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- Analysis of the relationship between the weather index of fire danger and occurrences of rural fires. Case study: centro region of PortugalPublication . Pedro, Nuno; Fernandez, Paulo; Bugalho, LourdesThe aim of this study was to design an approach for establishing a plausible relationship between FWI and the monthly average burned area (ABA) and the average number of ignitions (ANI) supported by geographic information systems (GIS). The application of these results will allow the projection of burned areas in forest fires in the future, making mitigation actions possible. This approach was applied to the region of Central Portugal, and to achieve the aims of the study, the following steps were completed: (1) geoprocessing the spatial data of the daily FWI indices, burned area and number of fire ignitions and (2) developing statistical regression models capable of reproducing the variability in burned area and ignition occurrence series from FWI data during the 2001–2017 period. The predicted equations for the burned area as a function of the FWI presented high coefficients of determination for most of the considered periods, thus allowing the projection, with a high degree of confidence, of the monthly burned area values according to the various future climate scenarios. The prediction of the average number of ignitions from the FWI values class proved to be effective for establishing highly adjusted forecast models for July and August. In the spatial analysis at the district level, the ABA and ANI estimation equations were obtained from the FWI values with determination coefficients above 0.90 for most of the districts. Significant differences were observed between the districts in the number of ignitions analysed.
- How will climate change impact maritime pine forest distribution and productivity in PortugalPublication . Alegria, Cristina Maria; Almeida, Alice M.; Ribeiro, Maria Margarida; Martins Roque, Natália; Fernandez, Paulo; Gerassis, Saki; Albuquerque, Maria TeresaPortuguese maritime pine forests are severely affected by forest fires. The study aimed at modelling: (1) species’ current distribution and productivity; and (2) species’ distribution for projected future climate change scenarios. The land cover, national forest inventory, and environmental data were used. A Bayesian Machine Learning (ML) analysis allowed exploring the most influential environmental variables. Species’ spatial productivity was modelled by stochastic Sequential Gaussian Simulation. Species’ potential distribution modelling was performed using two methodological approaches: (1) ML algorithms (Random Forest and Maximum Entropy); and (2) GIS map algebra (ecological envelopes) maps regarding a set of environmental variables and previously known thresholds. Results showed that species distribution was mainly determined by precipitation-related variables, but elevation and temperature related variables were important to differentiate species productivity. Species’ distribution for the present using ML modelling provided fitting efficiencies around 70% and matched well the species’ current distribution. The species ecological envelope map for the present was closer to the species’ empiric potential distribution. Climate change impacts on species’ future distributions by the ML approach were moderate with areas being relocated (47.3% regular-medium-high suitability area to 48.7%–48.3% in the future). The impacts in species’ ecological envelopes maps were higher and with greater future losses than the latter (76.5% regular-favourable-optimum suitability area to 58.2%–51.6% in the future). The two approaches showed a 44% concordance in the present, decreasing to 30%–35% in the future. These maps are key to support recommendations to set species' best suitability areas in planning future afforestation to attain fire-resilient landscapes, enhanced forest ecosystems biodiversity, functionality, and productivity under climate change scenarios.
- Assessing beekeeping potential in a Portuguese area with honey Protected Designation of OriginPublication . Silveira, Carlos; Roque, Natália; Fernandez, Paulo; Anjos, O.; Vilas-Boas, MiguelThis study aims to assess the beekeeping potential in a Portuguese area with honey protected designation of origin (PDO) following a multi-criteria decision analysis (MCDA) for supporting beekeepers in the selection of the best locations for apiaries, thus maximizing honey production and reducing the risk of bee colony losses.