Fig. 1. The locations of (a) 7orders of migratory birds and (b) 23
different IUCN categories species.
Climate data and the
scenarios
We
only used static environmental variables to simulate the potential
distribution of migratory birds, and focused on and analyzed the factors
the static environmental factors that are known to affect birds’
movement and habitat changes (Arribas et al., 2019; Beumer et al.,
2019). The data of bioclimate,
temperature and precipitation were imported to the model from
Worldclim
1.4 dataset(http://www.worldclim.org/ ) with 30 arc-seconds(about
1km)
resolution ratio.
Current
climate scenario(average 1970-2000)
is used to comprehend the latent distribution while two future
scenarios(RCP2.6 and RCP8.5 during average 2040-2060) are used to
estimate
the future distribution
shift.
The RCP2.6 is an optimistic
scenario
and the RCP8.5 is an pessimistic scenario (Boisvert-Marsh et al., 2019;
Rosen & Guenther, 2016). The land cover dataset and the NDVI dataset
were
downloaded
from
the resource and environment data cloud platform
(http://www.resdc.cn/) with
a resolution of 1 km. The reserves data were from the protected
planet(https://www.protectedplanet.net/).
The
distance of each grid to protected area was calculated by Euclidian
distance in ArcMap.
Altitude
and slope were
compiled
by using the Digital Elevation Model(DEM) from the Geospatial Data Cloud
(http://www.gscloud.cn).
To make the result reasonable, the Pearson Correlation Coefficient
(PCCs) was used to remove high correlated
variables(Table S3) Moreover, 13
variables were retained in the model(Table1) after removing high
correlated bioclimate variables(|r|>0.8).
Table1 List of variables used in MaxEnt modelling.