ORIGINAL PAPER

Spatial patterns of historical growth changes in Norway spruceacross western European mountains and the key effect of climatewarming

Marie Charru • Ingrid Seynave •

Jean-Christophe Herve •

Jean-Daniel Bontemps

Received: 30 May 2013 / Revised: 8 September 2013 / Accepted: 27 September 2013 / Published online: 23 October 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract

Key message Productivity changes in Norway spruce

show important regional and local spatial variations,

highlighting their context dependence at different spa-

tial scales. These variations suggest the enhancing role

of climate warming, and interplay with local water and

nutrient limitations.

Abstract While forest growth changes have been observed

in many places worldwide, their spatial variation and envi-

ronmental origin remain poorly documented. Analysis of

these historical changes in contrasted regional contexts, and

their mapping over continuous environmental gradients, may

help uncover their environmental causes. The approach was

tested on Norway spruce (Picea abies) in three western

European mountain contexts (Massif Central, Alps and Jura),

using National Forest Inventory (NFI) data. We explored the

environmental factors influencing stand basal area increment

(BAI) in each context. We then estimated and compared

mean regional changes in BAI and related these to the

regional environmental limitations evidenced. Within each

region, we further mapped local BAI trends using a geo-

graphically weighted regression (GWR) approach. In each

region, local estimates of BAI changes were finally correlated

to environmental indicators. We found an increase in BAI in

the three regions over 1980–2005, greater in the Massif

Central (?71 %) than in the Alps (?19 %) and the Jura

Mountains (?21 %). Inter-regional differences in BAI

changes suggested the release of a thermal constraint—found

more important in the Massif Central—by the strong tem-

perature increase over the period, and a limitation by water

availability in the Jura and the Alps Mountains. Spatial pat-

terns of BAI change revealed significant local variations in

the Massif Central and the Alps. From the correlation ana-

lysis, these were again found consistent with the hypothesis of

an enhancing effect of climate warming in these mountain

ranges. They were also related to local soil nutritional status

in the two regions, and negatively related to nitrogen depo-

sition level in the Massif Central. As a main outcome, a strong

context and spatial scale dependence of productivity changes

is emphasized. In addition, the enhancing effect of climate

warming on productivity is suggested, with local modulation

by climatic and nutritional conditions.

Keywords Forest growth � Growth changes �Environmental changes � Geographically weighted

regression � National Forest Inventory � Norway

spruce

Communicated by E. Liang.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00468-013-0943-4) contains supplementarymaterial, which is available to authorized users.

M. Charru � I. Seynave � J.-D. Bontemps (&)

AgroParisTech, Centre de Nancy, UMR 1092 INRA/

AgroParisTech Laboratoire d’Etude des Ressources Foret-Bois

(LERFoB), 14 rue Girardet, 54000 Nancy, France

e-mail: jdbontemps.agroparistech@gmail.com

M. Charru

e-mail: marie.charru@gmail.com

I. Seynave

e-mail: seynave@nancy.inra.fr

M. Charru � I. Seynave � J.-D. Bontemps

INRA, Centre de Nancy-Lorraine, UMR 1092 INRA/

AgroParisTech Laboratoire d’Etude des Ressources Foret-Bois

(LERFoB), 54280 Champenoux, France

J.-C. Herve

Institut National de l’Information Geographique et Forestiere,

IGN, 11 rue de l’Ile de Corse, 54000 Nancy, France

e-mail: jean-christophe.herve@ign.fr

123

Trees (2014) 28:205–221

DOI 10.1007/s00468-013-0943-4

Introduction

Forest growth changes have been reported widely over the

last decades, both at global (Nemani et al. 2003), conti-

nental (Boisvenue and Running 2006; Spiecker et al.

1996), national (Elfving and Tegnhammar 1996; Gsch-

wantner 2006), and regional scales (Bontemps et al. 2009;

Charru et al. 2010). Significant effort has also been paid on

the environmental drivers of these changes (increase in

atmospheric CO2 concentration and temperature, nitrogen

deposition and interactions between these factors; Hyvonen

et al. 2007; Kahle et al. 2008).

Growth changes have been shown to vary over space for

a given species (Bontemps et al. 2012; Bontemps et al.

2011; Kellomaki and Kolstrom 1994; Messaoud and Chen

2011). Such variation may result from a differential evo-

lution of the environmental drivers across space (Eastaugh

et al. 2011), and/or from differences in the locally limiting

factors (Lebourgeois et al. 2010), leading to contrasted

responses to environmental changes (Messaoud and Chen

2011). For example, historical changes in Aleppo pine

NDVI were shown to vary with site aridity in northern

Spain (Vicente-Serrano et al. 2010), and Bontemps et al.

(2011) attributed regional differences in productivity

changes of common beech to variations in the local nitro-

gen status and deposition.

Most studies concerning the spatial variation of

growth changes are based on relating regional levels of

change to environmental factors. However, no approach

has been provided to get a continuous spatial description

of these changes, thus preventing to explore the spatial

determinism of these variations. Such approach would

require an extensive dataset covering broad environ-

mental gradients with a fine resolution to investigate

spatial variations in growth changes and associated key

determinents (Makinen et al. 2002). Because national

forest inventories consist in a systematic forest moni-

toring over space and time, they form a promising tool

to investigate relationships between species behavior and

their environment. NFI data have hence proven useful to

explicit the environmental control of species productivity

at regional and national scales (Seynave et al. 2005; Ung

et al. 2001), and to estimate medium-term growth

changes (Charru et al. 2010; Elfving and Tegnhammar

1996; Gschwantner 2006). However, the study of Nel-

lemann and Thomsen (2001) on Norway spruce in

Norway is the single one using NFI data to have

investigated spatial variations in radial growth trends at a

national scale, which were found to vary with the level

of atmospheric deposition (acidifying and fertilizing

compounds).

Knowledge of growth–environment relationships in tree

species is also fundamental for the interpretation of growth

changes and their spatial variation. Species may react to

environmental changes only if these are limiting for growth

and if no other environmental factor is more limiting

(Bontemps et al. 2011). Furthermore, local limitations may

explain differential responses to the same environmental

changes in different regions. Thus, the approach would

permit to test whether identifying the environmental factors

that govern spatial variation in species growth allows

understanding their growth changes over time. However,

few studies have simultaneously considered species rela-

tionships to the abiotic environment, taken as a baseline to

account for historical growth trends and their spatial vari-

ations (Makinen et al. 2002). Vegetation–environment

relationships have also been found to depend on spatial

scale, climatic factors acting at large scale and soil factors

at local scale (Siefert et al. 2012). To our knowledge, the

effect of spatial scale on observed forest growth changes

and their environmental determinism has never been

studied.

Here, our objectives were twofold. First, we used the

French NFI data to test for the existence of regional vari-

ations in forest growth changes and to investigate their

spatial patterns, which remains unexplored to date. Second,

we analyzed how these patterns were related to spatial

patterns in environmental factors. We hence tested the

spatial correlations between the abiotic environment and

growth/growth changes. The hypotheses tested were that

the limiting factors identified over space on growth and

growth changes (1) depend on the regional contexts, and

(2) help understand temporal variations in growth at

regional and local scales.

