# Shrub growth in the Alps diverges from air temperature since the 1990s

Loïc Francon, Christophe Corona, Irène Till-Bottraud, Philippe Choler, Erwan Roussel, Bradley Z Carlson, Samuel Morin, Brigitte Girard, Markus Stoffel

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Loïc Francon, Christophe Corona, Irène Till-Bottraud, Philippe Choler, Erwan Roussel, et al.. Shrub growth in the Alps diverges from air temperature since the 1990s. Environmental Research Letters, 2021, 16 (7), 10.1088/1748-9326/ac0b67. [https://hal-03289150](https://hal.inrae.fr/hal-03289150)

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# Shrub growth in the Alps diverges from air temperature since the 1990s

To cite this article: Loïc Francon et al 2021 *Environ. Res. Lett.* **16** 074026

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ENVIRONMENTAL RESEARCH
LETTERS

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LETTER

Shrub growth in the Alps diverges from air temperature since the
1990s

Loïc Francon<fnref n="1" />,<fnref n="2" /> ID, Christophe Corona<fnref n="1" />,<fnref n="2" />, Irène Till-Bottraud<fnref n="3" />, Philippe Choler<fnref n="3" />, Erwan Roussel<fnref n="2" />, Bradley Z Carlson<fnref n="1" />, Samuel Morin<fnref n="5" />, Brigitte Girard<fnref n="6" /> and Markus Stoffel<fnref n="1" />,<fnref n="7" />,<fnref n="8" />

E-mail: loic.francon@unige.ch

**Keywords:** dendroecology, alpine, *Rhododendron ferrugineum*, climate change, divergence, topography, shrubs

Supplementary material for this article is available [online](#)

Abstract

In the European Alps, air temperature has increased almost twice as much as the global average over the last century and, as a corollary, snow cover duration has decreased substantially. In the Arctic, dendroecological studies have evidenced that shrub growth is highly sensitive to temperature—this phenomenon has often been linked to shrub expansion and ecosystem greening. Yet, the impacts of climate change on mountain shrub radial growth have not been studied with a comparable level of detail so far. Moreover, dendroecological studies performed in mountain environments did not account for the potential modulation and/or buffering of global warming impacts by topography, despite its possible crucial role in complex alpine environments. To fill this gap, we analyzed a network of eight sites dominated by the dwarf shrub *Rhododendron ferrugineum*. The sites selected for analysis represent the diversity of continentality, elevation and slope aspect that can be found across the French Alps. We quantified annual radial increment growth for 119 individuals, assembled meteorological reanalyzes specifically accounting for topographic effects (elevation, slope and aspect) and assessed climate-growth relations using a mixed modeling approach. In agreement with a vast majority of dendroecological work conducted in alpine and arctic environments, we find that the number of growing degree days during the snow-free period snow-free growing degree days (SFGDDs) is a strong and consistent driver of *R. ferrugineum* growth across all sites since 1960 until the late 1980s. We also document a marked loss of sensitivity of radial growth to increasing SFGDD since the 1990s, with this decoupling being more pronounced at the driest sites. Our observations of the spatial and temporal variability of shrub sensitivity to limiting factors can be compared to the ‘divergence’ problem observed in tree-ring series from circumpolar and alpine regions and, accordingly, sheds light on possible future trajectories of alpine shrub growth in response to ongoing climate change.

**1. Introduction**

Mountainous and Arctic regions are experiencing a
temperature increase that is about two times lar-
ger than the global average; as a corollary, obser-
vations also show a general decline in snow cover

(IPCC 2019). In the Arctic, higher temperatures dur-
ing summer lead to intense greening and increased
shrub productivity and cover, which ultimately drive
climate feedbacks (Myers-Smith et al 2015a, Berner
et al 2020). Likewise, the ongoing shrub expansion
has been shown to modify a range of ecosystem

<footnote n="1">¹ Climate Change Impacts and Risks in the Anthropocene (C-CIA), Institute for Environmental Sciences, University of Geneva, Geneva, Switzerland</footnote>

<footnote n="2">² Université Clermont Auvergne, CNRS, Geolab, F-63000 Clermont-Ferrand, France</footnote>

<footnote n="3">³ Université Grenoble Alpes, Université Savoie Mont-Blanc, CNRS, LECA, F-38000 Grenoble, France</footnote>

<footnote n="4">⁴ Centre de Recherches sur les Ecosystèmes d'Altitude (CREA), Observatoire du Mont-Blanc, 74400 Chamonix, France</footnote>

<footnote n="5">⁵ Université Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d'Études de la Neige, 38000 Grenoble, France</footnote>

<footnote n="6">⁶ Université Clermont Auvergne, INRAE, PIAF, 63000 Clermont-Ferrand, France</footnote>

<footnote n="7">⁷ Department of Earth Sciences, University of Geneva, Geneva, Switzerland</footnote>

<footnote n="8">⁸ Department F.-A. Forel for Environmental and Aquatic Sciences, University of Geneva, Geneva, Switzerland</footnote>
processes (Myers-Smith et al 2011) by altering surface albedo, snowpack depth, energy, water balance, or permafrost conditions (Sturm et al 2001, Liston et al 2002, Chapin 2005). By analyzing a network of circumpolar shrub rings, Myers-Smith et al (2015a) suggested that increasing summer temperature have enhanced shrub radial growth. Several arctic studies also showed that topography may have a positive buffering effect on changing summer temperatures by modifying soil moisture conditions (Myers-Smith et al 2015a). Ropars et al (2015) and Ackerman et al (2017) thereby demonstrated that the response of shrub individuals to climatic parameters can be strongly modulated by local topography. However, Bär et al (2008) and Ackerman et al (2018) did not detect any inconsistent responses of shrubs sampled in contrasting topographic situations to ongoing climate warming.

In the European Alps, increasing vegetation productivity and denser shrub cover above treeline have been documented through remote sensing or resurveys of vegetation plots (Dullinger et al 2003, Cannone et al 2007, Carlson et al 2017, Filippa et al 2019, Malfasi and Cannone 2020). Dendroecological studies have highlighted the negative role of winter precipitation and subsequent, delayed melt-out dates on radial growth of shrubs, but have also demonstrated the positive effect that increasing summer temperatures can have on shrub growth (Pellizzari et al 2014, Francon et al 2017). In the Italian Alps, a study suggested a positive, spatially consistent and stable effect of increasing growing season length on shrub radial growth (*Juniperus communis* nana; Carrer et al 2019). By contrast, two recent, albeit spatially-limited studies emphasized that elevation and slope aspect can significantly modulate *Rhododendron ferrugineum* growth response to increasing temperature significantly (Francon et al 2020a, 2020b). Slope aspect, as a local topographic factor, can mimic temperature differences of large elevational or latitudinal gradients over distances of just a few meters (Scherrer and Körner 2009) by modulating solar incidence angles, frost exposure and soil moisture conditions (Riihimäki et al 2017). Additionally, within mountain ranges, rain-shadow effects—controlling precipitation sums and aridity—were demonstrated to be of paramount importance for plant interactions and growth (Michalet et al 2003). However, no dendroecological study has so far specifically investigated the impacts of topoclimatic variability (defined as the local scale of a few hectares, where climate is driven by variations in topography; Aalto et al 2017) on shrub growth in mountain regions. This research gap is critical given that (a) topoclimatic heterogeneity in alpine environments (Dobrowski 2011) is likely to buffer the response of shrubs to global warming; and that (b) strong moisture gradients that exist over short distances due to the presence of rugged terrain (Scherrer and Körner

2011) could potentially modulate the productivity and performance of alpine vegetation more markedly than in the Arctic (Ernakovich et al 2014). Recent shrub growth and expansion in the European Alps is a critical and understudied topic, considering the various ecosystem 'disservices' expected to be conferred by the replacement of alpine meadows by dense shrub canopies, including: modifications of the microclimate (Mekonnen et al 2021) localized reductions in vascular plant diversity (Anthelme et al 2007, Boscutti et al 2018) diminished pasture resource and quality (Klein et al 2007), and altered soil chemical composition (Pornon et al 1996).

