Thesis title: Systemic analysis carried out on functional processes that are the bases for health status evaluation of ecosystems
The notion of "health" is generally used to indicate the vitality of individuals and populations. Assessments of ecosystem health are a fundamental part of ecosystem protection and monitoring. The concept of ecosystem health was first proposed by Rapport et al. (1985), who defined ecosystem health as the stability and sustainability of a system; that is, the ability of a system to maintain its organizational structure, self-regulate, and recover from stress. Costanza et al. (1992) proposed that "ecosystem health is closely related to the idea of sustainability, which is considered a global, multi-scale, and dynamic measure of system resilience, organization, and vigor." Based on these two definitions, mainly two types of approaches have been developed to assess the health of an ecosystem. In the first approach, the assessment is based on the structural and functional characteristics of different indicator species that reflect or predict the state and conditions of a particular ecosystem in a rather straightforward manner. However, this method has its limitations: the difficulty and lack of specific standards for selecting sensitive species, the lack of clarity, and the inability to include aspects of social, economic, and human health. In the second approach, the assessment is based on establishing a system of indicators. Indicators are measures that provide valuable information on various phenomena that are not directly accessible. They are used as a synthetic and meaningful way to represent reality and aim to facilitate concise and accurate information exchange between specialists and non-specialists (decision-makers). In general, ecological indicators are increasingly used to assess the environmental impact of various contaminants and the health conditions of ecosystems. Historically, the health of an ecosystem has been measured using a particular species or component as an index. Among the most common indicators, for example, are Ecosystem Services, a system of indicators easily accessible to non-specialist decision-makers, but it retains a view too closely tied to the concept of ecosystem health for humans. Such indices are inadequate because they are not comprehensive enough to reflect the complexity of ecosystems. Over the past twenty years, new approaches have been conceived and developed that better reflect the complexity of the ecosystem and its state of health. One of the most innovative approaches is based on exergy. Exergy represents the measure of the quality of energy; it represents the thermodynamic distance of a system from equilibrium with the surrounding environment and is a quantitative and qualitative measure of energy (mainly free energy in the context of ecosystems) embodied in a system. The application and development of the concept of exergy in ecology are mainly due to S.E. Jorgensen (2004), who introduces and defines structural exergy, or eco-exergy, determined by the relationship of total exergy to the total biomass of the analysed ecological system. The use of eco-exergy allows measuring certain aspects of an ecosystem, including the distance from thermodynamic equilibrium, structure and functions, survival capacity, energy use efficiency by ecosystem organisms, and the ability to regulate interactions between organisms or groups of organisms. However, eco-exergy deviates from the classical meaning of exergy and loses sight of a fundamental aspect of systems, namely being an indicator capable of reflecting the pure thermodynamic measures of work and energy. Eco-exergy does not reflect the actual work capacity of an ecological system.
Reflecting on this last point, the following doctoral work was conceived in such a way as to define the exergy of a Mediterranean forest system in a context of climate change, not by referring to the concept of structural exergy developed by S.E. Jorgensen but by referring to classical thermodynamic exergy.
To achieve the final goal, the thesis work was structured into a series of intermediate analyses.
