Modeling the spatial autocorrelation of pelagic fish abundance

The relationship between pelagic fish and ocean temperature is cited in many studies, the majority of which investigate correlations of pelagic species and sea surface temperatures (SST). While appropriate for surface-associated species, this may not be accurate for deep-diving fishes. A different way to examine this relationship is to model spatial autocorrelation of fish species and temperatures at an appropriate range of depths. Spatial autocorrelation, the distance at which data are interdependent, is a potential descriptor of the patch size of an organism. Here we modeled spatial autocorrelation for 5 pelagic species that inhabit different depths in the Gulf of Mexico: dolphinfish Coryphaena hippurus, wahoo Acanthocybium solandri, yellowfin tuna Thunnus albacares, swordfish Xiphias gladius, and bigeye tuna Thunnus obesus. Additionally, we modeled spatial autocorrelation for ocean temperatures at the surface, at 200, and 400 m. We hypothesized that autocorrelation distances will be greater for deeper water temperatures and for species that live at deeper depths due to greater homogeneity of deep waters over greater spatial ranges. Results show average distances of autocorrelation on the order of 55 to 60 km for wahoo and dolphinfish, 90 km for yellowfin tuna, and 135 to 145 km for swordfish and bigeye tuna; the same data for temperature were 75, 135, and 300 km for SST, and at 200 and 400 m depth, respectively. Autocorrelation distances of dolphinfish, wahoo, and yellowfin were correlated with that of of SST, while the autocorrelation distance of swordfish and bigeye were correlated with that of temperatures at 200 m. Results suggest that autocorrelation distances may be useful as a proxy for habitat delineation.