2.5 Model Parameter Values

2.5.1 SFM Stocks

Four¹ types of parameters were required to simulate the abundance and distribution of each stock:

1. The initial distribution of the age 3 cohort among fishing areas;

2. the survival from smolt to recruitment at age 3;

3. the initial number of smolts; and

4. the migration of fish between geographic regions.

The Phase 2 analysis assumed that the age 3 cohort of each stock was initially distributed among five geographic regions (model acronym provided in parentheses):

1. Strait of Georgia (GEOS);

2. South Puget Sound (SPSD);

3. Strait of Juan de Fuca - San Juan Islands (SJDF);

4. West Coast Vancouver Island (OCNN); and

5. Washington/Oregon Ocean (OCNS).

The Scott and Newman results were modified in three ways. First, the Puget Sound (PS) estimate was apportioned into the SJDF and SPSD regions using sport catch per unit effort (CPUE) in statistical weeks 1-24 for areas 5 and 6, and for areas 10, 11, and 13. Only weeks 1-24 were used in order to avoid the influence of adults returning later in the year. A second modification was necessary because individual stocks reported in Scott and Newman were aggregated in the SFM. To estimate the initial distribution of the SFM stocks, a weighted average of the distribution of the component stocks was calculated. The weights were the 1990 preseason forecasts of individual stock abundances used by the Salmon Technical Team of the PFMC (J. Banyard, WDFW, Pers. Comm.). Lastly, estimates of the Washington and Oregon proportions of Scott and Newman were aggregated to provide an estimate for the OCNS region. The estimates of the initial distribution of age 3 cohort in the Phase 2 analysis are given in Table 2.1.

2.5.1.2 Survival From Smolt to Age 3

It has been hypothesized that the survival of coho smolts varies annually in response to marine conditions encountered during the first four months of ocean life (Holtby et al. 1990). Since smolts from many stocks or regions (e.g., Columbia River, North Washington Coast, PS) may encounter similar marine conditions, survival rates among stocks are often correlated. For the stocks in the SFM, indicator stocks were selected which could be used to estimate the interdependence of survival rates among stocks. The primary criterion used to select the indicator stocks was the presence of a consistent, long-term record of CWT tagging. From the recoveries of CWTs, a survival rate could be estimated by dividing the estimated recoveries by the number of tagged fish released.

Table 2.1. Estimated number of smolts for each substock and the initial distribution of the age 3 cohort among geographic regions in the Phase 2 analysis, presented as a percentage of the total age 3 abundance.
Model Stock	Wild Smolts	Hatchery Smolts	Initial Distribution of Age 3 Cohort
			OCNN	OCNS	SJDF	GEOS	SSND
OutStk1	6,898,587	766,512	98%	1%	<1%	<1%	0%
OutStk2	14,969,950	8,419,358	60%	38%	1%	<1%	0%
OutStk3	0	8,168,508	6%	92%	1%	<1%	0%
InStk1	8,132,730	7,804,095	41%	1%	1%	58%	0%
InStk2	4,838,009	5,658,628	53%	16%	19%	12%	0%
InStk3	5,047,002	11,645,630	49%	20%	18%	<1%	12%

A preliminary review of tagging data indicated that not all model stocks had an appropriate indicator stock. For example, wild smolts from the South Fork Skykomish River were initially selected as the indicator stock for the wild component of InStk2, but tagging was found to have occurred only for the brood years 1976-1984. Similarly, Skagit Hatchery was initially selected to represent the hatchery component of InStk2, but no tagging occurred for brood years 1975-1980. As an alternative approach for these model stocks, the time trend of survival rates for the initial indicator stocks of InStk2 and InStk3 were compared for the years in which all indicator stocks were tagged. Since the analysis indicated that the trends were similar, and a longer time series of estimates was available for the indicator stocks for InStk3, these indicator stocks were also used to represent InStk2 in the subsequent analyses. In addition, since a wild indicator stock could not be found for OutStk1, Robertson Creek Hatchery was used for both the wild and hatchery components of this model stocks. Indicator stocks used in the analysis are provided below:

