Abstract
Key message
Minimum wood density exhibits strong responses to precipitation and, thus, it is a robust proxy of early season water availability.
Abstract
Tracheids fulfil most wood functions in conifers (mechanical support and water transport) and earlywood tracheids account for most hydraulic conductivity within the annual tree ring. Dry conditions during the early growing season, when earlywood is formed, could lead to the formation of narrow tracheid lumens and a dense earlywood. Here, we assessed if there is a negative association between minimum wood density and early growing-season (spring) precipitation. Using dendrochronology, we studied growth and density data at nine forest stands of three Pinaceae species (Larix sibirica, Pinus nigra, and Pinus sylvestris) widely distributed in three cool–dry Eurasian regions from the forest-steppe (Russia, Mongolia) and Mediterranean (Spain) biomes. We measured for each annual tree ring and the common 1950–2002 period the following variables: earlywood and latewood width, and minimum and maximum wood density. As expected, dry early growing season (spring) conditions were associated with low earlywood production but, most importantly, to high minimum density in the three conifer species. The associations between minimum density and spring precipitation were stronger (r = −0.65) than those observed with earlywood width (r = 0.57). We interpret the relationship between spring water availability and high minimum density as a drought-induced reduction in lumen diameter, hydraulic conductivity, and growth. Consequently, forecasted growing-season drier conditions would translate into increased minimum wood density and reflect a reduction in hydraulic conductivity, radial growth, and wood formation. Given the case-study-like nature of this work, more research on other cold–dry sites with additional conifer species is needed to test if minimum wood density is a robust proxy of early season water availability.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
In trees, wood fulfils multiple functions including mechanical support, transport, storage, and defense against biotic agents (Zobel and van Buijtenen 1989). The balances between these functions are reflected in changes of wood density between (Muller-Landau 2004) and within tree species (Martínez-Vilalta et al. 2009; Fajardo 2016). Trade-offs determining wood density result from the conflict between xylem filling with carbon-rich walls and parenchyma vs. leaving open conduit spaces (Carlquist 1975). A higher wood density provides greater strength but also entails higher construction costs (Niklas 1992). It is common that tree species forming narrow conduits and having high wood density show low growth rates (Chave et al. 2009). Conversely, conduit diameter increases in tree species with softer wood (Hacke and Sperry 2001; Bouche et al. 2014), thus providing a high hydraulic conductivity. However, presumed conflicts in wood functions may reflect evolutionary paths of co-varying traits, and caution must be taken to interpret such assumed trade-offs (Larjavaara and Muller-Landau 2010). In addition, wood density is a very conservative trait showing low variation along climatic gradients (Zhang et al. 2011). Nevertheless, since both radial growth and density determine forest carbon uptake, assessments of their responses to climate together with a better characterization of climate-growth-wood density associations are required (Bouriaud et al. 2015).
Conifers subjected to double stress during seasonal wood formation (i.e., growing under cold and dry conditions) are particularly suited to perform such assessments, because wood functions are mostly carried out by tracheids which are formed during a short growing season (Larson 1994). In conifers, wood density may be defined as the ratio of cell-wall thickness to transversal lumen diameter, which is a proxy of absolute wood density (Yasue et al. 2000). This thickness–span ratio is considered a surrogate of xylem hydraulic functions, since a large tracheid lumen (low thickness–span ratio) provides more hydraulic conductivity but increases the risk of frost- or drought-induced embolism (Hacke et al. 2001, 2015). Under dry conditions during the early growing season, conifers produce narrow earlywood tracheid lumens, which account for most hydraulic conductivity within the annual tree ring (Domec et al. 2009) and, therefore, a dense earlywood. However, large tracheid lumens have also been reported in Scots pine (Pinus sylvestris) provenances from dry sites (Martín et al. 2010) or in trees subjected to imposed dry conditions (Eilmann et al. 2011). These contradictory patterns require a better interpretation. Therefore, more research is needed to ascertain how density responds to changes in water availability during the growing season in conifers thriving in cold and seasonally dry climates.
A negative association between minimum wood density and early growing-season (spring) precipitation was already observed in the Cupressaceae Spanish juniper (Juniperus thurifera L.) subjected to cool–dry conditions (Camarero et al. 2014). Nevertheless, this relationship has to be investigated in other species as pines and larches which are widely distributed in cool–dry regions and form the world’s largest conifer forests (Richardson 1998). A reduction in hydraulic conductivity would be linked to narrower tracheid lumens and higher minimum density values (Pittermann et al. 2006). Thus, we hypothesize that dry spring conditions would increase minimum wood density. Specifically, we aim to: (1) analyse how seasonal growth (earlywood and latewood widths) and wood density components (minimum and maximum density) respond to climate (mean monthly and seasonal temperatures, and total precipitation); and (2) assess whether minimum wood density is consistently (negatively) associated with spring precipitation in Pinaceae species of two genera (Larix and Pinus) across a broad range of environmental conditions in Eurasia.
