Analysis of preliminary creel survey and recommendations for a creel survey of

Kootenay Lake, British Columbia.

Prepared for: Fish & Wildlife Compensation Program – Columbia Basin,* 103-333 Victoria Street, Nelson, B.C. V1L 4K3

by

Carl James Schwarz Department of Statistics and Actuarial Science

Simon Fraser University Burnaby, BC, V5A 1S6 cschwarz@stat.sfu.ca

2010-06-11

* The Fish & Wildlife Compensation Program (FWCP) was established in 1995 to offset the impacts resulting from construction of BC Hydro dams in the Columbia Basin, and works to deliver a wide range of conservation and enhancement projects for fish and wildlife, on behalf of its program partners BC Hydro, the BC Ministry of Environment (MOE) and the Department of Fisheries and Oceans Canada.

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Executive Summary

A survey of anglers was conducted on Kootenay Lake from October 2009 through March 2010 to provide data for designing a one year creel survey, and a preliminary estimate of angler effort and harvest. Eleven days were sampled at five access locations with overflight boat counts occurring on seven of the sampled days.

The expansion factor to adjust access interview data to total effort was about 1.4 and relatively stable over the sampled period. A model-assisted approach is used to deal with missing overflight information and the method of multiple imputation is recommended. This model-assisted method with multiple-imputation is very flexible and provides estimates that properly account for the uncertainty in the missing data.

Estimated angler effort during the October to March survey was 6870 (SE 984) angler days and 38,403 (SE 4850) rod hours. Bull trout and rainbow trout harvest were estimated as 1031 (SE 288) and 1016 (SE 303) respectively. Daily catch per unit effort (pooled over all sites) ranged from zero to 0.064 for bull trout and zero to 0.149 for rainbow trout. Average length and weight (range) of sampled bull trout was 60 cm (46 – 82) and 2.9 kg (1.0 – 7.0), and for rainbow trout 54 cm (34 – 78) and 2.6 kg (0.4 – 7.4). The log(weight) vs. log(length) relationship cannot be distinguished between the two species. A review of the proposed design for the year round creel survey suggested that effort be approximately split between weekend and weekdays, but that effort be shifted from sampling in the winter months to the summer months. Based on the stability of expansion factor during the off-peak season seen in the preliminary creel survey, the number of overflights can be reduced by about 50% during the off-peak months.

A simulator was used to investigate different designs for a one year survey using the feasibility data and other information to approximate the expected precision. The proposed design (and modifications) should provide estimates with relative standard errors (standard error/estimate) at the yearly level of 10% or less which would give 95% confidence intervals at the yearly level of ±20% or less of the estimates.

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Table of Contents Executive Summary........................................................................................................ ii Table of Contents........................................................................................................... iii 1. Introduction ................................................................................................................ 1 2. Analysis of Preliminary Survey................................................................................... 2

2.1 Estimates of catch and related variables................................................................. 2 2.2 CPUE.................................................................................................................... 9 2.3 Angler profile...................................................................................................... 12 2.4 Weight vs length for harvested fish. .................................................................... 14

3. Designing a yearly creel survey for Kootenay Lake................................................... 15 3.1 Comments on design decisions............................................................................ 17 3.2 Expected precision .............................................................................................. 21

4. Summary .................................................................................................................. 26 5. References ................................................................................................................ 26

Appendices A. Fish measurements taken from harvested fish during a creel survey on Kootenay

Lake from October 2009 to March 2010 B. Copy of Anonymous (2010) C. Locations of boats during overflights in preliminary creel survey

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1. Introduction This report analyzes the preliminary creel survey conducted in fall 2009 and winter 2010 on Kootenay Lake, British Columbia and then uses the information from the preliminary survey to suggest a design of a year long creel survey of the same lake.

The preliminary survey was conducted in October 2009 through March 2010. Briefly, the creel was sampled at 5 access points (Kaslo, Woodbury, Balfour, Queens Bay East, and Boswell) at 11 days during this period as shown in Table 1. [On 2 of the days, sampling was not done at Kaslo.] During the sampled days, all angling parties returning to the access point were interviewed to determine the number and species of fish kept and released, the start and end time of the angling trip, and other variables. On 7 of the sampled days, an aerial survey was also conducted at approximately noon that counted the number of active boats on the lake. The number was counted once as the airplane flew out and again on the return flight (Table 1). Additional details are provided in Anonymous (2010). Table 1: Summary of interviews conducted and air counts of active boats from the preliminary survey.

Date

24 Oct

2009

03 Nov

2009

21 Nov

2009

20 Dec

2009

09 Jan

2010 29 Jan

2010 16 Feb

2010 21 Feb

2010

04 Mar

2010

14 Mar

2010

20 Mar

2010 Day type1 WE WD WE WE WE WD WD WE WD WE WE Kaslo 7 * 2 0 0 0 3 5 * 3 7 Woodbury 8 2 2 2 1 0 1 3 2 5 3 Balfour 15 5 6 4 4 0 3 11 5 14 10 Queens Bay East 8 2 1 1 2 2 1 3 3 4 2 Boswell/Kuskanook 4 6 2 3 1 2 1 7 4 6 12 Total 42 15 13 10 8 4 9 29 14 32 34 Air counts Flight 1 41 20 9 27 12 36 32 Air counts Flight 2 38 14 9 33 15 36 37 Active Flight 12 33 9 7 20 11 22 28 Active Flight 2 33 9 7 22 10 25 28 * Interviews not conducted. 1 WE=Weekend, WD=Weekday 2 Number of access interviews where the start/end time of the boating trip overlapped the start/end time of the overflight.

All analyses in this report were done using SAS 9.2. Programs and results are available at: http://www.stat.sfu.ca/~cschwarz/Consulting/Kootenay/. Length and weight measurements for sampled fish are appended to this report.

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2. Analysis of Preliminary Survey

2.1 Estimates of catch and related variables. The preliminary survey is an example of an aerial-access survey as discussed by Pollock et al (1994) with non-randomized flight times. The interviews at the access point provide partial information about the total catch on the sampled day as other fishing parties may use different access points where creel clerks were not stationed. The aerial counts provide information on the fraction of the total effort that was sampled at the access point by comparing the number of active boats counted by the flight to the number of interviews that were active on the water during the flight time (determined by examining if the flight time occurred between the start and end of the trip) as outlined in Dauk and Schwarz (2001). For example, if 40 angling parties were interviewed on a date of which 30 were active during the overflight, and if the overflight counted 60 active parties, then it is estimated that only 30/60=0.50 of the total effort was interviewed and so the catch based on the 40 parties must be inflated by a factor or 2 (1/0.5) Unfortunately, estimates at the monthly level will be difficult to obtain for the feasibility data. Due to funding limitations, no sampling on weekdays was done in October 2009 and December 2010 which implies that estimates for weekdays for these months will have to based on perhaps the relationship between weekend and weekdays from other similar months. As well, no replicate day of the same daytype were taken in any month except March 2010 and even then only a weekend was sampled twice. Consequently, there is very little information on the variability among days of the same daytype within a month. One possible analysis would compute the variability among days of the same daytype over a longer period of time (e.g. seasons, see below) and then use this as an estimate of the variation within a month. For this reason, initial estimates will be provided at a larger unit of analysis than the month level. The analysis of the preliminary survey first stratifies the six months survey into two strata. The shoulder season is defined as October, February, and March. The winter season is defined as November, December and January. Within each of these strata, days of the week were subdivided into two daytypes -- weekdays (Monday to Friday) and weekends (Saturday and Sunday). Holiday Mondays and other statutory holidays were also defined as “weekend” days. This stratification ensured that at least 2 days of each type are measured in each season and provides a design-based estimate of variance based on replicate daytype. This avoids having to make additional assumptions about the variation across days of the same daytype in season when only one day of the daytype is sampled. It is assumed that days within each season-daytype stratum were selected at random. Normally in aerial-access surveys, aerial flights are conducted on all sample days; in this preliminary survey, only 7 of the 11 sampled days had flights conducted. It will be assumed that the days selected for aerial flights were a random sample of days selected for sampling.

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The usual assumptions are made about data being collected properly (both at the access points and during the aerial surveys). Estimates are formed as simple expansion of the average response for a daytype within a season by the number of daytypes within that season. The standard error at this first step is based on that for estimating a total from a simple random sample as outlined in many books on sampling and demonstrated by Pollock et al (1994). Let Ystd = Ystdi

interviews! be the response variable (e.g. total catch of species at all access

points) in season s, day-type t, and date d when summed over all interviews i for that day. Ordinarily, the expansion of the response variable using the aerial count would be done on each day; but because not every day had an overflight, this expansion will be left to the later steps. Step 1. Compute the average response and standard deviation of the response over multiple days of each daytype in each season: nst Number of days sampled of day-type t in season s.

Yst • =1nst

Ystdd! Average response for each season x day-type combination where nst is

the number of days of that day-type within the season.

s Yst •( ) Standard deviation of response over the days for each season x day-type combination

Step 2. Expand the average to estimate the total for the season-day-type combination.

Nmt Total number of days of day-type t in season s. total(Yst ) = NstYst Estimated total response for season s and

daytype t.

se total(Yst )[ ] = Nst

s Yst •( )2nst

1! nstNst

"#$

%&'

Estimated standard error for the total response for season s and day-type t.

