A0853
Title: Nc-mixture occupancy models
Authors: Wen-Han Hwang - National Tsing Hua University (Taiwan) [presenting]
Abstract: A class of occupancy models for detection/non-detection data is proposed to relax the closure assumption of N$-$mixture models. A community parameter $c$, ranging from $0$ to $1$, is introduced, which characterizes a certain portion of individuals being fixed across multiple visits. As a result, when $c$ equals $1$, the model reduces to the N$-$mixture model; this reduced model is shown to overestimate abundance when the closure assumption is not fully satisfied. Additionally, by including a zero-inflated component, the proposed model can bridge the standard occupancy model ($c=0$) and the zero-inflated N$-$mixture model ($c=1$). Then the behaviour of the estimators is studied for the two extreme models as $c$ varies from 0 to 1. An interesting finding is that the zero-inflated N$-$mixture model can consistently estimate the zero-inflated probability (occupancy) as $c$ approaches $0$, but the bias can be positive, negative, or unbiased when $c>0$ depending on other parameters. These results are also demonstrated through simulation studies and data analysis.
