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Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle sue Risorse

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Presentazione sul tema: "Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle sue Risorse"— Transcript della presentazione:

1 Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle sue Risorse Ecosystems and global services : an outlook on forest and mountain region

2 Welcome in the Anthropocene ! CO 2 CH 4 N2ON2O

3 2007 un anno per il Clima 4° Rapporto Intergovernativo sui Cambiamenti Climatici Premio Nobel per la Pace Artico si scioglie Il delfino Baiji è estinto Bush torna su i suoi passi ? Un film sul clima

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5 Regulating Benefits obtained from regulation of ecosystem processes Cultural Non-material benefits from ecosystems Provisioning Goods produced or provided by ecosystems What was unique? Ecosystem services Photo credits (left to right, top to bottom): Purdue University, WomenAid.org, LSUP, NASA, unknown, CEH Wallingford, unknown, W. Reid, Staffan Widstrand

6 Source: NASA

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8 2.4 Ocean Uptake Land Uptake 2.2 Land-Use Change 6.3 F Fuel, Cement Global C Budget Atmosphere Surface biosphere Atmospheric accumulation rate 3.2 GtC per year 1990s 2.9 Fast process (1 – 10 2 days) Slow process (10 3 – 10 4 days) Gruber et al 2003, SCOPE project

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10 Valentini, Dolman, Matteucci et al. Nature 2000

11 BIOSPHERE Source or sink ? VULNERABILITY OF BIOSPHERE (feed-backs with carbon cycle) Coupled carbon- climate models

12 Vulnerability of Carbon Pools Gruber et al Carbon in frozen soils: 400 PgC Carbon in wetlands: 450 PgC Carbon in tropical vegetation: 340 Pg Risk over the coming century of up to 200 ppm of atmospheric CO 2 Not included in most climate simulations.

13 1,7 MILIONI DI SPECIE CONOSCIUTE 15 MILIONI SPECIE STIMATE SULLA TERRA 90% DELLE SPECIE SCONOSCIUTE ……BIODIVERSITA IN CIFRE……

14 Change in Species Diversity Number per Thousand Species Extinctions (per thousand years) Number of Species Homogenization (e.g. growth in marine species introductions) North America Europe 100 to fold increase Source: Millennium Ecosystem Assessment

15 The experimental site is located in a farm (Malga Arpaco) at 1699 m a.s.l. Mean annual temperature: 5 °C Total annual rainfall: 1200 mm Soil type: Typic Hapludalfs, fine loamy (FAO) Ecosystem type: alpine semi-natural grassland Ecosystem management: extensive management, pasture from Jun to Sep Period of EC measurements: Eddy Covariance type: Metek USA-1, Li-cor 7500 Tower height: 2 m

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18 N2O emission and CH4 uptake was evaluated fortnightly, during 2003 and 2004 pasture season, using diffusion chambers. Gas samples conserved in vacuum vials were analysed through gaschromatography technique. For the N2O: ECD detector at 320°C; for the separation a capillary column Cromosob 1010 at 140°C was used, with a flux of helium at 30 kPa. For the CH4: FID detector at 180°C; for the separation a column 4m x ¼ OD Porapak q 80/100 MESH at 30° was used.

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21 The human foot print Data Magnani et al., 2007

22 Luyssaert et al., submitted

23 Annual mean : 35 M m3 of forest wood damaged by natural disturbances in Europe. 53% wind throw 16% fire 16% biotic (insects) 3% snow 5% other abiotic Extreme climate events or disturbances have a strong effect on biosphere-astmosphere exchanges Tatra Experiment CarboEurope

24 QUALCHE ESEMPIO Phythopthora cinnammomi, uno degli agenti causali del mal dellinchiostro del castagno, è attualmente ristretta a quelle aree in cui la temperatura minima non scende al di sotto di 0°C (vedi grafico a destra). Un aumento delle temperature minime di 2- 4°C, teoricamente verificabile nellarco di anni, porterebbe questa specie ad espandere il suo areale alle zone castanicole dove sono oggi presenti specie di Phytophthora meno aggressive quali P. cambivora, P. cactorum e P. citricola La spiccata polifagia di P. cinnamomi, permetterebbe inoltre al patogeno di colonizzare nuovi ospiti precedentemente non raggiungibili per limiti climatici. Malattie epidemiche causate da organismi introdotti Vannini, Anselmi et al Progetto CarboItaly

