Differentiation in phenology among and within natural populations of a South American Nothofagus revealed by a two-year evaluation in a common garden trial

Differentiation in phenology among and within natural populations of a South American Nothofagus revealed by a two-year evaluation in a common garden trial

Forest Ecology and Management 460 (2020) 117858 Contents lists available at ScienceDirect Forest Ecology and Management journal homepage: www.elsevi...

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Forest Ecology and Management 460 (2020) 117858

Contents lists available at ScienceDirect

Forest Ecology and Management journal homepage: www.elsevier.com/locate/foreco

Differentiation in phenology among and within natural populations of a South American Nothofagus revealed by a two-year evaluation in a common garden trial V.G. Duboscq-Carra


, J.A. Arias-Rios



, V.A. El Mujtar, P. Marchelli, M.J. Pastorino

Grupo de genética forestal, Instituto de Investigaciones Forestales y Agropecuarias Bariloche (IFAB), INTA – CONICET, Bote Modesta Victoria 4450 (8400), San Carlos de Bariloche, Río Negro, Argentina



Keywords: Bud burst Senescence Growing degree days Chilling hours Nothofagus alpina

Phenological traits are crucial for understanding adaptation to climate change due to their genetic control and association with abiotic factors. However, few data on phenology patterns are available for South American Nothofagus species, in particular for Nothofagus alpina, a key species of the temperate forests of Patagonia. Therefore, our aim was to analyze the variation among and within natural populations of N. alpina in two phenological traits (bud burst and foliar senescence), in growing season length and in relative growth height. We registered phenology in 65 open pollinated families of eight Argentinean natural populations installed in a common garden trial. Apical buds and foliar senescence were observed every three days in 6-year-old plants and again three years later in the same plants (N = 373). Day of the year until bud burst (DOY) and until the beginning (DOY10) and the end (DOY90) of foliar senescence were measured. Height was measured twice in a year in order to calculate the annual growth in both seasons. Growing degree days (GDD) and chilling hours (CH) until bud burst were also calculated, with two possible basal temperatures (5 °C and 7 °C) to evaluate their role in DOY. Significant differences among populations and years in DOY and growing season length were found using a linear mixed model (LMM), with the family factor explaining around 30% and 12% of the total variance respectively. The LMM for foliar senescence (DOY10 and DOY90) and the relative growth height (RGH) showed significant differences between years but not among populations. The family factor was significant for foliar senescence, although it only explained a small part of the total variance (DOY10: 4%; DOY90: 2%) and was not significant for relative growth height. A tight relationship between GDD and CH with DOY was found, and LMM showed significant differences among populations and years for both variables. The correlation between the altitude of natural populations and the mean DOY and GDD was high and positive. Our results reveal (i) the genetic control of bud burst and foliar senescence, and phenotypic plasticity of all analyzed traits, (ii) that GDD and CH are implicated in the DOY, and (iii) that altitude is probably conditioning thermal requirement of bud burst. This information suggests good perspectives to face the climate change scenario and highlight the importance of selecting appropriate populations and families for domestication and breeding of N. alpina at particular sites.

1. Introduction

can persist either by adaptation (change of the genetic frequencies of the populations towards the most favorable genes for the new environment), or by migration to places where the new environment resembles that of the original places. Still, the current generation can persist in situ through phenotypic plasticity, which is the adequacy of the phenotypes to the new environment without any genetic change

Climate change imposes new environmental conditions to which tree species can show different biological responses (Parmesan, 2006), the most drastic being the local extinction of populations. When the “biological strategy” is the survival of the next generation, the species

Abbreviations: DOY, day of the year until bud burst; DOY10, day of the year until the beginning of foliar senescence; DOY90, day of the year until the end of foliar senescence; GDD, growing degree days until bud burst; CH, chilling hours until bud burst; LMM, linear mixed model ⁎ Corresponding authors. E-mail addresses: [email protected] (V.G. Duboscq-Carra), [email protected] (J.A. Arias-Rios). 1 Both authors contributed equally to this manuscript. https://doi.org/10.1016/j.foreco.2019.117858 Received 5 November 2019; Received in revised form 27 December 2019; Accepted 30 December 2019 0378-1127/ © 2020 Elsevier B.V. All rights reserved.

