Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
agricultural and biological sciences
Ocean-scale prediction of whale shark distribution
Diversity and Distributions, Volume 18, No. 5, Year 2012
Notification
URL copied to clipboard!
Description
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. © 2011 Blackwell Publishing Ltd.
Authors & Co-Authors
Sequeira, A. M.M.
Australia, Adelaide
The University of Adelaide
Mellin, Camille
Australia, Adelaide
The University of Adelaide
Australia, Townsville
Australian Institute of Marine Science
Rowat, David R.L.
Seychelles
Marine Conservation Society
Meekan, Mark Gregory
Australia, Perth
The University of Western Australia
Bradshaw, Corey J.A.
Australia, Adelaide
The University of Adelaide
Australia, Adelaide
South Australian Research and Development Institute
Statistics
Citations: 105
Authors: 5
Affiliations: 5
Identifiers
Doi:
10.1111/j.1472-4642.2011.00853.x
ISSN:
13669516
e-ISSN:
14724642