Search

One of the most pressing challenges currently is climate change, and to face it, it is desirable to have the most detailed information possible on the distribution of climate metrics and the covariates that alter their fluctuations. This study investigated the spatial variations of the temperature and precipitation variables more sensitive to the unit change in elevation above sea level, considering the relative effects of latitude and solar radiation as covariates. For this, the analysis of covariance was applied to analyze data from 32,111 sampling units, systematically distributed in mainland Mexico, hypothesizing a constant and uniform rate of change of climate variables as the values of elevation above sea level increase and a non-significant effect of the covariates on the elevation-climate relationships. The results show that the rate of change of the studied variables varies at different latitudinal and altitudinal gradients. For example, without considering any covariate, for each meter that the elevation increases, the mean annual temperature decreases on average by 0.00473 °C, the mean temperature of the driest quarter decreases by 0.00488 °C, and the mean temperature of the warmest quarter decreases by 0.00449 °C. This means that for every kilometer of elevation, these variables decrease by 4.73, 4.88, and 4.49 °C, respectively. Yet, when the data were stratified by covariates, these values, as well as the rates of change in precipitation, varied significantly. There were also signs of a stronger correlation between altitude and precipitation for the driest month and driest quarter of over 30°N than in latitudes nearer to the equator. • How much does the temperature decrease for each meter increase in altitude? • Data from 32,111 plots distributed throughout Mexico were used • Regression slopes at different latitudinal strata were contrasted. • Signs that rainfall is associated with altitude, in some sectors of the conditioned distribution. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]