Radial growth variation of Norway spruce (Picea abies (L.) Karst.) across latitudinal and altitudinal gradients in central and northern Europe

Forest Ecology and Management 171 (2002) 243–259

Radial growth variation of Norway spruce (Picea abies (L.) Karst.) across latitudinal and altitudinal gradients in central and northern Europe Harri Ma¨kinena,*, Pekka No¨jda,1, Hans-Peter Kahleb,2, Ulrich Neumannc,3, Bjo¨rn Tveited,4, Kari Mielika¨inene,5, Heinz Ro¨hlec,6, Heinrich Spieckerb,7 a Finnish Forest Research Institute, P.O. Box 18, FIN-01301 Vantaa, Finland Institute of Forest Growth, University of Freiburg, Bertoldstr. 17, D-79085 Freiburg, Germany c Institute of Forest Growth and Forest Informatics, University of Dresden, Wilsdrufferstr. 18, D-01737 Tharandt, Germany d Norwegian Forest Research Institute, Hoegskolevn. 12, N-1432 A˚s, Norway e Finnish Forest Research Institute, Unioninkatu 40, FIN-00170 Helsinki, Finland b

Received 10 November 2000; received in revised form 8 August 2001; accepted 10 October 2001

Abstract Regional and temporal growth variation of Norway spruce (Picea abies (L.) Karst.) and its dependence on air temperature and precipitation were compared in stands across latitudinal and altitudinal transects in southwestern and eastern Germany, Norway, and Finland. The temporal variation of radial growth was divided into two components: medium- and high-frequency variation, i.e. decadal and year-to-year variation, respectively. The medium-frequency component was rather different between regions, especially the southern and northern ones. However, within each region the medium-frequency growth variation was relatively similar, irrespective of altitudinal and latitudinal differences of the sample sites. A part of the high-frequency variation was common to all four regions, which suggests that some factors synchronising tree growth are common for the entire study area. The high-frequency component of growth was more strongly related to monthly air temperature and precipitation than was the medium-frequency variation. The limiting effect of low temperatures was more significant at northern as well as high-altitude sites, while the importance of precipitation increased in the south and at low altitudes. # 2002 Elsevier Science B.V. All rights reserved. Keywords: Altitude; Climatic response; Dendrochronology; Growth variation; Latitude; Picea abies

*

Corresponding author. Tel.: þ358-9-85705308; fax: þ358-9-85705361. E-mail addresses: [email protected] (H. Ma¨kinen), [email protected] (P. No¨jd), [email protected] (H.-P. Kahle), [email protected] (U. Neumann), [email protected] (B. Tveite), [email protected] (K. Mielika¨inen), [email protected] (H. Ro¨hle), [email protected] (H. Spiecker). 1 Tel.: þ358-9-85705325; fax: þ358-9-85705361. 2 Tel.: þ49-761-2033739; fax: þ49-761-2033740. 3 Tel.: þ49-35-203381624; fax: þ49-35-203381628. 4 Tel.: þ47-64-949000; fax: þ47-64-942980. 5 Tel.: þ358-9-85705240; fax: þ358-9-85705478. 6 Tel.: þ49-35-203381614; fax: þ49-35-203381628. 7 Tel.: þ49-761-2033737; fax: þ49-761-2033740. 0378-1127/02/$ – see front matter # 2002 Elsevier Science B.V. All rights reserved. PII: S 0 3 7 8 - 1 1 2 7 ( 0 1 ) 0 0 7 8 6 - 1

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1. Introduction During the past decades, considerable attention has been focused on global climate change. Climate models predict that global warming will impact the whole Europe, especially the higher northern latitudes, even though the magnitude of the warming is very uncertain (e.g. Carter et al., 1995; Kattenberg et al., 1996). The climate of the North Atlantic region and bordering areas affects tree growth and forest productivity throughout central and northern Europe (e.g. Briffa et al., 1987). Changes of climate are known to have caused synchronous shifts in tree growth throughout Europe; such a change took place, for example, during the Little Ice Age (e.g. Grove, 1988; Briffa et al., 1990). Findings on a regional basis clearly show that climatic variation is a major driving force behind growth variation, as well as variation of tree mortality (Becker, 1989; Innes, 1994; Spiecker, 1995, 2000; Raitio, 2000; Ma¨ kinen et al., 2001). Investigations on growth trends of European forests have indicated increasing forest growth and forest site productivity in central Europe and southern Scandinavia (Kauppi et al., 1992; Elfving et al., 1996; Spiecker et al., 1996; Pretzsch, 1999). Standing volume per hectare and average age of stands have also increased considerably in recent decades (Kauppi et al., 1992; Kuusela, 1994; Spiecker et al., 1996). However, studies carried out in northernmost Europe have not revealed such trend-like changes in forest productivity (Mielika¨ inen and Timonen, 1996). The latter result suggests that growth trends in Europe may not have followed a uniform pattern and emphasises the need for investigating climate–growth relationships of trees in different areas. Spatial comparison of long time series of tree growth enables one to identify environmental driving forces of growth variation and underlying mechanisms behind it. Impacts of environmental factors on tree growth are known to change gradually across altitudinal, latitudinal, and longitudinal gradients. By comparing growth reactions in different regions representing different environmental conditions, more general knowledge about the effects of environmental factors on growth can be obtained. Spatial and temporal gradients also offer possibilities for formulating more specific hypotheses on the effects of changing