This paper assesses the impacts of global warming on shrub growth for a dominant dwarf shrub species in alpine heathlands, *R. ferrugineum* (L.), with a focus on the possible mediating role of topoclimatic conditions could have on shrub growth responses to climate change. To this end, we (a) assembled annual growth records from a network of eight sites designed to be representative of the contrasts in continentality, elevation and slope aspect across the French Alps. Analyses were performed (b) with SAFRAN-Crocus (Durand et al 2009a, 2009b, Vionnet et al 2012, Vernay et al 2019), a meteorological and snow cover reanalysis providing an estimation of meteorological and snow cover variables for different slope, aspects and per 300 m altitude steps within mountainous regions called 'massifs'. In a last analytical step, we (c) combined bootstrap correlation functions with mixed effects models applied to single *R. ferrugineum* shrubs to quantify threshold effects and shifts in shrub-climate relations resulting from ongoing global warming in the French Alps.

## 2. Material and methods

### 2.1. Shrub-ring chronology development

*R. ferrugineum* is an evergreen dwarf shrub species with highly branched trailing that can reach up to 0.8 m in height. It has a wide geographic range of occurrence across the European Alps (Ozenda 1985) and is in expansion in the treeline ecotone (Pornon et al 1996). Complementary sexual and vegetative reproductive strategies (Doche et al 2005). This strategy also allows *R. ferrugineum* to outcompete other plants in subalpine heaths (Theurillat and Schlüssel 2000), to accumulate large amounts of biomass per unit area and to form large heathlands that reach up to 90%–100% cover after 150–250 years (Pornon et al 1996, Pornon and Doche 1996)—explain its abundance in the subalpine belt. Given its (a) wide distribution from c. 1600–2500 m asl across the European Alps, (b) its longevity, as well as (c) the formation of clearly identifiable annual growth rings, *R. ferrugineum* is a reliable model to study shrub response to long-term climate variability (Francon et al 2017). We thus sampled *R. ferrugineum*
<figure><img src="image_4.png" /><figcaption>**Figure 1.** Location of the eight study sites of the *R. ferrugineum* network used in this study: CHAR refers to the site sampled in the Chartreuse massif; CN2400, SAL2000, SAL1800 to three sites with different slope aspects in the Taillefer massif at 2400, 2000 and 1800 m asl; LORIAZ to the site sampled in the Mont-Blanc massif; QGL, QN, QW refer to three sites with different slope aspects sampled at elevations ranging from 2350 to 2550 m asl in the Queyras massif. Details on site characteristics are given in table 1. Umbro-thermal diagrams (Chartreuse, Oisans, Mont-Blanc and Queyras) were obtained from the Safran-CROCUS (S2M) reanalysis (Météo France, 1981–2010).</figcaption></figure>

<table><caption>**Table 1.** Characteristics of the eight study sites with the name of the mountain massif (Massif), elevation of plots above sea level (Elev.), index of continentality ($\alpha$), and aspect (Asp.) as well as growth ring chronology characteristics and period covered (Length), number of individuals used (Nb.), signal strength (expressed population signal or EPS), mean intercorrelation between individual series inter-series correlation (rbar) and autocorrelation of order 1 (AC) included in the dendroecological network. The EPS, rbar and AC values have been computed for detrended chronologies.</caption><thead><tr><th>Massif</th><th>Site</th><th>Elev.</th><th>α</th><th>Asp.</th><th>Length</th><th>Nb.</th><th>Year<sub>EPS &gt; 0.8</sub></th><th>rbar</th><th>AC</th></tr></thead><tbody><tr><td rowspan="2">Mont-Blanc</td><td>LORIAZ</td><td>2225</td><td>50.70</td><td>135°</td><td>1905–2017</td><td>14</td><td>1960</td><td>0.42</td><td>0.07</td></tr><tr><td>CHAR</td><td>1800</td><td>41.40</td><td>270°</td><td>1900–2016</td><td>16</td><td>1958</td><td>0.29</td><td>0.21</td></tr><tr><td rowspan="3">Taillefer</td><td>CN2400</td><td>2400</td><td>60.00</td><td>45°</td><td>1896–2016</td><td>14</td><td>1959</td><td>0.31</td><td>0.01</td></tr><tr><td>SAL2000</td><td>2000</td><td>54.8</td><td>315°</td><td>1821–2015</td><td>24</td><td>1918</td><td>0.41</td><td>0.22</td></tr><tr><td>SAL1800</td><td>1800</td><td>53.20</td><td>315°</td><td>1862–2016</td><td>11</td><td>1963</td><td>0.32</td><td>0.14</td></tr><tr><td rowspan="3">Queyras</td><td>QGL</td><td>2550</td><td>71.50</td><td>30°</td><td>1940–2017</td><td>15</td><td>1954</td><td>0.41</td><td>0.24</td></tr><tr><td>QN</td><td>2350</td><td>69.50</td><td>30°</td><td>1906–2016</td><td>10</td><td>1954</td><td>0.42</td><td>0.22</td></tr><tr><td>QW</td><td>2350</td><td>68.70</td><td>270°</td><td>1918–2016</td><td>14</td><td>1946</td><td>0.57</td><td>0.13</td></tr></tbody></table>

individuals at eight sites across four mountain massifs of the French Alps. Sites were chosen along a continentality gradient and extend from the Chartreuse (total annual rainfall: 2045 mm) to the Queyras (868 mm) massifs (figure 1), i.e. from the wettest to the driest of the 23 massifs of the French Alps (Durand et al. 2009b). Topographic and climatic characteristics of all massifs considered are synthesized in figure 1 and table 1. At seven sites, substrate is composed of metamorphic or plutonic (i.e. acid rocks), whereas bedrock consists of fractured limestone favoring rapid water infiltration in the Chartreuse massif (CHAR). At all sites, vegetation height ranged between 30 and 50 cm.

Specimens of *R. ferrugineum* were sampled in heathlands or large patches at all sites except at CN2400 where individuals were growing isolated. When sampling, a minimum distance of 4 m was maintained between each sample selected for analysis so as to avoid replication of samples from the same individual (Escaravage et al. 1998). For each individual, one to three sections were taken from one or two stems to allow for a serial sectioning and repeat measurements of growth-ring series on the same individual (figure S2 (available online at stacks.iop.org/ERL/16/074026/mmedia)), but also to detect missing rings in growth-ring series as shrubs often produce very narrow, wedging growth rings
(Kolishchuk 1990). From each section, a micro-section was obtained with a sliding microtome; each section was then stained, colored with safranin and astra blue dye to enhance ring boundaries and finally mounted on slides (Schweingruber and Poschold 2005).

Cross-dating (i.e. the assignment of exact calendar years to the formation of each ring) was realized on the digitalized images of thin sections with CooRecorder and CDendro (CYBIS Elektonik & Data AB); this included a three-step procedure relying on the visual synchronicity between (a) elementary ring-width (RW) series measured at three radii of the same cross section, (b) mean RW series obtained for each section of the same individual, and finally (c) between individual chronologies at a given site. Visual cross-dating was validated statistically with COFECHA (Holmes 1994). In a subsequent step, we detrended each section chronology using a negative exponential function to eliminate non-climatic biological trends using ARSTAN (Cook 1987). After detrending, ring-width indices (RWIs) were averaged for each cross-dated section and individual with a bi-weighted robust mean; this step aimed at reducing the influence of outliers and at developing mean detrended site chronologies (Cook and Peters 1981). Finally, the eight detrended site chronologies were compared with each other using a correlation matrix to check for radial growth consistency between the sites.

We computed descriptive statistics including standard deviation, first-order AC, mean rbar and the running 35 years EPS for each detrended site chronology with ARSTAN (table 1), whereby EPS quantifies the strength of the common climate signal in growth-ring proxies (Wigley et al 1984). We used a quality threshold EPS ≥ 0.8 to evaluate reliability of our chronologies. Sample depth is shown on figure S2.

