ELECTRONIC SUPPLEMENTARY MATERIAL
Spatial patterns of historical growth changes in Norway spruce across Western
European mountains and the key effect of climate warming
Authors : Marie Charru1,2,*, Ingrid Seynave2,1, Jean-Christophe Hervé3, Jean-Daniel
Bontemps1,2
1
AgroParisTech, UMR 1092 Laboratoire d’Etude des Ressources Forêt-Bois (LERFoB), 14
rue
Girardet,
54000
Nancy,
France.
E-mail:
marie.charru@gmail.com;
jean-
daniel.bontemps@agroparistech.fr
2
INRA, Centre de Nancy-Lorraine, UMR 1092 Laboratoire d’Etude des Ressources Forêt-
Bois (LERFoB), 54280 Champenoux, France. E-mail: seynave@nancy.inra.fr
3
Institut National de l’Information Géographique et Forestière, 11 rue de l’Ile de Corse, 54000
Nancy, France. E-mail: jean-christophe.hervé@ign.fr
*Corresponding author. Tel.: (+33) 3 83 39 68 80 - Fax.: (+33) 3 83 39 68 78 – E-mail: jeandaniel.bontemps@agroparistech.fr
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Online resource 1: Details on the NFI data used
1.1. NFI sampling design
We used data from the French NFI collected between 1976 and 2008. The NFI sampling
method is based on temporary plots distributed over a systematic random grid of 1 km x 1 km,
thus ensuring that existing environmental gradients are encompassed. Between 1976 and
2004, the NFI sampling method was applied in each ‘département’ administrative unit
(average area of 590,000 ha, hereafter a.u.), and repeated approximately every 12 years
(Robert et al., 2010). The main drawback of this method is to induce a space-date imbalance
in the sampled environmental conditions. In 2004, the sampling design was changed in order
to cover the whole forested area each year, and is now based on a systematic grid of 10 km ×
10 km, shifted by 2 km each year, thereby ensuring a 2-km coverage every 5 years (1 km
coverage in 10 years). NFI sampling plots are organized into four circular concentric subplots
of 25 m-, 15 m-, 9 m- and 6 m-radius. Data collected include environmental data (topography,
soil characteristics and floristic survey), stand attributes, and tree attributes inventoried
exhaustively over a countable threshold at a diameter at breast height (dbh) of 7.5 cm (6 m-, 9
m- and 15 m-radius, for the small, medium, and large trees, respectively). Variables measured
on each countable tree include dbh, bark thickness at breast height, total tree height and 5-year
radial increment under bark (ri5) at breast height. Damaged or cut trees are inventoried when
the time elapsed since the event is assumed to not exceed five years.
1.2. Plot selection criteria and data homogenization
As in Charru et al (2010), we selected pure and even-aged plots of Norway spruce. We based
the selection procedure on dendrometric and stand characteristics. Together with the change
in the NFI sampling design, some variables and definitions were changed in 2004 which
could have affected plot selection criteria. We thus carefully homogenized the variables
2
between the two inventory methods. The main selection criteria are detailed here. For more
information, see Charru 20121.
1.2.1. Stand structure criteria
Stand pureness was guaranteed by selecting stands where Norway spruce represented more
than 70% of plot basal area.
Even-agedness was characterized by the NFI ‘stand structure’ variable. The definition of this
variable was changed in the new inventory method: whereas it was a qualitative appreciation,
it is now based on the relative cover of coppice and standard trees within the plot.
Consequently, we complemented the stand structure variable by a criteria on the proportion of
coppice trees (< 25% of plot basal area) and the relative standard deviation of tree height (<
30%).
We targeted plots with a stand cover over 50% to discard open or regenerating stands. The
NFI ‘stand cover’ variable was removed in the new inventory method but we could elaborate
a similar variable based on the relative cover of the different forest layers (coppice,
standards).
