Covariates
All covariates were extracted in ArcGIS (ESRI 2014). We used a Norwegian
and a Swedish vegetation map merged together to account for the cameras
along the border (Northern Research Institute’s vegetation map, Norway,
30 x 30 m resolution merged with Swedish Corine land cover map
Lantmäteriet, Sweden, 25 x 25 m resolution into a 25 x 25-m resolution
raster, Mattisson et al., 2013, Ordiz et al., 2015), from which we
extracted the proportion of agricultural land. Additionally, we obtained
human density (inhabitants/km2) from Statistics Norway
as a 250 m resolution raster (www.ssb.no).
However, this raster only contained data for urban areas. Given that the
cameras were placed at different distances from those areas, we used
that data to calculate a proxy for the direct human disturbance at the
camera sites. For this, we transformed the raster to a point layer,
which we used to predict a planar kernel density map set with bandwidth
= 2,000 and cell size = 200 m. We then extracted these two variables
(proportion of agricultural land and human disturbance) to a 1-km radius
circular buffer around each camera trap.
In order to assess primary productivity, we downloaded the 1-km
resolution monthly EVI from the MOD13A3 V061 product (Didan, 2021) using
the AppEEARS application (AppEEARS-Team, 2022). We downloaded monthly
data for years 2016-2020 and deleted 11 raster cells because of low
quality and missing data. We then merged the monthly EVI map layers into
one final raster containing average value of overlapping cells. We
masked out water to include only terrestrial cells and calculated the
mean-EVI for a 10-km radius circular buffer around each camera trap. We
used a 10-km buffer for primary productivity in order to focus on the
wider region rather than on the micro camera site. Using an
environmental productivity variable as a covariate such as EVI can help
to correct for biases when considering multiple sites (Hofmeester et
al., 2019).
We divided the camera trap data into two six-month periods, from October
to March (winter; roughly the months with snow cover) and from April to
September (summer; snow free months in most of the study area). We also
created an 8-level “study area” covariate based on geographic clusters
of camera traps (Figure 1).