## 2.2. Meteorological time series and topoclimatic characteristics of network sites

We extracted series of hourly temperature, hourly precipitation totals and daily snowpack depths at each of the 8 network sites from SAFRAN-Crocus (S2M) reanalyzes for the period 1959–2017 (Durand et al 2009a, 2009b, Vionnet et al 2012, Vernay et al 2019). They provide continuous time series of meteorological variables at hourly resolution, for different elevation bands, slope aspects and angles within masses, i.e. horizontally climatologically homogeneous regions. Furthermore, they also provide snow height and snow water equivalent values. Note however, that vegetation is represented in a simplified way in the simulations, assuming continuous grass cover to simulate typical snow conditions and underlying ground temperature for open areas (Vionnet et al 2012). Based on these reanalyzes, we derived snow melt-out dates at our study sites according to the elevation,

slope aspect and angle that had been measured in the field for each plot. Melt-out dates correspond to the date when snow cover depth reaches 10 cm for the last time within a snow cover period of at least 7 d. From the date of melt-out to August 31 (considered here as the last day of growth, based on multiple dendrometric series, unpublished data), we summed all above-zero daily mean temperatures to obtain snow-free growing degree days (SFGDDs). SFGDD summarize thermal conditions experienced during the period during which growth occurs. We also generated monthly and seasonal mean temperature series and precipitation totals. To account for the potential impact of summer drought on radial growth, we computed series of standardized precipitation-evapotranspiration indices (or SPEI, Vicente-Serrano et al 2010) for two month periods corresponding to the length of the growing season. Trends in summer precipitation and temperature, SFGDD and melt-out dates are shown in the supplementary materials attached to this manuscript (figure S3). Topographic characteristics (elevation and slope aspect) and the Gams continentality index (α, Gams 1932) were employed as additional variables to characterize each site, whereas the Northness index—ranging from—1 for a southern to 1 for a northern orientation—was given at each site as the cosine of slope aspect (α) and is used here as an indicator of rain-shadow effects while controlling for the effect of elevation. α was calculated following the equation:

$$ \cotg(\alpha) = P/A $$

where P and A represent the mean annual precipitation sum (mm) and the elevation (m asl), respectively, at each site (table 1). Elevation and slope aspect are known to affect temperature, light availability, solar incidence angles and soil conditions in mountainous terrain (Scherrer and Körner 2009, Bonet et al 2010, Riihimäki et al 2017).

## 2.3. Relationships between shrub radial growth and climate

Climate-growth analyses were performed in two steps: we first calculated bivariate correlation functions (BCFs) between the detrended site chronologies (RWI) and time series of monthly (and summer) temperature, precipitation, SPEI, melt-out dates and SFGDD. This preliminary analysis was intended to detect the main drivers of radial growth at each site. BCFs were computed separately for the periods 1960–1988 and 1989–2017 so as to detect potential shifts in climate-growth relationships related to global warming. The two periods of equal length cover almost 30 years each, in accordance with the definition of a climate normal. The second period also reflects the marked warming observed since the late 1980s (Lo and Hsu 2010, Reid et al 2016). This regime shift to warmer temperatures had a particularly significant
impact on biophysical environments including the physiology, development, behavior and phenology of living organisms (Oberhuber et al 2008, Reid et al 2016). Subsequently, relationships between SFGDD and log-transformed scaled raw RW of R. ferrugineum were investigated further using linear mixed models. The advantage of these models relies in (a) their ability to manage intra-site variance (Myers-Smith et al 2015b); (b) the possibility to compare shrub response between different topoclimatic conditions, thereby allowing to account for potential interactions between variables, for example topography and climate (Myers-Smith et al 2015b); (c) the possibility to use raw ring measurements, thus avoiding detrending that could remove meaningful ecological information from growth RW series (Ackerman et al 2018); (d) the detection of possible non-linear relationships and threshold effects in climate-growth relations by introducing, for example, quadratic effects; and in (e) the inference of predictions of general trends for sites that have not been sampled by using data of multiple sites.

Linear mixed models computed using the *nlme* package (Pinheiro and Bates 2000) in R (R Core Team 2016) are an extension of simple linear models to allow both fixed and random effects. The inclusion of random effects enables control of pseudo-replication, i.e. of the degree of dependence between observations within the same group. In our case, we chose multiple nested grouping levels for the random intercept structure of models: section nested within individual as well as individual within site as random terms (Harrison et al 2018). Fixed effects, equivalent to explanatory variables in standard linear regressions, are variables that we expect to affect RWs. These include cambial age, elevation, Gams continentality index, northness index and mean annual SFGDD (normalized and scaled). Cambial age (i.e. number of years) was introduced in the model to account for age-related trends in growth-ring series. Particular attention was also paid to interactions between SFGDD and other geographical variables (elevation, Gams continentality index and northness index) as they could modulate shrub response to increasing air temperature.

To detect potential threshold effects, we also explored second-order (non-linear) relationships between SFGDD and radial growth in the models. We therefore log-transformed and scaled mean raw RW measurements at the level of individual sections (hereafter referred to as RW) to obtain the required normality of residuals. An AC structure (AR1, first-order autoregressive process) was included as well so as to eliminate temporal biological memory from growth-ring series (Weijers et al 2018b). The predictive capacity of a single independent variable was then determined by the absolute value of Student's t statistics for its linear or quadratic effect on RW. We calculated a pseudo-R² of the selected models comprising

marginal ($R^2$m) and conditional ($R^2$c) values (Nakagawa and Schielzeth 2013). $R^2$m and $R^2$c account for the proportions of the variance explained by the fixed factors and by the whole model (i.e. fixed plus random factors), respectively. $R^2$m and $R^2$c values were calculated with the *MuMln* package (Barton 2019). Elimination of non-statistically significant terms (at $p \le 0.05$) was then used to select the most parsimonious model. The Akaike Information Criterion (AIC) and the Bayesian Information Criterion—imposing a stronger penalty for increasing model complexity—were then used to compare the resulting models. We considered the overall climate sensitivity to be the comparison in AIC value between the best model and a null model (Myers-Smith et al 2015a). Finally, multicollinearity issues where checked based on the variance inflation factor with a threshold set at three (Zuur et al 2010) in *car* package available in R. To detect potential impacts of global warming on climate-growth relationships, three models were implemented for the periods 1960–88, 1989–2017 and 1960–2017.

## 3. Results

Figure 2 provides a synthesis of changes in SFGDDs and RWI values at the eight sites sampled across the French Alps. The SFGDD series do not exhibit any significant trend from 1960 until the late 1980s. By contrast, and starting in the early 1990s, a clear shift of SFGDD is observed at each site along with an increasing trend in all SFGDD series over the period 1989–2017. No comparable trend is observed in the RWI chronologies over the same period (figure 2).

Using the growth RW series obtained from the 225 cross-sections sampled from 119 individuals of R. ferrugineum, we constructed eight detrended site chronologies (see table 1 and figures S1 and S2 for their characteristics) that were subsequently used for the analysis of climate-growth relationships. All chronologies exceed the 0.8 EPS threshold over the 1960–2017 period with the exception of SAL1800 (1963–2017) (table 1). Over the period 1960–89, BCFs (see figure S4) computed between meteorological variables and detrended chronologies reveal strong ($r$ values ranging between 0.39 and 0.75, figure 2) and statistically significant correlations ($p < 0.05$) between RWIs and SFGDD at all sites except at CHAR ($r = 0.22, p = 0.24$, figures 2 and S4). SFGDD was the main climatic driver of radial growth at SAL1800, CN2400, LORIAZ, QGL and QN. At SAL2000, comparable correlations ($r = 0.46, p < 0.05$) were computed between the shrub-ring chronology, SFGDD and summer temperature while at QW, correlation with SFGDD ranks second after the correlation with February precipitation. More generally, RWI correlates negatively with winter precipitation at a vast majority of sites (except for SAL1800 and SAL2000). Finally, mean late summer
<figure><img src="image_5.png" /><figcaption>**Figure 2.** Annual SFGDD series with positive and negative anomalies with respect to the 1960–1988 average indicated by red and blue colors, respectively as well as mean (RWIs, black line) (1960–2017) for each of the eight sites within the French Alps. SFGDD values were transformed into Z-scores with respect to their mean over the 1960–1988 reference period. Dashed and solid red lines represent linear trends (non-significant and significant at *p* ≤ 0.05) in SFGDD series for the time periods 1960–1988 and 1989–2017. Correlation coefficients between SFGDD and RWI are given for each period.</figcaption></figure>

and early fall temperature in the year preceding growth-ring formation (September–October) correlated positively with RWI at SAL2000, CN2400, LORIAZ and QGL (figure S4).

