermelin 6.5 0.2 0.2 0.09 0.04 2.530 Yard 33.1 0.0 0.6 9.03 0.01 2.2average 9.7 0.1 0.3 0.58 0.1 1.773Appendix 2: habitat measurements from Chapter 3 sites. Abbreviations follow Table 3.1.site region lp (cm) canopy (proportion) alder (proportion)1 angu C 30.5 0.60 0.072 bole I 35.3 0.17 0.153 brom C 60.8 0.63 0.594 carn C 5.3 0.10 0.095 chap C 84.0 0.14 0.106 cohn C 2.5 0.83 0.057 cohs C 7.4 0.71 0.178 cold I 64.0 0.02 0.019 colv C 2.6 0.59 0.0910 cook C 4.2 0.81 0.1111 craz I 21.5 0.02 0.0012 cris I 66.4 0.15 0.0113 dead I 53.2 0.11 0.0014 depo C 17.6 0.28 0.2815 elkk C 24.4 0.58 0.0816 fini C 29.0 0.88 0.8817 fred C 4.6 0.48 0.4318 guic I 23.0 0.28 0.0019 hifa C 25.8 0.45 0.2120 hunt C 27.4 0.40 0.3221 husd C 2.8 0.57 0.1122 kooa C 5.7 0.38 0.1523 koob C 0.6 0.49 0.0024 kooc C 1.0 0.38 0.0025 lemm I NA 0.10 0.0826 lems I NA NA NA27 lind I 24.6 0.13 0.0828 lost C 10.4 0.30 0.2929 maka I 73.2 0.03 0.0030 mash C 76.5 0.54 0.5431 moor I 2.1 0.14 0.1232 pach C 5.6 0.55 0.3733 rous C 13.0 0.08 0.0534 sand C 2.8 0.56 0.3835 sari C 10.4 0.13 0.0236 senn I 5.0 0.31 0.3137 shov C 43.1 0.24 0.2438 sila I 76.9 0.44 0.2139 silb I 18.0 0.73 0.1340 silh C 37.8 0.03 0.0041 sixm I 2.0 0.00 0.0042 sles C 78.7 0.04 0.0243 spiu I 82.3 0.04 0.0144 stag C 1.2 0.52 0.2545 verm I 12.9 0.34 0.1546 wake C 36.8 0.62 0.2147 weee C 1.8 0.88 0.8848 weym I 55.0 0.50 0.4049 yard I 20.3 0.00 0.0050 yola C 85.4 0.03 0.01average 28.7 0.35 0.1874Appendix 2: continuedsite pool (proportion) riffle (proportion) tavsm (oC) cond (uS)1 angu 0.35 0.65 16.2 55.42 bole 0.04 0.24 15.4 127.83 brom 0.08 0.63 16.0 17.24 carn 0.26 0.20 15.1 40.55 chap 0.04 0.26 16.3 20.56 cohn 0.17 0.52 16.1 53.67 cohs 0.23 0.28 16.1 63.48 cold 0.04 0.79 14.5 98.59 colv 0.05 0.22 17.0 116.510 cook 0.15 0.46 16.6 129.211 craz 0.00 0.30 16.0 24.412 cris 0.06 0.59 16.4 274.013 dead 0.14 0.16 15.6 234.014 depo 0.13 0.58 15.2 29.915 elkk 0.00 0.25 17.1 119.716 fini 0.70 0.19 15.3 NA17 fred 0.19 0.56 15.4 50.918 guic 0.01 0.00 16.2 55.219 hifa 0.01 0.75 15.9 10.020 hunt 0.20 0.55 15.4 90.321 husd 0.23 0.13 16.2 46.022 kooa 0.36 0.03 14.0 141.023 koob 0.24 0.01 14.0 96.324 kooc 0.00 0.00 14.0 34.925 lemm 0.00 0.54 17.3 201.026 lems 0.10 0.26 17.3 201.027 lind 0.02 0.37 17.1 66.028 lost 0.25 0.03 14.1 76.129 maka 0.00 0.70 14.9 138.630 mash 0.05 0.00 15.9 47.731 moor 0.18 0.00 15.9 220.032 pach 0.20 0.49 14.9 34.833 rous 0.32 0.46 15.2 42.534 sand 0.21 0.00 14.1 144.735 sari 0.46 0.22 15.6 47.636 senn 0.12 0.23 16.7 54.737 shov 0.01 0.53 15.2 28.938 sila 0.00 0.00 14.6 1.539 silb 0.01 0.34 17.0 210.040 silh 0.02 0.41 15.2 72.141 sixm 0.00 0.00 15.2 100.542 sles 0.01 0.60 15.2 53.943 spiu 0.00 0.00 14.6 163.344 stag 0.70 0.17 14.0 111.945 verm 0.17 0.24 15.0 13.046 wake 0.13 0.00 16.9 92.947 weee 0.25 0.20 16.3 73.648 weym 0.45 0.06 15.2 85.749 yard 0.01 0.64 16.3 31.850 yola 0.09 0.43 14.7 42.8average 0.15 0.31 15.6 87.575Appendix 