Comparing predictions of fisheries bycatch using multiple spatiotemporal species distribution model frameworks

Spatiotemporal predictions of bycatch (i.e., catch of nontargeted species) have shown promise as dynamic ocean management tools for reducing bycatch. However, which spatiotemporal model framework to use for generating these predictions is unclear. We evaluated a relatively new method, Gaussian Marko...

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Bibliographic Details
Published in:Canadian journal of fisheries and aquatic sciences 2020, Vol.77 (1), p.146-163
Main Authors: Stock, Brian C, Ward, Eric J, Eguchi, Tomoharu, Jannot, Jason E, Thorson, James T, Feist, Blake E, Semmens, Brice X
Format: Article
Language:English
Subjects:
Analysis
Aquatic reptiles
Bycatch
Catch composition
Distribution
Extrapolation
Fields (mathematics)
Fish industry
Fisheries
Fisheries management
Fishery management
Fishing
Forecasting
Forests
Freshwater
Geographical distribution
Geostatistics
Interpolation
Management tools
Marine
Modelling
Observers
Predictions
Sea turtles
Sedentary species
Species
Species diversity
Studies
Temporal distribution
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Summary:Spatiotemporal predictions of bycatch (i.e., catch of nontargeted species) have shown promise as dynamic ocean management tools for reducing bycatch. However, which spatiotemporal model framework to use for generating these predictions is unclear. We evaluated a relatively new method, Gaussian Markov random fields (GMRFs), with two other frameworks, generalized additive models (GAMs) and random forests. We fit geostatistical delta-models to fisheries observer bycatch data for six species with a broad range of movement patterns (e.g., highly migratory sea turtles versus sedentary rockfish) and bycatch rates (percentage of observations with nonzero catch, 0.3%–96.2%). Random forests had better interpolation performance than the GMRF and GAM models for all six species, but random forests performance was more sensitive when predicting data at the edge of the fishery (i.e., spatial extrapolation). Using random forests to identify and remove the 5% highest bycatch risk fishing events reduced the bycatch-to-target species catch ratio by 34% on average. All models considerably reduced the bycatch-to-target ratio, demonstrating the clear potential of species distribution models to support spatial fishery management.
ISSN:0706-652X
1205-7533
DOI:10.1139/cjfas-2018-0281