Step 3. Combined totals over daytypes within a season. total(Ys •• ) = total(Ys,we ) + total(Ys,wd ) Estimated total response for

season s.

se total(Ys •• )[ ] = se total(Ys,wd )!" #$2+ se total(Ys,we )!" #$

2

Estimated standard error for total response for season s.

Step 4. Grand total over all season. total(Y••• ) = total(YShoulder •• ) + total(YWinter •• ) Estimated grand total over all

season

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se total(Y••• )[ ] = se total(YShoulder •• )[ ]2 + . total(YWinter •• )[ ]2 Estimated standard error for grand total over all seasons

This procedure can be repeated for each variable of interest, e.g. number of fish kept, number of fish released, angler hours, rod hours, etc. A SAS program to do the computations is available. A summary of the results for preliminary survey before adjusting based on the overflights are shown in Table 2.

Table 2. Estimates from preliminary creel survey. No adjustments have been made for expansion based on the aerial overflights.

Stratum

2009/2010 - Shoulder 2009/2010 - Winter Grand total Response Variable

Est Total SE Total Est Total SE Total Est Total SE

Total

Angler- anglers 3417 392 1607 517 5024 649 Angler- hours 19220 2170 7267 2264 26487 3136 Angler- rod-hrs 20092 1925 7992 2424 28084 3095 Angler- rods 3699 378 1852 549 5551 667 Fish - BT - kept 560 163 194 125 754 206 Fish - BT - r/k 710 181 277 162 986 243 Fish - BT - rel 150 69 82 40 232 79 Fish - Oth - kept 0 0 0 0 0 0 Fish - Oth - r/k 0 0 0 0 0 0 Fish - Oth - rel 0 0 0 0 0 0 Fish - RT - kept 447 114 297 184 743 217 Fish - RT - r/k 710 181 277 162 986 243 Fish - RT - rel 615 269 378 271 992 382 Fish - Tot - kept 1007 197 491 301 1498 360 Fish - Tot - r/k 1771 374 951 600 2722 707 Fish - Tot - rel 764 259 460 300 1224 397

RT = Rainbow Trout; BT=Bull Trout; r/k = released + kept

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Dauk and Schwarz (2001) outlined how to expand the information from the access surveys for a non-randomized overflight. Briefly, define ! stdif = 0 /1 if the fishing party i in season s, day-type t, and sampling day d as not active/active during overflight f. For example, if the overflight occurred at 13:00, then the fishing party was active if the start and endtimes of their trip included 13:00. Then the expanded response variable for that day is found as:

Ystd

* = Ystdiinterviews! "

ostdfflights!

# stdifflights!

interviews!

where ostdf is the overflight count during flight f. For example, consider the data from Table 1 for 21 February 2010. There were 27 and 33 boats seen by the two overflights so

ostdfflights! = 27 + 33 = 60 . Of the 29 interviews, 20 and 22 were active during the two

overflights so ! stdifflights"

interviews" = 20 + 22 = 42 . The estimated expansion factor for that day

is 60 / 42 = 1.43 , i.e, inflate the total response over all interviews for that day by a factor of 1.43. If every sampled day had an overflight, the estimates (accounting for expansion) would be found by replacing Y

std by Y

std

* in Step 1 above. The final standard errors would automatically incorporate any variability in the expansion factors over the different days. However, not every day had an overflight in the preliminary survey. There are two (equivalent in large samples) ways in which the expansion factors can be applied in the case of missing data. In the first method, the estimates from the unadjusted counts are adjusted using an appropriate factor and the uncertainty in the factor. For example, if the expansion factor was roughly equal across all dates, the final totals could be expanded by the average expansion factor. Table 3 (and Figure 1) summarizes the estimated expansion factors over the seven days with overflights. Given the estimated uncertainty in each expansion factor, there is no evidence that the expansion factors differ across strata or by day type, and an average expansion factor based on all the overflight data will be used to expand the estimates presented in Table 2.

Table 3. Estimated expansion factors on sampled days with overflights.

Date Day-type

Air count 1

Air count 2

Active 1

Active 2

Expansion factor

SE1

24OCT09 WE 41 36 33 33 1.17 0.08 21NOV09 WE 20 14 9 9 1.89 0.43 09JAN10 WE 9 9 7 7 1.29 0.23

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Table 3. Estimated expansion factors on sampled days with overflights.

Date Day-type

Air count 1

Air count 2

Active 1

Active 2

Expansion factor

SE1

21FEB10 WE 27 33 20 22 1.43 0.17 04MAR10 WD 14 17 11 10 1.48 0.26 14MAR10 WE 36 36 22 25 1.53 0.19 20MAR10 WE 32 37 28 28 1.23 0.10

Average 179 182 130 134 1.37 0.06 1 Standard error was computed assuming binomial distribution for the proportion of interviews that was active during the overflight

Figure 1. Comparison of expansion factors across the surveyed dates.

The estimates in Table 4 are expanded by the average expansion factor (E) of 1.37 (SE 0.06). The standard error of the expanded estimates is approximated by : SE(est ! E) = SE(est)2E2 + est 2SE(E)2 . This assumes that the estimated expansion factor is independent of the estimate – this is only approximately true because some of the same data are used for both the estimate and the expansion factor, but the effects of

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non-independence are expected to be small. The expanded estimates are presented in Table 4a.

Table 4a. Estimates from preliminary creel survey after adjustment with average expansion factor of 1.37 (SE 0.06).

Stratum

2009/2010 - Shoulder 2009/2010 - Winter Grand total Response Variable

Est Total SE Total Est Total SE Total Est Total SE

Total

Angler- anglers 4672 609 2197 720 6870 984 Angler- hours 26282 3381 9938 3156 36219 4835 Angler- rod-hrs 27474 3130 10929 3382 38403 4850 Angler- rods 5058 604 2533 767 7591 1025 Fish - BT - kept 766 228 266 172 1031 288 Fish - BT - r/k 970 255 378 223 1348 343 Fish - BT - rel 204 95 112 55 317 110 Fish - Oth - kept 0 0 0 0 0 0 Fish - Oth - r/k 0 0 0 0 0 0 Fish - Oth - rel 0 0 0 0 0 0 Fish - RT - kept 611 160 406 253 1016 303 Fish - RT - r/k 970 255 378 223 1348 343 Fish - RT - rel 840 372 517 372 1357 529 Fish - Tot - kept 1376 283 671 414 2048 508 Fish - Tot - r/k 2421 533 1300 825 3721 994 Fish - Tot - rel 1045 360 629 413 1674 552

RT = Rainbow Trout; BT=Bull Trout; r/k = released + kept A second way to impute values for the missing expansion factors is the multiple imputation method as outlined in Little and Rubin (2002, Section 5.4). In simple imputation methods, an imputed value is used for any missing expansion factor (e.g. the mean of the expansion factors). The “complete” data are then analyzed in the standard way by replacing Y

std by Y

std

* in Step 1 above. As long as the imputed value is an unbiased estimate of the missing value, the final estimates will still be unbiased, but the reported standard errors will be too small. Under multiple imputations, a model for the missing expansion factors is first determined. In this case, it seems sensible that the missing expansion factors come from the same distribution as the observed expansion factors.

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Then a total of M imputed datasets are created. In each of the M imputed dataset, a new imputed value is chosen for each missing value. [In this case, you could randomly choose from the seven observed expansion factors for each missing flight, or you could generate these from a normal distribution with the same mean and standard deviation as the seven observed expansion factors.] For each of the “complete” datasets, compute the estimates of interest and the estimated variance (standard error squared) (e.g. estimated total catch), denoted as !̂i and Vi for i=1,… M respectively. The final estimate is the average of the estimates over the M “complete” datasets:

! =1M

!̂ii=1

M

"

The final standard error combines the average variance from the “complete” datasets plus a correction term for the extra variation in the estimates over the different imputations:

SE !( ) = 1M

Vi +M +1M

!̂i "!( )2i=1

M

#M "1i=1

M

#

These two methods are asymptotically equivalent. In our case, if a large number of “complete” datasets are imputed, the different values of the expansion factor will average out for the days with the missing expansion factor. Estimates for the preliminary survey using the multiple imputation method are presented in Table 4b. The estimates and estimated variances are very similar.

Table 4b. Estimates from preliminary creel survey after using multiple imputations (M=10) for the missing expansion factors.

Stratum

2009/2010 - Shoulder 2009/2010 - Winter Grand total Response Variable

Est Total SE Total Est Total SE Total Est Total SE

Total

Angler- anglers 4770 522 2319 733 7089 900 Angler- hours 26567 3096 10743 3534 37310 4698 Angler- rod-hrs 28020 3020 11794 3726 39814 4796 Angler- rods 5113 609 2775 853 7888 1048 Fish - BT - kept 808 239 272 182 1080 300 Fish - BT - r/k 1015 270 385 232 1400 356 Fish - BT - rel 211 100 116 61 327 117 Fish - Oth - kept 0 0 0 0 0 0 Fish - Oth - r/k 0 0 0 0 0 0

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Table 4b. Estimates from preliminary creel survey after using multiple imputations (M=10) for the missing expansion factors.

