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Heat waves (HWs) and wildfires are potentially considered highly correlated hazards with dramatic impacts on ecosystems and society. However, previous studies focused only on one single hazard leaving out potential compounding and cascading effects. The aim of this work is thus to investigate spatio-temporal patterns for both hazards in Mediterranean Europe and assess the potential influence of HWs on fire regime.The historical wildfire dataset was derived from the Global Wildfire Information System (GWIS). This dataset corresponds to georeferenced polygons detected between 2000 and 2021 by MODIS. To understand the relationship between fires and HWs, we extracted several fire regime metrics accounting for density, seasonality and severity. All the metrics were computed at the scale of 12 km square grid.Then, we characterised the HWs in terms of frequency, duration, seasonality and intensity for the same period based on a high resolution meteorological dataset (ERA5-land from Copernicus Climate Data Store). A HW was defined as a period of at least 6 days with temperatures greater than the 90th percentile whereas intensity was calculated as the total amount of degrees exceeding that percentile during the HW. Finally, we compared HWs occurrence with information about fire events. Compound and cascading events were analysed through hazard maps to identify simultaneous occurrences of the two hazards and look at different combinations of hazard sequences. Finally, multiple linear regression models were applied to explore the complex influence of HWs characteristics on fire metrics.The results highlighted the hotspot areas in Mediterranean countries where the common hazard occurrences were identified, and the role of HWs in the compound and cascading fire hazard events. The findings of this study could support the assessment of hazard patterns and, in turn, prevention and monitoring activities to support disaster risk reduction.

Assessing climate-related risks on key economic sectors is of paramount relevance to inform policy makers and support the decision making process in designing adaptation plans and strategies to cope with climate change. Agriculture in the Mediterranean area is already experiencing negative impacts of climate changes and urgent adaptation actions are required to increase the resilience of agricultural systems. This study applied the impact chain approach to assess the expected climate risks for the cereal and livestock sectors to support the development of the Regional Adaptation Strategy to Climate Change (SRACC) of Sardinia (Italy) Region. Statistical socio-economic indicators were integrated with crop simulation models output and climate change scenarios to 2050, to investigate the exposure, vulnerability and hazard components according to the IPCC framework. The results were elaborated at the regional and municipal level to provided information for policy-makers at different administrative levels. This information allows the identification of the areas with greatest impact and the most critical aspects for which to focus adaptation efforts.

Evapotranspiration (ET) is the process by which water is lost from the Earth's surface through the combined mechanisms of evaporation from surfaces and transpiration from plants. It is an important factor in the soil-plant-atmosphere (SPA) system and plays a key role in the functioning of ecosystems. In semi-arid regions such as the Mediterranean, ET is a major contributor to water loss. An accurate understanding of the spatiotemporal dynamics of ET is crucial for effective water resource management and conservation, particularly in the face of increasing water resource pressure and potential climate change. Remote sensing (RS) can provide long-term data with relatively high spatial and temporal resolution, which can be valuable for sustainable ecosystem management. Surface energy balance (SEB) techniques based on satellite RS data have proven useful for quantifying actual evapotranspiration (ETa eb) at various temporal and spatial scales. However, limitations such as the temporal resolution of satellite data and gaps in image acquisition due to cloud cover can limit the usefulness of RS. This study proposes a model-based approach for constructing daily crop actual evapotranspiration (ETc act) between Landsat 8 acquisition days. The modeling approach aims to simulate the dynamics in the SPA system that occur between two Landsat acquisitions in order to estimate the daily time series of ETc act. The model integrates ETa eb estimates by SEBAL model on Landsat-8 acquisition days, RS-derived vegetative biomass dynamics, field measurements of potential evapotranspiration, and a hydrological modeling approach using the transient flow Richards equation to estimate soil moisture in the root zone. The results show that the proposed approach is well suited for modeling the dynamics in the soil-plant-atmosphere system that occurs between two Landsat acquisitions to estimate the daily time series of ETc act. This approach can provide valuable information for water resource management, drought monitoring, and climate change research, moreover accurate ETc act estimates can make significant contributions to near real time irrigation modeling and scheduling.

