Bias Correction in Species Distribution Models: Pooling Survey and Collection Data for Multiple Species

TL;DR

This paper introduces a probabilistic model that combines presence-only and presence-absence data across multiple species to correct sampling biases and improve species distribution predictions, especially when data are scarce.

Contribution

The authors propose a joint likelihood approach that estimates and adjusts for sampling bias across species, leveraging shared bias assumptions to enhance inference from biased presence-only data.

Findings

Presence-only data show strong coastal and urban sampling bias.

Pooling data improves predictive accuracy for species with limited presence-absence data.

The method can unbiasedly estimate distributions of species with only presence-only data.

Abstract

Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence-absence or count data collected in systematic, planned surveys are more reliable but typically less abundant. We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence-absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting for the sampling bias affecting the presence-only data. By assuming that the sampling bias is the same for all species, we can borrow strength across species to efficiently estimate the bias and improve our inference from presence-only data. We evaluate our model's performance on data for 36 eucalypt species in southeastern Australia. We find that presence-only records exhibit a strong sampling bias toward the coast and toward Sydney, the largest city. Our data-pooling technique substantially improves the out-of-sample predictive performance of our model when the amount of available presence-absence data for a given species is scarce. If we have only presence-only data and no presence-absence data for a given species, but both types of data for several other species that suffer from the same spatial sampling bias, then our method can obtain an unbiased estimate of the first species' geographic range.