25 QUALCHE ESEMPIO Malattie endemiche causate da organismi nativi Biscogniauxia mediterranea, è un fungo Ascomycota che vive comunemente come endofita indifferente allinterno dei tessuti corticali e legnosi di querce mediterranee. Durante eventi particolarmente siccitosi, quando il potenziale idrico fogliare minimo dellospite raggiunge valori inferiori a -2.0 MPa, la popolazione endofitica va gradatamente aumentando (vedi grafico) fino a quando, a valori inferiori a -3.0 MPa, il fungo passa dalla fase endofitica a quella patogenetica aggredendo rapidamente i tessuti dellospite e causando il cosiddetto cancro carbonioso delle querce. Laumento delle temperature estive e la maggior frequenza di fenomeni estremi, tra cui la siccità, potrebbero attivare un alto numero di organismi comunemente silenti innescando pericolosi eventi di deperimento di cenosi forestali Vannini, Anselmi et al Progetto CarboItaly

26 Driving factors influencing distribution Actual species distribution Statistical analysis Probability of occurrence Future spatial distribution Scenarios of future driving factors Neighborhood criteria Spatial modelling of forest patterns in dependence by location characteristics is a reliable way to analyze the possible trajectories and shifts of species habitat in the near future if environmental conditions will change. Forest patterns

27 Forest Map of Italy (1:100000) raster 250 meters of resolution Physiognomic categories % 00 - Woody plantation in agricultural areas Oaks and other evergreen broadleaf forests Deciduous oak-dominant forests Chestnut-dominant forests Beech-dominant forests Hygrophyte species-dominant forests Other broadleaf deciduous autochthon species-dominant forests Exotic broadleaf-dominant forests and plantations Mediterranean pine and cypress dominant forests Oro-Mediterranean and mountain pine dominant forests Abies alba and Picea rubens dominant forests Larch and cembrus pine dominant forests Exotic needleleaf dominant forests Mixed needleleaf and broadleaf forests with prevalent beech Mixed needleleaf and broadleaf forests with prevalent oro-mediterranean and mountain pine Mixed needleleaf and broadleaf forests with prevalent Abies alba and/or Picea rubens Mixed needleleaf and broadleaf forests with other species prevalent Tall Mediterranean Macchia 3.35 Driving factors influencing distribution Scenarios future driving factors Actual species distribution Statistical analysis Probability of occurrence Future Spatial Distribution Calibration Error in rasterization -0.15% 26% of Italian territory is forest

28 Driving factors influencing distribution Actual species distribution Statistical analysis Probability of occurrence Future Spatial Distribution Scenarios future driving factors Calibration Mean annual precipitation (mm) Mean annual snow water equivalent (mm) Mean daily short wave net radiation (W/m 2 ) Mean of the annual dew point temperature (°K) Mean of the minimum annual temperature (°K) Mean of the maximum annual temperature (°K) DEM srtm Elevation values (m above sea level) Slope value (°) Aspect value (° clockwise from north) DMI F12 A2

29 where Pi is the probability for the occurrence of the considered forest type on location i and the x's are the location factors (independent variable values) forcing the presence/absence of forest classes. Driving factors influencing distribution Actual species distribution Statistical analysis Probability of occurrence Future Spatial Distribution Scenarios future driving factors Calibration Driving factors influencing distribution Actual species distribution Statistical analysis Probability of occurrence Future Spatial Distribution Scenarios future driving factors Calibration Mean ROC Logistic regression ROC i.e.ROC curve test for class 8 Accuracy

30 Driving factors influencing distribution Actual species distribution Statistical analysis Probability of occurrence Future Spatial Distribution Scenarios future driving factors Neighbooring criteraia Calibration Example of Euclidean distance gridExample of distance-based probability grid Pi v Driving factors influencing distribution Actual species distribution Statistical analysis Probability of occurrence Future Spatial Distribution Scenarios future driving factors Neighbooring criteraia Calibration

31 Altitude profiles of forest distribution Case a) Changed areas (red, 82%) considering only statistical analysis Case b) Changed areas (red, 77%) considering statistical analysis and neighborhood criteria Actual distribution Case a) Case b) Forest classes 00 - Woody plantation in agricultural areas 01 - Oaks and other evergreen broadleaf forests 09 - Oro-Mediterranean and mountain pine dominant forests 10 - Abies alba and Picea dominant forests 11 - Larch and cembrus pine dominant forests 12 - Exotic needleleaf dominant forests 13 - Mixed needleleaf and broadleaf forests with prevalent beech 14 - Mixed needleleaf and broadleaf forests with prevalent oro-mediterranean and mountain pine

32 CONCLUSIONS Climate change will impact mountain ecosystems in different and possible unexpected ways (increase productivity, decrease biodiversity…) The human dimension is still important Conservation of old forests preserve ecosystem services

33 You can observe a lot, just by watching. -Yogi Berra

34 Thank You


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