Modelling diameter distributions in radiata pine plantations in Spain with existing countrywide LiDAR data

We evaluated the use of low-density airborne laser scanning data to estimate diameter distributions in radiata pine plantations. The moment-based parameter recovery method was used to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. The fitted models explained more than 77% of the observed variability. This approach can be replicated every 6 years (temporal cover planned for countrywide LiDAR flights in Spain). Context: The estimation of stand diameter distribution is informative for forest managers in terms of stand structure, forest growth model inputs, and economic timber value. In this sense, airborne LiDAR may represent an adequate source of information. Aims: The objective was to evaluate the use of low-density Spanish countrywide LiDAR data for estimating diameter distributions in Pinus radiata D. Don stands in NW Spain. Methods: The empirical distributions were obtained from 25 sample plots. We applied the moment-based parameter recovery method in combination with the Weibull function to estimate the diameter distributions, considering LiDAR metrics as explanatory variables. We evaluated the results by using the Kolmogorov–Smirnov (KS) test and a classification tree and random forest (RF) to relate the KS test result for each plot to stand-level variables. Results: The models used to estimate average (dm) and quadratic (dg) mean diameters from LiDAR metrics, required for recovery of the Weibull parameters, explained a high percentage of the observed variance (77 and 80%, respectively), with RMSE values of 3.626 and 3.422 cm for the same variables. However, the proportion of plots accepted by the KS was low. This poor performance may be due to the strictness of the KS test and/or by the characteristics of the LiDAR flight. Conclusion: The results justify the assessment of this approach over different species and forest types in regional or countrywide surveys.Spanish Ministry of Science and Innovation (AGL2008-02259/FOR); Galician Government, Xunta de Galicia, Dirección Xeral de Montes (09MRU022291PR); Norvento (Multinational energy company) (PGIDT09REM023E); Eduardo González-Ferreiro was financially supported by the Plan galego de investigación, innovación e crecemento 2011-2015 (Plan I2C) (Official Journal of Galicia – DOG nº 52, 17/03/2014 p. 11343, exp: POS-A/2013/049): Galician Government (Dirección Xeral de Ordenación e Calidade do Sistema Universitario de Galicia – Consellería de Educación e Ordenación Universitaria) and European Social Fund. Manuel Arias-Rodil was financially supported by an FPU grant (AP2012-05337) from the Spanish Ministry of Education.Spanish Ministry of Science and Innovation (AGL2008-02259/FOR); Eduardo González-Ferreiro was financially supported by the Plan galego de investigación, innovación e crecemento 2011-2015 (Plan I2C) (Official Journal of Galicia - DOG nº 52, 17/03/2014 p. 11343, exp: POS-A/2013/049) - Galician Government (Dirección Xeral de Ordenación e Calidade do Sistema Universitario de Galicia - Consellería de Educación e Ordenación Universitaria) and European Social Fund. Manuel Arias-Rodil was financially supported by an FPU grant (AP2012-05337) from the Spanish Ministry of Education.S

Bicarbonate-Triggered In Vitro Capacitation of Boar Spermatozoa Conveys an Increased Relative Abundance of the Canonical Transient Receptor Potential Cation (TRPC) Channels 3, 4, 6 and 7 and of CatSper-γ Subunit mRNA Transcripts

Sperm capacitation is a stepwise complex biochemical process towards fertilization. It includes a crucial early calcium (Ca2+) transport mediated by CatSper channels and Canonical Transient Potential Channels (TRPC). We studied the relative abundance of mRNA transcripts changes of the CatSper β, γ and δ subunits and TRPC-channels 1, 3, 4, 6 and 7 in pig spermatozoa, after triggering in vitro capacitation by bicarbonate ions at levels present in vivo at the fertilization site. For this purpose, we analyzedfive5 ejaculate pools (from three fertile adult boars) before (control-fresh samples) and after in vitro exposure to capacitation conditions (37 mM NaHCO3, 2.25 mM CaCl2, 2 mM caffeine, 0.5% bovine serum albumin and 310 mM lactose) at 38 °C, 5% CO2 for 30 min. In vitro capacitation using bicarbonate elicits an increase in the relative abundance of mRNA transcripts of almost all studied Ca2+ channels, except CatSper-δ and TRPC1 (significantly reduced). These findings open new avenues of research to identify the specific role of each channel in boar sperm capacitation and elucidate the physiological meaning of the changes on sperm mRNA cargo

Regional adaptation of Muller cells in the chick retina. A Golgi and electron microscopical study

We report the morphological differences of Müller cells in relation to their topography, using the Golgi method. Müller cells in the central retina are long and slender, with numerous inner prolongations. In the peripheral retina, the morphology of the Müller cells adapts to the reduced thickness of the retina1 layers. In this zone, they are short and have thick inner prolongations which end in a large foot in the internal limiting membrane. In the optic disc margin, Müller cells have a particular morphology characterized by thick, arched prolongations that in general form a glial network between the retina and optic nerve. The ultrastructure of these cells is also described. The results are discussed with respect to the nature of Müller cells

