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Calibrating and Improving Mechanistic models of Biodiversity

Project description

Innovative method for biodiversity mechanistic models

Mechanistic community models emerged as a response to the restrictions of biodiversity modelling methods. While several options potentially act as bases for these mechanistic models, they often have limited use in advanced biodiversity modelling projects. The EU-funded CLIMB project proposes an innovative statistical method that answers pressing fundamental questions of ecology and biodiversity modelling and delivers pioneering perspectives to meet crucial environmental challenges. The project will test ample transfer functions allowing rapid measurement of mechanistic models with available quality data, making these models suitable for reliable biodiversity forecasting. CLIMB will develop apposite mechanistic community models, collect useful data for local grassland plant species and analyse empirical data.

Objective

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.

Keywords

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Coordinator

UNIVERSITAET REGENSBURG
Net EU contribution
€ 247 020,48
Address
Universitatsstrasse 31
93053 Regensburg
Germany

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Region
Bayern Oberpfalz Regensburg, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
Links
Other funding
€ 0,00

Partners (1)