The Biogeography, Evolution, and Function of Leaf-Out Phenology Studied with Experimental, Monitoring, and Phylogenetic Approach

Dissertation zur Erlangung des Doktorgrades der Naturwissenschaften (Dr. rer. nat.) an der Fakultät für Biologie der Ludwig-Maximilians-Universität München

THE BIOGEOGRAPHY, EVOLUTION, AND FUNCTION

OF LEAF-OUT PHENOLOGY STUDIED WITH EXPERIMENTAL,

MONITORING, AND PHYLOGENETIC APPROACHES

Constantin Mario Zohner

München, 20. August 2016

ii PREFACE

Statutory declaration

Erklärung

Diese Dissertation wurde im Sinne von §12 der Promotionsordnung von Prof. Dr. Susanne S. Renner betreut. Ich erkläre hiermit, dass die Dissertation nicht einer anderen Prüfungskommission vorgelegt worden ist und dass ich mich nicht anderweitig einer Doktorprüfung ohne Erfolg unterzogen habe.

Eidesstattliche Erklärung

Ich versichere hiermit an Eides statt, dass die vorgelegte Dissertation von mir selbstständig und ohne unerlaubte Hilfe angefertigt wurde.

Constantin Zohner, 20. August 2016 (Unterschrift)

1. Gutachter: Prof. Dr. Susanne S. Renner 2. Gutachter: Prof. Dr. Hugo Scheer

Tag der Abgabe: 25.08.2016 Tag der Disputation: 21.11.2016

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iv Note In this thesis, I present the results from my doctoral research, carried out in Munich from January 2014 to August 2016 under the guidance of Prof. Dr. Susanne S. Renner. My thesis resulted in four manuscripts, presented in Chapters 2 to 5, of which two have been published (Chapters 2 and 5), one is accepted (Chapter 3), and one is in review (Chapter 4). I also gave the conference talks listed below. I generated all data and conducted all analyses myself except for Chapter 5 for which I contributed the phenological data and conducted some analyses. Writing and discussion involved collaboration with S.S. Renner, with input from J.-C. Svenning (Chapters 3 and 4), and J.D. Fridley (Chapter 4). L. Muffler led the writing in Chapter 5, and I wrote all parts concerned with phenology.

Constantin Zohner Prof. Susanne S. Renner (Signature) (Signature)

List of publications Peer-reviewed journal articles

ZOHNER, C.M., BENITO, B.M., FRIDLEY, J.D., SVENNING, J.-C., RENNER, S.S. In review. Spring predictability explains different leaf-out times in the Northern Hemisphere woody floras. Nature.

ZOHNER, C.M., BENITO, B.M., SVENNING, J.-C., RENNER, S.S. In press. Photoperiod unlikely to constrain climate change-driven shifts in leaf-out phenology in northern woody plants. Nature Climate Change.

MUFFLER, L., BEIERKUHNLEIN, C., AAS, G., JENTSCH, A., SCHWEIGER, A.H., ZOHNER, C.M.,

KREYLING, J. 2016. Distribution ranges and spring phenology explain late frost sensitivity of 170 woody plants from the Northern hemisphere. Global Ecology and Biogeography. DOI: 10.1111/geb.12466, first published 29 May 2016

ZOHNER, C.M., AND RENNER, S.S. 2015. Perception of photoperiod in individual buds of mature trees regulates leaf-out. New Phytologist 208: 1023 – 1030.

vi Oral presentations

ZOHNER, C.M. Historical climates explain contrasting dormancy-breaking requirements in North American, Asian, and European woody species. EGU General Assembly, Vienna, Austria, 18 April 2016

ZOHNER, C.M. (Why) do temperate woody species use the photoperiod to regulate leaf-out? Phenology Workshop and Prep Meeting for AABG 4, Pont de Nant, Switzerland, 14 November 2015

ZOHNER, C.M. Perception of photoperiod in individual buds of mature trees regulates leaf-out. International Conference on Phenology, Kusadasi, Turkey, 8 October 2015

ZOHNER, C.M. (Why) do plants use the photoperiod to regulate leaf-out? Technical University Munich, Botanikertagung 2015, From molecules to the field, Freising, Germany, 2 September 2015

ZOHNER, C.M. Biogeographic history of Limonium (Plumbaginaceae), inferred with the first model that includes a parameter for speciation with dispersal. 16. Jahrestagung der Gesellschaft für Biologische Systematik (GfBS), Bonn, Germany, 19 March 2015

ZOHNER, C.M. The evolution and function of phenological signals and species-specific changes in leaf-out times. Seminar series at Department of Ecology, Environment and Plant Sciences, Stockholm, Sweden, 19 August 2014

Poster

ZOHNER, C.M., AND RENNER, S.S. Does biogeographic origin influence leaf-out phenology? International Biogeography Society: 7th Biennial Meeting, Bayreuth, Germany, Jan 8– 12, 2015.

ZOHNER, C.M., AND RENNER, S.S. Climate-change induced shifts in leaf-out times. Radiations 2014, Zurich, Switzerland, June 12–15, 2014.

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viii CONTENTS

Preface...... iii Statutory Declaration...... iii Erklärung...... iii Eidesstattliche Erklärung...... iii Note...... v List of publications...... v Oral presentations...... vi Poster...... vii Contents...... vii Summary...... 1 Chapter 1: General Introduction...... 3 Plant phenology...... 5 The ecological implications from phenological transitions in response to climate change..6 Experimental phenology...... 7 Leaf-out in woody plants and the environmental factors that regulate it...... 8 Causes of intra- and interspecific variation in leaf-out phenology...... 10 Research questions...... 13 Chapter 2: Perception of photoperiod in individual buds of mature trees regulates leaf-out...... 15 Chapter 3: Photoperiod unlikely to constrain climate change-driven shifts in leaf-out phenology in northern woody plants...... 29 Chapter 4: Spring predictability explains different leaf-out strategies in the Northern Hemisphere woody floras...... 73 Chapter 5: Distribution ranges and spring phenology explain late frost sensitivity of 170 woody plants from the Northern hemisphere...... 127 Chapter 6: General Discussion...... 147 6.1. Do temperate woody species use the photoperiod in spring as a cue for leaf-out?...... 149 6.2. Geographic variation in leaf-out strategies...... 151 6.3. Phenology and invasive success...... 153 ix

x 6.4. Herbarium phenology...... 154 6.5. Future research questions...... 156 6.5.1. Using latitudinal gradients to study intraspecific variability of leaf-out....156 6.5.2. Leaf senescence and vegetation periods...... 159 6.5.3. The molecular basis of dormancy release...... 160 References...... 162 Acknowledgements...... 171 Curriculum vitae...... 173

