Biology subjects

Vangi, E.

Publications and source records attributed to Vangi, E..

3 recordsLinked to original sources

bioRxiv2023

Integrating annual radial growth analyses and carbon isotope discrimination to forecast early warning of beech forest dieback across the Italian Peninsula

Tree mortality and forest dieback episodes are increasing due to drought and heat stress. Nevertheless, a comprehensive understanding of mechanisms enabling trees to withstand and survive droughts remains lacking. Our study investigated basal area increment (BAI), and {delta}13C- derived intrinsic water-use-efficiency (iWUE), to elucidate beech resilience across four healthy stands in Italy with varying climates and water availability. Additionally, fist-order autocorrelation (AR1) analysis was performed to detect early warning signals for potential tree dieback risks during extreme drought events. Results reveal a negative link between BAI and vapour pressure deficit (VPD), especially in southern latitudes. After the 2003 drought, BAI decreased at the northern site, with an increase in {delta}13C and iWUE, indicating conservative water-use. Conversely, the southern sites showed increased BAI and iWUE, likely influenced by rising CO2 and improved water availability. In contrast, the central site sustained higher transpiration rates due to higher soil water holding capacity (SWHC). Despite varied responses, most sites exhibited reduced resilience to future extreme events, indicated by increased AR1. Temperature significantly affected beech iWUE and BAI in northern Italy, while VPD strongly influenced the southern latitudes. The observed increase in BAI and iWUE in southern regions might be attributed to an acclimation response.

plant biology↗

bioRxiv2023

Stand age diversity dampens forests sensitivity to climate change

Stand age significantly influences the functioning of forest ecosystems by shaping structural and physiological plant traits, affecting water and carbon budgets. Forest age distribution is determined by the interplay of tree mortality and regeneration, influenced by both natural and anthropogenic disturbances. Thus, human-driven alteration of tree age distribution presents an underexplored avenue for enhancing forest stability and resilience. In our study, we investigated how age distribution impacts the stability and resilience of the forest carbon budget under both current and future climate conditions. We employed a biogeochemical model on three historically managed forest stands, projecting their future as undisturbed systems, i.e., left at their natural evolution with no management interventions. The model, driven by climate data from five Earth System Models under four representative climate scenarios and one baseline scenario, spanned 11 age classes for each stand. Our findings indicate that Net Primary Production (NPP) peaks in the young and middle-aged classes (16- to 50-year-old), aligning with ecological theories, regardless of the climate scenario. Under climate change, the beech forest exhibited an increase in NPP and maintained stability across all age classes, while resilience remained constant with rising atmospheric CO2 and temperatures. However, NPP declined under climate change scenarios for the Norway spruce and Scots pine sites. In these coniferous forests, stability and resilience were more influenced. These results underscore the necessity of accounting for age classes and species-specific reactions in evaluating the impacts of climate change on forest stability and resilience. We, therefore, advocate for customized management strategies that enhance the adaptability of forests to changing climatic conditions, taking into account the diverse responses of different species and age groups to climate.

plant biology↗

bioRxiv2023

Regional estimates of gross primary production applying the process-based model 3D-CMCC-FEM vs. multiple datasets

Process-based Forest Models (PBFMs) offer the possibility to capture important spatial and temporal patterns of both carbon fluxes and stocks in forests, accounting for ecophysiological, climate and geographical variability. Yet, their predictive capacity should be demonstrated not only at the stand-level but also in the context of large spatial and temporal heterogeneity. For the first time, we apply a stand scale process-based model (3D-CMCC-FEM) in a spatially explicit manner at 1 km spatial resolution in a Mediterranean region in southern Italy. Specifically, we developed a methodology to initialize the model that comprehends the use of spatial information derived from the integration of remote sensing (RS) data, the national forest inventory data and regional forest maps to characterize structural features of the main forest species. Gross primary production (GPP) is simulated over the period 2005-2019 and the multiyear predictive capability of the model in simulating GPP is evaluated both aggregated as at species-level by means of independent multiple data sources based on different RS-based products. We show that the model is able to reproduce most of the spatial ([~]2800 km2) and temporal (32 years in total) patterns of the observed GPP at both seasonal, annual and interannual time scales, even at the species-level. These new very promising results open the possibility of applying the 3D-CMCC- FEM confidently and robustly to investigate the forests behavior under climate and environmental variability over large areas across the highly variable ecological and bio- geographical heterogeneity of the Mediterranean region. Key PointsO_LIWe apply a process-based forest model on a regular grid at 1 km spatial resolution in a Mediterranean region. C_LIO_LIInitial forest state is estimated using spatially explicit input data derived from remote sensing and national forest inventory data. C_LIO_LIThe 3D-CMCC-FEM shows comparably estimates in simulating both spatial and temporally the gross primary production, when compared to independent satellite-based products. C_LI

ecology↗