FECIMOD - Forecasting ecological effects of climate change: integrating functional and correlative models

The demand for ecological forecasting is growing, but present models have severe limitations. Uncertainties derive from unrealistic model assumptions as well as the intrinsic stochasticity of natural processes. The proposed project will answer to these ch allenges by merging hypothesis testing of ecological mechanisms with applied climate scenario forecasting. Expected output is i) improved methods for forecasting ecological effects of climate change, ii) climate change scenarios for case study systems and iii) quantification and identification of main sources of uncertainties. We will build single-, two- and multi-species models forecasting impact of different climate scenarios. Northern freshwaters with their salmonid fishes and zooplankton prey will be used as case studies. These systems are ideal due to their relative simplicity, and extensive knowledge on physiological performance and the ecological functioning of main organism groups. The first part of the project will fill gaps in empirical knowledg e. In addition, several large affiliated projects will generate sub-models. Thereafter, derived and existing knowledge of physiological performance, adaptive variation and ecological mechanisms and will be integrated with correlative models. The approache s will be both within the Bayesian framework, and with models blending functional and correlative derived knowledge. Finally, we will compare the different sources of uncertainty, ranging from measurement error to process stochasticity and expert opinions on model performance. The project will promote recruitment to ecological climate change forecasting by including a PhD position in biostatistics and ecological modeling.