title: Theoretical predictions strongly support decision accuracy as a major driver of ecological specialization.
abstract: We examine the proposal that the high levels of ecological specialization seen in many animals has been driven by benefits in decision accuracy that accrue from this resource-use strategy. Using artificial analogs of real neural processing (artificial neural networks), we examine the relationship between decision accuracy, level of ecological specialization/generalization, and the punishment/reward for selecting non-host resources. We demonstrate that specialists make more accurate resource-use decisions than generalists when the consequences of using a non-host are neutral or positive but not very positive. Pronounced unsuitability of non-host resources in fact promotes higher decision accuracy in generalists. These unusual predictions can be explained by the special properties of neural processing systems and are entirely consistent with patterns of performance of many specialists in nature, where non-used resources are, curiously, often quite suitable for growth and reproduction. They potentially reconcile the long-observed discrepancy between the presence of high levels of ecological specialization in many animal groups and the absence of strong negative fitness correlations across resources. The strong theoretical support obtained here, and the equally good support in experimental studies elsewhere, should bring the "neural limitations" hypothesis to the forefront of research on the evolutionary determinants of ecological range.
authors.affiliation: Institute of Integrative and Comparative Biology, Faculty of Biological Sciences, University of Leeds, Leeds LS2 9JT, United Kingdom. c.r.tosh@leeds.ac.uk
authors.forename: Colin R
authors.initials: CR
authors.lastname: Tosh
authors.forename: Jens
authors.initials: J
authors.lastname: Krause
authors.forename: Graeme D
authors.initials: GD
authors.lastname: Ruxton
mesh_terms: D000818:Animals; D001522:Behavior, Animal; D005075:Biological Evolution; D003071:Cognition; D003198:Computer Simulation; D003657:Decision Making; D004463:Ecology; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D011678:Punishment; D012201:Reward