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http://www.bioon.net/biology/Ecology/446177.shtml

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Nature doi:10.1038/nature09116

Environmental context explains L¨¦vy and Brownian movement patterns of marine predators
Nicolas E. Humphries,Nuno Queiroz,Jennifer R. M. Dyer,Nicolas G. Pade,Michael K. Musyl,Kurt M. Schaefer,Daniel W. Fuller,Juerg M. Brunnschweiler,Thomas K. Doyle,Jonathan D. R. Houghton,Graeme C. Hays,Catherine S. Jones,Leslie R. Noble,Victoria J. Wearmouth,Emily J. Southall& David W. Sims

An optimal search theory, the so-called L¨¦vy-flight foraging hypothesis1, predicts that predators should adopt search strategies known as L¨¦vy flights where prey is sparse and distributed unpredictably, but that Brownian movement is sufficiently efficient for locating abundant prey2, 3, 4. Empirical studies have generated controversy because the accuracy of statistical methods that have been used to identify L¨¦vy behaviour has recently been questioned5, 6. Consequently, whether foragers exhibit L¨¦vy flights in the wild remains unclear. Crucially, moreover, it has not been tested whether observed movement patterns across natural landscapes having different expected resource distributions conform to the theory¡¯s central predictions. Here we use maximum-likelihood methods to test for L¨¦vy patterns in relation to environmental gradients in the largest animal movement data set assembled for this purpose. Strong support was found for L¨¦vy search patterns across 14 species of open-ocean predatory fish (sharks, tuna, billfish and ocean sunfish), with some individuals switching between L¨¦vy and Brownian movement as they traversed different habitat types. We tested the spatial occurrence of these two principal patterns and found L¨¦vy behaviour to be associated with less productive waters (sparser prey) and Brownian movements to be associated with productive shelf or convergence-front habitats (abundant prey). These results are consistent with the L¨¦vy-flight foraging hypothesis1, 7, supporting the contention8, 9 that organism search strategies naturally evolved in such a way that they exploit optimal L¨¦vy patterns.
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