Over the period 1989–2017, site-to-site climate-growth correlation profiles (figure S4) differ much more than over the period 1960–88. Interestingly, SFGDD remains the single most important driver of radial growth only at CN2400 (*r* = 0.42, *p* < 0.05, figure 2), whereas correlations between SFGDD and radial growth dropped close to zero at CHAR, LORIAZ, QGL, and SAL1800—and even become significantly negative at QW and QN (figure 2). Moreover, we find significant negative correlations between shrub chronologies and August air temperatures at CHAR, QN, QGL, and QW (figure S4). At QW, a negative impact of drought on *R. ferrugineum* radial growth is also evidenced by significant correlations between SFGDD (*r* = −0.29, *p* < 0.05) and summer SPEI (*r* = 0.55, *p* < 0.001). By contrast, detrended RW chronologies from SAL1800, SAL2000 and QGL are positively correlated with summer and early fall (September–October) temperatures. Finally, winter precipitation remains an important driver of radial growth at CN2400, LORIAZ, QGL and QN (figure S4).

The correlation matrices computed between site chronologies confirm the loss of consistency between both time periods (figure 3) and indicates that

median intercorrelation decreased from 0.36 to 0.13 from 1960–88 to 1989–2017, respectively. Whereas all chronologies—with exception of LORIAZ—were significantly and positively correlated between sites until 1988, we observe a shift to low and mostly statistically insignificant values for a majority of correlation coefficients over the period 1989–2017.

To further explore potential drivers of shrubs growth, we used a linear mixed modeling approach to associate RW with SFGDD and a suite of geographical variables (i.e. elevation, Gams continentality index and northness index). Prior to computing mixed models, we investigated relationships between RW and SFGDD (figure S5). Conditional *R*² values reveals that, regardless of the period, all models can explain ~50% of the interannual growth variability (44%, 45%, and 47% for 1960–2017, 1960–88, and 1989–2017, respectively; table 2). Marginal *R*² values (fixed factors) explain approximately half of the conditional *R*² showing that the proportions of variance explained by fixed and random variables remain comparable, regardless of the time period considered. According to the ΔAICnull vs model values, the models explain a greater part of the interannual growth variability than the null model. ΔAICnull vs model values yet are much higher for 1960–88 than for 1989–2017 (with values of 701.8 and 151.8, respectively).

For the period 1960–88, the model yields a positive, highly significant linear relation between
<figure><img src="image_6.png" /><figcaption>**Table 2.** Fixed effect coefficients and Student's *t* values of the most parsimonious mixed models predicting *R. ferrugineum* RWs (mm, log-transformed) over the French Alps for the periods 1960–2017, 1960–88 and 1989–2017. Conditional *R*² (*R*²c), marginal *R*² (*R*²m) and AIC differences between each model and a null model (ΔAIC<sub>null vs model</sub>) are given for each model. All variables are scaled, with the exception of stem age. *** (*p* < 0.001), ** (*p* < 0.01), * (*p* < 0.05) indicate the level of significance.</figcaption></figure>

<table><thead><tr><th rowspan="3">Parameter</th><th colspan="2">1960–2017</th><th colspan="2">1960–1988</th><th colspan="2">1989–2017</th></tr><tr><th colspan="2">R<sup>2</sup>c = 0.44; R<sup>2</sup>m = 0.23</th><th colspan="2">R<sup>2</sup>c = 0.45; R<sup>2</sup>m = 0.19</th><th colspan="2">R<sup>2</sup>c = 0.47; R<sup>2</sup>m = 0.19</th></tr><tr><th colspan="2">ΔAIC<sub>null vs model</sub> = 705.2</th><th colspan="2">ΔAIC<sub>null vs model</sub> = 701.8</th><th colspan="2">ΔAIC<sub>null vs model</sub> = 151.8</th></tr><tr><th></th><th>Value</th><th>t-value</th><th>Value</th><th>t-value</th><th>Value</th><th>t-value</th></tr></thead><tbody><tr><td>Intercept</td><td>0.584***</td><td>10.35</td><td>0.304***</td><td>4.05</td><td>0.494***</td><td>6.37</td></tr><tr><td>SFGDD</td><td>0.276***</td><td>21.78</td><td>0.404***</td><td>28.60</td><td>0.175***</td><td>8.63</td></tr><tr><td>(SFGDD)<sup>2</sup></td><td>-0.114***</td><td>-16.74</td><td>—</td><td>—</td><td>-0.065***</td><td>-5.75</td></tr><tr><td>Continentality</td><td>-0.141*</td><td>-2.69</td><td>—</td><td>—</td><td>-0.244**</td><td>-4.08</td></tr><tr><td>Cos(aspect)</td><td>-0.060</td><td>-1.24</td><td>—</td><td>—</td><td>-0.021</td><td>-0.38</td></tr><tr><td>SFGDD* continentality</td><td>-0.190***</td><td>-11.70</td><td>—</td><td>—</td><td>-0.208***</td><td>-7.94</td></tr><tr><td>SFGDD* cos(aspect)</td><td>0.108***</td><td>8.23</td><td>—</td><td>—</td><td>0.135***</td><td>7.39</td></tr><tr><td>(SFGDD)<sup>2</sup>* continentality</td><td>—</td><td>—</td><td>—</td><td>—</td><td>0.066***</td><td>7.54</td></tr><tr><td>(SFGDD)<sup>2</sup>* cos(aspect)</td><td>0.056***</td><td>8.92</td><td>—</td><td>—</td><td>—</td><td>—</td></tr><tr><td>Age</td><td>-0.016***</td><td>-17.55</td><td>-0.011***</td><td>-6.06</td><td>-0.012***</td><td>-9.72</td></tr></tbody></table>

SFGDD and RW, which is consistent with the fact that temperature during the snow-free period is the main driver of *R. ferrugineum* radial growth across the French Alps. By contrast, the independent variables slope aspect, elevation and continentality or the interaction with SFGDD were not significant predictors of RW and were removed from the final model for the period 1960–88. Comparison of the 1960–88 model with that obtained for 1989–2017 shows that, since 1989, *R. ferrugineum* radial growth is less constrained by SFGDD and that its response to temperature is increasingly modulated by continentality and slope aspect (table 2). The model computed over the entire period (1960–2017) indicates that the response of radial growth to SFGDD is non-linear and that it depends on slope aspect and continentality (table 2 and figure 4). The negative, statistically significant quadratic term

computed between SFGDD and RW further points to a decline of *R. ferrugineum* radial growth above a certain SFGDD threshold. The interactions between SFGDD, slope aspect and continentality—included as significant in the model—points to some modulation of threshold values by these geographic variables. Accordingly, maximum radial growth can be expected if SFGDD reaches 1600 °C in the humid masses characterized by a Gams angle of ~41°. The same threshold drops to 900 °C in the drier areas where the Gams angles are >68° (figure 4(A)). Similar differences are observed depending on slope aspect: That is, maximum RW values are modeled at 900 °C and 1700 °C on south- and north-facing slopes, respectively (figure 4(B)). By contrast, elevation does not seem to be a significant predictor in any model and therefore was eliminated as independent variable. This last result is probably due to the fact that snow
<figure><img src="image_7.png" /><figcaption>**Figure 4.** Linear mixed models of *R. ferrugineum* RWs (log-transformed and scaled RW values) as a function of SFGDDs and their interaction with continentality (A) and slope aspect (B) for the period 1960–2017. Ranges for northness and Gams continentality index are indicated to the right side of each panel.</figcaption></figure>

and meteorological variables already encapsulate a great dependence on elevation.