xii SUMMARY This dissertation deals with the timing of leaf unfolding in temperate woody plants, especially as regards the ongoing climate change. It particularly asks (i) if and how plants use photoperiod duration to regulate leaf-out, (ii) how adaptation to climate parameters relates to inter-specific variability in leaf-out strategies, (iii) if region-specific frost probabilities explain global biogeographic patterns in leaf-out phenology, and (iv) whether a species’ frost sensitivity is linked to its phenological strategy. To address these questions, I studied the phenology of 1600 woody species grown under common conditions in temperate gardens using experimental and monitoring approaches. The experiments particularly served to disentangle the three key drivers of leaf unfolding: photoperiod, chilling, and spring warming. I investigated the role of photoperiod and the extent of bud autonomy in leaf unfolding by applying in situ bagging experiments to three widespread European tree species (Chapter 2). I also conducted twig cutting experiments in ~200 species to study the effects of regional climate history on species’ photoperiod sensitivity (Chapter 3). I used monitoring and experimental data to study how chilling and spring warming requirements are shaped by species’ phylogenetic and biogeographic history. This provided an opportunity to infer region-specific phenological responses to climate change (Chapter 4). Lastly, I assessed the link between species’ leaf-out phenology and frost sensitivity by relating leaf-out observational data to information on the frost sensitivity (especially damage to their young leaves) in 170 species (Chapter 5). The experiments took me in the direction of proximate mechanisms, while most my other work focused on the ultimate (evolutionary) drivers of leaf unfolding. All my work combines experimental and statistical approaches typical of ecology with the comparative and data-mining approaches typical of systematics and macroecology. Photoperiod control of leaf unfolding in temperate woody species is poorly understood. To investigate when, where, and how photoperiod signals are perceived by plants to trigger leaf- out, I conducted in situ bagging experiments in Fagus sylvatica, Picea abies, and Aesculus hippocastanum. Twigs of nearby branches where kept under constant 8h short days or exposed to natural day-length increase. These experiments revealed that (i) the leaf primordia in each bud autonomously react to photoperiod signals, (ii) buds only react to photoperiod in late dormancy when air temperature increases, and (iii) the phytochrome system is mediating photoperiod control of leaf unfolding.

1 To investigate why the relative importance of photoperiod in regulating budburst differs among species, I tested for correlations between species’ photoperiod-sensitivity (as inferred from twig-cutting experiments) and their climate ranges. These analyses revealed that only 30% of temperate woody species use the photoperiod as a cue for leaf-out and these all come from regions with relatively short winter periods. In regions with long winters, increase in day length occurs too early (around the spring equinox) for frosts to be safely avoided. Species-specific leaf-out strategies evolved as a consequence of the trade-off between early carbon gain and avoidance of late frosts to prevent tissue damage. In regions where late frost events are common one can therefore expect conservative growth strategies (i.e., late leaf display) resulting from high chilling and/or high spring warming requirements. To test for this, I modeled the spring temperature variability across the Northern Hemisphere on the basis of gridded climate data over the past 100 years and correlated these data with phenological data on 1600 Northern Hemisphere species. The results showed that especially in eastern North America the late frost risk is high and, as a result, species from east North America have late leaf out strategies and high chilling requirements compared to species from regions with low late frost risk, such as East Asia. In eastern North America, species’ high chilling requirements should therefore counteract climate warming-induced advances in spring leaf unfolding, whereas opportunistic species from East Asia (that have lower winter chilling requirements) should be able to continuously track spring temperature increases. Chapter 5 of this dissertation deals with the biogeographic and phenological importance of late frost sensitivity. With colleagues from Bayreuth, I inferred the freezing-resistance of emerging leaves in 170 species, taking advantage of a natural extreme late frost event that occurred in the Bayreuth Ecological-Botanical Garden in May 2011 (-4.3°C after bud burst of all species). Frost-tolerant species flushed on average 2 weeks earlier than species sensitive to late frosts. Species’ phenological strategies therefore appear to reflect the frost sensitivity of their young leaves.

Chapter 1

GENERAL INTRODUCTION

4 Plant phenology One of the founders of the science of plant phenology was Carl von Linné (1751) who described methods for compiling calendars by recording species’ leaf-out times, flowering times, fruiting times, and leaf fall. The first phenological observations, however, go much further back and allowed people to predict sowing and harvesting times and also whether the climate in a particular year was different from ‘normal’ (Pfister, 1980). Quantification of climate parameters thus occurred long before the invention of the alcohol thermometer in 1709 and the mercury thermometer in 1714. Today’s driving force behind the collection of phenological data is still their utility for agriculture and other aspects of human wellbeing (e.g., forecasting fire hazard), but increasingly also a different goal: the forecasting of the ecological and economical consequences of anthropogenic climate change. Phenology is “the study of the timing of recurring biological events, the causes of their timing with regard to biotic and abiotic forces, and the interrelation among phases of the same or different species” (Lieth, 1974, p. 4). Modern phenological research is no longer restricted to the observation of life cycle events, e.g. flowering and fruiting, but aims to understand the environmental drivers underlying these observations and the relationships of different phenological events (both within and between species) to each other (Richardson et al., 2013). The basis for phenological research is data on multiple phenological events, over multiple years, and from a diverse set of taxonomic groups. Such data sometimes come from observations made by one person, such as Henry David Thoreau or the Marsham family (Miller-Rushing & Primack, 2008; Sparks & Menzel, 2002), but more often from networks of institutions or researchers, such as the Pan European Phenology Project (http://www.pep725.eu/) or the International Phenological Gardens network (http://ipg.hu-berlin.de). Correlative studies have shown earlier vegetation activity in spring in response to warming, with leaf-out in woody species advancing by 3–8 days for each 1°C increase in air temperature (Menzel & Fabian, 1999; Chmielewski & Rötzer, 2001; Parmesan, 2007; Zohner & Renner, 2014). How climate change may be affecting the end of the growing season is less clear, but many species (from the temperate zone) react to warmer autumn temperatures by shedding their leaves later in the season (Menzel & Fabian, 1999; Menzel, 2000; Menzel et al., 2006; Vitasse et al., 2009; Vitasse et al., 2011).

5 The ecological implications from phenological transitions in response to climate change Shifts in the leaf-out timing of temperate species may have a huge impact on ecosystem processes, such as carbon fixation, biomass accumulation, water cycling, microclimate and - animal interactions (Richardson et al., 2010; Richardson et al., 2013). Polgar and Primack (2011) provide an example of how changes in phenology may change competition for light and water resources between trees and shrubs. Once trees produce leaves, they are retaining most of the incoming rainfall. Advanced flushing under a warming climate would thus be associated with reduced through fall in spring, perhaps leading to decreased soil water and less soil evaporation. This might have adverse consequences for understory plants and could be reinforced by decreased light intensities in early spring caused by an earlier closure of the canopy. Such effects of the presence of leaves on water retention or run off are coupled with the albedo of leaf canopies, which depends on leaf area and other properties (White et al., 1999; Hollinger et al., 2010; Zha et al., 2010). Phenology is playing a key role in regulating such vegetation-atmosphere feedbacks (Richardson et al., 2013). By influencing temperature gradients, humidity, soil temperature and moisture, solar radiation, and precipitation retention and runoff, phenological transitions exert strong effects on microclimatic conditions. The global climate, too, is influenced by vegetation activity because phenological cycles affect water, energy, and carbon fluxes (Hogg et al., 2000; Schwartz & Crawford, 2001; Zha et al., 2010; Keenan et al., 2014). A lengthening of the growing season under a climate-warming scenario is unlikely to be associated with a proportional increase of carbon sequestration, however, because higher temperatures cause increased respiration. In northern ecosystems, carbon loss due to increased respiration has even been shown to exceed carbon gains from an extended growing season, thereby leading to a reduction of carbon concentration in forest ecosystems (Milyukova et al., 2002; Piao et al., 2008). Because of such antagonistic effects, prolonged growing seasons cannot readily be equated with an increase in carbon sequestration. Furthermore, the degree to which extended growing periods influence the rates of biomass accumulation is biome-specific. For instance, coniferous forests are expected to have a lower increase in biomass than deciduous forests (Richardson et al., 2009, 2010).

