Choosing prevention or cure when mitigating biodiversity loss: trade-offs under ‘no net loss’ policies

1. Biodiversity cannot always be conserved. Economic development activities can result in biodiversity losses, but also increase human wellbeing, so trade-offs must sometimes be made between conservation and development. An alternative strategy to avoidance of impacts through the strict protection of biodiversity (‘prevention’) is to permit certain biodiversity losses and fully compensate for them through offsets elsewhere (‘cure’). 2. Here, we build a stochastic simulation model to explore trade-offs between biodiversity loss prevention and cure, in the context of development under ‘no net loss’ (NNL) biodiversity policies. Our model implements a Management Strategy Evaluation framework, monitoring outcomes using four different performance metrics: total biodiversity, net biodiversity, total economic activity, and development activity. 3. We find that a "cure" strategy can potentially perform just as well as a prevention strategy in terms of biodiversity objectives, whilst outperforming the latter from an economic perspective. However, this does not undermine the need for a mitigation hierarchy, and the best-performing strategy depends strongly upon both the degree of compliance with the NNL policy and upon underlying ecological parameters. 4. Perhaps counterintuitively, when evaluated as advised by the technical literature (i.e. against an appropriate counterfactual scenario), we find that net biodiversity outcomes are highest when natural ecosystem recovery rates are slow (so long as development rates are also slow). 5. Finally, using the illustrative example of US wetlands, we suggest that real-world NNL policies could already be driving landscape-scale avoidance of development impacts under a "prevention" approach. 6. Policy implications. No net loss (NNL) biodiversity policy is currently being developed or implemented by over 100 countries worldwide and incorporated into environmental safeguards by multinational lenders. The socio-ecological model presented here can be used to advise decision makers about the best structure for nascent NNL policy on the basis of region-specific ecosystem recovery rates, development activity, legal compliance and monitoring uncertainty. Further, the model presents a means for estimating the degree to which biodiversity impacts are avoided by developers under NNL – an important monitoring consideration given that ensuring high levels of avoidance is crucial to robust NNL policy, but which has to date evaded assessment through purely empirical means.