Electrofishing for largemouth bass : an evaluation of Oklahoma's standardized sampling procedures.

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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