Quantifications of dendrochronological information from contrasting microdensitometric measuring circumstances of experimental wood samples

Applied Radiation and Isotopes 70 (2012) 1014–1023

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Quantifications of dendrochronological information from contrasting microdensitometric measuring circumstances of experimental wood samples g ¨ f,1, J. Merilainen ¨ S. Helama a,n, Y. Be´gin b,c, M. Vartiainen d, H. Peltola e, T. Kolstrom a

Arctic Centre, University of Lapland, Finland ´ tudes Nordiques, Universite´ Laval, Que´bec, Canada Centre d’E c Centre Eau Terre Environnement, Institut national de la recherche scientifique, Que´bec, Canada d Saima Centre for Environmental Sciences, Savonlinna, Finland e School of Forest Sciences, Joensuu, University of Eastern Finland, Finland f Mekrij¨ arvi Research Station, University of Eastern Finland, Ilomantsi, Finland g Kivel¨ ankatu 2 B 5, 57200 Savonlinna, Finland b

a r t i c l e i n f o

a b s t r a c t

Article history: Received 19 July 2011 Received in revised form 30 January 2012 Accepted 16 March 2012 Available online 23 March 2012

We analyzed how the pretreatment method of Scots pine (Pinus sylvestris L.) wood specimens together with X-ray methodology applied for density analyses affect resulting tree-ring data and derived proxybased climate information. We also evaluated whether these results from two contrasting laboratory circumstances could be homogenized by applying dendroclimatic statistical methods. For this study, we measured a pair of X-ray based microdensitometry datasets using double samples of subfossil and recent wood specimens. Dendrochronological information of earlywood and latewood series was examined to determine for alterations in the resulting data. We found that the level of overall density, its trend over cambial ages and the growth amplitude altered due to the sample pretreatment/density measuring exercise, which means that comparisons of heterogeneous datasets should be, in general, regarded cautiously. Dendrochronological standardization did, however, even out several potentially biasing influences from the differing overall densities and their trends. The two latewood (maximum) density chronologies yielded paleoclimatic reconstructions which both calibrated and verified satisfactorily with the instrumental warm-season (March–September) mean temperatures. The transfer functions were found to further equalize the differences between the two proxy records. We recommend (if no strictly homogenous data are available) reconciling similar data assemblages through transfer functions with multiple independent variables. & 2012 Elsevier Ltd. All rights reserved.

Keywords: Dendroclimatology Dendrodensitometry Paleoclimatology Scots pine Tree-ring

1. Introduction Tree-rings are high-resolution sources of past climate variability. They provide indirect (i.e., proxy) estimates of past climate and elongate the climatic records retrospectively over past centuries and millennia (Fritts, 1976). Tree-ring chronology is formed as an average record of tens to thousands of individual series from living trees. A temporal extension of the chronology is achieved by the additional use of dead wood of historical, archeological or paleontological origin. The tree-rings are beneficial in comparison to many other types of palaeoclimate proxy records, as they can be temporally calibrated with meteorological series and the dendroclimatic data provide information of past climatic events with exact dating control (Betancourt et al., 2002; Baillie, 2010; Helama et al., 2010a).

n Corresponding author. Current address: Finnish Forest Research Institute, P.O. Box 16, 96301 Rovaniemi, Finland. Tel.: þ 358 102115047; fax: þ358 102112103. E-mail address: samuli.helama@metla.fi (S. Helama). 1 Current address: Finnish Forest Research Institute, Vantaa, Finland.

0969-8043/$ - see front matter & 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.apradiso.2012.03.025

Briefly, tree-rings are the series that extend climatic records temporally, and critically strengthen our understanding about climate variability prior to any direct weather observation. Conventionally, tree-rings are observed under light-microscope, their widths recorded with the aid of a measuring table moving horizontally by a hand-wheel and the data of consecutive widths are saved to form dendrochronological time-series (e.g. Speer, 2010). However, the radial profile of tree stems exhibit highly varying wood density at intra-annual to longer scales. More sophisticated techniques have likewise been developed to recover this information, providing palaeoclimate research with dendrodensitometric time-series (Polge, 1970; Schweingruber et al., 1978, 1988). The importance of these data is the resulting accessibility for intra-annual profiles of seasonal density fluctuations (Rigling et al., 2001; Decoux et al., 2004; Campelo et al., 2006) and that in many regions the chronologies of tree-ring densities contain climatic signals that considerably improve the proxy-climate correlation in relation to ring-widths (Briffa et al., 1988, 2002a, 2002b).

