Bark beetles are a natural part of coniferous forests. Dendroctonus mexicanus Hopkins is the most widely distributed and most destructive bark beetle in Mexico, colonizing more than 21 pine species. The objectives of this study were to generate ecological niche models for D. mexicanus and three of its most important host species, to evaluate the overlap of climate suitability of the association Dendroctonus–Pinus, and to determine the possible expansion of the bark beetle. We used meticulously cleaned species occurrence records, 15 bioclimatic variables and ‘kuenm’, an R package that uses Maxent as a modeling algorithm. The Dendroctonus–Pinus ecological niches were compared using ordination methods and the kernel density function. We generated 1392 candidate models; not all were statistically significant (α = 0.05). The response type was quadratic; there is a positive correlation between suitability and precipitation, and negative with temperature, the latter determining climatic suitability of the studied species. Indeed, a single variable (Bio 1) contributed 93.9% to the model (Pinus leiophylla Schl. & Cham). The overlap of suitable areas for Dendroctonus–Pinus is 74.95% (P. leiophylla) and on average of 46.66% in ecological niches. It is observed that D. mexicanus begins to expand towards climates not currently occupied by the studied pine species.

Quantifying biomass is important for determining the carbon stores in land ecosystems. The objective of this study was to predict aboveground biomass (AGB) of Agave lechuguilla Torr., in the states of Coahuila (Coah), San Luis Potosí (SLP) and Zacatecas (Zac), Mexico. To quantify AGB, we applied the direct method, selecting and harvesting representative plants from 32 sampling sites. To predict AGB, the potential and the Schumacher–Hall equations were tested using the ordinary least squares method using the average crown diameter (Cd) and total plant height (Ht) as predictors. Selection of the best model was based on coefficient of determination (R2 adj.), standard error (Sxy), and the Akaike information criterion (AIC). Studentized residues, atypical observations, influential data, normality, variance homogeneity, and independence of errors were also analyzed. To validate the models, the statistic prediction error sum of squares (PRESS) was used. Moreover, dummy variables were included to define the existence of a global model. A total of 533 A. lechuguilla plants were sampled. The highest AGB was 8.17 kg; the plant heights varied from 3.50 cm to 118.00 cm. The Schumacher–Hall equation had the best statistics (R2 adj. = 0.77, Sxy = 0.418, PRESS = 102.25, AIC = 632.2), but the dummy variables revealed different populations of this species, that is, an equation for each state. Satisfying the regression model assumptions assures that the predictions of A. lechuguilla AGB are robust and efficient, and thus able to quantify carbon reserves of the arid and semiarid regions of Mexico.

Agave lechuguilla Torr., of the family Agavaceae, is distributed from southwestern United States to southern Mexico and is one of the most representative species of arid and semiarid regions. Its fiber is extracted for multiple purposes. The objective of this study was to generate a robust model to predict dry fiber yield ( Dfw ) rapidly, simply, and inexpensively. We used a power model in its linear form and bioclimatic areas as dummy variables. Training, generation (80%) and validation (20%) of the model was performed using machine learning with the package ‘ caret’ of R. Using canonical correlation analysis (CCA), we evaluated the relationship of Dwf to bioclimatic variables. The principal components analysis (PCA) generated two bioclimatic zones, each with different A . lechuguilla productivities. We evaluated 499 individuals in four states of Mexico. The crown diameter ( Cd ) of this species adequately predicts its fiber dry weight (R 2 = 0.6327; p < 0.05). The intercept (β 0 ), slope [ lnCd (β 1 )], zone [( β 2 )] and interaction [ lnCd :Zona ( β 3 )] of the dummy model was statistically significant (p < 0.05), giving origin to an equation for each bioclimatic zone. The CCA indicates a positive correlation between minimum temperature of the coldest month (Bio 6) and Dwf (r = 0.84 and p < 0.05). In conclusion, because of the decrease in Bio 6 of more than 0.5°C by 2050, the species could be vulnerable to climate change, and A . lechuguilla fiber production could be affected gradually in the coming years.

La evaluación precisa de la biomasa de árboles es necesaria para estimar los almacenes de carbono y entender la contribución de los ecosistemas de bosques en la regulación de las emisiones de gases de efecto invernadero. El objetivo de este estudio fue desarrollar una ecuación alométrica para predecir la biomasa del fuste (Bf) para Pseudotsuga menziesii (Mirb.) Franco, en la región de Arteaga Coahuila, en el noreste de México. El diámetro normal (Dn), la altura al punto directriz (Hp) y la altura total (Ht) fueron medidos con el Criterion RD1000®. El volumen del fuste (Sv) fue obtenido con la ecuación de Pressler y después fue trasformado a biomasa mediante la densidad básica de la madera. Los datos se ajustaron con el modelo potencial Ŷ = aXb en su forma logarítmica, donde Ŷ es Sb y X el Dn evaluando los supuestos de normalidad, homocedasticidad y no autocorrelación. Un total de 110 árboles de esta especie fueron muestreados. La ecuación ln(Bf) = −2.8732 (± 0.238) + 2.4237 (± 0.066) × ln(Dn) cumplió todos los supuestos de los modelos lineales (valor de P > 0.05), lo que asegura que la predicción de biomasa de fuste sea confiable. El diámetro normal explicó el 98 % de la biomasa del fuste de P. menziesii. El sesgo en la predicción de biomasa del fuste debido a la trasformación logarítmica debe corregirse multiplicándola por un factor de corrección de 1.018. La estimación indirecta de volumen a través del método de Pressler es una forma eficiente, de bajo costo y no destructiva para generar un modelo alométrico de biomasa de fuste.