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Analysis of biodiversity changes on structural and functional properties of marine ecosystems under cumulative human stressors


The objective of my research is to quantify how the structure of marine food-webs has changed due to biodiversity changes and develop an understanding of how these changes have impacted ecosystem functioning. To accomplish this objective I will use an interdisciplinary approach applying meta-analysis of historical and recent data on species loss and ecosystem changes, food-web modelling and laboratory experiments. This first trial run approach will provide complementary information on how biodiversity loss resulting from different anthropogenic disturbances such as fisheries exploitation, eutrophication and climate change has affected the network structure and functioning of marine food-webs at different temporal and spatial scales. The study will be divided into four parts: (A) In the first part I will make a synthesis of available data of long term changes on marine biodiversity (species decrease, loss, increase or recovery) under gradients of different human impacts (e.g. exploitation, increase of nutrient loading, habitat loss, temperature rise). I will also record observed/measured consequences of these changes from individual to ecosystem level (e.g. cascading effects, transfer efficiency changes, increase on detritus, oxygen depletion). (B) This data will be then analysed using ecological modelling techniques to explore the consequences of biodiversity changes in food web dynamics in time and space. (C) In parallel, I will develop aquatic microcosm experiments to reproduce observed patterns of human impacts on food webs and quantify ecosystem change. (D) Synthesis data, modelling results and microcosm analysis will be use to quantify general patterns of food web structural and functional changes due to biodiversity change by applying a meta-analysis approach. If I can successfully re-predict present-day food webs, I aim at forecasting past and current trends and develop ecosystem recovery scenarios into the future using modelling and laboratory experiments.

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