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Species diversity and genetic diversity, the most basic elements of biodiversity, have long been treated as separate topics, although populations evolve within a community context. Recent studies on community genetics and ecology have suggested that genetic diversity is not completely independent of species diversity. The Mexican Picea chihuahuana Martínez is an endemic species listed as “Endangered” on the Red List. Forty populations of Chihuahua spruce have been identified. This species is often associated with tree species of eight genera in gallery forests. This rare Picea chihuahuana tree community covers an area no more than 300 ha and has been subject of several studies involving different topics such as ecology, genetic structure and climate change. The overall aim of these studies was to obtain a dataset for developing management tools to help decision makers implement preservation and conservation strategies. However, this unique forest tree community may also represent an excellent subject for helping us to understand the interplay between ecological and evolutionary processes in determining community structure and dynamics. The AFLP technique and species composition data were used together to test the hypothesis that species diversity is related to the adaptive genetic structure of some dominant tree species (Picea chihuahuana, Pinus strobiformis, Pseudotsuga menziesii and Populus tremuloides) of the Picea chihuahuana tree community at fourteen locations. The Hill numbers were used as a diversity measure. The results revealed a significant correlation between tree species diversity and genetic structure in Populus tremuloides. Because the relationship between the two levels of diversity was found to be positive for the putative adaptive AFLP detected, genetic and species structures of the tree community were possibly simultaneously adapted to a combination of ecological or environmental factors. The present findings indicate that interactions between genetic variants and species diversity may be crucial in shaping tree communities.

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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.

Las coníferas son la fuente más importante de materia prima para la industria forestal mexicana, cumplen funciones ecológicas importantes y proveen bienes y servicios para el hombre. Para probar si es posible predecir la densidad de las especies de coníferas a partir de las variables ambientales, se hizo un análisis de regresión lineal múltiple por el método paso por paso (stepwise). Se estudiaron veinte especies de cinco géneros distintos y once variables ambientales (nueve variables climáticas, una fisiográfica y otra de suelo). En este trabajo se detectó una escasa relación lineal entre la densidad de especies y las variables predictivas; no obstante, algunos indicadores sugieren que en 60% de las especies, la densidad es afectada principalmente por al menos cuatro variables ambientales; entre las que destacan la precipitación de abril a septiembre, el fenómeno de las heladas, la altitud sobre el nivel del mar y la precipitación media anual.