S Helama et al. / Applied Radiation and Isotopes 70 (2012) 1014–1023

Producing the high-resolution wood density profiles require sample pretreatment methods and measuring devices of high complexity, in comparison to traditional research exploring the annual widths of rings. Already the pre-analytic protocols of dendrodensitometry are relatively complex and proceed through the scrutinized wood sampling, the removal of wood extractives and acclimatization of the samples before carrying out the measurements (Schweingruber et al., 1978). In addition, more than one method exists to produce the dendrodensitometry-based data or highly related information (e.g. Schweingruber et al., 1978; Cown and Clement, 1983; Bergsten et al., 2001; McCarroll et al., 2002; Schinker et al., 2003; Campbell et al., 2007). The divergent sample treatment and measuring techniques are known to yield differing results of wood density measurements in the context of the X-ray method (Schweingruber et al., 1978; Evertsen, 1982; Park et al., 1992). Even greater disparity of measurement data could be expected as the X-ray technique is not a single method of producing wood density data but actually a diversification of method is an inevitable process (Clauson and Wilson, 1991; Park and Telewski, 1993; Rinn et al., 1996; Ivkovich and Koshy, 1997; McCarroll et al., 2002; Schinker et al., 2003; Wang et al., 2003; Campbell et al., 2007; Mannes et al., 2007; Jackson et al., 2009). Meanwhile, the dendrodensitometry-based tree-ring data are stored in large amounts into the online archives making the information available for a global user community of dendrochronology-oriented researchers (e.g.: International Tree-Ring Data Base, ITRDB; Grissino-Mayer and Fritts, 1997). While the increase of similarly available data is an admirable goal and could highly benefit the research, the potential pitfalls may consider the lack of homogeneity of the data produced by multiple methods. Consequently, the palaeoclimate estimates derived using the shared datasets and their combinations could be in error. A recent study demonstrated how even the low amount wood extractives (i.e., non-structural and secondary constituents of wood (see Hillis, 1971, 1985)) could inflate both earlywood (a part of the annual ring, formed during the early part of the growing season, characterized by large thin-walled cells) and latewood (formed produced during the later part of the growing season, characterized by small and thick-walled cells) density values, and alter their trend lines, flatten their inter-annual variability, both in earlywood and latewood, and influence the estimation of intra-annual density variations (Helama et al., 2010b). If not extracted identically in the case of each sample set, or at all, the same lath of wood could, thus, provide densitometric data of altered dendrochronological information. Notably, the above study did not deal with any other treatment but the extraction. However, even more alterations could be hypothesized with a wider spectrum of methodologies applied for measurements and analyses. Altogether, tree-ring density has proven to provide improved dendroclimatic correlations in comparison to the conventionally examined tree-ring width. Furthermore, the reconstructions based on tree-ring density chronologies have found to produce more reliable estimates of past climates. However, technical diversification of the wood density determination is hypothesized to challenge the amalgamations of the old and new data. In the above context, the aim of this study was to analyze how the pretreatment method of wood specimens together with X-ray methodology applied for density analyses affect resulting tree-ring density data and proxybased climate information derived based on Scots pine (Pinus sylvestris L.) wood, which is a commonly studied species in dendroclimatology/paleoclimatology (see e.g. Briffa et al., 1988, 1992; Schweingruber et al., 1988; Savva et al., 2002; Campbell et al., 2007). The two different datasets measured under contrasting laboratory conditions differed both in terms of pretreatment method and X-ray densitometry methodology applied in density analyses. Importantly, we evaluated whether these intentionally altered

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laboratory circumstances could be homogenized by applying dendroclimatic statistical methods (e.g. Fritts, 1976). This was done in order to find out whether the tree-ring standardization and the transfer functions for palaeoclimate purposes could ‘‘filter out’’ from the dendrodensitometric data the effects of different treatment methodologies applied. For this study, we measured a pair of X-ray based microdensitometry datasets using double samples of Scots pine wood specimens of subfossil and modern origin. Dendrochronological information of earlywood and latewood series was examined to detect for alterations in the resulting data. The findings of this work could be valuable for the assessment of the potential pitfalls of palaeoclimate estimates reconstructed using a diverse source of measuring methods.

2. Material and methods 2.1. Study materials The study region from which the experimental materials come lies within the southern boreal forest zone (Ahti et al., 1968), not far from the town of Savonlinna, in southeastern Finland. Fifteen living Scots pine (P. sylvestris L.) trees were harvested and sawn into disks on a site with sandy soil in Varparanta (611590 N; 281450 E, 80 m a.s.l.). Subfossil samples of Scots pine were retrieved from the lacustrine sediment of small basins: Lake ¨ Herajarvi (61158’N; 28150’E), Kaivanto (61156’N; 28159’E), Liippilampi (611550 N; 291020 E), Isokortteikko (611580 N; 281520 E) and ¨ Valkeajarvi (611580 N; 281520 E). Also these samples were sawn into disks after lifting the megafossils to the surface. Subsequently, the trunks were returned to the lake. In all, this study included samples from 49 dead pines. The Lappeenranta weather station (611050 N, 281090 E, 105 m a.s.l.) provided the meteorological data, i.e. the time series of monthly mean temperatures, for the dendroclimatic analyses and, for comparison with the constructed tree-ring chronologies. 2.2. Microscopic dendrochronology The ring widths were measured under light microscope to the nearest one-hundredth of a millimeter. Careful cross-dating by means of the synchrony of the narrow and wide annual rings was carried out. This was done using the dendrochronological procedures of Aniol (1983) and Holmes (1983), along with visual comparison of the produced time series. Cross-dating is a routine procedure of dendrochronology that provides the annual dating control of each treering (Fritts, 1976). Dendrochronological crossdating was applied to both living tree and subfossil tree-ring series (Fig. 1). The subsequent densitometric analyses were performed on this cross-dated material. Tree-ring width data used here is a part of a much larger quality-controlled ring width ¨ dataset (Lindholm et al., 1998–1999; Merilainen and Timonen, 2004; Helama et al., 2005). 2.3. X-ray microdensitometry measurements The sample disks both for living trees and subfossil samples were first sawn from pith to bark into pieces of breadth 40 mm and height 27 mm. These samples were then carefully air dried to a moisture content of 12%. These pieces were further sawn into radial strips (rectangular laths) using a twin blade circular saw with an adjustment to reach an angle of 901 to the axis of the fiber direction (longitudinal tracheids). The studied samples were 5 mm in breadth in tangential direction and 5 mm in height in fiber direction. The radial length of the samples varied according to the actual distance from pith to bark in each sample. Each disk

