Characterisation of sampling sites using spatial, environmental
and landscape variables
A number of spatial, environmental and landscape variables were
calculated to characterize the sampling sites and quantify the distances
among localities, taking into account the high topographic complexity
and environmental/landscape heterogeneity of the study area. We
calculated pairwise weighted topographic distances
(SPATWD) based on a digital elevation model (DEM) at 90
m resolution using the topoDistance R package (Wang,
2020) as described in the Supplemental Information. We also generated a
set of high-resolution environmental variables (at 90 m resolution) for
Cyprus, by spatial interpolation of temperature and precipitation layers
at lower resolution using the aforementioned DEM (see Supplemental
Information). Specifically, we interpolated six WorldClim (annual mean
temperature, maximum temperature of warmest month, minimum temperature
of coldest month, annual precipitation, precipitation of wettest quarter
and precipitation of driest quarter; Fick & Hijmans, 2017) and three
ENVIREM (climatic moisture index, Thornthwaite aridity index and
topographic wetness index; Title & Bemmels, 2018) variables. These
variables are known to affect the water-energy dynamics and to explain
patterns of diversity in several organismal groups (Hawkins et al.,
2003). We extracted values of each interpolated variable along with the
elevation for all sampling sites and for 500 randomly distributed points
throughout Cyprus, to avoid potential biases resulting from only
considering conditions at focal sites. We then applied a principal
component analysis (PCA; Figure S1) to reduce the dimensionality of the
dataset and eliminate covariance among variables and we retained the two
first principal components (PCs). PC1 (accounting for 81.3% of
variation) was positively correlated with altitude and precipitation
variables and negatively with temperature variables and PC2 (8.0%) was
positively correlated with the topographic wetness index (Table S3). The
PC1 and PC2 scores for each sampling site were considered as
topoclimatic predictors (ENVPC1 and
ENVPC2) for downstream analyses. We also calculated a
Euclidean distance matrix among sampling points based on the obtained
scores of the two retained PCs, which was used as a topoclimatic
predictor (ENVPC1-2) for matrix regression analyses.
Finally, we applied a circuit theory approach (McRae, 2006) to quantify
habitat connectivity among the Qa sampling sites. We focused
specifically on the Qa habitat because it is broadly distributed
but highly fragmented across Troodos, in contrast to the Cb, Pn and Jn forests which are very narrowly distributed and
the Pb forest which is very extensive with largely continuous
distribution (Figure 1). Based on the assumption that dispersal among
isolated forest fragments can be impeded by the presumed lower habitat
suitability of the surrounding landscape (Brodie & Newmark, 2019), we
built an isolation-by-resistance (IBR) scenario of connectivity
(FRAIBR) defined by the distribution of Qa forest
patches according to the existing cartography (see Supplemental
Information). Two alternative IBR scenarios were constructed for
comparison: the TRIIBR representing the topographic
complexity of the study area as estimated by the terrain roughness index
(Title & Bemmels, 2018) and the NULLIBR representing a
completely “flat landscape” with a fixed resistance (=1) value
assigned to all cells. Resistance distances among all Qa sampling
points (n = 11) were calculated under each alternative IBR
scenario (FRAIBR, TRIIBR and
NULLIBR) in circuitscape v.4.0.5 (McRae &
Beier, 2007).