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We propose a novel mono-temporal framework with a physical basis and ecological consistency to retrieve fire severity at very high spatial resolution. First, we sampled the Composite Burn Index (CBI) in 108 field plots that were subsequently surveyed through unmanned aerial vehicle (UAV) flights. Then, we mimicked the field methodology for CBI assessment in the remote sensing framework. CBI strata were identified through individual tree segmentation and geographic object-based image analysis (GEOBIA). In each stratum, wildfire ecological effects were estimated through the following methods: (i) the vertical structural complexity of vegetation legacies was computed from 3D-point clouds, as a proxy for biomass consumption; and (ii) the vegetation biophysical variables were retrieved from multispectral data by the inversion of the PROSAIL radiative transfer model, with a direct physical link with the vegetation legacies remaining after canopy scorch and torch. The CBI scores predicted from UAV ecologically related metrics at the strata level featured high fit with respect to the field-measured CBI scores (R2 > 0.81 and RMSE < 0.26). Conversely, the conventional retrieval of fire effects using a battery of UAV structural and spectral predictors (point height distribution metrics and spectral indices) computed at the plot level provided a much worse performance (R2 = 0.677 and RMSE = 0.349). [ABSTRACT FROM AUTHOR]

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Understanding the influence of intrinsic (genetic and morphological) and extrinsic (geographical, environmental and social) factors on the performance and spatial differentiation of sexual signals, such as bird song, can help identify behavioural indicators of individual quality, habitat degradation and social environment. We used the Iberian Bluethroat Luscinia svecica azuricollis, a migratory bird that breeds in fragmented landscapes dominated by shrublands, as a case study to: (1) assess how a set of acoustic indicators of song performance are driven by intrinsic and extrinsic factors; and (2) contrast deterministic (adaptations to the environmental context and morphological constraints) vs. stochastic (differentiation by geographical isolation) explanations for song differentiation patterns. We explored acoustic indicators of song performance (spectral, temporal and song complexity) in relation to parameters related to genetic structure, body size, habitat type, habitat quality (assessed through a spatially explicit modelling approach) and social context (population abundance and songbird community composition). Then, we explored the contribution of genetic, geographical and environmental dissimilarity to song diversification across space. Our results highlight an association of song spectral variables with genetic structure and a significant connection between song complexity and duration with habitat quality. We found no relationship between social features and acoustic variables, or between song differentiation and genetic or geographical distances. There was, however, a correlation between song differentiation and environmental dissimilarity. We recommend the consideration of song complexity as an indicator of habitat quality. [ABSTRACT FROM AUTHOR]

Copyright of Ciencies: Cartafueyos Asturianos de Ciencia y Teunoloxía is the property of Universidad de Oviedo, Servicio de Publicaciones and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The structural complexity of plant communities contributes to maintaining the ecosystem functioning in fire‐prone landscapes and plays a crucial role in driving ecological resilience to fire. The objective of this study was to evaluate the resilience to fire off several plant communities with reference to the temporal evolution of their vertical structural diversity (VSD) estimated from the data fusion of C‐band synthetic aperture radar (SAR) backscatter (Sentinel‐1) and multispectral remote sensing reflectance (Sentinel‐2) in a burned landscape of the western Mediterranean Basin. We estimated VSD in the field 1 and 2 years after fire using Shannon's index as a measure of vertical heterogeneity in vegetation structure from the vegetation cover in several strata, both in burned and unburned control plots. Random forest (RF) was used to model VSD in the control (analogous to prefire scenario) and burned plots (1 year after fire) using as predictors (i) Sentinel‐1 VV and VH backscatter coefficients and (ii) surface reflectance of Sentinel‐2 bands. The transferability of the RF model from 1 to 2 years after wildfire was also evaluated. We generated VSD prediction maps across the study site for the prefire scenario and 1 to 4 years postfire. RF models accurately explained VSD in unburned control plots (R2 = 87.68; RMSE = 0.16) and burned plots 1 year after fire (R2 = 80.48; RMSE = 0.13). RF model transferability only involved a reduction in the VSD predictive capacity from 0.13 to 0.20 in terms of RMSE. The VSD of each plant community 4 years after the fire disturbance was significantly lower than in the prefire scenario. Plant communities dominated by resprouter species featured significantly higher VSD recovery values than communities dominated by facultative or obligate seeders. Our results support the applicability of SAR and multispectral data fusion for monitoring VSD as a generalizable resilience indicator in fire‐prone landscapes. [ABSTRACT FROM AUTHOR]

We aimed to compare the potential of physical-based models (radiative transfer and pixel unmixing models) for evaluating the short-term resilience to fire of several shrubland communities as a function of their regenerative strategy and burn severity. The study site was located within the perimeter of a wildfire that occurred in summer 2017 in the northwestern Iberian Peninsula. A pre- and post-fire time series of Sentinel-2 satellite imagery was acquired to estimate fractional vegetation cover (FVC) from the (i) PROSAIL-D radiative transfer model inversion using the random forest algorithm, and (ii) multiple endmember spectral mixture analysis (MESMA). The FVC retrieval was validated throughout the time series by means of field data stratified by plant community type (i.e., regenerative strategy). The inversion of PROSAIL-D featured the highest overall fit for the entire time series (R2 > 0.75), followed by MESMA (R2 > 0.64). We estimated the resilience of shrubland communities in terms of FVC recovery using an impact-normalized resilience index and a linear model. High burn severity negatively influenced the short-term resilience of shrublands dominated by facultative seeder species. In contrast, shrublands dominated by resprouters reached pre-fire FVC values regardless of burn severity. [ABSTRACT FROM AUTHOR]

