Bioclimatology of beech (Fagus sylvatica L.) in the Eastern Alps: spatial and altitudinal climatic signals identified through a tree-ring network

ORIGINALARTICLE

Bioclimatology of beech (Fagus sylvatica

L.) in the Eastern Alps: spatial and

altitudinal climatic signals identified

through a tree-ring network

Alfredo Di Filippo1*, Franco Biondi2, Katarina Cufar3, Martın de Luis4,

Michael Grabner5, Maurizio Maugeri6, Emanuele Presutti Saba1, Bartolomeo

Schirone1 and Gianluca Piovesan1

1DAF, Universita degli studi della Tuscia,

Viterbo, Italy,2DendroLab, Department of

Geography, University of Nevada, Reno, NV,

USA,3Department of Wood Science and

Technology, Biotechnical Faculty, University of

Ljubljana, Ljubljana, Slovenia,4Departamento

de Geografıa y Ordenacion del Territorio,

Universidad de Zaragoza, Zaragoza,

Spain,5University of Natural Resources and

Applied Life Sciences, Vienna, Austria,6Istituto

di Fisica Generale Applicata, Milan, Italy

* Correspondence: Alfredo Di Filippo, DAF,

Facolta di Agraria, Universita degli Studi della

Tuscia, Via SC de’ Lellis snc, 01100, Viterbo,

Italy.

E-mail: [email protected].

SUMMARY

Aim To11 identify the dominant spatial patterns of Fagus sylvatica radial growth in

the Eastern Alps, and to understand their relationships to climate variation and

bioclimatic gradients.

Location Fourteen beech stands in the Eastern Alps12 , growing between 200 and

1500 m a.s.l. in Italy, Slovenia and Austria.

Methods At each site, trees were sampled using increment borers or by taking

discs from felled trees. Cores and discs were processed by measuring and

crossdating ring width. Ring width series were standardized, averaged,

and prewhitened to obtain site chronologies. Hierarchical Cluster Analysis

(HCA) and Principal Components Analysis of prewhitened site chronologies were

used to identify spatial and altitudinal growth patterns, related to the bioclimatic

position of each stand. Bootstrap correlation and response functions were

computed between monthly climatic variables and either principal component

scores or composite chronologies from stands associated by HCA. The stability of

dendroclimatic signals was analyzed by moving correlation functions (MCF).

Correlation analysis (teleconnections) based on a data base of 37 Italian and

Slovenian beech tree-ring chronologies revealed the spatial extent of Principal

Component scores.

Results Sampled trees were 200–400 years old, representing the oldest beech

trees that have been crossdated for the Alps to date. Maximum age was directly

related to altitude and to the presence of historical forms of conservation. Tree-

ring parameters varied according to geographic patterns and the age of sampled

trees. Stands were bioclimatically organized according to their location, and with

reference to their elevation and distance from the Adriatic Sea. A direct response

to winter temperature was found at all elevations. The altitudinal gradient ranged

from low elevation stands, characterized by a Mediterranean-type, late spring–

summer drought signal, to mountain and high elevation stands, characterized by

a direct response to growing season temperature plus an inverse response to the

previous year’s July temperature. The mountain and high elevation signal was

evident in Austria, the Central Alps and Slovenia, while the low elevation signal

was confined to mountains adjacent to the Adriatic Sea. MCF revealed trends in

the response to climatic factors affecting tree-ring formation in mountain and

high mountain stands linked to climatic warming.

Main conclusions Dendroclimatic networks can be used for bioclimatic studies

of tree populations. A biogeographical separation emerged between the Alps and

the Apennines at the upper elevations, while different degrees of mediterraneity

distinguished sites at lower elevations. This information will be useful in assessing

Journal of Biogeography (J. Biogeogr.) (2007)

ª 2007 The Authors www.blackwellpublishing.com/jbi 1Journal compilation ª 2007 Blackwell Publishing Ltd doi:10.1111/j.1365-2699.2007.01747.x

The definitive version is available at www.blackwell-synergy.com

INTRODUCTION

The biogeographical study of plant–climate relationships has

been an important field of research since the 19th century, and

aims to explain vegetation patterns based on climate variation

(Woodward, 1987). In particular, the effect of climate on

species physiology and geographic range has provided the

causal mechanism linking climate with vegetation and biome

distribution (Walter, 1985). With respect to forest ecosystems,

dendroecology can contribute to bioclimatic studies by

improving the analysis of tree growth response to environ-

mental gradients, thereby refining the classifications that are

based on climate–vegetation interactions. This approach

considers tree-ring parameters as bioindicators that integrate

the environmental factors controlling forest growth. Tree-ring

records can show growth–climate relationships over space

(from a forest stand to a whole hemisphere) and time (from

seasons to centuries) (Fritts, 1976). The effect of climate

forcing on tree growth has been studied at local (Douglass,

1920), regional (Meko et al., 1993) and hemispheric (Briffa

et al., 2002) spatial scales. In this context, sampling stands of

the same species (e.g. beech) at different locations and altitudes

can provide an objective bioclimatic classification of tree

populations (Piovesan et al., 2005a).

A number of bioclimatic classification systems are currently

available (e.g. Walter, 1985; Bailey, 1996), with each system

defining bioclimatic units (e.g. ecoregions and zonobiomes)

based on different methods and types of data (e.g. Thompson

et al., 2005). Recently, the use of statistical techniques, digital

data bases and advanced spatial analytical approaches have

allowed the numerical classification of environmental variables

to clarify their role in affecting plant distribution (Laurent

et al., 2004; Metzger et al., 2005). However, especially at

regional scales, there is a need to integrate patterns of species

distribution with ecological processes, paying special attention

to the effects of climate variability (Whittaker et al., 2005).

Horizontal and vertical gradients in tree–climate relationships

provide the basis for defining bioclimatic units in terms of the

leading dendroclimatic signals. Such studies generate the basic

information necessary to perform climatic reconstructions

from tree rings (Frank & Esper, 2005), and can be the starting

point to define simulation models of plant community

response to a changing climate (Cook et al., 2001). In addition,

this way of classifying woody vegetation into bioclimatic units

offers a valuable tool for a science based management of forest

ecosystem, linking climate fluctuations to forest productivity

(Biondi, 1999). Finally, if long instrumental records33 are

available, it is possible to explore the temporal stability of

the observed climate–growth relationships (Biondi, 2000), and

formulate hypotheses about the potential effects of climatic

change on plant functioning and community dynamics (Jump

& Penuelas, 2005; Whittaker et al., 2005).

Dendroecologists have used European beech (Fagus sylvat-

ica L.) extensively during the last two decades (Eckstein &

Frisse, 1982; Gutierrez, 1988; Rozas, 2001; Dittmar et al.,

2003; Lebourgeois et al., 2005), taking advantage of the

widespread distribution, sensitivity to climate and longevity

of beech (Bourquin-Mignot & Girardclos, 2001; Piovesan

et al., 2005b). In Italy, investigations were carried out at local

(e.g. Biondi, 1993; Bernabei et al., 1996; Piutti & Cescatti,

1997; Piovesan et al., 2003) and regional (Biondi, 1992;

Biondi & Visani, 1996; Piovesan et al., 2005a) scales.

European beech approaches the southern edge of its

geographic range in Italy, where its altitudinal range extends

more than 1500 m, from about 300–400 to 2000–2100 m

a.s.l. in central and southern Italy, and from 200–300 to

1500–1600 m in the Alps. Our main hypothesis is that,

because beech is present in both the Alps and the Apennines,

its tree-ring records can be used to detect ecological

differences between these two mountain ranges. If a large

enough number of chronologies are available, it should also

be possible to distinguish dendroclimatic responses along

ecological gradients, such as elevation. To date, dendrocli-

matic networks in the Alps have focused on conifer species

growing at high elevation or at the tree line (e.g. Kienast

et al., 1987; Urbinati et al., 1997; Rolland, 2002; Frank &

Esper, 2005). In this study we tested our hypotheses using a

transnational network of 14 European beech stands sampled

at different elevations in the Eastern Alps (Italy, Slovenia and

Austria), which is a focus region for bioclimatic studies (e.g.

Walter, 1985; Ellenberg, 1988). Our main objectives were (1)

to identify the dominant spatial patterns of radial growth in

Fagus sylvatica, (2) to investigate how such patterns relate to

climatic variability and geographic location, and (3) to

improve the definition of bioclimatic gradients, and refine the

classification systems that rely on them.

any future climate-related bioclimatic shifts, especially for forests at ecotones and

along altitudinal gradients.

