An assessment of sampling designs using SCR analyses to estimate abundance of boreal caribou

Accurately estimating abundance is a critical component of monitoring and recovery of rare and elusive species. Spatial capture-recapture (SCR) models are an increasingly popular method for robust estimation of ecological parameters. We provide an analytical framework to assess results from empirical studies to inform SCR sampling design, using both simulated and empirical data from non-invasive genetic sampling of seven boreal caribou populations (Rangifer tarandus caribou) which varied in range size and estimated population density. We use simulated population data with varying levels of clustered distributions to quantify the impact of non-independence of detections on density estimates, and empirical datasets to explore the influence of varied sampling intensity on the relative bias and precision of density estimates.  Simulations revealed that clustered distributions of detections did not significantly impact relative bias or precision of density estimates. The genotyping success rate of our empirical dataset (n = 7,210 samples) was 95.1%, and 1,755 unique individuals were identified. Analysis of the empirical data indicated that reduced sampling intensity had a greater impact on density estimates in smaller ranges. The number of captures and spatial recaptures were strongly correlated with precision, but not absolute relative bias. The best sampling designs did not differ with estimated population density but differed between large and small ranges. We provide an efficient framework implemented in R to estimate the detection parameters required when designing SCR studies. The framework can be used when designing a monitoring program to minimize effort and cost while maximizing effectiveness, which is critical for informing wildlife management and conservation.

准确估算种群丰度是珍稀隐秘物种监测与恢复工作的核心环节。空间捕获-再捕获（Spatial capture-recapture, SCR）模型正日益成为稳健估算生态学参数的主流方法。本研究构建了一套分析框架，用于评估实证研究结果以指导SCR采样设计，所用数据涵盖模拟数据，以及来自7个北方驯鹿（Rangifer tarandus caribou）种群的非侵入式遗传采样实证数据——这些种群的分布范围与估算种群密度均存在差异。我们利用具有不同集群分布水平的模拟种群数据，量化检测非独立性对密度估算结果的影响；同时借助实证数据集，探究不同采样强度对密度估算的相对偏差与精准度的影响。模拟结果显示，检测的集群分布并不会显著影响密度估算的相对偏差与精准度。本实证数据集（n = 7,210 份样本）的基因分型成功率为95.1%，共鉴定出1755个独特个体。对实证数据的分析表明，采样强度降低对分布范围较小的种群密度估算影响更大。捕获次数与空间再捕获次数与估算精准度显著相关，但与绝对相对偏差无明显关联。最优采样设计并不随估算种群密度发生变化，却因分布范围的大小存在差异。我们开发了一套在R语言中实现的高效分析框架，用于估算SCR研究设计所需的检测参数。该框架可用于指导监测方案的设计，在最大化监测有效性的同时最小化工作量与成本，这对于野生动物管理与保护工作具有重要的指导意义。