Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 7601
Title: Space-time investigation of the effects of fishing on fish populations
Author: Kotaro Ono, Andrew O. Shelton, E. J. Ward, James T. Thorson, Blake E. Feist, R. Hilborn
Publication Year: 2015
Journal: Ecological Applications
Keywords: dover sole, spatial ecology, spatio-temporal model, INLA, spatial time-series,
Abstract:

Species distribution models (SDMs) are important statistical tools for obtaining ecological insight into species-habitat relationships, and providing advice for natural resource management. Many SDMs have been developed over the past decades, with a focus on space- and more recently, time-dependence. However, most of these studies have been on terrestrial species and applications to marine species have been limited. In this study, we used three large spatio-temporal data sources (habitat maps, survey-based fish density estimates, and fishery catch data) and a novel space-time model to study how the distribution of fishing may affect the seasonal dynamics of a commercially important fish species (Pacific Dover sole, Microstomus pacificus) off the US West coast. Dover sole showed a large scale change in seasonal and annual distribution of biomass and its distribution shifted from mid-depth zones to inshore or deeper waters during late summer/early fall. In many cases, the scale of fishery removal was small compared to these broader changes in biomass, suggesting that seasonal dynamics were primarily driven by movement and not by fishing. The increasing availability of appropriate data and space-time modeling software should facilitate extending this work to many other species - particularly those in marine ecosystems - and help tease apart the role of growth, natural mortality, recruitment, movement, and fishing on spatial patterns of species distribution in marine systems.
 

Theme: Recovery and rebuilding of marine and coastal species
Foci: Develop methods to use physiological, biological and behavioral information to predict population-level processes.
Characterize the population biology of species, and develop and improve methods for predicting the status of populations.