Abstract
A phenotypic combination linked to a maximal value of demographic rate at local scale defines a functional local optimality. The goal of this thesis is to understand the linkage between this optimality, the abundances and coexistence of species within communities, using both observational and modelling approaches. We first illustrate how community weighted means are influenced by the regional distribution of functional traits and deviates from the functional optimality along environmental gradients, leading to biases of interpretation. To avoid such biases, we propose a method to explicitly infer the parameters of the environmental filtering using a mechanistic model. We apply this method to plant communities distributed along a successional gradient with the objective to assess the community assembly parameters. We then study the signature of functional optimality across different spatial scales, through the structure of bipartite networks composed of communities and species. The emergent coherence of the assemblages within the network allows characterizing functional pools of species. This has been illustrated using a database of French grassland communities. The distribution of species’ occurrences between regional pools defines a novel metrics of ecological specialization. We show that the distance to functional optimality of specialist and generalist species is function of their competitive and stress-tolerance abilities. Generalist species are in average better competitors distant from the local optimality regarding their competitive traits while specialist species express greater stress-tolerance. Finally, we assess the link between abundances and distance to optimality under the joint influence of stochastic dynamics, environmental filtering and competitive interactions, as a function of the contribution of functional traits to these mechanisms. Thanks to the use of various assembly models, this thesis defines the notion of optimality and assesses its functional signature across spatial scales. Applications to distinct types of communities illustrate the potential of mechanistic approaches towards a better assessment of ecological and biogeographical drivers of biodiversity patterns.