Stereoselective Barbier Type Allylations and Propargylations Mediated by CpTiCl3

CpTiCl2, prepared in situ by manganese reduction of CpTiCl3, is an excellent new system for the Barbier-type allylation and propargylation of carbonyl compounds. It can be used in catalytic amounts when combined with Et3N•HBr/TMSBr, which act as regenerating system. The high regio- and stereoselectivity shown by this system makes it useful for prenylation and crotylation processes in synthesis of natural products

Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data

[EN] The fuel complex variables canopy bulk density and canopy base height are often used to predict crown fire initiation and spread. Direct measurement of these variables is impractical, and they are usually estimated indirectly by modelling. Recent advances in predicting crown fire behaviour require accurate estimates of the complete vertical distribution of canopy fuels. The objectives of the present study were to model the vertical profile of available canopy fuel in pine stands by using data from the Spanish national forest inventory plus lowdensity airborne laser scanning (ALS) metrics. In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, two different systems of models were fitted to estimate the canopy variables defining the vertical distributions; the first system related these variables to stand variables obtained in a field inventory, and the second system related the canopy variables to airborne laser scanning metrics. The models of each system were fitted simultaneously to compensate the effects of the inherent cross-model correlation between the canopy variables. Heteroscedasticity was also analyzed, but no correction in the fitting process was necessary. The estimated canopy fuel load profiles from field variables explained 84% and 86% of the variation in canopy fuel load for maritime pine and radiata pine respectively; whereas the estimated canopy fuel load profiles from ALS metrics explained 52% and 49% of the variation for the same species. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazardSIFunding was provided by projects DIABOLO (H2020 GA 633464) and GEPRIF (RTA 2014-00011-c06-04). The funders did not participate in designing the study, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to the Galician Government and European Social Fund (Official Journal of Galicia – DOG n° 52, 17/03/2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Dr Eduardo González-Ferreiro at different institutions. Copyright of LiDAR data, Instituto Geográfico Nacional-Xunta de Galici

Modelling the vertical distribution of canopy fuel load using national forest inventory and low-density airbone laser scanning data

The fuel complex variables canopy bulk density and canopy base height are often used to predict crown fire initiation and spread. Direct measurement of these variables is impractical, and they are usually estimated indirectly by modelling. Recent advances in predicting crown fire behaviour require accurate estimates of the complete vertical distribution of canopy fuels. The objectives of the present study were to model the vertical profile of available canopy fuel in pine stands by using data from the Spanish national forest inventory plus lowdensity airborne laser scanning (ALS) metrics. In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, two different systems of models were fitted to estimate the canopy variables defining the vertical distributions; the first system related these variables to stand variables obtained in a field inventory, and the second system related the canopy variables to airborne laser scanning metrics. The models of each system were fitted simultaneously to compensate the effects of the inherent cross-model correlation between the canopy variables. Heteroscedasticity was also analyzed, but no correction in the fitting process was necessary. The estimated canopy fuel load profiles from field variables explained 84% and 86% of the variation in canopy fuel load for maritime pine and radiata pine respectively; whereas the estimated canopy fuel load profiles from ALS metrics explained 52% and 49% of the variation for the same species. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazardWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia—DOG n° 52, 17/03/2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Dr Eduardo González-Ferreiro at different institutions. Copyright of LiDAR data, Instituto Geográfico Nacional-Xunta de GaliciaS

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

[EN] Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified.SIWe are grateful to the Galician Government and European Social Fund (Official Journal of Galicia DOG n° 52, 17 March 2014, p. 11343, exp: POS-A/2013/049) for financing the postdoctoral research stays of Eduardo González-Ferreiro at different institutions

Estimating Stand and Fire-Related Surface and Canopy Fuel Variables in Pine Stands Using Low-Density Airborne and Single-Scan Terrestrial Laser Scanning Data

