Time-varying natural mortality in fisheries stock assessment models: identifying a default approach

Author:

Johnson Kelli F.1,Monnahan Cole C.2,McGilliard Carey R.34,Vert-pre Katyana A.15,Anderson Sean C.6,Cunningham Curry J.1,Hurtado-Ferro Felipe1,Licandeo Roberto R.7,Muradian Melissa L.2,Ono Kotaro1,Szuwalski Cody S.1,Valero Juan L.18,Whitten Athol R.1,Punt A. E.1

Affiliation:

1. School of Aquatic and Fishery Sciences, University of Washington, Box 355020, Seattle, WA 98195-5020, USA

2. Quantitative Ecology and Resource Management, University of Washington, Box 352182, Seattle, WA 98195-2182, USA

3. National Marine Fisheries Service, Alaska Fisheries Science Center, 7600 Sand Point Way NE, Seattle, WA 98115, USA

4. Joint Institute for the Study of the Atmosphere and Ocean, University of Washington, Box 955020, Seattle, WA, USA

5. School of Forest Resources and Conservation, University of Florida, Box 110410, Gainesville, FL 32611, USA

6. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, Burnaby, BC, CanadaV5A 1S6

7. Fisheries Centre, Aquatic Ecosystems Research Laboratory, University of British Columbia, Vancouver, BC, CanadaV6T 1Z4

8. Center for the Advancement of Population Assessment Methodology, 8901 La Jolla Shores Drive, La Jolla, CA 92037, USA

Abstract

Abstract A typical assumption used in most fishery stock assessments is that natural mortality (M) is constant across time and age. However, M is rarely constant in reality as a result of the combined impacts of exploitation history, predation, environmental factors, and physiological trade-offs. Misspecification or poor estimation of M can lead to bias in quantities estimated using stock assessment methods, potentially resulting in biased estimates of fishery reference points and catch limits, with the magnitude of bias being influenced by life history and trends in fishing mortality. Monte Carlo simulations were used to evaluate the ability of statistical age-structured population models to estimate spawning-stock biomass, fishing mortality, and total allowable catch when the true M was age-invariant, but time-varying. Configurations of the stock assessment method, implemented in Stock Synthesis, included a single age- and time-invariant M parameter, specified at one of the three levels (high, medium, and low) or an estimated M. The min–max (i.e. most robust) approach to specifying M when it is thought to vary across time was to estimate M. The least robust approach for most scenarios examined was to fix M at a high value, suggesting that the consequences of misspecifying M are asymmetric.

Publisher

Oxford University Press (OUP)

Subject

Ecology,Aquatic Science,Ecology, Evolution, Behavior and Systematics,Oceanography

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