Stratum

2009/2010 - Shoulder 2009/2010 - Winter Grand total Response Variable

Est Total SE Total Est Total SE Total Est Total SE

Total

Fish - Oth - rel 0 0 0 0 0 0 Fish - RT - kept 582 125 434 271 1015 299 Fish - RT - r/k 1020 268 402 253 1422 369 Fish - RT - rel 815 319 567 401 1381 513 Fish - Tot - kept 1386 273 706 436 2092 515 Fish - Tot - r/k 2361 442 1345 834 3706 945 Fish - Tot - rel 1004 298 655 427 1659 521

RT = Rainbow Trout; BT=Bull Trout; r/k = released + kept The multiple imputation method requires the analysis of multiple complete datasets and some additional computations to roll-up the estimates from the multiple imputations but with modern software, this is relatively simple. A key advantage of the multiple imputation approach is that the model to generate the imputed values can be very general – it is not necessary to use the same distribution for all missing values if there is strong evidence that the missing values depends upon a covariate. For example, if the observed expansion factors showed a dependence upon weather variables, these weather variables could be used to impute a more appropriate expansion factor than the simple average. In the case of the preliminary survey, using the average expansion factor at the end of the analysis, or imputing based on a common distribution for all overflights is asymptotically equivalent. The method of multiple imputation could also be used to impute the missing access records for Kaslo on two dates. Given the small number of anglers typically using this access point during the shoulder and winter seasons, this was not done and the estimates are only slightly affected.

2.2 CPUE Only the interview data is required to estimate the CPUE. The CPUE was estimated for particular species and location/date as:

CPUE =CH

10

where C is the total catch and H is the total rod hours for the location/date. A standard error for the CPUE is estimated1 using the formula for the standard error of ratio estimator in simple random sampling as:

SE(CPUE) = 1n1H 2 var(Ci ) + CPUE

2 var(Hi ) ! 2CPUE cov(Ci ,Hi )( )

where n is the number of interviews;var(Ci ) is the variance in the catch; var(Hi ) is the variance in the rod hours; and cov(Ci ,Hi ) is the covariance between the catch and rod hours over the interviews for the location/date. Plots of the estimated CPUE by site across the 11 sampling dates is available at: http://www.stat.sfu.ca/~cschwarz/Consulting/Kootenay/prelim.cpue.pdf These plots show no evidence of a difference in CPUE among sites within each date because the very small sample sizes results in very large confidence intervals and low power to detect anything but major differences. The estimated CPUE by species for each date (pooled over sites) is presented in Table 5 and plotted in Figure 2. Given the large uncertainty in each estimate of CPUE, there is no evidence of a difference over the dates for each species.

Table 5. Estimated CPUE (fish/rod hour) pooled over all sites

Species1

BT RT Date and Daytype2

n CPUE SE n CPUE SE3

24OCT09

WE 42 0.026 0.007 42 0.149 0.032

03NOV09 WD 13 0.048 0.024 13 0.143 0.035 21NOV09 WE 13 0.000 0.000 13 0.085 0.026 20DEC09 WE 10 0.057 0.026 10 0.033 0.018 09JAN10 WE 8 0.047 0.038 8 0.066 0.026 29JAN10 WD 4 0.000 0.000 4 0.000 0.000 16FEB10 WD 9 0.027 0.015 9 0.053 0.024 21FEB10 WE 29 0.064 0.024 29 0.035 0.009 04MAR10 WD 14 0.047 0.016 14 0.024 0.012

1 Because all angling parties are interviewed at locations at the chosen sampling dates, the data are technically a census and no standard error is required. However, a superpopulation approach treats the anglers interviewed as a random sample of those anglers who could have fished that day.

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Table 5. Estimated CPUE (fish/rod hour) pooled over all sites

Species1

BT RT Date and Daytype2

n CPUE SE n CPUE SE3

14MAR10 WE 30 0.037 0.012 30 0.026 0.008 20MAR10 WE 34 0.009 0.005 34 0.039 0.010

1 BT=Bull trout; RT=Rainbow trout 2 WE=Weekend; WD=Weekday 3 SE are computed using SE(CPUE) equation presented earlier pooled

over all sites.

Figure 2. Estimated CPUE (fish/rod hour) by date pooled over all sites.

The CPUE was not broken out by guided or non-guided status as there were only 8 trips that were guided compared to the approximate 200 non-guided trips available in the sample over the entire preliminary survey.

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2.3 Angler profile The angler profile (the proportion of angling parties that were active at each hour of the day) is computed by simply finding the proportion of all interviews where the interval between the start of angling trip and the end of the angling trip included all or part of the hour of interest. For example, if an interviewed angling party had a start time of 7:30 and an end time of 12:00, then it was considered active in the hours 7:00-8:00, 8:00-9:00, 9:00-10:00, 10:00-11:00 and 11:00-12:00. The approximate standard error can be

determined from the standard error of a binomial distribution SE =p̂h (1! p̂h )

n where

!ph is the estimated profile at hour h.

Plots of the estimated angler profiles by site and by sampling date are available at: http://www.stat.sfu.ca/~cschwarz/Consulting/Kootenay/prelim.profile.pdf Given that the number of interviews collected on day or site are very small (typically 30 or less), the standard error is typically on the order of 0.10 and the 95% confidence interval at any hour is ±0.20. Consequently, there is no evidence of a difference in profiles among sites or dates. The profile plot for the two seasons and the overall profile plot is presented in Table 6 and Figure 3. The profiles for the two seasons are remarkably similar. Approximately 80% of boats are active when the overflights take place at 13:00. Notice that 1/0.80=1.25 is NOT the same as the expansion factor of 1.37 used earlier. The factor 1.25 would indicate that if you only counted boats that used the 5 landings at 13:00, you would miss about 20% of the activity. The expansion factor of 1.37 is larger than 1.25 because there are an additional 10% of effort that does not use the five landings in the survey.

Table 6. Estimated profile by season and pooled over seasons.

2009/2010 - Shoulder

2009/2010 - Winter

All sites/dates Start of

the hour % active % active % active

0:00-3:00 0.00 0.00 0.00 3:00 0.00 0.00 0.00 4:00 0.01 0.00 0.00 5:00 0.01 0.00 0.00 6:00 0.04 0.02 0.03 7:00 0.11 0.08 0.10 8:00 0.22 0.21 0.22 9:00 0.36 0.48 0.39 10:00 0.61 0.60 0.60

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Table 6. Estimated profile by season and pooled over seasons.

2009/2010 - Shoulder

2009/2010 - Winter

All sites/dates Start of

the hour % active % active % active

11:00 0.73 0.77 0.74 12:00 0.76 0.77 0.76 13:00 0.79 0.79 0.79 14:00 0.80 0.77 0.80 15:00 0.69 0.65 0.68 16:00 0.51 0.29 0.46 17:00 0.24 0.00 0.19 18:00 0.10 0.00 0.07 19:00 0.01 0.00 0.00 20:00-23:00 0.00 0.00 0.00

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Figure 3. Estimated profile plots by season and over both seasons in Kootenay Lake.

2.4 Weight vs length for harvested fish. A total of 43 BT and 59 RT harvest fish were measured in the creel for weight and length (Appendix A). An analysis of covariance failed to detect any differences in the log(weight) and log(length) relationship between the two species (p=0.16). A plot of the data with the fitted lines appears in Figure 4.

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Figure 4. log(weight) vs log(length) for harvest fish in Kootenay Lake.

3. Designing a yearly creel survey for Kootenay Lake A thorough review of the design and analysis of creel surveys is presented in Pollock et al (1994). The following are some of the considerations in the design and analysis of a creel survey. (a) Design- or model-based surveys. Design-based surveys rely upon randomization to ensure that the sample units selected are representative of the population and to provide estimates and measures of precision. For example, a design-based survey would randomly select weekdays within a month to survey and would obtain estimates at the monthly level by expanding the average over the sampled days by the number of weekdays in a month. Design-based methods would require a minimum of two replicates of each survey unit within a stratum to obtain estimates of precision. Each stratum is randomized separately and information is not shared among strata. A model-based method would augment the information from the randomized selection with a model to help assist in the estimation. For example, a smooth curve could be fit to the monthly values to interpolate values for months where no days were selected. Or the ratio of catch between weekend and weekday in months where both day types are sampled is used to interpolate catch in months where only one day-type is sampled. The key-advantage of model-based methods is that precision can be dramatically improved and/or sample size

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dramatically reduced to obtain the same precision as a design-based survey if the model used is correct. The danger with model-based methods is that inference can be severely biased if the model used is incorrect. (b) Model-assisted analyses. Even though a survey may be design-based, the analysis can often incorporate simple models to help improve precision or deal with problems in the data (such as missing values). This is known as model-assisted analyses. A simple example of a model-assisted approach is the use of a ratio-estimator to improve estimation when both the primary variable Y and an auxiliary variable X can be measured and there is a linear relationship between the primary and auxiliary variable. This linear relationship makes no assumption about normality and the model is very simple. A model-assisted approach is often the only way to deal with imputation of missing data. For example, the multiple-imputation method used to account for the missing overflight expansion estimator is a model-assisted approach. Here the influence of the model is restricted to imputing the missing expansion factors, but the rest of the analysis proceeds using design-based methods. By restricting the influence of the model to selected portions of the design and analysis, effects of errors in the model can be kept to acceptable limits. (c) Stratification. Stratification can help improve precision of the estimates if the survey units are not homogeneous in the population. For example, weekend and weekdays could have quite different numbers and types of anglers, as could summer vs. winter months. Typical stratification variables in creel surveys are weekend vs. weekdays, and months or season. If resources are not limiting, the stratification rarely degrades the precision of estimates. However, if stratification is too fine, this may require too many resources. For example, a design-based estimate with stratification by month and weekend/weekday would require a minimum of two survey units in each stratum for a minimum of 48 survey days which may make the cost of the survey prohibitive. (d) Effort-catch components. Angler surveys traditionally separate the estimation of effort and estimation of catch per effort. This is done because it is usually very difficult to have complete coverage of the survey area (Kootenay Lake) during a sampled day because there are too many access points or access is too diffuse. The catch component interviews angling parties, either following completed trips (access surveys) or during the fishing episode (roving surveys). This provides information about the catch per trip and other variables. The total effort is often obtained by an aerial overflight if fishing primarily takes place from boats. This flight can be done at a random time during the survey day or at a non-random time to maximize the number of active boats. In the latter case, the catch component must also collect information on the start and end time of the angling trip so that the angler effort profile can be constructed and the appropriate expansion factor computed for the non-randomized flight time. Dauk and Schwarz (2001) discuss estimation with non-randomized flight times under both completed and incomplete trip catch surveys.

