Cats, connectivity and conservation: incorporating datasets and integrating scales for wildlife management

Understanding resource selection and quantifying habitat connectivity are fundamental to conservation planning for both land-use and species management plans. However, datasets available to management authorities for resource selection and connectivity analyses are often highly limited and fragmentary. As a result, measuring connectivity is challenging, and often poorly integrated within conservation planning and wildlife management. To exacerbate the challenge, scale-dependent resource use makes inference across scales problematic, resource use is often modelled in areas where the species is not present, and connectivity is typically measured using a source-to-sink approach, erroneously assuming animals possess predefined destinations.
Here, we used a large carnivore, the leopard Panthera pardus, to characterise resource use and landscape connectivity across a vast, biodiverse region of southern Africa. Using a range of datasets to counter data deficiencies inherent in carnivore management, we overcame methodological limitations by employing occupancy modelling and resource selection functions across three orders of selection, and estimated landscape-scale habitat connectivity – independent of a priori source and sink locations – using circuit theory. We evaluated whether occupancy modelling on its own was capable of accurately informing habitat connectivity, and identified conservation priorities necessary for applied management.
We detected markedly different scale-dependent relationships across all selection orders. Our multi-data, multi-scale approach accurately predicted resource use across multiple scales and demonstrates how management authorities can more suitably utilise fragmentary datasets. We further developed an unbiased landscape-scale depiction of habitat connectivity, and identified key linkages in need of targeted management. We did not find support for the use of occupancy modelling as a proxy for landscape-scale habitat connectivity and further caution its use within a management context.
Synthesis and applications. Maintaining habitat connectivity remains a fundamental component of wildlife management and conservation, yet data to inform these biological and ecological processes are often scarce. We present a robust approach that incorporates multi-scale fragmentary datasets (e.g. mortality data, permit data, sightings data), routinely collected by management authorities, to inform wildlife management and land-use planning. We recommend that management authorities employ a multi-data, multi-scale connectivity approach—as we present here—to identify management units at risk of low connectivity.

资源选择（resource selection）解析与栖息地连通性（habitat connectivity）量化，是土地利用与物种管理规划中保护规划（conservation planning）的核心基础。然而，管理机构可用于资源选择与连通性分析的数据集往往极为有限且碎片化。这导致连通性测算难度极大，且常难以有效融入保护规划与野生动物管理工作。更棘手的是，尺度依赖的资源利用行为使得跨尺度推断存在缺陷；资源利用模型常构建于物种未分布的区域；连通性测算通常采用源-汇（source-to-sink）方法，错误假定动物拥有预设的活动终点。

本研究以大型食肉动物豹（Panthera pardus）为研究对象，在非洲南部广袤且生物多样性丰富的区域内，刻画其资源利用模式与景观连通性。针对食肉动物管理中固有的数据缺陷，研究团队通过整合三类选择等级下的占用模型（occupancy modelling）与资源选择函数（resource selection functions）突破了方法学局限，并采用电路理论（circuit theory）估算景观尺度的栖息地连通性——该方法无需依赖先验的源汇位置（a priori source and sink locations）。研究评估了单独使用占用模型能否准确支撑栖息地连通性分析，并明确了应用管理所需的保护优先级。

研究团队在所有选择等级中均发现了显著不同的尺度依赖关系。本研究提出的多数据、多尺度方法可精准预测多尺度下的资源利用模式，并展示了管理机构如何更合理地利用碎片化数据集。此外，研究构建了无偏的景观尺度栖息地连通性刻画结果，识别出需针对性管理的关键连通廊道。研究未发现证据支持使用占用模型作为景观尺度栖息地连通性的替代指标，并进一步提醒在管理场景中谨慎使用该方法。

综合与应用：维持栖息地连通性仍是野生动物管理与保护的核心内容，但支撑这些生物生态过程的相关数据往往极为匮乏。本研究提出了一套稳健的研究框架，整合了管理机构常规采集的多尺度碎片化数据集（如死亡记录、许可数据、目击数据），为野生动物管理与土地利用规划提供支撑。我们建议管理机构采用本研究提出的多数据、多尺度连通性分析方法，以识别连通性不足的风险管控单元。