Initially, a climatic analysis was conducted to determine the presence or absence of local climate change over a temporal data series (1951-2022) of temperature and precipitation from nearby weather stations and a meteorological station installed in the study area (operational since 2018). The analysis aimed to identify trends over time, which are also useful for predictive purposes. The first climatic analysis was performed using the Innovative Trend Analysis (ITA), a technique that allows for graphical evaluation of trends. To understand when a change occurs in the time series, a Changepoint analysis was used. This analysis can identify the point in time when certain statistical properties of a signal or sample in the dataset suddenly change. Once the trends were identified, an ARIMA model was used to obtain short-term (10-year) predictions of the climatic parameters. The climatic analyses confirmed the progressive increase in temperatures and extreme variability of precipitation locally. While the cumulative annual precipitation decreased, it increasingly manifested as concentrated episodes of extreme rainfall within a shorter time frame, resulting in an altered seasonality. The area is undergoing a local transition from a semi-arid to an arid climate. Subsequently, a framework was developed based on the integration of bottom-up and top-down approaches to quantify carbon and water fluxes at a local scale and assess the effect of climate change on these fluxes. The bottom-up approach involved measuring gas exchange and applying the MOCA model (Modelling for Carbon Assessment) to quantify photosynthesis and transpiration of the main vegetation species in the area. The top-down approach utilized satellite models C-fix and NDVI-CWS to calculate gross primary production (GPP), net primary production (NPP), and evapotranspiration (ETA) at the forest stand scale. The transition from leaf scale to stand scale was made possible by calibrating two crucial parameters for upscaling: light use efficiency (ε) and the vegetation coefficient of transpiration (Kcveg). The results for the study period (2018-2022) indicated a decrease in GPP, NPP, and ETA due to increased aridity conditions, particularly during the summer, caused by higher temperatures (16.60 vs. 16.98 °C) and significantly reduced local precipitation (1328 vs. 468 mm). The annual GPP in 2018 was 1186.83±351.40 gC/m2y, which decreased to 1128.67±331.21 gC/m2y in 2022. Considering the summer arid period, GPP ranged from 401.76±119.54 gC/m2y in 2018 to 347.39±101.62 gC/m2y in 2022. The annual ETA rates varied between 1184.86±351.86 mmH2O/m2y (2018) and 1304.92±384.07 mmH2O/m2y (2022). The summer ETA rates were strongly influenced by aridity conditions, decreasing from 638.73±190.67 mmH2O/m2y in 2018 to 390.35±108.83 mmH2O/m2y.
Once the carbon and water fluxes were defined, particularly after calculating evapotranspiration (ETA), it was possible to calculate the forest water balance by quantifying the variation in soil water content expressed as changes in Soil Water Content (ΔSWC, mm). The water balance calculation aimed to determine if there was a correspondence between climatic aridity and water conditions. The water balance calculations revealed that despite periods of aridity, both in summer and non-summer months, characterized by high temperatures and a lack of precipitation, the soil water content never dropped below the wilting point (Wp, 58.2 mm), even in the presence of a negative water balance. It is hypothesized that another factor within the water balance compensates for the significant water losses due to evapotranspiration that do not originate from meteoric input. Being a coastal site with a relatively constant relative humidity of around 80% and large diurnal temperature variations, especially during the arid summer period, and having a dense coexistence of shrub and tree species, microclimatic conditions may arise under the canopy that promote condensation, providing additional water input to the forest water balance.
Finally, after analysing how climatic and water conditions influence carbon and water fluxes, an exergetic model was applied at the forest stand scale to quantify system efficiency. The model is a simplified approach that assumes the entire canopy is assimilated as a single large leaf (Big Leaf approach). From the exergetic analysis, it is evident that the majority of the input energy (〖Ex〗_in), which represents the available or usable energy for performing useful work in the system, primarily comes from solar radiation absorption (approximately 95%), followed by energy from liquid water in the leaf (4%), and finally, to a negligible extent, from the energy of assimilated CO2 through the leaf (approximately 3*10-5%). The energy utilized for performing work (〖Ex〗_out) is used by the processes of sugar (〖Ex〗_su) and water (〖Ex〗_wL) formation to produce biomass (〖Ex〗_su+〖Ex〗_wL) (64%), diffusion of water in the absence of active transpiration (〖Ex〗_vap) (35%), and to a negligible extent, for heat convection (〖Ex〗_qk) (2*10-3%) and energy exchange (〖Ex〗_L) (1*10-4%) between the leaf and the atmosphere. Approximately 88% of the outgoing exergy is identified as lost exergy due to irreversible spontaneous processes. From the distribution of annual losses, most losses occur through radiative heat exchange between the leaf surface and the atmosphere 〖ex〗_cal (64.42%), evapotranspiration 〖ex〗_evap (35.26%), chemical reactions of photosynthesis 〖Ex〗_ch (0.20%), and heat convection between the leaf and the presence of the leaf boundary layer 〖Ex〗_qk (0.11%). Calculating the system efficiency based on the exergetic input-to-output ratios, an increase in efficiency was observed for the considered study period. The Bosco di Palo Laziale system, under conditions of extreme climatic variability tending towards aridity, does not appear to suffer from an energy perspective. On the contrary, it even manages to increase its capacity to withstand extreme climatic conditions by performing energetic work. This makes the system an ecosystem that can likely be defined as healthy.