# 4. Discussion

## 4.1. SFGDDs as the main driver of *R. ferrugineum* radial growth before 1989

Our results reveal strong, highly significant and consistent relationships between SFGDD and shrub radial growth until 1988 for all eight sites considered in the network. Our findings are consistent with the widely accepted concept that temperature—and more particularly growing degree days (Jochner et al 2018)—indeed are the single-most important drivers of plant growth in cold environments by driving cell division and phenological transitions (Körner 2003, Kudo and Suzuki 2003, Wipf 2010, Hoch 2015). Our findings are also in line with dendroecological studies realized in the Alps (Pellizzari et al 2014, Françon et al 2017, Carrer et al 2019). These studies demonstrated the positive effect of summer temperatures

and the negative effect of snowpack duration on shrub radial growth at their upper elevational limit. In the Arctic, the vast majority of dendroecological studies found summer temperatures to be the main driver of shrub growth (Blok et al 2011, Jørgensen et al 2015, Myers-Smith et al 2015a, Andreu-Hayles et al 2020). Furthermore, previous studies considered sensitivity to temperature was considered to be consistent over contrasted topographic contexts in most studies (Bär et al 2008, Ackerman et al 2018, Carrer et al 2019).

## 4.2. Temperatures and RWs diverge since 1989

Interestingly, the increasing number of growing degree days observed in our meteorological series after 1989 did not result in a release of radial growth rates from thermal limitations. On the contrary, our results suggest a divergence between increasing SFGDD and shrub radial growth and thus, point to non-linear relationships between the two variables. In other words, and in agreement with smaller scale studies carried out in the Taillefer and Queyras
massifs (Francon et al 2020a, 2020b), our data suggest a loss of sensitivity of shrub growth to SFGDD beyond a certain threshold, and that this threshold is indeed modulated by continentality and slope aspect. We hypothesize that the observed non-linear response of shrub growth is affected by a combination of two effects. On the one hand, the strong increase in growing season length makes shrubs less constrained by the availability of energy to complete their life cycle (Francon et al 2020a). On the other hand, however, this beneficial effect of rising temperatures on growth also seems to be compromised by the negative effects of drought, with the latter depending strongly on the position of the shrub along moisture gradients (Francon et al 2020b). Such non-linearity was not observed by Carrer et al (2019) who reported a consistent and negative role of winter precipitation on *J. communis nana* growth in the Italian Alps, despite the fact that a persistent and significant warming trend could be observed at their site as well. By contrast, warming climate negatively impacted alpine juniper shrub growth and shrub recruitment in the central Himalayas and Tibetan Plateau due to increasing drought stress (Lu et al 2019, Pandey et al 2020). Moreover, a loss of sensitivity to temperature has been observed since the 1990s in *Betula nana* and *Salix glauca* shrubs from Western Greenland (Gamm et al 2018), *Betula nana* and *Empetrum hermaphroditum* shrubs in the central Norwegian Scandes (Weijers et al 2018a) and in *Rumex alpinus* herbs in the Low Tatras of Slovakia (Dolezal et al 2020) which was attributed to global warming and its effects on increasing soil moisture limitation or more frequent late frost events. Similar negative effects of increasingly higher summer temperatures have been highlighted in terms of productivity of alpine meadows in the European Alps (de Boeck et al 2016, Cremonese et al 2017, Corona-Lozada et al 2019), underlining that, if combined with water deficit, heat waves will lead to vegetation browning. The loss of sensitivity to temperature that we highlight in this study echoes the ‘divergence problem’ that has been reported for trees growing in certain regions of circumpolar northern latitudes (Briffa et al 1998, Driscoll 2005, D’Arrigo et al 2008) and high-elevation sites (Büntgen et al 2008). This body of literature suggests—although with a focus on trees—complex and non-linear growth responses of trees to climate changes, thereby leading to a decrease of year-to-year sensitivity of tree growth in previously temperature-limited environments since the mid to late 20th century (D’Arrigo et al 2004, 2008, Wilmking 2005, Büntgen et al 2008, Oberhuber et al 2008, Leonelli et al 2009). Potential causes for this divergence include warming-induced thresholds of tree growth (D’Arrigo et al 2004) or increasing drought stress (Büntgen et al 2006). Assessment of divergence phenomena for shrubs has been only mentioned once at the Arctic tundra biome in response to warming and drying climate (Buchwal

et al 2020). In the same manner as for R. *ferrugineum*, Arctic shrubs display a pronounced growth response heterogeneity starting in the mid-1990s. However, a careful assessment of a divergence problem for alpine shrubs would require shrub ring chronologies covering longer periods, but also broader regions and accounting for other species.

### 4.3. Topographically driven moisture limitation as a possible driver of divergence

The fact that lower correlations are observed between the chronologies at all eight sites after 1989 and that greater heterogeneity emerges in the BCF profiles points to strong site effects that have become the key modulators of shrub growth responses to climate over the last few decades. Thus, we hypothesize that the recent loss of sensitivity and the non-linearity in the response of shrub growth to SFGDD, also referred to the ‘divergence problem’ in forestry and tree-ring research, could be explained by moisture limitations driven by topoclimate and microsite (Wilmking et al 2004, Wilmking 2005). The influence of topoclimate is confirmed by our mixed modeling approach which includes continentality and slope aspect variables in interaction with SFGDD as significant terms. Indeed, both variables directly control soil moisture both at the mesoscale (slope aspect) and at regional (continentality) scales. Therefore, shrubs growing on drier sites (characterized by higher Gams angles and lower northness indices) are more affected by the ongoing increase of air temperatures. Based on dendroecological studies realized in the Arctic, Myers-Smith et al (2015a) likewise postulated that topography, soil properties and snow-related moisture availability would modulate the positive effect of rising temperature on shrub growth. In other words, the sensitivity of shrubs to moisture stress has increased clearly over the last few decades, but this evolution still greatly depends on site characteristics. We are not aware of other studies reporting on topographical effects of moisture stress on shrub growth. In the Italian Alps, the absence of topographic effects on shrub growth reported by Carrer et al (2019) for *J. communis nana* can be explained by the its lesser sensitivity to drought as compared to R. *ferrugineum* (Gracia et al 2007) or to less pronounced topographic variability within the Italian site network. Besides moisture limitation, an increasing frequency of late frost events has been shown to cause both short-term (through damage to aerial parts of the shrub, Bokhorst et al 2009, Treharne et al 2019) and longer-term (through winter droughts, Phoenix and Bjerke 2016, Charrier et al 2017) stresses to shrub vegetation and could, thus, further exacerbate the observed divergence in growth responses. However, when comparing the RWI with the freezing degree days index (calculated as the sum of daily minimum temperature below −2 °C estimated from the moment of melt-out to the end of August), we do not detect any significant effects
<figure><img src="image_8.png" /><figcaption>**Figure 5.** Conceptual model synthesizing potential responses of *R. ferrugineum* radial growth along the moisture gradient to increasing SFGDD in a warmer climate. In wetter sites (top left panel), increasing SFGDD are expected to favor radial growth. By contrast, at drier sites (bottom left panel), a non-linear response is anticipated and will likely limit radial growth above a certain SFGDD threshold.</figcaption></figure>

of late frost on *R. ferrugineum* growth in our dataset. This absence of frost effects should yet be interpreted with caution as frost risk depends strongly on plant exposure and vulnerability, which in turn will be determined by microtopography, plant phenology and hardening (Bréda and Peiffer 2014). On this specific point, studies coupling high-resolution microclimatic and ecophysiological monitoring are yet to be realized to allow for more robust conclusions about effects of frost on shrub growth (Charrier et al 2017).

By contrast, the linear mixed model approach allowed determination, for the first time, of thresholds for the occurrence of divergence. Accordingly, values of 900 °C and 1600 °C represent SFGDD thresholds for *R. ferrugineum* radial growth in the dry (or eastern) and moist (or western) mountain massifs of the French Alps. The threshold also varies with changing aspect and could be set to 900 °C and 1700 °C for south-facing and north-facing slopes, respectively. However, one should keep in mind that the linear mixed model only explains 45% of the interannual RW variability with 24% attributed to the fixed effects. In that respect, taking both microsite (microtopography, soil properties) and (inter-)individual characteristics (competition, morphology, genetic/phenotypic variability) into account will facilitate the definition of potential drivers of shrubs growth further (Wipf et al 2006, Myers-Smith et al 2015a, Boulanger-Lapointe et al 2016, Bjorkman

et al 2018, Berner et al 2020) and thereby also enhance the reliability of our approach.

