Lake Nipissing Walleye Data Review and Harvest Level Recommendations for Nipissing First Nation

Richard Rowe, Fisheries Biologist

Nipissing First NationMarch 5, 2007

Executive Summary

A review of Lake Nipissing walleye assessment data suggests that at present, there is a high probability that the Lake Nipissing walleye population remains in a stressed to heavily stressed condition. Many trends in the data set and comparison to biological reference points suggest that the walleye population is showing signs of slight improvement, especially in the larger size classes. However, there is only a low probability (< 36%) that the fishery could be deemed healthy at this point in time.

Estimated total annual yield has been low for the past three years at 57% to 73% of the theoretical maximum sustainable yield. A stochastic simulation suggests that there is a 70-88% probability that this present yield is below the maximum sustainable yield reference point. Included in this present low yield is the Nipissing First Nation commercial fishery, which in 2006 harvested between 36,382 kg – 38,980 kg of walleye. Based on the present overall low yield, improving trends with larger walleye (decreasing mortality, increasing abundance), and regular production of strong walleye year classes, the 2006 Nipissing First Nation (NFN) commercial harvest level presently appears to be sustainable. However, a markedly lower catch of smaller exploitable walleye (30-40 cm) during 2006 Fall Walleye Index Netting suggests a recent depletion of this size class. Given this apparent depletion and the present tenuous status of the walleye fishery, an increase from present harvest level would be inadvisable and not recommended at this time. Therefore the recommended Nipissing First Nation commercial harvest quota for the 2007 fishing season is 38,000 kg, roughly identical to the harvest in 2006.

Present walleye assessment on Lake Nipissing appears to be for the most part, effective. There are significant predictive relationships in the data set including angler effort levels as a predictor of CUE of 30-40 cm walleye caught during Fall Walleye Index Netting (FWIN) and NFN annual harvest as a predictor of CUE of 35-50 cm walleye caught during FWIN. These predictive relationships and others suggest that responses to changes in angler effort and NFN harvest levels are reflected in annual Fall Walleye Index Netting. Assuming that these data sets continue to be perpetuated, it will be possible to derive detailed Lake Nipissing-specific biological performance indicators that should ultimately improve fisheries management and fisheries management planning on Lake Nipissing.

2

AcknowledgementsThank you to George Morgan and Scott Kaufman from Laurentian University Cooperative Freshwater Ecology Unit for providing scientific review of the final draft of this document. Scott Kaufman provided additional editorial comments which were very useful. North Bay District Ministry of Natural Resources is to be acknowledged for providing much of the data necessary for this report. It is hoped that they can continue their extremely critical annual data collection program on Lake Nipissing. Rodney Beaucage was particularly diligent in collecting the necessary data for the commercial fishery of Nipissing First Nation. Finally, the fishermen of Nipissing First Nation must be acknowledged for their efforts in accurately reporting their harvests. Without this data, formal management of the Lake Nipissing walleye fishery could not happen.

3

Table of Contents PageExecutive Summary………………………………………………………………………. 2Acknowledgements……………………………………………………………………….. 3Table of Contents…………………………………………………………………………. 4List of Figures…………………………………………………………………………….. 51.0 Introduction…………………………………………………………………………... 7

1.1 Data Collection Methods……………………………………………………. 71.1.1 Fall Walleye Index Netting (FWIN)…...………………………..….. 71.1.2 Angler Creel Surveys…………………………..…………………… 71.1.3 Nipissing First Nation Commercial Harvest Reports and Estimates 81.1.4 Biological Reference Points and Biological Performance Indicators 8

2.0 Results and Discussion………………………………………………………………. 92.1 Trend Analysis……………………………………………………………… 9

2.1.1 Fall Walleye Index Netting – Walleye Catch…………………........ 92.1.2 Catch of Exploitable Walleye during FWIN ………………………. 112.1.3 Mortality……………………………………………………………. 16

2.2 Biological Reference Point Analysis………………………………………... 182.2.1 Limit Reference Points……………………………………………… 18

2.2.1.1 Yield ………………………………………………………. 182.2.1.2 Abundance…………………………………………………. 202.2.1.3 Mortality………..………………………………………….. 202.2.1.4 Present Status of Fishery…………………………………… 21

2.2.2 Biological Performance Indicators: Morgan……………………2.2.3 Density Dependent Growth Responses……………………………

2226

3.0 State of the Lake Nipissing Walleye Resource ……………………………………... 27

4.0 Using Exploitation Patterns to Predict FWIN Results……………………………….. 27

5.0 Nipissing First Nation Commercial Walleye Harvest Level for 2007…..…………… 29

6.0 Present Approach to Walleye Assessment on Lake Nipissing………………………. 29

7.0 References…………………………………….……………………………………… 31

Appendix 1. Derivation of biological reference points for Lake Nipissing.…………..… 33

Appendix 2. Stochastic simulations used to generate probabilities of Lake Nipissing population parameters being over/under biological reference points as developed by Korver 2003……………………………………………………………………..……….. 36

4

List of Figures Page

Figure 1. Arithmetic mean walleye catch during FWIN 1998-2006 9

Figure 2. Arithmetic mean walleye catch by size class during FWIN 1998-2006 10

Figure 3. Mean walleye catch by size class expressed in biomass (kg/net) 10

Figure 4. Arithmetic mean exploitable walleye catch during FWIN 1998-2006 11

Figure 5. Exploitable walleye biomass catch during FWIN 1998-2006. 11

Figure 6a. Comparison of annual angler effort to exploitable walleye catch during FWIN.

Figure 6b. FWIN CUE exploitable walleye versus angler effort

12

13

Figure 7a. Comparison of NFN commercial harvest to exploitable walleye biomass caught during FWIN

Figure 7b. Comparison of NFN commercial harvest to exploitable walleye biomass caught during FWIN.

