A.Tenca, PhD Student, TeSAF Dept., University of Padua, Italy

alessandro.tenca@unipd.it

Forest ecosystems in the conditions of climate change:

biological productivity, monitoring and adaptation

28 June - 2 July, 2010

Yoshkar-Ola, Russia

Brief intro on the importance of the high altitudeenvironments for monitoring and survey;

overview of the high altitude survey areas and experiences set by UniPD in the last 15 years;

examples/preliminary results obtained in the Himalayan area.

Really “sensitive” ecotone:

monitoring global warming and climate changeeffects

Physiological driving forces still not well-known

Need and technical possibilities of long termmonitoring

Why surveying at the treeline??

Long term monitoring sites in:

Dolomites, NE Italy

Karakoram, Pakistan

E Himalayas, Nepal

Treeline with Larch and Swiss Stone Pine, 2200 m asl, Dolomites, Italy

Mean temperature MJJAS 7.5 C

JJA precipitation 500 mm

Max Vapour pressure deficit (VPD) < 12 hPa

Really sparse trees

(low competition)

Discontinuous, well draining soils

Treeline with Spruce, Betula and Rhododendron, 4100 m asl, Khumbu valley, Nepal

Treeline with Betula (and Juniper), 3800 m asl, Karakoram, Pakistan

Since 1996 we’ve been monitoring the most important ecophysiological

parameters of Pinus sylvestris, Larix decidua, Pinus cembra, Picea abies.

4 (along an altitudinal gradient) remote-controlled stations:

San Vito di Cadore (1100m asl)

Monte Croce (1600m asl)

5 Torri (2000 + 2100m asl)

and experiments on growth limitation factors at:

San Vito di Cadore (1100m asl)

Monte Rite (2100m asl)

Parameter St. 1 St. 2 Sensors When Type of sampling

T e umidità dell'aria ● ● Termo-igrometro Rotronic Tutto l'anno Media 15' dei valori misurati sul minuto

T del suolo ● ● Termocoppie Tutto l'anno Media 15' dei valori misurati sul minuto

T foglie, fusti e rami ● ● Termocoppie Tutto l'anno Media 15' dei valori misurati sul minuto

Flusso calore del suolo ● Heat flux plate HUKSEFLUX Tutto l'anno Media 15' dei valori misurati sul minuto

Radiazione netta ● Radiometro netto NR-Lite Tutto l'anno Media 15' dei valori misurati sul minuto

Radiazione globale ● ● Piranometro Li-Cor Tutto l'anno Media 15' dei valori misurati sul minuto

Rad. Fotosintetic. attiva ● Quantum sensor Li-Cor Fino al 1999 Media 15' dei valori misurati sul minuto

Velocità e dir. vento ● ● Gonio-anemometro Young Tutto l'anno Media 15' dei valori misurati sul minuto

Umidità del suolo ● ● Sonda TDR Tutto l'anno Valore orario

Pioggia ● ● Pluviometro Micros Estate-autunno Valore cumulato nell'ora

Densità flusso di linfa

(dm h-1) ● ● Sensori di Granier Periodo estivo Media 15' dei valori misurati sul minuto

Accrescim. fusto (mm) ● ● Dendrometri Tutto l'anno

Form. cellule legnose ● TrephorPeriodo

vegetativo Settimanale

Allung getti e foglie ●Periodo

vegetativo Settimanale

Conduttanza stomatica

e fotosintesi ●

Sensore di fotosintesi LCi,

ADC Bioscientific

Occasionale

Periodo

vegetativo

variabile

Micro-cores collection,

for wood formation

studies

Rossi et al 2006,

IAWA J.

Trephor

Patent UniPD

www.tesaf.unipd.it/Sanvito/index.htm

5 Torri 1 (2082m asl)

5 Torri 2 (2122 m asl)

Monitoring all the year round…

Growth limiting factors at the treeline: temperature

Hypothesis

• Apical buds are the thermo-sensitive organs.

• Apical buds control the formation of the xylem structure along the stem (Aloni 2001, 2004).

• Approaching the TBL, the optimization of the xylem structure cannot be maintained and hence the reduced compensation for the effect of hydraulic resistance with the increased height would lead to limitations to tree growth.

By enhancing the thermal conditions of the apical buds of trees at the treeline:

• The xylem structure should enhance (convergence to optimal conduit tapering

and/or increase in dimension of apical conduits).

• Tree growth (especially in height) should increase.

At the treeline, tree growth is limited by low temperatures: there is a thermal

boundary layer above which (T< 6-7°C) the formation of new cells is inhibited (e.g.

Rossi et al. 2007).

Trees at the treeline seemed to have a sub-optimal degree of conduit tapering (Coomes et al. 2007).

