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o~P4 ~ h ~ ~ ~ ~AA~ W 2800.7 FS32r/w no.29 F-SO-R-17 2010 c.1 FINAL REPORT FEDERAL AID GRANT NO. F-50-R-17 Fish Research for Oklahoma Waters PROJECT NO. 29 Electrofishing for largemouth bass: An evaluation of Oklahoma's standardized sampling procedures OKLAHOMA DEPARTMENT OF WILDLIFE CONSERV ATION JANUARY 1, 2010 through DECEMBER 31,2010 FINAL REPORT State: Oklahoma Grant Number: F-50-R-17 Grant Title: Fish Research for Oklahoma Waters Project Number: Project Title: Electrofishing for largemouth bass: An evaluation of Oklahoma's standardized sampling procedures Project Leader: Greg Summers Grant Period: From: January 1,2010 to: December 31, 2010 I. Project Objectives: Estimate precision of largemouth bass electro fishing data using 5, 10, and 15-min units of effort. Compare CPUE and precision of largemouth bass electro fishing data using a fixed site and random site sampling design. Review historical SSP electrofishing data and determine if sample sizes were adequate to detect a 25% or 50% change in mean CPUE. II. Summary of Progress A. Introduction Standardized sampling procedures are important in fisheries management and are required to evaluate fish populations over time and between lakes in a region or state. In 1977 the Oklahoma Department of Wildlife Conservation (ODWC) developed "Standardized Sampling Procedures (SSP) for Lake and Reservoir Management Recommendations" (Erickson 1978). Since that time, Oklahoma's SSP have been revised to obtain more accurate population estimates as well as maximize efficiency. Currently the SSP for electrofishing largemouth bass require discretel5-minute units of effort. Miranda et al. (1996) showed mean catch rates for largemouth bass did not change from durations of 5 to 60-min samples although variability increased as duration decreased. A thorough investigation of 5, 10 and 15 minute samples is need to determine if a decreased unit of effort will jeopardize catch-per-unit-effort (CPUE) data. The SSP protocol also requires sample locations to be fixed sites, selected by the biologist in known largemouth bass habitat. Fixed sites chosen in this manner are used to monitor changes in the population over time but are found to be more biased than random sites (Bonar et al. 2009). Biases in abundance and length frequencies can be found from fixed site sampling (Wilde and Fisher 1996, Larsen et al. 2001, Dauwalter et al. 2004). These sites may not be representative of the whole population and characteristics of these sites may change at a disproportional rate to the rest of the lake (Bonvechio in press, Noble et al. 2007). Random sampling alleviates these biases and allows for comparisons to be made between lakes. This is important, as CPUE for the whole population as well as the number oflarge fish (>21in or >8Ibs) is figured as criteria for ODWC Florida largemouth bass stockings. Random sampling would also give managers and Oklahoma anglers a more accurate assessment of how Oklahoma reservoirs compare. The downside to random sampling is the possibility of the data becoming more variable, consequently requiring more effort. Oklahoma currently uses a fixed number of samples needed for each lake depending on the size «500 acres = 6 sites, 500-999 acres = 12 sites, 1,000-9,999 acres = 18 sites, > 10,000 acres =24 sites). With this method, some lakes are adequately sampled to detect a 25% change in the total population while others are still unable to detect a 50% change. If precise changes in a population are not statistically detectable, the data is much less valuable. The Florida Fish and Wildlife Conservation Commission (Bonvechio in press) computes the number of samples needed for each lake from historical data to be able to detect a certain change in the population. Allocating effort in this approach more adequately distributes the effort where it is needed. B. Methods Duration Four reservoirs were electrofished during the spring of2009 and 2010. Two of the reservoirs, Arcadia Reservoir and Wes Watkins Reservoir historically had low largemouth bass catch rates « 50 fish/hr), while the other two, Konawa Reservoir and Dripping Springs Reservoir, had high catch rates (> 100 fish/hr). All four reservoirs were sampled between the end of March and beginning of May when water temperature range from 15 to 23°C which coincides with the pre-spawn and spawn for largemouth bass. Samples were collected with a double boomed electrofishing boat equipped with a 5.0 Smith-Root GPP set at 60 pulses/s of direct current with voltage and amperage set for optimal output depending on conductivity. Daytime samples were taken from 18 fixed sites that regional biologist had historically sampled as well an additional 18 random sites at each lake. Each sample had a total duration of 15 min which was divided into three 5 min units as it was collected. Largemouth bass from each 5 min sample were collected, counted, measured (TL:mm), and weighed (g). For analysis of the data, a 5 min unit was randomly selected from each of the 18 fixed and random sites. Also, two 5 min units were randomly selected from each of the samples to yield a 10 min unit of effort at each site. This allowed an equal number of 5 and 10 min samples (18) to be compared to the 15 min units of effort. CPUE (fish/hr) was determined for 5, 10 and 15 min samples of all fixed and random sites. The coefficient of variation of the mean (CVx; Cyr et al. 1992) was also calculated and compared for each of the three durations of fixed and random sites. Fixed vs. Random A stratified random sampling design was used to select random sites from each of the lakes. The sites were stratified by habitat type (good, fair, and poor). It was previously determined that a IS-minute unit of effort was approximately a 0.5 km transect of shoreline. Pryor to sampling, each lake was surveyed in 0.5 km transects to determine the overall habitat type of each. Habitat types were determined by three criteria, shape/structure of the bottom, substrate type, and type of cover present, to objectively classify the habitats. Different types of the three criteria were given a point value based on largemouth bass habitat preference during the spawn (Table 1; Gene Gilliland, ODWC, personal communication). The points from the three variables were then added together to determine habitat type (0 to 7 points - poor, 8 to 11 points - fair, 12 to 15 points - good). Catch rates (fish/sample) were determined for each 15 minute sample of the three habitat types at each lake and analyzed with a Krustal-Wallis one-way analysis of variance (ANOVA) to determine differences in habitat type since data were not normally distributed. Catch rates (fish/sample) were compared between historical fixed and newly selected random sites for each of the durations at the four lakes in 2009. Since results between fixed and random sites were similar among durations in 2009 and 10-minute duration seems optimal based on variability, fixed and random sites for only the 10-minute duration were analyzed in 2010. In addition catch rates oflargemouth bass> 14 inches were analyzed between fixed and random sites at the 10-minute duration. Data were not normally distributed and loge transformations did not normalize data, therefore a non-parametric Wilcoxon Rank Sum Test (a = .05) was used to determine differences in the medians and their distributions. CPUE (fish/hr) was also compared between fixed and random sites. The error estimate used to measure the precision of fixed and random sites was the CV x. In addition, the mean number of samples needed to achieve a target CV x = .25 and CV x = .125 was calculated (Cunningham and Cofer 2000) for fixed and random sites at each lake. A CVx =.25 and CVx = .125 correspond to a±.50x and±.25x, respectively. Length frequencies for largemouth bass were also compared between fixed and random sites at the 10 min duration. A Kolmogerov-Smirnov Test was used to determine if length frequencies were significantly different (a = .05). In addition length frequency histograms were assessed to identify specific differences. Sample Size Historical spring electro fishing data since 2003 were compiled and