6 Experimental phenology Most phenological studies are based on correlative analyses (Sparks & Carey, 1995; Menzel & Fabian, 1999; Menzel, 2000; Cook et al., 2012; Mazer et al., 2013; Zohner & Renner, 2014). A shortcoming of any such study is that correlations do not offer insights into ultimate or proximate mechanisms underlying responses to climate parameters, which prevents the development of mechanistic models. To better parameterize current models, experimental studies are needed to investigate the environmental cues that trigger phenological events, such as bud break, flowering, and leaf senescence, as well as the developmental-genetic pathways and physiological mechanisms. With respect to bud break, experimental insights have come from the so-called twig cutting method, in which twigs are cut from adult trees and brought into controlled conditions to detect and quantify the environmental factors that affect dormancy release and leaf unfolding (Heide, 1993a, b; Basler & Körner, 2012; Dantec et al., 2014; Laube et al., 2014a, b; Polgar et al., 2014; Primack et al., 2015). The method relies on the assumption that dormant buds in woody species react autonomously and do not depend on the twig being connected to the stem or the root system. A carefully controlled experimental study recently confirmed that cut twigs indeed show the same phenological response as their donor tree (Vitasse & Basler, 2014), validating the approach as an appropriate method for studying budburst cues. Another approach to studying the phenological behavior of trees is to use seedlings in climate chambers (e.g., Falusi & Calamassi, 1990). However, juveniles differ from adults in their reaction to environmental cues (Vitasse et al., 2014), complicating extrapolation from experiments with seedlings.

Leaf-out in woody plants and the environmental factors that regulate it As revealed by experiments of the type described above, there are three main cues used by plants to regulate budburst: winter chilling, spring warming, and photoperiod (Falusi & Calamassi, 1990; Heide, 1993a,b; Myking & Heide, 1995; Heide, 2003; Ghelardini et al., 2010; Basler & Körner, 2012; Laube et al., 2014a; Polgar et al., 2014). The relative role of these three factors depends on the species (Heide, 1993a, b; Körner, 2006; Körner & Basler, 2010; Polgar & Primack, 2011; Basler & Körner, 2012). ‘Chilling’ refers to an exposure of plants to cold temperatures in winter. During the winter period, buds of most temperate species are in a state of rest, a period with physiological arrested or slowed development (endodormancy), preventing bud burst regardless of the environmental conditions (Hänninen et al., 2007). In chilling-sensitive

7 species, an adequate duration of winter cooling (a sum of hours or days below a certain threshold temperature) is necessary to break the inactive phase (Körner, 2006). The molecular and physiological mechanisms underlying the perception of chilling signals are poorly understood (Cooke et al., 2012), and the specific temperatures adequate for the fulfillment of chilling requirements are not known, although they seem to range between zero and 12 degree Celsius (Heide, 2003). Accumulated chilling affects the amount of forcing in the spring for a plant to push out its leaves (Körner, 2006). Put simply, the less chilling in winter, the more warming is needed in spring for budburst. As an example, in a climate chamber experiment with twig cuttings of five elm species (Ulmus spp.), Ghelardini et al. (2010) found that the thermal time to budburst decreases with the number of chill days (= days with mean air temperatures below 5°C) they had experienced. This effect has since been found in at least 50 tree species (Laube et al., 2014a; Polgar et al., 2014). It predicts that, in plants employing a double control of budburst (chilling and warming requirements), the different effects of climate warming on the timing of leaf unfolding will cancel each other out: warmer springs are causing earlier leaf emergence because species’ temperature requirements are fulfilled earlier. Warmer winters, by contrast, will lead to delayed budburst, because the plants experience less chilling. Therefore, a continuing linear response to spring warming is not expected (Körner & Basler, 2010; Zohner & Renner, 2014; Fu et al., 2015). The relative importance of chilling and warming stimuli differs among and within species (Falusi & Calamassi, 1996; Ghelardini et al., 2006; Olson et al., 2013; Zohner & Renner, 2014), complicating the forecasting of phenological shifts in species-rich communities. Forecasting is made even more complex by additional factors that affect the timing of leaf unfolding, such as the time of dormancy induction in autumn, air humidity, and day length increase in spring (Heide, 2003; Körner, 2006; Laube et al., 2014b). That autumn senescence (dormancy induction) can affect leaf unfolding in the following spring was observed by Fu et al. (2014a), who found in Fagus sylvatica and Quercus robur that individuals that senesced earlier also leafed out earlier in the following year. The underlying mechanism of such carry-over effects is not understood, but might relate to earlier senescence allowing earlier perception of chilling signals and therefore earlier release from endodormancy (Fu et al., 2014a). The importance of water availability in influencing the timing of leaf unfolding in temperate woody species has rarely been analyzed (but see Fu et al., 2014b; Laube et al., 2014b; Shen et al., 2015). Using

8 twigs exposed to different levels of air humidity, Laube et al. (2014b) showed that in some species, humidity has an effect on leaf-out, with plants exposed to drier conditions showing delayed budburst. Because relative air humidity is highly correlated with air temperature, Laube and colleagues further suggest that, instead of sensing temperature, plants might perceive winter chilling and spring warming by tracking air humidity. Different from possible effects of autumn carry-over and air humidity, the role of day length increase for dormancy release in spring has received much attention (Heide 1993a,b; Körner & Basler, 2010; Vitasse & Basler, 2013; Laube et al., 2014a). While autumn-senescence of broad-leafed trees is largely determined by photoperiod signals, the role of photoperiod perception for bud break is less clear, perhaps in part reflecting experimental difficulties in adequately modifying day length when working with trees (Vitasse & Basler 2013, 2014). In a thought-provoking (and much cited) commentary, Körner and Basler (2010; also Körner, 2006 and Basler & Körner, 2012) hypothesized three main leaf-out strategies: In long-lived species, like Fagus sylvatica and Celtis occidentalis, photoperiod was thought to control both the induction and the release from dormancy, with temperature playing only a modulating role once the critical day-length has passed. The argument was that, “Because temperature is often an unreliable marker of seasonality, most long-lived plant species native to areas outside the tropics have evolved a second line of safeguarding against ‘misleading’ temperature conditions: photoperiodism. The significance of photoperiodism increases with latitude, not only because the annual variation of the photoperiod becomes more pronounced, but also because of its biological function. […] photoperiodism prevents phenology from following temperature as a risky environmental signal for development. […] It is an insurance against temperature-induced break of dormancy too early in the season. Thus photoperiodism constrains development to ‘safe periods’.” (Körner, 2006, p. 62). Shorter-lived species, like Betula pendula and Corylus avellana, were thought to be independent of day-length influences, which would allow them to respond more quickly to episodes of warm temperature in early spring, but also create more susceptibility to late frosts. Lastly, leafing out in ornamental plants from warmer climates, such as domestic cherries (Prunus spp.), was thought to depend almost exclusively on spring temperature, with no chilling and photoperiod requirements. Experiments in ~40 woody species from the Northern Hemisphere so far have not supported these ideas. Instead, most species studied so far show little or no response to photoperiod treatments (Laube et al., 2014a; Polgar et al., 2014). The one

9 exception is Fagus sylvatica, which under short days (8-h) takes twice as long to leaf out than under long days (16-h) (Heide, 1993a). Under climate warming, the flushing times of temperature-cued species are expected to change more than those of photoperiod-sensitive species. Fu et al.’s (2015) Figure 1 provides an example of this effect by showing that photosensitive F. sylvatica is responding significantly less to temperature changes than six other European tree species with low photoperiod requirements: per 1°C increase in spring air temperature, leaf-out in Fagus sylvatica advanced by only 2.8 days, whereas it advanced by an average of 3.5 days in the other species.

