PapersFlow Research Brief
Species Distribution and Climate Change
Research Guide
What is Species Distribution and Climate Change?
Species Distribution and Climate Change is the study of how climate change alters species geographic ranges through methods like species distribution modeling, MaxEnt, and ecological niche modeling to assess biodiversity impacts, habitat suitability, and range shifts.
This field encompasses 976,937 works on modeling species distributions under climate change using techniques such as MaxEnt and ecological niche modeling. It examines effects on biodiversity, habitat suitability, range shifts, and conservation, with contributions from citizen science. Key papers include 'Maximum entropy modeling of species geographic distributions' by Phillips et al. (2006) with 17,003 citations and 'Biodiversity hotspots for conservation priorities' by Myers et al. (2000) with 30,468 citations.
Topic Hierarchy
Research Sub-Topics
MaxEnt Species Distribution Modeling
This sub-topic covers the application of Maximum Entropy (MaxEnt) algorithms for predicting species geographic distributions under current and future climates. Researchers evaluate model performance using AUC and niche overlap metrics.
Ecological Niche Modeling under Climate Change
This sub-topic focuses on modeling species' realized niches and their shifts due to climate variables like temperature and precipitation. Researchers integrate bioclimatic variables and dispersal constraints in projections.
Species Range Shift Projections
This sub-topic examines predicted poleward and elevational range shifts in response to global warming scenarios. Researchers analyze velocity of climate change and biotic interactions influencing observed shifts.
Habitat Suitability Mapping
This sub-topic involves GIS-based mapping of suitable habitats using SDMs for invasive species and endemics. Researchers validate maps with field data and assess uncertainty in suitability thresholds.
Citizen Science in Biodiversity Monitoring
This sub-topic explores the integration of crowdsourced data from platforms like eBird into species distribution models. Researchers develop bias-correction methods and validate contributions to ecological research.
Why It Matters
Species distribution modeling under climate change informs conservation by projecting habitat suitability and range shifts for endangered species. For example, Phillips et al. (2006) in 'Maximum entropy modeling of species geographic distributions' introduced MaxEnt, now used in studies like projections for Dionysia diapensiifolia in Iran, revealing threats from narrow ecological niches under future climates. Recent work on 'Global 1-km habitat distribution for endangered species and its spatial changes under future warming scenarios' provides high-precision maps linking land-use and elevation to aid biodiversity actions for terrestrial vertebrates. Parmesan and Yohe (2003) in 'A globally coherent fingerprint of climate change impacts across natural systems' documented range shifts across natural systems, supporting global conservation efforts.
Reading Guide
Where to Start
'Maximum entropy modeling of species geographic distributions' by Phillips et al. (2006), as it introduces MaxEnt, a foundational method for species distribution modeling used across climate change studies.
Key Papers Explained
Phillips et al. (2006) in 'Maximum entropy modeling of species geographic distributions' established MaxEnt for presence-only data, foundational for projections. Myers et al. (2000) in 'Biodiversity hotspots for conservation priorities' identified priority areas vulnerable to range shifts modeled by MaxEnt. Parmesan and Yohe (2003) in 'A globally coherent fingerprint of climate change impacts across natural systems' provided empirical evidence of shifts, while Dormann et al. (2012) in 'Collinearity: a review of methods to deal with it and a simulation study evaluating their performance' addressed modeling challenges like predictor collinearity. Walther et al. (2002) in 'Ecological responses to recent climate change' built on this with specific response examples.
Paper Timeline
Most-cited paper highlighted in red. Papers ordered chronologically.
Advanced Directions
Recent preprints focus on high-resolution projections, such as 'Global 1-km habitat distribution for endangered species and its spatial changes under future warming scenarios' and rodent genera under SSP-RCP scenarios to 2100. 'Global dataset for realized thermal and aridity niche limits for terrestrial vertebrates' examines extinction risks from niche exceedance. Tools like Malpolon deep-SDM framework and sabinaNSDM for hierarchical models support these multi-scale analyses.