We focused on Norway spruce (Picea abies) as a major

tree species in Europe, and for which a strong spatial

variation in these changes across Europe has been reported:

growth has been found to increase in central Europe (Kahle

et al. 2008), in Austria (Eastaugh et al. 2011; Hasenauer

et al. 1999), in France (Badeau et al. 1996) and in Sweden

(Elfving and Tegnhammar 1996), and to decrease in other

places such as in Norway (Nelleman and Thomsen 2001)

and in Austria (Gschwantner 2006). In France, Norway

spruce is mainly found in mountain contexts, where strong

variations in environmental factors are usually observed

over short distances. We considered three mountain ranges,

the Alps, the Jura and the Massif Central (1,325 selected

plots), that differ in their average environmental conditions

for factors known to influence Norway spruce growth: soil

water content (positive effect; Seynave et al. 2005), sum-

mer temperature and water balance (either positive or

negative effect depending on elevation; Desplanque et al.

1998; Lebourgeois et al. 2010) and soil nutritional status

(Seynave et al. 2005). We could not consider the Vosges

Mountains because of spatio-temporal imbalances in past

NFI sampling designs.

206 Trees (2014) 28:205–221

123

Materials and methods

Material

NFI data and plot selection

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 9 1 km, thus ensuring that existing environmental

gradients are encompassed. Data collected include envi-

ronmental data (topography, soil characteristics and flo-

ristic surveys used for bio-indication), stand and tree

attributes including diameter (dbh), bark thickness and

5-year radial increment under bark (ri5) measured at breast

height, and total tree height, for trees over the countable

diameter threshold of 7.5 cm. More details on the NFI

sampling design are provided in Online resource 1.

To accurately separate between the effects of aging,

stand density and historical changes on growth, we targeted

pure ([70 % of stand basal area) and even-aged stands of

Norway spruce in three mountain ranges of contrasted

environmental conditions: the French Alps, the Jura

Mountains and the Massif Central (Fig. 1). The selection

criteria are detailed in Online resource 1. The number of

plots selected for each region is given in Table 1.

Calculation of dendrometric variables

5-year stand BAI was computed from tree inventory data

following Charru et al. (2010). The calculation method is

presented in Online resource 2. Stand developmental stage

was represented by the stand top height (H0) which is fairly

insensitive to thinning events, contrary to measures based

on diameter. It was calculated on each NFI plot as the mean

height of the n-1 thickest trees over n ares (Matern 1975).

Stand stocking level was assessed by a relative density

index (RDI; Reineke 1933), calculated from the self-thin-

ning relationship fitted on Norway spruce from NFI data in

Charru et al. (2012):

RDI ¼ N

Nmax

where Nmax ¼ expð10:043� 0:287ðln DgÞ2Þ;ð1Þ

where N is the number of trees/ha in the plot, and Dg is the

plot quadratic mean diameter. The average BAI was much

higher in the Massif Central (6.2 m2/ha/5 years) than in the

two other regions (around 4 m2/ha/5 years) whereas the top

height was lower in the Massif Central (21.6 m) than in the

two other regions (around 26 m) (Table 1). This suggested

that the stands were younger in the Massif Central. The

mean RDI was found lower in the Jura than in the two other

regions (Table 1).

Environmental data

We used two sets of environmental data: (1) permanent

(soil data) or average climatic (radiations, T and P) plot

attributes, (2) historical data corresponding to the envi-

ronmental conditions prevailing during the 5 years of each

BAI increment (assessed from climatic series, and soil

variables bioindicated from NFI plot vegetation surveys)

(Online resource 3).

Permanent or average site properties These data were

extracted from GIS maps. Data for average climate were

extracted from AURELHY maps (Benichou and Le Breton

1987), including monthly mean minimal (Tn, �C), maximal

Fig. 1 Distribution of Norway

spruce in France according to

the NFI data (left) and plots

selected over the three regional

contexts under study (right,

black dots). The grey areas

correspond to the distribution

area of Norway spruce

as modeled by Piedallu et al.

(2011), intersected with the

regions of interest. Grey lines

are the delimitations of the

French ‘departements’

administrative units (a. u.)

Trees (2014) 28:205–221 207

123

temperature (Tx, �C), and monthly precipitation sum (P,

mm). Total monthly solar radiations (Rad, MJ m-2) were

extracted from the HELIOS model (Piedallu and Gegout

2007). We computed monthly Turc potential evapo-tran-

spiration (PET, mm) (Turc 1961), and monthly climatic

water balance (CWB = P - PET, mm). We also extracted

soil water holding capacity (SWC, mm) estimates from

GIS information (Piedallu et al. 2011), and calculated

monthly soil water budget (SWB, mm) and soil water

deficit (SWD, mm) from SWC, P and PET. Soil pH, C:N

ratio (proxy for mineralization rate of organic matter) and

S:T ratio (base-cation saturation ratio) estimates were

extracted from national maps of bio-indicated values (see

Gegout 2008 for pH). Finally, modeled monthly levels of

total N deposition (N-NO3, N-NH4 and total N deposition)

over the period 1993–1998 were calculated following

Croise et al. (2005). The resulting values are current short-

term averages, static over time, but they provide valuable

information on the spatial pattern of nitrogen deposition.

Historical environmental data Historical climatic data for

monthly minimal (Tnh) and maximal temperature (Txh) and

monthly precipitation (Ph) corresponding to the period

covered by each 5-year BAI were extracted from the nearest

Meteo-France climatic record and corrected to avoid dis-

tance related bias (Online resource 4). Further climatic

indices (Turc PETh, CWBh) were computed assuming a

constant average monthly solar radiation (Piedallu and

Gegout 2007). When local NFI soil data were available, we

computed SWC on each plot (Piedallu et al. 2011), and

calculated monthly SWBh and SWDh. We also used pHh,

C:Nh and S:Th bio-indicated locally on each plot from the

Table 1 Summary statistics for (a) stand and (b) environmental characteristics in the selected plots in the three regions

(a) Stand characteristics

BAI (m2/ha/5 years) H0 (m) RDI Nb. of plots

Q5 Mean Q95 Q5 Mean Q95 Q5 Mean Q95

Massif Central 2.1 6.2 12.1 12.1 21.6 32.3 0.33 0.78 1.23 291 (90)

Alps 1.2 3.9 8.7 16.6 26.0 34.5 0.33 0.76 1.32 640 (299)

Jura 1.3 4.3 8.4 17.2 25.8 34.0 0.26 0.69 1.12 394 (196)

(b) Average site properties

Tn_an (�C) Tx_an (�C) P_an (mm)

Q5 Mean Q95 Q5 Mean Q95 Q5 Mean Q95

Massif Central 0.71 2.23 3.82 8.35 11.2 13.7 893 1,216 1,643

Alps -0.3 1.76 4.19 9.8 11.7 14.2 1,003 1,502 1,896

Jura -1.2 1.6 4.09 10.2 12.3 14.5 1,212 1,647 2,026

Tx6 (�C) P6 (mm) SWB6 (mm)

Q5 Mean Q95 Q5 Mean Q95 Q5 Mean Q95

Massif Central 14.5 17.4 20 73.9 92.4 118 34 78.3 115

Alps 16 18.5 21.5 82.2 135 175 22.9 59 92.5

Jura 16.3 19.1 22 102 142 169 15.1 37.4 78.1

SWC (mm) C:N pH Ndep (kg/ha/year)