Coffee is one of the most important agri-food systems from a global economic point of view. Most of the production takes place on small and medium-sized farms and is the main source of income for many rural families in several developing countries. Areas suitable for coffee production are very biodiverse and ecologically important, thus negative impacts should be minimized.Coffee production requires special environmental and climatic conditions. Current and future climate changes could cause problems for a sustainable production and result in lower yields. To overcome these problems, it is necessary to investigate the effectiveness of possible adaptation measures, such as intercropping with other tree species that can provide more shade to coffee plants and favour environmental sustainability. In order to study how such modifications could improve the resilience and sustainability of coffee production, the use of process-based models can be very useful. The DynACof model was developed specifically to simulate coffee farming systems, including phenological development, physiological processes related to flower and fruit production, carbon allocation, the effect of water availability, light and temperature, as well as management. We tested the DynACof model on some study areas in Mexico, Brasil and Rwanda and verified that the yield predictions were in line with the observations. We then developed a modelling tool where the model can be applied to entire geographical areas in a spatially explicit manner, using global climatic and soil datasets.We used this tool to simulate yields in Latin America and Africa, both for the period 1985-2014 and for the period 2036-2065 using climate projections. Comparing the two periods, the model predicts a decrease in yields of about 28% in Latin America and about 12% in Africa. We then simulated specific management options (e.g. agroforestry shading vs intensive monocropping) to assess their efficacy in enhancing environmental sustainability and resilience to climate risks. These impact analyses will be crossed with socio-economic indicators for a more comprehensive climate risk assessment to support adaptation recommendations.

Understanding how functional traits and diversity modulate plant interactions within forests is becoming a widespread research goal in ecology. We applied neighbourhood analysis to a Mediterranean biodiversity manipulation experiment (IDENT-Macomer) to assess the importance of functional traits in predicting tree diameter increments (DI) in a mixed forest. We used tree functional traits to weigh the neighbourhood competition index (NCI) and functional dispersion (FDis), which is a functional diversity metric. We found that functional traits affect competitive performance across species within a mixed forest and that resource acquisition is based primarily on trait hierarchy. We also found that traits related to competition for light, such as maximum plant height (Hmax), are the best predictors of DI. Our results reveal that NCI is a more reliable predictor than FDis, but the combination of both effects helps to better explain differences in DI. Finally, our findings show that gathering functional trait data is a practise that should be prioritised in mixed forest management due to the predictive importance of NCI and FDis in experiments with high density and species diversity.

The project OFIDIA2 (Operational FIre Danger preventIon plAtform 2), funded by the Interreg Greece-Italy 2014-2020 Programme, proposed a pragmatic approach to improve the operational capacity of the stakeholders to detect and fight forest wildfires. A data analytics system was designed and implemented within the project to manage, transform, and extract knowledge from heterogenous data sources, through forecasting models such as weather, fire danger, and fire behaviour models. The high-resolution weather forecasting network previously developed in OFIDIA1 was enhanced by using a mesoscale configuration of the WRF-ARW model over the Central Mediterranean Sea. A nested domain over the Southern Italy at ~2km horizontal resolution allows getting high-resolution weather forecasts (2x2km) and processing data into fire danger models. Fires, fuel, topography and weather data were collected from several sources and used to run and calibrate fire models (FlamMap and Wildfire Analyst) in Apulia region (Italy). Based on the analyses of recurrent weather conditions leading to large fires, fire metrics’ maps for prevention and fire-fighting activities were produced. Finally, a Decision Support System (DSS) was also developed to provide support for 1) the selection of fire behaviour scenarios by means of mathematical models; and 2) the prevention of emergencies thanks to weather forecast information with fire danger indices at high resolutions.

