DOI: 10.5683/sp3/r8jozi
title: Data for: Coexistence or conflict: black bear habitat use along an urban-wildland gradient

description: &lt;b&gt;Abstract&lt;/b&gt;&lt;br/&gt;&lt;p class="MsoNormal"&gt;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 (&lt;em&gt;Ursus americanus&lt;/em&gt;) 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.&lt;/p&gt;
description: &lt;b&gt;Methods&lt;/b&gt;&lt;br /&gt;&lt;p&gt;We set 54 camera traps within a 80 km&lt;sup&gt;2&lt;/sup&gt; 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.&lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;p&gt;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).&lt;/p&gt;
&lt;p&gt;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.&lt;/p&gt;
&lt;p&gt;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).&lt;/p&gt;
description: &lt;b&gt;Usage notes&lt;/b&gt;&lt;br /&gt;&lt;p&gt;Microsoft Excel, Google Sheets, R, or any program capable of reading .CSV files.&lt;/p&gt;
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