reviewed. The percent of lakes that would fall within a ±.50x and ±.25 x was calculated for the four sample size (6, 12, 18, or 24 samples) currently required to verify if currently used sample sizes were adequate. The mean number of samples needed to achieve these target levels of precision was also calculated to compare to the current sample sizes. C. Results/Discussion Duration CPUE did not change substantially for the 5, 10, and 15 min samples at each lake for either fixed or random sites (Figure 1). Slight increases and decreases across durations were observed depending on lake and year. Although subtle changes in CPUE were detected in both fixed and random sites, no overall trend was detected. CPUE was independent of sample duration which is consistent with results reported by Miranda et al. (1996). Although CPUE did not change with duration, variability among durations did. CV x was highest for the 5 min samples and lowest for the 15 min samples among all lake at both fixed and random sites (Figure 2). Three of the four lakes in both 2009 and 2010 had a greater difference in CV x from 5 to 10 min samples than from 10 to 15 min samples at fixed sites. The random sites at all four lakes also showed a similar pattern (Figure 2). In 2009 only the low catch lakes, Arcadia and Wes Watkins Reservoirs, had a greater difference in CV x from 5 to 10 min samples than from 10 to 15 min samples while three lakes exhibited this pattern in 2010. Overall, precision between 10 and 15 minutes at fixed and random sites was very similar. Habitat Classification Dripping Springs and Konawa Reservoirs, the high catch lakes, were classified with 43% good habitat, while the low catch lake, Arcadia and Wes Watkins Reservoirs, had 19% and 33% good habitat, respectively. Consequently, Arcadia had the highest proportion of poor habitat (39%) followed by Wes Watkins (22%). The high catch lakes, Dripping Spring and Konawa had 11% and 19% poor habitat, respectively. Fair habitat ranged from 38% to 46% on all four lakes. Mean CPUE was highest in good habitat and lowest poor habitat for Arcadia, Konawa, and Wes Watkins in 2009 and 2010 (Figure 3). Dripping Springs had a higher mean CPUE in the fair than the good habitat both years. In 2009 poor sites at Dripping Springs were sampled although they had changed between the time the habitat classification was made and the time they were sampled. They were reclassified from poor to fair at time of sampling because increased lake levels flooded shoreline vegetation that was not previously submerged, therefore no catch rates were computed for poor habitat. Median catch rates for habitat types at Arcadia, Wes Watkins, and Konawa Reservoirs in 2009 were significantly different (Krustal-Wallis one-way ANOVA; P = 0.037,0.049, and .05 respectively, Figure 3). In 2010 two lakes, Arcadia (P = 0.001) and Konawa (P = 0.017) had significantly different habitat types. Fixed vs. Random Median catch rates from 2009 were not significantly different between fixed and random sites for any duration at the four lakes (Wilcoxon Rank Sum Test, a = .05, Table 2). Although no differences were found in median catch rates, mean CPUE was lower at random sites than fixed sites (Figure 4). Mean CPUE of random samples at Arcadia Reservoir, decreasing by approximately 30% at random sites for the three durations. This was likely due to the high proportion of poor sites (39%) and low proportion of good sites (19%) sampled. Mean CPUE for random sites was also lower than fixed sites at the 10-minute duration for three of the four lakes in 2010. Only Wes Watkins had a higher mean CPUE at random sites. Wilcoxon Rank Sum tests indicated no significant differences between median catch rates at Arcadia, Dripping Springs, and Wes Watkins, although Konawa was significantly different (Table 3). Data collected from random sites were also less precise than the fixed sites in 2009 (Figure 5). The CV x was higher for random sites than fixed sites at all durations at each lake except the 5 min duration at Wes Watkins Reservoir. A target CVx :S0.2 was achieved at fixed sites for 10 and 15 min durations at all lakes. Only the high catch lakes, Dripping Springs and Konawa Reservoirs, achieved the target CVx :S0.2 for random sites which included all durations. In 2010 data were one again less precise in the random sites than the fixed sites at the 10 min duration. The target CV x :S0.2 was met at fixed and random sites at all lakes except for the random sites at Arcadia (Table 3). With the current required effort (18 samples), a ±50% change in the population, as indicated by mean CPUE of all sizes of LMB, could be detected at fixed sites for 10 and 15 min durations at low catch lakes, while it could be detected at all durations at the high catch lakes (Figure 6). A ±0.50% change could be detected at random sites for all durations at Dripping Springs and Konawa Reservoirs (high catch lakes) as well as Wes Watkins (low catch) Reservoir. This change in the mean was detected at Arcadia's random sites only with the duration of 15 min. The precision of the 10 min duration, random samples (18 units) at Arcadia was close to detecting a ±50% change in mean CPUE; 19 samples were needed. In 2010 a ±0.50% change in mean CPUE could be detected at the 10 min duration at all lakes for both fixed and random sites (Table 3). In 2009 the data were not precise enough to detect a ±0.25% change in CPUE for the low catch lakes, Arcadia and Wes Watkins Reservoirs, at either fixed or random sites for any of the three durations (Figure 7). Konawa Reservoir was able to detect this ±0.25% change at fixed sites for all three durations but this precision level was only achieved at the 15 min duration at Konawa's random sites. The 10 min duration at Konawa's random sites needed only one additional unit to detect a ±25% change in CPUE. The data at Dripping Springs Reservoir were the most precise as fixed and random samples for all three durations were able to detect this 25% change. In 2010 Arcadia was the only lake unable to detect a ±0.25% change in CPUE at fixed sites for the 10 min duration (Table 3). In addition only the high catch lakes, Dripping Springs and Konawa, were able to achieve this level of precision at the random sites. Fixed and random sites at the 10 min duration were also analyzed for largemouth bass >14 inches in 2009 and 2010. Once again CPUE >14 was lower for random sites than fixed sites at all lake except Wes Watkins in 2010. Median catch rates for largemouth bass> 14 inches were not significantly different (Wilcoxon Rank Sum Test, a = .05, Table 4) at the 10 min duration at Wes Watkins, Dripping Springs, and Konawa. Only Arcadia was significantly different in 2009 (P = 0.047) and 2010 (P = 0.023). For largemouth bass> 14 inches, a target CV = 0.2 was met at Arcadia for only the fixed sites in 2010. The other low catch lake, Wes Watkins, achieved this level of precision for fixed sites both years but failed to reach it on the random sites in 2009. The data for the high catch lakes, Dripping Springs and Konawa, met this level of precision for fixed and random sites both years (Table 4). Data were precise enough to detect a ±50% change in the population of largemouth bass > 14 inches for fixed sites at all lakes in 2009 and 2010. Wes Watkins also met this level of precision in random sites in 2010. Both Konawa and Dripping Drippings were also able to detect this change for random sites both years. Furthermore, Konawa and Dripping springs were able to detect a ±25% change in largemouth bass> 14 inches for fixed sites. Only Dripping Springs was able to detect this change at random sites in 2009 (Table 4). Length frequencies were similar between fixed and random sites (Figure 8). Although no major differences were obvious on any of the lakes in either 2009 or 2010, Kolmogerov- Smirnov tests indicated a significant difference (P = 0.046) in size structure between fixed and random sites at Dripping Springs in 2009. It is likely the significant difference was detected due to the larger sample size. These results suggest the size structure of largemouth bass collected at fixed and random sites is negligible. Sample Size Historical