INCREASING ARIDITY IS ENHANCING SILVER FIR (ABIES ALBA MILL.) WATER STRESS IN ITS SOUTH-WESTERN DISTRIBUTION LIMIT

INCREASING ARIDITY IS ENHANCING SILVER FIR(ABIES ALBA MILL.) WATER STRESS IN ITS SOUTH-WESTERN

DISTRIBUTION LIMIT

MARC MACIAS1,2,3, LAIA ANDREU2, ORIOL BOSCH2, J. JULIO CAMARERO4

and EMILIA GUTIERREZ2

1Department of Geology, University of Helsinki, Gustaf Hallstrominkatu 2 (P.O. Box 64). FI-00014University of Helsinki, Finland

E-mail: [email protected] d’Ecologia, Universitat de Barcelona, Avgda. Diagonal, 645. Barcelona 08028,

Catalonia, Spain3Finnish Forest Institute, Rovaniemi Research Station, Etelaranta 55. 96300-Rovaniemi, Finland4Unidad de Recursos Forestales, Centro de Investigacion Agroalimentaria, Gobierno de Aragon,

Apdo. 727, Zaragoza 50080, Aragon, Spain

Abstract. Tree populations located at the geographical distribution limit of the species may provide

valuable information about the response of tree growth to climate warming across climatic gradients.

Dendroclimatic information was extracted from a network of 10 silver-fir (Abies alba) populations

in the south-western distribution limit of the species (Pyrenees, NE Iberian Peninsula). Ring-width

chronologies were built for five stands sampled in mesic sites from the Main Range in the Pyrenees, and

for five forests located in the southern Peripheral Ranges where summer drought is more pronounced.

The radial growth of silver-fir in this region is constrained by water stress during the summer previous

to growth, as suggested by the negative relationship with previous September temperature and, to

a lesser degree, by a positive relationship with previous end of summer precipitation. Climatic data

showed a warming trend since the 1970s across the Pyrenees, with more severe summer droughts. The

recent warming changed the climate-growth relationships, causing higher growth synchrony among

sites, and a higher year-to-year growth variation, especially in the southernmost forests. Moving-

interval response functions suggested an increasing water-stress effect on radial growth during the

last half of the 20th century. The growth period under water stress has extended from summer up to

early autumn. Forests located in the southern Peripheral Ranges experienced a more intense water

stress, as seen in a shift of their response to precipitation and temperature. The Main-Range sites

mainly showed a response to warming. The intensification of water-stress during the late 20th century

might affect the future growth performance of the highly-fragmented A. alba populations in the

southwestern distribution limit of the species.

1. Introduction

Tree populations located at the limit of the species geographical distribution may beresponding more dramatically to climate change than those at the core of the range(Brubaker, 1986; Gaston, 2003). Several authors have noted a greater sensitivity ofradial growth in response to climate variability in marginal populations than in thosefound at the main range of species (Schulman, 1954; Fritts, 1976; Villalba et al.,1997; Biondi, 2000). This spatial variability interacts with temporal variability

Climatic Change (2006) 79: 289–313

DOI: 10.1007/s10584-006-9071-0 c© Springer 2006

290 M. MACIAS ET AL.

because climate-growth relationships are not stable through time (Gutierrez et al.,1998; Tardif et al., 2003).

Several tree species, such as Abies alba Mill., Pinus sylvestris L., Pinus uncinataRam., Fagus sylvatica L., etc., meet in the Iberian Peninsula their southern latitudi-nal limit of distribution. Silver fir (A. alba) main distribution area is found in CentralEurope. Silver-fir populations located in the south side of the main Pyrenean axis(hereafter Main Range) and nearby, less elevated ranges further south from theMain Range (hereafter Peripheral Ranges), constitute the south-western limit ofthe species (Jalas et al., 1999; Figure 1). It is a highly-fragmented distribution area,since most of these populations are very small (usually less than 50 ha) and farfrom each other.

A. alba stands are usually found on the highest quality and productivity sitesin the Pyrenees, where they form dense monospecific stands or coexist with F.sylvatica in the westernmost locations (Vigo and Ninot, 1987; Blanco et al., 1997).These zones may experience summer drought but receive abundant precipitationduring spring and autumn (Aussenac, 2002). In the study area, A. alba grows inhumid sites on north-facing, shady slopes with relatively deep soils, although oftenvery stony, where the risk of severe water stress in summer is lower than in thesurrounding areas often dominated by P. sylvestris forests. A. alba populations mayalso appear in valley bottoms, but always at elevations above 1200 m.a.s.l.

Silver fir has been historically subjected to regular logging in the Pyrenees,in some cases up to the late 1970s, when it was no longer used as a source oftimber (Kirby and Watkins, 1998; Cabrera, 2001). Managed forests pose a set ofproblems in the standardization process of ring-width series, which is a criticalstep in dendroclimatology and dendroecology (Fritts, 1976; Cook et al., 1990).However, dendroecological studies from these marginal stands are valuable toolsto assess the growth-climate relationships at the limit of the species distributionarea, where recent decline episodes have been described (Camarero, 2001). Toextract the climatic signal contained in tree-ring series from stands disturbed bylocal disturbances such as logging, new methodological approaches must be used.In addition, many trees should be sampled across a large geographical area to obtaina reliable growth pattern related to the regional climatic signal.

Orography and atmospheric circulation patterns create a rain shadow in theSouthern Pyrenees. Most of the rain carried by low pressure systems coming fromNorth Atlantic falls north of the Continental Divide (Plana, 1985). The rain shadowespecially affects Peripheral Southern Ranges, which receive less precipitationthan Pyrenean Main Range (Allue, 1990). As a whole, climate in the area has astrong Mediterranean influence and summer drought is not uncommon (Figure 1).This influence decreases westward. Easternmost Ranges get extra precipitationbecause cyclone formation is enhanced in the NW Mediterranean region due tothe combined effects of the Pyrenees and the Southern Alps to the westerly flow(Barry, 1992; Cuadrat, 2000). Also in this region, Mediterranean moisture enhancesthe growth of summer thunderstorms which soften summer drought. Overall, the

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Figure 1. Map of the Pyrenees showing the distribution of Abies alba (stained, Blanco et al., 1997). Note the scarce and scattered presence of silver fir

in Spanish Territory (S of the dotted line). Numbers are locations of the sites, displayed as in table I. Letters are meteorological stations used to build the

sub-regional series of temperature and precipitation (circles). Jacetania/Gallego: (a). Candanchu, (b). Canfranc, (c). Sallent de Gallego; Jacetania/Hoya:

(d). Jaca, (e). Sabinanigo, (f). Boltana, (g). Nocito; Pallars: (h). Estany Gento, (i). Cabdella, (j). Estany de Sant Maurici, (k). Espot, (l). Esterri d’Aneu, (m).