1.2.2. Stand dynamics criteria
Furthermore we targeted plots that were historically forested and where no change in species
was carried out within the last 40 years based on the ‘stand evolution’ variable in the previous
NFI method. In the new inventory method this variable was removed so we based our
selection on the variables ‘changes in species’ to ensure that no change in species had been
carried out over the last 15 years, and the variable ‘previous soil cover’ to ensure that the plot
was already forested 15 years before plot inventory.
1
Charru M (2012) Forest productivity in a changing environment: a multi-scale assessment of recent
productivity variations based on the National Forest Inventory (NFI) data and environmental
interpretation. PhD Thesis, Agroparistech, Nancy, 413p.
3
Because of the NFI countable threshold on dbh, tree inventory cannot be exhaustive in a
fraction of young stand plots. To avoid plots where total stand BAI may be underestimated,
we discarded plots of a quadratic mean diameter (Dg) below 10 cm. We also excluded plots
of a density of regeneration (trees not inventoried) above 500 trees/ha (previous inventory
method) or where the proportion of countable trees was below 10% (new inventory method).
The inventory of tree stumps (cut trees) is very important for the reconstitution of plot BAI, as
cut within the 5 years preceding the inventory have contributed to plot increment. In 2008 the
stumps are no more inventoried. Consequently, in order to avoid any underestimation of BAI
in plots with unaccounted cut trees, we discarded all plots where thinning had occurred within
the 5 years preceding inventory based on the ‘thinning type’ variable, for the 2008 fraction
only.
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Online resource 2: Stand BAI calculation on NFI plots
The total stand BAI was computed in three steps. In the first step individual tree basal area
increment under bark (bai) was calculated for living trees, according to the following formula:
bai 


dbh  2BT 2  dbh  2BT  2 ri 5 2
4

where bai is in m2/5-yr, dbh is tree diameter at breast height, BT is bark thickness and ri5 is
individual radial increment over the last 5 years at breast height.
In the second step we reconstituted the individual radial increment for ‘lost’ trees (dead, cut or
damaged within five years preceding inventory), from that reconstituted for living trees. It
was calculated as the plot mean quadratic increment (which allows an unbiased reconstitution
of a basal area increment), proportionally weighted by: (i) a relative social status index
defined as the ratio between individual dbh and plot mean quadratic diameter (Dhôte, 1999);
and (ii) the temporal fraction of the 5-year period preceding inventory during which lost trees
were estimated to have grown (IFN, 1994). Further details are given in the appendix of
Charru et al. (2010).
In the third step the stand level BAI was calculated as the sum of the individual bai, further
weighted by their relative area weight in order to obtain a per ha value. In order to account for
tree recruitment during the 5-year period (trees that crossed the countable threshold of 7.5 cm
at breast height), we only considered the share of the increment corresponding to a dbh over
7.5 cm.
BAI 



dbh i 2  max dbh i  2 ri 5 i  2BTi ,7.5  2BTi 

4 i
where i refer to each individual tree. Therefore, the stand BAI computed correspond to a gross
increment in basal area (m2/ha/5-yr) above the countable threshold of 7.5 cm at breast height,
over a 5-year period.
5
6
Thermal
indicators
Water indicators
Nutritional indicators
S:T
C:N
S:T ratio
C:N ratio
N deposition
(NO3, NH4, Ntot)
pH
mm
mm
SWB
SWD
mm
mm
SWC
CWB
pH
Soil water deficit
Monthly water
balance
Soil water
holding capacity
Soil water
budget
°C
Tx
Croisé et al., 2005
Piedallu, comm. pers.
Piedallu, comm. pers.
Gégout, 2008
Lebourgeois and Piedallu, 2005
Lebourgeois and Piedallu, 2005
Piedallu et al., 2011
Bénichou and Lebreton, 1987 ;
Turc, 1961
Bénichou and Lebreton, 1987
Bénichou and Lebreton, 1987
Piedallu and Gegout, 2007
MJ.m-2
°C
References
Unit
Tn
Rad
Monthly radiation
Monthly minimal
temperature
Monthly maximal
temperature
Abrev.
