'International Journal of Current Research in Science, Engineering & Technology (IJCRSET)'
Abstract
The aim of this study is to establish the spatial pattern of colonization and spread of Acacia saligna by predictive
modeling, susceptibility evaluation and to perform a cost-effective analysis in two sites of community
importance (Fernão Ferro/Lagoa de Albufeira and Arrábida/Espichel) in the Sesimbra County. The main goal is
to increase the knowledge on the invasive process and the potential distribution of the Acacia saligna in
Sesimbra County, namely in the Natura 2000 sites. The Artificial Neural Networks model was developed in
Open Modeller to predict the potential of occurrence of A. saligna, and is assumed to be conditioned by a set of
limiting factors that may be known or modeled. The base information includes a dependent variable (present
distribution of specie) and several variables considered as conditioning factors (topographic variables, land use,
soils characteristics, river and road distance), organized in a Geographical Information System (GIS) database.
This is used to perform spatial analysis, which is focused on the relationships between the presence or absence of
the specie and the values of the conditioning factors. The results show a high correspondence between higher
values of potential of occurrence and soils characteristics and distance to rivers; these factors seem to benefit the
specie’ invasion process. According to the conservation value of each cartographic unit, related to natural
habitats included in Habitats Directive (92/43/EEC), the coastal habitats (2130, 2250 and 2230) were the most
susceptible to invasion by A. saligna. The predicted A. saligna distribution allows for a more efficient
concentration and application of resources (human and financial) in the most susceptible areas to invasion, such
as the local and national Protected Areas and the Sites of Community Importance, and is useful to test
hypotheses about the specie range characteristics, habitats preferences and habitat partitioning