RELATIONSHIPS BETWEEN ELECTROFISHING CATCH RATE AND TROUT DENSITY IN WISCONSIN STREAMS. Paul Steven Bergman. A Thesis

Transcription

1 RELATIONSHIPS BETWEEN ELECTROFISHING CATCH RATE AND TROUT DENSITY IN WISCONSIN STREAMS By Paul Steven Bergman A Thesis Submitted in partial fulfillment of the Requirements of the degree MASTERS OF SCIENCE IN NATURAL RESOURCES (FISHERIES) College of Natural Resources UNIVERSITY OF WISCONSIN Stevens Point, Wisconsin May 2007

2 APPROVED BY THE GRADUATE COMJVITTTEE OF: Dr. Michael J. Hansen, Committee Chainnan Professor of Fisheries College of Natural Resources Dr. Katherine A. C a cy Assi aot Professor of Water Resources ;z"?k Dr. Timothy F. Ginnett Associate Professor of Wildlife College ofnarural Resources Fisheries Daaabase Coordinator Wisconsin Department ofnan1ral Resources

3 ABSTRACT The relative quickness and cost effectiveness of single pass electrofishing samples (catch per effort, CPE) argues for their use over multiple-pass estimates for enumerating stream fish populations. However, CPE is generally an imprecise index of fish density and therefore may be less useful than more-precise indices of abundance, such as markrecapture or depletion methods, for assessing trout populations. I evaluated the use of single pass electrofishing samples (catch/mi) as predictors of adult brook trout and brown trout density (number/mi) in Wisconsin streams by quantifying the relationship between electrofishing catch rate and adult trout density. First, I tested for linearity of the relationship between CPE and adult trout density, while accounting for measurement error. Next, I explained residual variability in the relationship using physical and biological attributes of the surveyed streams. I found that the relationship between CPE and trout density was proportional (linear) for both brook trout and brown trout. Electrofishing catchability did not differ significantly between trout species or among seasons or ecoregions. However, catchability was significantly greater for DC towedbarge electrofishing than backpack electrofishing. Trout density alone explained 83% of the variation in catch rate for backpack units and 92% of the variation in catch rate for DC towed-barge units. Stream habitat features such as stream slope, mean stream width, and pool density failed to explain additional variation in catchability. My results indicate that CPE is a precise and accurate measure of abundance in Wisconsin streams. Use of electrofishing CPE will permit Wisconsin fisheries managers to index trout abundance reliably, with less effort. iii

4 ACKNOWLEDGEMENTS I would like to thank the U.S. Geological Survey for providing funding for this project. I would like to thank the Wisconsin Department of Natural Resources for providing data for this project, and extend a special thanks to the multitude of employees who conducted the fieldwork and entered the data into statewide database. Dr. Michael Hansen, Dr. Katherine Clancy, Dr. Tim Ginnett, and Dr. Nancy Nate, the members of my graduate committee, provided constructive criticism and insight during my graduate work. I would especially like to thank Dr. Michael Hansen for his unending support and expert guidance. Many thanks to Dr. Nancy Nate for her assistance with data acquisition and her constant words of encouragement. I also would like to thank all my fellow graduate students for help along the way. Thank you to my family and friends for being an enduring source of support and inspiration. Lastly, I would like to thank my fiancée, Jen, for giving never-ending love and support and for always believing in me. iv

5 TABLE OF CONTENTS ABSTRACT... iii ACKNOWLEDGEMENTS... iv TABLE OF CONTENTS...v LIST OF TABLES... vi LIST OF FIGURES... vii INTRODUCTION...1 METHODS...7 Study Area...7 Field Sampling...8 Data Analysis...9 RESULTS...13 DISCUSSION...15 MANAGEMENT IMPLICATIONS...20 APPENDIX A: Water Body Intentification Code (WBIC), Stream Site Length (Miles), Electrofishing Gear Type, EPA level-iii Ecoregion, and Mark-Recapture Information Used to Evaluate the Relationship Between Adult Brook Trout (BRK) and Adult Brown Trout (BRN) Density and Electrofishing Catch Rates in Wisconsin Streams During APPENDIX B: Water Body Identification Code (WBIC), Stream Slope (m/km), Average Stream Width (m), and Pool Density (%) Used to Evaluate the Effect of Stream Habitat Characteristics on Catchability of Adult Trout to Electrofishing in Wisconsin Streams...61 REFERENCES...64 v

