A simulation model to investigate the potential effects of marine and freshwater fisheries on the Thompson River steelhead trout population (Oncorhynchus mykiss)

Robert Bison

Fish & Wildlife Branch, BC Ministry of Environment, Kamloops, BC, V2C 5Z5

August 8, 2007

Abstract: A simulation model was designed to monitor trends in fishing mortality as well as gain insight on the cumulative effects of fisheries that sequentially intercept the interior Fraser River steelhead population along its migration routes. The model uses well-known algorithms to determine cumulative losses due to immediate and latent fishing mortality effects. As input, the model uses effort data for the largest commercial, native and sport fisheries. Fishery parameters are based on salmon catch records and/or steelhead observer programs. Published information on steelhead and salmon biology, abundance data from field surveys, environmental records, results from tagging programs, and expert advice were used to determine the most likely values and the bounds of all parameters. Simulations are conducted using actual records of fishing schedules, and corresponding levels of effort, and sets of hypotheses concerning migration patterns for 1992-2006. Simulation results obtained under deterministic conditions indicate that exploitation rate declined considerably during 1994-2000, but has increased since then. Concurrent abundance trends are suggesting that abundance may be continuing to decline despite management efforts to date. For the most recent years, simulations results that account for some well-known sources of uncertainty show considerable variation in the plausible range of fishing effects. In light of such findings, and the low escapement levels observed in recent years, it seems advisable to seek opportunities to ensure that steelhead exploitation is reduced and remains low so as to stem further decline in abundance and foster a rebuilding of the stock. The model limitations are identified and recommendations are given on how to improve its performance given the seemingly unavoidable data gaps concerning fishery effects on this relatively small stock from the Fraser River. 1. Introduction

Steelhead that spawn in the Thompson River and its tributaries are considered as a ‘summer run’ population because they enter the Fraser River as immature fish during August- November (Anon. 1998). They approach the Fraser River using marine migration patterns that overlap with those of salmon species, and are intercepted by commercial net fisheries targeting salmon along the way. In recent years, increased conservation concerns over the state of the Fraser River summer runs lead the British Columbia Ministry of Environment (MoE)1 and the Canadian Department of Fisheries & Oceans (DFO) to develop and implement fishery management plans to reduce the steelhead by-catch. Initially, this was achieved through time and area restrictions. More recently this has been attempted by reducing handling mortality in gillnet and seine fisheries that have been reintroduced onto the mid and later portion of the steelhead run.

While considerable efforts have been made to improve the quality and quantity of data on the composition and abundance of steelhead populations that spawn in the Thompson River tributaries (collectively referred to as the Thompson River steelhead), data on exploitation patterns in marine waters is scarce, fragmented, and incomplete, with potential biases difficult to quantify. Anon. (1998) summarized the existing data, and provided preliminary figures on catches in the major gillnet and seine interception fisheries at Nitinat, Johnstone Strait, Juan de Fuca Strait, the Fraser River mouth, and in US waters of Juan de Fuca Strait and north Puget Sound. Steelhead catches in commercial fisheries are generally less than those of other salmon species caught simultaneously. And unlike major salmon production releases, most steelhead stocks are not routinely coded-wire tagged (CWT), or systematically sampled in commercial landings for physical and biological markers. Consequently, there are very few scientifically defensible catch estimates for most interception fisheries, which preclude the rigorous application of the conventional stock-assessment procedures as commonly used to determine exploitation rates on Pacific salmon stocks.

1 During 1996-2005, management of provincial fisheries was conducted by staff of the BC Ministry of Air, Water and Land Protection (WALP)

2 No rigorous monitoring program has been implemented to determine the overall effect of marine and fresh water fisheries on Fraser River steelhead populations since the implementation of the non-retention policy in 2002. Granted, implementing a scientifically credible, coast-wide steelhead catch monitoring program for this purpose may be hard to justify, given the costs involved, the low catches of steelhead relative to salmon in salmon fisheries, the absence of steelhead landing statistics, and the difficulty of obtaining reliable estimates of gear induced mortalities once steelhead are released.

To improve this state of affairs, a harvest simulation model was developed to provide a method of monitoring relative trends in fishing induced mortality as well as provide some insight on the ‘plausible’ level of fishing mortality on steelhead that spawn in the Fraser River tributaries upstream of Hell’s Gate (collectively termed the interior Fraser River steelhead stocks). The computational framework of this simulation model (termed ‘simulator’ in the following sections) is partly based on those developed to manage Fraser River sockeye fisheries (Cave and Gazey 1994), and Skeena River fisheries that intercept sockeye, coho and steelhead (Cox-Rogers 1994).

In this relatively ‘data-poor’ context, most of the steelhead-specific estimates of exploitation and gear-specific mortality required to parameterize the simulation model are characterized by considerable uncertainty, or are simply not available. When parameter values are lacking, investigators often rely on information from ‘meta-analyses’ (Fernandez-Duke and Valeggia 1994), which involves combining parameter estimates from similar species and under similar contexts. And for Bayesian analyses, when reliable priors are lacking, investigators often rely on ‘expert-based’ priors, which involves combining and weighting the opinions of several experts (see Morris 1977). For the present study, the ‘missing’ parameter values were determined through similar approaches which involved (i) obtaining pertinent figures from the literature, (ii) deriving these (if necessary) from available ones for co-migrating salmon species given similarities in behavior and morphology, and (iii) requesting the opinions of biologists and fishery scientists with considerable knowledge on the issues when doubt remained after the first two steps, so as to pinpoint the most plausible mode and range of parameter values.

3 The simulator uses information obtained via the above process, in conjunction with the actual fishery openings schedules, corresponding fishing effort levels, environmental conditions, and survey records with regard to run timing and migration rates to provide deterministic indices of relative exploitation rates separately for each year of interest. Obviously, one would expect some variation in the baseline parameter values used given annual changes in exploitation patterns, run timing, environmental conditions, fishing practices, and gear adjustments. The simulator allows users to explore the effects of some parameter variation on the predictions. The simulator was not used to quantify the combined impacts of a wide spectrum of parameters subject to variation, or describe the plausible impacts associated with a multitude of scenarios that could possibly occur in the future. But the effects of variation in some ‘key’ parameters are explored via Monte Carlo simulations for the post-2002 fishing seasons because of the additional uncertainty associated with the non-retention policy (must account for mortalities after release).

The results presented will show that the simulator is an investigative tool that can assist with the determination of data collection priorities, help gain better insight on the likely effect of the fisheries, and help gain better insight on how the overall fishing mortality rate is trending from year to year.

4 2. Symbols and notation a, b generic regression coefficients d 24 h interval corresponding to a specific calendar day f single gear fishery operating in a DFO or US fishing region, during a certain period k group of fish (or block) that migrate together during a period t time interval of a specified size (context specific) Cfdk catch in fishery f, obtained during day d, from block k D last calendar day of the fishing season E fishing effort in a single fishery and single day context Efd fishing effort deployed in fishery f, day d Ffdk instantaneous rate of fishing mortality applied by fishery f, during day d, on block k Nfdk number of fish available to fishery f, at the beginning of day d, from block k NDk number of fish alive on the last day of the fishing season, in block k ND+1 total escapement after the entire fishing season, all blocks combined N average abundance, between the start and the end of a period NE number of fish exposed to fishing NC number of fish not exposed to fishing N0 population size at the start of the fishing season N’0 number of fish that enter Georgia Strait through the northern route N”0 number of fish that enter Georgia Strait through the southern route Rfdk removals (deaths) caused by fishery f, during day d, on block k SD survival rate after D days o Td water temperature ( C), day d Uf exploitation rate of fishery f on the total population d fk average migration date (calendar day) through fishery f, by block k h harvest rate on fish in a single fishery and single day context hfdk harvest rate of fishery f, day d, on block k q catchability coefficient for a single fishery in a single day context qf catchability coefficient for fishery f rfk residence period (in days) in fishery f, for block k -1 mfd migration rate (km·d ) through fishery f, day d -1 mfdk migration rate (km·d ) through fishery f, day d, for block k u elemental harvest rate in a single fishery and single day context ufdk elemental harvest rate of fishery f, day d, on block k σfk standard deviation of the run time distribution (in days), through fishery f, for block k θf average mortality rate per encounter, in fishery f (immediate + subsequent deaths) θfd mortality rate per encounter, by fishery f, on day d (immediate + subsequent deaths) ε random error from a specified distribution Λ diversion rate, or portion of the total steelhead population that enters Georgia Strait through Johnstone Strait Rand() function that produces a random value from a specified distribution given a variable

5 3. Description of the simulator components

3.1 Model structure

Cave and Gazey (1994) described a procedure used to reconstruct sockeye stock sizes at various times during a fishing season, starting with abundance levels on spawning grounds, and recursively updating abundances backwards in time/space based on catch and effort of fisheries that intercept sockeye along their migration. Once the population sizes have been reconstructed prior to their entry into each fishery, and fishery-specific harvest rates have been estimated, forward projections of expected catches are made given a sequence of fishery openings to assess the effects on various fishery management scenarios.

The simulator is a simpler version of that described by Cave and Gazey (1994). It does not (and cannot) reconstruct population sizes because (i) historical steelhead catch records are unreliable and (ii) fishing effort acting on steelhead is unlikely to be identical to that on the salmon species targeted due to differences in spatial migration patterns. The simulator simply computes expected steelhead catches using the algorithm of Cave and Gazey (1994) using fishery-specific harvest rate estimates for other salmon stocks in the same fisheries, and hypothesized combinations of gear-specific mortality rates and migration patterns. The simulator does not determine the best combination of parameters, but can be used to determine the plausible effects of a set of hypotheses (like alternative fishing schedules), and determine data collection priorities for future evaluations.

3.2 Movement model

An algorithm is required to account for the movement of the population through the fisheries it traverses on its way to the spawning grounds. During the summer-fall, interior Fraser River steelhead stocks move through several marine and fresh water fisheries before reaching the spawning grounds during the fall. There are about 27 fisheries in southern BC that can intercept steelhead, but previous surveys indicate that only some of these have a substantial impact on the interior Fraser River stocks. The major marine fisheries (Fig. 1) take place between the mid

6 Fraser River and the entrances to Strait of Georgia, starting at Cape Caution on the north-east coast of Vancouver Island above Johnstone Strait (Area 11), and off Nitinat on the south-west coast of Vancouver Island near Juan de Fuca Strait (Area 21).

The terminology proposed by Cave and Gazey (1994) is used to describe how the simulator accounts for steelhead movement. All steelhead that move through a fishery on a given day are collectively referred to as a ‘block’. Successive blocks moving through a fishery (a unique combination of fishing region and gear) can be thought of as railroad cars moving past a train station, which explains the term ‘boxcar model’ often used to describe this modeling approach. All fish in a block are considered to be equally susceptible to harvest in the fishery they move through. The definition helps distinguish the impact of a fishery on a block (harvest rate) from its impact on the entire population (exploitation rate).

Fraser River steelhead return via Johnstone Strait (northern route) or via Juan de Fuca Strait (southern route). During the mid-1980s, Parkinson (1984) noted that Thompson River steelhead used mainly the southern route, while those from the Chilcotin River used mainly the northern route. The term ‘diversion rate’ denotes the percentage of the total population using the northern entrance. This rate still cannot be forecasted with certainty before each fishing season, so sensitivity analyses are conducted to determine its influence on steelhead exploitation rates. Given a known or hypothesized diversion rate, the population returning to the Fraser River must first be split into groups that enter through each route;

' (1) 0 NN 0 Λ=

" ' (2) 0 −= NNN 00

It is often assumed that salmon run timing through a region or fishery conforms to a normal distribution (Cave and Gazey 1994, Hilborn et al. 1999). The same assumption is made here. Given two groups (N’0, N”0) heading to Georgia Strait, the simulator first determines the numbers available for capture on day (d) in the first fisheries encountered.

7 ' 2 N ⎡ −− dd fk )( ⎤ (3) N = 0 exp⎢ ⎥ for d>0, k = 1 to x, f = 1 fdk 2σ 2 fk 2πσ ⎣⎢ fk ⎦⎥

" ⎡ 2 ⎤ N 0 −− dd fk )( (4) N = exp⎢ ⎥ for d>0, k = x+1 to K, f ≠ 1 fdk 2σ 2 fk 2πσ ⎣⎢ fk ⎦⎥

Since N’0 and N”0 are split into distinct and uniquely numbered blocks, that move through uniquely numbered fisheries, the is no further need for superscripts (‘ and “) when referring to blocks, so these are omitted from now on for purposes of clarity.

Run timing distribution parameters ( d ,σ fkfk ) are based on test-fishery records collected at Albion above the Fraser River mouth (Bison and Ahrens, 2003). These figures, used in conjunction with published estimates of steelhead migration rates, provide approximate dates of entry and passage times through the first two fisheries encountered (in Areas 12 and 21). Examination of past observer based catch records for these fisheries supports that the approximate dates are realistic. Passage times through these and other fisheries subsequently encountered are determined using migration rates.

