Data-Limited Stock Assessments (BIOL3305 Lecture) PDF

Data-limited stock assessment options: BSM, CMSY, AMSY, LBB Dirk Zeller Sea Around Us – Indian Ocean  “Basics of stock assessment” (Lecture 9) Basic steps in stock assessment 1. Define your unit stock 2. Collect catch data for...

Data-limited stock assessment options: BSM, CMSY, AMSY, LBB Dirk Zeller Sea Around Us – Indian Ocean  “Basics of stock assessment” (Lecture 9) Basic steps in stock assessment 1. Define your unit stock 2. Collect catch data for the whole fishery 3. Collect effort data for the whole fishery 4. [Combine 2 and 3 into a time series of CPUE (catch per unit of effort)] 5. Collect data on biological characteristics of stock a. Growth b. Age c. Mortality d. Recruitment (stock-recruitment relationship) 6. Derive estimates of expected catch (yield) under different conditions of fishing pressure a. Surplus production/surplus yield/biomass dynamics models b. Yield per recruit models c. Age-structured models 7. Provide stock and catch advice to managers  “Basics of stock assessment” (Lecture 9) Basic steps in stock assessment 1. Define your unit stock 2. Collect catch data for the whole fishery What if cannot 3. Collect effort datacollect allfishery for the whole these data, do not have the resources 4. Combine to series 2 and 3 into a time get ofthese data? CPUE (catch per unit of effort) 5. Collect data on biological characteristics of stock a. Growth - Financial b. Mortality constraints - Staff constraints c. Stock size - Historical d. Recruitmentconstraints (stock-recruitment relationship) 6. Derive estimates of expected catch (yield) under different conditions of fishing pressure Applies a. to developed Surplus andyield/biomass production/surplus developing countries. dynamics models See Alex Hesp’s lecture on WA fisheries b. Yield per recruit models c. Age-structured models 7. Provide stock and catch advice to managers  Too many stocks, too few data New legislation in Canada, US and Europe requires management of all exploited stocks… In Europe, of about 400 exploited stocks, only 100 have sufficient data for traditional age-structured stock assessment WA also such an issue… last week’s lectures Developing countries… virtually no stocks with such data 4/37 Thanks to Rainer Froese for material  “Status of World Fisheries” Slices: fraction of reported catch/species with assessment 5/37 Hilborn et al. (2020) PNAS 117(4):2218-2224  Too many stocks, too few data New, simple approaches are needed to get reasonable assessments with less time and data… Perfection is not the aim, reasonable estimates are New data-limited methods come to the rescue: BSM, CMSY, AMSY and LBB 6/37 Thanks to Rainer Froese for material  Too many stocks, too few data BSM: Bayesian Schaefer Model – Estimates stock status (B) and exploitation (F) from reliable catch and abundance data (CPUE) – Froese et al. (2017) CMSY: Catch Maximum Sustainable Yield – Estimates stock status (B) and exploitation (F) from reliable catch data and estimates of species-specific “resilience” (r – population growth rate) – Froese et al. (2017) [not “Catch-MSY” Martell & Froese 2013, Fish and Fisheries 14(4): 504-514] AMSY: Abundance Maximum Sustainable Yield – Estimates stock status (B) and exploitation (F) from abundance (CPUE) data and “resilience”, but available catch data are unreliable or if true stock boundaries are unknown (i.e., cannot use BSM) – Froese et al. (2020) LBB: Length-based Bayesian Biomass – Estimates status (B) from length frequency data – Froese et al. (2018) Froese et al. (2017) Fish and Fisheries 18(3): 506-526 Froese et al. (2018) ICES Journal of Marine Science 75(6): 2004-2015 7/37 Froese et al. (2020) ICES Journal of Marine Science 77: 527-538  Too many stocks, too few data These data-limited methods are often criticised by hard-core traditional stock assessment people…. Especially from the Ray Hilborn camp Yet, even WA fisheries uses them, even if they are their lowest tiered assessment type…. Why? Many cases where you have nothing else… Cover the broad basics Froese et al. (2017) Fish and Fisheries 18(3): 506-526 Froese et al. (2018) ICES Journal of Marine Science 75(6): 2004-2015 8/37 Froese et al. (2020) ICES Journal of Marine Science 77: 527-538  Already know the basics of surplus production Fishing effort Relevance of ½ k (50%) Schaefer (1954) Bulletin of the Inter-American Tropical Tuna Commission 1: 27-56 9/37 Reprinted as: Schaefer MB (1991) Bulletin of Mathematical Biology 53(1): 253-279  Bayesian statistics in a nutshell Bayesian statistics are a powerful, rigorous tool to update existing knowledge (prior) with new data to generate new knowledge (posterior) Knowledge