Tavascan; Alt Urgell: (n). Oliana, (o). Organya, (p). Adrall; Cerdanya: (q). Porte, (r). Puigcerda, (s). La Molina; Ripolles: (t). Nuria, (u). Ribes de Freser,

(v). Ripoll, (x). Vallter; Montseny: (y). Turo de l’Home, (z). Cardedeu and aa. Girona. Square: Pic du Midi station. Climatic diagrams are shown next to

each sub-regional series of temperature and precipitation.


N-S precipitation gradient in the Pyrenees (rain shadow) is stronger than the W-EAtlantic-Mediterranean gradient, as evidenced by climatic and floristic data (LopezVinyallonga, 2004).

The climatic trends in the Mediterranean Basin during the last 50 years werecharacterized by a rise of mean temperature (2–4 ◦C), and an increase in both the fre-quency and intensity of severe droughts (IPCC, 2001). Piervitali et al. (1997) noted a20% decrease in total precipitation between 1951 and 1995 in the Western Mediter-ranean Basin. In the Iberian Peninsula, the 1980–1995 period was characterizedby intense droughts, which caused the decline of several woody species (Penuelaset al., 2001). In the Central Pyrenees, mean annual temperature has increased by0.83 ◦C at the Pic du Midi meteorological station (2862 m.a.s.l). between 1882 and1970 (Bucher and Dessens, 1991; Dessens and Bucher, 1995). For Western-Europemountains, Diaz and Bradley (1997) reported a similar strong warming trend sincethe 1940s, resulting in the latest decades being much warmer than any other periodof the instrumental records. In conclusion, climate in the Iberian Peninsula duringthe 20th century has been characterized by exceptionally high temperatures with agreat interannual variability within the context of the last 500 years (Manrique andFernandez-Cancio, 2000; Camarero and Gutierrez, 2004).

The strong climate warming detected in the Pyrenees during the 20th centuryinvolves increasing aridity and should be detected among species sensitive to waterstress such as A. alba. Specifically, we hypothesize that silver-fir forests locatedin the southern Peripheral Ranges have experienced a more intense water stress inresponse to warming than stands located in the Main Range, where precipitationis higher than in the former sites. The climate-growth relationship might indicatewhen and where water stress is increasing. The objectives of this study were:(i) to assess the sensitivity of silver-fir populations located in contrasting sites(Peripheral vs. Main Ranges) in response to the regional climate warming observedin the Pyrenees, and (ii) to analyse the spatio-temporal variability of radial growthin these two contrasting groups of sites. To achieve this aim we have establishedthe first dendrochronological network of A. alba in the south-western limit of thespecies distribution (NE Iberian Peninsula).

2. Material and Methods

Ten silver-fir chronologies were produced for the present work (Table I, Figure 1).We sought for a homogeneous spatial distribution along the E-W axis of the Pyre-nees when selecting those sites where old trees could be found. In each stand,10–17 trees were selected and cored. At least two cores per tree were extractedat 1.3 m using an increment borer (28–40 cores per site). The cores were visuallycross-dated following Yamaguchi (1991). Then, ring widths were measured to thenearest 0.01mm using a semiautomatic ANIOL measuring device (Aniol, 1983)and the resulting series were checked statistically using the program COFECHA(Holmes, 1983).

SPATIOTEMPORAL VARIABILITY IN RADIAL GROWTH 293

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2.1. RADIAL GROWTH CHANGES, STANDARDIZATION AND CHRONOLOGY BUILDING

In order to reconstruct and infer the logging history of the studied sites, we calculatedthe percentage growth change for each individual tree-ring series in all the studysites. To identify growth releases we used the formula proposed by Nowacki andAbrams (1997): GC = 100 * [(M2–M1) / M1] × 100, where GC is the percentagegrowth change between preceding and subsequent 10-yr ring-width means, and M1and M2 are the preceding and subsequent 10-year means, respectively. A releaseor abrupt growth recovery along an individual tree-ring series was defined as anyGC > 75 %. We calculated the yearly relative frequency of releases per year for theMain-Range and Peripheral-Range groups of sites.

In the case of mesic sites with a long history of logging, the only reliablemethod able to produce complete chronologies without release signals was to fita very flexible smoothing spline (Cook and Peters, 1981), although it removeda considerable amount of long– and mid-term climatic signal from the resultingsite chronologies. Splitting the series at the release years, as Blasing et al. (1983)suggested, would create a missing period of several years in each series, whichwould leave little chances for an inter-chronology comparison and climate responseanalysis.

Residual chronologies were used in the study since our purpose was mainlydendroclimatic (Cook et al., 1990). Desplanque et al. (1998) showed that this stan-dardization method extracted relevant climatic information from A. alba ring-widthseries in Alps forests similar to ours. A spline length was needed which would beflexible enough to filter the series growth releases but still able to produce residualseries with climatic information. To solve this problem, we took the maximumsignal-to-noise ratio (SNR) criterion to the chronology level. SNR is a measure ofthe common variance in a chronology scaled by a measure of the total varianceof the chronology (Wigley et al., 1984; Cook et al., 1990). We compared our 10chronologies and quantified their common signal by means of the inter-chronologySNR for spline lengths ranging from 5 to 50 years (Figure 2, left). The maximumSNR for the residual chronologies was obtained for spline lengths of 15 years(Figure 2). However, declines in the spline stiffness lead to a decrease in the 1storder autocorrelation coefficient of the resulting standard series (Figure 2, right).SNR between residual chronologies started to decrease for splines shorter than 15yrs. In the case of the trees and conditions we are dealing with, residual chronolo-gies resulting from 15-yr spline standardizations constituted a trade-off betweenan efficient removal of the disturbance signal and problems related with adjustingtoo flexible curves to the growth-series (i.e. artificially creating negatively auto-correlated series (blue noise) by removing low frequencies). Thus, all subsequentanalyses were performed using the 15-yr spline residual chronologies, and all theresults presented were derived from them. We assume that such a spline fit mayremove much long- and mid-term variation from the series, so it must be notedthat our results are only referred to the short-term variation patterns of tree growth.


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Figure 2. Left: Mean inter-chronology Signal-to-noise-ratio (SNR) vs. spline length in years (com-

parisons between residual (squares) & standard chronologies (triangles)). Right: first-order autocor-

relation coefficients (AC) versus spline length (the thick line is the mean of the 10 chronologies).

Despite SNR between standard chronologies increased at lower values of splinestiffness (as low as 5 years) (Figure 2), such flexible splines would imply a clearloss of climatic signal. This was evidenced by an accelerated variance decrease inall chronologies for spline lengths shorter than 10 years.

