Modelling the effects of nitrogen deposition and carbon dioxide enrichment on forest carbon balance

Atmospheric CO2 concentration ([CO2]) has increased over the last 250 years, mainly due to human activities. Of total anthropogenic emissions, almost 31% has been sequestered by the terrestrial biosphere. A considerable contribution to this sink comes from temperate and boreal forest ecosystems of the northern hemisphere, which contain a large amount of carbon (C) stored as biomass and soil organic matter. Several potential drivers for this forest C sequestration have been proposed, including increasing atmospheric [CO2], temperature, nitrogen (N) deposition and changes in management practices. However, it is not known which of these drivers are most important. The overall aim of this thesis project was to develop a simple ecosystem model which explicitly incorporates our best understanding of the mechanisms by which these drivers affect forest C storage, and to use this model to investigate the sensitivity of the forest ecosystem to these drivers. I firstly developed a version of the Generic Decomposition and Yield (G’DAY) model to explicitly investigate the mechanisms leading to forest C sequestration following N deposition. Specifically, I modified the G’DAY model to include advances in understanding of C allocation, canopy N uptake, and leaf trait relationships. I also incorporated a simple forest management practice subroutine. Secondly, I investigated the effect of CO2 fertilization on forest productivity with relation to the soil N availability feedback. I modified the model to allow it to simulate short-term responses of deciduous forests to environmental drivers, and applied it to data from a large-scale forest Free-Air CO2 Enrichment (FACE) experiment. Finally, I used the model to investigate the combined effects of recent observed changes in atmospheric [CO2], N deposition, and climate on a European forest stand. The model developed in my thesis project was an effective tool for analysis of effects of environmental drivers on forest ecosystem C storage. Key results from model simulations include: (i) N availability has a major role in forest ecosystem C sequestration; (ii) atmospheric N deposition is an important driver of N availability on short and long time-scales; (iii) rising temperature increases C storage by enhancing soil N availability and (iv) increasing [CO2] significantly affects forest growth and C storage only when N availability is not limiting.

Chemical, biochemical and microbiological indicators to assess soil quality in temperate agro-ecosystems

Soil is a critically important component of the earth’s biosphere. Developing agricultural production systems able to conserve soil quality is essential to guarantee the current and future capacity of soil to provide goods and services. This study investigates the potential of microbial and biochemical parameters to be used as early and sensitive soil quality indicators. Their ability to differentiate plots under contrasting fertilization regimes is evaluated based also on their sensitivity to seasonal fluctuations of environmental conditions and on their relationship with soil chemical parameters. Further, the study addresses some of the critical methodological aspects of microplate-based fluorimetric enzyme assays, in order to optimize assay conditions and evaluate their suitability to be used as a toll to asses soil quality. The study was based on a long-term field experiment established in 1966 in the Po valley (Italy). The soil was cropped with maize (Z. mays L.) and winter wheat (T. aestivum L.) and received no organic fertilization, crop residue or manure, in combination with increasing levels of mineral N fertilizer. The soil microbiota responded to manure amendment increasing it biomass and activity and changing its community composition. Crop residue effect was much more limited. Mineral N fertilization stimulated crop residue mineralization, shifted microbial community composition and influenced N and P cycling enzyme activities. Seasonal fluctuations of environmental factors affected the soil microbiota. However microbial and biochemical parameters seasonality did not hamper the identification of fertilization-induced effects. Soil microbial community abundance, function and composition appeared to be strongly related to soil organic matter content and composition, confirming the close link existing between these soil quality indicators. Microplate-based fluorimetric enzyme assays showed potential to be used as fast and throughput toll to asses soil quality, but required proper optimization of the assay conditions for a precise estimation of enzymes maximum potential activity.

q-Hook length formulas for colored labeled forests

The major index has been deeply studied from the early 1900s and recently has been generalized in different directions, such as the case of labeled forests and colored permutations. In this thesis we define new types of labelings for forests in which the labels are colored integers. We extend the definition of the flag-major index for these labelings and we present an analogue of well known major index hook length formulas. Finally, this study (which has just apparently a simple combinatoric nature) allows us to show a notion of duality for two particular families of groups obtained from the product G(r,n)×G(r,m).

