Obiettivo
A crucial challenge of the next years will be to conserve biodiversity under climate changes. Anticipation is an essential facet to attack this challenge, where correlative models have a main role. To date, most species richness (SR) modeling methodologies have not accounted for real community assembly processes, or failed to capture the underlying mechanisms. We now need to generate more realistic SR models. The first step is to understand the mechanisms that drive the organization of species at community level, to reach this aim we will test the concept of carrying capacity and disentangle the importance of biogeographical large-scale processes and environmental filters local processes. After that, we could improve the outcome of SR models taking into account the previous point. In this project, I will develop, implement and test a framework for modeling species assemblages and obtaining spatially explicit projections by taking plants assemblages as model system. I will proceed step-by-step:
1) I will collect species and environmental data.
2) I will test the concept of carrying capacity and better account for community assembly rules in richness modeling with different tools (ecophylogenetics, remote sensing, and analysis of turnover of betadiversity).
3) I will perform stacked species distribution models.
4) I will define macroecological constraints on community properties through macroecological modeling.
5) Using data and results previously obtained, I will program rules to introduce real community assembly processes in the stacked species distributions models.
6) I will test the robustness and importance of methodological aspects running the models with different parameters.
7) I will apply the previous framework to develop a new generation of climate change projections.
Project results will be relevant for both theoretical science and conservation biology.
Campo scientifico
Argomento(i)
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FP7-PEOPLE-2013-IEF
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Meccanismo di finanziamento
MC-IEF - Intra-European Fellowships (IEF)Coordinatore
1015 LAUSANNE
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