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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: [email protected]
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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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References

REFERENCES

Canard, E., Mouquet, N., Mouillot, D., Stanko, M., Miklisova, D. and Gravel, D. (2014). Empirical evaluation of neutral interactions in host–parasite networks. The American Naturalist 183, 468–479.Google Scholar
Colautti, R. I., Ricciardi, A., Grigorovich, I. A. and MacIsaac, H. J. (2004). Is invasion success explained by the enemy release hypothesis? Ecology Letters 7, 721–733.CrossRefGoogle Scholar
Dallas, T. (2016). helminthR: an R interface to the London Natural History Museum's host–parasite database. Ecography 39, 391–393.Google Scholar
Daszak, P., Cunningham, A. A. and Hyatt, A. D. (2000). Emerging infectious diseases of wildlife–threats to biodiversity and human health. Science 287, 443–449.Google Scholar
Davies, T. J. and Pedersen, A. B. (2008). Phylogeny and geography predict pathogen community similarity in wild primates and humans. Proceedings of the Royal Society B: Biological Sciences 275, 1695–1701.Google Scholar
Elith, J., Leathwick, J. R. and Hastie, T. (2008). A working guide to boosted regression trees. Journal of Animal Ecology 77, 802–813.Google Scholar
Elith, J., Phillips, S. J., Hastie, T., Dudík, M., Chee, Y. E. and Yates, C. J. (2011). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17, 43–57.Google Scholar
Ezenwa, V. O., Price, S. A., Altizer, S., Vitone, N. D. and Cook, K. C. (2006). Host traits and parasite species richness in even and odd-toed hoofed mammals, Artiodactyla and Perissodactyla. Oikos 115, 526–536.CrossRefGoogle Scholar
Flach, P. A. (2010). ROC analysis. In Encyclopedia of Machine Learning (ed. Sammut, C. and Webb, G. I.), pp. 869–875. Springer US.Google Scholar
Froese, R. and Pauly, D. (ed.) (2000). FishBase 2000: Concepts, Design and Data Sources. ICLARM, Los Baños, Laguna, Philippines. 344 p.Google Scholar
Gibson, D., Bray, R. and Harris, E. (2005). Host–parasite database of the natural history museum, London. http://www.nhm.ac.uk/research-curation/scientific-resources/taxonomy-systematics/host-parasites/database/index.jsp Google Scholar
Han, B. A., Schmidt, J. P., Bowden, S. E. and Drake, J. M. (2015). Rodent reservoirs of future zoonotic diseases. Proceedings of the National Academy of Sciences of the United States of America 112, 7039–7044.CrossRefGoogle ScholarPubMed
Holmes, J. C. (1990). Helminth communities in marine fishes. In Parasite Communities: Patterns and Processes (ed. Esch, G. W., Bush, A. O. and Aho, J. M.), pp. 101–130. Springer, Netherlands.Google Scholar
Kennedy, C. (1990). Helminth communities in freshwater fish: structured communities or stochastic assemblages? In Parasite Communities: Patterns and Processes (ed. Esch, G. W., Bush, A. O. and Aho, J. M.), pp. 131–156. Springer, Netherlands.Google Scholar
Kennedy, C. (2009). The ecology of parasites of freshwater fishes: the search for patterns. Parasitology 136, 1653–1662.Google Scholar
Killen, S. S., Atkinson, D. and Glazier, D. S. (2010). The intraspecific scaling of metabolic rate with body mass in fishes depends on lifestyle and temperature. Ecology Letters 13, 184–193.Google Scholar
Krasnov, B. R., Fortuna, M. A., Mouillot, D., Khokhlova, I. S., Shenbrot, G. I. and Poulin, R. (2012). Phylogenetic signal in module composition and species connectivity in compartmentalized host–parasite networks. The American Naturalist 179, 501–511.Google Scholar
Liaw, A. and Wiener, M. (2002). Classification and regression by randomForest. R News 2, 18–22.Google Scholar