GROWTH LIMITATION AT THE TREELINE

Heating system

Policarbonate

cilinder with

internal

resistance

ΔT=10-5 C

---- Species

---- Environments

---- Treatments

---- Replicates

Picea abies Karst.

Forest Treeline

Cold Heated Cold Heated

5 5 5 5

Experiment repeated in 2006 and 2007

MEASUREMENTS:

• Annual longitudinal increments

• Dh at different distances along the stem

GROWTH LIMITATION AT THE TREELINEHeating experiment: Matherials & Methods

GROWTH LIMITATION AT THE TREELINEHeating experiment: Results

Paired T-Test: Incr. 2007 vs Avg. Incr. (2001-2005)

MONTE RITECOLD: p = 0.146

HEATED: p = 0.024SAN VITO

COLD: p = 0.346

HEATED: p = 0.239

MONTE RITE

0

5

10

15

20

25

30

1F 2F 3F 4F 5F 1R 2R 3R 4R 5R

L (

cm

)

2001-2005

2007

SAN VITO

0

5

10

15

20

25

30

1F 2F 3F 4F 5F 1R 2R 3R 4R 5R

L (

cm

)

2001-2005

2007

Longitudinal increment

Artificial warming promoted shoot elongation only at the treeline.

COLDCOLD HEATED HEATED

LTER AREAS TODAYAvailabilty

of new technologiesMore interest for:

- Precision- Fast sampling- Low costs

-Description of stand development and spatialstructures-Description of stand dynamics-Ecological role of disturbances-Application for close to nature selviculture

- Big extensions

- Tree to tree approach

- Different information layers

- Optimal time-scale to study

slow changing ecosystems,

with the “lowest noise”

Intensive monitoredarea,

along many years(regular intervals sampling)

LTER area

SPATIAL INTERACTIONS

Facilitation Competition

Constant in time and space??

Intra- inter- specific

Positive(spatial attraction)

Negative(spatial repulsion)

Monitoring along gradients

Treeline Timberline Subalpine forest

Since 1993 we’ve been monitoring the most important ecological processes

and dynamics throughout LTER areas.

LTER areas (along altitudinal gradient) in “Croda da Lago”:

- 1ha 2200m asl,

- 1ha 2000m asl

- 4ha, 2100m asl

3088 trees h>130cmSp.,dbh,h tot,canopy h and depth, age, position

Rakaposhi 1

Altitude: 3800m asl

Surface: 1.7ha

# of trees: 402 Density: 236 trees/ha

Slope aspect: WNW

Features: mainly Himalayan birch and

Juniper

Rakaposhi 2

Altitude: 3500m asl

Surface: 0.65

# of trees: 346 Density: 530 t/ha

Slope aspect: W

Features: mainly Himalayan blue

pine

Himalaya

Study areas: SNP

Ama Dalbam 1, 4050m

Ama Dalbam 2, 3820m

Ama Dablam 1

Ama Dablam 2

AMA DABLAM 1 AMA DABLAM 2 Localizzazione area: Pangboche Altitudine massima: 4050 m s.l.m. Esposizione: NW Pendenza: 25° Estensione: 1ha N piante: 444

Localizzazione area: Deboche Altitudine massima: 3820 m s.l.m. Esposizione: NW Pendenza: 26° Estensione: 1ha N piante: 1029

25%

1%

47%

27%

Sorbus microphylla

Juniperus recurva

Betula utilis

Abies spectabilis

27%

24%14%

35% Sorbus microphylla

Acer campbelii

Betula utilis

Abies spectabilis

Spatial statistics creates statistical models analysing data withgeographical coordinates.

In ecology we study the biological phenomena in their own spatialreference, to understand how space influences, drives and characterizes

every single observation.

How is a biological phenomenon distributed?

With groups? With gradients?

POINT PATTERN ANALYSIS

SURFACE PATTERN ANALYSIS

Spatial Point Patterns (x,y)

Just the position of everysingle tree is considered

Methods:

K-Ripley

O-ring

Geostatistical data (x, y, z)

It considers the position and another variable

(z=age, height, diameter) of each tree

Methods:

Moran’s I

Local G

Spatial statistical analysis is divided in two categories:

While Ripley’s K function determines aggregation or segregation up to a

certain distance,

O-ring statistics,

using rings instead of circles,

is able to determine aggregation o segregation at any given distance (r).

Point pattern analysis

O-ring statistics

That’s why O-ring is considere

an “upgraded” method compared to Ripley’s K,

which allows having

a better overview and interpretation of the results.

