Balance between climate change mitigation benefits and land use impacts of bioenergy: conservation implications for European birds.

Both climate change and habitat modification exert serious pressure on biodiversity. Although climate change mitigation has been identified as an important strategy for biodiversity conservation, bioenergy remains a controversial mitigation action due to its potential negative ecological and socio-economic impacts which arise through habitat modification by land-use change. While the debate continues, the separate or simultaneous impacts of both climate change and bioenergy on biodiversity have not yet been compared. We assess projected range shifts of 156 European bird species by 2050 under two alternative climate change trajectories: a baseline scenario, where the global mean temperature increases by 4°C by the end of the century, and a 2 degrees scenario, where global concerted effort limits the temperature increase to below 2°C. For the latter scenario, we also quantify the pressure exerted by increased cultivation of energy biomass as modelled by IMAGE2.4, an integrated land-use model. The global bioenergy use in this scenario is in the lower end of the range of previously estimated sustainable potential. Under the assumptions of these scenarios, we find that the magnitude of range shifts due to climate change is far greater than the impact of land conversion to woody bioenergy plantations within the European Union, and that mitigation of climate change reduces the exposure experienced by species. However, we identified potential for local conservation conflict between priority areas for conservation and bioenergy production. These conflicts must be addressed by strict bioenergy sustainability criteria that acknowledge biodiversity conservation needs beyond existing protected areas and apply also to biomass imported from outside the European Union.

Biodiversity funds and conservation needs in the EU under climate change.

Despite ambitious biodiversity policy goals, less than a fifth of the European Union's (EU) legally protected species and habitats show a favorable conservation status. The recent EU biodiversity strategy recognizes that climate change adds to the challenge of halting biodiversity loss, and that an optimal distribution of financial resources is needed. Here, we analyze recent EU biodiversity funding from a climate change perspective. We compare the allocation of funds to the distribution of both current conservation priorities (within and beyond Natura 2000) and future conservation needs at the level of NUTS-2 regions, using modelled bird distributions as indicators of conservation value. We find that funding is reasonably well aligned with current conservation efforts but poorly fit with future needs under climate change, indicating obstacles for implementing adaptation measures. We suggest revising EU biodiversity funding instruments for the 2014-2020 budget period to better account for potential climate change impacts on biodiversity.

Ensemble distribution models in conservation prioritization: from consensus predictions to consensus reserve networks.

AIM: Conservation planning exercises increasingly rely on species distributions predicted either from one particular statistical model or, more recently, from an ensemble of models (i.e. ensemble forecasting). However, it has not yet been explored how different ways of summarizing ensemble predictions affect conservation planning outcomes. We evaluate these effects and compare commonplace consensus methods, applied before the conservation prioritization phase, to a novel method that applies consensus after reserve selection. LOCATION: Europe. METHODS: We used an ensemble of predicted distributions of 146 Western Palaearctic bird species in alternative ways: four different consensus methods, as well as distributions discounted with variability, were used to produce inputs for spatial conservation prioritization. In addition, we developed and tested a novel method, in which we built 100 datasets by sampling the ensemble of predicted distributions, ran a conservation prioritization analysis on each of them and averaged the resulting priority ranks. We evaluated the conservation outcome against three controls: (i) a null control, based on random ranking of cells; (2) the reference solution, based on an expert-refined dataset; and (3) the independent solution, based on an independent dataset. RESULTS: Networks based on predicted distributions were more representative of rare species than randomly selected networks. Alternative methods to summarize ensemble predictions differed in representativeness of resulting reserve networks. Our novel method resulted in better representation of rare species than pre-selection consensus methods. MAIN CONCLUSIONS: Retaining information about the variation in the predicted distributions throughout the conservation prioritization seems to provide better results than summarizing the predictions before conservation prioritization. Our results highlight the need to understand and consider model-based uncertainty when using predicted distribution data in conservation prioritization.