data analysis indicated the number of samples currently taken based on lake size is adequate to detect a ±0.50% change in mean CPUE (target CV x :S0.25) in most sizes of reservoirs. With larger required samples in lakes ~1 0,000 acres, target CV x :S 0.25 were detected 100% of the time. Lakes less than 10,000 acres, even though they required smaller numbers of samples, still detected this change approximately 95% of the time (Table 5). A more precise detection level of ±0.25 x of CPUE was less often achieved in lakes throughout Oklahoma (Table 6). Again, lakes ::::'10,000acres, with larger sample sizes, were the most precise with as 81.8% of the historic electrofishing efforts detecting this level of change. Only 62.9% of the lakes from 1,000 acres to 9,999 acres were able to detect a ±0.25% change in mean CPUE. Smaller lakes :s 999 acres were the least likely to detect this change as approximately 46% achieved a target CV x :s 0.125. It is unknown whether this inverse relationship between lake size and precision is due to mere differences in required sample size or changes in variability within largemouth bass populations. It is conceivable that smaller lakes and lower density largemouth populations are more affected by environmental and made-made perturbations and therefore have inherently more variability. The mean number of samples needed to detect a ±0.25% change in mean CPUE was slightly higher for most lake size than the number currently required (Table 6). Small lakes «500 acres) and lakes 1,000 to 9,999 acres needed an additional four samples, while lake 500-999 acres required an additional seven samples. The mean number of samples needed for lake greater than 10,000 to detect a ±0.25% change in mean CPUE was 24, the number of samples required. D. Conclusions/Recommendations • CPUE did not change with duration in fixed or random sites at any of the four lakes sampled. • CV x changed more between 5 and 10 minute samples than between 10 and 15 minute samples. Shortening the duration from 15 to 10 min may not jeopardize the precision of data on most lakes. • Median catch rates generally were not significantly different between fixed and random sites although CPUE was usually lower for random sites than fixed sites, presumably due to sampling of poor habitat. • Precision was higher for fixed sites than random sites. A target CVx < 0.2 was achieved in 10 and 15 min samples at fixed sites at all four lakes. This precision was only consistently achieved at random sites for the high catch lakes, Dripping Spring and Konawa Reservoirs. • CPUE of largemouth bass> 14 inches was less precise than for the total population. Precision to detect a ±0.50% change in mean was achieved at fixed sites of all lakes. Random sites at high catch lakes were also able to achieve this level of precision although low catch lakes were inconclusive. • Approximately 95% of lakes sampled since 2003 were able to detect a ±0.50% change in mean CPUE of all size classes. • A ±0.25% change in mean CPUE is less likely to be detected in small lakes compared to larger lakes. If a precise changes in total population or specific length groups are desired, sampling should be based on variability not lake size. • All four lakes, Arcadia, Wes Watkins, Dripping Springs and Konawa Reservoirs, will be sampled again in the next segment to verify results. Habitat at Dripping Springs should be reclassified to verify correct classification. III. Significant Deviations: None Prepared by: _ Chas Patterson, Fisheries Biologist Date: :So HA12.. \ \ Approved by: Fisheries Division Administration Oklahoma Department of Wildlife Conservation IV. Literature Cited Bonar, S. A., S. Contreras-Balderas, and A. C. Iles. 2009. An introduction to standardized sampling. Pages 1-12 in Bonar, S. A., W. A. Hubert, and D. W. Willis, editors. Standard methods for sampling North American freshwater fishes. American Fisheries Society, Bethesda, Maryland. Bonvechio, K. 1. In Press. Standardized sampling manual. Florida Fish and Wildlife Conservation Commission. Tallahassee, Florida. Cyr, H., J.A. Downing, S. Lalonde, S.B. Baines, and M.L. Pace. 1992. Sampling larval fish populations: choice of sample number and size. Transactions of the American Fisheries Society 121:356-368. Cunningham, K. K., and L. M. Cofer. 2000. Evaluation of the related catch rates of hoop nets for sampling channel catfish. Proceeding of the Annual Conference of the Southeastern Association ofFish and Wildlife Agencies 54:80-87. Dauwater, D. C., W. L. Fisher, R. A. Marston, and D. K. Splinter. 2004. Random selection of stream sites: an important step in fluvial geomorphic and fishery surveys. Pages 30-33 in J. R. Copeland, F. Fiss, P. E. Balkenbush, and C. S. Thomason, editors. Warmwater streams symposium II. Available www.sdafs.org/wwstreams/wwscl.htm (June 2007). Erickson, K. E. 1978. Standardized sampling procedures for lake and reservoir management recommendations. Oklahoma Federal Aid Project F-38-R, Job 1. Performance Report. 27p. Larsen, D. P., P. R. Kaufmann, T. M. Kincaid, and N. S. Urquhart. 2004. Detecting persistent change in the habitat of salmon-bearing streams in the Pacific Northwest. Canadian Journal of Fisheries and Aquatic Sciences 61 :283-291. Miranda, L.E., W.D. Hubbard, S. Sangare, and T. Holman. 1996. Optimizing electrofishing sample duration for estimating relative abundance of largemouth bass in reservoirs. North American Journal of Fisheries Management 16:324-331. Noble, R. L., D. J. Austen, and M. A. Pegg. 2007. Fisheries management study design considerations. Pages 31-49 in C. S. Guy and M. L. Brown, editors. Analysis and Interpretation of Freshwater Fisheries Data. American Fisheries Society, Bethesda, Maryland. Wilde, G. R., and W. L. Fisher. 1996. Reservoir fisheries sampling and experimental design. Pages 397-409 in L. E. Miranda and D. R. DeVries, editors. Multidimensional approaches to reservoir fisheries management. American Fisheries Society Symposium 16, Bethesda, Maryland. Table 1. Habitat criteria used to determine habitat type of shoreline transects. Shape/Structure Point Value Point Value Flat in cove Points Moderate slope (""30° to 45°) Mainlake flat Steep slope (""45° to 60°) Cliff Unknown 5 4 3 2 1 o o Substrate Point Value Cover Gravel (smaller than fist) Clay Sand Rock Bedrock SiltlMud Unknown 5 4 3 2 1 oo Aquatic vegetation Timber/Brush Rock (large rock/boulders) None 5 4 3 o Poor v; 7 points Total Points for habitat rank: Fair = 8 to 11 points Good = 12 to 15 points Table 2. Mean and median catch rates (fish/sample) of four Oklahoma lakes sampled in 2009. Wilcoxon Rank Sum Tests (a = .05) indicated no differences between fixed and random sites for the three sample durations at the four lakes. Low Catch Sample Fixed / p- Sample Fixed / p- Lakes Duration Random N Mean Median value High Catch Lakes Duration (min) (min) Random N Mean Median value Arcadia 5 Fixed 18 1.33 0.5 0.49 Dripping Springs 5 Fixed 18 9.78 10.0 0.58 Arcadia 5 Random 18 0.83 0.0 Dripping Springs 5 Random 18 8.28 7.5 Arcadia 10 Fixed 18 3.11 3.0 0.10 Dripping Springs 10 Fixed 18 18.83 19.0 0.27 Arcadia 10 Random 18 1.83 1.0 Dripping Springs 10 Random 18 17.61 17.0 Arcadia 15 Fixed 18 3.44 5.0 0.12 Dripping Springs 15 Fixed 18 27.61 27.0 0.32 Arcadia 15 Random 18 3.22 1.5 Dripping Springs 15 Random 18 25.75 24.5 Wes Watkins 5 Fixed 18 1.28 0.5 0.21 Konawa 5 Fixed 18 12.78 12.5 0.66 Wes Watkins 5 Random 18 1.28 1.0 Konawa 5 Random 18 11.44 11.5 Wes Watkins 10 Fixed 18 2.39 2.0 0.39 Konawa 10 Fixed 18 26.44 29.0 0.69 Wes Watkins 10 Random 18 2.17 2.0 Konawa 10 Random 18 22.10 21.0 Wes Watkins 15 Fixed 18 3.55 3.0 0.32 Konawa 15 Fixed 18 39.10 42.5 0.61 Wes Watkins 15 Random 18 3.11 3.0 Konawa 15 Random 18 33.56 35.0 Table 3. Mean CPUE, CV x, and number of samples need to achieve a CV x = .25 and CV x = .125 for fixed and random sites at each of the four lake for the 10 min duration in 2010. The median catch rates are also given with corresponding P-values from Wilcoxon Rank Sum Test (a = .05). Arcadia Dripping Springs Fixed Random P-value Fixed Random P-value CPUE 22 16 CPUE 132 121 CVx 0.13 0.24 CVx 0.07 0.09 CVx=.25 5 17 CVx=.25 2 2 CVx=.125 19 67 CVx=.125 6 9 Median 16 6 0.305 Median 84 82 0.466 Wes Watkins Konawa Fixed Random P-value Fixed Random P-value CPUE 21 26 CPUE 188 149 CVx 0.12 0.14 CVx 0.07 0.11 CVx=.25 4 6 CVx=.25 1 3 CVx=.125 16 24 CVx=.125 6 13 Median 12 14 0.371 Median 124 88 0.046 Table 4. Mean CPUE, CV x, and number of samples need to achieve a CV x =.25 and CV x = .125 for fixed and random sites at each of the four lake for the 10 min duration in 2009 and 2010. The median catch rates are also given with corresponding P-values from Wilcoxon Rank Sum Test (a = .05). 