climate on tree growth over time. Therefore, such analyses are useful tools for assessing the responses of forest ecosystems to possible changes in future global climate. In discussions about long-term forest growth trends in Europe, considerable attention has been focused on Norway spruce (Picea abies (L.) Karst.). This is due to the economical importance of the species, as well as reports about increased mortality of Norway spruce at high altitudes in central Europe during the 1980s (e.g. Bosch et al., 1983; Zo¨ ttl and Mies, 1983; Papke, 1988). Four regions representing different climatic conditions were selected for this study: southwestern Germany, eastern Germany, Norway, and Finland. In all these areas, Norway spruce has an economically and ecologically important role in forest management. Southwestern Germany and Norway represent a more maritime climate, whereas in eastern Germany and Finland the conditions are more continental. In addition, in all these regions except Finland, spruce forests are found at widely varying altitudes. Our starting hypothesis was that high- and mediumfrequency changes in tree growth are connected to changes in precipitation and air temperature. Growth variation patterns and the influence of climate on Norway spruce growth were analysed in the four different geographical areas. The specific objectives were to determine: (1) how radial growth varies across the latitudinal gradient, and (2) which climate variables affect the variation in Norway spruce growth at low- and high-altitudes in each region. Total variation in ring-width series’ was divided into low-, medium-, and high-frequency variation in order to emphasise variations in different frequency ranges. Medium- and high-frequency growth variation and factors influencing them were analysed separately. The mediumfrequency increment series describe the main features of growth variation in a time scale of decades, while the high-frequency series represent the year-to-year variation. Low-frequency variation was not analysed because it was considered to be mainly caused by tree age, silvicultural treatments, etc.

2. Material and methods The study material was collected across a transect starting from southern Germany and extending to the

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Arctic spruce timberline in Fennoscandia. The four study regions were located in southwestern Germany, eastern Germany, Norway, and Finland. In addition to the south–north gradient, the sample includes maritime and more continental areas at fairly similar latitudes in southern and eastern Germany, as well as in Norway and Finland. Within regions, sampling was carried out across elevational gradients from lowlands up to mountains. In sampling, mature stands were preferred in order to cover as long common time period as possible. In the following, the term ‘region’ refers to these four geographical regions and the term ‘sub-region’ to the elevational and latitudinal groups of study stands within each region. In southwestern Germany, 10 study stands were selected across an elevational gradient from the upper Rhine valley (260 m a.s.l.) to the high altitudes of the southern Black Forest (1330 m a.s.l.). The stands were grouped into three sub-regions according to altitude (Table 1). The number of sample trees in a stand ranged from 4 to 5. In eastern Germany, 61 study stands were selected at elevations between 260 and 1000 m a.s.l. in the Ore mountains, Saxon, Switzerland and Zittauer mountains. Fertility of the stands on mineral soil ranged from low to medium, and water holding capacity of

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soil was assessed to be typical for the Norway spruce forests in Saxony. Most of the study stands (95%) are plantations, the planting density ranging between 1 m  1 m and 1:5 m  2 m. The rest of the stands were naturally regenerated. After the establishment, the stands were thinned from below following instructions of commercial forestry. The stands were grouped to four sub-regions representing different altitudes, but some overlap between the sub-regions was allowed (Table 1). In the overlap zones, sample plots on southfacing plots were grouped into lower sub-regions and north-facing plots into upper ones. The number of sample trees in a stand ranged from 5 to 12. The material is described in detail by Neumann (2001). In Norway, 81 study stands were selected from southern Norway (Telemark) and northern Norway (Helgeland). In both regions, widely varying altitudes were represented in the data (40–840 m a.s.l. in Telemark and 20–420 m a.s.l. in Helgeland), i.e. from lowlands to the uppermost natural Norway spruce forests. In Telemark, the sampling was centred around the Lifjell mountain, while in Helgeland the study stands were selected from a relative long south–north belt between the coast and the Swedish border within Nordland county. Three elevational sub-regions were sampled in the south, while only two sub-regions were

Table 1 Characteristics of the ring-width chronologies for each region Region

Sub-region

Latitude

Longitude

Altitude (a.s.l.)