Descriptive statistics were calculated for each chronology to allow comparisonsamong sites and with other dendroclimatic data sets (Fritts, 1976; Briffa and Jones,1990). These statistics were: first order autocorrelation (r1), the percentage of vari-ation explained by the first principal component (VARpc1), signal-to-noise ratio(SNR), as well as standard deviation (SD) and mean sensitivity (MS). A time seriesof year-to-year sensitivity indexes (St ) was also calculated for each chronology,based on the formula St = | (It+1 − It ) * 2 / (It+1 + It ) |, for It = residual indexvalue for the year t (Fritts, 1976), to analyse the variation of each chronology. Weused the Expressed Population Signal (EPS) to establish a criterion to select a com-mon period where all chronologies would be reliable enough (Wigley et al., 1984).The common period 1902–1999 was selected based on a minimum EPS value of0.85, which is a widely used threshold in dendroclimatic studies.

2.2. SPATIO-TEMPORAL VARIABILITY IN RADIAL GROWTH

Shared growth variability can be interpreted as a common response to regionalclimatic signals (Tardif et al., 2003; Macias et al., 2004), and its changes alongthe 20th century as a signal of climatic changes which have affected the growth ofsilver fir. Moreover, the spatial distribution of these relationships and their temporalchanges can also give information about homogeneous climatic areas or about whereclimatic gradients are more important for tree growth (Villalba et al., 1997).

First, we performed a Principal Component Analyses (PCA) based on the cor-relation matrix for the common period 1902–1999 to evaluate the shared varianceamong residual chronologies. Pearson correlation and PCA were also computed for


successive 20-yr periods with a 5-yr lag in order to evaluate the temporal changesof this shared variability. We also assessed the spatial variability of radial growththrough the relationship between distance and correlation for pairs of chronologiesfor the study period, as well as its changes along the 20th century. This is a goodway to analyze spatiotemporal relationships within a network of chronologies andthe existence (or not) of spatial gradients in Abies alba radial growth (Fritts, 1991).

2.3. RADIAL GROWTH RESPONSE TO CLIMATE

In mountainous regions such as the study area, temperature series have shownstronger inter-site relationships between distant sites than precipitation data, whichare more variable locally (Agustı-Panareda et al., 2000). Seven sub-regionalmonthly average temperature and precipitation data series were obtained from 27meteorological stations in the area (Figure 1, Table II). Meteorological stationswere grouped according to their homogeneity based on the Mann-Kendall test us-ing HOM routine from the Dendrochronology Program Library (DPL; Holmes,1996). Sub-regional datasets were then produced using the MET routine from thesame software. These calculations are based on the average and standard deviationof each month for each station. In addition, Main Range and Peripheral Ranges se-ries of temperature and precipitation were constructed by combining the differentsub-regional data sets.

Five sub-regional datasets allowed a response function analysis for the period1941–1994. Two of them, Montseny and Ripolles, started in 1951. Correlation andresponse function analyses were performed for such periods using the program Den-droclim2002 (Biondi and Waikul, 2003) to quantify the climate-growth relation-ships between the different sets of regional climate series (monthly mean temper-ature, monthly total precipitation) and the residual radial-growth chronologies. Inorder to avoid the problem of multi-collinearity, commonly found in multi-variablesets of meteorological data, Fritts (1976) introduced a stepwise multiple regressionon principal components to assess climate-growth relationships (response func-tion). The significance and stability of the calculated regression coefficients wereestimated based on 1000 bootstrap estimates obtained by random extraction withreplacement from the initial data set (Guiot, 1991). Climate-growth relationshipswere analyzed from the previous August up to September of the growth year. Inthese analyses, we used the sub-regional dataset corresponding to the area whereeach chronology was located.

We used evolutionary response functions to analyse how the growth-climate rela-tionships changed through time and to detect these changes in the climatic responseof Abies alba. First, a regional chronology was created by performing PrincipalComponent Analyses on the chronology network. Two sub-regional chronologieswere also built with the same procedure: one for the Main Range group (sites 1,4, 5 and 6 as in Figure 1) and one for the Peripheral Ranges group (sites 2, 3, 7, 8

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TABLE II

Sub-regional temperature (T) and precipitation (P) studied datasets.

Temperature PrecipitationTemperature Precipitation

Mean ◦C Annual sum (mm) m

Sub Regional

Climate

Series Total period 1941–1994 Total period 1941–1994 Station Latitude Longitude a.s.I. First Last First Last

Candanchu 42◦47′19′′ − 00◦32′14′′ 1613 1951 1975 1951 1975

Jacetania/

Gallego

1910–1999 7.4 1910–1999 1655 Canfranc 42◦44′03′′ − 00◦31′24′′ 1075 1910 1999 1910 1999

Sallent de Gallego 42◦46′26′′ − 00◦19′49′′ 1285 1953 1994 1960 1999

Jaca 42◦34′05′′ − 00◦34′05′′ 800 1943 1985 1930 1972

Jacetania/ 1941–1999 10.3 1941–1999 873 Sabinanigo 42◦31′08′′ − 00◦21′38′′ 790 1941 1995 1941 1999

Hoya Nocito 42◦19′24′′ − 00◦15′21′′ 931 1973 2000 – –

Boltana 42◦26′45′′ + 00◦04′00′′ 643 – – 1951 1999

Cabdella 42◦27′55′′ + 00◦59′28′′ 1270 1954 1992 1954 1994

Estany Gento 42◦30′28′′ + 01◦00′03′′ 2120 1930 1985 1925 1985

Pallars 1940–1994 6.9 1926–1999 984 Estany de St. Maurici 42◦34′50′′ + 01◦00′17′′ 1920 1953 2000 – –

Espot 42◦34′28′′ + 01◦05′21′′ 1310 1953 1991 1953 1991

Esterri d’ Aneu 42◦37′27′′ + 01◦07′31′′ 940 – – 1955 2000

Tavascan 42◦38′15′′ + 01◦15′07′′ 1100 1967 1994 – –

Oliana 42◦05′00′′ + 01◦18′10′′ 480 1931 1997 – –

Alt Urgell 1940–2000 11.5 1940–1996 660 Organya 42◦12′43′′ + 01◦19′46′′ 540 1972 1999 1915 1999

Adrall 42◦19′25′′ + 01◦23′39′′ 642 1940 1996 1933 1996

Porte Pimorent 42◦33′ + 01◦50′ 1600 – – 1966 1987

Cerdanya 1944–1996 7.9 1940–1994 902 La Molina 42◦20′02′′ + 01◦56′15′′ 1704 1929 1998 1927 1998

Puigcerda 42◦26′07′′ + 01◦56′16′′ 1145 1911 2000 1912 1974

Mean values are shown for the common period used in the study (1941–1994). Positive longitude: Eastern Hemisphere; negative longitude: Western

Hemisphere. (Continued on next page)


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and 10 as in Figure 1). Second, we built a long climatic series (1910–1999) usingmonthly meteorological data from Pic du Midi (Bucher and Dessens, 1991), andCanfranc stations (see Figure 1 for location), which characterize well the climaticvariability in the Central Pyrenees during the 20th century (Tardif et al., 2003).We performed evolutionary response functions based on the three different modesavailable in Dendroclim 2002 (Biondi, 1997, 2000): (1) moving response intervals,considering a 60-year fixed interval, and increasing the initial and final years ofthe analyses by one for each iteration; (2) forward evolutionary intervals, using thesame initial year of the interval but increasing by one for each iteration the final yearso that the intervals go progressively forward in time; (3) backward evolutionaryintervals, using a fixed final year of the interval, and decreasing by one for eachiteration the initial year, so that the intervals go progressively backward in time.