The Mediterranean islands’ blue economy and, more specifically, the tourism sector, largely regulate Europe’s gross product. Climate change threatens the ecological, societal, and economic sustainability of the islands in many ways, with increasing wildfires making up one of the most critical components of the climate change impacts on tourism. Here, we aim to identify and assess forest fire vulnerability and risk due to climate change for seven Mediterranean islands through the application of the “impact chain” conceptual framework. The backbone of this approach requires the integration of quantitative and qualitative data according to the three main risk components sensu the Intergovernmental Panel on Climate Change (IPCC), i.e., hazard, exposure, and vulnerability, with a structured participatory approach involving stakeholders and experts. Our results illustrate the islands with high potential for improvement in terms of adapting capacity and, by indicating the contribution of the different risk components, highlight the main environmental and socio-economic elements that affect the islands’ vulnerability and risk under climate change. The approach’s potentials and constraints are discussed, suggesting that the method can be handily used to point out the priorities that must be addressed by mitigation and adaptation policies and measures at the island level.

The FLUXNET2015 dataset provides ecosystem-scale data on CO2, water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-flled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the frst time in this paper. In addition, 206 of these sites are for the frst time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible., European Union (EU), United States Department of Energy (DOE)

Soil microbial communities (SMCs) are important drivers of forest ecosystem functions and services. To optimize afforestation practices, it is important to understand how pure and mixed plantations can affect SMCs and their functioning. Therefore, the objective of this work was to investigate the SMC structure and function of six Mediterranean woody species grown as monocultures, and of six selected 1:1 combinations. SMC structure was assessed through the abundance estimation of different culturable heterotrophic microbial populations (i.e. bacteria, fungi, actinomycetes, Pseudomoas spp. and aerobic spore-forming bacteria) and the Biolog community level physiological profile, while soil biochemical functioning was estimated by quantifying soil dehydrogenase (DHG) activity. Species richness (SR) did not increase the abundance of culturable heterotrophic microbial populations, nor the number of carbon sources metabolized by the microbial communities (Biolog richness) or DHG activity. However, SR significantly decreased the potential metabolic activity (Biolog AWCD) of soil microbial populations. Leaf type significantly affected soil microbial and biochemical features, particularly for monocultures where conifer plots were characterized by a significantly higher fungal/bacterial ratio, reduced AWCD and richness as well as reduced DHG values. Significant interactions between tree species in mixed plots were noticed as well as significant correlations between plant community weighted mean (CWM) traits and soil microbial and biochemical features. Both SMC structure and function were clearly influenced by tree plantations and, importantly, microbial populations of monocultures and mixed plantations, and their functioning, were governed by different CWM traits.

Environmental constraints on both functional traits and trait–trait correlation patterns are extremely relevant research questions to understand assembling mechanisms and scaling processes in ecology. In fact, community weighted mean (CWM) and functional diversity (FD) metrics assimilate meaningful complementary information from species to community level. Relating species abundance data from national forest inventories (France, Italy and Spain) with species trait values from published sources, we explored CWM and FD variations along climatic gradients for traits related to leaf, hydraulic and live history strategies, and how the CWM or the FD of single traits co-vary. Results show how the combined effects of water availability and temperature modulate CWM, and to a lower extent FD, of Western European forests. PCA on CWM traits data showed that main axis accounts for 46.6% of the variability and is represented by wood density, seed dry weight (SDW) and maximum height. This axis is similarly preserved in humid and dry areas and potentially related to competition ability, growth rates and successional status of tree species dominants. Furthermore, the second axis is associated with leaf wilting and cavitation resistance (P50), but not consistent in dry areas. Correlation patterns of FD data, which were partially preserved across climatic regions, showed one main axis involving the joint variability of all traits. However, between humid and dry areas determinants of SDW and P50 differ from all other traits. Results showed that at least a part of the co-variation patterns among traits change with climate conditions, suggesting scale-dependent effects which should be taken into account when scaling assembly mechanisms.

