Data for: Coexistence or conflict: black bear habitat use along an urban-wildland gradient

AbstractThe 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., MethodsWe set 54 camera traps within a 80 km2 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 >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)., Usage notesMicrosoft Excel, Google Sheets, R, or any program capable of reading .CSV files.

**摘要** 城野交错带（urban-wildland interface）正不断扩张，加剧了人兽冲突风险。部分野生动物会适应或回避人类聚居区域，另有一些则会利用人为活动产生的资源，因此更容易与人发生冲突。为推动人兽共存，野生动物与土地管理者需要明晰城野交错带内冲突与栖息地、资源利用之间的关联。本研究针对北美美洲黑熊（Ursus americanus）人兽冲突热点区域，调查了黑熊在人类干扰梯度下的栖息地利用情况。研究于2018年7月至2019年7月间利用相机陷阱（camera traps）监测黑熊活动，并将黑熊栖息地利用情况与环境、人为变量及冲突的时空概率进行对比分析。黑熊主要选择植被生产力较高的区域活动，回避人口密度较高的区域，并在人类活动附近提升夜行性比例。但在夏、秋两季，黑熊会更多利用高冲突区域，尤其是种植成熟作物的乡村用地。研究结果表明，黑熊通常会通过时空上规避人类的方式，在城野交错带调整自身行为，这或许有助于实现人兽共存。但冲突仍时有发生，尤其是在秋季，黑熊因秋季暴食期（hyperphagia）与作物成熟期的双重驱动，会被丰富的乡村食物资源吸引至此。为优化冲突防控措施，本研究建议针对季节性乡村诱源采取干预手段，例如提前采收果实、设置防熊堆肥设施，以及其他行为威慑方式。本研究将沿人为干扰梯度开展的大型食肉动物相机陷阱监测与冲突制图相结合，为基于实证优化人兽共存策略提供了研究框架。

**方法** 本研究在加拿大不列颠哥伦比亚省温哥华岛苏克（Sooke）及其周边80平方千米的区域内布设54台相机陷阱（camera traps），以评估黑熊在从城市到荒野的人类干扰梯度下，其分布与栖息地利用的时空变化。研究采用分层随机抽样设计布设相机，根据调查区域在城市、乡村、荒野三个分层中的占比，合理分配相机数量：城市层（n=11台）、乡村层（n=19台）、荒野层（n=24台）。相邻相机点位间距设置为不低于200米（平均间距446米，范围147~1467米），以保证空间独立性。各分层内的采样点位尽可能随机布设。由于区域内私有土地占比较高，城市与乡村层的相机点位均从当地环保非政府组织提供的参与地主候选名单中选取。乡村层点位涵盖农业用地或低开发区域，城市层点位则位于镇区且邻近其他住宅。荒野层点位位于受人类开发干扰极小的林区，其中21台布设于海至海区域公园（Sea to Sea Regional Park），另外3台设于未开发的特苏克族（T’Sou-ke Nation）领地。为在区域公园的主要可访问区块内随机布设采样点，研究将500×500米的网格叠加至公园步道地图中，并在包含步道的10个随机网格单元内布设相机。特苏克族林区点位与区域公园西北边缘点位仅能通过一条徒步步道抵达，因此相机间距不低于200米。为避免拍摄过多人类影像及隐私风险，研究避免将相机直接布设于公园与特苏克族领地的主徒步步道上，而是在选定的网格单元内选择邻近的野生动物步道与低人流量人行步道，或将相机设于主步道周边区域。相机布设时间为2018年7月18日至8月20日。为监测黑熊栖息地利用的季节变化，所有相机均持续布设约一年，于2019年7月16日至19日期间回收。研究采用三种型号的相机陷阱（Reconyx PC900、Reconyx HC600与Browning Strike Force HD Pro），并将其随机分配至各分层，以降低不同相机型号间检测概率差异带来的潜在影响。研究结合当地黑熊穿越城乡用地的活动路径知识，以及野生动物步道与痕迹分布情况，将相机布设于可最大化检测黑熊活动的点位。每个点位仅布设1台相机，安装于距地面约1米的树干上，设置为高灵敏度模式，触发间隔为1秒（每次触发仅拍摄1张照片，因黑熊体型较大无需连拍，此举可节省电池与存储卡空间），相机朝向开阔区域，例如草地、草坪或步道。美洲黑熊偏好使用低人流量的人行步道，因其移动便捷且周边灌丛可提供浆果类食物。如有可能，相机将朝向多条野生动物步道与/或低人流量人行步道的交汇处。研究团队每2~3个月巡查一次相机陷阱，下载影像、检查设备运行状态并按需更换电池。研究使用Timelapse Image Analyzer 2.0软件对所有黑熊的相机陷阱影像进行分类。由于无法对个体黑熊进行唯一标识，研究将间隔≥30分钟的影像定义为独立检测事件，以尽可能降低连续检测间的相关性。研究将带幼崽的母熊视为单个个体，因母熊主导了栖息地选择行为。研究统计每个相机点位每月的检测事件总数，以计算月度相机陷阱检测率作为栖息地利用的衡量指标（总样本量为702，即13个月×54台相机）。为将黑熊检测数据与环境、人为特征相关联，研究选取了一系列从空间数据集提取的相机点位专属自变量。选取的变量包括：人口密度、步道密度、道路密度、海拔，以及至农业用地与城市用地的距离，所有变量均以相机点位为中心，在150米半径加权缓冲区内取平均值，以避免缓冲区重叠。本研究采用的预测变量与此前针对包含苏克在内的首都地区（Capital Regional District, CRD）开展的美洲黑熊人兽冲突模型所用变量一致，以便直接对比这些变量在解释区域尺度（CRD）上报冲突与局域尺度（苏克）黑熊栖息地利用时的重要性差异。对于源自GIS栅格图层且栅格单元超出缓冲区范围的变量，其数值将按缓冲区范围内的栅格单元面积比例进行加权计算。此外，研究选用增强型植被指数（Enhanced Vegetation Index, EVI）作为植被生产力的衡量指标，以表征食物可获得性，而非采用至森林的距离，因所有相机点位均设于林区内。研究从MODIS 250米分辨率16天合成图层中提取EVI数据。研究基于16天MODIS合成窗口落在目标分析月份内的天数，以及每个栅格单元在相机点位150米缓冲区内的占比，计算加权平均值。额外的预测变量用于表征局域尺度上天然食物资源分布与近期冲突报告的变化：包括至淡水的距离（作为河岸植被与鱼类对黑熊重要性的替代指标，已有研究证实二者对黑熊具有关键意义）；每月统计相机点位150米缓冲范围内的鲑鱼产卵水域周边是否存在鲑鱼（鲑鱼是黑熊的季节性重要食物资源）；以及研究年度内相机点位500米缓冲范围内的上报冲突数量。针对后两项额外变量，研究参照Klees van Bommel等人（2020）的研究，测试了150米与500米两种缓冲区尺寸。

**使用说明** 可使用Microsoft Excel、Google Sheets、R或任意可读取.CSV文件的程序。