Dataset List

Vol.|Area

No.02 Vol.12,2025
No.01 Vol.12,2025
No.12 Vol.11,2024
No.11 Vol.11,2024
No.10 Vol.11,2024
No.09 Vol.11,2024
No.08 Vol.11,2024
No.07 Vol.11,2024
No.06 Vol.11,2024
No.05 Vol.11,2024
No.04 Vol.11,2024
No.03 Vol.11,2024
No.02 Vol.11,2024
No.01 Vol.11,2024
No.12 Vol.10,2023
No.11 Vol.10,2023
No.10 Vol.10,2023
No.09 Vol.10,2023
No.08 Vol.10,2023
No.07 Vol.10,2023
No.06 Vol.10,2023
No.05 Vol.10,2023
No.04 Vol.10,2023
No.03 Vol.10,2023
No.02 Vol.10,2023
No.01 Vol.10,2023
No.12 Vol.9,2022
No.11 Vol.9,2022
No.10 Vol.9,2022
No.09 Vol.9,2022
No.08 Vol.9,2022
No.07 Vol.9,2022
No.06 Vol.9,2022
No.05 Vol.9,2022
No.04 Vol.9,2022
No.03 Vol.9,2022
No.02 Vol.9,2022
No.01 Vol.9,2022
No.12 Vol.8,2021
No.11 Vol.8,2021
No.10 Vol.8,2021
No.09 Vol.8,2021
No.08 Vol.8,2021
No.07 Vol.8,2021
No.06 Vol.8,2021
No.05 Vol.8,2021
No.04 Vol.8,2021
No.03 Vol.8,2021
No.03 Vol.8,2021
No.02 Vol.8,2021
No.01 Vol.8,2021
No.09 Vol.7,2020
No.08 Vol.7,2020
No.07 Vol.7,2020
No.06 Vol.7,2020
No.05 Vol.7,2020
No.04 Vol.7,2020
No.03 Vol.7,2020
No.02 Vol.7,2020
No.01 Vol.7,2020
No.06 Vol.6,2019
No.05 Vol.6,2019
No.04 Vol.6,2019
No.03 Vol.6,2019
No.02 Vol.6,2019
No.01 Vol.6,2019
No.08 Vol.5,2018
No.07 Vol.5,2018
No.06 Vol.5,2018
No.05 Vol.5,2018
No.04 Vol.5,2018
No.03 Vol.5,2018
No.02 Vol.5,2018
No.01 Vol.5,2018
No.04 Vol.4,2017
No.03 Vol.4,2017
No.02 Vol.4,2017
No.01 Vol.4,2017
No.09 Vol.3,2016
No.08 Vol.3,2016
No.07 Vol.3,2016
No.06 Vol.3,2016
No.05 Vol.3,2016
No.04 Vol.3,2016
No.03 Vol.3,2016
No.02 Vol.3,2016
No.01 Vol.3,2016
No.02 Vol.2,2015
No.01 Vol.2,2015
No.02 Vol.1,2014
No.01 Vol.1,2014
China
Asia
Oceanic region
African
European
North American
South American
Arctic
Global Scale
Data Details

Dataset of Habitat Suitability and Richness of 285 Bird Species in China


LU Huiyuan1,2ZHANG Rui1JIANG Linlin3
1 Northwest Institute of Eco-Environment and Resources，Chinese Academy of Sciences，Lanzhou，Gansu 7300002 Suzhou University of Science and Technology，Suzhou，Jiangsu 2150093 Anhui Normal University，Wuhu，Anhui 241002

DOI:10.3974/geodb.2025.01.08.V1

Published:Jan. 2025

Visitors：730       Data Files Downloaded：27      
Data Downloaded：647.13 MB      Citations：

Key Words：

biodiversity,species distribution models,birds,remote sensing monitoring

Abstract：

China is one of the countries with the richest bird populations in the world. Based on bird observation data from the E-BIRD and GBIF platforms, along with digital elevation model (DEM), annual precipitation, annual mean temperature, and evapotranspiration data, the authors developed the dataset of habitat suitability and richness of 285 bird species in China using the Biomod2 platform with GLM, MAXENT, RF, and ensemble models. This dataset includes data of 285 bird species, such as the Northern Goshawk, Common Starling, and Eurasian Skylark, for the years 2000, 2005, 2010, 2015, and 2020, covering habitat suitability and species richness. Model validation results indicate high accuracy, with an average AUC of 0.9907 and an average TSS of 0.9225 for the test set. The dataset has a spatial resolution of 0.05° and a temporal resolution of 5 years in .img and .tif data formats of 1,430 data files with data size of 5.66 GB (Compressed into one single file with 23.9 MB).

Foundation Item：

Jiangsu Education Department (202210332083Y)

Data Citation：

LU Huiyuan, ZHANG Rui, JIANG Linlin. Dataset of Habitat Suitability and Richness of 285 Bird Species in China[J/DB/OL]. Digital Journal of Global Change Data Repository, 2025. https://doi.org/10.3974/geodb.2025.01.08.V1.

