Data from: Ocean-scale prediction of whale shark distribution

Aim: Predicting distribution patterns of whale sharks (Rhincodon typus, Smith 1828) in the open ocean remains elusive owing to few pelagic records. We developed multivariate distribution models of seasonally variant whale shark distributions derived from tuna purse-seine fishery data. We tested the hypotheses that whale sharks use a narrow temperature range, are more abundant in productive waters and select sites closer to continents than the open ocean. Location: Indian Ocean. Methods: We compared a 17-year time series of observations of whale sharks associated with tuna purse-seine sets with chlorophyll a concentration and sea surface temperature data extracted from satellite images. Different sets of pseudoabsences based on random distributions, distance to shark locations and tuna catch were generated to account for spatiotemporal variation in sampling effort and probability of detection. We applied generalized linear, spatial mixed-effects and Maximum Entropy models to predict seasonal variation in habitat suitability and produced maps of distribution. Results: The saturated generalized linear models including bathymetric slope, depth, distance to shore, the quadratic of mean sea surface temperature, sea surface temperature variance and chlorophyll a had the highest relative statistical support, with the highest percent deviance explained when using random pseudoabsences with fixed effect-only models and the tuna pseudo-absences with mixedeffects models (e.g. 58% and 26% in autumn, respectively). Maximum Entropy results suggested that whale sharks responded mainly to variation in depth, chlorophyll a and temperature in all seasons. Bathymetric slope had only a minor influence on the presence. Main conclusions: Whale shark habitat suitability in the Indian Ocean is mainly correlated with spatial variation in sea surface temperature. The relative influence of this predictor provides a basis for predicting habitat suitability in the open ocean, possibly giving insights into the migratory behaviour of the world's largest fish. Our results also provide a baseline for temperature-dependent predictions of distributional changes in the future.Fig4_Autum_PredictionsEnsemble predictions for Autumn (9-km resolution). File headings: X - longitude, Y - latitude, predP - habitat suitability, predSE - prediction error.Fig4_Spring_PredictionsEnsemble predictions for Spring (9-km resolution). File headings: X - longitude, Y - latitude, predP - habitat suitability, predSE - prediction error.Fig4_Summer_PredictionsEnsemble predictions for Summer (9-km resolution). File headings: X - longitude, Y - latitude, predP - habitat suitability, predSE - prediction error.Fig4_Winter_PredictionsEnsemble predictions for Winter (9-km resolution). File headings: X - longitude, Y - latitude, predP - habitat suitability, predSE - prediction error