Migrating steelhead travel about 17.0 km·d-1 in marine waters (Ruggerone et al. 1990). In freshwater, migration rates tend to vary according to river temperature (Renn et al. 2001). Over a typical range of river temperatures during the freshwater migration period, for example at temperatures of 18, 12, and 6oC, the respective migration rates computed by a regression relationship are 29, 17, and 6 km·d-1. These estimates are similar to those from tagging programs on Skeena River steelhead and coho (Lough 1981, Spence 1989, Koski et al. 1995). The freshwater migration rate is also similar to that of chum salmon in the Fraser River (Anderson and Beacham 1983). No evidence has been found to support the notion that steelhead may hold in areas at sea or in the Fraser River mouth. However, the simulator adjusts the freshwater migration rate using water temperature records, according to the radio-telemetry results obtained by Renn et al. (2001). The fishery-specific migration rates used to determine the next block locations are

8 −1 (5) m fd 0.17 ⋅= dkm marine fisheries (f = 1 to x)

−1 (6) m fd Td −= )1358.5875.1( ⋅ dkm in-river fisheries (f = x+1 to F)

As in Cave and Gazey (1994), the movement model is used to express space in units of time, so the length of each fishing region is described by the time required for a species to travel though. Eq. 5-6 also serves to move steelhead through regions with no fishing activity. The simulator simply sets fishing effort to zero, so no depletion occurs when blocks move through.

3.3 Exploitation model

Gulland (1983) relates catch, average abundance, harvest rate, fishing effort and gear catchability such that == EqNhNC . When the exploitation period can be split into short intervals (≤1d), and the catch is taken over a portion of each interval, there is no need to account for the average abundance. And in the absence of natural mortality, the catch can be computed from the abundance at the start of the interval (Hilborn and Walters 1992, p. 360)

(7) fdk = fdkfdk = qENhNC fdkfdkfdk

All gear-induced mortalities are assumed to occur shortly after capture or release, and before the next day, due to greater susceptibility to predation, accumulated stress, physiological problems (scale loss), or progressive loss of blood. The simulator keeps track of all block removals (i.e losses), the survivors, the overall exploitation rates, and the total escapement via

(8) fdk = CR θ fdfdk

(9) + )1( kdf = fdk − RNN fdk if fd+1 = fd

(10) N ++ )1(1 kdf = fdk − RN fdk if fd+1 ≠ fd

D K (11) R f = ∑∑R fdk for all k impacted by f d ==11k

9 R f (12) U f = N 0

K (13) D+1 = ∑ NN + ,1 kD k =1

3.4 Computational adjustments

The movement and exploitation models described in the preceding sections form the basic bookkeeping procedure of the simulator. However, two additional refinements are required to apply the boxcar model algorithms in the current context. First, Cave and Gazey (1994) forecast fishery-specific catches using harvest rate estimates obtained via the stock reconstruction procedure. Some of their published equations were found to be typographically incorrect, so the correct forms and derivations are given in the Appendix (Section 1). Irrespective of this minor finding, the authors decompose the harvest rate h because they hypothesize that a block (NE) that enters or leaves an area during a fishing period is subject to a lower harvest rate

than a block (NC) that remains in an area for the entire fishing period. The authors define an elementary harvest rate (u) for partially exposed blocks, to distinguish it from the harvest rate h acting on fully exposed blocks (see Appendix, Section 2 for derivations).

Many find this concept confusing (W. Gazey, pers. comm.), so an illustration is provided to help the reader visualize the process (see Appendix, Section 3). Given that steelhead and sockeye can move through some fisheries in 1 d, the simulator uses elementary harvest rates to compute catches in 1 d time steps. The relation between fishing effort, catchability rates, harvest rates, and elementary harvest rates is

(14) u fdk hfdk 1111 −−=−−= qE fdkfdk

Cave and Gazey (1994) account for changes in vessel distribution during a sockeye fishery opening. The authors apply context-specific relations between fishing effort and

catchability. When catchability increases linearly with effort, Eq. 14 holds since (u = qE). But for some fisheries, catchability seems to be limited by effort saturation (u = a + bLn(E)), so the

10 simulator substitutes Efdkqfdk by (af + bfLn(Efdk)) in Eq. 14, using the scaled fishery-specific

catchability coefficients (bf) published in annual reports.

A second adjustment is required because the average population size (as in Eq. 7) during fishing periods >1 d is rarely known. Usually, the size of the population before a fishery opening is estimated through some analytical procedure, and the instantaneous form of the catch equation is used given a corresponding rate of instantaneous fishing mortality. In the absence of natural mortality during short fishing periods (<1d), the standard equation reduces to

−F fdk (15) fdk fdk (1−= eNC )

As in Cox-Rogers (1994), the simulator uses Eq. 15 instead of Eq. 7 to compute daily

catches. For this purposes, it makes the necessary conversions between ufdk and hfdk, (or Efdkqfdk) before computing the rightmost component of Eq. 15 (see Appendix, Section 4 for derivations). Before doing the conversion, the simulator uses either a steelhead elementary harvest rate derived from the estimate for a salmon species in the same stratum (fishery/period), or the fishing effort level combined with a steelhead catchability derived from an estimate for a salmon species in the same stratum.

Since all steelhead released are thought to be subject to ‘delayed’ mortality, with perhaps a substantial, fishery-independent, over-wintering mortality after passing through the ‘terminal’ Thompson River sport fishery, the simulator uses Eq. 8 to compute all block removals and update the block sizes for purposes of consistency.

To help the reader visualize the sequential application of the simulator functions, Fig. 2 provides the pseudo-code used for the initialization, distribution, movement, depletion and computation of fishery impacts of steelhead populations moving through the marine and fresh water fisheries on their way up to the over-wintering and spawning grounds.

4. Parameter values and sources

11 The effort levels, elemental harvest rates and catchability coefficients used to compute the net fishery catches are given in Tables 1-2. Fisheries thought to have minor or unquantifiable impacts are not listed and not used to simulate recent fishery impacts. These include traditionally important ones such as gillnet and seine fisheries in Area 21, gillnet fisheries in Areas 4b-5-6c, the lower Fraser River sport, and First Nation fisheries upstream of Sawmill Creek.

The major data sources used include the results of sockeye exploitation assessments (Hill et al. 2000), sockeye run reconstruction results (Cave and Gazey 1994), published information on movement patterns of steelhead, sockeye and chum salmon, test-fishing statistics and observer records. What follows is a description of the procedures and justifications used to determine the exploitation and mortality parameters.

4.1 Exploitation parameters

4.1.1 Sport fisheries

The simulator accounts for the impacts of the major sport fishery in the Thompson River. For lack of sufficient data, the simulator does not account for the incidental catches of steelhead by anglers in marine waters and the lower Fraser although these fisheries are considered to have negligible effects. The Thompson River sport fishery has been rigorously monitored for over 20 years, and is the last (or ‘terminal’) major fishery before steelhead reach their over-wintering and spawning grounds. This fishery has been subject to catch-and-release restrictions since the late 1980’s. It used to be open by the time steelhead arrived, and was traditionally closed on December 31. Steelhead could be caught as soon as they entered the Thompson River. Observed catches (i.e. fish hooked/released) correlate well with escapement levels each year (Morris and Bison 2004), with slightly higher catch-to-escapement ratios in years of reduced flows and turbidity levels (Renn and Bison 2002).

Total angler catches are generally estimated using roving survey methods (Renn & Bison 2001). Post-season angler reports are also solicited annually by the BC Fish & Wildlife Branch. The figures reported are corrected for well-known reporting biases before the final catches are

12 estimated (DeGisi 1999). For seasons when no roving surveys were conducted, catches are computed based on regressions of previous roving surveys figures against previous post-season angler survey figures. Since total catches (and releases) are estimated directly, the simulator does not compute catches using elementary harvest rates, as for other fisheries.

4.1.2 Set-net fisheries

In the lower Fraser River between Sawmill Creek and Agassiz, there are aboriginal fisheries using set-nets (or stationary gillnets). The effectiveness of this gear is related to the morphology and swimming speed of the species caught. It is thought to be lower for steelhead than for sockeye because the former is larger and less readily gilled by nets with non-optimal mesh size. This assumption is supported by analyses of gillnet selectivity patterns for sockeye (Jim Cave, PSC, pers., comm.). For simulation purposes, set-nets are assumed to be ≈40% less effective at catching steelhead than sockeye, because the mesh size commonly used for sockeye fishing (5.75 in., 14.61 cm) is smaller than the most effective size for steelhead (6.75 in., 17.15 cm) in the size range captured (≈ 68-80 cm orbital-fork length). This assumption is considered ‘plausible’ (J. Cave, PSC, pers. comm.). Lower effectiveness is accounted for by first setting the steelhead elementary harvest rate to 60% of the sockeye estimate in same fisheries.

With regards to swimming speed, when nets are deployed in areas requiring >1 d to move through, the fastest swimming species should get caught in more nets than slower ones. Sockeye tend to travel faster than steelhead in fresh water (Renn et al. 2001). If sockeye travel twice as fast as steelhead, the elementary harvest rates for steelhead is 0.106 when that of sockeye is 0.2. The proof given in Appendix (Section 5) is based on the hypothesis that sockeye swim twice as fast as steelhead. This is not always the case since steelhead migration rates are adjusted for water temperatures, so the simulator adjusts the coefficient (b of Eq. 5.7) during the simulations given the ratio of swimming speeds between the two species in the fisheries traversed and the water temperatures at such times.

For in-river net fisheries above Mission that occur during the chum salmon fishing season (Table 2, categories 16-18), far fewer nets are deployed than during the sockeye fishing season.

13 Consequently, the derived harvest rates are further adjusted for those periods to account for the reduced effort. This is accomplished by multiplying the derived harvest rate by chum/sockeye fishing effort ratio. Typically, during the chum fishery, fishing effort is about 10-20% of that during the sockeye fishery, so the multiplicative factor (fishing effort ratio) that is determined dynamically by the simulator during each simulation is about ≈ 0.1-0.2.

4.1.3 Area 29 fisheries

For late season commercial and drift gillnet fisheries (after Sept. 20), sockeye estimates of elementary harvest rates cannot be used as they apply to fleet sizes much larger than those intercepting steelhead. So steelhead catchability figures are derived from those estimated for chum salmon.

The lower Fraser River test-fishing records are considered to be accurate and reliable. These were used with escapement records (Tables 3-4) and hypothesized movement patterns to determine the catchability coefficients for both species in the test-fishery, using the procedure described by Bison and Ahrens (2003). The authors assumed that test-fishery catches are subject to random variation (escapements are accurate), and that movement conforms to the model described by Hilborn et al. (1999). Given a normally distributed run timing, a total run size, and an error structure for variation in test-fishery catches, Bison and Ahrens (2003) compute the number of fish moving past the test-fishing area each day, the daily catch time series, and the overall gillnet catchability rate. Based on the fits obtained and the distribution of residuals, the pseudo-Poisson error assumption was rejected. Results obtained using a lognormal error structure showed good fit to non-zero catches, but the predicted run timing variance was unrealistically large. Given a normal error structure, sets with no steelhead catches could be used to compute the fits which were better, so this error structure was preferred. Since then, the error models in the original likelihood functions were substituted by the Poisson and the Negative Binomial (for testing purposes), and the Poisson error structure produced better fits between the predicted and observed values than all other error structures (see Bison 2006 for likelihood function). Consequently, this procedure was used to conduct the simulations described here.

14 Maximum likelihood estimates of peak migration dates past Albion, standard deviation in run timing, and catchability were generated for both species. Predicted tends conformed fairly well to the observed catch trends (Fig. 3), and the average catchability for steelhead was found to be ≈3.2 times greater than for chum (Table 5). This could be viewed as support for the hypothesis that chum are less susceptible to capture in the lower Fraser River gillnet fisheries because they swim closer to the bottom relative to steelhead (J.O. Thomas & Assoc. 1997, 1998) and are less vulnerable to gillnets when fishermen tend to avoid sweeping the bottom of the river for fear of snagging on submerged debris. Other hypotheses (size or behavior differences) might also explain the results, but even if this was the case, the test fishery catch-to-escapement ratios for both species indicate that steelhead catchability tends to be greater than that of chum salmon.

The 3.2:1 catchability ratio applies only to the test-fishery. For the commercial gillnet fishery, an overall daily catchability rate for chum salmon was determined using the gillnet catch and effort time series obtained at the peak of the chum run through Area 29 over 1990-2003. In 1998, a federally sponsored ‘buy-back program’ reduced the fleet size. It is not known if this changed the composition and effectiveness of the fleet, so estimates of daily catchability rates were generated for each period separately (pre/post 1998) by linearly regressing daily harvest rates against fishing effort (in boats). The best fitting chum catchability rates were 0.0010 for the first period, and 0.0014 for the later. Multiplying these by 3.2 translates into steelhead catchability rates of 0.0032 and 0.0045 respectively.

Given these coefficients, and total escapements and run timing estimates based on the Albion test-fishery data, commercial catches were computed for some Area 29 openings subject to catch monitoring by observers (Table 6). Chum catches based on the two catchability rates were 49-93% of observed catches for the first period, 71% during the second, and 71% over both periods. For steelhead, the figures are 70-93%, 134%, and 98% over both periods. Despite some discrepancy, the comparison to observed catches suggests that the approach is useful in estimating the magnitude of the catch. From a steelhead catch estimation perspective, this is encouraging given that conventional catch reporting is often biased to a degree where it is of little practical use.

15 Starting in 2002, the lower Fraser gillnet fishery changed, with a reduction in net length and soaking period. To account for changes in nominal effort (the area·time covered by a set), both catchability rates are linearly adjusted for changes in the nominal effort ratio (pre-post 2002). For example, if shorter nets are used (100 fathoms instead of 200), the baseline catchability is reduced by half before the simulator computes catches.

