Preferential sampling for presence/absence data and for fusion of presence/absence data with presence-only data

TL;DR

This paper clarifies the fundamental differences between presence/absence and presence-only data in species distribution modeling, emphasizing the importance of appropriate modeling strategies and demonstrating how preferential sampling can improve inference and data fusion.

Contribution

It introduces a modeling framework that treats presence/absence data as a probability surface and presence-only data as a point pattern, enabling coherent data fusion and improved inference.

Findings

Preferential sampling improves presence/absence surface estimation.

Proper modeling of data types enhances inference accuracy.

Fusion of data types benefits from shared process modeling.

Abstract

Presence/absence data and presence-only data are the two customary sources for learning about species distributions over a region. We illuminate the fundamental modeling differences between the two types of data. Most simply, locations are considered as fixed under presence/absence data; locations are random under presence-only data. The definition of "probability of presence" is incompatible between the two. So, we take issue with modeling strategies in the literature which ignore this incompatibility, which assume that presence/absence modeling can be induced from presence-only specifications and therefore, that fusion of presence-only and presence/absence data sources is routine. We argue that presence/absence data should be modeled at point level. That is, we need to specify a surface which provides the probability of presence at any location in the region. A realization from this surface is a binary map yielding the results of Bernoulli trials across all locations. Presence-only data should be modeled as a point pattern driven by specification of an intensity function. We further argue that, with just presence/absence data, preferential sampling, using a shared process perspective, can improve our estimated presence/absence surface and prediction of presence. We also argue that preferential sampling can enable a probabilistically coherent fusion of the two data types. We illustrate with two real datasets, one presence/absence, one presence-only for invasive species presence in New England in the United States. We demonstrate that potential bias in sampling locations can affect inference with regard to presence/absence and show that inference can be improved with preferential sampling ideas. We also provide a probabilistically coherent fusion of the two datasets to again improve inference with regard to presence/absence.