DOI: 10.5061/dryad.1c59zw3zf
title: Occupancy modeling of habitat use by white-tailed deer after more than a decade of exclusion in the boreal forest

description: The exclusion of herbivores in forest areas is a strategy used to reduce
 the impact of selective browsing and increase the regeneration of desired
 plant species. On Anticosti Island (Québec, Canada), selective browsing by
 white-tailed deer prevents the regeneration of balsam fir – white birch
 forests leading to their conversion into white spruce forests. Large deer
 exclosures were established for ca . 10 to 12 years in clear-cuts with
 patches of residual forest from 2001 to 2006 to assist in the natural
 regeneration of fir stands and to provide shelter and food resources for
 deer. Our objective was to assess how deer use exclosures after the
 removal of fences according to their spatial configuration and habitat
 composition. We randomly distributed automatic cameras for periods of 14
 days during summer in six exclosures ranging from 3.1 to 11.2 km2 (n=25
 cameras per exclosure) from which deer were reduced for 10 to 12 years. We
 compared candidate occupancy models that included spatial configuration
 and food resource variables while simultaneously controlling for variables
 affecting detection probability. We obtained weak evidence that deer
 habitat use increased by 19% when forage resources, represented by the
 cover of Cornus canadensis, increased from 0 to 100%. None of the other
 variables (distance between the border of exclosures and cameras and
 distance between forest patches and cameras) was retained, suggesting that
 the use of regenerating forests by deer in summer after a period of
 exclusion is related to forage availability and therefore, any forest
 management that improves food production during summer should help
 maintain or increase habitat use by deer.
description: During the summer of 2020 (25 June – 16 August), we distributed
 25 cameras (Bushnell Trophy, Spypoint Force 10, Reconyx PC800 and Spypoint
 Force 20) in each of the six exclosures according to a stratified random
 distribution in 3 different habitat types (regenerating cuts, residual
 mature forest without balsam fir [representing forest cover] and residual
 mature forest with balsam fir [representing forest cover and food
 resources]). We deployed each camera as accurately as
 possible to the random locations. We mounted cameras at level, 75 cm from
 the ground on trees with a diameter at breast height of 8 to 10 cm and
 facing the North quadrant. We removed vegetation obstructing the camera’s
 lens. Cameras recorded pictures for 14 days in each exclosure, taking a
 systematic picture every 5 minutes for a total of 288 pictures per
 day.  To classify automatically the presence and
 absence of white-tailed deer, we analyzed 485,338 pictures collected
 during 2,100 camera trap days using an artificial intelligence from Tan
 and Le (2019). We classified the pictures with the neural
 network/architecture EfficientNet from the EfficientNet-Pytorch library
 (Melas-Kyriazi 2020). We used a sample of 2968 pictures pre-classified as
 deer presence (n=1484) and absence (n=1484) to train the model and 636
 pictures to validate the model efficiency. The model classified the
 pictures with a degree of precision of 94%.  To
 estimate visibility and model the detection probability of each camera, we
 used a profile board composed of four panels of 50 x 25 cm to estimate the
 lateral cover in front of the camera. We took a measure at 5 and 10 meters
 in front of each camera, and then estimated the percentage of visibility
 at each site, following the approach in Ferron et al. (1996). The approach
 consist of averaging the grade of lateral cover of four squares on a
 blanket held vertically in front of the evaluator (1: 0-19% of visibility,
 2: 20-39%, 3: 40-59%, 4: 60-79%, 5: 80-100%), then multiplying it by 20
 and deducing 10, for example: ((1 + 3 + 4 + 4) / 4) * 20 – 10 = 50 % of
 lateral cover visibility at 5 m in front of the camera. The same approach
 was used at 10 m. We also recorded whether the camera was pointing at a
 deer trail or not. At each camera trap site, we
 surveyed all saplings and merchant trees present per species in a circular
 area of 100 m2 (5.64 m radius) centered on the camera. We tallied saplings
 per species and diameter at breast height (DBH, 1.3 m) classes to compute
 basal area per species. We measured vegetation of the
 ground layer in a 4 m2 circular plot located at 2 m in front of each
 camera. We estimated the percentage of cover of common herbaceous plants
 regularly browsed by deer on the island: Cornus canadensis, Maianthemum
 canadense, Chamaenerion angustifolium, Clintonia borealis, Rubus sp., and
 Poaceae sp. We kept only two species for the analysis (i.e., C. canadensis
 and Rubus spp.) because the global presence of the other species at all
 camera trap sites was less than 4%, and therefore their availability was
 not high enough to include these species in the analysis.
 To define spatial configuration, we used ArcMap tools (ArcGIS
 v10.4; Environmental Systems Research Institute, Redland, CA, USA), to
 measure the distance of the closest forest cover and the shortest distance
 to the exclosure border (m) for each camera trap site. When the camera was
 located under forest cover, we set the distance at 0.
 To obtain convergence, we scaled all the covariates (MacKenzie et
 al. 2018). Pairwise correlations between all variables were all below
 0.48. 
description: Data files require Excel to be open