4.1.4 Area 21 fisheries

Fishing vessels in this area target mainly chum salmon returning to the hatchery during October as they aggregate near the Nitinat River mouth. There used to be a sizeable by-catch of steelhead and coho that transit through this area during the fishing period. These two species stay further offshore than chum and are subject to different harvest rates (Anonymous 1996, Bison 1992). For these fisheries, overall catchability rates for steelhead could not be derived from those estimated for other species.

For lack of a better alternative, a fishery-specific elementary harvest rate for the gillnet fishery was determined based on a standard run-reconstruction method using the 1989 observer program results. Given a spawning population of 2200 steelhead, and accounting for sequential losses thought the fisheries ‘backward over time’, the size of the population that entered the Area 21 fishery was determined under the following hypotheses; (i) a fixed harvest rate of 30% in all ‘upstream fisheries’, established based on a review of the exploitation indices for that period, (ii) a 20% diversion rate (Parkinson 1984), and (iii) an interior Fraser stock contribution to Area 21 catches of 80% (Beacham et al. 1999). The fixed harvest rate figure was determined based on crude catch and escapements figures for the mid-1980s that indicated harvest rates of about 60%, which declined to <25% by 1993 according to the simulation results obtained with the present model. On this basis, the fixed harvest rate was arbitrarily set to 30%. Having reconstructed the abundances prior to the fishery, forward projections were done to determine catches in the Area 21 fishery over a range of harvest rates. For u = 0.1, the predicted and observed catches where very similar to the observed catch in 1989 (Table 7).

16 In 1996, the Nitinat gillnet fishing was limited to near-shore areas to minimize steelhead and coho interception further offshore. In 1998, the southern part of the fishing area was closed to reduce impacts on steelhead and coho that aggregate there. The fishery opening was delayed until October 1, beyond the historical peak migration period of Interior Fraser steelhead. Total fishing effort decreased to about 80% of what it was in the early 1990’s, largely due to unfavorable economic conditions (low prices, higher fuel costs, etc.). This fishery was largely reduced since 1999, so for subsequent seasons, the Area 21 GN fishery harvest rates were considered to be negligible, and are not accounted for by the model.

4.2 Induced mortality

Fishery-specific mortality impacts account for deaths when the fishing gear is retrieved, plus subsequent ones caused by injuries and handling stress after release. For gillnet fisheries, the figures used are largely based on observer records (Table 8). For commercial gillnet sets operated in a traditional manner (i.e. without operational adjustments intended to reduce mortality rates of bycatch), about half of the steelhead tend to die before retrieval. There are no standard figures on losses due to predation while the nets are soaking. Predation losses are highly variable, and often related to net handling practices. In the absence of survey data, average losses due to predation cannot be quantified with certainty, so no effort was made to account for them in the simulations.

Concerning post-release mortalities from gillnet fishing, some radio-telemetry studies (Anonymous 1992, Renn et al. 2001) showed that steelhead caught and released in brackish waters (estuaries) are subject to higher mortality than those released in marine or fresh water environments. During the 1990’s, new gillnet fishing practices were established to reduce by- catch mortality (assuming full compliance). For estuary sets, revival boxes and short set times were shown to reduce gillnet mortality for coho (Farrell et al. 2001) if implemented effectively. For gillnet fishing operations conducted in this manner, Winther (in prep) found that coho mortality was about 26% after being held for 24 h. The author found that mortality increased with fish size (0% for <45 cm, to 80% for >70 cm), because smaller fish were not close to the optimal size of capture for the mesh used. Based on figures reported in the literature and

17 discussions with regional biologists having experience in salmon tagging operations, it is assumed that traditional, commercial gillnet fishing practices induce, on average, about 80% mortality on the steelhead intercepted (all fishing locations combined). For First Nation gillnet fisheries, mortality is assumed to be 100%, because historically, there were no incentives to release steelhead in those fisheries.

Adjustments to mortality rates to account for improved gillnet fishing practices were provided by DFO staff and based on information compiled several years ago for a wide range of conditions (see Anon. 2001, 2002b). For Areas 12 and 20, the pre-1995 mortality rates of 80% are reduced to 50% for later years to account for the effects of new release regulations for by- catch species and stricter catch monitoring programs (Table 9). For Area 21, mortality rates are reduced over 1994-98 for the same reason. For the Area 29 commercial fishery, mortality rates are reduced in 1998 and 2000 to account for the effects of shorter soak periods and the mandatory use of revival tanks. For US fisheries (Areas 7-7a), mortality rates are reduced gradually during 1994-98 due to the implementation of practices to decrease by-catch, particularly of Puget Sound coho. For the aboriginal drift net fisheries, it is assumed that mortality rates are lower in 1993 and 1999, because of improved handling practices and greater awareness of stock conservation issues.

With regards to purse seine catches, Winther (in prep) found that mortality of chinook held for 24 h was inversely related to fish size, and ranged from ≈70% for <45 cm fish, to ≈18% for 70-100 cm fish. This suggests that mortality is lower for mature fish. Winther (in prep) found much lower mortality rates for coho. Radio-tagging operations conducted by Spence (1989) and Koski et al. (1995) showed mortality rates of about 30% for steelhead caught by purse seine vessels near the Skeena River mouth during the peak migration period. For radio-tagged coho, Koski et al. (1995) observed mortality rates of about 45%. Fraser River sockeye were also subject to radio-tagging operations like those described by Koski et al. (1995) and Spence (1989). Early-run sockeye mortality after release was 65% in 2002, and 54% in 2003 (English et al. 2004). Mortality rates tended to decrease as the season progressed; reaching 25% in 2002, and 33% in 2003 for late-run sockeye. Mortality rates of sockeye tagged with acoustic devices late in the season was only 19%.

18

Salmon mortality induced by seining operations is generally considered to be a function of the specific gear used and the catch retrieval method. Since 2000, brailing has been mandatory in Canadian seine fisheries, even in the south coast. In the DFO fishing areas 3 & 4 (near Prince Rupert), surveys have shown that brailing induces about 5-10% mortality, whereas ramping can be ≈30% (S. Cox-Rogers, DFO, pers. comm.). These figures are considered as minimal rates, since they account only for short term mortalities (died on board), and not for deaths after release. In principle, steelhead radio-tagging operations (Koski et al. 1995) can account for death after release, and provide better estimates of mortality. However, even those estimates may be biased due to the potential effects of tag regurgitation, tag malfunction, straying, losses due to predation, and fish selection (only fish that survive and are in top condition are radio-tagged). Furthermore, anecdotal reports suggest that in the absence of observers or taggers, brailing is generally done with less care, so the immediate and post release mortality rates are likely higher than observed under ideal conditions.

Steelhead mortality rates induced by marine purse seine fisheries were therefore set based on such considerations, and discussions with several regional fisheries scientists (DFO, MoE). For US fisheries where brailing is not common practice, the average induced mortality was set to 30%, with the lower and upper bounds being 20% and 40% respectively (distributions specified in following sections). For Canadian commercial seine fisheries, the average induced mortality was set to 20% with the lower and upper bounds being 10% and 40% respectively (Table 9).

Steelhead mortality caused by angling activities has been estimated on many occasions during hatchery broodstock collection operations conducted using gear and catch-and-release practices as commonly used by anglers (Anonymous 1998). Mortality-per-encounter was estimated to average ≈3% (range: 0.3-5.1%) for operations conducted in the Chilliwack, Thompson, Coquihalla and Squamish rivers. Some experts believe such figures are obtained under ideal conditions, and are not representative of fishing periods involving many non- experienced anglers and gear types. In a subsequent review of the Thompson River sport fishery, Labelle (2004) accounted for the mortality induced by different gear types (bait, lure, and flies), effort levels by angler category, and multiple recaptures. His simulation results indicated that

19 overall mortality induced by catch and release operations were about 5.4% (range: 4-7%), but decreased to 5.1% (range: 3.7-6.6%) from 2003 onwards with the implementation of fishery closure during October 1-15. This level of mortality affects only steelhead that are available to anglers during the fishing period. Telemetry studies indicate that a considerable portion of the population (20-30%) can move past the fishing areas prior to, or after the sport fishing season (Renn et al. 2001). Consequently, the above figures are thought to be slightly greater than the actual impact of this fishery on the escapements. So for modelling purposes, the lower bounds, means and upper bounds of the mortality were respectively set to 3%, 4.4% and 6% for years prior to 2003, and 2.7%, 4.1% and 5.6% for all years since then.

5. Monte Carlo simulations

For stochastic projections (years 2004, 2005 and 2006), the simulator uses values that can vary according to some distribution. Sometimes, the expert opinions solicited diverged considerably as to which distribution was most suitable. This is not surprising since there is little empirical data to identify the best distribution systematically. So final decisions were occasionally based on various considerations that include; does a certain distribution provide the random variates needed (continuous vs. discrete), can it serve to provide a most plausible value and associated bounds, are all parameter values equally likely (uniform vs. non-uniform), is the distribution likely skewed (symmetrical vs. asymmetrical), could some values be <0 (log-normal vs. others), and etc. After a ‘suitable’ set of distributions was selected, computations are made for each combination of randomly chosen parameter values. The parameters allowed to vary were (i) the peak date of arrival at the northern tip of Vancouver Island, (ii) the standard deviation of run timing, (iii) the diversion rate, (iv) the migration rate in marine waters, (v) the migration rate in fresh water above and below Hope, (vi) the gear catchability, and (vii) the gear- specific mortality. The justifications for selecting the particular distributions to account for patterns of variation are given below, with details summarized in Table 10.

5.1 Variation in run timing

20 The historical run timing patterns derived from test-fishery catches at Mission (Table 11), suggest that the peak of the steelhead run can be as much as two weeks earlier than average in some years, or up to 3 weeks later than average in others. The Weibull distribution was used to represent this positively skewed run timing patterns (Fig. 4). The peak arrival date for a particular realization of the fishery is taken as a random deviate from this distribution under the assumption there is no within-season variation. The average standard deviation in run timing is also closer to the minimum estimate that to the maximum estimate, so the variation in the standard deviation of run time is also assume to conform to a Weibull distribution.

5.2 Variation in diversion rate

The impacts of the marine fisheries are influenced by the diversion rate. This rate is thought to be highly variable and cannot be predicted before the season. There is no evidence to indicate that all steelhead ever enter the Strait of Georgia through a single passage. In light of such facts, a platykurtic Beta distribution is used to generate random deviates of diversion rates for each fishery realization under the assumption there is no within-season variation. Given parameter values of α = 1.7, β = 1.7 and scale = 1.0, this distribution has a nearly semi-circular form (Fig. 5). The greatest probability is associated with a diversion rate of 0.5. Lower probabilities are associated with diversion rate of 0.3-0.7, and zero probabilities of obtaining extreme diversion rates (0.0, 1.0).

5.3 Variation in migration rate

Instead of using a constant [baseline] marine migration rate of 17 km·d-1, the marine rate is represented by a random deviate from a normal distribution with a mean of 17, and a standard deviation of 0.6 (Fig. 6). A unique random value is generated for each fishery realization. The middle 95% percentile range of such a distribution of random values is ≈ 15.5-18.5 km·d-1, which amounts to ±1.5 km·d-1 or ± 9% of the baseline rate. The average migration rate used within each realization is subject to additional stochasticity to allow each block to migrate through a fishery at a slightly different rate during the same fishery realization. For this purpose, a series of

21 random error values (εfdk) are taken from a uniform distribution with the bounds being ±10% of the baseline rate. Block-specific daily migration rates in marine waters are thus obtained from

(14) m fdk = mRand fd )( + ε fdk

Daily fresh water migration rates are adjusted for water temperatures, which allows for variation in migration rates between days and seasons. Further stochastic variation is allowed under the assumption that block movements do not always conform to that predicted by the physiological-temperature model. For each day, a random error variate (εfdk) is obtained from a uniform distribution with bounds that are ±5% of the daily migration rate obtained with Eq. 6. Block-specific daily migration rates in fresh water are thus obtained from

(15) fdk = mm + ε fdkfd

5.4 Variation in gear catchability

Catchability of purse seine and gillnet gears are allowed to vary during the fishing periods within each realization, as if there is small day-to-day or set-to-set variation. Purse seine catchability is assumed to be more variable than that of gillnets. For purse seines fisheries, catchability variation is modeled by multiplying an elementary harvest rate by a random variate from a uniform distribution with bounds 0.8-1.2, so the adjusted daily harvest rates are within ±20% of the elementary rates. For gillnet fisheries, the same approach is used, with the bounds being 0.9 and 1.1, so the adjusted daily harvest rates are within ±10% of the elementary rates.

5.5 Variation in mortality

For some fisheries, the most reasonable figure proposed tended to be closer to lower bound (best conditions), than the upper (exceptionally bad conditions). Consequently, the distribution of mortality rates is represented by a Beta distribution, parameterized to have a

22 skewed profile for some fisheries, and a symmetrical profile for others. Stochastic simulations were conducted for years 2004-2006, so distributions chosen reflect the implementation of improved fishing practices. For the Area 12 gillnet fishery, the most plausible mortality was set to 0.5, with the bounds being 0.3-0.8 (Fig. 7). This set of distribution parameters is denoted as (0.3, 0.5, 0.8). As noted earlier, purse seine fisheries are thought to cause less mortality that gillnet fisheries, for the Area 12 PS fishery, the distribution parameters were (0.2, 0.3, 0.4). The set of parameters for the other fisheries; Area 13 PS (0.2, 0.3, 0.4), Area 13 GN (0.3, 0.5, 0.8), Area 21 PS (0.2, 0.3, 0.4), Area 21 GN (0.3, 0.5, 0.8), Area 20 PS (0.2, 0.3, 0.4), Area 7 Native PS (0.4, 0.5, 0.6), Area 7 non-Native PS (0.4, 0.5, 0.6), Area 7a Native PS (0.4, 0.5, 0.6), Area 29 GN (0.4, 0.6, 0.8), Area 29 to Port-Mann Aboriginal Driftnet AD (0.8, 0.9, 1.0), Port-Mann to Mission GN (0.4, 0.6, 0.8), Port-Mann to Mission AD (0.8, 0.9, 1.0), and Aboriginal SN from Mission to Hope were set to 1.0 (no substantial variation implied). For the sport fishery, the mortality induced by catch-and-release activities represented by a Beta distribution with parameters (0.027, 0.041, 2.0 and 2.1) for an average mortality rate of 4.1% since 2003.

