The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Environmental heterogeneity as a universal driver of species richness across taxa, biomes and spatial scales
Open accessAbstract
Environmental heterogeneity is regarded as one of the most important factors governing species richness gradients. An increase in available niche space, provision of refuges and opportunities for isolation and divergent adaptation are thought to enhance species coexistence, persistence and diversification. However, the extent and generality of positive heterogeneity–richness relationships are still debated. Apart from widespread evidence supporting positive relationships, negative and hump‐shaped relationships have also been reported. In a meta‐analysis of 1148 data points from 192 studies worldwide, we examine the strength and direction of the relationship between spatial environmental heterogeneity and species richness of terrestrial plants and animals. We find that separate effects of heterogeneity in land cover, vegetation, climate, soil and topography are significantly positive, with vegetation and topographic heterogeneity showing particularly strong associations with species richness. The use of equal‐area study units, spatial grain and spatial extent emerge as key factors influencing the strength of heterogeneity–richness relationships, highlighting the pervasive influence of spatial scale in heterogeneity–richness studies. We provide the first quantitative support for the generality of positive heterogeneity–richness relationships across heterogeneity components, habitat types, taxa and spatial scales from landscape to global extents, and identify specific needs for future comparative heterogeneity–richness research.
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Conducting Meta-Analyses in R with the metafor Package
Viechtbauer, W. (2010) · Journal of Statistical Software · doi:10.18637/jss.v036.i03 · Cited by 20,464
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009) · doi:10.1002/9780470743386 · Cited by 14,225
Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2021) · doi:10.1002/9781119558378 · Cited by 9,799
Terrestrial ecoregions of the world: a new map of life on Earth
Olson, D. M., Dinerstein, E., Wikramanayake, E. D., Burgess, N. D., Powell, G. V. N., Underwood, E. C., D'amico, J. A., Itoua, I., Strand, H. E., Morrison, J. C., et al. (2001) · BioScience · doi:10.1641/0006-3568(2001)051[0933:teotwa]2.0.co;2 · Cited by 8,488
The Handbook of Research Synthesis and Meta-Analysis
(2019) · doi:10.7758/9781610448864 · Cited by 4,553
Fixed-and random-effects models in meta-analysis.
Hedges, L. V., & Vevea, J. L. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.486 · Cited by 2,977
Peters, J. L., Sutton, A. J., Jones, D. R., Abrams, K. R., & Rushton, L. (2008) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2007.11.010 · Cited by 2,053
Rosenberg, M. S. (2005) · Evolution · doi:10.1111/j.0014-3820.2005.tb01004.x · Cited by 1,285
Handbook of Meta-analysis in Ecology and Evolution
(2013) · doi:10.1515/9781400846184 · Cited by 1,183
Methodological issues and advances in biological meta-analysis
Nakagawa, S., & Santos, E. S. A. (2012) · Evolutionary Ecology · doi:10.1007/s10682-012-9555-5 · Cited by 1,032
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Tanner-Smith, E. E., Tipton, E., & Polanin, J. R. (2016) · Journal of Developmental and Life-Course Criminology · doi:10.1007/s40865-016-0026-5 · Cited by 390
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Davies, A. B., & Asner, G. P. (2014) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2014.10.005 · Cited by 379
Tuanmu, M., & Jetz, W. (2015) · Global Ecology and Biogeography · doi:10.1111/geb.12365 · Cited by 331
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Nonato, F. A. S., Michelan, T. S., Freitas, P. V., Maia, C., & Montag, L. F. D. A. (2021) · Aquatic Ecology · doi:10.1007/s10452-020-09823-4 · Cited by 23
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Metadata
- Authors
- Stein, A., Gerstner, K., & Kreft, H.
- Venue
- Ecology Letters
- Publisher
- Wiley
- Published
- 2014-04-20
- Volume
- 17
- Issue
- 7
- Pages
- 866-880
- ISSN
- 1461-023X (ISSN-L)
- 1461-0248
- OpenAlex
- W2122131085
- Cited by
- 2,054
- References
- 13
- FWCI
- 69.61
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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