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Fig. 1. Nothofagus alpina natural distribution with the detailed location of the studied populations in Argentina. Chilean range from Gallo et al. (2004). Argentinean range modified from Sabatier et al. (2011).

autumn frost affecting functional leaves (Keskitalo et al., 2005). Therefore, plant phenology is a trade-off between competence and avoidance of cold damage, and is essentially driven by air temperature and photoperiod (Menzel, 2002). Active tissues in the buds are protected from frost damage by an endodormancy phase in winter, generally induced by photoperiod and released by chilling temperatures (Horvath et al., 2003; Campoy et al., 2012), followed by an ecodormancy phase released by heat temperatures (Lang, 1987). Within species, populations often have different chilling and heat requirements, mostly determined by adaptation to their home environment (Charrier et al., 2011; Polgar and Primack, 2011). The cessation of growth, formation of buds and subsequent senescence of leaves are adaptive responses to unfavorable conditions for photosynthesis and the occurrence of frost (Estrella et al., 2006). Therefore, trees adapted to colder climates concentrate their growth early in the season (Howe et al., 2003). Despite only a few studies have analyzed the effect of environmental factors on autumn phenology, it is generally assumed that under favorable conditions (i.e. no water or

(Aitken et al., 2008). Traits such as bud burst, frost resistance, growing season length and growth rate, may be variable among natural populations of a species and are key when selecting better-adapted genetic material (e.g. genotypes, populations) (Sotolongo Sospendra et al., 2010). These traits are considered as potentially adaptive, and knowledge about their variation is essential to promote species conservation, as well as for the development of domestication, breeding and restoration programs. Phenological traits are probably the most affected by climate change (Bertin, 2008). Early bud burst in temperate zones is advantageous to compete with other individuals of the same or different species for light and other resources. However, early leaf expansion also increases the risk of frost damage by late-spring freezing temperatures (Heide, 2003). On the other side, bud burst in late spring may avoid frost damage but with the cost of reducing the duration of the photosynthetically active foliage (Lechowicz, 1984; Leinonen and Hänninen, 2002). Likewise, late senescence can result in larger photosynthates storage, but can also increase the risks of incomplete nutrient remobilization due to early 2

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Table 1 Number of families and individuals analyzed in the current trial, location, altitude, mean annual precipitation (MAP), mean January and July temperatures (T °C) for the common garden and for the eight Nothofagus alpina analyzed populations. Population

Latitude S

Longitude W

Altitude (m asl)

MAPa [mm yr−1]

T °C. Jan.a

T °C Julya

N° fam

N° ind

Common garden Boquete (B) Curruhué (C) Paimún (P) Puerto Arturo (PA) Queñi (QE) Tren Tren (TT) Tromen Alto (TRA) Tromen Bajo (TRB)

41° 40° 39° 39° 40° 40° 40° 39° 39°

71° 71° 71° 71° 71° 71° 71° 71° 71°

415 910 1030 930 910 920 1040 1110 1064

908 1600 1400 1800 1200 2400 1400 1270 1492

16.5 13.1 13.2 12.1 14.6 9.7 13.8 13.5 13.0

3.7 1.4 0.9 0.9 1.9 0.0 1.6 1.0 0.9

– 5 9 6 12 7 6 13 7

– 35 52 37 63 49 34 67 36


59′ 01′ 50′ 42′ 01′ 09′ 11′ 36′ 34′

50′' 51′' 34′' 17″ 02′' 54″ 53′' 18″ 10″

31′ 34′ 30′ 33′ 22′ 45′ 25′ 20′ 26′

31′' 38′' 30′' 52″ 19′' 20″ 53′' 57″ 28″

Climatic data obtained from Bianchi and Cravero (2010).

National Park (Sabatier et al., 2011). A Mediterranean-type climate characterizes its Argentinean range, with dry summers of great thermal amplitude and mean annual precipitation from 3000 mm close to the international boundary, up to 1200 mm near the ecotone with the Patagonian steppe, with a distance of only 50 km between both extremes (Veblen et al., 1996). Specifically, the main objectives of this work were to analyze: (i) the variation in the date of bud burst, foliar senescence, growing season length and relative growth height (a) among natural populations and families within them and, (b) between two different growing seasons in a common garden trial. Additionally we also evaluated (ii) if growing degree days (GDD) and chilling hours (CH) have a role in bud burst date, and in case of having it, the variation of GDD and CH among populations / families and between years; (iii) the correlation between traits and geographical and environmental variables of the natural populations in order to discuss possible adaptation processes. Finally, an extra methodological objective was to evaluate the relationship between apical and lateral bud burst, to determine if lateral phenology could be a useful proxy of apical bud burst.