3. Results

3.1. CHRONOLOGY STATISTICS

Statistical characteristics of the chronologies are shown in Table III. For the studyperiod 1902–1999, EPS values were > 0.85 in all chronologies but in three: PenaOroel, Guara and Boavi showed values > 0.80 for this period. Their quality is stillvery good and they were kept. First-order autocorrelation was very close to zero formost of the standard chronologies due to the very flexible spline (15 years) used inthe standardization (Figure 2). Mean sensitivity (MS) values were generally higherfor chronologies located in the Peripheral Ranges (e.g., 0.18 for Moixero, 0.15 forOroel, 0.14 for Guara) and lower for chronologies located in the Main Range (e.g.,0.08 for Conangles, 0.11 for Boavi). Although this trend was observed, it was notpossible to separate two groups according to their MS values. However, time seriesof Sensitivity (St ) gave more information (discussed later).

3.2. SPATIAL VARIABILITY IN RADIAL GROWTH

The average Pearson correlation coefficient for the network of chronologies andfor the period 1902–1999 was 0.44 ± 0.09 (mean ± SD). All correlation pairswere positive and highly significant (p < 0.01). The lowest correlation values werefound when comparing sites located in the Main Range against sites located in thePeripheral Southern Ranges (sites 5 vs. 10 = 0.20; 4 vs. 8 = 0.28; 1 vs. 2 = 0.35;see Figure 1 for name and location). Low correlation coefficients were also foundbetween sites located at the western Peripheral Southern Ranges (wPSR) and siteslocated at the eastern Peripheral Southern Ranges (ePSR) (sites 2 vs. 10 = 0.32; 2vs. 8 = 0.31; 3 vs. 8 = 0.35). Highest values were achieved for pairs of chronologieswithin the wPSR (sites 2 vs. 3 = 0.61) or within ePSR (sites 7 vs. 8 = 0.61), as


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well as for a pair within the Main Range (sites 1 vs. 9 = 0.63). Despite the spatialcoherence of these extreme values, a look at the bulk of correlation pairs did notshow a clear zonation.

The variance explained by the first principal component (PC) of the PCA forall residual chronologies was 49.43 %. The first PC had positive loadings for allchronologies, and it was interpreted as the common variability of the network ofchronologies, that is, as a macroclimatic signal. Thus, the time series of the firstPC was used as a regional chronology. The second, third and fourth PCs explaineda cumulative variance of 25.2% and represented sub-regional to local sources ofvariability.

3.3. TEMPORAL VARIABILITY IN RADIAL GROWTH

As a result of the management history of the studied stands, we found a highfrequency of releases when looking at the non-standardized raw growth data, mostprobably due to logging, in the 1910s, 1920s and 1930s, which was higher in thePeripheral-Ranges than in the Main-Range groups of chronologies (Figure 3). A

Time (years)

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990

Fre

qu

en

cy o

f g

row

th-c

ha

ng

es

> 0

.75

(%

)

0

5

10

15

20Peripheral R.Main R.

Figure 3. Inferred logging history in the studied sites based on the yearly relative frequency of radial-

growth changes greater than 75%. Results are presented separately for the Peripheral-Ranges and

Main-Range sites.


y = 0.0132x + 0.3323

R2 = 0.6812

Fmodel=32.0477, p<0.0001

Slope: t=5.66, p<0.0001

y = 1.1224x + 41.429

R2 = 0.6648

Fmodel=29.745,p<0.0001

Slope:t=5.45,p<0.0001

0

10

20

30

40

50

60

70

19

02-1

92

0

191

1-19

30

192

1-19

40

193

1-1

950

194

1-1

960

195

1-1

970

196

1-1

980

197

1-1

990

198

1-19

99

%V

aria

nce

1st

PC

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Pe

ars

on C

orr

ela

tion C

oe

ffic

ien

t

Figure 4. Pearson correlation coefficients for the A. alba chronology network (20-yr periods with

a 5-yr lag, squares) and percentage of variance expressed by the 1st component of the PCA (20-yr

periods with a 5-yr lag, triangles). Linear regressions were applied in both cases. Continuous line box

contains statistics from the linear regression for the % of Variance of the 1st PC. Dashed line box

contains statistics from the linear regression for the Pearson Coefficients. Both cases showed highly

significant (p < 0.0001) models (F test) and slopes (t test).

high frequency of positive growth changes greater than 75% was also observedin the 1950s, this time being higher in the Main-Range sites. Since the 1960s thefrequency of releases has greatly decreased, except for a slight increase in the late1980s.

Both average Pearson correlation and variance explained by the first PC of theten residual chronologies showed a significant increase in the common variabilityof chronologies along the 20th century (Figure 4). Correlation values and varianceexplained by the first PC rose from r = 0.31 and 38.8 % in the beginning of the 20thcentury to r = 0.58 and 62.6 % in the end of the analyzed period. The cumulativevariance of the second, third and fourth PCs showed a steady and significant declineduring the 20th century from 38.7% to 26.3%, which implied a decrease in sub-regional to local variability (not shown). Both the linear regressions and their slopeswere highly significant (p < 0.0001). Thus, the common macroclimatic signal(common variability) has been increasing markedly during all the studied period.

The relationships between inter-site correlation and distance have also steadilyincreased during the 20th century (Figure 5). During the first half of the 20th century(1902–1950), correlation between pairs of chronologies decreased significantly (p< 0.05) with increasing distance between sites, i.e., there was a spatial gradient inthe growth of A. alba in the Pyrenees. However, the gradient disappeared during thesecond half of the century (1951–1999), when distant chronologies showed moresimilar growth patterns than before. Thus, for the first half of the 20th century, localor at least sub-regional features had a stronger effect on silver fir growth in the Pyre-nees than for the second half, when the signal of regional factors became dominant.


y = -0.0005x + 0.4373

R2 = 0.106

Fmodel=5.0969, p<0.05

Slope: t=-2.26, p<0.05

y = 3E-05x + 0.4996

R2 = 0.0003

Fmodel=0.0128, p>0.1

Slope: t=0.11, p>0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.0 50.0 100.0 150.0 200.0 250.0 300.0 350.0

Distance (km)

Pe

ars

on

Co

eff

icie

nt

Figure 5. Correlation coefficients vs distance between chronologies. The relationship was negative

and significant for the period 1902–1950 (squares), while it disappeared for the period 1951–1999

(triangles). Note also the higher correlation values for the second half 20th century.

