Gutierres, F.; Gil, A.; Reis, E.; Lobo, A.; Neto, C ; Calado, H. and Costa, J.C. 2011. Acacia saligna (La bill.) H. Wendl in the Sesimbra Council: Invaded habitats and potential distribution modeling. Journal of Coastal Research, SI 64 (Proceedings of the 11th Internation al Coastal Symposium), pg ‐ pg. Szczecin, Poland, IS SN 0749-0208 The aim of this study is to establish the spatial p attern 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 (Fernao Ferro/Lagoa de Albufeira and Arrabida/Espichel) in the Sesimbra County. The main goal is to increase the knowledge on the invasive process a nd the potential distribution of the Acacia saligna in Sesimbra County, namely in the Natura 2000 sites. Th e Artificial Neural Networks model was developed in Open Modeller to predict the potential of occurrenc e 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 (pre sent distribution of specie) and several variables consi dered as conditioning factors (topographic variable s, land use, soils characteristics, river and road distance), or ganized in a Geographical Information System (GIS) database. This is used to perform spatial analysis, which is focused on the relationships between the presence o r absence of the specie and the values of the conditioning facto rs. The results show a high correspondence between higher values of potential of occurrence and soils charact eristics and distance to rivers; these factors seem to benefit the specie’ invasion process. According to the conserva tion value of each cartographic unit, related to na tural habitats included in Habitats Directive (92/43/EEC), the coastal habitats (2130, 2250 and 2230) were th e most susceptible to invasion by A. saligna . The predicted A. saligna distribution allows for a more efficient concentration and application of resources (human a nd financial) in the most susceptible areas to inva sion, such as the local and national Protected Areas and the S ites of Community Importance, and is useful to test hypotheses about the specie range characteristics, habitats preferences and habitat partitioning.
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