Hydrodynamic Simulations of the Performance Landscape for Suction-Feeding Fishes Reveal Multiple Peaks for Different...

The complex interplay between form and function forms the basis for generating and maintaining organismal diversity. Fishes that rely on suction-feeding for prey capture exhibit remarkable phenotypic and trophic diversity. Yet the relationships between fish phenotypes and feeding performance on different prey types are unclear, partly because the morphological, biomechanical, and hydrodynamic mechanisms that underlie suction-feeding are complex. Here we demonstrate a general framework to investigate the mapping of multiple phenotypic traits to performance by mapping kinematic variables to suction-feeding capacity. Using a mechanistic model of suction-feeding that is based on core physical principles, we predict prey capture performance across a broad range of phenotypic trait values, for three general prey types: mollusk-like prey, copepod-like prey, and fish-like prey. Mollusk-like prey attach to surfaces, copepod-like prey attempt to escape upon detecting the hydrodynamic disturbance produced by the predator, and fish-like prey attempt to escape when the predator comes within a threshold distance. This approach allowed us to evaluate suction-feeding performance for any combination of six key kinematic traits, irrespective of whether these trait combinations were observed in an extant species, and to generate a multivariate mapping of phenotype to performance. We used gradient ascent methods to explore the complex topography of the performance landscape for each prey type, and found evidence for multiple peaks. Characterization of phenotypes associated with performance peaks indicates that the optimal kinematic parameter range for suction-feeding on different prey types are narrow and distinct from each other, suggesting different functional constraints for the three prey types. These performance landscapes can be used to generate hypotheses regarding the distribution of extant species in trait space and their evolutionary trajectories toward adaptive peaks on macroevolutionary fitness landscapes.