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The second half of the samples was analyzed at the Universite´ Laval, Centre d’E´tudes Nordiques in Canada. The samples were exposed to removal of wood extractives. This was done with a Soxhlet extractor with 96% volume alcohol for 48 h. A cellulose acetate wedge was used as a transparency standard for transforming wood density into optical darkness, since thickness and density of cellulose aceatate are constant with temperature and humidity conditions (1.274 mg/cm3). The wedge represents four layers of different thickness (0.234, 0.468, 0.936 and 1.404 mm) (e.g. Schweingruber et al., 1988). After removal of the extractives of wood samples, the wood laths, together with the wedge as the standard optical reference, were exposed to an X-ray source for a period of 50 min (11 kV, 20 mA, 20 1C, 50% relative humidity) over a double-sided emulsion film (Kodak RP/M). The film was then developed by an automatic processor (X-O-Mat for 90 s). Similarly to Wang et al. (2001, 2002), an X-ray densitometer (Dendro-2003, Walesch Electronic) was used to determine the wood density of tree-rings expressed as optical transparency on the developed films. For each measurement, the X-ray densitometer was first calibrated against the four transparencies of the acetate cellulosis wedge on each film to set up a standard optical reference. The densitometer then measured tree-ring transparency, moving 12 mm at each step along the radial direction of the stem disk samples. The total measurement error (wood lath thickness error (737 mg/cm3) and film grain-size error (79 mg/cm3)) was estimated to be 76 mg/cm3.

was sawn into two sample strips one upon the other, the ‘twin’ samples of each disk, thus, representing exactly the same radius of the stem. The counterparts were analyzed for radial density profiles by alternative methodologies used in two different wood science laboratories. Basically, the circumstances differed by extractive and moisture content and step size of the density scanning (Table 1). First, the removal of extractives has not been a prerequisite of the method in the past (Evertsen, 1982) and also the neglect of this pretreatment step has been suggested due to ¨ low proportion of extractives in specific samples (Morling, 2002; Jaakkola et al., 2005, 2006). Second, the acclimatization can bring the wood into alternative moisture contents with concomitant influences on the wood density (Schweingruber et al., 1978; Evertsen, 1982). The step size is an evolving technical property of the machinery. The first half of the samples were analyzed at the University of Eastern Finland, School of Forest Sciences (formerly University of Joensuu, Faculty of Forest Sciences; until end of 2009), in Finland. The samples were acclimatized to an ambient temperature of 20.1–20.9 1C and relative humidity of 61.0–62.0%, corresponding to wood moisture content of roughly 12%. The X-ray microdensitometry (ITRAX) available at the University of Eastern Finland is a commercially available X-ray microscopy developed by Cox Analytical Systems (Bergsten et al., 2001). In batch scan mode, samples were located in sample holders and it was possible to scan a maximum of 20 samples at the same time. ITRAX scanning was done using a step size of 25 mm intervals with the following settings for the X-ray tube: voltage of 30 kV, current of 25 mA and an exposure time of 20 ms (see Peltola et al., 2007).

2.4. Densitometric parameters

Fig. 1. Temporal fluctuations in the crossdated wood material, indicating the sample size of the double-measured series over the past 13 centuries. Modern and subfossil chronologies were determined for AD 1947–2000 and AD 1384–1594, ¨ respectively, for intervals with samples size of at least five (La¨ anelaid, 2000) trees (horizontal line).

Three to five rings near the pith were discarded when they contained reaction wood. The other portions were also discarded in the case of decayed wood (e.g. outer rings when the maximum density values on these rings decreased sharply, assuming a decay effect). The subsequent analyses were carried out using the segments with data from both laboratories. Subsequently, the average (median) ring number of the densitometry series was 72 (66) with a standard deviation of 35. The tree-rings of living Scots pines covered the period AD 1939–2000, and the rings of subfossil Scots pines covered the period AD 770–1722, with a void of data over AD 1099–1146 and AD 1168–1187 (Fig. 1). A total of 4593 rings were included from each of the density profiles, including altogether 9186 rings in the comparable data of both laboratories. The annual tree-ring widths were determined from the profiles of running density measurements. The width of each ring was measured as was the distance between the borders of the latewood and earlywood of the next year. This border was defined as the point where the sharp decline of the consecutive latewood density measurements ceases and the density growth evens out (Helama et al., 2008, 2010b). Series of tree-ring widths measured under the microscope and determined from the density profiles were matched. Subsequently, the earlywood minimum density and the latewood maximum density were determined for each ring. Thus, a total of 18,372 density values entered the analyses of dendrochronological statistical examinations.

