Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle sue Risorse [email protected]

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

mailto:[email protected]

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

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3

42a

1718

1516

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2423

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2321

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25

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-8.0

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-2.0

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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)

0

20

40

60

80

100

120

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

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-5

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Fc

[m

ol

m-2

s-1

]

EX

IF

NEX

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tem

ber

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ob

er

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vem

ber

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emb

er

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uar

y

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ruar

yr

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chr

Ap

ril

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e

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st

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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

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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


Scenarios future driving factors




FutureSpatial Distribution

Calibration

Error in rasterization

-0.15%

26% of Italian territory is forest

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.







Calibration







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







Neighbooring criteraia

Calibration

Example of Euclidean distance grid Example of distance-based probability grid Piv







Neighbooring criteraia

Calibration

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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

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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

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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

Documents

Riccardo Valentini Università della Tuscia Dipartimento di Scienze dellAmbiente Forestale e delle sue Risorse [email protected]