Research in Context
Species Distribution Modeling
Environmental Niche Modeling
1. INTRODUCTION
Methodologies have been developed and utilized by researchers in order to predict the performance of tree species in the future climate conditions. Analysis relies on the use of datasets providing environmental data on a global scale and for a number of bioclimatic and climatic parameters. Furthermore, conclusions diversify with the employment of various climate scenarios.
Image credit: Wix Studio Images Database
2. METHODOLOGIES AND MODELLING
Species Distribution Modelling (SDM).
This method is utilized in the fields of ecology and evolutionary biology. It has been applied in forestry in order to predict whether a tree species will survive in its natural habitat in future conditions. (Martin & Sjöman 2025).
The SDM models that are more frequently used in studies on urban tree species for the future, are MaxEnt and Ensemble.
MaxEnt Model
It has been used in a few studies which aim to predict future distribution of native species or tree species in the urban environment.
Ensemble
The ensemble species distribution model (eSDM) can integrate multiple models and improve prediction accuracy This approach provides a solution to intermodel variations that has been used in other fields is to utilize several models (herein termed ‘ensembles’) and use appropriate techniques to explore the resulting range of projections. (Araújo MB, and New M. 2007, Ramirez-Reyes C et al 2021, Song, Y. et al. 2024)
3. DATASETS
WorldClim2
This dataset provides climate data such as temperature and rainfall for many land areas globally. It incorporates data from thousands of weather stations and for a time period beginning from 1970 up to this day.
Bioclimatic variables are derived from the monthly climate values in order to generate more biologically meaningful variables. The 19 bioclimatic variables represent annual trends, seasonality (e.g., annual range in temperature and precipitation) and extreme or limiting environmental factors. (WorldClim documentation n.d.)
Temperature values
Precipitation values
Annual Mean Temperature
Annual Precipitation
Mean Diurnal Range (Mean of monthly (max temp - min temp))
Precipitation of Wettest Month
Isothermality (BIO2/BIO7) (×100)
Precipitation of Wettest Month
Temperature Seasonality (standard deviation ×100)
Precipitation Seasonality (Coefficient of Variation)
Max Temperature of Warmest Month
Precipitation of Driest Month
Min Temperature of Coldest Month
.
Temperature Annual Range (BIO5-BIO6)
.
Mean Temperature of Wettest Quarter
Precipitation of Wettest Quarter
Mean Temperature of Driest Quarter
Precipitation of Driest Quarter
Mean Temperature of Warmest Quarter
Precipitation of Warmest Quarter
Mean Temperature of Coldest Quarter
Precipitation of Coldest Quarter
The dataset has been used in many studies worldwide aiming to check tree species adaptability to future conditions (GU Jingxian et al 2025, Martin & Sjöman 2025, Örücü & Hoşgör 2022).
Environmental Rasters for Ecological Modeling (ENVIREM)
It can be used as a complement to WorldClim database when applying the Species Distribution Modelling methodology. (Envirem download website, Title and Bemmels 2018). It offers environmental variables that are thought to be relevant to species' ecology and geographic distribution. There are 16 climatic and 2 topographic variables. A selection is displayed in the following table:
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annualPET
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aridityIndexThornthwaite
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thermInd
-
tri
-
topoWet
-
annual potential evapotranspiration: a measure of the ability of the atmosphere to remove water through evapotranspiration processes, given unlimited moisture
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Thornthwaite aridity index: Index of the degree of water deficit below water need
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compensated thermicity index: sum of mean annual temp., min. temp. of coldest month, max. temp. of the coldest month, x 10, with compensations for better comparability across the globe
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terrain roughness index
-
SAGA-GIS topographic wetness index
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mm / year
-
-
-
℃
-
-
-
-
The Thornthwaite's Climate Moisture Index CMI describes the aridity or humidity of the soil and climate of a region, and is calculated from the collective effects of precipitation, evapotranspiration, soil water storage, moisture deficit and run off (Austroads, 2010).
Global Biodiversity Information Facility (GBIF)
GBIF is an international network and data infrastructure funded by the world's governments and aimed at providing anyone, anywhere, open access to data about all types of life on Earth. It provides occurrence data on trees and other living organisms. Occurrence data that can be extracted are latitude and longitude coordinates that correspond to where a species has been observed.