Climate change is a concerning phenomenon worldwide. The reduction in greenhouse gas emissions is the core of the mitigation strategies that are effective countermeasures to address a changing climate in the long term. Nevertheless, the need for short-term solutions regarding adaptation to climate change (ACC) has gained growing interest in the scientific community and in European institutions. European and national ACC principles are being integrated in strategies and plans. In Italy, some regions have adopted ACC principles in strategic plans, which influence the whole local planning system and persuade local communities to become more climate-resilient. This study focuses on the mainstreaming of ACC into strategies, plans, programs, and projects (SPPPs) adopted by the following Italian cities: Bologna, Milan, Naples, Rome, Turin, and Venice. We scrutinize the contents of SPPPs with respect to four criteria: (i) references to strategies or plans for ACC; (ii) inclusion of ACC objectives and (iii) measures; and (iv) references to—or the inclusion of—climatic analysis on historical series and/or future projections. We found out that most SPPPs adopted by the cities have considered ACC in a promising way, i.e., all the cities are inclined to promote ACC, despite three of them lacking a municipal ACC strategy or plan and a National Adaptation Plan not being in force.

Climate change scenarios together with the increasing request for sustainable crop farming push the irrigation sector toward a more efficient use of natural resources. This is particularly relevant for the wine industry in Mediterranean areas, where there is a growing interest for technologies and knowledge devoted to save water resources while maintaining wine production and quality. In this work, a set of plant water status related variables were continuously monitored using an automated platform and compared with stem water potential values measured with pressure chambers. The preliminary results showed a good potential for these new automated sensors for a real time monitoring of the vine water status.

Seasonal forecasts are medium-range climate predictions that, used for calculating agroclimatic indicators, might potentially help land managers for best decision making. To assess their reliability seasonal forecasts are commonly contrasted against observed datasets, e.g. gridded data coming from reanalysis, classifying yearly pixel conditions in into/out of the norm events (i.e. using the 33th and 66th percentiles along a time series to define the occurrence of out of the norm events). Potential differences in the shape of the probability distribution across observed climate datasets might influence the results in the validation procedure of seasonal forecasting since the definition of out of the norm events depends on the properties of the statistical distribution. Here, we assess for different agroclimatic indicators related with water availability, vegetation thermal needs and fire risk, the spatial patterns of skewness using a range of climate datasets, i.e. ERA5, E-OBS and WFDEI along a 30 year period. Skewness represents the degree of asymmetry of the probability distribution evidencing locations in which out of the norm events highly differ from mean conditions which might suggest a potentially higher detectability. Common spatial patterns of great skewness (either positive or negative) across observed dataset might suggest areas with high and consistent detectability whereas contrasting patterns might suggest higher uncertainty for the validation procedure.

Mediterranean region is recognized as an hot spot for negative effects due to climate change. The region is already experiencing an increase in frequency and intensity of extreme weather events (i.e. drought conditions, fire, floods, and heat waves) and climate projections indicate a general exacerbation of such phenomena.This work, performed in the framework of the Sardinia Region Adaptation Strategy to Climate Change and in collaboration with the LIFE Project Master-ADAPT, analyzed the impacts and the vulnerability to climate change in the Sardinia region (Italy) for three sectors: agricultural-forestry sector, the inland water systems, and the hydrogeological component.The analysis used the innovative approach of developing "Impact Chains” per each sector and analyzed risk. A selection of indicators has been used (at municipality level) as proxy to assess climate hazard (for past and future conditions) and the exposure, sensitivity and adaptation capacity of the Region. This analysis represents the knowledge base required by local administrations for developing adaptation policies to climate change.Results allowed to better understand climate vulnerability for the territory, and to guide the process of identifying adaptation objectives and options to face climate risk for each sector. Adaptation to climate change is a priority and local administrations need to work towards climate adaptation objectives and policies in order to strengthen climate resilience, reduce negative impacts due to climate change, and enable more effective management opportunities.

