Plant fire-adaptive traits mediate long-term fire recurrence impact on the potential supply capacity of ecosystem services and their resilience.

Fire-induced changes in vegetation composition due to fire-regime intensification are leading to alterations in ecosystem services that might threaten their future sustainability. Fire recurrence, in particular, could be a key driver shaping ecosystem service resilience in fire-prone ecosystems. This study evaluates the impact of fire recurrence, over twenty-four years, on the potential supply capacity of ten regulating, provisioning, and cultural services selected as critical services by stakeholders and experts. We assessed fire effects in four fire-prone landscapes dominated by species with different functional-traits response to fire (i.e., obligate seeder vs resprouter species). Trends in the potential supply capacity linked to fire recurrence were estimated by applying a supervised classification of Land Use and Land Cover (LULC) classes performed using Landsat imagery, associated to an ecosystem service capacity matrix adapted to the local socio-ecological context. In landscapes dominated by seeders, fire recurrence broke off the potential supply capacity of services traditionally associated to mature forest cover (i.e., the predicted probability of a decrease in the potential supply capacity of climate regulation, timber, wood fuel, mushroom production, tourism, landscape aesthetic, and cultural heritage occurred with high fire recurrence). In landscapes dominated by resprouter species, the effect of fire recurrence was partially buffered in the short-term after fire and no substantial differences in trends of change were found (i.e., equal predicted probability in the potential supply capacity of ecosystem services regardless of fire recurrence). We detected two new opportunities for ecosystems service supply associated to fire recurrence: livestock and honey production, especially in sites dominated by seeders. These findings provide valuable information aiming at recovering post-fire ecosystem service potential supply to partially counterbalance the loss in the socio-ecological system. When the main post-fire restoration goal is preserving ecosystem service resilience in fire-prone ecosystems, establishing management strategies focused on promoting resprouter species could aid mitigating the fire-driven loss of their supply capacity.

Monitoring post-fire neighborhood competition effects on pine saplings under different environmental conditions by means of UAV multispectral data and structure-from-motion photogrammetry.

In burned landscapes, the recruitment success of the tree dominant species mainly depends on plant competition mechanisms operating at fine spatial scale, that may hinder resource availability during the former years after the disturbance. Data acquisition at very high spatial resolution from unmanned aerial vehicles (UAV) have promoted new opportunities for understanding context-dependent competition processes in post-fire environments. Here, we explored the potentiality of UAV-borne data for assessing inter-specific competition effects of understory woody vegetation on pine saplings, as well as intra-specific interactions of neighboring saplings, across three burned landscapes located along a climatic/productivity gradient in the Iberian Peninsula. Geographic object-based image analysis (GEOBIA), including multiresolution segmentation and support vector machine (SVM) classification, was used to map pine saplings and understory shrubs at species level. Input data were, on the one hand, multispectral (11.31 cm·pixel-1) and Structure-from-Motion (SfM) canopy height model (CHM) data fusion, hereafter MS-CHM, and, on the other, RGB (3.29 cm·pixel-1) and CHM data fusion, hereafter RGB-CHM. A Random Forest (RF) regression algorithm was used to evaluate the effects of neighborhood competition on the relative growth in height of 50 pine saplings randomly sampled across the MS-CHM classified map. Circular plots of 3 m radius were set from the centroid of each target pine sapling to measure percentage cover, mean height of all individuals in the plot and mean height of individuals contacting the target sapling. Competing shrub species were differentiated according to their fire-adaptive traits (i.e. seeders vs resprouters). Object-based image classification applied on MS-CHM yielded higher overall accuracy for the three sites (83.67% ± 3.06%) than RGB-CHM (74.33% ± 3.21%). Intra-specific competitive effects were not detected, whereas increasing cover and height of shrub neighbors had a significant non-linear impact on the growth on pine saplings across the study sites. The strongest competitive effects of seeder shrubs occurred in open areas with low vegetation cover and fuel continuity, following a gap-dependent model. The non-linear relationships evidenced in this study between the structure of neighboring shrubs and the growth of pine seedlings/saplings have profound implications for considering possible competing thresholds in post-fire decision-making processes. These results endorse the use of UAV multispectral and SfM photogrammetry as a valuable post-fire management tool for measuring accurately the effect of competition in heterogeneous burned landscapes.

Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrieval in heterogeneous ecological systems after fire.

In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial resolution as compared to the standard use of coarser imagery. The study was conducted both at landscape and plant community levels within the perimeter of a megafire that occurred in western Mediterranean Basin. We developed a hybrid retrieval scheme based on PROSAIL-D RTM simulations to create a training dataset of top-of-canopy spectral reflectance and the corresponding FVC for the dominant plant communities. The machine learning algorithm Gaussian Processes Regression (GPR) was learned on the training dataset to model the relationship between canopy reflectance and FVC. The GPR model was then applied to retrieve FVC from WorldView-3 (spatial resolution of 2 m) and Sentinel-2 (spatial resolution of 20 m) surface reflectance bands. A set of 75 plots of 2x2m and 45 plots of 20x20m was distributed under a stratified schema across the focal plant communities within the fire perimeter to validate FVC satellite derived retrieval. At landscape scale, the accuracy of the FVC retrieval was substantially higher from WorldView-3 (R2 = 0.83; RMSE = 7.92%) than from Sentinel-2 (R2 = 0.73; RMSE = 11.89%). At community level, FVC retrieval was more accurate for oak forests than for heathlands and broomlands. The retrieval from WorldView-3 minimized the over- and under-estimation effects at low and high field sampled vegetation cover, respectively. These findings emphasize the effectiveness of high spatial resolution satellite reflectance data to capture FVC ground spatial variability in heterogeneous burned areas using a hybrid RTM retrieval method.

Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape.

The design and implementation of pre-fire management strategies in heterogeneous landscapes requires the identification of the ecological conditions contributing to the most adverse effects of wildfires. This study evaluates which features of pre-fire vegetation structure, estimated through broadband land surface albedo and Light Detection and Ranging (LiDAR) data fusion, promote high wildfire damage across several fire-prone ecosystems dominated by either shrub (gorse, heath and broom) or tree species (Pyrenean oak and Scots pine). Topography features were also considered since they can assist in the identification of priority areas where vegetation structure needs to be managed. The case study was conducted within the scar of a mixed-severity wildfire that occurred in the Western Mediterranean Basin. Burn severity was estimated using the differenced Normalized Burn Ratio index computed from Sentinel-2 multispectral instrument (MSI) Level 2 A at 10 m of spatial resolution and validated in the field using the Composite Burn Index (CBI). Ordinal regression models were implemented to evaluate high burn severity outcome based on three groups of predictors: topography, pre-fire broadband land surface albedo computed from Sentinel-2 and pre-fire LiDAR metrics. Models were validated both by 10-fold cross-validation and external validation. High burn severity was largely ecosystem-dependent. In oak and pine forest ecosystems, severe damage was promoted by a high canopy volume (model accuracy = 79%) and a low canopy base height (accuracy = 82%), respectively. Land surface albedo, which is directly related to aboveground biomass and vegetation cover, outperformed LiDAR metrics to predict high burn severity in ecosystems with sparse vegetation. This is the case of gorse and broom shrub ecosystems (accuracy of 80% and 77%, respectively). The effect of topography was overwhelmed by that of the vegetation structure portion of the fire triangle behavior, except for heathlands, in which warm and steep slopes played a key role in high burn severity outcome together with horizontal and vertical fuel continuity (accuracy = 71%). The findings of this study support the fusion of LiDAR and satellite albedo data to assist forest managers in the development of ecosystem-specific management actions aimed at reducing wildfire damage and promote ecosystem resilience.

Evaluation of fire severity in fire prone-ecosystems of Spain under two different environmental conditions.