nutrition stresses) the two main environmental factors that regulate the occurrence of leaf senescence are photoperiod and temperature (Estrella et al., 2006; Koike, 1990). Phenological traits also affect growth ability of plants (Howe et al., 2003), and have a great impact on functional processes (e.g. photosynthesis) (Tang et al., 2016). For deciduous trees, bud burst and leaf senescence determine the duration of the photosynthetically active foliage, defined as the lapse between both phenological events (Chmielewski and Rotzer, 2001; Kramer, 1995; Vitasse et al., 2009a), having a direct effect on the productivity of plants (Bennie et al., 2010; Tang et al., 2016). On the other hand, height growth (closely related to productivity) is the main morphological trait that characterizes plants ability for competition (Pinto et al., 2011). Potential adaptive traits are usually studied through common garden trials, establishing in the same site (homogeneous environmental conditions) different genetic entities (typically populations and families), under the assumption that phenotypic differences will be mainly of genetic origin. On the other hand, the latitude, longitude and altitude of the natural populations are proxy for environmental conditions at their place of origin (e.g. temperature, humidity and solar radiation), and somehow reflect adaptive patterns of quantitative traits to local conditions (Alberto et al., 2013). While several studies have demonstrated the genetic determinism of phenological traits (e.g. Chmura and Rozkowski, 2002, in Fagus sylvatica; Premoli et al., 2007, in Nothofagus pumilio; Barbero, 2014, in Nothofagus obliqua; Torres-Ruiz et al., 2019, in Quercus petraea), less knowledge has been gained related to the phenotypic plasticity of such characters (e.g. de Villemereuil et al., 2018 in Arabis alpina; Gárate-Escamilla et al., 2019, in F. sylvatica). To test phenotypic plasticity, it is necessary to grow the same genetic pool in different environments (i.e. clonal, progeny or even provenance assays in different sites). Also, it could be achieved by measuring traits repeatedly in the same trial for different years (“temporal” plasticity; e.g. Mijnsbrugge and Janssens, 2019), especially if the years are climatically different and the traits are little influenced by the ontogenic states reached in those years. The aim of this work was to study variation in phenological traits among natural populations of Nothofagus alpina (Poeppig & Endlicher) Oerst. (=Nothofagus nervosa (Phil.) Dim. et Mil.) growing in a common garden trial in climatically different years. Nothofagus alpina is a foundational and productive tree species of the temperate subantarctic forest from Chile and Argentina. Its very good quality wood (Tortorelli, 1956) and its breeding potential has led to develop genetic improvement programs both in Argentina and in Chile (Ipinza et al. 2000; Pastorino et al., 2016). Moreover, the species has been considered in Europe as a potential replacement of others of similar physiognomy but affected by climate change (e.g. Fagus sylvatica and Quercus spp.), although frost damage has been reported, probably caused by mistimed phenology (Mason et al., 2017). The species occurs mainly in Chile (Fig. 1), between 35° and 41° 30′ S, in both the Andes and Coastal Mountains, while in Argentina its range is almost restricted to the Lanín

2. Materials and methods 2.1. Common garden trial The study was performed in a provenance and progeny test of Nothofagus alpina installed in autumn 2011 in an INTA forest station located in Las Golondrinas (Table 1). A total of 1290 trees were planted at a spacing of 1 × 1 m in the field. The trial was laid out in a randomized complete block design, with single-tree plots and lineal blocks parallel to a pine windbreak in order to control its shading effect. After 7 years the number of surveyed trees was 373 (due to tree mortality, thinning treatment, and analysis exclusively on healthy undamaged trees), corresponding to 65 open pollinated families from eight natural populations that comprise the entire Argentinean range of the species, ordered in 9 blocks (at least 4 individuals per family) (Table 1, Fig. 1). Seven out of the 8 sampled stands must undoubtedly be considered as reproductive populations due to the distance (the average effective pollen dispersal of this species is < 35 m, Marchelli et al., 2012) and the rugged topography that separate them. However, in the case of the Tromen populations (Tromen Alto and Tromen Bajo) they are close to each other, but situated at different niches (one next to the lake and the other up in a hill). We kept these two stands separated for the analyses in order to check if the particular ecological settings impose differences in the evaluated traits. 2.2. Phenotypic trait measurements During the 2015–2016 and 2018–2019 growing seasons, the bud burst process was observed every 3 days in the apical buds of the trees. It was registered according to a subjective ordinal scale of five pheno3

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Table 2 Description of phases for phenological evaluation. Pheno-phase


Phase 1: “undeveloped bud”

Cataphylls (bud scales) closed; dark green leaves, if exist, located between cataphylls; without exudates; brown or dark green bud. Buds still in winter dormancy. With brown and light green grated design; with sticky exudate. Bud mainly light green; leaves appearing among brown cataphylls. Leaves flushing.a Some leaves totally in view; future stem not visible. All the leaves in sight; future stem visible.