Q5 Mean Q95 Q5 Mean Q95 Q5 Mean Q95 Q5 Mean Q95

Massif Central 55.6 95.1 118 16.1 18.6 20.9 5.04 5.35 5.78 5.7 8.3 10.2

Alps 35.5 62.2 92.9 14.4 16.2 18.6 5.66 5.97 6.29 0.2 7.4 10.9

Jura 15.1 38.9 86.3 12.6 14.2 17.5 4.87 6.07 6.35 9.2 10.9 11.9

BAI is basal area increment (m2/ha/5 years), H0 is top height (m), RDI is relative density index. The number of plots in parentheses corresponds

to the plots provided with local site NFI data (soil characteristics and floristic survey). Q5 and Q95 are the quantiles of variables distribution of

level 5 and 95 %, respectively. The environmental variables correspond to the average or permanent site properties (see ‘Environmental data’

and Online resource 3). Tn_an and Tx_an and P_an are annual averages of minimal and maximal temperature and annual precipitation sum,

respectively. Tx6, P6 and SWB6 are June average maximal temperature, precipitation sum and average soil water budget. SWC, C:N and pH are

the soil water capacity, organic carbon to total nitrogen ratio and soil pH. C:N and pH are extracted from GIS maps. Ndep is the total nitrogen

deposition level calculated from the model of Croise et al. (2005) over the period 1994–1998

208 Trees (2014) 28:205–221

123

NFI floristic surveys (Gegout et al. 2003) at the time of BAI

formation (Table 1). Since floristic surveys and soil data

were not available for all plots in the first two inventory

cycles, the number of plots including historical data is lower

than the initial number of selected plots (Table 1). Histor-

ical data on N deposition were not available.

Environmental characteristics of the selected NFI plots

The Massif Central sample was characterized by a high

soil water capacity, a high C:N ratio indicating poor

nitrogen availability and a low pH indicating acidic soils

(Table 1). In contrast, the Alps and the Jura samples had

an average lower soil water capacity, lower C:N ratio and

higher pH, around 6 pH units (Table 1). These two

samples showed similar annual average minimal (around

?1.7 �C) and maximal (around ?12 �C) temperature,

whereas the Massif Central sample revealed higher annual

average minimal temperature (?2.2 �C) and lower maxi-

mal temperature (?11.2 �C) (Table 1). The average

annual precipitation was the highest in the Jura, slightly

lower in the Alps and much lower in the Massif Central

(Table 1). In summer, maximal temperature and precipi-

tation sums were much lower in the Massif Central than

in the two other regions, but the SWB was the highest due

to the high SWC in this region (Table 1).

Methods

We first analyzed regional BAI–environment relationships

of Norway spruce in the three regional contexts considered.

We then evaluated regional temporal BAI changes, and

regional trends were related to the factors found to influ-

ence BAI in each region and compared across regions.

Finally, we mapped and tested for local spatial variations in

growth changes based on a spatial regression approach, and

analyzed how these related to environmental factors.

BAI response to the abiotic environment

Filtering of BAI from factors of stand dynamics To study

variations in BAI solely attributable to the environment,

BAI was filtered out from stand stocking level and devel-

opmental stage. As in Charru et al. (2010), we modeled

log-transformed BAI as a function of RDI and H0 using

OLS linear regression:

logðBAIÞ ¼ f 1ðRDIÞ þ f2ðH0Þ þ e; ð2Þ

where e is an error term assumed to follow a Gaussian

distribution with constant variance. The model was fitted

over each regional sample, by testing different

transformations of the independent variables. BAI was

then filtered out (BAIcor) from these estimated effects of

stocking level and developmental stage:

logðBAIÞcor ¼ logðBAIÞ � ðf1ðRDIÞ þ f2ðH0ÞÞ ¼ e: ð3Þ

Predicting the filtered BAI from environmental indicators

This corrected productivity was then predicted by the his-

torical environmental indicators collected:

logðBAIÞcor ¼X

i

giXiþ e; ð4Þ

where the Xi are the historical environmental indicators

(Online resource 3), and e is an error term assumed to follow

a Gaussian distribution with constant variance. We used a

partial least squares (PLS) regression approach (Geladi and

Kowalski 1986) that aims to identify the most predictive

variables in a high-dimensional set of partially correlated

variables, as is the case with climate-related data (Carrascal

et al. 2009). PLS regression consists in computing orthog-

onal linear combinations of the initial explanatory variables

(latent variables or components), defined to maximize the

share of explained variance in the dependent variable. It is

then possible to predict the Y back from the initial X vari-

ables. The model was fitted with the plsr package (Wehrens

and Mevik 2007) of the R software (R Development Core

Team 2009), using the kernel PLS algorithm on normalized

variables. We selected the most predictive components

from statistics of prediction (predictive power Q2, Tenen-

haus 1998) based on a leave-one-out cross-validation pro-

cedure (Geladi and Kowalski 1986) (see Online resource 5

for further details). The identification of the most important

environmental indicators in the prediction of productivity

was based on both their contribution to the goodness-of-fit

of the regression (partial R2) and their significance for the

model predictive accuracy, computed from generalized

jackknife tests associated to the cross-validation procedure

(Martens and Martens 2000) (see Online resource 5 for

details on the selection procedure).

Estimation of mean regional BAI changes

We estimated temporal changes in BAI of Norway spruce

at constant stocking level, developmental stage and the

permanent/average component of site fertility in each

region. Therefore, we extended the initial multiple OLS

regression model (Eq. 2) to ‘site fertility’ and to an effect

of calendar year (Charru et al. 2010):

logðBAIÞ ¼ f1ðRDIÞ þ f2ðH0Þ þ f3ðSFÞ þ f4ðDateÞ þ e;

ð5Þ

where SF is site fertility, Date is the median calendar year

of each 5-year BAI, and e is an error term assumed to

follow a Gaussian distribution with constant variance.

Trees (2014) 28:205–221 209

123

Permanent/average site fertility was described by the

environmental variables previously selected in the PLS

regression approach. To avoid absorption of the historical

change by environmental variables that would have changed

over time, we replaced the historical values of these envi-

ronmental variables by their corresponding permanent or

average value (see ‘‘Environmental data’’ and Online resource

3). The environmental variables were introduced in their order

of significance in the PLS regression and only those that were

significant (p \ 0.05) in the linear model were retained:

f3ðSFÞ ¼X

k

f3kðXkÞ; ð6Þ

where the Xk are the retained environmental variables. When

two variables were highly correlated (r [ 0.7), we retained

that leading to the highest R2. We also tested for interactions

between the variables introduced. Finally, we introduced the

effect of date that had one of the following forms:

f41ðDateÞ ¼ logð1þ aðDate� DaterefÞÞ ð7Þ

f42ðDateÞ ¼ logð1þ aðDate� DaterefÞþ bðDate� DaterefÞ2Þ; ð8Þ

where Dateref is a reference year for measuring the trend, so

that f4i(Dateref) = 0, and it is modeled as a linear (Eq. 7) or

quadratic (Eq. 8) multiplicative effect when back-trans-

formed on a BAI scale [and exp(f4i(Dateref)) = 1] so that it

expresses a relative change of the reference year productivity.

