UNIVERSITÁ DEGLI STUDI DI CAGLIARI Dipartimento Di Scienze .
UNIVERSITÁ DEGLI STUDI DI CAGLIARIDipartimento di Scienze della Vita e dell’AmbienteScuola di Dottorato inIngegneria e Scienze per l’Ambiente e il TerritorioCorso di Dottorato in Scienze e tecnologie della Terra e dell’AmbienteCoordinatore: Prof. Pierfranco LattanziPhilosophiæ Doctor Research ProposalThe endemic vascular flora of Sardinia: analyses, distribution patterns,ecological processes and implications for conservationCandidate: Mauro FoisTutor: Prof. Gianluigi Bacchetta1,Co-tutor: Doc. Giuseppe Fenu11Centro Conservazione Biodiversità (CCB), Dip. di Scienze della Vita e dell'Ambiente, Università di Cagliari
Table of contents:Introduction3Aim and objectives of the study5Materials and Methods6Species update, selection and data collection6Sardinian Biogeographical definition6Analysis of species richness distribution in the system of islets around Sardinia7Environmental drivers and species richness relationships8Sardinian Micro- and Nano-Hotspots identification9Species Distribution Models (SDM)9Research schedule12References132
IntroductionWith species extinction rates running 100 times the background rate and poised to increase another10-fold (Pimm et al., 2006), assessing the success of conservation efforts is vital (Joppa et al., 2007).Human impacts, such as agricultural expansion, logging, overexploitation, climate change andinvasive species, are widely recognized as the major drivers of the current global extinction crisis(Davies et al., 2006).In this context, the identification of biotic and abiotic parameters that determines the biodiversityrichness at different scales, the distribution patterns (Cañadas et al., 2014; Shatz et al., 2014) andthe prediction of biodiversity evolution are, now-a-days, a central issue. Several studies at largescale (e.g. Takhtajan 1978; Myers et al. 2000; Rivas-Martínez, 2007) have been performed; then, aninvestigation at a finer resolution has been developed in order to better satisfy the definition of taxadistributions and best conservation strategies. In particular, a local scale is necessary where there’san exceptional concentration of species in a small area (Fenu et al., 2010, Cañadas et al., 2014).The Mediterranean basin, with its 11.8 endemic taxa per 100 Km2, has been recognised as one ofthe priority regions for conservation in Europe and it has been identified as one of the 25/34 mostimportant “biodiversity hotspots” of the planet (Myers et al., 2000; Mittermeier et al., 2005).Specifically, Mediterranean islands and islets are not only singular for their species richness, but alsofor the high endemicity rates (Rosselló et al., 2009).Sardinia and its c. 400 circum-Sardinian islands (including four archipelagos), covering 24090 km2,are located in the western Mediterranean basin. The Sardinian flora consists of ca. 2408 taxa (Contiet al., 2005), 322 of which are endemic sensu lato (i.e., endemic of Sardinia and other insularterritories of the western Mediterranean subregion) and 180 are exclusive (Peruzzi et al. 2014).During last decades, modern qualitative researches methodologies generate a rich taxonomicinformation that has stimulated a floristic update even for Sardinia (e.g. Bacchetta et al., 2012a; b).Such floristic peculiarities, especially on its mountain massifs (Medail et Quezel, 1997), is mainly dueto its paleogeography, climatic conditions, habitat heterogeneity and the varying origins of the floraitself (e.g. Medail et Quezel, 1997; Thompson, 2005; Mansion et al. 2008; Blondel et Medail, 2009;Bacchetta et al., 2012c).According to this exceptional regional vascular flora variety and specificity (Tab. 1), further detailedinvestigations from a general biogeographical definition (e.g. Ladero Alvarez et al., 1987; RivasMartınez et al., 2002) and hotspots identification (e.g. Medail et Quezel, 1997; Myers et al., 2000)3
