Brodie, S., Hobday, A. J., Smith, J. A., Everett, J. D., Taylor, M. D., Gray, C. A., et al. (2015). Modelling the oceanic habitats of two pelagic species using recreational fisheries data. Fisheries Oceanography, 24(5), 463-477.
DOI: 10.1111/fog.12122
Species distribution modeling lends a useful tool for describing the environmental requirements of a species and understanding how a species may respond to a changing environment. As these models are built on a combination of presence records and environmental covariates, which are logistically difficult to collect for pelagic species, species distribution models are rarely developed for such species. Fishery catch records, which exist for some pelagic species, are no different from typical presence-only data, except that there is typically no way to quantify fishing effort and as such determining habitat suitability from this data is difficult. This paper seeks to develop a species distribution model for two pelagic species using presence-only fishery data. Poisson point process models are presence-only methods that model intensity of the points per unit area as a proxy for relative abundance. Data for presence of dolphinfish and kingfish were acquired from the New South Wales Department of Primary Industries catch and release program. Environmental covariates were extracted from the Spatial Dynamics Ocean Data Explorer for presence and pseudo-absence points. A PPM was constructed to predict the distribution of each species as a function of environmental covariates with the presence and pseudo-absence points acting as the binary response variable. All environmental covariates were retained by the model which predicted fish intensities for both species reasonably well (AUC 0.80 and 0.81 for dolphinfish and kingfish respectively). Dolphinfish intensity increased along the coast during the summer and autumn, while kingfish intensity shifted south during the summer and autumn. These results show a strong relationship between pelagic fish distribution and ocean environmental variables, along with seasonal shifts in distribution for these species. This study successfully implemented species distribution modeling with a novel data collection strategy by using fishery catch data. This approach to species distribution modeling can be particularly applicable to managers whom wish to understand the distribution of the species they are managing as well as the abundance of that species across the region.