The reference year was chosen as 1985 in the Massif Central,

and 1981 in the two other regions, depending on data avail-

ability. The trend fitted in the Massif Central was then

rescaled to the reference year 1981 for inter-regional com-

parisons. These regional models (Eq. 5) were fitted by OLS

non-linear regression using the nls() function in R software.

Local estimation and spatialization of BAI changes

We fitted an extended form of the regional models (Eq. 5)

using geographically weighted linear regression (GWR;

Fotheringham et al. 2002). GWR allows testing for spatial

non-stationarity in the regression parameters and providing

local estimates of these parameters, based on a local interpo-

lation approach. The mixed GWR approach is a refinement of

the classical GWR approach, where some of the parameters

can remain global (i.e., constant over space) while some others

are non-stationary (Fotheringham et al. 2002). Hence, we

tested for a spatial variation in the effect of date, whereas the

other parameters were assumed constant over space:

logðBAIÞðx;yÞ ¼ g1ðRDIÞ þ g2ðH0Þ þ g3ðSFÞþ g4ðx;yÞðDateÞ þ eðx;yÞ; ð9Þ

where (x, y) denotes geographic coordinates of the

observations, and e(x,y) is a Gaussian error term with

constant variance. As GWR provides only a linear

framework, the date effect was here specified as one of

the following polynomial effects to account for a historical

trend (Eq. 10) or identify an acceleration/deceleration

(Eq. 11):

g41ðx;yÞðDateÞ ¼ aðx;yÞðDate� DaterefÞ ð10Þ

g42ðx;yÞðDateÞ ¼ aðx;yÞðDate� DaterefÞ þ bðx;yÞðDate

� DaterefÞ2: ð11Þ

The reference years were chosen as aforementioned

and trends were then scaled to the reference year 1981.

GWR models are fitted locally in the neighbourhood of

each observation, and an adaptive k-nearest neighbor (k-

NN) bandwidth with bi-square weighting function was

selected and fitted using cross-validation (Fotheringham

et al. 2002, see Online resource 6 for further details).

Parameter non-stationarity was tested using Leung et al.’s

(2000) test using the significance threshold p value

(p \ 0.05). We calculated the total relative change in BAI

between 1981 and 2005 as:

RCðx;yÞ ¼ expðaðx;yÞð2005� 1981Þ þ bðx;yÞð2005

� 1981Þ2Þ: ð12Þ

RC was then mapped by kriging the local estimates

using ArcGis (version 9.3.1; ESRI Inc., Redlands, CA,

USA). To highlight sub-domains of regional maps where

the historical growth change was significant we also

mapped the t statistics of the parameters associated to the

effect of date (t surfaces; Fotheringham et al. 2002). We

represented the thresholds of 1.65, 1.96 and 2.58 for the

t surfaces that correspond to the 90, 95 and the 99 %

significance levels, respectively.

Relationships between local variations in BAI change

and the abiotic environment

Differences between regional BAI changes were inter-

preted based on the regional growth–environment rela-

tionships (see ‘‘Discussion’’). On a local scale, we

searched for relationships between local estimates of BAI

changes obtained in the GWR approach and local per-

manent/stationary site properties (Online resource 3), with

extension to the level of 1993–1998 level of N deposition,

using a second PLS regression approach comparable to

that aforementioned (see ‘‘BAI response to the abiotic

environment’’):

RCi ¼X

i

piXiþ e; ð13Þ

where RCi is the relative change in BAI observed at each

location (Eq. 12).

210 Trees (2014) 28:205–221

123

Results

BAI response to the abiotic environment

The models of BAI against stand characteristics (Eq. 2) are

summarized in Table 2. For the three regions, we fitted

logarithmic effects of RDI and H0, and the adjusted R2

amounted to 42, 26 and 42 % in the Massif Central, the

Alps and the Jura Mountains, respectively. Further infor-

mation on models adequacy is given in Online resource 7.

The most significant variables from the PLS regression

approach (Eq. 4) were grouped into seasonal effects and

their cumulated contribution was calculated (Table 3). The

amount of variance explained by the selected variables was

6, 11.7 and 15.5 % in the Massif Central, the Alps and the

Jura, respectively. In the Massif Central, maximal tem-

perature of June–July had a positive effect, as well as pH

and S:T ratio. In the Alps, SWB had a significant positive

effect all year long as well as soil water capacity. We also

found a significant and strong negative effect of the C:N

ratio (positive effect of nitrogen availability). Minimal

temperatures had a significant positive effect in April, June,

and in September, as well as June maximal temperatures.

In the Jura, all minimal temperatures had a significant

positive effect, as well as maximal temperatures of the

growing season and the soil water capacity.

Several factors that influence BAI were thus found

general across the three regions. Temperature always had a

positive effect, and maximal summer temperature was

found significant in the three regions (positive effect). Soil

water capacity had a positive effect in the Alps and the Jura

Mountains, and we found an effect of soil nutritional status

in the Alps (negative effect of the C:N ratio) and in the

Massif Central (positive effect of pH and S:T ratio).

However, the relative importance of these factors varied

from one region to another (Table 3). Water availability

seemed to be of major importance in the Alps as all

Table 2 Parameter estimates and summary statistics for the linear regression models of BAI (m2/ha/5 years) against RDI and H0 (m) for Norway

spruce for each of the regional contexts under study (Eq. 2)

Massif Central Alps Jura

Estimate Std. error P value Estimate Std. error P value Estimate Std. error P value

Intercept 4.52 0.49 1.75.10-14*** 3.67 0.45 1.16.10-14*** 5.12 0.45 \2.10-16***

logRDI (f1) 0.73 0.10 1.58.10-11*** 0.68 0.07 \2.10-16*** 0.89 0.08 \2.10-16***

logH0 (f2) -0.77 0.16 3.9.10-6*** -0.66 0.14 1.77.10-6*** -1.04 0.13 5.83.10-13***

Adj. R2 0.42 0.26 0.42

RSE 0.40 0.48 0.44

Adj. R2 adjusted coefficient of determination, RSE residual standard error (m2/ha/5 years)

Significance levels: ., \0.1; *, \0.05; **, \0.01; ***, \0.001

Table 3 Environmental variables selected in the PLS regression

approach of BAI for each of the regional contexts under study

Variable Sign Contribution (%) Statistics

Massif Central

Tx6-7_h 1.79 Ncomp = 2

pHh, S:Th 4.25 Q2 = 5 %

R2 = 24 %

Alps

SWBuh, 5.10

C:Nh 2.63 Ncomp = 2

Tn6_h, Tx6_h 1.53 Q2 = 22 %

Tn9_h 0.78 R2 = 26 %

SWCh 0.88

Tn4_h 0.77

Jura

Tn9-11_h 3.18

Tx4-7_h 4.12 Ncomp = 1

Tn4-8_h 4.79 Q2 = 31 %

Tn12-2_h 2.35 R2 = 33 %

SWCh 1.01

Variables were grouped seasonally when several successive months

of the same variable were significant, and were ranked according to

their degree of significance. Temperature is in �C, SWC is in mm,

C:N and S:T are dimensionless ratios. The sign of the jackknife

coefficient (see S5) is indicated for each group of variables by arrows

(grey upward = positive, black downward = negative) and their

contribution to the total R2 is also indicated. We reported the number

of selected components (Ncomp), predictive power assessed by cross-

validation (Q2, see S5) and R2 of the PLS regressions

Trees (2014) 28:205–221 211

123

monthly SWBu were significant and their cumulated con-

tribution was the highest. The effect of SWC was positive

in both the Alps and the Jura but no effect was found in the

Massif Central (Table 3). On the contrary summer tem-

perature was found the most significant in this region,

whereas it was of secondary importance in the two others.