Table 1 Characterization of the two measuring circumstances for our experimental wood samples in their X-ray microdensitometry analyses and the nomenclature of resulting datasets. Method/ dataset

Extraction

Acclimatization

Measuring step (mm)

Laboratory

Earlywood data

Latewood data

1

No treatment

25

MXD1

Alcohol extraction in Soxhlet apparatus

University of Eastern Finland Universite Laval

MND1

2

61–62% relative humidity 50% relative humidity

MND2

MXD2

12

S Helama et al. / Applied Radiation and Isotopes 70 (2012) 1014–1023

Characteristic differences between the counterpart series of differently pretreated and measured wood samples (datasets 1 and 2) were traced by their average densities (i.e. arithmetic mean), the standard deviations of the density, the first-order autocorrelations of the density series and the mean sensitivities (MS; Fritts, 1976). The mean sensitivity is  n1   1 X 2ðxRN þ 1 xRN Þ MS ¼ ð1Þ  n1 RN ¼ 1 xRN þ 1 þ xRN  where x is the tree-ring value of ring number RN in the series possessing n tree-rings. Compared to the standard deviation, mean sensitivity is a measure of the variation between consecutive years (i.e. variability at inter-annual timescales). These statistics were calculated for the maximal length of each series. A comparison between densities due to datasets 1 and 2 was done using the t-test for paired samples. In addition, mean density trends according to cambial age were calculated to characterize the density variations in the different radial portions of the stem. The individual series of minimum and maximum densities were aligned according to their cambial ages (instead of calendar years) and the average density values for each annual cambial age were calculated as the arithmetic mean. In practice, the cambial age is the ring number that runs from pith to bark (i.e. the ring closest to the pith is associated with ring number one and the ring closest to the bark gets the highest ring number). 2.5. Detrending and averaging the tree-ring series Tree-ring series of individual trees are generally known to exhibit trends in ring density development as the tree ages. Moreover, the series of earlywood densities, measured either as earlywood mean densities or minimum densities, are heteroskedastic (Helama et al., 2008, 2010b). Such trends make the initial tree-ring series non-stationary and they should be removed prior to chronology averaging and interpretation. In this study, the detrending of the series was carried out by fitting a regression line individually to each measurement series as follows: mRN ¼ bRN þc

ð2Þ

where b is the slope of the fitted linear regression line, RN is the ring number between 1 and n and c is the y-intercept. This function was thus expected to capture the age–size related trend in radial growth. The series of earlywood and latewood density indices were derived from the curve by division and subtraction, respectively, with an expected value of 1.0. The division was expected to stabilize the heteroskedastic variance of the earlywood series. No such stabilization was expected to be needed for the latewood series (Helama et al., 2008). In this condition, it has earlier been recommended to extract the indices from the curve by subtraction (see e.g. Cook and Peters, 1997). Thus, the dendrochronological indices were derived in the earlywood data as follows: iRN ¼ oRN =mRN

ð3Þ

and in the case of the latewood data as iRN ¼ oRN mRN þ1:0

ð4Þ

where iRN, ORN and mRN were the resulting index and the observed and modeled growth values for each ring number RN (indicating the biological age of the ring), respectively. Some of the samples series were fragmented. This problem (pertaining specifically to the low-frequency dendroclimate signal) was surmounted by inserting values to connect fragments of measurements prior to standardization (see e.g. Sheppard et al., 1997). In practice, the inserted values were the average value of ten rings preceding and following the fragment (Sheppard et al., 1997). After standardizing the series, the index values resulting from the inserted

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values were omitted with no inclusion in any subsequent calculations. After the standardization, the index series of earlywood and latewood densities were characterized by the standard deviations, the first-order autocorrelations and the mean sensitivities. This was done to trace the influence of the applied chronology construction procedures on the dendrochronological information. Tree-ring chronologies were produced by averaging the crossdated series into the mean chronologies by the bi-weight robust mean procedure (Cook et al., 1990b). The characterization of the chronologies was carried out using the same set of dendrochronological statistics: standard deviations, the first-order autocorrelations and the mean sensitivities. 2.6. Dendroclimatic comparisons and development of transfer functions Dendroclimatic correlations were first calculated between the monthly temperature means and the tree-ring chronologies. This was done using the Pearson product-moment correlation coefficient (r) measuring a statistical equation describing the linear relationship between two series. Thereafter, dendroclimatic transfer functions were built using linear models. This method was used here, as the robustness of linear regression as a reasonable model transforming the proxy values into palaeoclimatic estimates was demonstrated in a recent dendroclimatic study (Helama et al., 2009). The calibration and verification periods were defined by splitting the modern period 1947–2000 into two 27-year intervals, 1947–1973 and 1974–2000, providing the calibration/verification procedure with the early and late periods, respectively. The coefficient of determination (R2) was computed over the calibration period (first, 1947–1973). Statistics calculated over the verification period (first, 1974–2000) were the squared correlation coefficient (r2), reduction of error (RE) and coefficient of efficiency (CE) (Fritts, 1976; Briffa et al., 1988). In order to test the temporal stability of the relationship between dendrochronological data and climate, a cross-calibration/verification procedure (Gordon et al., 1982; Briffa et al., 1988) was carried out using the periods 1974–2000 and 1947–1973 for calibration and verification purposes, respectively. The significance of the dendroclimatic statistics, including the coefficient of correlation (r), the coefficient of determination (R2), the squared correlation coefficient (r2), reduction of error (RE) and coefficient of efficiency (CE), was calculated using a combination of frequency-domain modeling (Ebisuzaki, 1997) and Monte Carlo (Efron and Tibshirani, 1986) methods. One thousand (1000) pairs of surrogate time series with the same power spectrum as the original time series but with a random phase were generated and their corresponding statistics calculated. The empirical probability distribution of each statistic value was calculated and, hence, its significance for the single-tailed (R2, r2, RE and CE) and two-tailed (r) distribution. This method is robust as it accounts for autocorrelation structure in the time series (Macias Fauria et al., 2010; Macias-Fauria et al., 2012). The palaeoclimate model showing a combination of robust calibration and verification statistics was chosen as a final model that was calibrated over the common period (1947–2000). Finally, the past temperature variability was reconstructed using the dendrochronological information over the subfossil period (see Fig. 1).