6 LIST OF TABLES Table 1. Characteristics of the five EPA level III ecoregions in Wisconsin where trout are present from Omernik et al. 2000, where NLF = Northern Lakes and Forests, NCH = North Central Hardwood, WCB = Western Corn Belt Plains, DA = Driftless Area, and SE = Southeastern Wisconsin Till Plains...23 Table 2. Sample size (N) and minimum, maximum, and mean values of stream width (m), stream slope (m/km), and pool density (%) for surveyed streams for brook trout and brown trout to determine how stream habitat characteristics affected adult trout catchability to electrofishing Table 3. Sample size (N), mean, minimum, and maximum stream site length and width, and years sampled for surveys where trout catch rate and population density was estimated using one or two DC-towed barge electrofishing units or a backpack electrofishing unit Table 4. Mean, minimum, and maximum catch rate and population density for brook trout and brown trout in Wisconsin streams sampled using one or two DCtowed barge electrofishing units or a backpack electrofishing unit during March November, Table 5. Sample size (N), measurement error ratio (Y/X), bias-corrected slope (β +1), and catchability coefficient (α) estimated from the relationship between log e - transformed catch rate and log e -transformed population density of brook trout and brown trout, electrofishing gear types, seasons, and ecoregions (95% C.I. = 95% confidence interval) during March November, Table 6. Test statistics from un-paired t-tests comparing bias-corrected slopes (β +1) and intercepts (log e α) of relationships between log e (C/f) and log e (N/L) between species (brook trout and brown trout), electrofishing gear types (DC towedbarge and backpack), each pairing of seasons, and each pairing of Wisconsin EPA level III ecoregions during March November, Ecoregions are Northern Lakes and Forests (NLF), North Central Hardwood (NCH), Western Corn Belt Plains (WCB), and Driftless Area (DA) Table 7. Test statistics from un-paired t-tests comparing bias-corrected slopes (β +1) and intercepts (log e α) of relationships between log e (C/f) and log e (N/L) between each pairing of seasons during Table 8. Sample size (N), coefficient of determination (r 2 ), coefficient estimates, and test statistics from backward stepwise regression model that examined the effects of habitat characteristics on DC towed-barge electrofishing catchability of adult brook trout and adult brown trout during March November, Coefficients are defined as follows: α, catchability; β + 1, slope; stream slope (m/km); stream width (average stream width; m); pool density (%)...30 vi

7 LIST OF FIGURES Figure 1. Wisconsin stream site locations (dots) where trout catch rate and population density was estimated. Wisconsin EPA level III ecoregion boundaries are shown, where NLF = Northern Lakes and Forests, WCB = Western Corn Belt Plains, NCH = North Central Hardwood, and DA = Driftless Area...31 Figure 2. Electrofishing catch per mile versus population density of brook trout in Wisconsin streams sampled during March November, Observed values are shown as black dots. The ordinary-least-squares regression line (solid) and bias-corrected regression line (dashed) are shown as curves Figure 3. Electrofishing catch per mile versus population density of brown trout in Wisconsin streams sampled during March November, Observed values are shown as black dots. The ordinary-least-squares regression line (solid), and bias-corrected regression line (dashed) are shown as curves Figure 4. Electrofishing catch per mile versus population density of adult trout in Wisconsin streams sampled in 1981 during spring (triangles), summer (dots), and fall (x). Bias-corrected regression lines are shown as curves (upper = spring, middle = fall, lower = summer)...34 Figure 5. Electrofishing catch per mile versus population density of adult trout in Wisconsin streams sampled using a backpack electrofishing unit during March September, Observed values are shown as black dots. The bias-corrected regression line (solid) is shown as a curve...35 Figure 6. Electrofishing catch per mile versus population density of adult trout in Wisconsin streams sampled using one or two DC towed-barge electrofishing units during March November, Observed values are shown as black dots. The bias-corrected regression line (solid) is shown as a curve vii

8 INTRODUCTION When estimating abundance of fish populations, precision, accuracy, and available resources (mainly time) must be balanced (Simonson and Lyons 1995). Catch per effort (CPE) indices of relative abundance are quicker and less costly than multiplepass estimates of absolute abundance (mark-recapture or depletion) for indexing size of fish populations (Kruse et al. 1998). However, CPE is generally an imprecise index of fish abundance, and therefore may be less useful than more-precise estimates of abundance for assessing the status and trends of fish populations. Imprecision of CPE is caused by catchability varying with fish density and other factors, and measurement error in density estimates (Rogers et al. 2003; Hansen et al. 2004). Understanding factors that affect the relationship between CPE and fish density could help fishery managers to index fish abundance precisely with less effort using catch rates (Rogers et al. 2003). In Wisconsin, trout occur in over 2,900 streams totaling over 10,000 stream miles (WDNR 2002). The two most common trout species in Wisconsin streams are brook trout Salvelinus fontinalis and brown trout Salmo trutta. While both species occur across the state, brook trout are most common in central and northern Wisconsin, and brown trout in southern Wisconsin. Brook trout and brown trout require well-oxygenated, coldwater streams. Brook trout prefer colder, headwater portions of streams, whereas brown trout prefer slightly warmer, downstream portions of streams (Becker 1983; Brasch et al. 1982). Brook trout and brown trout both spawn in autumn, and prefer gravel riffles in spring-fed tributaries for spawning, where upwelling water of spring seepage provides oxygen for young to develop (Becker 1983). The primary prey of brook trout and brown trout are aquatic and terrestrial insects, crustaceans, and fish. 1

9 Brook trout attain an average size of 6 8 inches, and have a high natural mortality rate, so few fish live beyond three years (Brasch et al. 1982). Brown trout attain an average size of about 12 inches, and few live beyond four years (Brynildson et al. 1977). Brook trout and brown trout both support popular and intensive recreational fisheries in Wisconsin, with about 1 million of each species being caught in Wisconsin streams annually (McClanahan 2003). According to the National Survey of Fishing, Hunting, and Wildlife-Associated Recreation, 7.8-million anglers spent 83-million days fishing for trout in the U.S. in 2001 (USFWS 2003). In Wisconsin, 1.3-million anglers spent $1-billion fishing in inland waters. The large number of perennial streams in Wisconsin (over 22,000) precludes Wisconsin Department of Natural Resources (WDNR) fish biologists from monitoring each stream, so short reaches are typically surveyed from a sub-sample of streams annually. Stream sites are selected for sampling to assess the status and trends of trout populations and to address regional management needs. Typically, a single electrofishing pass (catch per effort, CPE) is conducted from downstream to upstream ends of an unblocked stream reach to index trout density. Catch per effort is often used to index fish abundance in fisheries surveys under the assumption that catch per effort is linearly related to fish abundance. If this assumption is true, the portion of a fish population removed by a single unit of fishing effort, defined as catchability (q), must remain constant with changing fish density, so the relationship between catch rate (C/f) and stock density (N/A) is linear: C f = q N A 2