Keywords

Alps, altitudinal gradient, bioclimatology, dendroclimatology, ecological gradi-

ent, ecotone, Fagus, old-growth forests, tree growth, tree-ring analysis.

A. Di Filippo et al.

2 Journal of Biogeography

ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd

METHODS

Study areas

A total of 14 European beech (Fagus sylvatica L.) forests were

sampled in the following ranges: Julian Alps in Italy and

Slovenia, Carnic Alps in Italy, and northern Alps in Austria

(Fig. 1). Sampled sites were located from 46.19� to 48.33� N

latitude, and from 12.75� to 15.43� E longitude, covering an

altitudinal range of 1300 m, from 200 to 1500 m a.s.l.

(Table 1). This region occupies the central-southern portion

of the geographic range of beech distribution, where this

species has a noticeable spread in altitude (Ellenberg, 1988).

According to the classification of Koppen–Trewartha (Tre-

wartha, 1968), the Carnic and the Austrian ranges pertain to

temperate climates (D), while the Julian mountains are at the

boundary between temperate and subtropical dry summer (Cs)

climates. A recent environmental classification of Europe

(Metzger et al., 2005) defined the central bulk44 of our network

as the Alpine Environmental Zone, the southernmost Julian

Alps as the boundary with the Mediterranean Mountains Zone,

and the northernmost Dunkelsteinerwald as within the Con-

tinental Zone.

Almost all stands were managed as high forests where beech

was the dominant species; only the lower elevation sites

(MOT, NIM, PEC, TOA and TOB; see Table 1 for acronyms)

(b)

(a)

Figure 1 (a) Geographic distribution of

Fagus sylvatica (von Wuehlisch., 2006), with

outline (rectangle) of the study area. (b)

Enlargement of the study area showing the

location of sampled sites.

Bioclimatology of beech in the Eastern Alps

Journal of Biogeography 3ª 2007 The Authors. Journal compilation ª 2007 Blackwell Publishing Ltd

included species (e.g. Acer spp., Fraxinus spp. And Ostrya

carpinifolia Scop.) that were managed as coppice-with-stand-

ards, and even at those sites beech maintained a monocormic

stem. These low-elevation beech stands, located at the

altitudinal limit of the species, cover a few hectares each,

and are found within mixed deciduous forests. Low elevation

forests on the Italian side of the Julian Alps and in Slovenia

(MOT, NIM, PEC, TOA and TOB) were privately owned. The

Slovenian sites were strongly affected by the battles of World

War I, and later on they were over-exploited by the local

population. Therefore well-preserved forests are only found in

remote areas that are difficult to access. All stands sampled in

the Carnic Alps were publicly owned, being situated above

towns for protection against avalanches and rock falls

(‘protection forests’). Some of these forests (TIM, CLE, LAT

and GRA), called ‘boschi banditi’ (Fig. 2), were maintained by

the Republic of Venice during the 16th century to produce

timber (especially masts) for ships, or set aside by local

inhabitants to provide shade and food (beech nuts and

understorey species) for livestock (Paiero et al., 1975). As a

final note, at several sites included in this network specifically

because of their relatively undisturbed conditions and old-

growth characteristics, land managers stipulated that only one

core per tree could be taken so as to minimize damage. The

Table 1 Geographical and structural features of the sampled beech sites.

Site

Alpine

range Country Code

Latitude

(N)

Longitude

(E)

Elevation*

(m a.s.l.) Aspect

Slope

(%)

d.b.h.�

(cm)

Ht.�

(m)

La Motta J I MOT 46.1886 13.3153 250 (200-300) SW 10–40 39–65 20

Tolmin A J S TOA 46.2000 13.7333 355 (290-420) SW 40–80 39–50 24

Nimis J I NIM 46.2006 13.2769 560 SE 45 34–65 27

Pechinie J I PEC 46.2807 13.2027 670 NE 10–30 36–62 20

Tolmin B J S TOB 46.2167 13.7500 821 (797-845) SW 40–80 39–53 27

Tolmin C J S TOL 46.2333 13.7666 1328 (1240-1415) S 40–80 33–55 22

Gracco C I GRA 46.5514 12.8519 825 (750-900) S 70–90 42–72 21

Cleulis C I CLE 46.5584 13.0006 930 NE 20–60 35–70 24

Tre Confini C I TRE 46.5039 13.5831 1100 – 20–60 35–55 30

Timau C I TIM 46.5817 13.0050 1160 (825-1500) S 70–90 40–95 25

Paularo C I PAU 46.5297 13.1172 1385 (1275-1500) W 70–90 52–100 27

Lateis C I LAT 46.4594 12.7489 1450 (1370-1530) S 75–110 33–70 28

Dunkelsteinerwald N A DSW 48.3333 15.4333 650 E 40–60 30–50 22

Hallstatt N A HSA 47.8333 13.7500 1400 SE 100–120 15–36 12

C, Carnic Alps; J, Julian Alps; N, Northern Austrian Alps. I, Italy; S, Slovenia; A, Austria.

*Mean with range in parentheses.

�Diameter at breast height range of sampled trees.

�Mean height of the three to four tallest trees in the stand.

Figure 2 View of the Gracco ‘protection

forest’ in Carnia, where beech age exceeds

300 years (Photograph by G. Piovesan).

COLOUR




same constraint was imposed in privately owned forests,

including most of the low-elevation beech stands.

Sampling and chronology building

In each stand, tree selection focused on dominant or

co-dominant trees, either isolated or grouped, with the best

combination of old age and trunk health. Trees were cored at

breast height (1.3 m from the ground) using an increment

borer, taking one or two cores per tree. At the Slovenian sites,

all samples were cross-sections taken from trees already felled

for other purposes. Tree-ring chronologies were developed

from wood samples using standard dendrochronological

procedures (Stokes & Smiley, 1996). After surfacing and

preliminary visual crossdating, tree-ring widths were measured

to the nearest 0.01 mm using the system CCTRMD (Aniol,

1987) and the program CATRAS (Aniol, 1983) or the LINTAB

measuring table and TSAP/X programme (Rinn, 1996). Tree-

ring series were visually and statistically compared with each

other to ensure accuracy of crossdating and measurement

(Holmes, 1983; Grissino-Mayer, 2001). Locally absent rings

(LAR), when detected, were given ring width equal to zero.

The percentage of locally absent rings was calculated on the

entire length of the tree-ring series. Dendrochronological

parameters (mean ring width, standard deviation, mean

sensitivity and first-order autocorrelation) were computed

for all measured ring-width series. The same parameters were

then computed on raw site chronologies, obtained by arith-

metically averaging the ring-width series by site. The years

1942–2001 were a common period that included at least three

ring-width series at each site. Site location and tree age were

then used to explain the variability of dendrochronological

parameters, computed for the 1942–2001 period using the raw

site chronologies.

Standardized tree-ring chronologies were produced for each

site using the following formula:

�It ¼

Pi¼nti¼1 ðw0:5 � yÞit

ntþ cit

with �It = chronology value at year t; nt = number of samples

for year t, with nt ‡ 3; w = crossdated ring width of sample i

in year t; y = value of sample i in year t computed by fitting

a modified negative exponential with asymptote ‡ 0 or a

straight line with slope £ 0 to the ith ring-width series;

cit = constant added to sample i in year t so that the

standardized chronology has mean equal to 1. Although

‘standardized indices’ used in dendrochronology are often

calculated as ratios between the measurement and the fitted

curve value, there is evidence that variance-stabilized

residuals should be preferred (Cook & Peters, 1997; Biondi,

1999; Helama et al., 2004). Each standardized chronology

was prewhitened by fitting autoregressive (AR) models

(Biondi & Swetnam, 1987) to remove any biological trend

and enhance the climatic signal (Cook et al., 2001). The

linear correlation between prewhitened site chronologies was

computed for the common period 1942–2001. Finally,

composite chronologies were obtained by pooling together

all ring-width series from sites whose prewhitened chronol-

ogies were associated by multivariate analysis (see next

paragraph). The same procedure used to compute prewhit-

ened site chronologies was used to compute prewhitened

composite chronologies. Chronology confidence was evalu-

ated by computing the expressed population signal (EPS)

(Wigley et al., 1984). EPS values were computed for the

period 1942–2001, 1952–2001 and 1962–2001 on the pre-

whitened site chronologies. These three periods were chosen

to show how the increase in the number of available samples

affects the EPS statistic. The limited extension of the low

elevation sites, together with the restrictions imposed on the

number of samples per tree, were responsible for a reduced

sample depth in the early part of these site chronologies. For

prewhitened composite chronologies (see Table S4 in Sup-

plementary Material), EPS values were calculated over the

entire length of the chronology using 50-year moving

windows with a 40-year overlap.

