New star on the stage: amount of ray parenchyma in tree rings shows a link to climate


Author for correspondence:

José Miguel Olano

Tel: +34 975 129485



  • Tree-ring anatomy reflects the year-by-year impact of environmental factors on tree growth. Up to now, research in this field has mainly focused on the hydraulic architecture, with ray parenchyma neglected despite the growing recognition of its relevance for xylem function. Our aim was to address this gap by exploring the potential of the annual patterns of xylem parenchyma as a climate proxy.

  • We constructed ring-width and ray-parenchyma chronologies from 1965 to 2004 for 20 Juniperus thurifera trees growing in a Mediterranean continental climate. Chronologies were related to climate records by means of correlation, multiple regression and partial correlation analyses.

  • Ray parenchyma responded to climatic conditions at critical stages during the xylogenetic process; namely, at the end of the previous year's xylogenesis (October) and at the onset of earlywood (May) and latewood formation (August).

  • Ray parenchyma-based chronologies have potential to complement ring-width chronologies as a tool for climate reconstructions. Furthermore, medium- and low-frequency signals in the variation of ray parenchyma may improve our understanding of how trees respond to environmental fluctuations and to global change.


Xylem is a complex tissue with multiple functions, including water, nutrient and hormone transport, plant support and reserve storage. Xylem anatomy has to adapt to the contrasting demands of these functions (Carlquist, 2001; Chave et al., 2009), and this involves trade-offs in terms of invested resources and of room devoted to the different tissue elements (Poorter et al., 2010). Despite a phylogenetic control of the xylem traits (Sperry, 2003; Willson et al., 2008), interspecific differences in xylem anatomy and function have been shown to be related to environmental factors such as climate (Myer, 1922; Lev-Yadun & Aloni, 1995; Martínez-Cabrera et al., 2009) and to contrasting plant life histories (Poorter et al., 2010). Xylem anatomy also shows intraspecific variability (Esteban et al., 2010; von Arx et al., 2012) and interannual variation within an individual along the different ontogenetic stages (Domec & Gartner, 2002).

It is thus not surprising that the quantitative analysis of xylem anatomical characteristics has become a promising field in dendrochronology (see Fonti et al., 2010). Most of the existing studies have focused on the analysis of conductive elements, both in conifers (e.g. Kirdyanov et al., 2003; Panyushkina et al., 2003; Olano et al., 2012; Xu et al., 2012) and angiosperms (e.g. Woodcock, 1989; García-González & Fonti, 2006; Fonti et al., 2007). This focus on the conductive function is probably related not only to its robust climate signal, but also to the increasing understanding of the relationships between the structures and functioning of conductive elements (Hacke & Sperry, 2001; Pittermann et al., 2006). By contrast, analysis of inter-year variability in xylem parenchyma traits and their relationships with environmental variation has remained largely unexplored (but see Lev-Yadun, 1998; von Arx et al., 2012), probably because a direct connection with the environmental and physiological mechanisms that induce parenchyma formation is not so evident.

The paucity of studies analysing xylem parenchyma contrasts with the key functions that are driven by this tissue. Ray parenchyma is the primary means of radial transport and as such is also involved in communication between xylem and phloem and the plant body in general, wound response, stem biomechanics, heartwood formation, water accumulation and reserve storage (van Bel, 1990; Gartner et al., 2000; Burgert & Eckstein, 2001; Pruyn et al., 2002; Spicer & Holbrook, 2007). The reserve storage function of the ray parenchyma may satisfy different needs, such as providing a sink for photoassimilates (Myer, 1922; Gartner et al., 2000) and establishing a pool from which to produce chemical defence compounds and energy for stem resprouting (Climent et al., 1998). Moreover, an interconnection between carbon starvation and plant mortality after stressful conditions such as drought has been highlighted (McDowell et al., 2008; McDowell & Sevanto, 2010). Recent research suggests a potential role of xylem parenchyma in the refilling of conductive elements after the occurrence of embolism (Holtta et al., 2006; Salleo et al., 2009; Nardini et al., 2011); a strong link is therefore seen to exist between xylem parenchyma and the adequate functionality of the hydraulic system (McDowell, 2011; McDowell et al., 2011).

