A comparative study on habitat models for adult bigeye tuna in the Indian Ocean based on gridded tuna longline fishery data

Abstract

Using the gridded fishery data to estimate the habitat preferences of bigeye tuna (Thunnus obesus) in the Indian Ocean is challenging, as it is still not clear what type of model is appropriate to make reliable habitat predictions. In this study, we tested two classes of habitat models: Generalized Additive Models (including Gaussian distribution GAM, Poisson distribution GAM, Negative Binomial distribution GAM, Tweedie class distribution GAM, and Zero‐inflated distribution GAM) and Maximum Entropy Model (MaxEnt) for forecasting the habitat of bigeye tuna in the Indian Ocean, using the 5°×5° monthly gridded fishery data from 2008 to 2015 provided by Indian Ocean Tuna Commission (IOTC) and the environmental factors (i.e., the vertical temperature, salinity, dissolved oxygen concentration, thermocline depth, vertical shear of ocean current, concentration of sea surface chlorophyll‐a (chl‐a), and the eddy kinetic energy (EKE)). We compared the models’ fitting ability, predictive performance, predicted results, and the ecological interpretation within the models. The results showed (1) GAMs provided better model fit and predictive performance than MaxEnt; (2) GA‐GAM showed the best model fit performance by using the log transformation of the standardized CPUE as response variable; (3) TW‐GAM was suitable for predicting the habitat of adult bigeye tuna with the over‐dispersed fishery datasets. (4) Results suggested that the vertical temperature, dissolved oxygen concentration, and thermocline depth could be used as reliable predictors in forecasting the spatial‐temporal distribution of the adult bigeye tuna in the Indian Ocean.

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Forecast‐ready models to support fisheries' adaptation to global variability and change

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