Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/13783
Título: Predicting the impacts of climate change on the distribution and conservation of endemic forest land snails of Madeira Island
Autor: Gouveia, Cátia Sofia Alves
Orientador: Cardoso, Pedro Miguel Bondoso
Cameron, Robert Andrew Duncan
Palavras-chave: Climate change
Predictive models
Land snails
MaxEnt
Geographic Information Systems
Madeira Island
Data de Defesa: 27-Jan-2014
Resumo: Climate change is emerging as one of the major threats to natural communities of the world’s ecosystems; and biodiversity hotspots, such as Madeira Island, might face a challenging future in the conservation of endangered land snails’ species. With this thesis, progresses have been made in order to properly understand the impact of climate on these vulnerable taxa; and species distribution models coupled with GIS and climate change scenarios have become crucial to understand the relations between species distribution and environmental conditions, identifying threats and determining biodiversity vulnerability. With the use of MaxEnt, important changes in the species suitable areas were obtained. Laurel forest species, highly dependent on precipitation and relative humidity, may face major losses on their future suitable areas, leading to the possible extinction of several endangered species, such as Leiostyla heterodon. Despite the complexity of the biological systems, the intrinsic uncertainty of species distribution models and the lack of information about land snails’ functional traits, this analysis contributed to a pioneer study on the impacts of climate change on endemic species of Madeira Island. The future inclusion of predictions of the effect of climate change on species distribution as part of IUCN assessments could contribute to species prioritizing, promoting specific management actions and maximizing conservation investment.
URI: http://hdl.handle.net/10362/13783
Designação: Mestrado em Ciência e Sistemas de Informação Geográfica
Aparece nas colecções:NIMS - Dissertações de Mestrado em Ciência e Sistemas de Informação Geográfica

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