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<DIV id=copyright class=copyright>Diversity and Distributions</DIV>
<DIV id=publishedOnlineDate>Article first published online: 19 OCT 2011</DIV>
<DIV id=doi>DOI: 10.1111/j.1472-4642.2011.00853.x</DIV></DIV>
<DIV class=articleTitle> </DIV>
<DIV class=articleTitle><FONT size=4>Ocean-scale prediction of whale shark
distribution</FONT></DIV>
<DIV id=cr1>Ana Sequeira<SUP><FONT size=1>1,*</FONT></SUP>, Camille
Mellin<SUP><FONT size=1>1,2</FONT></SUP>, David Rowat<SUP><FONT
size=1>3</FONT></SUP>, Mark G. Meekan<SUP><FONT size=1>4</FONT></SUP>, Corey J.
A. Bradshaw<SUP><FONT size=1>1,5</FONT></SUP></DIV>
<DIV id=publishedOnlineDate><SUP>1</SUP> The Environment Institute and School of
Earth and Environmental Sciences, University of Adelaide, Adelaide, SA 5005,
Australia</DIV>
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<DIV class=affiliation jQuery1319548932174="8"><SUP>2</SUP> Australian Institute
of Marine Science, PMB No. 3, Townsville MC, Townsville, Qld 4810,
Australia<SUP>3</SUP> Marine Conservation Society, Seychelles, PO Box 1299,
Victoria, Mahe, Seychelles<SUP>4</SUP> Australian Institute of Marine Science,
UWA Oceans Institute (MO96), 35 Stirling Hwy, Crawley, WA 6009,
Australia<SUP>5</SUP> South Australian Research and Development Institute, PO
Box 120, Henley Beach, SA 5022, Australia. Correspondence: Ana Sequeira, E-mail:
<!--TODO: clickthrough URL--><A title="Link to email address"
href="mailto:ana.martinssequeira@adelaide.edu.au" shape=rect><FONT
color=#007e8a>ana.martinssequeira@adelaide.edu.au</FONT></A></DIV>
<UL id=footnotes><FONT color=#007e8a></FONT></UL>
<DIV id=publicationHistoryDetails jQuery1319548932174="10">
<H4>Abstract</H4></DIV></DIV></DIV>
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<DIV id=abstract>
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<P><B>Aim </B> Predicting distribution patterns of whale sharks (<EM>Rhincodon
typus</EM>, 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.</P></DIV>
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<P><B>Location </B> Indian Ocean.</P></DIV>
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<P><B>Methods </B> We compared a 17-year time series of observations of whale
sharks associated with tuna purse-seine sets with chlorophyll <EM>a</EM>
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.</P></DIV>
<DIV class=para>
<P><B>Results </B> 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 <EM>a</EM> 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 <EM>a</EM> and temperature in all
seasons. Bathymetric slope had only a minor influence on the presence.</P></DIV>
<DIV class=para>
<P><B>Main conclusions </B> 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.</P></DIV></DIV></DIV></DIV></FONT></DIV><BR>
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