Point pattern analysis

Ama Dablam 1Ama Dablam 2

0

0,05

0,1

0,15

0,2

0,25

0 5 10 15 20 25 30 35 40 45 50

O 1

1 (

r)

Distanza (m)

AD2 O-ring

0

0,02

0,04

0,06

0,08

0,1

0,12

0 5 10 15 20 25 30 35 40 45 50

O 1

1 (

r)

Distanza (m)

AD1 O-ring

Aggregating trends at all the distance classes:

A first common pattern with the Alpine Areas.

Aggregation

Segregation

Aggregation

Segregation

Point pattern analysis for the main species

Ama Dablam 2 Ama Dablam 1

0

0,05

0,1

0,15

0,2

0,25

0 5 10 15 20 25 30 35 40 45 50

O 1

1 (

r)

Distanza (m)

AD2 Abies O-ring

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

0,045

0,05

0 5 10 15 20 25 30 35 40 45 50

O1

1 (

r)

Distanza (m)

AD1 Abies O-ring

0

0,01

0,02

0,03

0,04

0,05

0,06

0 5 10 15 20 25 30 35 40 45 50

O 1

1 (

r)

Distanza (m)

AD2 Betula O-ring

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0,1

0 5 10 15 20 25 30 35 40 45 50

O 1

1 (

r)

Distanza (m)

AD1 Betula O-ring

Point pattern analysis, Dbh classes

AD1 Betula

Dbh <=10

Dbh > 10

0

0,01

0,02

0,03

0,04

0,05

0,06

0,07

0,08

0,09

0 5 10 15 20 25 30 35 40 45 50

O1

1 (

r)

Distanza (m)

AD1 Betula Small

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0 5 10 15 20 25 30 35 40 45 50

O1

1 (

r)

Distanza (m)

AD1 Betula Big

Dbh <= 10

Dbh > 10

0

0,05

0,1

0,15

0,2

0,25

0,3

0 5 10 15 20 25 30 35 40 45 50

O1

1 (

r)

Distanza (m)

AD2 Abies Small

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

0,045

0 5 10 15 20 25 30 35 40 45 50

O1

1 (

r)

Distanza (m)

AD2 Abies Big

Considering the main species of the stands we analysed within different

size classes (Dbh > or < 10), the aggregation trend reaches lower

distance the bigger are trees: as in the Alps.

Point pattern analysis, Dbh classes

AD2 Abies

Point pattern analysis, bivariate, intraspecific, both the areas

0

0,005

0,01

0,015

0,02

0,025

0,03

0,035

0,04

0,045

0 5 10 15 20 25 30 35 40 45 50

o 1

2 (

r)

Distanza (m)

Treeline Betula Big vs Small

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0 5 10 15 20 25 30 35 40 45 50

O 1

2 (

r)

Distanza (m)

Timberline Abies Big vs Small

Point pattern analysis, bivariate, interspecific, treeline

0

0,005

0,01

0,015

0,02

0,025

0 5 10 15 20 25 30 35 40 45 50

O 1

2 (

r)

Distanza (m)

AD1 Abies Big vs Betula Small

Croda da Lago C2

-4

-2

0

2

4

0 10 20 30 40

Distanza (m)

L(t

)

Latemar

-4

-2

0

2

4

0 10 20 30 40

Distanza (m)

L(t

)

Aggregation: as it

happens for Swiss

stone pine and

Larch in the Alps.

Facilitation more than

competition?

0

0,002

0,004

0,006

0,008

0,01

0,012

0,014

0,016

0,018

0 5 10 15 20 25 30 35 40 45 50

O1

2 (

r)

Distanza (m)

AD1 Abies Big vs Betula Big

POSITIVE AUTOCORRELATION /

ATTRACTION

Similar values gruop together

NEGATIVE AUTOCORRELATION /

REPULSIONSimilar values do not gruop together

It determines the spatial autocorrelation:how a variable correlates with itself ,

in order to predict this variable’s values in given spatial points.

Surface pattern analysis

Moran’s I

-6

-4

-2

0

2

4

6

8

10

12

2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40

Z (

I)

Distanza (m)

Moran's I

Correlograms, diameter

-6

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Area timberline dbh

-6

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-2

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01

41

82

22

63

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43

84

24

65

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86

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47

88

28

69

09

49

8

Abies dbh

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Betula dbh

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Abies dbh

-5-4-3-2-1012345

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Area treeline dbh

Betula dbh

Surface pattern analysis

A homogeneous group structure, typical of the subalpine forests,

lights up within the timberline area, while at higher altitude, with more

limiting factors, the groups are not homogeneous.

Point pattern analysisGeneral aggregation trend,

decrising with bigger individuals, as it happens in the Alps, and

observed in all the specific and dimensional classes.

Just with 200m gradient it has been possible to catch and analyse

differences within survey areas close to each other, but also to make

comparisons with areas far away from each other, but really similar

from the ecological points of view:

a great feature in monitoring hign altitude ecosystems.

Conclusions