Multivariate analysis

The period 1942–2001, common to the 14 prewhitened beech

chronologies, was considered for multivariate analysis. Hier-

archical Cluster Analysis (HCA) and Principal Components

Analysis (PCA) were based on the correlation matrix between

chronologies. HCA was used for the first detection of

groupings among the 14 chronologies (Ludwig & Reynolds,

1988). Distance between variables was based on (1 – r), with

r = Pearson product–moment correlation coefficient, while

clusters were identified by means of the average distance

between all pairs of variables contained in two groups

(Stenson & Wilkinson, 2004). Because clusters were generated

according to the degree of growth affinity between chronol-

ogies, the HCA dendrogram, when interpreted with consid-

eration to the spatial and altitudinal location of each forest,

could reveal the bioclimatic organization of the network

(Piovesan et al., 2005a).

The main modes of common growth variability among

stands were represented by Principal Component (PC) scores,

or amplitudes (Piovesan et al., 2005a). Component loadings

(eigenvectors), which display the pattern of association of

chronologies with each component, were employed to detect

groupings in the tree-ring network. Selection of PCs was

guided by Kaiser’s Rule (Kaiser, 1992). The combination of

HCA and PCA is essential because HCA produces a clear-cut

bioclimatic classification of the network, and PCA allows the

break down (thus the description) of the dominant climatic

signals responsible for the observed classification. The spatial

extent of the common signals was investigated by telecon-

nection analysis (Fritts, 1976), performed by correlating PC

scores for the period 1942–88 with beech chronologies

developed for Austria, Italy and Slovenia, standardized and

prewhitened as in our site chronologies55 . Graphical represen-

tations of correlation maps were produced with the software

GMT (Wessel & Smith, 1998).



The climate–growth relationship

Dendroclimatic correlation and response functions (Biondi &

Waikul, 2004) were calculated between monthly climate

variables and two types of tree-ring bioindicators. One type

was the first and second principal component scores of

prewhitened site chronologies; the other type was the

prewhitened composite chronologies. The bootstrap method

(Efron & Tibshirani, 1986; Guiot, 1991) was used for

significance testing. Explanatory climate variables spanned a

17-month window, from October of the current growth year

to June of the previous year. Climatic data for the Italian and

Slovenian sites were developed under the research project

CLIMAGRI (Brunetti et al., 2006), and were organized in a

grid of 1� · 1� cells, with each cell containing the monthly

values of minimum and maximum temperature anomalies

Figure 3 Graphs of the 14 prewhitened site chronologies and of the number of samples per year. Plots were arranged according to

chronology length.




and of precipitation ratios. Climate data for Italy and

Slovenia were obtained by averaging the two grid cells

(46� N, 13� E and 46� N, 14� E) that included our network

sites. In Austria, monthly total precipitation and mean air

temperature were available for the period 1942–2001 from

the meteorological station of Kremsmunster (48.052� N,

14.127� E, 403 m a.s.l.) for the Hallstatt site, and from the

station of Hohe Warte (48.299� N, 16.356� E, 203 m a.s.l.)

for the Dunkelsteinerwald site.

As climatic series for Italy and Slovenia reached the

beginning of the 19th century, we calculated moving correla-

tion functions (MCF) (Biondi, 1997) with the longest and best

replicated composite chronologies, i.e. those for mountain and

high mountain sites (see Table S4 in Supplementary Material),

using a 70-year window. By doing so, we investigated the

temporal stability of climatic signals identified for those

bioclimatic units.

RESULTS

Characteristics of the tree-ring network

A total of 248 cores from 188 trees were used in dendro-

chronological analyses (Tables 1 and 2a). Several old trees,

often exceeding 200–300 years of age, were identified (Fig. 3).

Mean ring width (MW) was negatively correlated with

elevation, with annual increments ranging from >4 mm in

the lowlands to c. 1 mm at the highest elevations (Table 2a).

The maximum age (Nmax) at each stand had a positive

correlation with altitude (r = 0.75, P < 0.001; this linear

relationship is graphically represented in Fig. 4). The main

features of raw and prewhitened site chronologies (plotted in

Fig. 3) are summarized in Table 2b.

Latitude was also negatively correlated with MW (Table 3).

Latitude may affect MW because the southern stands, which

are closer to the Adriatic Sea, experience66 a milder climate than

the northern, more continental sites. Finally, MW was

negatively correlated with tree age, as old-growth beech forests

are characterized by trees showing long periods of reduced

growth, occurring when they occupy the suppressed layers of

the canopy (Piovesan et al., 2005b). MW values were also

negatively correlated with LAR percentage (r = )0.50,

P < 0.05), possibly because LARs were frequent in old-growth

stands, especially during the suppressed growth periods. As

most LARs were present in the younger portion of measured

cores during periods of slow growth, it is likely that their

positive correlation with altitude can be related to the presence

of older trees at higher elevations.

The standard deviation (SD) had mean values varying

between 0.29 and 1.75 (Table 2a), and was negatively corre-

lated with altitude as well as to age of a site (Length) and of

individual trees (Nmax) (Table 3). This may be due to the

positive correlation between MW and SD (r = 0.82,

P < 0.001). The mean sensitivity (MS), a measure of year-to-

year variability, was comprised between 0.19 and 0.36, with

minimum values at CLE and at low elevation sites on the

Julian Alps (Table 2a). MS was correlated positively with

latitude (Table 3), most likely because both Austrian chronol-

ogies had MS values above 0.3 (Table 2a). The first-order

autocorrelation coefficient (A1), a measure of the persistence in

time series, varied between 0.61 and 0.79 (Table 2a), and

decreased with latitude, longitude and altitude (Table 3), but

correlations with longitude and altitude were immediately

below common significance thresholds. It is interesting to note

that A1 was inversely correlated with MS (r = )0.62, P < 0.05).

The order (p) of the autoregressive model used for prewhit-

Table 2(a) Summary of the ring width series for each site.

Site Code Trees Cores

MW*

(mm year)1)

SD*

(mm year)1) MS* A1*

Nmax

(years) Period

LAR

(%)

La Motta MOT 19 19 4.05 (1.70–5.44) 1.75 (1.10–2.49) 0.26 (0.18–0.44) 0.70 (0.44–0.94) 77 1928–2004 0

Tolmin A TOA 9 9 2.60 (1.71–3.50) 1.00 (0.59–1.41) 0.22 (0.15–0.29) 0.76 (0.57–0.86) 122 1880–2001 0

Nimis NIM 13 13 3.09 (1.46–4.34) 1.44 (0.75–1.89) 0.26 (0.20–0.34) 0.76 (0.53–0.88) 120 1883–2003 0

Pechinie PEC 12 13 3.11 (2.11–5.77) 1.40 (0.62–2.47) 0.22 (0.13–0.40) 0.72 (0.39–0.91) 79 1926–2004 0

Tolmin B TOB 5 5 3.28 (2.34–4.78) 1.18 (0.88–1.50) 0.20 (0.14–0.24) 0.77 (0.66–0.84) 83 1919–2001 0

Tolmin C TOL 10 10 1.61 (0.74–2.63) 0.69 (0.45–1.00) 0.25 (0.20–0.33) 0.74 (0.56–0.87) 271 1731–2001 0

Gracco GRA 20 20 1.43 (0.74–2.59) 0.70 (0.41–1.29) 0.24 (0.19–0.29) 0.78 (0.52–0.87) 318 1685–2002 0.099

Cleulis CLE 23 23 1.37 (0.40–3.19) 0.50 (0.19–0.90) 0.19 (0.13–0.26) 0.79 (0.60–0.91) 260 1744–2003 0

Tre Confini TRE 12 12 1.64 (0.85–2.81) 0.71 (0.41–1.00) 0.24 (0.16–0.30) 0.74 (0.58–0.86) 172 1831–2002 0.195

Timau TIM 21 27 1.28 (0.70–3.79) 0.72 (0.36–1.33) 0.26 (0.16–0.33) 0.77 (0.23–0.96) 348 1655–2002 0.162

Paularo PAU 20 20 2.11 (0.88–4.08) 0.80 (0.47–1.44) 0.27 (0.16–0.35) 0.61 (0.14–0.85) 261 1742–2002 0.110

Lateis LAT 24 28 1.05 (0.50–2.08) 0.49 (0.23–1.00) 0.25 (0.16–0.36) 0.77 (0.48–0.89) 380 1625–2004 0.187

Dunkelsteinerwald DSW 14 28 1.27 (0.79–2.06) 0.59 (0.36–0.86) 0.32 (0.24–0.37) 0.65 (0.25–0.86) 177 1827–2003 0

Hallstatt HSA 11 21 0.54 (0.25–0.73) 0.29 (0.15–0.42) 0.36 (0.29–0.43) 0.67 (0.40–0.85) 252 1751–2002 –

MW, mean ring-width; SD, standard deviation; MS, mean sensitivity; A1, first-order autocorrelation; Nmax, maximum number of rings counted on a

single core; Period, period covered by at least one sample; LAR, percentage of locally absent rings.