The above figures account for US PS fisheries induced slightly greater mortalities than Canadian ones, and that aboriginal driftnet fisheries cause greater mortalities due to relatively greater retention rates and immediate losses. For each realization, the fishery-specific rates are allowed to vary independently from each other even if they have the same baseline parameter values. Each fishery-specific rate is then subject to some additional stochastic variation since it would likely not be constant throughout any given fishing period. This is achieved by taking a random error variate (εf) from a uniform distribution with bounds of ±10% of the fishery-specific rate. Omitting the subscript denoting a realization, the daily mortality rates are computed from the fishery-specific rate, subject period-specific (within season) variation

(16) θ = θ + ε fdffd

5.6 Stochastic simulations for 2004-2006

Stochastic simulations are conducted only for the 2004-2006 fishing season because they are the most recent, and the exploitation rates are the most uncertain given the assumed mortality

23 rate and harvest rate adjustments intended to account for improved fishing practices. Previous exploratory analyses indicated that the overall exploitation rates were influenced by well-known factors such as fishing effort levels, fishery opening schedules, diversion rates, peak migration times, and fresh water temperatures that influence migration rates. So the inter-seasonal changes in these conditions are described below to best interpret their combined impacts on the simulation results, and the resulting changes in exploitation rates.

In terms of run timing, peak migration at Albion was latest in 2004, earliest in 2005, and again later than average in 2006 (Oct. 22, 2 and 18 respectively), and consequently, so were the peak arrival dates at the northern tip of Vancouver Island (Sept. 27, 8 and 25 respectively). The run was much shorter in 2004 than during 2005-2006 (SD = 8.2, 13.0 and 21.0 d respectively). Average water temperatures in the Fraser River from Sept. 1 to November 20 (main steelhead run period) were substantially lower in 2004 than during 2005-2006 (10.2, 11.3, and 11.1oC respectively). From 2004 to 2006, there were more fishing days in Area 12 (PS &GN), in area 13 (PS & GN), in Area 7-7A (PS & GN), in Area 29 (GN), in the aboriginal fishery (DN) from Mission to Steveston, and in the set-net fishery from Mission to Sawmill Creek (Table 12). In all, there were 137 daily net fishery openings during 2004, 175 in 2005, and 218 in 2006 during the period of interior Fraser steelhead migration (Table 12). There was also a fishery opening in Johnstone Strait during mid-September, 2005, but none in the other years.

The Monte Carlo simulations involved conducting 10,000 fishery realizations. The first simulation was done with all-but-one stochastic process disabled. The second was done with all stochastic processes enabled. This shows the singular effect of each variable on the overall exploitation impact, and their combined effect under more realistic conditions (i.e, akin to doing a sensitivity analysis).

6. Results

For the years since the recent implementation of the non-retention policy, simulations were conducted with all stochastic processes enabled. The 2004 distribution of exploitation rates

24 had a mean of 20.0%, and a 95% inter-percentile range (or bounds) of ≈12-30%. For 2005, the mean was 14.5%, with bounds of ≈9-21%. For 2006, the mean was 19.0%, with bounds of ≈10- 29%. This suggests that exploitation effects varied between years, and by as much as 30% (or ≈ 5 percentage points) between 2005 and the two adjacent seasons (Fig. 8).

The number of fishery openings increased during 2004-2006, but the 2005 exploitation rate was lower. Sensitivity analysis showed that the major determinants of exploitation rates in 2004 were the peak migration date and the spread of the run (Table 13). For 2005, peak migration date was a distinctly important factor, but diversion rate and the marine migration rate were also notable factors. For 2006, the spread of the run was a distinctly important factor.

The sensitivity analysis indicated that peak migration date was negatively correlated in 2004 and positively correlated in 2005 and 2006. This is the result of the timing of the run relative to the timing of those fisheries having relatively large exploitation effects like the first Area 12/13 chum fishery and the first Area 29 chum fishery. It is apparent that if the steelhead peak date happens to occur late enough in the season, like in 2004, the peak of the run occurs on or after the occurrence of the larger fisheries and their effect diminishes as peak migration date is advanced. Under early or more average peak migration date scenarios, like 2004 and 2006 respectively, the correlation between peak migration date and exploitation rate is positive which is consistent with the observation that most fisheries tend to occur during the later part of the steelhead run when chum salmon are peaking in abundance.

The importance of the spread of the run in 2004 and 2006 is also a notable result. The importance of this parameter is related to the later-than-average peak migration dates for those years and the coinciding occurrence of the larger fisheries on those “blocks” containing relatively large numbers of fish. Increasing the spread of the run diminishes the number of fish in these blocks and since the fisheries having the larger exploitation effects are short in duration (usually one day openings, e.g. Johnstone Strait seine), the reduced number of fish in these blocks is quite pronounced and results in a reduction in the overall proportion of the run that is subjected to fishing effects. The importance of the spread of the run in 2006 is especially large because the value and variation in this parameter was largest in 2006 (Table 10). Whether this

25 parameter value was overestimated due to difficult test fishing conditions in 2006 is a possibility, but remains a matter of speculation. It is interesting therefore to conduct the 2006 Monte Carlo simulation using what may be a more realistic parameter value and variation for the spread of run. If we conduct the 2006 Monte Carlo simulation using the 2005 values for this parameter, spread of run remains the most important factor and remains negatively correlated with exploitation rate, but the coefficient is lower at -0.63 which is similar to the result for 2004. Interestingly, peak migration date increases in importance and ranks second with a coefficient of 0.37, again similar to the results for 2004. Mortality rates in Area 29 gillnet fishery also increased in importance ranking third and fourth for the area downstream and upstream of Port Mann, respectively indicating that there is potential for an increasing trend in the importance of this fishery on steelhead exploitation in the future should future values for spread of run be less than estimated for 2006.

The relative influence of strongly correlated factors is best viewed by manually adjusting their corresponding values in the simulator (one at the time), and comparing the deterministic outcomes (Table 14). For 2004, lower exploitation would have resulted from a more protracted run, earlier peak run time, and lower purse seine fishing effort in Area 12. For 2005, lower exploitation would result from an earlier run time, greater diversion rate, and greater marine migration rates, perhaps because it could reduce the number of fisheries encountered. For 2006, lower exploitation rates again would result from a more protracted run (as in 2004), lower gillnet fishing effort in Area 29, and reduced sport fishing effort.

The deterministic simulation results obtained with a fixed diversion rate of 50% suggest that relative exploitation rates decreased considerably during the 1990s, from >40% in 1994 to <10% in 2000 (Fig. 9). The exploitation indices increased progressively since then to reach about 19% in 2006. Given that little information exists on steelhead diversion rate, it is fortunate that this trend happens to be similar regardless of the diversion rate assumption used. Note that the annual figures differ from those computed with all stochastic processes enabled, because they do not account for the plausible variation of several factors operating simultaneously that can influence the overall impacts via multiplicative effects.

26 The relative exploitation rates computed deterministically also exhibit year-to-year variation, but suggest trends that are consistent with trends in fishing schedules. Third order polynomials were fitted to the exploitation and corresponding escapement time series. The trends show a progressive reduction in exploitation impacts during 1992-2001, but these have increased since which is consistent with the earlier scheduling of fisheries during the month of October in the lower Fraser River and in north Puget Sound. Escapement also decreased substantially up to 1999, remained relatively stable until 2004, and has been decreasing since then to reach the lowest level observed historically.

7. Discussion

Ideally, for in-season fishery management purposes, it would be desirable to rely on a fishery/stock assessment model, couched in a Bayesian framework that estimates key parameters while keeping track of the accumulating losses during the season. Unfortunately, in this data- poor context, there is too little information to apply an integrated assessment model. For lack of a better alternative, the simulator was designed and used to monitor relative trends in fishing induced mortality and the outcomes of fishery management plans by using information from various sources. Obviously, the results depend on many assumptions about movement patterns, gear-induced mortalities, fishery-specific catchability rates, and run timing patterns. If this simulator is to be relied upon to provide fishery management guidelines while more sophisticated procedures are being developed, it would seem desirable to focus attention on reducing the sources of uncertainty.

Peak migration date at the northern tip of Vancouver Island and the spread of run stand out as a major determinants of exploitation rates. Both parameters are derived from the Albion test fishery data. Because the test fishery is located “upstream” of fisheries in the lower Fraser River that affect the later segments of the steelhead runs, the peak migration date estimated from test fishery data could be biased. In spite of this limitation, and in the absence of test fishery data from interception fisheries in the two entrances to Georgia Strait, run timing data from the Albion test fishery will continue to serve as a crucial source of data for the present model.

27

Although diversion rate consistently ranks among the more important factors determining exploitation rate, the correlation coefficients in the sensitivity analysis are consistently much smaller than the most dominant factors: peak migration date and spread of run. This is fortunate since diversion rate is a parameter for which we admitted a large amount of uncertainty given that little information exists about it. The simulation results imply that the effect and timing of fisheries along both migration routes have been roughly similar, at least to date, and explains why the deterministic trends in exploitation rate over time are similar regardless of the diversion rate assumption used. This seems reasonable since almost all of the fisheries are targeting Fraser chum salmon so fisheries which happen to have roughly similar cumulative exploitation effects tend to be scheduled on roughly similar parts of the steelhead run regardless of the migration route.

The results of deterministic simulations indicate that considerable progress was made since 1992 to reduce the fishing mortality on the Thompson River steelhead population. Unfortunately, the simulation results suggest that exploitation rates have increased lately. In 2004, the DFO determined that maximum exploitation [or induced mortality] on steelhead should limited to 15%, while the MoE recommended that it be limited to 10%. The Integrated Fisheries Management Plan of the DFO for Southern BC salmon stocks for June 2006 to May 2007 states that [for Canadian fisheries] “the objective for Interior Fraser River Steelhead is to limit fishing mortality rates within Canada to recent year levels of less than 13%”. A substantial portion of the simulated exploitation impacts exceeded this figure in 2004-2006. As noted previously, the two sets of figures are not directly comparable. The simulation outputs are exploitation rate indices, and are not absolute measures of exploitation rates or even genuine estimates. However, they still provide a consistent index over the period of interest and when linked to the escapement trends, they can assist in alerting managers to seek additional opportunities to conserve steelhead in an attempt to reverse a declining trend in abundance in the event that the abundance is below a target or limit reference point (Johnston et al. 2002).

During October 2006, the simulator structure and the results produced were subject to a PSARC review. One of the major criticisms was that the simulator does not account for all the

28 underlying uncertainty of the system under study, including that associated with the harvest rate estimates obtained from various sources. However, few bio-statistical models (if any) account for all underlying uncertainties, since such models are by nature, simplified representations of reality (see Brown and Rothery 1993). Even complex and detailed ‘operational models’ of well-known fisheries and stock-assessment contexts do not (NRC 1988; Labelle 2002, 2005). Subsequent requests for additional data simply confirmed that the confidence intervals for harvest rates on sockeye salmon runs simply are unavailable. The Pacific Salmon Commission (PSC) staff does conduct ‘hindcasting’ studies to determine the extent of potential biases in harvest rates. But the variances of the fishery-specific harvest rates cannot be quantified as they cannot be attributed with certainty to the stock-reconstruction procedure, the pre-season planning model, other factors yet to be determined, or a mixture of these (see Cave 2006).

Another criticism expressed during the same PSARC review was that the simulator relies on a single set of harvest rate parameters to determine catches, but should use year-specific parameter values to determine fishery impacts each season. This would be the ideal situation, but subsequent requests for such data simply confirmed that the PSC has not had sufficient resources to determine and publish estimates of elementary harvest rates for each fishery/year shortly after each season. Furthermore, given the limitations of the methods used and various data set uncertainties, no efforts have been made to determine if there are statistical differences between the elementary harvest rates for different periods (J. Cave, PSC, pers. comm.).

During the PSARC review, it was also suggested that a backward reconstruction could provide more accurate results of the run timing distribution at Albion (and other fisheries ‘downstream’), because it can [theoretically] account for all losses further upstream starting from the escapement. In the absence of reliable estimates of catches and parameter values to conduct both types of analyses, it seems pointless to conjecture about the pseudo-merits of the backward reconstruction over the forward projection approach adopted years ago by DFO-MoE scientists. However, one should emphasize that in recent years, total incidental losses in fisheries upstream of Albion amount to <10% or less of the total population. In light of this fact, the uncertainty associated with the available statistics, and the low level of observer coverage, it seems doubtful that a backward reconstruction would provide a substantially better picture of the run-timing

29 distribution at Albion. However, this exercise still might be a useful, as it would provide alternative indices of exploitation trends that could eventually be used to compare the performance, merits and crucial data requirements of both approaches.