Reserve levels in parenchyma show strong seasonal variation, apparently with no associated anatomical changes (Sauter & Neumann, 1994; Cruz & Moreno, 2001; Palacio et al., 2007). However, the amount of ray parenchyma within a tree ring may be a good estimator of its potential maximum carbohydrate storage capacity (Spicer & Holbrook, 2007). Assessing the amount of xylem parenchyma on a year-by-year basis may provide information on the variation in a plant's stem resources storage capacity through its life. Annual sequences of the amount of ray parenchyma from several individuals from a population can be averaged into a chronology that could potentially be related to time series of environmental factors, in order to assess the external control of parenchyma formation. The aim of this study was to explore the potential of ray parenchyma as a new environmental proxy. Specifically, we wanted to evaluate the relationships between different parenchyma-based chronologies and climatic conditions, and to compare these relationships with those obtained from tree-ring width, the most widely used proxy. To that end, we constructed yearly ring-width and ray parenchyma chronologies for Spanish juniper (Juniperus thurifera) growing under continental Mediterranean conditions, and related them to climatic time series from the study area along a 40-yr period. Our specific questions were: which climatic factors influence the amount of ray parenchyma?; Have the ray parenchyma-based chronologies a climatic signal independent from ring width?; and Has this signal the potential to be used as a climate proxy?

Materials and Methods

Study species

Juniperus thurifera L. (Spanish juniper) is a long-lived evergreen tree endemic to the western Mediterranean basin. Radial growth is constrained by winter rainfall, low spring temperature, and summer drought (Bertaudière et al., 1999; Rozas et al., 2009; DeSoto et al., 2012). In our study area, its xylogenetic cycle starts in early May and ends in late October (Fig. 1), with latewood formation initiating in early August (Camarero et al., 2010). Parenchyma tissue in juniper species appears mainly as uniseriate rays and to a lesser extent as axial parenchyma (Richter et al., 2004; Wiedenhoeft & Miller, 2005).

Figure 1.

Climatic diagram obtained from Soria meteorological station for the period 1965–2010. Dark grey areas indicate water surplus periods, and pale grey water deficit period. Image in the bottom represents the timing of the main events in xylogenesis, onset of earlywood, onset of latewood and end of latewood formation based on Camarero et al. (2010).

Study area

The study area is a 3300 ha woodland located at Sierra de Cabrejas, 30 km west of Soria city, in north-central Spain (41°46′N, 02°49′W; 1100–1300 m altitude). The parent rock is Cretaceous limestone, and the soils are calcium-rich and shallow. Juniperus thurifera forms an open woodland characterized by a mean density of > 300 trees ha−1, coexisting with pines (Pinus sylvestris L. and Pinus pinaster Ait.) and holm oaks (Quercus ilex L.). The climate is subhumid Mediterranean continental. Records of mean monthly temperature and total monthly precipitation were obtained from the Soria meteorological station (41°46′N, 02°28′W; 1082 m altitude) for the period 1964–2004. Annual mean temperature is 10.4°C, the coldest month being January (mean daily minimum temperature of −1.8°C) and the warmest July (mean daily maximum temperature of 28.1°C). Average annual rainfall is 556 mm, with a summer drought period typically occurring during July and August (Fig. 1).

Dating tree rings, measuring anatomical features, and computing chronologies

We took one stem disc at 1.3 m above ground from each of 20 trees that had been felled during the winter of 2004–2005, based on a regular sampling design (for details, see Olano et al., 2008). These stem discs were mechanically surfaced and then manually polished with a series of successively finer grades of sandpaper until the xylem cellular structure was clearly visible under a binocular lens. On each disc, two radii were selected along sectors with more regular tree-ring growth, and tree-ring sequences along these radii were dated with the help of a robust chronology available for the same site (Rozas et al., 2009). Total ring widths were measured to the nearest 0.001 mm by using a Velmex sliding-stage micrometer interfaced with a computer. The software cofecha (Grissino-Mayer, 2001) was used to check the crossdating. As age and sex may affect Juniperus thurifera climatic response (Rozas et al., 2009), we chose 10 young (< 150 yr) and 10 old (> 150 yr) trees, and a similar number of male and female trees per age class (six males and four females for young trees, five per sex for old trees) to get a representative sampling of the species response, avoiding potentially biased results as a function of skewed age and sex distribution of the trees studied.