Materials and methods
Study sites and species
Sampling regions were selected to include Pinaceae species whose radial growth and wood formation were constrained by two stress types, namely low temperatures shortening the growing-season length and drought reducing the growth rates during the growing season. We selected four Eurasian regions from three countries subjected to cold and dry climatic conditions: southern Siberia and southern Urals in Russia, Khangai in Mongolia, and Sierra de Gúdar in eastern Spain (Table 1; Supporting Information, Fig. S1). Continental climate conditions (cold winters, ample temperature range) and warm conditions and low precipitation during the growing season constrain tree radial growth in all the study areas (Block et al. 2004, Dulamsuren et al. 2009, Velisevich and Kozlov 2006, Devi et al. 2008, Knorre et al. 2010, Camarero et al. 2015). In southern Siberia and Mongolia, we sampled Siberian larch (Larix sibirica Ledeb.), whilst Scots pine (Pinus sylvestris L.) was sampled in the Urals and Spain. In Spain, we also sampled Black pine (Pinus nigra Arn. subsp. salzmannii (Dunal) Franco), a typical Mediterranean tree species (Richardson 1998).
The northernmost study region is located in southern Siberian forest-steppe zone where open larch forests are typical (Dylis 1961, Knorre et al., 2010). The forest sampled in the Urals is situated in the Aldan plateau where open conifer forests predominate up to 1250 m (Devi et al. 2008). In Mongolian lowlands, sampled trees formed forest-steppe ecotones which constitute the lower treeline (Treter 2000, Dulamsuren et al. 2010). The southernmost study region is located in Sierra de Gúdar (southern Iberian Range, eastern Spain) and includes two Scots pine stands and two Black pine stands. In this region, the study sites were distributed along an altitudinal gradient, because Scots pine dominates at cold sub-Mediterranean sites situated at higher elevation, whereas Black pine is dominant at mid-elevation sites experiencing Mediterranean climate (Camarero et al. 2015). We sampled one site (LR in Table 1) where the two species co-occur. The selected study stands were not impacted by local anthropogenic disturbances (grazing, fires, and logging) since the 1960s. The understory is dominated by shrubs in most of the study sites (e.g., Juniperus communis L.). Soils are brown in the Russian and Mongolian sites, whereas basic and clayey soils appear in Spain.
Field sampling
We randomly selected and sampled 15–30 dominant trees per site in ca. 1-ha large sampling areas (Table 1). A 10-mm wide core per tree was extracted at 1.3 m for densitometry analyses using Pressler increment borers. We took special care for sampling this last core perpendicular to the main stem so as to capture the main fibre direction. We also measured diameter at 1.3 m and total height of each tree using tapes and clinometers, respectively. Trees selected for sampling had diameters ranging between 39.0 and 47.0 cm, heights between 9.3 and 10.5 m, and ages (estimated at 1.3 m) between 79 and 318 years (Table 1). Stands are relatively open, with basal area values ranging between 12 and 38 m2 ha−1.
We took two additional 5-mm wide cores from each tree for obtaining tree-ring width data so as to cross-date the densitometry samples. These cores were glued onto wooden mounts, sanded, visually cross-dated, and checked for dating accuracy using dendrochronology (Fritts 2001).
Width and density measurements
One radial X-ray density profile was obtained from each tree using indirect X-ray densitometry. Prior to further treatment, resin was extracted from the wood samples with alcohol in a Soxhlet extractor. Then, each core was cut carefully using a double-bladed saw to obtain ca. 1.5-mm-thick laths. These samples were air dried to moisture equilibrium and then subjected to X-ray exposure. The resulting X-ray films were scanned with a resolution of 10 µm using a microdensitometer DENDRO-2003 (Walesch Electronics Ltd., Switzerland).
The measured grey levels of the X-ray films were transferred to density values by comparing them to a standard of known physical and optical density also exposed on the same film. For each annual ring, the following variables were obtained from the tree-ring density profiles: earlywood (EW hereafter) and latewood widths (LW hereafter), and minimum (MN hereafter) and maximum wood densities (MX hereafter). Note that MN and MX are tightly related to earlywood and latewood mean densities, so we only analysed the former two variables, because they are easier to define and show a stronger response to climate variables (Camarero et al. 2014). To define the earlywood–latewood transition, we used the 50% level between the MN and MX values of each ring following Polge (1978), and confirmed this separation with a visual checking of the tree rings (Mäkinen and Hynynen 2014).
Chronology building
The accurately dated tree-ring series produced in the previous step (EW, LW, MN, and MX) were individually detrended to remove non-climatic biological growth trends (Cook and Kairiukstis 1990). Prior to trend removal, however, a power transformation was applied to the raw density data. A 2/3 cubic smoothing spline with 50% frequency–response cutoff was fitted to the individual records and indexed values were calculated. Then, indexed tree-ring series were subjected to autoregressive modelling to remove the first-order autocorrelation and produce residual indices. This autoregressive (pre-whitening) method was applied, because instrumental target data may reflect white noise behaviour (Büntgen et al. 2010). Finally, site chronologies for each species and variable were obtained by averaging the residual indices on a yearly basis using a bi-weight robust mean. These procedures were performed using the library dplR in the R platform (Bunn 2008, Bunn et al. 2016).
To compare the resulting chronologies, several statistics were calculated for the common period 1950–2002 considering either the raw site chronologies (mean; SD, standard deviation; AR1, first-order autocorrelation) or the residual chronologies (rbt, mean inter-series correlation; MSx, mean sensitivity, a measure of the relative variability between consecutive years; cf. Fritts 2011).