Background The characterization of surface and canopy fuel loadings in fire-prone pine ecosystems is critical for understanding fire behavior and anticipating the most harmful ecological effects of fire. Nevertheless, the joint consideration of both overstory and understory strata in burn severity assessments is often dismissed. The aim of this work was to assess the role of total, overstory and understory pre-fire aboveground biomass (AGB), estimated by means of airborne Light Detection and Ranging (LiDAR) and Landsat data, as drivers of burn severity in a megafire occurred in a pine ecosystem dominated by Pinus pinaster Ait. in the western Mediterranean Basin. Results Total and overstory AGB were more accurately estimated (R² equal to 0.72 and 0.68, respectively) from LiDAR and spectral data than understory AGB (R² ​= ​0.26). Density and height percentile LiDAR metrics for several strata were found to be important predictors of AGB. Burn severity responded markedly and non-linearly to total (R2 ​= ​0.60) and overstory (R² ​= ​0.53) AGB, whereas the relationship with understory AGB was weaker (R² ​= ​0.21). Nevertheless, the overstory plus understory AGB contribution led to the highest ability to predict burn severity (RMSE ​= ​122.46 in dNBR scale), instead of the joint consideration as total AGB (RMSE ​= ​158.41). Conclusions This study novelty evaluated the potential of pre-fire AGB, as a vegetation biophysical property derived from LiDAR, spectral and field plot inventory data, for predicting burn severity, separating the contribution of the fuel loads in the understory and overstory strata in Pinus pinaster stands. The evidenced relationships between burn severity and pre-fire AGB distribution in Pinus pinaster stands would allow the implementation of threshold criteria to support decision making in fuel treatments designed to minimize crown fire hazard. [ABSTRACT FROM AUTHOR]

Aim: In temperate mountain ranges, sharp spatial variations in habitat heterogeneity and climate provide a perfect study setup to assess genetic and phenotypic differentiation in bird populations. In this paper, we analysed morphological divergence patterns across geographical and environmental gradients, in correlation with genetic differentiation and geographical isolation, in the breeding grounds of a long‐distance migratory passerine. Location: Northwestern Iberian mountains. Taxon: The Iberian bluethroat, Luscina svecica azuricollis. Methods: We collected a sample of 625 Iberian bluethroats across their whole breeding range to measure body weight and tarsus length, as well as wing length and pointedness. Morphological differentiation across geographical (latitude and elevation) and environmental (climate and vegetation) gradients was assessed using generalized linear mixed models. Additionally, the role of genetic distance and geographical isolation as drivers of morphological differentiation was evaluated with Mantel tests. Results: Bird morphology varied significantly with latitude and elevation, but not with climate or vegetation. In the case of latitude, morphological differences in body size were contrary to Bergmann's rule statements. All biometric and morphometric variables diverged among localities. A similar trend was found for genetic clusters, except for wing shape. Body weight and wing length were both correlated with geographic distance, while only the former varied with genetic differentiation. The greatest genetic and phenotypic differentiation was detected in the southernmost mountain range, that holds the most geographically isolated genetic group. Main conclusion: Evidence suggests that the strong morphological differentiation observed in the Iberian breeding bluethroat across geographical gradients results from the combination of unambiguous deterministic forces, such as ecological selection mechanisms, and other factors, such as geographical isolation, that can be either deterministic, stochastic or both, all acting at different scales. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the transferability between fire recurrence scenarios of post‐fire vegetation cover models calibrated with satellite imagery data at different spatial resolutions within two Mediterranean pine forest sites affected by large wildfires in 2012. Location: The northwest and east of the Iberian Peninsula. Methods: In each study site, we defined three fire recurrence scenarios for a reference period of 35 years. We used image texture derived from the surface reflectance channels of WorldView‐2 and Sentinel‐2 (at a spatial resolution of 2 m × 2 m and 20 m × 20 m, respectively) as predictors of post‐fire vegetation cover in Random Forest regression analysies. Percentage vegetation cover was sampled in two sets of field plots with a size roughly equivalent to the spatial resolution of the imagery. The plots were distributed following a stratified design according to fire recurrence scenarios. Model transferability was assessed within each study site by applying the vegetation cover model developed for a given fire recurrence scenario to predict vegetation cover in other scenarios, iteratively. Results: For both wildfires, the highest model transferability between fire recurrence scenarios was achieved for those holding the most similar vegetation community composition regarding the balance of species abundance according to their plant‐regenerative traits (root mean square error [RMSE] around or lower than 15%). Model transferability performance was highly improved by fine‐grained remote‐sensing data. Conclusions: Fire recurrence is a major driver of community structure and composition so the framework proposed in this study would allow land managers to reduce efforts in the context of post‐fire decision‐making to assess vegetation recovery within large burned landscapes with fire regime variability. [ABSTRACT FROM AUTHOR]

Fire activity has significant implications for ecological communities, biogeochemical cycles, climate, and human lives and assets. Approximately over half of the Earth's land surface is susceptible to fire, with around 3% experiencing annual burning according to coarse-resolution satellites [[1]], a value that is probably much higher according to recent estimates from finer satellite imagery [[2]]. [Extracted from the article]