4.EXAMPLES OF APPLICATION WORLDWIDE
Kew Gardens
A team of researchers who are associated with the Kew Gardens have over the past years tried to address the issues of the preservation of Kew's tree collection and landscape. The results of their work and analysis gives answers to other urban environments and locations. They have adopted SDM methodology in order to determine the suitable species for the future and which ecosystems would be the best for seed sourcing.
With the employment of modelling methods and the use of datasets, Kevin Martin and Henrik Sjöman, evaluated the suitability of tree species at Kew's collection and urban area in the context of future climate scenarios. Their focus in their analysis of the occurrence locations were the Mean Annual Temperature MAT and the Climate Moisture Index CMI. The shifts in these variables, are considered to be the most significant for their effect on vegetation and ecosystems (Martin and Sjöman 2025).
Remarkable Trees in Royal Botanical Gardens, Kew. Image source: A. Vardakis 2023
Another significant aspect in this analysis is the employment of Euclidean distance calculations. Instead of identifying locations with suitable specific species, the aim is to identify specific areas with flora and ecosystems comparable to future conditions in Kew Gardens. As it is employed in this analysis, the Euclidean distance is actually the direct distance without obstacles between two locations (Utama 2020, Martin and Sjöman 2025).
An extended avenue with tree rows named after Euclid. Ontario, California. Image credit: The Desert Photo (2024).Tree lined Euclid Ave in Ontario California. [Online image]. Shutterstock. https://www.shutterstock.com/image-photo/tree-lined-euclid-ave-ontario-california-2422652587
Flanders (Belgium)
The bioscience engineer Vito Leyssens for his thesis evaluated the climate resilience of a large number of tree species, using the Ensemble SDM approach. The ecological and climatic niche for four climate scenarios and the region of Flanders was constructed. For the years 2050 and 2100. Climate data was downloaded from WorldClim database and species distribution data was extracted from GBIF.
Two lists of species were compiled, the potentially climate resilient and the traditional e.g. the main species used currently in the urban green spaces. The species lists were created with feedback from experts and professionals in urban green in Flanders who are expected to be the end users of research results.
For the "constructed" future conditions of the region, the quantitative analysis shows the number of natural occurrences for each species. The greater the number of occurrences, the species is assessed as the most suitable. In comparison, the Mediterranean species with small leaves show the greatest promise. (Leyssens 2021)
Brick building near trees, at the historical district Groot Begijnhof in Leuven, Belgium. Image credit: Selina Bubendorfer (2021). Groot Begijnhof Leuven, Groot Begijnhof, Löwen, Belgien. [Online image]. Unsplash.https://unsplash.com/photos/brown-brick-building-near-green-trees-during-daytime--9FzDqBdqHg
China
In order to predict the distribution areas and habitats of city trees under climate change, researchers at the Chongqing University, studied the distribution status of 50 urban tree species in China. The MaxEnt model was employed to determine the suitable distribution zones and habitats under the climate of 2070s. The study area was 147 cities across 10 climate zones including mid-temperate arid zones and mid-temperate semi-arid zones. Data on 19 climate variables were obtained from the WorldClim dataset (Gu et al 2025).
The landscape at the island campus of Chongqing University, China. Image credit: liyuhan (2023). Looking away from the vast lake, facing the teaching building on the university campus, on a sunny summer day . [Online image]. Shutterstock. https://www.shutterstock.com/image-photo/looking-away-vast-lake-facing-teaching-2360585039
It is to be noted that recently a collaboration of Danish and Chinese researchers has mapped changes in urban tree cover canopy across major Chinese cities since 2010 (Zhang et al 2025, Trees Outside Forests, n.d.).
Turkey
Researchers at the Universities of Turkey have studied the change in geographic distribution of native tree and shrub species. Most of them are commonly used in urban spaces worldwide: Laurus nobilis, Ostrya carpinifolia, Pinus brutia, Prunus laurocerasus, Quercus frainetto, Q. brantii. Similarly as in previous examples, the scientists utilize species distribution modeling, occurrence data and bioclimatic variables from WorldClim dataset (Örücü et al 2022, Örücü et al 2023, Akyol et al 2023, Arslan et al 2025).
