Functional traits as indicators of maize (Zea mays L.) strategies to cope with Zn, Pb and Cr heavy metal-induced stress

Heavy metals (HMs) contamination poses a significant threat to environmental matrices, particularly soil, which is essential for food security, agricultural productivity, and key ecosystem services. Understanding how crops respond to HMs is crucial for developing biomonitoring strategies to assess soil contamination and inform remediation efforts. Plants, including crops, exhibit a range of functional traits (FT) that can indicate HMs stress and contamination levels. In this study, we investigated the response strategies of Zea mays L. var. Limagrain 31455, widely cultivated throughout the region of Land of Fires, a critically polluted area of southern Italy, to different concentrations of Zn, Pb, and Cr, corresponding to moderate to severe soil contamination. Functional traits related to the photosynthetic machinery, including gas exchange, chlorophyll fluorescence and reflectance indices, were examined. Root morpho-histochemical analysis were also conducted to correlate early root alterations with any observed changes in these photosynthetic traits. Results revealed distinct response patterns: tolerance to Zn, without adverse effects on photosynthetic traits; resistance to Pb, mediated by increased RD and photoprotection through change in reflectance indices; and sensitivity to Cr highlighted by severe functional impairments of all the studied photosynthetic traits and structural root damages. Functional traits, such as chlorophyll fluorescence parameters and the photochemical reflectance index or normalized difference vegetation index, demonstrated high potential for monitoring HMs stress responses; in addition, morpho-anatomical traits of the root system provided insights into biomass allocation and the capacity of var. Limagrain 31455 to tolerate and adapt to HMs stress. These findings underscore the importance of integrating physiological, anatomical, and spectral analyses to improve the biomonitoring and management of polluted soils and detecting spatial variability in contamination via remote sensing.