## 5. Conclusion

The approach developed in this study relies on a network of *R. ferrugineum* RW series from eight contrasted topoclimatic locations across the French Alps. The unprecedented size and spatial coverage of the dataset, along with the temporal resolution of the meteorological and snow cover reanalyzes data used allowed facilitated the disentanglement of variables driving shrub growth at the individual level across sites. We demonstrated that a clear divergence emerged between shrub growth across the French Alps and increasing air temperatures after the early 1990s, a phenomenon that has previously not been demonstrated in the alpine environment (Carrer et al 2019), but has been detected in shrub rings in the arctic environment (Buchwal et al 2020). Our study also points to a likely modulation of the observed divergence by temperature thresholds, which in turn are controlled by differences in moisture availability resulting from contrasted topographical and geographical parameters. Given the increasing air temperatures and the earlier snow melt-out timing projected by climate model simulations (Gobiet et al 2014, Beniston et al, 2018, IPCC 2019), these thresholds will likely be exceeded
even more frequently during the 21st century. By syn-
thesizing the findings of this study, we offer a con-
ceptual model (figure 5) summarizing the potential
response of *R. ferrugineum* radial growth to increas-
ing SFGDD in a warmer climate for wetter and
moisture-limited sites. Individuals should benefit
from increasing temperatures on north-facing slopes
in the north-westernmost regions of the French Alps.
By contrast, we expect their productivity to decrease
as a result of increasingly severe moisture limita-
tions at the drier sites in the Southern Alps. Col-
lectively, our results underline the high sensitivity
of *R. ferrugineum* to climate change. We therefore
consider this shrub to be a valuable sentinel spe-
cies as it integrates the complex and multi-directional
effects that climate change will continue to have
on alpine ecosystems (Françon *et al* 2020a). Scal-
ing up, we encourage future studies to investig-
ate potential relationships between divergent growth
responses demonstrated by dendroecological analyses
and the greening or browning trends quantified with
remote sensing methods (Cunliffe *et al* 2020). At a
more micro-scale, further work is also need to disen-
tangle the underlying eco-physiological processes for
the non-linear relationships observed between ring
growth and temperature increase (de Swaef *et al* 2015,
de Micco *et al* 2019). The latter should necessarily be
complemented with high-resolution spatial imagery
(Myers-Smith *et al* 2020) to account for the hetero-
geneity of alpine environments. We encourage future
alpine studies to focus on the implications of the bid-
irectional evolution of shrub growth evidenced here
on shrub expansion, carbon transfers, radiative feed-
backs, and biodiversity.

Data availability statement

The data that support the findings of this study are
available upon reasonable request from the authors.

Acknowledgments

This research was supported by the program ProXyC-
liM (2016–2017) funded by the CNRS (Mission
interdisciplinarité—RNMSH) and by the ANR/FWF
ACOUFOLLOW project of the French Agence
Nationale de la Recherche (ANR-20-CE91-0008) and
the Austrian Science Found (FWF I 4918). We wish
to thank all who contributed to the meteorological
series data used in the analyses, especially Carlo Maria
Carmagnola (Météo-France—CNRS, CNRM, CEN).
The S2M data are provided by Météo-France—
CNRS, CNRM Centre d'Etudes de la Neige, through
AERIS. We acknowledge Olivier Voldoire (GEOLAB,
CNRS) for technical support. Rhododendron shrubs
were sampled with the help of Séverine Finet, Sébas-
tien Guillet, and Robin Mainieri. CNRM/CEN and
LECA are part of LabEX OSUG@2020.

ORCID iD

Loïc Françon https://orcid.org/0000-0002-2894-9774

References

Aalto J, Riihimäki H, Meineri E, Hylander K and Luoto M 2017
Revealing topoclimatic heterogeneity using meteorological
station data: revealing topoclimatic heterogeneity *Int.
J. Climatol.* **37** 544–56

Ackerman D E, Griffin D, Hobbie S E, Popham K, Jones E and Finlay J C 2018 Uniform shrub growth response to June temperature across the North Slope of Alaska *Environ. Res. Lett.* **13** 044013

Ackerman D, Griffin D, Hobbie S E and Finlay J C 2017 Arctic shrub growth trajectories differ across soil moisture levels *Glob. Change Biol.* **23** 4294–302

Andreu-Hayles L, Gaglioti B V, Berner L T, Levesque M, Anchukaitis K J, Goetz S J and D'Arrigo R 2020 A narrow window of summer temperatures associated with shrub growth in Arctic Alaska *Environ. Res. Lett.* **15** 105012

Anthelme F, Villaret J-C and Brun J-J 2007 Shrub encroachment in the Alps gives rise to the convergence of sub-alpine communities on a regional scale *J. Veg. Sci.* **18** 355–62

Bär A, Pape R, Bräuning A and Löffler J 2008 Growth-ring variations of dwarf shrubs reflect regional climate signals in alpine environments rather than topoclimatic differences *J. Biogeogr.* **35** 625–36

Barton K 2019 Package ‘MuMln’ Model selection and model averaging based on information criteria

Beniston M *et al* 2018 The European mountain cryosphere: a review of its current state, trends, and future challenges *Cryosphere* **12** 759–94

Berner L T *et al* 2020 Summer warming explains widespread but not uniform greening in the Arctic tundra biome *Nat. Commun.* **11** 4621

Bjorkman A D *et al* 2018. Plant functional trait change across a warming tundra biome. *Nature* **562** 57–62

Blok D, Sass-Klaassen U, Schaepman-Strub G, Heijmans M M P D, Sauren P and Berendse F 2011 What are the main climate drivers for shrub growth in Northeastern Siberian tundra? *Biogeosciences* **8** 1169–79

Bokhorst S F, Bjerke J W, Tømmervik H, Callaghan T V and Phoenix G K 2009 Winter warming events damage sub-Arctic vegetation: consistent evidence from an experimental manipulation and a natural event *J. Ecol.* **97** 1408–15

Bonet J A, Palahí M, Colinas C, Pukkala T, Fischer C R, Miina J and Martínez de Aragón J 2010 Modelling the production and species richness of wild mushrooms in pine forests of the Central Pyrenees in northeastern Spain *Can. J. For. Res.* **40** 347–56

Boscutti F, Casolo V, Beraldo P, Braidot E, Zancani M and Rixen C 2018 Shrub growth and plant diversity along an elevation gradient: evidence of indirect effects of climate on alpine ecosystems. Carcaillet C (ed) *PLoS One* **13** e0196653

Boulanger-Lapointe N, Lévesque E, Baittinger C and Schmidt N M 2016 Local variability in growth and reproduction of *Salix arctica* in the High Arctic *Polar Res.* **35** 24126

Bréda N and Peiffer M 2014 Vulnerability to forest decline in a context of climate changes: new prospects about an old question in forest ecology *Ann. For. Sci.* **71** 627–31

Briffa K R, Schweingruber F H, Jones P D, Osborn T J, Shiyatov S G and Vaganov E A 1998 Reduced sensitivity of recent tree-growth to temperature at high northern latitudes *Nature* **391** 678–82

Buchwal A *et al* 2020 Divergence of Arctic shrub growth associated with sea ice decline *Proc. Natl Acad. Sci.* **117** 33334–44

Büntgen U, Frank D C, Nievergelt D and Esper J 2006 Summer temperature variations in the European Alps, A.D. 755–2004 *J. Clim.* **19** 5606–23
Büntgen U, Frank D, Wilson R, Carrer M, Urbinati C and Esper J
2008 Testing for tree-ring divergence in the European Alps
Glob. Change Biol. 14 2443–53

Cannone N, Sgorbati S and Guglielmin M 2007 Unexpected
impacts of climate change on alpine vegetation *Front. Ecol.
Environ.* **5** 360–4

Carlson B Z, Corona M C, Dentant C, Bonet R, Thuiller W and
Choler P 2017 Observed long-term greening of alpine
vegetation—a case study in the French Alps *Environ. Res.
Lett.* **12** 114006

Carrer M, Pellizzari E, Prendin A L, Pividori M and Brunetti M
2019 Winter precipitation—not summer temperature—is
still the main driver for Alpine shrub growth *Sci. Total
Environ.* **682** 171–9

Chapin F S 2005 Role of land-surface changes in Arctic summer
warming *Science* **310** 657–60