*Mean values with range in parentheses.



ening varied between 1 and 5 (Table 2b), and was positively

correlated with chronology length (Table 3).

Mountain (three sites) and high mountain (five sites)

chronologies had EPS values >0.85. Of the six low elevation

sites, two had EPS > 0.85, and the other four chronologies

approached the EPS value in the most recent decades (see

Fig. S1). Given the overall high correlation between all tree-ring

chronologies (see Table S1), and the importance of our tree-ring

network for its ‘rear edge’ location (Hampe & Petit, 2005)

between Eurosiberian and Mediterranean regions, no chronol-

ogy was discarded based on its EPS value. Latitude and altitude

were directly correlated with EPS (Table 3), suggesting a

stronger climatic control on tree growth moving upward and

northward along the network. The highest correlationwas found

between EPS and stand age (Table 3), suggesting that old-

growth forests are characterized by greater synchronicity of

radial increment, perhaps because they have also escaped human

impacts for decades. For composite chronologies (see Table S4),

EPS values exceeded the 0.85 threshold during periods used to

quantify climate–tree growth relationships (Fig. 5).

Table 2(b) Summary of the raw and of the prewhitened site chronologies.

Site Code Trees Cores Period Length

Raw chronology Prewhitened

chronology

Entire length Common period (1942–

2001)

Entire length

MW

(mm

year)1)

SD

(mm

year)1) MS A1

MW

(mm

year)1)

SD

(mm

year)1) MS A1 p MW SD MS A1

La Motta MOT 19 19 1940–2004 65 2.99 1.66 0.21 0.93 3.50 1.47 0.16 0.88 2 1.00 0.16 0.19 )0.08

Tolmin A TOA 9 9 1910–2001 92 2.00 1.01 0.17 0.92 2.84 0.60 0.14 0.65 2 1.00 0.13 0.15 )0.03

Nimis NIM 13 13 1903–2003 101 2.64 1.08 0.18 0.86 3.47 0.69 0.13 0.62 2 1.00 0.16 0.18 )0.02

Pechinie PEC 12 13 1930–2004 75 2.64 1.34 0.16 0.92 3.08 1.20 0.14 0.83 1 1.00 0.17 0.16 )0.04

Tolmin B TOB 5 5 1934–2001 68 2.93 1.06 0.19 0.85 3.45 0.66 0.14 0.58 3 1.00 0.14 0.16 )0.03

Tolmin C TOL 10 10 1797–2001 205 1.25 0.57 0.22 0.85 1.53 0.36 0.16 0.64 4 1.00 0.11 0.13 0.00

Gracco GRA 20 20 1714–2002 289 1.15 0.39 0.16 0.83 1.25 0.29 0.13 0.70 2 1.00 0.09 0.10 )0.01

Cleulis CLE 23 23 1804–2003 200 1.08 0.41 0.16 0.90 1.41 0.28 0.12 0.66 1 1.00 0.07 0.09 )0.07

Tre Confini TRE 12 12 1850–2002 153 1.47 0.42 0.17 0.70 1.72 0.42 0.16 0.60 1 1.00 0.12 0.12 0.00

Timau TIM 21 27 1676–2002 327 1.16 0.65 0.19 0.83 1.58 0.46 0.13 0.75 3 1.00 0.10 0.11 )0.01

Paularo PAU 20 20 1824–2002 179 1.57 0.78 0.23 0.83 2.08 0.41 0.19 0.32 1 1.00 0.13 0.14 0.03

Lateis LAT 24 28 1626–2004 379 0.85 0.36 0.18 0.87 1.42 0.28 0.14 0.51 5 1.00 0.09 0.10 )0.01

Dunkelsteinerwald DSW 14 28 1828–2003 176 1.23 0.34 0.21 0.58 1.23 0.28 0.22 0.29 2 1.00 0.13 0.15 )0.02

Hallstatt HSA 11 21 1753–2002 250 0.57 0.22 0.28 0.58 0.57 0.17 0.25 0.49 3 1.00 0.12 0.13 0.00

Period, period covered by three or more samples; Length, number of years included in Period; MW, mean ring width; SD, standard deviation; MS,

mean sensitivity; A1, first-order autocorrelation; p, autoregressive model order.

Figure 4 Relationship between maximum age at each sampled forest and average elevation. ‘B’ indicates ‘boschi banditi’, forest stands

historically protected from logging.




Bioclimatic units and growth–climate relationships

Multivariate analysis was conducted on the 14 prewhitened site

chronologies. Hierarchical Cluster Analysis (HCA) produced a

dendrogram where three main clusters were identified

(Fig. 6a). The first cluster is made of beech stands growing

between 200 and 800 m a.s.l. on the Julian Alps, named ‘Low

Elevation Julian Alps’. In the second cluster there are stands

growing between 800 and 1200 m a.s.l. on the Carnic Alps, so

that it could be named ‘Mountain Carnic Alps’. In this context,

it is important to notice that TOB and GRA, although having

the same elevation and exposure, belong to different biocli-

matic units. The last cluster consists of chronologies developed

above 1300 m a.s.l., reaching the higher edge of the beech

altitudinal range, and are thus referred to as ‘High Mountain’.

TRE (near Tarvisio), although located at a relatively low

elevation, belongs to the ‘‘High Mountain’’ group, possibly

due to its exposure to cold northerly winds, low winter

Table 3 Correlation of dendrochronological parameters with stand geographical location and age. Bold: P < 0.01; italics: P < 0.05.

MW SD MS A1 p LAR EPS

Latitude )0.63 )0.47 0.81 )0.62 0.01 )0.01 0.50

Longitude )0.11 )0.13 0.63 )0.49 )0.05 )0.41 0.09

Altitude )0.68 )0.67 0.25 )0.45 0.42 0.65 0.60

Length )0.79 )0.68 0.01 )0.23 0.54 0.69 0.69

Nmax )0.79 )0.72 )0.03 )0.26 0.47 0.65 0.72

MW, SD, MS, A1 and EPS calculated on the period 1942–2001; MW, SD, MS and A1 were computed for the raw site chronologies; see Table 2b.

p, order of autoregressive model used to obtain the prewhitened chronologies; see Table 2b. LAR: see Table 2. EPS: see Figure S1. Latitude, longitude

and elevation: see Table 1; Length and Nmax: see Table 2.

Figure 5 Expressed population signal

(EPS) statistics for the (a) low-elevation, (b)

mountain and (c) high-mountain composite

chronologies (see Table S4 in Supplementary

Material for a summary of these chronol-

ogies). Running EPS values were calculated

over the entire length of the chronology using

a 50-year window with a 40-year overlap. The

dotted line is the 0.85 threshold (Wigley

et al., 1984); the light grey background shows

the period studied using moving correlation

functions; the grey background indicates the

years used to compute correlation and re-

sponse functions.



temperatures and generally cool summers (Mennella, 1967;

Trewartha, 1968). Mountain and high mountain stands could

be further combined into a single group. The northernmost

site, DSW (Fig. 1 and Table 1), located at 650 m a.s.l., had low

affinity with the rest of the network and remained isolated

from the previous clusters. This site is located at the beginning

of the Continental Environmental Zone (Metzger et al., 2005),

and this may explain its uniqueness. Based on these results,

three sites were selected from each elevation range, so that the

high-mountain composite chronology incorporated all sam-

ples from LAT, PAU, TOL; the mountain composite chronol-

ogy was developed using TIM, CLE and GRA; and the low-

elevation composite chronology consisted of MOT, NIM and

TOA.

The first two principal components of the 14 prewhitened

chronologies explained 50% of the total variance, and were

retained for describing the beech network (see Fig. S2).

