A state-space spatial survey-based stock assessment (SSURBA) model to inform spatial variation in relative stock trends

An age-structured, spatial survey-based assessment model (SSURBA) is developed and applied to the Grand Banks stock (NAFO Divisions 3LNO) of American plaice (Hippoglossoides platessoides) in Newfoundland and Labrador. The state-space model is fit to annual spatial (i.e., three divisions) stock size-at-age research vessel (RV) survey indices that are assumed to be proportional to abundance. We model index catchability (q) as a logistic function of fish length, which varies with age, cohort, and the time of the survey; therefore, the model facilitates the estimation of q values that change spatially and temporally following changes in fish growth and survey gears. The SSURBA model produces division-level estimates of fishing mortality rates (F), stock productivity, and stock size relative to the logistic catchability assumption with q = 1 for fully selected ages. The spatial model allows us to include additional survey information compared with the space-aggregated assessment model (all of 3LNO) that is currently used to assess stock status. The model can provide estimates of relative catch, which we compare with reported catch trends to partially validate the model.

[1]  Nan Zheng,et al.  A spatiotemporal Richards–Schnute growth model and its estimation when data are collected through length-stratified sampling , 2020, Environmental and Ecological Statistics.

[2]  N. Cadigan,et al.  Estimation of growth parameters based on length-stratified age samples , 2020 .

[3]  M. Morgan,et al.  Recruitment synchrony in spatially structured Newfoundland and Labrador populations of American plaice (Hippoglossoides platessoides) , 2019, Fisheries Research.

[4]  P. Snelgrove,et al.  Assessing connectivity patterns among management units of the Newfoundland and Labrador shrimp population , 2018, Fisheries Oceanography.

[5]  Saang-Yoon Hyun,et al.  Evaluating evidence for alternative natural mortality and process error assumptions using a state-space, age-structured assessment model , 2018 .

[6]  A. Punt,et al.  The effect of marine closures on a feedback control management strategy used in a spatially aggregated stock assessment: a case study based on pink ling in Australia , 2017 .

[7]  Anders Nielsen,et al.  Validation of ecological state space models using the Laplace approximation , 2017, Environmental and Ecological Statistics.

[8]  Facundo Muñoz,et al.  Fishery-dependent and -independent data lead to consistent estimations of essential habitats , 2016 .

[9]  K. Lorenzen.  Toward a new paradigm for growth modeling in fisheries stock assessments: Embracing plasticity and its consequences , 2016 .

[10]  Anders Nielsen,et al.  TMB: Automatic Differentiation and Laplace Approximation , 2015, 1509.00660.

[11]  Kevin R. Piner,et al.  Contemporary fisheries stock assessment: many issues still remain , 2015 .

[12]  Anders Nielsen,et al.  Estimation of time-varying selectivity in stock assessments using state-space models , 2014 .

[13]  P. Besbeas,et al.  Modelling Population Dynamics , 2014 .

[14]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[15]  Robin Cook,et al.  A fish stock assessment model using survey data when estimates of catch are unreliable , 2013 .

[16]  Richard D. Methot,et al.  Stock synthesis: A biological and statistical framework for fish stock assessment and fishery management , 2013 .

[17]  Pierre Petitgas,et al.  Fish stock assessments using surveys and indicators , 2009 .

[18]  Mountain Plover,et al.  COSEWIC Assessment and Status Report on the , 2009 .

[19]  Statistical catch-at-age analysis vs. ADAPT-VPA: the case of Gulf of Maine cod , 2008 .

[20]  Richard Law.  The Theoretical Biologist's Toolbox: Quantitative Methods for Ecology and Evolutionary Biology , 2007 .

[21]  R. G. Halliday,et al.  A HISTORY OF MARINE FISHERIES SCIENCE IN ATLANTIC CANADA AND ITS ROLE IN THE MANAGEMENT OF FISHERIES , 2006 .

[22]  M. Mangel.  The Theoretical Biologist's Toolbox: Quantitative Methods for Ecology and Evolutionary Biology , 2006 .

[23]  Mark N. Maunder,et al.  Interpreting catch per unit effort data to assess the status of individual stocks and communities , 2006 .

[24]  John M. Hoenig,et al.  Comparison of two approaches for estimating natural mortality based on longevity , 2005 .

[25]  D. Beare,et al.  Using survey data independently from commercial data in stock assessment: an example using haddock in ICES Division VIa , 2005 .

[26]  Paul J. Rago,et al.  FISHERY-INDEPENDENT SAMPLING : SURVEY TECHNIQUES AND DATA ANALYSES , 2004 .

[27]  Perry de Valpine,et al.  Review of methods for fitting time-series models with process and observation error and likelihood calculations for nonlinear, non-Gaussian state-space models , 2002 .

[28]  R. Myers,et al.  Is catch-per-unit-effort proportional to abundance? , 2001 .

[29]  S. Walsh.  Efficiency of Bottom Sampling Trawls in Deriving Survey Abundance Indices , 2001 .

[30]  Robin Cook,et al.  Stock trends in six North Sea stocks as revealed by an analysis of research vessel surveys , 1997 .

[31]  Walter H. F. Smith,et al.  Global Sea Floor Topography from Satellite Altimetry and Ship Depth Soundings , 1997 .

[32]  K. Lorenzen.  The relationship between body weight and natural mortality in juvenile and adult fish: a comparison of natural ecosystems and aquaculture , 1996 .

[33]  J. Wroblewski,et al.  Mortality Rate of Fishes in the Pelagic Ecosystem , 1984 .

[34]  Jm Hoenig,et al.  Empirical use of longevity data to estimate mortality rates , 1983 .