Assisting Ecosystem-Based Fisheries Management Efforts Using a Comprehensive Survey Database, a Large Environmental Database, and Generalized Additive Models

Statistical habitat models, such as generalized additive models (GAMs), are key tools for assisting ecosystem-based fisheries management (EBFM) efforts. Predictions from GAMs can be used, for example, to produce preference functions for the ecosystem-modeling platform Ecospace; preference functions permit a flexible representation of spatial distribution patterns in Ecospace by defining the preferences of marine organisms for certain environmental parameter values. Generalized additive model predictions can also be used to map species distributions for assisting marine protected area (MPA) planning. In this study, we applied a recently proposed methodology to produce preference functions for the fish and invertebrates represented in an Ecospace model of the West Florida Shelf (WFS) and to map the hotspots of juveniles and adults of three economically important species for informing future MPA planning in the WFS region. This proposed methodology consists of (1) compiling a comprehensive survey database blending all of the encounter and nonencounter data of the study ecosystem collected by the fisheries-independent and fisheries-dependent surveys that employ random sampling schemes, (2) developing a large environmental database to store all of the environmental parameters influencing the spatial distribution patterns of the marine organisms of the study ecosystem, (3) using the comprehensive survey database and the large environmental database to fit binomial GAMs that integrate the confounding effects of survey and year, and (4) making predictions with fitted GAMs to define preference functions for marine organisms and produce distribution and hotspot maps. All the GAMs we fitted were able to predict probabilities of encounter with reasonable or excellent discrimination and had a median adjusted coefficient of determination larger than the 0.1 threshold required for validation. The preference functions and hotspot maps produced using the fitted GAMs were generally in concordance with the literature. The methodology demonstrated in this study is timely, given the increasing interest in advancing EBFM worldwide.