[EN] In this study, we used data from a thinning trial conducted on 34 different sites and 102 sample plots established in pure and even-aged Pinus radiata and Pinus pinaster stands, to test the potential use of low-density airborne laser scanning (ALS) metrics and terrestrial laser scanning (TLS) metrics to provide accurate estimates of variables related to surface and canopy fires. An exhaustive field inventory was carried out in each plot to estimate the main stand variables and the main variables related to fire hazard: surface fuel loads by layers, fuel strata gap, surface fuel height, stand mean height, canopy base height, canopy fuel load and canopy bulk density. In addition, the point clouds from low-density ALS and single-scan TLS of each sample plot were used to calculate metrics related to the vertical and horizontal distribution of forest fuels. The comparative performance of the following three non-parametric machine learning techniques used to estimate the main stand-and fire-related variables from those metrics was evaluated: (i) multivariate adaptive regression splines (MARS), (ii) support vector machine (SVM), and (iii) random forest (RF). The selection of the best modeling approach was based on a comparison of the root mean square error (RMSE), obtained by optimizing the parameters of each technique and performing crossvalidation. Overall, the best results were obtained with the MARS techniques for data from both sensors. The TLS data provided the best results for variables associated with the internal characteristics of canopy structure and understory fuel but were less reliable for estimating variables associated with the upper canopy, due to occlusion by mid-canopy foliage. The combination of ALS and TLS metrics improved the accuracy of estimates for all variables analyzed, except the height and the biomass of the understory shrubs. The variability demonstrated by the combined use of both types of metrics ranged from 43.11% for the biomass of duff litter layers to 94.25% for dominant height. The results suggest that the combination of machine learning techniques and metrics derived from low-density ALS data, drawn from a single-scan TLS or a combination of both metrics, may represent a promising alternative to traditional field inventories for obtaining valuable information about surface and canopy fuel variables at large scalesSIThis research was funded by the projects GEPRIF (RTA2014-00011-C06-04) and VIS4FIRE (RTA2017-00042-C05-05) of the Spanish Ministry of Economy, Industry, and Competitiveness and a pre-doctoral grant of the first author funded by the “Consejería de Educación, Universidad y Formación Profesional” and the “Consejería de Economía, Empleo e Industria” of the Galician Government and the EU operational program “FSE Galicia 2014–2020”

Estimación de la distribución vertical de combustibles finos del dosel de copas en masas de Pinus sylvestris empleando datos LiDAR de baja densidad

[ES] La altura de la base de la copa, la carga de combustible disponible y la densidad aparente son características estructurales del dosel de copas utilizadas para predecir la actividad de fuego de copas. La medición directa en campo de estas variables es impráctica y por tanto sus valores se estiman habitualmente mediante el empleo de modelos predictivos. Avances en la modelización del comportamiento del fuego hacen que sea de gran interés explorar la posibilidad de estimar de forma precisa y a escala de paisaje la distribución vertical de los combustibles en el dosel de copas. En este sentido, este estudio pretende analizar el potencial de los datos obtenidos de sensores LiDAR (Light Detection and Ranging) aerotransportados para modelizar dicha distribución vertical en masas de pino silvestre en Galicia. Para ello se usaron datos del vuelo LiDAR del PNOA (Plan Nacional de Ortofotografía Aérea) con una densidad de 0,5 primeros retornos m–2 y datos de campo procedentes del Cuarto Inventario Forestal Nacional (IFN4). En un primer paso, la distribución vertical fue caracterizada empleando la función de densidad de probabilidad de Weibull para, en un segundo paso, ajustar un sistema de ecuaciones que relacionan las variables del dosel con métricas derivadas de los datos LiDAR. Las ecuaciones se ajustaron simultáneamente para corregir los posibles problemas de correlación entre errores. Las distribuciones verticales finalmente estimadas explicaron el 41% de la variabilidad observada en las parcelas de estudio. El sistema de ecuaciones propuesto puede ser usado también para evaluar la efectividad de diferentes alternativas de gestión del combustible para reducir el riesgo de fuego de copa en rodales de pino silvestre[EN] Canopy fuel load, canopy bulk density and canopy base height are structural variables used to predict crown fire initiation and spread. Direct measurement of these variables is not functional, and they are usually estimated indirectly by modelling. Advances in fire behaviour modelling require accurate and landscape scale estimates of the complete vertical distribution of canopy fuels. The goal of the present study is to model the vertical profile of available canopy fuels in Scots pine stands by using data from the Spanish national forest inventory and low-density LiDAR data (0.5 first returns m–2) provided by Spanish PNOA project (Plan Nacional de Ortofotografía Aérea). In a first step, the vertical distribution of the canopy fuel load was modelled using the Weibull probability density function. In a second step, a system of models was fitted to relate the canopy variables to Lidar-derived metrics. Models were fitted simultaneously to compensate the effects of the inherent cross-model correlation between errors. Heteroscedasticity was also analyzed, but correction in the fitting process was not necessary. The estimated canopy fuel load profiles from LiDAR-derived metrics explained 41% of the variation in canopy fuel load in the analysed plots. The proposed models can be used to assess the effectiveness of different forest management alternatives for reducing crown fire hazardS

Adaptación a la docencia online de las prácticas preclínicas de Cirugía Bucal I

Los cambios en la docencia debido a la pandemia por la COVID-19 ha llevado a la reducción de la presencialidad y a un aumento de la docencia online. Por ello se diseñó este proyecto, cuya finalidad fue la adaptación a la docencia online de las prácticas preclínicas de Cirugía Bucal I. Para el desarrollo de este trabajo se realizaron las rúbricas de evaluación de cada módulo de la asignatura, se elaboraron videos y documentación para subir al campus virtual antes de la realización de la práctica. Finalmente, se elaboraron cuestionarios de evaluación de la satisfacción de los estudiantes y profesorado con esta metodología. Los resultados mostraron una elevada satisfacción de ambos grupos, considerándola una herramienta de utilidad en el aprendizaje y de implementación en cursos posteriores