(A) Southwestern Germany

1 2 3

478500 –488030 478470 –488020 478480 –478510

78400 –78470 78450 –88210 78590 –88050

380 (260–490) 927 (910–930) 1275 (1200–1330)

(B) Eastern Germany

1 2 3 4

508730 –518110 508450 –508860 508460 –508860 508440 –508760

13806’–14885’ 128550 –148750 128570 –148730 128650 –138730

359 538 733 859

(C) Southern Norway

1 2 3

598260 –598380 598260 –598430 598250 –598430

88050 –98080 88020 –98020 88020 –98000

131 (40–200) 515 (470–550) 794 (730–840)

15 19 17

10 (10–12) 10 (10–10) 10 (10–11)

97 (67–124) 123 (83–186) 131 (98–182)

(C) Northern Norway

4 5

658320 –668250 658340 –668310

138080 –148260 138190 –148300

77 (20–120) 322 (230–420)

15 15

10 (9–11) 10 (9–10)

130 (76–199) 131 (76–164)

(D) Finland

1 2 3 4

608390 –608440 628490 –628500 668180 –678100 678350 –688130

238410 –238530 258290 –258290 258310 –268450 238590 –278110

116 167 256 297

8 8 14 18

10 10 11 10

134 127 203 168

(260–420) (410–610) (640–800) (760–1000)

(110–120) (162–175) (160–315) (202–410)

Number of plots 3 3 4 14 18 14 15

Number of trees/plot

Age at breast height

5 (4–5) 5 (5–5) 5 (5–5)

97 (82–114) 122 (95–151) 134 (58–224)

7 6 7 8

91 100 98 101

(6–12) (5–10) (5–10) (5–10)

(10–10) (9–10) (9–15) (6–11)

(70–119) (70–124) (78–142) (75–158)

(87–180) (93–192) (162–267) (72–221)

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chosen in the north due to the low timberline (Table 1). Within each sub-region trees were sampled from at least 15 stands of different forest owners (normally small farm forests) to level out the management effects. All study stands were normal commercial forests on mineral soil with unknown stand history. The number of sample trees in a stand ranged from 9 to 12. In Finland, the study sites were selected across a transect of over 850 km from southern Finland to the Arctic spruce timberline in northern Lapland. Unlike in the other regions, the sampled stands represent rather similar altitudes, simply because in Finland altitudinal differences are small. Sampling was carried out in 48 pure Norway spruce stands, mostly in national parks. In some cases, commercial forest without visible logging activities were chosen. The stands were grouped into four sub-regions from south to north: (1) southern Finland, (2) central Finland, (3)southernLapland,and(4)northernLapland(Table1). All sampled stands were growing on mineral soil, typical for Norway spruce in each sub-region. The number of sample trees in a stand ranged from 6 to 15. The material is described in detail in Ma¨ kinen et al. (2000, 2001). In each stand, dominant trees without visible signs of damage were randomly selected as sample trees. Increment cores or stem discs were taken at 1.3 m height from each tree. In southwestern Germany, ring widths of eight radii were measured from the stem discs to an accuracy of nearest 0.01 mm. In other regions, one to two radii were measured. The measurements were cross-dated visually with the help of pointer years. Standardisation of the individual ring-width series was carried out in order to remove variation due to tree maturation and stand dynamics. Prior to standardisation, the series were transformed to logarithmic values in order to stabilise the tendency of the variance to increase with increasing ring width. Standardisation was carried out in two steps in order to separate lowfrequency, medium-frequency, and high-frequency variation from each other. In the first phase of detrending, a stiff spline function with a 50% frequency cutoff in 75 years (Cook and Peters, 1981) was fitted to ring-width series in order to remove low-frequency variation (Fig. 1A). Radial increment indices were subsequently calculated as the ratio between the

Fig. 1. Detrending of the tree-ring series: (A) a stiff spline was fitted to a ring-width series (dashed and continuous line, respectively); (B) a more flexible spline was fitted to the ringwidth index chronology received from the first detrending (dashed and continuous line, respectively); (C) high-frequency indices received after the two stages of the detrending procedure.

observed and estimated values. In the second phase of detrending, a more flexible spline function with a 50% frequency cutoff in 10 years was fitted to the ringwidth index chronologies, which had been obtained as a result of the first phase of detrending. Values of this flexible spline were considered as medium-frequency variation (dashed line, Fig. 1B). Again, indices were

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calculated as the ratio between the indices calculated in the first phase of detrending and the flexible spline. These indices were considered as high-frequency variation in the subsequent analyses (Fig. 1C). An autoregressive (AR) model was used to remove remaining autocorrelation from the high-frequency chronologies (Box et al., 1994). Each series was modelled as an AR process where the order was selected for the individual series by searching the first minimum of the Akaike Information Criterion. In order to reduce bias caused by extreme individual observations that do not exist in ring-width chronologies of other trees, the bi-weight mean was used to calculate mean medium- and high-frequency chronologies for each stand (e.g. Mosteller and Tukey, 1977). The chronologies were constructed by using procedures in the ARSTAN software (Holmes et al., 1986). Mean medium- and high-frequency chronologies for each sub-region were calculated as an arithmetic mean of the stand series. Principal component analysis was used to identify common patterns of growth variation between chronologies representing the different sub-regions. The significance of principal

