The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Niches, models, and climate change: Assessing the assumptions and uncertainties
Open accessAbstract
As the rate and magnitude of climate change accelerate, understanding the consequences becomes increasingly important. Species distribution models (SDMs) based on current ecological niche constraints are used to project future species distributions. These models contain assumptions that add to the uncertainty in model projections stemming from the structure of the models, the algorithms used to translate niche associations into distributional probabilities, the quality and quantity of data, and mismatches between the scales of modeling and data. We illustrate the application of SDMs using two climate models and two distributional algorithms, together with information on distributional shifts in vegetation types, to project fine-scale future distributions of 60 California landbird species. Most species are projected to decrease in distribution by 2070. Changes in total species richness vary over the state, with large losses of species in some “hotspots” of vulnerability. Differences in distributional shifts among species will change species co-occurrences, creating spatial variation in similarities between current and future assemblages. We use these analyses to consider how assumptions can be addressed and uncertainties reduced. SDMs can provide a useful way to incorporate future conditions into conservation and management practices and decisions, but the uncertainties of model projections must be balanced with the risks of taking the wrong actions or the costs of inaction. Doing this will require that the sources and magnitudes of uncertainty are documented, and that conservationists and resource managers be willing to act despite the uncertainties. The alternative, of ignoring the future, is not an option.
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Do species’ traits predict recent shifts at expanding range edges?: Traits and range shifts
Angert, A. L., Crozier, L. G., Rissler, L. J., Gilman, S. E., Tewksbury, J. J., & Chunco, A. J. (2011) · Ecology Letters · doi:10.1111/j.1461-0248.2011.01620.x · Cited by 591
Predicting changes in the distribution and abundance of species under environmental change
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Chaloner, T. M., Gurr, S. J., & Bebber, D. P. (2021) · Nature Climate Change · doi:10.1038/s41558-021-01104-8 · Cited by 470
Which provenance and where? Seed sourcing strategies for revegetation in a changing environment
Breed, M. F., Stead, M. G., Ottewell, K. M., Gardner, M. G., & Lowe, A. J. (2013) · Conservation Genetics · doi:10.1007/s10592-012-0425-z · Cited by 461
Metadata
- Authors
- Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A., & Snyder, M. A.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2009-11-17
- Volume
- 106
- Issue
- supplement_2
- Pages
- 19729-19736
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2070750558
- Cited by
- 966
- References
- 7
- FWCI
- 22.31
- Type
- journal article
- Language
- en
- Open access
- green
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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.