Water use in the Mediterranean has been often pushed beyond sustainability, leading to water degradation and deterioration of ecosystem services. Different factors are interlinked with water management within a dynamically complex system (i.e. the Nexus) characterized by many feedbacks, trade-offs and high complexity of socioeconomic and environmental agents inducing non-linear responses hard to predict. Understanding such nexus systems requires innovative methodologies able to integrate different domains (e.g. hydrology, economics, planning, environmental and social sciences) and potential feedbacks, to support effective and targeted adaptation measures, taking into consideration uncertainty of climate change forecasts and associated impacts. Within the H2020 SIM4NEXUS project, water-land-energy-food-climate nexus links for Sardinia Island were represented with system dynamics modelling, together with relevant policy objectives, goals and measures. Sardinia, as many other Mediterranean regions, must implement a sustainable approach to water management, taking into account an equitable distribution of water resources between different sectors, economic needs, social priorities and ecology of freshwater ecosystems.For the Sardinia case study, the main focus was the representation of the reservoir water balance for the island, accounting predominantly for water supply and for water demand related to agricultural, hydro-power production, domestic/tourist consumption and environmental flows. With irrigated agriculture being the largest water consumer, this sector was modelled in more detail with crop specific distribution and projections. While water is the central focus, links with other nexus sectors including energy, climate, food and land use are included. Energy generation and consumption were also important along with the mode of generation and sector of consumption, as was modelling the change in crop types (i.e. land use and food production changes) and the crop water requirements associated with potential crop and cropped area changes, and in response to change in the local climate. Energy production is modelled from sources including oil, coal and methane, solar, wind and hydropower, while energy demand comes from the agricultural, domestic, industrial and service sectors (including transportation). The use of energy from the different sectors and using different energy sources, either renewable and not renewable, have different implication on GHG and climate change.While driven by strong interests to secure food provisions, an increase in irrigation in the Mediterranean may not be totally sustainable. Irrigation requirements of crops are projected to increase between 4 and 18% for 2050 compared to present conditions, limiting expansion of irrigated agriculture in Sardinia. Over the same period the inflow in the reservoirs can decrease between 5 and 20% and evaporation losses from reservoir surface bodies increase by 10%. Policy rules are tested and highlight how optimal allocation should be enforced in order to safeguard sustainability of natural resources over time, especially when considering climate variability. Natural resources are better preserved avoiding conflicts with strong seasonal peaks (i.e. summer). To meet these criticalities, new infrastructures and investments should increase use efficiency, All this would require changes in institutional and market conditions with a more cautious water management that includes prices and recycling policies.

The wine market is increasing in economic importance, so it is crucial for producers to be competitive, efficient, and productive. In addition, climate change requires the adoption of adaptive strategies for a more efficient management of natural resources. Especially in semi-arid regions, the limitation in water availability for crop farming requires adaptive strategies aiming to optimize water productivity. Knowing the optimal moment for irrigation and the water amount to apply is essential information for deficit irrigation of wine grapes. Stem water potential measurements, using the pressure chamber approach, provide an accurate technique for determining plant water status and timing irrigation. In combination with accurate ET measurements, the plant-based measurements offer the information needed to establish water saving deficit irrigation schedules. Collecting stem water potential data, however, is time-consuming and labour-intensive. This work presents the preliminary results of a comparison between new plant-based sensors, which continuously monitor the water status using an automated platform. A field study was conducted on a representative vineyard located in the Mediterranean Basin (Sardinia, Italy). Sensor data were compared to measurements of stem water potential. Two treatments were employed in the experiment: i) mild to moderate water stress conditions were applied from fruit set until ripening; ii) no irrigation from bunch closure until harvest, which resulted in moderate to severe water deficit conditions. In both treatments, stem water potential measurements were monitored weekly on adult leaves with a pump-up pressure chamber, while the T-Max method was used to determine the xylem sap flow. Leaf thickness, an indirect measurement of leaf turgor, was measured with a commercial sensor. Preliminary results showed a good potential for these promising techniques that may monitor proxies of the vine water status in an automated way, giving useful and user-friendly information for planning efficient irrigation schedules. In addition, micrometeorological measurements provide a method for assessing the actual ET rates between irrigation events, and this effort will be studied in future field experiments. Preliminary results showed a good potential for these promising techniques that may monitor proxies of the vine water status in an automated way that, in conjunction with reliable ET estimates, provide the information needed to determine user-friendly information for planning efficient irrigation schedules for deficit irrigated wine grapes.