A 5-mm wide piece was cut out from every disc along the best crossdated radius to obtain permanent histological preparations, following the method of Schweingruber & Poschlod (2005). Cross-sections thinner than 15 μm were cut with a sledge microtome (H. Gärtner/F. H. Schweingruber, WSL, Birmensdorf, Switzerland) and were then placed on a slide and stained with Alcian blue (Alcian blue 1% solution in acetic acid) and safranin (safranin 1% solution in ethanol), which resulted in unlignified cells appearing blue and lignified cells appearing red. Afterwards, the thin-sections were dehydrated using a series of ethanol solutions of increasing concentrations, washed with xylol, and then permanently preserved by embedding them into Eukitt glue (Kindler GmbH, Freiburg, Germany). Images of each annual ring were captured with a Nikon D90 digital camera mounted on a Nikon Eclipse 50i optical microscope with ×200 magnification. When an annual ring could not be captured in a single photograph, sequential images were merged (PTGUI, ver. 8.3.10 pro, New House Internet Services B.V., Rotterdam, the Netherlands). Image analysis was performed with imagej (ver. 1.44;; developed by W. Rasband, National Institutes of Health, Bethesda, MD, USA).

In a first step, five rings per tree were randomly selected (i.e. a total of 100 rings). The outline of every ray present in these rings was manually delineated to estimate the ray surface (Fig. 2). Ray surface was highly correlated with ray length (R2 = 0.898; < 0.001), with no significant effect of cambial age on this relationship. Thus, in order to speed up the process, in the remaining rings the ray surface was estimated as a function of ray length. In addition, we counted the total number of rays per mm of ring in a tangential direction, the number of rays that originated in a previous ring (continuing rays) and the number of rays initiating within the current ring (new rays) (Fig. 2). For every image we estimated the total ring area analysed and measured the ring width at three locations to obtain an averaged ring-width value. The total ring area analysed divided by the average ring width provided an estimate of measured sample width (Fig. 2). With these data, we then built annual chronologies for: (1) tree-ring width (RW), (2) percentage of the ring surface occupied by ray parenchyma (PERPAR), (3) total number of rays per mm of sample width (TOTRAY), (4) number of rays continuing from previous rings (CONRAY) per mm of sample width and (5) number of newly detected rays per mm of sample width (NEWRAY). In order to do a full assessment of all potential parameters, two additional parameters, total ray parenchyma surface per mm of sample width (TOTPAR) and number of ending rays (ENDRAY) per mm of sample width were also computed and are analysed in the Supporting Information, Fig. S1.

Figure 2.

Cross-section of Juniperus thurifera xylem indicating the parameters measured. Axial parenchyma, which has not been measured, is also shown. Picture corresponds to the year 1983 for a young male. Bar (upper left), 100 μm. [Correction added after online publication 14 January 2013: ‘Radial section’ has been corrected to ‘Cross-section’.]

Each individual series of interannual variation of raw ray parenchyma features and ring width was standardized using the arstan program (Cook & Holmes, 1996). All series were smoothed by spline functions characterized by a 50% frequency response of 32 yr, which was flexible enough to reduce the nonclimatic variance by preserving high-frequency climatic information (Cook & Peters, 1981). The year-by-year arithmetic mean for the time-series of standardized indices was calculated to obtain a chronology for each measured variable.

Statistical analysis

Pearson's correlations between pairs of chronologies were calculated to evaluate whether the five chronologies (RW, PERPAR, TOTRAY, CONRAY and NEWRAY) shared a similar high-frequency variation. As tree-ring width exerted a direct effect on most ray parameters, partial correlations were performed between all pairs of anatomical variables, controlling for ring-width effect.

