Cited by 938Statistical Methods and ApplicationsStatistical Methods and InferenceSpecies Distribution and Climate Change

Regression and model-building in conservation biology, biogeography and ecology: The distinction between – and reconciliation of – ‘predictive’ and ‘explanatory’ models

Mac Nally, Ralph·2000·Biodiversity & Conservation

Integrity signals

Registry

Crossref · 10.1023/a:1008985925162

Cites

51 of 51 references verified0 citation errors · 0 not checkable

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  • Automated regression techniques such as stepwise selection and backward elimination have issues including erroneous type I error rates and inflated R2 measures.

    “We did not utilize automated regression techniques, such as stepwise selection and backward elimination, due to a variety of issues including erroneous type I error rates and inflated R2 measures (33).”

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Authors
Mac Nally, R.
Venue
Biodiversity & Conservation
Published
2000-05
Volume
9
Issue
5
Pages
655-671
ISSN
  • 0960-3115 (ISSN-L)
  • 1572-9710
OpenAlex
W226176070
Cited by
938
References
51
Citations by year
2026: 13 · 2025: 13 · 2024: 13 · 2023: 27 · 2022: 19 · 2021: 37 · 2020: 41 · 2019: 22 · 2018: 30 · 2017: 43 · 2016: 47 · 2015: 48 · 2014: 65 · 2013: 64 · 2012: 55
FWCI
19.96
Type
journal article
Language
en
Open access
closed
Locations
Biodiversity and Conservation (publishedVersion)
Funders
Australian Research Council
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
  • Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
Keywords
regression models, multicollinearity, model selection, multiple linear regression, causal inference, conservation biology, biogeography, ecology

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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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