Mountain and high mountain chronologies have the highest

loadings on the first principal component, which explains 33%

of the total variance. The second principal component, which

accounts for 17% of the total variance, is mostly related to the

low elevation chronologies (see Fig. S2). Considering the

uniqueness of the Austrian site DSW, and the fact that gridded

climatic data covered the location of the Italian and Slovenian

sites, climate–tree growth relationships were analyzed sepa-

rately for the Austrian sites (see Tables S2 and S3). The first

two principal components of the 12 Italian and Slovenian

chronologies explained 55% of the total variance, and were

retained for describing the beech network (Fig. 6b,c). The first

component (PC1, 35.4% of the total variance) was mainly

related to mountain and high mountain chronologies; low

elevation stands were more related to the second component

(PC2, 19.3% of the total variance). Groups similar to those

identified by HCA were recognized by plotting the first and

second principal component loadings as a function of eleva-

tion (Fig. 6b,c).

Correlation and response function analysis of principal

component scores (Table 4) showed that radial growth in both

mountain and high mountain stands (PC1) was related to May

precipitation (negatively), September temperature (positively),

January minimum temperature (positively) and previous July

temperature (negatively). Radial growth in low-elevation beech

stands (PC2) was positively related to precipitation and

negatively to temperature in the months of May, July and

August; wood formation therefore appears to be drought-

limited during the growing season. Previous September and

October minimum temperatures were also negatively correla-

ted with low-elevation beech stands, while previous July and

November maximum temperatures appeared positively corre-

lated.

Correlation and response functions calculated for the period

1942–2001 using composite chronologies (see Table S4)

showed that growth in the high mountain forests was

positively correlated with May temperature and negatively

correlated with precipitation (Table 5), most likely because the

growing season at this elevation begins at the end of May

(Dittmar & Elling, 2005). July and August temperatures were

positively correlated with the high elevation composite,

whereas previous July temperature was inversely correlated.

No climatic variable for the growing season was significantly

correlated with the growth of mountain Carnic Alp stands, so

that the most important relationship was that with previous

summer temperatures. Thus, mountain and high mountain

chronologies, which were similar in terms of principal

component loadings, could be separated based on an eleva-

tion-related climatic signal. The low elevation composite was

R2

34.0=

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

0.1

004100210001008006004002

).l.s.am(edutitlA

Lo

ad

ing

(b)

(c)

PC1

PC2

R2

28.0=

0.1-

8.0-

6.0-

4.0-

2.0-

0.0

2.0

4.0

6.0

8.0

0.1

004100210001008006004002

).l.s.am(edutitlA

Lo

ad

ing

(a)

Figure 6 (a) Dendrogram showing results from the hierarchical cluster analysis made on the 14 prewhitened site chronologies (see

Table 2b). In parenthesis are reported the elevation and the alpine range of each site (see Table 1). Triangle = high-mountain forest;

circle = mountain forest; square = low elevation forest. Loadings of Italian and Slovenian prewhitened site chronologies on (b) the first and

(c) the second principal component are plotted as a function of site elevation.




correlated positively with June–July precipitation and negat-

ively with June–July temperature, pointing to summer drought

as the main climate signal. A positive correlation with

precipitation and a negative one with temperature also

appeared for the previous September. A positive correlation

with January minimum temperatures, which was reported for

PC1 (Table 4), was also found for low elevation and mountain

composites (Table 5). Climate–growth relationships for the

Austrian sites, DSW and HSA (see Tables S2 and S3),

reaffirmed their somewhat separate bioclimatic classification.

This separation could be the cause for the low correlations

between monthly precipitation recorded at the two meteoro-

logical stations in Austria and averaged over the two grid cells

for Italy and Slovenia (see Fig. S3).

Moving correlation function analysis was carried out on

mountain and high-mountain composite chronologies to

investigate the temporal stability of their main climatic

signals (Fig. 7). For mountain stands, the correlation in

growth with July maximum temperature during the previous

year has a progressive negative trend during the last

150 years (Fig. 7a), while the correlation with January

minimum temperature appears to increase in recent years

(Fig. 7b). With respect to high mountain sites, the inverse

correlations with May (Fig. 7c) and August precipitation

(data not shown) have emerged only in recent times. The

correlation of growth with August temperature has fluctu-

ated through time (Fig. 7d), with a tendency to higher

values in the most recent years, when correlation with July

and September temperature also becomes significant (data

not shown).

Teleconnection analysis

Teleconnection analysis of PCA scores served to evaluate the

geographic extent of climatic signals (Fig. 8). Correlations

between PC1 scores and other beech chronologies spread

mainly westward up to the Central Alps. It was less strong but

still significant eastward up to the Dinaric Mountains and the

hills of south-eastern Slovenia, and southward up to the

Northern Apennines (Fig. 8a). There was no significant

correlation with any beech chronologies further south, with

the only exception of Monte Cimino in Latium (r = 0.50,

P < 0.001). This might be due to the singularity of this site,

characterized by fertile volcanic soils, frequent fog and direct

exposure to northerly cold winds. Of the northernmost sites

DSW and HSA, which were excluded from the PC-based

dendroclimatic analysis, the former showed a less strong

correlation with PC1 scores (r = 0.32, P < 0.05) than the latter

(r = 0.47, P < 0.001). PC2 scores correlated eastward to the

Dinaric Mountains and south-eastern Slovenia, and southward

to low elevation stands in Central Italy (Latium) (Fig. 8b). In

that region, teleconnections could extend upward to 1050 m

a.s.l., where beech can grow in association with holm oak

(Quercus ilex L.) (Bernabei et al., 1996). No other Italian

chronologies from stands growing at higher elevations in the

Alpine region, the peninsula, or Sicily (Castorina et al., 2005),

Table 4 Bootstrap (a) correlation and (b) response function coefficients calculated for the period 1942–2001 between the scores of the first

two PCs of the 12 prewhitened chronologies from Italy and Slovenia, and the gridded climatic data covering the same area, over a 17-month

window. Coefficients with P < 0.05 are in bold.

Year preceding growth Year of growth

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

(a)

PC1

P 0.14 0.18 0.12 0.14 0.06 0.00 0.06 0.00 )0.23 0.04 0.06 )0.27 )0.06 0.06 )0.11 )0.06 0.11

Tmax 0.10 )0.33 )0.11 )0.12 )0.08 0.06 0.12 0.22 0.20 0.03 )0.14 0.12 )0.02 0.00 0.21 0.27 )0.01

Tmin 0.09 )0.39 )0.16 )0.04 0.10 )0.03 0.11 0.23 0.12 0.03 )0.17 0.15 0.08 0.04 0.22 0.30 0.10

PC2

P 0.01 0.07 )0.02 )0.19 0.25 0.15 )0.19 )0.17 )0.06 0.11 0.09 )0.22 )0.16 )0.40 )0.25 0.04 )0.12

Tmax )0.07 )0.24 0.09 0.25 0.01 )0.23 0.11 )0.07 )0.03 0.14 )0.07 0.34 0.18 0.33 0.37 0.13 0.11

Tmin )0.06 )0.18 0.09 0.26 0.24 )0.17 0.07 )0.14 )0.02 0.23 )0.05 0.35 0.21 0.31 0.36 0.04 )0.05

(b)

PC1

P 0.12 0.00 0.05 0.12 )0.02 0.01 0.03 0.00 )0.05 0.09 )0.02 )0.16 0.01 0.09 0.04 0.02 0.01

Tmax 0.05 )0.20 )0.04 )0.03 0.04 0.04 0.04 0.19 0.09 )0.05 )0.10 0.07 )0.01 0.02 0.09 0.26 )0.01

P 0.14 0.01 0.11 0.08 )0.04 )0.03 0.03 0.03 )0.07 0.12 0.00 )0.15 0.03 0.13 0.04 )0.02 0.06

Tmin 0.03 )0.28 )0.06 0.03 0.08 )0.03 0.03 0.23 0.08 )0.04 )0.09 0.11 0.04 0.09 0.13 0.26 0.03

PC2

P )0.02 0.02 0.15 )0.08 0.16 0.03 )0.09 )0.02 0.10 0.12 )0.05 )0.12 )0.08 )0.28 )0.10 0.02 )0.06

Tmax )0.07 )0.14 )0.02 0.13 0.02 )0.25 0.10 0.01 )0.05 0.16 )0.09 0.17 0.06 0.06 0.16 )0.07 0.01

P )0.02 0.05 0.11 )0.12 0.12 0.01 )0.09 )0.04 0.06 0.07 0.02 )0.11 )0.09 )0.24 )0.11 0.08 )0.08

Tmin )0.05 )0.11 0.00 0.13 0.10 )0.19 0.09 )0.07 0.04 0.13 )0.04 0.18 0.07 0.06 0.10 )0.11 )0.05

P, monthly precipitation; Tmax, monthly maximum temperature; Tmin, monthly minimum temperature.



were correlated with PC2 scores. There was also no correlation

between PC2 and the chronologies from the two Austrian sites,

DSW and HSA. Correlation between PC1 and PC2 scores for

the network presented here with PC1 and PC2 scores of the

central Italian network (Piovesan et al., 2005a) indicated that

the strongest teleconnection is found between low-elevation

beech chronologies (r = 0.48, P < 0.001, PC2 Eastern Alps vs.