Severe fires associated to climate change and land cover changes are becoming more frequent in Mediterranean Europe. The influence of environmental drivers on fire severity, especially under different environmental conditions is still not fully understood. In this study we aim to determine the main environmental variables that control fire severity in large fires (>500 ha) occurring in fire-prone ecosystems under two different environmental conditions following a transition (Mediterranean-Oceanic)-Mediterranean climatic gradient within the Iberian Peninsula, and to provide management recommendations to mitigate fire damage. We estimated fire severity as the differenced Normalized Burn Ratio, through images obtained from Landsat 8 OLI. We also examined the relative influence of pre-fire vegetation structure (vegetation composition and configuration), pre-fire weather conditions, fire history and topography on fire severity using Random Forest machine learning algorithms. The results indicated that the severity of fires occurring along the transition (Mediterranean-Oceanic)-Mediterranean climatic gradient was primarily controlled by pre-fire vegetation composition. Nevertheless, the effect of vegetation composition was strongly dependent on interactions with fire recurrence and pre-fire vegetation structural configuration. The relationship between fire severity, weather and topographic predictors was not consistent among fires occurring in the Mediterranean-Oceanic transition and Mediterranean sites. In the Mediterranean-Oceanic transition site, fire severity was determined by weather conditions (i.e., summer cumulative rainfall), rather than being associated to topography, suggesting that the control exerted by topography may be overwhelmed by weather controls. Conversely, results showed that topography only had a major effect on fire severity in the Mediterranean site. The results of this study highlight the need to prioritise fuel treatments aiming at breaking fuel continuity and reducing fuel loads as an effective management strategy to mitigate fire damage in areas of high fire recurrence.

Using Unmanned Aerial Vehicles in Postfire Vegetation Survey Campaigns through Large and Heterogeneous Areas: Opportunities and Challenges.

This study evaluated the opportunities and challenges of using drones to obtain multispectral orthomosaics at ultra-high resolution that could be useful for monitoring large and heterogeneous burned areas. We conducted a survey using an octocopter equipped with a Parrot SEQUOIA multispectral camera in a 3000 ha framework located within the perimeter of a megafire in Spain. We assessed the quality of both the camera raw imagery and the multispectral orthomosaic obtained, as well as the required processing capability. Additionally, we compared the spatial information provided by the drone orthomosaic at ultra-high spatial resolution with another image provided by the WorldView-2 satellite at high spatial resolution. The drone raw imagery presented some anomalies, such as horizontal banding noise and non-homogeneous radiometry. Camera locations showed a lack of synchrony of the single frequency GPS receiver. The georeferencing process based on ground control points achieved an error lower than 30 cm in X-Y and lower than 55 cm in Z. The drone orthomosaic provided more information in terms of spatial variability in heterogeneous burned areas in comparison with the WorldView-2 satellite imagery. The drone orthomosaic could constitute a viable alternative for the evaluation of post-fire vegetation regeneration in large and heterogeneous burned areas.

Prescribed burning and clear-cutting effects on understory vegetation in a Pinus canariensis stand (Gran Canaria).

Prescribed fires are a powerful tool for reducing fire hazards by decreasing amounts of fuel. The main objective is to analyze the effects of prescribed burning on the understory vegetation composition as well as on the soil characteristics of a reforested stand of Pinus canariensis. The study attempts to identify the effects of the preburning treatment of cutting understory vegetation on the floristic parameters of the vegetation community. This study was carried out for two years following a prescribed fire in a Canarian pine stand. Cutting and burning treatment affected species composition and increased diversity. Burnt and cut plots were characterized by a diverse array of herbaceous species and by a lower abundance of Teline microphylla (endemic legume), although burning apparently induced its germination. Cut treatment was more consistently differentiated from the control plots than burnt treatment. Soil K decreased after both treatments, pH slightly decreased after cutting, while P and Ca increased after fire. From an ecological point of view, prescribed burning is a better management practice than cutting the woody species of the understory. However, long-term studies would be necessary to evaluate the effects of fire intensity, season and frequency in which the prescribed burning is applied.