Nutritional variables had no significant effect in the Jura,

whereas their contribution and significance were high in

the Alps and the Massif Central.

Estimation of mean regional BAI changes

Because of strong correlations in the environmental vari-

ables selected from the PLS regression (Table 3), we could

not include more than two variables and their interaction in

the regional models (Eq. 5) to control for site fertility

(Table 4). An effect of temperature was included in the

three regions, with a concave quadratic effect of summer

maximal temperature in the Massif Central, and a positive

effect of summer minimal and maximal temperature in the

Alps and the Jura Mountains, respectively. We also

included a positive effect of log-transformed SWC in the

Alps and the Jura Mountains and a positive effect of the

S:T ratio in the Massif Central (Table 4). The negative

interaction between SWC and Tx4-7 in the Jura suggested

a stronger limitation by temperature where SWC was the

lowest, i.e., at high elevation (data not shown). The nega-

tive interaction between S:T and Tx6-7 in the Massif

Central indicated a stronger effect of the S:T ratio where

temperature was the lowest.

In the three regions we observed a significant positive

effect of the effect of calendar year (Table 4). In the Jura

and the Alps only the linear term was significant, whereas a

significant quadratic effect of Date was found in the Massif

Central, indicating an acceleration of growth in recent

years (Table 4; Fig. 2). The relative change in BAI was

much higher in the Massif Central (?71 % between 1981

and 2005) than in the Alps (?19 %) and the Jura (?21 %)

(Fig. 2).

Local estimation and spatialization of BAI changes

The proportions of k-NN selected were 23, 6 and 10 % for

the Massif Central, the Alps and the Jura, respectively

(Table 5), representing an average distance of 21, 9 and

12 km, respectively. Some of the environmental variables

used to control site fertility were no longer significant in

the GWR models (Table 5, Massif Central and Jura) which

may indicate that local environmental gradients in the k-

NN neighborhood were not large enough to identify envi-

ronmental effects on BAI. Spatial non-stationarity in the

historical growth change was significant in the Massif

Central and the Alps. Ta

ble

4P

aram

eter

esti

mat

esan

dsu

mm

ary

stat

isti

csfo

rth

em

od

els

of

BA

I(m

2/h

a/5

yea

rs)

incl

ud

ing

ah

isto

rica

ltr

end

for

No

rway

spru

cefo

rea

cho

fth

ere

gio

nal

con

tex

tsu

nd

erst

ud

y

Mas

sif

Cen

tral

Alp

sJu

ra

Est

imat

eS

td.

erro

rP

val

ue

Est

imat

eS

td.

erro

rP

val

ue

Est

imat

eS

td.

erro

rP

val

ue

(In

terc

ept)

-1

5.1

63

3.5

58

2.7

7.1

0-

5***

(Inte

rcep

t)1.9

18

0.3

21

3.6

6.1

0-

9***

(Inte

rcep

t)-

3.0

01

1.0

45

0.0

04

3*

*

log

RD

I(f

1)

0.6

36

0.0

53

2.1

0-

16*

**

log

RD

I(f

1)

0.7

60

0.0

43

2.1

0-

16*

**

logR

DI

(f1)

0.8

05

0.0

45

2.1

0-

16*

**

log

H0

(f2)

-1

.72

90

.32

92

.91.1

0-

7*

**

log

H0

(f2)

-0

.86

20

.081

2.1

0-

16*

**

logH

0(f

2)

-0

.927

0.0

83

2.1

0-

16*

**

1/H

0(f

2)

-1

4.1

47

5.6

48

0.0

12

8*

Tn

60

.40

20

.060

4.1

0.1

0-

1*

**

Tx

4-7

0.3

87

0.0

59

1.3

.10

-10*

**

Tx

6-7

1.9

31

0.2

91

1.6

3.1

0-

10*

**

log

SW

C0

.08

90

.012

1.0

.10

-12*

**

logS

WC

1.7

85

0.2

88

1.4

3.1

0-

9*

**

Tx

6-7

2-

0.0

39

0.0

07

5.4

7.1

0-

8*

**

dat

e0

.79

50

.288

0.0

06

**

Tx

4-7

:lo

gS

WC

-0

.087

0.0

16

1.9

2.1

0-

7*

**

S:T

0.1

19

0.0

42

0.0

05

**

Dat

e0

.886

0.2

24

8.9

1.1

0-

5*

**

Tx

6-7

:S:T

-0

.00

60

.00

20

.006

**

Dat

e0

.51

20

.25

60

.046

*

Dat

e21

5.2

53

3.6

41

3.7

5.1

0-

5*

**

Ad

j.R

20

.58

Ad

j.R

20

.42

Ad

j.R

20

.67

RS

E0

.36

RS

E0

.43

RS

E0

.34

H0

isin

m,

tem

per

atu

rein

�C,

SW

Cin

mm

,d

ate

iny

ears

,S

:Tan

dR

DI

are

dim

ensi

on

less

rati

os

Ad

j.R

2ad

just

edco

effi

cien

to

fd

eter

min

atio

n,

RS

Ere

sid

ual

stan

dar

der

ror

(m2/h

a/5

yea

rs)

Sig

nifi

cance

lev

els:

.,\

0.1

;*

,\0

.05;

**

,\0

.01;

**

*,\

0.0

01

212 Trees (2014) 28:205–221

123

Massif Central

The linear effect of date was very significantly non-station-

ary (p value \10-16, Table 5) whereas the quadratic term

was stationary (p value = 0.99, Table 5). The t surfaces

(Fig. 3b) indicated that the linear term was significant in the

south-west and south-east of the area (positive) and in the

north-west (negative). The quadratic term was significant in

the north-west, the south and the east. At locations where

both terms were significant, the local trends displayed a

convex relationship of varying intensity (Fig. 4), consistent

with the average regional trend (Fig. 2). The BAI change

varied from ?5 to ?350 % between 1981 and 2005 (Fig. 3a.

A NW–SE gradient was identified in this spatial variation

(Fig. 3a), with the most important changes in the south-east.

Alps

The linear term of the date effect was significantly non-

stationary (p value \10-3, Table 6). The t surfaces indi-

cated that the date effect was significant in the south-west

(negative) and in the north-west (positive) of the area

(Fig. 5b). The BAI change varied from -60 to ?300 %

between 1981 and 2005. The BAI change was highest in

the north-west (Fig. 5a), whereas a significant negative

trend was identified in the south-west.

Jura

The linear date effect was not significantly non-stationary,

indicating a homogeneous positive trend not depending on

the location (Table 5).

Relationships between local variations in BAI change

and the abiotic environment

The PLS regression models of the local level of BAI

change against permanent or average site properties

(Eq. 13) reached a high goodness-of-fit (R2 of 82 % and

60 % in the Massif Central and the Jura, respectively,

Table 6). The most significant and most contributive

environmental variables related to spatial variations in

growth changes in the Massif Central and the Alps were

mainly related to thermal and nutrient indicators.