PC2 Latium–Abruzzi).

DISCUSSION

Several trees almost four centuries old were discovered in this

Alpine region. These are heretofore the oldest beech stands

scientifically dated in the Alps (Biondi & Visani, 1996; Piutti &

Cescatti, 1997; Dittmar et al., 2003). Because beech longevity

can reach and exceed 500 years in the Apennines (Piovesan

et al., 2005b) and the Pyrenees (Bourquin-Mignot & Girard-

clos, 2001), beech trees even older than those reported here

might occur in the Alps. Carnia yielded the oldest trees of the

network within the so-called ‘boschi banditi’, where the

protection of these forests over the past few centuries has

allowed the conservation of old individuals, reaching a

maximum age at breast height of 380 years at77 the Lateis site

(Table 1). Limits imposed on local harvesting or industrial

logging, for instance by creating nature reserves, have greatly

contributed to the survival of old-growth beech stands

(Piovesan et al., 2005b).

Maximum tree age increased with altitude, as was also found

for other beech forests in the rest of Italy (Piovesan et al.,

2005a). Lower growth rates, which were observed at the upper

limit of the beech altitudinal range, may be responsible for the

increased longevity of these trees, possibly because of enhanced

wood durability or reduced maintenance and repair costs

(Penuelas, 2005).

Mean radial increment and its standard deviation decreased

when stand age increased. Older trees are often found in stands

where shade-tolerant species can undergo long periods of

suppression before canopy attainment (Canham, 1990). In

particular, European beech can survive several decades in a

suppressed status (Piovesan et al., 2005b), a period during

Table 5. Bootstrap correlation (a) and response (b) function coefficients calculated for the period 1942–2001 between the three composite

chronologies (see Table S4 in Supplementary Material), and the gridded climatic data covering the same area, over a 17-month window.

Coefficients with P < 0.05 are in bold.

Year preceding growth Year of growth

Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

(a)

Low elevation stands

P 0.04 0.06 0.12 0.26 )0.16 )0.21 0.19 0.13 )0.11 )0.03 )0.12 0.08 0.28 0.27 0.15 )0.08 0.17

Tmax 0.11 0.04 )0.14 )0.25 )0.06 0.27 0.02 0.17 0.12 )0.11 0.00 )0.22 )0.26 )0.26 )0.17 0.06 )0.11

Tmin 0.12 )0.03 )0.18 )0.20 )0.16 0.15 0.03 0.23 0.03 )0.16 )0.08 )0.19 )0.20 )0.21 )0.12 0.15 0.08

Mountain stands

P 0.22 0.17 0.21 0.12 0.14 )0.13 0.02 0.15 )0.21 )0.02 0.07 )0.18 0.01 0.01 )0.02 )0.12 0.07

Tmax )0.04 )0.45 )0.12 )0.14 )0.03 )0.01 0.13 0.26 0.12 0.03 )0.10 0.04 )0.08 )0.09 0.15 0.17 )0.02

Tmin 0.01 )0.45 )0.15 )0.07 0.15 )0.08 0.13 0.23 0.04 )0.01 )0.16 0.10 0.06 )0.04 0.17 0.19 0.03

High mountain stands

P )0.02 0.19 )0.03 0.02 0.16 0.17 0.00 )0.16 )0.21 0.19 0.09 )0.40 )0.16 )0.10 )0.27 )0.02 0.09

Tmax 0.18 )0.26 0.00 0.16 )0.08 )0.01 0.16 0.16 0.26 0.08 )0.08 0.28 0.13 0.26 0.41 0.25 )0.04

Tmin 0.14 )0.31 )0.04 0.22 0.15 )0.12 0.15 0.13 0.15 0.14 )0.06 0.32 0.17 0.28 0.42 0.25 0.08

(b)

Low elevation stands

P 0.07 0.01 )0.06 0.12 )0.14 )0.07 0.06 0.02 )0.13 )0.04 )0.05 0.03 0.17 0.20 0.09 0.02 0.10

Tmax 0.08 0.02 )0.03 )0.11 )0.03 0.24 )0.05 0.07 0.08 )0.14 0.02 )0.07 )0.10 )0.04 )0.05 0.18 0.00

P 0.07 0.01 )0.01 0.14 )0.11 )0.07 0.07 0.04 )0.11 0.02 )0.09 0.05 0.22 0.19 0.11 )0.05 0.14

Tmin 0.05 )0.03 )0.06 )0.07 )0.08 0.15 )0.06 0.16 0.00 )0.09 )0.04 )0.06 )0.07 0.00 0.02 0.23 0.08

Mountain stands

P 0.15 )0.02 0.16 0.06 0.09 )0.07 )0.04 0.17 )0.05 0.06 )0.01 )0.13 0.04 0.05 0.08 )0.04 0.04

Tmax )0.03 )0.33 0.01 0.00 0.08 )0.05 0.08 0.21 0.00 )0.06 )0.01 0.07 )0.03 )0.03 0.13 0.15 )0.04

P 0.19 )0.02 0.19 0.03 0.05 )0.12 )0.04 0.16 )0.05 0.09 0.00 )0.11 0.07 0.09 0.09 )0.06 0.08

Tmin )0.02 )0.38 0.01 0.02 0.12 )0.08 0.05 0.21 0.05 )0.07 )0.05 0.09 0.04 0.03 0.15 0.16 0.00

High mountain stands

P )0.03 0.08 0.03 0.09 0.03 0.05 0.02 )0.10 0.02 0.22 0.01 )0.21 )0.05 0.00 0.01 )0.02 )0.01

Tmax 0.05 )0.16 )0.08 0.14 0.02 )0.08 0.05 0.16 0.16 0.01 )0.11 0.11 0.02 0.13 0.12 0.18 )0.06

P )0.02 0.08 0.04 0.01 )0.01 0.02 0.03 )0.07 )0.02 0.21 0.04 )0.21 )0.06 0.04 )0.01 )0.02 0.04

Tmin 0.04 )0.23 )0.10 0.19 0.04 )0.18 0.08 0.16 0.14 0.04 )0.04 0.14 0.05 0.20 0.15 0.18 0.01

P, monthly precipitation; Tmax, monthly maximum temperature; Tmin, monthly minimum temperature.




which growth is reduced to a minimum and locally absent

rings are more frequent in the lower stem of the tree (Lorimer

et al., 1999).

Geographic patterns characterized beech radial growth in

the Eastern Alps, as was previously reported for Apennine

forests (Piovesan et al., 2005a). Latitude was negatively

Figure 7 Moving correlation function coefficients calculated between selected climatic variables and composite mountain (a, b) and high-

mountain (c, d) beech chronologies. Period: 1803–2001; moving window: 70 years. See Table S4 in Supplementary Material for a summary

of the composite chronologies.

(a) (b)

Figure 8 Correlation map (teleconnections) between first (a) and second (b) principal component scores of 12 prewhitened chronologies

with Italian, Austrian and Slovenian beech site chronologies. Symbols relate to bioclimatic position as in Fig. 6, and were based on HCA of

different data sets (see Fig. 6a in this study, Fig. 2 in Piovesan et al., 2005a; and Fig. 3 in Di Filippo, 2006). Dimension of each symbol is

proportional to correlation value; in solid grey are correlations with P-value < 0.01. Calculations were made on the period 1942–1988.



correlated with mean ring-width, and was positively correlated

with mean sensitivity. Proximity to the Adriatic Sea most likely

generates a milder climate in the Julian Alps, and these

influences seemed to reach the south-facing Carnic valleys as

well. Relatively warmer temperatures, together with the

exceptional high levels of precipitation in this southern range

of the Eastern Alps (>2000–3000 mm year)1, Desiato et al.,

2005), create favourable conditions for beech growth, as shown

by the low mean sensitivity of these chronologies. The higher

mean sensitivity of the Austrian chronologies can be explained

by a progressive increase in continentality with latitude. In

particular, a shift from a Mediterranean to a Continental

Environmental Zone, passing through the Alpine one, can be

observed in our network going from south to north (Metzger

et al., 2005).

