MEPS 478:185-195 (2013)  -  DOI: https://doi.org/10.3354/meps10166

ABSTRACT: The processes driving temporal distribution and abundance patterns of whale sharks Rhincodon typus remain largely unexplained. We present an analysis of whale shark occurrence in the western Indian Ocean, incorporating both spatial and temporal elements. We tested the hypothesis that the average sighting probability of sharks has not changed over nearly 2 decades, and evaluated whether variance in sightings can be partially explained by climate signals. We used a 17 yr dataset (1991 to 2007, autumn only) of whale shark observations recorded in the logbooks of tuna purse-seiners. We randomly generated pseudo-absences and applied sequential generalized linear mixed-effects models within a multi-model information-theoretic framework, accounting for sampling effort and random annual variation, to evaluate the relative importance of temporal and climatic predictors to sighting probability. After accounting for seasonal patterns in distribution, we found evidence that sighting probability increased slightly in the first half of the sampling interval (1991-2000) and decreased thereafter (2000-2007). The model including a spatial predictor of occurrence, fishing effort, time² and a random spatial effect explained ~60% of the deviance in sighting probability. After including climatic predictors, we found that sighting probability increased slightly with rising temperature in the central Pacific Ocean and reduced temperatures in the Indian Ocean. The declining phase of the peak, concurrent with recent accounts of declines in population size at near-shore aggregations and with the most pronounced global warming, deserves continued investigation. Teasing apart the legacy effects of past exploitation and those arising from on-going climate changes will be a major challenge for the successful long-term management of the species.

KEY WORDS: Temporal trends · Rhincodon typus · Tuna purse-seine fisheries · Generalized linear mixed-effects models · Spatial distribution · Satellite data