The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
ΔTrait SDMs: species distribution models that account for local adaptation and phenotypic plasticity
Open accessAbstract
Summary: Improving our understanding of species ranges under rapid climate change requires application of our knowledge of the tolerance and adaptive capacity of populations to changing environmental conditions. Here, we describe an emerging modelling approach, ΔTrait SDM, which attempts to achieve this by explaining species distribution ranges based on phenotypic plasticity and local adaptation of fitness‐related traits measured across large geographical gradients. The collection of intraspecific trait data measured in common gardens spanning broad environmental clines has promoted the development of these new models – first in trees but now rapidly expanding to other organisms. We review, explain and harmonize the main findings from this new generation of models that, by including trait variation over geographical scales, are able to provide new insights into future species ranges. Overall, ΔTrait SDM predictions generally deliver a less alarming message than previous models of species distribution under new climates, indicating that phenotypic plasticity should help, to a considerable degree, some plant populations to persist under climate change. The development of ΔTrait SDM s offers a new perspective to analyse intraspecific variation in single and multiple traits, with the rationale that trait (co)variation and consequently fitness can significantly change across geographical gradients and new climates.
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
Accelerating extinction risk from climate change
Urban, M. C. (2015) · Science · doi:10.1126/science.aaa4984 · Cited by 2,436
Anderegg, W. R. L., Klein, T., Bartlett, M., Sack, L., Pellegrini, A. F. A., Choat, B., & Jansen, S. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1525678113 · Cited by 882
Cited by
Evolutionary genomics can improve prediction of species’ responses to climate change
Waldvogel, A.-M., Feldmeyer, B., Rolshausen, G., Exposito-Alonso, M., Rellstab, C., Kofler, R., Mock, T., Schmid, K., Schmitt, I., Bataillon, T., et al. (2020) · Evolution Letters · doi:10.1002/evl3.154 · Cited by 348
Neale, R. E., Barnes, P. W., Robson, T. M., Neale, P. J., Williamson, C. E., Zepp, R. G., Wilson, S. R., Madronich, S., Andrady, A. L., Heikkilä, A. M., et al. (2021) · Photochemical & Photobiological Sciences · doi:10.1007/s43630-020-00001-x · Cited by 225
Self-citationBarnes, P. W., Robson, T. M., Zepp, R. G., Bornman, J. F., Jansen, M. A. K., Ossola, R., Wang, Q.-W., Robinson, S. A., Foereid, B., Klekociuk, A. R., et al. (2023) · Photochemical & Photobiological Sciences · doi:10.1007/s43630-023-00376-7 · Cited by 113
Self-citationSubstantial declines in urban tree habitat predicted under climate change
Burley, H., Beaumont, L. J., Ossola, A., Baumgartner, J. B., Gallagher, R., Laffan, S., Esperon-Rodriguez, M., Manea, A., & Leishman, M. R. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2019.05.287 · Cited by 90
Forecasting trait responses in novel environments to aid seed provenancing under climate change
Putra, A. R., Yen, J. D. L., & Fournier‐Level, A. (2023) · Molecular Ecology Resources · doi:10.1111/1755-0998.13728 · Cited by 7
Metadata
- Authors
- Benito Garzón, M., Robson, T. M., & Hampe, A.
- Venue
- New Phytologist
- Publisher
- Wiley
- Published
- 2019-02-27
- Volume
- 222
- Issue
- 4
- Pages
- 1757-1765
- ISSN
- 0028-646X (ISSN-L)
- 1469-8137
- OpenAlex
- W2912413427
- Cited by
- 280
- References
- 2
- FWCI
- 49.80
- 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: ΔTrait SDMs: species distribution models that account for local adaptation and phenotypic plasticity. https://app.cochat.ai/papers/10.1111/nph.15716
- The paper
- Benito Garzón, M., Robson, T. M., & Hampe, A. (2019). ΔTrait SDMs: species distribution models that account for local adaptation and phenotypic plasticity. New Phytologist. https://doi.org/10.1111/nph.15716
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.