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Table 2 Summary of the top resource selection function model quantifying the environmental effect on goose presence

From: Reducing human pressure on farmland could rescue China’s declining wintering geese

  NCP YRF
TBG GWFG TBG GWFG
Fixed effects Estimate SE Estimate SE Estimate SE Estimate SE
Intercept −5.36 0.324*** −5.77 0.197*** −14.03 0.828*** −14.78 0.421***
HP 0.35 0.189 −0.21 0.047*** −0.45 0.165** −3.06 0.643***
RoostDist (km) −10.83 0.360*** −11.50 0.289*** −22.42 0.937*** −22.43 0.589***
LC −0.43 0.136** 1.61 0.255*** 1.39 0.262*** 3.48 0.200***
HP*RoostDist 1.03 0.299*** −4.57 0.929***
HP*LC 0.24 0.130 −1.54 0.230***
RoostDist*LC 1.80 0.409***
Random effects Variance SD Variance SD Variance SD Variance SD
BirdID 0.130 0.361 0.221 0.470 0.977 0.988. 0.390 0.624
Year 0.100 0.317 4.94E-9 7.03E-5 0.848 0.921 0.030 0.172
  1. NCP Northeast China Plain, YRF Yangtze River Floodplain, TBG tundra bean goose (Anser serrirostris), GWFG greater white-fronted goose (A. albifrons), HP human pressure (refers to the standardized human footprint with a range of 0–50), RoostDist distance to the roost, LC land cover type (farmland and wetland/grass, with farmland as the baseline), BirdID bird individual ID, SE standard error, SD standard deviation, no value as the variable is not selected in the best model
  2. ***: P < 0.001; **: P < 0.01; *: P < 0.05