OutStk1H: Robertson Creek Hatchery;
OutStk1W: Robertson Creek Hatchery;
OutStk2H: Quinault National Fish Hatchery ;
OutStk2W: Queets River Wild;
OutStk3H: Big Creek Hatchery;
InStk1H: Quinsam Hatchery;
InStk1W: Black Creek Wild;
InStk2H: South Sound Pens and Minter Creek Hatchery;
InStk2W: Deschutes River Wild and Big Beef Creek Wild;
InStk3H: South Sound Pens;
InStk3W: Deschutes River Wild and Big Beef Creek Wild.

Table 2.2. Correlation coefficients for survival rates of representative indicator stocks and approximate p-value.
	InStk3W	InStk3H	InStk1W	InStk1H	OutStk1H	OutStk2W	OutStk2H
InStk2H	+0.86 p=0.006	-	-	-	-	-	-
InStk1W	+0.66 p=0.071	+0.73 p=0.039	-	-	-	-	-
InStk1H	+0.70 p=0.055	+0.74 p=0.034	+0.81 p=0.016	-	-	-	-
OutStk1H	+0.23 p=0.591	+0.08 p=0.845	+0.41 p=0.318	+0.36 p=0.385	-	-	-
OutStk2W	+0.51 p=0.201	+0.18 p=0.676	+0.26 p=0.538	+0.02 p=0.957	+0.16 p=0.702	-	-
OutStk2H	-0.04 p=0.924	-0.14 p=0.735	-0.21 p=0.612	-0.22 p=0.596	-0.01 p=0.978	+0.58 p=0.130	-
OutStk3H	+0.40 p=0.321	+0.22 p=0.603	+0.29 p=0.482	+0.61 p=0.110	+0.20 p=0.643	+0.36 p=0.379	+0.46 p=0.253

In order to simulate the correlation of survival rates among the PS and GS stocks, a model of survival rates for these stocks was developed which included both a year specific and a stock specific effect:

Table 2.3. Parameter estimates for the survival from smolt to age 3 for model substocks with dependent survival rates.
Model Stock	CWT Survival Indicator Stock	Mean	Common Distribution Variance of Yj	Stock Specific Distribution Variance of Xij
InStk1H	Quinsam Hatchery	0.0696	0.1468	0.00023
InStk1W	Black Creek Wild	0.1172	0.1468	0.00082
InStk2H and InStk3H	South Sound Pens Minter Creek Hatchery	0.1064	0.1468	0.00065
InStk2W and InStk3W	Deschutes River Wild Big Beef Creek Wild	0.1381	0.1468	0.00154

Survival rates for each of the other stocks in the SFM (hatchery and wild components of OutStk1, OutStk2, and OutStk3) were varied independently using a normal distribution (Table 2.4).

Table 2.4. Parameter estimates for the survival from smolt to age 3 for model substocks without dependent survival rates.
Model Stock	CWT Survival Indicator Stock	Stock Specific Distributions of Survival
		Mean	Variance
OutStk1H,W	Robertson Creek Hatchery	0.0453	0.00060
OutStk2H	Quinault National Fish Hatchery	0.0200	0.00021
OutStk2W	Queets River Wild	0.0265	0.00019
OutStk3H	Big Creek Hatchery	0.0315	0.00049

2.5.1.3 Number of Smolts

The number of smolts (Table 2.1) was estimated using the following three steps:

1. Estimate the age 3 cohort;

2. partition the cohort into hatchery and wild components by multiplying the age 3 cohort by the estimated proportion wild; and

3. estimate the number of smolts by dividing the cohort sizes computed in (2) by the smolt survival rates provided in Tables 6.6 and 6.7 [not included in this document].