Phase 2: “bud elongating” Phase 3: “open bud” Phase 4: “bud in expansion” Phase 5: “bud fully expanded” a

Bold shows the phase used in this work to indicate bud burst.

(GSL) was calculated in days for each individual as the difference between the dates of beginning of senescence and of the open bud stage for the same season. Aiming to analyze possible relationships between growth and duration of the growing season, the total height was measured for all individuals during the winter season of 2015, 2016, 2018 and 2019 with an accuracy of 0.5 cm. The relative growth height (RGH) of each growing season was calculated as:

phases developed for N. obliqua by Barbero (2014) and adjusted by observations of the apical bud burst made on N. alpina trees from the common garden trial in 2014 (Table 2). Observations were made from the beginning of the season until all plants reached the phase 5 (from September 20th to November 6th in 2015, and from September 25th to November 13th in 2018). Finally, the analyzed variable was the day of the year (DOY: number of days from January 1) until pheno-phase 3 (“open bud”), which is a doubtless and clear stage. Although we concentrated the analysis on the intermediate pheno-phase, monitoring until the completion of the process, that is, pheno-phase 5, was important to gain confidence in all stages. In the 2018–2019 growing season, lateral buds were additionally observed, considering the distal third of the first sprouted branch of each tree. The growing degree days (GDD) and chilling hours (CH) until bud burst were also calculated. GDD is the number of grades of air temperature in the trial above a biological meaningful temperature (basal temperature: Tb) accumulated per day from July 1st to the occurrence of the pheno-phase of reference (open bud in this case). This accumulation of warm temperatures is required to break the ecodormancy of the buds. GDD was calculated according to Dantec et al., (2014) by the following formulae:




RGH = (Ht f − Hti )/Hti where RGH is the relative growth height; Htf is the height after the growing season (2016 or 2019 winter); and Hti is the height before the beginning of the growing season (2015 or 2018 winter). 2.3. Weather characterization of the trial site The analysis of phenotypic plasticity calls for a comparative characterization of the different environments of the assay. In this case of temporal plasticity, just a weather characterization of each season is required due to the constancy of the other environmental features (e.g. soil type, photoperiod). We considered three temporal periods of interest, according to the analyzed phenological traits: 1) pre-bud-burst, from May 1st (beginning of growing degree days accumulation) to the sprouting of the last tree for both years, 2) growing, from bud burst to beginning of leaves senescence of the last tree, and 3) senescence, from beginning to the end of senescence of the last tree. For these three periods, we calculated the mean air temperature, the mean maximum temperature, the mean minimum temperature, the number of freezing hours (below 0 °C), and the accumulated precipitation. The data logger HOBO® of the trial was utilized for the temperature data. Precipitation data were not available for the trial site, therefore data from the station 87,800 of the Servicio Meteorológico Nacional (41° 57′ 0″ S, 71° 32′ 0″ W, 337 m asl, located at 5 km from the trial site) were obtained from the INTA database (http://siga.inta.gob.ar/#/).

y (T )

T − Tb, T > Tb y (T ) = ⎧ ⎨ ⎩ 0, T ≤ Tb where GDD is growing degree days, t1 is the start date of heat accumulation (July 1st), t2 is the end date of heat accumulation (the bud burst date), y(T) is the accumulation unit, T is the mean temperature of each date, estimated as the average of all daily data, and Tb is the basal temperature. CH is the sum of hours with temperatures between 2 °C and each basal temperature (5 °C or 7 °C) from May 1st to the occurrence of the open bud (Basler and Körner 2014; Basler 2016). The basal temperature is a property of each species, and a particular study is required to determine it. However, the range of possible Tb values is small, and they are commonly taken from previous studies on related species. Hence, we considered two Tb in order to attempt a sensibility analysis: 7 °C according to the only study specifically performed for N. alpina (García et al., 2013; although in this study Tb was arbitrarily set), and 5 °C according to a study in two temperate species (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) that compared two different Tb (Dantec et al., 2014). In order to obtain accurate data of air temperature, measurements were taken at the trial site itself, every 30 min between May 2015 and June 2019. A data logger HOBO® was utilized located in a corner of the trial at a height of 1.5 m. The process of leaf senescence was also followed, observing the crowns of all trees every three days from March 1st to June 3rd in 2016, and from March 21th to May 27th in 2019. A qualitative scale of two levels was used, considering “beginning” of senescence when 10% of the crown had autumnal leaves (reddish or yellowish colors), and “ending” of senescence when 90% of the crown presented autumnal leaves. The variable was the day of the year until these two phenophases (DOY10 and DOY90). Subsequently, the growing season length