2009 2010 Arcadia Arcadia Fixed Random P-value Fixed Random P-value CPUE 17 7 CPUE 12 6 CVx 0.24 0.3 CVx 0.18 0.36 CVx=.25 16 27 CVx=.25 10 38 CVx=.125 64 106 CVx=.125 15 151 Median 8 2 0.047 Median 8 0 0.023 Wes Watkins Wes Watkins Fixed Random P-value Fixed Random P-value CPUE 13 9 CPUE 11 13 CVx 0.18 0.28 CVx 0.15 0.15 Cvx=.25 9 22 CVx=.25 6 7 CVx=.125 35 87 CVx=.125 25 27 Median 8 4 0.132 Median 8 8 0.504 Konawa Konawa Fixed Random P-value Fixed Random P-value CPUE 75 62 CPUE 101 79 CVx 0.1 0.15 CVx 0.11 0.16 CVx=.25 3 6 CVx=.25 3 7 CVx=.125 12 25 CVx=.125 14 28 Median 40 40 0.446 Median 66 44 0.216 Dripping Springs Dripping Springs Fixed Random P-value Fixed Random P-value CPUE 31 26 CPUE 24 21 CVx 0.1 0.12 CVx 0.16 0.16 CVx=.25 11 18 CVx=.25 4 7 CVx=.125 4 7 CVx=.125 17 28 Median 20 16 0.388 Median 16 14 0.423 Table 5. Percent oflakes in Oklahoma since 2003 that obtained a CVx :S.25 categorized by lake size. This level of precision allows a detection of ±0.50 x. The mean number of samples needed to obtain a CV x = .25 is also indicated. Lake Size # of Samples % Lakes Mean Samples Needed (acres) Required N CVx <.25 (CVx =.25) < 500 6 80 96.25% 2 500 - 999 12 26 95.83% 5 1,000-9,999 18 59 91.94% 6 >10,000 24 22 100.00% 6 Table 6. Percent oflakes in Oklahoma since 2003 that obtained a CVx :S.125 categorized by lake size. This level of precision allows a detection of ±0.25 x. The mean number of samples needed to obtain a CV x = .125 is also indicated. Lake Size # of Samples % Lakes Mean Samples Needed (acres) Required N CVx :S.125 (CVx =.125) < 500 6 80 47.50% 10 500 - 999 12 26 45.83% 19 1,000-9,999 18 59 62.90% 22 >10,000 24 22 81.82% 24 Figure 1. Mean CPUE (fish/hr) at 5, 10 and 15 min durations from four Oklahoma lakes in 2009 and 2010 at fixed and random sites. 2009 Fixed 180 160 - - - - - - 140 --Arcadia 120 --Wes Watkins :.<;:: 100 - - - - - - - - DrippingSprings o 80 - Konawa "o' 60 40 20 0 5 10 15 duration (min) Random 160 140 :.c::11802000 ~ 60 u 402~~~~~====~~~ - - - - ... 5 10 15 duration (min) ••• Konawa -Arcadia -Wes Watkins n - - DrippingSprings 200 180 160 140 120 ~ 100 '" 80 60 40 20 160 140 120 100 s:~ 80 '" 60 40 20 2010 Fixed ---------- .. 10 duration (min) Random 10 duration (min) 15 15 --Arcadia --Wes Watkins - Dripping Springs - ..•.• Konawa --Arcadia --Wes Watkins - Dripping Springs - •.••• Konawa Figure 2. CVx at 5,10 and 15 min durations from four Oklahoma lakes in 2009 and 2010 at fixed and random sites. 0.35 0.30 0.25 . 0.20 >o 0.15 2009 Fixed 0.10 0.05 0.00 +------.-----,__----~ 5 10 duration (min) Random 0.35 0.30 0.25 > 0.20 U 0.15 0.10 0.05 0.00 -j----,-------.-----, - - -- -- - - - - 5 10 duration (min) 15 15 --Arcadia --Wes Watkins DrippingSprings - - -Konawa -Arcadia ---Wes Watkins • Dripping Springs • Konawa 0.35 0.3 0.25 0.2 :> c..i 0.15 0.1 0.05 0.35 0.3 0.25 0.2 :> c..i 0.15 0.1 0.05 2010 Fixed .. ... - - - - - 10 duration (min) Random - - - - - - o+------~-----,__----~ 10 duration (min) 15 15 --Arcadia --Wes Watkins - Dripping Springs - •.• - Konawa --Arcadia --Wes Watkins - Dripping Springs - ..• - Konawa Figure 3. CPUE (fish/hr) of three habitat types (good, fair, poor) for four Oklahoma lakes in 2009 and 2010. Lakes denoted with * had significantly different catch rates for the three habitats (Krustal- Wallis one-way ANOVA, a = .05). 2009 180 114600 .. .. .. •• •• .. 120 .. n _ •• t---Arcadia* ~--Wes Watkins* m Dripping Springs - Konawa* - -- .. 100 •• .. .. 80 •• 60 40 2o°l__ ~===='~====::-- =_=_~~~ Good Fair Poor 2010 250 200 ... •• •• ---Arcadia* ---Wes Watkins - u • Dripping Springs 150 •• ... .. .. .. .• .. '" "'" ~ ~-.•.. -..- - - 100 .. .• - - - Konawa* 50 a +-------------~r_------------_,------~----__. Good Fair Poor Figure 4. Mean CPUE (fish/hr) for fixed and random sites at 5, 10 and 15 min durations for four Oklahoma lakes in 2009. Arcadia Wes Watkins 20 19 19 20 •.. 15 •.. 15 15 15 14 s: s: :2 10 :2 10 .I•II. <I;I:I: 5 5 0 0 5 10 15 5 10 15 duration (min) duration (min) Dripping Springs Konawa 120 117 200 .. 111105 •.. 150 153 159 156 .r:: s: :2 105 :2 100 III III <;:: 100 <;:: 95 50 90 0 5 10 15 5 10 15 duration (min) duration (min) Figure 5. CVx for fixed and random sites at 5, 10 and 15 min durations for four Oklahoma lakes in 2009. Arcadia Wes Watkins 0.40 0.35 0.30 0.30 0.25 > 0.20 > 0.20 0 0 0.15 0.10 0.10 0.05 0.00 0.00 5 10 15 5 10 15 duration (min) duration (min) Dripping Springs Konawa 0.10 0.15 0.13 0.08 0.06 0.10 U> 0.04 >0 0.05 0.02 0.00 0.00 5 10 15 5 10 15 duration (min) duration (min) Figure 6. Number of samples need to achieve a CVx =.25 for fixed and random sites at 5,10 and 15 min durations for four Oklahoma lakes in 2009. This level of precision allows a detection of ±0.50x. 160 140 "C 120 Q) ~ 100 Q) ~ 80 ~Q . 60 E~ 40 20 o ~ 2 "Ca: 1.5 c: Vl Q) Co cEu 0.5 Vl Arcadia 144 5 10 duration (min) 15 Dripping Springs 2.5 2 2 2 2 o 5 10 15 duration (min) 30 al 25 al 20 Q) c: VI 15 .!!! 10 Co ~ 5 VI o 6 al 5 al 4 Q) c: 3 VI .!!! 2 Co ~ 1 VI o Wes Watkins 27 5 10 15 duration (min) Konawa 6 5 10 15 duration (min) Figure 7. Number of samples need to achieve a CVx = .125 for fixed and random sites at 5,10 and 15 min durations for four Oklahoma lakes in 2009. This level of precision allows a detection of ±0.25 x. Arcadia Wes Watkins 60 120 110 "C 50 "C 100 Q) Q) "QC) 40 36 "QC) 80 Q) Q) ~ 30 c: 60 III ~ 20 Q) c.. c.. 40 ~ 10 IEII 20 f/l f/l 0 0 5 10 15 5 10 15 duration (min) duration (min) Dripping Springs Konawa 12 10 25 22 "C 10 "C Q) Q) 20 "C 8 "C Q) Q) sQ::): Q) 15 6 c: f/l f/l Q) 10 4 Q) Q. c.. E 2 E 5 III III f/l f/l 0 0 5 10 15 5 10 15 duration (min) duration (min) Figure 8. Length frequency histograms from four Oklahoma lakes for fixed and random sites in 2009 and 2010. Kolmogerov-Smirnov tests indicated a significant difference (P = 0.046) in size structure between fixed and random sites at Dripping Springs in 2009. Arcadia 2009 16.00% 14.00% 12.00% 10.00% .c. 8.00% ee a. 6.00% 4.00% 2.00% 0.00% 14.00% 12.00% 10.00% ce 8.00% ea . 6.00% 4.00% ••..~.•..k.. ,.~5.."~ ,,~ ~ ,,~ ~ ~~ ~ ,,':'3~ -, ~~ •..~..~ "i>'~ "~,,~-,,,~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ lenght group Wes Watkins 2009 25.00% 25.00% 5.00% 20.00% 20.00% 15.00% C "u ~ 10.00% a. e 15.00% '"~~ 10.00% " 5.00% Dripping Springs 2009 ,,',:,"'-,!'"," ,,~.," ,,~f\." "x"," -,':<5:>" -,?J"," ","'j"," •...J•>•.••"• ,'P'"," "P<:>" "'<.?"," ,,<0"," ...1..:•l••"• ~ ~ ~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ ~ lenght group Konawa 2009 lenght group ,,~~,,~~,,~~,,~~,,~~,,~~,~~,,~~ ,,~~,,~~ ,,~~•.....~~ ,,~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ percent Dripping Springs 2010 Figure 8 continued. Length frequency histograms from four Oklahoma lakes for fixed and random sites in 2009 and 2010. 2.00% 0.00% I..""" " ,11,11 Arcad ia 2010 16.00% 14.00% 12.00% 10.00% 8.00% 1.e:.: 6.00% Q. 4.00% 2.00% 0.00% 12.00% 10.00% 1:: ~ 8.00% :; Q. 6.00% 4.00% 2.00% 0.00% II ,11,11,11,11,11,1 ,11,11,11,11,11,11,11,11,11,11,1·1,11,11,11,11, ,11,1 , "..~.:'- ",/'~ .J..'~ ~'V~ ,,~~ ",'"~'3 •..?•:J.~ ,,~ ~ ~ ~ ~ ~ ,,<,~j ,,':l~ ,,<.;)~ ~ ~ ~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ lenghl group Wes Watkins 2010 14.00% 12.00% 10.00% Q. .e",. 8.00% :.E,. 6.00% -.". 4.00% 2.00% 0.00% 20.00% 18.00% 16.00% 14.00% _ 12.00% .e". 10.00% Q. 8.00% 6.00% 4.00% ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ ~ ~ percent ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 10·..:. ,,:-. ••.~•.. ,,~ ,,'1; ",'V •.•?..J ,.".':i •.•s..;. .Ji' ..J.i... •.•<.:'J. ,,~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ ~ ~ lenghl group Konawa 2010 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ <:0": <:-, ,,~ •..~.. ,,~ <:1); ,,':':J •.•r.:.3 -, ~ ~ ,,> ,,':J •.5..'5. -: Y5 ~ ~ ~ v ~ ~ ~ ~ ~ ~ ~ ~ ~ lenghl group
