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Bibliografia : f. 61

Bibliografia : f. 39

Contém referências bibliográficas

Dissertação de Mestrado em Sistemas de Informação Geográfica, em Recursos Agro-Florestais e Ambientais, Especialização em Análise de Informação Geográfica

Disponível na Biblioteca da ESACB na cota C30-21025TFCER.

Relatório do Trabalho de Fim de Curso de Engenharia Rural e Ambiente apresentado à Escola Superior Agrária do Instituto Politécnico de Castelo Branco.

Dissertação apresentada à Escola Superior Agrária do Instituto Politécnico de Castelo Branco para obtenção do Grau de Mestre em Sistemas de Informação Geográfica - Recursos Agro-Florestais e Ambientais.

International Conference on Multidisciplinary Research – CMR

Resumos "International Conference on Multidisciplinary Research – CMR"

Research Highlights: Modelling species’ distribution and productivity is key to support integrated landscape planning, species’ afforestation, and sustainable forest management. Background and Objectives: Maritime pine (Pinus pinaster Aiton) forests in Portugal were lately affected by wildfires and measures to overcome this situation are needed. The aims of this study were: (1) to model species’ spatial distribution and productivity using a machine learning (ML) regression approach to produce current species’ distribution and productivity maps; (2) to model the species’ spatial productivity using a stochastic sequential simulation approach to produce the species’ current productivity map; (3) to produce the species’ potential distribution map, by using a ML classification approach to define species’ ecological envelope thresholds; and (4) to identify present and future key factors for the species’ afforestation and management. Materials and Methods: Spatial land cover/land use data, inventory, and environmental data (climate, topography, and soil) were used in a coupled ML regression and stochastic sequential simulation approaches to model species’ current and potential distributions and productivity. Results: Maritime pine spatial distribution modelling by the ML approach provided 69% fitting efficiency, while species productivity modelling achieved only 43%. The species’ potential area covered 60% of the country’s area, where 78% of the species’ forest inventory plots (1995) were found. The change in the Maritime pine stands’ age structure observed in the last decades is causing the species’ recovery by natural regeneration to be at risk. Conclusions: The maps produced allow for best site identification for species afforestation, wood production regulation support, landscape planning considering species’ diversity, and fire hazard mitigation. These maps were obtained by modelling using environmental covariates, such as climate attributes, so their projection in future climate change scenarios can be performed.

Climate change is already a reality, and habitat loss is affecting relentlessly tree species distributions. The strawberry tree (Arbutus unedo L., Ericaceae) is a Mediterranean evergreen tree used in this article as a case study to evince its expected threatening situation in the upcoming age. This research work seeks to identify the bioclimatic and physiographic variables that have the most impact on the strawberry tree’s spatial distribution in Portugal, acquiring vital knowledge for the design of accurate conservation and afforestation plans for the use and conservation of strawberry tree, working as a guide under a climate change scenario. For that, twenty-one bioclimatic variables, two physiographic attributes (altitude and slope), and the Emberger Index (EI) were used together with 318 observations of strawberry trees, to build a scalable Bayesian procedure, based on machine learning techniques, aimed to assess the species’ future habitat evolution through three temporal scenarios: (i) Control Run (1960–1990); (ii) 2050 and (iii) 2070. The results indicate for 2050 a 30% loss of the humid subregion and a 35% increase in the semi-arid sub-region towards the north. In 2070, it is expected a 2% recuperation for the sub-humid area, but an 8% loss of the humid sub-area. Under these extreme climate change conditions, it is anticipated an almost complete loss of habitat for the strawberry tree in the south of Portugal. The expected ecological evolvability may trigger future migration paths and new refuges’ settlement in the northern sub-region for the succeeding decades and suggesting after 2070 the possibility of habitat switch and species drifting.

Obligate coastline taxa generally occupy very limited areas, especially when there is a close affinity with a specific coast type. Climate change can be a meaningful threat for them, reducing suitable habitat or forcing migration events. Cistus ladanifer subsp. sulcatus is an endemic plant of Portugal, known to occur only in the top of its south-western coast’s prominent cliffs. In spite of being included in the annexes II and IV of the European Habitats Directive of Natura 2000 Network, this taxon is still understudied, especially regarding the effects of climate change on its distribution. To overcome such gap, Maxent was used to model the current distribution of C. ladanifer subsp. sulcatus and project its future distribution considering different General Circulation Models, periods (2050 and 2070) and Representation Concentration Pathways (4.5 and 8.5). The results suggested an extensive range contraction in the future, and extinction is a possible scenario. The proximity to littoral cliffs is crucial for this plant’s occurrence, but these formations are irregularly distributed along the coast, hindering range expansions, further inhibited by a small dispersal capacity. Cistus ladanifer subsp. sulcatus will probably remain confined to south-western Portugal in the future, where it will continue to face relevant threats like human activity, reinforcing the need for its conservation.