Comparison of physical-based models to measure forest resilience to fire as a function of burn severity

Producción CientíficaWe 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.Ministerio de Economía y Competitividad y Fondo Europeo de Desarrollo Regional (FEDER) - (project AGL2017-86075-C2-1-R)Junta de Castilla y León - (project LE005P20)British Ecological Society - (project SR22-100154

Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Producción CientíficaPrescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes.Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (project AGL2017-86075-C2-1-R)Junta de Castilla y León (project LE001P17

Evolutionary divergences in Luscinia svecica subspecies complex - New evidence supporting the uniqueness of the Iberian bluethroat breeding populations

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 subspecies with a Mantel test. Biometric differences were 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 shorter wings 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. This article was originally published in Ornis Fennica. © 2017 Finnish Ornithological Societ

Evaluation of Prescribed Fires from Unmanned Aerial Vehicles (UAVs) Imagery and Machine Learning Algorithms

Prescribed fires have been applied in many countries as a useful management tool to prevent large forest fires. Knowledge on burn severity is of great interest for predicting post-fire evolution in such burned areas and, therefore, for evaluating the efficacy of this type of action. In this research work, the severity of two prescribed fires that occurred in “La Sierra de Uría” (Asturias, Spain) in October 2017, was evaluated. An Unmanned Aerial Vehicle (UAV) with a Parrot SEQUOIA multispectral camera on board was used to obtain post-fire surface reflectance images on the green (550 nm), red (660 nm), red edge (735 nm), and near-infrared (790 nm) bands at high spatial resolution (GSD 20 cm). Additionally, 153 field plots were established to estimate soil and vegetation burn severity. Severity patterns were explored using Probabilistic Neural Networks algorithms (PNN) based on field data and UAV image-derived products. PNN classified 84.3% of vegetation and 77.8% of soil burn severity levels (overall accuracy) correctly. Future research needs to be carried out to validate the efficacy of this type of action in other ecosystems under different climatic conditions and fire regimes

Maximum entropy niche-based modelling of seasonal changes in little bustard (Tetrax tetrax) distribution

The effects of habitat fragmentation on species may change seasonally mainly due to variations in resource availability and biotic interactions. In critical periods, such as winter, when the importance of intraspecific competition diminish, species may relax their environmental requirements widening their ecological niche to exploit the scarcer trophic resources more efficiently in comparison with spring. Those variations in niche width may implicate seasonal expansions/retractions in species distribution. In this sense, an integrated knowledge on the spatial arrangement of breeding and wintering suitable patches is essential to infer seasonal movements (migratory connectivity). This paper shows that little bustard environmental preferences were more predictable and complex (controlled by a larger number of environmental factors) in spring than in winter, when potential distribution and ecological niche width were slightly larger. In spring, habitat variables (i.e. percentage of dry crops and pasturelands and altitude) ruled species’ distribution; while, winter pattern was driven by mixed criteria, based on both habitat and climate (i.e. percentage of dry crops and wastelands and winter rainfall). Suitable patches were more connected across spatial scales in winter than in spring, i.e. landscape was perceived as less fragmented. The overlap between potential breeding and wintering distribution areas was high. In fact, most of the predicted wintering areas coincided or showed high connectedness with predicted breeding patches. Conversely, there were significant breeding patches that were predicted with low suitability, showing little connectedness with potential winter areas. Spring habitat was a better predictor of little bustard’s wintering range than vice versa, which has clear management implications (preserving breeding sites closer to wintering areas ensures the conservation of a larger proportion of the total distribution range). This is an example of how predictive large-scale modeling procedures can contribute to the optimization of land management aimed at species conservation.<br/