Leveraging Citizen Science To Assess Richness, Diversity, And Abundance In Ant Communities

AO_SCPLOWBSTRACTC_SCPLOWCitizen science is a key resource in overcoming the logistical challenges of monitoring biodiversity. While datasets collected by groups of volunteers typically have biases, recent methodological and technological advances provide approaches for accounting for such biases, particularly in the context of modelling species distributions and diversity. Specifically, data integration techniques allow for the combination of scientifically collected datasets with haphazardly sampled presence-only datasets created by most citizen science initiatives. Here, we use a hierarchical Bayesian framework to integrate a set of ant presences collected by citizen scientists in the Vaud canton (Switzerland) with ant colony density data collected concurrently in the same region following a scientific sampling design. The community-level Poisson point process model included species-specific responses to the local (1.2 m2) and regional (1 km2) environment, with the presence-only samples incorporated at the regional scale to predict local and regional ant communities. At the regional scale, species richness followed a hump-shaped pattern and peaked near 1000 m while abundance increased with elevation. Low elevation and montane ant communities were composed of distinct species assemblages. At the local scale, the link between elevation and richness, diversity, and abundance was weak. At low elevations, local plots varied both in total abundance and species composition, while at higher elevations, the species composition was less variable. The citizen science dataset showed a general tendency toward under-representation of certain species, and heavy spatial sampling bias. Nonetheless, the inclusion of the citizen science data improved predictions of local communities, and also reduced susceptibility to over-fitting. Additionally, the citizen science dataset included many rare species not detected in the structured abundance dataset. The model described here illustrates a framework for capitalizing on the efforts of citizen scientists to better understand the patterns and distribution of biodiversity.