Modelling the spatial distribution of multi-habitat species is challenging since they show multi-dimensional environmental responses that may vary sharply through habitats. Hence, for these species, the achievement of realistic models useful in conservation planning may depend on the appropriate consideration of habitat information in model calibration. We aimed to evaluate the role of different types of habitat predictors, along with habitat-partitioning, to improve model inference, detect non-stationary responses across habitats and simulate the impact of sampling bias on spatial predictions. As a case study, we modelled the occurrence of the multi-habitat plant species bilberry (Vaccinium myrtillus) in the Cantabrian Mountains (NW Spain), where it represents a basic trophic resource for threatened brown bear and capercaillie. We used MaxEnt to compare a baseline model approach calibrated with topo-climatic variables against three alternative approaches using explicit habitat information based on vegetation maps and remote sensing data. For each approach, we ran non-partitioned (all habitats together) and habitat-partitioned models (one per habitat) and evaluated model performance, overfitting and extrapolation. The highest performance was for habitat-partitioned models including habitat predictors. The lowest overfitting was for the baseline non-partitioned model, at the cost of achieving the highest predicted fractional area. The extrapolation success of habitat-partitioned models was low, with the highest performance for the baseline approach. Our results highlight that multi-habitat species responses are non-stationary across habitats, with habitat-biased data resulting in weak spatial predictions. When modelling the distribution of multi-habitat species at regional scale, we recommend using habitat-partitioned models including habitat predictors, either vegetation maps or remote sensing data, to improve the realism of spatial outputs and its applicability in regional conservation planning. [ABSTRACT FROM AUTHOR]

This study analyses the suitability of remote sensing data from different sources (Landsat 7 ETM+, MODIS and Meteosat) in evaluating the effect of fuel conditions on fire severity, using a megafire (11 891 ha) that occurred in a Mediterranean pine forest ecosystem (NW Spain) between 19 and 22 August 2012. Fire severity was measured via the delta Normalized Burn Ratio index. Fuel conditions were evaluated through biophysical variables of: (i) the Visible Atmospherically Resistant Index and mean actual evapotranspiration, as proxies of potential live fuel amount; and (ii) Land Surface Temperature and water deficit, as proxies of fuel moisture content. Relationships between fuel conditions and fire severity were evaluated using Random Forest models. Biophysical variables explained 40% of the variance. The Visible Atmospherically Resistant Index was the most important predictor, being positively associated with fire severity. Evapotranspiration also positively influenced severity, although its importance was conditioned by the data source. Live fuel amount, rather than fuel moisture content, primarily affected fire severity. Nevertheless, an increase in water deficit and land surface temperature was generally associated with greater fire severity. This study highlights that fuel conditions largely determine fire severity, providing useful information for defining pre-fire actions aimed at reducing fire effects. Potential live fuel amount had more influence on fire severity than fuel moisture content in pine forest ecosystems. The Visible Atmospherically Resistant Index, as a proxy of live fuel amount, showed the strongest association with fire severity. Remote sensing has high potential for determining fuel characteristics susceptible to influencing fire severity, although spatial resolution may constrain the utility of fire severity models. [ABSTRACT FROM AUTHOR]

We analyse the effects of burn severity on individual soil properties and soil quotients in Mediterranean fire-prone pine forests immediately after a wildfire. Burn severity was measured in the field through the substrate stratum of the Composite Burn Index and soil samples were taken 7–9 days after a wildfire occurred in a Pinus pinaster Ait. ecosystem. In each soil sample, we analysed physical (size of soil aggregates), chemical (pH, organic C, total N and available P) and biological (microbial biomass C, β-glucosidase, urease and acid phosphatase activities) properties. Size of aggregates decreased in the areas affected by high burn severity. Additionally, moderate and high severities were associated with increases in pH and available P concentration and with decreases in organic C concentration. Microbial biomass C showed similar patterns to organic C along the burn severity gradient. The enzymatic activities of phosphatase and β-glucosidase showed the highest sensitivity to burn severity, as they strongly decreased from the low-severity scenarios. Among the studied soil quotients, the C : N ratio, microbial quotient and β-glucosidase : microbial biomass C quotient decreased with burn severity. This work provides valuable information on the impact of burn severity on the functioning of sandy siliceous soils in fire-prone pine ecosystems. We analysed the impacts of burn severity on soil properties immediately after fire. Physical (mean weight diameter), chemical (pH, available P, organic C) and biological (microbial biomass C, β-glucosidase and acid phosphatase activities) soil properties, as well as soil quotients (C : N, microbial quotient, β-glucosidase : microbial biomass C) were significantly affected by burn severity. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Land abandonment and the loss of traditional farming practices are thought to control land cover dynamics, and hence the ecosystem service supply in traditionally managed mountain landscapes. We evaluate the impact of land cover changes in Cantabrian Mountains (NW Spain), over 1990-2012, on the potential supply capacity of ecosystem services (regulating, provisioning, and cultural) at both regional and local scales. We also analyze trends in the use of ecosystem services at the local scale. Land cover changes were estimated from CORINE Land Cover database. Patterns of potential ecosystem service supply were assessed by applying an ecosystem service supply capacity matrix and trends in their actual use by using field data. Main trajectories of land cover change encompassed woody vegetation spread in semi-natural open systems and agricultural expansion in the most suitable areas. The capacity of landscape to provide ecosystem services improved during 1990-2012 at both scales. We detected trade-offs between the potential supply of ecosystem services associated to natural systems and those linked to traditional land uses, at both regional and local scales. Changes in the potential supply of ecosystem services matched trends in ecosystem service use. This study could help develop future scenarios to address upcoming challenges in ecosystem service supply. [ABSTRACT FROM AUTHOR]