13

14

Figure 8. Walleye year class strength as observed by FWIN catches. 14

Figure 9. Abundance of walleye > 40 cm as represented by FWIN catch 15

Figure 10. Abundance of walleye > 40 cm as represented by FWIN biomass 16

Figure 11. Robson-Chapman Total Annual Adult Mortality Estimate 17

Figure 12. Robson-Chapman Total Annual Mortality Estimate for Exploitable Walleye

17

Figure 13. Total estimated walleye yield from Lake Nipissing 2001-2006 19

Figure 14. Geometric mean exploitable walleye biomass from FWIN 1998-2006 20

Figure 15. Robson-Chapman total annual mortality estimate for exploitable female walleye

21

Figure 16. Status of Lake Nipissing walleye population based on combined probabilities of observed abundance and mortality exceeding or falling below limit reference points

22

Figure 17. FWIN Catch Per Unit Effort of walleye greater than 450 mm 23

5

List of Figures

Figure 18. Number of age classes in FWIN sample

Page

23

Figure 19. Maximum age of walleye in FWIN catch 24

Figure 20. Shannon-Diversity Index 24

Figure 21. Overall Morgan Score

Figure 22. Length at age for walleye aged 1-4

25

26

6

1.0 IntroductionThe fisheries of Lake Nipissing have traditionally been managed by the Ontario Ministry of Natural Resources (MNR) on a 5-6 year cycle, approximating the generation time of the lake’s top predator: the walleye. Each management cycle begins with a fisheries data review, in which the most current fisheries assessment data is compiled and analyzed. The data review serves as a biological foundation for MNR’s fisheries management plans and fishing regulations. Updates to MNR’s management plans and fishing regulations are based on conclusions and recommendations from the fisheries data review. The last data review was produced in 2003.

Nipissing First Nation (NFN) has begun to develop its own formal management plan that will address issues related to its commercial fisheries. Because the intensity of a commercial fishery can change rapidly from year to year due to changes in the market and number of participating fishermen, it is necessary for NFN to review fisheries data each year. This annual review allows Nipissing First Nation’s commercial fishery to be managed on a one-year cycle, with modifications to fisheries laws including quotas to be made immediately if necessary, based on the present state of the fishery resource. The focus of this report is walleye, which comprises over 50% of the commercial catch and over 80% of the commercial market.

The purpose of this report is to determine the status of the Lake Nipissing walleye fishery in order to recommend a sustainable harvest quota for the Nipissing First Nation commercial walleye fishery for the 2007 season. A secondary purpose is to evaluate the effectiveness of the present walleye assessment approach on Lake Nipissing that uses a combination of annual Fall Walleye Index Netting (FWIN), roving angler creel surveys and NFN commercial harvest reports.

1.1 Data Collection Methods

1.1.1 Fall Walleye Index Netting (FWIN)FWIN is a standardized gill netting technique used provincially to assess walleye populations (Morgan 2002) and has been recommended for use on Lake Nipissing to assess the walleye population lake-wide (Rowe & Seyler 2000). Fall Walleye Index Netting (FWIN) is conducted in two depth strata: a deep stratum (5 m – 15 m depth) and a shallow stratum (2 m – 5 m). FWIN data has been collected each year from 1998 to 2006 inclusive. Estimates of and trends in abundance, mortality and life history characteristics can be derived from this data set.

1.1.2 Angler Creel SurveysRoving angler creel surveys have been used to assess the angling fishery on Lake Nipissing since 1969. This method involves counting fishing boats and anglers (or active ice huts and anglers in the winter) to estimate annual angling effort and interviewing anglers on the water while they are fishing to estimate catch rate and ultimately total fish harvest. The roving creel survey has been precise at estimating catch and effort for the three most exploited fish species: walleye, northern pike and yellow perch. For more details on Lake Nipissing roving creel surveys, refer to Rowe & Seyler (2000).

7

1.1.3 Nipissing First Nation Commercial Harvest Reports and EstimatesEstimates of NFN commercial walleye harvest from 2001 to 2003 are based on fish sale records as well as anecdotal information from NFN fishermen and NFN fisheries technicians. Estimates for 2004 and 2005 were based on fishing effort and harvest information taken from 457 on-water reports by MNR patrols during these fishing seasons (Rowe unpublished).

In 2006, the Nipissing First Nation Fisheries Department received or compiled a total of 600 daily harvest reports from NFN commercial fishermen for the 2006 fishing season. Harvest reports included numbers of all walleye killed or harvested, whether they were sold or not. These reports were received from twelve different commercial fishermen, or groups of fishermen that fish together. An additional two fishermen or groups of commercial fishermen did not report their catch. One other fisherman did not report their catch, but it could not be confirmed whether this fisherman even fished in 2006.

A total of 114 walleye were measured from randomly selected commercial nets throughout the year (May to November). The average size of walleye caught was 43.0 cm total length (+/- 9.0 cm (95% confidence limits)), with an average weight of 790 grams (+/- 50 g).

In order to estimate total annual commercial harvest, the mean catch of the twelve reporting groups of fishermen was assigned to the 2 (possibly 3) non-reporting groups. The sum total of all reported fish plus the estimated fish from the non-reporting fishers was then compiled and multiplied by the average weight and the 95% confidence limits of the weight to generate the range of the 2006 NFN commercial walleye harvest.

1.1.4 Biological Reference Points and Biological Performance IndicatorsSets of biological reference points or biological performance indicators have been developed for evaluating the state of the walleye resource (Lester & Morgan 2003, Gangl & Perreira 2003, Lester unpublished). The reference points derived by Lester & Morgan (2003, Lester unpublished) are estimated values of walleye abundance, mortality, harvest and fishing effort that would be observed if the fishery was operating at maximum sustainable yield. Biological performance indicators developed by Morgan (personal communication) estimate an index of population health based on abundance of large fish (>450 mm), maximum age, number of age classes and age diversity of reproductive females caught during Fall Walleye Index Netting. Due to timing and software limitations, detailed length at age and other density dependent growth analyses used as biological performance indicators by Gangl and Perreira (2003) and suggested by Korver (unpublished) have not yet been performed.

8

2.0 Results and Discussion

2.1 Trend Analysis2.1.1 Fall Walleye Index Netting – Walleye CatchThe overall walleye catch per net during FWIN showed a slight but steady decrease from 1998 to 2002, then a slight but steady increase from 2003 to 2005. A decline was observed in walleye catch from 2005 to 2006 (figure 1).

Figure 1. Arithmetic mean walleye catch during FWIN 1998-2006

Walleye catch calibrated for habitat

0

2

4

6

8

10

12

14

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

tota

l me

an

ca

tch

(#

pe

r n

et)

When examining CUE of different size classes, it appears that much of the recent upward trend was attributable to high young of the year or yearling catches (figure 2). CUE of walleye < 30 cm closely mirrors the overall walleye catch. The 2006 decrease appears to be attributable to lower catches of 30-40 cm walleye and walleye <30cm, but particularly in catch of walleye 30-35 cm in size. This same general pattern holds for walleye biomass per net, however the trends in larger walleye were amplified by using biomass instead of numbers of fish caught (figure 3).

9

Figure 2. Arithmetic mean walleye catch by size class during FWIN 1998-2006.