have been performed. In particular, several studies have identified biogeographical units (sectors,subsectors and districts) within the Sardinia island (e.g. Bacchetta et Pontecorvo, 2005; Fenu etBacchetta, 2008; Angius et Bacchetta, 2009; Fenu et al., 2010; Bacchetta et al., 2013) and newmethods in hotspot settings and identification at regional scale are defined basing on the Sardiniacase of study (Fenu et al., 2010; Bacchetta et al., 2012c; Cañadas et al., 2014). Besides that, animportant part of the aspects that determines the actual and future species distribution in Sardinia(e.g. Species Area Relationships - SARs, Climate change impacts) still lacks of a methodological andsystematic investigation.In particular, the islets compendium surrounding Sardinia is, biogeographically andconservationally, one of the most important of the Mediterranean area. Several studies have beenperformed in other Mediterranean contexts such as the Aegean archipelago (e.g. Panitsa etTzanoudakis, 1998; Triantis et al., 2005; Kougioumoutzis et Tiniakou, 2014); besides that, except forsome conclusions originated by floristic and zoological studies (e.g. Arrigoni et Bocchieri, 1996;Bocchieri, 1996; Poggesi et al., 1996), still unknown the ecological factors driving plant speciesdiversity in circumsardinian islands. At the same time, although the high rate of rare plants and theexposition in future climate change projections (IPCC, 2007; Ruiz-Labourdette et al., 2013) there isa no scientific and comprehensive knowledge about the climate change effect on plant richness inSardinia.Extent (km²)Endemic plant density (taxon per 100 km2)Human population density (people/ km²)Protected area (km²)MedHotspotSardinia2.382.000a11,8a111a42.132 (1,7%)a24.09015,468b4.668 (19%)bTab.1: Comparison between the Mediterranean hotspot (MedHotspot) and the Sardinian maincharacteristics. a:Mayers et al., 2000; b: Gambino et al., 20024
Aim and objectives of the studyWith the development of pattern analysis instruments, several processes of investigation willcarried out in order to contribute the implementation of a useful knowledge for the developmentof conservation planning both at administrative and biogeographical level, according to the criterionof “regional responsibility” (Bacchetta et al., 2012c, Mattana et al. 2012).The objectives of the research project to conduct during the PhD program are:1. To develop a methodology for a comprehensive and detailed biogeographic definition ofSardinian territories within the Mediterranean context;2. To analyze the species compendium of the circumsardinian islets and the explanatoryvariables that determine current distribution patterns even in relation with the main Island;3. To investigate the relationships between environmental drivers and species-richness for theidentification of the factors that mostly influence their distribution patterns at regional level.4. To define the most important areas for the phytodiversity conservation through theidentification of micro- and nano-hotspots.5. To model, through the study of current and future environmental drivers related with actualtaxa presence, the distribution of some interesting species from a conservational andecological point of view.5
Materials and MethodsEach methodology chapter below illustrated is divided following the five main objectives based onthe different principal topics of studies. However, for some of them, the final outcome will beachieved reaching intermediate results that will be also illustrate.Species update, selection and data collectionA preliminary work will consist in a necessary update of the checklist of Sardinian endemic vascularplants (Bacchetta et al., 2012c, d). A chorological definition and selection of the analyzed taxa willbe done afterwards. Principal researches will structured on the vascular endemic plants, thus, formore detailed studies, it will be evaluate case by case the addition of additional selected vasculargroups distribution data (e.g. Orchidaceae). All the presence information from the availableliterature (e.g. Bacchetta et Pontecorvo, 2005; Bacchetta, 2006; Fenu et Bacchetta, 2008; Angius etBacchetta, 2009; Fenu et al., 2010; Bacchetta et al., 2013), as well as from herbarium specimensconserved in several botanical museums (CAG, CAT, FI, RO, SASSA, SS, TO) will be digitalized by aGIS Software into a 1-km2 grid cells. All records will constitute the first Sardinian flora geodatabaseand the methodology will be assumed as a usual practice of the Centre of investigation “Centre ofConservation of Biodiversity” (CCB) in order to constantly continue the database updating.Sardinian Biogeographical definitionA biogeographical analysis will be performed for the Sardinian territory and a comprehensivesubdivision in sectors and subsectors units will be done. Then, a more detailed biogeographicalanalysis will be deeper developed for the most isolated and well known unit previously defined.Accordingly to the shift from a regional to a local scale of study, two different methodologies will betested.Because of the high geomorphological and ecological diversity spread through the Sardinianterritory, a previous identification of main biogeographical units will be necessary. The basicgeological units will grouped into homogeneous polygons through the integration of geological(Carmignani et al., 2001) and geomorphological maps of Sardinia (Ulzega, 1988), with a minimumsurface area of 500 km2; some limits will be adjusted using elevation or other cartographic sources(e.g. hydrographic and land-use maps).6