about a certain trait or parameter is best expressed as a distribution described by, e.g., a mean and a standard deviation Hence “prior distribution” and “posterior distribution” in Bayesian stats Bayesian statistics combine the prior distribution with the distribution derived from the new data (new evidence) to obtain the posterior distribution 10/37 Thanks to Rainer Froese for material  Update prior beliefs with new evidence to generate posterior beliefs 11/37 Thanks to Rainer Froese for material  BSM in a nutshell Bayesian Schaefer Model Given a time series of reliable catch and CPUE, the parameters r = rmax and k are estimated from   =  +   1 − −   where Ct is catch in year t, B is biomass (= CPUE / q, q is the catchability coefficient), r is resilience (population growth rate), and k is the unexploited stock size Using a Bayesian approach, the r-k combination that minimizes the difference between the observed biomass and the one predicted by the equation is chosen as best estimate 12/37 Froese et al. (2017) Fish and Fisheries 18(3): 506-526  Advantages of BSM Can utilize short and interrupted time series of abundance (CPUE) Can estimate catchability q Gives the desired fisheries reference points MSY, Bmsy and Fmsy Gives the ecological reference points rmax and k Gives stock size as B and status as B/Bmsy Gives exploitation as F and F/Fmsy Gives time series of biomass and exploitation 13/37 Froese et al. (2017) Fish and Fisheries 18(3): 506-526  Sole (Solea solea) in the Irish Sea: Graphical results of BSM analysis for use by management 14/37 Froese et al. (2017) Fish and Fisheries 18(3): 506-526  Data-limited assessments priors Need priors for intrinsic rate of population increase rmax (also called “resilience”) Can get from dedicated studies for your species of interest…. or FishBase and SeaLifeBase provide priors for resilience 15/37  rmax Prior for European Anchovy in FishBase 16/37  rmax Prior for speckled shrimp in SeaLifeBase 17/37  CMSY in a nutshell Catch Maximum Sustainable Yield Estimates stock status and exploitation from reliable catch time series and resilience (r – population growth rate) It needs priors for resilience r 18/37 Froese et al. (2017) Fish and Fisheries 18(3): 506-526  Life history correlates of resilience rmax r = 2*Fmsy (Schaefer 1954) r ~ 2*M because M ~ Fmsy (Gulland 1971) r ~ 3*K because K ~ 2/3 M (Jensen 1996) r ~ 9/tmax because tmax ~ 3/K (Taylor 1958) r ~ 3.3/tgen because tgen ~ 1/K (Roff 1984) r = f*(Fecundity < 1000) (Musick 1999) These relationships were used to predict r in FishBase for many species Qualitative resilience and their r ranges are available for all species: Very low: 0.015-0.1 Low: 0.05-0.5 Medium: 0.2-0.8 High: 0.6-1.5 19/37 Froese et al. (2017) Fish and Fisheries 18(3): 506-526  rmax Prior for European Anchovy in FishBase Qualitative r ranges : Very low: 0.015-0.1 Low: 0.05-0.5 Medium: 0.2-0.8 20/37 High: 0.6-1.5  CMSY in a nutshell If abundance is unknown, a prior range for r is derived from life history traits, a prior range for k is derived from maximum catch, and prior ranges for Bt/k (beginning and end of catch time series) are derived from expert knowledge   =  +   1 − −   All r-k combinations that are compatible with the life history traits (r, M, K), the catches (Ct) and the expert knowledge (Bt/k) are identified by a Monte-Carlo approach An r-k combination representative of high r values is chosen as best estimate 21/37 Froese et al. (2017) Fish and Fisheries 18(3): 506-526  Flathead grey mullet (Mugil cephalus) in the southern Gulf of Mexico 22/37  AMSY Abundance Maximum Sustainable Yield Estimates stock status and exploitation from abundance (CPUE) data and resilience, if catch data are unreliable or if true stock boundaries are unknown (i.e., cannot use BSM) Catch-per-unit-of-effort (CPUE) data quite common at useful levels for assessment Why would there be only CPUE data? - Catch data may be unknown, aggregated (e.g., “small pelagics”) or unreliable - CPUE may be available from research data or from a reliable fisher’s log-book Standardization only needed within the stock/fishery - Kg per hour - Kg per day - Numbers per 100 hooks per day - Catch per fuel used - Time series of acoustic survey - Any arbitrary abundance index - Only the trend must be correct and reliable (min 10 years) 23/37 Froese et al. (2019) ICES Journal of Marine Science 77: 527-538  AMSY in a nutshell AMSY uses CPUE data combined with independent prior knowledge about the resilience or productivity of the species (r) and prior perceptions or estimates of stock status for the year with the best available estimate   =  +   1 − −   CPUE as proxy for B (where q is