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Understanding patterns of species covariation using a trait based approach with field data and models

Subject Area Ecology and Biodiversity of Animals and Ecosystems, Organismic Interactions
Term from 2012 to 2015
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 215987020
 
Final Report Year 2017

Final Report Abstract

This project was based on observational data (phytoplankton in Lake Constance and Saidenbach Reservoir) and ecological modelling revealing important causes and consequences of changes in functional diversity within ecological communities. It contributes to a deeper understanding of the processes sustaining functional diversity at different hierarchical levels. It shows that trait adaptation via adaptive evolution or adaptive phenotypic plasticity may strongly promote species coexistence in natural systems by influencing niche- and fitness differences among species that codetermine their coexistence. It also reveals that species richness can influence the reliability of ecosystem functions in various and highly interrelated ways. Most importantly, compensatory dynamics at one trophic level may result from strong competition and go along with compensatory or synchronous dynamics at an adjacent trophic level. Our work further emphasizes that the causes and consequences of biodiversity are interrelated and cannot be studied independently. Hence, we strongly focused on potential feedbacks between the community’s functional diversity and its environment. However, given the astonishing diversity and overwhelming complexity of natural systems, different biological components influence each other in numerous ways. This work contributes to current theory by revealing important mechanisms that may promote the maintenance of functional diversity and thus the reliability of ecosystem functions in natural systems. It provides also substantial methodological improvements. The development of sophisticated trait-based models and critical assessment of existing model types were complemented by advanced data analysis and rigorous mathematical considerations.

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