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components was determined using the Kaiser–Guttman criterion ðeigenvalues > 1Þ. Standard deviations and mean sensitivities were calculated in order to characterise the variability in ring-width series. Mean sensitivity is a measure of the relative intensity of year-to-year changes in growth. Standard deviation describes the magnitude of lowand medium-frequency variation (Fritts, 1976). The mean sensitivity of each ring-width series was calculated as the absolute difference between the increments of current and preceding year divided by the mean of these two increments. Differences in standard deviation, mean sensitivity and first-order autocorrelation between the sub-regions were evaluated by t-tests, using the sequential Bonferroni procedure (Holm, 1979). In it, the individual tests are treated simultaneously as a multiple statistical inference problem, and the probability of rejecting any true null hypotheses is kept small in order to keep experimentwise error rate to a < 0:05. Raw monthly air temperature and monthly precipitation sum from measurement stations in the neighbourhood of the stands were obtained from the

Fig. 2. Mean air temperatures and precipitation sums of summer months (May–August) in the sub-regions (1961–1990). For symbols refer to Table 1.

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Deutscher Wetterdienst, Norwegian Meteorological Institute, and Finnish Meteorological Institute. Monthly temperatures and precipitation sums were interpolated for each stand from the meteorological data using trend surface estimation based on altitudinal, latitudinal, and longitudinal differences between the stands and weather stations. Methods of the spatial interpolation are described in detail in Ojansuu and Henttonen (1983) and Kahle (1994). For the data set from eastern Germany, the weather data of the nearby stations was, however, used as such, i.e. without interpolation. Air temperature and precipitation series for each of the sub-regions were calculated as an arithmetic mean of the stand series. Mean temperatures and precipitation sums during summer months (May–August) are presented for the sub-regions in Fig. 2. Southwestern Germany was the warmest and most humid region. Eastern Germany and Norway were fairly similar, eastern Germany being on average slightly warmer than Norway. Finland had driest summers and differences in summer precipitation were small within the country. Relationships between the climatic variables and ringwidth chronologies were analysed by cross-correlation analysis (Box et al., 1994).

3. Results The medium-frequency increment chronologies showed rather similar pattern of variation between the sub-regions of each region (Fig. 3). Between the regions growth development differed more, although some common patterns were also observed. The most pronounced growth depression occurred during the late 1970s and early 1980s in eastern Germany. It was followed by an equally strong recovery. At the same time, a growth decline was also observed in southwestern Germany and Norway, even though the reduction was not as severe. Another long growth reduction took place around 1950 in southwestern Germany. A somewhat similar phenomenon was observed also at lower altitudes in eastern Germany, as well as in southern and central Finland. It did not occur at higher altitudes in eastern Germany and in Norway. In northern Finland, a more evident reduction took place some years later, at the end of 1950s. On the other hand, a growth decline was observed during the mid-1940s in northern Norway, but not in

Fig. 3. Medium-frequency variation in southwestern Germany (A), eastern Germany (B), Norway (C), and Finland (D). Within each region, the sub-regions are presented from north to south and from high to low altitudes, from above downwards.

the other sub-regions. In recent years, no irreversible growth reductions have been observed in any of the sub-regions, except at the lowermost elevation in southern Germany. The high-frequency variation of the chronologies also had more similarities within regions than between them (Fig. 4). The indices showed some well-known pointer years with above or below average growth in each sub-region. For example, the drought in 1976 can

Fig. 4. High-frequency variation. The sub-regions are presented in the same order as that of Fig. 3.

Table 2 Regional averages of standard deviation, mean sensitivity, and first-order autocorrelation of the mean ring-width series of individual stands (non-standardised) for the maximum common overlap period 1910–1995, as well as p-values of the t-test for the regional differences. The symbols of the regions are explained in Table 1a Mean

p-Values A1

A2

A3

B1

B2

B3

B4

C1

C2

C3

C4

C5

D1

D2

D3

Standard deviation A1 78.80 – A2 52.97 0.12 A3 55.26 0.13 B1 61.14 0.17 B2 68.74 0.42 B3 72.74 0.64 B4 74.65 0.74 C1 72.20 0.60 C2 44.07 0.01 C3 36.34 0.00 C4 35.60 0.00 C5 37.42 0.00 D1 46.75 0.02 D2 42.52 0.01 D3 14.90 0.00 D4 17.45 0.00

– 0.88 0.52 0.21 0.12 0.09 0.13 0.48 0.19 0.17 0.22 0.65 0.44 0.00 0.00

– 0.61 0.23 0.13 0.09 0.14 0.31 0.09 0.08 0.12 0.49 0.30 0.00 0.00

– 0.29 0.13 0.07 0.14 0.02 0.00 0.00 0.00 0.11 0.04 0.00 0.00

– 0.58 0.40 0.62 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00

– 0.80 0.94 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

– 0.74 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

– 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

– 0.25 0.22 0.34 0.75 0.86 0.00 0.00

– 0.92 0.88 0.23 0.47 0.00 0.01

– 0.80 0.21 0.43 0.01 0.01

– 0.30 0.56 0.00 0.00

– 0.67 0.00 0.00

– 0.00 0.00

– 0.72

Mean sensitivity A1 0.17 – A2 0.14 0.04 A3 0.13 0.00 B1 0.18 0.63 B2 0.18 0.51 B3 0.17 0.86 B4 0.17 0.81 C1 0.14 0.01 C2 0.14 0.00 C3 0.15 0.04 C4 0.19 0.21 C5 0.21 0.01 D1 0.16 0.22 D2 0.15 0.06 D3 0.18 0.63 D4 0.19 0.14