Climatic factors

To identify the climatic factors that were related to the indexed chronologies for the common period 1965–2004, we calculated Pearson's correlations with monthly average temperature and total precipitation records from the previous September up to the current September. Partial correlations between the significant climatic factors and the different ray parenchyma chronologies controlling for tree-ring width were performed, to assess whether the existing information from rays differed from that present in ring widths. Multiple regressions were then performed between the chronologies and monthly temperature and precipitation records, which were included in the model according to Wald's forward stepwise procedure. The cut-off value for inclusion in the model was 0.05, and it was 0.1 for excluding a previously included variable. Analyses were performed with PASW Statistic (SPSS v18, Chicago, IL, USA).


Characteristics of tree-ring width and ray parenchyma chronologies

The ring width series showed moderately high values of mean sensitivity (msx), mean correlation between trees (rbt) and signal-to-noise ratio (SNR), and an expressed population signal (EPS) value > 0.85 (Table 1), suggesting adequate replication and a high common signal shared by trees. The mean ring width was 0.79 mm. Ray parenchyma covered 6.67% of the ring area. An average of 7.87 rays per mm of sample width were counted, from which 4.89 were rays already present in the previous ring, while 2.98 were new rays. All parenchyma chronologies showed a suboptimal statistical quality (Fig. 3).

Figure 3.

Individual chronologies (grey) and mean chronologies (red) of Juniperus thurifera standardized indices for RW, PERPAR, TOTRAY, CONRAY and NEWRAY (see Table 1 for acronyms). Vertical lines reflect pointer years corresponding to the 1971 and 1997 wide rings, and the 1979, 1991 and 2001 narrow rings.

Table 1. Summary statistics for the common period 1965–2004 for Juniperus thurifera ring-width and parenchyma chronologies based on 20 individuals
ParameterAcronymMean ± SDmsx r bt SNREPS
  1. msx, mean sensitivity; rbt, mean correlation between trees; SNR, signal-to-noise ratio; EPS, expressed population signal.

Ring-width (μm)RW783 ± 4150.2890.2957.9480.888
% of ray parenchyma areaPERPAR6.67 ± 1.890.211−0.005−0.098−0.109
Number of rays (rays mm−1)TOTRAY7.87 ± 4.260.2410.0010.0200.020
Continuing rays (rays mm−1)CONRAY4.89 ± 1.520.2560.0100.0190.016
New rays (rays mm−1)NEWRAY2.98 ± 4.180.2460.0300.0550.036

Relationship among chronologies

Pairwise correlations between chronologies were significant and positive for most of the cases (Table 2). The highest correlation occurred between RW and TOTRAY, suggesting a strong dependence of this parameter on the ring width. NEWRAY also showed a high correlation with RW. By contrast, marginally significant or nonsignificant correlations with RW were found for PERPAR and CONRAY, respectively. PERPAR was positively related to the three other ray parameters (TOTRAY, CONRAY and NEWRAY). TOTRAY showed a stronger correlation with NEWRAY than with CONRAY, whereas NEWRAY and CONRAY were uncorrelated. Removing the RW effect through partial correlation resulted in the disappearance of significant correlations between NEWRAY and PERPAR, and the emergence of a significant negative correlation between CONRAY and NEWRAY.

Table 2. Pairwise Pearson's correlations between the six Juniperus thurifera chronologies, upper right half of the matrix indicates Pearson's r for correlations between chronologies, bottom left half indicates partial correlations between chronologies after removing RW effect
  1. Coefficients in bold type are significant at < 0.001, underlined coefficients are significant at < 0.01 and coefficients in italic are significant at < 0.05.

 RW0.310 0.770 0.117 0.653
Controlled for RWPERPAR  0.684 0.454 0.370
TOTRAY 0.734   0.392 0.668
CONRAY 0.442 0.477  −0.189
NEWRAY0.233 0.383 −0.352  

Climatic influence on ring-width and ray-parenchyma chronologies

RW was positively correlated with rainfall in summer (June–July), negatively so with rainfall in late winter (February–March), and marginally negatively correlated with June temperature (Fig. 4). The multiple regression model was highly significant (R2 = 0.555; < 0.001) and included February, March and June rainfall, plus an additional negative effect of rainfall in the previous December (Table 3). PERPAR was positively correlated with May rainfall and negatively so with the previous October temperature, with an additional negative effect of August temperature (Fig. 4). The multiple regression model also explained a large proportion of variance (R2 = 0.561; < 0.001), including the previously stated factors plus a positive effect of March temperature.