Estimate of Initial Age 3 Cohort. The initial age 3 cohort for each model stock (hatchery plus wild) was estimated using the nonlinear estimator tool in Microsoft Excel. This tool selects the model parameters which minimize a user defined objective function. To estimate the age 3 cohort size, an objective function was defined as the sum of the squared differences between the predicted and the target catch distributions plus nine times the sum of the squared differences between the predicted and target exploitation rates:

where p_ij is the observed proportion of the catch of the ith stock in the jth fishery and u_i is the observed exploitation rate of the ith stock. The squared exploitation rate deviations were weighted since the catch distribution array had nine times as many values as the exploitation rate array (6 stocks times 9 fisheries compared to 6 stocks).

Inputs to the estimator were:

1. Model Fishery Catches. Since not all coho stocks contributing to the fisheries in 1990 were included in the SFM input data set, the catch for each model fishery was adjusted to remove the proportion of the catch not represented by the model stocks. This proportion was estimated by the ratio of the catch of model stocks to the catch of all stocks using an unconstrained least squares model (TCCoho (94)-1; B. Tweit, WDFW, Pers. Comm. (Table 2.5)).

2. Initial Distribution. The methods used to estimate the initial distribution of the age 3 cohort were provided above.

3. Target Exploitation Rates. Target exploitation rates were calculated from 1990 CWT recoveries of representative hatchery releases. Tag codes used to represent each stock are listed below:

   OutStk1:

   025418, 025419, 024161 (Robertson Creek)

   OutStk2:

   635255 (Grays Harbor); 634749 (Humptulips);

   OutStk3:

   074156, 074157, 074158, 074244, 074247, 074410, 074412, 074445, 074454, 074457, 074458, 074501, 074606, 074607, 074608, 074609, 074610, 074611, 074703, 074705, 074706, 074744, 635256, 635507 (Columbia River Early); 634747 (Columbia River Late);

   InStk1:

   025235, 025508, 025719, 025720, 025721, 05722, 025723, 025724 (Big Qualicum); 024820, 024832, 025137, 025138 (Chilliwack River);

   InStk2:

   635516 (Nooksack); 630149, 630216, 630219, 630221, 630222 (Skagit); 630155 (Stillaguamish/Snohomish); and

   InStk3:

   212852 (Nisqually).

   Table2.5. Actual fisheries used as guidelines for input data development and the pseudonyms used in the Phase 2 analysis. The catch adjustment factor is defined as the proportion of the actual fishery's catch represented by selective fishery model stocks.
   Fishery Pseudonym	Fishery Used As Guideline	Catch Adjustment Factor	Comments
   OutTr1	West Coast Vancouver Island Troll	0.92	Areas 21, 23-27
   OutTr2	Washington Ocean Troll	0.89	Areas 1-4
   OutSp1	Washington Ocean Recreational	0.85	Areas 1-4
   OutNt1	Washington Coastal Net	1.00	Grays Harbor and North Washington Coastal rivers
   OutNt2	Columbia River Freshwater Net	0.92	All Fisheries
   InTr1	Strait of Georgia Troll	1.00	-
   InSp1	Strait of Georgia Recreational	0.99	Areas 13-19B, 28-29
   InSp2	U.S. Juan de Fuca Recreational	0.84	Areas 5&6
   InSp3	South Puget Sound Recreational	0.98	Areas 10, 11, and 13
   InNt1	Canadian Juan de Fuca Net	0.85	Area 20
   InNt2	U.S. San Juan Net	0.93	Areas 7 and 7A
   InNt3	North Puget Sound Net	1.00	Nooksack, Samish, Skagit, Stillaguamish & Snohomish terminal areas (marine & rivers)
   InNt4	South Puget Sound Net	0.88	South Puget Sound terminal areas including Areas 10 & 11

   To calculate the target exploitation rates, the 1990 estimated recoveries in all of the fishery strata represented by the model and escapement were tallied. From these data, an exploitation rate was estimated as the ratio between the fishery recoveries and the sum of fishery and escapement recoveries. For stocks represented by more than one tag code, a ratio was calculated by summing recoveries across all codes.

