Capitalising on Opportunistic Data for Monitoring Species Relative Abundances

TL;DR

This paper introduces a statistical framework that combines opportunistic citizen science data with structured sampling data to accurately estimate species relative abundances, especially benefiting rare species and improving precision.

Contribution

It develops a generalized linear model-based approach to integrate diverse data sources for better abundance estimation under structural assumptions.

Findings

More precise estimates than using known-effort data alone.

Significant gains in precision with abundant opportunistic data.

Ability to estimate abundances for species recorded only in opportunistic data.

Abstract

With the internet, a massive amount of information on species abundance can be collected under citizen science programs. However, these data are often difficult to use directly in statistical inference, as their collection is generally opportunistic, and the distribution of the sampling effort is often not known. In this paper, we develop a general statistical framework to combine such "opportunistic data" with data collected using schemes characterized by a known sampling effort. Under some structural assumptions regarding the sampling effort and detectability, our approach allows to estimate the relative abundance of several species in different sites. It can be implemented through a simple generalized linear model. We illustrate the framework with typical bird datasets from the Aquitaine region, south-western France. We show that, under some assumptions, our approach provides estimates that are more precise than the ones obtained from the dataset with a known sampling effort alone. When the opportunistic data are abundant, the gain in precision may be considerable, especially for the rare species. We also show that estimates can be obtained even for species recorded only in the opportunistic scheme. Opportunistic data combined with a relatively small amount of data collected with a known effort may thus provide access to accurate and precise estimates of quantitative changes in relative abundance over space and/or time.