Utilize este identificador para referenciar este registo: http://hdl.handle.net/10348/4306
Título: Testing a novel spatially-explicit dynamic modelling approach in the scope of the laurel forest management for the endangered Azores bullfinch (Pyrrhula murina) conservation.
Palavras-chave: Azores bullfinch
Habitat management
Habitat management
Invasive plant species
Laurel forest conservation
Spatially dynamic modelling
Stochastic dynamic methodology
Data: 2012
Citação: Bastos R. et al, 2012
Resumo: The Azores bullfinch (Pyrrhula murina) is an endemic bird of São Miguel island (Azores Archipelago, Portugal), currently threatened by two of the major causes of biodiversity loss worldwide: invasion of native habitats by exotic plants and habitat destruction by land use changes. The aim of this research was to develop and test a novel spatially explicit modelling framework that predicts the Azores bullfinch responses to alternative realistic scenarios of native forest management. This was done by integrating Multi-Model Inference statistical analysis, Stochastic-Dynamic Modelling and Geographic Information Systems under a common framework relating bird population trends to changes in the surrounding habitats. Overall, in the next 25 years, the Azores bullfinch breeding population was predicted to increase around 19% as a consequence of habitat management actions already implemented (‘‘LIFE Priolo’’ project) or around 27% in the context of realistic future habitat restoration scenarios. These results represent, respectively, a supplementary increase of more 6% or more 13% in the Azores bullfinch abundance when compared with the trends simulated for the scenario without management. Nevertheless, those actions seemed to be relatively ineffective in promoting the expansion of the species from the actual restricted favourable area, essentially due to local forestry dynamics and on-going plant invasion processes. This novel integrative approach provides a promising baseline to support ecological models with increased realism and predictive power, making the outputs more useful and intuitive to decision-makers and environmental managers.
Revisão por Pares: yes
URI: http://hdl.handle.net/10348/4306
Tipo de Documento: Artigo
Aparece nas colecções:DEBA - Artigo publicado em Revista Científica Indexada

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32. Bastos et al. 2012.pdf
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