Resistance of soil bacterial communities from montane heathland ecosystems in the Cantabrian mountains (NW Spain) to a gradient of experimental nitrogen deposition.

Elevated atmospheric nitrogen (N) deposition on terrestrial ecosystems has become one of the most important drivers of microbial diversity loss on a global scale, and has been reported to alter the soil function of nutrient-poor, montane Calluna vulgaris heathlands in the context of global change. In this work we analyze for the first time the shifts of bacterial communities in response to experimental addition of N in Calluna heathlands as a simulation of atmospheric deposition. Specifically, we evaluated the effects of five N addition treatments (0, 10, 20, and 50 kg N ha-1 yr-1 for 3-years; and 56 kg N ha-1 yr-1 for 10-years) on the resistance of soil bacterial communities as determined by changes in their composition and alpha and beta diversities. The study was conducted in montane Calluna heathlands at different development stages (young and mature phases) in the southern side of the Cantabrian Mountains (NW Spain). Our results evidenced a substantial increase of long-term (10-years) N inputs on soil extractable N-NH4+, particularly in young Calluna stands. The alpha diversity of soil bacterial communities in mature Calluna stands did not show a significant response to experimental N addition, whereas it was significantly higher under long-term chronic N addition (56 kg N ha-1 yr-1 for 10-years) in young Calluna stands. These bacterial community shifts are mainly attributable to a decrease in the dominance of Acidobacteria phylum, the most representative in montane Calluna ecosystems, in favor of copiotrophic taxa such as Actinobacteria or Proteobacteria phyla, favored under increased N availability. Future research should investigate what specific ecosystem functions performed by soil bacterial communities may be sensitive to increased nitrogen depositions, which may have substantial implications for the understanding of montane Calluna ecosystems' stability.

High resilience of soil bacterial communities to large wildfires with an important stochastic component.

Wildfires alter the structure and functioning of ecosystems through changes in their biotic and abiotic components. A deeper understanding recovery process concerning diverse communities and properties within these components can provide valuable insights into the ecological effects of wildfires. Therefore, it is appropriate to enhance our understanding of the resilience of bacterial communities after wildfires within Mediterranean ecosystems. In this research, soil bacterial community resilience was evaluated in three types of ecosystems for two fire severities, two years after a large wildfire in Mediterranean ecosystems. The resilience of the soil bacterial community refers to its ability to return to original state after disturbance. This capacity can be estimated by the study of its recovery over time. In this study we evaluated the resilience using the variables: alpha diversity, beta diversity and the changes in abundance of both OTUs (Operational Taxonomic Units) and principal bacterial taxa (phyla, classes or orders). Our results showed that resilience depends on fire severity and type of ecosystem. We studied three ecosystems with different stage in the secondary succession: low maturity shrublands and heathlands, and high maturity oak forests. In general, high resilience in the soil bacterial community was observed in heathlands under low and high fire severity conditions. The other two ecosystems were resilient only under low fire severity. Stochastic replacement of the abundance of the OTUs was observed in all three ecosystems, with a notable impact on oak forests, under during high-severity conditions.

Wildland-urban interface typologies prone to high severity fires in Spain.

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016-2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Short-term responses of ecosystem multifunctionality to fire severity are modulated by fire-induced impacts on plant and soil microbial communities.