Climate data
Local climate data (monthly total precipitation and mean temperature) were retrieved from the nearest meteorological stations for all study sites (Table 2; Supporting Information, Fig. S2), excepting Mongolian sites which are located far away from climate stations with long and homogeneous precipitation records. There, precipitation was obtained from the nearest 0.5° grid box to each sampling site of the high-resolution gridded climate data set (Climatic Research Unit, CRU TS 3.22; Harris et al. 2014). In Spain, local data from ten stations situated in the study area were converted into a regional climate series and elevation differences were corrected by calculating regressions between the station elevation and mean annual temperature or total annual precipitation. To take into account the elevation difference between climate stations and sampling sites, we corrected the regional mean temperature and precipitation data considering a mean lapse rate of −7.8 °C km−1 and +420 mm km−1, respectively (see more details in Sangüesa-Barreda et al. 2014). Climate data were obtained for the common period 1950–2002 when tree-ring data were available for all sites (Table 1).
To estimate the water balance at each study site, we calculated the potential evapotranspiration (PET) following the Hargreaves–Samani method (Hargreaves and Samani 1982). Then, we calculated the annual water balance as the sum of the monthly differences between precipitation and PET. We also calculated the Conrad continentality index to characterize the temperature range at each site (Tuhkanen 1980).
Statistical analyses
To compare tree-ring statistics (means, r bt, MSx) of width and density variables, we performed one-way analysis of variance (ANOVA) in cases when data were normal (means, r bt) or Kruskal–Wallis test when normality could not be assumed (MSx). These comparisons were followed by Tukey’s Honest Significant Difference test for normally distributed data or Mann–Whitney U test otherwise.
To evaluate how tree-ring variables were related, we calculated Pearson correlation coefficients between site chronologies for each species considering the common 1950–2002 period. We also performed separate Principal Component Analysis (PCA) on variance–covariance matrices of width and density chronologies to summarize their variability into a few principal components (Jolliffe 2002). Then, we used Pearson correlations to characterize climate–growth associations by relating either tree-ring width or density variables or the first (PC1) and second (PC2) principal components of their corresponding PCAs (summarizing their variability) with monthly climate data (mean temperature and precipitation). The first two PCA axes were retained as they accounted for at least 50% of the total variance (Jolliffe 2002). Climate-growth relationships were analysed from September prior to tree-ring formation to October of the growth year based on previous studies in the study regions (Kirdyanov et al. 2007, Camarero et al. 2010, 2015). Partial correlation analyses were calculated to discern if some variables (EW and MN) were more related to early season precipitation than to temperature data in sites where significant (P < 0.05) correlations were found for those variables. Analyses were done using the vegan package (Oksanen et al. 2013) in the R environment (R Development Core Team, 2015).
Results
General features of width and density site chronologies
Overall, Scots pine showed higher EW and LW, followed by Siberian larch and Black pine (Table 3). Conversely, Black pine presented the highest MN values, whereas larch showed the highest MX values. On average, MN showed the lowest mean autocorrelation (0.35), whilst EW showed the highest one (0.64). The coherence between trees (r bt) was significantly (F = 2.53, P = 0.03) higher in the case of seasonal width variables (EW 0.49; LW 0.51) than in the case of density variables (MN, 0.31; MX, 0.36). Note that the highest rbt values for MN were observed for the Mediterranean Black pine. Finally, the year-to-year variability (MSx) was significantly (K = 0.99, P < 0.001) higher in the case of width variables (EW 0.44; LW 0.55) as compared with density variables (MN 0.14; MX 0.13).
Associations between tree-ring variables
EW and LW records were positively associated for all species and sites, being stronger in the case of larch (r = 0.70–0.87), followed by Black pine (r = 0.45–0.60) and Scots pine (r = 0.28–0.39) (Table 4). In contrast, EW and MN records were negatively related with stronger associations found for Black pine (r = −0.73 to −0.81), followed by larch (r = −0.66 to −0.75) and Scots pine (r = −0.38 to −0.71) (Table 4). More latewood production (higher LW) was also related to heavy latewood (higher MX), but this association was again particularly strong for larch and Black pine, which explains the strong positive EW-MX and the negative LW-MN associations observed for these species. MN and MX records were negatively associated, with this relationship being significant in all larch forests and one Black pine (AC site) and one Scots pine (CA site) sites (Table 4).
Common patterns in width and density data between sites and species
According to the Principal Component Analyses (PCA), the variance captured by the first two axes was about the same for width and density variables, with the first principal component (PC1) accounting for about 35% of the total variance (Fig. 1). The second principal component (PC2) accounted for more variance in width than in density variables (21 vs. 15%; Fig. 1). The loadings of EW and LW data of the same species and sites were grouped together in the PCA diagram with maximum loadings on the PC1 corresponding to LW data from the low-elevation Spanish sites (Fig. 1a). In turn, EW-LW data for the larch sites had the highest loadings on the PC2 (Fig. 1a). Considering density data, larch MX and MN chronologies showed the highest and lowest PC1 loadings, respectively (Fig. 1b). The minimum and maximum PC2 loadings were observed for MN chronologies from the Black pine in AC site and Siberian larch in M7 site, respectively.