In the meantime, the results suggest improvements are best achieved by improving our understanding of the migration biology of Thompson steelhead and in particular improving estimates of run timing parameters. One viable option would be to simply collect more data at Albion. The test fishing activities were reduced in 1998, supposedly to reduce the incidental kill of coho (Anonymous, 2002). There is no evidence that the incidental kill on total coho escapement has effectively been reduced, but reduced test fishing has substantially reduced the amount of data available for the assessment and in-season management of steelhead. Ironically, it was noted years ago that “the value of the test fishery for management of Fraser River watershed salmon stocks, far out weights the impact it could have on any individual coho or steelhead stock in [the] watershed” (Anon. 2002). The Thompson River sport fishery has recently been subject to severe gear/time/area regulations to reduce incidental mortality in this catch-and-release fishery, and recent escapement levels are near minimum conservations levels, so there is now an even greater need for reliable escapement indices from the Albion test-fishery. Resuming daily test fishing operations would help increase sample size, provide more information on the shape of the run timing curve, and provide more data for the application of the in-season sport fishing management model used by the MoE. Alternatively, run timing parameters could also be obtained via the use of fish wheels. If the catch records from fishwheels over a few trial years end up being highly correlated with those of the test fishing gear, it may be possible to infer abundance information from these data. Steelhead radio-tagging operations should also be conducted below Albion to provide better estimates of the catchability coefficient of this test-fishing gear, and losses due to in-river fisheries above Mission. Tagging operations could also provide information on the behavior, catchability and run strengths of weak coho stocks subject to conservation concerns that co-migrate with steelhead stocks.

Another potentially viable option would be to tag surviving steelhead kelts with acoustic transmitters in order to directly measure diversion rate and migration rate parameters upon their second returning migration to the Fraser and Thompson rivers. The operation of acoustic

30 listening lines along the North American coast as part of the Pacific Ocean Shelf Tracking project (POST) makes this option possible. In the absence of any practical means of capturing steelhead at sea prior to reaching the North American coast, tagging kelts is the next best potential alternative while technologies continue to improve.

As currently structured, the simulator accounts for more of the underlying uncertainties than the original (PSARC approved) model used by the DFO staff in North Coast for similar purposes (see Cox-Rogers, 1994). The later has since been updated each year with the addition of new data and assumptions (Cox-Rogers, 2007). Similar adjustments could be made to the simulator if additional efforts were made to obtain key data for this purpose.

8. Acknowledgements

Sincere thanks are owed to several persons who made substantial contributions to this investigation: Marc Labelle for developing much of the text and conducting the Monte Carlo simulations, Ryan Hill for developing the original model, Tom Johnston for statistical advice, and Vic Palermo for suggesting other model improvements. Data on gear-specific mortality rates were provided by Leroy Hop Wo (southern seines), Melanie Sullivan (southern gillnets), Barbara Mueller (Area 29 gillnets), Ivan Winther (northern gillnet and seine), Jim Renn (other gillnet). Pieter Van Will, Michael Folkes, Don Noviello, Jim Cave, Steve Cox-Rogers, Paul Starr, and William Gazey provided background data and reviews. Paul Ryall, Al Martin, Ian McGregor, and Ted Down provided support for this investigation. Kim Haytt provided some encouragements to pursue the investigation following the initial PSARC review of a prior draft in October 2006. Financial assistance was provided by the Habitat Conservation Trust Fund (HCTF) for the steelhead migration studies, escapement monitoring and angler surveys in the Thompson River. Additional funds were provided by the BC Ministry of the Environment, and the Pacific Salmon Foundation (PSF).

31 9. References

Anderson, A.D., and T.D. Beacham 1983. The migration and exploitation of chum salmon stocks of the Johnstone Strait – Fraser River study area, 1962-1970. Can. Tech. Rep. Fish. Aquat. Sci. 1166: 125 pp.

Anonymous. 1998. Review of Fraser River Steelhead Trout. Unpublished MS. Ministry of Environment, Lands and Parks and Department of Fisheries and Oceans, Kamloops, BC. 54 p.

Anonymous. 1998b. 1997 Fraser Selective Salmon Fisheries Final Report. Unpublished Report prepared for the BC Ministry of Environment, Lands and Park, Kamloops, BC. J.O. Thomas & Associates Ltd., Vancouver, BC.

Anonymous. 2001. Working document report recommendations for interim changes of coho post-release mortality rates to the salmon working group by the post-release mortality subcommittee, March 19. 2001. Internal Report. Dept. of Fisheries & Oceans. 35 pp. [Copy provided to the authors by Leroy Hop Wo (DFO)].

Anonymous. 2002. Mortality rates of coho and steelhead salmon captured incidentally in the Fraser River chum test fishery, 2001. Investigation report produced for the Dept. of Fisheries and Oceans. [Copy provided to the authors by Melanie Sullivan (DFO)].

Anonymous. 2002b. Assessment of the 2001 performance measures for post-release mortality rates for coho and chinook, and recommendations to the development of the integrated fisheries management plan for 2002 fisheries. Working Document Report. Dept. of Fisheries & Oceans. 21 pp. [Copy provided to the authors by Leroy Hop Wo (DFO)].

Beere, M.C. 1991. Steelhead migration behavior and timing as evaluated from radio tagging at the Skeena River test fishery 1989. Unpublished MS. Ministry of Environment, Smithers, BC. 18 p.

32

Bison, R.G. 1992. The interception of steelhead, chinook, and coho salmon during three commercial gillnet openings at Nitinat, 1991. Unpublished MS. BC Environment, Lands and Parks, Fisheries Branch, Kamloops, BC. 16 p.

Bison, R., and R. Ahrens. 2003. Estimating Fraser steelhead abundance from the Albion test fishery catches. Unpublished MS. BC Ministry of Water, Land and Air Protection, Fish & Wildlife Science and Allocation Branch, Kamloops, BC. 19 p.

Bison, R. 2006. Estimation of steelhead escapement to the Nicola River watershed. Unpublished Report. BC Ministry of Environment, Sept. 2006. 34 p.

Brown, D., and P. Rothery. 1993. Models in Biology: Mathematics, Statistics and Computing. John Wiley & Sons. NY. USA. 688 pp.

Cave, J.D., and W.J. Gazey. 1994. A preseason simulation model for fisheries on Fraser River sockeye salmon. Can. J. Fish. Aquat. Sci. 51(7): 1535-1549.

Cave, J.D. 2006. Hindcasting with the Pre-Season Planning Model 2002-2004. Unpublished MS PowerPoint presentation of an investigation conducted by the Pacific Salmon Commission. 20 pp.

Cox, S.P. 2000. Angling quality, effort response, and exploitation in recreational fisheries: field and modeling studies on British Columbia rainbow trout (Oncorhynchus mykiss) lakes. Ph.D. Thesis. University of British Columbia.

Cox-Rogers, S. 1994. Description of a daily simulation model for the Area 4 (Skeena River) commercial gillnet fishery. Can. MS. Rep. Fish Aquat. Sci. 2256: iv + 26 p.

33 Cox-Rogers, S. 2003. 2003 Skeena sockeye, coho, and steelhead post season review. Memorandum. Fisheries & Oceans Canada. Stock Assessment Division. Prince Rupert, BC. 18 pp. [Copy provided to the authors by the S. Cox-Rogers].

Cox-Rogers, S. 2007. A short history of the Skeena Management Model. Unclassified Memorandum. Fisheries & Oceans Canada. Stock Assessment Division. Prince Rupert, BC. 3 pp dated March 9, 2007. [Copy provided to the authors by S. Cox-Rogers].

Davis, M.W. 2002. Key principles for understanding fish bycatch discard mortality. Can. J. Fish. Aquat. Sci. 59: 1834-1843.

DeGisi, J. 1999. Precision and bias of the British Columbia Steelhead Harvest Analysis. Unpublished report prepared for the BC Ministry of Environment, Lands and Parks, Smithers, BC.

English, K.K., C. Sliwinski, M. Labelle, W.R. Koski, R. Alexander, A. Cass and J. Woodey. 2004. Migration timing and in-river survival of late-run Fraser River sockeye in 2003 using radio-telemetry techniques. LGL Report prepared for the Canadian Dept. of Fisheries and Oceans, Nanaimo, BC, Canada. 108 pp.

Gulland, J.A. 1983. Fish Stock Assessment. A Manual of Basic Methods. John Wiley & Sons, New York. 223 p.

Farrell, A.P., P.E. Gallaugher, J. Fraser, D. Pike, P. Bowering, A.K.M. Hadwin, W. Parkhouse, and R. Routledge. 2001. Successful recovery of the physiological status of coho salmon on board a commercial gillnet vessel by means of a newly designed revival box. Can. J. Fish. Aquat. Sci. 58: 1932-1946.

Fernandez-Duque, E., and C. Valeggia. 1994. Meta-analysis: a valuable tool in conservation research. Conservation Biology 8:555-561.

34 Haddon, M. 2001. Modelling and Quantitative methods in Fisheries. Chapman and Hall. New York. 406 pp.

Hargreaves, N.B., and C. Tovey. 2001. Mortality rates of coho salmon caught by commercial salmon gillnets and the effectiveness of revival tanks and reduced soak time for decreasing coho mortality rates. Fisheries and Oceans Canada, Canadian Science Advisory Secretariat, Research Document 2001/154.

Hilborn, R., B.G. Bue, and S. Sharr. 1999. Estimating spawning escapements from periodic counts: a comparison of methods. Can. J. Fish. Aquat. Sci. 56:888-896.

Hill, R., R. Bison, and A. Tautz. 2000. A daily simulation model of catch, mortality and escapement for Fraser-Thompson steelhead stocks. Unpublished MS. BC Ministry of Environment, Lands and Parks, Kamloops, BC.

Johnston, N.T., E.A. Parkinson, A.F. Tautz, and B.R. Ward. 2002. A conceptual framework for the management of steelhead, Oncorhynchus mykiss. Fisheries Project Report No. RD101. B.C. Ministry of Water, Land and Air Protection. BC Fisheries Branch. 26 pp.

Koski, W.R., R.F. Alexander, and K. English. 1995. Distribution, timing and numbers of coho salmon and steelhead returning to the Skeena watershed in 1994. Unpublished MS prepared for the BC Ministry of Environment, Lands and Parks. Victoria, BC. LGL Limited, Sidney, BC.

Labelle, M. 2002. An operational model to evaluate assessment and management procedures for the North Pacific swordfish fishery. U.S. Department of Commerce, NOAA-TM-NMFS- SWFC-341. 53 pp.

Labelle, M. 2004. Some comments of the procedure used for the in-season management of the steelhead (Onchorhynchus mykiss) sport fishery in the Thompson River. LGL report

35 prepared for the BC Ministry of Water, Land, and Air Protection, Victoria, BC, Canada. 29 pp.

Labelle, M. 2005. Testing the MULTIFAN-CL assessment model using simulated tuna fisheries data. Fisheries Research. 71(2005) 311-334.

Lewynsky, V.A. 1986. Evaluation of special angling regulations in the Coeur d’Alene River trout fishery. MSc. Thesis. University of Idaho.

Lough, M.J. 1981. Commercial interceptions of steelhead trout in the Skeena River - radio telemetry studies of stock indentification and rates of migration. British Columbia Fish & Wildlife Branch, Smithers, BC. 33 p.

McGregor, I. and D. Carson. 1993. Steelhead Observer Program October 27, 1992. File letter. Ministry of Water, Land and Air Protection, Kamloops, BC.

Morris, P.A. 1977. Combining expert judgments: A Bayesian Approach. Management Science. 23(7): 679-693.

Morris, A. R. and R. Bison. 2004. 2003 Thompson River Steelhead Creel Survey. BC Ministry of Water, Land and Air Protection, Fish & Wildlife Science and Allocation Section. Kamloops, BC. 80 p.

National Research Council (NRC). 1998. Improving fish stock assessments. Committee on Fish Stock Assessment Methods. National Research Council. National Academy Press, Washington D.C. 1998. 176 p.

Parkinson, E. 1984. Identification of steelhead stocks in the commercial net fishery of the southern BC coast. Ministry of Environment, Fish & Wildlife Branch, Fisheries Management Report No. 81; 16 p.

36 Renn, J.R., R.G. Bison, J. Hagen, and T. Nelson. 2001. Migration characteristics and stock composition of interior Fraser steelhead as determined by radio telemetry, 1996-1999. BC Ministry of Water, Land and Air Protection, Kamloops, BC. 135p.

Renn, J. and R. Bison. 2002. Assessment of the 2001 Thompson River steelhead sport fishery. BC Ministry of Water, Land and Air Protection, Kamloops, BC. 25 p.

Ruggerone, G.T., T.P. Quinn, I.A. McGregor and T.S. Wilkinson. 1990. Horizontal and vertical movements of adult steelhead trout, Oncorhynchus mykiss, in Dean and Fisher channels, British Columbia. Can. J. Fish. Aquat. Sci. 47: 1963-1969.

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Winther, I. (in prep). Estimates of chinook and coho mortality rates after catch and release. Internal Report. Department of Fisheries and Oceans. Prince Rupert. BC. Canada.

37

10. TABLES

38

Table 1. Effort figures (in vessels) for fisheries targeting chum salmon, 1992-2002. These effort values are also used to quantify steelhead exploitation levels, after adjustments (see text for details). Areas 7-7a equivalencies based on 1 purse seine boat·day equals 5 gillnet boat·days on summer run sockeye, and 10 gillnet boat·days on late run sockeye.