Charrier G, Nolf M, Leitinger G, Charra-Vaskou K, Losso A,
Tappeiner U, Amélio T and Mayr S 2017 Monitoring of
freezing dynamics in trees: a simple phase shift causes
complexity *Plant Physiol.* **173** 2196–207

Cook E R 1987 The decomposition of tree-ring series for
environmental studies *Tree Ring Bull.* **47** 37–59

Cook E R and Peters K 1981 The smoothing spline: a new
approach to standardizing forest interior tree-ring width
series for dendroclimatic studies *Tree Ring Bull.* **41** 45–53

Corona-Lozada M C, Morin S and Choler P 2019 Drought offsets
the positive effect of summer heat waves on the canopy
greenness of mountain grasslands *Agric. For. Meteorol.*
**276-277** 107617

Cremonese E, Filippa G, Galvagno M, Siniscalco C, Oddi L, Morra
Di Cella U and Migliavacca M 2017 Heat wave hinders green
wave: the impact of climate extreme on the phenology of a
mountain grassland *Agric. For. Meteorol.* **247** 320–30

Cunliffe A M, Assmann J J, Daskalova G, Kerby J T and
Myers-Smith I H 2020 Aboveground biomass corresponds
strongly with drone-derived canopy height but weakly with
greenness (NDVI) in a shrub tundra landscape *Environ. Res.
Lett.* **15** 125004

D'Arrigo R, Kaufmann R K, Davi N, Jacoby G C, Laskowski C,
Myneni R B and Cherubini P 2004 Thresholds for
warming-induced growth decline at elevational tree line in
the Yukon Territory, Canada: thresholds for warming-
induced growth decline *Glob. Biogeochem. Cycles*
**18** n/a–n/a

D'Arrigo R, Wilson R, Liepert B and Cherubini P 2008 On the
'Divergence Problem' in Northern Forests: a review of the
tree-ring evidence and possible causes *Glob. Planet. Change*
**60** 289–305

de Boeck H J, Bassin S, Verlinden M, Zeiter M and Hiltbrunner E
2016 Simulated heat waves affected alpine grassland only in
combination with drought *New Phytol.* **209** 531–41

de Micco V, Carrer M, Rathgeber C B K, Julio Camarero J,
Voltas J, Cherubini P and Battipaglia G 2019 From
xylogenesis to tree rings: wood traits to investigate
tree response to environmental changes *IAWA J.*
**40** 155–82

de Swaef T, de Schepper V, Vandegehuchte M W and Steppe K
2015 Stem diameter variations as a versatile research tool in
ecophysiology Way D (ed) *Tree Physiol.* **35** 1047–61

Dobrowski S Z 2011 A climatic basis for microrefugia: the
influence of terrain on climate: a climatic basis for
microrefugia *Glob. Change Biol.* **17** 1022–35

Doche B, Franchini S, Pornon A and Lemperiere G 2005 Changes
of humus features along with a successional gradient of
*Rhododendron ferrugineum* (L.) Populations (Subalpine
Level, Northwestern Alps, France) *Arctic, Antarct. Alpine Res.*
**37** 454–64

Dolezal J, Kurnotova M, Stastna P and Klimesova J 2020 Alpine
plant growth and reproduction dynamics in a warmer world
*New Phytol.* **228** 1295–305

Driscoll W W 2005 Divergent tree growth response to recent
climatic warming, Lake Clark National Park and Preserve,
Alaska *Geophys. Res. Lett.* **32** L20703

Dullinger S, Dirnböck T and Grabherr G 2003 Patterns of Shrub
invasion into high mountain grasslands of the Northern
Calcareous Alps, Austria *Arctic, Antarct. Alpine Res.*
**35** 434–41

Durand Y, Giraud G, Laternser M, Etchevers P, Mérindol L and
Lesaffre B 2009a Reanalysis of 47 years of climate in the
French Alps (1958–2005): climatology and trends for snow
cover *J. Appl. Meteorol. Climatol.* **48** 2487–512

Durand Y, Laternser M, Giraud G, Etchevers P, Lesaffre B and
Mérindol L 2009b Reanalysis of 44 Yr of climate in the
French Alps (1958–2002): methodology, model validation,
climatology, and trends for air temperature and
precipitation *J. Appl. Meteorol. Climatol.* **48** 429–49

Ernakovich J G, Hopping K A, Berdanier A B, Simpson R T,
Kachergis E J, Steltzer H and Wallenstein M D 2014
Predicted responses of arctic and alpine ecosystems to
altered seasonality under climate change *Glob. Change Biol.*
**20** 3256–69

Escaravage N, Questiau S, Pornon A, Doche B and Taberlet P 1998
Clonal diversity in a *Rhododendron ferrugineum* L.
(Ericaceae) population inferred from AFLP markers *Mol.
Ecol.* **7** 975–82

Filippa G, Cremonese E, Galvagno M, Isabellon M, Bayle A,
Choler P, Carlson B Z, Gabellani S, Morra Di Cella U and
Migliavacca M 2019 Climatic drivers of greening trends in
the Alps *Remote Sens.* **11** 2527

Francon L et al 2020a Assessing the effects of earlier snow
melt-out on alpine shrub growth: the sooner the better?
*Ecol. Indic.* **11** 106455

Francon L, Corona C, Roussel E, Lopez Saez J and Stoffel M 2017
Warm summers and moderate winter precipitation boost
*Rhododendron ferrugineum* L. growth in the Taillefer massif
(French Alps) *Sci. Total Environ.* **586** 1020–31

Francon L, Corona C, Till-Bottraud I, Carlson B Z and Stoffel M 2020b Some (do not) like it hot: shrub growth is hampered by heat and drought at the alpine treeline in recent decades *Am. J. Bot.* **107** 607–17

Gamm C M, Sullivan P F, Buchwal A, Dial R J, Young A B,
Watts D A, Cahoon S M P, Welker J M and Post E 2018
Declining growth of deciduous shrubs in the warming
climate of continental western Greenland Cornelissen H
(ed) *J. Ecol.* **106** 640–54

Gams H 1932 Die Klimatische Begrenzung von Pflantzenarealen
une die verteilung der hygrischen Kontinentalitat in der
Alpen Zeitschr. D. Gesell. F. Erdkunde **56** 52–68

Gobiet A, Kotlarski S, Beniston M, Heinrich G, Rajczak J and Stoffel M 2014 21st century climate change in the European Alps—a review *Sci. Total Environ.*
**493** 1138–51

Gracia M, Montané F, Piqué J and Retana J 2007 Overstory
structure and topographic gradients determining diversity
and abundance of understory shrub species in temperate
forests in central Pyrenees (NE Spain) *For. Ecol. Manage.*
**242** 391–7

Harrison X A, Donaldson L, Correa-Cano M E, Evans J,
Fisher D N, Goodwin C E D, Robinson B S, Hodgson D J
and Inger R 2018 A brief introduction to mixed effects
modelling and multi-model inference in ecology *Peer
J.* **6** e4794

Hoch G 2015 Carbon reserves as indicators for carbon limitation
in trees *Progress in Botany* ed U Lüttge and W Beyschlag
(Cham: Springer) pp 321–46

Holmes R L 1994 *Dendrochronology Program Library User's Manual* (Tucson, AZ: Laboratory of Tree-Ring Research, University of Arizona)

IPCC 2019 *The Ocean and Cryosphere in a Changing Climate*
Pörtner H O *et al* (Geneva: IPCC)

Jochner M, Bugmann H, Nötzli M and Bigler C 2018 Tree growth
responses to changing temperatures across space and time: a
fine-scale analysis at the treeline in the Swiss Alps *Trees*
**32** 645–60

Jørgensen R H, Hallinger M, Ahlgrimm S, Friemel J, Kollmann J
and Meilby H 2015 Growth response to climatic change over
120 years for *Alnus viridis* and *Salix glauca* in West
Greenland. Michalet R (ed) *J. Veg. Sci.* **26** 155–65

Klein J A, Harte J and Zhao X-Q 2007 Experimental warming, not grazing, decreases rangeland quality of the Tibetan Plateau *Ecol. Appl.* **17** 541–57