Thus, a presence–absence matrix will be built by ascribing the georeferenced data of endemic taxato the simplified geological units.The data matrix will be analyzed by a hierarchical cluster analysis using “hclust” function, includedin the R vegan package (Oksanen et al., 2012). Euclidean distance and arithmetic averages will beused as clustering options, since they provided interpretable results. This analysis will be performedfollowing the procedure successfully tested by other authors (Reyjol et al., 2007; Reygondeau etal.,2012), by selecting two cut-off levels to define two levels of biogeographical units (sectors andsubsectors). The resulting units will be named in relation to local toponymy, according to the systemproposed by Rivas-Martínez et al. (1997).As previous recognized in literature (e.g. Heikkinen et al.,1998; Echeverry et Morrone, 2010;Medina-Cazorla et al., 2014), a more detailed biogeographical definition of the territory is seldomnecessary to better understand the real ecological processes that determine a specific locale speciesdistribution. Starting from the previous results, a new methodological approach (in Sardinia) for thedefinition of smaller biogeographical units will be tested on the best previously defined area.According to the classification proposed by Rivas-Martínez et al. (1997), the biogeographic sublevelunits will be recognized as “districts”. As stated by previous same-scale studies (see previously citedliterature), a coherent geomorphological units identification is not possible in this case, thus, theanalysis will be performed from a matrix of species presence in a homogenous 1 x 1 km grid. Thehierarchical cluster analysis will be performed comparing the results of the two most universallyaccepted agglomerative methods for small and spatially homogenous units: the UnweightedPairGroup Method using arithmetic Averages - UPGMA (e.g. Màrquez et al., 2001; Moreno Saiz etLobo, 2008; Kreft et Jetz, 2010; Luna-Vega et al., 2013) and the Parsimony Analysis -PA (e.g.Morrone, 1994; Medina-Cazorla et al., 2010, Moreno Saiz et al., 2013; Sillero 2009, 2014). To havea more performant result at a high detailed scale, the overall endemic flora will be used instead theprevious restricted number of entities and it will be evaluated the contribution of the addition offurther occurrence data (e.g. vegetation map and Orchidaceae taxa). All the analysis will beperformed by open source Softwares as the R vegan package (Oksanen et al., 2012), PAST (Hammeret al., 2001).and TNT 1.1 (Goloboff et al., 2000).Analysis of species richness distribution in the system of islets around Sardinia7
An additional study will be focused on the circumsardinian flora; in particular, the analysis will affectthe 57 islets with almost one vascular endemic taxa. Species presence data is available frombibliographic sources (e.g. Biondi et al., 1995; Argano, Manicastri,1996; Bocchieri, 1996; Poggesi etal., 1996; Taiti, Argano, 2011) and explicative variables will be calculated by Spatial analysis tools:Some different species richness will be computed for each islet:St: Total species richness examined;Svt: total vascular species richness;Svesa-Sveti: Exclusive endemic species richness and Tyrrhenian species richness;Svt\Svesa-Svt\Svesa: Vascular species ratio between the total and endemic richnessesThus, following variables will be correlated with the species richnesses:A: Islet Area in km2;Dm-Ds: As stated in previous works (e.g. Panitsa et al. 2010; Troia et al., 2012; Weigelt et al., 2013),it will be evaluated the most significant kind of distance calculated from the mainland (Sardinia; Dm)or closest possible source (Islet with more than 10 vascular endemisms; Ds);H: Habitat diversity; the number of biotypes present in each islet will be assessed from the CORINEBIOTYPES map of Sardinia (ISPRA, 2005);E: Elevation, altitude manually calculated from the 1:25000 Istituto Geografico Militare (IGM) map;The modelling process will be conducted in two steps: firstly, simple regression analyses will beperformed in order to assess separately the relationship between Species Richness (SR) and eachexplanatory variable; secondly, a series of Multivariate Models resulting from the combination ofthe variables showing