10 where C = catch, f = fishing effort, N = fish abundance, and A = area occupied by the fish stock when catchability is constant (Ricker 1975; Peterman and Steer 1981; Hilborn and Walters 1992; Quinn and Deriso 1999). However, catchability may vary inversely with fish density when searching is non-random or when gear becomes saturated (Peterman and Steer 1981; Arreguin-Sanchez 1996; Shardlow et al 1985; Hansen et al. 2000, 2004; Rogers et al. 2003; Newby et al. 2000). Additionally, CPE and density are measured with error, thereby biasing estimates from ordinary least squares (OLS) regression and hindering testing for nonlinear catchability (Peterman and Steer 1981; Shardlow et al 1985; Rogers et al. 2003; Hansen et al. 2004, 2005). Finally, many factors cause catchability to vary, thereby causing the relationship between CPE and density to be noisy (Hilborn and Walters 1992; Rogers et al. 2003; Hansen et al. 2004; Edwards et al. 1997; Kruse et al. 1998; McInery and Cross 2000). The relationship between catch rate and population density can take three forms: proportional, hyperstability, and hyperdepletion (Hilborn and Walters 1992). A proportional relationship occurs when catch rate varies linearly with population density due to low gear saturation and random searching. Hyberstability, the most common form of density dependent catchability, occurs when catch rate stays high as abundance declines and occurs in fisheries where gear saturation is high or searching is nonrandom (Peterman and Steer 1981; Hilborn and Walters 1992; Rogers et al. 2003). Hyperdepletion occurs when catch rate declines faster than density due to differential response to the gear, such that a small, highly vulnerable subset of fish is depleted, leaving behind a less vulnerable, abundant subset of fish (Hilborn and Walters 1992). 3

11 The relationship between catch rate and population density must be understood to use catch rate as an index of abundance, but catch rate and population density are both measured with error, thereby biasing estimates from ordinary least squares regression (Fuller 1987; Hilborn and Walters 1992; Rogers et al. 2003; Hansen et al. 2005). Because population density is usually estimated with error, estimates derived from OLS linear regression are biased because OLS linear regression relies on the assumption that population density is estimated with much less error than catch rate (Ricker 1975; Shardlow et al. 1985; Hilborn and Walters 1992; Rogers et al. 2003; Hansen et al. 2005). If measurement errors of catch rate and population density are similar, geometric mean regression is appropriate (Ricker 1975). If measurement errors of catch rate and population density are both large, Monte Carlo simulations and measurement-error models must be used to estimate parameters of the relationship between catch rate and population density (Fuller 1987; Hilborn and Walters 1992; Quinn and Deriso 1999; Hansen et al. 2005). Many chemical and physical attributes of waterbodies can cause electrofishing catchability to vary (Reynolds 1996). Water conductivity is the most important environmental factor affecting electrofishing catchability (Reynolds 1996; McInery and Cross 2000; Hansen et al. 2004; Speas 2004). Water conductivity is directly related to total ion concentration and water temperature, and alters the conductance of electrical energy from the water through the fish (Reynolds 1996). Waterbody size also influences electrofishing catchability, with fish generally less vulnerable to capture in larger waterbodies than smaller ones (Kruse et al. 1998; Heggenes 1990). Stream slope has a bell-shaped relationship to electrofishing catchability, with high stream slope reducing 4

12 fish visibility to dip-netters, and low stream slope increasing fish avoidance (Reynolds 1996; Kruse et al. 1998). Many biological factors of the fish community and technical factors used in the electrofishing operation can cause electrofishing catchability to vary (Reynolds 1996). Vulnerability to electrofishing varies among fish species due to differences in morphology, physiology, and behavior. Electrofishing also tends to select for larger fish because smaller fish are not immobilized as effectively as larger fish (Reynolds 1996; Thompson and Rahel 1996). Finally, many technical factors involved in electrofishing sampling can cause electrofishing catchability to vary, including personnel, equipment, and organization (Reynolds 1996). Seasonal fluctuations in hydraulic and thermal regimes in riverine systems cause electrofishing catchability to vary (Zalewski and Cowx 1990). In flowing waters, electrofishing is often most effective at lower flows due to decreased water velocity and increased water clarity (Reynolds 1996). Seasonal variations in water temperature, turbidity, and stream flow affect electrofishing catchability, including warmwater species in the upper Mississippi River (Pierce et al. 1985), smallmouth bass in Wisconsin streams (Lyons and Kanehl 1993), and brown trout and rainbow trout in the Colorado River (Speas et al. 2004). Electrofishing catchability may vary spatially across a landscape. Water conductivity, the most important environmental factor affecting electrofishing catchability, mainly depends on predominant rock formations in the watershed (Zalewski and Cowx 1990). Geology helps to define ecoregion boundaries (Omernik et al. 2000), so electrofishing catchability may vary among ecoregions. Ecoregion boundaries also 5

