Nondetection sampling bias in marked presence-only data

TL;DR

This paper introduces a statistical framework to correct nondetection sampling bias in species distribution models using presence-only data, accounting for animal aggregation and detection probabilities to improve model reliability.

Contribution

The authors develop a marked inhomogeneous Poisson point process model that explicitly accounts for nondetection bias and aggregation in presence-only species data, filling a gap in SDM methodology.

Findings

Methods perform well on simulated data when nondetection is corrected.

Ignoring nondetection leads to poor model performance.

Auxiliary data on detection probabilities are essential for bias correction.

Abstract

1. Species distribution models (SDM) are tools used to determine environmental features that influence the geographic distribution of species' abundance and have been used to analyze presence-only records. Analysis of presence-only records may require correction for nondetection sampling bias to yield reliable conclusions. In addition, individuals of some species of animals may be highly aggregated and standard SDMs ignore environmental features that may influence aggregation behavior. 2. We contend that nondetection sampling bias can be treated as missing data. Statistical theory and corrective methods are well developed for missing data, but have been ignored in the literature on SDMs. We developed a marked inhomogeneous Poisson point process model that accounted for nondetection and aggregation behavior in animals and tested our methods on simulated data. 3. Correcting for nondetection sampling bias requires estimates of the probability of detection which must be obtained from auxiliary data, as presence-only data do not contain information about the detection mechanism. Weighted likelihood methods can be used to correct for nondetection if estimates of the probability of detection are available. We used an inhomogeneous Poisson point process model to model group abundance, a zero-truncated generalized linear model to model group size, and combined these two models to describe the distribution of abundance. Our methods performed well on simulated data when nondetection was accounted for and poorly when detection was ignored. 4. We recommend researchers consider the effects of nondetection sampling bias when modeling species distributions using presence-only data. If information about the detection process is available, we recommend researchers explore the effects of nondetection and, when warranted, correct the bias using our methods.