Regarding the width chronologies, low EW and LW values of the driest Black pine site (the low-elevation AC site) correspond to severe droughts (e.g., 1986 and 1994; Fig. 2). Low EW and LW values were also observed during the 1980s in Russian and Mongolian sites (Fig. 2). Regarding density data, high MN was found during dry years (e.g., 1994 in Spain, 1979–1980 in Russia and Mongolia, 1986–1987 in Mongolia), and this pattern was most evident in dry sites as the Spanish AC site (Fig. 3a). Low MX values were observed during cool summers in cold sites (e.g., 1972 in the high-elevation PN Scots pine site; 1976 in Russian and Mongolian sites; Fig. 3b).
Relationships between climate, width, and density variables
The first axis of the PCA based on climate–growth or climate–density relationships accounted for a high common variance in the case of LW (50%) and MN (46%) (see Supporting Information, Fig. S3). In both cases, Spanish sites grouped together and showed the highest PC1 scores, albeit the high-elevation PN Scots pine site showed a low PC1 score in the case of MN. Mongolian and Russian larch sites also grouped together and showed high PC2 loadings in the LW and MN. In the PCAs based on EW and MX data, the PC1 separated the PN site from the rest of Spanish sites suggesting a different impact of climate on the determination of EW and MX at this high-elevation location. Similarly, Siberian larch chronologies showed higher PC2 loadings compared to Mongolian sites.
Wet and cool spring (May to June) conditions enhanced EW formation but lead to low MN values, i.e., dry spring conditions were associated with low EW and high MN across sites and species (Fig. 4; see also Supporting Information, Table S1). LW formation was enhanced by wet–cool conditions during the growing season (May–September), particularly in Spanish pinewoods (excluding the high-elevation PN site) and Mongolian larch sites. MX increased in response to high May–June precipitation values and low June–July temperatures (Fig. 4; Table S1).
The strongest associations between climate and width or density variables were found for May or June precipitation and MN (Supporting Information, Table S1). The negative precipitation–MN relationship was consistently observed across species and countries. The strongest correlations were detected either in May (Spain, Black pine AC site; Russia, Scots pine CA site) or in June (e.g., Mongolia Siberian larch M7 site) (Fig. 5). May or June precipitation explained ca. 32–42% of the MN variance in these drought-prone sites (Fig. 5). Finally, partial correlation analyses confirmed that EW and MN responded more strongly to May–June precipitation than to temperatures. Partial correlations of EW and MN with May–June precipitation remained significant at the 0.01 significance after controlling for temperature effects (Supporting Information, Table S2), whilst partial correlations with May–June temperatures after controlling for precipitation effects were not significant (P > 0.05).
Discussion
In agreement with our hypothesis, dry spring conditions were associated with high MN values and, consequently, to a dense earlywood across species and sites in Eurasia. Such strong response of MN to precipitation indicates that it is a robust proxy of early season water availability Water deficit at the beginning of the growing season, when the earlywood is formed, leads to a dense earlywood (Vaganov et al. 2009). Such dense earlywood is characterized by tracheids with narrow lumens and a decreased hydraulic conductivity (Domec et al. 2009).
Our results confirm the previous studies showing that: (1) seasonal density data better reflect the moisture status of conifer species during the growing season than width variables, and (2) this difference is noticeable in drought-prone sites (Cleaveland 1986). Such precipitation-density coupling, possibly mediated by adjustments in hydraulic conductivity, could also explain the decrease in earlywood production observed in response to water deficit in drought-prone sites as showed for forest-steppe and Mediterranean biomes (e.g., Dulamsuren et al. 2010; Camarero et al. 2015). According to ecophysiological studies, photosynthesis rates decrease in both winter and summer in these cool–dry regions, and growing-season drought stress caused by elevated atmospheric vapour pressure deficit and low soil water availability leads to radial growth reduction (Dulamsuren et al. 2009; Gimeno et al. 2012). Nevertheless, the correlations between spring precipitation and MN were always stronger, in absolute terms, than those detected between precipitation and EW (Fig. 4; Table S1). Consequently, MN exhibits strong response to precipitation and, thus, it is a robust proxy of early season water availability. This agrees with empirical approaches demonstrating that an improved water status is linked to a lower density in Norway spruce (Picea abies) (Lundgren 2004).
The correlations found between MN and spring precipitation in pine and larch (Fig. 5) were similar to those observed in Spanish juniper (Juniperus thurifera) (Camarero et al. 2014), explaining on average 37% of variability in minimum density. Indeed, further investigations should explore the nature of climate–MN associations in more detail. This research could explicitly consider wood anatomy to disentangle whether MN changes are mainly due to lumen modifications, as we assumed here, or to changes in cell-wall thickness. For instance, in drought-exposed Norway spruce trees, wood density augmented as a consequence of the formation of thicker cell walls (Jyske et al. 2010), whilst increased tracheid lignin content was observed in drought-prone Austrian Black pine forests (Pinus nigra) (Gindl 2001). Climate–MN associations could also reflect the extent of plastic responses to drought stress. For instance, Douglas fir (Pseudotsuga menziesii) trees showing high resistance to xylem cavitation and lower drought-induced mortality also presented the highest minimum wood density values (Dalla-Salda et al. 2009; Ruiz Diaz Britez et al. 2014), which could reflect narrower tracheid lumens. Nevertheless, other parameters affecting cavitation and hydraulic conductivity should be also considered, including conduit length, pit size, and the numbers of tracheids per unit area (Carlquist 1975).
