Chemical de-staining and the delta correction for blue intensity measurements of stained lake subfossil trees

1Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, Rimouski, G5L 3A1, Canada 2Centre d’études nordiques, Université du Québec à Rimouski, Rimouski, G5L 3A1, Canada 3Département de Géographie, GEOTOP and Centre d’études nordiques, Université du Québec à Montréal, Montréal, H3A 0B9, Canada 10 4Key Laboratory of Tree-ring Physical and Chemical Research and Xinjiang Laboratory of Tree-ring Ecology, Institute of Desert Meteorology, China Meteorological Administration, Urumqi, 830002, China 5Canada Research Chair in geochemistry of coastal hydrogeosystems, Québec-Océan, UQAR/ISMER, Rimouski, G5L 3A1, Canada 6Département de Géographie, Université Laval, Quebec City, G1V 0A6, Canada 15 7Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, 100101, China


Introduction
The recently developed blue intensity (BI) technique is potentially a valuable alternative to more expensive maximum latewood density (MXD) data for dendroclimatology (Björklund et al., 2019;McCarroll et al., 2002). MXD is the most suitable tree-ring parameter for summer temperature reconstructions in northern and high-altitude regions Frank and Esper, 2005). However, compared to less climate-sensitive ring-width data, millennial MXD series have been 35 much less frequently developed worldwide, mainly due to the expensive X-ray densitometric facilities (Anchukaitis et al., 2017;St. George and Esper, 2019;Wilson et al., 2016). In contrast, BI is a cheap measure of blue light reflectance of treering images generated using high-resolution flatbed scanners and image analysis software . Several studies reported excellent coherences between latewood BI (LBI) and MXD data measured from living-tree materials of several coniferous species across the northern hemisphere (Campbell et al., 2007;Kaczka et al., 2018;Österreicher et al., occur (Arseneault et al., 2013;Grudd et al., 2002). All the above color issues may potentially alter the measurement of BI 50 data (e.g. LBI) and introduce biases in BI-based climate reconstructions, particularly for the low-frequency domain (Björklund et al., 2014;Wilson et al., 2019). Therefore, it is critical to develop unbiased BI data from a variety of wood materials, in particular from LSTs, to help the promising BI technique to be widely applied in future dendroclimatic reconstructions.
Some solutions have been proposed to overcome the sapwood-heartwood color issue. Sheppard and Wiedenhoeft 55 (2007) used hydrogen peroxide to bleach wood colors and attenuate the sapwood-heartwood difference, although an earlier study claimed that chemical bleaching likely degraded the climate signal (Sheppard, 1999). A later and seemingly more promising approach consists in a simple delta mathematical correction computed as the BI difference between the earlywood and latewood, (i.e. the delta BI; hereafter DBI) of each tree ring. DBI is suitable to recover low-frequency (decadalcentennial) variations distorted by the sapwood-heartwood color issues (Björklund et al., 2014(Björklund et al., , 2015. Only a few studies 60 attempted to use BI data measured from LSTs to develop long-term temperature reconstructions, but the staining issue was not addressed and these BI series were not directly compared to MXD data. For example, Rydval et al. (2017) combined the high-frequency temperature signals extracted from LBI with the low frequency from ring-width data of LSTs in order to reconstruct temperatures, leaving the low-frequency domain unimproved by the more temperature-sensitive BI data.
In this study, we explore the potential of generating unbiased BI series from stained black spruce LSTs from two from L20 (54.56º N, 71.24º W) were previously included in a millennial temperature reconstruction (Gennaretti et al., 2014c). L105 (50.81º N, 67.80º W) is a new lake where more than one thousand black spruce LSTs were extracted, and many of them were successfully cross-dated to develop a millennial ring-width chronology (unpublished data).
LSTs in the eastern Canadian boreal forest are frequently stained to various blue-gray intensities (Fig. S1). When dry, 85 these stains correspond well to the Munsell Soil Color Chart 2009-5Y-Chroma I. In total, 78% and 79% of the cross-dated LSTs at L20 and L105, were stained to some degrees of gray, respectively (Fig. 1b, c). Very few LSTs display additional colors, for example due to fungi invasion. Stained LSTs are distributed throughout the timespan of the two millennial chronologies with increasing proportions back in time, particularly before year 1800 CE (Fig. 1b, c), suggesting that staining issues are unavoidable when using BI measurements from this material.

