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We have developed a set of tools that operate within an aquatic geographic information system to improve the accessibility, and usability of remote-sensed satellite and computer-modeled oceanographic data for marine science and ecosystem-based management. The tools form the Pelagic Habitat Analysis Module (PHAM), which can be applied as a modeling platform, an investigative aid in scientific research, or utilized as a decision support system for marine ecological management. Applications include fisheries, marine biology, physical and biological oceanography, and marine spatial management. The GIS provides a home for diverse data types and automated tools for downloading remote sensed and global circulation model data. Within the GIS environment, PHAM provides a framework for seamless interactive four-dimensional visualization, for matching between disparate data types, for flexible statistic or mechanistic model development, and for dynamic application of user developed models for habitat, density, and probability predictions. Here we describe PHAM in the context of ecosystem-based fisheries management, and present results from case study projects which guided development. In the first, an analysis of the purse seine fishery for tropical tuna in the eastern Pacific Ocean revealed oceanographic drivers of the catch distribution and the influence of climate-driven circulation patterns on the location of fishing grounds. To support management of the Common Thresher Shark (Alopias vulpinus) in the California Current Ecosystem, a simple empirical habitat utilization model was developed and used to dynamically predict the seasonal range expansion of common thresher shark based on oceanographic conditions.

There is no consensus on the population structure of Pacific swordfish, and current-working hypotheses based on genetic and fisheries data include variations of two, three, and four stocks. In this study the hypothesis of panmixia for Pacific swordfish was tested using multilocus analyses of single-copy nuclear DNA loci on 891 swordfish collected in 16 localities. Sampling coverage differs from all previous genetic studies by offering a more comprehensive representation of tropical areas and by including early life history stages (ELS). Accordingly, samples of larvae, juveniles, and adults were characterized at 10 loci containing 20 informative SNPs, 19 of which were genotyped using high resolution melting analysis (HRMA) and one as a restriction fragment length polymorphism (RFLP). Additionally, two short sequence repeat (SSR) loci were characterized. Exact tests of genetic differentiation, analyses of molecular variance (AMOVA), principal coordinates analysis (PCoA), Bayesian analyses using STRUCTURE and GENELAND conducted on these data all rejected the hypothesis of panmixia for Pacific swordfish, and identified Taiwan as the most highly differentiated sample. Samples from temperate areas differed from most tropical samples (SST > 24 °C), but genetic heterogeneity was also detected among several tropical samples. By contrast, no differences were detected among temperate samples despite the considerable geographic distances (up to 18,000 km) separating them. Further, Bayesian analyses using both STRUCTURE and GENELAND suggested a pattern of genetic differentiation in Pacific swordfish more complex than a simple separation of tropical and temperate regions, with no clear delineation of geographic boundaries as those displayed by Atlantic swordfish. AMOVAs conducted to test models of population structure based on fisheries data failed to yield significant proportions of among-group variance. The observed patterns of genetic differentiation of Pacific swordfish are discussed in reference to studies of reproductive biology, tagging experiments, and previous genetic studies conducted on this species.

We developed an iterative sequential random utility model to investigate the social and environmental determinants of the spatiotemporal decision process of tuna purse-seine fishery fishing effort in the eastern Pacific Ocean. Operations of the fishing gear mark checkpoints in a continuous complex decision-making process. Individual fisher behavior is modeled by identifying diversified choices over decision-space for an entire fishing trip, which allows inclusion of prior and current vessel locations and conditions among the explanatory variables. Among these factors are vessel capacity; departure and arrival port; duration of the fishing trip; daily and cumulative distance travelled, which provides a proxy for operation costs; expected revenue; oceanographic conditions; and tons of fish on board. The model uses a two-step decision process to capture the probability of a vessel choosing a specific fishing region for the first set and the probability of switching to (or staying in) a specific region to fish before returning to its landing port. The model provides a means to anticipate the success of marine resource management, and it can be used to evaluate fleet diversity in fisher behavior, the impact of climate variability, and the stability and resilience of complex coupled human and natural systems.