Modeling the expansion of the invasive grass Brachiaria sp. inside a protected area

• Grant number: 11/04873-3
• Support Opportunities: Scholarships in Brazil - Master
• Effective date (Start): August 01, 2011
• Effective date (End): February 28, 2013
• Field of knowledge: Biological Sciences - Ecology - Theoretical Ecology
• Principal Investigator: Rafael Dias Loyola
• Grantee: Clara Luz Braga Santanna
• Host Institution: Instituto de Biologia (IB). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil

Abstract

The global ecosystems have been under great pressure as a function to human activities, causing enormous loss of biodiversity and high rate of extinction, which makes Conservation Biology highly relevant to the maintenance of species and populations, as well the establishment and proper management of Conservation Units (Cus). Despite the importance, the CUs face several problems, among them, the biological invasion by exotic/alien species. Because of its aggressiveness, alien species can replace native ones leading to loss of biodiversity at species level and ecological processes. In this process the "Cerrado", or Brazil's savannas, is badly damaged. This Domain, a key area for the conservation of global biodiversity (Biodiversity Hotspot), lost about 80% of its primary vegetation due to advancing agricultural land use and anthropogenic activities. In addition, it faces the problem of invasion by African grasses, such Melinis minutiflora and Brachiaria sp., that replacing native vegetation and contributes to the uncontrolled fire. Recently, the Emas National Park (ENP), an area of great representation of the "Cerrado", but also affected by invasive grasses, lost in a large fire over 90% of its protected area. So, in the light of the processes of re-establishment of native vegetation and alien grass, the aim of this master degree project is to model using Cellular Automata (CA) the dynamic expansion of these alien grasses in the ENP, which in turn will allow the development of proposed zoning, based on spatial prioritization of areas for alien management and conservation of local biodiversity.