Oak trees at the Zagros Mountains. Persian oak Quercus brutii is the most characteristic species of the forest steppe.
Image credit: Wisteria50 (2024). The beautiful plains of Khuzestan with oak tree cover at the foothills of the Zagros Mountains. [Online image]. Shuttertock. https://www.shutterstock.com/image-photo/beautiful-plains-khuzestan-oak-tree-cover-2545032049
Grove with drought-hardy laurels Laurus nobilis at the NTUA campus.
Agroforestry in Africa
Roeland Kindt is a senior ecologist with CIFOR-ICRAF (Center for International Forestry Research and World Agroforestry). He been leading research on tree species selection for restoration and agroforestry. His work focuses on with a focus on ecological modelling. He combines ensemble suitability modelling algorithms (integrated in the BiodiversityR package) with data on species distribution and assemblages of ecoregions and potential natural vegetation.
A series of tools has been therefore produced that support decision making on tree species selection across diverse landscapes. Roeland Kindt has led the development of several resources, datasets and interactive maps such as:
- GlobalUsefulNativeTrees
- Tree Globally Observed Environmental Ranges
- EcoregionsTreeFinder dataset
- Agroforestry Species Switchboard
- Vegetationmap4Africa
- What to Plant Where in Ethiopia
- Africa Tree Finder
- Climate change atlases for Central America and Africa. (CIFOR-ICRAF 2025, Africa Platform 2023 )
REFERENCES
PAPERS
Akyol, A., Örücü, Ö.K., Arslan, E.S. et al. 2023.
Predicting of the current and future geographical distribution of Laurus nobilis L. under the effects of climate change.
Environ Monit Assess 195, 459 (2023).
https://doi.org/10.1007/s10661-023-11086-z
Araújo MB, New M. 2007
Ensemble forecasting of species distributions.
Trends Ecol Evol. 2007;22:42–7
https://doi.org/10.1016/j.tree.2006.09.010
Arslan, E.S., Örücü, Ö.K., Gülcü, S. et al. 2025.
Prediction of the shift of the distribution of Pinus brutia Ten. Under future climate model. New Forests 56, 25 https://doi.org/10.1007/s11056-025-10092-y
Booth, Trevor H. 2018.
Species distribution modelling tools and databases to assist managing forests under climate change,
Forest Ecology and Management,
Volume 430, 2018, Pages 196-203,ISSN 0378-1127
https://doi.org/10.1016/j.foreco.2018.08.019.
谷婧娴,杨永川,牟文博,靳程.气候变化下中国城市市树适宜分布区及适生规律[J].土木与环境工程学报（中英文）, 2025,47(2):197~208
GU Jingxian , YANG Yongchuan , MOU Wenbo, JIN Cheng.
Distribution and suitability of city trees in China under climate change [J].
Journal of Civil and Environmental Engineering, 2025, 47(2):197~208. (in Chinese)
http://qks.cqu.edu.cn/cqdxxben/article/abstract/202502022
Leyssens, Vito 2021
Finding Climate Resilient Urban Tree Species for Flanders (Master Thesis)
KU Leuven - Faculty of Bioscience Engineering
https://www.kpb-isa.nl/images/vakblad-archief/Masterproef_VitoLeyssens.pdf
Martin, Kevin & Sjöman, Henrik. (2025).
Navigating the future: unveiling the resilience of trees in evolving UK climates.
Acta Horticulturae. 77-84. 10.17660/ActaHortic.2025.1429.9.
http://dx.doi.org/10.17660/ActaHortic.2025.1429.9
Örücü, Ömer and Hoşgör, Ecem. 2022.
Karayemiş’in (Prunus laurocerasus L.) İklim Değişikliği Senaryolarına Göre Günümüz ve Gelecekteki Yayılış Alanlarının Analizi
Analyses of current and future distribution areas of Prunus laurocerasus L. under the climate change scenarios
TÜCAUM 2022 International Geography Symposium12-14 Ekim 2022 /12-14 October 2022, Ankara (in Turkish)
https://tucaum.ankara.edu.tr/wp-content/uploads/sites/280/2022/12/TM-34-Omer-K.-ORUCU-455-467.pdf
Örücü, Ömer K & Azadi, Hossein & Örücü, Seda & Aksoy, Özgür & Choobchian, Shahla & Stefanie, Horatiu. 2023.