   InStk1, InStk2, and OutStk3 included several stock components which might experience different exploitation rates (e.g., the Nooksack River, Skagit River, and Stillaguamish River stocks are aggregated in the SFM into InStk2). In these instances, the target exploitation rate for each model stock was estimated by weighting the component exploitation rates by the preseason estimate of abundance used by the PFMC Salmon Technical Team (J. Banyard, WDFW, Pers. Comm.).

4. 1990 Preterminal Catch Distribution. The preterminal catch distribution was estimated from recoveries of CWTs in 1990. The catch of each stock in each fishery was divided by the total catch of that stock across all model fisheries to obtain the catch distribution for that stock. The four terminal fisheries in the SFM (InNt3, InNt4, OutNt1, and OutNt2) were not included in calculating the preterminal catch distribution.

Estimates of Number of Smolts. The number of smolts for each hatchery and wild substock was back-calculated from the age 3 cohort by dividing the cohort size by the estimated smolt survival rates in Tables 6.7 and 6.8 [not included in this document]. Since the estimated survival rates differ for some hatchery and wild components in the SFM, the hatchery to wild ratios for smolts may not be identical to the ratios presented above for the age 3 cohort. The estimated number of smolts for each substock in the Phase 2 simulation is provided in Table 2.1.

2.5.1.4 Migration

Several simplifying assumptions were made to estimate the proportion of each stock in each area migrating in each time step:

1. No migration occurred between geographic regions during statistical weeks 1 through 32;

2. all migration was directed toward the river of origin (Fig. 2.2);

3. hatchery and wild fish had the same migration timing and pathway;

4. 33% of InStk1 in the OCNN region migrated around the north end of Vancouver Island to the GEOS region; and

5. catch per unit effort in a fishery provided an unbiased measure of stock abundance.

Figure 2.2. Assumed migration pathways for model stocks, by fishery, and the initial distribution (proportion of the total initial abundance, from Table 6-4) [not included in this document]. The migration pathway is toward the escapement block.

With these assumptions, an optimization program was constructed in Microsoft Excel which estimated migration rates for each stock among the five geographic regions discussed above as well as the North Puget Sound Terminal (NSND), South Puget Sound Terminal (SSND), North Washington Coast Terminal (WCTM), Columbia River Terminal (CRTM), and escapement populations (ESCP). The following types of data were used in the estimator:

1. Distribution at Week 32. Estimates of the distribution of each SFM stock at the start of week 32 were obtained by running the SFM with the initial cohort and distribution parameters discussed above, SFM catches in each fishery, and the middle of the range of estimates for dropoff mortality.

2. Abundance. Methods used to estimate the abundance or relative abundance differed by SFM stock and region (Table 2.6). The abundance in terminal areas (NSND, SSND, WCTM, and CRTM) was estimated from run reconstructions which utilized terminal catch, entry timing, and exit timing data. In contrast, CPUE was used in preterminal areas (GEOS, SJDF, OCNS) as an index of abundance since estimates of actual abundance were not available.

3. Catch by Fishery and Time. The catch of each SFM stock in each fishery obtained from the Excel spreadsheet used to estimate the initial distribution and abundance (see Section 2.5.1.3) was apportioned to weekly time steps using the temporal distribution of actual catch in 1990.

4. Fishery Exit Timing. The following sources of data were used to estimate the timing of escapement from the SFM fisheries for each of the stocks:

   a) OutStk1. The exit timing for OutStk1 was estimated based upon the SWVI net CPUE in the years 1972 and 1979. These years were selected based upon the relatively protracted fishing schedule.

   b) OutStk2. Trap counts in the West Hoquiam River (Scott Chitwood, QDNR, Pers. Comm.) and in Bingham Creek (D. Seiler, WDFW, Pers. Comm.) in 1990 were used to estimate the exit timing of OutStk2 from the North Washington terminal population.

   c) OutStk3. Counts of coho salmon at the Bonneville Dam (D. Simmons, USFWS, Pers. Comm.) were used to estimate the time at which OutStk3 exited from the Columbia River terminal population.