13 often correspond to abiotic stream characteristics that can affect elecrofishing catchability, including water conductivity, water clarity, stream slope, waterbody size, stream flow, and fish cover (Lyons and Kanehl 1993; Lyons 1989; Whittier et al 1988; Rohm et al. 1987). Regional patterns in electrofishing catchability may vary predictably along ecoregion boundaries due to relatively homogenous stream characteristics present in a particular ecoregion. Whenever catchability varies with fish density and other factors, CPE generally imprecisely indexes fish abundance (Rogers et al. 2003; Hansen et al. 2004). Such lack of precision in CPE of trout density may inhibit making sound management decisions. Rather than relying solely on CPE for assessing trout population size, mark-recapture population estimates are often used. Mark-recapture procedures allow an estimate of absolute abundance, which may be more useful for assessing a population than a crude index of abundance. Reliable estimates of population density are needed to accurately track changes in abundance through time and provide information necessary for managers to help assess population status and determine management actions. An estimate of fish population size is a common population characteristic used to assess population status, along with size structure, body condition, growth, and mortality (Van Den Avyle and Hayward 1999). Such population attributes are important for describing fluctuations in abundance, measuring responses to exploitation or other perturbations, evaluating the impact of management activities, and defining fishery components for management (Van Den Avyle and Hayward 1999). 6

14 The value of CPE as an index of fish density decreases with increased variation in catchability (Arreguin-Sanchez 1996; McInery and Cross 2000; Rogers et al. 2003). A better understanding of factors that cause catchability of fishes to vary may make CPE more useful as a predictor of fish density, or to develop a predictive model based on catch rates (Lyons and Kanehl 1993; Rogers et al. 2003). A predictive model of trout density would permit fishery managers to index trout abundance reliably, with less effort. My objective was to determine if electrofishing catch rate reliably predicts trout density in Wisconsin streams. First, I determined if catchability of brook trout and brown trout of Wisconsin streams was density dependent by testing linearity of the relationship. Because both CPE and trout density are measured with error, I used Monte Carlo methods to estimate bias-corrected slopes and intercepts for relationships between catch rate and density, and an errors-in-variables model to estimate the measurement error ratio between catch rate and density. Next, I explained residual variability in the relationship between catch rate and density using physical and biological attributes of the surveyed streams, Wisconsin ecoregions, electrofishing gear type, and seasons. METHODS Study Area Brook trout and brown trout survey events were queried from the Wisconsin Department of Natural Resource (WDNR) Fish and Habitat Database and other WDNR historic files. The dataset consisted of 751 survey events, located on 275 stream sites on 118 streams across Wisconsin (Figure 1). Survey events occurred in 30 of 67 Wisconsin counties where trout were present, and all five Wisconsin EPA level III ecoregions 7

15 (Omernik and Gallant 1988) where trout were present (Table 1). Surveyed streams had wild or hatchery-reared populations of brook trout, brown trout, or both species. Field Sampling Stream sites were sampled during spring (March-May), summer (June- September), and fall (October-November), Stream sites were selected to assess the status and trends of trout populations and to address regional management needs. Population abundance was estimated by mark-recapture for adult brook trout and brown trout ( 5 inches) with the Chapman modification of the Petersen estimator (Ricker 1975). Because Chapman density estimates are nearly unbiased when the number of fish recaptured exceeds 7 (Van Den Avyle and Hayward 1999), only sites where more than 7 adult trout were recaptured were included in my analysis. Adult trout catch rates were estimated from a single electrofishing pass from downstream to upstream ends of an unblocked stream reach where electrofishing crews attempted to net all trout seen. Stream reaches were typically selected between natural barriers (blocks), such as from riffle to riffle. Adult trout were marked by removal of a fin after capture by 1 2 DC towed-barge electrofishing units or a backpack electrofishing unit. A DC towedbarge electrofishing unit consisted of a generator, output control mechanism, two or three anodes, and a cathode mounted on a small boat that was towed by two to three dip netters (Lyons 1992). Sampling with backpack electrofishing units was conducted with one anode and one or two dip netters. Electrofishing operators adjusted DC output to optimize shocker effectiveness. The recapture sample was collected 1 30 days (mean = 4 days) after the first electrofishing survey using the same sampling methods. 8

16 Habitat was surveyed on a sub-sample of 58 stream sites using a standardized protocol (Simonson et al. 1993; Table 2). Among the habitat characteristics measured were average stream width (m), stream slope (m/km), and pool density (the percentage of the stream site length observed as pools). Data Analysis Electrofishing catch rate (C/f; fish/mile) of adult brook trout and brown trout was modeled as a non-linear function of population density (N/L; number of fish/mile): C f β +1 N ε = α e ; L where α provides an estimate of catchability near the origin and β expresses the degree of curvature in the relationship between C/f and N/L (Peterman and Steer 1981). Parameters were estimated from the log e -transformed model: C N loge = b + b1 log e + ε f L 0 ; where the intercept b 0 = log e α and the slope b 1 = β + 1. When b 1 = 1, the relationship between catch rate and density is linear (proportional), when b 1 < 1, density increases at a faster rate than catch rate (hyperstability), and when b 1 > 1, catch rate increases at a faster rate than density (hyperdepletion). Population density and catch rate are both estimated with error, so Monte Carlo methods were used to estimate bias-corrected parameters of relationships between catch rate and population density (Rogers et al. 2003; Hansen et al. 2004). First, 1,000 random values of the total number of recaptured fish for each survey event were generated from a 9