Predicting the distribution of European Hop Hornbeam: application of MaxEnt algorithm and climatic suitability models.
European Journal of Forest Research.
https://link.springer.com/article/10.1007/s10342-023-01543-2
Ramirez-Reyes C, Nazeri M, Street G, Jones-Farrand DT, Vilella FJ, Evans KO. 2021.
Embracing ensemble species distribution models to inform At-Risk species Status assessments.
Journal of Fish and Wildlife Management 1 June 2021; 12 (1): 98–111.
https://doi.org/10.3996/JFWM-20-072
Song, Y., Xu, GB., Long, KX. et al. 2024.
Ensemble species distribution modeling and multilocus phylogeography provide insight into the spatial genetic patterns and distribution dynamics of a keystone forest species, Quercus glauca. BMC Plant Biol 24, 168
https://doi.org/10.1186/s12870-024-04830-1
Title P.O., Bemmels J.B. 2018.
ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling.
Ecography. 41:291–307.
https://nsojournals.onlinelibrary.wiley.com/doi/epdf/10.1111/ecog.02880
Utama, D.N. 2020.
Fuzzy decision support model for determining plants planted in specific suitable areas in Indonesia. Int. J. Emerg. Trends Eng. Res. 8 (5), 1517–1522
https://doi.org/10.30534/ijeter/2020/07852020
Zhang, X., Brandt, M., Tong, X., Tong, X., Zhang, W., Reiner, F., Li, S., Tian, F., Yue, Y., Zhou, W., Chen, B., Xiao, X., & Fensholt, R. 2025.
A strong but uneven increase in urban tree cover in China over the recent decade.
Nature Cities.
https://doi.org/10.1038/s44284-025-00227-9
SOFTWARE, MODELS
Steven J. Phillips, Miroslav Dudík, Robert E. Schapire.
Maxent software for modeling species niches and distributions (Version 3.4.1). http://biodiversityinformatics.amnh.org/open_source/maxent/
Maxent. (n.d.).
https://www.gbif.org/tool/81279/maxent
Ensemble (n.d.).
biomod2: Ensemble Platform for Species Distribution Modeling
https://biomodhub.r-universe.dev/biomod2
QGIS. (2025)
QGIS Geographic Information System. Open Source Geospatial Foundation Project.
DATASETS
Bioclimatic variables — WorldClim 1 documentation. (n.d.). 2020-2025, worldclim.org.
https://worldclim.org/data/bioclim.html
ENVIREM. (n.d.).
Global Biodiversity Information Facility. (2024). GBIF
PROJECT WEBSITES
Trees Outside Forest
https://www.treesoutsideforests.com/
Center for International Forestry Research and World Agroforestry CIFOR-ICRAF. (2025, August 4).
Research staff
https://www.cifor-icraf.org/research-staff/roeland-kindt/
Africa platform, Ghent University. 2023
"A Climate Change Atlas for Africa to Guide Tree Planting in the Continent", experte seminar by Kindt Roeland
WEBINARS
Martin, Kevin (2025, September).
What are the climate risks to trees and therefore which urban trees should we plant for the expected future climate? [Video].
TDAG Seminar on the theme Resilience and climate risk – the role of urban trees. https://www.youtube.com/watch?v=z8N2UKdO5X8
GU eResearch (Griffith University). 2021, August 3.
Module 1 - Introduction to Species Distribution Modelling
https://www.youtube.com/watch?v=T5_ZShgTxJI
CONFERENCE PRESENTATIONS
Kindt, Roeland & Dawson, Ian & McMullin, Stepha & Jamnadass, Ramni & Graudal, Lars. (2020).
High resolution species distribution modelling across Africa.
10.13140/RG.2.2.32044.90248.
MAPS
China Urban Tree Change. (2025).
Earthengine.app.
https://ee-xzrscph.projects.earthengine.app/view/china-urban-tree-change