2.5.2 SFM Fisheries

The 13 model fisheries used in the Phase 2 analysis represent a range of potential stock locations (outside, migratory transition, and inside) and gear types. The actual fisheries used as guidelines for input data development and the pseudonyms used in the Phase 2 analysis are given in Table 2.7 [not included in this document].

2.5.2.1 SFM Fishery Definition

The Phase 2 analysis required up to five types of information for each fishery:

1. Base catch;

2. base effort;

3. catchability coefficients;

4. expected annual variability in fishing effort;

5. escapement goals for stocks used to control the terminal area fisheries that are modelled to achieve an escapement goal.

Base Catch. Base catch data for the fisheries were previously described (see Section 2.5.1.3).

Base Effort Data. Base effort data for the SFM fisheries were compiled and adjusted using the same methods as described for the base catch.

Catchability Coefficients. The catchability coefficient, an estimate of gear efficiency, is required for simulating selective fisheries, bag limit effects, or fisheries controlled by defined effort levels. For the SFM, catchability coefficients were estimated using the base catch and effort data and the equation

where K_a,g is the estimated scalar to account for the presence of a bag limit. The estimated catchability coefficients are provided in Table 2.8 [not included in this document].

To simulate the effects of bag limits in SFM fisheries, the catchability coefficient was scaled to simulate a reduction in gear efficiency. To simulate this correctly, the original catchability coefficient must reflect the "true" efficiency of the gear. Since the catchability coefficients were calculated from actual fishery data, the coefficients calculated from recreational fisheries where bag limits were in effect may not reflect the true efficiency of the gear. This will be the case for those fisheries where bag limits greatly increase the likelihood that an angler will be required to halt a fishing trip prematurely due to catching the allowable number of fish. For these fisheries the calculated catchability coefficients need to be corrected to remove the bag limit effect.

In fisheries operating without a bag limit, no correction to the catchability coefficient was made. It was assumed that catches represent all fish brought to the boat. This assumption may not be valid for cases where fish are released (e.g., more proficient anglers). Also, the "true" catchability may be not be represented by catches in fisheries that have size limits.

In fisheries with bag limits, the effectiveness of the bag limit was estimated by comparing the duration of trips with and without landed limits. This methodology assumes that anglers terminate their fishing trip once they catch the bag limit. The specific methods for each SFM recreational fishery are briefly described below.

1. Washington Ocean Recreational (OutSp1). Although in general, the sampling data from the ocean recreational fishery does not include information on the duration of a boat trip, fortuitously the return times for charter boats were collected at Westport (Area 2) in 1990. Trip length was defined as the difference between the start and return times, assuming a common starting time for all boats of 5 AM, and subtracting an assumed round trip travel time to the fishing grounds of two hours. The average trip length in the presence of a bag limit computed using these methods was 6.46 hours, and the trip length in the absence of a bag limit was assumed equal to 9 hours. To compute what the catch would be in the absence of a bag limit, an expansion factor for the catch was computed which was equal to the ratio of the trip length without a bag limit to the trip length with a bag limit:

   

   Application of this factor to the Washington Ocean recreational fishery assumes that charter boat angling behavior is representative of all anglers. Charter boats represented roughly one-half of the total angler trips for the fishery.

2. Strait of Georgia Recreational (InSp1). Daily bag limit data were examined for the GS recreational fishery for the 1987 catch year. This was a year of moderate coho salmon catch (641,000) and effort (590,000 boat trips). The bag limit for salmon is four fish per day of which a maximum of two can be chinook salmon. The maximum number of coho salmon is therefore four per day, but this would be reduced by any chinook salmon caught during that day. In 1990, only 1.8% of the 72,250 anglers surveyed caught the maximum possible number of four coho salmon per day. The effect of the chinook salmon catch on the daily coho salmon catch is believed to be small; chinook salmon catch is approximately 25% of the coho salmon catch. Due to the small proportion of anglers catching four coho salmon per day the catchability coefficient for this fishery was not adjusted in the Phase 2 SFM input file.