Abstract: Aim: We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities. Location: Europe. Methods: We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models (CDMs) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling (GDM) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions. Results: For the two community types, CDMs computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability. Main conclusions: Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDMs with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales. [ABSTRACT FROM AUTHOR]

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 theWorldView-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. [ABSTRACT FROM AUTHOR]

Aim Species distribution models based on breeding occurrence data allow for identifying both environmental drivers and geographic areas potentially relevant for breeding. However, the interpretation of model predictions in terms of reproductive performance should be further investigated, as this information is crucial for conservation planning. We evaluated the strengths and weaknesses of a correlative modelling approach based on breeding occurrence data (presence-absence) against another approach based on vital rates' data (breeding success) for gaining insights on species persistence in the case of Great Bustards ( Otis tarda). Location Spain. Methods Breeding occurrence and breeding success were independently modelled using generalized linear models and multimodel inference analyses. Sensitivities to the way in which the population parameter (breeding success) was defined were explored by building five versions of the dependent variable. We evaluated differences in model performance and identified areas of congruence for breeding occurrence and breeding success. Results The agreement between the spatial predictions achieved by breeding occurrence and breeding success models differed substantially across databases, with the largest differences in occupied breeding areas. The deviance explained by the breeding occurrence model was 64.98% and ranged from 7.83% to 62.27% for the breeding success models. Model performance was higher for models calibrated within potential than within occupied breeding areas. Main conclusions The combination of data on both breeding occurrence and breeding success into a species distribution modelling framework showed the limitations of breeding occurrence models for inferring reproductive parameters. The definition of the population parameter as dependent variable was a key factor that strongly affected the inference of vital rates' models. The approach allowed for discriminating between areas and landscape attributes necessary for the long-term species persistence from others that may be relevant, but not so much for reproductive performance. [ABSTRACT FROM AUTHOR]

The assessment of evolutionary divergences within subspecies complexes provide an effective short-cut for estimating intraspecific genetic diversity, which is relevant for conservation actions. We explore new evidence supplementing the existing knowledge about the singularity of Iberian bluethroats within the Luscinia svecica subspecies mosaic. We compared biometric traits of Iberian males (L. s. azuricollis) to the closest subspecies (L. s. cyanecula, L. s. namnetum and L. s. magna) using general linear models and analysed the correlations between biometric and genetic differentiation (based on nuclear microsatellites) among the target subspecieswith aMantel test. Biometric differenceswere calculated using 63 museum skins and 63 live specimens. Genetic distances were estimated in a sample of 136 individuals. An additional characterisation of the plumage of Iberian males was shaped from 22 live specimens. We highlight the distinctiveness of Iberian birds within the subspecies mosaic since L. s. azuricollis had longer wings than L. s. cyanecula and L. s. namnetum, but shorterwings than L. s. magna. Indeed, L. s. azuricollis had longer tarsus and bill than L. s. namnetum, but shorter bill than L. s. magna. Biometric divergence was not significantly associated with genetic distance. Iberian males showed an all-blue plastron in 77% of specimens, a mostly non-marked black band and no white band, which distinguished them from males of L. s. cyanecula and L. s. namnetum. We conclude the importance of considering phenotypic and genotypic differences at subspecies level, which is essential for designing realistic conservation strategies addressed to preserve species genetic diversity patterns. [ABSTRACT FROM AUTHOR]

Aim Species distribution modelling ( SDM) allows identification of suitable areas where the species might occur. The Grinnellian approach relates species occurrence to abiotic limiting factors, such as topography and climate, measured at present or averaged over time at large spatial scales, whilst Eltonian approaches relate processes as reproductive rates or species abundance to factors as resource availability or functional traits described at a finer spatio-temporal resolution. In this study, we aimed to compare the performance and reliability achieved by Eltonian and Grinnellian models developed at geographical scale when addressing two questions that may affect species distribution: breeding site selection and historical local extinctions. Location Spain. Methods We used a temporal series (1988-2010) of breeding success data of Great Bustards ( Otis tarda) across Spain to create: (1) a yearly series of correlative Eltonian models based on satellite data (1-km pixel Normalized Difference Vegetation Index derived from NOAA- AVHRR) as a proxy for resource availability and (2) a static topo-climatic Grinnellian model. We applied two modelling algorithms (MaxEnt and Bootstrapped Generalized Mixed-Effects Models) calibrated and validated across different distributional ranges to investigate breeding success in abiotically suitable areas and recent extinctions within the accessible range. Results Eltonian models showed higher performance and reliability than Grinnellian models at geographical scale and allowed for comparisons of breeding habitat suitability through the study period. Then, satellite data were a more successful predictor of the species breeding success than topography and climate. Breeding was possible out of the range currently occupied by the species, but habitat suitability was significantly lower in non-occupied areas, on where local extinctions have occurred in recent historical times. Main conclusions This study highlights the advisability of using remote-sensing explanatory variables at high temporal resolution matched with population fitness data in Eltonian approaches to improve the knowledge of the relationship between realized and potential distributions of the species at geographical scale. This is a fundamental question in biogeography and a key issue for designing comprehensive conservation strategies. [ABSTRACT FROM AUTHOR]

