Ecological equivalence assessment: The potential of genetic tools, remote sensing and metapopulation models to better...

Within the framework of the mitigation hierarchy, biodiversity offsetting is the main tool promoted to reach No Net Loss. One of the determining factors of offsetting success is the evaluation of ecological equivalence. Various equivalence assessment methods (EAMs) have been developed to provide a framework to evaluate the balance between expected biodiversity losses and gains. In the context of achieving No Net Loss, EAMs must address challenges of Operationality, Currency, Uncertainty, Spatial scale and Time frame. In this study, we investigated the way the most widely used EAMs address these challenges, positing that certain tools from ecological science could limit the trade-offs between these challenges and improve the ecological assessment process. To this end, we analysed the risks and benefits associated with the inclusion of genetic tools (landscape genetics and eDNA), remote sensing and metapopulation models in selected EAMs. Our results revealed trade-offs between these five challenges, in particular between Operationality and Currency. The EAMs varied strongly in these two aspects, depending on the general assessment approach and the biodiversity component they focus on. To a lesser degree, Time frame and Spatial scale also differed between the methods. We identified that the integration of the different tools differs among them, being easier for remote sensing and metapopulation models than for the genetic tools. Nevertheless, the integration resulted in benefits compared to the current use of the methods - benefits that included improving the objectivation of the assessment and the automatization potential. The tools also show potential for automatization, which could have major benefits for operationality. In terms of risks, the integration of these tools increases the technical complexity of the methods, requiring new skills, and would change the overall approach of the ecological assessment.