Some series of year-to-year sensitivity (St ) showed great variability, whereasothers were more regular throughout the 20th century (Figure 6). Two groups wereformed according to the variances of St of each chronology (t-test, p < 0.005),which were different by one order of magnitude: a high variance of St Group:sites 2, 3, 7, 8, 9 and 10 (see Figure 1 and Table I for names and location), anda low variance of St Group: sites 1, 4, 5 and 6. Note that all chronologies of thelow variance group are located in the Main Range, whereas all chronologies of thehigh variance group but one (Setcases, site 9) are located in the Peripheral Ranges.Although being located in the Main Range, Setcases has the particularity of beingthe closest site to the Mediterranean Sea, only ca. 65 km from it, and so a higherMediterranean influence in terms of precipitation regime is expected for this sitethan for the other Main Range sites.

We found peaks in St around 1930s, 1960s and 1980s, which reached muchhigher values in the high-variance than in the low-variance group. Sensitivityincreased during the 20th century (p < 0.05), but the increase was stronger in thehigh-variance group (Figure 6). During the second half of the 20th century, therewas a strong and highly significant (p < 0.0001) increase in St in the high-variancegroup, whereas no significant increase could be detected in the low-variance one.

3.4. RADIAL GROWTH-CLIMATE RELATIONSHIPS

All sub-regional datasets showed a Mediterranean influence characterized by pre-cipitation maxima in spring and autumn, and a relative minimum in summer(Figure 1). Annual precipitation was highest in the westernmost site (Jacetania-


High Variance Group

y = 0.0004x - 0.6775

R2 = 0.0808

Fmodel=7.644, p=0.007Slope t=2.76, p=0.007

0

0.1

0.2

0.3

0.4

0.5

0.6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Low Variance Group

y = 0.0002x - 0.3729

R2 = 0.0807

Fmodel=7.635,p=0.007

Slope: t=2.76, p=0.007

0

0.1

0.2

0.3

0.4

0.5

0.6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

y = 0.0021x - 3.995R2 = 0.3725

Fmodel=26.114, p<0.0001

Slope: t=5.11, p<0.0001

y = 0.0005x - 0.8849R 2 = 0.0564

Fmodel=2.628, p>0.1Slope: t=1.62, p>0.1

Year-

to-y

ear

sensitiv

ity(

St)

y = 0.0004x - 0.6775

R2 = 0.0808

. ,

t= , p=0.007

0

0.1

0.2

0.3

0.4

0.5

0.6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

y = 0.0002x - 0.3729

R2 = 0.0807

odel=7.635,p=0.007

e: t=2.76, p=0.007

0

0.1

0.2

0.3

0.4

0.5

0.6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

y = 0.0021x - 3.995R2 = 0.3725

el=26.114, 0001

e: t=5.11, 0. 1

y = 0.0005x R 2 = 0.

= .6 p>0.1Slope: t=1.62, p>0.1

2

. ,

0

0.1

0.2

0.3

0.4

0.5

0.6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

y = 0.0002x - 0.3729

R2 = 0.0807

odel=7.635,p=0.007

e: t=2.76, p=0.007

0

0.1

0.2

0.3

0.4

0.5

0.6

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

y = 0.0021x - 3.995R2 = 0.3725

el=26.114,e: t=5.11, 1

y = 0.0005x R 2 = 0.

p>0.1Slope: t=1.62, p>0.1

Figure 6. Year-to-year sensitivity time series (St ) for the two groups of chronologies: high variance

of St (up), low variance of St (down). The thick line is a 10-yr centred running mean. The dashed

straight line is a linear regression for the period 1902–1999, whereas the continuous straight line

is a linear regression for the sub-period 1950–1999. Note that the maximum values observed in the

high-variance group were much higher than those reached by the low-variance group.

Gallego: Table II, Figure 1), where climate is under greater oceanic influence, andlowest in the stations far from the sea (Alt Urgell). We noted a general process ofaridification in all datasets during the 20th century. Taking the period 1941–1994as the study period, annual mean temperature has been rising, especially since the1970s (Figure 7.A, B). The temperature increase was observed both in winter (De-cember to March) and summer (July, August) months. The temperature increase inwinter was more pronounced in the Western and Central Main-Range sub-regions,


Figure 7. (A). Recent trends (slope of the linear regression; b in y = a + bx) of monthly mean

temperature and monthly total precipitation for the seven sub-regional datasets and Pic du Midi

climate data (abbreviations follow Figure 1). Squares correspond to p < 0.05. The period analysed is

1941–1994, except for Montseny and Ripolles (1952–1994) (B). Two examples of the recent climatic

trends starting in the 1980s (aridification) corresponding to the average departures for the seven sub-

regional datasets (see Figure 1) in mean July temperature and total August precipitation from the

1941–1994 averages.

whereas the summer warming was more intense in the Central Pyrenees sub-regions.In some cases, precipitation sums have also been declining, especially in summer(July, August) and March. Positive trends in precipitation were observed in Octobereverywhere and their value decreased eastwards. The two easternmost sub-regions(Ripolles and Montseny) did not show these trends, but a process of temperatureincrease was evident since the 1970s. Generally, the temperature rise and the precip-itation decrease in February, March, July and August have been more pronouncedsince the 1980s (Figure 7.B)..


Table IV summarizes the general climate response for all sites based on therelationships between A. alba regional chronology (the first Principal Componentof the chronology network) and the regional climatic series for all the study area, andalso separately for the Main Range and the Peripheral Ranges. Variance explainedby the first PC was 58.6% and 52.8%, for the Main-Range and Peripheral-Rangesgroups, respectively. As mentioned in methods, these PCs were used as the Main-Range and Peripheral-Ranges sub-regional chronologies.

All chronologies showed significant responses to the climate conditions of thelate summer prior to the growth year, especially a negative and generalized re-sponse to September temperature (Table IV). During the year previous to tree-ringformation, tree-growth showed significant (p < 0.05) positive relationships withAugust precipitation for the Main Range chronology, and, to a lesser degree, withSeptember precipitation for the Peripheral Ranges chronology. During the growthyear, radial growth was positively related to February temperature and to July pre-cipitation (restricted to the Peripheral Ranges Chronologies). Response functionsperformed for each individual site with the climatic data of the closest sub-regionclimate series showed similar results, with the only difference being a more ex-tended negative response to previous late summer temperature by the Main RangeChronologies (August to October) (not shown).