1. In Mediterranean mountainous areas, forests have expanded in recent decades because traditional management practices have been abandoned or reduced. However, understanding the ecological mechanisms behind landscape change is a complex undertaking because the influence of land use may be reinforced or constrained by abiotic factors such as climate. In this work, we evaluated their combined effects on recent forest expansion across climatic, topographic and management gradients. 2. We used orthorectified aerial photographs from the second half of the twentieth century (1956, 1974, 1983, 1990 and 2004) to monitor changes in forest distribution in a set of 20 head-water basins in the Cantabrian Mountains of north-west Spain, at the Eurosiberian-Mediterranean limit. In particular, we evaluated the role of land-use history (comparing natural vs. anthropic basins) and microclimate (comparing shaded vs. sunny aspects) of forest gain/loss rates and spatial distribution shifts. Finally, we applied Species Distribution Modelling techniques (MaxEnt and BIOMOD) in the stated scenarios of land-use history and microclimate, to assess habitat suitability for forest expansion on the basis of topography, soil properties and mesoclimatic variables. 3. Forest cover increased from 10.72% in 1956 to 27.67% in 2004 in the area. The rate of expansion was significantly higher in natural basins and, particularly, on shaded slopes. In all cases, the mean elevation of new forest patches increased during the study period, which was particularly evident on natural sunny slopes. The performance of the models and the magnitude of the effects varied across land-use histories and microclimatic conditions. Soil properties and temperature and precipitation in late spring and early summer were the main drivers of forest expansion in modelling exercises, although expansion rates and upward altitudinal shifts were primarily controlled by landuse history and the biogeographic origin of the forests. 4. Synthesis. The combination of monitoring and modelling techniques used in this work contributed to the understanding of forest expansion in cultural systems, indicating that ecological succession is not a homogeneous process, but varies spatially due to human and abiotic constraints since historical times. [ABSTRACT FROM AUTHOR]

Scaling is a key process in modelling approaches since it allows for translating information from one scale to another. However, the success of this procedure may depend on 'source' and 'target' scales, but also on the biogeographic/ecological context of the study area. We aimed to quantify the performance and success of scaling species distribution model (SDM) predictions across spatial resolution and extent along a biogeographic gradient using the Iberian mole as study case. We ran separate MaxEnt models at two extents (national and regional) using independent datasets (species locations and environmental predictors) collected at 10 km and 50 m resolutions respectively. Model performance and success of scaling SDMs were quantified on the basis of accuracy measures and spatial predictions. Complementarily, we calculated marginality and tolerance as indicators of habitat availability and niche truncation along the biogeographic gradient. Model performance increased with resolution and extent, as well as from north to south (mainly for high resolution models). When regional models were validated at different scales, their performance reduced severely, particularly in the case of coarse resolution models (some of them performed worse than random). However, when the 10 km-national model was downscaled within regions, it performed better (AUCtest: 0.82, 0.85 and 0.55 respectively for Galicia, Madrid and Granada) than models specifically calibrated within each region at 10 km (0.47, 0.65, 0.44). Indeed, it also had a better accuracy when projected at 50 m (0.77, 0.91, 0.79) than models fitted at that resolution (0.62, 0.83, 0.96) in two of the three cases. The success of scaling model predictions decreased along the biogeographic gradient, being these differences associated to niche truncation. Models representing non-truncated niches were more successfully scaled across resolutions and extents (particularly in areas not offering all possible habitats for species), which has important implications for SDM applications. [ABSTRACT FROM AUTHOR]

Heathlands are considered biodiversity hotspots of high conservation interest. However, they are at risk of degradation and disappearance in most parts of Europe mainly due to land abandonment, degradation, and conversion to other land uses. Heathlands are semi-natural systems: their maintenance and survival depends on specific practices such as extensive grazing or burning. Traditionally they provide a wide range of goods and services to societies. In this study we used the ecosystem services (ES) framework to analyze the changes in the demand for and delivery of ES for the heathland landscapes of the Cantabrian Mountains (NW Spain), since the 1950s. Particularly, we analyzed how the social changes since the 1950s have determined changes in stakeholders' demand for provisioning, cultural and regulating services and how these changes have influenced the vegetation dynamics and conservation status of these systems. We identified a general shift from the provisioning of grazing facilities and local products for the local-regional market to the provisioning of conservation services to satisfy national-international demand. For the present situation we found a clear mismatch between the conservation demand, management practices, and land-use forms. This mismatch threatens to lead to further landscape changes and loss of biodiversity. The results of our multi-scale and -services study can help to increase awareness of the value of currently obtainable benefits from heathlands among stakeholders and managers. The ES approach can improve understanding of the functioning of the socio-ecological heathland system, and inform the development of new management strategies for heathland protection. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

