Mean Annual Habitat Quality and Its Driving Variables in China (1990–2018)

This CSV file 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.<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 &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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., &amp; 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>