A multi-peak performance landscape for scale biting in an adaptive radiation of pupfishes.

The physical interactions between organisms and their environment ultimately shape diversification rates, but the contributions of biomechanics to evolutionary divergence are frequently overlooked. Here, we estimated a performance landscape for biting in an adaptive radiation of Cyprinodon pupfishes, including scale-biting and molluscivore specialists, and compared performance peaks with previous estimates of the fitness landscape in this system. We used high-speed video to film feeding strikes on gelatin cubes by scale eater, molluscivore, generalist and hybrid pupfishes and measured bite dimensions. We then measured five kinematic variables from 227 strikes using the SLEAP machine-learning model. We found a complex performance landscape with two distinct peaks best predicted gel-biting performance, corresponding to a significant non-linear interaction between peak gape and peak jaw protrusion. Only scale eaters and their hybrids were able to perform strikes within the highest performance peak, characterized by larger peak gapes and greater jaw protrusion. A performance valley separated this peak from a lower performance peak accessible to all species, characterized by smaller peak gapes and less jaw protrusion. However, most individuals exhibited substantial variation in strike kinematics and species could not be reliably distinguished by their strikes, indicating many-to-many mapping of morphology to performance. The two performance peaks observed in the lab were partially consistent with estimates of a two-peak fitness landscape measured in the wild, with the exception of the new performance peak for scale eaters. We thus reveal a new bimodal non-linear biomechanical model that connects morphology to performance to fitness in a sympatric radiation of trophic niche specialists.

Multiple performance peaks for scale-biting in an adaptive radiation of pupfishes.

The physical interactions between organisms and their environment ultimately shape their rate of speciation and adaptive radiation, but the contributions of biomechanics to evolutionary divergence are frequently overlooked. Here we investigated an adaptive radiation of Cyprinodon pupfishes to measure the relationship between feeding kinematics and performance during adaptation to a novel trophic niche, lepidophagy, in which a predator removes only the scales, mucus, and sometimes tissue from their prey using scraping and biting attacks. We used high-speed video to film scale-biting strikes on gelatin cubes by scale-eater, molluscivore, generalist, and hybrid pupfishes and subsequently measured the dimensions of each bite. We then trained the SLEAP machine-learning animal tracking model to measure kinematic landmarks and automatically scored over 100,000 frames from 227 recorded strikes. Scale-eaters exhibited increased peak gape and greater bite length; however, substantial within-individual kinematic variation resulted in poor discrimination of strikes by species or strike type. Nonetheless, a complex performance landscape with two distinct peaks best predicted gel-biting performance, corresponding to a significant nonlinear interaction between peak gape and peak jaw protrusion in which scale-eaters and their hybrids occupied a second performance peak requiring larger peak gape and greater jaw protrusion. A bite performance valley separating scale-eaters from other species may have contributed to their rapid evolution and is consistent with multiple estimates of a multi-peak fitness landscape in the wild. We thus present an efficient deep-learning automated pipeline for kinematic analyses of feeding strikes and a new biomechanical model for understanding the performance and rapid evolution of a rare trophic niche.