3. Results 3.1. Influence of laboratory routines Differences between the wood densities in datasets 1 and 2 (see Table 1) were compared and the resulting dendrochronological information was found to be pervasively altered. On average, dataset 2 resulted in significantly (po0.001) lower density of both

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earlywood (Table 2a) and latewood (Table 2b). In the case of latewood, dataset 1 resulted in an increase of the total spread of density values, quantified as standard deviations (po0.001), as well as the inter-annual density variability, quantified as mean sensitivities (po0.001). In the case of earlywood, the alterations in density information were more complex as dataset 1 resulted in significantly (po0.01) increased spread of density values, but significantly (po0.001) lowered inter-annual density variability. The first-order autocorrelation was found to be less affected and was significantly (po0.05) altered only for the latewood density values. Increasing cambial age yielded differently varying minimum and maximum density trend patterns. More precisely, the earlywood densities exhibited more or less linear trends (Fig. 2A), especially onwards from ring number 20, whereas a convexly shaped growth trend was observed for the latewood densities (Fig. 2B). Moreover, a positive trend in the difference between earlywood densities of datasets 1 and 2 was detected (Fig. 2C).

Conversely, a negative trend was discernible in the corresponding difference of latewood densities (Fig. 2D). These results would suggest that different methods influence the density figures differently in different radial portions of the tree stem. To explore whether the density difference occurred due to difference of the measuring step (see Table 1), the minimum and maximum density differences were plotted as a function of annual ring width (Fig. 3). This showed that dataset 1 could occasionally exhibit lower maximum density values in comparison to dataset 2 in the case of narrow but not wide rings. No such discrepancy was observed in the case of minimum densities. Since dataset 1 was actually expected to exhibit higher densities depending on higher moisture and extractive content (Table 2b), this type of deviation (Fig. 3) could indeed most reasonably be explained by restrictions of the device measuring dataset 1 with its relatively long step size of 25 mm (see Table 1) that may have caused bypassing of the shortest (pertaining to the radial dimension of tree stem) density maximum peaks.

Table 2 Characterization of the individual series of (a) minimum earlywood densities (MND) and (b) maximum latewood densities (MXD) for datasets 1 and 2. The comparison includes the overall density mean of the series (i.e. arithmetic mean), standard deviation (SD), the first-order autocorrelation (AR1), and mean sensitivity (MS; Eq. (1)). Differences between the wood characteristics of two types were compared using a t-test with accompanying statistical significances (p). The comparison was repeated subsequent to the dendrochronological standardization (c, d). Mean

SD

AR1

MS

Mean

SD

AR1

MS

(a)

MND1 MND2 p

0.278 0.218 o 0.001

0.024 0.022 0.004

0.452 0.463 0.634

0.061 0.072 o0.001

(c)

1.000 1.000 0.634

0.071 0.082 o0.001

0.287 0.300 0.541

0.061 0.071 o 0.001

(b)

MXD1 MXD2 p

0.756 0.695 o 0.001

0.109 0.059 o 0.001

0.479 0.408 0.011

0.106 0.070 o0.001

(d)

1.000 1.000 0.178

0.093 0.051 o0.001

0.347 0.247 o0.001

0.078 0.047 o 0.001

Fig. 2. Average growth as determined for (A) earlywood minimum density (MND1 and MND2 for datasets 1 and 2) and (B) latewood maximum density (MXD1 and MXD2) of modern and subfossil data as a function of cambial age (ring number) and the differences between the corresponding density values (C, D). Trends in the differences were determined by regression lines and Pearson correlations (r). The series are shown here over the cambial years with a sample size of at least ten trees.

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observed by comparing the values for datasets 1 and 2, whether calculated for individual series or mean chronology of modern (Table 3a and b) and subfossil (Table 3c and d) periods. Over the subfossil data, however, the averaging resulted in relative disparity for the spread of the earlywood chronology values as the comparison between the standard deviations by the two types of chronologies evidenced statistically significant differences (Table 3c). The earlywood and latewood chronologies were constructed over the modern (Fig. 4) and subfossil (Fig. 5) periods. In all cases, the chronologies between datasets 1 and 2 were highly correlative. Peculiarly negative growth estimates were however evident in the latewood dataset 1 for some years (e.g. 1408 and 1985).

