|Document Type:||Journal Article|
|Title:||Accounting for spatiotemporal variation and fisher targeting when estimating abundance from multispecies fishery data|
|Author:||James T. Thorson, Robert Fonner, M. A. Haltuch, Kotaro Ono, Henning Winker|
|Journal:||Canadian Journal of Fisheries and Aquatic Sciences|
|Keywords:||Multivariate spatio-temporal model, spatial dynamic factor analysis, fishery catch-per-unit-effort, index standardization, fishing tactics, fisher targeting, logbook subsetting, technical interactions, fishery-dependent index,|
Estimating trends in abundance from fishery catch rates is one of the oldest endeavors in fisheries science. However, many jurisdictions do not analyze fishery catch rates due to concerns that these data confound changes in fishing behavior (adjustments in fishing location or gear operation) with trends in abundance. In response, we developed a spatial dynamic factor analysis (SDFA) model that decomposes covariation in multispecies catch rates into components representing spatial variation and fishing behavior. SDFA estimates spatiotemporal variation in fish density for multiple species and accounts for fisher behavior at large spatial scales (i.e., choice of fishing location) while controlling for fisher behavior at fine spatial scales (e.g., daily timing of fishing activity). We first use a multispecies simulation experiment to show that SDFA decreases bias in abundance indices relative to ignoring spatial adjustments and fishing tactics. We then present results for a case study involving petrale sole (Eopsetta jordani) in the California Current, for which SDFA estimates initially stable and then increasing abundance for the period 1986¿2003, in accordance with fishery-independent survey and stock assessment estimates.
|Full Text URL:||http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2015-0598#.WfDAb_-nGUk|
|Theme:||Recovery and rebuilding of marine and coastal species|
Describe the relationships between human activities and species recovery, rebuilding and sustainability.
Develop methods to use physiological, biological and behavioral information to predict population-level processes.