Beyer, Jan

Kainge, Paulus Inekela

Köster, Fritz

Online Research Database In Technology

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Downloaded from orbit.dtu.dk on: Aug 06, 2016Introducing state-space stock assessment (SAM), split species issues and spatialmodellingKöster, Fritz; Kainge, Paulus Inekela; Beyer, JanPublication date:2011Document VersionPublisher's PDF, also known as Version of recordLink to publicationCitation (APA):Köster, F., Kainge, P. I., & Beyer, J. (2011). Introducing state-space stock assessment (SAM), split speciesissues and spatial modelling. Paper presented at 3rd Benguela Current Commission Annual Science Forum,Swakopmund, South Africa.Introducing state-space stock assessment (SAM), split species issues and spatial modellingFritz Köster, Paulus Kainge and Jan BeyerDevelopment of ecological sustainable fisheries practises in the Benguela Current Large Marine ecosystem (ECOFISH)ECOFISH Work Package 1: Stock assessmentPartners: DTU Aqua, UCT, NatMIRC, DAFF and INIP“Fishermen”Capacity building“fishermen”“scientists”WP2phase I phase II CatchabilityStock structureTrophodynamicsWP4 TrainingStock assessment IIStock assessment IWP1 Model based scientific knowledgeWP3 Shareholder knowledge & acceptanceExperience based knowledgeWP2 Data based scientific knowledgeGrowthWP3 Decision support toolsWork Package linkages“Fishermen”Capacity building“fishermen”“scientists”WP2phase I phase II CatchabilityStock structureTrophodynamicsWP4 TrainingStock assessment IIStock assessment IWP1 Model based scientific knowledgeWP3 Shareholder knowledge & acceptanceExperience based knowledgeWP2 Data based scientific knowledgeGrowthWP3 Decision support toolsWork Package linkagesLarry“Fishermen”Capacity building“fishermen”“scientists”WP2phase I phase II CatchabilityStock structureTrophodynamicsWP4 TrainingStock assessment IIStock assessment IWP1 Model based scientific knowledgeWP3 Shareholder knowledge & acceptanceExperience based knowledgeWP2 Data based scientific knowledgeGrowthWP3 Decision support toolsWork Package linkagesLarryBarbara“Fishermen”Capacity building“fishermen”“scientists”WP2phase I phase II CatchabilityStock structureTrophodynamicsWP4 TrainingStock assessment IIStock assessment IWP1 Model based scientific knowledgeWP3 Shareholder knowledge & acceptanceExperience based knowledgeWP2 Data based scientific knowledgeGrowthWP3 Decision support toolsWork Package linkagesLarryBarbaraOur talkObjectives and target species• Objectives:Improve reliability of stock assessments through using alternative models for processes, such as selectivity, growth and considering species and stock structure.Establish trans-boundary assessment models for hake that could provide a basis for regional management advice.Improve understanding of hake spatial dynamics.Establish improved statistical stock-assessment methodologies for horse mackerel and sardinella that take due account of the data available.• Target species:HakeHorse mackerelSardinella• Task 1.1 (2011-2012)– Set up of state space assessment model (SAM) for hake– Data preparation for 1. hake stock assessment workshop (28.11.-02.12.2011)– 1. hake stock assessment workshop (28.11-2.12.2011):- comparing results from SAM to Statistical catch at age model (SCAA),- data compilation for transboundary analyses, review of data, data compilation procedures, e.g. split of species.• Task 1.2 (2011-2012)– Modify SCAA assessment model for SA hake to a spatial-box model with movement• Task 1.3 (2011-2012)– 1. assessment/training workshop with focus on horse mackerel and sardinella (planned for 5.-9.12.2011)- review of existing information, data, assessments and methodology- possibilities and necessity to improve ?- training on SAM application– Compilation of stock assessment data for horse mackerel and sardinella, inclusive relevant quality measuresWorkplan WP1 (phase I)Further tasks are starting in the 2nd half 2012 (report next forum)State space assessment model (SAM)States are the stochastic variables that we don't observe, e.g. stock sizes, fishing mortality ratesObservations are the variables that we do observe, e.g. catch and survey catch ratesParameters are survey catchabilities, stock-recruitment parameters, process and observation variancesMethod has not been more frequently used in stock assessment, as software  to handle these models has not been availableApplication increasingly used in ICES as standard stock assessment model (for model comparison see WKADSAM Report 2010, ICES CM 2010/SSGSUE:10)State space assessment model (SAM)Model:•Build upon classical standard equations for stock, catch and survey indices •Extension to full parametric statistical models with model fitting based on maximum likelihood estimation•Estimation of uncertainties an integrated part of model and unobserved random variables included•Flexible models with low number of model parametersExample: fishing mortality (F) at age as a random walk with yearly variance (σ), only one parameter (σ) to be estimated and F’s for all years are predicted once the paramerters are estimated, i.e. no need to make assumptions about selectivity each F is part of a interrelated series not estimated in isolation replaces the idea about shrinkage as tool to fix lack of information on last years F in classical VPA type assessments (more objective tuning)•Nicely handling of missing observationsAdvantages compared to other assessment models Feature Deterministic standard modelsParametric statistical modelsState-space modelProportionality to catchYes No NoObservation noise includedNo Yes YesTransparent model No Yes YesObjective modeltuningNo Yes YesOver-parametrisized Some times Often Can be avoidedOutput includes uncertaintyNo Yes YesPresent assessment model for hake in South Africa: Statistical Catch at Age (SCAA) model (Rademeyer et al. 2008); in Namibia an age-structured production model, being a simpler version of the SCAA  (Kirchner 2011) fully state or the