Effect of different management intensities on natural and human-induced carbon fluxes in a golf course in northern Italy

Turfgrasses are ubiquitous in urban landscape and their role on carbon (C) cycle is increasing important also due to the considerable footprint related to their management practices. It is crucial to understand the mechanisms driving the C assimilation potential of these terrestrial ecosystems Several approaches have been proposed to assess C dynamics: micro-meteorological methods, small-chamber enclosure system (SC), chrono-sequence approach and various models. Natural and human-induced variables influence turfgrasses C fluxes. Species composition, environmental conditions, site characteristics, former land use and agronomic management are the most important factors considered in literature driving C sequestration potential. At the same time different approaches seem to influence C budget estimates. In order to study the effect of different management intensities on turfgrass, we estimated net ecosystem exchange (NEE) through a SC approach in a hole of a golf course in the province of Verona (Italy) for one year. The SC approach presented several advantages but also limits related to the measurement frequency, timing and duration overtime, and to the methodological errors connected to the measuring system. Daily CO2 fluxes changed according to the intensity of maintenance, likely due to different inputs and disturbances affecting biogeochemical cycles, combined also to the different leaf area index (LAI). The annual cumulative NEE decreased with the increase of the intensity of management. NEE was related to the seasonality of turfgrass, following temperatures and physiological activity. Generally on the growing season CO2 fluxes towards atmosphere exceeded C sequestered. The cumulative NEE showed a system near to a steady state for C dynamics. In the final part greenhouse gases (GHGs) emissions due to fossil fuel consumption for turfgrass upkeep were estimated, pinpointing that turfgrass may result a considerable C source. The C potential of trees and shrubs needs to be considered to obtain a complete budget.

Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach

Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.

Impact of anthropogenic stressors on the microbial communities in marine holobionts and ecosystems

Free-living or host-associated marine microbiomes play a determinant role in supporting the functioning and biodiversity of marine ecosystems, providing essential ecological services, and promoting the health of the entire biosphere. Currently, the fast and restless increase of World’s human population strongly impacts life on Earth in the forms of ocean pollution, coastal zone destruction, overexploitation of marine resources, and climate change. Thanks to their phylogenetic, metabolic, and functional diversity, marine microbiomes represent the Earth’s biggest reservoir of solutions against the major threats that are now impacting marine ecosystems, possibly providing valuable insights for biotechnological applications to preserve the health of the ocean ecosystems. Microbial-based mitigation strategies heavily rely on the available knowledge on the specific role and composition of holobionts associated microbial communities, thus highlighting the importance of pioneer studies on microbial-mediated adaptive mechanisms in the marine habitats. In this context, we propose different models representing ecologically important, widely distributed, and habitat-forming organisms, to further investigate the ability of marine holobionts to dynamically adapt to natural environmental variations, as well as to anthropogenic stress factors. In this PhD thesis, we were able to supply the characterization of the microbial community associated with the model anthozoan cnidaria Corynactis viridis throughout a seasonal gradient, to provide critical insights into microbiome-host interactions in a biomonitoring perspective. We also dissected in details the microbial-derived mitigation strategies implemented by the benthonic anthozoan Anemonia viridis and the gastropod Patella caerulea as models of adaptation to anthropogenic stressors, in the context of bioremediation of human-impacted habitats and for the monitoring and preservation of coastal marine ecosystems, respectively. Finally, we provided a functional model of adaptation to future ocean acidification conditions by characterizing the microbial community associated with the temperate coral Balanophyllia europaea naturally living at low pH conditions, to implement microbial based actions to mitigate climate change.