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Taxonomic resolution affects host-parasite association model performance.

Tad A Dallas1, Daniel J Becker2

  • 1Department of Biological Sciences, Louisiana State University, Baton Rouge, LA70802, USA.

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PubMed
Summary

Taxonomic resolution matters for predicting host-parasite associations. Taxon-specific models, focusing on helminth groups like Acanthocephalans, consistently outperform broader models, improving parasite distribution and transmission predictions.

Keywords:
Boosted regression treesparasite macroecologyphylogenetic scale

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Area of Science:

  • Ecology and Evolutionary Biology
  • Parasitology
  • Biodiversity Science

Background:

  • Host-parasite interactions are crucial for understanding species distributions and cross-species transmission.
  • Phylogenetic relatedness influences host-parasite associations, but specificity varies with parasite traits.

Purpose of the Study:

  • To investigate how taxonomic resolution impacts the accuracy of predictive models for host-parasite associations.
  • To determine if using broader datasets or specific parasite subsets yields better predictions.

Main Methods:

  • Utilized the London Natural History Museum's host-helminth interaction database.
  • Employed boosted regression trees to build and compare predictive models.
  • Analyzed models at different taxonomic resolutions (full dataset vs. taxon-specific subsets).

Main Results:

  • Taxon-specific models for Acanthocephalans, Nematodes, and Platyhelminthes consistently outperformed full models in predicting mammal-helminth associations.
  • Model performance did not vary at finer spatial resolutions, indicating scale-dependent tradeoffs.
  • Key host and parasite covariates were identified across all models.

Conclusions:

  • Taxonomic resolution significantly influences the predictive power of host-parasite models.
  • Using taxonomic subsets of data can enhance predictions of parasite distributions and cross-species transmission.
  • Future models should consider desired spatial and phylogenetic scales, potentially using ensemble methods.