Papers at a Glance
| # | Paper | Year | Venue | Citations | Open Access |
|---|---|---|---|---|---|
| 1 | Biodiversity hotspots for conservation priorities | 2000 | Nature | 30.5K | ✕ |
| 2 | phyloseq: An R Package for Reproducible Interactive Analysis a... | 2013 | PLoS ONE | 20.7K | ✓ |
| 3 | Maximum entropy modeling of species geographic distributions | 2005 | Ecological Modelling | 17.0K | ✕ |
| 4 | Measures of the Amount of Ecologic Association Between Species | 1945 | Ecology | 11.6K | ✕ |
| 5 | Red and photographic infrared linear combinations for monitori... | 1979 | Remote Sensing of Envi... | 10.9K | ✕ |
| 6 | A globally coherent fingerprint of climate change impacts acro... | 2003 | Nature | 10.9K | ✕ |
| 7 | Ecological Diversity and Its Measurement | 1988 | — | 10.9K | ✕ |
| 8 | Institutional Ecology, `Translations' and Boundary Objects: Am... | 1989 | Social Studies of Science | 10.0K | ✕ |
| 9 | Ecological responses to recent climate change | 2002 | Nature | 9.8K | ✕ |
| 10 | Collinearity: a review of methods to deal with it and a simula... | 2012 | Ecography | 9.7K | ✓ |
In the News
Global dataset for realized thermal and aridity niche limits for terrestrial vertebrates
Climate changes are altering temperature and aridity regimes, posing serious challenges for many species globally. Particularly, environmental changes that create conditions that push species beyon...
Global 1-km habitat distribution for endangered species and its spatial changes under future warming scenarios
Implementing biodiversity and climate actions for endangered terrestrial vertebrates is hampered by a lack of high-precision habitat maps. Therefore, we developed a dataset by linking the suitable ...
Machine learning applied to global scale species distribution models
key challenge. In this study, we apply Bayesian Additive Regression Trees (BART), a non-parametric machine learning algorithm, to estimate and forecast the global distribution of marine turtle spec...
SDMapCH : a Comprehensive database of >7,500 modelled species habitat suitability maps for Switzerland
Species Distribution Models (SDMs; also commonly referred to as Habitat Suitability Models HSMs) have become essential tools in biodiversity and conservation science, generalizing information on sp...
Odonata Foundation Launches AUD $2.5 Million Species ...
In addition to supporting the work across Odonata’s sanctuary network, Amazon’s funding will support the stewardship and research happening at new and existing sites. This includes species distribu...
Code & Tools
Developed as part of the European GUARDEN (ID: 101060693) and MAMBO (ID: 101060639) projects, Malpolon is a framework facilitating the training and...
The **sabinaNSDM** R package generates **spatially-nested hierarchical species distribution models (NSDMs)** that integrates species distribution m...
`enmSdmX` is a set of tools in **R** for implementing species distribution models (SDMs) and ecological niche models (ENMs), including: bias correc...
This package includes functions for ensemble species distribution modelling. The package can be used to run a suite of models on your data and sele...
Species Distribution Modeling (SDM) is a field of increasing importance in ecology 1 . Several popular applications of SDMs are understanding clima...
Recent Preprints
How Will Environmental Conditions Affect Species ...
Species are disappearing worldwide and the expectation is that this will increase in the future. This review summarizes information on the reasons for the global reduction in biodiversity and what ...
Potential impacts of climate change on the geographic ...
*Dionysia diapensiifolia*Boiss., a rare and ecologically specialized rocky plant endemic to Iran, faces increasing threats from climate change due to its narrow ecological niche and restricted habi...
Global dataset for realized thermal and aridity niche limits for terrestrial vertebrates
Climate changes are altering temperature and aridity regimes, posing serious challenges for many species globally. Particularly, environmental changes that create conditions that push species beyon...
High-resolution global distribution projections of 10 rodent genera under diverse SSP-RCP scenarios, 2021–2100
urgent. Ecological Niche Modeling (ENM) is an effective approach for projecting species distributions, helping researchers understand spatial distribution patterns and project species’ potential ra...
