Data coverage, biases, and trends in a global citizen-science resource for monitoring avian diversity

Abstract

Aim

Understanding and addressing the global biodiversity crisis requires ecological information compiled continuously from across the globe. Data from citizen science initiatives are useful for quantifying species' ecological niches and geographical distributions but can be difficult to apply towards biodiversity monitoring. The presence of fixed geographical locations reduces the opportunistic nature of citizen science data, allowing for more reliable and nuanced trend estimation. The eBird citizen-science program contains predefined locations whose bird assemblages are sampled across years (‘hotspots’). For hotspots to function as a biodiversity monitoring resource, issues related to data coverage, biases, and trends need to be addressed.

Location

Global.

Methods

We estimated the survey completeness of species richness at 300,500 eBird hotspots during 2002–2022. We documented sampling biases at eBird hotspot and non-hotspot locations during 2022 based on protection status, temperature, precipitation, and landcover.

Results

A total of 10,410 bird species (ca. 96.9% of total) were recorded at hotspots. The number of hotspots, checklists, and participants and the quality of species richness estimates increased worldwide with the Nearctic containing the strongest and most consistent trends. Compared to non-hotspots, hotspots oversampled areas with higher protection status. Hotspots and non-hotspots oversampled warmer and wetter locations in the Antarctic, Nearctic, and Palearctic, and cooler locations in the Afrotropics, Australasia, and the Neotropics. Hotspots and especially non-hotspots oversampled urban areas. Hotspots and non-hotspots undersampled shrublands in Australasia. Hotspots and especially non-hotspots undersampled forests in the Afrotropics, Indomalaya, Neotropics, and Oceania.

Main Conclusions

Hotspots have captured a large component of the world's avian diversity but have done so inconsistently across space and time. Data quantity and quality are increasing in many regions, but the presence of regionally specific sampling biases and spatial uncertainty in hotspot locations should be addressed when applying the data.