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Age­-structured assessment of the Togiak herring stock, 1978-­1992, and preliminary forecast of abundance for 1993. Alaska Department of Fish and Game, Division of Commercial Fisheries. Regional Information Report 5J92-­11. Alaska Department of Fish and Game, Juneau, Alaska.

Authors:
Fritz Funk at Backwater Research
Fritz Funk
  • Backwater Research
Linda K Brannian
Linda K Brannian
Linda K Brannian
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Katherine A Rowel
Katherine A Rowel
Katherine A Rowel
  • This person is not on ResearchGate, or hasn't claimed this research yet.
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An age-structured model is used to assess the abundance of herring spawning at Togiak, Alaska, forecast the abundance for 1993, and recommend harvest levels for the commercial fishery.
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... Perception used here refers to a belief in the accuracy of information. Where harvest can be sufficiently sampled for age structure and auxiliary information is available, catch-age analysis (Deriso et al. 1985;Funk et al. 1992) can be a useful analytical tool for modeling the interaction of the fish stock with a recreational fishery. We use this tool with the humpback whitefish (Coregonus pidschian) population in the Chatanika River, Alaska, which is subject to a recreational spear fishery. ...
... Four systems for weighting annual observations are examined: equal (hereafter referred to as the base model), CV -2 , perceptions of accuracy by a knowledgeable individual, and a combination of perception and CV -2 (hereafter referred to as the combined model). Equal weighting of data has often been used in catch-age analysis (Funk et al. 1992;Zheng et al. 1993). ...
... We used an Excel spreadsheet form of the catch-age analysis approach of Deriso et al. (1985), developed in Funk et al. (1992). The specific form of the catch-age model used in this paper is given in Merritt (1995). ...
Using perceptions of data accuracy and empirical weighting of information: Assessment of a recreational fish population
Article
Full-text available
Recreational fisheries management is often compromised by limited information of variable quality from several sources. We develop a form of catch-age analysis to combine uncertain information from creel surveys, age composition, and mark-recapture estimates of abundance. Four systems are used in weighting annual observations: equal, inverse of squared coefficients of variation (CV-2), perceptions of accuracy, and a combination of the latter two. The model is applied to a humpback whitefish (Coregonus pidschian) population in Alaska and evaluated for model fit, parameter uncertainty, conservative forecasts of exploitable abundance, and biological plausibility. The probability of forecasted stock abundance occurring below a threshold level defined by an agency management plan is evaluated for various recruitment and exploitation scenarios. The perception model is judged to be best with the use of the analytic hierarchy process, a decision-making technique. By incorporating perceptions into fisheries decision-making, beliefs in the accuracy of uncertain information are made explicit. In a conservative context, fishery management decisions should include reducing risk to the stock in the setting of harvest policy and in the selection of the assessment model.
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... The sum of squares measuring the goodness of fit of the run biomass was based on the differences between ASA and aerial survey estimates of run biomass: rrrrvey where B~ is the aerial survey biomass estimate in year y, w,, is the weight at age a in year y (Appendix E), pa is the propoltion mature at age a (equation 6) , and Nu, is the ASA estimate of total abundance at age a in year y (equation 2). Though there were too few abundance estimates to evaluate the appropriateness of the log transformation in equation (7, ASA models have been fit with and without the log transformation, with the results not being sensitive to this assumption (Funk et al. 1992). We chose to use a log transformation in our model because a lognormal error structure is commonly found when dealing with abundance data. ...
Forecast of the Kamishak Herring Stock in 1994. Regional Information Report 2A94-12. Alaska Department of Fish and Game, Juneau, Alaska.
Article
Full-text available
  • Mar 1994
The 1994 abundance of Pacific Herring Clupea pallasi in the Kamishak Bay District of Lower Cook Inlet, Alaska, was forecasted for the first time from an age structured analysis model. This model estimates values of survival, maturity, gear selectivity, and initial population abundance that minimize differences between predicted and observed age composition and run biomass estimates. Estimated survival and adjusted initial population abundances were used to project the 1994 abundances. A regression model was used to predict 1994 weight at age from 1993 data. The 1993 aerial surveys of run biomass were interrupted by bad weather. Therefore, the 1993 run biomass estimate was derived from daily aerial survey estimates of biomass divided by an estimate of expected daily proportion, The difference between the run biomass estimate, 32,439 tons, and the harvest, 3,570 tons, was escapement biomass. No late season age composition data was collected during 1993. A biomass of 25 thousand tons of herring is forecast to return to the Kamishak Bay District in 1994. Herring mean weight is predicted to be 189 g. The 1988 year class is forecast to represent 70% of the run biomass and 69% of the individuals. The 1994 recommended total allowable harvest is 3.8 thousand tons and represents an exploitation rate of 15%. In accordance with the Kamishak Bay Herring Management Plan the harvest allocation is 3.4 thousand tons for the Kamishak spring sac roe fishery and 380 tons for the Shelikof Strait fall food and bait fishery. KEY WORDS: Clupea pallasi, herring, forecast, Lower Cook Inlet, age structured analysis
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... We used an age-structured model for Pacific herring from Norton Sound, Alaska developed by Williams and Quinn (1998). This model incorporates catch and total-run age composition data and aerial survey estimates of abundance for years 1981 to 1996 and ages 3 to 10+, similar to other agestructured models for Pacific herring (Funk et al. 1992, Brannian et al. 1993, Yuen et al. 1994. Sample sizes for the gillnet and total run age compositions were usually large, ranging from about 400 to over 6,000 (Table 1). ...
