Biodiversity is declining worldwide due to anthropogenic pressures resulting from economic activities. Corporate biodiversity impact assessments are a key tool to quantify biodiversity losses induced by companies, including their upstream supply chains. However, several challenges must be overcome to improve their robustness, in particular when used to support claims of no net loss (NNL) or net positive impact on biodiversity. This thesis addresses three aspects related to these challenges. First, many metrics are available for biodiversity impact assessments, yet how the choice of a particular metric influences outcomes of NNL assessments is poorly understood. We compared three globally applicable biodiversity impact assessment metrics in a corporate NNL context, and showed that attaining NNL as measured with one metric does not guarantee its attainment as measured with another that tracks different facets of biodiversity. Thus, using several complementary biodiversity impact metrics is key. Second, currently available globally applicable models to quantify biodiversity impacts have a limited granularity to represent the impacts of fine-grained management practices or corporate activities, particularly those relating to agricultural land use. Diversified agricultural practices, such as the inclusion of natural elements (e.g., hedgerows), crop diversification, and the retention of complex native vegetation structures, can mitigate impacts of agricultural activities on biodiversity. However, their effect is poorly characterised. We performed a meta-analysis to investigate the extent to which agricultural diversification mitigates the adverse effects of agriculture on bird diversity in agroforestry sites, cropland, forest plantations, and pastures. We found that diversification measures can mitigate the effects of agriculture, but not consistently, as bird responses differ depending on the diversity metric used, the location of the site and individual species characteristics. Third, the inherent limitations of global biodiversity models and metrics induce uncertainties in corporate biodiversity impact assessments, which may harm a company’s reputation and lead to adverse outcomes for biodiversity if compensatory claims are not sufficiently supported by scientific arguments. To address this, we evaluated various sources of uncertainty in corporate value-chain loss measurement and local gain measurement, particularly those relating to the inherent limitations of metrics and models. We then suggested a simple framework to identify the risk of inaccurate compensatory claims at the corporate value-chain level, depending on the degree of uncertainty in estimates of losses and gains. We illustrate how corporates can very rarely robustly claim they have compensated for a value-chain level biodiversity loss. The framework provides a path to making cautious compensatory claims that are coherent with the numerous uncertainties in the assessment process. Overall, this thesis contributes to improving models of biodiversity impact that are relevant for corporate assessments and beyond, and provides important guidance for corporates on choosing appropriate metrics and making robust statements on their net impacts on biodiversity.
