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.

Ante lo prioritario que resulta la evaluación de los impactos de la deforestación sobre las variaciones del clima local en los bosques mexicanos, se planteó el estudio que se describe a continuación con el propósito de precisar la variabilidad de la temperatura (máxima, mínima y media) en algunas asociaciones de coníferas y áreas vecinas deforestadas en México, así como sus diferencias diurnas y nocturnas. Se ubicaron seis sitios en pinares, oyametales y en zonas sin arbolado próximas a los mismos en los estados de Tlaxcala, Coahuila, Michoacán, Jalisco y Durango, donde se colocaron Estaciones Meteorológicas Automáticas (EMA). Los resultados indican que las zonas deforestadas presentan mayor temperatura que las arboladas (de 0.43 °C a 0.69 °C). En el bosque de Pinus michoacana se registraron los valores más altos de temperatura, mientras que en el de Abies vejarii los menores. Entre las temperaturas diurnas y nocturnas se observó una diferencia media de 3 °C y predominó una temperatura superior en las territorios deforestados con 1.4 %, respecto a los cubiertos por árboles. Finalmente, las áreas con Pinus durangensis tuvieron la mayor oscilación de temperatura entre el día y la noche (arriba de 4 °C) y las de Abies religiosa, la más baja, con valores inferiores a 1.5 °C.

Los sensores remotos en combinación con información derivada de los inventarios forestales estiman variables de interés con precisión y bajo costo. El objetivo de este trabajo fue estimar el área basal (AB), volumen maderable (VTA) y biomasa aérea (B) en diferentes ecosistemas de selvas mediante información Landsat ETM+ e Inventario Nacional Forestal y de Suelos (INFyS) en Quintana Roo, México. Se generó una matriz de correlación entre datos del INFyS e información espectral, posteriormente, un modelo de regresión lineal múltiple. Con las ecuaciones seleccionadas se generaron mapas de distribución espacial de AB (m2 ha-1), VTA (m3 ha-1) y B (Mg ha-1). El inventario total se estimó mediante tres enfoques: i) estimadores de razón (ERaz), ii) estimadores de regresión (EReg) y iii) estimadores del muestreo simple al azar. Los dos primeros enfoques corresponden al inventario alternativo mediante sensores remotos y el tercero al inventario tradicional. El coeficiente de correlación resultó mayor del índice de diferencia normalizada con 0.35, 0.39 y 0.39 para AB, VTA y B. Los modelos de regresión presentaron coeficientes de determinación ajustada de 0.28, 0.32 y 0.32 para estimar AB, VTA y B, respectivamente. Los tres estimadores son estadísticamente diferentes y muestran que el EReg es el más conservador y con precisión en AB, VTA y B de 2.73%, 2.92% y 2.71%, respectivamente, además de intervalos de confianza de menor amplitud que el MSA y ERaz. Con la actualización del inventario mediante sensores remotos se mejora el proceso de evaluación de los recursos forestales y su planificación.

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.