17 Poisson distribution using the actual number of recaptured fish for the rate parameter, λ. Second, randomly generated recapture values were used to calculate 1,000 estimates of abundance (N) for each survey event using the Chapman modification of the Peterson estimator (Ricker 1975). Third, each estimate of N (i.e. 1,000 randomly generated estimates for each survey event) was converted to density (N/L) by dividing by stream length (L = miles). Fourth, each estimate of density was log e -transformed (i.e. 1,000 randomly generated estimates for each survey event). Fifth, observed catch rates were regressed against each random series of log e density with OLS to estimate 1,000 biased slopes. Lastly, a bias-corrected slope (b bc ) was estimated for each of the 1,000 biased estimates of the slope: b bc = b ols + ( bols bmc ); where b ols is the single slope or intercept estimated by linear regression for the original data, and the b mc is the slope estimate for each of the 1,000 biased estimates of the slope. To test for density dependence in catchability, bias-corrected slopes were tested for a significant difference from 1.0 using the upper and lower percentiles of the distribution of bias-corrected slopes (i.e., approximate 95% confidence interval, CI). I concluded that the relationship was linear if the simulated 95% CI of the slopes included 1.0 and nonlinear if the simulated 95% CI of the slopes did not include 1.0. To examine relative precision of estimates of electrofishing catch rate and population density, measurement-error ratios between electrofishing catch rate and population density were estimated with an errors-in variables model: 2 1 m xy β = 1( m yy δmxx ) δmxy 0 β ; 10

18 where 1 ˆβ is the median bias-corrected slope estimate, myy is the estimated variance in Y, m xx is the estimated variance in X, m xy is the estimated covariance between X and Y, and δ is the Y/X measurement error ratio estimated iteratively (Fuller 1987). Un-paired t-tests were used to determine if bias-corrected slopes (β+1) or intercepts (α) of log e -transformed relationships between electrofishing catch rate and population density differed between gear types (DC tow-barge or backpack) or fish species (brook trout or brown trout), and among ecoregions (Figure 1) or seasons (Spring: March May, Summer: June September, or Fall: October November) in Wisconsin streams during : t b b 1 2 = ; 2 2 s1 s2 n 1 + n 2 where t is the t-statistic, b 1 and b 2 are bias-corrected slopes or intercepts, and s 2 /n are variances of bias-corrected slopes or intercepts estimated using an errors-in variables model (Fuller 1987) when the ratio of measurement variances was known. To test for significance, degrees of freedom (v) were estimated (Zar 1999): 2 2 s1 s 2 + n1 n2 v = s 1 s2 n1 n2 + n n Catchability was considered different if intercepts of the relationships differed significantly from one another. Data were combined for subsequent analysis if slopes or 11

19 intercepts did not differ significantly between gear types or fish species, or among ecoregions or seasons. To examine the effect of sampling year on tests of differences in log e -transformed relationships between catch rate and density among seasons, I tested for differences among seasons for a single sampling year (year effect held constant) when sample size was large enough. Un-paired t-tests were used to determine if bias-corrected slopes (β+1) or intercepts (α) of log e -transformed relationships between electrofishing catch rate and population density differed among seasons for a single sampling year. I modeled factors that affected electrofishing catchability of brook trout and brown trout by incorporating habitat features of surveyed streams into a multiple regression model: C F = α N L β +1 X b X bn n e ε ; where X variables were mean stream width (m), stream slope (m/km), and pool density (%). Parameters were estimated from the log e -transformed model: C N log e = b + b1 log e + b2 log e X b F L 0 n log e X n + ε ; where b 0 = log e (α), b 1 = β+1, and b 2 b n = coefficients that describe how each X variable affected catchability (Hansen et al. 2004; Rogers et al. 2003). Models were fitted using backward stepwise linear regression, where explanatory variables were rejected if P > 0.05, added if P 0.05, and retained in the final model if P

20 RESULTS Electrofishing catch rate (fish/mile) and population density (N/mile) of adult brook trout or adult brown trout was estimated for 751 segments in 118 streams. Mean stream site length was 612 meters for DC towed-barge electrofishing surveys and 276 meters for backpack electrofishing surveys. Mean stream width was 7 meters for DC towed-barge electrofishing surveys and 3 meters for backpack electrofishing surveys (Table 3). Maximum trout density was 7,129/mile and lowest trout density was 19/mile (Table 4). Mean population density and mean catch rate were higher for brown trout than for brook trout (Table 4). Mean population density and mean catch rate were higher for DC towed-barge electrofishing surveys than for backpack electrofishing surveys (Table 4). Mean population density and mean catch rate were highest in spring and lowest in summer (Table 3). Mean population density and mean catch rate were highest in the Western Corn Belt Plains Ecoregion and lowest in the Northern Lakes and Forests Ecoregion (Table 4). Electrofishing catch rate was linearly related to adult trout density in Wisconsin streams for brook trout during and for brown trout (Figures 2 3). Bias-corrected slopes of relationships between log e (C/f) and log e (N/L) did not differ significantly from 1.0 for brook trout (95% CI = ) or brown trout (95% CI = ), so relationships between electrofishing catch rate and density were linear (proportional) for both species (Table 5). Electrofishing catch rate was linearly related to density for each electrofishing gear type, season, and ecoregion. Measurement-error ratios between electrofishing catch rate and population density varied between species and electrofishing gear types and among seasons and ecoregions 13