2: continuedsite elev (m) wb (m) cover (proportion) grav (proportion)1 angu 20 8.59 0.16 0.372 bole 720 9.79 0.07 0.253 brom 260 4.99 0.12 0.064 carn 20 9.06 0.06 0.845 chap 60 20.07 0.06 0.106 cohn 40 1.48 0.21 0.327 cohs 80 2.73 0.14 0.688 cold 880 16.06 0.07 0.289 colv 20 2.89 0.15 0.5210 cook 20 2.41 0.10 0.5411 craz 400 17.55 0.02 0.4512 cris 540 8.01 0.15 0.0813 dead 660 11.42 0.08 0.2514 depo 640 11.85 0.08 0.2815 elkk 40 4.18 0.03 0.1416 fini 40 1.74 0.08 0.3117 fred 40 9.75 0.07 0.6118 guic 600 8.20 0.11 0.1219 hifa 60 10.55 0.04 0.1920 hunt 20 8.11 0.11 0.2421 husd 80 2.57 0.21 0.4022 kooa 20 3.52 0.20 0.5923 koob 20 3.20 0.12 0.0724 kooc 20 2.37 0.23 0.1725 lemm 400 21.10 0.02 0.2726 lems 400 6.70 0.03 0.3027 lind 400 6.87 0.06 0.3428 lost 40 7.79 0.09 0.2629 maka 820 9.39 0.04 0.3430 mash 80 8.15 0.14 0.1931 moor 640 4.21 0.18 0.5732 pach 40 4.68 0.08 0.8133 rous 40 10.14 0.07 0.6034 sand 40 4.48 0.20 0.2135 sari 20 15.37 0.08 0.7136 senn 380 3.27 0.12 0.0837 shov 160 13.56 0.05 0.3038 sila 860 6.85 0.09 0.1339 silb 400 5.66 0.08 0.1140 silh 440 17.37 0.03 0.0941 sixm 740 3.10 0.08 0.1042 sles 240 20.77 0.05 0.2043 spiu 860 16.66 0.05 0.0344 stag 20 2.22 0.10 0.0945 verm 660 6.52 0.14 0.4646 wake 20 6.34 0.10 0.2047 weee 40 1.30 0.10 0.3148 weym 780 4.18 0.10 0.0949 yard 380 33.11 0.18 0.0950 yola 480 15.93 0.15 0.08average 294 8.74 0.10 0.3076Appendix 2: continuedsite org (proportion) grad (%) lwd (pieces/m) lwd_pf(pieces/m)1 angu 0.06 1.6 0.03 0.002 bole 0.04 2.1 0.01 0.003 brom 0.00 5.1 0.00 0.004 carn 0.05 1.0 0.04 0.015 chap 0.00 1.3 0.01 0.006 cohn 0.18 1.5 0.23 0.037 cohs 0.12 1.8 0.14 0.028 cold 0.00 1.4 0.01 0.009 colv 0.31 0.5 0.11 0.0010 cook 0.14 0.7 0.14 0.0211 craz 0.01 2.3 0.00 0.0012 cris 0.01 3.3 0.04 0.0413 dead 0.02 1.6 0.01 0.0014 depo 0.05 0.9 0.08 0.0015 elkk 0.00 3.4 0.02 0.0016 fini 0.05 8.0 0.26 0.0017 fred 0.23 0.7 0.02 0.0018 guic 0.01 2.4 0.00 0.0019 hifa 0.00 1.6 0.02 0.0020 hunt 0.00 2.7 0.02 0.0021 husd 0.14 0.4 0.24 0.0322 kooa 0.20 0.8 0.20 0.0423 koob 0.28 0.1 0.09 0.0024 kooc 0.40 0.7 0.18 0.0025 lemm 0.00 1.5 0.00 0.0026 lems 0.36 1.0 0.01 0.0027 lind 0.13 0.9 0.03 0.0028 lost 0.37 1.3 0.03 0.0029 maka 0.00 1.9 0.00 0.0030 mash 0.00 1.3 0.02 0.0031 moor 0.01 0.5 0.00 0.0032 pach 0.04 1.2 0.03 0.0133 rous 0.10 0.8 0.01 0.0034 sand 0.13 0.0 0.13 0.0135 sari 0.01 0.5 0.01 0.0036 senn 0.17 1.7 0.05 0.0237 shov 0.00 2.7 0.02 0.0038 sila 0.01 4.0 0.02 0.0039 silb 0.04 3.6 0.04 0.0040 silh 0.06 1.6 0.01 0.0041 sixm 0.50 1.0 0.00 0.0042 sles 0.01 1.8 0.00 0.0043 spiu 0.00 1.6 0.00 0.0044 stag 0.49 1.2 0.11 0.0045 verm 0.06 2.5 0.10 0.0146 wake 0.00 1.9 0.03 0.0047 weee 0.16 3.6 0.19 0.0448 weym 0.00 4.1 0.00 0.0049 yard 0.01 2.2 0.02 0.0050 yola 0.01 2.0 0.01 0.00average 0.10 1.8 0.06 0.0177Appendix 2: continuedsite shock_area (m2) YOY (#) rainbow (#) coho (#)1 angu 185.4 178 NA 1092 bole 96.1 117 74 53 brom 123.5 44 