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Title | Electrofishing for largemouth bass 2010 |
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Full text | o~P4 ~ h ~ ~ ~ ~AA~ W 2800.7 FS32r/w no.29 F-SO-R-17 2010 c.1 FINAL REPORT FEDERAL AID GRANT NO. F-50-R-17 Fish Research for Oklahoma Waters PROJECT NO. 29 Electrofishing for largemouth bass: An evaluation of Oklahoma's standardized sampling procedures OKLAHOMA DEPARTMENT OF WILDLIFE CONSERV ATION JANUARY 1, 2010 through DECEMBER 31,2010 FINAL REPORT State: Oklahoma Grant Number: F-50-R-17 Grant Title: Fish Research for Oklahoma Waters Project Number: Project Title: Electrofishing for largemouth bass: An evaluation of Oklahoma's standardized sampling procedures Project Leader: Greg Summers Grant Period: From: January 1,2010 to: December 31, 2010 I. Project Objectives: Estimate precision of largemouth bass electro fishing data using 5, 10, and 15-min units of effort. Compare CPUE and precision of largemouth bass electro fishing data using a fixed site and random site sampling design. Review historical SSP electrofishing data and determine if sample sizes were adequate to detect a 25% or 50% change in mean CPUE. II. Summary of Progress A. Introduction Standardized sampling procedures are important in fisheries management and are required to evaluate fish populations over time and between lakes in a region or state. In 1977 the Oklahoma Department of Wildlife Conservation (ODWC) developed "Standardized Sampling Procedures (SSP) for Lake and Reservoir Management Recommendations" (Erickson 1978). Since that time, Oklahoma's SSP have been revised to obtain more accurate population estimates as well as maximize efficiency. Currently the SSP for electrofishing largemouth bass require discretel5-minute units of effort. Miranda et al. (1996) showed mean catch rates for largemouth bass did not change from durations of 5 to 60-min samples although variability increased as duration decreased. A thorough investigation of 5, 10 and 15 minute samples is need to determine if a decreased unit of effort will jeopardize catch-per-unit-effort (CPUE) data. The SSP protocol also requires sample locations to be fixed sites, selected by the biologist in known largemouth bass habitat. Fixed sites chosen in this manner are used to monitor changes in the population over time but are found to be more biased than random sites (Bonar et al. 2009). Biases in abundance and length frequencies can be found from fixed site sampling (Wilde and Fisher 1996, Larsen et al. 2001, Dauwalter et al. 2004). These sites may not be representative of the whole population and characteristics of these sites may change at a disproportional rate to the rest of the lake (Bonvechio in press, Noble et al. 2007). Random sampling alleviates these biases and allows for comparisons to be made between lakes. This is important, as CPUE for the whole population as well as the number oflarge fish (>21in or >8Ibs) is figured as criteria for ODWC Florida largemouth bass stockings. Random sampling would also give managers and Oklahoma anglers a more accurate assessment of how Oklahoma reservoirs compare. The downside to random sampling is the possibility of the data becoming more variable, consequently requiring more effort. Oklahoma currently uses a fixed number of samples needed for each lake depending on the size «500 acres = 6 sites, 500-999 acres = 12 sites, 1,000-9,999 acres = 18 sites, > 10,000 acres =24 sites). With this method, some lakes are adequately sampled to detect a 25% change in the total population while others are still unable to detect a 50% change. If precise changes in a population are not statistically detectable, the data is much less valuable. The Florida Fish and Wildlife Conservation Commission (Bonvechio in press) computes the number of samples needed for each lake from historical data to be able to detect a certain change in the population. Allocating effort in this approach more adequately distributes the effort where it is needed. B. Methods Duration Four reservoirs were electrofished during the spring of2009 and 2010. Two of the reservoirs, Arcadia Reservoir and Wes Watkins Reservoir historically had low largemouth bass catch rates « 50 fish/hr), while the other two, Konawa Reservoir and Dripping Springs Reservoir, had high catch rates (> 100 fish/hr). All four reservoirs were sampled between the end of March and beginning of May when water temperature range from 15 to 23°C which coincides with the pre-spawn and spawn for largemouth bass. Samples were collected with a double boomed electrofishing boat equipped with a 5.0 Smith-Root GPP set at 60 pulses/s of direct current with voltage and amperage set for optimal output depending on conductivity. Daytime samples were taken from 18 fixed sites that regional biologist had historically sampled as well an additional 18 random sites at each lake. Each sample had a total duration of 15 min which was divided into three 5 min units as it was collected. Largemouth bass from each 5 min sample were collected, counted, measured (TL:mm), and weighed (g). For analysis of the data, a 5 min unit was randomly selected from each of the 18 fixed and random sites. Also, two 5 min units were randomly selected from each of the samples to yield a 10 min unit of effort at each site. This allowed an equal number of 5 and 10 min samples (18) to be compared to the 15 min units of effort. CPUE (fish/hr) was determined for 5, 10 and 15 min samples of all fixed and random sites. The coefficient of variation of the mean (CVx; Cyr et al. 1992) was also calculated and compared for each of the three durations of fixed and random sites. Fixed vs. Random A stratified random sampling design was used to select random sites from each of the lakes. The sites were stratified by habitat type (good, fair, and poor). It was previously determined that a IS-minute unit of effort was approximately a 0.5 km transect of shoreline. Pryor to sampling, each lake was surveyed in 0.5 km transects to determine the overall habitat type of each. Habitat types were determined by three criteria, shape/structure of the bottom, substrate type, and type of cover present, to objectively classify the habitats. Different types of the three criteria were given a point value based on largemouth bass habitat preference during the spawn (Table 1; Gene Gilliland, ODWC, personal communication). The points from the three variables were then added together to determine habitat type (0 to 7 points - poor, 8 to 11 points - fair, 12 to 15 points - good). Catch rates (fish/sample) were determined for each 15 minute sample of the three habitat types at each lake and analyzed with a Krustal-Wallis one-way analysis of variance (ANOVA) to determine differences in habitat type since data were not normally distributed. Catch rates (fish/sample) were compared between historical fixed and newly selected random sites for each of the durations at the four lakes in 2009. Since results between fixed and random sites were similar among durations in 2009 and 10-minute duration seems optimal based on variability, fixed and random sites for only the 10-minute duration were analyzed in 2010. In addition catch rates oflargemouth bass> 14 inches were analyzed between fixed and random sites at the 10-minute duration. Data were not normally distributed and loge transformations did not normalize data, therefore a non-parametric Wilcoxon Rank Sum Test (a = .05) was used to determine differences in the medians and their distributions. CPUE (fish/hr) was also compared between fixed and random sites. The error estimate used to measure the precision of fixed and random sites was the CV x. In addition, the mean number of samples needed to achieve a target CV x = .25 and CV x = .125 was calculated (Cunningham and Cofer 2000) for fixed and random sites at each lake. A CVx =.25 and CVx = .125 correspond to a±.50x and±.25x, respectively. Length frequencies for largemouth bass were also compared between fixed and random sites at the 10 min duration. A Kolmogerov-Smirnov Test was used to determine if length frequencies