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Cited by 747

The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.

Local Processes and Regional Patterns: Appropriate Scales for Understanding Variation in the Diversity of Plants and Animals

Huston, Michael A.·1999·Oikos
DOI 10.2307/3546645
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Abstract

Determining the causes of variation in species diversity requires linking the scales at which variation in diversity is measured to the scales at which the processes hypothesized to affect diversity actually operate. Published analyses of the relative effects of local versus regional processes on species diversity have failed to measure diversity at spatial scales relevant to local processes. The effects of local processes. such as competition, can only be detected at appropriately small local scales, and are obscured by large samples that aggregate environmental heterogeneity. Relatively few ecological and evolutionary processes can be identified as uniquely regional in scale. and local processes are expected to produce regional-scale differences between regions that differ consistently in environmental conditions that affect local processes. The relative contributions of regional properties and local processes are hypothesized to vary predictably along certain environmental gradients, and to produce locally regulated patterns of species diversity at scales ranging from a few millimeters to the entire globe.

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Metadata

Authors
Huston, M. A.
Venue
Oikos
Publisher
JSTOR
Published
1999-09
Volume
86
Issue
3
Pages
393
ISSN
0030-1299 (ISSN-L)
DOI
10.2307/3546645
OpenAlex
W2138303671
Cited by
747
References
44
FWCI
31.79
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Local Processes and Regional Patterns: Appropriate Scales for Understanding Variation in the Diversity of Plants and Animals. https://app.cochat.ai/papers/10.2307/3546645
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Huston, M. A. (1999). Local Processes and Regional Patterns: Appropriate Scales for Understanding Variation in the Diversity of Plants and Animals. Oikos. https://doi.org/10.2307/3546645

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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.