– 0.47 0.00 0.00 0.02 0.02 0.87 0.80 0.53 0.00 0.00 0.22 0.57 0.00 0.00

– 0.00 0.00 0.00 0.00 0.42 0.46 0.09 0.00 0.00 0.02 0.12 0.00 0.00

– 0.77 0.27 0.21 0.00 0.00 0.00 0.19 0.00 0.01 0.00 0.99 0.08

– 0.14 0.10 0.00 0.00 0.00 0.28 0.00 0.00 0.00 0.77 0.12

– 0.90 0.00 0.00 0.00 0.02 0.00 0.11 0.01 0.28 0.00

– 0.00 0.00 0.00 0.01 0.00 0.12 0.01 0.23 0.00

– 0.89 0.16 0.00 0.00 0.03 0.26 0.00 0.00

– 0.10 0.00 0.00 0.02 0.20 0.00 0.00

– 0.00 0.00 0.31 0.98 0.00 0.00

– 0.01 0.00 0.00 0.20 0.70

– 0.00 0.00 0.00 0.02

– 0.37 0.01 0.00

– 0.00 0.00

– 0.09

First-order autocorrelation A1 0.78 – A2 0.72 0.45 – A3 0.76 0.77 0.61 B1 0.69 0.16 0.67 B2 0.72 0.31 0.97 B3 0.77 0.82 0.46 B4 0.83 0.44 0.08 C1 0.82 0.59 0.13 C2 0.79 0.94 0.29 C3 0.70 0.20 0.77 C4 0.71 0.23 0.82 C5 0.57 0.00 0.02 D1 0.78 0.97 0.38 D2 0.80 0.82 0.26 D3 0.68 0.13 0.57 D4 0.66 0.06 0.37

– 0.24 0.46 0.89 0.21 0.31 0.63 0.30 0.34 0.00 0.74 0.54 0.19 0.09

– 0.48 0.05 0.00 0.00 0.01 0.80 0.72 0.00 0.05 0.02 0.82 0.43

– 0.17 0.00 0.01 0.04 0.63 0.73 0.00 0.15 0.07 0.35 0.11

– 0.09 0.19 0.59 0.07 0.10 0.00 0.78 0.51 0.03 0.00

– 0.70 0.20 0.00 0.01 0.00 0.25 0.45 0.00 0.00

– 0.39 0.00 0.00 0.00 0.40 0.66 0.00 0.00

– 0.01 0.02 0.00 0.88 0.80 0.01 0.00

– 0.91 0.00 0.07 0.03 0.63 0.27

– 0.00 0.09 0.04 0.56 0.23

– 0.00 0.00 0.00 0.01

– 0.73 0.04 0.01

– 0.01 0.00

– 0.59

a Between-region differences that are significant when error rate is held to a ¼ 0:05, as determined by the sequential Bonferroni procedure (Holm, 1979) are marked in bold.

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be seen as growth reduction in all the chronologies from southwestern and eastern Germany, as well as from southern Norway. In Germany, the reduction was more severe at lower altitudes. In most sub-regions, a synchronous growth reduction was also observed in 1948. On the other hand, many distinct growth depressions, such as those in 1928, 1951, 1975, and 1982, in northern Norway and Finland were not observed in the southern sub-regions. The average standard deviation, mean sensitivity, and first-order autocorrelation of the original (nonstandardised) ring-width series of the individual sub-regions are shown in Table 2. Standard deviation decreased moving northwards from the southernmost sub-regions. Statistically significant differences between the sub-regions were also found in mean sensitivity and first-order autocorrelation. However, no clear altitudinal, latitudinal, or longitudinal trends were observed. The degree of similarity of medium- and highfrequency growth variation was studied between the 16 sub-regions with principal component analysis. The first three principal components together accounted for 67% of the total variation in the mediumfrequency chronologies (Fig. 5). The loadings of the first principal component mainly described the differences of growth variation between eastern Germany and the other regions. It shows that medium-frequency growth variation was rather similar at all altitudes in eastern Germany and the degree of similarity decreased with increasing distance from eastern Germany. The Finnish chronologies in particular had nothing in common with the chronologies from eastern Germany. In southwestern Germany, the chronology of high altitude was most similar with the ones from eastern Germany. The second principal component, accounting for 22% of the total variation in the medium-frequency chronologies, mainly separated the chronologies of Fennoscandia from the ones from central Europe. The third component accounted for 17% of the total variation, and reflected differences between the chronologies from southern Germany and Norway that were not already included in the other components. The first three principal components of the highfrequency variation also accounted for over 60% of the total variation (Fig. 6). The loadings of the second principal component, accounting for 22% of the total

