TY - JOUR
T1 - Untangling the dynamics of persistence and colonization in microbial communities
AU - Ranjeva, Sylvia L.
AU - Mihaljevic, Joseph R.
AU - Joseph, Maxwell B.
AU - Giuliano, Anna R.
AU - Dwyer, Greg
N1 - Funding Information:
Acknowledgements Earlier versions of this manuscript were improved by comments from Joshua Weitz, Jonathan Dushoff, and an anonymous reviewer. SLR was funded by two grants from the US National Institutes of Health (NIH F30AI124636 and T32GM00728). JRM was funded by a US Department of Agriculture (USDA) National Institute of Food and Agriculture (NIFA) Postdoctoral Fellowship (2014-67012-22272). ARG was supported by funding from the National Cancer Institute (NCI) R01 CA214588.
Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here, we introduce a dynamic statistical model, based on probit regression, that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We first validate our modeling framework with several simulation studies. Then, we apply the approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.
AB - A central goal of community ecology is to infer biotic interactions from observed distributions of co-occurring species. Evidence for biotic interactions, however, can be obscured by shared environmental requirements, posing a challenge for statistical inference. Here, we introduce a dynamic statistical model, based on probit regression, that quantifies the effects of spatial and temporal covariance in longitudinal co-occurrence data. We separate the fixed pairwise effects of species occurrences on persistence and colonization rates, a potential signal of direct interactions, from latent pairwise correlations in occurrence, a potential signal of shared environmental responses. We first validate our modeling framework with several simulation studies. Then, we apply the approach to a pressing epidemiological question by examining how human papillomavirus (HPV) types coexist. Our results suggest that while HPV types respond similarly to common host traits, direct interactions are sparse and weak, so that HPV type diversity depends largely on shared environmental drivers. Our modeling approach is widely applicable to microbial communities and provides valuable insights that should lead to more directed hypothesis testing and mechanistic modeling.
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U2 - 10.1038/s41396-019-0488-7
DO - 10.1038/s41396-019-0488-7
M3 - Article
C2 - 31444482
AN - SCOPUS:85071318351
SN - 1751-7362
VL - 13
SP - 2998
EP - 3010
JO - ISME Journal
JF - ISME Journal
IS - 12
ER -