2.4. Data analysis The comparative environmental characterization of both years in the common garden was performed by testing differences in mean temperatures with a Student’s t-test for each period (pre-bud-burst, growing and senescence) and through the direct comparison of the accumulated precipitation. In order to test differences among populations, between years, interaction between these two factors and the variability among families of each population, an ANOVA test was performed for bud burst (DOY), foliar senescence (DOY10 and DOY90), growing season length (GSL) and relative growth height (RGH). A linear mixed effects model was tested, using the “lme4” package and R 3.3.0 software (Bates et al., 2016), with the following equation:

yijkl = μ + γi + ρj + γi ∗ ρj + φk (ρj ) + βl + εijkl where: yijkl is the observation in the individual of the kth family of the jth population in the lth block measured in the ith year; μ is the general mean of the whole trial for the measured variable; γi is the fixed effect of the 4

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ith year;ρj is the fixed effect of the jth population; φk (ρj ) is the random

of the total variance respectively. The block factor was significant only for GSL. Interaction between both fixed factors was not detected, in fact for any of the analyzed traits. According to the Tukey test (Table S1, Supplementary material), two extreme behaviors can be recognized among populations with respect to DOY and GSL: on the one hand we have Boquete population, with earlier bud burst and longer duration of the growing season; while on the other hand we have Tren Tren and Tromen Alto populations, with later bud burst and shorter duration of growing season. The other populations were in the middle of these extremes (Fig. 2). On average for the whole trial (Table S1, Supplementary material), leaf senescence began in March 7th in 2016 (66 ± 7 DOY) and in April 8th in 2019 (97 ± 12 DOY), while it finished in April 27th in 2016 (116 ± 21 DOY) and in May 8th in 2019 (127 ± 11 DOY). Relative growth height was on average 0.31 ± 0.14 for 2015 and 0.19 ± 0.09 for 2018 (Table S1, Supplementary material). These variables showed significant differences between years (p < 0.001) (Fig. S2, supplementary material) but not among populations, and family factor was not significant (pLRT > 0.05). The block factor was significant for both traits.

effect of the kth family nested into the jth population; βl is the random effect of the lth block; ε ijkl is the residual error ~ NID (0, σ2). The normality of the data was assessed using a histogram and qqplot for each variable. Homoscedasticity was checked by a graph of residuals versus predicted values. In case of significant differences among the levels of fixed effects factors, a post-hoc Tukey test was performed. The significance of the family and block variance was evaluated through a likelihood ratio test (LRT) considering the complete model and a model without the family or block factor. In order to evaluate the role of GDD and CH in DOY a multiple linear regression was performed considering data for both years together. Subsequently, an ANOVA test was performed for GDD and CH to test the differences among populations, between years, the interaction between these two factors and the variability among families of each population. A linear mixed effects model was performed using the same package, software and model previously described. For traits showing differences among populations in LMM analyses, a Pearson correlation test for each year was performed between population averages of each trait and the already presented main geographic and environmental variables of each natural population (Table 1). A correlation analysis was planned to analyze the relationship between relative growth height and duration of the growing season. However, the correlation test was at last not performed because significant differences among populations were not detected for RGH (see Results). Finally, correlation between apical and lateral DOY to bud burst was tested.

