Goodness-of-fit methods in spatial capture-recapture

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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.