The DOI matches its Crossref record; 125 of the 125 references it cites were found in a registry or an index.
The recent past and promising future for data integration methods to estimate species’ distributions
Open accessAbstract
With the advance of methods for estimating species distribution models has come an interest in how to best combine datasets to improve estimates of species distributions. This has spurred the development of data integration methods that simultaneously harness information from multiple datasets while dealing with the specific strengths and weaknesses of each dataset. We outline the general principles that have guided data integration methods and review recent developments in the field. We then outline key areas that allow for a more general framework for integrating data and provide suggestions for improving sampling design and validation for integrated models. Key to recent advances has been using point‐process thinking to combine estimators developed for different data types. Extending this framework to new data types will further improve our inferences, as well as relaxing assumptions about how parameters are jointly estimated. These along with the better use of information regarding sampling effort and spatial autocorrelation will further improve our inferences. Recent developments form a strong foundation for implementation of data integration models. Wider adoption can improve our inferences about species distributions and the dynamic processes that lead to distributional shifts.
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
Maximum entropy modeling of species geographic distributions
Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006) · Ecological Modelling · doi:10.1016/j.ecolmodel.2005.03.026 · Cited by 15,396
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
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
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
ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., & Langtimm, C. A. (2002) · Ecology · doi:10.1890/0012-9658(2002)083[2248:esorwd]2.0.co;2 · Cited by 4,481
Pearson, R. G., & Dawson, T. P. (2003) · Global Ecology and Biogeography · doi:10.1046/j.1466-822x.2003.00042.x · Cited by 4,252
Introduction to Distance Sampling: Estimating Abundance of Biological Populations
Buckland, S. T., Anderson, D. R., Burnham, K. P., Laake, J. L., Borchers, D. L., & Thomas, L. (2001) · doi:10.1093/oso/9780198506492.001.0001 · Cited by 3,055
Explicit estimates from capture-recapture data with both death and immigration-stochastic model
Jolly, G. M. (1965) · Biometrika · doi:10.1093/biomet/52.1-2.225 · Cited by 2,605
Hierarchical Modeling and Analysis for Spatial Data
Banerjee, S., Carlin, B. P., & Gelfand, A. E. (2014) · Chapman & Hall/CRC eBooks · doi:10.1201/b17115 · Cited by 2,500
Cited by
(2021) · doi:10.1111/ddi.13271 · Cited by 407
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
Botella, C., Joly, A., Bonnet, P., Munoz, F., & Monestiez, P. (2021) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13565 · Cited by 17
Metadata
- Authors
- Miller, D. A. W., Pacifici, K., Sanderlin, J. S., & Reich, B. J.
- Venue
- Methods in Ecology and Evolution
- Publisher
- Wiley
- Published
- 2019-01
- Volume
- 10
- Issue
- 1
- Pages
- 22-37
- ISSN
- 2041-210X (ISSN-L)
- OpenAlex
- W2911448778
- Cited by
- 283
- References
- 125
- FWCI
- 43.98
- Type
- journal article
- Language
- en
- Open access
- hybrid
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: The recent past and promising future for data integration methods to estimate species’ distributions. https://app.cochat.ai/papers/10.1111/2041-210x.13110
- The paper
- Miller, D. A. W., Pacifici, K., Sanderlin, J. S., & Reich, B. J. (2019). The recent past and promising future for data integration methods to estimate species’ distributions. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210x.13110
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.
