The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity.

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Land abandonment and the loss of traditional farming practices are thought to control land cover dynamics, and hence the ecosystem service supply in traditionally managed mountain landscapes. We evaluate the impact of land cover changes in Cantabrian Mountains (NW Spain), over 1990-2012, on the potential supply capacity of ecosystem services (regulating, provisioning, and cultural) at both regional and local scales. We also analyze trends in the use of ecosystem services at the local scale. Land cover changes were estimated from CORINE Land Cover database. Patterns of potential ecosystem service supply were assessed by applying an ecosystem service supply capacity matrix and trends in their actual use by using field data. Main trajectories of land cover change encompassed woody vegetation spread in semi-natural open systems and agricultural expansion in the most suitable areas. The capacity of landscape to provide ecosystem services improved during 1990-2012 at both scales. We detected trade-offs between the potential supply of ecosystem services associated to natural systems and those linked to traditional land uses, at both regional and local scales. Changes in the potential supply of ecosystem services matched trends in ecosystem service use. This study could help develop future scenarios to address upcoming challenges in ecosystem service supply.

The estimated potential of landscape metrics as a surrogate for biodiversity is strongly dependent on the spatial analytical unit used for evaluation. We assessed the relationship between terrestrial vertebrate species richness (total and taxonomic) and structural landscape heterogeneity, testing the impact of using different spatial analytical units in three mountain systems in Spain. Landscape heterogeneity was quantified through an additive partitioning of the Shannon diversity index of landscape classes. Both landscape heterogeneity and species richness were calculated using two spatial analytical unit approaches: eco-geographic vs. arbitrary (i.e., watersheds vs. square windows of different sizes 20x20 km, 50x50 km, 100x100km). We predicted species richness on the basis of landscape heterogeneity by fitting separate linear models for each spatial analytical unit approach. The main results obtained showed that landscape heterogeneity influenced terrestrial vertebrate species richness. However, the emerging relationships were dependent on the spatial analytical unit approach. The eco-geographic approach showed significant relationships between landscape heterogeneity and total and taxonomic species richness in almost all cases (except mammals). Considering the arbitrary approach, landscape heterogeneity appeared as a predictor of species richness only for mammals and breeding birds and at the coarsest spatial scales. Our results claim for further consideration of ecogeographical spatial analytical unit approaches in biodiversity studies and show that the methods of this study offer a valuable cost-effective framework for biodiversity management and spatial modeling, with potential to be adapted to national and global applications.

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