An examination of saltatory predation strategies employed by fish larvae foraging in a variety of different turbulent regimes

Marine Ecology Progress Series

S. I. Bala¹  D. M. Lewis¹ 

关键词: Larval foraging;    Turbulence;    Restricted perception fields;    Net energy gain;   

DOI  :  10.3354/meps07337

学科分类：海洋学与技术

来源: Inter-Research

【 摘 要 】

ABSTRACT: Saltatory or pause-travel predation strategies are exhibited by a wide variety of different species of fish larvae (e.g. Atlantic cod Gadus morhua and herring Clupea harengus, white crappie Pomoxis annularis and red drum Sciaenops ocellatus), usually classified as visual predators. Given the frequency that saltatory predation behaviour is observed, one would expect that this type of foraging provides an optimal balance between the energy expended whilst swimming, as against the potential energy to be gained upon successfully capturing some suitable prey. However, visual predators must live and forage in the upper 10s of metres of the ocean mixed layer, where light levels are relatively high, which means that their ability to find and capture prey is strongly influenced by small-scale turbulence. The strength of the turbulence regime characterising the water motion of the mixed layer is subject to considerable variation, but given the commonalty of saltatory predation, one would suspect that it remains a robust strategy under a variety of different conditions. This paper seeks to address this issue by studying the impact of saltatory predation over a wide cross section of turbulent environments. By means of a combination of kinematic simulations and theoretical predictions, the paper demonstrates that saltatory predation is indeed a robust and optimal strategy in the sense described, provided the larva’s travel speed is not too high. However, when subject to very high levels of turbulence a larva’s net energy gain peaks and then falls, leading to a prediction that it would either cease foraging altogether, or seek an escape from such an environment.

【 授权许可】

Unknown