Variable
1 km
1 km
1 km
1 km
1 km
1 km
500 m
1 km
1 km
1 km
1 km
Spatial
Résolution
Average site properties (SIG data)
C:Nh
S:Th
15 yrs average
(1989 - 2004)
15 yrs average
(1989 - 2004)
5 yrs average
(1993 - 2004)
pHh
SWDh
SWBh
SWCh
CWBh
Tx h
Tnh
Radh
Abrev.
15 yrs average
(1989 - 2004)
30 yrs average
(1961 - 1990)
30 yrs average
(1961 - 1990)
15 yrs average
30 yrs average
(1961 - 1990)
Temporal
resolution
30 yrs average
(1971 - 2000)
30 yrs average
(1961 - 1990)
30 yrs average
(1961 - 1990)
mm
mm
mm
mm
°C
°C
MJ.m-2
Unit
NFI soil data + Bio-indication
(Gegout et al 2003)
NFI soil data + Bio-indication
(Gegout et al 2003)
NFI soil data + Bio-indication
(Gegout et al 2003)
Lebourgeois and Piedallu, 2005
Lebourgeois and Piedallu, 2005
NFI soil data + Piedallu et al., 2011
Météo-France historical data
Météo-France historical data
Météo-France historical data
Piedallu and Gegout, 2007
References
Historical environmental data
punctual
punctual
punctual
1 yr
1 yr
punctual
1 yr
1 yr
1 yr
Temporal
resolution
30 yrs average
(1971 - 2000)
Online resource 3: Summary of the average site properties variables and historical
environmental variables
Online resource 4: Extraction of historical climatic data
We used historical climatic series coming from Météo-France meteorological stations. We
attributed to each plot the nearest station for which precipitation and/or temperature data was
available for the 5-year period covered by the plot’s BAI increment. Over the three regions
under study, we used 111 stations for precipitation data and 91 stations for temperature data.
The number of stations considered in each region is given in Table OR4-1, and their location
is represented on Figure OR4-1.
Distance between
Number
plot and nearest
of stations
station for P (m)
used
min
max mean
Distance between
Number of
plot and nearest
stations
station for T (m)
used
min
max mean
Massif Central 214
29747 10419
42
214
29747 11512
40
Alps
522
46305 13864
32
522
29867 13351
23
Jura
806
29996 10505
38
985
34408 12585
28
Table OR4-1: Number of meteorological stations used in each of the three regions under
study, and summary statistics on the distance between each NFI plot and the nearest
station for precipitation (left) and temperature (right) data.
The average distance between each NFI plot and the nearest station varied between 10 and 14
km depending on the region (Tab OR4-1). The minimum distance was 214 m and the
maximum distance was 46 km (Tab OR4-1).
Once a meteorological station was attributed to each plot, we extracted the average monthly
temperature and precipitation over the 5-year period covered by the plot’s BAI increment. In
order to avoid bias related to the distance (and differences in climatic conditions) between the
plot and the station, we first corrected the climatic series of each station from the difference
between the average value of the variable over the period 1961-1990 (Bénichou and Le
Breton 1987) at the station location, and this average value at the plot location.
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The extracted historical climatic variables are thus representative of the average climate over
the 5-year period covered by each BAI increment.
Figure OR4-1: Localization of the meteorological stations used (black dots) for
precipitation (left) and temperature data (right) in the three regions under study. The
NFI selected plots are represented with grey dots.
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Online resource 5: Procedure of components and variables selection in the PLS
regression approach.
The selection of the most predictive components was based on a leave-one-out crossvalidation procedure (Geladi and Kowalski, 1986). The predictive power Q2 (Tenenhaus,
1998) was defined as:
Q2 h  1 -
PRESSh
RSSh-1
where PRESSh is the prediction error sum of square of the model with h components, and
RSSh-1 is the residual sum of square of the same model with h-1 components (see Tenenhaus,
1998 for further details). Q2 was therefore computed for an increasing number of components,
and we selected the number of components allowing reaching 80% of the maximum
predictive power.