Altitude exerts an even stronger control on mean ring width,

with values increasing three to four times from high to low

elevation sites. This pattern is frequently observed along

altitudinal gradients (Monserud & Sterba, 1996; Piovesan

et al., 2005a). It can be explained considering that growing

season length and ecosystem productivity are closely linked

(e.g., White et al., 1999). Dittmar & Elling (2005), observed

that in Bavaria, beech growing season decreases by about 2–

3 days for every 100-m increase in elevation. Moreover, beech

growth may be negatively impacted by frequent late-frost

damage at higher elevations (Dittmar et al., 2006).

Multivariate analysis applied to tree-ring series has been

demonstrated to detect the effects of environmental gradients

on the growth of forest species (e.g. for Europe: Biondi &

Visani, 1996; Makinen et al., 2002; Dittmar et al., 2003;

Linderholm et al., 2003; Tardif et al., 2003; Frank & Esper,

2005; Piovesan et al., 2005a). Despite the elevational range of

this study, most beech forests in the network showed a

common climatic signal, characterized by the importance of

conditions at the beginning and the end of the growing period,

the risk of winter cold damage and the one-year lagged effect88 of

floral induction. Statistical relationships pointed to a negative

effect of May precipitation, which could be due to beech

sensitivity to soil saturation (Nielsen & Jørgensen, 2003),

especially considering the high precipitation levels at the study

areas (Desiato et al., 2005) and the fact that snow melt

typically occurs in late spring (Mennella, 1967). In addition,

cloudiness during May is another factor that could reduce

growth through light limitation (e.g. Graham et al., 2003).

During September, warmer temperatures can contribute to

latewood cell wall thickening (Lebourgeois et al., 2005). Before

the beginning of the growing season, minimum temperature in

January could be related to tree growth because extremely low

winter temperature can cause cold damage, such as freezing

embolism (Lemoine et al., 1999).

The highest correlations were found with previous July

temperature, which may be related to floral induction,

stimulated by a hot and dry summer in the year preceding

masting (Piovesan & Adams, 2001; Schmidt, 2006). This signal

was more pronounced in mountain and high mountain stands,

and less so at lower elevations. Previous summer conditions

were most important for mountain chronologies, while the

climate of the growing season gains significance at higher

elevations (Dittmar & Elling, 1999). This beech response to

previous summer conditions has been found throughout

Europe, e.g. in the Pyrenees (Gutierrez, 1988; Dittmar et al.,

2003), Cantabria (Rozas, 2001), Apennines (Piovesan &

Schirone, 2000), French hills (Lebourgeois et al., 2005) and

central Europe (Dittmar et al., 2003). Its widespread occur-

rence suggests a connection with climate controls on physio-

logical processes involved in resource accumulation (e.g. starch

reserves) and bud development (with particular reference to

leaf primordia) and differentiation of flower buds (see

Nakawatase & Peterson, 2006). Thus, the number of differen-

tiated flower buds should influence the amount of photosynt-

hates assigned to reproduction, rather than to growth, during

the following season. Beech mast years are known to have a

negative effect on wood production, and are sometime

responsible for the formation of ‘pointer years’ (see Figs

19.112 and 19.114 in Schweingruber, 1996; Piovesan &

Bernabei, 1997). Studies that have employed a long-term data

set have usually found trade-offs between radial growth,

masting and climate (Woodward et al., 1994; Piovesan &

Bernabei, 1997; Selas et al., 2002; Monks & Kelly, 2006).

Relationships between reproduction and growth in plants are

not easily detected (Banuelos & Obeso, 2004). Recently, Monks

& Kelly (2006) identified such relationships in Nothofagus,

which has a reproductive behaviour very similar to Fagus

(Richardson et al., 2005). Other authors had already reported a

negative relationship between growth and previous summer

temperature in Nothofagus menziesii without providing an

explanation for it (Cullen et al., 2001). On the other hand,

Fagus crenata does not always show a tree-ring response to

mast years (Yasumura et al., 2006). Mechanistic explanations

will most likely need to focus on the partitioning of carbon

allocation between reproductive and vegetative pathways (see

Hoch, 2005; and Yasumura et al., 2006). In our study, moving

correlations suggested an increased importance of this inter-

action during recent times, possibly due to the combination of

climatic warming (Schmidt, 2006) and the progressive aging

and development of sampled trees.

Close to the upper edge of its altitudinal range, beech

benefits from higher temperature and suffers from excessive

precipitation in late spring (May) and during summer. The

same behaviour was observed in central European high-

elevation beech populations (830–1240 m a.s.l.) (Dittmar

et al., 2003), and can be related to a thermal limit imposed

by altitude, plus the associated negative effect of cloudiness

and soil saturation. A similar temperature response during the

growing season is typical of conifer species growing at high

elevations in the Alps (Frank & Esper, 2005) and Pyrenees

(Tardif et al., 2003), or at high latitudes in Europe (e.g.,

Makinen et al., 2002;99 Linderholm et al., 2003). Moving

correlations showed a transient response of the main climatic

signals, and similar behaviour was recently reported in a

dendroclimatic study of Larix decidua in the same region

(Carrer & Urbinati, 2006).




A word of caution is needed on the interpretation of MCF.

In fact, it is simplistic to consider these changes in moving

correlations as evidence of deviation from the ‘principle of

uniformitarianism’ (Camardi, 1999), which assumes that

modern natural processes have acted similarly in the past,

and is equivalent to the statistical assumption of ‘stationarity’.

First, because results are based on empirical relationships,

changes in data quality (both dendrochronological and

instrumental) can be responsible for different correlation

values over time. Second, the ‘uniformity principle’ commonly

refers to the fact that limiting factors controlled tree-ring

parameters in the past just as they do today, but the role of

different factors at a single location or over an entire region

could change over time. This possibility has been raised, for

example, to explain the ‘divergence’ between temperature and

ring parameters (width and maximum latewood density)

during the late 20th century (Jacoby & D’Arrigo, 1995; Briffa

et al., 1998). In Alaska, recent increases in air temperature are

not reflected in tree-ring thickness because water (that is,

drought stress) has become the limiting factor (Barber et al.,

2000; Lloyd & Fastie, 2002; Wilmking & Juday, 2005). In

Siberia, on the other hand, reduced correlation of growth rates

with summer temperature has been attributed to increasing

winter precipitation, which leads to delayed snowmelt in

permafrost environments, thus shortening the tree growing

season (Vaganov et al., 1999).

The second PC of the 12 site chronologies in the Eastern

Alps showed contrasting growth patterns for low and high

mountain sites. Dendroclimatic results pointed to an environ-

mental gradient of increasing summer drought with low

elevation sites negatively influenced and high elevation ones

impacted positively (Table 5). Reversing tree-ring responses to

a certain climatic factor along an altitudinal gradient were

previously reported for European beech in the central Apen-

nines (Piovesan et al., 2005a), for Douglas fir and mountain

hemlock in western North America (Fagre et al., 2003; Zhang

& Hebda, 2004), and for birch in Japan (Takahashi et al.,

2005). Environmental differences associated with elevation

gradient, such as a decrease in temperature and in the length of

the growing season with increase in elevation, may lead to such

spatial variation in radial growth. With future warming, we

hypothesize a reduction of growth at the lower elevations,

whereas high-mountain beech productivity could increase due

to a milder and longer vegetative period (see also Nakawatase

& Peterson, 2006). At the lower elevations, reduced temper-

atures in previous summer–early autumn can favour harden-

ing-related processes that reduce the risk of early frost damage,

whereas mildness in November could enhance the storage of

reserves for the next growing season (Barbaroux & Breda,

2002)1010 .