Figure 4.

Correlations between Juniperus thurifera ring width and parenchyma chronologies (see Table 1 for acronyms) and monthly climatic variables (mean temperature (grey bars) and accumulated precipitation (closed bars)) for the period 1965–2004. Dashed lines indicate < 0.05, and dotted lines indicate < 0.01 under two-tailed hypothesis. Lowercase and uppercase letters correspond to the months of the previous and current growth year, respectively. Asterisks indicate the parameters with significant values in multiple regression models.

Table 3. Results of multiple forward stepwise regressions between the six Juniperus thurifera chronologies and monthly climatic parameters for average temperature and accumulated rainfall for the time-window ranging from September of the previous year (lowercase letters) to September of the target year (uppercase letters)
  1. See Table 1 for acronyms. All models are significant at < 0.001. Standardized beta coefficients are shown. Coefficients in bold type are significant at < 0.001, coefficients in italic are significant at < 0.01 and underlined coefficients are significant at < 0.05. Only months included in at least one of the models are shown.

Model R20.5550.5610.3720.2330.594
RainfallDEC −0.255
JAN −0.371 −0.469
FEB −0.314 −0.371
MAR −0.323 0.368
MAY 0.374 0.475
JUN 0.418
AUG 0.280
TemperatureSEP −0.289
OCT −0.440 −0.277
MAR 0.366
JUN −0.330
AUG −0.554

TOTRAY was positively correlated with May rainfall and February temperature, and negatively correlated with previous October temperature and February–March rainfall (Fig. 4). Its multiple regression model was highly significant (R2 = 0.372; < 0.001), including May rainfall and temperature from the previous September and October. CONRAY was positively correlated with January rainfall and negatively correlated with March rainfall (Fig. 4), and the multiple regression model included both factors (R2 = 0.233; < 0.001). Finally, NEWRAY was negatively correlated with December to February rainfall (Fig. 4), while its multiple regression model (R2 = 0.594; < 0.001) included a negative effect of January–February rainfall and a positive effect of August rainfall, plus a negative effect of June temperature.

Link between climate and ray-parenchyma chronologies after removing RW effect

Removing the RW effect in the TOTRAY chronology resulted in its showing a positive correlation with May rainfall. This signal was also present in PERPAR (Fig. 5) and TOTPAR (Fig. S1). PERPAR chronology also showed a negative relationship with temperature in October of the previous year and with August temperature. CONRAY climatic signal did not change, and showed a positive effect of January rainfall and a negative effect of March rainfall. However, the negative correlation of previous December rainfall with NEWRAY disappeared and the effect of March rainfall became positive.

Figure 5.

Partial correlations between Juniperus thurifera parenchyma chronologies (see Table 1 for acronyms) and monthly climatic variables (mean temperature (grey bars) and accumulated precipitation (black bars)) for the period 1965–2004 after controlling for ring-width. Dashed lines indicate < 0.05, and dotted lines indicate < 0.01 under two-tailed hypothesis. Lowercase and uppercase letters correspond to the months of the previous and current growth year, respectively.


This study indicates that the annual variation of J. thurifera ray parenchyma abundance is strongly linked to high-frequency climate signals. This link is similar in strength to those observed for ring width, but differs significantly in the timing of the climatic parameters involved. This indicates independent climatic signals in ring width-based and ray parenchyma-based chronologies.

Strength of parenchyma chronologies

The classical estimators for chronology quality were weak for ray parenchyma time-series, with values close to zero or even negative (Table 1). This situation is common in chronologies based on anatomical features (Eckstein & Frisse, 1982; Yasue et al., 2000; Fonti & García-González, 2004; Olano et al., 2012) and has been attributed to either a reduced interannual variation in anatomical parameters owing to functional constraints, to a great interindividual variability, or to the reduced area examined in anatomical studies, as they are commonly based on microscopic preparations (García-González & Fonti, 2008). However, it has been shown that merging individual ring-width chronologies into a mean chronology may produce a robust climatic signal, even if individual trees have lower climatic signals or no significant responses to these limiting factors (Carrer, 2011; Rozas & Olano, 2013). The concomitant poor chronology statistics and robust climatic signals in our data and other chronologies based on anatomy (Yasue et al., 2000; Campelo et al., 2010) might respond to the same phenomenon of common climatic signal enhancement. A preliminary analysis of our data shows that the climatic signal strongly increases with larger sample sizes for those climatic factors that significantly influence wood anatomy, although remains unchanged for nonsignificant factors (see Fig. S2).

