Integrating the underlying structure of stochasticity into community ecology
Research output: Journal contributions › Journal articles › Research › peer-review
Authors
Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics.
| Original language | English |
|---|---|
| Article number | e02922 |
| Journal | Ecology |
| Volume | 101 |
| Issue number | 2 |
| Number of pages | 17 |
| ISSN | 0012-9658 |
| DOIs | |
| Publication status | Published - 01.02.2020 |
Bibliographical note
Funding Information:
LGS and LLS contributed equally to this manuscript, and authorship was determined by a single draw from a Bernoulli distribution. This work is a joint effort of the working group sNiche (Expanding neo-Chessonian coexistence theory towards a stochastic theory for species rich communities) supported by sDiv, the Synthesis Centre of iDiv (DFG FZT 118). We thank editor Dr. Tom Miller, two anonymous reviewers, and the Snyder and Abbott lab groups for comments that greatly improved the quality of this manuscript. During the completion of this work, LLS was supported by the Legislative-Citizen Commission on Minnesota Resources (LCCMR) Environmental and Natural Resources Trust Fund (ENRTF) grant (M.L. 2016, Ch. 186, Sec. 2, Subd. 08b), and startup funds from the University of Minnesota provided to Allison K. Shaw; LGS was supported by the James S. McDonnell Foundation grant 220020513; KCA was supported by a Complex Systems Scholar Award from the James S. McDonnell Foundation; MMM was supported by an ARC Future Fellowship (FT140100498); NJBK was supported by the National Science Foundation (DEB 1644641); XW was supported by the National Natural Science Foundation of China (31722010) and the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB31030000); CC was supported by the National Natural Science Foundation of China (31570426 and 31622014); QY was supported by a postgraduate scholarship from the Irish Research Council (GOIPG/2013/1474). LLS, LGS, and KCA led the manuscript, LLS, LGS, ID, JSC, RJW, and MMM contributed substantially to writing, modeling, and/or word association analyses. All authors contributed to idea development and provided comments on earlier drafts.
Publisher Copyright:
© 2019 The Authors. Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America
- autocorrelation, demographic stochasticity, distribution, diversity, environmental stochasticity, population dynamics, scale, uncertainty
- Ecosystems Research
- Environmental Governance