Pastoral wildfires in the Mediterranean: Understanding their linkages to land cover patterns in managed landscapes

The pastoral use of fire to regenerate rangelands is a major cause of wildfires in many Mediterranean countries. Despite producing important environmental impacts, this phenomenon has hardly ever been studied separately from other wildfire ignition causes. As extensive livestock breeding relies on the available pasture resources, we hypothesised that a higher rate of pastoral wildfire ignitions could be associated with land cover patterns, as these reflect the spatial arrangement of human activities in managed landscapes. To investigate these patterns, we studied landscape structure and the pastoral wildfires recorded between 1988 and 2000 in 24 Nature Park landscapes in Andalusia (Spain). The CORINE Land Cover map was reclassified according to five levels of grazing use and landscape metrics were calculated. Neural networks were developed to model the relationship between landscape metrics and pastoral wildfires, obtaining a set of significant variables which are discussed in the frame of land and livestock management in the region. We conclude that pastoral wildfire ignitions are more likely in landscapes where the pattern of being dominated by a matrix composed of several large patches of low to moderate grazing use, and having abundant small and elongated patches of higher grazing use, is more extreme. This pattern could be reflecting the persistence of numerous small livestock farms within an increasingly abandoned agrarian landscape. To prevent pastoral wildfires, land management could attempt to enlarge and merge those small patches of higher grazing use, reducing the amount of interface and their intermixture with the surrounding poorer pasture resources.

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► Land cover patterns are analysed in 24 representative landscapes in Andalusia (Spain).
► The arrangement of pastoral resources is characterised through landscape metrics.
► Metrics are correlated to a 13-year pastoral wildfire dataset through Neural Networks.
► Significant variables are discussed in the frame of land and livestock management.
► Most important patterns are highlighted and implications for management derived.