3. U.S. Juan de Fuca Recreational (InSp2). Catch data from 1986, 1990, and 1991 were used to analyze the effect of bag limits on the catch in the Strait of Juan de Fuca recreational fishery (catch areas 5 and 6). Although 11% - 32% of the boats had limits during the high catch period (July - September), the average difference in trip duration was small (< 45 minutes). Because of the similar trip durations, the catchability coefficient for this fishery was not adjusted in the Phase 2 selective fishery input file.

4. South Puget Sound Recreational (InSp3). For a range of years (1986 - 1987, 1990 - 1992), a very low percentage of boats landed with catch limits, even during the peak catch months of August through October (< 2%). As a result, the catchability coefficient for this fishery was not adjusted in the Phase 2 selective fishery input file.

Although these results may be adequate as guidelines for the Phase 2 analysis, additional analysis of bag limit effects will be required to accurately model impacts of bag limit regulations in selective or nonselective fisheries.

Error and Annual Variability in Fishing Effort. Management error occurs in fisheries controlled by catch quotas, escapement goal objectives, and effort limitations and is simulated in the SFM using a gamma distribution. The gamma distribution was chosen because it allows one to create a wide variety of single mode densities. Methods used to estimate the parameters for the gamma distribution for each fishery are discussed below and summarized in Table 2.9.

1. Net Fisheries Controlled by Escapement Objectives. Error in the management of net fisheries controlled by escapement objectives (e.g., many PS net fisheries) can result from both: (1) an inability to precisely control the harvest, and/or (2) imprecise estimates of the actual number of fish available for harvest. Inseason and postseason estimates of the allowable catch for PS net fisheries managed for escapement objectives (Teresa Clocksin, WDFW, Pers. Comm.) were used to estimate the parameters for a gamma distribution of the ratio of actual to desired catch (Table 2.9). Identical parameter values were used for the two net fisheries in the model controlled by escapement goal objectives in the simulations (InNt3, OutNt2).

2. Fisheries Controlled by Catch Ceilings. The inability to precisely control the catch may result in the actual catch exceeding the management ceiling. For OutTr1, the parameters of a gamma distribution were estimated by comparing the actual catches with the ceiling in effect in each year. The annual catch was divided by the catch ceiling for those years following implementation of the treaty where the coho salmon catch was constrained by the catch ceiling or management actions for species other than coho salmon. This ratio represents the annual deviation around the ceiling. The mean and standard deviation of the annual deviations was used to estimate the parameters of the gamma distribution.

3. Fisheries Controlled by Effort Limitations. A number of fisheries, such as sport fisheries in PS and GS and net fisheries in many coastal rivers of the Washington coast, are controlled using limitations on the fishing effort. The control may be either direct (e.g., a specified number of boat days) or indirect (e.g., bag limits and minimum size limits). In the latter case, since inseason management of the effort does not occur, the actual effort varies on an annual basis in response to catch rates, weather, and other factors.

   For all model fisheries except OutNt2 and InNt3, the weekly effort values for weeks when coho salmon were landed were averaged across years. The effort in each week was then divided by the mean effort for that week to obtain weekly deviation in each year. The mean and a weighted measure of the standard deviation of the weekly deviations were computed to obtain the moment estimates of the gamma parameters. A weighted measure of the standard deviation was computed since the amount of effort that occurs in an individual week is quite variable. The weights were calculated by dividing the weekly effort totals by the total annual effort for each year. The weights used to calculate the standard deviation are the weekly averages, across years, of these proportions.

   The parameters for the gamma distribution of effort in the InSp1 fishery were calculated in a similar manner to that described above. However, since weekly effort information was not available for this fishery, a preliminary step was required to partition the monthly effort data into weekly estimates. This was done by partitioning the monthly effort estimates equally among the number of statistical weeks in each month.

