A workflow for impact indicators of alien species for policy: A demonstration with Acacia species

Abstract

The negative impact of alien species is recognised as a major threat to biodiversity. However, there is a lack of evidence-based impact indicators that adhere to the Findable, Accessible, Interoperable, and Reusable (FAIR) Data Principles and employ standardised workflows. As a result, the impacts of alien species cannot be systematically estimated over time, posing a major hurdle for policymaking. We introduce an open-source workflow for computing impact indicators of alien species, combining occurrence data from the Global Biodiversity Information Facility (GBIF) with assessments of Environmental Impact Classification for Alien Taxa (EICAT). To operationalise the workflow, we developed an R package, impIndicator , which allows users to compute and visualise impact indicators for individual species and sites, as well as derive a regional estimate of alien species impacts. This tool can support ecological research and management by providing timely, accurate insights into where alien species pose the greatest threats and whether current interventions are effectively reducing them. Such information is directly relevant to policy frameworks, including Target 6 of the Convention on Biological Diversity’s Kunming–Montreal Global Biodiversity Framework and the Sustainable Development Goals. We demonstrate the workflow using Acacia species in the Iberian Peninsula, South Africa and California, showcasing the spatiotemporal dynamics of their impacts and highlighting sites with higher impact risk. The results were sensitive to variation in sampling effort in the underlying occurrence data, indicating that impact indicators depend strongly on data completeness. This highlights the importance of well-sampled and systematically curated occurrence datasets for computing robust impact indicators.