Forest ecosystems in the conditions of climate change...
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Transcript of Forest ecosystems in the conditions of climate change...
A.Tenca, PhD Student, TeSAF Dept., University of Padua, Italy
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??
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
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
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
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
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
-4
-2
0
2
4
6
8
2 6 10
14
18
22
26
30
34
38
42
46
50
54
58
62
66
70
74
78
82
86
90
94
98
Area timberline dbh
-6
-4
-2
0
2
4
6
2 61
01
41
82
22
63
03
43
84
24
65
05
45
86
26
67
07
47
88
28
69
09
49
8
Abies dbh
-6
-4
-2
0
2
4
6
2 6 10
14
18
22
26
30
34
38
42
46
50
54
58
62
66
70
74
78
82
86
90
94
98
Betula dbh
-6
-4
-2
0
2
4
6
8
2 6
10
14
18
22
26
30
34
38
42
46
50
54
58
62
66
70
74
78
82
86
90
94
98
-6
-4
-2
0
2
4
6
2 6 10
14
18
22
26
30
34
38
42
46
50
54
58
62
66
70
74
78
82
86
90
94
98
Abies dbh
-5-4-3-2-1012345
2 6 10
14
18
22
26
30
34
38
42
46
50
54
58
62
66
70
74
78
82
86
90
94
98
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