The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Stochasticity in Structural and Functional Characteristics of an Indiana Stream Fish Assemblage: A Test of Community Theory
Abstract
In general, ecological assemblages and communities appear to be regulated primarily by either deterministic or stochastic processes. It is currently important to quantify the relative frequencies of these two types of assemblages since most ecological theory is applicable only to deterministic systems. We attempted to distinguish the mechanism regulating species abundances and trophic structure in an Indiana stream fish assemblage. Samples collected over a 12-yr period were separated by season (spring, summer, and autumn) and species were then assigned to a trophic group based on published dietary data. Analyses showed a total lack of persistence for the ranks of species abundances and the ranks of trophic groups for all seasons. Consequently, we conclude that this assemblage is probably regulated by stochastic factors. This finding is concordant with many other studies which have documented the substantial effects of environmental unpredictability (i.e., floods and droughts) on other stream taxocenes.
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Linkages between flow regime, biota, and ecosystem processes: implications for river restoration
Palmer, M., & Ruhi, A. (2019) · Science · doi:10.1126/science.aaw2087 · Cited by 791
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Developments in Stream Ecosystem Theory
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Metadata
- Authors
- Grossman, G. D., Moyle, P. B., & Whitaker
- Venue
- The American Naturalist
- Publisher
- University of Chicago Press
- Published
- 1982-10
- Volume
- 120
- Issue
- 4
- Pages
- 423-454
- ISSN
- 0003-0147 (ISSN-L)
- 1537-5323
- OpenAlex
- W1996247834
- Cited by
- 411
- References
- 60
- FWCI
- 18.61
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