were significantly different (a = .05). In addition length frequency histograms were assessed to identify specific differences. Sample Size Historical spring electro fishing data since 2003 were compiled and reviewed. The percent of lakes that would fall within a ±.50x and ±.25 x was calculated for the four sample size (6, 12, 18, or 24 samples) currently required to verify if currently used sample sizes were adequate. The mean number of samples needed to achieve these target levels of precision was also calculated to compare to the current sample sizes. C. Results/Discussion Duration CPUE did not change substantially for the 5, 10, and 15 min samples at each lake for either fixed or random sites (Figure 1). Slight increases and decreases across durations were observed depending on lake and year. Although subtle changes in CPUE were detected in both fixed and random sites, no overall trend was detected. CPUE was independent of sample duration which is consistent with results reported by Miranda et al. (1996). Although CPUE did not change with duration, variability among durations did. CV x was highest for the 5 min samples and lowest for the 15 min samples among all lake at both fixed and random sites (Figure 2). Three of the four lakes in both 2009 and 2010 had a greater difference in CV x from 5 to 10 min samples than from 10 to 15 min samples at fixed sites. The random sites at all four lakes also showed a similar pattern (Figure 2). In 2009 only the low catch lakes, Arcadia and Wes Watkins Reservoirs, had a greater difference in CV x from 5 to 10 min samples than from 10 to 15 min samples while three lakes exhibited this pattern in 2010. Overall, precision between 10 and 15 minutes at fixed and random sites was very similar. Habitat Classification Dripping Springs and Konawa Reservoirs, the high catch lakes, were classified with 43% good habitat, while the low catch lake, Arcadia and Wes Watkins Reservoirs, had 19% and 33% good habitat, respectively. Consequently, Arcadia had the highest proportion of poor habitat (39%) followed by Wes Watkins (22%). The high catch lakes, Dripping Spring and Konawa had 11% and 19% poor habitat, respectively. Fair habitat ranged from 38% to 46% on all four lakes. Mean CPUE was highest in good habitat and lowest poor habitat for Arcadia, Konawa, and Wes Watkins in 2009 and 2010 (Figure 3). Dripping Springs had a higher mean CPUE in the fair than the good habitat both years. In 2009 poor sites at Dripping Springs were sampled although they had changed between the time the habitat classification was made and the time they were sampled. They were reclassified from poor to fair at time of sampling because increased lake levels flooded shoreline vegetation that was not previously submerged, therefore no catch rates were computed for poor habitat. Median catch rates for habitat types at Arcadia, Wes Watkins, and Konawa Reservoirs in 2009 were significantly different (Krustal-Wallis one-way ANOVA; P = 0.037,0.049, and .05 respectively, Figure 3). In 2010 two lakes, Arcadia (P = 0.001) and Konawa (P = 0.017) had significantly different habitat types. Fixed vs. Random Median catch rates from 2009 were not significantly different between fixed and random sites for any duration at the four lakes (Wilcoxon Rank Sum Test, a = .05, Table 2). Although no differences were found in median catch rates, mean CPUE was lower at random sites than fixed sites (Figure 4). Mean CPUE of random samples at Arcadia Reservoir, decreasing by approximately 30% at random sites for the three durations. This was likely due to the high proportion of poor sites (39%) and low proportion of good sites (19%) sampled. Mean CPUE for random sites was also lower than fixed sites at the 10-minute duration for three of the four lakes in 2010. Only Wes Watkins had a higher mean CPUE at random sites. Wilcoxon Rank Sum tests indicated no significant differences between median catch rates at Arcadia, Dripping Springs, and Wes Watkins, although Konawa was significantly different (Table 3). Data collected from random sites were also less precise than the fixed sites in 2009 (Figure 5). The CV x was higher for random sites than fixed sites at all durations at each lake except the 5 min duration at Wes Watkins Reservoir. A target CVx :S0.2 was achieved at fixed sites for 10 and 15 min durations at all lakes. Only the high catch lakes, Dripping Springs and Konawa Reservoirs, achieved the target CVx :S0.2 for random sites which included all durations. In 2010 data were one again less precise in the random sites than the fixed sites at the 10 min duration. The target CV x :S0.2 was met at fixed and random sites at all lakes except for the random sites at Arcadia (Table 3). With the current required effort (18 samples), a ±50% change in the population, as indicated by mean CPUE of all sizes of LMB, could be detected at fixed sites for 10 and 15 min durations at low catch lakes, while it could be detected at all durations at the high catch lakes (Figure 6). A ±0.50% change could be detected at random sites for all durations at Dripping Springs and Konawa Reservoirs (high catch lakes) as well as Wes Watkins (low catch) Reservoir. This change in the mean was detected at Arcadia's random sites only with the duration of 15 min. The precision of the 10 min duration, random samples (18 units) at Arcadia was close to detecting a ±50% change in mean CPUE; 19 samples were needed. In 2010 a ±0.50% change in mean CPUE could be detected at the 10 min duration at all lakes for both fixed and random sites (Table 3). In 2009 the data were not precise enough to detect a ±0.25% change in CPUE for the low catch lakes, Arcadia and Wes Watkins Reservoirs, at either fixed or random sites for any of the three durations (Figure 7). Konawa Reservoir was able to detect this ±0.25% change at fixed sites for all three durations but this precision level was only achieved at the 15 min duration at Konawa's random sites. The 10 min duration at Konawa's random sites needed only one additional unit to detect a ±25% change in CPUE. The data at Dripping Springs Reservoir were the most precise as fixed and random samples for all three durations were able to detect this 25% change. In 2010 Arcadia was the only lake unable to detect a ±0.25% change in CPUE at fixed sites for the 10 min duration (Table 3). In addition only the high catch lakes, Dripping Springs and Konawa, were able to achieve this level of precision at the random sites. Fixed and random sites at the 10 min duration were also analyzed for largemouth bass >14 inches in 2009 and 2010. Once again CPUE >14 was lower for random sites than fixed sites at all lake except Wes Watkins in 2010. Median catch rates for largemouth bass> 14 inches were not significantly different (Wilcoxon Rank Sum Test, a = .05, Table 4) at the 10 min duration at Wes Watkins, Dripping Springs, and Konawa. Only Arcadia was significantly different in 2009 (P = 0.047) and 2010 (P = 0.023). For largemouth bass> 14 inches, a target CV = 0.2 was met at Arcadia for only the fixed sites in 2010. The other low catch lake, Wes Watkins, achieved this level of precision for fixed sites both years but failed to reach it on the random sites in 2009. The data for the high catch lakes, Dripping Springs and Konawa, met this level of precision for fixed and random sites both years (Table 4). Data were precise enough to detect a ±50% change in the population of largemouth bass > 14 inches for fixed sites at all lakes in 2009 and 2010. Wes Watkins also met this level of precision in random sites in 2010. Both Konawa and Dripping Drippings were also able to detect this change for random sites both years. Furthermore, Konawa and Dripping springs were able to detect a ±25% change in largemouth bass> 14 inches for fixed sites. Only Dripping Springs was able to detect this change at random sites in 2009 (Table 4). Length frequencies were similar between fixed and random sites (Figure 8). Although no major differences were obvious on any of the lakes in either 2009 or 2010, Kolmogerov- Smirnov tests indicated a significant difference (P = 0.046) in size structure between fixed and random sites at Dripping Springs in 2009. It is likely