Future climate projections indicate a northward shift of suitable agricultural areas with a potential intensification of cropping systems in northern Europe, and a decrease in crop productivity in southern Europe mainly due to extreme temperatures and reduction in precipitation and water availability. Furthermore, the uncertainty in projecting climate change impacts on agricultural productivity at the regional scale affects the choice of appropriate adaptation strategies.The main objective of this study is to assess the potential risk for cereal production and food security in the Euro-Mediterranean area and North Africa due to climate change, integrating multiple factors. Simulations were carried out using the CSM-CERES-Wheat and CSM-CERES-Maize crop models implemented in the DSSAT (Decision Support System for Agrotechnology Transfer) software. A spatially distributed routine integrating DSSAT with large geodatasets characterizing the environment (climate and edaphic) conditions and management options (e.g. agronomic practices, irrigation, fertilization) was applied to perform the simulation of grain yield for durum wheat, common wheat, and maize in each grid cell. An ensemble of climate projections from ISIMIP (Inter-Sectoral Impact Model Intercomparison Project) were used as input to the crop models. The uncertainty and model agreement of projected changes in crop yield, under current and future CO2 values (according to RCP8.5) were evaluated and new potential and high risk areas for cereal production were identified. Moreover, socio-economic indicators were considered to evaluate the exposure and adaptive capacity of the system and estimate the potential risk for cereal production. The result is a four dimensions single map that combine the selected variable and indices. Results show a potential decrease in cereal production per capita in the west Mediterranean area, North Africa and Turkey. However, the potential risk differ across these regions, according with the adaptive capacity of each area. Results also show a potential increase in cereal production per capita in north eastern Europe. Overall, there is high agreement across models.

High severity wildfires can have many negative impacts on ecosystems. In this work, we coupled wildfire spread and erosion prediction modelling to evaluate the effects of fuel reduction treatments in preventing soil runoff in Mediterranean ecosystems. The study was carried out in a 68,000-ha forest area located in Northern Sardinia, Italy. We treated 15% of the study area, and compared no-treatment conditions vs alternative strategic fuel treatments. We estimated pre- and post-treatment fire behaviour by using the Minimum Travel Time (MTT) fire spread algorithm. For each fuel treatment scenario, we simulated 25,000 wildfires replicating the historic weather conditions associated with severe wildfires in the 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 the fuel treatment scenarios tested. The treatments realized nearby roads were the most efficient. We also evaluated the effects of 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 optimize proactive risk management activities aimed at reducing post-fire erosion in Mediterranean areas.

Satellite remote sensing-based surface energy balance (SEB) techniques have emerged as useful tools for quantifying spatialized actual evapotranspiration at various temporal and spatial scales. However, discontinuous data acquisitions and/or gaps in image acquisition due to cloud cover can limit the applicability of satellite remote sensing (RS) in agriculture water management where continuous time series of daily crop actual evapotranspiration (ETc act) are more valued. The aim of the research is to construct continuous time series of daily ETc act starting from temporal estimates of actual evapotranspiration obtained by SEB modelling (ETa eb) on Landsat-TM images. SEBAL model was integrated with the FAO 56 evaporation model, RS-retrieved vegetative biomass dynamics (by NDVI) and on-field measurements of soil moisture and potential evapotranspiration. The procedure was validated by an eddy covariance tower on a vineyard with partial soil coverage in the south of Sardinia Island, Italy. The integrated modeling approach showed a good reproduction of the time series dynamics of observed ETc act (R2 =0.71, MAE=0.54 mm d-1, RMSE=0.73 mm d-1). A daily and a cumulative monthly temporal analysis showed the importance of integrating parameters that capture changes in the soil-plant-atmosphere (SPA) continuum between Landsat acquisitions. The comparison with daily ETc act obtained by the referenced ET fraction (ETrF) method that considers only weather variability (by ETo) confirmed the lead of the proposed procedure in the spring/early summer periods when vegetation biomass changes and soil water evaporation have a significant weight in the ET process. The applied modelling approach was also robust in constructing the missing ETc act data under scenarios of limited cloud-free Landsat acquisitions. The presented integrated approach has a great potential for the near real time monitoring and scheduling of irrigation practices. Further testing of this approach with diverse dataset and the integration with the soil water modeling is to be analyzed in future work.