We investigate how fire regime may influence both temporal patterns and drivers of vegetation regeneration in a Natural Park (NW Spain) affected by a long history of fire events and human activities. To address this issue, we evaluate the suitability of five spectral indices derived from Landsat imagery (for the period 1992-2005) for estimating biophysical properties of vegetation and monitoring post-fire recovery. Complementarily, we assess the role of the observation level (pixel and patch) on the results, identifying which of them is more informative for land management. Tasselled Cap Wetness was the best-performing index and total cover was the vegetation property more closely related to spectral data. Most post-fire recovery occurred within the 2 years following the fire event. Fire recurrence did not influence patch extent or shape nor did it affect ecosystem resilience. However, patch extent and shape affected resilience. The relevance of the environmental drivers behind vegetation recovery was not related to fire recurrence and changed over time. Prior vegetation status and rainfall were the most important drivers, while topography and vegetation type had a more secondary role. Our results advocate the consideration of patches as the most appropriate organisational unit when monitoring vegetation recovery. Using a temporal series of Landsat imagery, we demonstrated that fire recurrence did not influence other components of fire regime such as patch extent or shape, or ecosystem resilience. The relevance of the different environmental drivers behind vegetation recovery processes was not related to fire recurrence and changed over time. The conclusions have implications in habitat management applications. [ABSTRACT FROM AUTHOR]

Aim We aim to map the distribution of four heath and shrub formations constituting habitats of high conservation priority in Europe, whose occurrence is strongly dependent on human activities. Specifically, we assess whether the use of LANDSAT data in habitat distribution modelling may account for land use management, allowing accurate mapping of real distribution patterns. In particular, we explore whether reflectance values may be a better alternative to other remote sensing data traditionally used in modelling approaches (i.e. spectral vegetation indices and classified land cover maps). Finally, we test whether modelling performance is affected by the ecological traits of the dominant species of the target formations. Location Cantabrian Mountains (NW Spain). Methods We generated maps for the four formations (two specialists vs. two generalists) using MaxEnt. First, we ran the models with environmental predictors only (topography, climate, lithology and human disturbances). Then, we compared the advantages of including, in turn, different data derived from LANDSAT imagery: reflectance values (corresponding to different wavelength channels of the multispectral image), a spectral index and a land cover map. We assessed changes in explanatory power and also in the formation's predicted distribution patterns. Results Formations dominated by specialist species were accurately mapped on a base of environmental variables only, whereas those dominated by generalists were overpredicted. Average mean temperature, southness and distance to urban areas were the variables contributing most in predictions of environmental models. LANDSAT channels increased the accuracy of all models, but mainly those for formations dominated by generalist species. They showed advantages against other remote sensing data traditionally used in modelling approaches. Main conclusions Habitat distribution models allowed accurate mapping of heath and shrub formations. The use of reflectance values as predictors improved the accuracy of the models, particularly for formations dominated by generalist species, supplying environmental information that was otherwise unavailable. [ABSTRACT FROM AUTHOR]

The effects of habitat loss and fragmentation on the genetic structure and variability of wild populations have received wide empirical support and theoretical formalization. By contrast, the effects of habitat quality seem largely underinvestigated, partly due to technical difficulties in properly assessing habitat quality. In this study, we combine geographic information system (GIS)-based habitat-quality modelling with a landscape genetics approach based on mitochondrial DNA markers to evaluate the possible influence of habitat quality on the levels and distribution of genetic diversity in a range of natural populations ( n = 15) of Otis tarda throughout Spain. Ninety-three percent of the population represented by our countrywide sample lives in good-quality habitats, while 4.5% and 2.5% occur respectively in intermediate and poor habitats. Habitat quality was highly correlated with patch size, population size and population density, indicating the reliability and predictive power of the habitat suitability model. Genetic diversity was significantly correlated with habitat quality, size and density of the population, but not with patch size. Three of a total of 20 existing matrilineages from the species' current genetic pool are restricted to poor-quality habitats. This study therefore highlights the importance of considering both population genetics and habitat quality in a species of high conservation priority. [ABSTRACT FROM AUTHOR]

In this study we developed a methodology aimed at improving the assessment of inter-annual land cover dynamics from hard classified remotely sensed data in heterogeneous and resilient landscapes. The methodology is implemented for the Spanish Natural Park of Sierra de Ancares, where human interference during the last century has resulted in the destruction and fragmentation of the original land cover. We ran supervised classifications, with a maximum likelihood algorithm (Maxlike), on a temporal series of Landsat images (1991–2005), followed by an uncertainty assessment using fuzzy classifications and confusion indices (CIs). This allowed us to show how much (and where) of the resulting maps contained a substantial amount of error, distinguishing data that might be useful to measure land change from data that are not particularly useful when applying a post-classification comparison methodology. In this way, we can detect true changes not skewed by the effects of uncertainty. Even if patterns of change were always coherent amongst years, they were more realistic after reducing uncertainty, in spite of a substantial decrease in the number of available pixels (i.e. unmasked by the method). We then computed land cover dynamics by means of a model specifically designed to determine the frequency of disturbances (mainly fire events) and the vegetation recovery time during the study period. Model outputs showed correlated landscape patterns at a broad scale and provided useful results to explore land cover change from pattern to process. [ABSTRACT FROM AUTHOR]

The article discusses a study on the use of DNA analyses as an alternative and more accurate technique to classify the sex of Iberian Water Rails. It also explores the possibility of using simple external morphometric cues for sex determination of an Iberian-breeding Water Rail population. The site consisted of four wet areas located in the León Province, Spain, Villadangos del Páramo and San Andrés. It advises against commonly used measurements in classifying the sex of Iberian Water Rails unless validated by other criteria.