This study represents a first attempt to shed light into the mechanisms that modulate the response of ecosystem multifunctionality (EMF) to fire severity in post-fire landscapes. We specifically investigated the role played by fire-induced changes on above and belowground communities in the modulation of EMF responses at short-term after fire. For this purpose, we estimated EMF using an averaging approach from three ecosystem functions (carbon regulation, decomposition and soil fertility) and their standardized functional indicators in field plots burned at low and high fire severity 1-year after a wildfire occurred in a Mediterranean ecosystem in the central region of Spain. Plant taxonomic and functional richness, and the bacterial and fungal taxonomic richness, were measured in the plots as community properties with a potential intermediate control over fire severity effects on EMF. The ecological effects of fire severity on above and belowground communities were important in shaping EMF as evidenced by Structural Equation Modeling (SEM). Indeed, the evidenced shrinkage exerted by high fire severity on EMF at short-term after fire was not direct, but modulated by fire-induced effects on the plant functional richness and the microbial taxonomic richness. However, EMF variation was more strongly modulated by indirect effects of fire severity on the biodiversity of soil microbial communities, than by the effects on the plant communities. Particularly, the fungal community exerted the strongest intermediate control (standardized SEM ß coefficient = 0.62), which can be linked to the differential response of bacterial (ß = -0.36) and fungal (ß = -0.84) communities to fire severity evidenced here. Our findings demonstrate that the effects of fire severity on above and belowground communities are important drivers of short-term ecosystem functioning. Efforts tailored to secure the provision of multiple functions should be focused on promoting the recovery on soil microbial communities under high-severity scenarios.

Estimates of fine fuel litter biomass in the northern Great Basin reveal increases during short fire-free intervals associated with invasive annual grasses.

Exotic annual grasses invasion across northern Great Basin rangelands has promoted a grass-fire cycle that threatens the sagebrush (Artemisia spp.) steppe ecosystem. In this sense, high accumulation rates and persistence of litter from annual species largely increase the amount and continuity of fine fuels. Here, we highlight the potential use and transferability of remote sensing-derived products to estimate litter biomass on sagebrush rangelands in southeastern Oregon, and link fire regime attributes (fire-free period) with litter biomass spatial patterns at the landscape scale. Every June, from 2018 to 2021, we measured litter biomass in 24 field plots (60 m × 60 m). Two remote sensing-derived datasets were used to predict litter biomass measured in the field plots. The first dataset used was the 30-m annual net primary production (NPP) product partitioned into plant functional traits (annual grass, perennial grass, shrub, and tree) from the Rangeland Analysis Platform (RAP). The second dataset included topographic variables (heat load index -HLI- and site exposure index -SEI-) computed from the USGS 30-m National Elevation Dataset. Through a frequentist model averaging approach (FMA), we determined that the NPP of annual and perennial grasses, as well as HLI and SEI, were important predictors of field-measured litter biomass in 2018, with the model featuring a high overall fit (R2 = 0.61). Model transferability based on extrapolating the FMA predictive relationships from 2018 to the following years provided similar overall fits (R2 ≈ 0.5). The fire-free period had a significant effect on the litter biomass accumulation on rangelands within the study site, with greater litter biomass in areas where the fire-free period was <10 years. Our findings suggest that the proposed remote sensing-derived products could be a key instrument to equip rangeland managers with additional information towards fuel management, fire management, and restoration efforts.

Short-term effects of burn severity on ecosystem multifunctionality in the northwest Iberian Peninsula.

Severe wildfires cause important changes in vegetation and soil properties in Mediterranean ecosystems. The aim of this work was to evaluate ecosystem multifunctionality through the study of burn severity short-term effects on different ecosystem functions and services. We selected the Cabrera wildfire (2017) in northwest Spain. Burn severity was quantified using CBI index, differentiating four categories: unburned, low, moderate, and high severity. We established a total of 126 field plots, where one year after fire the vegetation was evaluated and soil samples for the analysis of chemical, biochemical, and microbiological properties were collected. Sentinel-2 images were used to obtain vegetation biophysical variables. Vegetation and soil variables were directly applied as indicators, or used to calculate other indicators, which were standardized and selected to define ecosystem functions and services: (1) photosynthetic activity, soil fertility, nutrient cycling, and soil quality (supporting ecosystem service); (2) grass production for livestock and wood production (provisioning ecosystem service); (3) climate regulation and erosion protection (regulating ecosystem services), and (4) woody species diversity and aesthetic value (cultural ecosystem services). The combination of these functions and services defined ecosystem multifunctionality. The main results showed that burn severity negatively affected most ecosystem functions, as well as the ecosystem services of supporting, provisioning, and regulating, and hence, ecosystem multifunctionality. However, the soil fertility function significantly increased with high burn severity, while woody species diversity and aesthetic value functions and, consequently, the cultural ecosystem service, only decreased under the effect of moderate severity. These results provide a starting point to study burn severity effects from a multifunctional approach in Mediterranean ecosystems.

