Point pattern analysis

Forest ecosystems in the conditions of climate change:
biological productivity, monitoring and adaptation
28 June - 2 July, 2010
Yoshkar-Ola, Russia
A.Tenca, PhD Student, TeSAF Dept., University of Padua, Italy
[email protected]
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Brief intro on the importance of the high altitude
environments 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.
Why surveying at the treeline??
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Really “sensitive” ecotone:
monitoring global warming and climate change
effects
Physiological driving forces still not well-known
Need and technical possibilities of long term
monitoring
Long term monitoring sites in:
Karakoram, Pakistan
E Himalayas, Nepal
Dolomites, NE Italy
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
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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
Umidità del suolo
●
●
Sonda TDR
Tutto l'anno
Valore orario
Pioggia
●
●
Pluviometro Micros
Estate-autunno
Valore cumulato nell'ora
●
●
Sensori di Granier
Periodo estivo
Media 15' dei valori misurati sul minuto
Accrescim. fusto (mm)
●
●
Dendrometri
Tutto l'anno
Form. cellule legnose
●
Allung getti e foglie
●
Densità flusso di linfa
(dm h-1)
Conduttanza stomatica
e fotosintesi
●
●
Trephor
Periodo
vegetativo
Periodo
vegetativo
Sensore di fotosintesi LCi,
ADC Bioscientific
Media 15' dei valori misurati sul minuto
Settimanale
Settimanale
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
GROWTH LIMITATION AT THE TREELINE
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).
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.
GROWTH LIMITATION AT THE TREELINE
Heating experiment: Matherials & Methods
---- Species
Picea abies Karst.
Forest
---- Environments
Treeline
Cold
Heated
Cold
Heated
---- Treatments
5
5
5
5
---- Replicates
Experiment repeated in 2006 and 2007
Policarbonate
cilinder with
internal
resistance
ΔT=10-5 C
Heating system
MEASUREMENTS:
• Annual longitudinal increments
• Dh at different distances along the stem
GROWTH LIMITATION AT THE TREELINE
Heating experiment: Results
Longitudinal increment
30
MONTE RITE
30
2001-2005
SAN VITO
2001-2005
2007
25
25
20
20
L (cm)
L (cm)
2007
15
15
10
10
5
5
0
0
1F 2F 3F 4F 5F 1R 2R 3R 4R 5R
COLD
HEATED
1F 2F 3F 4F 5F 1R 2R 3R 4R 5R
COLD
HEATED
Paired T-Test: Incr. 2007 vs Avg. Incr. (2001-2005)
MONTE RITE
COLD: p = 0.146
HEATED: p = 0.024
SAN VITO
COLD: p = 0.346
HEATED: p = 0.239
Artificial warming promoted shoot elongation only at the treeline.
LTER AREAS TODAY
More interest for:
Availabilty
of new technologies
-Description of stand development and spatial - Precision
structures
- Fast sampling
-Description of stand dynamics
- Low costs
-Ecological role of disturbances
-Application for close to nature selviculture
LTER area
Intensive monitored
area,
along many years
(regular intervals sampling)
- Big extensions
- Tree to tree approach
- Different information layers
- Optimal time-scale to study
slow changing ecosystems,
with the “lowest noise”
Monitoring along gradients
SPATIAL INTERACTIONS
Intra- inter- specific
Positive
Negative
(spatial attraction)
(spatial repulsion)
Facilitation
Competition
Constant in time and space??
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>130cm
Sp.,
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
Study areas: SNP
Himalaya
Ama Dalbam 1, 4050m
Ama Dalbam 2, 3820m
Ama Dablam 1
Ama Dablam 2
AMA DABLAM 1
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Localizzazione area: Pangboche
Altitudine massima: 4050 m s.l.m.
Esposizione: NW
Pendenza: 25°
Estensione: 1ha
N piante: 444
AMA DABLAM 2
Localizzazione area: Deboche
Altitudine massima: 3820 m s.l.m.
Esposizione: NW
Pendenza: 26°
Estensione: 1ha
N piante: 1029
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25%
27%
27%
Sorbus microphylla
1%
35%
Sorbus microphylla
Juniperus recurva
Acer campbelii
Betula utilis
Betula utilis
Abies spectabilis
Abies spectabilis
14%
47%
24%
Spatial statistics creates statistical models analysing data with
geographical coordinates.
In ecology we study the biological phenomena in their own spatial
reference, to understand how space influences, drives and characterizes
every single observation.
How is a biological phenomenon distributed?
With groups? With gradients?
Spatial statistical analysis is divided in two categories:
POINT PATTERN
ANALYSIS
Spatial Point Patterns (x,y)
Just the position of every
single tree is considered
Methods:
K-Ripley
O-ring
SURFACE PATTERN
ANALYSIS
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
Point pattern analysis
O-ring statistics
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).