21 ( ; Table 5). Measurement-error ratios were mostly greater than one (10 of 11), so catch rate was generally measured with greater error than population density. Measurement-error ratios were similar for brook trout and brown trout. The measurement-error ratio for DC towed-barge units was nearly two times higher than the measurement-error ratio for backpack units, so catch rate was measured with greater error for DC-towed-barge units than for backpack units. Measurement-error ratios were greater in spring than summer or fall, so catch rates were measured with greater error during spring than summer or fall. Measurement-error ratios were greatest in the Driftless Area Ecoregion and lowest in the Western Corn Belt Ecoregion, so catch rates were measured with the greatest error in the Driftless Area Ecoregion and with the least error in the Western Corn Belt Plains Ecoregion. Catchability (α) varied between species and electrofishing gear types and among seasons and ecoregions in Wisconsin streams during (Table 6). Catchability was significantly greater for DC towed-barge units than for backpack units, which explains why catch rates were greater for DC towed-barge units than backpack units. The exponent (β +1) of the relationship between catch rate and density did not differ significantly between gear types or species or among seasons or ecoregions. Catchability did not differ significantly between species or among seasons or ecoregions. The relationship between catch rate and density varied among seasons in Wisconsin streams during 1981 (Table 7; Figure 4). The exponent (β +1) of the relationship between catch rate and density was significantly higher in spring than summer. The relationship between catch rate and density did not differ significantly between spring and fall or between summer and fall. 14

22 Adult trout density explained significant variation in catch rate of adult trout to backpack electrofishing in Wisconsin streams (F 1, 44 = 222; P < 0.001): C f N = L In the log-log model, adult trout density explained 83% of observed variation in catch rate of adult trout to backpack electrofishing. For backpack units, catch rate was linearly related to adult trout density (Figure 5). No habitat data were available to evaluate effects of stream habitat characteristics on catchability of adult trout to backpack electrofishing. Adult trout density explained signficant variation in catch rate of adult trout to DC towed-barge electrofishing in Wisconsin streams (F 1, 710 = 7747; P < 0.001): C f N = L In the log-log model, adult trout density explained 92% of observed variation in catch rate of adult trout tow towed-barge electrofishing. For DC towed-barge units, catch rate was linearly related to adult trout density (Figure 6). Stream slope, mean stream width, and pool density each failed to describe significant variation in the relationship between DC towed-barge log e (C/f) and log e (N/L) (Table 6). DISCUSSION I found that electrofishing catchability did not differ significantly between brook trout and brown trout, whereas catch rate of brown trout was greater than catch rate of brook trout because brown trout density was generally higher than brook trout density. Electrofishing catchability often varies between fish species due to differences in 15

23 morphology, physiology, and behavior (Reynolds 1996). For example, electrofishing catchability varied among species in Wisconsin lakes (Schoenebeck et al. 2005) and Illinois lakes (Bayley and Austin 2002). In contrast, electrofishing catchability did not differ between stream-dwelling brown trout and rainbow trout in southern Ontario streams (Jones and Stockwell 1995), as in my study. Stream-dwelling trout species have similar body shapes and sizes that likely lead to similar electrofishing catchability. I found that catch rate was greater for DC towed-barge units than for backpack units because electrofishing catchability and population density were greater for DC towed-barge units than for backpack units. Electrofishing range, and hence effectiveness, increases with increased power (Novotny 1990). Catchability of DC towed-barge units was likely higher than for backpack units because DC towed-barge units have a larger power supply (generator) than backpack units (12-V battery). Adult trout density was higher for DC towed-barge units than for backpack units likely because DC towed-barge units were used in wider streams, where population density (fish/mile) is often higher, to maximize electrofishing effectiveness. Catch rate and population density varied among seasons and ecoregions, but I found that electrofishing catchability did not differ among seasons or ecoregions. Electrofishing catchability varied with seasonal changes in water clarity and stream flow for smallmouth bass in Wisconsin streams (Lyons and Kanehl 1993) and brown trout and rainbow trout in the Colorado River (Speas et al. 2004). Ecoregion boundaries also correspond to abiotic stream characteristics that affect elecrofishing catchability in Wisconsin streams, such as water conductivity (Lyons and Kanehl 1993) and stream slope and water clarity (Lyons 1989). I likely did not detect a difference in electrofishing 16