Fig. 5. Loadings of principal components PC 1, PC 2, and PC 3 in medium-frequency variation in southwestern Germany (A), eastern Germany (B), Norway (C), and Finland (D). Within each region, the sub-regions are presented from south to north and from low to high altitudes, from left to right.

variation, were positive for all the chronologies. The common variance accounted for by the second principal component suggests that there are large-scale environmental factors that affect the whole study area. High-frequency growth variation appears to be affected by such factors more than medium-frequency variation. Latitudinal differences in high-frequency growth variation were clear. The first and third principal components accounted for 28 and 12% of the total variation, respectively. Together they describe major differences between the chronologies. As was the case with the medium-frequency chronologies, the

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Fig. 7. Cross-correlation analysis between medium-frequency chronologies from southwestern Germany and monthly precipitation (left) and temperature (right) from May to August of current year (Lag 0) and three previous years (Lag 1–Lag 3). From highest (A3) to lowest altitude (A1).

Fig. 6. Loadings of principal components PC 1, PC 2, and PC 3 in high-frequency variation. The sub-regions are presented in the same order as that of Fig. 5.

high-frequency chronologies of northern Norway and Finland differed from the ones from central Europe (PC 1). In addition, the chronologies of southern Norway had a pattern of variation that was not found in the others (PC 3). Cross-correlation analysis was used for assessing the relationship between the medium- and high-frequency chronologies and monthly temperature and precipitation values for the maximum common overlap period (1910–1995). In southwestern Germany, medium-frequency variation was negatively correlated with air temperature of the summer months of current and preceding years at all altitudes (Fig. 7).

Precipitation of current, as well as preceding summers, was clearly positively correlated with increment indices at low altitudes, but the correlations decreased at higher altitudes. At the lowest altitude, correlations between weather variables and high-frequency chronology were similar to what was found for the medium-frequency chronology: negative with summer temperature and positive with summer precipitation (Fig. 8). At higher altitudes, relationship between weather variables and high-frequency increment chronology was, however, completely different than that of the medium-frequency chronology: temperature of the current summer was positively and precipitation negatively correlated with increment indices. In eastern Germany, no individual weather variable was significantly correlated with medium-frequency

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Fig. 8. Cross-correlation analysis between high-frequency chronologies from southwestern Germany and monthly precipitation (crosshatched, left) and temperature (line shaded, right) from April of previous year to September of current year. From highest (A3) to lowest altitude (A1).

chronology at lower altitudes (Fig. 9). Correlations between summer temperatures and increment chronologies increased at higher altitudes, and at the highest altitude they were statistically significant. The effect of precipitation on medium-frequency growth variation was not as pronounced as in southwestern Germany. Due to lower summer temperature, evapotranspiration is probably lower in eastern Germany. However, the trend of association between weather variables and high-frequency growth variation was similar across altitudinal gradients in different parts of Germany. Current summer precipitation was positively and temperature negatively correlated with increment indices at lower altitudes (Fig. 10). However, the positive relationship with precipitation and the negative relationship with temperature disappeared at higher altitudes.

Fig. 9. Cross-correlation analysis between medium-frequency chronologies from eastern Germany and monthly precipitation (left) and temperature (right) from May to August of current year (Lag 0) and three previous years (Lag 1–Lag 3). From highest (B4) to lowest altitude (B1).

In southern Norway, medium-frequency variation was positively related to summer precipitation of current and preceding years (Fig. 11). Other significant correlations were also found, but they are difficult to explain logically. High-frequency variation was positively correlated with temperatures of current spring and summer, and correlations increased with

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Fig. 10. Cross-correlation analysis between high-frequency chronologies from eastern Germany and monthly precipitation (crosshatched, left) and temperature (line shaded, right) from April of previous year to September of current year. From highest (B4) to lowest altitude (B1).

increasing altitude and latitude (Fig. 12). In addition, precipitation of the current summer was negatively related to high-frequency variation at northern latitudes. This could be due to the negative correlation between air temperature and precipitation. In Finland, precipitation and temperature were not correlated with medium-frequency variation (Fig. 13). As in Norway, high-frequency variation

Fig. 11. Cross-correlation analysis between medium-frequency chronologies from Norway and monthly precipitation (left) and temperature (right) from May to August of current year (Lag 0) and three previous years (Lag 1–Lag 3). (C5) highest and (C4) lowest altitude in northern Norway, (C3) highest, (C2) intermediate, and (C1) lowest altitude in southern Norway.

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Fig. 13. Cross-correlation analysis between medium-frequency chronologies from Finland and monthly precipitation (left) and temperature (right) from May to August of current year (Lag 0) and three previous years (Lag 1–Lag 3). From northernmost (D4) to (D1) southernmost latitude.

Fig. 12. Cross-correlation analysis between high-frequency chronologies from Norway and monthly precipitation (crosshatched, left) and temperature (line shaded, right) from April of previous year to September of current year. (C5) highest and (C4) lowest altitude in northern Norway, (C3) highest, (C2) intermediate, and (C1) lowest altitude in southern Norway.

was significantly correlated with current summer temperature, especially the June temperature (Fig. 14). Furthermore, the strength of association between radial increment and June temperature increased from south to north. In all sub-regions, the effect of precipitation on high-frequency variation was less pronounced than the effect of air temperature, even though significant correlations were found.