the significant difference was detected due to the larger sample size. These results suggest the size structure of largemouth bass collected at fixed and random sites is negligible. Sample Size Historical data analysis indicated the number of samples currently taken based on lake size is adequate to detect a ±0.50% change in mean CPUE (target CV x :S0.25) in most sizes of reservoirs. With larger required samples in lakes ~1 0,000 acres, target CV x :S 0.25 were detected 100% of the time. Lakes less than 10,000 acres, even though they required smaller numbers of samples, still detected this change approximately 95% of the time (Table 5). A more precise detection level of ±0.25 x of CPUE was less often achieved in lakes throughout Oklahoma (Table 6). Again, lakes ::::'10,000acres, with larger sample sizes, were the most precise with as 81.8% of the historic electrofishing efforts detecting this level of change. Only 62.9% of the lakes from 1,000 acres to 9,999 acres were able to detect a ±0.25% change in mean CPUE. Smaller lakes :s 999 acres were the least likely to detect this change as approximately 46% achieved a target CV x :s 0.125. It is unknown whether this inverse relationship between lake size and precision is due to mere differences in required sample size or changes in variability within largemouth bass populations. It is conceivable that smaller lakes and lower density largemouth populations are more affected by environmental and made-made perturbations and therefore have inherently more variability. The mean number of samples needed to detect a ±0.25% change in mean CPUE was slightly higher for most lake size than the number currently required (Table 6). Small lakes «500 acres) and lakes 1,000 to 9,999 acres needed an additional four samples, while lake 500-999 acres required an additional seven samples. The mean number of samples needed for lake greater than 10,000 to detect a ±0.25% change in mean CPUE was 24, the number of samples required. D. Conclusions/Recommendations • CPUE did not change with duration in fixed or random sites at any of the four lakes sampled. • CV x changed more between 5 and 10 minute samples than between 10 and 15 minute samples. Shortening the duration from 15 to 10 min may not jeopardize the precision of data on most lakes. • Median catch rates generally were not significantly different between fixed and random sites although CPUE was usually lower for random sites than fixed sites, presumably due to sampling of poor habitat. • Precision was higher for fixed sites than random sites. A target CVx < 0.2 was achieved in 10 and 15 min samples at fixed sites at all four lakes. This precision was only consistently achieved at random sites for the high catch lakes, Dripping Spring and Konawa Reservoirs. • CPUE of largemouth bass> 14 inches was less precise than for the total population. Precision to detect a ±0.50% change in mean was achieved at fixed sites of all lakes. Random sites at high catch lakes were also able to achieve this level of precision although low catch lakes were inconclusive. • Approximately 95% of lakes sampled since 2003 were able to detect a ±0.50% change in mean CPUE of all size classes. • A ±0.25% change in mean CPUE is less likely to be detected in small lakes compared to larger lakes. If a precise changes in total population or specific length groups are desired, sampling should be based on variability not lake size. • All four lakes, Arcadia, Wes Watkins, Dripping Springs and Konawa Reservoirs, will be sampled again in the next segment to verify results. Habitat at Dripping Springs should be reclassified to verify correct classification. III. Significant Deviations: None Prepared by: _ Chas Patterson, Fisheries Biologist Date: :So HA12.. \ \ Approved by: Fisheries Division Administration Oklahoma Department of Wildlife Conservation IV. Literature Cited Bonar, S. A., S. Contreras-Balderas, and A. C. Iles. 2009. An introduction to standardized sampling. Pages 1-12 in Bonar, S. A., W. A. Hubert, and D. W. Willis, editors. Standard methods for sampling North American freshwater fishes. American Fisheries Society, Bethesda, Maryland. Bonvechio, K. 1. In Press. Standardized sampling manual. Florida Fish and Wildlife Conservation Commission. Tallahassee, Florida. Cyr, H., J.A. Downing, S. Lalonde, S.B. Baines, and M.L. Pace. 1992. Sampling larval fish populations: choice of sample number and size. Transactions of the American Fisheries Society 121:356-368. Cunningham, K. K., and L. M. Cofer. 2000. Evaluation of the related catch rates of hoop nets for sampling channel catfish. Proceeding of the Annual Conference of the Southeastern Association ofFish and Wildlife Agencies 54:80-87. Dauwater, D. C., W. L. Fisher, R. A. Marston, and D. K. Splinter. 2004. Random selection of stream sites: an important step in fluvial geomorphic and fishery surveys. Pages 30-33 in J. R. Copeland, F. Fiss, P. E. Balkenbush, and C. S. Thomason, editors. Warmwater streams symposium II. Available www.sdafs.org/wwstreams/wwscl.htm (June 2007). Erickson, K. E. 1978. Standardized sampling procedures for lake and reservoir management recommendations. Oklahoma Federal Aid Project F-38-R, Job 1. Performance Report. 27p. Larsen, D. P., P. R. Kaufmann, T. M. Kincaid, and N. S. Urquhart. 2004. Detecting persistent change in the habitat of salmon-bearing streams in the Pacific Northwest. Canadian Journal of Fisheries and Aquatic Sciences 61 :283-291. Miranda, L.E., W.D. Hubbard, S. Sangare, and T. Holman. 1996. Optimizing electrofishing sample duration for estimating relative abundance of largemouth bass in reservoirs. North American Journal of Fisheries Management 16:324-331. Noble, R. L., D. J. Austen, and M. A. Pegg. 2007. Fisheries management study design considerations. Pages 31-49 in C. S. Guy and M. L. Brown, editors. Analysis and Interpretation of Freshwater Fisheries Data. American Fisheries Society, Bethesda, Maryland. Wilde, G. R., and W. L. Fisher. 1996. Reservoir fisheries sampling and experimental design. Pages 397-409 in L. E. Miranda and D. R. DeVries, editors. Multidimensional approaches to reservoir fisheries management. American Fisheries Society Symposium 16, Bethesda, Maryland. Table 1. Habitat criteria used to determine habitat type of shoreline transects. Shape/Structure Point Value Point Value Flat in cove Points Moderate slope (""30° to 45°) Mainlake flat Steep slope (""45° to 60°) Cliff Unknown 5 4 3 2 1 o o Substrate Point Value Cover Gravel (smaller than fist) Clay Sand Rock Bedrock SiltlMud Unknown 5 4 3 2 1 oo Aquatic vegetation Timber/Brush Rock (large rock/boulders) None 5 4 3 o Poor v; 7 points Total Points for habitat rank: Fair = 8 to 11 points Good = 12 to 15 points Table 2. Mean and median catch rates (fish/sample) of four Oklahoma lakes sampled in 2009. Wilcoxon Rank Sum Tests (a = .05) indicated no differences between fixed and random sites for the three sample durations at the four lakes. Low Catch Sample Fixed / p- Sample Fixed / p- Lakes Duration Random N Mean Median value High Catch Lakes Duration (min) (min) Random N Mean Median value Arcadia 5 Fixed 18 1.33 0.5 0.49 Dripping Springs 5 Fixed 18 9.78 10.0 0.58 Arcadia 5 Random 18 0.83 0.0 Dripping Springs 5 Random 18 8.28 7.5 Arcadia 10 Fixed 18 3.11 3.0 0.10 Dripping Springs 10 Fixed 18 18.83 19.0 0.27 Arcadia 10 Random 18 1.83 1.0 Dripping Springs 10 Random 18 17.61 17.0 Arcadia 15 Fixed 18 3.44 5.0 0.12 Dripping Springs 15 Fixed 18 27.61 27.0 0.32 Arcadia 15 Random 18 3.22 1.5 Dripping Springs 15 Random 18 25.75 24.5 Wes Watkins 5 Fixed 18 1.28 0.5 0.21 Konawa 5 Fixed 18 12.78 12.5 0.66 Wes Watkins 5 Random 18 1.28 1.0 Konawa 5 Random 18 11.44 11.5 Wes Watkins 10 Fixed 18 2.39 2.0 0.39 Konawa 10 Fixed 18 26.44 29.0 0.69 Wes Watkins 10 Random 18 2.17 2.0 Konawa 10 Random 18 22.10 21.0 Wes Watkins 15 Fixed 18 3.55 3.0 0.32 Konawa 15 Fixed 18 39.10 42.5 0.61 Wes Watkins 15 Random 18 3.11 3.0 Konawa 15 Random 18 33.56 35.0 Table 3. Mean CPUE, CV x, and number of samples need to achieve a CV x = .25 and CV x = .125 for fixed and random sites at each of the four lake for the 10 min duration in 2010. The median catch rates are also given with corresponding P-values from Wilcoxon Rank Sum Test (a = .05). Arcadia Dripping Springs Fixed Random P-value Fixed Random P-value CPUE 22 16 CPUE 132 121 CVx 0.13 0.24 CVx 0.07 0.09 CVx=.25 5 17 CVx=.25 2 2 CVx=.125 19 67 CVx=.125 6 9 Median 16 6 0.305 Median 84 82 0.466 Wes Watkins Konawa Fixed Random P-value Fixed Random P-value CPUE 21 26 CPUE 188 149 CVx 0.12 0.14 CVx 0.07 0.11 CVx=.25 4 6 CVx=.25 1 3 CVx=.125 16 24 CVx=.125 6 13 Median 12 14 0.371 Median 124 88 0.046 Table 4. Mean CPUE, CV x, and number of samples need to achieve a CV x =.25 and CV x = .125 for fixed and random sites at each of the four lake for the 10 min duration in 2009 and 2010. The median catch rates are also given with corresponding P-values from Wilcoxon Rank Sum Test (a = .05). 