Tree-ring width and density data often provide redundant information (Kirdyanov et al. 2007; Büntgen et al. 2010; Vaganov and Kirdyanov 2010, Galván et al. 2015). This redundancy could apply to MN, which is negatively related to EW (Tables 4) and, in turn, both variables show opposite associations with precipitation (Fig. 4). Note that we opted for analysing residual growth and density chronologies without removing dependences between them (e.g., EW–MN and LW–MX relationships), since our purpose was to assess how climate distinctively influenced seasonal growth and density data. A pro of analysing MN is that it offers indirect clues on wood phenology. For example, the shift in the strongest precipitation–MN association from May (Spanish and Russian sites) to June (Mongolian sites; Table S1) could be caused by a late peak of earlywood formation in Mongolian sites. This agrees with our knowledge of xylogenesis, with, e.g., maximum rates of tracheid production occurring from May to July (Antonova and Stasova 1993; Vaganov et al. 2006; Camarero et al. 2010).
In cold northern Siberian larch forests, width and density variables mainly respond to temperature changes during the short growing season (June–July) (Esper et al. 2010), and indirectly to winter precipitation which affects the length of snow melt season and soil temperatures (Vaganov et al. 1999; Kirdyanov et al. 2003). Our results also evidenced that MN was negatively related to spring precipitation in southern Siberia sites subjected to water shortage during that season (Fig. 4), suggesting that earlywood tracheid expansion is constrained by water availability. Low soil temperature can increase water viscosity leading to a high root hydraulic resistance, decreasing water flow from the soil to the tree and reducing stomatal conductance (Wan et al. 2001). Spring cold conditions could therefore constrain the radial enlargement of earlywood tracheids. Despite variables as EW and MN showed significant correlations with May–June temperatures, the associations between them and precipitation were stronger in absolute terms suggesting a secondary role of thermal factors (Fig. 4, Supporting Information, Table S1). Partial correlation analyses and the aforementioned arguments on xylogenesis confirm that temperature does not influence EW and MN in the study sites as much as precipitation does.
In cool–arid inner Asian forests, wet conditions during the growing season enhance radial growth (Poulter et al. 2013). However, Mongolian forest-steppe ecotones are experiencing warmer and drier conditions since the 1940s, causing growth decline and the retreat of larch forests (Dulamsuren et al. 2010). Increased aridity and drought stress are major restrictions of tree growth and functioning in these biomes where larches show low shoot water potentials and high stomatal conductance (Dulamsuren et al. 2009). This agrees with our findings and emphasizes the key role of sufficient moisture availability for tree growth and wood formation.
In Mediterranean pine forests, wet conditions during the previous winter and the current spring are associated with improved earlywood production (De Luis et al. 2007; Martín-Benito et al. 2008; Pasho et al. 2012) and a decrease in minimum density (Olivar et al. 2015). Wet conditions favour tracheid division which results in low minimum density (Bouriaud et al. 2005), as we found in the low-elevation Spanish sites (Fig. 5a; Table S1). Under more continental and colder conditions, cambial resumption is delayed and summer conditions become more relevant as drivers of Scots pine growth (Sánchez-Salguero et al. 2015). This explains why earlywood density may not be related to spring precipitation in some Mediterranean Scots pine stands (Olivar et al. 2015). In contrast, the positive association between summer temperatures and maximum density found in high-elevation Scots pine stands (PN site) (Table S1) is in agreement with other studies of cold biomes such as boreal and subalpine forests (e.g., Briffa et al. 1998). This relationship has previously been reported for pine species in Mediterranean environments (Olivar et al. 2015), and it is due to an improved thickening and lignification of latewood cell walls in response to warm summer and fall conditions (Gindl et al. 2000; Yasue et al. 2000).
Contrary to most previous studies at cold sites which found that summer temperatures and maximum latewood density are positively associated (e.g., Briffa et al. 2004), we found significant and negative correlations of MX with May-to-July temperatures (Fig. 4). We assume that such negative relationships are driven by water shortage caused by warm spring-to-summer temperatures. This is a typical climate effect observed in Mediterranean forests such as the Spanish study sites, but we also found such effect in other drought-prone sites from Russia (Scots pine CA site) and Mongolia (Siberian larch M7 site). We interpret this negative association as a drought-related reduction in the thickening and lignifications rates of latewood tracheids. This is confirmed by the positive effect of wet June conditions on MX observed in most of those sites, whilst MX was mainly negatively related to July temperatures, because water deficit usually peaks during that month.
An increasing influence of climate warming on temporal coherence in ring width records (spatial synchrony) has been observed across some of the studied Eurasian regions (i.e., Iberian Peninsula and Siberia; Shestakova et al. 2016). It could be tested if this enhanced synchrony holds too for density records at similar sub-continental scales, since wood density seasonal components (MN and MX) respond to different climate variables, and wood density is a fundamental variable to estimate forest carbon uptake and woody biomass pools (Bouriaud et al. 2015). It is expected that growing-season precipitation would drive earlywood density and hydraulic conductivity (Pacheco et al. 2016). Consequently, we predict that warmer and drier conditions during the most active phase of the growing season would reduce earlywood production, but increase earlywood density. These predictions must be taken with caution because of the case-study-like nature of this investigation. It should be underlined that much more research is needed to prove the validity and generality of our findings. If they turn to be true for other cold–dry sites where other Pinaceae species dominate, this would imply that minimum wood density could be a valuable proxy of early season precipitation.
