Forest are highly vulnerable to global change drivers, such as an increase in wildfire events. Learning more about how and why different post‐fire management strategies regulate the ability of forest ecosystem properties (e.g. plant diversity and function) to simultaneously recover after wildfire and provide multiple ecosystem functions is of critical importance.
This study aims to evaluate how unburned, burned managed and burned unmanaged plots regulate the responses of multiple forest ecosystem properties (e.g. plant diversity, nutrient cycling, soil carbon stocks, water regulation, decomposition and wood production) and overall multifunctionality to wildfires. In September 2017, we selected two post‐fire management strategies in a 3‐km2 watershed previously affected by a wildfire in July 2012: contour‐felled log debris (CFD), log erosion barriers area (LEB), and also unburned and unmanaged plots (BNA). We randomly distributed 12 plots among the three post‐fire management strategies (three plots per treatment) and unburned.
The results showed that multiple forest ecosystem properties were significantly affected by wildfire and that specific post‐fire management treatment (e.g. LEB and CFD) can be used to efficiently support plant diversity and ecosystem functioning. Our results revealed that the general indicators of ecosystem functions decreased in Mediterranean forests after wildfires and post‐fire management strategies (LEB and CFD) significantly helped to recover the ecosystems’ short‐term community‐level properties and ecosystem functions (5 years after a wildfire event) to pre‐fire levels.
Synthesis and applications. These findings demonstrate that multiple ecosystem functions are affected by wildfires in Mediterranean forests and show that post‐fire management treatments can promote multifunctionality and plant diversity. Our results unfold the potential of log erosion barriers (LEB) and contour‐felled log debris (CFD) as effective strategies for recovering community‐level properties and forest functions in the short term.
Forest fires and post-fire practices influence sediment connectivity (SC). In this study, we use the 'aggregated index of connectivity' (AIC) to assess SC in five Mediterranean catchments (198-1090 ha) affected by a wildfire in 2012 in south-eastern Spain. Two temporal scenarios were considered, immediately after the fire and before post-fire management, and 2 years after the fire including all practices (hillslope barriers, check-dams, afforestation, salvage logging and skid trails). One LiDAR (light detection and ranging)-derived digital elevation model (DEM, 2 m  2 m resolution) was generated, per scenario. The five catchment outlets were established as the computation target (AIC OUT ), and structural and functional SC were calculated. Index outputs were normalized to make the results of the non-nested catchments comparable (AIC N-OUT ). The output analysis includes the SC distribution along the catchments and at local scale (929 sub-catchments, 677 in the burned area), the hillslope and channel measures' effect on SC, and a sedimentological analysis using observed area-specific sediment yield (SSY) at 10 new (built after post-fire practices) concrete check-dams located in the catchments (SSY = 1.94 Mg ha À1 yr À1 ; σ = 1.22). The catchments with more circular shapes and steeper slopes were those with higher AIC N-OUT . The structural SC mapsremoving the rainfall erosivity influenceallowed evaluating the actual role played by the post-fire practices that reduced SC (x= À 1.19%; σ = 0.41); while functional SC was linked to the actual change of SC (x= + 5.32%; σ = 0.62). Hillslope treatments resulted in significant changes on AIC N-OUT at sub-catchment scale with certain disconnectivity. A good and positive correlation was found between the SSY and the changes of AIC N-OUT .However, the coarse DEM resolution explained the lack of effect of the rock check-damslocated on the secondary channelson AIC N-OUT . AIC N-OUT proved to be a useful tool for decision making in post-fire restoration, but an optimal input data is still necessary to refine calculations.
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