MODELING SPATIAL PATTERNS IN FISHERIES BYCATCH: IMPROVING BYCATCH MAPS TO AID FISHERIES MANAGEMENT

Abstract

Fisheries bycatch, or incidental take, of large vertebrates such as sea turtles, seabirds, and marine mammals, is a pressing conservation and fisheries management issue. Identifying spatial patterns of bycatch is an important element in managing and mitigating bycatch occurrences. Because bycatch of these taxa involves rare events and fishing effort is highly variable in space and time, maps of raw bycatch rates (the ratio of bycatch to fishing effort) can be misleading. Here we show how mapping bycatch can be enhanced through the use of Bayesian hierarchical spatial models. We compare model-based estimates of bycatch rates to raw rates. The model-based estimates were more precise and fit the data well. Using these results, we demonstrate the utility of this approach for providing information to managers on bycatch probabilities and cross-taxa bycatch comparisons. To illustrate this approach, we present an analysis of bycatch data from the U.S. gill net fishery for groundfish in the northwest Atlantic. The goals of this analysis are to produce more reliable estimates of bycatch rates, assess similarity of spatial patterns between taxa, and identify areas of elevated risk of bycatch.