The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
sdm: a reproducible and extensible R platform for species distribution modelling
Abstract
sdm is an object‐oriented, reproducible and extensible, platform for species distribution modelling. It uses individual species and community‐based approaches, enabling ensembles of models to be fitted and evaluated, to project species potential distributions in space and time. It provides a standardized and unified structure for handling species distributions data and modelling techniques, and supports markedly different modelling approaches, including correlative, process‐based (mechanistic), agent‐based, and cellular automata. The object‐oriented design of software is such that scientists can modify existing methods, extend the framework by developing new methods or modelling procedures, and share them to be reproduced by other scientists. sdm can handle spatial and temporal data for single or multiple species and uses high performance computing solutions to speed up modelling and simulations. The framework is implemented in R, providing a flexible and easy‐to‐use GUI interface.
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An evaluation of methods for modelling species distributions
Segurado, P., & Araújo, M. B. (2004) · Journal of Biogeography · doi:10.1111/j.1365-2699.2004.01076.x · Cited by 875 · cited 3×
“Given the difficulties in comparing the results of different models, conclusions from model comparison studies are difficult to generalise beyond the specific case studies (Segurado and Araújo 2004, Elith et al. 2006).”
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Bio‐ORACLE v2.0: Extending marine data layers for bioclimatic modelling
Assis, J., Tyberghein, L., Bosch, S., Verbruggen, H., Serrão, E. A., & De Clerck, O. (2018) · Global Ecology and Biogeography · doi:10.1111/geb.12693 · Cited by 931
Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019) · Science of The Total Environment · doi:10.1016/j.scitotenv.2018.10.064 · Cited by 787
Hao, T., Elith, J., Guillera‐Arroita, G., & Lahoz‐Monfort, J. J. (2019) · Diversity and Distributions · doi:10.1111/ddi.12892 · Cited by 674
Want to model a species niche? A step-by-step guideline on correlative ecological niche modelling
Sillero, N., Arenas-Castro, S., Enriquez‐Urzelai, U., Vale, C. G., Sousa-Guedes, D., Martínez-Freiría, F., Real, R., & Barbosa, A. M. (2021) · Ecological Modelling · doi:10.1016/j.ecolmodel.2021.109671 · Cited by 456
Kaky, E., Nolan, V., Alatawi, A., & Gilbert, F. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101150 · Cited by 432
Metadata
- Authors
- Naimi, B., & Araújo, M. B.
- Venue
- Ecography
- Publisher
- Wiley
- Published
- 2016-03
- Volume
- 39
- Issue
- 4
- Pages
- 368-375
- ISSN
- 0906-7590 (ISSN-L)
- 1600-0587
- OpenAlex
- W2324360419
- Cited by
- 1,137
- References
- 8
- FWCI
- 20.43
- Type
- journal article
- Language
- en
- Open access
- closed
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- Naimi, B., & Araújo, M. B. (2016). sdm: a reproducible and extensible R platform for species distribution modelling. Ecography. https://doi.org/10.1111/ecog.01881
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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.