Causes of intra- and interspecific variation in leaf-out phenology The following paragraph will introduce the potential ultimate (evolutionary) causes of phenological differences in leaf unfolding between and within species. The timing of leaf emergence in temperate woody species can vary up to four months between species grown at the same site (Zohner & Renner 2014). Trade-offs between greater productivity and a higher frost risk may play an important role in this variation since temperate plants have to adapt to opposite selective forces: protection against the cold season and effective use of the growing season. Early-leafing species are able to replenish nutrient supplies and to start growth before late flushers do. An early-leafing strategy, however, also creates a greater susceptibility to late spring frosts. How different species solve such trade-offs may have to do with latitudinal climate differences (Lechowicz, 1984), and studying this question is important for understanding and predicting ongoing and future changes in the phenology of forest communities, the composition of which depends on latitude. However, we are far from understanding either species’ empirical behavior or the underlying climatic forces (e.g., duration of winter, spring warming, or late spring frosts) that may select for particular strategies. This is seen in contradicting results in common garden studies: In Acer saccharum and Populus balsamifera, plants originating from southern populations leaf out later than individuals from more northern populations when grown together (Kriebel, 1957; Olson et al., 2013). By contrast, in Juglans nigra and Ulmus minor populations of more southern origin started growth earlier in the year compared to more northern plants (Bey, 1979; Ghelardini et al., 2006).

10 Many insights into species-specific phenologies have come from observations conducted in botanical gardens (Panchen et al., 2014; Zohner & Renner, 2014). Botanical gardens permit both observations and experiments, and constitute a common garden setup, at least to the extent that observations can also be obtained or inferred for the same plants’ behavior in the wild (‘non- common’) situation. Botanical gardens therefore provide the opportunity to study species-specific phenological behavior and shifts in response to climate change in a representative sample of the world’s temperate species (Primack & Miller-Rushing, 2009; Panchen et al., 2014; Zohner & Renner, 2014). Using biannual leaf-out observations on ~500 temperate woody species grown together in the Munich Botanical Garden, I showed in my M.Sc. thesis that (under identical conditions) species from regions with cold climates leaf-out earlier than species from Southern climates because they are adapted to lower energy/temperature resources (Zohner & Renner, 2014). This led me to the prediction that advances in the timing of leaf unfolding will be counteracted by the floristic change expected under climate warming, because a northward expansion of southern species will increase the number of late flushers in the North. My study further revealed that adaptation to local climates explains a significant portion of the variation in phenological strategies between species from different geographic regions. As noticed by Lechowicz (1984), however, also within regions there is marked interspecific variability in the timing of leaf-out, and even within a forest, leaf unfolding can vary by several weeks among coexisting native trees. Lechowicz suggested that the high degree of species-specificity in the timing of leaf unfolding might be explained by phylogenetic/historical and adaptive patterns. However, it took 30 years for Lechowicz’s hypothesis to be tested (Panchen et al., 2014; Zohner & Renner, 2014). Phylogeny may influence leaf unfolding because development and architecture have large genetic components and are inherited from ancestors. Phylogenetically-informed analyses of leaf-out times in woody plants from the Northern Hemisphere have found evidence of such phylogenetic inertia (Panchen et al., 2014). Thus Panchen et al. (2014) found that Ericaceae, Fabaceae, Fagaceae, and Pinaceae tend to flush late, while Dipsacaceae and Rosaceae mostly flush early. Similarly, species from lineages with a more southern background may retain (sub)- tropical habitat requirements, such as a relatively low frost tolerance, and should therefore leaf- out late, compared to species with a mainly temperate distribution (Lechowicz, 1984). I provided support for this in my M.Sc. thesis, in which I showed that late leafing species cluster in genera

11 that evolved primarily under subtropical conditions, such as Carya, Diospyrus, Fagus, Juglans, Liquidambar, Liriodendron, Nyssa, Platanus, and Tilia (Graham, 1972; Tiffney & Manchester, 2001), underscoring the phylogenetic component of leaf-out phenology. The timing of leaf unfolding contributes in an essential way to the survival of temperate plants, yet only a few adaptive explanations for leaf unfolding strategies have been postulated or inferred (Lechowicz, 1984; Panchen et al., 2014). Firstly, unfolding strategies depends on growth habit, with shrubs leafing out significantly earlier than trees when grown under common conditions (Panchen et al., 2014). A possible explanation for this pattern is competition for sunlight: shrubs might profit from an early flushing strategy in spring to maximize photosynthetic activity, because the light availability in the undergrowth of forests is highly reduced once canopy trees emerge their leaves. Secondly, the timing of leaf unfolding correlates with wood anatomical traits. As hypothesized by Lechowicz (1984), species with large vessels generally leaf-out late in spring (Panchen et al., 2014), most likely because large diameter vessels are more prone to embolisms caused by freeze-thaw events early in spring (Michelot et al., 2012). Lastly, the timing of leaf unfolding correlates with leaf longevity, with deciduous species usually preceding evergreen species that can make use of past years leaves for photosynthesis early in spring (Davi et al., 2011; Panchen et al., 2014).

Research questions To explore species-specific differences in leaf-out strategies in a biogeographic context, I made use of the broad taxonomic range of temperate woody species cultivated in the Munich Botanical Garden. My sample included species from all over the Northern Hemisphere, with North American, European, and East Asian species in roughly equal proportion. The common garden setup and the permission to carry out twig cutting experiments allowed me to study phenological adaptations to climate conditions: the leaf-out times of the studied species should still reflect their native thresholds for chilling, forcing, and photoperiod because woody plants in the Munich garden have had no opportunity for natural propagation, precluding evolutionary adaptation. I observed the leaf-out times of 498 species over five years (2012–2015) and used these observations, together with observations on another 1400 species carried out in five Northern Hemisphere gardens (see Panchen et al., 2014), to test if the leaf-out times of photosensitive species show less inter-annual variation than those of species flushing independent of

12 photoperiod (Chapter 3) and to explore species-specific adaptations to climate factors such as spring temperature variability (Chapter 4). Because plants have to time their leaf unfolding to maximize carbon gain while at the same time minimizing frost damage (above, section ‘Causes of intra- and interspecific variation in leaf-out phenology’), I asked whether species from regions in which spring temperatures are highly unpredictable (implying a high late frost probability) show more conservative growth strategies than species from regions with predictable spring climates. The leaf-out data additionally allowed me to directly study the relationship between leaf-out strategy and frost sensitivity of leaves, the latter of which was inferred for 170 species from an extreme late frost event that occurred in the Bayreuth Botanical Garden in 2011 (Chapter 5). To disentangle the relative effects of photoperiod, chilling, and spring warming on the timing of leaf unfolding, I conducted twig-cutting experiments on 144 of the 498 monitored species. Placing the results in a biogeographic context also allowed me to test for regional (climatic) differences in species’ relative use of photoperiod (Chapter 3) and chilling (Chapter 4) as leaf-out triggers. I specifically asked whether photoperiod and chilling requirements are influenced by species’ latitudinal occurrence, degree of continentality of the climate they are adapted to, or spring temperature variability in their native range. I also wanted to know which organs or tissues perceived photoperiod signals and at which period during dormancy plants perceive light signals. I therefore conducted in situ bagging experiments on three species (Aesculus hippocastanum, Fagus sylvatica, and Picea abies) for which previous studies have shown a high degree of photosensitivity (Chapter 2). These last experiments took me in the direction of proximate mechanisms, while most my other work focused on ultimate mechanisms.

Chapter 2

PERCEPTION OF PHOTOPERIOD IN INDIVIDUAL BUDS OF MATURE TREES REGULATES LEAF-OUT.

Zohner, C.M. and S.S. Renner

New Phytologist 208: 1023–1030 (2014)

16 Research

Perception of photoperiod in individual buds of mature trees regulates leaf-out

Constantin M. Zohner and Susanne S. Renner Systematic Botany and Mycology, Department of Biology, Munich University (LMU), 80638 Munich, Germany

Summary Author for correspondence: Experimental data on the perception of day length and temperature in dormant temperate Constantin M. Zohner zone trees are surprisingly scarce. Tel: +49 89 17861 285 In order to investigate when and where these environmental signals are perceived, we car- Email: [email protected] ried out bagging experiments in which buds on branches of Fagus sylvatica, Aesculus Received: 4 March 2015 hippocastanum and Picea abies trees were exposed to natural light increase or kept at con- Accepted: 15 May 2015 stant 8-h days from December until June. Parallel experiments used twigs cut from the same trees, harvesting treated and control twigs seven times and then exposing them to 8- or 16-h New Phytologist (2015) days in a glasshouse. doi: 10.1111/nph.13510 Under 8-h days, budburst in Fagus outdoors was delayed by 41 d and in Aesculus by 4 d; in Picea, day length had no effect. Buds on nearby branches reacted autonomously, and leaf pri-

Key words: chilling, day length, high- mordia only reacted to light cues in late dormancy after accumulating warm days. Experi- resolution spectrometry, leaf-out, phenology, ments applying different wavelength spectra and high-resolution spectrometry to buds photoperiod, trees. indicate a phytochrome-mediated photoperiod control. By demonstrating local photoperiodic control of buds, revealing the time when these signals are perceived, and showing the interplay between photoperiod and chilling, this study contributes to improved modelling of the impact of climate warming on photosensitive species.

spring (Heide, 1993a,b; Ghelardini , 2010; Basler & K€orner, Introduction et al. 2012; Laube et al., 2014). A problem with these experiments is In temperate zone trees and shrubs, winter dormancy release and that twigs cut later experience longer chilling and longer, contin- budburst are mediated by temperature and photoperiod (Heide, uously increasing photoperiods than those cut earlier (Table 1). 1993a,b; K€orner & Basler, 2010; Polgar & Primack, 2011; Basler The change in day length between 14 December and 14 March &K€orner, 2012; Laube et al., 2014). Although leaf senescence in in the temperate zone is considerable; for example, in Munich it autumn is usually regulated by photoperiod (Cooke et al., 2012), is 3.5 h, and buds on twigs cut on these two dates and moved to the role of photoperiod in the regulation of bud burst varies an 8-h light regime indoors therefore experience vastly different among species (Basler & K€orner, 2012; Laube et al., 2014; jumps in photoperiod. The failure to control for this, and also Zohner & Renner, 2014). Of the 44 temperate zone tree species for possible effects of gradual vs sudden day length increase, may investigated, spring leaf-out is influenced by photoperiod in 18, have led to an under-appreciation of the effects of photoperiod whereas in the remaining species, winter and spring temperatures on the timing of budburst (Laube et al., 2014; Polgar et al., alone regulate bud burst (Heide, 1993b; Basler & K€orner, 2012; 2014). Laube et al., 2014). The species-specific importance of photope- Here we experimentally study the effects of day length and riod as a leaf-out cue probably arises from the trade-off between chilling on leaf-out in three large, temperate tree species – Fagus frost prevention and selection for early photosynthesis: photope- sylvatica, Aesculus hippocastanum and Picea abies. For Fagus riod tracking protects species against leafing out during brief sylvatica, studies based on cut twigs or seedlings all report a day warming periods and thus reduces the risk of frost damage. By length-dependent leaf-out strategy (Heide, 1993a; Basler & contrast, a day length-independent leaf-out strategy allows species K€orner, 2012; Caffarra & Donnelly, 2011; Vitasse & Basler, to use early warm days, but exposes them to damage from late 2013; Laube et al., 2014). Evidence for the other two species is frosts (K €orner & Basler, 2010; Zohner & Renner, 2014). equivocal. Although Basler & K€orner (2012) find a day length- Experimental studies focusing on the impact of day length on dependent flushing strategy in Picea and no photoperiod require- dormancy release in trees have used seedlings cultivated indoors ments in Aesculus, Laube et al. (2014) conclude the opposite, with (Falusi & Calamassi, 1990; Caffarra & Donnelly, 2011) or buds Aesculus in their study being the species with the highest photope- on cut twigs brought indoors at different times during winter/ riod threshold of 36 species analysed. 17 Ó 2015 The Authors New Phytologist (2015) 1 New Phytologist Ó 2015 New Phytologist Trust www.newphytologist.com New 2 Research Phytologist

Table 1 Experimental set-up of the experiments on leaf-out in cut twigs of Aesculus hippocastanum, Fagus sylvatica and Picea abies

Start of experiment (Collection date) 21 Dec 29 Jan 11 Feb 24 Feb 10 March 21 March 4 April

Day length outside at start of experiment (h) 8 9.4 10 10.6 11.6 12 13 Chilling status: Chill days (<5°) 38 64 75 83 92 95 101 Day-degrees (> 5°C) at start of experiment 0 24 29 43 64 119 195

