Spatial and Temporal Patterns of Poaching in Brazil

• Grant number: 25/17419-1
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: December 01, 2025
• End date: March 31, 2026
• Field of knowledge: Biological Sciences - Ecology - Applied Ecology
• Principal Investigator: Márcio Leite de Oliveira
• Grantee: Nádia Gabriela da Silva
• Supervisor: Marcus Rowcliffe
• Host Institution: Universidade de Araraquara (UNIARA). Araraquara , SP, Brazil
• Institution abroad: Institute Of Zoology, England
• Associated to the scholarship: 24/22487-3 - Characterization of hunting activity in Brazil, BP.IC

Abstract

Understanding poaching practices in Brazil is essential to evaluate their environmental, social, and economic impacts. Poaching in Brazil, especially in tropical regions, has historically been significant for local communities, serving as a source of food and income. However, poaching poses a major threat to ecological balance, contributing to the spread of zoonotic diseases and the loss of native species, even within legally protected areas. A deeper understanding of this practice is crucial to mitigate its impacts, support more effective public policies, and develop targeted conservation strategies. This study aims to characterize poaching in Brazil by identifying its spatial and temporal patterns and potential drivers. Data will be collected through online news articles retrieved using the Google News platform. The keywords "caça" and "apreensão" will be employed to find relevant information, in addition to an existing dataset of 120 observations produced using the words "caça" and "calibre." Extracted data will include locations, dates, and hunted species. Additional variables such as habitat cover, forest loss, and socioeconomic indexes will also be incorporated into the dataset. The collected data will be organized into a database and analyzed using techniques such as kernel density estimation for temporal patterns and spatial density mapping. Frequency analyses will be conducted for species data, and a generalized linear mixed model (GLMM) will be used to test the influence of covariates on a poaching index (reports per human density). The study is expected to identify patterns of illegal hunting in Brazil, including the most affected regions, the most hunted species, critical periods for this activity, and its drivers. The results will provide systematic data to support public policies, enforcement strategies, and conservation efforts, contributing to the mitigation of poaching impacts on Brazilian biodiversity.