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Anonymous (2010) presented a preliminary basic outline for the creel survey of Kootenay Lake which is appended. In brief, this plan indicated that five days of sampling will be done each month (for a total of 60 days of sampling), with 3 weekdays and 2 weekend randomly sampled each month. Clerks will be stationed at the five main public access points (Kaslo, Woodbury, Balfour, Queens Bay, and Boswell) to interview angling parties after completed trips. Aerial flight will be conducted on 42 of the 60 sampled days with four flights during the six peak summer months and 3 flights/month on the off-peak month. Survey forms for data collections are also provided.

3.1 Comments on design decisions. Access survey for catch data. For the reasons outlined in Anonymous (2010), an access design to measure catch of complete trips is recommended. The main reason of favoring an access design over a roving survey is safety – rough wave conditions often preclude good coverage of the lakes. Locations of access points. The key assumption being made is that the anglers who are interviewed at the access points are a representative sample of all anglers on the lake during that day. The analysis of the preliminary survey data failed to show any differences in CPUE between the different access sites, but sample sizes were so small that only gross differences were likely to be detected. The map of the boat locations during the overflight is shown in Appendix C. The current five access locations are well situated for the majority of boat that were active during the overflight except perhaps for boats in the very northern portion of the lake. However, the effort in the very northern part of the lake appears to be relatively small compared to the total effort and unless the anglers there have very different catches and/or behaviour than the remaining anglers, any bias is expected to be small. Aerial survey for effort data. As summarized in Anonymous (2010) total effort will be estimated using an aerial survey. Counts will be done during or near peak daily activity (Figure 3) to maximize the number of boats seen and to reduce the variability in the expansion factor. [The variability in the expansion factor increases as the proportion of boats active decreases.] Based on Figure 3, the time of the overflight is not that critical – any time between 10:30 and 16:00 should provide good coverage. [Notice that the preliminary survey data did not have information on trip timing during summer months with longer days, so the actual profile may be different in those months.] While the results of the preliminary creel survey did not show any large differences in the expansion factor across October -> March, a similar analysis of the Arrow Lakes creel surveys showed that the expansion factor was substantially larger (about 2x larger) during June/July/August than other months in the year. Consequently, the survey should ensure that adequate flights take place during the summer months and imputation of missing expansion factors should be based on similar months as the missing data. For example, it would not be a sensible plan to use the expansion factors from the winter months to impute the expansion factors for the summer months. This is discussed again later in the

18

report. Number of flights per day. The preliminary Kootenay creel survey used a single overflight at approximately 13:00 and counted boats on the water during the outbound and return trips. In most cases, the outbound and return trip were sufficiently close together that the count was almost the same. During the summer months, there may be two peaks of effort with separate angling trips in the morning and late afternoon. It may be advantageous in future years to allow for multiple aerial flight – an analysis of the first years data should reveal if this is cost effective. Dauk and Schwarz (2001) show how to incorporate multiple overflights into the analysis. Number of aerial surveys. If every sample day has a corresponding overflight, then the analysis of the final survey is relatively straightforward. The expanded values of the variable of the interest is used as “data” in a standard stratified sampling analysis. In the proposed design, not every sample day will have an aerial overflight. The method of multiple imputation (Little and Rubin, 2002) as outlined in the analysis of the preliminary creel survey, provides a general way to deal with these missing values. Based on the days with aerial overflights, a model will be built for the expansion factor as a function of covariates such as weather. [In the preliminary survey, a very simple model of a common distribution with a mean and variance was used.] Then a set of imputed “complete” datasets is constructed, and the final estimates can be computed to properly account for the uncertainty in the expansion factors. This provides substantial flexibility in the scheduling of the overflights. For example, if it is reasonable to believe that the expansion factor is relatively stable within a season (e.g. summer months), then only a few flights need to be conducted each month as pooling over all of the summer months will provide substantial information. However, if the expansion factor is though to be highly variable even among adjacent months, then more flights must be scheduled so that an adequate model can be built. Anonymous (2010) suggested a total of 42 of the 60 sampled days have overflights. This should provide sufficient number of expansion factors to ensure that a sensible model for the imputation of the missing expansion factors can be done. Based on the preliminary survey, the expansion factor was reasonably stable in October to March and didn’t appear to differ between weekend and weekdays. Consequently, the number of overflights could be reduced from 18 to about 10 for the same period. Unfortunately, there is no information on the variability of the expansion factor for the summer months, so no recommendation can be made for a reduction in these months. In Anonymous (2010), it was proposed that 3 flights be taken in the off-peak months. To ensure good coverage of weekend/weekdays, some months should have 1 weekend/2 weekdays overflown, and other months have 2 weekend/1 weekdays overflown. Allocation of effort between weekends/weekdays. The analysis of the preliminary creel survey and that of the Arrows Lakes creel surveys shows that angling effort per day is

19

approximately twice as large during weekend days compared to weekdays. Assuming the catch rates are approximately equal between weekday and weekend anglers, this implies that a stratification by weekends/weekday should be pursued. There are about 2.5 times as many weekdays as weekends in a typical month and standard sampling theory would imply that more weekdays be sampled than weekends. However, the Arrow Lake creel survey and the preliminary Kootenay Lake creel survey also showed that the standard deviation in the number of anglers over replicate days of the same daytype is roughly proportional to the mean number of interviews. Neyman allocation (Cochran, 1977) implies then that the allocation of effort to weekend/weekdays to get the best possible precision should be proportional to the product of the number of weekend/weekdays in a month and the standard deviation in the number of anglers, or in this case, to the product of the number of days and the mean number of anglers. This implies that approximately equal number of days should be sampled from weekends and weekdays with slightly more effort in weekdays. Anonymous (2010) proposed allocating three days of sampling per month to weekdays and 2 days of sampling to weekends in each month for a total of 60 days sampled over the year as shown in Table 7. Table 7. Suggested sampling allocation by Anonymous (2010). Month J F M A M J J A S O N D WD access 3 3 3 3 3 3 3 3 3 3 3 3 WE access 2 2 2 2 2 2 2 2 2 2 2 2 WD 1 flights 1 2 1 2 2 2 2 2 2 2 1 2 WE flights 2 1 2 2 2 2 2 2 2 1 2 1 1 The report is not specific about the allocation of overflights in the off-peak months so a reasonable pattern was assumed. Allocation of effort among months. An analysis of the Arrow Lake creel survey (report in preparation) and feedback from S. Arndt (person communication) indicates that angling effort/day on weekends may be about 8x larger in the summer months (May-August) compared to winter months (November-January), and 4x in the shoulder months (March, April, Sept, October) compared to winter months. Again assuming that the standard deviation in the number of anglers per day is proportional to the mean, this implies that more effort should be expended in the summer months rather than the winter months. A possible allocation based on Neyman principles (Cochran, 1977) as shown in Table 8 that also incorporates a reduced number of overflights during the off peak periods based on the consistency seen in the preliminary survey.

