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Title: Microbial community composition predicts bacterial production across ocean ecosystems
Abstract Microbial ecological functions are an emergent property of community composition. For some ecological functions, this link is strong enough that community composition can be used to estimate the quantity of an ecological function. Here, we apply random forest regression models to compare the predictive performance of community composition and environmental data for bacterial production (BP). Using data from two independent long-term ecological research sites—Palmer LTER in Antarctica and Station SPOT in California—we found that community composition was a strong predictor of BP. The top performing model achieved an R2 of 0.84 and RMSE of 20.2 pmol L−1 hr−1 on independent validation data, outperforming a model based solely on environmental data (R2 = 0.32, RMSE = 51.4 pmol L−1 hr−1). We then operationalized our top performing model, estimating BP for 346 Antarctic samples from 2015 to 2020 for which only community composition data were available. Our predictions resolved spatial trends in BP with significance in the Antarctic (P value = 1 × 10−4) and highlighted important taxa for BP across ocean basins. Our results demonstrate a strong link between microbial community composition and microbial ecosystem function and begin to leverage long-term datasets to construct models of BP based on microbial community composition.  more » « less
Award ID(s):
1846837
PAR ID:
10541172
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
Oxford University Press
Date Published:
Journal Name:
The ISME Journal
Volume:
18
Issue:
1
ISSN:
1751-7362
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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