A new modeling approach to evaluate the effects of climate change on plant functional diversity in the Amazon rainforest

• Grant number: 23/10503-1
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Effective date (Start): January 21, 2024
• Effective date (End): January 04, 2025
• Field of knowledge: Biological Sciences - Ecology - Ecosystems Ecology
• Principal Investigator: David Montenegro Lapola
• Grantee: Bárbara Aparecida Pereira da Rocha Cardeli
• Supervisor: Thomas Hickler
• Host Institution: Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura (CEPAGRI). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Research place: Senckenberg - Leibniz Institution for Biodiversity and Earth System Research, Germany
• Associated to the scholarship: 22/00194-9 - Functional Diversity and provisioning Ecosystem Services: An analysis of the Amazon Forest in the face of climate changes, BP.DR

Abstract

Climate change is impacting all regions of the world, particularly tropical ecosystems like the Amazon rainforest. The Amazon rainforest, being the largest tropical forest globally, plays a crucial role in acting as a carbon sink and mitigating the effects of climate change. However, studies indicate that the increasing levels of CO2 in the atmosphere can disrupt the ability of ecosystems to act as effective carbon sinks. The use of vegetation models, known as dynamic global vegetation models (DGVMs), has become increasingly frequent in understanding the impact of climate change on vegetation through computer simulation of ecological and physiological processes. Some DGVM models, referred to as trait-based vegetation models, also allow the representation of different plant functional strategies within an ecological unit. Recent studies have shown that trait-based vegetation models can explore the role of various components of plant functional diversity on ecosystem functioning. Given the immense influence of mega-diverse ecosystems like the Amazon rainforest on the global carbon cycle and atmosphere CO2 concentrations, it is crucial to study the connections between the forest's ecosystem functioning and climate change. In this research, we propose a novel approach called inverse modeling. Conventional vegetation modeling approaches treat traits as parameters (i.e. independent variables) and carbon storage and productivity as outputs (i.e. dependent variables), however, in this proposal, we will invert this arrangement through inverse modeling. So, through the development of an inverse modeling framework for DGVMs, we aim to identify the combination of functional traits that best maintain the Amazon forest's capacity as a carbon sink and ensure essential processes such as evapotranspiration, which impacts rainfall and the water cycle at the local level. This analysis will help us understand how predicted climate changes can alter ecosystem functioning and plant functional diversity. By investigating the relationships between ecosystem functioning, climate change, and plant functional traits, this research will contribute to discussions on effective management and restoration techniques. Additionally, it will explore the potential of DGVMs in improving our understanding of complex ecological systems and the impacts of climate change on the Amazon rainforest, highlighting the significance of plant functional traits in sustaining ecosystem functioning and resilience. (AU)