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Cited by 1,479

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Habitat Suitability and Distribution Models: With Applications in R

Guisan, Antoine, Thuiller, Wilfried, Zimmermann, Niklaus E.·2017·Cambridge University Press eBooks

Abstract

This book introduces the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity. Beginning with the main theory behind ecological niches and species distributions, the book proceeds through all major steps of model building, from conceptualization and model training to model evaluation and spatio-temporal predictions. Extensive examples using R support graduate students and researchers in quantifying ecological niches and predicting species distributions with their own data, and help to address key environmental and conservation problems. Reflecting this highly active field of research, the book incorporates the latest developments from informatics and statistics, as well as using data from remote sources such as satellite imagery. A website at www.unil.ch/hsdm contains the codes and supporting material required to run the examples and teach courses.

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Metadata

Authors
Guisan, A., Thuiller, W., & Zimmermann, N. E.
Venue
Cambridge University Press eBooks
Published
2017-09-14
OpenAlex
W2958856141
Cited by
1,479
References
1
FWCI
5.82
Type
book
Language
en
Open access
closed

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CoChat. (2026). Citation check: Habitat Suitability and Distribution Models: With Applications in R. https://app.cochat.ai/papers/10.1017/9781139028271
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Guisan, A., Thuiller, W., & Zimmermann, N. E. (2017). Habitat Suitability and Distribution Models: With Applications in R. Cambridge University Press eBooks. https://doi.org/10.1017/9781139028271

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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.