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.

Local genetic structure on breeding grounds of a long-distance migrant passerine: the bluethroat (Luscinia svecica) in Spain.

Breeding site fidelity can be determined by environmental features, which depending on their heterogeneous distribution may shape the genetic landscape of a population. We used 10 microsatellite loci to study the genetic variation of 83 bluethroats (Luscinia svecica azuricollis) across 14 localities within the Spanish breeding population and assess the relative influence of different habitat characteristics (physiography and vegetation) on genetic differentiation. Based on the genetic variation of this population, we identified 3 geographically consistent genetic clusters that on average showed a higher genetic differentiation than among other north European populations, even those belonging to different subspecies. The inferred genetic clusters occurred in geographic areas that significantly differed in elevation. The highest genetic differentiation was observed between sites at different mountain ranges, as well as between the highest altitude sites in the northeastern locale, whereas vegetation type did not explain a significant percentage of genetic variation. The lack of correlation between geographic and genetic distances suggests that this pattern of genetic structure cannot be explained as a consequence of isolation by distance. Finally, we discuss the importance of preserving areas encompassing high environmental and genetic variation as a means of preserving evolutionary processes and adaptive potential.

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.

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.

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.

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.