Walleye catch calibrated for habitat

0

1

2

3

4

5

6

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

tota

l m

ean

cat

ch (

# p

er n

et)

CUE < 30 cm CUE 30-34.9 cm CUE 35-39.9

CUE 40-44.9 CUE 45-49.9 CUE > 50

Figure 3. Mean walleye catch by size class expressed in biomass (kg/net).

Biomass comparison

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

1.4000

1.6000

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

kg/n

et

Weighted Biomass 30-34.9 Weighted Biomass 35-39.9

Weighted Biomass 40-44.9 Weighted Biomass 45-49.9

Weighted Biomass >50

10

2.1.2 Catch of Exploitable Walleye during FWINOverall, catch of exploitable walleye (>30cm) steadily increased from 2002 to 2005. The 2006 catch was the first decrease since 2002 (figure 4). This same pattern was noticed in exploitable biomass, but the upward trend began one year later in 2003 (figure 5).

Figure 4. Arithmetic mean exploitable walleye catch during FWIN 1998-2006.

CUE exploitable walleye (> 30 cm)

0

1

2

3

4

5

6

7

8

9

10

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

CU

E (

wal

leye

/net

)

Figure 5. Exploitable walleye biomass catch during FWIN 1998-2006.

Weighted Exploitable Biomass

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

3.0000

3.5000

4.0000

4.5000

5.0000

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

kg/n

et

11

This one year discrepancy between catch by numbers versus catch by biomass trends may be attributable to an initial reduction in catch of smaller walleye, followed by a subsequent reduction in larger walleye. The depletion of smaller walleye would go relatively unnoticed while monitoring biomass because the overall depletion by weight is much less than the overall depletion by numbers. Conversely, when there is a decrease in catch of larger, heavier fish in the FWIN catch, it is quite visible when monitoring biomass trends, but not apparent when monitoring catch by numbers, as incoming year classes cause CUE by number trends to show overall increases in catch. This explanation is consistent with FWIN, angler and Nipissing First Nation harvest observations made during 2002 and 2003 (figures 6a & 7a).

Angler creel data indicates that angler-harvested walleye are relatively small exploitable walleye (mean TL 354 mm, mean weight 438 g). Data from Nipissing First Nation commercial harvest indicate that commercially caught walleye are larger exploitable walleye (mean TL 430 mm, mean weight 791 g). Given these observations, FWIN trends in number of exploitable walleye per net and exploitable biomass per net could be reflecting a heavy harvest of smaller fish by anglers in 2002, followed by a heavy harvest by NFN commercial fishing in 2003.

Figure 6a. Comparison of annual angler effort to exploitable walleye catch during FWIN.

0

100000

200000

300000

400000

500000

600000

700000

1998 1999 2000 2001 2002 2003 2004 2005 2006

An

gle

r E

ffo

rt (

ho

urs

)

0

1

2

3

4

5

6

7

8

9

10

Exp

loit

able

Wal

leye

Cat

ch D

uri

ng

FW

IN (

#/n

et)

Total Angler Effort (hrs) CUE Exploitable

12

Figure 7a. Comparison of NFN commercial harvest to exploitable walleye biomass caught during FWIN.

0

10

20

30

40

50

60

70

80

2001 2002 2003 2004 2005 2006

Est

imat

ed o

r O

bse

rved

NF

N H

arve

st (

kg x

100

0)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

Exp

loit

able

Wal

leye

Bio

mas

s (k

g/n

et)

du

rin

g

FW

IN

NFN Harvest (1000s kg) biomass

Further analysis shows significant relationships between angler effort and FWIN CUE of exploitable walleye per net (p = 0.017 , r2= 0.58)(figure 6b) and NFN commercial harvest and exploitable biomass per net (p = 0.03, r2= 0.71 )(figure 7b). A strong 2001 year class entered the exploitable portion of the fishery around 2003-2004, which is consistent with the observation that while CUE of exploitable walleye increased in 2003, exploitable walleye biomass decreased (figures 4 & 5).

Figure 6b. FWIN CUE exploitable walleye versus angler effort

0

1

2

3

4

5

6

7

8

9

10

0 100000 200000 300000 400000 500000 600000 700000

Angler Effort (hrs)

FW

IN C

UE

Ex

plo

ita

ble

Wa

lley

e (

#/n

et)

13

Figure 7b. FWIN exploitable biomass versus NFN commercial harvest

0

0.5

1

1.5

2

2.5

3

0 10 20 30 40 50 60 70 80

Estimated or Observed NFN Commercial Harvest (000s of kg)

Ex

plo

ita

ble

Wa

lley

e B

iom

as

s F

WIN

(k

g/n

et)

Given the above trends and relationships, it seems apparent that exploitation remains the most significant population stress on Lake Nipissing walleye (Rowe 2003, Rowe & Seyler 2000, Dunlop 1997, Jorgensen & Reckahn 1995, Anthony & Jorgensen 1976). Year class strength also appears to have a significant influence, as regular strong year classes continue to replenish the exploitable part of the fishery (figure 8). Present walleye assessment techniques on Lake Nipissing appear to be effective at monitoring these relationships. Results indicate that trends in smaller size classes are most effectively monitored by tracking numbers of walleye caught per net, while trends in larger walleye are best monitored by tracking biomass per net.

Figure 8. Walleye year class strength as observed by FWIN catches.

YOY and Yearling Walleye Catches - FWIN

0

0.5

1

1.5

2

2.5

3

3.5

4

1998 1999 2000 2001 2002 2003 2004 2005

Year

CU

E (

wal

leye

/net

)

CUE YOYs

CUE yearlings

14

The trends in both FWIN CUE and biomass per net suggest that there was a depletion of walleye in the smaller exploitable size class (30-34.9 cm) between 2005 and 2006. A very strong year class in 2003 likely increased the catch of 30-34.9 cm walleye to its high level in 2005, but this effect did not last into 2006, as both 30-34.9 cm and 35-39.9 size classes both show a decreasing trend for that year. It is conceivable that angler harvest has led to this depletion, since angler harvest has been largely focused on these size classes since 2004 slot size regulations (40-60 cm) were implemented. This harvest needs to be closely monitored in the near future, as adjustments to the regulation may be warranted if an insufficient number of exploitable fish survive to be protected by the slot size.

Beginning in 2004, a full-year protected slot size regulation of 40 cm to 60 cm was implemented for anglers. That same year, Nipissing First Nation implemented its first ever moratorium on gill net fishing during the spring walleye spawn and has since lowered its overall harvest level. While both fisheries experienced some level of non-compliance, these regulations should have led to a decrease in overall harvest of walleye greater than 40 cm since 2003. Since this time there has been an increasing trend of walleye greater than 40 cm observed in FWIN catches (figures 9 & 10).