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Fig. 14. Cross-correlation analysis between high-frequency chronologies from Finland and monthly precipitation (crosshatched, left) and temperature (line shaded, right) from April of previous year to September of current year. From northernmost (D4) to (D1) southernmost latitude.

4. Discussion This study represents a geographical approach to growth variation of Norway spruce across latitudinal and altitudinal transects and the relationships between growth and weather variation. The studied latitudinal gradient ranged from the Arctic spruce timberline in Fennoscandia to the temperate zone in central Europe. In each region (excluding Finland), the altitudinal

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gradient extended from lowlands almost to the elevational forest limit of Norway spruce. Growth variation was divided into different frequency components and the variation of each component was analysed separately. The medium-frequency increment chronologies gave insight to differences and similarities in medium-term growth between the sub-regions. In individual stands, these trends are affected by climate, but also by silvicultural treatments and other local disturbances. When several stand chronologies are averaged, the influence of local trends that are not present in other stands are likely to be reduced. Therefore, the average trends may disclose growth changes of a more general nature. Medium-frequency growth variations were more similar within the four regions than between them, which suggests that climatic variations and the resulting growth reactions have low spatial similarity. A similar pattern of variation was also observed by Hofgaard et al. (1999) in Picea mariana and Pinus banksiana across a latitudinal gradient in eastern Canada. Medium-frequency growth variation is probably affected by extreme weather events, and, therefore, regional differences may partly be caused by region-specific physiological differences in susceptibility of trees to extreme climatic events (e.g. LaMarche, 1974; Kienast and Schweingruber, 1986; Oberhuber and Kofler, 2000). Although the medium-frequency growth response to weather variation was primarily determined by regional features, relatively large common high-frequency variation (22%) suggests that there are also common limiting and favouring factors for tree growth throughout the entire study area. Large-scale changes in positions of air masses have caused similar weather conditions in larger areas bordering the North Atlantic (Briffa et al., 1987; Hurrell, 1995). Tree growth may be affected by common stress years that are caused by this type of large scale weather pattern. A disadvantage of cross-correlation analysis, as well as most other statistical methods, is that they are unable to render evidence about less frequent growth limiting factors. These less frequent events are often years with extreme weather conditions that have an effect on tree growth also for subsequent years, such as severe frosts. One of the limitations of using mean monthly air temperature and precipitation data is that short-term, extreme climatic events are

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not adequately described by them. Furthermore, because air temperature and precipitation of individual months are often correlated, their effects on growth are difficult to separate (e.g. Kienast et al., 1987). The calculation of cross-correlations did not provide information about the mode of response between weather variables and radial growth. Methods that are not restricted to studying only linear relationships between weather and growth may lead to findings that are not revealed by the more traditional type of analysis (Neumann, 2001). Even though, the observed correlations with weather variables were in most cases rather low, a common factor that caused synchronous medium-frequency growth variation throughout each of the regions is evident. Prolonged growth reductions have been observed after severe droughts or frosts, for example after the drought of year 1976 in central Europe (Fig. 3, see also Mikola, 1950; Kahle, 1994; Kahle and Spiecker, 1996; Abrams et al., 1998; Oberhuber et al., 1998). Possible reasons for growth reductions of long duration are leaf, branch, and root dieback, xylem caviation, reduced leaf photosynthetic capacity, and changes in carbon allocation patterns between tree parts (Orwig and Abrams, 1997). Fritts et al. (1965) demonstrated that values of ringwidth variation can be used as a basis for evaluating growth responses of trees to their environment. They observed increasing growth variability towards xeric sites along a moisture gradient in Arizona. They concluded that climatic control of growth increases towards the limits of tree growth, i.e. trees growing on more favourable sites do not respond as strongly to drought as trees on dry sites. Phipps (1982) and Abrams et al. (1998) have observed a similar variation pattern in sites of different water holding capacity. In contrast to their results, there was not always a clear trend across the altitudinal or latitudinal gradient in this study. Accordingly, Kahle (1994) and Tardif and Bergeron (1997) found no major changes in ringwidth variability on sites with different soil moisture. No straightforward biologically plausible explanation for these contradictory results is available. Some chronologies of this study originate from sites in transition zones between climatic extremes. Therefore, the relationship between tree growth, climatic conditions, and site characteristics can be very complex and the factors that effect tree growth most