Lindenfors, P., Nunn, C. L., Jones, K. E., Cunningham, A. A., Sechrest, W. and Gittleman, J. L. (2007). Parasite species richness in carnivores: effects of host body mass, latitude, geographical range and population density. Global Ecology and Biogeography 16, 496–509.Google Scholar
Locke, S. A., McLaughlin, J. D. and Marcogliese, D. J. (2013). Predicting the similarity of parasite communities in freshwater fishes using the phylogeny, ecology and proximity of hosts. Oikos 122, 73–83.Google Scholar
Locke, S. A., Marcogliese, D. J. and Valtonen, E. T. (2014). Vulnerability and diet breadth predict larval and adult parasite diversity in fish of the Bothnian bay. Oecologia 174, 253–262.Google Scholar
Nunn, C. L. and Altizer, S. M. (2005). The global mammal parasite database: an online resource for infectious disease records in wild primates. Evolutionary Anthropology: Issues, News, and Reviews 14, 1–2.CrossRefGoogle Scholar
Pilosof, S., Morand, S., Krasnov, B. R. and Nunn, C. L. (2015). Potential parasite transmission in multi-host networks based on parasite sharing. PLoS ONE 10, e0117909.CrossRefGoogle ScholarPubMed
Poulin, R. (2005). Relative infection levels and taxonomic distances among the host species used by a parasite: insights into parasite specialization. Parasitology 130, 109–115.Google Scholar
Poulin, R. (2011). Interactions between species and the parasite niche. In Evolutionary Ecology of Parasites, pp. 188–207. Princeton University Press, New Jersey, USA.Google Scholar
Poulin, R. and Rohde, K. (1997). Comparing the richness of metazoan ectoparasite communities of marine fishes: controlling for host phylogeny. Oecologia 110, 278–283.CrossRefGoogle ScholarPubMed
Poulin, R. and Valtonen, E. T. (2002). The predictability of helminth community structure in space: a comparison of fish populations from adjacent lakes. International Journal for Parasitology 32, 1235–1243.Google Scholar
Rall, B. C., Brose, U., Hartvig, M., Kalinkat, G., Schwarzmüller, F., Vucic-Pestic, O. and Petchey, O. L. (2012). Universal temperature and body-mass scaling of feeding rates. Philosophical Transactions of the Royal Society B 367, 2923–2934.CrossRefGoogle ScholarPubMed
Ridgeway, G. (2015). gbm: Generalized Boosted Regression Models. R package version 2.1.1. https://CRAN.R-project.org/package=gbm Google Scholar
Rohde, K. (2002). Ecology and biogeography of marine parasites. Advances in Marine Biology 43, 1–83.Google Scholar
Rohr, R. P., Naisbit, R. E., Mazza, C. and Bersier, L. F. (2016). Matching–centrality decomposition and the forecasting of new links in networks. Proceedings of the Royal Society B: Biological Sciences 283, 20152702.Google Scholar
Sasal, P., Trouvé, S., Müller-Graf, C. and Morand, S. (1999). Specificity and host predictability: a comparative analysis among monogenean parasites of fish. Journal of Animal Ecology 68, 437–444.Google Scholar
Strona, G. and Lafferty, K. D. (2012 a). FishPEST: an innovative software suite for fish parasitologists. Trends in Parasitology 28, 123.Google Scholar
Strona, G. and Lafferty, K. D. (2012 b). How to catch a parasite: parasite Niche Modeler (PaNic) meets Fishbase. Ecography 35, 481–486.Google Scholar
Strona, G., Palomares, M. L. D., Bailly, N., Galli, P. and Lafferty, K. D. (2013). Host range, host ecology, and distribution of more than 11 800 fish parasite species. Ecological Archives: E094-045 94, 544–544.Google Scholar
Walton, L., Marion, G., Davidson, R. S., White, P. C., Smith, L. A., Gavier-Widen, D., Yon, L., Hannant, D. and Hutchings, M. R. (2016). The ecology of wildlife disease surveillance: demographic and prevalence fluctuations undermine surveillance. Journal of Applied Ecology. doi: 10.1111/1365-2664.12671.CrossRefGoogle Scholar
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