ALOS-2 L-band SAR backscatter data improves the estimation and temporal transferability of wildfire effects on soil properties under different post-fire vegetation responses.

Remote sensing techniques are of particular interest for monitoring wildfire effects on soil properties, which may be highly context-dependent in large and heterogeneous burned landscapes. Despite the physical sense of synthetic aperture radar (SAR) backscatter data for characterizing soil spatial variability in burned areas, this approach remains completely unexplored. This study aimed to evaluate the performance of SAR backscatter data in C-band (Sentinel-1) and L-band (ALOS-2) for monitoring fire effects on soil organic carbon and nutrients (total nitrogen and available phosphorous) at short term in a heterogeneous Mediterranean landscape mosaic made of shrublands and forests that was affected by a large wildfire. The ability of SAR backscatter coefficients and several band transformations of both sensors for retrieving soil properties measured in the field in immediate post-fire situation (one month after fire) was tested through a model averaging approach. The temporal transferability of SAR-based models from one month to one year after wildfire was also evaluated, which allowed to assess short-term changes in soil properties at large scale as a function of pre-fire plant community type. The retrieval of soil properties in immediate post-fire conditions featured a higher overall fit and predictive capacity from ALOS-2 L-band SAR backscatter data than from Sentinel-1 C-band SAR data, with the absence of noticeable under and overestimation effects. The transferability of the ALOS-2 based model to one year after wildfire exhibited similar performance to that of the model calibration scenario (immediate post-fire conditions). Soil organic carbon and available phosphorous content was significantly higher one year after wildfire than immediately after the fire disturbance. Conversely, the short-term change in soil total nitrogen was ecosystem-dependent. Our results support the applicability of L-band SAR backscatter data for monitoring short-term variability of fire effects on soil properties, reducing data gathering costs within large and heterogeneous burned landscapes.

The effect of sheep grazing abandonment on soil bacterial communities in productive mountain grasslands.

Livestock grazing abandonment entails important shifts on the overall ecosystem function, but the effects of this land-use change on specific bacterial taxa remain poorly understood in mountain grasslands. Moreover, we currently lack knowledge about the feedbacks between changes in ecosystem functions affected by livestock abandonment in mountain grasslands and the soil bacterial communities. Here, we evaluated the behavior of bacterial communities' structure and composition at taxa level as a function of short (1-year) and long-term (15-years) grazing abandonment in a mountain grassland. We also linked the observed responses in the bacterial communities to changes in several ecosystem functions (i.e. primary production, plant species biodiversity, carbon stocks and soil fertility). The alpha diversity of the bacterial communities did not show a significant response as a consequence of grazing abandonment. However, we identified significant changes on the overall composition of soil bacterial communities between the long-term abandoned grassland areas and grazed or abandoned areas in the short term. We also evidenced a balance between the number of operational taxonomic units (OTUs) whose relative abundance is favored by livestock grazing (19.51 %) and those with higher relative abundances in long-term grazing exclusion areas (20.23 %) that could behave as indicators of grazing abandonment. Structural Equation Modeling analyses proved that several bacterial taxa associated with relevant ecosystem functions, such as Rhodospirillales order within Alphaproteobacteria phylum, featured significant changes in their relative abundance between grazing treatments. The direct and indirect effects of grazing exclusion on woody species encroachment and soil organic carbon were strongly linked to the changes in the abundance of bacterial taxa indicators. The assessment of the bacterial community response to livestock abandonment in mountain grasslands may thus provide early warning signs before subtle changes in ecosystem functions occur.