To conclude, minimum wood density of three conifer species (L. sibirica, P. nigra, and P. sylvestris) reflects changes in growing-season (spring) precipitation in cool–dry Eurasian regions. An increase in minimum wood density in response to dry spring conditions was observed in drought-prone sites from the forest-steppe and Mediterranean biomes. The associations between minimum wood density and precipitation were stronger than those observed with seasonal width variables. Future drier conditions during the growing season may increase minimum wood density and reduce radial growth of Eurasian conifer forests, negatively affecting their potential to fix and store carbon pools as stem wood.
Author contribution statement
JJC conceived the study and lead the writing. LFP, TAS, AVK, and VVK carried out wood densitometry and statistical analyses. JV, AAK, and JJC collected data in the field. All authors contributed to the discussion and the writing of the manuscript.
References
Antonova GF, Stasova VV (1993) Effects of environmental factors on wood formation in Scots pine stems. Trees 7:214–219
Block J, Magda VN, Vaganov EA (2004) Temporal and spatial variability of tree growth in mountain forest steppe in Central Asia. In: Trace, Proceedings of the Dendrosymposium, vol 2, pp 46–53. Utrecht, Netherlands, May the 1st–3rd, 2003
Bouche PS, Larter M, Domec JC, Burlett R, Gasson P, Jansen S, Delzon S (2014) A broad survey of hydraulic and mechanical safety in the xylem of conifers. J Exp Bot 65:4419–4431
Bouriaud O, Leban J, Bert D, Deleuze C (2005) Intra-annual variations in climate influence growth and wood density of Norway spruce Intra-annual variations in climate influence growth and wood density. Tree Physiol 25:651–660
Bouriaud O, Teodosiu M, Kirdyanov AV, Wirth C (2015) Influence of wood density in tree-ring-based annual productivity assessments and its errors in Norway spruce. Biogeosciences 12:6205–6217
Briffa KR, Schweingruber FH, Jones PD, Osborn TJ, Shiyatov SG, Vaganov EA (1998) Reduced sensitivity of recent tree-growth to temperature at high northern latitudes. Nature 391:678–682
Briffa KR, Osborn TJ, Schweingruber FH (2004) Large-scale temperature inferences from tree rings: a review. Glob Planet Chang 40:11–26
Bunn AG (2008) A dendrochronology program library in R (dplR). Dendrochronologia 26:115–124
Bunn A, Korpela M, Biondi F, Campelo F, Mérian P, Qeadan F, Zang C (2016) dplR: Dendrochronology Program Library in R. R package version 1.6.4. https://CRAN.R-project.org/package=dplR
Büntgen U, Frank D, Trouet V, Esper J (2010) Diverse climate sensitivity of Mediterranean tree-ring width and density. Trees 24:261–273
Camarero JJ, Olano JM, Parras A (2010) Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytol 185:471–480
Camarero JJ, Rozas V, Olano JM (2014) Minimum wood density of Juniperus thurifera is a robust proxy of spring water availability in a continental Mediterranean climate. J Biogeogr 41:1105–1114
Camarero JJ, Gazol A, Tardif JC, Conciatori F (2015) Attributing forest responses to global-change drivers: limited evidence of a CO2-fertilization effect in Iberian pine growth. J Biogeogr 42:2220–2233
Carlquist S (1975) Ecological strategies of xylem evolution. University of California Press, Berkeley
Chave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE (2009) Towards a worldwide wood economics spectrum. Ecol Lett 12:351–366
Cleaveland MK (1986) Climatic response of densitometric properties in semiarid site tree rings. Tree-Ring Bull 46:13–29
Cook ER, Kairiukstis LA (1990) Methods of dendrochronology: applications in the environmental science. Kluwer, Dordrecht
Dalla-Salda G, Martínez-Mier A, Cochard H, Rozenberg P (2009) Variation of wood density and hydraulic properties of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) clones related to a heat and drought wave in France. For Ecol Manage 257:182–189
De Luis M, Gričar J, Čufar K, Raventós J (2007) Seasonal dynamics of wood formation in Pinus halepensis from dry and semi-arid ecosystems in Spain. IAWA J 28:389–404
Devi N, Hagedorn F, Moiseev P, Bugmann H, Shiyatov S, Mazepa V, Rigling A (2008) Expanding forests and changing growth forms of Siberian larch at the Polar Urals treeline during the 20th century. Glob Chang Biol 14:1581–1591
Domec JC, Warren JM, Meinzer FC, Lachenbruch B (2009) Safety factors for xylem failure by implosion and air-seeding within roots, trunks and branches of young and old conifer trees. IAWA J 30:101–120
Dulamsuren Ch, Hauck M, Bader M, Osokhjargal D, Oyungerel S, Nyambayar S, Runge M, Leuschner C (2009) Water relations and photosynthetic performance in Larix sibirica growing in the forest-steppe ecotone of northern Mongolia. Tree Physiol 29:99–110