De-staining experiments and chemical analyses
We selected stem cross sections from five (evenly and heavily) stained LSTs and five unstained lakeshore living trees from each of the two studied lakes. Using a twin-blade saw (DendroCut, Walesch Electronic), we transversally cut sixteen 1mm-thick laths along the radii of each subfossil tree (total of 160 laths), along with 2 laths from each living tree (total of 20 laths). All laths were pretreated using 95% ethanol in Soxhlet extractors for 48 hours to remove 95 resins and then air-dried and weighted. 16 pretreated laths from each LST were divided into eight pairs. Seven pairs were immersed in 50 mL of one of the seven chemical solutions (Table 1) in Falconâ 50mL tubes (see an example in During de-staining treatments, we sequentially sampled reaction solutions to construct temporal Fe dissolution curves (the most abundant metal element detected in preliminary tests) for MixA, MixB and MixC, the most effective de-staining reagents (see results below). 1mL of solution was sampled at 0, 1, 3, 6, 12 and 24 hours for MixA and lath. Fe concentrations in this study represent the total of ferrous and ferric Fe which cannot be distinguished by MP-

AES.
The second lath of each pair was air-dried, finely sanded (to 1000 grits) and scanned using the SilverFast 8.0 115 software (LaserSoft Imaging) and an Epson V800 flatbed scanner. In order to obtain optimal calibration results, sanded laths were scanned to RGB images of 3200 dpi along with a color IT8.7/2 calibration target (LaserSoft Imaging). The scanner was covered by a black plastic box to avoid inferences of external light. It should be noted that the actual image resolution is approximately 2580 dpi (horizontal) by 1825 dpi (vertical) according to the USAF-1951 resolution target. Wood RGB intensities (definition 1-3 in Table 2), were then measured using the CooRecorder 8.1 software 120 (Cybis Dendrochronology). RGB values were subtracted from a value of 256 such that the inverted data are consistent to real-world observations, i.e. lower RGB values correspond to lighter colors. RGB intensities were compared among treatments (seven treatments plus two controls) to assess the efficiency of the de-staining reagents.

Chemical treatments for dendroclimatic assessment
In order to perform dendroclimatic assessments of the most effective de-staining treatments, we selected 57 trees of 125 different types (stained and unstained LSTs as well as living trees) cross-dated after the year 1600 CE from L20 and L105 (28 and 29 trees, respectively; Fig. S4). We cut eight 1mm-thick laths from two radii of each tree to acquire four pairs of laths per tree (Fig. S5). Laths were pretreated using 95% ethanol in Soxhlet extractors for 48 hours. We then treated three pairs of laths per tree using the MixA, MixB and MixC, respectively, while keeping the fourth lath as an untreated control.
The conditions of de-staining treatments were the same as explained above, except that the treatments lasted for six hours, 130 which is the time required for optimal de-staining according to the Fe dissolution curves (Fig. S2). Treated laths were then air-dried for LBI, DBI and MXD measurements.

Tree-ring data and chronology development
Measured wood and tree-ring parameters are explained in Table 2. Wood laths for dendrochronological assessments were firstly X-rayed to generate MXD data prior to being sanded and scanned for BI measurements. X-ray densitometry 135 experiments were conducted in a controlled environment with relative humidity of 50% and room temperature of 20ºC. Xray films were developed using the DendroXray2 system (Walesch Electronic) and MXD series were measured using the Dendro2003 system (Walesch Electronic). LBI and DBI were measured using the same procedure as the measurement of wood RGB intensities. We subtracted the raw LBI values from a value of 256 in order to make the LBI positively correlated with MXD data, according to Rydval et al. (2014) and Wilson et al. (2019). Before data analysis, LBI, DBI and MXD data 140 were averaged by tree (i.e. by pair of laths) for each treatment (MixA, MixB, MixC, Control). We used regional curve standardization (RCS) to remove the biological trends from tree-ring series in order to retain low-frequency (decadal-centennial) climatic variations (Briffa and Melvin, 2011;Helama et al., 2017). Age-dependent spline with an initial stiffness of 2 years was used to estimate the regional curve. Standardized tree-ring series were computed as ratios between raw data and the smoothed regional curve. In total, we standardized 24 groups of tree-ring data 145 by site (L105 and L20), parameter (LBI, DBI and MXD) and treatment (MixA, MixB, MixC and Control). We excluded data from chemically treated, unstained trees (unstained LSTs plus living trees) because we found BI data of unstained trees tend to be altered by reagents, particularly the MixC (Fig. S8). Consequently, we pooled the data of stained LSTs that were chemically treated, plus the data of the untreated, unstained trees for each standardization. In addition, living-tree data of L20 after the year 1950 CE were excluded as BI diverged from MXD data ( Fig. S4 and S7), likely due to the sapwoodheartwood color issue and unhealthy tree growth (see discussions below).
Regional chronologies were generated by pooling standardized series from both sites using the Tukey's bi-weight robust mean for each tree-ring parameter and treatment. This approach was selected because of the limited tree replication per site (Fig. S2). Regional MXD chronologies from the four treatments were similar and thus averaged to one reference chronology. All regional chronologies were truncated at 1655 CE to ensure a minimum replication of five trees (Fig. S2c).