Machine learning per l'idrologia: applicazione del metodo random forests per la previsione degli eventi di piena fluviale con un approccio probabilistico

Le tecniche di Machine Learning sono molto utili in quanto consento di massimizzare l’utilizzo delle informazioni in tempo reale. Il metodo Random Forests può essere annoverato tra le tecniche di Machine Learning più recenti e performanti. Sfruttando le caratteristiche e le potenzialità di questo metodo, la presente tesi di dottorato affronta due casi di studio differenti; grazie ai quali è stato possibile elaborare due differenti modelli previsionali. Il primo caso di studio si è incentrato sui principali fiumi della regione Emilia-Romagna, caratterizzati da tempi di risposta molto brevi. La scelta di questi fiumi non è stata casuale: negli ultimi anni, infatti, in detti bacini si sono verificati diversi eventi di piena, in gran parte di tipo “flash flood”. Il secondo caso di studio riguarda le sezioni principali del fiume Po, dove il tempo di propagazione dell’onda di piena è maggiore rispetto ai corsi d’acqua del primo caso di studio analizzato. Partendo da una grande quantità di dati, il primo passo è stato selezionare e definire i dati in ingresso in funzione degli obiettivi da raggiungere, per entrambi i casi studio. Per l’elaborazione del modello relativo ai fiumi dell’Emilia-Romagna, sono stati presi in considerazione esclusivamente i dati osservati; a differenza del bacino del fiume Po in cui ai dati osservati sono stati affiancati anche i dati di previsione provenienti dalla catena modellistica Mike11 NAM/HD. Sfruttando una delle principali caratteristiche del metodo Random Forests, è stata stimata una probabilità di accadimento: questo aspetto è fondamentale sia nella fase tecnica che in fase decisionale per qualsiasi attività di intervento di protezione civile. L'elaborazione dei dati e i dati sviluppati sono stati effettuati in ambiente R. Al termine della fase di validazione, gli incoraggianti risultati ottenuti hanno permesso di inserire il modello sviluppato nel primo caso studio all’interno dell’architettura operativa di FEWS.

Coastal Forests of Kenya-Ecology, Biodiversity & Conservation

Aims: the broad objective of this study is to investigate the ecological, biodiversity and conservation status of the coastal forests of Kenya fragments. The specific aims of the study are: (1) to investigate current quantitative trends in plant diversity; (2) develop a spatial and standardised vegetation database for the coastal forests Kenya; (3) investigate forest structure, species diversity and composition across the forests; (4) investigate the effect of forest fragment area on plant species diversity; (5) investigate phylogenetic diversity across these coastal remnants (6) assess vulnerability and provide conservation perspectives to concrete policy issues; (7) investigate plant and butterfly diversity correlation. Methods: I performed various analytical methods including species diversity metrics; multiple regression models for species-area relationship and small island effect; non-metric multidimensional scaling; ANOSIM; PERMANOVA; multiplicative beta diversity partitioning; species accumulation curve and species indicator analysis; statistical tests, rarefaction of species richness; phylogenetic diversity metrics of Phylogenetic diversity index, mean pairwise distance, mean nearest taxon distance, and their null-models: and Co-correspondence analysis. Results: developed the first large standardised, spatial and geo-referenced vegetation database for coastal forests of Kenya consisting of 600 plant species, across 25 forest fragments using 158 plots subdivided into 3160 subplots, 18 sacred forests and seven forest reserves; species diversity, composition and forest structure was significantly different across forest sites and between forest reserves and sacred forests, higher beta diversity, species-area relationship explained significant variability of plant diversity, small Island effect was not evident; sacred forests exhibited higher phylogenetic diversity compared to forest reserves; the threatened Red List species contributed higher evolutionary history; a strong correlation between plants and butterfly diversity. Conclusions: This study provides for the first time a standardized and large vegetation data. Results emphasizes need to improve sacred forests protection status and enhance forest connectivity across forest reserves and sacred forests.