The different models that predict the distribution of species are a useful tool for the evaluation and monitoring of forest resources as they facilitate the planning of their management in a changing climate environment. Recently, a significant number of algorithms have been proposed for this purpose, making it difficult to select the most appropriate to use. The evaluation of performance and predictive stability of these models can elucidate this problem. Distribution data of 17 pine species with high economic importance for Mexico were collected and distribution models were carried out. We carried out a pre-modeling design to select the prediction variables (climatic, edaphic and topographic), after which nine algorithms and an ensemble model were contrasted against one another. The true skill statistic (TSS) and the area under the curve (AUC) were used to evaluate the predictive performance of the models, and the coefficient of variation of the predictions was used to evaluate their stability. The number of predictive variables in the final models fluctuated from 6 to 12; the mean diurnal range and the maximum temperature of warmest month were included in the models for most species. Random forests, the ensemble model, generalized additive models and MaxEnt were the ones that best described the distribution of the species (AUC >0.92 and TSS >0.72); the opposite was found in Bioclim and Domain (AUC<0.75 and <0.82; and TSS<0.5 and 0.055); on the contrary, Maxent and the ensemble model attained high predictive stability (CV<0.015). The ensemble model obtained greater performance and predictive stability in the predictions of the distribution of the 17 species of pines. The differences found in performance and predictive stability of the algorithms suggest that the ensemble model has the potential to model the distribution of tree species.

Locating potential tree-planting sites and analyzing tree canopy cover is important in the planning and management of urban forests. This paper reports the quantification of potential planting sites as well as tree canopy cover in the urban area of Mexico City, estimated by means of SPOT (Satellite Pour l’Observation de la Terre) 6 satellite images and a supervised pixel-based classification approach. Results showed an estimated area of 3100.7 ha of potentially useful sites, including places with bare soil and grass-covered areas such as median strips, roundabouts and parks. An average tree canopy cover of 10.6% and an average impervious surface of 79.2% for the 15 boroughs were also analyzed. The area of potential planting sites would represent a 5% gain for the current tree canopy cover if it were to be planted. With an overall accuracy of 92.4%, the use of both images from the SPOT 6 sensor and the classification approach proved to be appropriate for obtaining thematic covers in the urban environment of Mexico City.

The proper estimation of above-ground biomass (AGB) stocks of managed forests is a prerequisite to quantifying their role in climate change mitigation. The aim of this study was to analyze the spatial variability of AGB and its uncertainty between actively managed pine and unmanaged pine-oak reference forests in central Mexico. To investigate the determinants of AGB, we analyzed variables related to forest management, stand structure, topography, and climate. We developed linear (LM), generalized additive (GAM), and Random Forest (RF) empirical models to derive spatially explicit estimates and their uncertainty, and compared them. AGB was strongly influenced by forest management, as LiDAR-derived stand structure and stand age explained 80.9% to 89.8% of its spatial variability. The spatial heterogeneity of AGB varied positively with stand structural complexity and age in the managed forests. The type of predictive model had an impact on estimates of total AGB in our study site, which varied by as much as 19%. AGB densities varied from 0 to 492 ± 17 Mg ha⁻¹ and the highest values were predicted by GAM. Uncertainty was not spatially homogeneously distributed and was higher with higher AGB values. Spatially explicit AGB estimates and their association with management and other variables in the study site can assist forest managers in planning thinning and harvesting schedules that would maximize carbon stocks on the landscape while continuing to provide timber and other ecosystem services. Our study represents an advancement toward the development of efficient strategies to spatially estimate AGB stocks and their uncertainty, as the GAM approach was used for the first time with improved results for such a purpose.

Mexico, as a center of origin of maize, presents a high diversity of maize races. With the objective of spatially demarcating regions with high concentration of intraspecific diversity in Mexico, as a fundamental measure for the in situ conservation of their agrobiodiversity, Geographic Information Systems (GIS) tools have been used to generate diversity and richness indexes for 64 maize races cultivated in Mexico, using indexes to demarcate relationships to environmental factors such as temperature, precipitation, and altitude; the presence of indigenous groups; and the type of maize used. These relations allowed defining seven environmental units spatially, with characteristic maize races in each of them, which constitute priority areas for in situ conservation. In addition, a close relationship was found between the diversity of maize races and of ethnic groups, from the center to the south of the country, associated with the differences in maize use in different ethnic groups. This geographical demarcation of races and uses of maize will favor food security through specific measures of in situ conservation, as well as an increase in added value of maize by-products based on specific maize races conserved by local ethnic groups.

The methods for measuring vegetation cover in Mexican forest surveys are subjective and imprecise. The objectives of this research were to compare the sampling designs used to measure the vegetation cover and estimate the over and understory cover in different land uses, using digital photography. The study was carried out in 754 circular sampling sites in central Mexico. Four spatial sampling designs were evaluated in three spatial distribution patterns of the trees. The sampling designs with photographic captures in diagonal form had lower values of mean absolute error (MAE < 0.12) and less variation in random and grouped patterns. The Carbon and Biomass Sampling Plot (CBSP) design was chosen due to its smaller error in the different spatial tree patterns. The image processing was performed using threshold segmentation techniques and was automated through an application developed in the Python language. The two proposed methods to estimate vegetation cover through digital photographs were robust and replicable in all sampling plots with different land uses and different illumination conditions. The automation of the process avoided human estimation errors and ensured the reproducibility of the results. This method is working for regional surveys and could be used in national surveys due to its functionality.

Maize is the grain cereal that is the basis of human and animal diets in Mexico and Latin America; it constitutes an essential crop for global food security. The objective of this study was to analyze the spatial–temporal evolution of scientific production on the theme of GMO maize, through a bibliometric analysis of the texts available in the main editorial houses (Elsevier, Scopus, and Springer), open access journal articles database (Conricyt, Scielo, Redalyc, Latindex, Claryvate Analytics, Periodica, and DOAJ), and freely accessible web search engine Google Scholar, to determine the factors that influence the impact of the studies. From 1991 to 2019, 917 texts were found whose spatial–temporal evolution showed a linear growth that concentrated in Latin America (58.56%). The low impact (measured by the number of bibliographic citations) of scientific studies developed in countries of Latin America was related to their publication in journals edited in their own countries and in Spanish, which restricts the constructive criticism of peer review. For the case of Mexico, a spatial discrepancy was also found between research centers and production areas, which limits the transference of technology; and no specialized author in theme of GMO maize was found; the researchers responded to “scientific trends” in agreement with the agrarian policies of the time.