Department of Ecology, Montana State University, Bozeman, Montana, United States of America.
Montana Department of Fish, Wildlife and Parks, Bozeman, Montana, United States of America.
PLoS One. 2019 Apr 19;14(4):e0215458. doi: 10.1371/journal.pone.0215458. eCollection 2019.
Spatial capture-recapture (SCR) models have improved the ability to estimate densities of rare and elusive animals. However, SCR models have seldom been validated even as model formulations diversify and expand to incorporate new sampling methods and/or additional sources of information on model parameters. Information on the relationship between encounter probabilities, sources of additional information, and the reliability of density estimates, is rare but crucial to assessing reliability of SCR-based estimates. We used a simulation-based approach that incorporated prior empirical work to assess the accuracy and precision of density estimates from SCR models using spatially unstructured sampling. To assess the consequences of sparse data and potential sources of bias, we simulated data under six scenarios corresponding to three different levels of search effort and two levels of correlation between search effort and animal density. We then estimated density for each scenario using four models that included increasing amounts of information from harvested individuals and telemetry to evaluate the impact of additional sources of information. Model results were sensitive to the quantity of available information: density estimates based on low search effort were biased high and imprecise, whereas estimates based on high search effort were unbiased and precise. A correlation between search effort and animal density resulted in a positive bias in density estimates, though the bias decreased with increasingly informative datasets. Adding information from harvested individuals and telemetered individuals improved density estimates based on low and moderate effort but had negligible impact for datasets resulting from high effort. We demonstrated that density estimates from SCR models using spatially unstructured sampling are reliable when sufficient information is provided. Accurate density estimates can result if empirical-based simulations such as those presented here are used to develop study designs with appropriate amounts of effort and information sources.
空间捕获-再捕获（SCR）模型提高了对稀有和难以捉摸的动物密度进行估计的能力。然而，即使模型公式多样化并扩展到包含新的采样方法和/或模型参数的其他信息来源，SCR 模型也很少得到验证。关于遭遇概率、额外信息来源与密度估计可靠性之间的关系的信息很少，但对于评估基于 SCR 的估计的可靠性至关重要。我们使用了一种基于模拟的方法，该方法结合了先前的经验工作，以评估使用空间非结构化采样的 SCR 模型的密度估计的准确性和精度。为了评估数据稀疏和潜在偏差源的后果，我们根据三种不同的搜索努力水平和搜索努力与动物密度之间的两种相关性水平，模拟了六种情况下的数据。然后，我们使用包含从收获个体和遥测获得的越来越多的信息的四个模型来估计每个场景的密度，以评估额外信息来源的影响。模型结果对可用信息量敏感：基于低搜索努力的密度估计值偏高且不准确，而基于高搜索努力的密度估计值则无偏且准确。搜索努力与动物密度之间的相关性导致密度估计值出现正偏差，但随着信息量更大的数据集的增加，偏差会减小。从收获个体和遥测个体中添加信息可以改善基于低努力和中等努力的密度估计，但对于高努力产生的数据集几乎没有影响。我们证明了使用空间非结构化采样的 SCR 模型的密度估计在提供足够信息时是可靠的。如果使用这里提出的基于经验的模拟来制定具有适当努力和信息来源的研究设计，则可以获得准确的密度估计值。
