Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function
Abstract
Abstract Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. We test these assertions using methane (CH 4 )‐cycling processes in soil samples collected along a wetland‐to‐upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high‐affinity CH 4 oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scalemore »
- Authors:
-
- Univ. of Oregon, Eugene, OR (United States); Univ. of California, Berkeley, CA (United States)
- Univ. of Oregon, Eugene, OR (United States)
- Publication Date:
- Research Org.:
- Univ. of Oregon, Eugene, OR (United States)
- Sponsoring Org.:
- Gabon-Oregon Transnational Center on Environment and Development; Smithsonian Conservation Biology Institute; Shell Gabon; National Science Foundation (NSF); USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1801803
- Alternate Identifier(s):
- OSTI ID: 1630762
- Grant/Contract Number:
- SC0008092; SC0012088; DEB 14422214; 14422214; DE‐SC0008092; DE‐39SC0012088
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Molecular Ecology
- Additional Journal Information:
- Journal Volume: 29; Journal Issue: 10; Journal ID: ISSN 0962-1083
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; biochemistry & molecular biology; environmental sciences & ecology; evolutionary biology; biodiversity-ecosystem function; Congo Basin; methane; methanogenesis; methanotrophy; microbial ecology
Citation Formats
Meyer, Kyle M., Hopple, Anya M., Klein, Ann M., Morris, Andrew H., Bridgham, Scott D., and Bohannan, Brendan J. M. Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function. United States: N. p., 2020.
Web. doi:10.1111/mec.15442.
Meyer, Kyle M., Hopple, Anya M., Klein, Ann M., Morris, Andrew H., Bridgham, Scott D., & Bohannan, Brendan J. M. Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function. United States. https://doi.org/10.1111/mec.15442
Meyer, Kyle M., Hopple, Anya M., Klein, Ann M., Morris, Andrew H., Bridgham, Scott D., and Bohannan, Brendan J. M. Mon .
"Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function". United States. https://doi.org/10.1111/mec.15442. https://www.osti.gov/servlets/purl/1801803.
@article{osti_1801803,
title = {Community structure – Ecosystem function relationships in the Congo Basin methane cycle depend on the physiological scale of function},
author = {Meyer, Kyle M. and Hopple, Anya M. and Klein, Ann M. and Morris, Andrew H. and Bridgham, Scott D. and Bohannan, Brendan J. M.},
abstractNote = {Abstract Belowground ecosystem processes can be highly variable and difficult to predict using microbial community data. Here, we argue that this stems from at least three issues: (a) complex covariance structure of samples (with environmental conditions or spatial proximity) can make distinguishing biotic drivers a challenge; (b) communities can control ecosystem processes through multiple mechanisms, making the identification of these controls a challenge; and (c) ecosystem function assessments can be broad in physiological scale, encapsulating multiple processes with unique microbially mediated controls. We test these assertions using methane (CH 4 )‐cycling processes in soil samples collected along a wetland‐to‐upland habitat gradient in the Congo Basin. We perform our measurements of function under controlled laboratory conditions and statistically control for environmental covariates to aid in identifying biotic drivers. We divide measurements of microbial communities into four attributes (abundance, activity, composition, and diversity) that represent different forms of community control. Lastly, our process measurements differ in physiological scale, including broader processes (gross methanogenesis and methanotrophy) that involve more mediating groups, to finer processes (hydrogenotrophic methanogenesis and high‐affinity CH 4 oxidation) with fewer mediating groups. We observed that finer scale processes can be more readily predicted from microbial community structure than broader scale processes. In addition, the nature of those relationships differed, with broad processes limited by abundance while fine‐scale processes were associated with diversity and composition. These findings demonstrate the importance of carefully defining the physiological scale of ecosystem function and performing community measurements that represent the range of possible controls on ecosystem processes.},
doi = {10.1111/mec.15442},
journal = {Molecular Ecology},
number = 10,
volume = 29,
place = {United States},
year = {Mon Apr 13 00:00:00 EDT 2020},
month = {Mon Apr 13 00:00:00 EDT 2020}
}
Web of Science
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