Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle...
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Transcript of Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle...
Riccardo ValentiniUniversità della TusciaDipartimento di Scienze dell’Ambiente Forestale e delle sue [email protected]://gaia.agraria.unitus.it
Ecosystems and global services : an outlook on forest and mountain region
Welcome in the Anthropocene !
CO2
CH4
N2O
2007 un anno per il Clima4° 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
RegulatingBenefits obtained from regulation of
ecosystem processes
CulturalNon-material benefits from ecosystems
ProvisioningGoods 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
Source: NASA
2.4OceanUptake
Land Uptake
2.2Land-UseChange
6.3 F Fuel, Cement
Global C Budget
Atmosphere
Surface biosphere
Atmospheric accumulation rate3.2 GtC per year 1990s
2.9
Fast process (1 – 102 days) Slow process (103 – 104 days)
Gruber et al 2003 , SCOPE project
1 2
6
5 7
913
1014
8
11
3
42a
1718
1516
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2423
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2321
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25
-10.0
-8.0
-6.0
-4.0
-2.0
0.0
2.0
4.0
35 40 45 50 55 60 65 70
Latitude (°N)
NEE (t
C h
a-1 y
-1)
Valentini, Dolman, Matteucci et al. Nature 2000
CO2-equivalent emissions
0
20
40
60
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120
140
1970 1990 2010 2030 2050 2070 2090
GtCO2/yr
Baseline A2
IMAGE S550eIMAGE S650e
BIOSPHERE
Source or sink ?
VULNERABILITY OF BIOSPHERE(feed-backs with carbon cycle)
Coupled carbon-climate models
Vulnerability of Carbon Pools
Gruber et al. 2004
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 CO2
• Not included in most climate simulations.
1,7 MILIONI DI SPECIE CONOSCIUTE
15 MILIONI SPECIE STIMATE SULLA TERRA
90% DELLE SPECIE SCONOSCIUTE
……BIODIVERSITA’ IN CIFRE……
Change in Species Diversity
0,1
1
10
100
1000
10000
Fossil Recent Future
Number per Thousand Species
Extinctions(per thousand years)
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60
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100
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140
1790-1819 1820-1849 1850-1879 1880-1909 1910-1939 1940-1969 1970-19991790 1900 2000
Number of Species
Homogenization(e.g. growth in marine species
introductions)
North America
Europe100 to 1000-fold increase
Source: Millennium Ecosystem Assessment
The experimental site is located in a farm (Malga Arpaco) at 1699 m a.s.l.Mean annual temperature: 5 °CTotal annual rainfall: 1200 mmSoil type: Typic Hapludalfs, fine loamy (FAO)Ecosystem type: alpine semi-natural grasslandEcosystem management: extensive management, pasture from Jun to SepPeriod of EC measurements: 2003-2007Eddy Covariance type: Metek USA-1, Li-cor 7500Tower height: 2 m
N2O emission and CH4 uptake was evaluated fortnightly, during2003 and 2004 pasture season, using diffusion chambers. Gassamples conserved in vacuum vials were analysed through gaschromatographytechnique.For the N2O: ECD detector at 320°C; for the separation a capillarycolumn Cromosob 1010 at 140°C was used, with a flux of heliumat 30 kPa. For the CH4: FID detector at 180°C; for the separation acolumn 4m x ¼’’ OD Porapak q 80/100 MESH at 30° was used.
The human foot print
Data
Magnani et al., 2007
Luyssaert et al., submitted
Annual mean 1850-2000: 35 M m3 of forest wood damaged by natural disturbances in Europe.