In the Massif Central, among the most significant vari-

ables, those with the highest contribution to the local level

of BAI change were a negative effect of winter maximal

temperature, a positive effect of February to June temper-

ature and summer solar radiation, and a negative effect of

nitrogen deposition (NH4 and total N deposition, Table 6).

We also found a negative effect of autumn solar radiation

and minimal temperature, a negative effect of summer

water availability and a positive effect of soil C:N ratio

(Table 6).

In the Alps, among the most significant variables, those

with the highest contribution to the local level of BAI

change were a positive effect of January–February minimal

temperature and February maximal temperature, a negative

effect of early autumn temperature and a positive effect of

the C:N and the S:T ratios (Table 6). As in the Massif

Central, we also found a positive effect of June maximal

temperature and radiation (although we also found a neg-

ative effect of May radiation), and a negative effect of

December maximal temperature (Table 6). Finally, we

found an effect of CWB, positive in September and

Fig. 2 Historical BAI changes relative to the reference year 1981 for

Norway spruce in the three regional contexts under study (Eqs. 7 and

8, black lines). Grey dots correspond to the partial residuals of date.

Black dots correspond to annual averages of these partial residuals.

The grey areas correspond to the 95 % level bilateral confidence

intervals for the fitted trends relatively to the reference year 1981 for

which they reach 1 by construction

Trees (2014) 28:205–221 213

123

negative in spring, and a positive effect of August SWD

(Table 6).

Discussion

The first objective of this study was to investigate spatial

variations in stand BAI changes at regional and local scale.

The second objective was to relate these spatial variations

to environmental data to uncover the possible footprint of

some environmental factor known to affect the growth of

Norway spruce.

Adequacy of the approach

NFI data were selected for this study for different reasons:

(1) the broad ecological gradients covered allowed us to

investigate the growth–environment relationships of Nor-

way spruce in three contrasted mountain regions

(Table 3), (2) the frequency of the inventories allowed

assessing regional historical changes in BAI (Fig. 2), (3)

and the spatial resolution of the sampling design also

allowed investigating local variations of the trends

(Figs. 3a, 5a). Although the NFI sampling design was

changed in 2004 a careful homogenization of the data was

carried out to avoid any artifact in the models elaborated.

The only differences between the two inventory methods

lies (a) in the density of plots available for a given

inventory cycle (lower in the new method of inventory

because only 4 annual fractions were used), which is not

likely to affect our results, (b) in the change of some

forest stand variable definitions, which were carefully

homogenized (see Online resource 1). The models of BAI

against stand dendrometric variables (Eq. 2) had a fair

explicative power (Table 2) and did not reveal any mis-

specification (Online resource 7) as the explanatory vari-

ables showed effects consistent with the processes they

represent (positive effect of stand stocking, negative effect

of stand developmental stage; Table 2). The PLS regres-

sion approach helped identifying the main factors of

Norway spruce productivity, and their regional differ-

ences, with a satisfactory explanatory power (Table 3). As

H0 may be suspected to absorb some of the environmental

signal, we tested the correlation between H0 and the set of

environmental indicators used in the study. In the Jura and

the Alps, the correlation was never higher than 0.2

showing that H0 is not dependent on environmental fac-

tors. In the Massif Central, we found some higher but still

moderately positive correlations with temperature vari-

ables (r between 0.2 and 0.5) and a few variables related

to water availability in autumn (r between -0.2 and -0.3,

showing that H0 may hardly absorb most of the

Table 5 Parameter estimates and summary statistics for the mixed

GWR models fitted for Norway spruce for each of the regional con-

texts under study

Estimate Std. error P value1 P value2

Massif Central

(Intercept) -7.448 \2.10-16***

logRDI (g1) 0.613 0.053 \2.10-16***

logH0 (g2) -1.794 0.423 2.97.10-5***

1/H0 (g2) -17.3 7.335 0.019*

Tx6-7 1.505 0.466 0.001**

Tx6-72 -0.032 0.011 0.004**

S:T 0.034 0.051 0.505

Tx6-7:S:T -0.003 0.002 0.120

t85 -0.002 \2.10-16***

t852 0.002 0.993

R2 0.69

Adapt.quant. (%)

23(about68 of291plots)

Alps

(Intercept) -1.261 \2.10-16***

logRDI (g1) 0.744 0.043 \2.10-16***

logH0 (g2) -0.784 0.082 \2.10-16***

Tn6 0.999 0.236 2.7.10-5***

logSWC 0.161 0.037 1.23.10-5***

t81 0.004 2.17.10-4***

R2 0.54

Adapt.quant. (%)

6.4 (about 41of 640plots)

Jura

(Intercept) -2.564 \2.10-16***

logRDI (g1) 0.725 0.044 \2.10-16***

logH0 (g2) -0.877 0.083 \2.10-16***

Tx4-7 0.363 0.161 0.024*

logSWC 0.955 0.573 0.096.

Tx4-7:logSWC

-0.042 0.032 0.193

t81 0.005 0.817

R2 0.73

Adpat.Quant. (%)

10 (about 38of 394plots)

H0 is in m, temperature in �C, SWC in mm, date in years, S:T andRDI are dimensionless ratios

The intercept and date effects were considered as local (with asso-ciated Leung et al. test of the significance of non-stationarity,P value1), whereas the other effects were assumed global (withassociated global t test, P value2; see Eq. 9). We indicated theadaptative quantile (adapt. quant.) corresponding to the proportion ofobservations considered as nearest neighbors (see Eq. 12)

Significance levels: ., \0.1; *, \0.05; **, \0.01; ***, \0.001

214 Trees (2014) 28:205–221

123

environmental signal in this region. These correlations are

not likely to affect the BAI models.

Regional BAI models have already proven useful to

estimate historical BAI changes (Charru et al. 2010).

Nevertheless, using environmental variables selected from

a prior autecological analysis to control site fertility is an

originality of this approach.

The GWR approach can be considered as a further

refinement of the latter model where the spatial stationarity

of the year effect was tested, allowing spatialization of BAI

change. To our knowledge, such maps have never been

presented in the literature, but form an appealing output.

From a statistical perspective, however, such spatial

approach must also be viewed as a means to reveal the

insufficiencies of the average model, arising either from

possible misspecification in the explanatory variable effects,

or from a lack of important explanatory variables (Fother-

ingham et al. 2002). Accordingly, local variation in BAI

trends was here considered as an environmental signal to be

correlated to spatial environmental information. We here

hypothesized that only the date effect was likely to vary over

space (mixed GWR). Potential non-stationarity in other

parameters, including those associated to environmental

variables, was not tested, but this may open perspectives as to

the local significance of co-limitations in species produc-

tivity and to the associated role of spatial scale. This, how-

ever, falls out of scope from the present exploratory study.

Growth response to the abiotic environment

The explanatory power of the environmental variables

selected in the PLS regression was low as compared to the

dendrometric variables (RDI, H0). Besides the fact that

dendrometric variables account for first-order effects of

stand dynamics that overcome environmental effects, this

may result from two sources of uncertainty: (a) NFI plot

coordinates are blurred to respect forest owners’ privacy,

and are given with an uncertainty of ±500 m in each

direction, which affects plot localization and thus the

attribution of environmental variables to each plot,

(b) interpolation methods were used both in the estimation

of historical climatic variables (see Online resource 4) and

in the elaboration of environmental GIS layers (permanent/

average environmental variables), which is likely to

increase further the uncertainty in environmental variables

and to lower the amount of variance they explain.