Figure 9. Abundance of walleye > 40 cm as represented by FWIN catch.

CUE > 40 cm (walleye/net)

0

0.5

1

1.5

2

2.5

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

ca

tch

pe

r n

et

(wa

lley

e)

15

Figure 10. Abundance of walleye > 40 cm as represented by FWIN biomass.

Biomass > 40 cm

0.0000

0.5000

1.0000

1.5000

2.0000

2.5000

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

kg/n

et

Subsequent FWIN projects on Lake Nipissing should carefully monitor whether this increase in spawning biomass leads to a consistent increase in young of the year production and ultimately, an increase in overall walleye abundance. Such an increase, if it occurs may offset the high angler exploitation and apparent depletion of 30-40 cm walleye observed in 2006. The apparently abundant 2005 year class should be tracked closely, as it represents the highest young of the year FWIN catch in the 8-year data set.

2.1.3 MortalityAnnual adult mortality (A), the proportion of fish that dies in one year, was estimated using Robson and Chapman’s method (Ricker 1975 p. 31):

S =T

N + T - 1 where S is annual adult survival, T = N1 + 2N2 + 3N3…, N = N0 + N1 + N2 +…, and N is the number of fish whose age is greater than or equal to age 5. Annual adult mortality (A) was then calculated as A = 1 – S and expressed as a percent. This same method was used to calculate mortality of exploitable walleye (walleye > 300 mm TL) and exploitable female walleye (female walleye > 300 mm TL). N0 used for these calculations was age 2, when an average size > 300 mm was attained (mean 314 mm +/- 13 mm (95% confidence limits)).

Trends in mortality show an overall decrease in adult mortality (figure 11), but a steady rise in exploitable walleye mortality (figure 12).

16

Figure 11. Robson-Chapman Total Annual Adult Mortality Estimate.

Robson-Chapman Total Annual Adult Mortality Estimate

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1998 1999 2000 2001 2002 2003 2004 2005

Year

An

nu

al M

ort

alit

y (%

)

Figure 12. Robson-Chapman Total Annual Mortality Estimate for Exploitable Walleye.

Robson-Chapman Total Annual Mortality Estimate for Exploitable Walleye

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1998 1999 2000 2001 2002 2003 2004 2005

Year

Mo

rtal

ity

(%)

17

These observations suggest that initially, there is a high mortality of exploitable walleye but once (or if) they reach adult age (or size), their survival rates increase.

Mortality observations are consistent with trends in abundance; where there is an upward trend in walleye > 400 mm (adults) there is a downward trend in adult mortality (> 400 mm). There is an upward trend in exploitable mortality (age 2+) where there is a noticeable downward trend in numbers of exploitable walleye (>300 mm; age 2). These observations also support the possibility that recent management actions which protect or reduce harvest of walleye > 400 mm seem to be having a positive effect. The increasing trend in exploitable walleye mortality suggests that harvest focused on smaller exploitable walleye may be too high and may affect adult recruitment of walleye in the future if this trend continues, or if it is not offset by stronger incoming year classes.

2.2 Biological Reference Point Analysis

2.2.1 Limit Reference PointsBiological reference point formulae for yield (Lester et al. 2004), walleye abundance, and mortality (Lester unpublished) were used to calculate Lake Nipissing specific reference points (Appendix 1). These values were then compared to values observed from present FWIN data.

Because these limit reference points tend to have a high level of uncertainty, stochastic simulation software developed by Korver (unpublished) was used to estimate probabilities that observed FWIN values were over/under the limit reference values (Appendix 2).

2.2.1.1 YieldTotal walleye yield was calculated by adding the two main sources of walleye harvest in years where there were estimates of both (angler harvest + NFN commercial harvest). The last three years have observed the three lowest total yields in this relatively small dataset (figure 13).

18

Figure 13. Total estimated walleye yield from Lake Nipissing 2001-2006.

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

2001 2002 2003 2004 2005 2006

Year

To

tal

Est

imat

ed Y

ield

(kg

/ha)

When compared to the biological reference point value of 1.13 kg/ha, there is 70% - 88% probability that observed yield has been below this reference point and at a sustainable level for the past three years. A 20% relative standard error was assumed for this measure, which seems reasonable considering that angler harvest data estimates have consistently had an RSE below 15% and when RSE calculations were possible for the NFN commercial fishery, they were well below 20%.

Estimated annual yield has been between 57%-73% of the theoretical Maximum Sustainable Yield from 2004 to 2006. This yield includes the Nipissing First Nation commercial walleye harvest, which yielded between 36,382 kg – 38,980 kg (NFN 2007). If the 95% confidence intervals are applied from the sub-sample of fish weighed, the harvest range becomes 34,022 kg – 41,378 kg of walleye. A Lake Nipissing walleye management objective set in 2003 was to keep total yield below 70% of the MSY, as the fishery was believed to be stressed at the time (Rowe 2003). At present there is a high probability that this management objective is being met with current harvest levels.

19

2.2.1.2 AbundanceGeometric mean biomass per net of exploitable walleye (> 30 cm) was calculated for each FWIN from 1998 to 2006 (figure 14). This value was compared to the Lake Nipissing biological reference point (2.64 kg/ha) and the probability that the observed value exceeded the biological reference point criterion was estimated.

Figure 14. Geometric mean exploitable walleye biomass from FWIN 1998-2006.

Exploitable Biomass Trend in FWIN

0

0.5

1

1.5

2

2.5

3

3.5

4

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

FW

IN c

atc

h E

xp

loit

ab

le

bio

ma

ss

(k

g/n

et)

geometric mean Upper 95% CL

Lower 95% CL BRP (Lester et al.)

Linear (geometric mean)

While there is a slight upward trend in exploitable walleye biomass, the probability that the biological reference point is being exceeded (ie. at a sustainable level) is only 49%. Therefore at this point in time, one cannot conclude definitively that exploitable biomass (or abundance) of walleye is either above or below a sustainable level.

It is also likely that two successive strong year classes in 2001 and 2002 combined with reduced yields were largely responsible for the highest exploitable biomass observations in 2004 and 2005. While there is presently a low probability that the Lake Nipissing walleye population has reached or exceeded its biomass at MSY, yield remained low in 2006 and the general trend upward trend in exploitable biomass is an encouraging observation.

2.2.1.3 MortalityThe limit reference point for walleye mortality is expressed in terms of total annual mortality of exploitable females (30 cm total length or greater) using the Robson &

20

Chapman estimate (Figure 15). Estimated total annual mortality in 2005 was 49.8%. The Lake Nipissing biological reference value is 52%.