3.2. Role of growing degree days and chilling hours in date of bud burst and its variation among populations/families and years The multiple linear regression between day of the year until bud burst as dependent variable and growing degree days and chilling hours as independent variables was significant for both years (p < 0.001). The multiple regression models adjusted very tight (R2Tb5 = 0.92; R2Tb7 = 0.90) and GDD and CH were both significant for both basal temperatures. Considering the temperature requirements approach, with Tb 5 °C buds needed an accumulation of 128 ± 32 GDD and 1076 ± 38 CH in 2015, and 136 ± 35 GDD and 991 ± 32 CH in 2018; while with Tb 7 °C an accumulation of 52 ± 18 GDD and 1648 ± 55 CH in 2015, and 71 ± 22 GDD and 1503 ± 55 CH in 2018, as an average of the whole trial (Table S1, Supplementary material). Analysis for both GDD and CH showed significant differences in both fixed factors: population and year (p < 0.001). Family (random factor) had also a significant effect (pLRT < 0.001) and, for Tb 5 °C, explained 26.7% and 24.1% of the total variance of GDD and CH respectively. A similar result was determined for Tb 7 °C, with significant differences in population and year, and family explaining 29.2% and 29.9% of the total variance of GDD and CH respectively. The block factor was significant only for GDD Tb7. Interaction between both fixed factors was not detected. According to the Tukey test (Table S1, Supplementary material), the same two extreme behaviors previously described for DOY and GSL can be recognized among populations with respect to GDD and CH. Boquete population, with lower accumulation of growing degree days and chilling hours; and Tren Tren and Tromen Alto populations, with greater accumulation of GDD and CH. The other populations were in the middle of these extremes (Fig. 2).

3. Results 3.1. Phenotypic trait variation among populations/families and years Variation in temperature and precipitation were determined at the trial between both evaluated years (Fig. S1, Supplementary material). The mean temperatures of each period were higher in 2015 than in 2018 (Table 3), and the t-test showed significant differences in mean and maximum temperatures for the pre-bud-burst period and maximum temperature for the senescence period. The accumulated freezing hours were lower during the pre-bud-burst period for 2015/16, in accordance with the feature observed for the mean values, but on the contrary, higher (more than four times) for the growing period. The accumulated precipitation for pre-bud burst and senescence periods were similar between years, but in the growing period the precipitation of 2015/16 was less than half of that of 2018/19 (Table 3). On average for the entire trial (Table S1, Supplementary material), open bud (pheno-phase 3) occurred in October 15th in 2015 (287 ± 8 DOY) and in October 21th in 2018 (293 ± 7 DOY). The mean growing season length across the whole trial was 144 ± 10 days for 2015 and 169 ± 14 days for 2018 (Table S1, Supplementary material). Analysis for both, DOY and GSL, showed significant differences in both fixed factors: population and year (p < 0.001). Also, family (random factor) had a significant effect (pLRT < 0.001) and explained 29.3% and 12.2%

Table 3 Mean air temperature, mean maximum temperature, mean minimum temperature, number of freezing hours (below 0 °C) and accumulated precipitation for pre-budburst, growing and senescence periods in each year. Period


mean T°

mean min. T°

mean max. T°

Freezing hours


Pre-bud burst Pre-bud-burst Growing Growing Senescence Senescence

May/01/2015 – Nov/09/2015 May/01/2018 – Nov/09/2018 Nov/10/2015 – May/06/2016 Nov/10/2018 – May/06/2019 May/07/2016 – Jun/03/2016 May/07/2019 – Jun/03/2019

5.95 ± 3.69* 4.85 ± 3.90* 14.11 ± 5.04 13.34 ± 3.91 5.24 ± 2.60 4.46 ± 3.21

1.28 0.69 5.56 5.39 1.64 0.79

12.10 ± 5.92* 10.56 ± 6.00* 24.82 ± 7.36 24.10 ± 6.62 10.30 ± 2.43* 8.70 ± 3.19*

585 884 118 28.5 76 109

707.8 671.3 125.1 309.3 30.8 34.7

* Significant difference between years at p = 0.05. 5

± ± ± ± ± ±

3.11 3.47 3.90 3.10 2.89 3.07

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Fig. 2. Boxplot of day of the year (DOY), growing degree days (GDD) and chilling hours (CH) to bud burst and growing season length (GSL) between populations in each year. Lower and upper box boundaries represent first and third quartiles respectively, line inside the box is the median and lower and upper error lines represent minimum and maximum respectively, dots are outliers.

3.4. Correlation between apical and lateral buds

3.3. Correlation between traits and geographical and environmental variables of natural populations

The burst of lateral buds in 2018 occurred on average 7 ± 5 days before the apical ones. The correlation between the dates of apical and lateral bud burst was significant (p < 0.001) and moderately high (r = 0.79) (Fig. 4) revealing that for future measurements bud phenology can be follow through lateral buds in tall trees.