20

Table 8. Proposed allocation after accounting to different angler effort and variation in effort among days and assuming that expansion factor during the off-peak months is relatively constant. Month J F M A M J J A S O N D WD access 1 1 2 3 4 4 4 4 3 2 1 2 WE access 2 1 2 2 4 4 4 4 2 2 1 1 WD flights 1 1 1 2 2 2 2 2 2 1 1 1 WE flights 1 0 1 2 2 2 2 2 2 0 1 1 Note that to obtain a design-based estimator of variance, it will be necessary to pool the winter months (November-February) to ensure that at least 2 days of each daytype are measured. Sub-sampling within selected days. Some care will be needed if it is not possible to sample the entire day at all the access points consistently across the year. If not all the access points can be sampled, but sampling still occurs over the entire day, then the analysis is not that difficult to adjust. If an aerial overflight occurs on that day, there is no problem as the usual expansion factor can be used directly. If the expansion factor must be imputed because no overflight occurs, then the existing data can be “thinned” to match the set of access points used to obtain suitable expansion factors. If the entire day can be sampled, but there are too many anglers to interview, then no problems are introduced by doing a systematic subsample of the angling parties (e.g. every third angling party). If an aerial overflight takes place on that day, the expansion factor can be determined directly; if it must be imputed, then data from the other days can be “thinned” to match the process for this day. If the entire day cannot be sampled, then this may introduce difficulties as there is no easy way to extrapolate from a half-day’s sampling to the entire day without strong assumptions. For example, on days with complete days sampling, the ratio of total effort to effort before noon may provide a way to expand these partial days sampling events. Consequently, if sampling must be reduced during the day, it would be preferable to cover the entire day at fewer access points and let the overflight survey provide the appropriate expansion factor. If the access effort is reduced on days with no overflight scheduled, than a “thinning” of the data for days with overflight should provide sufficient information for a valid imputation. Other concerns not discussed in Anonymous (2010) Public vs. Private access points. There are a large number of private access points that are used mainly in the warmer months. If the angler behavior and experience is the same as at the public access points, this does not cause any major problems as the overflight will still capture boats using private access points. It may be possible to capture some of

21

the effort from the private access points through a roving survey to verify this assumption separately from the main survey. Consequently, the expansion factor may not be as stable as seen in the preliminary creel survey and it likely needs to be estimated at least monthly or seasonally. Shore based anglers. Anonymous (2010) did not consider estimation of catch from shore based anglers. I do not have sufficient information to know if shore based anglers catch a substantial portion of the catch. Discussions with Ministry of Environment (MOE) staff suggest that this could be significant during the summer and shoulder seasons. Known areas of shore fishing should be counted during the flights and sampled separately if the budget permits. Derbies. Anonymous (2010) also did not consider the impact of derbies. S. Arndt (personal communication) indicates that there is a substantial effort during several fishing derbies. These are best handed in a separate stratum (with some effort being transferred from the regular sampling program). If registration is required for the derby, there may be an opportunity to collect information from the registration desk in addition to the creel clerk.

3.2 Expected precision To assist in the planning process for a year round creel survey of Kootenay Lake, a simulator was built to help predict the expected precision of different survey designs. Information on catch and effort was based on the preliminary creel survey analyzed in the previous section and discussions with FWCP and MOE biologists. The basic features of the simulator are: (a) Number of angling parties/day. Based on the analysis of the preliminary creel survey and correspondence with S. Arndt, Table 9 provides an estimate of the average number of anglers that would be interviewed at the five access points proposed in Anonymous (2010). Based on data from the Arrow Lakes creel survey, the standard deviation in the number of interviews across days of the same daytype is approximately ½ of the mean. The simulated number of completed interviews on a sampling day will be generated using a (rounded to integers) normal distribution with the mean values shown in Table 9 and the standard deviation set to half of the mean.

22

Table 9. Baseline mean number of interview of completed trips on a selected day by month based on 5 access points.

Month Day type1

1 2 3 4 5 6 7 8 9 10 11 12

WD 10 20 20 25 40 30 30 30 25 20 10 10 WE 10 40 40 50 80 80 80 80 50 40 10 10 1 WD=Weekday; WE=Weekend

The actual catch and other variables is randomly selected (with replacement) from the actual and simulated catch records in (b). (b) Simulated catch and other variables. The simulated catch and other variables is constructed in two parts. The interviews for shoulder season (October 2009, February 2010, and March 2010) in the preliminary Kootenay Lake survey are used for these three months partitioned by weekend/weekday. Similarly, the pooled interviews for November 2009 to January 2010 are used for these months partitioned by weekend/weekday. Based on discussions with FWCP, the data from the shoulder season are also used for simulated interviews conducted in April, May, June and September (also partitioned by weekend/weekday). Finally in July and August, the catch rate for bull trout was (randomly) set to about half of that in the shoulder season; while the catch rate for rainbow trout was (randomly) set to about double the shoulder catch rate. The number of real/simulated interviews that were randomly selected with replacement to match the overall number of interviews conducted during a simulated day is shown in Table 9. Unfortunately, there is little information on how to adjust the start and end time of the fishing episodes for the summer months. These are currently not adjusted and reflect the start and end times in the preliminary survey data. (c) Time of overflight. The overflight is assumed to take place at 13:00 (but this can be changed). (d) Expansion factor. Based on the analysis of the preliminary survey data, an average expansion factor of 1.37 is used for all months except May-August. Based on the analysis of the Arrow Lake creel survey, the average expansion factor is doubled during May-August. The simulator allows for a single overflight during the sampling day at an hour of choice. The total number of boats seen during the overflight is generated as a negative binomial distribution based on the expansion factor and the number of active boats in the simulated data. As in any simulation, there are limitations to the simulator:

23

(a) The estimates are obtained only using design-based methods and require multiple sampling days within each stratum to obtain estimates of precision. The simulator currently does not use a model-assisted approach to impute expansion factors for missing overflights. However, if the model for the expansion factors is correct, the resulting estimators should have similar precision compared to a design where the expansion factors are estimated monthly so the results from the simulator are still useful. Simulation Results: Anonymous (2010) suggested a sampling plan with 5 days per month (3 weekday and 2 weekend) at the same five sampling locations as in the preliminary survey. This corresponds to interviewing anglers at the five access points and the sample weekend/weekday as shown in Table 7. The predicted precision (SE) from the simulator is shown in Table 10. The simulator gives predictions that are about 30% higher than the preliminary creel survey results in Table 4 for the comparable period but with similar relative standard errors. The estimates are not that important in the simulator, but rather the relative precision and the impact of different design criteria upon the precision should be examined.

24

Table 10. Simulated results with 3 WD/2 WE per month with overflights on all months.

Stratum

01 02 03 04 05 06 07 08 09 10 11 12 9999

Est Est Est Est Est Est Est Est Est Est Est Est Est

Variable

696 1948 2847 1858 11172 8389 12006 7174 2942 2496 670 765 52962 Angler- anglers

SE 71 123 202 156 790 638 760 588 199 161 65 78 1467 3483 11587 16961 11117 66460 50992 68763 41516 17328 14563 3416 3816 310002 Angler- rod-hrs

SE 352 738 1227 942 4981 4160 4329 3207 1219 991 393 468 8985 80 338 491 326 1924 1608 788 460 483 389 76 88 7050 Fish - BT - kept

SE 31 52 72 58 219 221 106 94 78 63 29 35 375 115 413 607 405 2359 1873 1090 617 608 507 116 126 8836 Fish - BT - r/k

SE 41 70 98 83 309 268 161 121 96 88 41 42 528 35 75 116 79 435 265 302 157 125 118 40 38 1786 Fish - BT - rel

SE 20 35 50 39 157 94 94 58 44 46 21 17 235 118 244 361 236 1423 1005 3557 2076 371 333 123 137 9982 Fish - RT - kept

SE 36 45 63 50 217 189 505 335 68 74 35 42 754 275 568 850 576 3392 2427 8583 4959 905 773 268 311 23885 Fish - RT - r/k

SE 64 92 131 109 479 397 1161 849 166 154 62 74 1744 156 324 489 340 1969 1422 5027 2883 534 440 145 174 13903 Fish - RT - rel

SE 42 74 101 83 323 291 863 644 124 110 40 48 1245 390 980 1456 981 5751 4300 9673 5576 1512 1280 384 437 32721 Fish - Tot - r/k

SE 86 122 180 142 671 544 1231 875 217 199 85 90 1963

25

The predicted precision is quite good at the yearly level with relative standard errors (SE/estimate) of about 10% (which implies that the 95% relative confidence intervals will be about ±20% of the estimates). The simulated results under revised allocation of Table 8 with an overflight for each day are presented in Table 11. The estimates are equal (within simulation error) to those in Table 10, and the standard errors are abut 25% smaller because of the revised allocation. Table 11. Simulated results with revised allocation of sampling dates proposed in Table 8 with overflights on all sampling days

Stratum

03 04 05 06 07 08 09 10 11/12/ 01/02 9999

Est Est Est Est Est Est Est Est Est Est

Variable

1976 4036 9364 8181 8432 9001 2968 2998 4066 51022 Angler- anglers SE 162 217 582 422 493 455 227 205 290 1071

12241 23217 54445 48352 50278 51955 17397 17329 21556 296770 Angler- rod-hrs SE 1102 1382 3400 2609 3129 2792 1385 1203 1717 6677

419 615 1432 1423 561 597 487 447 549 6531 Fish - BT - kept SE 79 77 158 143 75 78 93 73 114 309

467 822 1869 1757 711 797 629 603 749 8404 Fish - BT - r/k SE 91 104 212 187 95 105 136 109 139 407

47 207 438 334 150 200 143 156 200 1874 Fish - BT - rel SE 30 68 100 89 45 53 75 61 56 196

223 537 1252 1033 2273 2633 393 404 655 9402 Fish - RT - kept SE 63 80 146 131 289 321 84 80 105 558

533 1286 2959 2469 5611 6311 919 958 1521 22567 Fish - RT - r/k SE 123 188 320 295 664 645 196 157 240 1213

310 749 1707 1436 3338 3678 527 555 866 13165 Fish - RT - rel SE 90 142 233 236 537 499 148 117 181 899

1000 2108 4829 4226 6322 7107 1549 1561 2270 30971 Fish - Tot - r/k

SE 171 245 435 400 709 676 259 201 306 1345

26

4. Summary The analysis of the preliminary creel survey on Kootenay Lake found that the expansion factor to adjust the access interview data for total effort to be about 1.4 and relatively stable over the October -> March period. However, based on the results of the Arrow Lake creel survey, the expansion factor in the summer months is expected to be larger. This implies that aerial flights need to be conducted during these peak periods rather than relying on an average expansion factor for the entire year. The peak of angling activity occurred approximately between 13:00 and 16:00 during the October to February period and is relatively flat. This provides considerable latitude in scheduling the overflights during these months. The methods of multiple imputation is recommended as the analysis method for the year-long survey. A model for the expansion factors as a function of weather and other covariates can be developed to provide estimates of the missing values that vary among different “completed” datasets. The multiple imputation method generates estimates with proper standard errors that account for the uncertainty in the imputed values. The basic framework for the yearlong creel survey as outlined by Anonymous (2010) was reviewed and should provide estimates that meet the stated precision requirements. A revised allocation that places more sampling effort in the summer months and less in the winter months (rather than equal effort across all months) has the potential to provide more precise estimates at the same cost. If the stability of the expansion factor in the offpeak months seen in the preliminary survey again occurs, the number of overflights in the offpeak periods can be reduced by about 50%. At the moment, there is little information on the stability of the expansion factor in the peak months. Additional consideration is needed to deal with shore angler and fishing derbies.

