BACTERIAL COMMUNITIES AS BIOINDICATORS OF CLIMATE CHANGE FOR TROPICAL FRESHWATER ECOSYSTEMS: A MACHINE LEARNING APPROACH

• Grant number: 24/17327-7
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
• Start date: February 25, 2026
• End date: August 24, 2026
• Field of knowledge: Biological Sciences - Ecology - Ecosystems Ecology
• Principal Investigator: Valeria Maia Merzel
• Grantee: Daniel di Pace Penna Soares
• Supervisor: Syrie M Hermans
• Host Institution: Centro Pluridisciplinar de Pesquisas Químicas, Biológicas e Agrícolas (CPQBA). Universidade Estadual de Campinas (UNICAMP). Paulínia , SP, Brazil
• Institution abroad: Auckland University of Technology, New Zealand
• Associated to the scholarship: 22/13597-4 - Microorganisms as climate change proxies in tropical aquatic ecosystems: a molecular approach, BP.DD

Abstract

Climate change is the defining challenge of the 21st century, driven by rising global temperatures and extreme weather events. These changes pose severe threats to ecosystems, leading to habitat destruction, biodiversity loss, and negative impacts on human health. Even under lower emission scenarios, temperature increases can disrupt biodiversity and ecological functions, altering population dynamics and trophic networks. Microorganisms, particularly bacteria, respond quickly to environmental shifts, making them promising bioindicators in freshwater environments. However, identifying suitable bioindicators and dealing with the complexity of microbial communities remains a critical challenge. Machine learning (ML) offers a powerful solution to analyze complex patterns and predict climate change impacts on tropical freshwater ecosystems. By applying ML models, it is possible to develop community indices based on bacterial communities, improving the ability to monitor and assess climate change effects on a global scale. This project aims to utilize ML approaches, such as the random forest algorithm, to create a model that uses bacterial communities as bioindicators or ecological indicators of climate change in tropical freshwater ecosystems. The goal is to develop a robust model and a community index to classify new environments and evaluate their climate-related impacts. Additionally, comparing tropical samples with those from other climates will help determine whether microbial responses to climate change are consistent across different environments. (AU)