catchability)  =  ∗  Thus   =  +   1 − −    24/37 Froese et al. (2019) ICES Journal of Marine Science 77: 527-538  AMSY in a nutshell Results: Reasonable uncertainty around B/BMSY – Suitable for management advice High uncertainty around F/FMSY – Reasonable as no data on catch used – These prediction of exploitation to be used with caution Assumes a direct proportionality between CPUE and exploited biomass May not always hold, especially if management actions impacted effort 25/37 Froese et al. (2019) ICES Journal of Marine Science 77: 527-538  LBB: Length-based Bayesian Biomass Length-frequency data quite common in fisheries science, especially in tropical countries LBB estimates asymptotic length, length at first capture, relative natural mortality, and relative fishing mortality Standard fisheries equations can then be used to approximate current exploited biomass relative to unexploited biomass In addition, these parameters allow the estimation of a proxy for the relative biomass capable of producing maximum sustainable yield 26/37 Froese et al. (2018) ICES Journal of Marine Science 75(6): 2004-2015  LBB in a nutshell LBB results for relative biomass or stock status have been validated against simulations and against 34 real stocks LBB reproduced the “true“ parameter values of the simulations LBB gave stock status results similar to the full stock assessments for 34 real stocks LBB gives preliminary estimates of stock status based on length frequency data from the fishery LBB results provide objective B/B0 priors for other assessment methods such as CMSY and AMSY 27/37 Froese et al. (2018) ICES Journal of Marine Science 75(6): 2004-2015  Example: Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins Biomass trends of 1320 fish and invertebrate populations for 483 species exploited in the 232 coastal Marine Ecoregions (MEs) 28/37 Palomares et al. (2020) Estuarine, Coastal and Shelf Science 243: 106896  Example: Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins 29/37 Spalding et al. (2007) Marine Ecoregions of the World: A Bioregionalization of Coastal and Shelf Areas. Bioscience 57(7): 573-583  Example: Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins 30/37 Palomares et al. (2020) Estuarine, Coastal and Shelf Science 243: 106896  Example: Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins Biomass trends of 1320 fish and invertebrate populations for 483 species exploited in the 232 coastal Marine Ecoregions (MEs) Biomass trends derived using CMSY method applied to global fisheries catches for 1950–2014 (Sea Around Us) 31/37 Palomares et al. (2020) Estuarine, Coastal and Shelf Science 243: 106896  Example: Fishery biomass trends of exploited fish populations in marine ecoregions, climatic zones and ocean basins Only 18% of all the assessed populations deemed healthy in terms of biomass status Biomass above the level deemed optimal for MSY (based on original Schaefer 0.5 B0) 82% are not 32/37 Palomares et al. (2020) Estuarine, Coastal and Shelf Science 243: 106896 33/37 Zeller et al. (2023) Annual Review of Marine Science 15  BMSY 34/37 Zeller et al. (2023) Annual Review of Marine Science 15 35/37 Zeller et al. (2023) Annual Review of Marine Science 15  Conclusion Data-limited methods briefly introduced here have huge applicability across all developed and developing countries Use is growing rapidly… not perfect, but neither are the alternatives (see Hilborn et al. paper concerns regarding “global”) BSM gives good estimates of stock status and exploitation based on reliable catch and abundance data CMSY gives reasonable estimates of stock status and exploitation based on reliable catch and species-specific resilience (population growth rate r) in data-poor situations AMSY gives reasonable estimates of stock status, but uncertain estimates of exploitation based on abundance data and resilience (r) in the absence of reliable catch data LBB gives preliminary estimates of stock status based on length frequency data from the fishery. Results provide objective B/B0 priors for CMSY or AMSY If plan on using… always contact the authors or coding development team for newest version… as constantly being refined and updated 36/37  “CMSY update” now “CMSY++” package Froese et al. (2023) New developments in the analysis of catch time series as the basis for fish stock assessments: The CMSY++ method. Acta Ichthyologica et Piscatoria 53: 173-189. https://doi.org/10.3897/aiep.53.e105910 This includes an AI addition in the form of Artificial Neural Network assist for informative prior selection. Paper has a good introduction and discussion around assessment methods and challenges. Sea Around Us – Indian Ocean