Table 2.9. Data sources and parameter estimates for the gamma distribution used for each fishery.
Fishery	Data Type	Years Included	Gamma Parameters
OutTr1	Effort	1986-1987,1989-1991	4.22	4.22
OutTr1	Catch	1986-1987, 1989-1991	215.39	200.67
OutTr2	Effort	1985-1990	0.97	0.97
OutSp1	Effort	1985-1990	6.99	6.99
OutNt1	Effort	1985-1990	6.89	6.89
OutNt2 and InNt3	Target and Actual Catch	1986-1992	99.47	108.40
InTr1	Effort	1985-1993	5.27	5.27
InSp1	Effort	1985-1993	23.59	23.59
InSp2	Effort	1985-1990	8.73	8.73
InSp3	Effort	1985-1990	6.41	6.41
InNt1	Effort	1985-1993	1.17	1.17
InNt2	Effort	1985-1990	3.70	3.70
InNt4	Effort	1985-1990	7.78	7.78

The gamma distribution parameters were also calculated in a similar manner for the OutSp1 fishery; however, only those weeks where all four ocean management areas were simultaneously open were included in the calculation.

For the OutTr2 fishery, the Washington Treaty troll landings were transformed into days fished using sampling data from the WDFW Ocean Sampling Program.

2.5.2.2 Parameters for Bag Limit Effect

The SFM includes an algorithm to simulate the effect of a bag limit on the daily catch in recreational fisheries. Using procedures derived from Porch and Fox (1991), the model algorithm initially uses a negative binomial distribution to simulate the distribution of CPUE in the absence of a bag limit. Catch for a fishery regulated by a bag limit is then computed by truncating this CPUE distribution at the limit. The distribution is computed using the mean CPUE and the variance to mean ratio, a parameter that relates the shape of the distribution to the CPUE.

From our initial analysis, the Washington ocean recreational fishery was the only fishery that was effectively constrained by a bag limit. We estimated that in 1990, the two salmon bag limit reduced the catch by 29%. Based on that analysis, a two salmon bag limit was specified for the OutSp1 fishery. We assumed that the bag limit also effectively reduced the catch in OutSp1 by 29%. This reduction was approximated in the SFM by a variance to mean ratio of two. No alternative bag limits were considered for any fisheries in this analysis. If bag limit effects are simulated for additional model fisheries, or if bag limit changes are to be simulated, then estimates for the mean to variance ratio appropriate for those fisheries will need to be calculated.

2.5.3 Release Mortality

Release mortality is defined as the probability that a fish brought back to the fisher and released in a selective fishery will subsequently die as a result of the catch-and-release process. The mortality of fish released is likely to depend upon the gear and technique used to capture the fish, the location of capture (e.g., ocean, estuary, or freshwater), and the species and size of the fish released (TCChinook (87)-4; WDF et al. 1993). Of these factors, the effect of the type of gear upon the mortality rate has received the greatest study. Three release mortalities were used in the SFM:

Recreational, trap, and beach seine (7% - 15%); marine recreational hook-and-line fisheries for adult coho and legal-sized chinook (62 cm fork length), traps, and beach seines. The midpoint - 11% - was used in the SFM;

troll and purse seine (20% - 30%); troll fisheries and purse seine fisheries in which a small number of fish are caught per set. The midpoint - 25% - was used in the SFM; and

gillnet and purse seine (30% - 70%); gillnet fisheries and purse seine fisheries in which a large number of fish are caught per set. The two extremes - 30 % and 70% - were used in the SFM.

Dropoff mortality is defined as the probability that a fish that encounters the gear and subsequently drops off will die. Within the SFM, the dropoff mortality is controlled by the number of encounters which are not brought to the boat, expressed as a proportion of the landed catch, and the probability that a fish which drops off the gear dies as a result of the encounter. For input into the SFM, the product of these two parameters was specified and termed the dropoff mortality rate.