Fishery 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Area 12 gillnet 200 200 200 200 200 200 200 200 200 200 200 Area 12 purse seine 200 200 200 200 200 200 200 200 200 200 200 Area 13 gillnet Effort converted to seine effort equivalencies, and included in the figures below Area 13 purse seine 100 100 100 100 100 100 100 100 100 100 100 Areas 7-7a, non-treaty gillnet Effort converted to seine effort equivalencies, and included in the figures below Areas 7-7a, non-treaty purse seine 40-90 40-50 40-50 40-50 40-50 40-50 40-50 40-50 40-50 40-50 14-14 Areas 7-7a, treaty gillnet Effort converted to seine effort equivalencies, and included in the figures below Areas 7-7a, treaty purse seine 100-50 100-50 100-50 100-50 100-50 100-50 100-50 100-50 100-50 100-50 10-10 Area 29 to Port Mann, driftnet effort by individual opening within each season Area 29 to Port Mann, gillnet 200 200 200 200 200 200 200 150 100 100 100 Port Mann to Mission driftnet effort by individual opening within each season Port Mann to Mission, gillnet 150 150 150 150 150 150 150 110 80 80 80 Mission to Agassiz, setnet effort by individual opening within each season Agassiz to Hope, setnet effort by individual opening within each season

39

Table 2. Exploitation parameter values for all net fisheries (GN = gillnet, PS = purse seine) used by the simulator. The total fishing effort deployed is denoted by label E.

Category Fishery Exploitation parameters Estimate source Sockeye run reconstructions (Cave and Gazey 1994), revised by the Pacific [1a] Area 12 GN u = (0.0002015*E) + 0.01424 Salmon Commission in 2000.

[1b] Area 12 GN after Sept. 20 u = 1-SQRT(1-(0.0003*E+ 0.0013)) Chum run reconstruction estimates (VanWill, pers. comm.).

[2] Area 12 SN u = (0.0012*E) + 0.2334 As in [1a].

Sockeye run reconstructions (Cave and Gazey 1994), revised by the Pacific [3a] Area 13 GN u = 1-SQRT(1-(0.0003*E+ 0.0013)) Salmon Commission in 2000.

[3b] Area 13 GN after Sept. 20 u = (0.0002015*E) + 0.01424 Chum run reconstruction estimates (VanWill, pers. comm.).

Sockeye run reconstructions (Cave and Gazey 1994), revised by the Pacific [4] Area 13 SN u = (0.001*E) + 0.2243 Salmon Commission in 2000.

[5] Area 21 GN u = 0.1 Least-squares fit to 1989 observed steelhead catches. See text for details.

Average value, based on sockeye run reconstructions (Cave and Gazey [6] Area 20 PS u = 0.5 1994), revised by the Pacific Salmon Commission for a range of diversion rates.

[7] Area 7 Indian u = (0.0928*Ln(E)) - 0.1823 From sockeye run reconstruction (Cave and Gazey 1994).

[8] Area 7 Non-indian u = (0.00088*E) + 0.0443 As in [6].

[9] Area 7a Indian u = (0.212*Ln(E)) - 0.3602 As in [6].

[10] Area 7a Non-indian u = (0.00378*E) + 0.0405 As in [6].

Forward projection fits to chum catches, adjusted for steelhead:chum [11] Area 29 GN (pre-1998) q = 0.0032 catchability ratio.

Forward projection fits to chum catches, adjusted for steelhead:chum [12] Area 29 GN (1998 onwards) q = 0.0045 catchability ratio.

[13] Area 29 GN (100 fathom nets) q = 0.0023 As in [11], adjusted for a reduction in net length.

[14] Drift GN Aboriginal Area 29 to Port Mann q = 0.00192 As in [11], adjusted for fishery duration and net length.

[15] Drift GN Aboriginal Port Mann to Mission q = 0.0008 As in [11], adjusted for smaller net length.

Baseline figure from 1995 sockeye run reconstructions. Further adjusted [16] Set GN Aboriginal Mission to Agassiz u = 0.036 for differences in morphology, plus swimming speed (during sockeye fishery), plus sockeye/chum effort ratio (after sockeye fishery)

[17] Set GN Aboriginal Agassiz to Hope u = 0.03 As in [16].

[18] Set GN Aboriginal Hope to Sawmill Cr. u = 0.20 As in [16].

40

Table 3. Albion test fishery catches and total escapements of Interior Fraser River steelhead.

Spring spawners 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 Thompson 1135 3544 2426 1675 1500 1671 1200 1200 900 2955 2660 2591 1019 1470 2419 835 1880 1672 1490 1000 2200 1800 1000 Chilcotin 1133 3149 1992 2328 2342 610 403 466 542 1546 917 830 500 1373 672 744 739 1258 1113 917 254 384 552 Thompson Chilcotin 2268 6693 4418 4003 3842 2281 1603 1666 1442 4501 3577 3421 1519 2142 3163 1574 3138 2785 2407 1254 2584 2352 Test Fishing Steelhead Catch Data Date 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 01-Sep 000 00 00 00 02-Sep 0100 0 03-Sep 000 00 0000 04-Sep 0100 0 05-Sep 000 12 1001 06-Sep 0000 0 07-Sep 000 01 10000 08-Sep 0000 0 09-Sep 000 11 1000 10-Sep 0103 1 11-Sep 100 00 11000 12-Sep 2101 1 13-Sep 100 00 00000 14-Sep 1001 2 15-Sep 0010020 02 01000 16-Sep 01110102 8 17-Sep 0040000 03 00020 18-Sep 00300000 1 19-Sep 160000 10 00011 20-Sep 03101001 1 21-Sep 2260000 10 11040 22-Sep 0 802003 2 23-Sep 3262001 10 00000 24-Sep 01001100 0 25-Sep 0161310 112 30040 26-Sep 03410202 0 27-Sep 2202100 17 12010 28-Sep 21820201 2 29-Sep 0020031 01 31001 30-Sep 14310502 0 01-Oct 17 07031130080 11 41020 02-Oct614 2 320220003 1 2 03-Oct 11 02400410160 13 00100 04-Oct21410 024225201 0 2 05-Oct 421 000231280 28 110112 06-Oct01911 116151202 0 1 07-Oct10 803012205120 9 00001 08-Oct1 6 023140204 0 09-Oct15 444011122010102 00110 10-Oct0 1 210 24103 2 11-Oct073301150202620412 00010 12-Oct02712 205202461 3 13-Oct107 22200104311502 10011 14-Oct1357 123220102 2 15-Oct054 0201 503120022 00020 16-Oct2660 01 00413 1 17-Oct0410 03201211020111 20000 18-Oct1198 204433322 2 0 19-Oct22041 413400150218 20010 20-Oct116 001330110 022 21-Oct227403213413410 011300100 22-Oct1714 3142 20309 0120 210 23-Oct0410 012 000332121000011 24-Oct03302150101112 1000 410 25-Oct2420011 1111012000100310 26-Oct15230121110000 00000 1 0 27-Oct092511142 2203200010 000 28-Oct13010159100512 000120200 29-Oct011413311002120210000210 30-Oct29240021210104 00030000 31-Oct01342110 0110030010110000 01-Nov210600300011017 000000000 02-Nov0921032 11105531200000 0 03-Nov191 0011000053 1 0000200 04-Nov044121200131110100000003 05-Nov1631250 003004 011001000 06-Nov20520000031000 300011000 07-Nov11500130110000 00001000 08-Nov022011101300040100011100 09-Nov044 0100 12001 00000001 10-Nov050400 0220101110010000 11-Nov030212 001110 000100000 12-Nov05220100 040021 00001002 13-Nov05121100010003 000000001 14-Nov000010101100120000000001 15-Nov01104100101001 100022001 16-Nov012010000211000100000000 17-Nov01100001002000 000000000 18-Nov0241000 0200021000000000 19-Nov02200010 03000 00000000 20-Nov001000110030000000000000

41

Table 4. Albion test fishery catches and total escapements of Fraser River chum salmon. Blank cells indicate that no records are available. Actual escapements not reported.

Test Fishing Chum Catch Data Date 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 01-Sep 010 00 50010 02-Sep 0100 0 03-Sep 010 12 50001 04-Sep 0201 1 05-Sep 100 21 5100 06-Sep 1102 2 07-Sep 210 54 71065 08-Sep 0004 4 09-Sep 111 63 70567 10-Sep 1512 8 11-Sep 7 1 0 6 4 15 2 4 116 16 12-Sep 1406 6 13-Sep 6 4 1 146 180131619 14-Sep 81013 12 15-Sep 315780 35 36462320 16-Sep 4223107220 23 17-Sep 6332865 283 505025102 18-Sep 54510441 23 19-Sep 1812174 80107910737122 20-Sep 4101 9107 719 38 21-Sep 8 9 11 26 17 6 10 61 22 58 22 30 122 158 22-Sep 12 616120 232 27 23-Sep 13 2 4 18 15 9 4 72 25 91 44 36 83 190 24-Sep 40 17 0 16 33 21 4 47 51 25-Sep 38 20 5 13 42 17 58 50 157 60 39 126 150 26-Sep 29 22 9 11 18 20 7 78 88 27-Sep 30 16 2 18 28 31 8 150 83 149 134 51 100 115 28-Sep 40 6 9 10 65 32 9 135 90 29-Sep 35 21 6 36 53 11 135 133 195 189 66 267 190 30-Sep 31 51 22 15 36 67 15 168 249 01-Oct 101 123 30 24 95 40 117 3 188 149 266 162 57 186 219 02-Oct 53 35 97 80 70 36 137 27 90 323 252 03-Oct 0 0 72 121 41 27 180 57 108 4 264 182 436 175 136 199 232 04-Oct 36 74 24 151 75 20 125 26 34 280 351 05-Oct 0 0 59 119 59 28 84 38 115 10 162 282 323 135 204 176 325 06-Oct 75 93 104 123 115 55 123 58 52 540 260 07-Oct 0 63 100 105 97 62 156 65 75 2 243 449 247 325 227 386 08-Oct 54 0 154 136 65 45 167 42 100 518 09-Oct 68 118 130 126 46 67 158 48 52 26 530 208 182 519 263 390 371 787 10-Oct 131 0 138 48 54 152 99 100 282 11-Oct 49 147 184 148 84 70 160 96 83 126 416 261 188 488 210 305 233 337 12-Oct 0 73 224 300 46 106 159 127 76 523 13-Oct 91 93 219 327 21 40 175 131 92 87 931 230 166 239 352 341 313 420 14-Oct 97 96 258 226 106 118 224 121 52 310 15-Oct 116 107 256 126 88 160 94 147 138 504 322 265 645 300 463 256 322 16-Oct 120 142 131 118 314 64 156 479 17-Oct 82 63 272 253 148 96 386 183 145 51 471 313 377 596 205 621 332 424 18-Oct 132 66 233 292 137 116 380 372 160 156 442 19-Oct 95 155 125 239 157 155 245 182 95 20 476 463 283 573 502 459 384 289 20-Oct 86 198 101 149 239 189 302 258 84 648 449 775 21-Oct 57 160 158 151 74 144 224 656 161 701 466 117 404 714 6 680 532 440 22-Oct 92 139 276 106 71 347 346 99 677 272 269 165 400 652 409 23-Oct 39 121 43 77 105 315 359 90 57 376 478 278 358 294 43 429 712 24-Oct 82 133 50 71 157 86 379 205 70 432 232 222 406 574 362 305 25-Oct 62 56 39 84 201 745 219 48 414 329 423 126 365 351 73 729 233 286 26-Oct 108 36 168 75 136 253 315 204 108 284 362 134 294 449 455 227 27-Oct 140 86 286 113 124 285 231 13 302 353 118 307 319 456 657 202 288 28-Oct 73 104 246 166 126 70 367 248 29 334 363 201 471 717 293 401 313 385 29-Oct 83 78 347 127 112 69 442 181 116 239 435 227 143 641 801 176 533 214 333 30-Oct 116 124 246 202 150 140 308 195 162 242 302 152 493 395 502 420 519 31-Oct 32 296 190 117 142 479 170 24 276 44 67 321 374 298 477 323 314 01-Nov 44 243 368 57 252 123 167 171 286 257 108 322 259 134 529 237 195 02-Nov 36 110 62 215 65 118 118 79 414 164 224 65 201 167 299 414 228 03-Nov 35 76 90 25 146 148 156 204 301 19 205 167 866 225 173 04-Nov 50 252 89 14 358 269 165 169 147 44 83 150 24 146 97 458 178 478 05-Nov 152 174 6 140 418 152 86 114 106 57 14 213 132 379 292 198 363 06-Nov 140 120 33 36 111 166 15 159 106 141 27 44 531 315 181 383 88 234 07-Nov 189 155 117 71 22 103 275 245 75 87 28 61 511 257 278 224 79 08-Nov 122 108 168 93 50 163 189 67 116 320 76 33 60 33 248 56 29 09-Nov 77 64 75 53 137 150 85 61 12 370 56 56 128 22 10-Nov 37 51 94 136 127 98 274 173 61 198 82 13 217 163 175 65 252 11-Nov 76 85 148 50 77 217 575 229 221 35 14 111 163 94 139 18 166 12-Nov 21 180 48 120 77 386 76 66 1 1 70 100 98 56 41 250 13-Nov 16 90 6 81 58 45 109 243 105 35 5 2 90 102 45 14 124 14-Nov 52 174 4 109 47 13 42 57 16 287 30 2 2 156 86 64 0 119 15-Nov 23 264 45 166 114 26 20 18 110 104 15 1 4 156 95 28 8 105 16-Nov 8 237 203 108 91 49 19 76 46 22 33 35 3 42 72 128 28 8 5 17-Nov 19 107 244 9 27 80 51 122 71 86 17 4 86 36 76 34 6 108 18-Nov 119 97 27 75 120 61 3 31 19 25 1 9 52 75 38 33 3 29 19-Nov 45 123 110 90 136 21 38 5 23 1 38 11 41 4 11 18 20-Nov 46 44 177 45 84 84 10 46 2 34 11 22 6 13 31 17 12 7 3 21-Nov 38 75 131 91 33 58 21 11 3 16 35 0 8 25 10 37 4 19 22-Nov 38 12 153 64 18 17 9 1 4 21 8 1 8 12 6 17 2 3 23-Nov 6129 7153346662603510451403 24-Nov 10126 54301314006010252000 25-Nov 11550 237 91169018307122000 26-Nov 14048 54112203909211810362000 27-Nov 15747 20713410119112003810080000 28-Nov 12353 2061301252520001500071100 29-Nov 7793 615300108000010000 30-Nov 52181 6731115020610056000

42

Table 5. Estimates of catchability coefficients for steelhead and chum for the Albion test-fishery. For some years, no figures are provided, as none were obtained, or those obtained were considered to be unreliable or un-informative.