art !Why a new model ?SAM is easier to use, e.g. less tuning and estimation outside model necessarySAM comes along with a web-interface which allows utilisation without being ”the” stock assessment expertSAM has some technical advantages with respect to evolving selectivity by commercial fleets (modelled otherwise period specific) and missing data handlingSAM is potentially easier to transform into a spatially explicit stock assessment modelProblems:Fishing mortality is determined for each age independently and may also jump from year to year depending on the variance (σ) Using a different distribution than (log-)normal Introducing time trends or cross age correlations, but one looses the independence in estimating selectivity somewhatAt present no fleets includes, but can be done.Test application for Namibian hake2000150010005000Spawning stock biomass1970 1980 1990 2000 20101970 1980 1990 2000 2010Comparison of time trendsin stock indices from SAM and SCAA (Kirchner 2011) Needs further workFishing mortality (ages 2-4)Spawning stock biomass 0.60.50.30.20Fishing mortality 0.40.1Incl. 95% confidence intervals Web interface for SAMShould be possible for all involved to:- see all details of the implementation- run the assessment- experiment with data- experiment with model assumptions- everyone is running the same version and same data- makes update assessment very easyIntroduction 5-9.12.2011 at assessment/training workshop in Swakopmund    Assessment of mixed stocksApplication of state space model for combined assessments of two stocks, partly sharing the same distribution area and thus fished together.Example:Application for herring in NorthSea (autumn spawners) andwestern Baltic (spring spawners)with mixing in between (blue area)Proportions commercial catchesknown from otolith characteristics.Surveys stock specific.Basic assumption:- Same F in same age-group inmixing area  for both stocksPhD project: Casper BergWestern Baltic stockNorth Sea stockIn red: standard assessment (ICA, applying splitted catches),In black: mixed stock SAMDifference in North Sea up to 300.000 t, deviations go both ways, i.e. under- and over-estimation Spawning stock biomass (in t)Assessment of mixed stocks: output herring exampleAssessment of mixed stocks: lessons learnedPossible to estimate stock composition, abundance and fishing mortality within mixing area.Simpler alternative to models which explicitly incorporate migration.Continuous time/space resolution needs improvement:• needed are cross-area surveys with same catchability (i.e. overlap in space and time).• Account for when and where samples/catches are taken more specifically.• Modelling of migrations within years would be an advantage.Application to hake species in different depths strata and hake stocks of the same species in different areas is possible !However, not a spatially explicit assessment model !Model build-up1. Set-up of dynamic size (or age) structured population model (can be any stock assessment model, e.g. SAM)2. Distribute individuals randomly in space in a way such that the density varies continuously as function of size (or age), space and time3. Consider size-continuous noise for each sample to account for schooling and other small-scale behaviour related to size (or age)4. Consider gear selectivity (of commercial fishing and survey gears used)Results from step 1.+2. represent the hidden surface (i.e. population dynamic in space and time)Results from  3.+4. represent the measurement noise and selectivityGoal: estimate the entire set of parameters for population and noise in space and time by maximum likelihood and predict stock sizes, catch rates, fishing mortalities in space and timeSpatially explicit population models (GeoPop)Kasper Kristensen (2011)Perception of spatial stock dynamics from bottom trawl survey and commercial catch rates of cod in the eastern North Sea/Skagerrak/Kattegat:1. Simple size spectrum population model: Four parameters - growth, mortality, recruitment and noise variance.2. The large scale spatio-  temporal field: Four parameters - time-correlation, spatial correlation, size-correlation and variance.3. The small scale variability: One parameter - the variance representing a size-correlated nugget effect.4. Gear selection: Five parameters - L50 and Selection range for each gear plus a gear efficiency factor of one of the gears (efficiency of one gear can only be estimated relative to the other).14 model parameters are estimated and output investigated for differences in perceived cod distribution dynamics as well as testing for the impact of changes in any of the above parameters.Spatially explicit population models: exampleModel system can be applied to any stock assessment under step. 1 (e.g. hake) and be extended with and tested for environmental forcing !Spatially explicit population models: examplePrediction of the spatial distribution of population (size interval 0-30 cm) (process 1+2).Prediction of the spatial distribution in the commercial gear (size interval 0-30 cm) (process 1+2+4)SummarySAM offers a flexible, easy to use, full statistically based dynamic stock assessment model and comes with a web application.Has advantages in relation to estimation of F when catchability by commercial and/or survey gears changes.Has been developed into a relatively simple mixed stock assessment model, which appears to be applicable to hake (and other species) in the Benguela system.Can be developed into a fully spatially explicit population dynamic model handling also mixed and transboundary stocks in Benguela region.… a long way to go – but by 2014-2015 is a realistic goal!Thank you for listening !

Introducing state-space stock assessment (SAM), split species issues and spatial modelling

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Introducing state-space stock assessment (SAM), split species issues and spatial modelling

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