Figure 15. Robson-Chapman total annual mortality estimate for exploitable female walleye.

Robson Chapman Total Annual Mortality Estimate for Exploitable Female Walleye

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1998 1999 2000 2001 2002 2003 2004 2005

Year

Mo

rtal

ity

(%)

There is a 52% probability that the observed mortality estimate in 2005 is below the biological reference value and considered sustainable. Therefore, similar to the abundance reference point, there is a large amount of uncertainty as to whether population mortality is at a sustainable level.

2.2.1.4 Present Status of FisheryThe present status of the fishery can be estimated by combining the abundance and mortality probabilities of exceeding or falling below limit reference points (figure 16).

21

Figure 16. Status of Lake Nipissing walleye population based on combined probabilities of observed abundance and mortality exceeding or falling below limit reference points.

Stage 1: Healthy

2003: 18.1% x 52.8% = 9.1%2004: 61.7% x 61.4% = 37.9%2005: 70.1% x 51.7% = 36.2%

Mor

tali

ty (

%)

Stage 2: Stressed; Early Overexploitation

2003: 18.1% x 47.2% = 8.5%2004: 61.7% x 38.6% = 23.8%2005: 70.1% x 48.3% = 33.9%

Abundance (kg/net)

2003: 81.9% x 47.2% = 38.7%2004: 38.3% x 38.6% = 14.8%2005: 29.9% x 51.7% = 15.5%

Stage 3: Heavily Stressed; Late Overexploitation

2003: 81.9% x 52.8% = 43.2%2004: 38.3% x 61.4% = 23.5%2005: 29.9% x 51.7% = 15.4%

Stage 4: Degraded or Recovering

Based on these combined probabilities related to biological reference points, there is only a 36% probability that the Lake Nipissing walleye population was healthy in 2005. There is a 50% probability that the walleye population is stressed to heavily stressed.

Maximum Sustainable Yield (MSY), on which these reference points are based upon, is a theoretical value of fish harvest which can occur safely, without leading to a collapse in fish stocks to the point where future production is compromised. However, fisheries scientists have realized that this definition is not completely accurate, as using MSY as a target level for harvest makes fish stocks vulnerable to overfishing and collapse (Lester et al. 2003). MSY estimates are better used as limit reference points, defining thresholds which should never be exceeded.

From a management perspective, based on these probabilities and the conservative evaluation necessary for comparison to MSY values, it must be concluded that the Lake Nipissing walleye population is not yet at a healthy state.

2.2.2 Biological Performance Indicators: MorganFWIN results since 1998 were plotted against biological performance indicators developed by Morgan (pers. comm.) as another index of Lake Nipissing walleye population health (figures 17-21).

22

Figure 17. FWIN Catch Per Unit Effort of Walleye Greater than 450 mm

FWIN CUE Walleye >450 mm

0

0.5

1

1.5

2

2.5

1998 1999 2000 2001 2002 2003 2004 2005 2006

Year

CU

E >

450m

m

Healthy/Stable

Stressed/Unstable

Unhealthy/Collapsed

Figure 18. Number of age classes in FWIN sample.

Number of Walleye Age Classes in FWIN Sample

0

2

4

6

8

10

12

14

16

1998 1999 2000 2001 2002 2003 2004 2005

Year

Nu

mb

er o

f A

ge

Cla

sses

Healthy/Stable

Stressed/Unstable

Unhealthy/Collapsed

* - very large fish (>70 cm TL) not aged

*

23

Figure 19. Maximum age of walleye in FWIN catch

Maximum Age of Walleye in FWIN

0

2

4

6

8

10

12

14

16

18

1998 1999 2000 2001 2002 2003 2004 2005

Year

Max

imu

m A

ge

Healthy/Stable

Stressed/Unstable

Unhealthy/Collapsed

*

* - very large fish (>70 cm TL) not aged

Figure 20. Shannon-Diversity Index

Shannon Diversity Index Mature Females

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1998 1999 2000 2001 2002 2003 2004 2005

Year

S.D

.I.

Healthy/Stable

Stressed/Unstable

Unhealthy/Collapsed

24

Figure 21. Overall Morgan Score

Morgan Score

0

0.5

1

1.5

2

2.5

3

3.5

4

1998 1999 2000 2001 2002 2003 2004 2005

Year

Sco

re

Healthy/Stable

Stressed/Unstable

Unhealthy/Collapsed

Trends in the Morgan Index indicate that the Lake Nipissing walleye fishery is in a stressed condition. Trends in CUE of walleye > 450 mm are a surrogate for spawning stock abundance, and while Lake Nipissing is well below the healthy performance standard, there has been an upward trend since 2003.

Number of age classes represented in FWIN catch and maximum age are reasonable surrogates for mortality and exploitation on the walleye population (Fruetel 1999). The latest performance measures (2005) for maximum age and number of age classes may be artificially low, as a very large walleye (>70 cm) was not aged that year and likely would have increased these scores. However, overall scores and trends still suggest a stressed walleye population.

The Shannon Diversity index, a measure of spawning stock age diversity, suggests that with the exception of 2003, the walleye population has been in a relatively healthy state since 2001.

The overall combination of these measures classifies the Lake Nipissing walleye population in the stressed category, which is consistent with conclusions from comparing the population to MSY biological reference points.

25

2.2.3 Density Dependent Growth ResponsesLength at age analysis for walleye aged 1-4 showed significant increases in the growth rate of 2-year olds (p = 0.04) and 3-year olds (p = 0.02) (figure 22). Changes in growth rates of 1-year olds and 4-year olds were not significant.

Figure 22. Length at age for walleye aged 1-4.

Length at Age Walleye

200

250

300

350

400

450

1998 1999 2000 2001 2002 2003 2004 2005

TL

(m

m)

1-Yr Old

2-Yr Old

3-Yr Old

4-Yr Old

Linear (1-Yr Old)

Linear (2-Yr Old)

Linear (4-Yr Old)

Linear (3-Yr Old)

More analysis is necessary to determine whether these growth rate changes are most attributable to exploitation or local weather patterns. Walleye aged 2 and 3 are the two most exploited age classes from angling and therefore could be exhibiting a response to exploitation levels. Length at age analysis for older age classes is not possible past age 5 due to a low number of samples.

While not analyzed in depth for this report, the importance of monitoring density dependent growth responses of the Lake Nipissing walleye population cannot be overlooked or overstressed. Population recovery and rate of recovery are greatly affected by its ability to respond to density-dependent changes, most often due to high levels of exploitation

Typically, a walleye population will exhibit changes in growth rate, age to maturity, and fecundity in response to changes in harvest levels. With these responses, population recovery from stress due to harvest is more likely and much quicker, assuming the harvest stress has been appropriately reduced. Without these responses, populations are at greater risk if harvest levels remain too high as there is little compensation possible in the form of increases in growth and ultimately replenishment. Population recovery is much slower without growth responses, once stresses on the population are reduced.

