The world is currently witnessing a sharp decline in biodiversity, caused largely by human activity, and thought to be resulting in a corresponding loss of ecosystem services. However, the economic development activities that are a key driver of this loss of biodiversity also provide substantial benefits to human wellbeing. It is consequently increasingly clear that an optimal balance must be found between development and biodiversity conservation.
One relatively new principle that shows promise in trying to balance this trade-off is that of ‘No Net Loss’ (NNL) – i.e. implementing mechanisms in which biodiversity losses from development are measured, and active conservation interventions are implemented that fully compensate for those losses, resulting in no net loss of biodiversity alongside development. Understanding NNL is therefore of significant importance to policymakers, businesses, and the general pubic. However, it is not yet clear how widely NNL has been implemented, or whether NNL of biodiversity and ecosystem services can be achieved in tandem. Further, NNL is not a trivial objective when ecological dynamics (e.g. climate driven habitat change) and social dynamics (e.g. economic growth) are taken into account. It is crucial that we establish whether there are general principles that can be applied to achieving NNL across different ecosystems, and if not, identify key peculiarities for each system.
In the Fellowship, we sought to compile disparate data so as to create the first global database on the current use of NNL type interventions, including spatial (e.g. location, size, form of habitat disturbance) and non-spatial (e.g. absolute species losses, species gains) information. Using these data, we have analysed existing NNL interventions for the first time. Correspondingly, we have been expanding theory on the use of ‘frames of reference’ in NNL interventions. Building on this improved empirical and theoretical basis, we have developed bespoke simulation models, with which we are exploring the implementation of different types of NNL policy mechanism. Our simulations consider not only biological components, but also feedback between social and ecological components of the system, drivers of environmental change, and sources of uncertainty.
Our original objectives were:
1. To collate a database of the implementation of NNL-type interventions worldwide
2. Establish quantitative landscape frames of reference for case study ecosystems (Europe, US, Australia), as a basis for evaluating the outcomes of simulation models.
3. Build or implement models to simulate the implementation of different NNL type interventions in the ecosystems for which a frame of reference has been developed above
4. Identify the system drivers, dynamics, feedbacks and sources of uncertainty that determine NNL outcomes
5. Develop a general theory for achieving NNL in dynamic systems, if one is possible
6. Apply any general theory developed to European ecosystems for which data exist (e.g. forests, mountains, marine systems).
Thee objectives have largely been met, and the results published in an ongoing series of academic papers, some of which have received considerable media interest.
