No abstract

As species distribution models, and similar techniques, have emerged in marine ecology, a vast array of predictor variables have been created and diverse methodologies have been applied. Marine fish are vital food resources worldwide, yet identifying the most suitable methodology and predictors to characterize spatial habitat associations, and the subsequent distributions, often remains ambiguous. Our objectives were to identify knowledge gaps in fish guilds, identify research themes, and to determine how data sources, statistics, and predictor variables differ among fish guilds. Data were obtained from an international literature search of peer-reviewed articles (2007–2018; n = 225) and research themes were determined based on abstracts. We tested for differences in data sources and modeling techniques using multinomial regressions and used a linear discriminant analysis to distinguish differences in predictors among fish guilds. Our results show predictive studies increased over time, but studies of forage fish, sharks, coral reef fish, and other fish guilds remain sparse. Research themes emphasized habitat suitability and distribution shifts, but also addressed abundance, occurrence, stock assessment, and biomass. Methodologies differed by fish guilds based on data limitations and research theme. The most frequent predictors overall were depth and temperature, but most fish guilds were distinguished by their own set of predictors that focused on their specific life history and ecology. A one-size-fits-all approach is not suitable for predicting marine fish distributions. However, given the paucity of studies for some fish guilds, researchers would benefit from utilizing predictors and methods derived from more commonly studied fish when similar habitat requirements are expected. Overall, the findings provide a guide for determining predictor variables to test and identifies novel opportunities to apply non-spatial knowledge and mechanisms to models.

Appendix 1. Description of the Ecopath with Ecosim modeling approach and application to the Catalan Sea.

Environmental variability changes the distribution, migratory patterns, and susceptibility to various fishing gears for highly migratory marine fish. These changes become especially problematic when they affect the indices of abundance (such as those based on catch-per-uniteffort: CPUE) used to assess the status of fish stocks. The use of simulated CPUE data sets with known values of underlying population trends has been recommended by ICCAT (International Commission for the Conservation of Atlantic Tunas) to test the robustness of CPUE standardization methods. A longline CPUE data simulator was developed to meet this objective and simulate fisheries data from a population with distinct habitat preferences. The simulation was used to test several statistical hypotheses regarding best practices for index standardization aimed at accurate estimation of population trends. Effort data from the US pelagic longline fleet was paired with a volume-weighted habitat suitability model for blue marlin (Makaira nigricans) to derive a simulated time series of blue marlin catch and effort from 1986-2015 with four different underlying population trends. The simulated CPUE data were provided to stock assessment scientists to determine if the underlying population abundance trend could accurately be detected with different methods of CPUE standardization that did or did not incorporate environmental data. While the analysts' approach to the data and the modeling structure differed, the underlying population trends were captured, some more successfully than others. In general, the inclusion of environmental and habitat variables aided the standardization process. However, differences in approaches highlight the importance of how explanatory variables are categorized and the criteria for including those variables. A set of lessons learned from this study was developed as recommendations for best practices for CPUE standardization.