Whale shark abundance forecast: the interannual hotspot effect.
Each year, whale sharks (Rhincodon typus) aggregate in the northern Gulf of California at Bahia de los Angeles, a small coastal village that recently developed whale shark swimming as economic activity during the summer and autumn. Since the number of animals using the bay fluctuates annually, we aimed to predict it 1 month before each whale shark season. We based our modelling approach on the hypothesis that interannual warming conditions would spatially restrict whale shark habitats, triggering larger aggregations in areas that remain productive. We photo-identified whale sharks while surface feeding in Bahia de los Angeles during a 13-year period, which allowed us to estimate a local abundance index per year through mark-recapture techniques. Then, we fitted these abundances as a function of several competing model structures, based on means of sea surface temperature and chlorophyll-a, as well as their interannual anomalies from the linear trend, during spring, within the best-known range of the species in the northern Gulf of California. The results of the ecological model showed that whale sharks visit Bahia de los Angeles in larger numbers during extreme warming conditions, with relatively high surface chlorophyll-a concentrations, which was especially evident during the 2014-2016 northeast Pacific Marine Heatwave and El Niño. Presumably, the overall reduction in surface productivity associated with these events forced whale sharks to restrict their summer-autumn habitat to productivity hotspots, including Bahia de los Angeles. Our model allowed us to predict the number of animals visiting the site before each season starts, which has crucial proactive management implications, such as allowing authorities to regulate the ecotourism effort dynamically to reduce the probability of ship strikes and/or excessive stress to the animals. We propose an abundance baseline from the prediction for neutral interannual conditions. Synthesis and applications. We recommend local authorities apply the model described in this study during the late spring of each year to obtain predictions of the upcoming whale shark abundance and establish the maximum ecotourism effort allowed accordingly.