Urban ecosystems are increasingly shaped by multiple environmental stressors, which may threaten both biodiversity and human well-being. We summarised the current knowledge on the ecological and social consequences of seven major urban pressures: air pollution, freshwater degradation, biological invasions, noise pollution, habitat fragmentation, soil pollution and climate crisis. Air and soil pollution, largely driven by traffic and industrial activities, compromises vegetation functions, reduces ecosystem services, and affects human health. Urban freshwater systems face contamination from stormwater runoff, wastewater, and microplastics, leading to biodiversity loss, altered ecosystem processes, and reduced water availability. Biological invasions, facilitated by human activities and habitat disturbances, reshape ecological communities, outcompete native species, and impose socio-economic costs, while management requires integrated monitoring and citizen engagement. Noise pollution disrupts animal communication, alters species distributions, and poses significant risks to human physical and mental health. Simultaneously, habitat fragmentation and loss reduce ecological connectivity, impair pollination and dispersal processes, and heighten extinction risks for both plants and animals. Collectively, these stressors interact synergistically, amplifying ecological degradation and exacerbating health and social inequalities in urban populations. The cumulative impacts highlight the need for systemic and adaptive approaches to urban planning that integrate biodiversity conservation, public health, and social equity. Nature-based solutions, ecological restoration, technological innovation, and participatory governance emerge as promising strategies to enhance urban resilience. Furthermore, fostering citizen science initiatives can strengthen monitoring capacity and create community ownership of sustainable urban environments. Addressing the combined pressures of urban environmental stressors is thus pivotal for building cities that are ecologically robust, socially inclusive, and capable of coping with the challenges of the climate crisis and global urbanization.

Agroforestry systems (AFSs) enhance long-term carbon sequestration through tree biomass accumulation. As the European Union’s Carbon Farming Certification (CRCF) Regulation now recognizes AFSs in carbon farming (CF) schemes, accurate tree biomass estimation becomes essential for certification. This review examines field-destructive and remote sensing methods for estimating tree aboveground biomass (AGB) in AFSs, with a specific focus on their advantages, limitations, timing, and associated costs. Destructive methods, although accurate and necessary for developing and validating allometric equations, are time-consuming, costly, and labour-intensive. Conversely, satellite- and drone-based remote sensing offer scalable and non-invasive alternatives, increasingly supported by advances in machine learning and high-resolution imagery. Using data from the INNO4CFIs project, which conducted parallel destructive and remote measurements in an AFS in Tuscany (Italy), this study provides a novel quantitative comparison of the resources each method requires. The findings highlight that while destructive measurements remain indispensable for model calibration and new species assessment, their feasibility is limited by practical constraints. Meanwhile, remote sensing approaches, despite some accuracy challenges in heterogeneous AFSs, offer a promising path forward for cost-effective, repeatable biomass monitoring but in turn require reliable field data. The integration of both approaches might represent a valid strategy to optimize precision and resource efficiency in carbon farming applications.

Tropospheric ozone (O ₃ ) is a pervasive stressor that impairs forest biomass and alters carbon allocation strategies. This study assessed biomass responses across 17 woody taxa under free‐air controlled exposure (FACE), integrating a decade of experiments conducted with an analogous exposure regime applied to deciduous and evergreen species. The analysis provided a comparative evaluation of existing flux‐based metrics. Statistical analyses revealed consistent reductions in relative total (RTB), aboveground (RTAB), and belowground (RTBB) biomass with increasing O ₃ uptake in terms of phytotoxic ozone dose (POD ₁ mmol m ⁻² ). Deciduous species reached the 4% biomass reduction threshold (CL ₄ ) at lower POD ₁ levels for RTBB (10.21), RTAB (13.16), and RTB (10.77) and displayed relatively small values for RTBB (2.75), RTAB (5.70), and RTB (3.31), where represents the increment in O ₃ uptake required to reach the CL ₄ threshold. In contrast, evergreen species showed higher CL ₄ for RTBB (11.48), RTAB (15.40), and RTB (13.86) and larger values for RTBB (8.40), RTAB (12.32), and RTB (10.78), reflecting a slower biomass decline. Contrasting relationships suggest that leaf habit‐specific patterns are associated with divergent carbon allocation strategies under O ₃ stress. In deciduous species, POD ₁ and Leaf Index Flux (LIF) were negatively correlated with shoot‐to‐root ratio (S/R), whereas in evergreen species, both indices were positively correlated with leaf area ratio (LAR) and S/R. In conclusion, flux‐based metrics provided a biologically robust framework for quantifying O ₃ ‐induced biomass losses, revealing higher sensitivity in deciduous species than in evergreens and highlighting the root as the most vulnerable compartment under O ₃ exposure. The findings should be interpreted considering the spatial and temporal constraints of a single‐site FACE experiment and the focus on O ₃ as a stand‐alone stressor without interaction effects. Future research should combine O ₃ uptake with multi‐stressor frameworks to better predict biomass and carbon responses in complex field conditions.

In this study, we aimed to evaluate whether candidate genes for abiotic stresses in Fagus sylvatica L. are also candidate genes for herbaceous plants, with the purpose of better defining the abiotic stress response model of F. sylvatica. Therefore, a meta-analysis was performed on published papers related to abiotic stress. Firstly, we carried out a systematic review regarding the activity of 24 candidate genes selected for F. sylvatica under abiotic stress reported in 503 articles. After choosing the inclusion criteria, 73 articles out of 503, regarding 12 candidate genes, were included in this analysis. We performed an exploratory meta-analysis based on the random-effect model and the combined effect-size approach (Cohen’s d). The results obtained through Forest and Funnel plots indicate that the candidate genes for F. sylvatica are considered to be candidate genes in other herbaceous species. These results allowed us to set up models of plants’ response to abiotic stresses implementing the stress models in forest species. The results of this study will serve to bridge knowledge gaps regarding the pathways of response to abiotic stresses in trees based on the meta-analysis. The study approach used could be extended to observe larger gene databases and different species.