Plants face various abiotic stresses in their natural environments that trigger the production of reactive oxygen species (ROS), leading to oxidative stress and potential cellular damage. This comprehensive review examines the interplay between plant antioxidant defense systems and ROS under abiotic stress conditions. We discuss the major enzymatic antioxidants, including superoxide dismutase, catalase, reductases, and peroxidases, as well as non-enzymatic antioxidants, such as ascorbic acid, glutathione, polyphenols, and flavonoids, which play crucial roles in ROS detoxification. This review elaborates on different types of ROS, their production sites within plant cells, and their dual role as both damaging oxidants and key signaling molecules. We discuss how various abiotic stresses—including heat, cold, drought, flooding, salinity, and heavy metal toxicity—induce oxidative stress and trigger specific antioxidant responses in plants. Additionally, the mechanisms of ROS generation under these abiotic stress conditions and the corresponding activation of enzymatic and non-enzymatic scavenging systems are discussed in detail. This review also discusses recent advances in understanding ROS signaling networks and their integration with other stress-response pathways. This knowledge provides valuable insights into plant stress-tolerance mechanisms and suggests potential strategies for developing stress-resistant crops by enhancing antioxidant defense systems. Moreover, the strategic ROS modulation through priming, exogenous antioxidants, nanoparticles, or genetic tools can enhance plant resilience. Integrating these methods with agronomic practices (e.g., irrigation management) offers a sustainable path to climate-smart agriculture. Our review reveals that ROS accumulation can be detrimental; however, the coordinated action of various antioxidant systems helps plants maintain redox homeostasis and adapt to environmental stress.

Global changes cause widespread forest fragmentation, which, in turn, has given rise to many ecological problems; this is especially true if the forest carbon stock is profoundly impacted by fragmentation levels. However, the way in which forest carbon uptake changes with different fragmentation levels and the main pathway through which fragmentation affects forest carbon uptake are still unclear. Remote sensing data, vegetation photosynthesis models, and fragmentation models were employed to generate a time series GPP (gross primary productivity) dataset, as well as forest fragmentation levels for forest landscapes in Zhejiang province, China. We analyzed GPP variation with forest fragmentation levels and identified the relative importance of the phenology (carbon uptake period—CUP) and physiology (maximum daily GPP—GPP_max) control pathways of GPP under different fragmentation levels. The results showed that the normalized mean annual GPP data of highly fragmented forests during the period from 2000 to 2018 were significantly higher than those of other fragmentation levels, while there was almost no significant difference in the annual GPP trend of forest landscapes with all fragmentation levels. Moreover, the percentage area of the control variable, GPP_max, gradually increased with fragmentation levels; the mean GPP_max between 2000 and 2018 of high-level fragmentation was higher than that of other fragmentation levels. Our results demonstrate that the carbon uptake capacity per unit area was enhanced in highly fragmented forest areas, and the maximum photosynthetic capacity (physiology-based process) played an important role in controlling carbon uptake, especially in highly fragmented forest landscapes. Our study calls for a better and deeper understanding of the potential of forest carbon uptake, and it is necessary to explore the mechanism by which forest fragmentation changes the vegetation photosynthetic process.

Zhejiang Province, a pivotal economically developed region within China’s Yangtze River Delta, requires systematic investigation of spatiotemporal vegetation dynamics and their drivers to formulate targeted ecological protection policies and optimize vegetation restoration strategies. Utilizing the Google Earth Engine (GEE) platform, this study applied the Kernel Normalized Difference Vegetation Index (kNDVI) to assess vegetation responses to climate variability and human activities in Zhejiang Province from 2000 to 2022. Analytical methods included simple linear regression, Theil Sen trend analysis (Sen), Mann Kendall test (MK), Hurst index, partial correlation analysis, and correlation analysis. The results show: (1) The kNDVI exhibited a significant upward trend (0.001/year), covering 61.5% of the province. The Hurst index analysis revealed that 69.1% of vegetation changes exhibited anti-sustainability characteristics, with future vegetation degradation areas (56.4%) projected to exceed improvement areas (28.1%). (2) Human activities (57.11%) contributed more to kNDVI changes than climate change (42.89%). (3) Against the backdrop of climate change, kNDVI demonstrated a positive partial correlation with temperature (coefficient: 0.44) but exhibited a negative correlation with precipitation (coefficient: −0.056), confirming temperature as the dominant climatic driver. Overall, vegetation dynamics in Zhejiang Province from 2000 to 2022 were jointly driven by climate change and human activities.

Acid rain has many negative effects on the ecological environment and poses serious abiotic stress onto plants, resulting in substantial ecological and economic impairments annually. Ilex chinensis, a well-known medicinal plant, is sensitive to acid rain, but its response mechanisms are unclear. In this study, we simulated sulfuric acid rain (SAR), mixed acid rain (MIX), and nitric acid rain (NAR) at different pH values to investigate their effects on growth condition, photosynthesis, antioxidants, and nitrogen metabolites. We also explored the metabolic pathways and key genes involved in the response of I. chinensis to acid rain through transcriptome analysis. Physiological analysis showed that I. chinensis suffered the most significant inhibition at pH 3.0, which is manifested in the decrease in height growth rate, specific leaf weight, photosynthetic pigments content, net photosynthetic rate, stomatal conductance, and transpiration rate; the increase in MDA content and SOD activity; and the reduction in glutamine synthetase activity, nitrogen content, and proline content. Transcriptome analysis isolated 314 and 21 shared differentially expressed genes (DEGs) from I. chinensis treated with acid rain at pH 3.0 for 5 d and 15 d, respectively. KEGG enrichment analysis found that different types of acid rain caused changes in multiple metabolic pathways of I. chinensis, and the shared DEGs in 5 d treatment were mainly enriched in ribosomes, oxidative phosphorylation, and glycolysis/glycolysis, etc. The shared DEGs in 115 d treatment were mainly enriched in sulfur metabolism, RNA polymerase, cysteine and methionine metabolism, etc. Further research on gene regulatory networks at the two time points showed that the key pathways of I. chinensis, in response to acid rain stress, include plant–pathogen interaction, MAPK signaling pathway-plant, protein processing in the endoplasmic reticulum, ubiquitin mediated proteolysis, etc., in which 6 hub genes were identified, including TRINITY_DN13584_c0_g1, TRINITY_DN164_c0_g4, TRINITY_DN654_c0_g1, TRINITY_DN13611_c1_g2, TRINITY_DN21290_c0_g2, TRINITY_DN44216_c0_g1. Our findings provide a basis for exploring the regulatory mechanisms of I. chinensis in response to acid rain at the physiological and molecular levels, and for identifying candidate genes with acid tolerance potential.