Dulamsuren Ch, Hauck M, Leuschner C (2010) Recent drought stress leads to growth reductions in Larix sibirica in the western Khentey, Mongolia. Glob Chang Biol 16:3024–3035
Dylis NV (1961) Larch in Eastern Siberia and the Far East: variation and natural diversity (in russian). Akad. Nauk SSSR, Moscow
Eilmann B, Zweifel R, Buchmann N, Pannatier EG, Rigling A (2011) Drought alters timing, quantity, and quality of wood formation in Scots pine. J Exp Bot 62:2763–2771
Esper J, Frank D, Büntgen U, Verstege A, Hantemirov RM, Kirdyanov AV (2010) Trends and uncertainties in Siberian indicators of 20th century warming. Global Chang Biol 16:386–398
Fajardo A (2016) Wood density is a poor predictor of competitive ability among individuals of the same species. For Ecol Manage 372:217–225
Fritts HC (2001) Tree rings and climate. Caldwell, New York
Galván DJ, Büntgen U, Ginzler C, Grudd H, Gutiérrez E, Labuhn I, Camarero JJ (2015) Drought-induced weakening of growth-temperature associations in high-elevation Iberian pines. Glob Planet Chang 124:95–106
Gimeno TE, Camarero JJ, Granda E, Pías B, Valladares F (2012) Enhanced growth of Juniperus thurifera under a warmer climate is explained by a positive carbon gain under cold and drought. Tree Physiol 32:326–336
Gindl W (2001) Cell-wall lignin content related to tracheid dimensions in drought-sensitive Austrian pine (Pinus nigra). IAWA J 22:113–120
Gindl W, Grabner M, Wimmer R (2000) The influence of temperature on latewood lignin content in treeline Norway spruce compared with maximum density and ring width. Trees 14:409–414
Hacke UG, Sperry JS (2001) Functional and ecological xylem anatomy. Perspect Plant Ecol Evol Syst 4:97–115
Hacke UG, Sperry JS, Pockman WT, Davis SD, McCulloh KA (2001) Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia 126:457–461
Hacke UG, Lachenbruch B, Pittermann J, Mayr S, Domec JC, Schulte PJ (2015) The hydraulic architecture of conifers. In: Hacke UG (ed) Functional and ecological xylem anatomy. Springer, Berlin, Heidelberg, pp 39–75
Hargreaves GH, Samani ZA (1982) Estimating potential evapotranspiration. J Irrig Drain Eng 108:225–230
Harris I, Jones PD, Osborn TJ, Lister DH (2014) Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int J Climatol 34:623–642
Jolliffe I (2002) Principal component analysis. Springer, New York
Jyske T, Hölttä T, Mäkinen H, Nöjd P, Lumme I, Spiecker H (2010) The effect of artificially induced drought on radial increment and wood properties of Norway spruce. Tree Physiol 30:103–115
Kirdyanov AV, Hughes MK, Vaganov EA, Schweingruber F, Silkin P (2003) The importance of early summer temperature and date of snow melt for tree growth in Siberian Subarctic. Trees 17:61–69
Kirdyanov AV, Vaganov EA, Hughes MK (2007) Separating the climatic signal from tree-ring width and maximum latewood density records. Trees 21:37–44
Knorre AA, Siegwolf RTW, Saurer M, Sidorova OV, Vaganov EA, Kirdyanov AV (2010) Twentieth century trends in tree ring stable isotopes (δ13C and δ18O) of Larix sibirica under dry conditions in the forest steppe in Siberia. J Geophys Res Biogeosci 115:G03002
Larjavaara M, Muller-Landau HC (2010) Rethinking the value of high wood density. Funct Ecol 24:701–705
Larson PR (1994) The vascular cambium: development and structure. Springer, New York
Lundgren C (2004) Microfibril angle and density patterns of fertilized and irrigated Norway spruce. Silva Fenn 38:107–117
Mäkinen H, Hynynen J (2014) Wood density and tracheid properties of Scots pine: responses to repeated fertilization and timing of the first commercial thinning. Forestry 87:437–447
Martín JA, Esteban LG, de Palacios P, Fernández FG (2010) Variation in wood anatomical traits of Pinus sylvestris L. between Spanish regions of provenance. Trees 24:1017–1028
Martín-Benito D, Cherubini P, Del Río M, Cañellas I (2008) Growth response to climate and drought in Pinus nigra Arn. trees of different crown classes. Trees 22:363–373
Martínez-Vilalta J, Cochard H, Mencuccini M, Sterck F, Herrero A, Korhonen J, Llorens P, Nikinmaa E, Nolè A, Poyatos R (2009) Hydraulic adjustment of Scots pine across Europe. New Phytol 184:353–364
Muller-Landau HC (2004) Interspecific and inter-site variation in wood specific gravity of tropical trees. Biotropica 36:20–32
Niklas KJ (1992) Plant biomechanics. University of Chicago Press, Chicago
Oksanen J, Blanchet FG, Kindt R, Legendre P, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Wagner H (2013) vegan: Community Ecology Package. R package version 2.0-7. http://CRAN.R-project.org/package=vegan
Olivar J, Rathgeber C, Bravo F (2015) Climate change, tree-ring width and wood density of pines in Mediterranean environments. IAWA J 36:257–269