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 1
Ama Dablam 2
0,25
0,12
AD2 O-ring
Aggregation
Aggregation
0,08
O 11 (r)
O 11 (r)
0,2
0,15
AD1 O-ring
0,1
0,1
0,06
0,04
0,05
0,02
Segregation
0
0
5
10
15
20
25
30
Distanza (m)
35
40
Segregation
0
45
50
0
5
10
15
20
25
30
35
Distanza (m)
Aggregating trends at all the distance classes:
A first common pattern with the Alpine Areas.
40
45
50
Point pattern analysis for the main species
Ama Dablam 2
Ama Dablam 1
0,25
AD2 Abies O-ring
0,05
0,04
0,035
0,15
O11 (r)
O 11 (r)
AD1 Abies O-ring
0,045
0,2
0,1
0,03
0,025
0,02
0,015
0,05
0,01
0,005
0
0
5
10
15
20
25
30
35
40
45
0
50
0
5
10
15
Distanza (m)
0,06
25
30
35
40
45
50
Distanza (m)
0,1
AD2 Betula O-ring
AD1 Betula O-ring
0,09
0,05
0,08
0,07
O 11 (r)
0,04
O 11 (r)
20
0,03
0,02
0,06
0,05
0,04
0,03
0,02
0,01
0,01
0
0
0
5
10
15
20
25
30
Distanza (m)
35
40
45
50
0
5
10
15
20
25
30
Distanza (m)
35
40
45
50
Point pattern analysis, Dbh classes
AD1 Betula
0,09
AD1 Betula Small
0,08
Dbh <=10
0,07
0,05
0,04
0,03
0,02
0,01
0
0
5
10
15
20
25
30
35
40
45
50
0,035
Distanza (m)
AD1 Betula Big
0,03
Dbh > 10
0,025
O11 (r)
O11 (r)
0,06
0,02
0,015
0,01
0,005
0
0
5
10
15
20
25
30
Distanza (m)
35
40
45
50
Point pattern analysis, Dbh classes
AD2 Abies
0,3
AD2 Abies Small
0,25
Dbh <= 10
0,15
0,1
0,05
0
0
5
10
15
20
25
30
35
40
Distanza (m) 0,045
45
50
AD2 Abies Big
0,04
0,035
Dbh > 10
0,03
O11 (r)
O11 (r)
0,2
0,025
0,02
0,015
0,01
0,005
0
0
5
10
15
20
25
30
35
40
45
50
Distanza (m)
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, bivariate, intraspecific, both the areas
0,045
Treeline Betula Big vs Small
0,04
0,035
o 12 (r)
0,03
0,025
0,02
0,015
0,01
0,005
0
0
5
10
15
20
25
30
35
40
45
50
Distanza (m)
0,014
Timberline Abies Big vs Small
0,012
O 12 (r)
0,01
0,008
0,006
0,004
0,002
0
0
5
10
15
20
25
30
Distanza (m)
35
40
45
50
Point pattern analysis, bivariate, interspecific, treeline
0,018
AD1 Abies Big vs Betula Big
AD1 Abies Big vs Betula Small
0,0250,016
0,01
0,015
0,008
0,006
0,01
0,004
0,0050,002
0
0
0
5
5
10
10
15
15
20
20
30
35
25Distanza
30 (m) 35
25
40
40
45
45
50
50
Facilitation more than
competition?
Distanza (m)
Latemar
Croda da Lago C2
4
4
2
2
L(t)
0
L(t)
O 12 (r)
O12 (r)
0,014
0,020,012
Aggregation: as it
happens for Swiss
stone pine and
Larch in the Alps.
0
0
-2
-2
-4
-4
0
10
20
30
Distanza (m)
40
0
10 20 30
Distanza (m)
40
Surface pattern analysis
Moran’s I
It determines the spatial autocorrelation:
how a variable correlates with itself ,
in order to predict this variable’s values in given spatial points.
Z (I)
Moran's I
12
10
8
6
4
2
0
-2
-4
-6
POSITIVE AUTOCORRELATION /
ATTRACTION
Similar values gruop together
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
NEGATIVE AUTOCORRELATION /
REPULSION
Distanza (m)
Similar values do not gruop together
6
4
4
2
2
0
0
-2
-2
-4
-4
-6
-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
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
8
6
4
2
0
-2
-4
-6
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
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
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
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
Correlograms, diameter
8
6
4
2
0
-2
-4
-6
Area treeline dbh
Abies dbh
Betula dbh
Betula dbh
5
4
3
2
1
0
-1
-2
-3
-4
-5
Conclusions
Point pattern analysis
General aggregation trend,
decrising with bigger individuals, as it happens in the Alps, and
observed in all the specific and dimensional classes.
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.
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.