Survey steelhead chum ratio year (q) (q) steel/chum 1990 0.038 0.0106 3.6 1991 0.029 0.0085 3.4 1992 0.022 0.0078 2.8 1993 1994 0.018 0.0071 2.5 1995 0.031 0.0055 5.6 1996 1997 0.010 0.0076 1.3 1998 0.022 0.0055 4.0 1999 2000 0.036 0.0115 3.1 2001 0.027 0.0093 2.9 Average 0.026 0.008 3.2

Table 6. Predicted and observed catches of chum and steelhead in the Area 29 commercial gillnet fishery, 1990-2001. Reported catch figures adjusted for sale slip omissions.

Catchability Run size Species Opening Effort Predicted Observed (q) (No) dd/mm/yy (vessels) catches catches 0.0010 1,000,000 Chum 23/10/90 520 49,088 n/a 0.0032 2,400 Steelhead 23/10/90 520 354 425 0.0010 900,000 Chum 28/10/91 346 28,096 30,751 0.0032 2000 Steelhead 28/10/91 346 178 266 0.0010 800,000 Chum 27/10/92 362 26,674 54,895 0.0032 5,300 Steelhead 27/10/92 362 305 227 0.0014 1,900,000 Chum 08/11/01 180 20,057 28,359 0.0045 3,600 Steelhead 08/11/01 180 46 n/a

43 Table 7. Predicted and observed catches of steelhead in the Area 21 commercial gillnet fishery, in 1989. Figures obtained with an entering run of 3440 subject to u of 0.1.

Opening Effort (# vessels each day) Predicted catch Observed catch Sept. 18-21 335, 176, 105, 80 79 229 Sept. 25-28 275, 277, 200, 125 91 95 Oct. 4-10 200, 315, 234, 210, 170, 190, 165 141 82

Table 8. Estimates of steelhead mortality rates based on observer records and radio-tagging program results. Figures that are not available denoted by ‘n/a’.

Gear Fishing Survey Investigation Total Dead at Dead Condition of live fish Post-release type location year caught capture Poor Good mortalities Gill net Skeena 1989 Beere 1991 113 55 49% 9 49 41 Gill net Skeena 1992 Anonymous 1992 62 45 73% n/a n/a n/a Gill net Fraser 1992 McGregor 1993 227 149 66% n/a n/a n/a Gill net Nitinat 1996 Anonymous 1996 160 95 59% 57 8 8 Gill net Nitinat 1997 Anonymous 1997 26 17 65% n/a n/a n/a Gill net Nitinat 1998 Anonymous 1998 39 20 51% n/a n/a n/a

Table 9. Mortality rate per encounter figures used for simulation purposes, by fishery and year. Fishery gear types and labels are; gillnets (GN), drift net (DN), set net (SN) and purse seine (PS). Mortality rates of 1.0 indicates no post-release survival (all catches retained).

Fishery (Area & Gear) 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Area 12 GN 0.8 0.8 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Area 12 PS 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 Area 13 PS 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 Area 20 GN 0.8 0.8 0.8 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Area 20 PS 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 Area 21 GN 1.0 1.0 1.0 0.9 0.8 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Area 21 PS 1.0 1.0 1.0 0.9 0.8 0.6 0.5 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Areas 7-7a, Non-Treaty GN 1.0 1.0 1.0 0.9 0.8 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Areas 7-7a, Non-Treaty PS 1.0 1.0 1.0 0.9 0.8 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Areas 7-7a, Treaty GN 1.0 1.0 1.0 0.9 0.8 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Areas 7-7a, Treaty PS 1.0 1.0 1.0 0.9 0.8 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 Area 29 to Port Mann DN 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 Area 29 to Port Mann GN 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.6 0.6 0.6 0.6 0.6 0.6 0.6 Port Mann to Mission DN 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 Port Mann to Mission SN 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.8 1.0 0.8 0.8 Port Mann to Mission GN 0.8 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.6 0.6 0.6 0.6 0.6 0.6 0.6 Mission to Agassiz SN 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 Agassiz to Hope SN 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0

44

Table 10. Assumed parameter distributions and values used to represent stochastic processes for simulation purposes. Lower and upper bounds respectively denoted by lb and ub. Weibull location parameters are expressed either as the mean date of entry into top end of Johnstone Straight, or as the number of days.

Variable Distribution Parameters Values Peak date of entry Johns. Strait (2004) Weibull Location, Scale, Shape Sept.22, 10.0, 2.2 Peak date of entry Johns. Strait (2005) Weibull Location, Scale, Shape Aug. 27, 15.0, 4.0 Peak date of entry Johns. Strait (2006) Weibull Location, Scale, Shape Sept.8, 21.0, 3.4 SD run time Johns. Strait (2004) Weibull Location, Scale, Shape 5.5, 5.0, 1.6 SD run time Johns. Strait (2005) Weibull Location, Scale, Shape 9.5, 5.3, 1.9 SD run time Johns. Strait (2006) Weibull Location, Scale, Shape 10.0, 15.0, 2.0 Diversion rate Beta lb, ub, alpha, beta 0.0, 1.0, 2.0, 2.0 Daily migration rate in marine fisheries Normal mean, SD 17.0, 0.6 Daily migration rate in fresh water Regr. + uniform lb, ub 0.95, 1.05 Catchabilty PS gear Uniform lb, ub 0.8, 1.2 Catchability GN gear Uniform lb, ub 0.9, 1.1 Area 12 GN mortality Beta lb, ub, alpha, beta 0.3, 0.8, 2.0, 2.6 Area 12 PS mortality Beta lb, ub, alpha, beta 0.1, 0.3, 2.0, 2.0 Area 13 PS mortality Beta lb, ub, alpha, beta 0.1, 0.3, 2.0, 2.0 Area 13 GN mortality Beta lb, ub, alpha, beta 0.3, 0.8, 2.0, 2.6 Area 21 PS mortality Beta lb, ub, alpha, beta 0.2, 0.4, 2.0, 2.0 Area 21 GN mortality Beta lb, ub, alpha, beta 0.3, 0.8, 2.0, 2.6 Area 20 PS mortality Beta lb, ub, alpha, beta 0.2, 0.4, 2.0, 2.0 Area 7 Native PS mortality Beta lb, ub, alpha, beta 0.4, 0.6, 2.0, 2.0 Area 7 non-Native PS mortality Beta lb, ub, alpha, beta 0.4, 0.6, 2.0, 2.0 Area 7a Native PS mortality Beta lb, ub, alpha, beta 0.4, 0.6, 2.0, 2.0 Area 7a non-Native PS Mortality Beta lb, ub, alpha, beta 0.4, 0.6, 2.0, 2.0 Area 29 GN mortality Beta lb, ub, alpha, beta 0.4, 0.8, 2.0, 2.0 Area 29 to Port-Mann AD mortality Beta lb, ub, alpha, beta 0.8, 1.0, 2.0, 2.0 Port-Mann to Mission GN Beta lb, ub, alpha, beta 0.4, 0.8, 2.0, 2.0 Port-Mann to Mission AD Beta lb, ub, alpha, beta 0.8, 1.0, 2.0, 2.0 Terminal Sport (1992-2003) Weibull Location, Scale, Shape 0.026, 0.020, 3.0 Terminal Sport (2004-2006) Weibull Location, Scale, Shape 0.023, 0.020, 3.0

45

Table 11. Estimates of steelhead run timing patterns based on Albion test fishery catches and escapement records (based on Bison and Ahrens 2003). The mean date corresponds to the number of days after August 31. Standard deviations expressed in number of days. Figures that are not-available are represented by blank cells. Estimates based on the Poisson error structure are used for simulation purposes. Estimates based on the normal error structure are provided for reference purposes.

Escapement Test fishing Normal Error Normal Error Poisson Error Poisson Error Coeff. Year Year Mean date Standard Dev. Mean Date Standard Dev. Variation 1984 1983 1985 1984 1986 1985 1987 1986 31.2 24.2 32.4 20.2 0.624 1988 1987 1989 1988 1990 1989 1991 1990 1992 1991 44.7 14.1 44.1 14.8 0.336 1993 1992 39.5 19.7 40.9 21.8 0.535 1994 1993 1995 1994 44.3 11.2 43.9 11.9 0.272 1996 1995 46.8 17.0 44.8 17.7 0.396 1997 1996 1998 1997 59.3 9.5 59.5 15.7 0.264 1999 1998 34.2 21.9 35.2 22.2 0.630 2000 1999 36.7 18.2 36.1 17.9 0.497 2001 2000 32.6 10.9 31.4 11.5 0.364 2002 2001 29.3 20.8 31.0 17.3 0.556 2003 2002 29.7 19.4 28.5 26.9 0.946 2004 2003 2005 2004 53.9 2.7 52.2 8.0 0.153 2006 2005 31.8 11.5 32.6 12.8 0.392 2007 2006 46.7 28.0 48.1 20.8 0.432 Mean 40.0 16.4 40.0 17.1 0.457 sd 9.5 6.7 9.1 5.1 min 29.3 2.7 28.5 8.0 0.153 max 59.3 28.0 59.5 26.9 0.946

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Table 12. The number of daily net fishery openings of discrete fisheries (as defined in the model) during the Thompson steelhead migration period. Area 12/13 is defined as containing 4 discrete fisheries (2 areas and 2 net types). Area 7/7a is defined as containing 4 fisheries (2 areas each divided by "Treaty" and "Non-Treaty"). Area 29 is defined as containing 2 discrete fisheries (2 areas, one upstream of Port Mann and one downstream). Lower Fraser Drift Net is defined as containing 2 fisheries (one upstream of Port Mann and one downstream of Port Mann). Lower Fraser Set Net is defined as containing 3 fisheries, Mission to Agassiz, Agassiz to Hope, and Hope to Sawmill Cr.).

Year Area 12/13 Area 7/7a Area 29 Lower Fraser Drift Net Lower Fraser Set Net Total (Aug 15-Oct 30) (Sept 1-Nov 20) (Sept 1-Nov 20) (Steveston to Mission, (Mission to Sawmill, Sept 1-Nov 20) Sept 1-Nov 20) 2004 24 56 4 25 28 137 2005 32 69 4 30 40 175 2006 40 82 8 38 50 218

Table 13. Effects of each variable on overall exploitation for the 2004-2006 fishing seasons. Coefficients of the rank correlations between each variable and the exploitation rate are given under the label Corr. See text for additional details.

Rank Variable (2004) Corr. Variable (2005) Corr. Variable (2006) Corr. 21 Aborig. driftnet mort u/s P.Mann 0.00 Slope of migr.-temp. regression 0.00 Catchability noise for seine 0.00 20 Area 7 mortality Treaty Indian -0.01 Area 7a mortality Treaty Indian 0.00 Catchability noise for gillnet 0.00 19 Area 21 gillnet mortality -0.01 Aborig. driftnet mortality u/s P.Mann -0.01 Aborig. driftnet mortality u/s P.Mann 0.01 18 Area 29 mortality u/s P.Mann 0.01 Catchability noise for seine -0.01 Area 7 mortality Non-Indian 0.01 17 Area 7a mortality Non-indian 0.01 Catchability noise for gillnet -0.01 Area 7a mortality Treaty Indian 0.01 16 Slope of migr.-temp. regression -0.01 Area 13 gillnet mortality 0.01 Area 21 gillnet mortality 0.02 15 Marine migration rate 0.02 Spread of run 0.02 Area 7a mortality Non-Indian 0.02 14 Area 7 mortality Non-Indian 0.02 Area 21 gillnet mortality 0.02 Slope of migr.-temp. regression -0.02 13 Area 13 gillnet mortality 0.02 Area 7 mortality Non-Indian 0.03 Area 7 mortality Treaty Indian 0.02 12 Catchability noise for gillnet -0.02 Area 7a mortality Non-Indian 0.03 Aborig. driftnet mortality d/s P.Mann 0.03 11 Catchability noise for seine 0.02 Area 7 mortality Treaty Indian 0.03 Area 13 gillnet mortality 0.03 10 Area 12 gillnet mortality 0.04 Area 29 gillnet mortality u/s P.Mann 0.05 Area 13 seine mortality 0.03 9 Sport mortality 0.05 Area 29 gillnet mortality d/s P.Mann 0.05 Area 29 gillnet mortality u/s P.Mann 0.05 8 Aborig. driftnet mortality d/s P.Mann 0.06 Area 12 gillnet mortality 0.06 Marine migration rate -0.06 7 Area 7a mortality Treaty Indian 0.06 Aborig. driftnet mortality d/s P.Mann 0.06 Area 12 gillnet mortality 0.07 6 Area 13 seine mortality 0.07 Sport mortality 0.12 Area 12 seine mortality 0.07 5 Area 29 mortality d/s P.Mann 0.07 Area 12 seine mortality 0.13 Peak migration date 0.10 4 Diversion rate 0.10 Area 13 seine mortality 0.15 Diversion rate -0.14 3 Area 12 seine mortality 0.13 Marine migration rate -0.20 Sport mortality 0.15 2 Peak migration date -0.45 Diversion rate -0.21 Area 29 gillnet mortality d/s P.Mann 0.16 1 Spread of the run -0.69 Peak migration date 0.84 Spread of run -0.90

47

Table 14. Relative influence of the major determinants of overall exploitation for the 2004-2006. Baseline values correspond to most likely parameter value implied. Figures under the label ‘Δ Expl. Rate’ correspond to the relative change ((adjusted – likely) / likely)). All figures based on a 50% diversion rate.