significant bivariate relationships with SR will be carried on. The best modelswill be selected basing on the best explained species richness EVPR variability by means of thecorrected Akaike Information Criterion (AICc). The differences between the AIC value of the bestmodel and the values of each model ranked below it (ΔAIC) provide information for evaluating whichmodels in a set are as plausible as the best model.Environmental drivers and species richness relationshipsSpecies pattern distributions will be overlaid on several environmental data acquired from opensource databases. Specifically, the climatic variables and altitude will be downloaded from theWorldClim database (version 1.4 years 1950–2000; Hijmans et al., 2005). Additional information willbe obtained from the REGIONE SARDEGNA website (e.g. Land use map, Geological map, 2008a;2008b) and from other authors (e.g. Vegetation map, Lithological map, Morphological map; Ulzega,8
1988; Aru et al., 1992; Bacchetta et al. 2009).Simple regression analyses between EVPR and of Multivariate Models resulting from thecombination of the most explanatory variables will follow the same methodology before illustratedfor the small islands species richness linear and multivariate models.Sardinian Micro- and Nano-Hotspots identificationAs it’s recently published for Sierra Nevada and Gennargentu massifs (Cañadas et al., 2014), a studyfocused on the micro (5-km2 grid cells) and nano-hotspots (1-km2 grid cells; Fenu et al., 2010)identification will be developed for the complete Sardinian territory; in this way, it will be definedthe distribution of most important areas for conservation. To assess endemic vascular-plant richness(EVPR), a presence/absence data matrix for the complete Sardinian territory will be built on theendemic plant geodatabase information before described. Thus, the number of taxa per grid (1 km2and 5 km2 respectively for the Micro- and Nano-Hotspots identification) will be evaluated. Acomparison between the Micro-Nano-hotspots and the current protected areas will be performedin order to evaluate the efficiency of the actual conservation plans. As it is previously tested, a gapanalysis comparing Natura 2000 vs National Protected Area network will performed for both microand nano-hotspots in order to have an exhaustive tool for the conservation areas designing inSardinia.Species Distribution Models (SDM)Species distribution models (SDMs) have emerged as an effective tool in conservation management(Raxworthy et al., 2003; Rushton et al., 2004; Elith et al., 2006) and future threats prediction (e.g.Thuiller et al., 2005; Elith et al., 2006; Tabor et Williams, 2010). Thus, in particular when includingfine scale environmental variables, SDMs are a useful tool for the evaluation of suitable habitats andfor new population discovery. First SDM will carried out for some interesting plants test. In relationwith actual knowledge and conservation planning priorities, it will be chosen an orophil plantexclusive of the Gennargentu massifs (i.e. Gentiana lutea L.). The analysis, finalized to theidentification of further habitat suitable areas, will be performed with few known presence points(less than 10) and the same environmental drivers previously described. Basing on first results,models will validate through several field surveys and evaluated by the Area Under the ROC(Receiver Operating Characteristic) Curve (AUC) statistical values (Lobo et al., 2008). Thepresence-only data will be employed to fit the model and the Maximum Entropy (MaxEnt) algorithm9
to estimate the extent of occurrence of G. lutea. Maxent software 3.2.1, which uses an algorithm ofthe same name, has demonstrated good results when using only presence data of species withlimited biological information (Gogol-Prokurat, 2011, and Phillips et al., 2006) and it’s particularlyappropriated for analyses with small sample sizes (Hernandez et al. 2006; Pearson et al., 2007).The same Maximum Entropy (MaxEnt) algorithm will be employed for two temporal variables:current and future climate scenarios. Current data will derive from Worldclim (years omthewebdataportalhttp://ccr.aos.wisc.edu/climate modeling/ipcc/index.html (years 2050, 2090; Tabor et Williams,2010). Excess predictors in a Maxent model can cause over-fitting and hence bias the responsesunder future scenarios by over-weighting certain drivers over others (Warren et Seifert 2010).Hence, following Warren et al. (2013), the number of predictors in the Maxent model for specieswill be reduced to the vascular endemic taxa with low numbers