Temperature reconstruction
We performed temperature reconstructions using the regional LBI, DBI and MXD chronologies to further quantify the influence of the de-staining protocols. Instrumental summer (May to August) temperature data were obtained from the CRU TS 4.02 0.5° gridded monthly mean temperature dataset (Harris et al., 2014) and averaged from the four grid cells closest to each lake in order to generate a regional temperature target. Reconstructions were based on the scaling method (Esper et al.,

Data analysis
Data were analyzed using the R program (R Core Team, 2018). We conducted linear regressions between wood RGB intensities and logarithmic residual Fe of both treated and untreated LSTs in order to determine the roles of Fe on the 165 staining issue. For chronology assessments, we generated several high-pass and low-pass LBI, DBI and MXD series using the Butterworth filter available in the "dplR" R package (Bunn, 2008). Pearson correlation coefficients were used to assess the degrees of coherence among all the time series. Reconstruction performances were assessed following a regression-based calibration-verification procedure using the "treeclim" R package (Zang and Biondi, 2015). Since our chronologies showed higher replication during 1901-1960 (Fig. S4), this time period was used for calibration, while 1961-2015 period was used 3. Results

Effects of chemical de-staining
LSTs displayed a variety of color changes after the seven de-staining treatments (Fig. 3a). NaAsc resulted in very similar colors as the untreated stained samples, representing the weakest de-staining effect. Conversely, MixA, MixB and 175 MixC showed dramatic effects and almost completely removed the gray stain. MixC was the most effective de-staining solution based on wood RGB intensities, although the resultant colors still slightly differed from the living-tree standards ( Fig. 3a, b). BI is less variable with varying degrees of post-treatment stains in comparison to red and green intensities (Fig.   3b), suggesting a potentially weaker influence of wood stain on the BI data.

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Chemical analyses showed strong links between Fe concentrations and color intensities of wood, especially for green and red ntensities (Fig. 3a, c). Total Fe concentration was highest for untreated stained LSTs and near zero for living trees (Fig. 3c). MixA, MixB and MixC could remove 94.1%, 92.5% and 96.2% of Fe relative to the amount measured in untreated stained LSTs, respectively. Although Fe dissolution curves stabilized after 6-12 hours (Fig. S2), minor quantities of residual Fe at the end of the treatments (24-48 hours) indicated that all de-staining reactions are incomplete. Significant (p<0.001) 185 linear relationships existed between the log of residual Fe and post-treatment color (RGB) intensities of earlywood and latewood treated (Fig. 3d, e). However, such linearity markedly weakens for delta RGB intensities, especially for delta BI (p=0.087, Fig 3f), indicating that DBI of LSTs is insignificantly affected by the staining issue (Note that wood delta BI and DBI are not exactly the same as explained in definition No. 3 and 5 Table 2, respectively).

