Abstract Understanding biodiversity pressures associated with recreation and tourism is a major challenge for conservation planning and landscape management. While estimates of landscape use are often collected using mechanisms such as park entry fees and traffic density estimates, these data do not provide substantial detail about the spatial location or intensity of recreation and tourism across biodiversity management areas. To better predict patterns of recreation and tourism likelihood to support conservation planning, we used social network data from Facebook(™), Flickr(™), Google(™), Strava(™), and Wikilocs(™) along with a suite of remote-sensing-derived environmental covariates in a maximum entropy (MaxEnt) presence-only modelling framework. Social network samples were compiled and processed to reduce sampling bias and spatial autocorrelation. Road access, climate data, and remote sensing covariates describing vegetation greenness, disturbance, topography, and moisture were used as predictor variables in the MaxEnt modelling framework. Our focus site was a grizzly bear (Ursus arctos) management area in west-central Alberta, Canada. Individual models were developed for each social network dataset, as well as a combined model including all the samples . Mean cross-validated AUC, partial ROC, and true skill statistics (TSS) were used to evaluate model accuracy. Results indicated that the covariates proposed were able to best model Strava and Wikilocs activity (TSS = 0.69 and 0.50, respectively), while samples from Flickr or the combination of all social networks were least accurate (TSS = 0.32). The “access” covariate was most important for MaxEnt training gain across a number of social network models, highlighting the importance of access for recreation and tourism likelihood. The summer heat moisture index and normalized burn ratio were also useful spatial covariates in many predictions. Recreation and tourism likelihood maps were combined with grizzly bear telemetry data to examine how recreation and tourism may affect grizzly bear behaviour. All social network models found a similar influence on grizzly bear behaviour, with increasing recreation and tourism use resulting in decreased foraging behaviour and increased rapid movement, suggesting that the models developed here are useful tools for predicting grizzly bear behaviour and planning conservation strategies for the species.

中文翻译：

---

使用社交网络数据和最大熵模型绘制灰熊恢复区的娱乐和旅游使用图

摘要 了解与娱乐和旅游相关的生物多样性压力是保护规划和景观管理的主要挑战。虽然景观利用的估计通常是使用公园入场费和交通密度估计等机制收集的，但这些数据并未提供有关跨生物多样性管理区域的空间位置或娱乐和旅游强度的大量细节。为了更好地预测娱乐和旅游的可能性模式以支持保护规划，我们使用了来自 Facebook(™)、Flickr(™)、Google(™)、Strava(™) 和 Wikilocs(™) 的社交网络数据以及一套最大熵（MaxEnt）仅存在建模框架中的遥感衍生环境协变量。社会网络样本被编译和处理以减少抽样偏差和空间自相关。道路通道、气候数据和描述植被绿度、干扰、地形和湿度的遥感协变量被用作 MaxEnt 建模框架中的预测变量。我们的重点地点是加拿大艾伯塔省中西部的灰熊 (Ursus arctos) 管理区。为每个社交网络数据集开发了单独的模型，以及包含所有样本的组合模型。平均交叉验证的 AUC、部分 ROC 和真实技能统计 (TSS) 用于评估模型准确性。结果表明，提出的协变量能够最好地模拟 Strava 和 Wikilocs 活动（分别为 TSS = 0.69 和 0.50），而来自 Flickr 或所有社交网络组合的样本最不准确（TSS = 0.32）。在许多社交网络模型中，“访问”协变量对于 MaxEnt 培训收益最重要，突出了访问对娱乐和旅游可能性的重要性。在许多预测中，夏季热湿指数和归一化燃烧率也是有用的空间协变量。娱乐和旅游可能性地图与灰熊遥测数据相结合，以研究娱乐和旅游如何影响灰熊行为。所有社交网络模型都发现对灰熊行为的影响类似，随着娱乐和旅游使用的增加导致觅食行为减少和快速移动增加，