of occurrences.Models performance will evaluated with using the Area under the ROC (Receiver OperatingCharacteristic) Curve of the test data (AUCTest), calculated as the average AUCTest of the 10 runs.Results of different model predictions will be useful to assess the impact of climate change in speciesassemblages (protected areas, ISA) and in individual species (IIS).For the ISA evaluation, the species turnover will be calculated with a modified Broennimann Index(Broennimann et al., 2006)species turnover 100 (number of species gained or -1 number of species lost)/ speciesrichness)This turnover index has a lower limit of -100 when projected species richness (PSR) is 0 and 100 when PSR is the double; in case that the species richness is unchanged, the Index Valuewill be 0.To estimate the sensitivity to climate change at the species level for both migration periods, thecurrent and future climatic niches will be intersected and the climatic niche persistencecalculated. This is defined as the percentage of area that remains suitable in relation to the totalarea in the current climatic niche.10
OutcomesThe main expected outcome for this project is to build and then analyse a species occurrencedataset that could be constantly implemented and updated through the years. This kind ofinstrument will have a great quantity of potential outcomes that will vary from the conservationassessment issues to the species distribution modelling for the prediction of new rare plantsoccurrences or future scenarios.Principal expected results could be briefly resumed as follows:1. The elaboration of a comprehensive
UNIVERSITÁ DEGLI STUDI DI CAGLIARI Dipartimento di Scienze della Vita e dell’Ambiente Scuola di Dottorato in Ingegneria e Scienze per l’Ambiente e il Territorio Corso di Dottorato in Scienze e tecnologie della Terra e dell’Ambiente Coordinatore: Prof. Pierfranco Lattanzi Philosophiæ Doctor Research Proposal
cagliari, 10/10/2014 . universitÀ degli studi di cagliari . dipartimento di sanita’ pubblica, medicina clinica e molecolare . scuola di specializzazione di medicina del lavoro . documento di valutazione dei rischi per la salute e la sicurezza degli studenti della facolta’ di medicina e chirurgia
UNIVERSITÀ DEGLI STUDI DI CAGLIARI DIPARTIMENTO DI PEDAGOGIA, PSICOLOGIA, FILOSOFIA VERBALE DELLA COMMISSIONE INCARICATA DELL'AFFIDAMENTO DEGLI INCARICHI DI INSEGNAMENTO V ACANTI PER L' A.A. 2013/14 SSD M-PSI/01, M-PED/01, M-PED/03 La Commissione, nominata dal Dipartimento di Pedagogia, Psicologia, Filosofia, è composta da: Pro f.
UNIVERSITA’ DEGLI STUDI DI CAGLIARI DIPARTIMENTO DI STUDI STORICI GEOGRAFICI E ARTISTICI DOTTORATO DI RICERCA IN STORIA MODERNA E CONTEMPORANEA XXIII CICLO L’INFANZIA ABBANDONATA NELLA SARDEGNA MODERNA: IL PADRE D’ORFANI Settore scientifico disciplinare di afferenza: M-STO/2 – Storia moderna
Desidero ringraziare il Dipartimento di Filologie e Letterature Moderne dell’Università degli Studi di Cagliari per il supporto ottenuto in questi quattro anni, in particolare le mie tutores, le proff.sse Luisa Mulas e Maria Antonietta Cortini. È stato un anno difficile,ma siamo arrivati in porto.
Matita Tutorial ANDREA ASPERTI DISI: Dipartimento di Informatica, Universit a degli Studi di Bologna and . DISI: Dipartimento di Informatica, Universit a degli Studi di Bologna This tutorial provides a pragmatic introduction to the main functionalities of the Matita interac-tive theorem prover, o ering a guided tour through a set of not so .
3 Scienti c and Organizing Committees 3.1 Scienti c Committee ( ) Liliane Basso Barichello (UFRGS, Porto Alegre, lbaric@mat.ufrgs.br)( ) Piermarco Cannarsa (Universit a di Roma Tor Vergata, Roma,cannarsa@axp.mat.uniroma2.it)( ) Ciro Ciliberto (Universit a di Roma Tor Vergata, Roma, cilibert@axp.mat.uniroma2.it)- co-chair ( ) Giorgio Fotia (Universit a di Cagliari, Giorgio.Fotia@crs4.it)
UNIVERSITY of CAGLIARI Department of Physics Address: Dipartimento di Fisica ‐ Cittadella Universitaria, 09042 Monserrato (Ca) Phone: 070 675 4703 Fax: 070 510 171 E‐mail: direttore.fisica@dsf.unica.it 3 1. OUTLOOK The Department of Physics (DSF) of the University of Cagliari (UniCa) is the only academic
others are just rough paths. Details are given in a document called the Hazard Directory. 1.3 Signals Most running lines have signals to control the trains. Generally, signals are operated from a signal box and have an identifying number displayed on them. Signals are usually attached to posts alongside the track but can also be found on overhead gantries or on the ground. Modern signals tend .
