DOI: 10.5061/dryad.nvx0k6dvf
title: Data for: Coexistence or conflict: black bear habitat use along an urban-wildland gradient

description: The urban-wildland interface is expanding and increasing the risk of
 human-wildlife conflict. Some wildlife species adapt to or avoid living
 near people, while others select for anthropogenic resources and are thus
 more prone to conflict. To promote human-wildlife coexistence, wildlife
 and land managers need to understand how conflict relates to habitat and
 resource use in the urban-wildland interface. We investigated black bear
 (Ursus americanus) habitat use across a gradient of human disturbance in a
 North American hotspot of human-black bear conflict. We used camera traps
 to monitor bear activity from July 2018 to July 2019, and compared bear
 habitat use to environmental and anthropogenic variables and
 spatiotemporal probabilities of conflict. Bears predominantly used areas
 of high vegetation productivity, avoided higher human densities, and
 increased their nocturnality near people. Still, bears used more
 high-conflict areas in summer and autumn, specifically rural lands with
 ripe crops. Our results suggest that bears are generally modifying their
 behaviours in the urban-wildland interface through spatial and temporal
 avoidance of humans, which may facilitate coexistence. However, conflict
 still occurs, especially in autumn when hyperphagia and peak crop
 availability attract bears to abundant rural food resources. To improve
 conflict mitigation practices, we recommend targeting seasonal rural
 attractants such as with pre-emptive fruit picking, bear-proof compost
 containment, and other forms of behavioural deterrence. By combining
 camera-trap monitoring of a large carnivore along an anthropogenic
 gradient with conflict mapping, we provide a framework for evidence-based
 improvements in human-wildlife coexistence.
description: We set 54 camera traps within a 80 km<sup>2</sup>
 area in and adjacent to Sooke, Vancouver Island, BC, Canada to assess
 spatial and temporal variation in bear distribution and habitat use along
 a gradient of human disturbance from urban to wild. We deployed cameras
 following a stratified random design to representatively allocate cameras
 based on the proportion of the survey area falling within each of three
 strata: urban (n = 11 cameras), rural (n = 19), or wild (n = 24). We aimed
 for &gt;200 m between neighboring camera sites (mean = 446 m, range =
 147-1467 m) to maintain spatial independence. Within strata, sampling
 distribution was randomized where possible. Due to the abundance of
 private land, urban and rural camera sites were selected from a candidate
 list of participating landowners provided by the local environmental
 non-governmental organization. Rural sites were either within agricultural
 land cover or low development areas, while urban sites were in town and
 close to other homes. Wild sites were in forested areas with minimal
 disturbance from human development, consisting of 21 in Sea to Sea
 Regional Park and three on undeveloped T’Sou-ke Nation lands. To randomize
 sampling locations within the main accessible block of the regional park,
 a 500 by 500 m grid was overlaid on park trail maps and cameras were
 placed in 10 random cells that contained a trail. The T’Sou-ke Nation
 forest sites and regional park sites on the northwest edge were only
 accessible by a single hiking trail, so cameras were set a minimum of 200
 m apart. To avoid excessive human photos and privacy concerns, we avoided
 setting cameras directly on the main hiking trails in the park and
 T’Sou-ke Nation lands, and either targeted adjacent game and low-use human
 trails within the selected cell or set cameras off the main trail.
 Deployment occurred between July 18- August 20, 2018. To detect any
 seasonal variation in black bear habitat use, all cameras remained
 deployed for approximately one year, and were retrieved between July
 16-19, 2019. We used a combination of three camera trap models (Reconyx
 PC900, Reconyx HC600, and Browning Strike Force HD Pro) randomly allocated
 across strata to reduce potential effects of different detectability
 between camera models. We set cameras at locations to
 maximize the probability of detecting bears that occurred there, using
 local knowledge of where bears moved across urban or rural properties, or
 the presence of animal trails and sign. Per site, one camera was set on a
 tree, approximately one metre above the ground, at high sensitivity, with
 a one second delay between triggers (one image per trigger as bears are
 large enough to be captured without a sequence and this saves battery and
 memory card space), and facing open spaces such as meadows, lawns, or
 trails. Black bears have shown a preference for using low-use human paths
 because of the ease of movement and increased shrub vegetation containing
 berries. Where possible, cameras faced an intersection of multiple animal
 and/or low-use human trails. We visited camera traps
 every 2-3 months to download images, check functionality and replace
 batteries as needed. We used Timelapse Image Analyzer 2.0 to classify all
 camera trap images of black bears. We defined independent detection events
 as those separated by ≥30 minutes to minimize correlation among
 consecutive detections as individual bears were not uniquely identifiable.
 We counted sows with cubs as single individuals because sows determined
 the habitat use. We summed the number of detection events at each camera
 site for each month to calculate the monthly camera trap detection rate as
 a measure of habitat use (n = 702; 13 months x 54 cameras).
 To relate bear detections to environmental and anthropogenic
 features, we considered a suite of camera-specific independent variables
 extracted from spatial datasets. We included human density, trail density,
 road density, elevation, and distances to agriculture and urban land
 cover, averaged within a 150 m radius weighted buffer centred on camera
 locations in order to avoid overlapping buffers. These are the same
 predictors used in previous research to model human-black bear conflict in
 the Capital Regional District that include Sooke (CRD) to allow for direct
 comparison of their importance in explaining reported conflicts at the
 regional scale (CRD) versus bear habitat use at the local scale (Sooke).
 For variables derived from GIS raster layers with cells that extended
 beyond the buffer boundary, values were proportionally weighted to the
 cell areas within the buffer. Additionally, we used the Enhanced
 Vegetation Index (EVI) as a measure of vegetation productivity to indicate
 forage availability, rather than distance-to-forest because all camera
 locations were set within treed areas. We extracted EVI from MODIS 250m
 16-day layers. We used a weighted average based on number of days the
 16-day MODIS window had within our focal calendar month of analysis and
 the amount each raster cell fell into a 150m buffer around each camera
 site. Additional predictors captured local-scale
 variation in natural food occurrence and recent conflict reports. We used
 distance-to-freshwater as a proxy for the documented importance of
 riparian vegetation and fish for black bears; presence/absence of salmon
 at camera sites near (within 150 m buffer radius) salmon-bearing water by
 month, given their importance as a seasonal food resource for bears; and
 the number of reported conflicts within a 500 m buffer of a camera site
 within the study year. The buffer size for the latter two additional
 variables were tested at 150 and 500 m as in Klees van Bommel et al.
 (2020).
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