Riccardo Valentini Università della Tuscia Dipartimento di Scienze dell’Ambiente Forestale

1. Ecosystems and global services : an outlook on forest and mountain region Riccardo Valentini Università della Tuscia Dipartimento di Scienze dell’Ambiente Forestale e delle sue Risorse rik@unitus.it http://gaia.agraria.unitus.it

2. CO2 CH4 N2O Welcome in the Anthropocene !

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

5. What was unique?Ecosystem services • Regulating • Benefits obtained from regulation of ecosystem processes • Provisioning • Goods produced or provided by ecosystems • Cultural • Non-material benefits from ecosystems 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

8. Fast process (1 – 102 days) Slow process (103 – 104 days) Global C Budget Atmospheric accumulation rate 3.2 GtC per year 1990s Atmosphere Surface biosphere 6.3 F Fuel, Cement 2.2 Land-Use Change 2.9 Land Uptake 2.4 Ocean Uptake Gruber et al 2003 , SCOPE project

10. Valentini, Dolman, Matteucci et al. Nature 2000

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

12. Carbon in tropical vegetation: 340 Pg Carbon in wetlands: 450 PgC Carbon in frozen soils: 400 PgC • Risk over the coming century of up to 200 ppm of atmospheric CO2 • Not included in most climate simulations. Vulnerability of Carbon Pools Gruber et al. 2004

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

14. Number of Species North America Europe 1790 1900 2000 Homogenization (e.g. growth in marine species introductions) Change in Species Diversity Number per Thousand Species 100 to 1000-fold increase Extinctions (per thousand years) 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: 2003-2007 Eddy Covariance type: Metek USA-1, Li-cor 7500 Tower height: 2 m

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.

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

22. Luyssaert et al., submitted

23. Extreme climate events or disturbances have a strong effect on biosphere-astmosphere exchanges Annual mean 1850-2000: 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 Tatra Experiment CarboEurope

24. Vannini, Anselmi et al. 2007 Progetto CarboItaly QUALCHE ESEMPIO Malattie epidemiche causate da organismi introdotti Phythopthora cinnammomi, uno degli agenti causali del mal dell’inchiostro 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 nell’arco di 20-40 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.

25. QUALCHE ESEMPIO Vannini, Anselmi et al. 2007 Progetto CarboItaly Malattie endemiche causate da organismi nativi Biscogniauxia mediterranea, è un fungo Ascomycota che vive comunemente come endofita indifferente all’interno dei tessuti corticali e legnosi di querce mediterranee. Durante eventi particolarmente siccitosi, quando il potenziale idrico fogliare minimo dell’ospite 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 dell’ospite e causando il cosiddetto “cancro carbonioso delle querce”. L’aumento 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

26. Forest patterns 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. Actual species distribution Driving factors influencing distribution Statistical analysis Probability of occurrence Future spatial distribution Neighborhood criteria Scenarios of future driving factors

27. Actual species distribution Calibration Statistical analysis Driving factors influencing distribution Probability of occurrence Future Spatial Distribution Scenarios future driving factors Forest Map of Italy (1:100000) raster 250 meters of resolution Error in rasterization -0.15% 26% of Italian territory is forest

28. Actual species distribution Calibration Statistical analysis Driving factors influencing distribution Probability of occurrence Future Spatial Distribution Scenarios future driving factors DEM srtm DMI F12 A2 Driving factors • Elevation values (m above sea level) • Slope value (°) • Aspect value (° clockwise from north) • Mean annual precipitation (mm) • Mean annual snow water equivalent (mm) • Mean daily short wave net radiation (W/m2) • Mean of the annual dew point temperature (°K) • Mean of the minimum annual temperature (°K) • Mean of the maximum annual temperature (°K)

29. Actual species distribution Actual species distribution Calibration Calibration Statistical analysis Statistical analysis Driving factors influencing distribution Driving factors influencing distribution Probability of occurrence Probability of occurrence Future Spatial Distribution Scenarios future driving factors Future Spatial Distribution Scenarios future driving factors Logistic regression 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. Accuracy ROC 0.973 i.e.ROC curve test for class 8 Mean ROC 0.855

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

31. Altitude profiles of forest distribution Actual distribution Case a) Changed areas (red, 82%) considering only statistical analysis Case a) Case b) Changed areas (red, 77%) considering statistical analysis and neighborhood criteria Case b)

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