24 catchability among seasons or ecoregions during because inter-annual variation in catchability within seasons and ecoregions was too large. In contrast, when year effect was held constant (1981 data only), the relationship between catch rate and trout density differed between spring and summer. Therefore, combining more than thirty years of data may have limited my ability to detect seasonal changes because stream characteristics likely varied among years. My ability to detect differences in electrofishing catchability among ecoregions may also have been limited by using stream sites that were near ecoregion boundaries that exhibited characteristics of neighboring ecoregions. The influence of neighboring ecoregions may have been reduced by excluding stream sites that were near ecoregion boundaries (Lyons 1989). I found that catch rate was proportionally (linearly) related to population density for adult brook trout and adult brown trout, so gear saturation was not evident at the highest observed trout densities. No one has previously tested for linearity of the relationship between catch rate and population density for stream-dwelling trout, though proportional relationships seemed evident for cutthroat trout and rainbow trout in Wyoming streams (Kruse et al. 1998) and brown trout in Spanish streams (Lobon-Cervia and Utrilla 1993). Proportional relationships between catch rate and population density usually occur when search is random and gear saturation is minimal (Hilborn and Walters 1992). In my study, search was random because the entire length of the stream site was sampled, and capture efficiency did not decrease with increasing population density even though trout densities were high (up to 7129/mile). Extremely high catching power of commercial fishing vessels may also cause catchability to remain constant as fish density increases (Peterman and Steer 1981). In my study, the use of a backpack electrofishing 17

25 unit with one or two dip netters in very small streams (1 8 meters wide), and one or two DC towed-barge electrofishing units each with two or three dip netters in larger streams (2 18 meters wide), likely resulted in high catching power that limited gear saturation. Measurement error ratios (Y/X) between electrofishing catch rate and population density were greater than 1.0 for 10 of 11 models tested, so catch rate was generally measured with greater error than population density. Measurement error ratios were also greater than 1.0 for electrofishing catch rates of age-0 and adult walleyes in Wisconsin lakes (Hansen et al. 2004; Rogers et al. 2003), and adult walleye, largemouth bass, smallmouth bass, northern pike, and muskellunge in Wisconsin lakes (Schoenebeck et al. 2005). Lower measurement errors of density estimates than of catch rates indicate that mark-recapture methods provide a more precise index of brook trout and brown trout density than CPE in Wisconsin streams. Catchability models for brook trout and brown trout were similar for OLS and bias-corrected methods (Figure 1; Figure 2), so OLS regression methods produced nearly unbiased estimates of intercepts and slopes (Hansen et al. 2005). I found that electrofishing catch rate provided a precise index of brook trout and brown trout density in Wisconsin streams, with logarithms of mark-recapture density estimates explaining more variation in logarithms of catch rates from backpack (83%) and DC towed-barge (92%) units than has previously been quantified in similar studies. For example, abundance explained only 52% of the variation in catch rate of adult brown trout in Welsh streams (Strange et al. 1989), % of the variation in catch rate of rainbow trout and brown trout in southern Ontario streams (Jones and Stockwell 1995), 67% of the variation in catch rate of adult brown trout in Spanish streams (Lobon-Cervia 18

26 and Utrilla 1993), and 69% of the variation in catch rate of various coldwater, coolwater, and warmwater species in Wisconsin streams (Simonson and Lyons 1998). Kruse et al. (1998) found a similar high level of precision as in my study, with abundance explaining 94% of the variation in catch rate of trout in Wyoming streams, and attributed their high level of precision to low trout densities and limited instream cover in small mountain streams. In my study, high precision of the relationship between catch rate and density was not likely caused by low trout density because I observed higher trout density (7129/mile) than was observed by Kruse et al. (1998; 805/mile), and habitat complexity was likely high for 118 streams across Wisconsin. Because electrofishing does not immobilize smaller fish as effectively as larger fish (Reynolds 1996; Thompson and Rahel 1996), my high precision may be partly attributed to only including adult trout ( 5.0 inches) in my analysis. Increased precision in my study may also be a result of variable sampling effort. All previous studies relied on data collected for research purposes, so sampling effort was held constant over a wide range of stream sizes, whereas data for my study came from surveys where sampling effort was varied to maximize trout catch rate. As stream size increased, electrofishing power supply and numbers of electrofishing units and dipnetters increased, thereby increasing precision in the relationship between catch rate and density. I found that stream habitat features such as stream slope, mean stream width, and pool density failed to explain additional variation in catchability, unlike Rosenberger and Dunham (2005) and Kruse et al. (1998) who found a negative relationship between electrofishing catchability of trout and stream size. I may have failed to detect the effects of such stream habitat features on electrofishing catchability because trout density 19

27 predicted catch rates so well that habitat features had little effect on the strength of the relationship. Other stream characteristics not examined in my analysis that have affected electrofishing catchability of trout in previous studies, such as instream cover (Peterson and Cederholm 1984; Thompson and Rahel 1996; Rosenberger and Dunham 2005) and turbidity (Speas et al. 2004), may have affected catchability of trout to electrofishing in Wisconsin streams. However, additional stream characteristics were not necessary to strengthen the relationship, as found by Kruse et al. (1998). MANAGEMENT IMPLICATIONS My results suggest that electrofishing catch rate provides an accurate and precise index of adult trout density in Wisconsin streams. Only when catchability is density independent and the effects of environmental factors on catchability are known, can catch rate be used to estimate population density (Schoenebeck et al. 2005; Rogers et al. 2003; McInery and Cross 2000). Because I found a strong proportional relationship between catch rate and population density, my results suggest that single-pass electrofishing catch rate can be used to accurately and precisely index trout density in Wisconsin streams. Population density of adult trout in Wisconsin streams can be estimated by dividing catch rate by the catchability coefficient for backpack electrofishing or DC towed-barge electrofishing (Table 4), depending on which gear type was used. The upper and lower 95% confidence limit of the density estimate can be calculated by dividing catch rate by the upper and lower 95% confidence limit of the catchability coefficient. I recommend that when DC towed-barge units are used, single-pass electrofishing catch rate be used to estimate adult trout density in Wisconsin streams. For DC towedbarge units, the model-based density estimates provided a similar level of high precision 20

