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A new theoretical performance landscape for suction feeding reveals adaptive kinematics in a natural population of reef damselfish

Citation

Holzman, Roi (2022), A new theoretical performance landscape for suction feeding reveals adaptive kinematics in a natural population of reef damselfish, Dryad, Dataset, https://doi.org/10.5061/dryad.59zw3r2b5

Abstract

Understanding how organismal traits determine performance andultimatelyfitness is a fundamental goal of evolutionary ecomorphology. However, multiple traits can interact in non-linear and context-dependent ways to affect performance, hindering efforts to place natural populations with respect to performance peaks or valleys. Here, we used an established mechanistic model of suction-feeding performance (SIFF) derived from hydrodynamic principles to estimate a theoretical performance landscape for zooplankton prey capture. This performance space can be used to predict prey capture performance for any combination of six morphological and kinematic trait values. We then mapped in situ high-speed video observations of suction feeding in a natural population of a coral reef zooplanktivore, Chromis viridis, onto the performance space to estimate the population’s location with respect to the topography of the performance landscape. Although the kinematics of the natural population closely matched regions of high performance in the landscape, the population was not located on a performance peak. Individuals were furthest from performance peaks on the peak gape, ram speed and mouth opening speed trait axes. Moreover, we found that the trait combinationin the observed population were associated with higher performance than expected by chance, suggesting that these combinations are under selection. Our results provide a framework for assessing whether natural populations occupy performance optima.

Methods

Data is feeding kinematics for 110 Chromis viridis individuals filmed underwater while feeding undisturbed on natural prey. data was obtained using a pair of high-speed underwater cameras that were calibrated to generate 3D coordinates. from digitized landmarks we determined: (1) maximum gape, defined as the maximum diameter of the fish’s mouth; (2) time to peak gape (TTPG), defined as the time to open its mouth from 20% to 95% of peak gape; (3) maximum jaw protrusion measured during the strike from the fish’s frame of reference; (4) time to peak jaw protrusion (TTPJP), defined as the time it took the fish to protrude its jaws from 20% to 95% of maximum jaw protrusion; (5) ram speed, defined as the fish’s swimming speed during the strike (i.e. from mouth opening to closing); and (6) timing of peak protrusion, defined as the difference between the time of maximum gape and time of maximal jaw protrusion . Calculations were made as described in Holzman et al. (2008)

Funding

United States-Israel Binational Science Foundation, Award: 2016136