26

Quick management responses and greater reductions in harvest are necessary when there is little density-dependent response exhibited by a population.

Past reports on the Lake Nipissing walleye population showed very little growth rate response under different levels of exploitation (Jorgensen et al. 1996). Early growth rate and fecundity have exhibited changes recently, but these changes may be attributable to changes in weather patterns as measured by cumulative growing degree days (Rowe 2004).

Given the management implications of density-dependent growth responses, these must be gleaned from past data and monitored closely in the future to assist with setting sustainable harvest levels and projecting future population recovery and rate of recovery.

3.0 State of the Lake Nipissing Walleye Resource Based on the above analyses, it can be concluded that the Lake Nipissing walleye population cannot at this point in time be considered ‘healthy’. However, upward trends in larger size classes, production of regular strong year classes and a present total yield well below the maximum sustainable yield suggest that the population should continue to improve as long as harvest stays at its present level.

One trend of concern which warrants close study is the apparent depletion of smaller exploitable walleye in 2006. Given that virtually all angler harvest is targeted at this size of walleye, it is conceivable that this harvest may limit walleye recruitment into adulthood. Consistent monitoring is required to study this, as the recent increase in spawning stock biomass may lead to subsequent increased production that may offset the observed depletion.

4.0 Using Exploitation Patterns to Predict FWIN Results Angler creel data indicates that since a full-year protected slot size regulation of 40 cm to 60 cm was implemented in 2004, over 90% of all angler-harvested walleye are between 30cm – 40cm. Data from Nipissing First Nation commercial harvest and other 3 ¾ inch mesh catches on Lake Nipissing indicate that 88% of walleye harvest is between 35cm – 50 cm.

Significant relationships exist between estimates of fish harvest and catch results from Fall Walleye Index Netting. Most notable are:

1. Angler Effort as a predictor of FWIN CUE 30-40 cmSignificant at p = 0.03; r2=0.50

FWIN CUE 30-40 cm = 10.07 – 1.047 x 10-5(Effort)

This relationship is even stronger when the FWIN results have been corrected for selectivity biases (Significant at p = 0.01; r2=0.73). Selectivity corrections have not yet been performed for 2005 or 2006.

27

2. NFN harvest as a predictor of FWIN CUE 35-50 cmSignificant at p = 0.04; r2=0.69

FWIN CUE 35-50cm = 5.75 – 5.11 x 10-5(NFN harvest)

3. Angler Effort as a predictor of CUE exploitable walleye (#/net)Significant at p = 0.02; r2=0.58

CUE Exploitable Walleye = 12.09 – 1.02 x 10-5(Effort)

4. NFN Harvest as a predictor of FWIN exploitable biomass (kg/net)Significant at p = 0.03; r2=0.71

Exploitable biomass (kg/net) = 5.54 - 4.4 x 10-5(NFN harvest)

In addition to exploitation patterns, FWIN catch of young of the year and yearling walleye has significant predictive power for future FWIN catch of 30-35 cm walleye: 5. Young of the year FWIN catch as a predictor of FWIN catch 30-35 cm two years later (t+2)Significant at p = 0.05; r2=0.58

FWIN CUE(30-35cm)t+2 = 1.02 + 1.54(FWIN CUE YOY)

6. Yearling FWIN catch as a predictor of FWIN catch 30-35 cm 1 year later (t+1)Significant at p = 0.02; r2=0.65

FWIN CUE(30-35 cm)t+1 = 0.882 + 0.852(FWIN CUE YEARLINGS)

The above predictive equations can be used as another mechanism to assess the potential effects of different harvest levels on the walleye population, assuming that fish harvest estimates are accurate and FWIN results reflect the true status of the walleye population.

These equations predict that, if the 2006 harvest levels are maintained in 2007:

CUE of walleye 30-40 cm will be 4.88 walleye per net ( 61st percentile in data set) CUE of walleye 35-50 cm will be 3.76 walleye per net (82nd percentile in data set) CUE of exploitable walleye will be 7.03 walleye per net (88th percentile in data

set) CUE of exploitable walleye will be 3.82 kg per net (89th percentile in data set)

Incoming year class strength (FWIN CUE YOYs) predicts that the catch of 30-35 cm walleye will be 6.06 walleye/net (highest on record) in 2007.

This set of predictive equations suggests that there could be a slight improvement in the walleye population if total harvest remains at 2006 levels.

28

5.0 Nipissing First Nation Commercial Walleye Harvest Level for 2007Based on the data analyses contained within this report and the desired state of a healthy walleye fishery, it is recommended that the 2007 harvest level for the Nipissing First Nation commercial walleye fishery be 38,000 kg; close to the midpoint of the estimated 2006 harvest.

Decreasing this harvest level for 2007 appears to be unwarranted, as there is a high probability that overall yield appears to be at a safe level, with the chances of this changing in the immediately future unlikely. In addition, upward trends in the targeted portion of this fishery continue in FWIN catches (upward trends in biomass 35-50 cm; upward trend in walleye >40 cm) and strong year classes continue to move through the fishery.

Increasing this harvest level for 2007 is inadvisable, as there is still a low probability that the Lake Nipissing walleye fishery is in a healthy state. All analyses thus far suggest that the walleye population is in a stressed state. An increase in harvest level should not be considered until there is a high probability that the walleye population is in a healthy state. It is also not known how the apparent decrease in 30-40 cm walleye noticed in the 2006 FWIN will affect future recruitment of walleye into adulthood. It is also not yet known whether the apparent increasing trend in adult walleye (>40 cm) will lead to surplus production and subsequent increased recruitment of exploitable walleye. These trends and the abundance of incoming year classes need to be closely monitored over the next few years at known harvest levels before an appreciable increase in walleye harvest can be considered.

6.0 Present Approach to Walleye Assessment on Lake NipissingThe present approach to walleye assessment on Lake Nipissing appears to be yielding useful predictive relationships between walleye harvest and walleye assessment using Fall Walleye Index Netting. It is recommended that this dataset, which includes FWIN, open water and winter angling creels, and Nipissing First Nation commercial harvest reporting continues on an annual basis. Annual monitoring is necessary as long as the fishery remains in a precarious ‘stressed’ condition, and will also allow predictive relationships to develop further, increasing the ability to monitor and manage the fishery in the future. It may also be possible to develop a Lake Nipissing-specific set of biological performance indicators which will further enhance management of the Lake Nipissing walleye population. Recommended improvements in annual data collection include a better understanding on how FWIN operates in Lake Nipissing with respect to capture of fish > 50 cm, an investigation into accuracy of roving creel harvest estimates, improved biological sampling of Nipissing First Nation commercial catches and enhanced collection of density-dependent growth data.

