title: A test of the critical assumption of the sensory bias model for the evolution of female mating preference using neural networks.
abstract: The sensory bias model for the evolution of mating preferences states that mating preferences evolve as correlated responses to selection on nonmating behaviors sharing a common sensory system. The critical assumption is that pleiotropy creates genetic correlations that affect the response to selection. I simulated selection on populations of neural networks to test this. First, I selected for various combinations of foraging and mating preferences. Sensory bias predicts that populations with preferences for like-colored objects (red food and red mates) should evolve more readily than preferences for differently colored objects (red food and blue mates). Here, I found no evidence for sensory bias. The responses to selection on foraging and mating preferences were independent of one another. Second, I selected on foraging preferences alone and asked whether there were correlated responses for increased mating preferences for like-colored mates. Here, I found modest evidence for sensory bias. Selection for a particular foraging preference resulted in increased mating preference for similarly colored mates. However, the correlated responses were small and inconsistent. Selection on foraging preferences alone may affect initial levels of mating preferences, but these correlations did not constrain the joint evolution of foraging and mating preferences in these simulations.
authors.affiliation: School of Integrative Biology, University of Illinois, 104 Shelford Vivarium, Champaign, IL 61820, USA. fuller@life.uiuc.edu
authors.forename: Rebecca C
authors.initials: RC
authors.lastname: Fuller
mesh_terms: D000818:Animals; D005075:Biological Evolution; D002755:Choice Behavior; D003116:Color; D005247:Feeding Behavior; D005260:Female; D005399:Fishes / Q000033:anatomy & histology / Q000502:physiology; D008297:Male; D052586:Mating Preference, Animal; D008954:Models, Biological; D016571:Neural Networks, Computer; D012641:Selection, Genetic