Table S2. Description of the landscape predictors that were calculated with the SPA for the training
of the regional SDMs. “*” refer to the metrics calculated with the raster package of the R software
instead of Fragstats 4.
Variable
Description
age_mean
This predictor was built starting from the map of the mean
forest age per cell provided by LANDIS-II. It reports the mean
of these age values within the moving window.
age_sd
Same as previous, but starting from the map of the forest age
unevenness.
species_richness
Same as previous, but starting from the map of the number of
forest species.
ai
This predictor is a measure of the dispersion level of the
habitat patch (ai ranges from 0 when habitat is maximally
disaggregated to 100 when habitat is formed by a single
compact patch.
amount*
This predictor measures the number of habitat cells in the
moving window.
area_am
It measures the area-weighted mean dimension of habitat
patches based on the proportional abundance of the patch (i.e.
larger patches are more important in defining landscape
characteristics)
area_cv
It is a measure of heterogeneity. Specifically, it indicates the
patch size coefficient of variation: low values are
characteristics of homogeneous landscape while high values
mean heterogeneity (i.e., patches of different sizes).
cohesion
This predictor provides a measure of the structural
connectivity of the habitat. It approaches to 0 as the proportion
of habitat in landscape decreases and became less connected;
and it increases monotonically as the proportion of the habitat
in the landscape increases.
ed
It is a measure of the density of habitat edges in the landscape.
It serves as a measure of fragmentation: high ed values mean
high fragmentation level.
enn_am
This predictor quantifies the area-weighted mean nearest
neighbour distance between two habitat patches. Enn_am
provides a measure of habitat isolation.
enn_cv
Same as previous, but it measure the coefficient of variation of
the Euclidean nearest neighbour distance.
iji
It quantifies the extent to which different patch types are
interspersed. Higher values result from landscapes in which
patch types are well interspersed, i.e. equally adjacent to each
other, whereas lower values are characteristic of poor
interspersed patch types.
lpi
This predictor quantifies the percentage of total landscape area
comprised by the largest patch.
shape_am
It is a measure of patch geometric complexity. Higher values
denote geometrically complex patches.
shape_cv
Same as previous, but it measure the coefficient of variation of
the shape index.
shdi
This index refers to the variety and abundance of different land
cover types within a landscape. Higher values indicate more
landscape diversity.