However, BAI–environment relationships indicated that

the growth of Norway spruce was favoured by high SWC,

Fig. 3 a Spatial pattern of the relative change of BAI in Norway

spruce (Eq. 12) between 1981 and 2005 in the Massif Central and

b t surfaces (see ‘‘Methods’’) of the linear and quadratic terms of the

date effect. As in Fotheringham et al. (2002), we used the thresholds

of 1.65, 1.96 and 2.58 for the t surfaces that correspond to the 90, 95

and the 99 % significance levels, respectively

Trees (2014) 28:205–221 215

123

elevated temperature, in particular during the growing

season, and good nutritional status (Table 3). These find-

ings agree with the literature, as SWC and mineralization

rate have been shown to have positive effects on Norway

spruce site index (Seynave et al. 2005), and summer tem-

perature is known to have a positive effect on radial growth

at high elevations or latitudes (Desplanque et al. 1998;

Lebourgeois et al. 2010; Babst et al. 2013). In the literature,

Norway spruce growth has also been found to be influenced

by the previous growing season temperature [September in

Lebourgeois et al. (2010), summer in Desplanque et al.

(1998) and Babst et al. (2013)]. We could not test such

carry-over effects because we only considered NFI cumu-

lated 5-year increments, with no possibility to consider

annual increments.

The relative importance of the main influential factors

varied from one region to another in accordance with their

regional level. The effect of summer temperature was

hence found the most significant in the Massif Central,

where average summer temperature is the lowest (Table 1),

whereas it was among the least significant in the Alps

(Table 3), where average summer temperature is higher

(Table 1). In the Jura range, average minimal temperatures

had a very important cumulated contribution and signifi-

cance (Table 3), consistently with their low average level

in this region (Table 1). In the Massif Central, the positive

effects of pH and S:T ratio were in accordance with the

Fig. 4 Representation of the local quadratic BAI trends fitted in the

mixed GWR approach in the Massif Central. We only represented local

trends for which both the linear and the quadratic term of the date effect

are significant according to the t surfaces (t [ 1.96). The reference year

for expressing BAI changes is 1981 with reference level 1

Table 6 Environmental variables having a significant effect on local

variations in BAI trends in the Massif Central and in the Alps (Eq. 13)

Variable Sign Contribution (%) Statistics

Massif Central

Tx11-1 3.91

Tn2-6, 11 5.76

rad9-10 3.02

rad5-6 4.60

Tn8-9 2.93 Ncomp = 6

CWB6 1.41 Q2 = 74 %

NH4_dep, Ntot_dep 4.53 R2 = 82 %

SWB6-7 2.72

C:N 2.14

CWB7 1.19

Tx6 0.52

Alps

Tn1-2, Tx2 3.54

Tn8-9, Tx8-9 2.66

rad5 1.07

Tx12 1.03

rad6 1.23 Ncomp = 14

Tx6 0.57 Q2 = 54 %

C:N 2.16 R2 = 60 %

S:T 1.63

CWB9 1.05

CWB3-4 1.29

SWD8 1.28

Variables were grouped seasonally when several months of the same

variable were significant, and were ranked by degree of significance.

The sign of the jackknife coefficient (see S5) is indicated for each

group of variables by arrows (grey upward = positive, black down-

ward = negative) and their contribution to the total R2 is also indi-

cated. We reported the number of selected components (Ncomp),

predictive power assessed by cross-validation (Q2, see S5) and R2 of

the regressions. Temperature is in �C, SWB, SWD and CWB in mm,

radiations in MJ m-2, nitrogen deposition in kg ha-1 year-1 and S:T,

and C:N are dimensionless ratios

216 Trees (2014) 28:205–221

123

lower level of pH in this region (Table 1). In the Alps, the

negative effect of C:N was the most significant, in accor-

dance with the higher average C:N ratio in this area

(Table 1), indicating a poor nitrogen mineralization.

However, we did not find any effect of nitrogen deposition.

SWC had no significant effect in the Massif Central, con-

sistently with its high average level. However, it had the

most contributing and significant effect in the Alps

(Table 3), although the average SWC is intermediate

between the two other regions (Table 1). SWC also had a

surprisingly little significant effect in the Jura (Table 3), in

spite of its very low average level in this region (Table 1).

This finding suggests that the low level of minimal tem-

peratures may be more limiting for growth than SWC in the

Jura Mountain.

Regional changes in BAI and relationship with abiotic

environment

In the three regions, we found an increase in BAI (Fig. 2), in

line with previous studies in France (Badeau et al. 1996).

This is a timely finding, since there is no agreement in the

literature on growth changes in Norway spruce across Eur-

ope. Unfortunately NFI data only allow investigating

medium-term productivity trends and assessing the role of

climatic events in featuring the changes observed (Charru

et al. 2010) is difficult at this timescale. For example, low

increments were observed in the Alps for Norway spruce in

the late 1970s (Buntgen et al. 2008). The trends observed

may thus be partly due to a recovery after unfavorable years.

We found inter-regional differences in the magnitude of

these changes (Fig. 2), highlighting their context depen-

dence. In particular, productivity increased much more in

the Massif Central than in the two other regions. Norway

spruce productivity was particularly limited by summer

temperature in the Massif Central (Table 2), and June

temperature has increased by ?4.1 �C to ?4.5 �C between

the mid-point years 1981 and 2005 in the three regions

(?0.17 to ?0.18 �C/year, from a linear regression on

Meteo-France climatic series in each region, R2 between 30

and 37 %). This warming may have favored growth more

in the Massif Central where average summer temperatures

is lower than in the two other regions. Lower changes in

BAI in the Alps and Jura mountain may be related to the

stronger limitation by water in these two regions (Table 2),

where SWC is less favorable (Table 1). In particular in the

Jura mountain, SWC is much lower at a high altitude where

the temperature limitation is the most important (data not

Fig. 5 a Spatial pattern of the relative change in Norway spruce BAI

(Eq. 12) between 1981 and 2005 in the Alps and b t surfaces (see

‘‘Methods’’) of the linear term of the date effect. As in Fotheringham

et al. (2002), we used the thresholds of 1.96 and 2.58 for the t surfaces

that correspond to the 95 and the 99 % significance levels,

respectively

Trees (2014) 28:205–221 217

123

shown), as suggested by the negative interactions between

temperature and SWC on productivity (Table 4). This may

have limited the positive effect of warming in this region.

Regional differences in BAI changes were thus consistent

with differences in regional limitations for temperature and

water availability.

Soil pH and S:T ratio also had a positive effect on

Norway spruce productivity in the Massif Central

(Table 3), suggesting the possible role of an improvement

in nutritional conditions. This could not be directly tested

as no data were available as to changes in nutritional

conditions over time. Although we may expect an effect of

N deposition on productivity changes (Kahle et al. 2008;

Solberg et al. 2009), regional BAI changes were not cor-

related to regional average levels of N deposition over the

period 1993–1998, amounting to 8.1, 7.2 and 10.8 kg N/

year in the Massif Central, the Alps and the Jura, respec-

tively. These average levels of deposition, however, remain

close and moderate (Kahle et al. 2008).