3.5. TEMPORAL INSTABILITY OF GROWTH-CLIMATE RELATIONSHIPS

We analysed the moving-interval response functions based on 60-yr intervals for theregional chronology, and for the Main-Range and Peripheral-Ranges sub-regionalchronologies (Figure 8). The negative response to previous August temperature hasextended until October, and a positive response to previous August precipitation hasshown to be important, extending into September during the 1980s. Both changessuggest a longer water-stress season during the year prior to growth at the endof the 20th century than at the beginning of the past century. When performingthe sub-regional analyses, we found slightly different results for the Main Rangeand the Peripheral Ranges chronologies. From 1986 to 1999, negative responsesto previous October temperature appeared in the Main Range, whereas positiveresponses to previous August precipitation became stronger at the end of the 20thcentury. Positive responses to previous summer precipitation extended from Augustto September in the Peripheral Ranges from 1986 to 1993; for the same period,negative responses to previous September temperatures were also found in thePeripheral Ranges. During the year of tree-ring formation, the negative effect ofAugust precipitation in the Main Range was not significant since 1982. The positiveeffect of current July precipitation in the Peripheral Ranges weakened and Juneprecipitation became more important for tree growth at the end of the 20th century.The analyses based on forward and backward evolutionary intervals confirmedthese findings (results not presented).

SP

AT

IOT

EM

PO

RA

LV

AR

IAB

ILIT

YIN

RA

DIA

LG

RO

WT

H3

07

TABLE IV

Correlation (C) and response (R) functions based on bootstrap correlation and orthogonal regression on residual chronologies

and monthly climate data from previous September to current August (months abbreviated by capital letters correspond to the

year of growth). Results correspond to the regional (up), the Main-Range (middle) and the Peripheral-Ranges chronologies

(down). Only significant values are presented (p < 0.05)

Year t − 1 Year t

Chronology Variable Analysis a s o n d J F M A J J A S

Regional T C −0.52 0.37

R −0.23

P C 0.31 0.29 0.21

R

Main range T C −0.35 −0.49 0.35

R −0.21

P C 0.31 0.29

R 0.23

Peripheral range C −049 0.34

T

R −0.26

C 0.28 0.31

P

R

The analyses are based on regional climatic series (T, mean monthly temperature; P, total monthly precipitation) for all the

study area, the Main Range and the Preipheral Ranges covering the 1941–1994 period. The window starts with August of

previous year (t − 1) and ends with September of the year of growth (year t, months abbreviated by capital letters). Only

bootstrap correlation (C) and response (R) significant values are displayed (p < 0.05).


Main Range Peripheral Ranges

T

Time (60-year intervals)

1970 1975 1980 1985 1990 1995

Tim

e (

mo

nth

s)

ye

ar

t-1

ye

ar

t

a

s

o

n

d

J

F

M

A

M

J

J

A

S P

1970 1975 1980 1985 1990 1995 2000

Regional chronology

A

B

P

1970197519801985199019952000

T

197019751980198519901995

P

197019751980198519901995

T

Time (60-year intervals)

197019751980198519901995

year

t-1

year

t

a

s

o

n

d

J

F

M

A

M

J

J

A

S

Figure 8. Moving-interval response functions showing the significant coefficients (p < 0.05) based

on the relationships between mean monthly temperature (T) or total monthly precipitation (P) and the

Regional (up), Main-Range (down and left) and Peripheral-Ranges (down and right) chronologies.

Months abbreviated by capital letters correspond to the year t of growth, and the rest of months

correspond to the previous year t − 1. The displayed years in the abscissa axis correspond to the last

year of 60-yr moving intervals. The symbol type indicates the type of relationship: circles, negative

coefficients; squares, positive coefficients. The symbol colour indicates the strength of the relationship:

grey symbols, coefficient = 0.1–0.2; black symbols, coefficient = 0.2–0.3.


4. Discussion

Standardizing the series with a flexible spline produced reliable chronologies sinceA. alba climatic signal was successfully extracted, as seen in the response functions,which show similar patterns and magnitudes to those found in the Alps for the samespecies (Rolland, 1993; Desplanque et al., 1998; Rolland et al., 1999). The spatialsynchrony between sites was low in 1902–1950 (r < 0.4 at distances > 100 km)but high in 1951–1999 (r > 0.5 at distances > 200 km), which partially agreeswith results presented by Rolland (2002). These findings emphasize the greaterlocal variability in radial growth of low-elevation species from mesic sites suchas A. alba, which caused the low spatial synchrony between sites in the first halfof the 20th century. However, during the second half of the last century, A. albashowed a greater similarity in radial growth at long distances, which suggests thata regional climatic factor was modulating radial growth (Tardif et al., 2003). Thislast behaviour was similar to that usually observed for conifers from high-elevationand harsh sites such as P. uncinata, which usually show greater inter-site similarityin radial growth at long distances (>500 km). Spatial synchrony between distantchronologies is a valuable property for dendroclimatic reconstructions, but ourstudy has demonstrated that it may not be ascribed only to species from harsh sites.

The studied area has shown a trend towards an aridification during the 20thcentury, especially since the 1970s and 1980s (Figure 7). In NE Iberian Peninsula,a greater drought stress might have been induced by the recent warming detectedin the Pyrenees starting in the 1980s, which caused severe summer droughts inthe 1980s and 1990s (Bucher and Dessens, 1991; Tardif et al., 2003; Brunet et al.,2005). Southern-Pyrenean areas close to the Mediterranean Sea did not show adecrease in precipitation (Pinol et al., 1998).

Silver fir response to summer drought seems to be general as inferred fromthe presented response functions (Table IV). Drought during the summer priorto growth was the most limiting factor for radial growth, which agrees with the“drought-avoidance” strategy of the species (Rolland et al., 1999; Aussenac, 2002).A. alba has lower water-use efficiency than other fir species from more xeric areas(Guehl and Aussenac, 1987; Guehl et al., 1991). Thus, it is expected that A. albawould be a species highly sensitive to drought.

Increasing water stress during the second half of the 20th century might be thecause of the higher synchrony in tree growth (Figure 4) and the increase in year-to-year sensitivity (Figure 6). An elongation of the period of water stress might explainwhy silver-fir growth shifted in the mid 1980s from being sensitive to Augusttemperature to being sensitive up to September and even October temperature(Figure 8). Other authors noted similar growth responses in the Pyrenees in othersubalpine conifers (P. uncinata) during this decade (Gutierrez et al., 1998; Tardifet al., 2003).

The stands in the Peripheral Ranges have experienced these changes with specialintensity, as seen in the higher increase of sensitivity when compared with the


Main Range chronologies, which showed an increase in the length of previouslate summer temperature response. Peripheral chronologies showed this responseand also a shift in the response to precipitation from previous August to previousSeptember in the second half of the 20th century (Figure 8). Besides, they showedpositive relationships with current early summer precipitation, which changed fromJuly at the beginning of the study period to June at its end, also suggesting anelongation of the water stress period (Figure 8). These results are logical since theseperipheral forests grow under the most stressful conditions (drier climatology).

Main Range sites are usually located at the bottom of upper valleys or on N-NW slopes, at the base of high mountains (2500 to 3000 m). Snowmelt duringlate spring and summer could guarantee water supply in the soils of these sites, asthey do not show any positive response to current late spring-summer precipitation.However, Peripheral Ranges are lower (usually less than 2000 m) and dryer, withsilver fir forests located in northern slopes not far away from the summit. In thiscase, there is probably not much water supply coming from snowmelt and PeripheralChronologies show a current late spring-summer positive response to precipitation.