Climatic factors affecting ray parenchyma formation

Our results suggest that ray parenchyma formation in J. thurifera depends on conditions before and during its formation, and this concurs with previously observed effects of climate on cambial activity, xylogenesis and tracheid traits (Camarero et al., 2010; Olano et al., 2012). Parameters linked to parenchyma total quantity (TOTPAR, PERPAR and TOTRAY) shared climatic signals at critical stages of the xylogenetic process, mainly at the end of previous year xylogenesis (October), radial growth resumption (May) and latewood initiation after summer arrest (August). Interestingly, parameters related to ray formation (NEWRAY) or to the continuity of existing rays (CONRAY) responded to winter rainfall conditions, probably through their effect on winter levels of carbohydrates stored in the stem.

Higher temperatures at the end of the previous growing season exerted a negative effect on PERPAR and TOTRAY (Figs 4, 5). This is the time of latewood tracheids’ wall thickening (Camarero et al., 2010), a process that is usually positively related to temperature (Yasue et al., 2000; Wang et al., 2002). Thus, high October temperatures may induce carbohydrate investment in lignin deposition in latewood cell walls (Gindl et al., 2000), reducing carbohydrate levels and inducing a subsequent decline of parenchyma production the following growth season. [Correction added after online publication 14 January 2013: ‘low’ has been replaced with ‘high’ relating to the October temperatures.]

The detrimental influence of winter rainfall on TOTRAY may be a side-effect of its detrimental effect on ring width, as is suggested by the absence of a winter rainfall signal in the parenchyma chronologies after removing the RW effect (Fig. 4). Winter rainfall exerts a strong negative effect on Juniperus thurifera secondary growth in our study area (Rozas et al., 2009), and in general throughout the northern sector of its Spanish distribution range (DeSoto et al., 2012). Rainy conditions and cloudiness may notably reduce solar radiation, photosynthetic rates and the amount of glucose assimilated by evergreen conifers (Medlyn et al., 2002). A decrease of winter recharge of stem carbohydrates under rainy/cloudy conditions may lead to lower reserve levels at the beginning of the growing season (DeSoto, 2010; Gimeno et al., 2012), a factor that usually induces reduced rates of xylem increment the next year (Hoch et al., 2003; Daudet et al., 2005). However, the negative effect of winter rainfall on the appearance of new rays is slightly different because it occurs within an earlier window, that is, December–February, and remains strong even after the RW effect has been removed (Fig. 4). This finding indicates that the formation of new rays in response to dry winters is independent from ring width. A tentative hypothesis may be that the formation of new rays is promoted by high carbohydrate levels at the beginning of the growing season. Moreover, the positive effect of January rainfall on CONRAY and the negative correlation between CONRAY and NEWRAY, after removing the RW effect, also indicates that the formation of new rays is partly related to the end of previously existing rays.

Summer drought is a major factor constraining secondary growth in Mediterranean environments (Cherubini et al., 2003). In our study area, water deficit occurs in June and July in 50% and 70% of the years analysed, respectively. Consequently, June and July rainfall signals are commonly reflected in juniper tree-ring increment (Fig. 3; Notes S1; Table S1; Rozas et al., 2009; DeSoto et al., 2012; Gimeno et al., 2012; Rozas & Olano, 2013) and tracheid size (Olano et al., 2012). By contrast, May conditions are generally optimal for photosynthesis and growth, with water deficits occurring in only 5% of years, leading to a weak signal of May rainfall in the RW chronology. An explanation other than water limitation should be sought to interpret the strong positive effect of May rainfall on PERPAR and TOTRAY. Ray parenchyma formation derived from high concentrations of nonstructural carbohydrates produced under optimal photosynthetic conditions (Gartner et al., 2000) is a possible explanation that should, however, be discarded, because active secondary growth in May (Camarero et al., 2010) depletes storage carbohydrate levels in J. thurifera (DeSoto, 2010).