A climate suitability index for species distribution modelling ...
# A climate suitability index for species distribution modelling applied to terrestrial arthropods in the Mediterranean region
Latest Developments
Recent developments in species distribution and climate change research include the application of machine learning to global species distribution models as of October 2025, and studies using species distribution models (SDMs) to assess impacts of climate change on various species, such as amphibians in the Pacific Northwest and medicinal plants in China, published in 2025 and 2023 respectively (nature.com, frontiersin.org, nwcasc.uw.edu). Additionally, recent reviews highlight the increasing impact of climate variability on species distributions, emphasizing the challenges for conservation planning and protected area effectiveness (mdpi.com, nature.com, published in 2025).
Sources
Frequently Asked Questions
What is species distribution modeling?
Species distribution modeling predicts species geographic ranges based on environmental variables and occurrence data. Phillips et al. (2006) in 'Maximum entropy modeling of species geographic distributions' developed MaxEnt, a maximum entropy approach widely used for this purpose. It assesses habitat suitability under scenarios like climate change.
How does MaxEnt work in ecological niche modeling?
MaxEnt estimates species distributions by maximizing entropy subject to constraints from presence-only data and environmental variables. Phillips et al. (2006) demonstrated its effectiveness for species geographic distributions in 'Maximum entropy modeling of species geographic distributions'. It handles collinearity issues noted by Dormann et al. (2012) in 'Collinearity: a review of methods to deal with it and a simulation study evaluating their performance'.
What are the impacts of climate change on species ranges?
Climate change drives range shifts and habitat loss, as shown by Parmesan and Yohe (2003) in 'A globally coherent fingerprint of climate change impacts across natural systems' with evidence across natural systems. Walther et al. (2002) in 'Ecological responses to recent climate change' reported shifts in species distributions. Recent preprints project losses for rodents under SSP-RCP scenarios through 2100.
Why are biodiversity hotspots important?
Biodiversity hotspots identify priority areas for conservation due to high endemism and threat levels. Myers et al. (2000) defined them in 'Biodiversity hotspots for conservation priorities', cited 30,468 times. They guide efforts to protect species vulnerable to climate-driven range shifts.
What role does citizen science play?
Citizen science provides occurrence data for species distribution models. Star and Griesemer (1989) in 'Institutional Ecology, `Translations' and Boundary Objects: Amateurs and Professionals in Berkeley's Museum of Vertebrate Zoology, 1907-39' explored amateur-professional collaborations. It enhances data for modeling biodiversity under climate change.
How is collinearity addressed in models?
Collinearity inflates variance in regression parameters for ecological data. Dormann et al. (2012) reviewed methods in 'Collinearity: a review of methods to deal with it and a simulation study evaluating their performance', recommending variance inflation factors and principal components. This improves reliability of species distribution predictions.
Open Research Questions
- ? How will climate change push terrestrial vertebrates beyond their realized thermal and aridity niche limits, varying interspecifically?
- ? What are the projected spatial changes in high-resolution habitat distributions for endangered species under future warming scenarios?
- ? How effective are machine learning methods like Bayesian Additive Regression Trees for global-scale marine species distribution forecasts?
- ? What factors beyond climate, such as land use, most strongly drive future biodiversity reductions?
- ? How can spatially-nested hierarchical models address niche truncation in multi-scale species distribution predictions?
Recent Trends
Recent preprints emphasize high-resolution ecological niche modeling, including projections for Dionysia diapensiifolia in Iran and rodent genera under SSP-RCP scenarios to 2100.
'Global dataset for realized thermal and aridity niche limits for terrestrial vertebrates' addresses interspecific variation in climate responses.
2025News highlights 'SDMapCH: a Comprehensive database of >7,500 modelled species habitat suitability maps for Switzerland' and machine learning like BART for global marine turtle distributions.
Tools such as Malpolon and sabinaNSDM advance deep learning and hierarchical SDMs.
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