Different collection dates of twigs equate with different degrees of chilling. Chill days were calculated as days with mean temperature below 5°C since November 1 (following Murray et al., 1989; Laube et al., 2014). Photoperiod treatments for cut twigs were 8 or 16 h of light per day. Knowledge about the underlying molecular mechanisms of light-tight bags placed around the twigs every day at 17:00 h and photoperiodic dormancy regulation in trees is fragmentary. Phyto- removed the next morning at 09:00 h (Supporting Information chromes and the clock system (LHY and TOC genes) interact with Fig. S1). This ensured an 8-h photoperiod. Simultaneously, the CO/FT signalling network to regulate flowering, and this translucent bags of the same size and plastic thickness were placed pathway likely is also involved in regulating dormancy release on another 10 twigs on the same tree individuals. Climate data (Cooke et al., 2012). Photoreceptors and clock genes are found in were obtained from Hobo data loggers (Onset Computer Corp., all (living) plant cells, and their action can differ between organs Bourne, MA, USA), placed inside each type of bag for each treat- (James et al., 2008; Arabidopsis; Cooke et al., 2012: review). In ment, on openly exposed control twigs, and in the glasshouse tobacco, Thain et al. (2000) showed that parts of single leaves can (below). The percentage of leaf-out under both types of bags as independently reset their clock systems in reaction to different well as on naturally exposed twigs was monitored every 3 d light cues and that circadian rhythms in one leaf are independent (100% leaf-out was achieved when all buds on the observed 10 of entrainment in other leaves. Cooke et al. (2012) therefore sug- branches per treatment had leafed out; Fig. 1). gest that buds also might independently entrain to light (and/or For the cutting experiments, we sampled 30 replicate twigs per temperature) cues. Such a mechanism would enable each bud to species on seven dates during winter/spring 2013/14 (cutting react autonomously to environmental cues. To our knowledge, dates: 21 December, 29 January, 11 February, 24 February, 10 this hypothesis has never been tested in trees. March, 21 March and 4 April; see Table 1). After cutting, twigs In order to address the twin questions of the extent of bud were disinfected with sodium hypochlorite solution (200 ppm autonomy and of the interaction between chilling and photope- active chlorine), re-cut a second time to c. 40 cm, and then placed riod, we conducted experiments in mature individuals of the in 0.5-l glass bottles filled with 0.4 l cool tap water enriched with 1 three species mentioned above. These are the first reported in situ the broad-spectrum antibiotic gentamicin sulfate (40 lglÀ ; experiments on how photoperiod affects bud burst and leaf-out Sigma-Aldrich; Basler & K€orner, 2012; Larcher et al., 2010). in adult trees. We kept some buds under constant day length, Twigs were subsequently kept under short day (8 h) or long day while letting others (on the same tree) experience the natural (16 h) conditions. Temperatures in the glasshouse ranged from increase in day length during spring. Still using the same trees, 18°C during the day to 14°C at night. Water was changed twice we cuttreated and untreated twigs seven times during the winter a week, and twigs were trimmed weekly by c. 2 cm. Additionally, and spring and exposed them to 8- or 16-h light regimes indoors on 11 February and 21 March, 16 twigs per species from each of to test at which time the photoperiod signal becomes relevant as the bagging treatments (translucent and light-tight bag) were cut a leaf-out trigger, and to what extent photoperiod interacts with chilling status and warming temperatures. Combining the in situ experiment with the twig-cutting approach also allowed us to address effects of sudden vs gradual day length changes given different chilling status.

Materials and Methods

Experiments on buds on outdoor trees and buds on cut twigs brought indoors The study took place in the botanical garden of Munich between 21 December 2013 and 1 June 2014. Cutting and bagging experiments were conducted on Aesculus hippocastanum L., Fagus sylvatica L. and Picea abies L. (H.Karst.) trees growing permanently outdoors. Leaf-out of individual buds was deﬁned as the date when the bud scales had broken and the leaf had pushed Fig. 1 Percentage of budburst per day-degree under 8-h day length (light- out all the way to the petiole. For the bagging experiments, which tight bag) and naturally increasing day length (translucent bag and ran from 1 January 2014 until the day of leaf-out in the respec- without bag) for Aesculus hippocastanum (red), Fagus sylvatica (blue) and tive species, we covered 10 branches per species with 1 m-long Picea abies (green). 18 New Phytologist (2015) Ó 2015 The Authors www.newphytologist.com New Phytologist Ó 2015 New Phytologist Trust New Phytologist Research 3 and transferred to a glasshouse chamber, where they were exposed six (with bagging) that leafed out were recorded. A twig was to experimental photoperiods as described above (see Fig. 2a for a scored as having leafed out when three buds had their leaves scheme of treatment conditions). For all three species and all pushed out all the way to the petiole. treatments, bud development was monitored every second day, We conducted repeated-measures ANOVA to test for effects and the leaf-out dates of the ﬁrst 10 twigs (without bagging) or of naturally increasing day length vs constant short-day treatment

(a)

(b)

Fig. 2 (a) Explanation of treatment conditions for twig cuttings. Colour coding refers to (b). Outside, natural increase in day length (NDL) until collection date vs constant 8-h daylength via bag treatment (8 h); Collection, twig collection (transfer from field to glasshouse at seven different times; see Table 1); Glasshouse, fixed day length (8 or 16 h) in glasshouse chambers. (b) Correlation between collection date (left panels), chilling (right panels) and thermal time to budburst (day-degrees > 5°C) under 8 and 16 h day length for twig cuttings of Aesculus hippocastanum, Fagus sylvatica and Picea abies. For explanation of treatment conditions see (a). For statistical analysis see Table 2. Points and error bars represent the mean SE of thermal time to budburst. Æ Twigs of Fagus and Picea were collected seven times during winter/spring 2013/ 2014; those of Aesculus were collected only six times because leaf-out of Aesculus in the field had preceded the last cutting date on 4 April. Thermal time to budburst did not increase when twigs were kept under a constant day length of 8 h in the field (light- tight bags) before collection (repeated measures ANOVA: P > 0.1; see coloured points). 19 Ó 2015 The Authors New Phytologist (2015) New Phytologist Ó 2015 New Phytologist Trust www.newphytologist.com New 4 Research Phytologist

before twigs were cut and brought indoors. ANCOVA was used leaf tissue. To test for the quantity and quality of light they trans- to test for interactions between chilling and photoperiod treat- mit, we carried out transmission analyses, using the HR4000 ments. Accumulated day-degrees (> 5°C) until leaf-out (= sum of high-Resolution Spectrometer (Ocean Optics, Dunedin, FL,

day-degrees accumulated outside after 1 January and in the cli- USA), which is responsive from 200 to 1100 nm. We therefore mate chamber) were used as response variable. All statistical bisected the buds and removed leaf primordial tissue inside the analyses relied on R (R Core Team, 2014). buds of A. hippocastanum, F. sylvatica and P. abies and measured light transmission through all remaining bud scales (Fig. 4), and through a single bud scale (Fig. S3). For each species, we calcu- Light perception and transmission through buds lated the mean of the transmission spectra of 10 buds. We also In order to test for the light spectrum that plants use to regulate measured the light transmission of the bags used in our in situ budburst, we exposed twigs of the photosensitive species experiment to ensure that translucent bags transmitted across the A. hippocastanum and F. sylvatica to: the entire light spectrum; entire spectrum while light-tight bags efficiently filtered out light red light (> 575 nm); and far-red light (> 700 nm) (Fig. 3). Fif- across the spectrum. teen twigs were collected per species and treatment, using the same cutting procedure as above and the leaf-out dates of the first Results 10 twigs were recorded. The cutting date was 5 March 2015, and twigs were exposed to 16 h of light per day. Additional twigs were Effects of photoperiod on buds outdoors and on cut twigs kept under 8- or 12-h day length (and exposed to the entire light brought indoors spectrum) to test their photoperiod sensitivity. A Tukey-Kramer test was conducted to test for differences in thermal time to bud- Buds of F. sylvatica kept under constant 8-h day length (achieved burst among the treatments. by bagging twigs of outdoor trees every evening and unbagging Bud scales consist of thick cuticle-like material and hardly them every morning) achieved 100% budburst 41 days later than allow for transmission of light that might be sensed by subjacent those that experienced the natural day length increase (Figs 4, 5, S2). The same conditions delayed budburst in A. hippocastanum by four days and had no effect on budburst in P. abies. Twigs that had experienced constant 8-h days or naturally increasing day length were harvested at seven different times (Table 1) and brought into the glasshouse where they received the experimental treatments summarized in Fig. 2(a). Later cutting dates equate with plants having reached a higher chilling status and having accumulated more day-degrees (Table 1).

