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      Detecting parasite associations within multi-species host and parasite communities

      1 , 2 , 1 , 3 , 1 , 4
      Proceedings of the Royal Society B: Biological Sciences
      The Royal Society

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          Abstract

          Understanding the role of biotic interactions in shaping natural communities is a long-standing challenge in ecology. It is particularly pertinent to parasite communities sharing the same host communities and individuals, as the interactions among parasites—both competition and facilitation—may have far-reaching implications for parasite transmission and evolution. Aggregated parasite burdens may suggest that infected host individuals are either more prone to infection, or that infection by a parasite species facilitates another, leading to a positive parasite–parasite interaction. However, parasite species may also compete for host resources, leading to the prediction that parasite–parasite associations would be generally negative, especially when parasite species infect the same host tissue, competing for both resources and space. We examine the presence and strength of parasite associations using hierarchical joint species distribution models fitted to data on resident parasite communities sampled on over 1300 small mammal individuals across 22 species and their resident parasite communities. On average, we detected more positive associations between infecting parasite species than negative, with the most negative associations occurring when two parasite species infected the same host tissue, suggesting that parasite species associations may be quantifiable from observational data. Overall, our findings suggest that parasite community prediction at the level of the individual host is possible, and that parasite species associations may be detectable in complex multi-species communities, generating many hypotheses concerning the effect of host community changes on parasite community composition, parasite competition within infected hosts, and the drivers of parasite community assembly and structure.

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          Most cited references56

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          How to make more out of community data? A conceptual framework and its implementation as models and software.

          Community ecology aims to understand what factors determine the assembly and dynamics of species assemblages at different spatiotemporal scales. To facilitate the integration between conceptual and statistical approaches in community ecology, we propose Hierarchical Modelling of Species Communities (HMSC) as a general, flexible framework for modern analysis of community data. While non-manipulative data allow for only correlative and not causal inference, this framework facilitates the formulation of data-driven hypotheses regarding the processes that structure communities. We model environmental filtering by variation and covariation in the responses of individual species to the characteristics of their environment, with potential contingencies on species traits and phylogenetic relationships. We capture biotic assembly rules by species-to-species association matrices, which may be estimated at multiple spatial or temporal scales. We operationalise the HMSC framework as a hierarchical Bayesian joint species distribution model, and implement it as R- and Matlab-packages which enable computationally efficient analyses of large data sets. Armed with this tool, community ecologists can make sense of many types of data, including spatially explicit data and time-series data. We illustrate the use of this framework through a series of diverse ecological examples.
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            Species interactions in a parasite community drive infection risk in a wildlife population.

            Most hosts, including humans, are simultaneously or sequentially infected with several parasites. A key question is whether patterns of coinfection arise because infection by one parasite species affects susceptibility to others or because of inherent differences between hosts. We used time-series data from individual hosts in natural populations to analyze patterns of infection risk for a microparasite community, detecting large positive and negative effects of other infections. Patterns remain once variations in host susceptibility and exposure are accounted for. Indeed, effects are typically of greater magnitude, and explain more variation in infection risk, than the effects associated with host and environmental factors more commonly considered in disease studies. We highlight the danger of mistaken inference when considering parasite species in isolation rather than parasite communities.
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              Emphasizing the ecology in parasite community ecology.

              In natural systems, individuals are often co-infected by many species of parasites. However, the significance of interactions between species and the processes that shape within-host parasite communities remain unclear. Studies of parasite community ecology are often descriptive, focusing on patterns of parasite abundance across host populations rather than on the mechanisms that underlie interactions within a host. These within-host interactions are crucial for determining the fitness and transmissibility of co-infecting parasite species. Here, we highlight how techniques from community ecology can be used to restructure the approaches used to study parasite communities. We discuss insights offered by this mechanistic approach that will be crucial for predicting the impact on wildlife and human health of disease control measures, climate change or novel parasite species introductions.
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                Author and article information

                Journal
                Proceedings of the Royal Society B: Biological Sciences
                Proc. R. Soc. B
                The Royal Society
                0962-8452
                1471-2954
                October 02 2019
                October 09 2019
                October 02 2019
                October 09 2019
                : 286
                : 1912
                : 20191109
                Affiliations
                [1 ]Organismal and Evolutionary Biology Research Programme, University of Helsinki, PO Box 65, Helsinki 00014, Finland
                [2 ]Department of Biological Sciences, Louisiana State University, Baton Rouge, LA, USA
                [3 ]Department of Evolutionary Biology and Environmental Studies, University of Zürich, Zürich 8057, Switzerland
                [4 ]Centre for Biodiversity Dynamics, Department of Biology, Norwegian University of Science and Technology, Trondheim 7491, Norway
                Article
                10.1098/rspb.2019.1109
                6790755
                31575371
                c67ffbaf-08ef-4a03-a847-2e1811250c11
                © 2019

                https://royalsociety.org/journals/ethics-policies/data-sharing-mining/

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