Banca de DEFESA: MIRIAM LECÍLIA FARIAS RIBEIRO

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : MIRIAM LECÍLIA FARIAS RIBEIRO
DATE: 26/02/2025
TIME: 14:00
LOCAL: https://meet.google.com/opv-yexk-ggn
TITLE:

Incorporation of Spatial Autocorrelation for the Construction of Isoscapes Using Machine Learning Techniques

KEY WORDS:

Spatial Interpolation, Geostatistics, Machine Learning, Isotope, Leaf.

PAGES: 58
BIG AREA: Ciências Agrárias
AREA: Agronomia
SUMMARY:

Atoms are the fundamental building blocks that make up matter and form the basis of everything we observe in the universe. They are composed of protons, neutrons, and electrons. Isotopes are versions of a chemical element (a set of atoms) that share the same number of protons in the atomic nucleus but differ in the number of neutrons. They can be classified into different categories based on their characteristics, including stable, radioactive, natural, artificial, cosmogenic, and industrial. The use of stable isotopes is based on the fact that the isotopic ratio is altered by biogeochemical processes that govern the movement of carbon, nitrogen, and water compounds between soil-plant-atmosphere systems. This alteration in the relative abundance of isotopes is called fractionation or isotopic discrimination, which is usually caused by a kinetic effect due to the small mass difference between the heavy and light isotope. The isotopic methodology consists of creating isoscapes from georeferenced isotopic compositions. Many studies use traditional spatial modeling and geostatistical techniques, such as spatial autoregressive regressions and Kriging methods. However, recent research indicates that machine learning (ML) algorithms, such as Random Forest (RF), provide more accurate results than previous methodologies. The importance of this technique has led to the incorporation of stable isotopes into the routine investigations of various security agencies worldwide and to the creation in 2002 of the Forensic Isotope Ratio Mass Spectrometry Network (FIRMS), which brings together academics, forensic experts, private companies, and government agencies working in this field of application. The objective of this study is to generate isoscapes of δ13C and δ15N from C3 plants for Brazil using geostatistical techniques and, mainly, machine learning to improve the accuracy of geographic isotopic patterns. This work used a dataset with 6,480 leaf samples from trees in 57 georeferenced field plots distributed throughout the Brazilian territory. The δ13C and δ15N isoscapes showed spatial patterns consistent with eco-physiological predictions in ML techniques. There are several potential applications for the isoscapes proposed here. One of them is to track the illegal wildlife trade (IWT), illegal logging, and the certification of natural products related to flora.

COMMITTEE MEMBERS:
Interno - ANTONIO SAMUEL ALVES DA SILVA
Externo à Instituição - JOÃO PAULOS SENA SOUZA - UNIMONTES
Presidente - PAULO JOSE DUARTE NETO