2009 2010 Arcadia Arcadia Fixed Random P-value Fixed Random P-value CPUE 17 7 CPUE 12 6 CVx 0.24 0.3 CVx 0.18 0.36 CVx=.25 16 27 CVx=.25 10 38 CVx=.125 64 106 CVx=.125 15 151 Median 8 2 0.047 Median 8 0 0.023 Wes Watkins Wes Watkins Fixed Random P-value Fixed Random P-value CPUE 13 9 CPUE 11 13 CVx 0.18 0.28 CVx 0.15 0.15 Cvx=.25 9 22 CVx=.25 6 7 CVx=.125 35 87 CVx=.125 25 27 Median 8 4 0.132 Median 8 8 0.504 Konawa Konawa Fixed Random P-value Fixed Random P-value CPUE 75 62 CPUE 101 79 CVx 0.1 0.15 CVx 0.11 0.16 CVx=.25 3 6 CVx=.25 3 7 CVx=.125 12 25 CVx=.125 14 28 Median 40 40 0.446 Median 66 44 0.216 Dripping Springs Dripping Springs Fixed Random P-value Fixed Random P-value CPUE 31 26 CPUE 24 21 CVx 0.1 0.12 CVx 0.16 0.16 CVx=.25 11 18 CVx=.25 4 7 CVx=.125 4 7 CVx=.125 17 28 Median 20 16 0.388 Median 16 14 0.423 Table 5. Percent oflakes in Oklahoma since 2003 that obtained a CVx :S.25 categorized by lake size. This level of precision allows a detection of ±0.50 x. The mean number of samples needed to obtain a CV x = .25 is also indicated. Lake Size # of Samples % Lakes Mean Samples Needed (acres) Required N CVx <.25 (CVx =.25) < 500 6 80 96.25% 2 500 - 999 12 26 95.83% 5 1,000-9,999 18 59 91.94% 6 >10,000 24 22 100.00% 6 Table 6. Percent oflakes in Oklahoma since 2003 that obtained a CVx :S.125 categorized by lake size. This level of precision allows a detection of ±0.25 x. The mean number of samples needed to obtain a CV x = .125 is also indicated. Lake Size # of Samples % Lakes Mean Samples Needed (acres) Required N CVx :S.125 (CVx =.125) < 500 6 80 47.50% 10 500 - 999 12 26 45.83% 19 1,000-9,999 18 59 62.90% 22 >10,000 24 22 81.82% 24 Figure 1. Mean CPUE (fish/hr) at 5, 10 and 15 min durations from four Oklahoma lakes in 2009 and 2010 at fixed and random sites. 2009 Fixed 180 160 - - - - - - 140 --Arcadia 120 --Wes Watkins :.<;:: 100 - - - - - - - - DrippingSprings o 80 - Konawa "o' 60 40 20 0 5 10 15 duration (min) Random 160 140 :.c::11802000 ~ 60 u 402~~~~~====~~~ - - - - ... 5 10 15 duration (min) ••• Konawa -Arcadia -Wes Watkins n - - DrippingSprings 200 180 160 140 120 ~ 100 '" 80 60 40 20 160 140 120 100 s:~ 80 '" 60 40 20 2010 Fixed ---------- .. 10 duration (min) Random 10 duration (min) 15 15 --Arcadia --Wes Watkins - Dripping Springs - ..•.• Konawa --Arcadia --Wes Watkins - Dripping Springs - •.••• Konawa Figure 2. CVx at 5,10 and 15 min durations from four Oklahoma lakes in 2009 and 2010 at fixed and random sites. 0.35 0.30 0.25 . 0.20 >o 0.15 2009 Fixed 0.10 0.05 0.00 +------.-----,__----~ 5 10 duration (min) Random 0.35 0.30 0.25 > 0.20 U 0.15 0.10 0.05 0.00 -j----,-------.-----, - - -- -- - - - - 5 10 duration (min) 15 15 --Arcadia --Wes Watkins DrippingSprings - - -Konawa -Arcadia ---Wes Watkins • Dripping Springs • Konawa 0.35 0.3 0.25 0.2 :> c..i 0.15 0.1 0.05 0.35 0.3 0.25 0.2 :> c..i 0.15 0.1 0.05 2010 Fixed .. ... - - - - - 10 duration (min) Random - - - - - - o+------~-----,__----~ 10 duration (min) 15 15 --Arcadia --Wes Watkins - Dripping Springs - •.• - Konawa --Arcadia --Wes Watkins - Dripping Springs - ..• - Konawa Figure 3. CPUE (fish/hr) of three habitat types (good, fair, poor) for four Oklahoma lakes in 2009 and 2010. Lakes denoted with * had significantly different catch rates for the three habitats (Krustal- Wallis one-way ANOVA, a = .05). 2009 180 114600 .. .. .. •• •• .. 120 .. n _ •• t---Arcadia* ~--Wes Watkins* m Dripping Springs - Konawa* - -- .. 100 •• .. .. 80 •• 60 40 2o°l__ ~===='~====::-- =_=_~~~ Good Fair Poor 2010 250 200 ... •• •• ---Arcadia* ---Wes Watkins - u • Dripping Springs 150 •• ... .. .. .. .• .. '" "'" ~ ~-.•.. -..- - - 100 .. .• - - - Konawa* 50 a +-------------~r_------------_,------~----__. Good Fair Poor Figure 4. Mean CPUE (fish/hr) for fixed and random sites at 5, 10 and 15 min durations for four Oklahoma lakes in 2009. Arcadia Wes Watkins 20 19 19 20 •.. 15 •.. 15 15 15 14 s: s: :2 10 :2 10 .I•II. 0.20 > 0.20 0 0 0.15 0.10 0.10 0.05 0.00 0.00 5 10 15 5 10 15 duration (min) duration (min) Dripping Springs Konawa 0.10 0.15 0.13 0.08 0.06 0.10 U> 0.04 >0 0.05 0.02 0.00 0.00 5 10 15 5 10 15 duration (min) duration (min) Figure 6. Number of samples need to achieve a CVx =.25 for fixed and random sites at 5,10 and 15 min durations for four Oklahoma lakes in 2009. This level of precision allows a detection of ±0.50x. 160 140 "C 120 Q) ~ 100 Q) ~ 80 ~Q . 60 E~ 40 20 o ~ 2 "Ca: 1.5 c: Vl Q) Co cEu 0.5 Vl Arcadia 144 5 10 duration (min) 15 Dripping Springs 2.5 2 2 2 2 o 5 10 15 duration (min) 30 al 25 al 20 Q) c: VI 15 .!!! 10 Co ~ 5 VI o 6 al 5 al 4 Q) c: 3 VI .!!! 2 Co ~ 1 VI o Wes Watkins 27 5 10 15 duration (min) Konawa 6 5 10 15 duration (min) Figure 7. Number of samples need to achieve a CVx = .125 for fixed and random sites at 5,10 and 15 min durations for four Oklahoma lakes in 2009. This level of precision allows a detection of ±0.25 x. Arcadia Wes Watkins 60 120 110 "C 50 "C 100 Q) Q) "QC) 40 36 "QC) 80 Q) Q) ~ 30 c: 60 III ~ 20 Q) c.. c.. 40 ~ 10 IEII 20 f/l f/l 0 0 5 10 15 5 10 15 duration (min) duration (min) Dripping Springs Konawa 12 10 25 22 "C 10 "C Q) Q) 20 "C 8 "C Q) Q) sQ::): Q) 15 6 c: f/l f/l Q) 10 4 Q) Q. c.. E 2 E 5 III III f/l f/l 0 0 5 10 15 5 10 15 duration (min) duration (min) Figure 8. Length frequency histograms from four Oklahoma lakes for fixed and random sites in 2009 and 2010. Kolmogerov-Smirnov tests indicated a significant difference (P = 0.046) in size structure between fixed and random sites at Dripping Springs in 2009. Arcadia 2009 16.00% 14.00% 12.00% 10.00% .c. 8.00% ee a. 6.00% 4.00% 2.00% 0.00% 14.00% 12.00% 10.00% ce 8.00% ea . 6.00% 4.00% ••..~.•..k.. ,.~5.."~ ,,~ ~ ,,~ ~ ~~ ~ ,,':'3~ -, ~~ •..~..~ "i>'~ "~,,~-,,,~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ lenght group Wes Watkins 2009 25.00% 25.00% 5.00% 20.00% 20.00% 15.00% C "u ~ 10.00% a. e 15.00% '"~~ 10.00% " 5.00% Dripping Springs 2009 ,,',:,"'-,!'"," ,,~.," ,,~f\." "x"," -,':<5:>" -,?J"," ","'j"," •...J•>•.••"• ,'P'"," "P<:>" "'<.?"," ,,<0"," ...1..:•l••"• ~ ~ ~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ ~ lenght group Konawa 2009 lenght group ,,~~,,~~,,~~,,~~,,~~,,~~,~~,,~~ ,,~~,,~~ ,,~~•.....~~ ,,~~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ percent Dripping Springs 2010 Figure 8 continued. Length frequency histograms from four Oklahoma lakes for fixed and random sites in 2009 and 2010. 2.00% 0.00% I..""" " ,11,11 Arcad ia 2010 16.00% 14.00% 12.00% 10.00% 8.00% 1.e:.: 6.00% Q. 4.00% 2.00% 0.00% 12.00% 10.00% 1:: ~ 8.00% :; Q. 6.00% 4.00% 2.00% 0.00% II ,11,11,11,11,11,1 ,11,11,11,11,11,11,11,11,11,11,1·1,11,11,11,11, ,11,1 , "..~.:'- ",/'~ .J..'~ ~'V~ ,,~~ ",'"~'3 •..?•:J.~ ,,~ ~ ~ ~ ~ ~ ,,<,~j ,,':l~ ,,<.;)~ ~ ~ ~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ lenghl group Wes Watkins 2010 14.00% 12.00% 10.00% Q. .e",. 8.00% :.E,. 6.00% -.". 4.00% 2.00% 0.00% 20.00% 18.00% 16.00% 14.00% _ 12.00% .e". 10.00% Q. 8.00% 6.00% 4.00% ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ ~ ~ percent ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ 10·..:. ,,:-. ••.~•.. ,,~ ,,'1; ",'V •.•?..J ,.".':i •.•s..;. .Ji' ..J.i... •.•<.:'J. ,,~ ~ ~ ~ 0/ ~ ~ ~ ~ ~ ~ ~ ~ lenghl group Konawa 2010 ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ <:0": <:-, ,,~ •..~.. ,,~ <:1); ,,':':J •.•r.:.3 -, ~ ~ ,,> ,,':J •.5..'5. -: Y5 ~ ~ ~ v ~ ~ ~ ~ ~ ~ ~ ~ ~ lenghl group |
Date created | 2011-06-09 |
Date modified | 2011-10-28 |
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