Details:

Journal Title:
Fisheries Research
Personal Author:
Merino, G. ; Urtizberea, A. ; Fu, D. ; Winker, H. ; Cardinale, M. ; ... More +
Merino, G. ; Urtizberea, A. ; Fu, D. ; Winker, H. ; Cardinale, M. ; Lauretta, M.V. Less -
NOAA Program & Office:
NMFS (National Marine Fisheries Service) ; SEFSC (Southeast Fisheries Science Center)
NMFS (National Marine Fisheries Service) ; SEFSC (Southeast Fisheries Science Center) Less -
Description:
Integrated stock assessments consist of fitting several sources of catch, abundance, and auxiliary biological information to estimate parameters of equations that describe the population dynamics of fish stocks. Stock assessments are subject to uncertainty, and it is a common practice to characterize uncertainty using alternative hypotheses and assumptions within an ensemble of models to develop scientific advice for fisheries management. In this context, there is the need to assign levels of plausibility to each of the combinations of factors that ultimately reflect the uncertainty on different biological and fishery processes. In this study, we describe and apply a model diagnostic to identify trends in process error in recruitment deviation estimates within ensembles of integrated assessment models of tropical tunas. We demonstrate that assessment model ensembles for tropical tunas contain distinct scenarios with significant trends in process error that are overlooked, with the associated implications for fisheries management. Using the Indian Ocean yellowfin as a case study, we found that trends in recruitment deviates are linked to extreme productivity scenarios which strongly diverged in scale from deterministic models fitted without recruitment deviates. This indicates that when recruitment deviates show an increasing trend, these can compensate for the loss of biomass in periods of high catch beyond the surplus production. In these cases, variation in recruitment is not a random process, but rather takes the function of a compensatory, systematic driver in productivity. Significant trends in recruitment were positively correlated with increased standard deviations and auto-correlation coefficient, non-random residual pattern in fits to abundance indices, and particularly poor performance of the Age-Structured Production Model (ASPM) diagnostic. We suggest that trends in recruitment deviates can be caused by misspecification of the biological parameters used as fixed values in integrated assessment models. The process error diagnostic described here can provide a statistical criterion in support for hypotheses and assumptions when using ensembles of models to develop fisheries management advice.
Keywords:
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Fisheries Fishery Management Fish Stock Assessment Process Error Recruitment Uncertainty
Source:
Fisheries Research 256 (2022) 106478
DOI:
https://doi.org/10.1016/j.fishres.2022.106478
Format:
PDF
Document Type:
Journal Article
Rights Information:
Accepted Manuscript
Compliance:
Submitted
Main Document Checksum:
[+]
urn:sha256:3cb16bcc4f849c2506ff32225fef15942c8e6d08097b2e7f1e7519adde287cd4
Download URL:
https://repository.library.noaa.gov/view/noaa/52235/noaa_52235_DS1.pdf
File Type:

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