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Predictability of helminth parasite host range using information on geography, host traits and parasite community structure

Published online by Cambridge University Press:  20 October 2016

TAD DALLAS ,
ANDREW W. PARK Open the ORCID record for ANDREW W. PARK [Opens in a new window]  and
JOHN M. DRAKE
TAD DALLAS*
Affiliation:
Odum School of Ecology, University of Georgia, Athens, GA 30602, USA Department of Environmental Sciences and Policy, University of California, Davis, CA 95616, USA
ANDREW W. PARK
Affiliation:
Odum School of Ecology, University of Georgia, Athens, GA 30602, USA Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
JOHN M. DRAKE
Affiliation:
Odum School of Ecology, University of Georgia, Athens, GA 30602, USA Center for the Ecology of Infectious Diseases, University of Georgia, Athens, GA 30602, USA
*
*Corresponding author: University of Georgia, Odum School of Ecology, 140 East Green Street, Athens, GA 30606, USA. E-mail: tdallas@uga.edu
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Summary

Host–parasite associations are complex interactions dependent on aspects of hosts (e.g. traits, phylogeny or coevolutionary history), parasites (e.g. traits and parasite interactions) and geography (e.g. latitude). Predicting the permissive host set or the subset of the host community that a parasite can infect is a central goal of parasite ecology. Here we develop models that accurately predict the permissive host set of 562 helminth parasites in five different parasite taxonomic groups. We developed predictive models using host traits, host taxonomy, geographic covariates, and parasite community composition, finding that models trained on parasite community variables were more accurate than any other covariate group, even though parasite community covariates only captured a quarter of the variance in parasite community composition. This suggests that it is possible to predict the permissive host set for a given parasite, and that parasite community structure is an important predictor, potentially because parasite communities are interacting non-random assemblages.

Keywords

FishPESTspecies distribution modelboosted regression treeparasite niche
Type
Research Article
Information
Parasitology , Volume 144 , Issue 2 , February 2017 , pp. 200 - 205
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Copyright
Copyright © Cambridge University Press 2016 

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