The drastic reduction in catch of walleye > 50 cm during the 2003 FWIN appears to be an anomaly. Based on all other data for this year, it is likely that the abundance of this size class did decrease, but the magnitude of the decrease and subsequent re-emergence of this size class in the 2004 FWIN suggests that some type of external influence led to an

29

under-representation of these walleye in the 2003 FWIN. Accurate monitoring of the abundance of this size class is critical to many of the biological reference points used to assess the state of the walleye resource. Variability of FWIN catch of this size class greatly influences estimates of biomass per net, mortality, walleye catch > 450 mm, maximum age, number of age classes, and Shannon-Diversity index of spawning stock. Therefore, to improve future assessment efforts and interpretation of results, the 2003 FWIN data set should be investigated further for environmental influences that may have influenced the catch of walleye >50 cm that particular year. These influences may include water temperature, weather patterns, slimy detritus accumulation on net panels or depths of sampling. Other independent estimates of walleye >50 cm such as an End of Spring Trapnetting could be attempted to verify past or present FWIN results.

During the winter of 2000, roving angler creel harvest estimates were compared to estimates from a complete trip access creel survey (Rowe unpublished). Walleye catch and harvest estimates from the access point creel were more than double those generated by the roving creel estimates and well within acceptable precision levels (~5%). Results from the access creel survey suggest that winter angling harvest may be greatly underestimated using the roving creel method. Further investigation is recommended, as accurate effort and harvest data are critical to an effective fisheries assessment program.

Biological sampling of the NFN commercial walleye harvest represented only 0.3% of the total commercial harvest. If harvest reporting is to continue using a biological sub-sampling method, then an increase in sub-sampling to a 5% level is recommended as a starting point for the 2007 season. Based on 2006 numbers, this would increase biological sampling to 1,970 walleye per year. This sampling should be random, and spread across all fishermen and across each month of the fishing season to assure the most representative and accurate results.

For reasons stated in section 2.2.3 above, more emphasis needs to be placed upon the detection of density-dependent growth responses. Age data should be collected from all possible sources including FWIN, angler creel and Nipissing First Nation commercial catch sampling. This would provide a larger sample of older fish, enabling accurate length at age analysis across a larger number of age classes. In addition, fecundity data from FWIN should be continuously collected, as it represents an additional density-dependent growth parameter.

30

7.0 ReferencesAnthony, D.D. and C.R. Jorgensen. 1976. Factors in the declining contribution of

walleye (Stizostedion vitreum vitreum) to the fishery of Lake Nipissing, Ontario, 1960-1976. J. Fish. Res. Board Can. 34: 1703-1709.

Dunlop, W.I. 1997. Lake Nipissing Walleye Fishery Data Review, 1997. OMNR Technical Report. 32 p.

Fruetel, M. 1999. The effect of recruitment variability on estimating survival rates. Oshki On Line, Boreal Science Newsletter Volume 4, Number 1. OMNR.

Gangl, R.S. & D.L. Perreira. 2003. Biological performance indicators for evaluating exploitation of Minnesota’s large-lake walleye fisheries. North American

Journal of Fisheries Management. 23:1303-1311.

Jorgensen, C.R. and J.A. Reckahn. 1995. The effects of a change in the angling season on the walleye (Stizostedion vitreum vitreum) fishery of Lake Nipissing, Ontario, 1968 to 1992 (25 years). in The Lake Huron Ecosystem: Ecology, Fisheries and Management. Eds. M. Munawar, T Edsall, J. Leach. SPB Academic Publishing, Amsterdam, The Netherlands, pp. 427-445.

Jorgensen, C.R., T.R. Stronks, and G. Preston. 1996. Lake Nipissing 1995 Data Review. OMNR internal report. 84 p.

Korver, Rob, 2003. Personal communication.

Korver, Rob, 2006. Personal communication.

Lester N.P. In Prep. Biological reference points for walleye. OMNR unpublished.

Lester, N.P., A.J. Dextrase, R.S. Kushneriuk, M.R. Rawson and P.A. Ryan. 2004. Light and temperature: key factors affecting walleye abundance and production.

Transactions of the American Fisheries Society 133: 588-605.

Lester, N.P., and G.E. Morgan. 2003. Biological reference points for management of walleye fisheries. In Proceedings of Percis III: The Third International Percid Fish Symposium (Barry, T.P., and J.A. Malison Eds.). University of Wisconsin Sea Grant Institute, Madison, WI.

Lester, N.P., P.A. Ryan, R.S. Kushneriuk, A.J. Dextrase and M.A. Rawson. 2002. The effect of water clarity on walleye (Stizostedion vitreum vitreum) habitat and yield. Percid Community Synthesis, Ontario Ministry of Natural Resources, Peterborough, Ontario.

31

Lester, N.P., B.J. Shuter, R.S. Kushneriuk, and T.R. Marshall. 2000. Life history variation in Ontario walleye populations: implications for safe rates of fishing. Percid Community Synthesis, Ontario Ministry of Natural Resources, Peterborough, Ontario.

Morgan, G. 2002. Manual of instructions – Fall Walleye Index Netting (FWIN). Percid Community Synthesis, Diagnostics and Sampling Standards Working Group, Ontario Ministry of Natural Resources, Peterborough, Ontario.

Nipissing First Nation. 2007. Nipissing First Nation Commercial Harvest ReportFor 2006 Fishing Season. Nipissing First Nation Fisheries Department, Garden Village, Ontario.

Prager, M.H., C.E. Porch, K.W. Shertzer and J.F. Caddy. 2003. Targets and limits for management of fisheries: a simple probability-based approach. North Amer. J. Fish. Manage. 23: 349-361.

Ricker, W.E. 1975. Computation and interpretation of biological statistics of fish populations. Bull. Fish. Res. Board Can. 191: 382 p.

Rowe, R. and J. Seyler. 2000. Fisheries Assessment on Lake Nipissing; 2000-2009. OMNR-A/OFRC Technical Report.

Rowe, R. 2004. Lake Nipissing Walleye Data Review 2003. OMNR Technical Report, January 28, 2004.

Rowe, R. 1998. Lake Nipissing Fisheries Management Plan 1999-2003. OMNR Technical Report.

