Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables
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Climate variables are often highly correlated and can generate spurious results when used in regression.
“Climate variables are often highly correlated and can generate spurious results when used in regression (34, 134).”
From Climate-driven declines in arthropod abundance restructure a rainforest food web (2018) · cites this paper as [34]
Cites
Model selection and multimodel inference: a practical information-theoretic approach.
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Non‐parametric multivariate analyses of changes in community structure
Clarke, K. R. (1993) · Australian Journal of Ecology · doi:10.1111/j.1442-9993.1993.tb00438.x · Cited by 14,196 · see citing works
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828 · see citing works
Randomization, Bootstrap and Monte Carlo Methods in Biology
Manly, B. F. J. (2018) · Chapman & Hall/CRC eBooks · doi:10.1201/9781315273075 · Cited by 4,233 · see citing works
Model Selection and Inference: A Practical Information-Theoretic Approach
Strawderman, R. L., Burnham, K. P., & Anderson, D. R. (2000) · Journal of the American Statistical Association · doi:10.2307/2669574 · Cited by 4,194 · see citing works
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Randomization, Bootstrap and Monte Carlo Methods in Biology.
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Buckland, S. T., Burnham, K. P., & Augustin, N. H. (1997) · Biometrics · doi:10.2307/2533961 · Cited by 1,717 · see citing works
Mac Nally, R. (2000) · Biodiversity & Conservation · doi:10.1023/a:1008985925162 · Cited by 938 · see citing works
Self-citationChevan, A., & Sutherland, M. (1991) · The American Statistician · doi:10.1080/00031305.1991.10475776 · Cited by 761 · see citing works
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Hierarchical partitioning as an interpretative tool in multivariate inference
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Nally, R. M., Bennett, A. F., & Horrocks, G. (2000) · Biological Conservation · doi:10.1016/s0006-3207(00)00017-3 · Cited by 164 · see citing works
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The art of modelling range-shifting species: <i>The art of modelling range-shifting species</i>
Elith, J., Kearney, M., & Phillips, S. (2010) · Methods in Ecology and Evolution · doi:10.1111/j.2041-210x.2010.00036.x · Cited by 2,813 · see citing works
Climate-driven declines in arthropod abundance restructure a rainforest food web
Lister, B. C., & Garcia, A. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1722477115 · Cited by 730 · see citing works
Creamer, R. E., Hannula, S. E., Leeuwen, J. P. V., Stone, D., Rutgers, M., Schmelz, R. M., Ruiter, P. C. D., Hendriksen, N. B., Bolger, T., Bouffaud, M. L., et al. (2016) · Applied Soil Ecology · doi:10.1016/j.apsoil.2015.08.006 · Cited by 275 · see citing works
Olea, P. P., Mateo-Tomás, P., & de Frutos, Á. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011698 · Cited by 109 · see citing works
- Authors
- Mac Nally, R.
- Venue
- Biodiversity & Conservation
- Published
- 2002-08
- Volume
- 11
- Issue
- 8
- Pages
- 1397-1401
- ISSN
- 0960-3115 (ISSN-L)
- 1572-9710
- OpenAlex
- W276691513
- Cited by
- 554
- References
- 13
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 14 · 2023: 21 · 2022: 13 · 2021: 20 · 2020: 19 · 2019: 15 · 2018: 19 · 2017: 30 · 2016: 35 · 2015: 39 · 2014: 40 · 2013: 30 · 2012: 44
- FWCI
- 11.92
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Biodiversity and Conservation (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Keywords
- multiple linear regression, predictor selection, multicollinearity, hierarchical partitioning, randomization, model selection criteria, Bayes factor, species richness, conservation biology, ecological inference
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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.
Statistics and arithmetic are recomputed, not judged.
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