Lookup NU author(s): Dr Dan Reed,
Dr Colin Tosh
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Specialist animals are at a greater risk of extinction in the face of environmental change than generalist ones. The inability of some specialist taxa to expand host range through evolution may exacerbate or cause their high extinction risk. Here we use connectionism (a framework for modelling animal behaviour) to predict the environmental and physiological factors that predispose some specialist taxa to an ‘evolutionary dead-end’. Neural networks are evolved to become resource-specialised in a resource-abundant and resource-diverse ‘historical’ environment while losing ‘genes’ that should restrict their ability to expand host range. Networks are subsequently challenged to escape their dead-end by expanding host range in a ‘contemporary’ environment that may have depleted resource abundance and diversity (as many human impacted environments do). Loss of diversity in available resources universally constrains the ability of networks to expand host range and this effect is very robust to network conformation. Environmental resource abundance is more variable in its effect. Networks are generally robust to loss of genetic diversity during the evolution of specialisation except at very high rates of loss. By omitting historical specialisation, we show that the effect of resource diversity on host range expansion is not a universal network property but something that is often specific to specialist organisms. Historical specialisation also slightly reduces the robustness of networks in the contemporary environment to loss of genetic diversity during the specialisation process. Fundamentally, simulations predict that loss of local resource diversity will further increase the vulnerability of specialists to extinction by constraining their ability to expand host range in the face of environmental change.
Author(s): Reed DT, Tosh CR
Publication type: Article
Publication status: Published
Journal: Journal of Theoretical Biology
Print publication date: 07/09/2019
Online publication date: 24/05/2019
Acceptance date: 23/05/2019
Date deposited: 29/05/2019
ISSN (print): 0022-5193
ISSN (electronic): 1095-8541
Publisher: Academic Press
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