The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Regression models for spatial prediction: their role for biodiversity and conservation
Overview
Integrity signals
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Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography124,125.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [125]
Citations
Cites
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Brown, R. A., Hastie, T. J., & Tibshirani, R. J. (1991) · Biometrics · doi:10.2307/2532174 · Cited by 8,263
Predictive habitat distribution models in ecology
Guisan, A., & Zimmermann, N. E. (2000) · Ecological Modelling · doi:10.1016/s0304-3800(00)00354-9 · Cited by 7,359
Systematic conservation planning
Margules, C. R., & Pressey, R. L. (2000) · Nature · doi:10.1038/35012251 · Cited by 4,631
Rare species, the coincidence of diversity hotspots and conservation strategies
Prendergast, J. R., Quinn, R. M., Lawton, J. H., Eversham, B. C., & GIBBONS, D. W. (1993) · Nature · doi:10.1038/365335a0 · Cited by 1,110
Franklin, J. (1995) · Progress in Physical Geography Earth and Environment · doi:10.1177/030913339501900403 · Cited by 909
Predicting species occurrences : issues of accuracy and scale
Scott, J. M. (2002) · Cited by 806
Generalized additive models in plant ecology
Yee, T. W., & Mitchell, N. D. (1991) · Journal of Vegetation Science · doi:10.2307/3236170 · Cited by 716
Mapping Spatial Pattern in Biodiversity for Regional Conservation Planning: Where to from Here?
Ferrier, S. (2002) · Systematic Biology · doi:10.1080/10635150252899806 · Cited by 707
Does size matter? The relationship between pond area and biodiversity
Oertli, B., Joye, D. A., Castella, E., Juge, R., Cambin, D. G., & Lachavanne, J. (2002) · Biological Conservation · doi:10.1016/s0006-3207(01)00154-9 · Cited by 704
Research works printed but not linked: 4
- Ferrier S. and Watson G. 1997. An evaluation of the effectiveness of environmental surrogates and modelling techniques. In: Predicting the Distribution of Biological Diversity, Environment Australia, Canberra, Australia.
- Heegaard E. and Hangelbroek H.H. 1999. The distribution of Ulota crispa at a local scale in relation to both dispersal-and habitat-related factors. Lindbergia 24: 65–74.
- Overton J.McC., Leathwick J.R. and Lehmann A. 2000. Predict first, classify later – a new paradigm of spatial classification for environmental management: a revolution in the mapping of vegetation, soil, land cover, and other environmental information. 4th International Conference on Integrating GIS and Environmental Modelling (GIS/EM4), Canada (http://www.Colorado.EDU/research/cires/banff/upload/80/).
- Reid W.V. 1998. Biodiversity hotspots. TREE 13: 275–280.
Cited by
Sorted by citations
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740
Metadata
- Authors
- Lehmann, A., Overton, J. M., & Austin, M. P.
- Affiliations
- info fauna (CH)
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Corresponding author
- Lehmann, A.
- Venue
- Biodiversity & Conservation
- Published
- 2002-12
- Volume
- 11
- Issue
- 12
- Pages
- 2085-2092
- ISSN
- 0960-3115 (ISSN-L)
- 1572-9710
- OpenAlex
- W1531035825
- Cited by
- 111
- References
- 31
- Citations by year
- 2026: 2 · 2025: 2 · 2023: 1 · 2022: 4 · 2021: 2 · 2020: 2 · 2019: 1 · 2018: 1 · 2017: 4 · 2016: 2 · 2015: 5 · 2014: 2 · 2013: 9 · 2012: 5
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biodiversity and Conservation (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Keywords
- regression models, spatial prediction, biodiversity assessment, conservation management, spatially explicit analysis, computational power, biodiversity conservation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Regression models for spatial prediction: their role for biodiversity and conservation. https://app.cochat.ai/papers/10.1023/a:1021354914494
- The paper
- Lehmann, A., Overton, J. M., & Austin, M. P. (2002). Regression models for spatial prediction: their role for biodiversity and conservation. Biodiversity & Conservation. https://doi.org/10.1023/a%3A1021354914494
