This is a repository copy of Tropical tree growth driven by dry-season climate variability.

Accurate description of forest fuels is necessary for developing appropriate fire management strategies aimed at reducing fire risk. Although field surveys provide accurate measurements of forest fuel load estimations, they are time consuming, expensive, and may fail to capture the inherent spatial heterogeneity of forest fuels. Previous efforts were carried out to solve this issue by estimating homogeneous response areas (HRAs), representing a promising alternative. However, previous methods suffer from a high degree of subjectivity and are difficult to validate. This paper presents a method, which allows eliminating subjectivity in estimating HRAs spatial distribution, using artificial intelligence machine learning techniques. The proposed method was developed in the natural protected area of “Sierra de Quila,” Jalisco, and was replicated in “Sierra de Álvarez,” San Luis Potosí and “Selva El Ocote,” Chiapas, Mexico, to prove its robustness. Input data encompassed a set of environmental variables including altitude, average annual precipitation, enhanced vegetation index, and forest canopy height. Four, three, and five HRAs with overall accuracy of 97.78%, 98.06%, and 98.92% were identified at “Sierra de Quila,” “Sierra de Álvarez,” and “Selva El Ocote,” respectively. Altitude and average annual precipitation were identified as the most explanatory variables in all locations, achieving a mean decrease in impurity values greater than 52.51% for altitude and up to 36.02% for average annual precipitation. HRAs showed statistically significant differences in all study sites according to the Kruskal–Wallis test (p-value < 0.05). Differences among groups were also significant based on the Wilcoxon–Mann–Whitney (p-value < 0.05) for all variables but EVI in “Selva El Ocote.” These results show the potential of our approach to objectively identify distinct homogeneous areas in terms of their fuel properties. This allows the adequate management of fire and forest fuels in decision-making processes.

L a erosión del suelo es un fenómeno natural que las actividades agrícolas aceleran. En México se estima que de 29 a 97 % de las tierras tienen algún grado de erosión hídrica, donde es bien conocida la acción protectora de la cobertura de la vegetación sobre el suelo; pero cultivos como agave tequilero o inclusive en maíz o pasto, requiere documentarse el proceso erosivo en lapsos de tiempo cuando el suelo está expuesto al efecto erosivo de la lluvia o escurrimiento. El objetivo del presente trabajo es describir el proceso de erosión hídrica en maíz, pasto, agave tequilero y suelo desnudo. Se utilizaron lotes de escurrimiento con maíz, agave tequilero, pasto y suelo desnudo, donde se midió la pérdida de suelo (PS) en cinco años. Los resultados mostraron que la mayor PS se tuvo en agave tequilero y suelo desnudo, intermedia en maíz y la más baja en pasto. La PS acumulada en agave tequilero siguió la tendencia de los eventos de lluvia. La PS acumulada en maíz es alta sólo hasta antes de que se cubra por completo el suelo, mientras que en pasto la erosión hídrica fue baja en todos los años de estudio.