Ensemble forecasting of species distributions
Abstract
Concern over implications of climate change for biodiversity has led to the use of bioclimatic models to forecast the range shifts of species under future climate-change scenarios. Recent studies have demonstrated that projections by alternative models can be so variable as to compromise their usefulness for guiding policy decisions. Here, we advocate the use of multiple models within an ensemble forecasting framework and describe alternative approaches to the analysis of bioclimatic ensembles, including bounding box, consensus and probabilistic techniques. We argue that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.
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The ensemble of replicate maps used the median value of each pixel, reducing the effect of outliers.
“The same function then projected all models onto the speci fi ed future environments, across all RCP scenarios, and created the ensemble of all replicate maps using the median value of each pixel, thus reducing the effect of outliers (Arau ́ jo and New, 2007; Shipley et al., 2022).”
From Projecting future climate change impacts on the distribution of the ‘Octopus vulgaris species complex’ (2022) · cites this paper as Araújo and New, 2007
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“The same function then projected all models onto the specified future environments, across all RCP scenarios, and created the ensemble of all replicate maps using the median value of each pixel, thus reducing the effect of outliers (Araújo and New, 2007; Shipley et al., 2022).”
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Mendonça, P., Carneiro, L. S., Leandro‐Silva, V., Aleixo, A., Araripe, J., & Rêgo, P. S. (2025) · Ecology and Evolution · doi:10.1002/ece3.70331
- Authors
- ARAUJO, M., & NEW, M.
- Affiliations
- University of Oxford (GB)
- ORCID
- NEW, M
- Venue
- Trends in Ecology & Evolution
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 22
- Issue
- 1
- Pages
- 42-47
- ISSN
- 0169-5347 (ISSN-L)
- OpenAlex
- W2056868695
- Cited by
- 3,640
- References
- 2
- Citations by year
- 2026: 357 · 2025: 312 · 2024: 292 · 2023: 244 · 2022: 279 · 2021: 257 · 2020: 261 · 2019: 198 · 2018: 169 · 2017: 173 · 2016: 149 · 2015: 139 · 2014: 136 · 2013: 172 · 2012: 139
- FWCI
- 26.81
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Trends in Ecology & Evolution (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Natural Environment Research Council (tynd10001)
- 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
- Life Sciences › Agricultural and Biological Sciences › Plant Science › Botany and Plant Ecology Studies
- Keywords
- ensemble forecasting, species distribution modeling, bioclimatic models, range shifts, climate change, model uncertainty, consensus methods, probabilistic forecasting, biodiversity projections
- MeSH terms
- Forecasting, Geography, Models, Biological, Greenhouse Effect, Ecosystem
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