53% wind throw16% fire16% biotic (insects)3% snow5% other abiotic
Extreme climate events or disturbances have a strong effect on biosphere-astmosphere exchanges
Mean day on monthly base
-25
-20
-15
-10
-5
0
5
10
Fc
[m
ol
m-2
s-1
]
EX
IF
NEX
Sep
tem
ber
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ob
er
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vem
ber
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emb
er
Jan
uar
y
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ruar
yr
Mar
chr
Ap
ril
May
Jun
e
July
Au
gu
st
Sep
tem
ber
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ob
er
Tatra Experiment CarboEurope
QUALCHE ESEMPIOQUALCHE ESEMPIO
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. citricolaLa 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
P. cinn
amom
i
P. cam
bivor
a
P. citr
icola
P. cac
toru
m-5
0
5
10
Tem
per
atu
re °
C
Vannini, Anselmi et al. 2007Progetto CarboItaly
QUALCHE ESEMPIOQUALCHE ESEMPIO
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
5
10
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30
35
-4,0 -3,5 -3,0 -2,5 -2,0 -1,5 -1,0
MWP (MPa)
% d
'iso
lab
ilam
ento
Vannini, Anselmi et al. 2007Progetto CarboItaly
Driving factors influencing distribution
Actual species distribution
Statistical analysis
Probability of occurrence
Future spatialdistribution
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
Forest Map of Italy (1:100000) raster 250 meters of resolution
Physiognomic categories %
00 - Woody plantation in agricultural areas 0.55
01 - Oaks and other evergreen broadleaf forests 9.07
02 - Deciduous oak-dominant forests 24.45
03 - Chestnut-dominant forests 8.85
04 - Beech-dominant forests 11.52
05 - Hygrophyte species-dominant forests 0.85
06 - Other broadleaf deciduous autochthon species-dominant forests 10.28
07 - Exotic broadleaf-dominant forests and plantations 1.85
08 - Mediterranean pine and cypress dominant forests 2.46
09 - Oro-Mediterranean and mountain pine dominant forests 2.75
10 - Abies alba and Picea rubens dominant forests 7.71
11 - Larch and cembrus pine dominant forests 3.06
12 - Exotic needleleaf dominant forests 0.10
13 - Mixed needleleaf and broadleaf forests with prevalent beech 2.19
14 - Mixed needleleaf and broadleaf forests with prevalent oro-mediterranean and mountain pine 2.24
15 - Mixed needleleaf and broadleaf forests with prevalent Abies alba and/or Picea rubens 1.93
16 - Mixed needleleaf and broadleaf forests with other species prevalent 6.77
17 - Tall Mediterranean Macchia 3.35
Driving factors influencing distribution
Scenarios future driving factors
Actual species distribution
Statistical analysis
Probability of occurrence
FutureSpatial Distribution
Calibration
Error in rasterization
-0.15%
26% of Italian territory is forest
Driving factors
Driving factors influencing distribution
Actual species distribution
Statistical analysis
Probability of occurrence
FutureSpatial Distribution
Scenarios future driving factors
Calibration
•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)
DEM srtm•Elevation values (m above sea level)•Slope value (°)•Aspect value (° clockwise from north)
DMI F12 A2
nni
i xxxxP
P
...1
log 3322110
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
FutureSpatial Distribution
Scenarios future driving factors
Calibration
Driving factors influencing distribution
Actual species distribution
Statistical analysis
Probability of occurrence
FutureSpatial Distribution
Scenarios future driving factors
Calibration
Mean ROC 0.855
1.00.80.60.40.20.0
1 - Specificity
1.0
0.8
0.6
0.4
0.2
0.0
Sen
siti
vity
Diagonal segments are produced by ties.
ROC Curve
Logistic regression
ROC 0.973
i.e.ROC curve test for class 8
Accuracy
vvss PiwiPiwiPi
Driving factors influencing distribution
Actual species distribution
Statistical analysis
Probability of occurrence
FutureSpatial Distribution
Scenarios future driving factors
Neighbooring criteraia
Calibration
Example of Euclidean distance grid Example of distance-based probability grid Piv
Driving factors influencing distribution
Actual species distribution
Statistical analysis
Probability of occurrence
FutureSpatial Distribution
Scenarios future driving factors
Neighbooring criteraia
Calibration
0
500
1000
1500
2000
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3000
0 500 1000 1500 2000 2500 3000
Number of pixels
m a
sl
class 0
class 1
class 2
class 3
class 4
class 5
class 6
class 7
class 8
class 9
class 10
class 11
class 12
class 13
class 14
class 15
class 16
0
500
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0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Number of pixels
m a
sl
class 2
class 3
class 4
class 5
class 6
class 7
class 8
class 9
class 14
class 15
class 16
Altitude profiles of forest distribution
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0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 5500
Number of pixels
m a
sl
class 0
class 2
class 3
class 4
class 5
class 6
class 7
class 8
class 9
class 10
class 14
class 15
class 16
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
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
“You can observe a lot, just by watching.”
-Yogi Berra
Thank You