Using trait-based biodiversity to identify critical risk indicators in ecology

Abstract:

The interconnected nature of human-natural systems results in spill-overs of risk across domains. Understanding and predicting catastrophic outcomes from these cumulative interactions begins with quantifying the approach to adverse outcomes, defined here as a critical risk indicator (CRI), within domains. A widely recognized adverse outcome within ecology is biodiversity loss, which is typically measured by spatiotemporal changes in abundance, richness, and evenness across species within communities. On their own, each of these measures represent an incomplete picture of ecosystem function. Alternatively, a trait-based approach utilizes phenotypic traits that link organism performance to the biotic and abiotic environment, thus reflecting the processes that structure communities, which are direct responses to drivers of change. In particular, Trait Driver Theory utilizes differences in biomass-weighted trait distribution among assemblages to understand how the composition of trait-based groups in plant communities change along spatial gradients (e.g., elevation, latitude, temperature) and over time. In this study, we apply Trait Driver Theory to bird species, and demonstrate the utility of temporal changes in trait composition between spatial units (e.g., towns, states, regions) as an indicator of ecological risk. We integrate relative species abundance estimates from USGS Breeding Bird Survey and eBird data with recently derived morphological and niche-based traits to quantify spatiotemporal changes in biomass-weighted trait distributions. This trait-based approach allows us to evaluate risk based on ecosystem functions, which may show stronger connections with risks in other domains than that based on species abundances alone.