The correlations tested for each year between the population means of the traits (DOY, GSL, GDD and CH) and the main geographic and environmental variables of the home range were not significant in almost all cases. However, altitude was significantly correlated with DOY and GDD (for both basal temperatures) in 2018, and marginally correlated with these traits in 2015, being those correlations moderately (Fig. 3).

4. Discussion 4.1. Phenotypic trait variation among populations/families and years The two analyzed growing seasons varied in climatic conditions: 6

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Fig. 3. Correlation between altitude and day of the year (DOY) and growing degree days (GDD) considering 5 °C and 7 °C basal temperatures.

different environmental conditions. Additionally, the lack of significance of the interaction between year and population (environmental and genetic factors, respectively) should be interpreted as the lack of genetic control of the detected plasticity itself (Whitman & Anagrawal, 2009). It is important to mention the possible ontogenic effect on the inter-annual variation, what would prevent considering it as evidence of plasticity (that is, the differences between years would be an effect of the different age of the trees). However, this is not probable, since the two surveys have only a difference of three years, both ages correspond to the same development stage (juvenile trees) and the traits considered are expected to be not deeply influenced by the age of the trees. Therefore, the differences found in the date of bud burst between both

2018–2019 was colder than 2015–2016 in general, although the number of freezing hours during the growing period was four times higher in 2015–2016. Actually, significant differences between 2015–2016 and 2018–2019 were proved for mean and mean maximum temperatures of the pre-bud-burst period, and for the mean maximum temperature of the senescence period. On the other hand, 2015–2016 was drier than 2018–2019, during the growing period when the precipitation for 2015–2016 was less than half of that of 2018–2019. This constitutes a good scenario to test temporal phenotypic plasticity. The significance of the environmental factor (year) in the linear mixed models tested is evidence of phenotypic plasticity, since the traits surveyed varied for the same genotypes when they were exposed to 7

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Fig. 4. Correlation between day of the year (DOY) of lateral and apical buds burst in 2018.

differences between those two stands apparently did not lead to an adaptation process as initially presumed, at least with respect to those traits.

seasons would indicate a phenotypic plasticity, giving to the current generation of trees a capacity to adequate to a certain new climatic condition. This idea is more discussed in the next section. The moment of foliar senescence marks the end of the productive period in the season of a deciduous tree (Estiarte and Peñuelas, 2015). In our trial, the dates of both the beginning and the end of senescence were not different between populations, which is a result also found in other forest tree species, such as Quercus robur (Baliuckas and Pliura, 2003), Abies alba, Acer pseudoplatanus, Fagus sylvatica, Fraxinus excelsior, Ilex aquifolium and Quercus petraea (Vitasse et al., 2009b). These results show a lack of genetic control for the timing of these phenological phases. At present, there is no agreement among researchers on a correct model for senescence (Schaber and Badeck, 2003), although the date of leaf fall has been observed to be quite consistent from year to year (Lee et al., 2003), which suggests that senescence may be controlled by photoperiod rather than temperature. However, in our work the dates of beginning and end of senescence varied between years. This result was also in agreement with previous studies, reporting that the photoperiodic control of the phenological senescence can be driven by temperature at varying intensities (Tanino et al., 2010). The thermal control has also been demonstrated by delays in leaf senescence in a single year downwards across altitudinal gradients with uniform photoperiods (Richardson et al., 2006; Vitasse et al., 2011, 2009b). This could be the key to understand the observed behavior in our trial. Foliar senescence occurred earlier in 2016 than in 2019. Coincidently, the growing period before 2016-senescence had four times more freezing hours than 2019. Additionally, the growing period before 2016-senescence was particularly dry, since precipitation was less than half of that before 2019-senescence, even removing the day of highest rainfall in the last year (Fig. S1, Supplementary material). May be this condition had an influence on the anticipation of senescence verified in 2016. On the other hand, the significant differences detected among populations and the variance among families in date of bud burst and growing season length, are evidence of its genetic control as well as its probable adaptive value. Adaptation in phenological traits were verified in studies of other forest species (e.g. Alberto et al., 2011, in Q. petraea; Premoli et al., 2007, in Nothofagus pumilio; Vitasse et al., 2009a, in A. alba, A. pseudoplatanus, F. sylvatica, F. excelsior, I. aquifolium and Q. petraea). Regarding N. alpina antecedents in quantitative traits, differentiation among populations and low variability among families have been previously reported for several morphological seedling traits (Duboscq-Carra et al., 2018). Concerning the Tromen stands taken as two different populations, we did not find evidence of differentiation in any of the studied traits. Therefore, the observed environmental