The positive influence of February temperature on growth (Table IV) is probablycomparable to the positive response to current March temperature in the French Alpsfor the same species (Rolland et al., 1999). Warmer Februaries might acceleratesnowmelt (or at least stop snow accumulation), favouring soil warming and thusenhancing an earlier start of the growing season. However, the effect of cold winterson A. alba growth over the area might not be strong and general given the fact thatsuch relationship did not show to be significant when performing the bootstrapresponse functions and only showed significance in the less restrictive correlationfunctions.

Silver-fir populations have been receding in the Iberian Peninsula after theirpost-glacial expansion (Huntley and Birks, 1983) due to climate, but also becauseof competition with Fagus sylvatica in the last four millennia (Blanco et al., 1997)and intense logging in the last centuries (Jalut, 1988). Nowadays, A. alba reaches itsSW distribution limit in the Pyrenees, where previous summer drought is a majorlimiting factor for silver fir growth. Due to the ecological requirements of silver fir,most of the Iberian Peninsula under Mediterranean influence constitutes a ‘desert’to the species, which only finds ‘oasis’ in very special places such as northwardslopes and valley bottoms where slope aspect, moist climate and deep soils allowit to strive. These places are mainly located in the Main Range, where most of A.alba forests are found. The scattered small stands in the southern Peripheral Rangesform relict populations of former glacial refuges whose future growth performanceis uncertain under current warming trends in the light of our results. The presentedclimatic signal extracted from Abies alba growth series makes this species a reliablemonitor of the effects of climate change on forests in the Pyrenees. More effortsshould be put to improve the present network of chronologies, especially in thecentral Pyrenees, and to relate their growth patterns to current climatic patternsincluding synoptic situations, with the aim of assessing their potential association


with different phases of the North Atlantic Oscillation and other regional to globalclimate patterns relevant in the area.

Acknowledgements

This study was funded by EU project For MAT (Sensitivity of tree-growth to climatechange and growth modelling from past to future), contract ENV4-CT97-0641, aswell as by a CICyT (AMB95-0160) project. Marc Macias Fauria acknowledgesthe support of a CIMO – Centre for International Mobility – fellowship, codeTM-03-1535, given by the Finnish Ministry of Education, as well as the goodadvices of Samuli Helama at the Department of Geology of the University ofHelsinki, and Aslak Grinsted and John Moore at the Arctic Centre in Rovaniemi.J. J. Camarero acknowledges the support of an INIA-Gob.Aragon post-doctoralcontract. We also thank Montse Ribas and Elena Muntan for their help in core sam-pling and cross-dating, as well as for their contribution in the climatic dataset. Wethank S.H. Schneider, two anonymous referees and F. Biondi for their constructivereviews.

References

Agustı-Panareda, A., Thompson, R. and Livingstone, D. M.: 2000, ‘Reconstructing temperature vari-

ations at high elevation lake sites in Europe during the instrumental period’, Verh. Int. Ver. Limn.27, 479–483.

Allue, J. L.: 1990, Atlas fitoclimatico de Espana, Ministerio de Agricultura Pesca y Alimentacion,

Instituto Nacional de Investigaciones Agrarias, Madrid.

Aniol R. W.: 1983, ‘Tree-ring analysis using CATRAS’, Dendrochronologia 1, 45–53.

Aussenac, G.: 2002, ‘Ecology and ecophysiology of circum-Mediterranean firs in the context of

climate change’, Ann. For. Sci. 59, 823–832.

Barry, R. G.: 1992, Mountain Weather & Climate, Routledge Physical Environment Series, London,

2nd ed., 402 pp.

Biondi, F. and Waikul, K.: 2003, ‘DENDROCLIM 2002: A C++ program for statistical calibration of

climate signals in tree-ring chronologies’, Computers & Geosciences 30, 303–311.

Biondi, F.: 1997, ‘Evolutionary and moving response functions in dendroclimatology’, Den-drochronologia 15, 139–150.

Biondi, F.: 2000, ‘Are climate-tree growth relationships changing in north-central idaho, USA?’,

Arctic, Antarctic and Alpine Research 32(2), 111–116.

Blanco, E., Casado, M. A., Costa, M., Escribano, R., Garcıa, M., Genova, M., Gomez, A., Gomez,

F., Moreno, J. C., Morla, C., Regato, P. and Sainz, H.: 1997, Los bosques ibericos, Ed. Planeta,

Barcelona, p. 572.

Blasing, T. J., Duvick, D. N. and Cook, E. R.: 1983, ‘Filtering the effects of competition from ring-

width series’, Tree-Ring Bulletin 43, 19–30.

Briffa, K. R. and Jones, P. D.: 1990, ‘Basic chronology statistics and assessment’, in Cook, E. and

Kairiukstis, L. (eds)., Methods of Dendrochronology: Applications in the Environmental Science,

Kluwer Academic Publishers, Dordrecht, pp. 137–152.


Brubaker, L. B.: 1986, ‘Responses of tree populations to climatic change’, Vegetatio 67, 119–130.

Brunet, M., Sigro, J., Saladie, O., Aguilar, E., Jones, P. D., Moberg, A., Walther, A. and Lopez, D.:

2005, ‘Spatial patterns of long-term Spanish temperature change’,Geophys. Res. Abstr. 7, 04007.

Bucher, A. and Dessens, J.: 1991, ‘Secular trend of surface temperature at an elevated observatory in

the Pyrenees’, Journal of Climate 4, 859–868.

Cabrera, M.: 2001, ‘Evolucion de abetares del Pirineo aragones’, Cuad. Soc. Esp. Cien. For. 11,

43–52.

Camarero, J. J.: 2001, ‘El decaimiento del abeto (Abies alba Miller) en los Pirineos aragoneses’,

Depto. Medio Ambiente, Gobierno de Aragon, Zaragoza.

Camarero, J. J. and Gutierrez, E.: 2004, ‘Pace and pattern of recent treeline dynamics: Response of

ecotones to climatic variability in the Spanish Pyrenees’, Climatic Change 63, 181–200.

Cook, E. R. and Peters, K.: 1981, ‘The smoothing spline: A new approach to Standardizing forest

interior tree-ring width series for dendroclimatic studies’, Tree-Ring Bulletin 41, 45–53.

Cook, E. R., Briffa, K. R., Shijatov, S. G. and Mazepa, V.: 1990, ‘Tree-ring standardization and

growth-trend estimation’, in Cook, E. and Kairiukstis, L. (eds)., Methods of Dendrochronology:Applications in the Environmental Science, Kluwer Academic Publishers, Dordrecht, pp. 104–

123.

Cuadrat, J. M.: 2000, Climatologıa, Coleccion Geografıa, Ed. Catedra, Madrid, 2nd ed.

