Ignoring species availability biases occupancy estimates in single‐scale occupancy models

Abstract

Abstract Most applications of single‐scale occupancy models do not differentiate between availability and detectability, even though species availability is rarely equal to one. Species availability can be estimated using multi‐scale occupancy models; however, for the practical application of multi‐scale occupancy models, it can be unclear what a robust sampling design looks like and what the statistical properties of the multi‐scale and single‐scale occupancy models are when availability is less than one. Using simulations, we explore the following common questions asked by ecologists during the design phase of a field study: (Q1) what is a robust sampling design for the multi‐scale occupancy model when there are a priori expectations of parameter estimates? (Q2) what is a robust sampling design when we have no expectations of parameter estimates? and (Q3) can a single‐scale occupancy model with a random effects term adequately absorb the extra heterogeneity produced when availability is less than one and provide reliable estimates of occupancy probability? Our results show that there is a tradeoff between the number of sites and surveys needed to achieve a specified level of acceptable error for occupancy estimates using the multi‐scale occupancy model. We also document that when species availability is low (<0.40 on the probability scale), then single‐scale occupancy models underestimate occupancy by as much as 0.40 on the probability scale, produce overly precise estimates, and provide poor parameter coverage. This pattern was observed when a random effects term was and was not included in the single‐scale occupancy model, suggesting that adding a random‐effects term does not adequately absorb the extra heterogeneity produced by the availability process. In contrast, when species availability was high (>0.60), single‐scale occupancy models performed similarly to the multi‐scale occupancy model. Users can further explore our results and sampling designs across a number of different scenarios using the RShiny app https://gdirenzo.shinyapps.io/multi‐scale‐occ/. Our results suggest that unaccounted for availability can lead to underestimating species distributions when using single‐scale occupancy models, which can have large implications on inference and prediction, especially for those working in the fields of invasion ecology, disease emergence, and species conservation.