Goodness-of-fit methods in spatial capture-recapture

空间捕获-再捕获（SCR）模型提供了空间参考的遭遇数据的动物密度的估计，并已成为最广泛采用的方法估计密度。尽管空间捕获再捕获方法的发展和应用的快速增长，评估模型拟合的方法得到了很少的关注相比，生态学中的其他类别的层次模型。在这个项目中，我们开发了一种方法来测试拟合优度（GoF）的频率SCR模型使用蒙特卡罗模拟。我们评估这些GoF测试的能力，以诊断缺乏适合在一系列的假设违反的情况下。我们还试图了解这些测试是否与模型估计的偏差和精度相关。

Spatial capture-recapture (SCR) models provide estimates of animal density from spatially referenced encounter data and has become the most widely adopted approach for estimating density. Despite the rapid growth in the development and application of spatial capture recapture methods, approaches for assessing model fit have received very little attention when compared to other classes of hierarchical models in ecology. In this project, we develop an approach for testing goodness-of-fit (GoF) for frequentist SCR models using Monte Carlo simulations. We assess the ability of these GoF tests to diagnose lack of fit under a range of assumption violating scenarios. We also seek to understand if these tests correlate with bias and precision of the model estimates.