Mechanistic community models have been advocated as a response to the conceptual and practical limitations of correlative approaches to modeling biodiversity. Building from ecological theory, there are multiple frameworks that could potentially act as a basis for such mechanistic models. However, these options often include a the large number of demographic rates to estimate in species-rich ecosystems, and their direct connection to empirical data has often been limited to simplified settings, something that strongly limits their use for ambitious biodiversity-modeling projects. With CLIMB, we propose an innovative statistical methodology to overcome this challenge: we will connect community data with functional trait data in an array of carefully designed mechanistic community models.
More precisely, CLIMB aims to propose and test adequate transfer function(s) that allow
rapid calibration of mechanistic models with available trait data and make these models suitable for reliable biodiversity predictions. The CLIMB framework will be developed and tested with simulations and two empirical study cases of temporal dynamics of grassland plant communities dynamics in two different biomes. CLIMB consists in an outgoing phase focused on (1) studying the theoretical fundations of the framework and developing appropriate mechanistic community models; and (2) collecting functional data for local grassland plant species. The return phase will focus on (3) completing the development of the modelling framework and (4) analyzing empirical data.
Ultimately, CLIMB will answer pressing fundamental questions of ecology and biodiversity modelling and will offer the ground-breaking perspectives necessary to meet key environmental challenges faced by society today.
Field of science
- /natural sciences/biological sciences/ecology
- /natural sciences/biological sciences/ecology/ecosystems
Call for proposal
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