Teleconnection analysis revealed that the Eastern Alps

climatic signal is still found in the Central Alps, spreads

northward in Austria, eastward to Slovenia, and southward to

the northern Apennines. The northern Apennines occupy an

intermediate position, as beech chronologies from that area are

correlated with both Alpine and central-southern Apennine

chronologies (Piovesan et al., 2005a). This mountain range is

placed in a transition zone between the Mediterranean and

Temperate climate (e.g. Walter, 1985; Bailey, 1996). From a

climatic point of view, the distinction between the Apennines

and the Alps is mainly a difference in precipitation regime

(Trewartha, 1968), as was confirmed by a recent climatic

zonation of Italy (Brunetti et al., 2006). As a possible

consequence, beech growth in central-southern Italy is char-

acterized by a dominant response to summer drought (Piov-

esan et al., 2005a), while in the Alps the response shifts

towards thermal factors (Di Filippo, 2006). Considering the

autoecology and biogeography of European beech, it is

reasonable to expect higher exposure to drought stress toward

the southern limit of the geographic range of the beech

(Becker, 1981), and an increased importance of temperature

moving northward and upward (Dittmar & Elling, 2005;1111 Fang

& Lechowicz, 2006). Evidence for the spatial separation

between these two bioclimatic zones emerged even in a study

of the climatic patterns influencing conifer tree rings across the

Northern Hemisphere (Fig. 7 in Briffa et al., 2002). Further

confirmation can be found in the bioclimatic classification of

Italy proposed by Pignatti (1979) according to vegetation

analysis.

Low elevation chronologies were correlated through central

and northern Italy and in Slovenia, suggesting the existence

of common climatic factors controlling growth at these

altitudes. Late spring–summer drought emerged as important

for tree-ring formation in both central Italy (Piovesan et al.,

2005a) and low elevation stands in the Julian Alps. From the

teleconnection analysis, a similar influence extended to the

Dinaric Mountains and south-east Slovenian hills. In partic-

ular, May climate seems to be important at hilly sites,

possibly because higher temperature at the beginning of the

growing season allows for early crown development, exposing

beech to water stress during the summer (Piovesan et al.,

2005a). This expansion of the Mediterranean region up to the

Pre-Alps is consistent with placing the Julian Alp sites at the

boundary between the Mediterranean Mountains and the

Alpine Environmental Zones (Metzger et al., 2005; see also

Zohary, 1973). Thus, TOB (Julian Alps) and GRA (Carnic

Alps), even though located at the same elevation on a

southern exposure, belong to, respectively, the low elevation

and the mountain range. Additional evidence of Mediterra-

nean influences at these sites comes from the presence upon

rocky cliffs of the evergreen holm oak (Quercus ilex L.), and

from the cultivation of the olive tree (Olea europea L.). These

two species reach the northernmost limit of distribution (the

former) and cultivation (the latter) in north-eastern Italy

(Pignatti, 1998). In low-elevation Julian stands, the response

to drought appears combined with one to early-frost damage.

A mixed response to summer drought stress and previous

autumn–winter temperature was found in hilly beech tree-

ring chronologies of central and eastern Europe (Dittmar

et al., 2003). In French beech sites, such responses are

generally observed below 600 m a.s.l. (Lebourgeois et al.,

2005).



Genetic research has showed a different origin of the

Apennine and Alpine beech populations (Vettori et al., 2004;

Magri et al., 2006). The Apennines and the southern part of

the Balkan Peninsula were Mediterranean refuge areas for

European beech during the ice ages. These refugia were

separated from Central European populations, from which the

Alpine Fagus sylvatica stands originated (Magri et al., 2006).

Even though beech radial growth is sensitive to summer

drought both in the Apennines and at low elevation in the

Alps, the Mediterranean populations seem more drought

resistant (Nahm et al., 2006). Because summer drought is

becoming increasingly important for beech forest dynamics

(and eventually for natural selection) in central Europe

(Czajkowski et al., 2005) and in the Mediterranean Basin

(Jump et al., 2006), it is vital to understand how beech

populations with varying genetic material have responded to

drought in the past.

CONCLUSION

The Alps and the Apennines belong to two different biocli-

matic zones, both possessing a clear vertical dendroclimatic

zonation in altitudinal ranges, each with its own distinctive

climatic signal (Dittmar et al., 2003; Piovesan et al., 2005a). In

both zones, response to low temperature increases with

altitude. However, this phenomenon is more evident in the

Alps than the Apennines, probably because at high elevations

low temperatures can be limiting even in the middle of the

growing season (Dittmar et al., 2003). In the high-mountain

Mediterranean environment, temperature response is concen-

trated in early spring (late-frost damage and/or temperature

requirements for growth reactivation). Alpine high-elevation

beechwoods have dendroclimatic signals opposite those for

low elevation ones, which correlate with precipitation in May

and coolness in summer (Dittmar & Elling, 1999; Dittmar

et al., 2003). The cause may be the altitudinal difference in

temperature regime and, in particular, an altitude-mediated

phenological shift in growing season onset (Dittmar & Elling,

2005). While drought in the Apennines is a limiting factor

across all Mediterranean settings, from hilly to tree-line beech

stands (Piovesan et al., 2005a), its influence in the Alps

remains limited to the low elevation environments. Thus,

despite similar elevational response, summer influence on tree-

ring chronologies marks the bioclimatic separation between

the Apennines and the Alps.

Is this bioclimatic difference stable through time? Moving

response functions suggest a recent expansion of the

growing season for Alpine high-elevation beechwoods. As

for plant phenological changes (e.g. Parmesan & Yohe,

2003), the observed changes can be explained by the positive

temperature trend reported for Italy over the last 130 years

(Brunetti et al., 2006). This phenomenon is emphasized

when considering that the first decades used in moving

correlations fall within the Little Ice Age, which ended in

central Europe during the second half of the 19th century

(Xoplaki et al., 2005). Under a changing climate, bioclimatic

shifts could characterize vegetation arranged along altitudinal

gradients or at ecotonal boundaries (e.g. Penuelas & Boada,

2003). The beech old-growth network presented here will

therefore be useful to assess any future changes in forest

growth related to climate. As dendroecological data sets

provide ground-truth information on spatial and temporal

variability of climate–forest interactions, they can also

benefit the successful implementation of adaptive manage-

ment strategies.

ACKNOWLEDGEMENTS

We are grateful to Livio Silverio and to the Forest Service of

Friuli–Venezia Giulia for their support during the sampling

excursions, and to Anze Rutar for his contribution to beech

research at the Tolmin site. FB thanks Stanford University for

sabbatical support. KC thanks the Ministry of Education,

Science, and Sport of the Republic of Slovenia, Research

Program ‘Lesarstvo’, for financial support. The comments of

the Handling Editor and two anonymous referees helped in

improving on an earlier version of this manuscript.

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SUPPLEMENTARY MATERIAL

The following supplementary material is available for this

article online:

Table S1 Correlation matrix for the 14 prewhitened beech site

chronologies (period 1942–2001).

Table S2 Bootstrap correlation (a) and response (b) function

coefficients calculated between the Dunkelsteinerwald pre-

whitened site chronology and the 17-month climatic window

for the period 1942–2001.

Table S3 Bootstrap correlation (a) and response (b) function

coefficients calculated between the Hallstatt prewhitened site

chronology and the 17-month climatic window for the period

1942–2001.

Table S4 Summary of composite tree-ring chronologies,

which were developed using three sites each.

Figure S1 Expressed population signal (EPS) statistics for the

14 prewhitened site chronologies arranged according to their

length.

Figure S2 Eigenvectors (loadings) of the first two components

for the 14 prewhitened site chronologies (a). Eigenvalues

(percentage of explained variance) of the first two components

(b, total; c, PC1; d, PC2).

Figure S3 Correlation between climatic data used in dendro-

climatic analyses.

This material is available as part of the online article

from: http://www.blackwell-synergy.com/doi/abs/10.1111/j.

1365-2699.2007.01747.x

Please note: Blackwell Publishing are not responsible for the

content or functionality of any supplementary materials

supplied by the authors. Any queries (other than missing

material) should be directed to the corresponding author for

the article.

BIOSKETCHES

Alfredo Di Filippo conducted this research as a part of his

PhD thesis. He is currently working at the Universita della

Tuscia (Italy) as a research assistant, conducting investigations

in the fields of dendroclimatic networks and dendroecology

applied to the study of old-growth forests and disturbance

regimes.

Franco Biondi directs the DendroLab at the University of

Nevada, Reno (USA). His experience and interests are in

dendrochronology, climate change at multiple spatial and

temporal scales, numerical analysis of proxy climate data, and

late-Holocene dynamics in forests, woodlands and mountains.

Gianluca Piovesan is professor of Dendrology and Ecolog-

ical Forest-Landscape Management at the Universita della

Tuscia (Italy). His research interests include old-growth forest,

masting, dendroecology, forest response to climatic variability

and restoration of forest ecosystems.

Editor: Mark Bush




Bioclimatology of beech (Fagus sylvatica L.) in the Eastern Alps: spatial and altitudinal climatic signals identified through a tree-ring network

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