Revisiting resource selection probability functions and single-visit methods: Clarification and extensions

TL;DR

This paper clarifies the conditions for resource selection probability function estimation and extends single-visit methods to broader models, addressing biases in population estimates caused by detection probability issues.

Contribution

It provides a detailed mathematical clarification of RSPF conditions, analyzes the limitations of existing methods, and proposes an extended single-visit approach with a multinomial extension for improved estimation.

Findings

Multiple-visit methods can be biased under scaled logistic detection functions.

The class of models for RSPF is broad and applicable.

The extended single-visit method can handle scaled detection functions and covariate-dependent scaling.

Abstract

Models accounting for imperfect detection are important. Single-visit methods have been proposed as an alternative to multiple-visits methods to relax the assumption of closed population. Knape and Korner-Nievergelt (2015) showed that under certain models of probability of detection single-visit methods are statistically non-identifiable leading to biased population estimates. There is a close relationship between estimation of the resource selection probability function (RSPF) using weighted distributions and single-visit methods for occupancy and abundance estimation. We explain the precise mathematical conditions needed for RSPF estimation as stated in Lele and Keim (2006). The identical conditions, that remained unstated in our papers on single-visit methodology, are needed for single-visit methodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of the RSPF or the single-visit methodology. To complement the work by Knape and Korner-Nievergelt, we study the performance of multiple-visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function multiple-visits methods also lead to biased estimates. As a solution to this problem, we extend the single-visit methodology to a class of models that allows use of scaled probability function. We propose a Multinomial extension of single visit methodology that can be used to check whether the detection function satisfies the RSPF condition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated.