ORIGINAL PAPER

Wood anatomical traits highlight complex temperature influenceon Pinus cembra at high elevation in the Eastern Alps

Marco Carrer1 & Lucrezia Unterholzner1 & Daniele Castagneri1

Received: 6 March 2018 /Revised: 11 May 2018 /Accepted: 21 June 2018# ISB 2018

AbstractClimate sensitivity of populations at the margins of their distribution range is of key importance to understand species’ responsesto future warming conditions. Pinus cembra is of particular interest being a typical high-elevation taxon, spread with mostlyscattered populations within its actual range, but still overlooked in traditional dendrochronological researches due to low tree-ring variability and climate sensitivity. With a different approach, we analyzed time series of xylem anatomical traits, split intointra-ring sectors, and used daily climate records over 89 years (1926–2014) aiming to improve the quality and time resolution ofthe climate/growth associations. From nine trees growing at their altitudinal limit and on 1.5 × 106 tracheids, we measured ringwidth (MRW), cell number per ring, lumen area (LA), and cell-wall thickness (CWT). We then computed correlations withmonthly and fortnightly climate data. Late-spring and summer temperature emerged as the most important factors. LA andespecially CWTshowed a stronger temperature response thanMRW, starting in mid-May and early June, respectively. CWTalsoevidenced the longest period of correlations with temperature and a significant difference between latewood radial and tangentialwalls. Analysis of xylem anatomical traits at intra-ring level and the use of daily temperature records proved to be useful for highresolution and detailed climate/growth association inferences in Pinus cembra.

Keywords Climate/growth associations . Pinus cembra . Lumen area . Cell number . Cell-wall thickness . Dendroanatomy .

Tree ring

Introduction

Forests have a prominent role in the terrestrial carbon cy-cle. However, the current dramatic climate changes, whichinclude increasing temperature together with the frequencyand intensity of extreme events such as storms and severedroughts, are challenging this role all around the planet(Anderegg et al. 2015; Reichstein et al. 2013; Xia et al.2014). A better knowledge of tree response strategies atdifferent levels therefore represents a scientific priority(Allen et al. 2015; Park et al. 2014).

In the last years, we have been accumulating evidence thatmodern climate change is reshuffling the geographic distribu-tions and shifting the phenological phases of plant speciesworldwide (Parmesan and Yohe 2003). In this context, rearedge populations are considered disproportionately importantfor the long-term conservation of genetic diversity and, some-times, for the cultural history of species and this is the reasonwhy they deserve a high priority in investigation and conser-vation (Hampe and Petit 2005; Sánchez-Salguero et al. 2017).Within the scientific community, there is ample convergencein considering the dynamics of populations at the margins oftheir distribution range of key importance to understand andpossibly forecast the fate and species’ responses to future cli-mate conditions (Hampe and Petit 2005). In fact, marginalpopulations frequently include individuals operating at theextremes of their physiological limit, therefore more sensitiveto environmental variability and usually prone to react moresignificantly to any climatic changes than population growingwithin the core of their distribution area (Fritts 1976). Treesgrowing at high-elevation edge of species distribution aremostly limited by temperature (Tranquillini 1979), which,

Electronic supplementary material The online version of this article(https://doi.org/10.1007/s00484-018-1577-4) contains supplementarymaterial, which is available to authorized users.

* Marco Carrermarco.carrer@unipd.it

1 Università degli Studi di Padova, Dip. TeSAF – Agripolis,I-35020 Legnaro, PD, Italy

International Journal of Biometeorologyhttps://doi.org/10.1007/s00484-018-1577-4

virtually certainly, will increase in the next future (IPCC2013). However, the mechanisms responsible of high alti-tude limitation to tree growth are still under debate, sincemany physiological and environmental factors are in-volved, including temperature and hydraulic constraint tocell growth and tissue formation (Hoch et al. 2002; Petit etal. 2011; Körner 2012).

Swiss stone pine (Pinus cembra L.) is the only five-needlepine species on the European continent; nowadays, it grows intwo disjunct regions: the Alps, the actual core natural range,where it represents the most typical high-elevation tree spe-cies, and the Carpathians, with isolated populations. This bio-geographical pattern likely results from the repeated climaticfluctuations during the glacial/interglacial periods togetherwith the interaction with other tree species (Huntley 1990)and, from the human pressure (mostly fire-clearing, selectiveexploitation and pasturelands establishment) in the lastmillennia (Ali et al. 2005). As a result, Swiss stone pine sig-nificantly reduced its range and retreated to marginal habitatsmostly at high elevation (Caudullo and de Rigo 2016).However, reduced human pressure and increased temperaturemight enhance its distribution in the subalpine area in the nextdecades (Vittoz et al. 2008).

Despite the peculiarity of its biogeographical history, andthe potential of being long-lived, the use of this species withinthe framework of tree-ring researches has been rather limited,especially if compared to the co-occurring larch (Larixdecidua Mill.) (but see Carrer et al. 2007; Oberhuber 2004and references therein; Saulnier et al. 2011). Swiss stone pinecould potentially be more suited to climate/growth associationinvestigations, because it is not the host species of any signif-icant defoliating insects. On the contrary, larch is stronglyaffected by larch budmoth (Zeiraphera diniana Guénée) theoutbreaks of which periodically alter its year-to-year tree-ringpattern (Buntgen et al. 2009; Esper et al. 2007). However,despite this potential, the overly even ring width and minimalvariability in latewood density prevented the extensive use ofthe species in ring width and densitometric analyses(Schweingruber and Johnson 1993). Within this setting, wetested a different approach using dendroanatomy, which is theanalysis of wood anatomical traits in dated tree rings for geo-eco- and climatological investigations.

The analysis of xylem anatomical features has long beenconsidered a promising method to extract novel and high-resolution information from tree rings (Fonti et al. 2010).Yet, methodological improvements have only recentlyallowed to fully access the wealth of the xylem anatomicalarchive, allowing a sample depth, both in terms of both num-ber of elements per year and number of trees per site, togetherwith a length of time series, never reached before (Carrer et al.2016; Castagneri et al. 2017; von Arx et al. 2016). In thisresearch, we tested the potential of dendroanatomy in Swissstone pine with the major objective of improving the quality

and time resolution of climate/growth associations with re-spect to the classical ring width measurement. This could shedlight on the mechanisms that regulate xylem growth of a typ-ical high-elevation species at its distribution limit. For this, wemeasured three anatomical traits, cell number per ring, cellsize and cell-wall thickness, from nine stone pine trees grow-ing at high elevation, and contrasted the resulting time serieswith temperature using monthly and fortnightly timewindows.

Materials and methods

Study site

The sampling site is located on a north-east facing slope athigh elevation in the Eastern Italian Alps (46° 29′N, 12° 06′E,2100 m a.s.l.) within a mixed stand of Swiss stone pine (Pinuscembra L.), European larch (Larix deciduaMill.), and sporad-ic Norway spruce (Picea abies L. Karst.). It features the typ-ical uneven age forest structure of high-elevation forests withlow tree density and irregular spatial distribution. Soils areshallow and calcareous. Mean annual temperature fromCortina d’Ampezzo station (1230 m a.s.l., located less thanfour kilometers from the sampling site), is 6.7 °C with month-ly (daily) extremes ranging from 4.1 (− 25) °C in January to22.7 (+ 30) °C in July. Mean annual precipitation is1080 mm, with a maximum in June (125 mm), and usuallyfalls as snow from November to early May. This stationprovides the longest (1926–2014) daily record in the re-gion, and is fully representative of the day-to-day maxi-mum temperature variability in the surrounding area, in-cluding the sample site (Carrer et al. 2017).

Sample collection and processing

Two increment cores were extracted at breast height with aPressler borer from 15 undamaged dominant or codominantstanding trees. Ring widths were measured to the nearest0.01 mm using TsapWin (Rinntech, Heidelberg, Germany),crossdated using standard dendrochronological procedures(Stokes and Smiley 1968), and checked for dating and mea-surement errors with the COFECHA program (Holmes 1983).Nine cores collected from different trees were then selected foranatomical measurements among those without any visiblefaults such as nodes, fibers torsion, reaction wood, rotten, ormissing parts (von Arx et al. 2016). These cores were split in4–5 cm long pieces, from which thin (10 μm) transversalsections were obtained with a rotary microtome (Leica,Heidelberg, Germany). Sections were then stained with safra-nin (1% in distilled water), fixed on permanent slides withEukitt (BiOptica, Milan, Italy), and finally scanned using aD-sight 2.0 System (Menarini Diagnostics, Florence, Italy)

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at × 100 magnification, with a resolution of 1.99 pixels/μm.Resulting images were then processed with the image analysissoftware ROXAS v3.0 (Prendin et al. 2017; von Arx andCarrer 2014). Ring borders were manually identified and abrief manual fine-editing was performed to remove wronglyidentified or broken cells. The software provided the lumensize, cell-wall thickness, and relative position within the datedannual ring for each of the > 4.5 × 106 measured tracheids.With this information, we created tree-ring anatomical profilesrepresenting the variation of different anatomical traits withinthe ring (Fig. 1), and divided each ring into 10 sectors of equalwidth along the tangential direction (Carrer et al. 2016;Pacheco et al. 2016). We considered the splitting into 10 sec-tors a good compromise between our target to increase thetemporal resolution of the analysis and maintaining a goodsample size (i.e. cell number) within each sector. Moreover,Pinus cembra xylem is characterized by very thin latewoodwith minimal interannual variability which restrains the clas-sical splitting between earlywood and latewood and actuallyrepresents the main reason why this species is considered notvery suitable for densitometric analysis (Frank and Esper2005; Schweingruber and Johnson 1993). For each sector,we computed (1) the radial (CWTRad) and (2) tangential(CWTTan) mean cell-wall thickness (Prendin et al. 2017),and (3) the 90th percentile of the cell lumen area (LA)(Castagneri et al. 2017). For each tree, we therefore built 10LA, 10 CWTRad, and 10 CWTTan time series, which rangedfrom earlywood to latewood, and should represent successivetime windows within the growing season. We also computed(4) the total number of cells per ring (CN) and (5) the meanring width (MRW) to derive the corresponding individual treeCN and MRW series.

TRW and CN series were standardized to remove the typ-ical age-size related trends with a spline detrending with a50% frequency cutoff response at 100 years (Cook et al.1990). Anatomical series, especially of cell lumen size, alsousually exhibit an increasing age trend related to heightgrowth. This trend is mostly evident during the early stagesof tree life, when the height growth rate peaks, and ceases

when the tree reaches its maximum height (Carrer et al.2015). Given the mean age of the sampled trees of 326 years(all trees were > 150 years; Supplementary Table 1), and theabsence of any evident long-term ontogenetic trend in all LAand CWT anatomical series (Supplementary Fig. 1),detrending was not considered necessary. We therefore justapplied just Z-score transformation (mean subtraction and di-vision by the standard deviation) to remove dimensional offsetand stabilize the variances among series. Some descriptivestatistics, within the 1926–2014 investigation period, werecomputed to compare and describe the chronologies(Supplementary Table 2).

Principal component analysis (PCA) (Jolliffe 2002) basedon the correlation matrix was used as an exploratory techniqueto investigate the similarities and differences between the 32MRW and anatomical trait chronologies of the ring sectors.The significance of the principal components (PCs) was ver-ified with a randomization test and applying the Rnd-Lambdastopping rule (Peres-Neto et al. 2005). Scatter plots of the PCweighting coefficients were used to display and assess therelationships among variables.

Climate/growth associations were quantified contrastingmonthly climate records and the chronologies by 10,000-boot-strap replication correlations. Each correlation coefficient wasconsidered significant at the 95% level if the mean value wasat least twice, in absolute value, the standard deviation of its10,000 replications (Efron 1992; Guiot 1991). To go beyondthe conventional monthly climate data aggregation (Fonti etal. 2010) and striving for a higher detail in the resolution of themost important associations, we also computed running cor-relations at sub-monthly scale. We tested 5, 7, 10, 15, 20, and30-day windows, and finally we selected the 15-day windowconsidering that very short intervals provide spurious and un-stable results, and because this window length is similar tothose usually applied in cambium dynamics analyses androughly correspond to the mean time of cell enlargementphase for trees at these elevation (Rossi et al. 2006, 2008).All correlations were computed between April 1st andSeptember 30th of the ring formation year.

Fig. 1 Profile for a lumen areaand b cell-wall thickness(computed on 1.5 × 106 cells)along Pinus cembra tree ring. Theblack lines represent the meanvalues for nine trees over 89 years(1926–2014), and the shadowedareas delimit the 99.9%confidence interval

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Results

Swiss stone pine features the typical tree-ring structure ofconifers in cold and temperate environments, with a monoton-ic reduction in cell lumen area and a parallel increase in wallthickness from earlywood to latewood. These general trendsdiffer just at the beginning of the ring for LA, with an evidentincrease at the very first part, and for tangential CWT in late-wood, showing a reduction at the closing of the ring, whichdoes not occur in radial CWT (Fig. 1).

Principal component analysis performed with the TRWandanatomical trait chronologies resulted in three significant com-ponents: together they explained 82% of the variance andhighlighted different year-to-year patterns reflected also intheir descriptive statistics (Table 1; Supplementary Table 1).The first component allows to clearly separate LA and CWTchronologies (Fig. 2) while loadings ordination along the thirdcomponent permits to create a smooth and ordered transitionfrom the first- to last-sector chronologies. Combination of thesecond and third component permits the directional CWTmeasurements to be separated. MRW and CN chronologiesare very close to each other in every plot.

Correlations computed with the conventional monthly cli-mate data (Figs. 3 and 4) point out that temperature was themain climate driver for both MRW and anatomical parame-ters, while precipitation had a marginal role. LA sensitivity totemperature was confined to the last tree-ring sectors for June(positively) and August (negatively). On the contrary, for boththe radial and tangential direction, CWT shows a significantand positive response to temperature along most of the ringsectors, with higher correlations for the radial than the tangen-tial CWT in August in the last three sectors. The higher reso-lution of the 15-day running correlations retrieves more de-tailed information (Figs. 5 and 6). LA shows positive associ-ation with temperature from mid-May in the first ring sectorsto the end of June in the last sectors. LA of the last ring sectoralso shows negative associations with late-July and Augusttemperature. CWT chronologies, while corroborating theoverall stronger correlation values with respect to LA, revealan evident propagation of the significant response over theseason and sequential sectors. The first earlywood sectorsshow earlier significant values than the last latewood ones, amore evident pattern for CWTRad, where the lag between the

peaks of significant correlation from the first to the tenth sec-tor covers 57 days, with respect to 14 days in the CWTTan.

Discussion

Tree rings are the most important source of long-term infor-mation related to tree growth and health, but they also give theaccess to the retrospective analysis of a wealth of environmen-tal factors such as past climate, geo- or hydrological variability(Fritts 1976) or terrestrial carbon cycle (Babst et al. 2014).This work falls within this framework; we introduced somenovel methodological and analytical aspects on xylem traits togain a better understanding of the Swiss stone pine sensitivityto temperature variability. We focused on this underrated pinespecies comparing the classical ring width measurements(MRW) with several anatomical traits related to different xy-lem functions and xylogenetic phases, such as water transportand cell enlargement (LA), mechanical support andwall thick-ening (CWT), with CN straddling both functions and relatedto cambial division.

The anatomical trait profiles (Fig. 1) highlight both typicaland novel features in the tree-ring structure. The lumen areashows an initial increase, followed by a shrinking trend, moreevident in the last sector (corresponding to the latewood). Inthe earlywood (sectors 1 to 9), both the radial and the tangen-tial cell-wall thickness feature a monotonic increase, follow-ing a pattern observed in many other conifer species (Carrer etal. 2016; Cuny et al. 2014; Vysotskaya and Vaganov 1989).However, our directional measurement of CWT evidenced ananisotropic pattern between radial and tangential CWT, neverthoroughly assessed before. Indeed, in most of the previoustree-ring-related investigations, CWT was mostly consideredjust on the tangential side (Prendin et al. 2017; Vaganov et al.2006). Although the principal drivers for this difference arestill not clear, some hypotheses are related to the additionaldeposition of cell-wall material on the radial walls of tra-cheids at ray contact areas, to the stronger reduction fromearly- to latewood in the radial with respect to tangentialwidth of the tracheid or to the uneven distribution of bor-dered pits, which are found more frequently in radial andearlywood cell walls (Brändström 2001; Rosner 2013;Sirviö and Kärenlampi 1998).

Table 1 Results from the PCAapplied onMRW, CN, the 30 LA,CWTTan and CWTRadchronologies

Axis Eigenvalue Explained variance (%) Cum.% of variance P value of randomization

1 14.581 45.6 45.6 < 0.0001

2 7.433 23.2 68.8 < 0.0001

3 4.224 13.2 82.0 < 0.0001

4 1.202 3.8 85.8 > > 0.05

5 0.942 2.9 88.7 > > 0.05

The last useful axis was the third one, according to the Rnd-Lambda, Avg-Rnd, and BS stopping rules [27]

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Fig. 2 Scatter plots of weighting coefficients for the three significantprincipal components (PCs) computed on tree-ring width andanatomical trait chronologies for 10 tree-ring sectors. Color shading of

the circles refers to sectors ranging from earlywood (light) to latewood(dark). Axis labels report the percentage of variance expressed by eachcomponent

Fig. 3 Bootstrap correlation coefficients between monthly temperature(April to September, 1926–2014) and mean ring width (MRW), cellnumber (CN) (upper-right panel), and for the 10 sector chronologies oflumen area (LA), radial and tangential cell-wall thickness (CWTRad andCWTTan). Color of the circles refers to the month (see the key). Circleswithin the gray area indicate non-significant (p > 0.05) correlations

Fig. 4 Bootstrap correlation coefficients as in Fig. 3 but consideringmonthly precipitation (April to September, 1926–2014). All thevariables, color coding, and significance levels are the same as in theprevious figure

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Observed differences in the ring profiles are fully conveyedwithin the year-to-year variability described by PCA and thefollowing temperature/association analysis. Despite the usuallower performance in some chronology-quality statistics ofthe anatomical traits with respect to MRW (Carrer et al.2017; Olano et al. 2013), here we appreciate not only thatdiverse xylem traits can provide distinct environmental infor-mation, but also that different seasonal signals can be extract-ed within the same trait (Carrer et al. 2017; Fonti et al. 2013).The regular displacement of the PCA loadings of LA,

CWTRad and CWTTan are, indeed, well translated into thecorresponding shift in the significant correlations with temper-ature, which emerged as the key climate driver of ring forma-tion, in accordance with previous studies on Pinus cembra insimilar environmental conditions (Babst et al. 2013; Carrer etal. 2007; Carrer and Urbinati 2004; Saulnier et al. 2011). Thissequential propagation of the sensitivity window to tempera-ture across the sectors should not be taken for granted as wewere using a very short 15-day interval. Over 89 years, tem-perature variability certainly affects the pace and pattern of

Fig. 5 Associations between temperature and anatomical chronologies(as in Fig. 3) over the period 1926–2014. Correlation coefficients havebeen assessed on 15-day windows, represented at sliding daily steps fromApril to September and coded according to the color key on the right.

Gray cases are not significant, colored boxes are significant (p < 0.05),black marks highlight the peak of correlation in the 1st and 10th CWTsectors

Fig. 6 Associations between temperature and mean ring width, cellnumber, radial and tangential cell-wall thickness of the 10th sectoranatomical chronologies (as in Fig. 5) over the period 1926–2014. Cell-wall thickness correlation profiles of the 10th sector (as in Fig. 5) are

shown side by side to ease the comparison. Gray cases are notsignificant, colored boxes are significant (p < 0.05) and coded accordingto the same color key as in Fig. 5, black marks highlight the peak ofcorrelation in the last two CWT sectors

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tree growth, inducing appreciable anticipation, delay andchange in the rate of various phenological phases, probablyeven larger than the time window adopted (Rossi et al. 2008).This stems from a seeming overlap between contiguous sec-tors nonetheless, they proved able to clearly catch the keymean-temporal window likely related to the xylogenetic pro-cess of trait development (Castagneri et al. 2017). This ex-plains the temporal offset between LA and CWT response toclimate but also the much longer period of significant corre-lations of CWT which closely corresponds to the differenttiming and duration of the wall-thickening phase recordedfor the same species and site with cambial activity investiga-tions (Rossi et al. 2006).

Focusing on the last part of the ring (Fig. 6), we observedan interchange between periods of significant temperaturesensitivity for the two facing cell walls, i.e., radial and tangen-tial CWT. This suggests a different timing of thickening, an-ticipated and/or faster for the tangential than radial walls.Future investigations should consider anisotropy of the pro-cesses and timing of the cell wall-thickening phase.

Our intra-seasonal analysis indicates that temperature var-iation along the growing season not only controls ring width,which mostly depends on the number of tracheids produced(Castagneri et al. 2015; Dufour and Morin 2013), but alsoaffects the tracheid wall thickness, i.e., the amount ofcarbon-rich compounds in each cell. Interestingly, in Pinuscembra, climate influence on cell wall is evident for all thetracheids, and not just for latewood ones, as for Picea abies(Castagneri et al. 2017) and Larix decidua (Carrer et al. 2016),the two other species occurring at the high-elevation vegeta-tion belts in the Alps (San-Miguel-Ayanz et al. 2016).Considering that CN and CWT are the two key traits closelyrelated to trees’ C-sink activity, detailed information on thewood anatomical traits association with climate could helpin understanding the physiological mechanisms behind en-vironmental limitation to tree growth at high elevation, andin models to predict high-elevation forest distribution androle in the C-cycle under future climate scenarios. Intra-ring analysis could also contribute in investigating treesresponses to short-term (e.g., extreme) events, which aregetting a prominent role for their pervasive, significant andlong-lasting effects within many forest ecosystems (Franket al. 2015; Reichstein et al. 2013).

Conclusions

The different parameters measured in tree rings have alreadyproved to be multifaceted and with a wide temporal range.Dendroanatomy can represent a step toward a better exploita-tion of this potential, leading to higher temporal resolution ofthe information extracted, but also a deeper delving into newand more detailed nuances. Analyzing xylem anatomical traits

together with daily temperature records we were able to im-prove the quality of the inferences compared to the classicalapproach, which adopts monthly values and yearly mean ringwidth. This allowed us to evidence a strong climate influenceon the growth processes of Swiss stone pine, a species so farrather neglected in tree-ring research.

Despite these promising results, areas still exist for contin-ued development, for example, better tuning the time win-dows of analysis and therefore allowing for yearly climateidiosyncrasies or better considering the species-specific eco-physiological traits. This study can contribute to improvedunderstanding of temperature/growth associations for a typi-cal and relict high-elevation tree species and can improve theforecasting of future pine responses and timberline stand dy-namics on projected changes in climate.

Acknowledgments We are very grateful to Maria Elena Gelain,Department of Comparative Biomedicine and Food Safety, Universityof Padova, for giving us access to the D-sight 2.0 System automaticscanner (Grandi Attrezzature Fund, University of Padova).

References

Ali AA, Carcaillet C, Talon B, Roiron P, Terral J-F (2005) Pinus cembraL. (arolla pine), a common tree in the inner French Alps since theearly Holocene and above the present tree line: a synthesis based oncharcoal data from soils and travertines. J Biogeogr 32:1659–1669.https://doi.org/10.1111/j.1365-2699.2005.01308.x

Allen CD, Breshears DD, McDowell NG (2015) On underestimation ofglobal vulnerability to tree mortality and forest die-off from hotterdrought in the Anthropocene. Ecosphere 6:1–55. https://doi.org/10.1890/ES15-00203.1

AndereggWRL, Schwalm C, Biondi F, Camarero JJ, Koch G, Litvak M,Ogle K, Shaw JD, Shevliakova E, Williams AP, Wolf A, Ziaco E,Pacala S (2015) Pervasive drought legacies in forest ecosystems andtheir implications for carbon cycle models. Science 349:528–532.https://doi.org/10.1126/science.aab1833

Babst F, Poulter B, Trouet V, Tan K, Neuwirth B, Wilson R, Carrer M,Grabner M, Tegel W, Levanic T, Panayotov M, Urbinati C,Bouriaud O, Ciais P, Frank D (2013) Site- and species-specific re-sponses of forest growth to climate across the European continent.Glob Ecol Biogeogr 22:706–717. https://doi.org/10.1111/geb.12023

Babst F, Alexander MR, Szejner P, Bouriaud O, Klesse S, Roden J, CiaisP, Poulter B, Frank D, Moore DJP, Trouet V (2014) A tree-ringperspective on the terrestrial carbon cycle. Oecologia 176:307–322. https://doi.org/10.1007/s00442-014-3031-6

Brändström J (2001) Micro-and ultrastructural aspects of Norway sprucetracheids: a review. IAWA J 22:333–353

Buntgen U et al (2009) Three centuries of insect outbreaks across theEuropean Alps. New Phytol 182:929–941. https://doi.org/10.1111/j.1469-8137.2009.02825.x

Carrer M, Urbinati C (2004) Age-dependent tree-ring growth responsesto climate in Larix decidua and Pinus cembra. Ecology 85:730–740

Carrer M, Nola P, Eduard JL, Motta R, Urbinati C (2007) Regional var-iability of climate-growth relationships in Pinus cembra high eleva-tion forests in the Alps. J Ecol 95:1072–1083. https://doi.org/10.1111/j.1365-2745.2007.01281.x

Carrer M, von Arx G, Castagneri D, Petit G (2015) Distilling allometricand environmental information from time series of conduit size: thestandardization issue and its relationship to tree hydraulic

Int J Biometeorol

architecture. Tree Physiol 35:27–33. https://doi.org/10.1093/treephys/tpu108

Carrer M, Brunetti M, Castagneri D (2016) The imprint of extreme cli-mate events in century-long time series of wood anatomical traits inhigh-elevation conifers. Front Plant Sci 7. https://doi.org/10.3389/fpls.2016.00683

Carrer M, Castagneri D, Prendin AL, Petit G, von Arx G (2017)Retrospective analysis of wood anatomical traits reveals a recentextension in tree cambial activity in two high-elevation conifers.Front Plant Sci 8. https://doi.org/10.3389/fpls.2017.00737

Castagneri D, Petit G, Carrer M (2015) Divergent climate response onhydraulic-related xylem anatomical traits of Picea abies along a 900-m altitudinal gradient. Tree Physiol 35:1378–1387. https://doi.org/10.1093/treephys/tpv085

Castagneri D, Fonti P, von Arx G, Carrer M (2017) How does climateinfluence xylem morphogenesis over the growing season? Insightsfrom long-term intra-ring anatomy in Picea abies. Ann Bot 119:1011–1020. https://doi.org/10.1093/aob/mcw274

Caudullo G, de Rigo D (2016) Pinus cembra in Europe: distribution,habitat, usage and threats. In: San-Miguel-Ayanz J, de Rigo D,Caudullo G, Houston Durrant T, Mauri A (eds) European Atlas ofForest Tree Species. Publ. Off. EU, Luxembourg, pp 120–121

Cook ER, Briffa K, Shiyatov S, Mazepa V (1990) Tree-ring standardiza-tion and growth-trend estimation. In: Cook ER, Kairiukstis LA (eds)Methods of Dendrochronology: Applications in the EnvironmentalSciences. Kluwer Academic Publisher, Dordrecht, The Netherlands,pp 104–123

Cuny HE, Rathgeber CBK, Frank D, Fonti P, Fournier M (2014) Kineticsof tracheid development explain conifer tree-ring structure. NewPhytol 203:1231–1241. https://doi.org/10.1111/nph.12871

Dufour B, Morin H (2013) Climatic control of tracheid production ofblack spruce in dense mesic stands of eastern Canada. TreePhysiol 33:175–186. https://doi.org/10.1093/treephys/tps126

Efron B (1992) Bootstrap methods: another look at the jackknife. In: KotzS, Johnson NL (eds) Breakthroughs in statistics: methodology anddistribution. Springer New York, New York, NY, pp 569–593.https://doi.org/10.1007/978-1-4612-4380-9_41

Esper J, BüntgenU, FrankDC,Nievergelt D, LiebholdA (2007) 1200 yearsof regular outbreaks in alpine insects. Proc R Soc B 274:671–679

Fonti P, von Arx G, Garcia-Gonzalez I, Eilmann B, Sass-Klaassen U,Gartner H, Eckstein D (2010) Studying global change through in-vestigation of the plastic responses of xylem anatomy in tree rings.New Phytol 185:42–53. https://doi.org/10.1111/j.1469-8137.2009.03030.x

Fonti P, Bryukhanova MV, Myglan VS, Kirdyanov AV, Naumova OV,Vaganov EA (2013) Temperature-induced responses of xylem struc-ture of Larix sibirica (Pinaceae) from the Russian Altay. Am J Bot100:1332–1343. https://doi.org/10.3732/ajb.1200484

Frank D, Esper J (2005) Characterization and climate response patterns ofa high-elevation, multi-species tree-ring network in the EuropeanAlps. Dendrochronologia 22:107–121. https://doi.org/10.1016/j.dendro.2005.02.004

Frank DA, Reichstein M, Bahn M, Thonicke K, Frank D, Mahecha MD,Smith P, van der Velde M, Vicca S, Babst F, Beer C, Buchmann N,Canadell JG, Ciais P, Cramer W, Ibrom A, Miglietta F, Poulter B,Rammig A, Seneviratne SI, Walz A, Wattenbach M, Zavala MA,Zscheischler J (2015) Effects of climate extremes on the terrestrialcarbon cycle: concepts, processes and potential future impacts. GlobChang Biol 21:2861–2880. https://doi.org/10.1111/gcb.12916

Fritts HC (1976) Tree rings and climate. Academic Press, LondonGuiot J (1991) The bootstrapped response function. Tree-Ring Bull 51:

39–41Hampe A, Petit RJ (2005) Conserving biodiversity under climate change:

the rear edgematters. Ecol Lett 8:461–467. https://doi.org/10.1111/j.1461-0248.2005.00739.x

Hoch G, Popp M, Körner C (2002) Altitudinal increase of mobile carbonpools in Pinus cembra suggests sink limitation of growth at theSwiss treeline. Oikos 98:361–374

Holmes RL (1983) Computer-assisted quality control in tree-ring datingand measurement. Tree-Ring Bull 43:69–78

Huntley B (1990) European post-glacial forests: compositional changesin response to climatic change. J Veg Sci 1:507–518. https://doi.org/10.2307/3235785

IPCC (2013) Climate change 2013: the physical science basis. In: StockerT et al (eds) Contribution of Working Group I to the FifthAssessment Report of the Intergovernmental Panel on ClimateChange. Cambridge Univ Press, Cambridge, p 1535

Jolliffe IT (2002) Principal component analysis. Springer, New YorkKörner C (2012) Alpine Treelines: functional ecology of the global high

elevation tree limits. Springer, BaselOberhuber W (2004) Influence of climate on radial growth of Pinus

cembra within the alpine timberline ecotone. Tree Physiol 24:291–301. https://doi.org/10.1093/treephys/24.3.291

Olano JM, Arzac A, Garcia-Cervigon AI, von Arx G, Rozas V (2013)New star on the stage: amount of ray parenchyma in tree rings showsa link to climate. New Phytol 198:486–495. https://doi.org/10.1111/nph.12113

Pacheco A, Camarero JJ, Carrer M (2016) Linking wood anatomy andxylogenesis allows pinpointing of climate and drought influences ongrowth of coexisting conifers in continental Mediterranean climate.Tree Physiol 36:502–512. https://doi.org/10.1093/treephys/tpv125

ParkA, Puettmann K,Wilson E,Messier C, Kames S, Dhar A (2014) Canboreal and temperate forest management be adapted to the uncer-tainties of 21st century climate change? Crit Rev Plant Sci 33:251–285. https://doi.org/10.1080/07352689.2014.858956

Parmesan C, Yohe G (2003) A globally coherent fingerprint of climatechange impacts across natural systems. Nature 421:37–42. https://doi.org/10.1038/nature01286

Peres-Neto PR, Jackson DA, Somers KM (2005) How many principalcomponents? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal 49:974–997. https://doi.org/10.1016/j.csda.2004.06.015

Petit G, Anfodillo T, Carraro V, Grani F, Carrer M (2011) Hydraulicconstraints limit height growth in trees at high altitude. New Phytol189:241–252. https://doi.org/10.1111/j.1469-8137.2010.03455.x

Prendin AL, Petit G, Carrer M, Fonti P, Björklund J, von Arx G (2017)New research perspectives from a novel approach to quantify tra-cheid wall thickness. Tree Physiol 37:976–983. https://doi.org/10.1093/treephys/tpx037

Reichstein M, Bahn M, Ciais P, Frank D, Mahecha MD, Seneviratne SI,Zscheischler J, Beer C, Buchmann N, Frank DC, Papale D, RammigA, Smith P, Thonicke K, van der Velde M, Vicca S, Walz A,Wattenbach M (2013) Climate extremes and the carbon cycle.Nature 500:287–295. https://doi.org/10.1038/nature12350

Rosner S (2013) Hydraulic and biochemical optimization in Norwayspruce trunkwood—a review. IAWA J 34:365–390. https://doi.org/10.1163/22941932-00000031

Rossi S, Deslauriers A, Anfodillo T (2006) Assessment of cambial activ-ity and xylogenesis by microsampling tree species: an example atthe alpine timberline. IAWA J 27:383–394

Rossi S, Deslauriers A, Anfodillo T, Carrer M (2008) Age-dependentxylogenesis in timberline conifers. New Phytol 177:199–208.https://doi.org/10.1111/j.1469-8137.2007.02235.x

Sánchez-Salguero R, Camarero JJ, Carrer M, Gutiérrez E, Alla AQ,Andreu-Hayles L, Hevia A, Koutavas A, Martínez-Sancho E, NolaP, Papadopoulos A, Pasho E, Toromani E, Carreira JA, Linares JC(2017) Climate extremes and predicted warming threatenMediterranean Holocene firs forests refugia. Proc Natl Acad Sci US A 114:E10142–E10150. https://doi.org/10.1073/pnas.1708109114

Int J Biometeorol

San-Miguel-Ayanz J et al. (2016) European atlas of forest tree species.Publications Office of the European Union. doi:citeulike-article-id:13984530 https://doi.org/10.2788/4251

Saulnier M, Edouard J-L, Corona C, Guibal F (2011) Climate/growthrelationships in a Pinus cembra high-elevation network in theSouthern French Alps. Ann For Sci 68:189–200. https://doi.org/10.1007/s13595-011-0020-3

Schweingruber FH, Johnson S (1993) Trees and wood in dendrochronolo-gy: morphological, anatomical, and tree-ring analytical characteristicsof trees frequently used in dendrochronology. Springer, New York

Sirviö J, Kärenlampi P (1998) Pits as natural irregularities in softwoodfibers. Wood Fiber Sci 30:27–39

StokesMA, Smiley TL (1968) Introduction to tree-ring dating. Universityof Chicago Press, Chicago

Tranquillini W (1979) Physiological ecology of the alpine timberline. vol31. Ecological studies. Springher-Verlag, Berlin

Vaganov EA, Hughes K, Shashkin AV (2006) Growth dynamics of coni-fer tree rings: images of past and future environments vol 183.Ecological Studies. Springer

Vittoz P, Rulence B, Largey T, Freléchoux F (2008) Effects of climate andland-use change on the establishment and growth of cembran pine(Pinus cembra L.) over the altitudinal treeline ecotone in the CentralSwiss Alps. Arct Antarct Alp Res 40:225–232. https://doi.org/10.1657/1523-0430(06-010)[VITTOZ]2.0.CO;2

von Arx G, Carrer M (2014) ROXAS–a new tool to build centuries-longtracheid-lumen chronologies in conifers. Dendrochronologia 32:290–293. https://doi.org/10.1016/j.dendro.2013.12.001

von Arx G, Crivellaro A, Prendin AL, Cufar K, Carrer M (2016)Quantitative wood anatomy-practical guidelines. Front Plant Sci 7.https://doi.org/10.3389/fpls.2016.00781

Vysotskaya L, Vaganov E (1989) Components of the variability of radialcell size in tree rings of conifers. IAWA J 10:417–426

Xia J, Chen J, Piao S, Ciais P, Luo Y, Wan S (2014) Terrestrial carboncycle affected by non-uniform climate warming. Nat Geosci 7:173–180. https://doi.org/10.1038/ngeo2093

Int J Biometeorol