Introduction - SEAFA



MEMORANDUMState of AlaskaDEPARTMENT OF FISH AND GAMETO:Karla Bush, F&G Coordinator- RIDATE: November 14, 2017Joe Stratman, Lead Crab Biologist-RITHRU:Adam Messmer, Kellii Wood, Tessa BergmannFILE NO.:S:\Shellfish\Research\Golden\Golden King Crab surplus production_final_2017.docxTELEPHONE NO.465-4226FAX NO.465-4944FROM:Katie Palof, Shellfish Biometrician- R1Andrew Olson, (former) Shellfish Biologist SUBJECT:Golden king crab surplus production model analysisNON_CONFIDENTIALIntroductionRecent declines in the Golden King crab fishery have enlisted a review of current management practices, specifically how biologically relevant the guideline harvest ranges (GHRs) are. The goal of this analysis was to establish a biologically based maximum sustainable yield (MSY) from historic fisheries catch and effort data to inform the GHRs. Guideline Harvest RangeCurrently the golden king crab (GKC) fishery in southeast Alaska is managed with season specific guideline harvest levels (GHLs) in each of the seven management areas (East Central, North Stephens Passage, Northern, Icy Strait, Mid Chatham Strait, Lower Chatham Strait, and Southern Areas). The current management areas were established in regulation prior to the 2005/06 season. The GHLs are annually adjusted based on recent fishery performance and the implied health of the population. The GHLs must fall within the regulatory GHRs established for each area. These range from 0 to an upper limit that was determined based on historical harvest (Table1). The GHRs were last modified at the 2009 Board of Fisheries meeting; upward adjustments were made to the GHRs for the East Central, Northern, and Icy Strait Areas based on harvests that had exceeded the former upper ranges of those areas. Biomass-Dynamic modelsBiomass dynamics models are some of the simplest fisheries models used in an attempt to apply basic population dynamics to data on harvest (Shertzer et al. 2008). They are not the models of choice for most assessments and management due to their many assumptions and caveats; however they are advantageous because the only data needed is a time series of harvest and an index of abundance (which in most cases can be fishery CPUE). Their cautious usage can aid in understanding stock dynamics for which you only have fisheries dependent data, however literature warns against using only these models for management purposes since they are considered to be a non-conservative approach. They are mostly used in fisheries management to aid in setting a maximum sustainable yield or MSY. The MSY of a stock is the largest yield (or harvest) that can be taken from a species’ stock over an indefinite period of time. The basic assumption of these models is that the biomass or index of biomass for the population is related to the biomass in the previous year with the addition of recruitment and growth and the subtraction of catch and natural mortality. Any movement between populations is ignored, along with any inter-annual variability. These models also assume that catch is related to available biomass, meaning that harvest is not limited by GHLs or number of days. Our model violates this assumption but can still give useful information as to how the stocks/areas have responded to various levels of fishing pressure over the last 30 years. Another major assumption is that the population remains in a similar “state of growth” during the entire time period. There is only one parameter estimated for growth of the population, or production of the population, this parameter incorporates all aspects of production – recruitment, growth of individuals, and mortality. If any of these production aspects vary over time their variation is not accounted for in the model. Due to these and a few other assumptions, surplus production models provide a non-conservative or over inflated estimate of MSY (maximum sustainable yield or harvest). MethodsBiomass dynamic models were applied to all GKC fishing areas using a time series of harvest and effort from fish ticket and logbook data. Effort in the GKC fishery can be described in a number of ways, models were investigated using: number of permits fished per year, number of pot lifts per year, season length, and number of landings per year. The number of pots lifts per year or season was determined to be the most consistent measure of effort in most areas/stocks. The models discussed here use the number of pot lifts per season as the measure of effort and the biomass (pounds, lb) of crab caught as the time series of harvest. Yield, or harvest in pounds, for each year was obtained from fish ticket data; while effort, or pot lifts, was taken from fish tickets prior to 2000 and from logbook data from 2000 through 2016. Schaefer models were fit using a time series approach in the R package TropFishR (R Core Team 2017, Mildenberger et al. 2017) and the program ASPIC (version 7, Prager 2016, Prager 1994). This is the most common type of biomass dynamic model used with minimum data available, such as a single time series of effort and yield (Schaefer 1957, Hilborn and Walters 1992). The models fit in R are simpler and do not provide an estimate of the error associated with the point estimate of MSY. However, in data sets with low contrast, meaning there is not a wide range of variability in the relationship between harvest and effort, these simpler models will provide reasonable estimates, whereas the more complex models in ASPIC will not converge on a reasonable point estimate of MSY. Therefore both programs were used in this analysis. ResultsThe models in R were able to estimate a point estimate for MSY, while the models in ASPIC produced a point estimate and Bayesian credible intervals (similar to confidence intervals). Effort used was the number of pot lifts per fishery year, which prior to 2000 was taken from fish tickets and after 2000 from logbook data. The best estimates of MSY for each area are provided in Table 1 and graphically represented in Figure 1. For two areas, Icy Strait and Southern, the contrast in the data was weak and therefore the more advanced models in the ASPIC program were not able to converge at an estimate of MSY. For these areas a simpler version of the model was used in R to obtain a point estimate of MSY. Table 1: AreaUpper GHR (lb)2017 GHL (lb)2017 Harvest (lb)Avg. harvest (2000-2017)MSY(lb)80% Lower Credible Interval80% Upper Credible IntervalEast Central300,00015,000972208,469211,000197,800222,000Northern175,00010,0005,610114,575138,800120,100149,600Icy Strait75,00010,0007,00743,60453,800*North Stephens Passage25,0008,00016,55816,38622,80018,50039,200Mid-Chatham Strait150,00020,000**79,81090,60072,900104,600Lower Chatham50,00023,000**15,51821,70017,41030,840Southern25,00019,00016,72215,07822,800**Data contrast is limited, no credible intervals are available.** Data is confidential due to less than three permit holders.Discussion Under equilibrium or normal population conditions the MSY estimates obtained from these models should be treated as an upper limit of the sustainable harvest for each area. Compared to the current GHRs, the MSY estimates provide support for the downward adjustment of these maximum harvest levels in most management areas. It is also worth noting that the average harvest in each area for the last 16 years is fairly similar to the MSY calculations. This provides support that the MSY estimates are reasonable biologically and for management purposes. FiguresFigure 1: Harvest (lb) over time for each management area. The dashed lines represent MSY estimates from this analysis (Table 1). Due to inconsistent recording of effort (pot lifts) prior to 1985, only data from 1985 on was used for this analysis. References:Hilborn, R. and C. J. Walters. 1992. Quantitative Fisheries Stock Assessment: choice, dynamics, and uncertainty. Springer USMildenberger T. K., M. H. Taylor, and M. Wolff. 2017. TropFishR: Tropical Fisheries Analysis with R. R package version 1.1.4. <, M. H. 1994. A suite of extensions to a nonequilibrium surplus-production model. Fishery Bulletin 92:374-389.Prager, M. H. 2016. User’s guide for ASPIC suite, version 7: a stock-production model incorporating covariates and auxiliary programs. Prager Consulting. Portland, Oregon. R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. <, M. B. 1957. A study of the dynamics of the fishery for yellowfin tuna in the eastern tropical Pacfic Ocean. Bulletin of the Inter-American Tropical Tuna Commission 2:247-268.Shertzer, K. W., M. H. Prager, D. S. Vaughan, and E. H. Williams. 2008. Fishery Models. Entry in Encyclopedia of Ecology. Elsevier, Amsterdam. ................
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