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
alessandro.tenca@unipd.it

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??
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:
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
Discontinuous, well draining soils
Really sparse trees
(low competition)

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 ● Trephor
Allung getti e foglie ●
Conduttanza stomatica
e fotosintesi
●
Sensore di fotosintesi LCi,
ADC Bioscientific
Periodo
vegetativo
Periodo
vegetativo
Occasionale
Periodo
vegetativo
Settimanale
Settimanale
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 system
Policarbonate
cilinder with
internal
resistance
ΔT=10-5 C
Picea abies Karst.
Forest Treeline
Cold Heated Cold Heated
5 5 5 5
Heating experiment: Matherials & Methods
MEASUREMENTS:
---- Species
---- Environments
---- Treatments
---- Replicates
Experiment repeated in 2006 and 2007
• Annual longitudinal increments
• Dh at different distances along the stem

GROWTH LIMITATION AT THE TREELINE
L (cm)
30
25
20
15
10
5
0
MONTE RITE
MONTE RITE
1F 2F 3F 4F 5F 1R 2R 3R 4R 5R
Paired T-Test: Incr. 2007 vs Avg. Incr. (2001-2005)
COLD: p = 0.146
HEATED: p = 0.024
Longitudinal increment
2001-2005
2007
L (cm)
30
25
20
15
10
5
0
SAN VITO
SAN VITO
Heating experiment: Results
2001-2005
2007
1F 2F 3F 4F 5F 1R 2R 3R 4R 5R
COLD HEATED COLD HEATED
COLD: p = 0.346
HEATED: p = 0.239
Artificial warming promoted shoot elongation only at the treeline.

LTER AREAS TODAY
More interest for:
-Description of stand development and spatial
structures
-Description of stand dynamics
-Ecological role of disturbances
-Application for close to nature selviculture
Availabilty
of new technologies
- Precision
- Fast sampling
- Low costs

LTER area
- Big extensions
- Tree to tree approach
- Different information layers
- Optimal time-scale to study
slow changing ecosystems,
with the “lowest noise”
Intensive monitored
area,
along many years
(regular intervals sampling)

Monitoring along gradients
Positive
(spatial attraction)
SPATIAL INTERACTIONS
Intra- inter- specific
Negative
(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

Altitude: 3800m asl
Surface: 1.7ha
Rakaposhi 1
# 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 2, 3820m
Ama Dablam 1
Ama Dalbam 1, 4050m
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
27%
47%
25%
1%
Sorbus microphylla
Juniperus recurva
Betula utilis
Abies spectabilis
Localizzazione area: Deboche
Altitudine massima: 3820 m s.l.m.
Esposizione: NW
Pendenza: 26°
Estensione: 1ha
N piante: 1029
35%
14%
27%
24%
Sorbus microphylla
Acer campbelii
Betula utilis
Abies spectabilis

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.

O 11 (r)
0,25
0,2
0,15
0,1
0,05
0
Ama Dablam 2
AD2 O-ring
Aggregation
Segregation
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
Point pattern analysis
O 11 (r)
0,12
0,1
0,08
0,06
0,04
0,02
0
Ama Dablam 1
AD1 O-ring
Aggregation
Segregation
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
Aggregating trends at all the distance classes:
A first common pattern with the Alpine Areas.

O 11 (r)
O 11 (r)
0,25
0,2
0,15
0,1
0,05
0
0,06
0,05
0,04
0,03
0,02
0,01
0
Point pattern analysis for the main species
Ama Dablam 2 Ama Dablam 1
AD2 Abies O-ring
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
AD2 Betula O-ring
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
O11 (r)
O 11 (r)
0,05
0,045
0,04
0,035
0,03
0,025
0,02
0,015
0,01
0,005
0,1
0,09
0,08
0,07
0,06
0,05
0,04
0,03
0,02
0,01
0
0
AD1 Abies O-ring
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
AD1 Betula O-ring
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)

O11 (r)
0,09
0,08
0,07
0,06
0,05
0,04
0,03
0,02
0,01
0
Point pattern analysis, Dbh classes
AD1 Betula
AD1 Betula Small
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
O11 (r)
0,035
0,03
0,025
0,02
0,015
0,01
0,005
0
Dbh 10

O11 (r)
0,3
0,25
0,2
0,15
0,1
0,05
0
Point pattern analysis, Dbh classes
AD2 Abies
AD2 Abies Small
0 5 10 15 20 25 30 35 40 45 50
Distanza (m) 0,045
O11 (r)
0,04
0,035
0,03
0,025
0,02
0,015
0,01
0,005
0
Dbh 10
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.

o 12 (r)
O 12 (r)
0,045
0,04
0,035
0,03
0,025
0,02
0,015
0,01
0,005
0,014
0,012
0,01
0,008
0,006
0,004
0,002
0
0
Point pattern analysis, bivariate, intraspecific, both the areas
Treeline Betula Big vs Small
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)
Timberline Abies Big vs Small
0 5 10 15 20 25 30 35 40 45 50
Distanza (m)

O 12 (r)
O12 (r)
0,018
0,0250,016
0,014
0,020,012
0,01
0,015
0,008
0,006
0,01
0,004
0,0050,002
0
0
Point pattern analysis, bivariate, interspecific, treeline
AD1 Abies Big vs Betula Big
AD1 Abies Big vs Betula Small
0 5 10 15 20 25 30 35 40 45 50
0 5 10 15 20 25
Distanza
30
(m)
35 40 45 50
L(t)
4
2
0
-2
-4
Distanza (m)
Croda da Lago C2
0 10 20 30 40
Distanza (m)
L(t)
4
2
0
-2
-4
Aggregation: as it
happens for Swiss
stone pine and
Larch in the Alps.
Facilitation more than
competition?
Latemar
0 10 20 30 40
Distanza (m)

Z (I)
12
10
8
6
4
2
0
-2
-4
-6
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.
Moran's I
2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40
Distanza (m)
POSITIVE AUTOCORRELATION /
ATTRACTION
Similar values gruop together
NEGATIVE AUTOCORRELATION /
REPULSION
Similar values do not gruop together

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
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
-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
-1
0
1
2
3
4
5
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

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.