Partitioning detectability components in populations subject to within-season temporary emigration using binomial mixture models

摘要: Detectability of individual animals is highly variable and nearly always < 1; imperfect detection must be accounted for to reliably estimate population sizes trends. Hierarchical models can simultaneously abundance effective probability, but there are several different mechanisms that cause variation in detectability. Neglecting temporary emigration lead biased estimates because availability conditional probability confounded. In this study, we extend previous hierarchical binomial mixture account multiple sources The state process the model describes ecological generate spatial temporal patterns abundance, while observation accounts nature counting individuals due false absences. We illustrate our model’s potential advantages, including allowance between sampling periods, with a case study southern red-backed salamanders Plethodon serratus. fit standard counts terrestrial surveyed at 40 sites during 3–5 surveys each spring fall 2010–2012. Our generated similar parameter models. Aspect was best predictor salamander study; increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased surface activity (i.e. sampling), higher amounts woody cover objects rocks capture, given an animal exposed sampling). By explicitly accounting both components detectability, congruence statistical modeling understanding system. stress importance choosing survey locations protocols maximize species increase reliability.