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Dust particles lifting and discharge from Africa to Europe is a recurring phenomenon linked to air circulation conditions. The possibility that microorganisms are conveyed across distances entails important consequences in terms of biosafety and pathogens spread. Using culture independent DNA-based analyses via next generation sequencing of the 16 S genes from the airborne metagenome, the atmospheric microbial community was characterized and the hypothesis was tested that shifts in species diversity could be recorded in relation to dust discharge. As sampling ground the island of Sardinia was chosen, being an ideal cornerstone within the Mediterranean and a crossroad of wind circulation amidst Europe and Africa. Samples were collected in two opposite coastal sites and in two different weather conditions comparing dust-conveying winds from Africa with a control situation with winds from Europe. A major conserved core microbiome was evidenced but increases in species richness and presence of specific taxa were nevertheless observed in relation to each wind regime. Taxa which can feature strains with clinical implications were also detected. The approach is reported as a recommended model monitoring procedure for early warning alerts in frameworks of biosafety against natural spread of clinical microbiota across countries as well as to prevent bacteriological warfare.

Soil erosion varies greatly over space and is commonly estimated using the revised universal soil loss equation (RUSLE). Neglecting information about estimation uncertainty, however, may lead to improper decision-making. One geostatistical approach to spatial analysis is joint stochastic simulation, which draws alternative, equally probable, joint realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error propagation analysis.The objective of this paper was to assess spatial uncertainty of a soil erodibility factor (K) model resulting from the uncertainties in the input parameters (texture and organic matter). The 500 km 2 study area was located in central-eastern Sardinia (Italy) and 152 samples were collected. A Monte Carlo analysis was performed where spatial cross-correlation information through joint turning bands simulation was incorporated. A linear coregionalization model was fitted to all direct and cross-variograms of the input variables, which included three different structures: a nugget effect, a spherical structure with a shorter range (3500 m) and a spherical structure with a longer range (10 000 m). The K factor was then estimated for each set of the 500 joint realizations of the input variables, and the ensemble of the model outputs was used to infer the soil erodibility probability distribution function. This approach permitted delineation of the areas characterized by greater uncertainty, to improve supplementary sampling strategies and K value predictions.

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In Mediterranean agropastoral areas, land abandonment is a key driver of wildfire risk as fuel load and continuity increase. To gain insights into the potential impacts of land abandonment on wildfire risk in fire-prone areas, a fire-spread modeling approach to evaluate the variations in wildfire potential induced by different spatial patterns and percentages of land abandonment was applied. The study was carried out in a 1200 km2 agropastoral area located in north-western Sardinia (Italy) mostly covered by herbaceous fuels. We compared nine land abandonment scenarios, which consisted of the control conditions (NA) and eight scenarios obtained by combining four intensity levels (10, 20, 30, 40%) and two spatial patterns of agropastoral land abandonment. The abandonment scenarios hypothesized a variation in dead fuel load and fuel depth within abandoned polygons with respect to the control conditions. For each abandonment scenario, wildfire hazard and likelihood at the landscape scale was assessed by simulating over 17,000 wildfire seasons using the minimum travel time (MTT) fire spread algorithm. Wildfire simulations replicated the weather conditions associated with the largest fires observed in the study area and were run at 40 m resolution, consistent with the input files. Our results highlighted that growing amounts of land abandonment substantially increased burn probability, high flame length probability and fire size at the landscape level. Considering a given percentage of abandonment, the two spatial patterns of abandonment generated spatial variations in wildfire hazard and likelihood, but at the landscape scale the average values were not significantly different. The average annual area burned increased from about 2400 ha of the control conditions to about 3100 ha with 40% land abandonment. The findings of this work demonstrate that a progressive abandonment of agropastoral lands can lead to severe modifications in potential wildfire spread and behavior in Mediterranean areas, thus promoting the likelihood of large and fast-spreading events. Wildfire spread modeling approaches allow us to estimate the potential risks posed by future wildfires to rural communities, ecosystems and anthropic values in the context of land abandonment, and to adopt and optimize smart prevention and planning strategies to mitigate these threats.

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Wildfires are known to change post-fire watershed conditions such that hillslopes can become prone to increased erosion and sediment delivery. In this work, we coupled wildfire spread and erosion prediction modelling to assess the benefits of fuel reduction treatments in preventing soil runoff. The study was conducted in a 68000-ha forest area located in Sardinia, Italy. We compared no-treatment conditions v. alternative strategic fuel treatments performed in 15% of the area. Fire behaviour before and after treatments was estimated by simulating 25000 wildfires for each condition using the minimum travel time fire-spread algorithm. The fire simulations replicated historic conditions associated with severe wildfires in the study area. Sediment delivery was then estimated using the Erosion Risk Management Tool (ERMiT). Our results showed how post-fire sediment delivery varied among and within fuel treatment scenarios. The most efficient treatment alternative was that implemented near the road network. We also evaluated other factors such as exceedance probability, time since fire, slope, fire severity and vegetation type on post-fire sediment delivery. This work provides a quantitative assessment approach to inform and optimise proactive risk management activities intended to reduce post-fire erosion.