Rowe, R. Unpublished. Using enforcement patrol reports to estimate commercial walleye harvest by Nipissing First Nation during the 2005 open water season on Lake Nipissing. OMNR Internal Report, June 8, 2006.

Rowe, R. Unpublished. Lake Nipissing winter access creel 2000: feasibility and comparison study. OMNR Technical Report, September 18, 2003.

32

Appendix 1. Derivation of biological reference points for Lake Nipissing.Key observed parameters for Lake Nipissing:

Area = 87,325 haMean depth = 4.5 mMaximum depth = 20 m* Secchi Depth = 2.5 mNumber of Growing Degree Days > 5 C = 1,856 (actual average past 6 years)Mean Annual Temperature = 5 CTotal dissolved solids (TDS) = 50 mg/L

* - Because maximum depth of Lake Nipissing is listed as 52 m, it represents the basin shape of Lake Nipissing to be a cone, rather than the ‘frying pan’ that it actually is. It is my opinion that this oversimplified cone shape puts much of the lake bottom out of the TOHA zone, giving the lake an artificially low value for Thermal Optical Habitat Area, which then leads to an artificially low value for MSY. 99% of the lake area of Lake Nipissing is under the 20 m contour and therefore 20 m was used as the maximum depth.

Using equation 3 from Lester et al. (2004) and the above values, an MSY value was calculated for Lake Nipissing with the Yield Calculator software (v. 2.1) produced by OMNR Northwest Science & Information:

MSY(Eq3;Table3) = (EXP(0.946*LN(TOHA)+0.531*LN(TDS)-3.961)/Area

MSY = 1.13 kg/ha; Upper 95% CL = 2.38 kg/haLower 95% CL = 0.53 kg/ha

With this estimate and using Lester’s Biological Reference Points for Walleye paper (believed to be still in progress), other biological reference points can be derived:

i) Maximum Sustainable Mortality Estimation

Zmsy = Fext

+ M2

Zmsy = instantaneous mortality rate at maximum sustainable yield

Fext = fishing mortality rate that would cause extinction of population

M = natural mortality rate

33

Fext = -0.855 + 1.397G – 0.256G2

= -0.855 + 1.397(1.856) – 0.256(1.856)2

= 0.85598

M = 0.0485h0

h0 = expected pre-maturation growth rate (cm/yr) of an unexploited population

h0 = -2.802 + 6.29G – 0.767G2

= 6.230127

M = 0.0485(6.230127)

= 0.3022

Zmsy = 0.85598

+ 0.30222

Zmsy = 0.7302

Therefore Total Annual Mortality at MSY (Amsy )

= 1 – e- Zmsy

= 1 - e- 0.7302

Amsy = 0.518 (total Annual Mortality)

34

Exploitable Biomass at MSY (Bmsy)

Bmsy =Ymsy

Fmsy

Bmsy =1.13

0.428

= 2.640 kg/ha

The Biological Reference Points Used for Lake Nipissing:

Ymsy = 1.13 kg/ha/year

Amsy = 0.52

Bmsy = 2.64 kg/ha

35

Appendix 2. Stochastic simulations used to generate probabilities of Lake Nipissing population parameters being over/under biological reference points as developed by Korver 2003

Objective: To compare in a meaningful way, measured values of Lake Nipissing walleye yield, mortality and abundance to different criteria or

benchmarks.

Criteria or benchmarks usually have high variability associated with them; the 95% confidence limits around these criteria usually span an order of magnitude. Due to this large statistical error, direct comparison of observed values to criterion values has little meaning or defensibility. Therefore, the relationship of the observed values to the criteria was described by probability of whether observed values were above or below the criteria they were being measured against.

The probability of being over or under a criterion value was calculated using a ‘Monte Carlo’-type analysis developed by Rob Korver. It was assumed that both the criterion and the observed value are distributed normally on a log scale. Random values from each normal probability distribution are generated and a ‘random’ measured value is compared to a ‘random’ criterion value. One thousand (1000) iterations are done and the proportion of iterations where measured values exceed criterion values is calculated. The proportion of iterations where measured values exceed criterion values approximates the probability that this measured value is indeed greater than the criterion value given the high statistical error. This approximate probability is an indication of how confident the fisheries manager can be that an observed value, be it an estimate of yield, abundance, mortality etc., exceeds a defined criterion value (MSY, abundance at MSY, abundance prior to regulation change, etc.).

Required input into this stochastic simulation model was the log(mean) of the criterion value and a standard deviation which caused 95% confidence limits to span an order of magnitude, and the log (mean) of the measured value and its standard deviation. The standard deviation values are used to generate the normal probability distributions in the simulation.

The standard deviation used to generate the normal probability distribution for the observed values was manipulated manually to reflect observed 95% confidence limits. The model was run twice for each measured vs. criterion comparison: the first time the standard deviation input value was manipulated such that the lower observed 95% confidence limit was simulated in the model; and the second time the observed upper 95% confidence limit was simulated. By using both the upper and lower confidence limit values, a range of estimated probabilities were generated for each measured versus criterion pair.

36

Example calculation:

Observed Yield from 2006 versus Maximum Sustainable Yield

Criterion Value:

Estimated MSY using equation 3 from Lester et al. (2004):

MSY(Eq3;Table3) = (EXP(0.946*LN(TOHA)+0.531*LN(TDS)-3.961)/Area

MSY = 1.13 kg/ha;

Generated confidence intervals from Yield Estimator software (v. 2.1) produced by OMNR Northwest Science & Information:

Upper 95% CL = 2.38 kg/haLower 95% CL = 0.53 kg/ha

Observed Value: Total 2006 Yield = 0.712 kg/ha/yr Used 20% RSE for 95% confidence limits

Upper = 0.9968 kg/ha/yr; Lower = 0.4272 kg/ha/yr

Log(criterion) = 0.053 criterion mean = 1.13

Log(standard deviation) = 0.162 (produced confidence intervals identical to those from Yield Estimator software.

Log(measured) = -0.1474 measured mean = 0.712

standard dev. used for Run 1 = 0.073 (simulated the observed upper 95% CL of 0.997)

(lower confidence interval was 0.509)

standard dev. used for Run 2 = 0.111 (simulated the observed lower 95% CL of 0.427)

(upper confidence interval was 1.187)

Probability that measured value > criterion (after 1000 random iterations):

Run 1: 12.4%; Therefore a 87.6% probability that observed yield < criterion.

Run 2: 15.0%; Therefore an 85.0% probability that observed yield < criterion.

37

Therefore, there is an 85-88% probability that the observed yield from 2006 was below the estimated maximum sustainable yield.

38