Biodiversity dynamics in the Anthropocene: Re-purposing air quality networks for global scale species monitoring

Biodiversity on land provides huge benefits to humanity through carbon capture, resilient ecosystems, provision of food, and effects on human health. To manage and conserve our resources we need to understand how biodiversity is changing in response to the environment and human impacts. Moreover, we have implemented global targets to measure and protect nature through recent international agreements. Commonly used methods of reporting (e.g., datasets generated by citizen scientists) have limitations in terms of spatial, temporal and taxonomic resolution. This is problematic given that ecological communities are naturally dynamic systems in which both species and their interactions change, and these kinds of data are necessary to assess nature recovery. Both positive and negative human impacts on nature happen on relatively short timescales, but data are not collected with sufficient regularity for us to understand and manage these rapid changes. Coverage within datasets is biased towards popular animals (e.g., birds), with greater records coverage in densely inhabited areas.

I have pioneered an approach that will deliver widespread, rapid understanding of biodiversity and ecological community dynamics on land. My initial work has shown that airborne environmental DNA (eDNA) is shed from animals, plants and fungi into the atmosphere, and that this broadly reflects the composition of the surrounding community. I have recently shown that this material is being continuously collected by an existing globally distributed infrastructure: air quality monitoring networks. These networks are filtering the air at daily or weekly intervals to measure particulates and pollutants but are also inadvertently collecting information on biodiversity.

My overall aims are to understand how ecological communities are responding to human impacts globally and deliver technological solutions to the problem of scale in biodiversity monitoring using the air quality networks. I will do this by working with the environmental departments of four countries to analyse the molecular information from their air quality networks over three years. I will explain finescale temporal variation in species richness, turnover, community composition and ecological network "modules" (groups of tightly co-occurring species), and how these change over time according to environmental variation and human impacts. In addition, the recent COVID-19 pandemic offers the opportunity to quantify the effects of reducing human activity on biodiversity dynamics within the framework of a natural longitudinal experiment. While usual biodiversity monitoring was halted during these years, air quality monitoring continued, and these filters have been stored in long-term archives. I will work with an additional five countries (Switzerland and four countries in S America and Asia from the GAPS megacities project) to quantify the impacts of the "Anthropause" on biodiversity dynamics and ecological network properties.

I will harness a multidisciplinary approach to further study the nature of airborne environmental DNA itself. Working with atmospheric scientists, I will study the particulate size range of airborne eDNA, in addition to the effects of sampling time and local weather conditions on samples. A combination of field experiments and particulate dispersal modelling will allow me to understand how far eDNA disperses from animal populations. These experiments will be used to inform other work packages in the FLF, but will also be of great interest to end-users implementing the technology.

This work will have multiple beneficiaries from many sectors because society increasingly recognises that biodiversity preservation is both a cross-sector responsibility and challenge. I will work with policy stakeholders to explore how these data inform the UK's initiatives to protect and restore nature, monitor invasive species and forecast responses to future environmental changes.