The identification of important predictors in the prediction of Y can be based on their
contribution to the global explicative power. However, in order to also detect predictors
which effect was stable in the prediction, we additionally performed generalized jackknife
tests for each predictor over the selected set of components, based on the cross-validation
approach (Martens and Martens, 2000). Due to lack of knowledge on the distribution of the
variance estimates (Mevik and Wehrens, 2007), p-values resulting from these tests cannot be
used as absolute values. The predictors were ranked according to their p-values, and their
cumulative explicative power was calculated based on their global contributions to Y. We
calculated the explicative power reached by the variables whose p-value was under the
heuristic threshold of 5%, and selected the variables allowing reaching 50% of this explicative
power as the most influent.
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Online resource 6: Details on GWR models fitting
In practice, the GWR model is fitted as many times as there are observations in the data,
taking each observation and an appropriate spatial neighborhood as a fitting subset
(Fotheringham et al., 2002). The neighborhood was here characterized by an adaptive radius,
based on a k-nearest neighbor (k-NN) approach (Fotheringham et al., 2002), which was thus
larger where the observations are locally sparse and smaller where observations are denser.
Within the neighborhood, each data point was weighted according to its proximity to the local
observation of reference using a bi-square function (Fotheringham et al., 2002), defined as:
2
  d 2 
ij
w ij   1     if j is one of the k nearest neigbours of i
 R  
  ij  
w ij  0 otherwise
where Rij is the distance between the regression point i and its kth nearest neighbor.
The optimal proportion of nearest neighbors to consider was selected based on the crossvalidation procedure (Fotheringham et al., 2002).
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Online resource 7: Adequacy of the dendrometric models.
The approach developed in this paper is based on models relating stand BAI to its main
determinants (Charru et al., 2010). In a first step, we modeled stand BAI as a function of
stocking level (RDI) and stand developmental stage (H0). As all the subsequent steps depend
on the correct formulation of these effects, we present here statistics on the main dendrometric
variables considered as well as tests concerning the correct representation of these effects in
Eq 2.
We tested the correlation between H0 and RDI to ensure that they could be incorporated
simultaneously in the dendrometric model. No strong correlation was found between these
two variables (Tab OR7-1).
Region
Massif Central
Pearson correlation
between RDI and H0
0.33
P-value
1.5.10-3
Jura
0.38
4.8.10-8
Alps
0.30
1.6.10-7
Tab OR7-1 : Pearson correlation between H0 and RDI in the three regions under study.
Figure OR7-1 presents the evolution of logBAI, RDI and H0 over the inventory cycles. In
each case we observe an increase of logBAI over time, but no similar evolution of RDI and
H0 except a very moderate positive trends in RDI and H0 in the Alps. The amplitude of
variation of these variables for each inventory cycle is always larger than the average
temporal evolution of each variable, which prevents that the effects of the dendrometric
variables be confounded with temporal effects.
To ensure that the effects of stand stocking and developmental stage were correctly taken into
account in the fitted models (Tab 2), we plotted the residuals of these models against RDI and
H0 (Fig. OR 7-2). The plots reveal no residual effect of these variables. Furthermore, No
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particular trend was detected in the residuals when plotted against fitted values (Fig. OR 7-2).
These tests show that the fitted models not only have a fair explicative power (Tab. 2) but
that the effects of the dendrometric variables are correctly taken into account. Consequently
residual dendrometric effects are unlikely to be transferred to the effects of environmental
variables or calendar date in the following steps.
Figure OR 7-1: Evolution of logBAI (m2/ha/5-yrs), stand stocking level (RDI) and stand
developmental stage (H0, m) over the NFI cycles. The X-axis corresponds to the inventory
cycle number since the beginning of NFI. Cycles 2, 3 and 4 correspond to the old method of
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inventory. NM corresponds to the new method of inventory, and encompasses the 4 fractions
2005, 2006, 2007 and 2008.
Figure OR 7-2: Residuals of the dendrometric models of logBAI (Eq. 2, Tab 2) against
fitted value, RDI and H0 (m) for the 3 regions under study.
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