Predicting potential wildfire severity across Southern Europe with global data sources.

The large environmental and socioeconomic impacts of wildfires in Southern Europe require the development of efficient generalizable tools for fire danger analysis and proactive environmental management. With this premise, we aimed to study the influence of different environmental variables on burn severity, as well as to develop accurate and generalizable models to predict burn severity. To address these objectives, we selected 23 wildfires (131,490 ha) across Southern Europe. Using satellite imagery and geospatial data available at the planetary scale, we spatialized burn severity as well as 20 pre-burn environmental variables, which were grouped into climatic, topographic, fuel load-type, fuel load-moisture and fuel continuity predictors. We sampled all variables and divided the data into three independent datasets: a training dataset, used to perform univariant regression models, random forest (RF) models by groups of variables, and RF models including all predictors (full and parsimonious models); a second dataset to analyze interpolation capacity within the training wildfires; and a third dataset to study extrapolation capacity to independent wildfires. Results showed that all environmental variables determined burn severity, which increased towards the mildest climatic conditions, sloping terrain, high fuel loads, and coniferous vegetation. In general, the highest predictive and generalization capacities were found for fuel load proxies obtained though multispectral imagery, both in the individual analysis and by groups of variables. The full and parsimonious models outperformed all, the individual models, models by groups, and formerly developed predictive models of burn severity, as they were able to explain up to 95%, 59% and 25% of variance when applied to the training, interpolation and extrapolation datasets respectively. Our study is a benchmark for progress in the prediction of fire danger, provides operational tools for the identification of areas at risk, and sets the basis for the design of pre-burn management actions.

Global application of an unoccupied aerial vehicle photogrammetry protocol for predicting aboveground biomass in non-forest ecosystems.

Non-forest ecosystems, dominated by shrubs, grasses and herbaceous plants, provide ecosystem services including carbon sequestration and forage for grazing, and are highly sensitive to climatic changes. Yet these ecosystems are poorly represented in remotely sensed biomass products and are undersampled by in situ monitoring. Current global change threats emphasize the need for new tools to capture biomass change in non-forest ecosystems at appropriate scales. Here we developed and deployed a new protocol for photogrammetric height using unoccupied aerial vehicle (UAV) images to test its capability for delivering standardized measurements of biomass across a globally distributed field experiment. We assessed whether canopy height inferred from UAV photogrammetry allows the prediction of aboveground biomass (AGB) across low-stature plant species by conducting 38 photogrammetric surveys over 741 harvested plots to sample 50 species. We found mean canopy height was strongly predictive of AGB across species, with a median adjusted R 2 of 0.87 (ranging from 0.46 to 0.99) and median prediction error from leave-one-out cross-validation of 3.9%. Biomass per-unit-of-height was similar within but different among, plant functional types. We found that photogrammetric reconstructions of canopy height were sensitive to wind speed but not sun elevation during surveys. We demonstrated that our photogrammetric approach produced generalizable measurements across growth forms and environmental settings and yielded accuracies as good as those obtained from in situ approaches. We demonstrate that using a standardized approach for UAV photogrammetry can deliver accurate AGB estimates across a wide range of dynamic and heterogeneous ecosystems. Many academic and land management institutions have the technical capacity to deploy these approaches over extents of 1-10 ha-1. Photogrammetric approaches could provide much-needed information required to calibrate and validate the vegetation models and satellite-derived biomass products that are essential to understand vulnerable and understudied non-forested ecosystems around the globe.

Wildfire effects on diversity and composition in soil bacterial communities.