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LBI chronologies of the four retained treatments (MixA, MixB, MixC plus Control) diverged relative to the reference MXD chronology (Fig. 4a) prior to year 1900 CE when stained LSTs dominate the chronologies (Fig. S4c). Correlation analyses showed that coherences between LBI and MXD chronologies are only robust for the 10-year high-pass filtered data (r>0.89), and decreased at longer timescales (Fig. 4c). In contrast, DBI chronologies are very similar to the reference MXD chronology for all treatments (Fig. 4b). Correlations with MXD data are strong and stable among all frequencies tested 195 (r>0.82) (Fig. 4d). In addition, little differences were found between untreated control DBI series and chemically treated 4. Discussion

Causes of stain
Significant relationships between post-treatment wood RGB intensities and residual Fe (Fig. 3), along with the rapid post-sampling staining (Fig. 5b), support our hypothesis that oxidation of Fe is a major cause of stain in our lake subfossil material. Fe is abundant in natural aquatic systems as dissolved and particulate fractions (Bortleson and Lee, 1974;Davison, 1993;Nürnberg and Dillon, 1993). Briefly, dissolved Fe, mainly in Fe(II) state, is reduced and mobilized in porewater in anoxic sediments. Dissolved Fe can migrate upward to oxic bottom water to form particulate Fe(III)-oxides (Davison, 1993;Davison et al., 1982). This cycle results in much higher concentrations of dissolved Fe in anoxic sediments compared to 205 oxygenated freshwaters (Zaw and Chiswell, 1999). Soluble forms of Fe in anoxic sediments can readily penetrate into buried wood tissues. When buried LSTs are extracted from lakes, cut and exposed to air, dissolved Fe is rapidly oxidized to colored Fe-oxides (oxyhydroxides, hydroxides and more crystalline Fe(III)-oxides) which may combine to wood (Pelé et al., 2015).
This process is also supported by the fact that fresh cuts from buried portions were heavily stained while exposed (but submerged) portions of the same LST were not (Fig. 5a). Photo-oxidation is assumed to be less likely, yet not improbable, 210 because the stain contaminated both surface and inner portions of LSTs.
Furthermore, we found that amorphous and crystalline Fe-oxides are likely produced during the oxidation of dissolved Fe. About 63.4% of Fe is removed by the NaAsc reagent, although post-treatment colors of LSTs only slightly lightened (Fig. 3a, c). The neutral NaAsc, similar to buffered ascorbates, only extracts the most reactive amorphous Fe-oxides (Anschutz et al., 2005;Hyacinthe et al., 2006;Kostka and Luther, 1994). The less reactive crystalline phases can be removed 215 by EDTA, HAsc, MixA, MixB and MixC, each of which removed at least 25% more Fe than NaAsc. In fact, EDTA, ascorbic acid and sodium dithionite, which are the active chemicals in these solutions, are known as useful extractants of both crystalline and amorphous Fe-oxides (Borggaard, 1982;Hyacinthe et al., 2006;Kostka and Luther, 1994;Tessier et al., 1979). The notable de-staining effect of these solutions (Fig. 4a) also implies that crystalline Fe-oxides are more colorreflective than amorphous ones. In our experiment, HAc extracted less Fe than NaAsc but with a better de-staining effect 220 (Fig. 3a, c). A probable explanation is that acid-soluble Fe-oxides extracted by HAc (Chester and Hughes, 1969;Gupta and Chen, 1975;Tessier et al., 1979) are in amorphous and crystalline phases which have stronger color reflectivity, whereas NaAsc only extracted less color-reflective amorphous Fe-oxides.
Other metal elements most likely have a negligible staining effect on LSTs compared to Fe. Our preliminary analyses demonstrated that, among 15 potential metal elements (including iron, manganese, chromium, cobalt, copper, lead and etc.),

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Fe was the only element present at high concentrations. Although manganese is relatively abundant in the samples (several times higher than other metals), its concentration is still approximately 20 times lower than Fe. In addition, we did not detect any copper and lead from our samples. On the other hand, Fe complexes bound to sulfur and phosphorus might also be responsible for the staining of LSTs along with Fe-oxides. However, the MP-AES instrument used in this study is not sensitive to detect sulfur and phosphorus.