Kolishchuk V 1990 Dendroclimatological study of prostrate woody plant *Methods of Dendrochronology, Applications in the Environmental Sciences* (Dordrecht: Springer) pp 51–5

Körner C 2003 *Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems* 2nd edn (Berlin: Springer)

Kudo G and Suzuki S 2003 Warming effects on growth, production, and vegetation structure of alpine shrubs: a five-year experiment in northern Japan *Oecologia* **135** 280–7

Leonelli G, Pelfini M, Battipaglia G and Cherubini P 2009 Site-aspect influence on climate sensitivity over time of a high-altitude *Pinus cembra* tree-ring network *Clim. Change* **96** 185–201

Liston G E, Mcfadden J P, Sturm M and Pielke R A 2002 Modelled changes in arctic tundra snow, energy and moisture fluxes due to increased shrubs *Glob. Change Biol.* **8** 17–32

Lo T-T and Hsu H-H 2010 Change in the dominant decadal patterns and the late 1980s abrupt warming in the extratropical Northern Hemisphere *Atmos. Sci. Lett.* **11** 210–5

Lu X, Liang E, Wang Y, Babst F, Leavitt S W and Julio Camarero J 2019 Past the climate optimum: recruitment is declining at the world's highest juniper shrublines on the Tibetan Plateau *Ecology* **100** e02557

Malfasi F and Cannone N 2020 Climate warming persistence triggered tree ingression after shrub encroachment in a high Alpine Tundra *Ecosystems* **23** 1657–75

Mekonnen Z A *et al* 2021 Arctic tundra shrubification: a review of mechanisms and impacts on ecosystem carbon balance *Environ. Res. Lett.* **16** 053001

Michalet R, Rolland C, Joud D, Gafta D and Callaway R M 2003 Associations between canopy and understory species increase along a rainshadow gradient in the Alps: habitat heterogeneity or facilitation? *Plant Ecol.* **165** 145–60

Myers-Smith I H *et al* 2011 Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities *Environ. Res. Lett.* **6** 045509

Myers-Smith I H *et al* 2015a Climate sensitivity of shrub growth across the tundra biome *Nat. Clim. Change* **5** 887–91

Myers-Smith I H *et al* 2015b Methods for measuring arctic and alpine shrub growth: a review *Earth Sci. Rev.* **140** 1–13

Myers-Smith I H *et al* 2020 Complexity revealed in the greening of the Arctic *Nat. Clim. Change* **10** 106–17

Nakagawa S and Schielzeth H 2013 A general and simple method for obtaining $R^2$ from generalized linear mixed-effects models *Methods Ecol. Evol.* **4** 133–42

Oberhuber W, Kofler W, Pfeifer K, Seeber A, Gruber A and Wieser G 2008 Long-term changes in tree-ring-climate relationships at Mt. Patscherkofel (Tyrol, Austria) since the mid-1980s *Trees* **22** 31–40

Ozenda P G 1985 La végétation de la chaîne alpine: dans l'espace montagnard européen. Masson: Paris u.a

Pandey J, Sigdel S R, Lu X, Salerno F, Dawadi B, Liang E and Camarero J J 2020 Early growing-season precipitation drives radial growth of alpine juniper shrubs in the central Himalayas *Geogr. Ann.* A **102** 317–30

Pellizzari E, Pividori M and Carrer M 2014 Winter precipitation effect in a mid-latitude temperature-limited environment: the case of common juniper at high elevation in the Alps *Environ. Res. Lett.* **9** 104021

Phoenix G K and Bjerke J W 2016 Arctic browning: extreme events and trends reversing arctic greening *Glob. Change Biol.* **22** 2960–2

Pinheiro J C and Bates D M 2000 *Mixed-effects Models in S and S-PLUS* (Berlin: Springer)

Pornon A, Bligny R, Gout E and Doche B 1996 Growth rates and nutrition status of an open and a closed population of

*Rhododendron ferrugineum* L. in the northwestern Alps (France). *Trees* **11** 91–98

Pornon A and Doche B 1996 Age structure and dynamics of *Rhododendron ferrugineum* L. populations in the northwestern French Alps *J. Veg. Sci.* **7** 265–72

R Core Team 2016 R: a language and environment for statistical computing. R foundation for statistical computing. R foundation for statistical computing Vienna, Austria (available at: www.R-project.org/)

Reid P C *et al* 2016 Global impacts of the 1980s regime shift *Glob. Change Biol.* **22** 682–703

Riihimäki H, Heiskanen J and Luoto M 2017 The effect of topography on arctic-alpine aboveground biomass and NDVI patterns *Int. J. Appl. Earth Obs. Geoinf.* **56** 44–53

Ropars P, Lévesque E and Boudreau S 2015 How do climate and topography influence the greening of the forest-tundra ecotone in northern Québec? A dendrochronological analysis of *Betula glandulosa* *J. Ecol.* **103** 679–90

Scherrer D and Körner C 2009 Infra-red thermometry of alpine landscapes challenges climatic warming projections: thermometry of alpine landscapes *Glob. Change Biol.* **16** 2602–13

Scherrer D and Körner C 2011 Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming: topographical control of thermal-habitat differentiation buffers alpine plant diversity *J. Biogeogr.* **38** 406–16

Schweingruber F H and Poschold P 2005 Growth rings in herbs and shrubs: life span, age determination and stem anatomy *For. Snow Landsc. Res.* **79** 195–415

Sturm M, Holmgren J, McFadden J P, Liston G E, Chapin F S and Racine C H 2001 Snow–shrub interactions in Arctic Tundra: a hypothesis with climatic implications *J. Clim.* **14** 336–44

Theurillat J P and Schlüssel A 2000 Phenology and distribution strategy of key plant species within the subalpine-alpine ecotone in the Valaisan Alps (Switzerland) *Phytocoenologia* **30** 439–56

Treharne R, Bjerke J W, Tommervik H, Stendardi L and Phoenix G K 2019 Arctic browning: impacts of extreme climatic events on heathland ecosystem CO₂ fluxes *Glob. Change Biol.* **25** 489–503

Vernay M, Lafaysse M, Hagenmuller P, Nheili R, Verfaillie D and Morin S 2019 The S2M meteorological and snow cover reanalysis in the French mountainous areas (1958—present) (available at: https://en.aeris-data.fr/metadata/?865730e8-edeb-4c6b-ae58-80f95166509b) (Accessed 8 December 2020)

Vicente-Serrano S M, Beguería S and López-Moreno J I 2010 A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index *J. Clim.* **23** 1696–718

Vionnet V, Brun E, Morin S, Boone A, Faroux S, Le Moigne P, Martin E and Willemet J-M 2012 The detailed snowpack scheme Crocus and its implementation in SURFEX v7.2 *Geosci. Model Dev.* **5** 773–91

Weijers S, Beckers N and Löffler J 2018a Recent spring warming limits near-treeline deciduous and evergreen alpine dwarf shrub growth *Ecosphere* **9** e02328

Weijers S, Pape R, Löffler J and Myers-Smith I H 2018b Contrasting shrub species respond to early summer temperatures leading to correspondence of shrub growth patterns *Environ. Res. Lett.* **13** 034005

Wigley T M L, Briffa K R and Jones P D 1984 On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology *J. Clim. Appl. Meteorol.* **23** 201–13

Wilmking M 2005 Increased temperature sensitivity and divergent growth trends in circumpolar boreal forests *Geophys. Res. Lett.* **32**
Wilmking M, Juday G P, Barber V A and Zald H S J 2004 Recent climate warming forces contrasting growth responses of white spruce at treeline in Alaska through temperature thresholds *Glob. Change Biol.* 10 1724–36

Wipf S 2010 *Phenology, growth, and fecundity of eight subarctic tundra species in response to snowmelt manipulations* *Plant Ecol.* **207** 53–66

Wipf S, Rixen C and Mulder C P H 2006 Advanced snowmelt causes shift towards positive neighbour interactions in a subarctic tundra community *Glob. Change Biol.* **12** 1496–506

Zuur A F, Ieno E N and Elphick C S 2010 A protocol for data exploration to avoid common statistical problems: data exploration *Methods Ecol. Evol.* **1** 3–14