5. References Anonymous (2010). Kootenay Lake Angler Survey: Draft Methods for Review.

Document dated February 2010. Cochran, W. G. (1977). Sampling techniques. Wiley. New York. Dauk, P.C. and C.J. Schwarz. 2001. Catch estimation with restricted randomization in the

effort survey. Biometrics 57, 461-468. Little, R.J.A. and Rubin, D.B. (2002). Statistical analysis with missing data, 2nd Edition.

27

Wiley. New York. Pollock, K. H., Jones, C. M. and Brown, T.L. (1994). Angler survey methods and their

applications in fisheries management. American Fisheries Society Special Publication 25, Bethesda.

28

Appendix A. Fish measurements taken from harvested fish during a creel survey on Kootenay Lake from October 2009 to March 2010.

Record No. Date

Access Location Species*

Fork Length (cm)

Weight (g) Comments

5 24-0ct-09 Kaslo BT 78 na 6 24-0ct-09 Kaslo BT 73 na 7 24-0ct-09 Kaslo BT 46 na 7 24-0ct-09 Kaslo BT 49 na 8 24-0ct-09 Woodb/Ains RB 42 800 8 24-0ct-09 Woodb/Ains RB 34.5 500

21 24-0ct-09 Riondel/ Fish Hawk RB 70.1 3500

23 24-0ct-09 Riondel/ Fish Hawk RB NA 1000

ANGLER REPORTED DATA

26 24-Oct-09 Bosw/Kusk RB 42 500 27 24-Oct-09 Bosw/Kusk RB 76 4900 27 24-Oct-09 Bosw/Kusk BT 59 2100 27 24-Oct-09 Bosw/Kusk RB 40 600 28 24-Oct-09 Bosw/Kusk RB 50 2000 32 24-Oct-09 Balfour BT 52 na 32 24-Oct-09 Balfour RB 38 na 32 24-Oct-09 Balfour RB 60 na 32 24-Oct-09 Balfour RB 48 na 35 24-Oct-09 Balfour RB 54 2000 39 24-Oct-09 Balfour RB 34 na 39 24-Oct-09 Balfour BT 70 2500 42 24-Oct-09 Balfour RB 46 na 42 24-Oct-09 Balfour RB 40 na

44 24-Oct-09 Balfour BT NA 3400 guide (Kerry) reported weight

44 24-Oct-09 Balfour RB NA 5800 guide (Kerry) reported weight

44 24-Oct-09 Balfour RB NA 4500 guide (Kerry) reported weight

44 24-Oct-09 Balfour RB NA 3600 guide (Kerry) reported weight

44 24-Oct-09 Balfour RB NA 3600 guide (Kerry) reported weight

44 24-Oct-09 Balfour RB NA 2200 guide (Kerry) reported weight

52 03-Nov-09 Riondel/ Fish Hawk RB 49 1100

45 03-Nov-09 Bosw/Kusk BT 55 1600 45 03-Nov-09 Bosw/Kusk BT 60 2350

47 03-Nov-09 Bosw/ Kuskanook RB 43 800

47 03-Nov-09 Bosw/Kusk RB 42 700 47 03-Nov-09 Bosw/Kusk BT 60.5 1900 49 03-Nov-09 Bosw/Kusk RB NA 1100 ANGLER REPORTED

29

DATA

50 03-Nov-09 Bosw/ Kuskanook RB 69 4300

50 03-Nov-09 Bosw/Kusk RB 35 450

58 03-Nov-09 Balfour BT 46.2 1000 guide (Kerry) reported weight

60 03-Nov-09 Balfour BT NA 1900

66 21-Nov-09 Riondel/ Fish Hawk RB NA 4500

ANGLER REPORTED DATA

68 21-Nov-09 Kaslo RB 61 5100 76 21-Nov-09 Bosw/Kusk RB 37 400 77 20-Dec-09 Woodb/Ains BT 66.1 4300 77 20-Dec-09 Woodb/Ains BT 53.4 2000 77 20-Dec-09 Woodb/Ains RB 61.6 3700

80 20-Dec-09 Balfour BT 61 2495 weighed on angler's scale

87 20-Dec-09 Riondel/ Fish Hawk BT 55 na gutted weight 1500

88 09-Jan-10 Riondel/ Fish Hawk RB

filleted, angler est 80 cm/4800 g

88 09-Jan-10 Riondel/ Fish Hawk RB

filleted, angler est 40 cm/900 g

88 09-Jan-10 Riondel/ Fish Hawk RB

filleted, angler est 70 cm/2200 g

88 09-Jan-10 Riondel/ Fish Hawk BT

filleted, angler est 70 cm 2700 g

90 09-Jan-10 Woodb/Ains BT 57 2500 90 09-Jan-10 Woodb/Ains BT 59 2900 91 09-Jan-10 Balfour RB 72 5200

104 16-Feb-10 Woodb/Ains BT 60.8 2700

measurements supplied by angler (fish cleaned on arrival)

105 16-Feb-10 Bosw/Kusk RB 54.5 1588 105 16-Feb-10 Bosw/Kusk RB 44 907 111 16-Feb-10 Balfour BT 63.4 1800

113 21-Feb-10 Kaslo BT 82 6991.6 weighed on angler's scale

114 21-Feb-10 Kaslo BT 66.5 3178 weighed on angler's scale

118 21-Feb-10 Bosw/Kusk RB 54 2500 122 21-Feb-10 Bosw/Kusk RB 65 2500 123 21-Feb-10 Bosw/Kusk RB 67 3600 123 21-Feb-10 Bosw/Kusk BT 62 2800 125 21-Feb-10 Balfour RB 70.6 4000 Gary measurements

141 21-Feb-10 Riondel/Fish Hawk BT 59.7 2000

questionable measurement (Greg and was on anglers scale)

143 04-Mar-10 Riondel/Fish Hawk BT 51 2000

149 04-Mar-10 Balfour RB 4082

assume this is angler-supplied weight as there was no length with it

151 04-Mar-10 Woodb/Ains BT 58 2500 154 04-Mar-10 Bosw/Kusk BT 63 3600

30

154 04-Mar-10 Bosw/Kusk BT 64 3100 155 04-Mar-10 Bosw/Kusk RB 60 2300 155 04-Mar-10 Bosw/Kusk BT 62 2800 155 04-Mar-10 Bosw/Kusk BT 59 2200 157 04-Mar-10 Bosw/Kusk BT 54 1300 169 14-Mar-10 Balfour BT 63 169 14-Mar-10 Balfour BT 56 176 14-Mar-10 Balfour BT 67 3200 177 14-Mar-10 Balfour BT 53.5 177 14-Mar-10 Balfour RB 54.5 1300 177 14-Mar-10 Balfour BT 72.5 3600 178 14-Mar-10 Balfour BT 64.5 3100 180 14-Mar-10 Woodb/Ains RB 58.4 2200 angler supplied info? 180 14-Mar-10 Woodb/Ains BT 45.7 1100 angler supplied info? 182 14-Mar-10 Woodb/Ains RB 78 7371 188 14-Mar-10 Bosw/Kusk RB 52 1800 191 20-Mar-10 Woodb/Ains RB 68 3400 fat

202 20-Mar-10 Riondel/Fish Hawk RB 59 3000

length is Greg's estimate after gutting; weight is angler's

206 20-Mar-10 Bosw/Kusk RB 54 1750 206 20-Mar-10 Bosw/Kusk RB 57.5 2000 206 20-Mar-10 Bosw/Kusk RB 51 1200 209 20-Mar-10 Bosw/Kusk BT 61 2400 210 20-Mar-10 Bosw/Kusk BT 57 2200 210 20-Mar-10 Bosw/Kusk RB 51 1400 212 20-Mar-10 Bosw/Kusk RB 55 1750 212 20-Mar-10 Bosw/Kusk RB 50 1400 212 20-Mar-10 Bosw/Kusk RB 66 4400 214 20-Mar-10 Bosw/Kusk RB 70 3600 217 20-Mar-10 Balfour RB 65 Kerry (guide) 222 20-Mar-10 Balfour RB 60 222 20-Mar-10 Balfour RB 50.5 222 20-Mar-10 Balfour BT 57

* BT = bull trout, RB = rainbow trout

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Appendix B. Copy of Anonymous (2010).