3.2. Influence of standardization Dendrochronological standardization of the individual series clearly homogenized the density difference caused by different methods, in the case of both earlywood (Table 2c) and latewood (Table 2d). The mean differences of the series were found to be statistically non-significant at any reasonable level. The results regarding the earlywood and latewood standard deviations, mean sensitivities and the first order autocorrelations (Table 2c and d) remained mostly unchanged, in comparison to results prior to the standardization (see Table 2a and b). However, the standardization inflated the standard deviations of the earlywood series (Table 2c). The averaging of individual series into the mean chronology did not largely influence the dendrochronological statistics. The differences between the dendochronological statistics were consistent as

Fig. 4. Tree-ring chronologies over the modern period (AD 1947–2000) of (A) earlywood minimum densities (MND1 and MND2 for datasets 1 and 2) and (B) latewood maximum densities (MXD1 and MXD2). Pearson correlation (r) was calculated between the records.

Fig. 3. (A) Minimum and (B) maximum density values as a function of the annual widths of each measured ring.

Table 3 Characterization of the mean chronologies of (a) minimum earlywood densities (MND1 and MND2 for datasets 1 and 2) and (b) maximum latewood densities (MXD1 and MXD2) (b) constructed from living Scots pines (AD 1947–2000). The comparison includes the overall density mean of the series (i.e. arithmetic mean), standard deviation (SD), the first-order autocorrelation (AR1), and mean sensitivity (MS; Eq. (1)). Differences between the wood characteristics of datasets 1 and 2 were compared using a t-test with accompanying statistical significances (p), except the SDs that were compared using F-test and AR1s for which the statistically significant difference was not determined (n.d.). The comparison was repeated for subfossil chronology (AD 1384–1594) (c, d). Mean

SD

(a)

MND1 MND2 p

0.997 0.998 0.649

0.040 0.045 0.329

(b)

MXD1 MXD2 p

1.000 0.999 0.906

0.047 0.027 o0.001

AR1 0.187 0.113 n.d.  0.169  0.025 n.d.

MS

Mean

SD

AR1

MS

0.040 0.048 0.007

(c)

1.000 1.002 0.446

0.043 0.052 0.007

0.264 0.325 n.d.

0.043 0.049 o 0.001

0.058 0.030 o 0.001

(d)

1.002 1.002 0.903

0.059 0.033 o 0.001

0.469 0.381 n.d.

0.050 0.029 o 0.001

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Fig. 6. Warm-season (April through September) temperature reconstructions WSTR1 and WSTR2 as calculated from datasets 1 and 2) over the subfossil and modern periods (see Fig. 1). Instrumental data are shown over the modern period and the coefficient of determination (R2) quantified the proxy-climate calibrations (1947–2000).

Accordingly, the final calibration models were recalibrated using the entire period of overlapping latewood chronologies and the instrumental data averaged over the April through September season. This resulted in transfer functions of the following form:

Fig. 5. Tree-ring chronologies over the subfossil period (AD 1384–1594) of (A) earlywood minimum densities (MND1 and MND2 for datasets 1 and 2) and (B) latewood maximum densities (MXD1 and MXD2). Pearson correlation (r) was calculated between the records.

While the higher densities were characteristics of dataset 1 (Table 1b), the exceptionally lower densities in these years could be reasonably associated with the longer measuring step and bypassing of the very short density maximum peaks, as discussed above (see Section 3.1). 3.3. Influence of transfer functions Dendroclimatic correlations were calculated (Supplementary Table 1). Earlywood chronologies correlated significantly only with the mean temperature of May for living trees in years 1947–2000. Highly significant dendroclimatic correlations were found between the latewood chronologies and the mean temperatures of July and August. Relatively high correlations were also observed between the latewood chronologies and the mean temperatures of March, April and September in 1947–2000. These findings guided us to derive dendroclimatic transfer functions. The warm-season temperature variability was reconstructed using the information of latewood chronologies as independent variables over the modern period (e.g. 1947–2000). Calibration was obtained for the reconstruction of April through September temperatures, this model indicating reliable reconstruction over the early and late periods. This was the season that provided calibration and verification with statistically significant (p o0.01) results in the case of both chronologies, based on datasets 1 and 2 (Supplementary Table 2).

WSTR1t ¼ 9:730ð 71:685Þ  MXD1t þ 1:610ð 71:688Þ

ð5Þ

WSTR2t ¼ 18:378ð 7 3:110Þ  MXD2t 7:040ð 73:112Þ

ð6Þ

where WSTR1t and WSTR2t were the warm-season temperatures reconstructed for year t using the yearly information of MXD1t and MXD2t chronologies, respectively. When the transfer functions were applied over the periods of 1947–2000 (modern samples) and 1384–1594 (subfossil samples), it resulted in new palaeoclimate information showing evidence for considerable temperature variability at various timescales (Fig. 6). The reconstructions accounted for approx. 40% of the total climate variance. Interestingly, the two reconstructions showed decreasingly small differences in their statistical characteristics (Table 4). This became evident as the calculation of the mean, standard deviation, the autocorrelation and mean sensitivity of the reconstructions did not show statistically significant (po0.05) differences in these properties between the WSTR1 and WSTR2, with the exception of the mean sensitivity over the modern period indicating higher inter-annual temperature variability in the case of WSTR2. It appeared that, in the case of the present sample, the transfer functions (Eqs. (5) and (6)) did actually equilibriate the differences evidently present in the latewood chronologies, MXD1 and MXD2 (see Table 3). It is noteworthy that the transfer functions did not alter the timedependent characteristics of the latewood chronologies. The higher level of standard deviations and autocorrelations as well as the lower level of the mean sensitivities during the subfossil period, relative to the modern period (see Table 3), remained as the properties of the two reconstructions (see Table 4).

