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Trait-based modeling of the impacts of climate change on plant phenology-related functionality

Grant number: 19/06487-5
Support type:Scholarships in Brazil - Master
Effective date (Start): May 01, 2019
Effective date (End): December 31, 2020
Field of knowledge:Biological Sciences - Ecology
Principal Investigator:David Montenegro Lapola
Grantee:Gabriela Martins Sophia
Home Institution: Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura (CEPAGRI). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:15/02537-7 - AmazonFACE/ME: the Amazon-FACE Model-Experiment integration project - the role of biodiversity and climate feedbacks, AP.PFPMCG.JP

Abstract

Several models of the terrestrial biosphere have been developed to better understand the response of vegetation to human perturbations, namely Dynamic Global Vegetation Models - DGVMs. However, these models yield a substantially variable response on the role of the biosphere to the global carbon cycle in light of climate change scenarios. This variation can be partly explained by the generalization made in DGVMs with respect to the functional diversity of plants (i.e. a high number of growth strategies are currently represented by only a few functional types in current DGVMs). Models based on functional attributes rise as a promising alternative to a better characterization of surviving mechanisms and growth strategies adopted by plants. This MS project will contribute to the development of the Carbon and Ecosystem functional Trait Evaluation (CAETÊ) model, more specifically with the implementation of a phenology module in the source code. This development aims at better representing the seasonality of vegetation and thereafter impacts of changing environmental variables (e.g. climate change) on it, using the multi-plant life strategies logic of the CAETÊ model. The beneficiary of this fellowship will use an already existing phenology scheme and adapt to the CAETÊ model, which uses a trade-offs heuristic to simulate varying plant traits.