Season Factor Adjustment Value Expl. Rate Δ Expl. Rate 2004 Spread of the run x 0.9 7.38 24.1% 4.3% x 1.0 8.20 23.1% 0.0% x 1.1 9.02 22.2% -3.9%

Peak date 1 wk earlier 20-Sep 19.2% -16.9% Peak 27-Sep 23.1% 0.0% 1 wk later 04-Oct 20.4% -11.7%

Area 12 purse seine mortality x 0.9 0.18 22.9% -0.9% x 1.0 0.20 23.1% 0.0% x 1.1 0.22 23.3% 0.9%

2005 Peak date 1 wk earlier 01-Sep 10.5% -21.8% Peak 08-Sep 13.5% 0.0% 1 wk later 15-Sep 17.5% 29.7%

Diversion rate x 0.9 0.45 13.6% 0.5% x 1.0 0.50 13.5% 0.0% x 1.1 0.55 13.4% -0.4%

Marine migration rate x 0.9 15.30 15.4% 14.5% x 1.0 17.00 13.5% 0.0% x 1.1 18.70 11.9% -11.6%

2006 Spread of the run x 0.9 18.90 16.4% 5.8% x 1.0 21.00 15.5% 0.0% x 1.1 23.10 14.7% -5.2%

Area 29 comm. GN mortality d/s P.Mann x 0.9 0.540 15.0% -3.2% x 1.0 0.600 15.5% 0.0% x 1.1 0.660 15.9% 2.6%

Sport mortality x 0.9 0.027 15.2% -1.9% x 1.0 0.030 15.5% 0.0% x 1.1 0.033 15.7% 1.3%

48

11. FIGURES

49

Figure 1. Map of fishery management regions where steelhead and salmon species are caught.

50 Set initial population size (No = 1, or use hypothesized value) ↓ Determine numbers entering through northern and southern routes N'o = No*diversion rate, N"o = No - N'o ↓ Determine start run time at Albion ↓ Determine dates of arrival of N'o to Area_11, and N"o to Area_21 x days earlier = Albion date - (distance*migration speed) ↓ Given normal distribution (mean, SD), split N'o and N"o into blocks i.e., determine series of N'fdk and N"fdk ↓ Start loop, block =1, day = 1 ↓ ↑→ Update block number and/or day number ↑↓ ↑ Move blocks sequentially upstream towards upper Thompson River ↑ Adjust migration rate using FW temperature if in Fraser R. ↑↓ ↑ Derive q or u; set corresponding h and F ↑↓ ↑ Determine numbers in each block intercepted in each fishery ↑↓ ↑ Determine incidental kill due to fishery f, on block k, day d ↑↓ ↑ Reduce abundance of each block impacted given incidental kill ↑↓ ↑ Accumulate incidental kill by fishery ↑↓ ↑ Finished moving all K blocks through all fisheries day d ? ↑↓↓ ↑←←← ← No Yes ↑↓ ↑ day = D ? ↑↓↓ ↑←←← ← No Yes ↓ Determine overall fishery-specific impact (i.e., Sum fishery-specific incidental kills over initial population size) ↓ Determine overall fishing impact (i.e., Sum incidental kills for all fisheries over initial population size)

Figure 2. Pseudo-code used by the simulator to distribute the initial population specified, move and deplete the initial population and compute fishery impacts.

51

Chum salmon 600

500

400 h 300 Catc 200

100

0 24-Aug 13-Sep 3-Oct 23-Oct 12-Nov 2-Dec 22-Dec Date

Steelhead 14 12 10

h 8

Catc 6 4 2 0 24-Aug 13-Sep 3-Oct 23-Oct 12-Nov 2-Dec 22-Dec Date

Figure 3. Observed (dots) and predicted (lines) test fishery catches of chum salmon and steelhead during 2000. Predictions based on escapements figures considered to be accurate, using a normal observation error, as described by Bison and Ahrens (2003).

52

Figure 4. Hypothesized probability distribution of peak arrival date at the northern tip of Vancouver Island. Peak date here is Sept. 13th, with the lower and upper bounds being Sept. 1st and Oct. 5th respectively.

Figure 5. Hypothesized probability distribution of steelhead diversion rates. There is a zero probability of observing extreme diversion rates (0.0 or 1.0).

53

Figure 6. Hypothesized probability distribution of the average migration rate of steelhead as they move through marine fisheries.

Figure 7. Hypothesized probability distribution of the mortality rates of steelhead released after capture for the Area 12 GN fishery.

54

Forecast: Overall Exploitation

10,000 Trials Frequency Chart 69 Outliers

.023 226

.017 169.5

.011 113

.006 56.5

.000 0

12% 16% 21% 25% 30% Exploitation Rate

Forecast: Ov erall Exploitation

10,000 Trials Frequency Chart 89 Outliers

.023 230

.017 172.5

.012 115

.006 57.5

.000 0

8.66% 11.81% 14.95% 18.09% 21.23% Exploi tati on Rate

Forecast: Overall Exploitation

10,000 Trials Frequency Chart 90 Outliers

.024 243

.018 182.2

.012 121.5

.006 60.75

.000 0

9.64% 14.62% 19.59% 24.56% 29.53% Exploitation Rate

Figure 8. Monte Carlo simulation results for the 2004-2006 fishing seasons (in chronological order from top to bottom). Scales differ across plots to facilitate viewing.

55

% Expl. Poly. (% Expl.)

50% 45% 40% 35% 30% 25% 20% 15% 10% 5% 0% 1992 1994 1996 1998 2000 2002 2004 2006

Spawners Poly. (Spawners)

4000 3500 3000 2500 2000 1500 1000 500 0 1992 1994 1996 1998 2000 2002 2004 2006

Figure 9. Time series of exploitation rate indices and corresponding escapements (year indicates fishing season) for the Thompson River steelhead population. All figures computed deterministically using a fixed diversion rate of 50%. The smooth black lines are third degree polynomial fitted to each data series. No escapement figures are available for 1997 due to flood conditions that prevented field surveys to be conducted.

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12. APPENDIX

Unless specified otherwise in one of the following sections, the symbols and notation used in this Appendix conform to those described in Section 2 of this manuscript. Only in the odd case (for simplification and descriptive purposes only), is a superscript or subscript modified to help the reader understand the basic principle.

Section 1: The following clarifications are made to the equations reported by Cave and Gazey (1994) following a discussions with the co-author during June 2005. Consider a case where two block are subject to exploitation for one day by one fishery (so no need for subscripts f,d) Starting with the corrected form of their Eq. 7, with the denominator changed, the combined catch of fully exposed block (NC) and partially exposed block (NE) blocks in a 24 h opening can be obtained using the average abundance for the two blocks combined

hN (1.1) C = −+ h)1(1 x

Substituting N in Eq (1.1) by Cave and Gazey’s paper Eq. (4) and Eq. (6), translates into

+ NNh )( −+ x NhNh ))1(( −+ )1( x NhhhN (1.2) C = EC = C C = C C −+ h)1(1 x −+ h)1(1 x −+ h)1(1 x

Which after elimination of NC and NE, and substitution of NC by N reduces to

−+ hhN x ))1(1( (1.3) C = = hN −+ h)1(1 x

57

Section 2: Cave and Gazey (1994) consider the harvest rate on a partially exposed (or vulnerable) block (u) is about half of that acting a fully exposed block (h), and model the relation between both variables using a contrast coefficient (x) of 0.5 (chosen in an ad hoc fashion)

(2.1) hu x h 5.0 11)1(1)1(1 −−=−−=−−= h

(2.2) 11−=− hu (2.3) 2 1)1( −=− hu

(2.4) 2 112 −=++ huu (2.5) 2 2 =− huu

Section 3: The following illustration describes the procedure used to account for the exploitation and survival of partially and fully exposed blocks of fish

Block Day Fishery Size Harvest Survival Survival k 1 of 1 Area_A 1 day after 1 d after 2 d 1 u 1-u 0 u 1-u

1 of 2 Area_A Area_B 2 u 1-u 1-u 1 2u -u ^2 (1-u )^2 (1-u )^2 0 u 1-u 1-u 2 of 2 3 u 1-u 1-u 2 2u -u ^2 (1-u )^2 (1-u)^3 1 u (1-u )^2 (1-u)^3

The arrows indicate the direction of movement for each block during a 24 h interval. Shaded areas are those occupied by a ‘fully exposed block’ when fishing began. The top diagram concerns a fishery that requires 1 d to travel through. Block 0 occupies Area A when the fishery opens, and has moved out of it 24 h later. Block 1 moves into Area A during this period. Both

58 blocks are partially exposed to fishing when entering or leaving the area, and are subject to elementary harvest rate u, with a (1-u) survival rate after 1 d of exploitation.

The two lower diagrams concerns a fishery requiring 2 d to travel through, and is split into two areas (A, B), each requiring 1 d to travel through. When the fishery opens, blocks 0 and 1 occupy adjacent areas. During the first day, block 0 moves out of Area B, and is subject to harvest rate u. Block 1 moves out of Area A to replace block 0. It is fully exposed to fishing, is subject to harvest rate h = 2u-u2, with a survival rate after 1 d of exploitation of 1-h (or 1-(2u-u2) = 1-2u+u2 = (1-u)2). Block 2 enters Area A to replace block 1, and is subject to harvest rate u.

On day 2, all blocks move again, but block 0 is no longer subject to exploitation, and a new one (block 3) moves into Area A. After 2 d of fishing, block 2 has been subject to an overall rate of u + ((1-u).(2u-u2)), or 3u–3u2+u3, because the harvest rate on the second day (2u-u2) applies only to the portion of the block that survives the first day of exploitation (1-u). This translates into an overall survival rate of (1-u)3. Same for block 1, except that the magnitude of the impacts on each day is reversed. So during a two day fishery, 4 blocks are involved, but only 3 are simultaneously subject to exploitation each day. Note that by the end of day 2, block 2 still occupies Area B, so if fishing was to continue in that area the next day, block 2 would be harvested again at rate u while it moves out, for a total survival rate of (1-u)4. Therefore, survival over a period depends on the block location with regards to that of the fishery when exploitation begins, the migration time through the fishery, and the fishery opening schedule.

Section 4: As illustrated by Haddon (2001, p.43), translating annual harvest rates into instantaneous harvest rates allows for rounding errors whose magnitude depends on the stratification level used. For protracted fishing periods (≤ 1d), one can omit subscript d for purposes of clarity, so the relation C=Nh=NEq is used here for illustrative purposes. If 10 fish are alive at beginning of a ≤1 d fishery, a 20% harvest rate translates into two deaths and eight survivors by the end of the fishing period. To use the instantaneous form of the catch equation, h or Eq are usually transformed to produce an equivalent estimate of instantaneous fishing mortality

59 (4.1) = − − = − − EqLnhLnF )1()1(

After accounting for the above equivalencies and natural mortality that may occur during the exploitation period, one obtains the instantaneous form of the catch equation

(4.2) (1−= eNC +− MF )( ) or alternatively (4.3) (1−= eNC −F ) when M = 0

As a simple example, let E=1, so q=Eq and h=q. Converting h with Eq. 4.1 gives F = 0.223, which translates into the correct catch of 2.0 fish based on Eq. 4.3.

Section 5: Let subscripts 1 and 2 denote two species subject to the same overall harvest rate in a fishery. If so, both survival rates should be the same after passing through the fishery. For a one day fishery impacting a ‘fully exposed’ block, survival is (1-u)2. But if the second species travels at half the speed, its survival rate should be (1-u)4, since it is fully exposed twice. Substitute exponents 2 and 4 by a and ab to represent the travel times in relative terms, such that for b=2, sockeye move twice as fast a steelhead (as in this case). Elementary harvest rates for steelhead are then derived from those of sockeye as follows

ab a (5.1) u2 −=− u1)1()1(

(5.2) − 2 = − uLnauLnab 1)1()1(

(5.3) − 2 = − uLnuLnb 1)1()1(

(5.4) − uLn 2 = ( ( − 1 )/1exp)1(exp( buLn )

(5.5) − 2 = ( ( − 1 )/1exp1 buLnu )

(5.6) u 2 = − ( ( − 1 )/1exp1 buLn )

b −uLn 1 )1( b (5.7) 2 1−= eu 11 −−= u1

60