22 NA4 carn 322.0 418 NA 4145 chap 85.1 89 46 426 cohn 31.1 93 NA 297 cohs 28.8 108 NA 198 cold 320.3 139 87 119 colv 81.2 142 NA 2810 cook 29.8 61 NA 311 craz 126.5 87 57 2412 cris 118.6 90 80 113 dead 179.0 80 79 NA14 depo 77.2 54 NA 3515 elkk 74.8 123 NA 6916 fini 35.6 20 NA NA17 fred 207.3 130 29 9518 guic 129.2 50 2 819 hifa 110.9 45 44 NA20 hunt 188.1 86 32 5421 husd 84.4 71 NA 2322 kooa 78.7 194 NA 15623 koob 90.5 71 NA 1024 kooc 59.9 132 NA 12525 lemm 48.3 22 20 226 lems 109.2 211 72 18727 lind 88.9 298 NA 29528 lost 371.3 208 NA 15729 maka 99.1 18 16 130 mash 78.4 93 60 2431 moor 108.0 23 23 NA32 pach 135.2 90 17 6733 rous 213.3 178 74 10234 sand 192.1 49 NA 3535 sari 304.5 91 9 7836 senn 37.6 142 62 6937 shov 150.4 290 227 4838 sila 71.0 21 5 NA39 silb 89.2 41 24 NA40 silh 79.4 49 37 NA41 sixm 60.0 28 22 NA42 sles 167.0 232 169 4843 spiu 135.0 94 73 244 stag 41.8 61 NA 445 verm 26.5 36 24 446 wake 110.2 105 NA 8847 weee 29.5 180 NA 4348 weym 70.7 55 44 NA49 yard 168.6 28 9 450 yola 188.5 123 76 NAaverage 120.8 108 52 6578Appendix 2: continued.site YOY/m2 rainbow/m2 coho/m2 drift (dry mg/m3)1 angu 0.96 NA 0.59 NA2 bole 1.22 0.77 0.05 NA3 brom 0.36 0.18 NA 71.164 carn 1.3 NA 1.29 15.805 chap 1.05 0.54 0.49 14.726 cohn 2.99 NA 0.93 NA7 cohs 3.75 NA 0.66 36.148 cold 0.43 0.27 0.03 NA9 colv 1.75 NA 0.34 16.8310 cook 2.05 NA 0.1 38.8011 craz 0.69 0.45 0.19 46.9412 cris 0.76 0.67 0.01 84.2113 dead 0.45 0.44 NA NA14 depo 0.7 NA 0.45 NA15 elkk 1.65 NA 0.92 91.5516 fini 0.56 NA NA 9.2617 fred 0.63 0.14 0.46 NA18 guic 0.39 0.02 0.06 NA19 hifa 0.41 0.4 NA 1062.0220 hunt 0.46 0.17 0.29 NA21 husd 0.84 NA 0.27 10.7922 kooa 2.47 NA 1.98 NA23 koob 0.78 NA 0.11 NA24 kooc 2.2 NA 2.09 NA25 lemm 0.46 0.41 0.04 61.2426 lems 1.93 0.66 1.71 161.6927 lind 3.35 NA 3.32 77.9528 lost 0.56 NA 0.42 51.7029 maka 0.18 0.16 0.01 NA30 mash 1.19 0.77 0.31 NA31 moor 0.21 0.21 NA 135.2032 pach 0.67 0.13 0.5 NA33 rous 0.83 0.35 0.48 51.1434 sand 0.26 NA 0.18 22.7935 sari 0.3 0.03 0.26 NA36 senn 3.77 1.65 1.83 34.8237 shov 1.93 1.51 0.32 57.1638 sila 0.3 0.07 NA NA39 silb 0.46 0.27 NA 12.5540 silh 0.62 0.47 NA 57.2941 sixm 0.47 0.37 NA 57.8842 sles 1.39 1.01 0.29 137.0443 spiu 0.7 0.54 0.01 283.6244 stag 1.46 NA 0.1 NA45 verm 1.36 0.9 0.15 33.5946 wake 0.95 NA 0.8 52.2047 weee 6.1 NA 1.46 NA48 weym 0.78 0.62 NA NA49 yard 0.17 0.05 0.02 119.2050 yola 0.65 0.4 NA 7.88average 1.20 0.47 0.60 97.1179Appendix 3: UBC Research Ethics Board Certificate of Approval?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ????????????????????? ?????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????? ????????????????????????????????????????????????????????????????????????????????????????????????????????? ?????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????????? ??????????????? ???????????????????????????????????????????? ????????? 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