4. Discussion and conclusions 4.1. Wood anatomy and dendrochronological information 4.1.1. Overall density differences The initial density results (Tables 2a and b) were in agreement with the previous studies showing that the removal of extractives

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1021

Table 4 Characterization of the warm season temperature reconstructions (WSTR1 and WSTR2 for datasets 1 and 2) built using the latewood maximum density chronologies due to datasets 1 and 2 (a) over the period of 1947–2000 for living trees and (b) period of 1384–1594 for subfossil samples. The comparison included the temperature mean of the reconstructed temperatures (i.e. arithmetic mean), standard deviation (SD), the first-order autocorrelation (AR1), and mean sensitivity (MS; Eq. (1)). Differences between the calculated statistics were compared using a t-test with accompanying statistical significances (p), except the SDs that were compared using an F-test and AR1s for which the statistically significant difference was not determined (n.d.).

(a)

WSTR1 WSTR2 p

Mean

SD

AR1

MS

11.344 11.344 1.000

0.473 0.480 0.919

 0.134  0.068 n.d.

0.050 0.056 0.025

of pine wood will generally result in significantly lower density values (Lloyd, 1978; Kanowski and Wright, 1985; Bergsten et al., 2001). Moreover, the lowered moisture content of the tree-ring samples is known to cause a parallel effect (Schweingruber et al., 1978). Dendrochronological standardization did, however, result in negligible density difference (Tables 2c and d). Such removal of the density difference could be illustrated from Monserud (1986) who alternatively understood the relationship (Eq. (3)) between the observed and modeled tree-ring values as follows: iRN ¼ 1:0 þ r RN =mRN

(b)

Mean

SD

AR1

MS

11.372 11.419 0.074

0.594 0.623 0.496

0.439 0.346 n.d.

0.045 0.047 0.115

standardization method did, however, capture the linear trends (Eq. (2)) present in the ring series (Figs. 2A and B) and the calculation of the mean chronology resulted in highly correlated tree-ring chronologies of modern (Fig. 4) and subfossil (Fig. 5) periods. The situation is in line with the dendrochronological paradigm whereby the fluctuations occurring with wavelengths longer than the individual series are not preserved in the standardized series (Cook et al., 1995). Consequently, the removal of the potential bias could be expected, given that the trend-line or curve is fitted individually to each series.

ð7Þ

where rRN is the residual from the growth trend. Combination of the relationships (Eqs. (4) and (7)) demonstrates the essence of the resulting tree-ring indices as a series of deviants from the longterm level of 1.0 and, thus, illustrates the path for homogenizing the density alteration due to the extractive and moisture contents. Consequently, the usage of standardization methods based on a single averaged trend curve (i.e., regional curve standardization (RCS); Briffa and Melvin, 2011) cannot be recommended to be applied simultaneously for multiple datasets with no certainty of common densitometry methods. The reason for this originates from the assumption that the averaged curve is an invariable feature of the radial growth (Cook et al., 1995; Briffa et al., 1996). This assumption is invalid for differently measured density values (see Figs. 2A and B). As a consequence, the single RCS curve would systematically over- or underestimate the measured density not only because of changing climate (Cook et al., 1995), but also laboratory conditions. 4.1.2. Behavior of density trends The trends of the density profiles differed by their overall shapes (Fig. 2). These results were in agreement with those of a previous study also showing an increasing (decreasing) density difference between the unextracted and extracted earlywood (latewood) as a function of ring number (Helama et al., 2010b). In the present study, the divergences (Fig. 2) were not only because of extraction but also moisture content, conceivably resulting in highly pronounced cambial trends of the disparity. In this regard, earlywood is also known to exhibit both radially and tangentially increasing lumen diameter with increasing age in Scots pine whereas the latewood is known to show an increasing proportion of narrow and thick-walled tracheids in the same direction (Hannrup et al., 2001). These wood anatomical trends (Hannrup et al., 2001) could in some degree explain the opposite trends between the earlywood and latewood regarding their unextracted and extracted density values (Fig. 3). Also the measuring steps of X-ray equipment used may result in density divergences, i.e. dataset 1 was measured with a double measuring step compared to dataset 2 (Table 1). The narrow rings will be measured with relatively coarser resolution and especially the maximum ring density can be underestimated (Fig. 3B). The trends of divergence progressed through the pith-to-bark profiles (Figs. 2C and D) the dimensions of the phenomena, thus, exceeded the lengths of the available series. The dendrochronological

