Modelagem da diversidade funcional e resiliência da floresta Amazônica às mudanças climáticas além dos estoques de carbono

Author(s)

Bianca Fazio Rius

• Document type: Doctoral Thesis
• Press: Campinas, SP.
• Institution: Universidade Estadual de Campinas (UNICAMP). Instituto de Biologia
• Defense date: 2024-07-19

Examining board members

David Montenegro Lapola; Tomas Ferreira Domingues; Simone Aparecida Vieira; Celso von Randow; Carolina Casagrande Blanco

• Advisor: David Montenegro Lapola

Abstract

The Amazon forest has exhibited concerning signs of diminishing resilience in recent decades. Given its critical role in preserving biodiversity, sequestering carbon, regulating the climate, and offering a myriad of ecosystem services on a global scale, comprehending the resilience of the Amazon rainforest amidst shifting climatic conditions is of utmost importance. Nonetheless, significant uncertainties persist in this area of study. This thesis explores the impacts of reduced precipitation on the Amazon's ecosystem functioning and plant functional diversity using a trait-based vegetation model, CAETÊ. In the first chapter, we present, for the first time the model, investigate how plant trait diversity affects vegetation carbon storage and net primary productivity (NPP) under current and low precipitation scenarios. Two modeling approaches are compared: a plant functional type (PFT) approach with three PFTs and a trait-based approach representing 3000 plant life strategies (PLSs). Our findings reveal that including trait variability improves the model’s accuracy in representing NPP and vegetation carbon storage. Under reduced precipitation, both approaches simulate significant C storage loss (~60%), but the trait-based approach shows a more nuanced response with the emergence of rare trait combinations and a higher root-to-shoot ratio. These results underscore the importance of accounting for plant functional diversity in evaluating the Amazon's sensitivity to climate change. The second chapter focuses on the resilience of Amazon forests under increased drought frequency and intensity. We simulate a 30% reduction in precipitation applied at two frequencies: every seven years (7-year frequency) and alternately every other year (1-year frequency), using 6000 PLSs defined by traits such as wood density, specific leaf area (SLA), and maximum stomatal conductance (g1). Our results indicate that frequent droughts lead to ecosystem collapse (for the 1-year frequency) and diminished resilience (7-year frequency), resulting in notable shifts in ecosystem configuration and functional composition. Various ecosystem indicators such as NPP, evapotranspiration, water use efficiency (WUE), and the diversity of surviving PLSs exhibit diverse sensitivities to drought. This research emphasizes the critical role of multiple ecosystem indicators, beyond carbon stocks, in evaluating resilience and suggests that tropical forests may be more susceptible to climate impacts than previously assumed. Together, these chapters offer a comprehensive insight into the functional diversity and resilience of the Amazon forest, highlighting the necessity for holistic conservation strategies to address the challenges posed by climate change (AU)

• FAPESP's process: 19/04223-0 - Modeling functional diversity and resilience of Amazon Forest to climate change beyond carbon stocks
• Grantee: Bianca Fazio Rius
• Support Opportunities: Scholarships in Brazil - Doctorate