Knowledge on environmental variability and how it is affected by disturbances is crucial for understanding patterns of biodiversity and determining adequate conservation strategies. The aim of this study is to assess environmental variability in patches undergoing post-fire vegetation recovery, identifying trends of change and their relevant drivers. We particularly evaluate: the value of three spectral indices derived from Landsat satellite data [Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI) and Wetness Component of the Tasseled Cap Transformation (TCW)] for describing secondary succession; the effectiveness of three metrics (diversity, evenness and richness) as indicators of patch variability; and how thematic resolution can affect the perception of environmental variability patterns. While the system was previously characterised as highly resilient from estimations of vegetation cover, here we noted that more time is required to fully recover pre-fire environmental variability. Using mean diversity as indicator of patch variability, we found similar patterns of temporal change for the three spectral indices (NBR, NDVI and TCW). Analogous conclusions could be drawn for richness and evenness. Patch variability, measured as diversity, showed consistent patterns across thematic resolutions, although values increased with the number of spectral classes. However, when the variance of diversity was plotted against thematic resolution, different scale dependencies were detected for those three spectral indices, yielding a dissimilar perception of patch variability. In general terms, NDVI was the best performing spectral index to assess patterns of vegetation recovery, while TCW was the worst. Finally, burned patches were classified into three classes with similar trends of change in environmental variability, which were strongly related to fire severity, elevation and vegetation type. [ABSTRACT FROM AUTHOR]

Despite the general success of species envelope models, capturing the fine-scale detail of patchiness in the distributions of some species is problematic. For great bustards in Spain, apparently suitable habitat patches remain unoccupied and cannot be distinguished from occupied patches in current distribution models. We consider philopatry and conspecific attraction as main behavioural mechanisms which could account for this patchiness, and then look for evidence of their influence on the distribution of great bustards across the whole of Spain. We compared the characteristics of habitat patches classed as suitable by a distribution model according to whether they were actually used or not. Occupied patches were larger than unoccupied patches and over-used in proportion to their size, suggesting aggregation and a metapopulation structure. Arguing that conspecific attraction may serve to transfer information about site history and environmental predictability (at least over a short time period), we compared the coefficients of variation in time-series of vegetation and climatic factors at occupied and unoccupied sites. Great bustards chose sites which were more environmentally stable at critical periods in the breeding cycle, “public information” that can only be gained from others rather than through sampling. There is thus evidence that both metapopulation dynamics and conspecific attraction influence the large scale distribution of great bustards in Spain. We discuss how alternative predictor variables and multi-stage analyses may help us to incorporate behavioural mechanisms into distribution models, but acknowledge that there are limits to the value of species envelope models for animal species making decisions. [ABSTRACT FROM AUTHOR]

Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non-stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non-stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non-stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better-fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss. [ABSTRACT FROM AUTHOR]

Global climate change generated by human activities is likely to affect agroecosystems in several ways: reinforcing intensification in northern and western Europe, and extensification in the Mediterranean countries. If we are to predict the consequences of global warming for wildlife, distribution models have to include climate data. The METEOSAT temporal series from EWBMS offers an attractive alternative to using climatic surfaces derived from ground stations. The aim of this paper is to test whether this climatic satellite data can improve the distribution models obtained previously by Suárez-Seoane et al. using habitat variables for three agro-steppe bird species: great bustard, little bustard and calandra lark in Spain. Rainfall, radiation balance, evapotranspiration and soil moisture images were incorporated together with the other variables used as predictors in the published stepwise GAM models. Changes in the predicted distributions from the habitat only and climate-habitats models were assessed by reference to the CORINE land cover categories. Inclusion of climatic variables from METEOSAT led to statistically superior models for all three species. There were large differences in the climatic variables selected and the original variables dropped among the species. Evapotranspiration variables were the most frequently selected. Maps of the differences between the habitat and climate-habitat models showed very different patterns for the three species. Inclusion of climate variables led to a wider range of land cover types being deemed suitable. Despite the statistical superiority of models, care is needed in deciding whether to use climatic variables because they may emphasize the fundamental rather than the realized niche. Used together, however, habitat and climate models can provide new insights into factors limiting species distributions and how they may respond to climate change. [ABSTRACT FROM AUTHOR]

This paper analyses how landscape pattern detection changes when different spatial and temporal scales and several levels of detail of the cartography are used to describe a landscape affected by land abandonment in northern Spain. In order to integrate landscape composition and structure at different temporal and spatial scales in the same framework, a multiple correspondence factorial analysis was ran for each typology of landscape units. Annual rates of change and scale dependencies were calculated for each typology from the Euclidean distances in the factorial space. Finally, the potential assessment of habitat utilisation by species with different landscape perception and movement capacity was modelled for the range of typologies. >The amount of variance explained by the factorial analysis decreased with the complexity of the typology. Annual rates of change appeared different according to the time span and the detail of the landscape unit typology used. For all typologies, changes were faster during 1983–95, a period characterised by massive land abandonment. However, when the whole period (1956–95) was considered, annual changes were much lower, showing differences between typologies. As a general trend, the variance of the mean annual change decreased with the size of the analysis units. In response to land abandonment, different scale dependencies were found for different levels of detail of the cartography. Coarser typologies are suitable when analysing highly mobile species. However, species with small movement capacity or with a preference for homogeneous habitats perceive more detail in landscape. In this case, a detailed typology is more appropriate. [ABSTRACT FROM AUTHOR]

