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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