DOI: 10.6084/m9.figshare.29086178
title: Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018)

description: This dataset uses the variables listed in Table 1 to train four machine learning models—Linear Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting—to explain the mean annual habitat quality in China from 1990 to 2018. The best-performing model (XGBoost) achieved an R² of 0.8411, a mean absolute error (MAE) of 0.0862, and a root mean square error (RMSE) of 0.1341. All raster data were resampled to a 0.1º spatial resolution using bilinear interpolation and projected to the WGS 1984 World Mercator coordinate system.The dataset includes the following files:A CSV file containing the mean annual values of the dependent variable (habitat quality) and the independent variables across China from 1990 to 2018, based on the data listed in Table 1.(HQ: Habitat Quality; CZ: Climate Zone; FFI: Forest Fragmentation Index; GPP: Gross Primary Productivity; Light: Nighttime Lights; PRE: Mean Annual Precipitation Sum; ASP: Aspect; RAD: Solar Radiation; SLOPE: Slope; TEMP: Mean Annual Temperature; SM: Soil Moisture)<br>A Python script used for modeling habitat quality, including mean encoding of the categorical variable climate zone (CZ), multicollinearity testing using Variance Inflation Factor (VIF), and implementation of four machine learning models to predict habitat quality.<br><br>Table 1. Variables used in the machine learning models<br><b>Dataset</b><b>Units</b><b>Source</b>Habitat Quality-Calculated based on landcover map(Yang and Huang, 2021)Gross Primary ProductivitygC m-2 d-1(Wang et al., 2021)TemperatureºC(Peng et al., 2019)Precipitation0.1mm(Peng et al., 2019)Downward shortwave radiationW m−2(He et al., 2020)Soil moisturem3 m−3(K. Zhang et al., 2024)Nighttime lightDigital Number(L. Zhang et al., 2024)Forest fragmentation index-Derived from landcover map (Yang & Huang, 2021)Digital Elevation Modelm(CGIAR-CSI, 2022)AspectDegreeDerived from DEM(CGIAR-CSI, 2022)SlopeDegreeDerived from DEM(CGIAR-CSI, 2022)Climate zones-(Kottek et al., 2006)<b>References</b>CGIAR-CSI. (2022). SRTM DEM dataset in China (2000). In <i>National Tibetan Plateau Data Center</i>. National Tibetan Plateau Data Center. https://dx.doi.org/He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., & Li, X. (2020). The first high-resolution meteorological forcing dataset for land process studies over China. <i>Scientific Data</i>, <i>7</i>(1), 25. https://doi.org/10.1038/s41597-020-0369-yKottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006). World Map of the Köppen-Geiger climate classification updated. <i>Meteorologische Zeitschrift</i>, <i>15</i>(3), 259–263. https://doi.org/10.1127/0941-2948/2006/0130Peng, S., Ding, Y., Liu, W., & Li, Z. (2019). 1 km monthly temperature and precipitation dataset for China from 1901 to 2017. <i>Earth System Science Data</i>, <i>11</i>(4), 1931–1946. https://doi.org/10.5194/essd-11-1931-2019Wang, S., Zhang, Y., Ju, W., Qiu, B., & Zhang, Z. (2021). Tracking the seasonal and inter-annual variations of global gross primary production during last four decades using satellite near-infrared reflectance data. <i>Science of The Total Environment</i>, <i>755</i>, 142569. https://doi.org/10.1016/j.scitotenv.2020.142569Yang, J., & Huang, X. (2021). The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019. <i>Earth System Science Data</i>, <i>13</i>(8), 3907–3925. https://doi.org/10.5194/essd-13-3907-2021Zhang, K., Chen, H., Ma, N., Shang, S., Wang, Y., Xu, Q., & Zhu, G. (2024). A global dataset of terrestrial evapotranspiration and soil moisture dynamics from 1982 to 2020. <i>Scientific Data</i>, <i>11</i>(1), 445. https://doi.org/10.1038/s41597-024-03271-7Zhang, L., Ren, Z., Chen, B., Gong, P., Xu, B., & Fu, H. (2024). A Prolonged Artificial Nighttime-light Dataset of China (1984-2020). <i>Scientific Data</i>, <i>11</i>(1), 414. https://doi.org/10.1038/s41597-024-03223-1<br><br><br>