Mining waste often contains elevated concentrations of V, Cr, As, Sb, and Pb. Stibnite mining, during extraction and processing, generates waste that is deposited directly onto soil without vegetation cover, potentially leading to environmental pollution. This study assessed concentrations of potentially toxic elements (PTEs) in the rhizoids and stems-phyllidia of terrestrial mosses near antimony mines and used these mosses as biomonitors of soil contamination. Results obtained via energy-dispersive X-ray fluorescence spectrometry showed the highest concentrations of As, Sb, and Pb in mosses growing on mining rubble, reflecting elevated contaminant levels in the study area. Concentrations of As, Sb, and Pb differed significantly between mosses from mine rubble sites and those from forest and meander sites. Both rhizoids and stems/phyllidia of mosses from rubble sites showed high enrichment factors (EFs) for As, Sb, and Pb. Notably, PTEs concentrations in mosses from the forest area were lower than values reported for other regions, whereas concentrations in the mine rubble area exceeded those reported for other mining-polluted regions worldwide.

Understanding the spatial structure of tree communities is fundamental for evaluating ecological interactions and management dynamics in agroforestry systems. However, the structural complexity and small spatial scale of traditional agroecosystems often limit the use of conventional remote sensing approaches. Recent advances in drone-based photogrammetry offer new opportunities to reconstruct the three-dimensional structure of vegetation at high spatial resolution and to quantify tree-level structural attributes. In this study, we applied aerial photogrammetry from unmanned aerial vehicles (UAVs) to characterize the spatial structure of agroforestry systems in traditional home gardens (THGs) in the Yucatan Peninsula, Mexico. The immediate neighborhood structure of the tree community of 20 THGs distributed along a south–north precipitation gradient was analyzed using two focal species as anchor references: Spondias purpurea and Annona muricata. High-resolution orthomosaics and three-dimensional point cloud models were generated to estimate structural attributes, including tree height, crown area, crown surface area, and canopy volume, which were combined with field measurements of diameter at breast height. Spatial indices describing aggregation, dominance, and neighborhood diversity were calculated to evaluate tree spatial organization and potential interaction patterns. The UAV-derived structural metrics revealed significant differences in canopy architecture across regions and between focal species. Regardless of the focal species, trees in the southern region exhibited greater height, crown diameter, and canopy volume than those in the northern region. Moreover, the spatial arrangement of tree communities also differed depending on which focal species was considered as the anchor, suggesting contrasting strategies of canopy dominance and spatial coexistence. Finally, our results validate the use of drone-based photogrammetry as an effective approach for capturing fine-scale spatial structure in complex agroforestry systems. By enabling detailed three-dimensional reconstruction of tree canopies, UAV remote sensing offers an affordable, simple approach to investigate neighborhood interactions, management effects, and structural dynamics in traditional agroecosystems that are difficult to assess using conventional field- or satellite-based methods.