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.