As with release mortality, the dropoff mortality rate is likely to depend upon a number of factors, including the type of gear, the fishing technique, and the number of predators in the vicinity of the gear. Since the fate of the fish lost typically cannot be observed, the parameter is difficult to estimate. The dropoff mortality rate was stratified using categories similar to those previously identified for release mortality:

Recreational (1% - 5%); in developing input data for a simulation model of coho fisheries, Hunter (1985) assumed that the dropoff mortality rate was equal to 5% of the landed catch for recreational fisheries. Discussions with biologists familiar with sport fisheries indicated that the proportion of fish which drop off might range from 1:3 to 2:3 of the fish that are successfully brought to the boat (J. Packer, WDFW, Pers. Comm.; P. Lawson, ODFW, Pers. Comm.). When the range of parameter values was computed for the sensitivity analysis, it was assumed that fish which dropoff are likely to be less severely wounded and/or subject to less handling than fish which are landed. Hence, the dropoff mortality rate was computed by multiplying 50% of the release mortality rate for recreational gear by the range of estimates for the number of fish lost before landing at the boat. The mid-point - 3% - was used in the SFM.

Troll (3% - 9%); the simulation model developed by Hunter (1985) used a dropoff mortality rate of 5% for troll fisheries. For the sensitivity analysis, a range of 3% to 9% was calculated using the same methods as described for recreational hook-and-line fisheries. The mid-point - 6% - was used in the SFM.

Net (10% - 30%); several studies have indicated that the dropoff mortality in net fisheries can be high, particularly if predators remove fish from the net (Geiger 1985; Beach et al. 1981). For example, harbor seal interactions with a gillnet fishery for chinook salmon in South Puget Sound in 1982 resulted in an estimated dropoff mortality rate of 87% (January 18, 1983 letter from Jack Rensel to WDF). A technical team which assessed Puget Sound gillnet fisheries (WDF and NWIFC 1984) indicated that the rate was likely to vary depending upon the predators in the areas, the species, the intensity of fishing, and the type of gear. Depending upon the fishing area, recommended rates in that report for coho salmon ranged from 2% to 23%. The wide range selected by the committee (10%-30%) reflects both the between fishery variability in this parameter and the uncertainty in the value estimated for any particular fishery. The mid-point - 20% - was used in the SFM.

The retention error rate is defined as the probability that an unmarked fish will be retained in a selective fishery. Failure to release a fish not marked for selective removal could occur if:

1. Naturally occurring marks are identical to the mass mark;

2. a fisher fails to identify the lack of a mark; or

3. a fisher does not comply with regulations.

2.5.5 Marked Recognition Error Rate

The marked recognition error rate is defined as the probability that a marked fish will be inadvertently released. The error rate will depend upon the mark which is used to identify fish for selective removal. Fins which are likely to be regenerated or are difficult to observe will result in a higher rate of error. Unpublished studies of ventral-clipped coho salmon by WDFW indicate that at return to the hatchery, 3-4% of the fish had a completely regenerated ventral fin, 20% had less than 50% of the ventral fin missing, and 15% had more than 50% of the ventral fin missing but less than completely removed (L. Blankenship, WDFW, Pers. Comm.). The marked recognition error rate was modelled at 6% for the adipose clip simulations and at either 10% or 30% for the ventral clip simulations.

2.5.6 Mark Induced Mortality Rate

Mark induced mortality is defined as the incremental mortality associated with marking fish for identification in a selective fishery. The mortality will vary depending upon the mark which is used and the size of fish at release; fish marked at a smaller size will have a higher mortality rate. Estimates range from 0%-8% for an adipose clip and 5%-20% for a ventral clip (Ad-hoc Selective Fishery Evaluation Committee 1995). The mark induce mortality rate was modelled at 4% for the adipose clip simulations and at either 6% or 20% for the ventral clip simulations.

Selective Fishery Simulation Model Specifications