Recent studies with Arabidopsis mutants show that interfascicular cambium, which contains ray initials, is activated through the tension generated from dividing fascicular cambium and an associated hormonal triggering by jasmonate (Sehr et al., 2010; Agusti et al., 2011). Under this hypothesis, the interfascicular cambium divides as a response to cambial pressure generated by the dividing cells of the fascicular cambium, and this process is regulated by hormonal signals related to a mechanical disturbance. Ethylene, a hormone related to mechanical disturbance, has been linked to a higher production of parenchyma rays after wounds or external tensions (Andersson-Gunneras et al., 2003; Chehab et al., 2009): it is known that ethylene plays a clear role in both cambial activity (Love et al., 2009) and the control of interfascicular cambium activity (Lev-Yadun & Aloni, 1995), and elevated ethylene levels have been described in conifer stems during the period of maximal cambial activity (Klintborg et al., 2002). If the same mechanism is applied to J. thurifera, it would offer a plausible explanation, as abundant May rainfall may increase stem internal water pressure, increasing jasmonate and ethylene levels, and activating fascicular cambial divisions, with this process leading to the activation of ray initials. The observed positive effect of May rainfall on PERPAR and TOTRAY fits with this hypothesis, with the timing in the climatic response occurring during the period of cambial activity initiation in J. thurifera (Camarero et al., 2010).

Late summer weather conditions in the current growth year did not play a significant role for J. thurifera growth, probably because of the reduced contribution of latewood to the final ring width (Fig. 1; Olano et al., 2012). However, our results suggest that the conditions during August, that is, at the onset of latewood formation after the arrest of mid-summer growth (Camarero et al., 2010), are relevant for parenchyma formation. The development of a greater number of new rays is associated with a relatively humid August, whereas a consistently warm August has a strong detrimental effect on PERPAR, probably through the additional effect of ray finalization. Interestingly, no effects from the conditions in or after the current September were observed, which is consistent with the end of latewood forming cambial divisions in late August (Camarero et al., 2010).

Potential use of ray-parenchyma chronologies

In comparison with other anatomical parameters, ray parenchyma is relatively quick to measure, especially in conifers with uniseriate rays. This fact is extremely relevant as the need for time-consuming procedures precludes the widespread use of other anatomical proxies based on conductive elements (Fonti et al., 2010). The potential to analyse large sample sizes is critical to effectively evaluate the climatic sensitivity observed in the chronologies, as common climatic signal is generally enhanced by increasing sample size (Fig. S2; Carrer, 2011). In addition, the interest of parenchyma chronologies may go well beyond its value as a climatic proxy. Medium- and low-frequency signals in the interannual variation of ray parenchyma may provide keys that enable the interpretation of plant responses to diverse abiotic factors (such as disturbances, fires, avalanches and extreme climatic events) and biotic factors (such as competition, herbivory and defoliator outbreaks). As such, they may contribute to a more thorough understanding of the response of trees to global climatic change through retrospective analysis of both growth dynamics and reserve levels. Increasing the potential of this technique requires a better understanding of the physiological process controlling ray parenchyma formation and its relationships with the vegetative phenology and environmental conditions.


We are indebted to Juan Carlos Rubio for his assistance with stem disc preparation and Gonzalo Juste for cross-sections preparation. Cesefor kindly authorized us to use their stem discs collection. The manuscript benefited from a thorough review from Ignacio García-González. We thank Alison Macalady for providing an extensive literature review on xylem parenchyma and its role as an indicator of environmental conditions and vigour in trees. Ian Woodward and three anonymous referees provided valuable comments on a previous version. English was edited by David Brown. This work was supported by a FPI-EHU grant to A.A., a FPI-MICINN grant to A.I.G-C., a CSIC research contract to V.R. and projects CGL2009-13190-C03-03 (Spanish Ministry of Science and Innovation) and VA006A10-2 (Junta de Castilla y León).