Chemical de-staining versus delta correction
Divergent trends of the three chemically treated LBI chronologies (MixA, MixB, MixC) compared to the reference MXD chronology demonstrate that none of the de-staining treatments can generate satisfactory and robust LBI data (Fig.   4a). Although MixC is the most effective protocol with very few residual Fe (<5% relative to the stained LSTs), the corresponding LBI chronology still displays a significant long-term bias before 1830 CE (Fig. 4a), leading to a discrepancy 235 of about +2 ºC compared to the MXD-based reconstruction (Fig. 6a). If no chemical treatment had been applied, such temperature discrepancy would be amplified to about +4 ºC (not shown). These errors are caused by the extreme color sensitivity of LBI values as direct measures of blue light reflectance. Therefore, any stain contributing to the wood color will strongly contaminate the LBI data, especially in the low-frequency domain (Björklund et al., 2014). These results discourage the use of LBI in the presence of stained subfossils woods. By contrast, high-frequency variability of LBI data seems 240 unaffected by the Fe stain (Fig. 4b). Coherence of high-pass filtered MXD series with LBI data is slightly higher than with DBI data measured from untreated stained samples (Fig. 4b, d), however, such results are based on a relatively small replication.
Unlike LBI data, DBI is unaffected by the Fe stain from annual to centennial timescales, which is shown by the high and stable coherence between DBI and MXD chronologies (Fig. 4b, d) and the non-significant linear relationships between 245 log of residual Fe concentrations and delta BI data (Fig. 3f). The linearity between DBI and MXD remained almost unaffected by de-staining treatments, for example the most efficient MixC protocol (Fig. S9). Furthermore, compared to the Control DBI chronology, no chemical treatments substantially improved the correlation of DBI with MXD data (Fig. 4d), resulting in nearly identical trends in the corresponding temperature reconstructions in comparison with MXD, except for some periods where tree replication is less than 10 (Fig. 6c, d). These evidences suggest that DBI, is not only excellent to 250 resolve the sapwood-heartwood color biases, but is also efficient to resolve the low-frequency biases caused by the Fe stain in black spruce LSTs from the eastern Canadian boreal forest.
We observed that LBI as well as DBI diverged from MXD data after 1950 CE at L20 (Fig. S7). Divergence of LBI is due to slight color differences between heartwood and sapwood of selected living trees although this issue is generally not serious for black spruce compared to pine or larch species Sheppard, 1999;Yang, 2007). DBI is 255 theoretically sufficient to solve the sapwood-heartwood color issue (Björklund et al., 2014) but DBI only recovered some negative trend (Fig. S7). A large proportion of unhealthy old living trees were collected from lakeshore forests at L20 and they often displayed declined ring widths compared to healthy trees sampled at other sites (not shown). DBI of L20 is likely influenced by the ring-width declines because DBI of black spruce is not only similar to MXD but also the ring-width data (Wang et al., in preparation). We thus speculate the divergence of DBI only reflects the site-specific unhealthy tree growth.
based reconstruction shows weaker verification statistics against instrumental temperature (Table S1) and some slight instability during poorly replicated periods (Fig. 6c, d). When replication is above 15 trees during 1901-1960 (Fig. S4c), the 265 calibration r 2 is similar for DBI and MXD data against temperature (Table S1). Secondly, Björklund et al. (2014Björklund et al. ( , 2015 suggested some multi-centennial biases in DBI data due to the heterogeneous wood color. Although this phenomenon is not obvious in our case regardless of the Fe stain, our reconstruction only spanned the last three centuries and future attentions are needed to verify this potential bias.

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Our study indicates that the simple delta correction of differentiating latewood and earlywood BI values is more effective to resolve the staining biases of BI data from LSTs than the much more complex and time-consuming chemical destaining protocols tested here. DBI of black spruce LSTs is unaffected by the Fe stain from annual to centennial timescales and allows robust temperature reconstructions similar to MXD data. Consequently, DBI from stained black spruce LSTs is a promising proxy for developing millennial temperature reconstructions in the eastern Canadian boreal forests, a region with 275 very few long MXD series (Wang et al., 2001). On the contrary, LBI is very color sensitive and appears problematic in retaining the low-frequency climatic signals.
The chemical de-staining experiments, though not satisfactory regarding the robustness of LBI data, suggests the mechanisms of the staining issue through post-sampling chemical Fe oxidation. Since Fe is so abundant in the Earth's systems, our results may be representative of much wider regions. On the other hand, excellent Fe extraction abilities 280 (removal of >90% Fe) of three chemical mixtures also suggest that they, in particular the MixC, can be further used as Fe extraction protocols for waterlogged archeological artifacts which are also facing Fe-staining issues (Fors et al., 2014(Fors et al., , 2012Pelé et al., 2015;Zhang and Xi, 2003 (Fig. S6b).

MXD
The maximum value of measured tree-ring latewood density.

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The gray shades in (a) corresponds to the distribution range of black spruce. The "other stain" category includes a variety of additional colors such as red or dark brown.