4.1.3. Growth amplitudes One more consistent issue arising from the results was demonstrated as the mean sensitivities (i.e., measure of variability) were influenced decisively dissimilarly between earlywood and latewood (Tables 2 and 3). This showed that the differing measurement circumstances influence the density values disproportionately in anatomically different wood cells and that the increased extractive and moisture content clearly flattened the earlywood density variability. This could be understood in the context of larger radial tracheid size and decreased cell wall proportion in Scots pine earlywood relative to latewood of the same year (Decoux et al., 2004). The diverging measures of latewood variability, on the other hand, appear consistent with the observation that dataset 1 may show lowered latewood densities in the case of narrow rings (Fig. 3), these deviations being most reasonably explained by restrictions of the device measuring dataset 1 with its relatively long step size of 25 mm (see Table 1) that may have caused bypassing of the shortest (pertaining to the radial dimension of tree stem) density maximum peaks. Expressing the dendrochronological standardization in tailored form (Eq. (7)) shows the scaling of the growth residuals (rRN) by 1/mRN (Cook et al., 1995). This approach further demonstrates the second purpose of standardization that is to make the resulting growth estimates (iRN) homoscedastic (Fritts, 1976; Cook and Peters, 1997; Helama et al., 2004). This is to divide the higher observed value by the higher expected value to stabilize their higher variance (Cook et al., 1990a). Here, the standardization of earlywood series were produced through the division (Eq. (3)) actually resulted in inflated standard deviations (Table 2c). Potentially, procedures more sophisticated than those applied conventionally for tree-ring standardization (e.g. Cook and Peters, 1997) could have resulted in more stabilized data. 4.1.4. From proxy to paleoclimate data The transfer functions were found to proportion the latewood chronologies with regards to the proxy variations (Table 4). The transfer functions were applied here with regression slopes that were lower and higher for the MXD1 and MXD 2 chronologies 1 and 2 (Eqs. (5) and (6)), respectively, thus metamorphosing the higher and lower density amplitudes in the corresponding chronologies (Table 3b). In fact, the amplitudes of the reconstructed temperature are highly linked with the regression statistics, particularly, the coefficient of determination (R2). The proxy

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records contain variability unrelated to the climate and the higher the degree of this non-climatic variability is present in the chronology, the lower the reconstructed temperature amplitude is attained (von Storch et al., 2004; Esper et al., 2005). More precisely, the reconstructed temperature amplitude is reduced by the square root of unexplained variance in the regression (Esper et al., 2005). That the coefficients of determination were highly similar for both datasets (Fig. 6) could be associated with the reduced difference in the measures of variability in the case of the two warm-season temperature reconstructions. Yet, the differing treatment methods resulted in altered tree-ring information by significantly higher level of autocorrelations as measured from latewood of higher extractive/moisture content (Table 2). Notably, the transfer function may be capable of adjusting the structure of autocorrelation to greatly mimic the corresponding structure in the targeted climate series. Such transformation is facilitated by, for example, using the transfer functions in the form of multiple regression or artificial neural networks employing not only the tree-ring data of the year concurrent to climate but preceding and/or following years (Helama et al., 2009). Here, an inclusion of multiple tree-ring values did not markedly improve the regression statistics and the simple regression line was applied to transform the proxy into paleoclimate reconstructions. Another approach for transforming the proxy data into paleoclimate estimates (free of regression-based pitfalls) would scale the tree-ring chronology to the mean and variance of climatic time-series (Esper et al., 2005; Lee et al., 2008). This approach was not however examined here as it has been shown to result in inflated paleoclimate estimates (Helama et al., 2009). Of note, the scaling would be unable to adjust for the higher level of autocorrelation in tree-ring data into the lower level of autocorrelation in climatic series. 4.2. Recommendations for chronology construction The number of tree-ring chronologies relevant for dendroclimatic interpretations and palaeoclimate reconstructions from various regions on the globe is increasing. Meanwhile, large quantities of data are stored in the online databases. There is consequently an increasing need for the comparison, compilation, reconciliation, and synthesis of this information. Here we performed a comparison designed to detail the dendrodensitometry data from contrasting microdensitometric treatments. Based on our results and interpretations, a set of recommendations can be drawn. The method-dependent density difference can be evened out by using the dendrochronological standardization in so far as the expected growth curve is fitted individually to each series and the tree-ring comparisons analyzed in terms of the resulting dimensionless indices. Fortunately, this type of standardization also accounts for the differential behavior of the density trends. Consequently, these types of standardization methods ought to be preferred in the case of comparisons between dendrodensitometry datasets if no well-documented laboratory protocols exist. However, even in the case of standardization of this type the growth amplitudes can differ due to laboratory methods alone. This is less than optimal for comparisons of typical dendroecological and dendroclimatic measures such as the mean sensitivity, standard deviation and autocorrelation. Clearly, these findings indicate that comparisons and syntheses of heterogeneous datasets should be regarded with great caution. Usage of a single mean standardization curve (e.g. RCS) should be avoided as a detrending method in the case of potentially heterogeneous datasets. In theory, the calculation of the RCS curve separately for each assumingly homogenous dataset could, at least partially, eliminate the problem of inherently different growth trends due to sample treatment/density measuring practices. The hypothesis remains to be evaluated with datasets with enlarged dimensions.

Notwithstanding the contrasting sample treatment and measuring circumstances, the reconstructed estimates of the paleoclimate information became substantially comparable in relation to the initial proxy values. The finding shows the relative feasibility of the transformed information for dendroclimatic and paleoclimate comparisons and reconciliations. This emphasizes the role of transfer functions in integrating paleoclimate information of multiple proxies. Based on our findings, we are confident in suggesting an approach of proxy reconciliation whereby several tree-ring density chronologies are simultaneously included in the transfer functions (e.g. multiple linear regression) as independent variables. This approach, however, presumes highly similar climate versus growth correlativity among the exploited tree-ring chronologies.

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