Journal of Experimental Biology · 2011 · 86 citations · 59 references
Life SciencesEcologyZoologyFish BiologyFish EcologyFish PhysiologyEnergeticsPredator-prey InteractionsFish BiomechanicsHydrodynamic Force ModelingHydrodynamic Interaction EffectsHydrodynamic ModellingHydrodynamics Of Fish SwimmingPrey Capture MechanismsSkull MechanicsSuction Force
Research on suction-feeding performance has mostly focused on measuring individual underlying components such as suction pressure, flow velocity, ram or the effects of suction-induced forces on prey movement during feeding. Although this body of work has advanced our understanding of aquatic feeding, no consensus has yet emerged on how to combine all of these variables to predict prey-capture performance. Here, we treated the aquatic predator-prey encounter as a hydrodynamic interaction between a solid particle (representing the prey) and the unsteady suction flows around it, to integrate the effects of morphology, physiology, skull kinematics, ram and fluid mechanics on suction-feeding performance. We developed the suction-induced force-field (SIFF) model to study suction-feeding performance in 18 species of centrarchid fishes, and asked what morphological and functional traits underlie the evolution of feeding performance on three types of prey. Performance gradients obtained using SIFF revealed that different trait combinations contribute to the ability to feed on attached, evasive and (strain-sensitive) zooplanktonic prey because these prey types impose different challenges on the predator. The low overlap in the importance of different traits in determining performance also indicated that the evolution of suction-feeding ability along different ecological axes is largely unconstrained. SIFF also yielded estimates of feeding ability that performed better than kinematic traits in explaining natural patterns of prey use. When compared with principal components describing variation in the kinematics of suction-feeding events, SIFF output explained significantly more variation in centrarchid diets, suggesting that the inclusion of more mechanistic hydrodynamic models holds promise for gaining insight into the evolution of aquatic feeding performance.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Phylogenies and the Comparative Method
Joseph Felsenstein · The American Naturalist · 1985 · 10.1K citations