Camera trapping
We used three years of data (2018-2020) from 618 camera trap locations
as part of an ongoing long-term camera trapping study of lynx (SCANDCAM,
viltkamera.nina.no). The SCANDCAM
project has volunteer-run camera traps (HC500, HC600, PC800, PC850,
PC900 and HP2X, Reconyx, Holmen, Wisconsin, USA), which are distributed
with one camera per 50 km2 grid cell, covering 30,950
km2 in Norway (Figure 1). Local volunteers, in
cooperation with trained technicians, placed a minimum of one camera
trap inside each grid cell. To maximize the probability of detecting
lynx and other predators, the cameras were preferably located on forest
roads, trails or natural movement routes for wildlife. Each camera trap
was placed 60-120 cm above the ground and aimed at the landscape feature
of interest. Memory cards and batteries were switched at least four
times a year. All camera traps were set to take a daily time-lapse image
at 8 a.m., in addition to being activated by an animal passing, in order
to check if the unit functioned correctly and if the field of view was
clear. A deep convolutional neural network trained with previous images
from the SCANDCAM project was used to classify all images using
TensorFlow. All species identifications were in addition manually
verified by trained staff and students. All images of humans and
vehicles were automatically removed to conform to Norwegian privacy
regulations, but we retained information of their passing. A detailed
explanation of the pre-processing and classification workflow can be
found in Hofmeester et al. (2021).
We calculated species encounter rate as the number of days in which an
animal (lynx, wolf, red fox, badger or pine marten) was detected by a
camera trap per year and season, corrected for camera effort (i.e.,
number of days during which the camera trap was active). This encounter
rate results from a combination of both local density and activity of
predators (Carbone et al., 2001). This is useful for our study because
it not only reflects the number of individuals present, but also the
intensity of use of a specific area.