may even change from year-to-year. The result may also indicate that air temperature and precipitation influence chronology characteristics in a different manner. Environmental factors influencing tree growth and their temporal variation may naturally be rather different in the four regions. It is logical to expect a decline in radial growth and changes in factors controlling tree growth at the northern sites in comparison with the southern ones. Indeed, the principal component analysis of high-frequency growth variation showed that there are marked differences between the northern and southern regions. PC 1 of highfrequency variation included positive loadings for the chronologies at latitudes of about 608N to 688N and negative loadings for the chronologies from the more southern regions. There are differences in the timing and duration of the growing season, as well as in climatic conditions, between the sub-regions, most noticeably from north to south (e.g. Briffa et al., 1987). Therefore, climatic prerequisites for tree growth are expected to change gradually from south to north throughout the temperate and boreal zones (cf. Hofgaard et al., 1999). The results are consistent with the results of previous findings that growth variation of trees in central and northern Fennoscandia is mainly related to current summer temperatures (e.g. Hustich and Elfving, 1944; Mikola, 1950; Henttonen, 1984; Ma¨ kinen et al., 2000, 2001). Even though, the distance along the west–east transect from the Atlantic Ocean across the Scandes is hundreds of kilometres, high-frequency growth variation in northern Norway and in Finland was rather similar. In both regions, a negative correlation was found between current summer precipitation and tree growth despite great differences in average summer precipitation. A positive growth reaction to drought is less commonly encountered, and the correlation is probably due to an inverse relationship between precipitation and air temperature (cf. Tardif and Bergeron, 1997). In the northern regions, low evaporation due to low summer temperatures and longlasting snow cover make drought an unlikely event. It is evident that this common signal in both regions is caused by summer temperature, which is the dominant growth-determining factor on both sides of the Scandes (cf. Kirchhefer, 1999). Although the Scandes strongly affect the spatial pattern of precipitation, they

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do not cause totally different patterns of summer temperature on their oceanic and continental sides. Generally, the limiting effect of low air temperature is known to decrease and the importance of precipitation on tree growth increase moving southwards from the northern forest limit (Jonsson, 1969; Tuhkanen, 1984). In this study, the annual growth of Norway spruce was mainly related to precipitation in central Europe, where dry and warm summers were connected to lower growth rates (cf. Eckstein et al., 1989; Spiecker, 1991; Kahle, 1994). At all latitudes, a continuous change in the factors controlling Norway spruce growth was observed as altitude changes. At low altitudes, the lack of water was the main growth limiting factor. At intermediate altitudes, growth variation was not as strongly related to individual weather variables. Probably, growth controlling factors change from year-to-year at these altitudes. In the highest sub-regions, a clear correlation was found between air temperature and growth of Norway spruce. A similar altitudinal trend in the weather–growth relationship has also been observed in some previous studies in central Europe and Norway (e.g. Sla˚ stad, 1957; Dittmar and Elling, 1999). In southern Finland, low growth of Norway spruce has often coincided with production of a rich seed crop, which requires large amounts of photosynthetic products (Mielika¨ inen et al., 1998). The negative correlation between previous summer temperatures and tree growth found in several sub-regions can be partly explained by the fact that high temperature during the latter part of previous summer promotes flowering during next summer (Tiren, 1935; Leikola et al., 1982). The observed decline in Norway spruce growth in eastern Germany during the 1970s and 1980s coincided with the period when the phenomenon termed Novel Forest Damage was intensively debated. The phenomenon was also studied scientifically and many hypotheses were proposed to explain it. Air pollution, especially SO2 deposition, was brought up as a likely contributing factor. It was also pointed out that tree growth in central Europe had been affected by the severe drought in 1976. While growth has subsequently recovered, the decline appears to have been the worst during the 20th century in eastern Germany, a heavily polluted area during the 1970s and 1980s. However, growth recovered already during

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the mid-1980s, several years before the reduction of SO2 emissions. One of the expected effects of the climate change is an increase in temperatures. Warmer temperatures may increase tree growth in northern Europe, but this type of effect is unlikely in central Europe. A positive correlation between summer precipitation and growth variation was evident in central Europe. Therefore, if the possible climate change does not lead to increased precipitation, rising temperatures may even have a detrimental effect on forest ecosystems in central Europe due to an increase of evapotranspiration. However, one can expect different growth responses at different altitudes. The presented hypotheses should be regarded only as general indicators of possible growth changes, not as a highly probable one, because growth will also be affected by other types of mechanisms, such as extreme climatic events. The changes in average temperature and precipitation induced by the possible climate change may not necessarily be the most influential factors regulating tree growth in the future. Possible changes in severity and frequency of extreme stresses may influence growth more.

5. Conclusions The variation of annual growth of Norway spruce, especially the medium-frequency variation, was rather different between the four regions, although certain similarities were observed. However, within each individual region the medium-frequency growth variation was relatively similar, irrespective of altitudinal and latitudinal differences of the sample sites. Medium-frequency variation is probably affected by extreme weather events resulting in regional growth patterns. The high-frequency component of growth was more strongly connected to temperature and precipitation than medium-frequency variation. Part of the highfrequency variation was common to all four regions, which suggests that some factors synchronising tree growth are common for the entire study area. However, the results of this study indicate that the growth of Norway spruce is often influenced by rather different climatic variables in different geographic regions and at differing altitudes. The limiting effect of low precipitation on Norway spruce growth

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decreased and the effect of air temperature increased with increasing latitude and altitude.

Acknowledgements We thank the field and laboratory assistants for their work during the study. The research was supported by the European Union (Fair3-CT96-1310).

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