[OANNES Foro] Ocean-scale prediction of whale shark distribution

[Mario Cabrejos]

Diversity and Distributions
Article first published online: 19 OCT 2011
DOI: 10.1111/j.1472-4642.2011.00853.x

Ocean-scale prediction of whale shark distribution
Ana Sequeira1,*, Camille Mellin1,2, David Rowat3, Mark G. Meekan4, Corey J. A. Bradshaw1,5
1 The Environment Institute and School of Earth and Environmental Sciences, University of Adelaide, Adelaide, SA 5005, Australia
2 Australian Institute of Marine Science, PMB No. 3, Townsville MC, Townsville, Qld 4810, Australia3 Marine Conservation Society, Seychelles, PO Box 1299, Victoria, Mahe, Seychelles4 Australian Institute of Marine Science, UWA Oceans Institute (MO96), 35 Stirling Hwy, Crawley, WA 6009, Australia5 South Australian Research and Development Institute, PO Box 120, Henley Beach, SA 5022, Australia. Correspondence: Ana Sequeira, E-mail: ana.martinssequeira en adelaide.edu.au
Abstract
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 pseudo-absences 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 pseudo-absences with fixed effect-only models and the tuna pseudo-absences with mixed-effects 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.



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