Mexico is home to the highest species diversity of pines: 46 species out of 113 reported around the world. Within the great diversity of pines in Mexico, Pinus culminicola Andresen et Beaman, P. jaliscana Perez de la Rosa, P. maximartinenzii Rzed., P. nelsonii Shaw, P. pinceana Gordon, and P. rzedowskii Madrigal et M. Caball. are six catalogued as threatened or endangered due to their restricted distribution and low population density. Therefore, they are of special interest for forest conservation purposes. In this paper, we aim to provide up-to-date information on the spatial distribution of these six pine species according to different historical registers coming from different herbaria distributed around the country by using spatial modeling. Therefore, we recovered historical observations of the natural distribution of each species and modelled suitable areas of distribution according to environmental requirements. Finally, we evaluated the distributions by contrasting changes of vegetation in the period 1991–2016. The results highlight areas of distribution for each pine species in the northeast, west, and central parts of Mexico. The results of this study are intended to be the basis of in situ and ex situ conservation strategies for the endangered Mexican pines.

Various spatial modelling methods and tools have been used in ecology and biogeography. The application of these options serves a dual function: first, they offer information about the potential distribution of species to understand the richness and diversity of unassessed areas. Second, spatial modelling methods employ these predictions to select relevant sites to determine natural conservation areas. In this study, we compared three methods for modelling the spatial distribution of Egg-cone Pine (Pinus oocarpa Schiede), an important non-timber pine in Mexico. The final goal is to estimate suitable areas for the conservation and reproduction of superior individuals (plus trees) of P. oocarpa as a conservation strategy outside the known distribution since this species possesses a high ecological and economic value. The model used were a generalised linear model (GLM) as a parametric regression method, random forest (RF) as a machine-learning method, and the MaxEnt model, a standard procedure, implemented using the Kuenm R package. The results suggest that the models used performed well since the AUROC was between 0.95 and 0.98 in all cases. MaxEnt and random forest approaches provided more conservative predictions for the distribution of suitable areas of plus trees of P. oocarpa than the generalised linear model, but the random forest algorithm achieved the best performance. The results of the study allowed the determination of ex situ conservation areas for P. oocarpa plus trees outside of their known distribution.

The design of a sampling unit, whether a simple plot or a subplot within a clustered structure, including shape and size, has received little attention in inferential forestry research. The use of auxiliary variables from remote sensing impacts the precision of estimators from both model-assisted and model-based inference perspectives. In both cases, model parameters are estimated from a sample of field plots and information from pixels corresponding to these units. In studies assisted by remote sensing, the shape of the plot used to fit regression models (typically circular) often differs from the shape of the population elements for prediction, where the area of interest is divided into equal tessellated parts. This raises interest in understanding the effect of the sampling unit shape on the mean of variables in forest stands of interest. Therefore, the objective of this study was to evaluate the effect of circular and square subplots, concentrically overlapped and arranged in an inverted Y cluster structure, over tree density, basal area, and aboveground biomass in a managed temperate forest in central Mexico. We used a Multivariate Generalised Linear Mixed Model, which considers the Gamma distribution of the variables and accounts for spatial correlation between Secondary Sampling Units nested within the Primary Sampling Unit. The main findings of this study indicate that the type of secondary sampling unit of the same area and centroid, whether circular or square, does not significantly affect the mean tree density (trees), basal area (m²), and aerial biomass.

Every year, Danaus plexippus (Linnaeus, 1758) travels to hibernate in oyamel fir forests located between the limits of the states of Michoacán and Mexico in Mexico. Climate change and anthropogenic actions are diminishing oyamel fir forests in Mexico, putting pressure on the habitats of monarch butterflies. In the last decade, new colonies outside their usual range have been predicted through modeling and reported by the National Commission on Protected Areas of Mexico. The objectives of the study were to recover information on the historical and new hibernation sites, reported or modeled, from different literature sources. We also aimed to perform a bioclimatic and forest biometric characterization of new monarch butterfly colonies located in Sierra Nevada in Mexico to provide information to aid in conservation strategies for the monarch butterfly population. We conducted field trips to georeference the colonies at sites located in the Atlautla municipality in Mexico State. Climatic, topographic, and forest biometric variables were used to characterize the sites physically. It was found that the butterfly’s roosts occurred at a higher elevation than those recorded by other sources. The locations where the monarch’s colonies were established, in the east of Mexico State, provide information relevant to defining and developing policies for their conservation.

Los bosques de alta montaña ofrecen importantes servicios ecosistémicos de los cuales destaca el almacenamiento de bióxido de carbono (CO2), uno de los principales gases de efecto invernadero causantes del calentamiento global. Cuantificar el CO2 en este tipo de ecosistemas ayudaría a entender cómo funcionan y cómo contribuyen a la mitigación del cambio climático. El objetivo de este trabajo fue ajustar las ecuaciones alométricas para Pinus hartwegii en plantaciones de reforestación en el Parque Nacional Izta-Popo Zoquiapan. Las variables utilizadas fueron diámetro de la base, altura total y altura a la primera rama viva para relacionarlas con la biomasa. Se seleccionaron 90 brinzales y se calculó la biomasa de cada uno, así como la biomasa por componente: follaje, ramas, tallo y raíz. Se estimó el contenido de carbono para determinar la cantidad de este elemento capturado en los propios brinzales. Las ecuaciones resultantes para el diámetro a la base fue B=0.0092*DB2.7226, con un coeficiente de determinación de R2= 0.9521; y para la altura total fue de B=0.7546*hT2.5819, con un coeficiente de determinación de R2= 0.8644. La variable de altura a la primera rama viva obtuvo un coeficiente de determinación bajo, R2= 0.3203. El contenido de carbono promedio para los brinzales fue de 50.81 %.

Estimating aboveground biomass (AGB) is crucial for understanding the carbon cycle in terrestrial ecosystems, particularly within the context of climate change. Therefore, it is essential to research and compare different methods of AGB estimation to achieve acceptable accuracy. This study modelled AGB in temperate forests of central Mexico using active and passive remote sensing data combined with machine learning techniques (Random Forest and XGBoost) and compared the estimations against a traditional method, such as linear regression. The main goal was to evaluate the performance of machine learning techniques against linear regression in AGB estimation and then validate against an independent forest inventory database. The models obtained acceptable performance in all cases, but the machine learning algorithm Random Forest outperformed (R²_cv = 0.54; RMSE_cv = 19.17) the regression method (R²_cv = 0.41; RMSE_cv = 25.76). The variables that made significant contributions, in both Random Forest and XGBoost modelling, were NDVI, kNDVI (Landsat OLI sensor), and the HV polarisation from ALOS-Palsar. For validation, the Machine learning ensemble had a higher Spearman correlation (r = 0.68) than the linear regression (r = 0.50). These findings highlight the potential of integrating machine learning techniques with remote sensing data to improve the reliability of AGB estimation in temperate forests.