Kootenay Lake Angler Survey

Draft Methods for Review

February 2010

1

I. Introduction The Fish and Wildlife Compensation Program: Columbia Basin (FWCP:CB) Steering Committee has recently approved a task for the 2010/11 fiscal to do an angler survey on Kootenay Lake to quantify the annual fishing effort, catch, and harvest. The approval was on condition the Large Lakes Committee of MOE review and comment on the design. This document outlines a basic approach that will be used, and is provided for review and comment. It is intended that methods will be finalized after a feasibility study (described below) has been completed and analyzed. Background The FWCP funds two major initiatives on Kootenay Lake: the north arm nutrient restoration project, and Meadow Creek kokanee spawning channel. These projects are primarily aimed at restoration and maintenance of Gerrard rainbow trout, bull trout, and their kokanee prey. To properly evaluate fish responses, it is necessary to measure both spawner escapement (i.e., ongoing kokanee counts, Gerrard rainbow counts, unfunded bull trout redd counts) and the harvest that occurs before spawning. In fiscal 2009/10, a small feasibility study (Kootenay Lake creel survey design) was funded to test methods and collect data to help design a lake-wide survey of annual angler effort, catch, and harvest. Due to the limited funds available, this study is focused on the fall to spring fishery (October 24/09 – March 20/10) when it is believed most angling for gerrard rainbows and bull trout occurs. Initial discussions with regional MOE1 and FWCP staff resulted in a consensus that access-based surveys combined with aerial boat counts were more likely to succeed than roving surveys. Roving (boat-based) surveys were considered unsuitable because (1) rough wave conditions on the lake can often prevent good coverage, and (2) it is not feasible to do biological sampling without interfering with trolling boats and trying to transfer large fish or personnel from one boat to another in rough wave conditions. Biological sampling of predator species is considered very important because fish growth and condition factor are important indicators for evaluating the FWCP compensation projects, and for fisheries management purposes. An access point survey has been conducted on the Arrow Lakes since the late 1970s in a relatively consistent manner except that the number of monitored access points has changed over time with budget constraints. Recently this survey was augmented by limited funding for aerial boat counts to allow for expansion of the access point CPUE data to whole-lake estimates of effort and harvest using ratio type estimators (Dauk and Schwarz 2001). Analyses for 2003-2009 are in progress in collaboration with Carl Schwarz (Simon Fraser University). The intent of the Kootenay Lake feasibility study is to test methods similar to those used on Arrow on Kootenay Lake, which has many more private and public access locations than Arrow Lake. Funding (and MOE in kind contributions) will allow for a total of 11 days to be sampled at 5 access locations. Aerial boat counts will be done during seven of the access-sampled days. Objectives include: 1 J. Bell, C. Spence, J. Burrows, E. Schindler

2

1) Determine the feasibility of using an access point survey and instantaneous boat

counts similar to that on Arrow Lakes Reservoir to obtain angler effort and harvest estimates for Kootenay Lake, and identify suitable access points for such a survey and the percentage of total effort captured at each site.

2) Obtain descriptions of daily angler activity patterns that can be used to plan timing of flights for a whole-lake survey accounting for the majority of effort.

3) Plot the locations of boats counted from the air to determine whether the five chosen access locations are likely to sample all significant components of the main lake fishery.

4) Obtain access point CPUE estimates for comparing to the Arrow Lakes Reservoir and correlating to the KLRT2 surveys. Describe temporal (among days, months) and spatial (access locations) patterns in CPUE.

5) Test the feasibility of counting shore anglers from the air. 6) Collect biological data (length, weight, age) on harvested fish. 7) Determine approximate costs of access and air sampling. 8) Review creel surveys from the past to ensure consistency and limitations.

Early results (three completed flights) have shown that boats can be successfully counted from a SuperCub airplane flying from Creston to the end of the north arm, and more than 50% of the boats at large on the lake during the fall/winter fishery can be contacted by monitoring 5 access areas (Fig. 1). Weather has delayed flights on one occasion, but this should not be a critical concern when ratio estimators are used. Shore anglers are not easily spotted from the air and will have to be excluded from the survey or sampled independently. Shore angling is believed to be a minor component of the total fishery. Catch per unit effort (CPUE) variability is high among the different access locations and dates sampled with no consistent pattern in the first five days sampled (Fig. 2) and overall distributions are skewed towards zero (Fig. 3). There is a clear trend of decline in boat effort from late October to January (Fig. 4). Peak daily fishing activity occurs from approximately 11:00 to 15:00 at this time of year (Fig. 5).

2 The Kootenay Lake Rainbow Trout survey is a mail out survey funded periodically by HCTF. It is sent only to anglers that purchase a special tag for retaining rainbow trout over 50 cm.

3

y = 1.1757x + 2.0315R2 = 0.9914

y = 1.5092x + 3.7141R2 = 0.9392

0

5

10

15

20

25

30

35

40

45

0 5 10 15 20 25 30 35

Interviewed boats fishing during the count

Air

Coun

t

Count 1Count 2

Fig.1. Number of boats interviewed at five access points compared to the total counted from the air on Kootenay Lake. Two counts were done for each of three days (24 October, 21 November 2009, and 9 January 2010).

2009-10-24

2009-11-03

2009-11-21

2009-12-20

2010-01-09

2010-01-29

Date

0.0

0.1

0.2

0.3

0.4

CP

UE

(fis

h /ho

ur)

Woodb/AinsRiondel/FishKasloBosw/KuskBalfour

Fig.2. Catch per unit effort (bull and rainbow trout combined) for five access locations on Kootenay Lake on five sampled dates. Missing values indicate no fishing from the access on that date. CPUE was calculated from total rod-hours and fish caught (all boats) at each access.

4

0.0 0.1 0.2 0.3 0.4CPUE (fish/hr)

0

2

4

6

8

10

Cou

ntCombinedRainbowBull

Fig.3. Distribution curves fitted to five days of catch per unit effort from five access locations on Kootenay Lake.

0

5

10

15

20

25

30

35

40

45

50

24-Oct 03-Nov 21-Nov 20-Dec 09-Jan 29-Jan 16-Feb 21-Feb 04-Mar 14-Mar 20-Mar

Date Sampled

Num

ber o

f Boa

ts

Boswell/KuskanookQueens EastBalfourWoodburyKaslo

41

20

9

Fig.4. Total number of boats interviewed at five access locations on sampled dates since October 24, 2009. Numbers above bars are whole-lake “instantaneous” boat counts done at mid-day. Dates with no bars have not yet been sampled.

5

0

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15

20

25

30

35

06:00

- 07:0

0

07:00

- 08:0

0

08:00

- 09:0

0

09:00

- 10:0

0

10:00

- 11:0

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0

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18:00

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0

Time of Day

Num

ber o

f boa

ts fi

shin

gTotalKasloWoodburyQueens EastBosw/KuskBalfour

Fig. 5. Daily activity profile for October 24, 2009 based on number of interviewed boats that reported fishing from five access locations on Kootenay Lake.

II. Preliminary Sampling Plan – to be modified when the feasibility data is complete A summary of the feasibility study with recommendations for whole lake annual methods will be completed soon after the sampling is finished in March 2010, and sampling methods will be finalized based on the test data, consultation with a qualified statistician, and received comments. Regional MOE staff will be consulted to get input on how to deal with the higher boat traffic (much of it non-fishing) and additional access locations during the warmer months. The following outline is essentially the same as the feasibility study and may be changed (e.g., the proportion of funds allocated to air counts versus ground sampling). Spatial Boundaries The area included in the survey will be the main body of Kootenay Lake including the north and south arms and Queens Bay. The west arm will not be included in the flights because the kokanee fishery in that zone is quantified by a separate survey, catch of large rainbow trout and bull trout is relatively rare there, and costs would increase substantially (for flights and additional access points). However, during the summer months, some anglers that fish on the west arm will be interviewed incidentally at the Balfour access. This data will be recorded and can be summarized for CPUE and catch composition. A potential problem will be anglers that spend part of a day fishing on the main lake and part in the west arm. In this case, the clerk will have to determine the

6

times spent in each area. The boundary between the west arm and main lake will be the boundary markers described in the freshwater fishing regulations. Field Methods (ground and aerial) Boat angling parties will be interviewed at the end of their trips by clerks stationed at five access locations (Table 1, Fig. 6). Some of the locations include more than one boat ramp that can (at least during the fall to spring fishery) be checked by one clerk. See datasheet (Appendix A) for data collected during the feasibility survey. Additional questions could be added on the human dimensions and economic sides if desired, keeping in mind that during busy times a quicker interview will allow more boats to be contacted. Table 1. Access locations used for the Kootenay Lake creel survey test from October 2009 to March 2010. Access Locations Description Kaslo Shipyards dock and Kaslo Bay Resort in Kaslo near the north end of the North

Arm Woodbury Woodbury Resort and Ainsworth moorages on the west side of the North Arm Balfour Ramp maintained by Recreation Commission, public launch near Ministry boat

house, Lang’s marina, (and others? – check with Gary) Queens Bay East Riondel moorage, Kootenay Bay ramp, and Fish Hawk Marina all located to

the east of Queens Bay (central Kootenay Lake) Boswell/Kuskanook Boswell public ramp, Kuskanook moorage and ramp facility on east side of

South Arm. Aerial boat counts will be done flying out of Creston and making one count as the airplane travels to the end of the north arm, and a second count on the return flight. Start and end times of the counts will be recorded and locations of fishing and non-fishing boats (Appendix B). A SuperCub 165 h.p. airplane works very well for this due to the relatively slow air speed. Counts will be done during or near peak daily activity (Fig. 5) rather than randomly to ensure that flights are unlikely to be done during periods of zero activity. The boat counts will be used with the fishing times recorded in interview data to estimate a ratio of interviewed to total boats (Dauk and Schwarz 2001), and combined with access CPUE to estimate catch by species (Fig. 7). It is expected that the ratio of total/interviewed boats will change over the course of the year because of the high number of private access points that are used mainly in the warmer months. Consequently it will likely need to be estimated monthly, or at least seasonally.

