Figure 4 Breeding habitats of the
Eastern population of the Lesser White-fronted Goose based on the
minimum training presence threshold. Projection: Asia North Albers Equal
Area Conic. Background: World Imagery from ESRI
(http://services.arcgisonline.com/arcgis/rest/services).
Effects of environmental factor on the
summering range of A.
erythropus
Of the nine environmental variables included in model building,
elevation was the most important, strongly contributing to the scaling
of the Maxent model (59.7% based on the model gain and 50.1% based on
re-evaluation of the random permutation of training presence and
background data, Table 2). Other highly influential variables (with more
than 5% permutation contribution) include precipitation of the warmest
quarter, distance to streams, and mean temperature of the warmest
quarter (Table 2).
Although highly correlated environmental predictors were excluded from
model fitting, there are still collinearities in the remaining
variables. For example, the Pearson r between Bio10 (precipitation of
the warmest quarter) and Bio18 (mean temperature of the warmest quarter)
is relatively high (-0.82) in the study area. Thus, the variable
contributions in Table 2 should be interpreted with caution (Phillips
2005).
Table 2 Relative contributions of the environmental variables to the
breeding habitat distribution of A. erythropus ranked by
permutation importance.