The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Incorporating uncertainty in predictive species distribution modelling
Open accessAbstract
Motivated by the need to solve ecological problems (climate change, habitat fragmentation and biological invasions), there has been increasing interest in species distribution models (SDMs). Predictions from these models inform conservation policy, invasive species management and disease-control measures. However, predictions are subject to uncertainty, the degree and source of which is often unrecognized. Here, we review the SDM literature in the context of uncertainty, focusing on three main classes of SDM: niche-based models, demographic models and process-based models. We identify sources of uncertainty for each class and discuss how uncertainty can be minimized or included in the modelling process to give realistic measures of confidence around predictions. Because this has typically not been performed, we conclude that uncertainty in SDMs has often been underestimated and a false precision assigned to predictions of geographical distribution. We identify areas where development of new statistical tools will improve predictions from distribution models, notably the development of hierarchical models that link different types of distribution model and their attendant uncertainties across spatial scales. Finally, we discuss the need to develop more defensible methods for assessing predictive performance, quantifying model goodness-of-fit and for assessing the significance of model covariates.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A globally coherent fingerprint of climate change impacts across natural systems
Parmesan, C., & Yohe, G. (2003) · Nature · doi:10.1038/nature01286 · Cited by 11,265
Novel methods improve prediction of species’ distributions from occurrence data
Elith*, J., H. Graham*, C., P. Anderson, R., Dudík, M., Ferrier, S., Guisan, A., J. Hijmans, R., Huettmann, F., R. Leathwick, J., Lehmann, A., et al. (2006) · Ecography · doi:10.1111/j.2006.0906-7590.04596.x · Cited by 9,088
The file drawer problem and tolerance for null results.
Rosenthal, R. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.3.638 · Cited by 9,068
Extinction risk from climate change
Thomas, C. D., Cameron, A., Green, R. E., Bakkenes, M., Beaumont, L. J., Collingham, Y. C., Erasmus, B. F. N., de Siqueira, M. F., Grainger, A., Hannah, L., et al. (2004) · Nature · doi:10.1038/nature02121 · Cited by 7,695
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
Species Distribution Models: Ecological Explanation and Prediction Across Space and Time
Elith, J., & Leathwick, J. R. (2009) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev.ecolsys.110308.120159 · Cited by 6,707
Predicting species distribution: offering more than simple habitat models
Guisan, A., & Thuiller, W. (2005) · Ecology Letters · doi:10.1111/j.1461-0248.2005.00792.x · Cited by 6,587
Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation
Phillips, S. J., & Dudík, M. (2008) · Ecography · doi:10.1111/j.0906-7590.2008.5203.x · Cited by 5,890
Ensemble forecasting of species distributions
ARAUJO, M., & NEW, M. (2007) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2006.09.010 · Cited by 3,640
Hanski, L. (1999) · doi:10.1093/oso/9780198540663.001.0001 · Cited by 3,535
Cited by
Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity
Hortal, J., de Bello, F., Diniz-Filho, J. A. F., Lewinsohn, T. M., Lobo, J. M., & Ladle, R. J. (2015) · Annual Review of Ecology, Evolution, and Systematics · doi:10.1146/annurev-ecolsys-112414-054400 · Cited by 1,743
Norberg, A., Abrego, N., Blanchet, F. G., Adler, F. R., Anderson, B. J., Anttila, J., Araújo, M. B., Dallas, T., Dunson, D., Elith, J., et al. (2019) · Ecological Monographs · doi:10.1002/ecm.1370 · Cited by 573
Multidimensional biases, gaps and uncertainties in global plant occurrence information
Meyer, C., Weigelt, P., & Kreft, H. (2016) · Ecology Letters · doi:10.1111/ele.12624 · Cited by 572
The Predictive Performance and Stability of Six Species Distribution Models
Duan, R.-Y., Kong, X.-Q., Huang, M.-Y., Fan, W.-Y., & Wang, Z.-G. (2014) · PLoS ONE · doi:10.1371/journal.pone.0112764 · Cited by 259
Species Distribution Modeling for Machine Learning Practitioners: A Review
Beery, S., Cole, E., Parker, J., Perona, P., & Winner, K. (2021) · ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3460112.3471966 · Cited by 100
Metadata
- Authors
- Beale, C. M., & Lennon, J. J.
- Venue
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2012-01-19
- Volume
- 367
- Issue
- 1586
- Pages
- 247-258
- ISSN
- 0962-8436 (ISSN-L)
- 1471-2970
- OpenAlex
- W2163833366
- Cited by
- 320
- References
- 93
- FWCI
- 10.30
- Type
- journal article
- Language
- en
- Open access
- green
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Incorporating uncertainty in predictive species distribution modelling. https://app.cochat.ai/papers/10.1098/rstb.2011.0178
- The paper
- Beale, C. M., & Lennon, J. J. (2012). Incorporating uncertainty in predictive species distribution modelling. Philosophical Transactions of the Royal Society B: Biological Sciences. https://doi.org/10.1098/rstb.2011.0178
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
