Premium
Transmission risk predicts avoidance of infected conspecifics in Trinidadian guppies
Author(s) -
Stephenson Jessica F.,
Perkins Sarah E.,
Cable Joanne
Publication year - 2018
Publication title -
journal of animal ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.134
H-Index - 157
eISSN - 1365-2656
pISSN - 0021-8790
DOI - 10.1111/1365-2656.12885
Subject(s) - guppy , biology , transmission (telecommunications) , sociality , avoidance response , avoidance behaviour , poecilia , predator avoidance , host (biology) , ecology , predation , psychology , developmental psychology , fish <actinopterygii> , predator , computer science , telecommunications , neuroscience , fishery
Abstract Associating with conspecifics afflicted with infectious diseases increases the risk of becoming infected, but engaging in avoidance behaviour incurs the cost of lost social benefits. Across systems, infected individuals vary in the transmission risk they pose, so natural selection should favour risk‐sensitive avoidance behaviour that optimally balances the costs and benefits of sociality. Here, we use the guppy Poecilia reticulata–Gyrodactylus turnbulli host–parasite system to test the prediction that individuals avoid infected conspecifics in proportion to the transmission risk they pose. In dichotomous choice tests, uninfected fish avoided both the chemical and visual cues, presented separately, of infected conspecifics only in the later stages of infection. A transmission experiment indicated that this avoidance behaviour accurately tracked transmission risk (quantified as both the speed at which transmission occurs and the number of parasites transmitting) through the course of infection. Together, these findings reveal that uninfected hosts can use redundant cues across sensory systems to inform dynamic risk‐sensitive avoidance behaviour. This correlation between the transmission risk posed by infected individuals and the avoidance response they elicit has implications for the evolutionary ecology of infectious disease, and its explicit inclusion may improve the ability of epidemic models to predict disease spread.