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.

The purpose of this study was to analyze the effects of altitude, the position of the trees along a gradient of canopy cover, and the orientation of their crown on leaf traits of 18 deciduous woody species belonging to 10 families in the Fandoglo forest region in Ardabil, North West of Iran. We measured eight leaf traits (leaf width, length, area, thickness, water content, leaf mass per area, specific leaf area, and dry matter concentration) of trees sampled at sites subjected to different light regimes (forest edge, forest understory, and isolated trees). All traits were measured on more than 3600 leaves from 90 trees sampled in two altitudinal ranges (low: 1300-1500 m a.s.l.; high: 1500-1700 m a.s.l.). A two-way ANOVA and t-test for independent samples were applied to test for differences in leaf traits between different altitudes and degree of canopy cover. The results confirmed that species’ leaf traits were more strongly correlated with the altitude and canopy cover rather than the orientation of the crown. No relationship between leaf traits and crown orientation was detected. All leaf traits had significantly higher values at low than at high elevation, indicating that environmental factors such as atmospheric CO2 concentration, temperature, light, irradiance, and wind deeply impact on foliar morphology and function; however, water content and specific leaf area showed an opposite trend. Also, species with different positions along the gradient of canopy cover could have different responses to elevation. Our results indicate that the variation of functional (morphological and physiological) traits in different tree species are affected by altitude and light regime. This might provide a theoretical basis for afforestation and forest management activities in the Fandoghlo forest region.

Effective landscape-scale fuel management strategies are essential for reducing wildfire risk in Mediterranean fire-prone areas. In this study, the minimum travel time (MTT) fire-spread algorithm as implemented in FlamMap was applied to assess the potential of alternative fuel treatments for lowering wildfire losses in a 5,740-ha study area in eastern Sardinia, Italy. Twenty-seven wildfires at 10-m resolution were simulated considering three wind speeds (15, 18, and 21 km h⁻¹) to compare fuel treatments: no treatment (NT), irrigated agroforestry areas with shrub clearing (T1), prescribed fire in eucalyptus stands (T2), and irrigated grasslands (T3). The simulations replicated a recent large wildfire that occurred in the study area (Orrì wildfire, 2019) and considered the weather and fuel moisture conditions associated with this event. The average wildfire exposure outputs (burned area, probability of burning, conditional flame length, potential crown fire occurrence, and surfaces withflame lengths above 2.5 m) decreased after fuel treatments, compared to no treatment. T1 was the most effective strategy in mitigating wildfire hazards and provided the most significant performance for several wildfire exposure indicators. Treating only 0.5% of the study area (~ 30 ha) resulted in a decrease in all wildfire exposure metrics to ~ 10% within the study area. In addition, the total surface characterized by high flame length (average > 2.5 m) was the lowest in the T1 treatment. This study can help land and fire managers optimize fuel treatment opportunities and wildfire risk mitigation strategies in Mediterranean areas.

Aim of the study:Forest fuel classification and characterization is a critical factor in wildfire management. The main purpose of this study was to develop custom fuel models for accurately mapping wildfire spread compared to standard models.Area of study: The study was conducted at a replanted forest dominated by coniferous species, in the Arabdagh region,GolestanProvince, northernIran.Material and methods: Six custom fuel models were developed to characterize the main vegetation types in the study area. Fuel samples were collected from 49 randomly selected plots. In each plot, the fuel load of 1-hr, 10-hr, 100-hr, 1000-hr, live herbs, live woody plants, surface area volume ratio, and fuel depth were estimated using the Fuel Load (FL) sampling method along three transects. Canopy fuel load was calculated for each fuel model. The performance of the custom fuel models versus standard fuel models on wildfire behavior simulations was compared using the FlamMap MTT simulator.Main results: The results showed that, despite the similarity in the burned area between observed and modeled fires, the custom fuel models produced an increase in simulation accuracy. Compared to the observed fire, simulation results did not give realistic results to the crown fire. The simulation using standard fuel models did not result in crown fire, while the simulation using custom fuel models showed a moderate rate of crown fire with a Kappa coefficient of 0.54.Research highlights: The results demonstrated the importance of developing custom fuel models to simulate wildfire maps with higher accuracy for wildfire risk management.Keywords: custom fuel model; FlamMap; replantation; vegetation type; wildfire behavior.