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Host traits and environmental factors shape infection heterogeneity in wild rat-protozoa networks.

Journal articles  - Journal Article
Markfeld, M; Talpaz, I; Biton, B; Maheriniaina Randriamoria, T; Soarimalala, V; Goodman, SM; Nunn, CL; Titcomb, G; Pilosof, S
Published in: ISME communications
January 2026

The occurrence of microbes in animal hosts is highly heterogeneous, shaped by interactions among host traits, environmental context, and microbial diversity. Understanding this heterogeneity is particularly critical for endoparasite infections, where some hosts harbor diverse, high-burden assemblages that elevate disease spread and spillover risk. Yet the mechanisms underlying such heterogeneity remain poorly understood in wild systems, especially at the individual-host level. We addressed this challenge by studying protozoan infections in introduced black rats (Rattus rattus) across environmental gradients in Madagascar. Using network-based stochastic block modeling, we identified three infection profiles capturing meaningful variation in protozoan richness and composition, providing a structured framework for understanding heterogeneity. To uncover the predictors of these profiles, we trained machine-learning models incorporating host traits with environmental variables. Our models consistently outperformed no-skill baselines, with host traits contributing [Formula: see text]40% more to predictions than environmental factors. Body mass and gut microbiome composition emerged as the strongest host predictors, while rat and other non-native species densities were the most influential environmental predictors. These results show that infection heterogeneity arises from the interplay of intrinsic host traits and extrinsic environmental conditions. Our approach illustrates how combining network analysis with predictive modeling can (i) uncover latent heterogeneity in host-microbe associations, (ii) identify the relative contribution of the factors driving this heterogeneity, and (iii) predict host infection profiles. Our framework advances microbial ecology by linking host traits, microbial communities, and environmental context, while also informing disease ecology at human-animal interfaces where zoonotic pathogens circulate.

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Published In

ISME communications

DOI

EISSN

2730-6151

ISSN

2730-6151

Publication Date

January 2026

Volume

6

Issue

1

Start / End Page

ycag026

Related Subject Headings

  • 3107 Microbiology
  • 3103 Ecology
 

Citation

APA
Chicago
ICMJE
MLA
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Markfeld, M., Talpaz, I., Biton, B., Maheriniaina Randriamoria, T., Soarimalala, V., Goodman, S. M., … Pilosof, S. (2026). Host traits and environmental factors shape infection heterogeneity in wild rat-protozoa networks. ISME Communications, 6(1), ycag026. https://doi.org/10.1093/ismeco/ycag026
Markfeld, Matan, Itamar Talpaz, Barry Biton, Toky Maheriniaina Randriamoria, Voahangy Soarimalala, Steven Michael Goodman, Charles L. Nunn, Georgia Titcomb, and Shai Pilosof. “Host traits and environmental factors shape infection heterogeneity in wild rat-protozoa networks.ISME Communications 6, no. 1 (January 2026): ycag026. https://doi.org/10.1093/ismeco/ycag026.
Markfeld M, Talpaz I, Biton B, Maheriniaina Randriamoria T, Soarimalala V, Goodman SM, et al. Host traits and environmental factors shape infection heterogeneity in wild rat-protozoa networks. ISME communications. 2026 Jan;6(1):ycag026.
Markfeld, Matan, et al. “Host traits and environmental factors shape infection heterogeneity in wild rat-protozoa networks.ISME Communications, vol. 6, no. 1, Jan. 2026, p. ycag026. Epmc, doi:10.1093/ismeco/ycag026.
Markfeld M, Talpaz I, Biton B, Maheriniaina Randriamoria T, Soarimalala V, Goodman SM, Nunn CL, Titcomb G, Pilosof S. Host traits and environmental factors shape infection heterogeneity in wild rat-protozoa networks. ISME communications. 2026 Jan;6(1):ycag026.

Published In

ISME communications

DOI

EISSN

2730-6151

ISSN

2730-6151

Publication Date

January 2026

Volume

6

Issue

1

Start / End Page

ycag026

Related Subject Headings

  • 3107 Microbiology
  • 3103 Ecology