Evaluating evidence for alternative natural mortality and process error assumptions using a state-space, age-structured assessment model
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Evaluating evidence for alternative natural mortality and process error assumptions using a state-space, age-structured assessment model

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  • Journal Title:
    Canadian Journal of Fisheries and Aquatic Sciences
  • Personal Author:
  • NOAA Program & Office:
  • Description:
    State-space models explicitly separate uncertainty associated with unobserved, time-varying parameters from that which arises from sampling the population. The statistical aspects of formal state-space models are appealing and these models are becoming more widely used for assessments. However, treating natural mortality as known and constant across ages continues to be common practice. We developed a state-space, age-structured assessment model that allowed different assumptions for natural mortality and the degree of temporal stochasticity in abundance. We fit a suite of models where natural mortality was either age-invariant or an allometric function of mass and interannual transitions of abundance were deterministic or stochastic to observations on Gulf of Maine – Georges Bank Acadian redfish (Sebastes fasciatus). We found that allowing stochasticity in the interannual transition in abundance was important and estimating age-invariant natural mortality was sufficient. A simulation study showed low bias in annual biomass estimation when the estimation and simulation model matched and the Akaike imformation criterion accurately measured relative model performance, but it was important to allow simulated data sets to include the stochasticity in interannual transitions of abundance-at-age.
  • Source:
    Canadian Journal of Fisheries and Aquatic Sciences, 75(5), 691-703
  • DOI:
  • ISSN:
    0706-652X;1205-7533;
  • Format:
    pdf
  • Publisher:
    Canadian Science Publishing
  • Document Type:
    Journal Article
  • Funding:
    Grant no. NA14NMF4720170
  • Rights Information:
    Other
  • Compliance:
    Library
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