Pacheco A, Camarero JJ, Carrer M (2016) Linking wood anatomy and xylogenesis allows pinpointing of climate and drought influences on growth of coexisting conifers in continental Mediterranean climate. Tree Physiol 36:502–512
Pasho E, Camarero JJ, Vicente-Serrano SM (2012) Climatic impacts and drought control of radial growth and seasonal wood formation in Pinus halepensis. Trees 26:1875–1886
Pittermann J, Sperry JS, Wheeler JK, Hacke UG, Sikkema EH (2006) Mechanical reinforcement of tracheids compromises the hydraulic efficiency of conifer xylem. Plant Cell Env 29:1618–1628
Polge H (1978) Fifteen years of wood radiation densitometry. Wood Sci Technol 12:187–196
Poulter B, Pederson N, Liu H, Zhu Z, D’Arrigo R, Ciais P, Davi N, Frank D, Leland C, Myneni R, Piao S, Wang T (2013) Recent trends in inner Asian forest dynamics to temperature and precipitation indicate high sensitivity to climate change. Agric For Meteorol 178–179:31–45
R Development Core Team (2015) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/
Richardson DM (1998) Ecology and biogeography of pinus. Cambridge University Press, Cambridge
Ruiz Diaz Britez M, Sergent A-S, Martinez Meier A, Bréda N, Rozenberg P (2014) Wood density proxies of adaptive traits linked with resistance to drought in Douglas fir (Pseudotsuga menziesii (Mirb.) Franco). Trees 28:1289–1304
Sánchez-Salguero R, Camarero JJ, Hevia A, Madrigal-González J, Linares JC, Ballesteros-Canovas JA, Sánchez-Miranda A, Alfaro-Sánchez R, Sangüesa-Barreda S, Galván JD, Gutiérrez E, Génova M, Rigling A (2015) What drives growth of Scots pine in continental Mediterranean climates: drought, low temperatures or both? Agric For Meteorol 206:151–162
Sangüesa-Barreda G, Camarero JJ, García-Martín A, Hernández R, de la Riva J (2014) Remote-sensing and tree-ring based characterization of forest defoliation and growth loss due to the Mediterranean pine processionary moth. For Ecol Manage 320:171–181
Shestakova TA, Gutiérrez E, Kirdyanov AV, Camarero JJ, Génova M, Knorre AA, Linares JC, Resco de Dios V, Sánchez-Salguero R, Voltas J (2016) Forests synchronize their growth in contrasting Eurasian regions in response to climate warming. PNAS 113:662–667
Treter U (2000) Stand structure and growth patterns of the larch forests of Western Mongolia—a dendrochronological approach. Geowiss Abh Reihe A 205:60–66
Tuhkanen S (1980) Climatic parameters and indices in plant geography. Acta Phytogeogr Suec 67:1–110
Vaganov EA, Kirdyanov AV (2010) Dendrochronology of larch trees growing on Siberian permafrost. In: Osawa A, Zyryanova OA, Matsuura Y, Kajimoto T, Wein RW (eds) Permafrost ecosystems: Siberian Larch forests, ecological studies, vol 209. Springer, Heidelberg, pp 347–363
Vaganov EA, Hughes MK, Kirdyanov AV, Schweingruber FH, Silkin PP (1999) Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature 400:149–151
Vaganov EA, Hughes MK, Shashkin AV (2006) Growth dynamics of conifer tree rings. Images of past and future environments. Springer, Berlin, Heidelberg
Vaganov EA, Schulze ED, Skomarkova MV, Knohl A, Brand WA, Roscher C (2009) Intra-annual variability of anatomical structure and δ13C values within tree rings of spruce and pine in alpine, temperate and boreal Europe. Oecologia 161:729–745
Velisevich SN, Kozlov DS (2006) Effects of temperature and precipitation on radial growth of Siberian Larch in ecotopes with optimal, insufficient, and excessive soil moistening. Russ J Ecol 37:241–246
Wan X, Zwiazek JJ, Lieffers VJ, Landhäusser SM (2001) Hydraulic conductance in aspen (Populus tremuloides) seedlings exposed to low root temperatures. Tree Physiol 21:691–696
Yasue K, Funada R, Kobayashi O, Ohtani J (2000) The effects of tracheid dimensions on variations in maximum density of Picea glehnii and relationships to climatic factors. Trees 14:223–229
Zhang SB, Ferry Silk JW, Zhang JL, Cao KF (2011) Spatial patterns of wood traits in China are controlled by phylogeny and the environment. Glob Ecol Biogeogr 20:241–250
Zobel BJ, van Buijtenen JP (1989) Wood variation. Its causes and control. Springer, Berlin
Acknowledgements
We acknowledge the support of Spanish Ministry of Economy Projects (Fundiver, CGL2015-69186-C2-1-R). Tree-ring density data were obtained and analysed under support of Russian Science Foundation (Project 14-14-00295).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by E. Liang.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Camarero, J.J., Fernández-Pérez, L., Kirdyanov, A.V. et al. Minimum wood density of conifers portrays changes in early season precipitation at dry and cold Eurasian regions. Trees 31, 1423–1437 (2017). https://doi.org/10.1007/s00468-017-1559-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00468-017-1559-x