Desplanque, C., Rolland, C. and Michalet, R.: 1998, ‘Dendroecologie comparee du sapin blanc (Abiesalba) et de l’epicea commun (Picea abies) dans une vallee alpine de France’, Can. J. For. Res 28,

737–748.

Dessens, J. and Bucher, A.: 1995, ‘Changes in minimum and maximum temperatures at Pic du Midi

in relation with humidity and cloudiness, 1882–1984’, Atmospheric Research 37, 147–162.

Diaz, H. F. and Bradley, R. S.: 1997, ‘Temperature variations during the last century at high elevation’,

Climatic Change 36, 254–279.

Fritts, H. C.: 1976, Tree Rings and Climate, Academic Press, London.

Fritts, H. C.: 1991, Reconstructing Large-Scale Climatic Patterns From Tree-Ring Data: A DiagnosticAnalysis, The University of Arizona Press, Tucson & London, p. 286.

Gaston, K. J.: 2003, The Structure and Dynamics of Geographic Ranges, Oxford University Press,

New York.

Guehl, J. M. and Aussenac, G.: 1987, ‘Photosynthesis decrease and stomatal control of gas ex-

change in Abies alba Mill. in response to vapour pressure difference’, Plant Physiol. 83, 316–

322.

Guehl, J. M., Aussenac, G., Bouachrine, J., Zimmermann, R., Pennes, J. M., Ferhi, A. and

Grieu, P.: 1991, ‘Sensitivity of leaf gas exchange to atmospheric drought, soil drought, and

water-use efficiency in some Mediterranean Abies species’, Can. J. For. Res. 21, 1507–

1515.

Guiot, J.: 1991, ‘The bootstrapped response function’, Tree-Ring Bull. 51, 39–41.

Gutierrez, E., Camarero, J. J., Tardif, J., Bosch, O. and Ribas, M.: 1998, ‘Tendencias recientes del

crecimiento y la regeneracion en bosques subalpinos del Parque Nacional d’Aiguestortes i Estany

de Sant Maurici’, Ecologıa 12, 251–283.

Holmes, R.: 1996, Dendrochronology Program Library Version 1992–1, Lab. of Tree-Ring Research,

The Univ. of Arizona. Tucson.

Holmes, R. L.: 1983, ‘Computer-assisted quality control in tree-ring dating and measurement’, Tree-Ring Bulletin 43, 68–78.

Huntley, B. and Birks, H. J. B.: 1983, An Atlas of Past and Present Pollen Maps of Europe: 0 – 13000years ago, Cambridge University Press.

IPCC: 2001, Climate Change 2001: The Scientific Basis, Cambridge Univ. Press, Cambridge.

Jalas, J., Suominen, J. and Lampinen, R.: 1999, Atlas Florae Europaeae, Available at http://www.fmnh.

helsinki.fi/english/botany/afe/index.htm.


Jalut, G.: 1988, ‘Les principales etapes de l’histoire de la foret pyreneenne francaise depuis 15000

ans’, in Homenaje a Pedro Montserrat, Monografıas I.P.E. (C.S.I.C). 4, 609–615.

Kirby, K. J. and Watkins, C. (eds): 1998, The Ecological History of European Forests, CAB Interna-

tional, Wallingford.

Lopez Vinyallonga, S.: 2004, ‘Proposta de sectorialitzacio biogeografica del vessant meridional dels

Pirineus’, in Diploma of Advanced Studies in Botany, University of Barcelona, Barcelona.

Macias, M., Timonen, M., Kirchhefer, A. J., Lindholm, M., Eronen, M. and Gutierrez, E.: 2004,

‘Growth variability of Scots pine along a West-East gradient across Northern Fennoscandia: A

dendroclimatic approach’, Arct. Antarct. Alp. Res. 36, 565–574.

Manrique, E. and Fernandez-Cancio, A.: 2000, ‘Extreme climatic events in dendroclimatic recon-

structions from Spain’, Climatic Change 44, 123–138.

Nowacki, G. J. and Abrams, M. D.: 1997, ‘Radial-growth averaging criteria for reconstructing dis-

turbance histories from presettlement-origin oaks’, Ecol. Monogr. 67, 225–249.

Penuelas, J., Lloret, F. and Montoya, R.: 2001, ‘Severe drought effects on Mediterranean woody flora’,

For. Sci. 47, 214–218.

Piervitali, E., Colacino, M. and Conte, M.: 1997, ‘Signals of climatic change in the Central-Western

Mediterranean Basin’, Theor. Appl. Climatol. 58, 211–219.

Pinol, J, Terradas, J. and Lloret, F.: 1998, ‘Climate warming, wildfire hazard, and wildfire occurrence

in coastal eastern Spain’, Climatic Change 38, 345–357.

Plana Castellvı, J. A.: 1985, Estudi Climatic i Balanc Hıdric de la Conca de la Noguera Ribagorcana,

Institut d’Estudis Catalans, Barcelona.

Rolland, C.: 1993, ‘Tree-ring and climate relationships for Abies alba in the Internal Alps’, Tree-RingBulletin 53, 1–11.

Rolland, C.: 2002, ‘Decreasing teleconnections with inter-site distance in monthly climatic data and

tree-ring width networks in a mountainous Alpine area’, Theor. Appl. Climatol. 71, 63–75.

Rolland, C., Michalet, R., Desplanque, C., Petetin, A. and Aime, S.: 1999, ‘Ecological requirements of

Abies alba in the French Alps derived from dendro-ecological analysis’, J. Veg. Sci. 10, 297–306.

Schulman, E.: 1954, ‘Dendroclimatic changes in semiarid regions’, Tree-Ring Bull. 29, 26–28.

Tardif, J., Camarero, J. J., Ribas, M. and Gutierrez, E.: 2003, ‘Spatiotemporal variability in tree growth

in the central Pyrenees: Climatic and site influences’, Ecol. Monogr. 73, 241–257.

Vigo, J. y, and Ninot, J. M.: 1987, ‘Los Pirineos’, in Peinado Lorca, M. y Rivas-Martınez, S. (eds), LaVegetacion de Espana, Servicio de Publicaciones de la Universidad de Alcala de Henares, Alcala

de Henares, pp. 351–384.

Villalba, R., Boninsegna, J. A., Veblen, T. T., Shmelter, A. and Rubulis, S.: 1997, ‘Recent trends in

tree-ring records from high elevation sites in the Andes of northern Patagonia’, Climatic Change36, 425–454.

Wigley, T. M. L., Briffa, K. R. and Jones, P. D.: 1984, ‘On the average value of correlated time series,

with applications in dendroclimatology and hydrometeorology’, Journal of Climate and AppliedMeteorology 23, 201–210.

Yamaguchi, D. K.: 1991, ‘A simple method for cross-dating increment cores from living trees’,

Canadian Journal of Forest Research 21, 414–416.

(Received 26 October 2004; in revised form 16 December 2005)