On site occupancy models with heterogeneity

TL;DR

This paper investigates how heterogeneity in detection and presence probabilities affects site occupancy models, revealing potential biases and proposing robust estimation methods for ecological studies.

Contribution

It introduces a conditional likelihood approach to estimate detection parameters under heterogeneity and provides a consistent estimator for average presence probability.

Findings

Ignoring heterogeneity biases presence probability estimates.

Detection heterogeneity affects estimation depending on its relationship with presence.

Proposed methods improve robustness and accuracy in occupancy modeling.

Abstract

Site occupancy models are routinely used to estimate the probability of species presence from either abundance or presence-absence data collected across sites with repeated sampling occasions. In the last two decades, a broad class of occupancy models has been developed, but little attention has been given to examining the effects of heterogeneity in parameter estimation. This study focuses on occupancy models where heterogeneity is present in detection intensity and the presence probability. We show that the presence probability will be underestimated if detection heterogeneity is ignored. On the other hand, the behavior is different if heterogeneity in the presence probability is ignored; notably, an estimate of the average presence probability may be unbiased or over- or under-estimated depending on the relationship between detection and presence probabilities. In addition, when heterogeneity in the detection intensity is related to covariates, we propose a conditional likelihood approach to estimate the detection intensity parameters. This alternative method shares an optimal estimating function property and it ensures robustness against model specification on the presence probability. We then propose a consistent estimator for the average presence probability, provided that the detection intensity component model is correctly specified. We illustrate the bias effects and estimator performance in simulation studies and real data analysis.