The fire danger (FD) defines the conditions less or more favourable for a fire ignition success and its propagation. FD indexes, that integrates environmental variables related to FD in more or less complex equations and systems, are widely used in wildfire prone countries for both scientific and operational purposes. Assessing the performance of FD indexes is challenging and this issue is quite debated within the fire community, which has been trying to apply several methodologies to evaluate FD indexes. The main aim of this work is to give a contribution to this effort. The analysis was conducted using data from a fire-prone Mediterranean area (Sardinia island, Italy), where 8 FD indexes were evaluated and compared using different statistical approaches. We calculated the daily FD values for the period 2000-2007 over the study area. A set of statistical tools (namely Spearman rank correlation, Index Value Distribution and Percentile Analysis, and Logistic Regression) were applied to evaluate the performance of each FD index by comparing FD values with fire occurrence indicators. The statistical tests revealed a large variability in FD indexes performance, depending also on fire activity conditions. Our results showed that two of the tested FD indexes reached a good overall performance. Findings from this study can help both the scientific community and local fire managers, supporting the evaluation of early warning systems and fire prevention strategies in the Mediterranean Basin.

Wildfire spread and behavior can be limited by fuel treatments, even if their effects can vary according to a number of factors including type, intensity, extension, and spatial arrangement. In this work, we simulated the response of key wildfire exposure metrics to variations in the percentage of treated area, treatment unit size, and spatial arrangement of fuel treatments under different wind intensities. The study was carried out in a fire-prone 625 km2 agro-pastoral area mostly covered by herbaceous fuels, and located in Northern Sardinia, Italy. We constrained the selection of fuel treatment units to areas covered by specific herbaceous land use classes and low terrain slope (

Reforestation with multiple tree species is a promoted strategy to mitigate global change and to improve forest resistance against natural hazards. Dryland reforestation often fails because seedlings suffer from harsh conditions in degraded areas. Positive species interactions can overcome recruitment drawbacks by ameliorating environmental stress, but there is a strong need to advance functional insights from well-designed experiments. We studied the vitality of 19,712 tree seedlings from 12 species in a Mediterranean common garden experiment (Sardinia). Vitality was assessed as an integrated index of foliage discoloration and defoliation measures, which are in dry areas potential indicators of early plant performance. The experimental design properly replicated all monocultures and a selection of mixed communities with different levels of species richness (SR) and functional diversity (FD). From the second year onwards, a water availability treatment (irrigated versus non-irrigated) was added to the design. In the second year, seedling vitality was strongly determined by species identity and irrigation, but ecological interactions between trees were not relevant. In the third year, however, broad-leaved species were significantly more vigorous in mixed assemblages. Importantly, FD was identified as a seven times stronger predictor compared to SR. This suggests that a certain degree of trait diversification is essential to benefit from facilitative interactions. The positive FD effects were principally mediated by the presence of pines (P. pinea, P. pinaster and P. halepensis) in the neighborhood of broad-leaved trees. The latter had, on average, a 23% greater likelihood to have the highest vitality score in mixture with pines. The creation of a favorable physical and biotic neighborhood by pines is likely caused by their fast juvenile growth and adequate crown light transmission. FD effects on seedling vitality were positive, but contrary to the stress-gradient hypothesis, they were of similar magnitude in both irrigated and non-irrigated blocks. We conclude that local neighborhood facilitation provides essential assistance for broad-leaved trees passing a critical seedling stage in semi-arid regions. This knowledge can contribute to increased success rates in forest rehabilitation in these regions.