Fig. 3 Thermal time (day-degrees > 5°C) to budburst under different light spectra for Fagus sylvatica. Twigs were collected on 5 March 2015 and exposed to 8-, 12- and 16-h day length under: the entire light spectrum; Fig. 4 Transmission spectra of buds of Aesculus hippocastanum, Fagus red light (> 575 nm); or far-red light (> 700 nm). Buds exposed to red or sylvatica and Picea abies. Buds were bisected and leaf primordial tissue far-red light reacted no differently from those exposed to the entire light was removed before measurements, thus the graph reﬂects the quality spectrum. Treatments differed signiﬁcantly from the 16 h, full light and quantity of light that could be sensed by photoreceptors located in spectrum treatment: *, P < 0.05. Error bars represent the mean SE of leaf primordia. Dashed lines indicate the absorption maxima for Æ thermal time to budburst. Phytochrome a (Pr and Pfr). 20 New Phytologist (2015) Ó 2015 The Authors www.newphytologist.com New Phytologist Ó 2015 New Phytologist Trust New Phytologist Research 5

Table 2 Results of ANCOVA to test for the effect of day length on species’ forcing requirements, while controlling for the effect of chilling status

Explanatory Fagus Aesculus Picea factor (n = 13) (n = 12) (n = 12)

Chilling F(1,12) = 91.3 F(1,11) = 320.8 F(1,11) = 25.7 P < 0.001 P < 0.001 P < 0.001 Photoperiod F(1,12) = 1440.2 F(1,11) = 3.8 F(1,11) = 0.05 P < 0.001 P = 0.09 P = 0.83 Interaction F(1,12) = 140.6 F(1,11) = 0.9 F(1,11) = 0.03 Chilling 9 P < 0.001 P = 0.39 P < 0.87 Photoperiod

n, refers to the number of treatments (Chilling (number of collection dates) 9 Photoperiod (8 or 16 h)); see also Fig. 2(b). P values < 0.1 are shown in bold.

the full light spectrum, only red light or only far-red light Fig. 5 Development of Fagus sylvatica buds kept under translucent (upper (P > 0.15; see Fig. 3 for F. sylvatica), even though in both species, twig) or light-tight bags (lower twig) on 25 April 2014. under far-red conditions, leaves appeared pale due to lack of chlorophyll. Day lengths of 8 or 12 h delayed budburst in Fagus by Leaf-out date in all the species was unaffected by whether twigs 42 or 15 d and in Aesculus by 3 or 1 d. experienced a gradual (natural) day length increase (up to 12 h Transmission spectra of the entire bud scale tissue were similar days) or constant 8-h short days (bag treatment) before being among species, but the relative amplitudes of transmission bands brought indoors (repeated measures ANOVA: P > 0.1 and differed (Fig. 4). In the range between 600 and 800 nm, bud scales Fig. 2(b), compare the blue and red dots to the black and white of Aesculus and Fagus transmitted two to three times more light dots, respectively): Buds on twigs cut in February or late March, than those of Picea. In all three species, light transmission increased when they had already experienced quite long days outdoors, and with longer wavelengths. Between 400 and 500 nm, transmission brought into 8- or 16-h glasshouse conditions underwent bud- was < 2%, whereas above 500 nm it steeply increased, reaching burst at the same time as buds on twigs kept under a constant 8-h 100% at 900 nm. In Aesculus, the transmission spectrum shows a day until then (see Fig. 2b). local minimum c. 670 nm, likely due to chlorophylls located in the In F. sylvatica, the day-degrees until leaf-out accumulated by inner surface of bud scales in this species, whereas Fagus and Picea buds kept under 8-h day length were correlated exponentially bud scales are dead and do not contain any chlorophyll. For trans- with collection date (Fig. 2b, left top panel: curve fitting white mission spectrum analysis of single bud scales, see Fig. S3. and red dots), whereas the association between day-degrees and accumulated chill days was linear (Fig. 2b, right top panel). For Discussion buds on twigs kept under 16-h day length, collection date and chill days were linearly and negatively correlated with accumu- Photoperiod signal perceived at the local bud level lated day-degrees (Fig. 2b, top panels: curves fitting black and blue dots). The effect of day length treatment on forcing Animals have central circadian pacemakers in the brain that requirements was highly significant, and there was also a highly entrain peripheral clocks (Liu & Reppert, 2000). This leads to a significant interaction between chilling status and day length close coupling between the circadian clocks of individual cells treatment, with higher chilling reducing day length require- and increases the precision of timing in vivo (Thain et al., ments and longer days reducing chilling requirements (see 2000). Sessile organisms, such as most plants, by contrast have Table 2; Fig. 2b). largely autonomous or weakly coupled circadian clocks that In Aesculus , day length barely affected forcing requirements allow for independence among a plant’s modules in the en- (Table 2, P = 0.09), and chilling status did not affect photoperiod trained phases of circadian rhythms. Using in vivo reporter gene requirements. Collection date and chilling status were linearly imaging in tobacco, Thain et al. (2000) found that the clock correlated with required day-degrees until leaf-out (Table 2; systems even of sections within leaves are functionally indepen- Fig. 2b, middle panels). In Picea, day length had no significant dent. Our experiments on the effect of photoperiod on bud effect on forcing requirements (Table 2), and collection date and break on nearby twigs of single individuals of F. sylvatica, chilling status were linearly correlated with day-degrees until leaf- A. hippocastanum and P. abies provide evidence for the extent of out (Fig. 2b, lower panels). local control (Fig. 5). The light signal likely is perceived by receptors just below the bud scales, and the genetic system involved in leaf-out regulation must therefore be located in the Light perception and transmission analyses of buds young leaf primordial cells. This allows each bud to react auton- In A. hippocastanum and F. sylvatica, leaf-out date under 16-h omously to cues by maintaining an independent circadian clock days did not differ regardless of whether buds were exposed to system during winter and to respond to day length increase in 21 Ó 2015 The Authors New Phytologist (2015) New Phytologist Ó 2015 New Phytologist Trust www.newphytologist.com New 6 Research Phytologist

spring (Thain et al., 2000; tobacco; James et al., 2008; For F. sylvatica, Vitasse & Basler (2013) put forward two Arabidopsis ; our Fig. 5 for F. sylvatica). hypotheses for how photoperiod may modulate the relationship Compared to light perception, even less is known about the between chilling status and thermal time (day-degrees) to bud-