In recent years, the Mediterranean area has witnessed an increase of both the frequency and severity of large fires, which appears to be intimately associated with climate and land use changes. To measure the impact of wildfires on living organisms, diverse indicators have been proposed. These indicators of fire severity traditionally rely on quantifying the damage caused to the vegetal component of ecosystems. However, the use of bacterial communities as severity indicators has received less attention. Here, we studied the differences between bacterial communities of three different Mediterranean ecosystems, two shrubby and one arboreal, two months after a large wildfire. Two levels of severity were compared to a control unburnt soil. The results showed that greater fire severity triggers a reduction in the diversity of soil bacterial communities. In high-severity fires, this reduction reached 40.6 and 58.6% of the control values for richness and Shannon's diversity, respectively. We also found that the greatest differences between communities could be attributed first to the severity of the fire, and second to the ecosystem from which they originated. Importantly, species of just five families of bacteria: Oxalobacteraceae, Micrococcaceae, Paenibacillaceae, Bacillaceae and Planococcaceae, became dominant in all three ecosystems. The average frequency increase for particular species was 100 times. However, due to random uncontrolled factors, the species that became dominant in each community were not always the same.

Fire regimes shape diversity and traits of vegetation under different climatic conditions.

Changes in climate and land use are altering fire regimes in many regions across the globe. This work aims to study the influence of wildfire recurrence and burn severity on woody community structure and plant functional traits under different environmental conditions. We selected three study sites along a Mediterranean-Oceanic climatic gradient, where we studied the fire history and burn severity of the last wildfire. Four years after the last wildfire, we established 1776 1-m2 plots where the percentage cover of each woody species was sampled. We calculated (i) structural parameters of the community such as total cover, alpha species richness, evenness and diversity (Shannon diversity index), and (ii) vegetation cover of each functional group (differentiating life forms, eco-physiological traits and regenerative traits). Focusing on community structure, results showed increases in species richness and diversity as wildfire recurrence increased, but this effect was partially counterweighted in the areas affected by high severity. In relation to functional groups, we found that increases in recurrence and severity fostered transition from tree- to shrub-dominated ecosystems. Non-arboreal life form, high specific leaf area, N2-fixing capacity, resprouting ability and heat-stimulated germination were advantageous traits under high recurrences and severities, and low seed mass was advantageous under high recurrence situations. We suggest that the strength of the effects of wildfire recurrence and burn severity on vegetation structure and traits might vary with climate, increasing from Oceanic to Mediterranean conditions. In the Mediterranean site, recurrence and severity were strongly related to traits associated with germination (seed mass and heat-stimulated germination), whereas in the Oceanic site the strongest relationships were found with a resprouting-related trait (bud location). This study identifies changes in vegetation structure and composition in scenarios of high recurrence and severity, and provides useful information on plant traits that could be key in enhancing vegetation resilience.

Linking species functional traits of terrestrial vertebrates and environmental filters: A case study in temperate mountain systems.

Knowledge on the relationships between species functional traits and environmental filters is key to understanding the mechanisms underlying the current patterns of biodiversity loss from a multi-taxa perspective. The aim of this study was to identify the main environmental factors driving the functional structure of a terrestrial vertebrate community (mammals, breeding birds, reptiles and amphibians) in a temperate mountain system (the Cantabrian Mountains; NW Spain). Based on the Spanish Inventory of Terrestrial Vertebrate Species, we selected three functional traits (feeding guild, habitat use type and daily activity) and defined, for each trait, a set of functional groups considering vertebrate species with common functional characteristics. The community functional structure was evaluated by means of two functional indexes indicative of functional redundancy (species richness within each functional group) and functional diversity. Ordinary least squares regression and conditional autoregressive models were applied to determine the response of community functional structure to environmental filters (climate, topography, land cover, physiological state of vegetation, landscape heterogeneity and human influence). The results revealed that both functional redundancy and diversity of terrestrial vertebrates were non-randomly distributed across space; rather, they were driven by environmental filters. Climate, topography and human influence were the best predictors of community functional structure. The influence of land cover, physiological state of vegetation and landscape heterogeneity varied among functional groups. The results of this study are useful to identify the general assembly rules of species functional traits and to illustrate the importance of environmental filters in determining functional structure of terrestrial vertebrate communities in mountain systems.