Dey Soumen, Bischof Richard, Dupont Pierre P A, Milleret Cyril
Faculty of Environmental Sciences and Natural Resource Management Norwegian University of Life Sciences Ås Norway.
Ecol Evol. 2022 Feb 15;12(2):e8600. doi: 10.1002/ece3.8600. eCollection 2022 Feb.
Spatial capture-recapture (SCR) analysis is now used routinely to inform wildlife management and conservation decisions. It is therefore imperative that we understand the implications of and can diagnose common SCR model misspecifications, as flawed inferences could propagate to policy and interventions. The detection function of an SCR model describes how an individual's detections are distributed in space. Despite the detection function's central role in SCR, little is known about the robustness of SCR-derived abundance estimates and home range size estimates to misspecifications. Here, we set out to (a) determine whether abundance estimates are robust to a wider range of misspecifications of the detection function than previously explored, (b) quantify the sensitivity of home range size estimates to the choice of detection function, and (c) evaluate commonly used Bayesian -values for detecting misspecifications thereof. We simulated SCR data using different circular detection functions to emulate a wide range of space use patterns. We then fit Bayesian SCR models with three detection functions (half-normal, exponential, and half-normal plateau) to each simulated data set. While abundance estimates were very robust, estimates of home range size were sensitive to misspecifications of the detection function. When misspecified, SCR models with the half-normal plateau and exponential detection functions produced the most and least reliable home range size, respectively. Misspecifications with the strongest impact on parameter estimates were easily detected by Bayesian -values. Practitioners using SCR exclusively for density estimation are unlikely to be impacted by misspecifications of the detection function. However, the choice of detection function can have substantial consequences for the reliability of inferences about space use. Although Bayesian -values can aid the diagnosis of detection function misspecification under certain conditions, we urge the development of additional custom goodness-of-fit diagnostics for Bayesian SCR models to identify a wider range of model misspecifications.
空间捕获再捕获（SCR）分析如今在野生动物管理和保护决策中被常规使用。因此，我们必须理解SCR模型常见误设的影响并能够诊断它们，因为有缺陷的推断可能会延伸到政策和干预措施中。SCR模型的检测函数描述了个体的检测在空间中的分布情况。尽管检测函数在SCR中起着核心作用，但对于源自SCR的丰度估计和家域大小估计对误设的稳健性却知之甚少。在此，我们着手（a）确定丰度估计对于比之前探索范围更广的检测函数误设是否稳健，（b）量化家域大小估计对检测函数选择的敏感性，以及（c）评估用于检测其误设的常用贝叶斯p值。我们使用不同的圆形检测函数模拟SCR数据，以模拟广泛的空间使用模式。然后，我们将具有三种检测函数（半正态、指数和半正态平台）的贝叶斯SCR模型拟合到每个模拟数据集。虽然丰度估计非常稳健，但家域大小估计对检测函数的误设很敏感。当出现误设时，具有半正态平台和指数检测函数的SCR模型分别产生最可靠和最不可靠的家域大小。对参数估计影响最大的误设很容易被贝叶斯p值检测到。仅将SCR用于密度估计的从业者不太可能受到检测函数误设的影响。然而，检测函数的选择可能会对关于空间使用推断的可靠性产生重大影响。虽然贝叶斯p值在某些条件下可以帮助诊断检测函数的误设，但我们敦促为贝叶斯SCR模型开发更多定制的拟合优度诊断方法，以识别更广泛的模型误设。