A Monte Carlo Evaluation of the Stock Synthesis Assessment Program
Article
  • Jan 1998
Stock assessments for many U.S. Pacific coast groundfish stocks are de-veloped using the catch at age method known as Stock Synthesis. The Stock Synthesis computer program attempts to reconstruct the demograph-ic history of a stock from observed changes in fish age or size distribu-tions, coupled with auxiliary information such as an index of stock biomass developed from a research survey or an index of fishing mortality based on fishing effort. In this study Monte Carlo simulation techniques were used to generate fishery and survey data with known characteristics. The simulated data were then analyzed with the age-structured version of the Stock Synthesis program and results from the program were compared with the true values to evaluate the influence of measurement errors on the accuracy of the Stock Synthesis results. Data sets were constructed with low and high levels of random error in each of four types of sample data (fishery age composition, a fishing effort index, survey age composi-tion, and a survey index of stock biomass). A series of experiments, based on a fractional factorial design, was conducted to examine the importance of eight factors: low versus high rates of natural mortality; constant ver-sus variable annual recruitment; low versus high rates of increase in fish-ing mortality; dome-shaped versus asymptotic fishery selectivity; short versus long data series; low versus high variability in the fishing effort index; low versus high variability in the survey biomass index; and small versus large samples for age composition. On average the Stock Synthesis estimates for total biomass, exploitable biomass, recruitment, and fishing mortality in the ending year were slightly positively biased (3.5-6.1%) but less variable than the input data. In general, the number of years in the data series and the size of the age samples were the most influential fac-tors, with increased amounts of data producing less biased and less vari-able estimates.
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... We used a log transformation in our model because a lognormal error structure is commonly found when dealing with abundance data. Though there were too few abundance estimates to evaluate the appropriateness of the log transformation in equation (6), fits with and without log transformation indicate ASA models are not sensitive to this assumption (Funk et al. 1992). ...
FORECAST OF THE KAMISHAK HERRING STOCK IN 1996
Article
Regional Infomation ~ e ~ o f i ' No. 2A96-01 Alaska Department of Fish and Game Division of Commercial Fisheries Management and Development 3 3 3 Raspberry Road Anchorage, Alaska 995 18-1599 January 1996 I The Regional Information Report Series was established in 1987 to provide an information access system for all unpublished divisional reports. These reports frequently s e w diverse ad hoc informational purposes or archive basic uninterpreted data. To accommodate timely reporting of rcccntly collected information, reports in this series may contain preliminary data; this information may be subsequcntly finalized and published in the formal literature. Consequently, these reports should not be cited l~ithout prior approval of the author or the Division of Commercial Fisheries Management and Development.
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Pacific herring, Clupea pallasi, recruitment in the Bering Sea and north-east Pacific Ocean, I: Relationships among different populations
Article
  • Dec 2000
We examined recruitment and average weight-at-age time series for Pacific herring (Clupea pallasi) populations from the Bering Sea and north-east Pacific Ocean to determine similarities. Statistical correlation and multivariate clustering methods indicated Pacific herring populations form large-scale groups. Large year classes occur synchronously among several Pacific herring populations. Multivariate cluster analyses of recruitment and weight-at-age data indicated that Bering Sea herring populations are distinct from north-east Pacific Ocean populations. Within the NE Pacific Ocean, there appear to be three groups of herring populations: a British Columbia group, a south-east Alaska coastal group, and an outer Gulf of Alaska group. Jackknife and randomization tests indicate these groups are robust and not the result of random chance. Deviations from observed herring population groups were examined for indications of anthropogenic perturbations. The Prince William Sound herring populations did not show any strong deviations corresponding to the oil spill of 1989. There might not yet be enough data since the spill to detect changes in the recruitment or weight-at-age data since that time, particularly if oil spill effects were concentrated on the early life history stages.
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Pacific herring, Clupea pallasi, recruitment in the Bering Sea and north-east Pacific Ocean, II: Relationships to environmental variables and implications for forecasting
Article
  • Dec 2000
  • FISH OCEANOGR
Previous studies have shown that Pacific herring populations in the Bering Sea and north-east Pacific Ocean can be grouped based on similar recruitment time series. The scale of these groups suggests large-scale influence on recruitment fluctuations from the environment. Recruitment time series from 14 populations were analysed to determine links to various environmental variables and to develop recruitment forecasting models using a Ricker-type environmentally dependent spawner–recruit model. The environmental variables used for this investigation included monthly time series of the following: southern oscillation index, North Pacific pressure index, sea surface temperatures, air temperatures, coastal upwelling indices, Bering Sea wind, Bering Sea ice cover, and Bering Sea bottom temperatures. Exploratory correlation analysis was used for focusing the time period examined for each environmental variable. Candidate models for forecasting herring recruitment were selected by the ordinary and recent cross-validation prediction errors. Results indicated that forecasting models using air and sea surface temperature data lagged to the year of spawning generally produced the best forecasting models. Multiple environmental variables showed marked improvements in prediction over single-environmental-variable models.
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