Predictive habitat distribution models in ecology
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A GAM enables fitting non-linear models for a wide family of statistical distributions.
“A GAM enables fitting non-linear models for a wide family of statistical distributions, and thus is preferred to some other habitat modelling approaches (Guisan and Zimmermann, 2000).”
From Global habitat preferences of commercially valuable tuna (2015) · cites this paper as Guisan and Zimmermann, 2000
Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography.
“Spatially explicit linear regression models have been conventionally used to predict species and community distribution based on explanatory variables such as climate and topography124,125.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [124]
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- Authors
- Guisan, A., & Zimmermann, N. E.
- Affiliations
- info fauna (CH)
- Swiss Federal Institute for Forest, Snow and Landscape Research (CH)
- Corresponding author
- Guisan, Antoine
- Zimmermann, Niklaus E.
- Venue
- Ecological Modelling
- Publisher
- Elsevier BV
- Published
- 2000-12
- Volume
- 135
- Issue
- 2-3
- Pages
- 147-186
- ISSN
- 0304-3800 (ISSN-L)
- OpenAlex
- W2120160157
- Cited by
- 7,359
- References
- 294
- Citations by year
- 2026: 224 · 2025: 315 · 2024: 303 · 2023: 274 · 2022: 359 · 2021: 386 · 2020: 389 · 2019: 317 · 2018: 336 · 2017: 363 · 2016: 358 · 2015: 340 · 2014: 347 · 2013: 386 · 2012: 380
- FWCI
- 61.22
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Ecological Modelling (publishedVersion)
- DORA WSL (Swiss Federal Institute for Forest, Snow and Landscape Research) (submittedVersion)
- Topics
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Nature and Landscape Conservation › Ecology and Vegetation Dynamics Studies
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Keywords
- species distribution modeling, generalized linear models, artificial neural networks, generalized additive models, Bayesian model, receiver operating characteristic, model evaluation, model selection, predictor selection, realized niche, conservation biology, climate change, biogeography, model generalization
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