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Title: A general framework for quantitatively assessing ecological stochasticity

Abstract

Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio ( NST ), was developed with 50% as the boundary point between more deterministic (<50%) and more stochastic (>50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic ( NST = 21%) tomore » more stochastic ( NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic ( NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.« less

Authors:
ORCiD logo; ; ORCiD logo;
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1547822
Alternate Identifier(s):
OSTI ID: 1580904
Grant/Contract Number:  
AC02-05CH11231; SC0014079; SC0016247; SC0010715
Resource Type:
Published Article
Journal Name:
Proceedings of the National Academy of Sciences of the United States of America
Additional Journal Information:
Journal Name: Proceedings of the National Academy of Sciences of the United States of America Journal Volume: 116 Journal Issue: 34; Journal ID: ISSN 0027-8424
Publisher:
National Academy of Sciences
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; stochasticity; community assembly; microbial ecology

Citation Formats

Ning, Daliang, Deng, Ye, Tiedje, James M., and Zhou, Jizhong. A general framework for quantitatively assessing ecological stochasticity. United States: N. p., 2019. Web. doi:10.1073/pnas.1904623116.
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Ning, Daliang, Deng, Ye, Tiedje, James M., & Zhou, Jizhong. A general framework for quantitatively assessing ecological stochasticity. United States. https://doi.org/10.1073/pnas.1904623116
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Ning, Daliang, Deng, Ye, Tiedje, James M., and Zhou, Jizhong. Wed . "A general framework for quantitatively assessing ecological stochasticity". United States. https://doi.org/10.1073/pnas.1904623116.
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@article{osti_1547822,
title = {A general framework for quantitatively assessing ecological stochasticity},
author = {Ning, Daliang and Deng, Ye and Tiedje, James M. and Zhou, Jizhong},
abstractNote = {Understanding the community assembly mechanisms controlling biodiversity patterns is a central issue in ecology. Although it is generally accepted that both deterministic and stochastic processes play important roles in community assembly, quantifying their relative importance is challenging. Here we propose a general mathematical framework to quantify ecological stochasticity under different situations in which deterministic factors drive the communities more similar or dissimilar than null expectation. An index, normalized stochasticity ratio ( NST ), was developed with 50% as the boundary point between more deterministic (<50%) and more stochastic (>50%) assembly. NST was tested with simulated communities by considering abiotic filtering, competition, environmental noise, and spatial scales. All tested approaches showed limited performance at large spatial scales or under very high environmental noise. However, in all of the other simulated scenarios, NST showed high accuracy (0.90 to 1.00) and precision (0.91 to 0.99), with averages of 0.37 higher accuracy (0.1 to 0.7) and 0.33 higher precision (0.0 to 1.8) than previous approaches. NST was also applied to estimate stochasticity in the succession of a groundwater microbial community in response to organic carbon (vegetable oil) injection. Our results showed that community assembly was shifted from more deterministic ( NST = 21%) to more stochastic ( NST = 70%) right after organic carbon input. As the vegetable oil was consumed, the community gradually returned to be more deterministic ( NST = 27%). In addition, our results demonstrated that null model algorithms and community similarity metrics had strong effects on quantifying ecological stochasticity.},
doi = {10.1073/pnas.1904623116},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
number = 34,
volume = 116,
place = {United States},
year = {2019},
month = {8}
}
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Journal Article:
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https://doi.org/10.1073/pnas.1904623116
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Cited by: 12 works
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Figures / Tables:

Fig. 1 Fig. 1: Consistency between the estimated and expected stochasticity with different methods based on the simulated communities with various levels of expected stochasticity. The simulation model was spatially implicit. Red indicates NST, green indicates ST, and blue indicates NP. STexp.ab (black), expected abundance-based stochasticity in the simulated communities. NST andmore » ST were calculated based on (A) Ružička and (B) modified Gower. The inner tables show accuracy coefficient (χa) and precision coefficient (ρ), which are derived from concordance correlation coefficient (SI Appendix, Eqs. S21 and S22). See SI Appendix, Supplementary Text C, Table S1, and Fig. S1A, for more details about the simulation model; SI Appendix, Table S2, for the results of other similarity metrics; and SI Appendix, Table S3, for the definition of each metric.« less
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