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Scale-dependent effects of host patch traits on species composition in a stickleback parasite metacommunity.

ABSTRACT: A core goal of ecology is to understand the abiotic and biotic variables that regulate species distributions and community composition. A major obstacle is that the rules governing species distributions can change with spatial scale. Here, we illustrate this point using data from a spatially nested metacommunity of parasites infecting a metapopulation of threespine stickleback fish from 34 lakes on Vancouver Island, British Columbia. Like most parasite metacommunities, the composition of stickleback parasites differs among host individuals within each host population, and differs between host populations. The distribution of each parasite taxon depends, to varying degrees, on individual host traits (e.g., mass, diet) and on host-population characteristics (e.g., lake size, mean host mass, mean diet). However, in most cases in this data set, a given parasite was regulated by different factors at the host-individual and host-population scales, leading to scale-dependent patterns of parasite-species co-occurrence.

SUBMITTER: Bolnick DI

PROVIDER: S-EPMC7757261 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Scale-dependent effects of host patch traits on species composition in a stickleback parasite metacommunity.

Bolnick Daniel I DI Resetarits Emlyn J EJ Ballare Kimberly K Stuart Yoel E YE Stutz William E WE

Ecology 20201001 12

A core goal of ecology is to understand the abiotic and biotic variables that regulate species distributions and community composition. A major obstacle is that the rules governing species distributions can change with spatial scale. Here, we illustrate this point using data from a spatially nested metacommunity of parasites infecting a metapopulation of threespine stickleback fish from 34 lakes on Vancouver Island, British Columbia. Like most parasite metacommunities, the composition of stickle ...[more]

PMID: 32880940

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Project description:Supplemental food provided to wildlife by human activities can be more abundant and predictable than natural resources, and subsequent changes in wildlife ecology can have profound impacts on host-parasite interactions. Identifying traits of species associated with increases or decreases in infection outcomes with resource provisioning could improve assessments of wildlife most prone to disease risks in changing environments. We conducted a phylogenetic meta-analysis of 342 host-parasite interactions across 56 wildlife species and three broad taxonomic groups of parasites to identify host-level traits that influence whether provisioning is associated with increases or decreases in infection. We predicted dietary generalists that capitalize on novel food would show greater infection in provisioned habitats owing to population growth and food-borne exposure to contaminants and parasite infectious stages. Similarly, species with fast life histories could experience stronger demographic and immunological benefits from provisioning that affect parasite transmission. We also predicted that wide-ranging and migratory behaviours could increase infection risks with provisioning if concentrated and non-seasonal foods promote dense aggregations that increase exposure to parasites. We found that provisioning increased infection with bacteria, viruses, fungi and protozoa (i.e. microparasites) most for wide-ranging, dietary generalist host species. Effect sizes for ectoparasites were also highest for host species with large home ranges but were instead lowest for dietary generalists. In contrast, the type of provisioning was a stronger correlate of infection outcomes for helminths than host species traits. Our analysis highlights host traits related to movement and feeding behaviour as important determinants of whether species experience greater infection with supplemental feeding. These results could help prioritize monitoring wildlife with particular trait profiles in anthropogenic habitats to reduce infectious disease risks in provisioned populations.

Project description:BackgroundHost-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases.ObjectiveMany studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose.MethodsThe various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed.ResultWe discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information.ConclusionThis review presents useful knowledge and future directions on the subject matter.

Dataset Information

Scale-dependent effects of host patch traits on species composition in a stickleback parasite metacommunity.

Publications

Scale-dependent effects of host patch traits on species composition in a stickleback parasite metacommunity.

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