A predictive model of arthropod biodiversity resilience to inform conservation strategy

Resilience is the ability of an ecosystem to withstand or absorb external stress without undergoing a significant or irrevocable state change. Given the current stress humans are imposing on global biodiversity both directly, through land use practices, pollution, and ecosystem modifications, and indirectly, through changes in temperature and precipitation regimes as an outcome of global climate change, a more precise and quantifiable understanding of biodiversity resilience is of critical importance. A reliable index of biodiversity resilience will allow us to identify habitats or ecosystems to target for protection or remediation efforts, and to understand whether ecological communities will persist or collapse in response to natural or human-mediated disturbances. High-throughput biodiversity genetic inventories (e.g., metabarcoding/eDNA) coupled with global remote sensing data allow for characterizing the structure of ecological communities as well as the environmental and human-land-use associated correlates of pristine and disturbed systems. Here we will demonstrate a Biodiversity Resilience Index (BRI), which utilizes community-scale intraspecific genetic diversity as may be obtained from contemporary broad-spectrum metabarcoding approaches. We will also describe a process-based model of biodiversity resilience which is informed by a holistic understanding of eco-evolutionary dynamics to capture the signatures of biodiversity resilience and community genetic health. We will use arthropod community metabarcode data from Hawaii show how this model can be used to make predictions of BRI under competing management scenarios, to inform conservation strategy.