References：


     [1] Ding, Z. H., Cao, J. J., Wang, Y. The construction and optimization of habitat networks for urban-natural symbiosis: a case study of the main urban area of Nanjing [J]. Forests, 2023, 14(1): 18.
     [2] Buxton, R. T., Pearson, A. L., Lin, H. Y., et al. Exploring the relationship between bird diversity and anxiety and mood disorder hospitalisation rates [J]. Geo-Geography and Environment, 2023, 10(2): 8.
     [3] Lees, A. C., Haskell, L., Allinson, T., et al. State of the world's birds [J]. Annual Review of Environment and Resources, 2022, 47: 231-260.
     [4] Xu, C. L., Yu, Q., Wang, F., et al. Identifying and optimizing ecological spatial patterns based on the bird distribution in the Yellow River Basin, China [J]. Journal of Environmental Management, 2023, 348: 13-23.
     [5] Lu, H. Y., Shang, Z. Y., Ruan, Y. L., et al. Study on urban expansion and population density changes based on the inverse S-shaped function [J]. Sustainability, 2023, 15(13): 19-35.
     [6] Moller, A. P., Rubolini, D., Lehikoinen, E. Populations of migratory bird species that did not show a phenological response to climate change are declining [J]. Proceedings of the National Academy of Sciences of the United States of America, 2008, 105(42): 16195-16200.
     [7] Liu, Z. X., Zhang, W. W., Lu, H. Y., et al. Exploring evolution characteristics of eco-environment quality in the Yangtze River Basin based on remote sensing ecological index [J]. Heliyon, 2023, 9(12): 14-29.
     [8] Zhu, B. R., Verhoeven, M. A., Velasco, N., et al. Current breeding distributions and predicted range shifts under climate change in two subspecies of black-tailed godwits in Asia [J]. Global Change Biology, 2022, 28(18): 5416-5426.
     [9] Virkkala, R., Rajasärkkä, A., Heikkinen, R. K., et al. Birds in boreal protected areas shift northwards in the warming climate but show different rates of population decline [J]. Biological Conservation, 2018, 226: 271-279.
     [10] Rousseau, J. S., Betts, M. G. Factors influencing transferability in species distribution models [J]. Ecography, 2022, 45(7): 13-25.
     [11] Gaul, W., Sadykova, D., White, H. J., et al. Data quantity is more important than its spatial bias for predictive species distribution modelling [J]. PeerJ, 2020, 8: e27.
     [12] Yu, H., Cooper, A. R., Infante, D. M. Improving species distribution model predictive accuracy using species abundance: application with boosted regression trees [J]. Ecological Modelling, 2020, 432: 11-23.
     [13] Thuiller, W., Lafourcade, B., Engler, R., et al. BIOMOD: a platform for ensemble forecasting of species distributions [J]. Ecography, 2009, 32(3): 369-373.
     [14] Neate-Clegg, M. H. C., Horns, J. J., Adler, F. R., et al. Monitoring the world's bird populations with community science data [J]. Biological Conservation, 2020, 248: 7-15.
     [15] Tejeda, I., Medrano, F. eBird as a tool to improve the knowledge of Chilean birds [J]. Revista Chilena de Ornitologia, 2018, 24(2): 85-94.
     [16] Peng, S. Z., Ding, Y. X., Wen, Z. M., et al. Spatiotemporal change and trend analysis of potential evapotranspiration over the Loess Plateau of China during 2011-2100 [J]. Agricultural and Forest Meteorology, 2017, 233: 183-194.
     [17] Fick, S. E., Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas [J]. International Journal of Climatology, 2017, 37(12): 4302-4315.
     [18] Peng, S. 1-km monthly mean temperature dataset for China (1901-2023) [DS]. National Tibetan Plateau / Third Pole Environment Data Center, 2024.
     [19] Peng, S. 1-km monthly precipitation dataset for China (1901-2023) [DS]. National Tibetan Plateau / Third Pole Environment Data Center, 2024.
     [20] Peng, S. 1-km monthly potential evapotranspiration dataset for China (1901-2023) [DS]. National Tibetan Plateau / Third Pole Environment Data Center, 2024.
     [21] Phillips, S. J., Anderson, R. P., Schapire, R. E. Maximum entropy modeling of species geographic distributions [J]. Ecological Modelling, 2006, 190(3-4): 231-259.
     [22] Xu, L., Fan, Y., Zheng, J. H., et al. Impacts of climate change and human activity on the potential distribution of Aconitum leucostomum in China [J]. Science of the Total Environment, 2024, 912: 12-27.
     [23] Adeyemo, S. M., Granger, J. J. Habitat suitability model and range shift analysis for American chestnut (Castanea dentata) in the United States [J]. Trees, Forests and People, 2023, 11: 13-25.
     [24] Lobo, J. M., Jiménez-Valverde, A., Real, R. AUC: a misleading measure of the performance of predictive distribution models [J]. Global Ecology and Biogeography, 2008, 17(2): 145-151.
     [25] Zhang, W. W., Liu, Z. X., Qin, K., et al. Long-term dynamic monitoring and driving force analysis of eco-environmental quality in China [J]. Remote Sensing, 2024, 16(6): 22-35.
     

Data Product:

ID Data Name Data Size Operation
1 HabitatSuitability&Richness2000-2020.rar 24543.18KB
Co-Sponsors
Superintend