4.2. Role of growing degree days and chilling hours in date of bud burst and its variation Plant phenology is essentially driven by air temperature and photoperiod (Menzel, 2002), and the impact of these drivers can change between species. Our experimental design (two growing season survey of a common garden trial) allowed us to determine that growing degree days and chilling hours have a role in date of bud burst in N. alpina, but not to test the effect of photoperiod. Manipulative experiments or common garden trials located at different latitudes are needed to test photoperiod effect. Furthermore, significant differences detected among populations and the variance among families for growing degree days and chilling hours suggest that temperature requirements in bud burst processes could be related with local adaptation. 4.3. Correlation between traits and geographical and environmental variables of natural populations With respect to the explored relationships between populations mean values of the variables and home conditions of the natural populations, a high positive correlation was only found for date of bud burst and growing degree days with altitude. This finding was observed in other common garden experiments (Gomory and Paule, 2011, in F. sylvatica; Vitasse et al., 2009b, in A. alba, A. pseudoplatanus, F. sylvatica, F. excelsior, I. aquifolium and Q. petraea). Thus, the high-altitude populations (Tromen Alto and Tren Tren) showed a later bud burst and needed more growing degree days than the low-altitude population (Boquete), both in 2015 and in 2018. As suggested by Alberto et al. (2011) and Vitasse et al. (2009b), the late bud burst of the higher-altitude populations could be the result of adaptation to avoid the damage caused on leaves by late spring frosts, which occur most frequently at high altitudes. The date of bud burst would be regulated by the growing degree days required, which seems to be conditioned by the altitude at the site of origin of the population. 5. Conclusions Our results showed that in N. alpina: bud burst, growing season length and temperature requirements until bud burst are under genetic control; plasticity plays a role in all analyzed traits; and bud burst and 8

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heat temperature requirements are conditioned by altitude. Both the phenotypic plasticity and family genetic variation of bud burst timing are good perspectives to face the climate change scenario. Plasticity would help the present generation of trees to adequate the date of bud break to the new climate, while family genetic variation will allow to adapt the related genetic frequencies in the next generations. On the other hand, the variation among populations suggest potential local adaptation for bud burst and growing season length. This knowledge has relevant consequences on both the persistence of the natural populations of the species despite of climate change and its use for commercial afforestation and the development of restoration programs. Niche modelling predictions suggest that N. alpina would be highly vulnerable under future climatic conditions, and would probably endure only at western locations like Boquete (Marchelli et al., 2017). Eastern locations might become climatically milder in the future, but also extreme events like late frost might occur more often. Under this scenario, our results can help in the selection of genetic material for plantations, either for domestication or restoration purposes. Early sprouters might be preferred for more mild but stable locations; while late sprouters can be used for high altitude areas and also for places where amplitude in temperature suggest a more probable occurrence of early frosts.

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Funding This work was supported by the projects “Variación genética de poblaciones naturales argentinas de Raulí (Nothofagus nervosa) y Roble Pellín (Nothofagus obliqua) en caracteres adaptativos tempranos relevantes para domesticación” PIP 2008 N° 112-200801-02867 CONICET; “Subprograma Nothofagus” PROMEF – BIRF 7520 AR; “Mejoramiento Genético de Especies Forestales Nativas de Alto Valor” PNFOR 110463 INTA and “Restauración ecosistémica y domesticación de especies forestales nativas patagónicas con gran potencialidad productiva: bases genéticas de la adaptación a estres hídrico y térmico” PICT 2016 1116 ANPCyT. CRediT authorship contribution statement V.G. Duboscq-Carra: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft. J.A. Arias-Rios: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing - original draft. V.A. El Mujtar: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Visualization, Writing review & editing. P. Marchelli: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Visualization, Writing - review & editing. M.J. Pastorino: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Validation, Visualization, Writing review & editing. Acknowledgements We would like to thank Fernando Barbero for his collaboration in seed collection and seedling production, Mario Huentú and Abel Martínez for their help in the installation of the trial, the staff of the Campo Forestal General San Martin for helping with trial maintenance and Fernando Duran for his kind help for collecting data and trial maintenance work. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.foreco.2019.117858. 9

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