Local variation of BAI changes and relationship

with abiotic environment

We found significant and large spatial variation of histor-

ical BAI changes in the Massif Central and in the Alps

(Figs. 3a, 5a) leading to local trend inversions in the Alps

(Fig. 5a). This is a major outcome of this study, as it

highlights that an average regional trend may have no local

relevance. The role of scale in depicting forest growth

changes is therefore demonstrated.

Local BAI changes were related to local site properties

in an exploratory approach based on PLS regression

(Table 6). In the two regions, the most significant and

contributive variables were mainly temperature and nutri-

tional variables. Among the thermal effects, we found a

positive effect of temperature from February to June in the

Massif Central, and of January–February and June in the

Alps. We also found a positive effect of radiation in May

and June in the Massif Central, and in May in the Alps.

These effects are thus again compatible with an effect of

climate warming at this scale. However, in both regions we

also found a negative effect of winter maximal temperature

(November to January in the Massif Central, December in

the Alps) on the level of BAI change. This may suggest the

need of cold temperature during winter dormancy. We also

found a negative effect of August–September temperature

in both regions, and a negative effect of autumn radiation

in the Massif Central, which may indicate that increasing

temperature had a stronger effect at locations where the

growing season was the shortest.

Interestingly, we found significant and contributive

effects of nutrient variables on local BAI changes. In the

Massif Central, we found a negative effect of the local

nitrogen deposition level over the period 1993–1998

(Croise et al. 2005). It may suggest local nitrogen satura-

tion at locations with the highest deposition levels (Aber

et al. 1998; Dise and Wright 1995), leading to acidification

in already poor and acidic soils (Table 2), and to a local

reduction of the level of BAI change. Since we found a

positive effect of the C:N ratio, indicating a larger BAI

increase at locations with a poor mineralization rate, our

results may suggest an interaction between the level of N

deposition and the C:N ratio, with N saturation not attained

on sites with low N mineralization. In the Alps, we found a

positive effect of the C:N and S:T ratios, indicating a

higher level of change where mineralization rate was low

and soil base saturation was high. As the C:N ratio had a

negative effect on productivity in this region (Table 3) and

because we found no effect of nitrogen deposition in this

region, it may correspond to an indirect effect of warming

on organic matter decomposition (Melillo et al. 2011).

Finally, among the most significant variables, water

variables were the least contributive and their effects are

difficult to interpret.

This observational study intended to provide indications

on the role of environmental factors on Norway spruce BAI

changes. Because of uncertainty in the data, we could not

provide any direct evidence of the role of climate warming

on Norway spruce BAI changes. However, the inter-com-

parison between regions, and the local analyses, converged

to reveal that the clearest signal is the footprint of tem-

perature and it comes in support of the hypothesis of cli-

mate warming enhancing effect on Norway spruce

productivity. In addition, our results suggest a modulation

of the trends by other potential drivers of change such as

local nutritional status (soil mineralization rate, nitrogen

deposition, S:T ratio) and water status. Even if these effects

are not always easily interpretable, our results point out to a

scale dependence of the environmental determinism of

spatial variations in productivity trends: whereas inter-

regional variations appear to be mainly linked to temper-

ature and water availability, intra-regional variations were

also related to soil nutritional conditions and atmospheric

nitrogen deposition, as suggested in Siefert et al. 2012.

Other potential drivers of BAI changes

The effect of atmospheric CO2 increase was not tested in

this study, because relating a monotonous and spatially

homogenous CO2 trend to any increasing BAI trend may

lead to trivial correlations. Although the long-term effect of

CO2 fertilization on growth of adult trees is under debate, it

might have interacted with site conditions, in particular

with water stress, temperature, and nitrogen availability

(Huang et al. 2007). In addition to the average level of N

deposition over the period 1993–1998 (Croise et al. 2005),

218 Trees (2014) 28:205–221

123

changes in N deposition levels may also have played a role

in the productivity increase (Eastaugh et al. 2011; Kahle

et al. 2008) but regional BAI trends were not in agreement

with regional levels of nitrogen deposition. This could be

due to the lower level of N deposition over the period

studied, or to the limited temporal range of the sample that

may allow observe the effect of medium-term climatic

fluctuations better than the long-term effect of N deposi-

tion. However, our results suggested local nitrogen satu-

ration in the Massif Central (Table 6), in line with previous

results suggesting nitrogen saturation in coniferous stands

(Magill et al. 2004; Pinto et al. 2007), although the regional

level of N deposition remains moderate in this region

(8.1 kg N/year according to Croise et al. 2005).

Changes in forest management may possibly cause

apparent growth changes. For a species such as Norway

spruce, these changes may concern the genetic material

used in plantations, or the intensity of the thinning regimes.

Little information is currently available as regards the first

aspect, but Norway spruce is found in its natural range in

the Alps and Jura mountain ranges (EUFORGEN 2009),

and possible genetic-driven growth changes should be

restricted to the Massif Central. In addition, the time period

under study is restricted relative to the usual rotation length

in this species (more than 80 years according to NFI data),

offering little possibility of a significant impact of such

practices. Second, the careful selection of pure and even-

aged stands that were historically forested and not regen-

erating (see Online resource 1) should limit the effect of

management practices. Third, all BAI models include a

control of stand stocking level by means of the RDI, i.e., a

control of possible silvicultural intensification over time. In

addition, this intensification is very unlikely in these

mountain ranges, as (1) the average level of RDI is high in

each region (from 0.69 in the Jura up to over 0.78 in the

two other regions) indicating highly stocked stands on

average, and (2) we investigated the correlation between

calendar year and RDI in each region (r between 0.02 and

0.12, p value between 0.08 and 0.68) and found no trend.

Conclusion

We highlighted regional differences in the productivity–

environment relationships of Norway spruce, in accordance

with the contrasted environmental conditions found in each

area. We observed positive trends in BAI in the three

regions, and found regional differences in their magnitude.

On a local scale, we also reported spatial variations in some

areas, which is a strong original outcome of this contri-

bution. Our results thus highlight the context dependence

of productivity changes, down to local contexts. Adaptive

forest management strategies may therefore be primarily

meaningful at local scale and attempts at regional assess-

ments of such strategies may have somewhat lower sig-

nificance, with a risk to omit hot/bluespots in forest growth.

Regional differences in the magnitude of growth chan-

ges and limitations in temperature and water conditions

suggested a significant role of climate warming on Norway

spruce growth. Local spatial variations of these changes in

the Massif Central and the Alps also supported a positive

effect of climate warming and further modulations by

environmental factors such as soil nutritional conditions

and nitrogen deposition.

It is thus demonstrated that knowledge on productivity–

environment relationships at different scales, from local to

regional, provides useful insight of past growth changes

origin. Thanks to NFI data, the approach would gain from

being generalized on a wide range of species and contexts

in Europe.

Acknowledgments The authors wish to thank the National Institute

of Geographic and Forest Information (IGN) for project funding and

data provision. This work was also funded by the French Research

Agency (ANR) through the ‘‘Oracle’’ project (CEP&S call, 2010). We

are grateful to Christian Piedallu for his help in the use and inter-

pretation of GIS environmental data, and to Francois Lebourgeois for

helpful discussions on the interpretation of environmental effects on

Norway spruce productivity.

Conflict of interest The authors declare that they have no conflict

of interest.

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