28 as mark-recapture surveys. The coefficient of variation (CV) for the model-based density estimate was Similarly, CVs for mark-recapture density estimates ranged (median = 0.08). Because single-pass electrofishing surveys are quicker and less costly than multiple-pass estimators of abundance (Kruse et al.), use of electrofishing CPE will permit Wisconsin fisheries managers to accurately and precisely index adult trout density, with less effort. For trout streams, time and effort may be reduced by 50% if single-pass catch rates are used instead of mark-recapture density estimates, thereby allowing more surveys in more streams over the same period. For backpack units, mark-recapture density estimates provided a higher level of precision than model-based density estimates. The range of CVs for mark-recapture density estimates was (median = 0.10), while the CV for the model-based density estimates was Therefore, for backpack surveys, mark-recapture methods may be needed to attain a precise estimate of adult trout density in Wisconsin streams. However, single-pass electrofishing catch rate may still be useful if a course index of relative abundance is suitable for the intended purpose. Low precision of model-based density estimates is likely due to relatively small sample size (N = 46) of the backpack catchability model. More backpack surveys may be needed to increase precision of model-based density estimates. My backpack and DC towed-barge catchability models may also be useful to fisheries managers beyond Wisconsin to determine appropriate sample size for markrecapture surveys of stream-dwelling trout. By dividing a single-pass electrofishing estimate of trout abundance by my catchability coefficient for backpack or DC towedbarge units, an initial estimate of trout population size can be calculated. Next, charts 21

29 created by Robson and Regier (1964) can be used to determine numbers of marked fish (M) and numbers of fish examined for marks (C) required to produce mark-recapture estimates with a desired level of precision. 22

30 Table 1. Characteristics of five EPA level III ecoregions in Wisconsin where trout are present (from Omernik et al. 2000), where NLF = Northern Lakes and Forests, NCH = North Central Hardwood, WCB = Western Corn Belt Plains, DA = Driftless Area, and SE = Southeastern Wisconsin Till Plains. Ecoregion Land Surface Form Potential Natural Vegetation Land Use Streams Characteristics NLF Undulating till plains, Great Lakes spruce/fir, Woodland and forest Mostly perennial streams moraine hills, Great Lakes pine and with low productivity, lacustrine basins, and northern hardwoods originating from lakes and sandy outwash plains wetlands 23 NCH Nearly level to rolling Northern hardwoods and Mosaic of forests, Low gradient streams with till plains, lacustrine maple/basswood forests wetlands and lakes, intermediate productivity basins and outwash cropland agriculture, between NLF and DA plains and pasture ecoregions WCB Nearly level to gently Predominantly tall-grass Cropland agriculture Low gradient, productive rolling till plains prairie with mixed and forage for streams with fine (sand, hardwoods livestock silt) substrates DA Relatively broad, flat Oak forests and savanna, Mixed agriculture/ Relatively high gradient valley bottoms with large prairie grasslands, woodlands with streams with high steep crested ridges and mixed hardwoods livestock farming productivity SE Irregular plains Hardwood forest, Cropland Low gradient, high oak savanna, and productivity tall-grass prairies

31 Table 2. Sample size (N) and minimum, maximum, and mean values of stream width (m), stream slope (m/km), and pool density (%) for surveyed streams for brook trout and brown trout to determine how stream habitat characteristics affected adult trout catchability to electrofishing. Species N Variable Min Max Mean Brook trout 28 Stream width Stream slope Pool density Brown trout 30 Stream width Stream slope Pool density

32 Table 3. Sample size (N), mean, minimum, and maximum stream site length and width, and years sampled for surveys where trout catch rate and population density was estimated using one or two DC-towed barge electrofishing units or a backpack electrofishing unit. Site length (m) Site Width (m) a N Mean Min Max Mean Min Max Years DC tow-barge Backpack a Site width statistics calculated from 140 DC towed-barge electrofishing surveys and 14 backpack electrofishing surveys. 25

33 Table 4. Mean, minimum, and maximum catch rate and population density for brook trout and brown trout in Wisconsin streams sampled using one or two DC-towed barge electrofishing units or a backpack electrofishing unit during March November, Catch rate (fish/mile) Density (fish/mile) N Mean Min Max Mean Min Max Species Brook trout Brown trout Gear Type DC Tow-Barge Backpack Season Spring Summer Fall a Ecoregion NLF NCH WCB DA a Twelve survey sites located in the Southeastern Wisconsin Till Plains Ecoregion were not included in the analysis due to small sample size. 26

34 Table 5. Sample size (N), measurement error ratio (Y/X), bias-corrected slope (β +1), and catchability coefficient (α) estimated from the relationship between log e -transformed catch rate and log e -transformed population density of brook trout and brown trout, electrofishing gear types, seasons, and ecoregions (95% C.I. = 95% confidence interval) during March November, β +1 α N Y/X Estimate 95% C.I. Estimate 95% C.I. Species Brook trout Brown trout Gear Type DC towed-barge Backpack Season Spring Summer Fall a Ecoregion NLF NCH WCB DA a Twelve survey sites located in the Southeastern Wisconsin Till Plains Ecoregion were not included in the analysis due to small sample size. 27