7

Fig. 6. Proposed access locations (red dots) for interviewing returning boat anglers on Kootenay Lake.

8

Fig.7. Outline of the Kootenay Lake access-aerial survey estimation components. Data from angler interviews and aerial boat counts used to estimate both average catch rates and total effort. Estimates will be stratified by time (weekday/weekend, month). (Diagram after Fisheries and Oceans Creel Survey bulletin # 1, 10 June 2009.)

9

Timing and Sampling Frequency The survey will start in April or May 2010 and run for a 12 month period (funds carried over to next fiscal). This will allow all of the feasibility data to the end of March to be considered in the final design. A stratified random approach will be used with 5 spatial strata and two daytypes within months. The tentative sampling frequency is five days per month at each of the five access locations (Table 2). Sampled days will be randomly selected within the time strata. Period monitored on sampled days will be from two hours after official sunrise to dark with the assumption that this captures the complete angling day. Table 2. Tentative time and access strata for a Kootenay Lake creel survey. Day Type Weekend 2 days per month Weekday 3 days per month Access Locations Kaslo 5 days per month Woodbury 5 days per month Balfour 5 days per month Queens Bay East 5 days per month Boswell/Kuskanook 5 days per month Expected data • Completed trip CPUE by species for comparison to Arrow Lakes Reservoir access

dataset, KLRT mailed survey, and other BC lakes; can be calculated based on targeted species or as a whole

• Total boat counts made during peak angling activity • Annual estimate of angler effort (angler-days, angler-hours), catch (including released

fish) and harvest by species made by combining access interview CPUE and aerial boat counts. It will be possible to stratify estimates by day-type (weekend/weekday), month, or season (winter, fall/spring, summer).

[Note: an option is to do the estimates using non-guided boats only, and use guide log

books as the estimate for guided parties.] • Spatial distribution of effort by dates surveyed • Description of anglers and preferences (residence, targeted species, and use by

resident category at different times of year) • Estimates of economic impact (based on estimated angler days X expenditure per day

estimates from DFO or FFWBC surveys) • Biological data (fish size and condition) for comparison to other BC lakes and for

indicating Kootenay Lake feeding conditions for piscivores; this data will contribute to the

10

understanding of large lake ecology and interactions between spawning channels and nutrient additions.

• Scales/otoliths for age determination will be collected III. Budget and assumptions Approved funding level not including FWCP staff salary or MOE contributions is $164,000 and estimated expenditures are listed in Table 3. Table 3. Tentative budget allocation for the Kootenay Lake creel survey. Component Unit Cost Amount Total Cost Comments

Access Sampling $400/day 300 days $120,000

5 days/month at five sites; combination of MOE auxiliary staff and independent contractor

Airplane Boat Counts $700/flight*

42 flights $29,400

4/month for 6 peak months; 3/month for rest

Flight Observer/recorder $250/flight

24 flights $6,000

remainder of flights done by FWCP/MOE staff

Travel/mileage $33/day 200 $6,600 assumes some MOE vehicle trips in kind Data analysis assistance - - $2,000 Field supervision, contract admin., data entry, reporting

FWCP staff salary

Includes some field sampling, flight observer, contractor monitoring in the field, ongoing coordination of project

*summer flights may take longer because there will be a large number of non-fishing boats on the lake that will have to be checked for fishing lines. Reference Dauk, P.C. and C.J. Schwarz. 2001. Catch estimation with restricted randomization in the effort

survey. Biometrics 57: 461-468.

11

Appendix A. KOOTENAY LAKE CREEL SURVEY DATA SHEET

Observer __________________ Access Point(s) __________________________________ Date________________________ Time of Arrival1 ________ Time of Departure1 ________ Circle day of week: Sun Mon Tues Wed Thur Fri Sat

Number

of Anglers

Number of Rods

Start Time2

Finish Time2

Species Sought

Species Released (# Spp.)

Species Kept

Length (cm)

Weight (kg)

Guided Yes/No

Angler Residence4

Access5

Weather (include wave conditions): Comments:__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

1 The time when the observer arrives at the access point at the beginning of the day and leaves at the end of the day. 2 Please indicate AM or PM or use 24 hour time (i.e. 6:00 PM or 18:00)

3 Record number of any tagged fish 4 Residence categories are: Local – West Kootenay

R – BC resident outside local area NRC – non-resident Canadian

NRA – non-resident outside Canada 5 Indicate which boat ramp/marina is used for each interviewed party if more than one location is checked

12

Suggested Interview Questions:

1) START with a friendly greeting, identify who you work for, and ask if you can ask a few questions for a survey on the fishing in the lake. (The Fish & Wildlife Compensation Program and MOE want to see how the lake fertilization and spawning channel are affecting the fishing quality.)

2) Ask – What time did you get your lines in the water today? 3) What time did you stop fishing? 4) How many rods were you fishing with? (you may be able to see this without asking) 5) What species were you fishing for? 6) Did you release any fish today? (If yes – follow up with recording the number for each species) 7) Did you keep any fish today? Do you mind if I measure them? (If they are in a big rush just get as

much information as you can without seeing or measuring the fish.). Record the fork length and weight in the appropriate columns. If you happen to see a tagged fish record the number beside the row.

8) Do you live in the local area? If not, where? [Local (West Kootenay), BC Resident, Canada, outside Canada). This is recorded for each person. (e.g., you might record 1 Local, 2 NRC for a party of 3)

9) You may need to ask if this is a guided party although this should be obvious from the boat markings. 10) THANK THEM FOR THEIR TIME AND INFORMATION

Other Considerations:

- Try to approach the anglers at a time when they are not pre-occupied with other things but before they are all packed up and ready to leave. For example, if the person is having a hard time getting the boat on the trailer wait until they’re safely pulled out.

- Sometimes one person is less busy than another; approach him/her first. - Since we are not enforcement people, we don’t try to force anyone to give us answers or show us

their fish. It is voluntary. (If you see suspicious actions feel free to record as much information as possible and report to the conservation officers later.)

- Try to give some information back to them. For example, describe what other people have been catching during that day or last time you surveyed.

- Refer specific questions that you can’t answer to the appropriate people. Useful numbers below: o Eva Schindler – lake fertilization coordinator 250-354-6338 o Jeff Burrows – Regional fisheries biologist 250-354-6928 o James Baxter – Fish & Wildlife Compensation Program Senior Fish Bio 250-352-6874 o Steve Arndt – Fish & Wildlife Compensation Program Fish Bio 250-352-6874

Example:

Number of

Anglers

Number of Rods

Start Time2

Finish Time2

Species Sought

Species Released (# Spp.)

Species Kept

Length (cm)

Weight (kg)

Guided Yes/No

Residence4 Access5

1 2 8:00 13:30 RB 3 BT RB 55 2.4 NO Local Ainsworth 4 4 8:30 16:30 RB/BT 4 BT, 2

RB RB 65 3.6 YES 1 Local, 3

R Woodbury

RB 40 0.8 BT 68 3.9

2 2 12:30 17:00 RB None RB 66 3.5 NO 1 local, 1 R Ainsworth

13

Appendix B. Count 1 Map - Mark the location of all boats and shore anglers on the map below and record coordinates if possible. Indicate whether boats are fishing or non-fishing.

Date: ______________ Weather: ____________________ Estimated Wave Height (feet): ________ Observer: ____________________ Start Time _________ Finish Time _________ # Fishing Boats ______ # Non-fishing Boats ___ # Shore Anglers _______

32

Appendix C Locations of boats during overflights in preliminary creel survey

ML

ML

ML

ML

ML

ML

ML

ML

MLKaslo

Balfour

Kuskanook

Boswell

KootenayBay

FishHawk

Riondel

Ainsworth

Woodbury

490000

490000

500000

500000

510000

510000

520000

520000

530000

530000

54

40

00

0

54

40

00

0

54

50

00

0

54

50

00

0

54

60

00

0

54

60

00

0

54

70

000

54

70

000

54

80

00

0

54

80

00

0

54

90

00

0

54

90

00

0

55

000

00

55

000

00

55

10

00

0

55

10

00

0

55

20

00

0

55

20

00

0

55

30

00

0

55

30

00

0

55

40

00

0

55

40

00

0

55

50

00

0

55

50

00

0

55

60

00

0

55

60

00

0

Date of Creel Survey

24/10/2009

21/11/2009

09/01/2010

21/02/2010

04/03/2010

14/03/2010

20/03/2010

ML Boat Launch

Kootenay Lake Creel Survey

Projection: UTM, zone 11Date created: May 11, 2010

0 6 123

Kilometers