The degree to which burn severity influences the recovery of aboveground carbon density (ACD) of live pools in shrublands remains unclear. Multitemporal LiDAR data was used to evaluate ACD recovery three years after fire in shrubland ecosystems as a function of burn severity immediately after fire across an environmental and productivity gradient in the western Mediterranean Basin. Two large mixed-severity wildfires were assessed: an Atlantic site, dominated by resprouter shrubs and located at the most productive extreme of the gradient, and a Mediterranean site, dominated by obligate seeders and located at the less productive extreme. Initial assessment of burn severity was performed using the differenced Normalized Burn Ratio index computed from Landsat imagery. Thresholds for low and high burn severity categories were established using the Composite Burn Index (CBI). LiDAR canopy metrics were calibrated with field measurements of mean shrub height and cover at plot level in a post-fire situation. Pre-fire and post-fire ACD estimates, and their ratio (ACDr) to calculate carbon stock recovery, were computed from the predictions of LiDAR grid metrics at landscape level using shrubland allometric relationships. Overall, ACDr decreased both with high burn severity and low productivity, although the burn severity impact was not homogeneous within the gradient. In the Atlantic site, ACDr was similar under low and high burn severity, whereas it decreased with burn severity in the Mediterranean site. These results suggest that carbon cycling models could be biased by not accounting for both fire severity and species composition of shrublands under different environmental conditions. [ABSTRACT FROM AUTHOR]

Heterogeneous and patchy landscapes where vegetation and abiotic factors vary at small spatial scale (fine-grained landscapes) represent a challenge for habitat diversity mapping using remote sensing imagery. In this context, techniques of spectral mixture analysis may have an advantage over traditional methods of land cover classification because they allow to decompose the spectral signature of a mixed pixel into several endmembers and their respective abundances. In this work, we present the application of Multiple Endmember Spectral Mixture Analysis (MESMA) to quantify habitat diversity and assess the compositional turnover at different spatial scales in the fine-grained landscapes of the Cantabrian Mountains (northwestern Iberian Peninsula). A Landsat-8 OLI scene and high-resolution orthophotographs (25 cm) were used to build a region-specific spectral library of the main types of habitats in this region (arboreal vegetation; shrubby vegetation; herbaceous vegetation; rocks–soil and water bodies). We optimized the spectral library with the Iterative Endmember Selection (IES) method and we applied MESMA to unmix the Landsat scene into five fraction images representing the five defined habitats (root mean square error, RMSE ≤ 0.025 in 99.45% of the pixels). The fraction images were validated by linear regressions using 250 reference plots from the orthophotographs and then used to calculate habitat diversity at the pixel (α-diversity: 30 × 30 m), landscape (γ-diversity: 1 × 1 km) and regional (ε-diversity: 110 × 33 km) scales and the compositional turnover (β- and δ-diversity) according to Simpson's diversity index. Richness and evenness were also computed. Results showed that fraction images were highly related to reference data (R2 ≥ 0.73 and RMSE ≤ 0.18). In general, our findings indicated that habitat diversity was highly dependent on the spatial scale, with values for the Simpson index ranging from 0.20 ± 0.22 for α-diversity to 0.60 ± 0.09 for γ-diversity and 0.72 ± 0.11 for ε-diversity. Accordingly, we found β-diversity to be higher than δ-diversity. This work contributes to advance in the estimation of ecological diversity in complex landscapes, showing the potential of MESMA to quantify habitat diversity in a comprehensive way using Landsat imagery. [ABSTRACT FROM AUTHOR]

The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires. [ABSTRACT FROM AUTHOR]

In Mediterranean fire-prone ecosystems, plant functional traits and burn severity have decisive roles in post-fire vegetation recovery. These traits may reflect plant fitness to fire regimes in the Mediterranean Basin. The aim of this study was to evaluate the effect of burn severity on post-fire vegetation regeneration through plant functional (physiological and regenerative) traits in two Mediterranean ecosystems: one more humid and colder (Cabrera in León province, NW Spain), and another characterized by a longer summer drought (Gátova in Valencia province, SE Spain). A total of 384 and 80 field plots (2 m × 2 m) were fixed in Cabrera and Gátova, respectively. In each burned plot, we quantified burn severity by means of the composite burn index (CBI), differentiating three severity levels (low, moderate, and high), and evaluated post-fire vegetation regeneration one and two years after wildfires. We measured the percentage cover of each species and classified them according to physiological (specific leaf area and N2-fixing capacity) and regenerative traits (reproductive strategy, bud bank location, and heat-stimulated germination). The main results showed that in Cabrera, burn severity had significant effects on vegetation cover independently of plant functional traits. In Gátova, burn severity effects differed among functional traits. In this site, the cover of plants with low specific leaf area and without heat-stimulation and N2-fixing capacity was negatively related to burn severity. On the contrary, the cover of N2-fixers and species with resprouting ability and heat-stimulated germination rose with increasing burn severity. In general, vegetation cover showed a more pronounced increased over time in the more humid area, mainly under the effect of high severity. The results of this research highlighted the importance of the use of plant functional traits as a driver to understand the response of different ecosystems to current fire regimes, which could be relevant for pre- and post-fire management. [ABSTRACT FROM AUTHOR]