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Mapping urban waterbird habitats using machine learning and citizen science: A multi-scale analysis in Brussels, Belgium.

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Název: Mapping urban waterbird habitats using machine learning and citizen science: A multi-scale analysis in Brussels, Belgium.
Autoři: Jiang X; Department of Geography, Ghent University, Ghent, 9000, Belgium., Zhang L; Xinjiang Uygur Autonomous Region Climate Center, Urumqi, 830002, China; State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China. Electronic address: zhangliancheng22@mails.ucas.ac.cn., Feng K; Department of Geography, Ghent University, Ghent, 9000, Belgium; State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China., Qin Y; Department of Geography, Ghent University, Ghent, 9000, Belgium., Liang H; Department of Geography, Ghent University, Ghent, 9000, Belgium; State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China., Wang J; College of Life Sciences and Oceanography, Shenzhen University, 518060, Shenzhen, China., Bonte D; Department of Ecology, Ghent University, Ghent, 9000, Belgium., Van de Voorde T; Department of Geography, Ghent University, Ghent, 9000, Belgium.
Zdroj: Journal of environmental management [J Environ Manage] 2025 Dec; Vol. 395, pp. 128006. Date of Electronic Publication: 2025 Nov 18.
Způsob vydávání: Journal Article
Jazyk: English
Informace o časopise: Publisher: Academic Press Country of Publication: England NLM ID: 0401664 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1095-8630 (Electronic) Linking ISSN: 03014797 NLM ISO Abbreviation: J Environ Manage Subsets: MEDLINE
Imprint Name(s): Original Publication: London ; New York, Academic Press.
Výrazy ze slovníku MeSH: Citizen Science* , Machine Learning* , Ecosystem* , Birds*, Animals ; Belgium ; Cities ; Humans ; Conservation of Natural Resources
Abstrakt: Competing Interests: Declaration of competing interest The authors declare they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.
This study integrates citizen science (eBird) data, which provides the backbone of species with remote sensing variables to explore the spatial distribution of waterbird habitats in the urban region of Brussels. Machine learning, including XgBoost (XgB), Random Forest (RF), Extra Trees (ET), LightGBM (LGB), and CatBoost (CB), was applied to assess the influence of environmental factors at 5 scales (5, 10, 30, 50, and 100 m resolution). We found that landcover factors as highly correlated with species habitat suitability, with all models exhibiting high predictive accuracy. Notably, the 10 m resolution data combined with XgB and RF models demonstrated robust performance, making them promising tools for spatial modeling of waterbird habitats in urban or large-scale regions. Additionally, the inclusion of the impervious density map (IDM) improved prediction accuracy by reducing over estimations in highly urbanized areas. The study also highlighted the significant role of bio-climatic factors such as Bio 14 (precipitation seasonality), Bio 17 (precipitation of the wettest quarter), and Bio 18 (precipitation of the warmest quarter), as well as human activities, particularly NP (noise pressure), in shaping waterbird habitats. These results can directly guide urban biodiversity management in Brussels and highlight the necessity of developing targeted conservation strategies, which protect key habitats, enhance habitat connectivity, and mitigate the human-induced environmental stresses to ensure the long-term habitat sustainability.
(Copyright © 2025 Elsevier Ltd. All rights reserved.)
Contributed Indexing: Keywords: Ecosystem services; Machine learning; Urban landscape; Waterbirds ecology
Entry Date(s): Date Created: 20251117 Date Completed: 20251203 Latest Revision: 20251203
Update Code: 20251203
DOI: 10.1016/j.jenvman.2025.128006
PMID: 41248577
Databáze: MEDLINE
Popis
Abstrakt:Competing Interests: Declaration of competing interest The authors declare they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.<br />This study integrates citizen science (eBird) data, which provides the backbone of species with remote sensing variables to explore the spatial distribution of waterbird habitats in the urban region of Brussels. Machine learning, including XgBoost (XgB), Random Forest (RF), Extra Trees (ET), LightGBM (LGB), and CatBoost (CB), was applied to assess the influence of environmental factors at 5 scales (5, 10, 30, 50, and 100 m resolution). We found that landcover factors as highly correlated with species habitat suitability, with all models exhibiting high predictive accuracy. Notably, the 10 m resolution data combined with XgB and RF models demonstrated robust performance, making them promising tools for spatial modeling of waterbird habitats in urban or large-scale regions. Additionally, the inclusion of the impervious density map (IDM) improved prediction accuracy by reducing over estimations in highly urbanized areas. The study also highlighted the significant role of bio-climatic factors such as Bio 14 (precipitation seasonality), Bio 17 (precipitation of the wettest quarter), and Bio 18 (precipitation of the warmest quarter), as well as human activities, particularly NP (noise pressure), in shaping waterbird habitats. These results can directly guide urban biodiversity management in Brussels and highlight the necessity of developing targeted conservation strategies, which protect key habitats, enhance habitat connectivity, and mitigate the human-induced environmental stresses to ensure the long-term habitat sustainability.<br /> (Copyright © 2025 Elsevier Ltd. All rights reserved.)
ISSN:1095-8630
DOI:10.1016/j.jenvman.2025.128006