Temporal patterns of tropical leaf phenology and climate change: investigating drivers across communities and leaf functional groups

• Grant number: 24/07226-9
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Effective date (Start): August 01, 2024
• Effective date (End): February 28, 2026
• Field of knowledge: Biological Sciences - Ecology - Ecosystems Ecology
• Acordo de Cooperação: National Natural Science Foundation of China (NSFC)
• Principal Investigator: Leonor Patricia Cerdeira Morellato
• Grantee: Marcel Caritá Vaz
• Host Institution: Instituto de Biociências (IB). Universidade Estadual Paulista (UNESP). Campus de Rio Claro. Rio Claro , SP, Brazil
• Associated research grant: 22/07735-5 - The impact of climate variability and weather extremes on plant phenology and its implications on biodiversity, AP.PFPMCG.TEM

Abstract

Drivers of leafing phenology are still unknown for most plant systems, especially in the tropics (Morellato et al. 2016, Abernethy et al. 2018). A wide range of leafing patterns strategies has been observed for the tropics, as well as climate seasonality varies in the intensity and length of dry season (Reich, 1995, Camargo et al., 2018). Identifying the proximate drivers regulating tropical leaf phenology is crucial to understand ecosystem dynamics and efficiently forecast impacts of climate changes. We propose to monitor simultaneous sites of tropical vegetations through a multi-scale approach with the use of satellite sources and on-the-ground sensors as phenocameras and drones , aiming to assess: (i) the temporal patterns of leaf phenology and the functional diversity (leafing timing and length) of plant phenological strategies present across vegetation types and biomes; (ii) and the environmental drivers regulating leaf community patterns and plant functional groups. The proposal focusses on the sites monitored by the e-Phenology network, that encompasses several vegetation types across different biomes with varying patterns of seasonality (Amazon Forest, Atlantic Rainforest, Cerrado and Caatinga). Cutting-edge approaches will be applied for the data modelling using time series derived from satellites (MODIS, LANDSAT and SENTINEL), drones and digital cameras, along with environmental variables, as well as to assess leaf functional traits based on image features extracted from the high resolution imagery database. (AU)