mechanisms of temperature sensing during bud dormancy release, burst: Either, a fixed photoperiod threshold has to be reached to although experiments on one-node cuttings prove that bud auton- allow for perception of thermal time or else forcing require- omy also exists for forcing and chilling requirements (Vitasse & ments continuously decrease with increasing photoperiod. Our Basler, 2014), and there is evidence that circadian clocks are experiments suggest that both hypotheses are partially correct. involved (Rensing & Ruoff, 2002; Cooke et al., 2012). Findings in On the one hand, insufficiently chilled buds require that a cer- Populus of an upregulation of the clock gene LHY under cold contain photoperiod threshold be exceeded before bud development ditions and of low LHY expression causing delayed budburst (buds on twigs did not leaf-out under low chilling and 8-h day (Ibanez~ et al., 2010) point to a connection between the circadian length; see Fig. 2b). Buds that had passed their chilling thresh- clock and chilling fulfilment. This would permit extremely fine- old, on the other hand, leafed out under short days, but even in scale leaf-out regulation and acclimation to the microclimate these buds, longer days significantly reduced the thermal time differences commonly experienced by large, perennial individuals required for budburst. (Augspurger, 2004; Vitasse & Basler, 2013). In short, Fagus obligatorily requires a minimal day length to allow for budburst when chilling requirements are not met, and long days partially substitute for unmet chilling requirements. Interplay between chilling and photoperiod Aesculus shows a constant delay in leaf-out under short days, The three tree species studied here behaved differently in terms of does not obligatorily require a certain day length, and shows no the extent to which chilling status and warming temperatures modulating effect of day length on chilling requirements or vice (degree day) interacted with day length. In A. hippocastanum, versa. In Picea we found no effect of photoperiod on budburst. delayed budburst under short days probably is merely a conse- Our results for Aesculus and Picea are in agreement with those quence of slower growth as a result of lower light availability. By of Laube et al. (2014), but contradict Basler & K€orner (2012) contrast, in F. sylvatica, day length had a huge effect on forcing who found day length-dependent flushing in Picea and day requirements, and leaf-out was not possible under short days and length-independent budburst in Aesculus. Laube et al. (2014) low chilling (Fig. 2b, top panel). The correlation between cutting and the present study used only individuals at low elevation, time and thermal time to budburst has a different slope for 8- and whereas Basler & K€orner (2012) analysed trees along an eleva- 16-h day length treatments (Fig. 2b). This demonstrates that the tional gradient of 1000 m and found that low-elevation Picea extent of chilling fulfilment influences photoperiod requirements were less sensitive to photoperiod than high-elevation individu- and vice versa, with chilling partially substituting for unmet photo- als. This points to ecotypic differentiation of photoperiod period requirements (see also Laube et al., 2014) and increasing requirements. Intraspecific phenological plasticity or ecotypes day length substituting for a lack of chilling. That exposure of buds deserve further study. to natural day length (12 h day length on 21 March) or 8-h days (bag treatment) before 21 March did not affect the leaf-out dates Experimental implications on twigs brought to the glasshouse (see Fig. 2b) indicates that photoperiod signals do not cause irreversible molecular responses in Our experiments control for a possible artefact in previous buds and that day length influences only the late phase of dor- studies that used buds on twigs transferred to vases in glass- mancy, when substantial forcing has accumulated. Long days houses: Twigs cut later during the winter experience increasing occurring during cold periods with little accumulation of warm day lengths and higher chilling than those cut earlier days therefore have no effect on subsequent forcing requirements. (Table 1). Twigs cut at different times are thus not strictly This can be seen in Fig. 2(b), where there is no difference in ther- comparable in chilling status because the photoperiod effect is mal requirements between buds that had experienced a gradual day not controlled for. Our experiments (in all three species), length increase (up to 12 h light per day) and buds that were kept however, revealed that buds on twigs cut on 21 March and under constant 8-h day length until 21 March (compare the red or brought into a glasshouse for 16- or 8-h light treatments blue to the white or black points, respectively). behaved no different regardless whether they had experienced Our experiments also reveal that for F. sylvatica there is a lin- naturally increasing day lengths (up to 12-h day length) or ear, negative relationship between accumulated chill days and had been kept under a constant 8-h day (by the outdoor bag- forcing requirements (day-degrees required), whereas collection ging experiments; Fig. 2b). This indirectly validates the results date under short days (8 h) was nonlinearly correlated with day- of earlier studies in which twigs were cut early or late in degrees (Fig. 2b, top panel), probably because late in spring the spring to study the effects of chilling, but without controlling number of predictably cold days varies greatly. This implies that for the day length increase experienced before they were cut using chill-days in leaf-out models will more accurately forecast (Laube et al., 2014; Polgar et al., 2014). That buds in situ and leaf-out behaviour than will day-of-year models, although the on cut twigs react similarly to similar treatments as shown exact temperature threshold and the precise physiological and here (see also Vitasse & Basler, 2014) underlines the utility of molecular mechanisms that lead to chilling fulfilment are not yet the twig cutting method for inferring woody species’ responses understood (Cooke et al., 2012). to photoperiod. 22 New Phytologist (2015) Ó 2015 The Authors www.newphytologist.com New Phytologist Ó 2015 New Phytologist Trust New Phytologist Research 7

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We from trees to ecosystems. New Phytologist 191: 926–941. found that: dormancy release is controlled at the bud level, with Pukacki P, Giertych M. 1982. Seasonal changes in light transmission by bud – light sensing (and probably also temperature sensing) occurring scales of spruce and pine. Planta 154: 381 383. R Core Team. 2014. R: a language and environment for statistical computing. inside buds; leaf primordia only react to light cues during the late Vienna, Austria: R Foundation for Statistical Computing. [WWW document] phase of dormancy release when they have begun accumulating URL http://www.R-project.org/. [accessed 10 January 2014]. warm days; in Fagus, but not the other species, photoperiod can Rensing L, Ruoff P. 2002. Temperature effect on entrainment, phase shifting, partially substitute for a lack of chilling and vice versa; and and amplitude of circadian clocks and its molecular bases. Chronobiology – red light triggers the day length response, with bud scales International 19: 807 864. Solymosi K, B€oddi B. 2006. Optical properties of bud scales and filtering-out most of the remaining light spectrum received by protochlorophyll(ide) forms in leaf primordia of closed and opened buds. Tree the primordia. Physiology 26: 1075–1085. Thain SC, Hall A, Millar AJ. 2000. Functional independence of circadian clocks that regulate plant gene expression. Current Biology 10: 951–956. Acknowledgements Vitasse Y, Basler D. 2013. What role for photoperiod in the bud burst phenology of European beech. European Journal of Forest Research 132:1–8. We thank V. Sebald and M. Wenn for help with branch bagging, Vitasse Y, Basler D. 2014. Is the use of cuttings a good proxy to explore C. Bolle and A. Pazur for help in measuring light transmission, phenological responses of temperate forests in warming and photoperiod and H. Y. Yeang and three anonymous reviewers for comments experiments? Tree Physiology 00:1–10. on the manuscript. Zohner CM, Renner SS. 2014. Common garden comparison of the leaf-out phenology of woody species from different native climates, combined with herbarium records forecasts long-term change. Ecology Letters 17: 1016–1025. References Augspurger CK. 2004. Developmental versus environmental control of early leaf Supporting Information phenology in juvenile Ohio buckeye (Aesculus glabra). Canadian Journal of Botany/ Journal Canadien de Botanique 82: 31–36. Additional supporting information may be found in the online Basler D, K€orner C. 2012. Photoperiod sensitivity of bud burst in 14 temperate version of this article. forest tree species. Agricultural and Forest Meteorology 165: 73–81. Caffarra A, Donnelly A. 2011. The ecological significance of phenology in four Fig. S1 Treatment of twigs with light-tight bags, to ensure an 8-h different tree species: effects of light and temperature on bud burst. photoperiod and translucent bags as control. International Journal of Biometeorology 55: 711–721. 23 Ó 2015 The Authors New Phytologist (2015) New Phytologist Ó 2015 New Phytologist Trust www.newphytologist.com New 8 Research Phytologist

Fig. S2 Bud development of Aesculus hippocastanum (on 3 April Please note: Wiley Blackwell are not responsible for the content 2014) and Fagus sylvatica (on 22 April 2014) on mature trees or functionality of any supporting information supplied by the kept under 8-h day length and naturally increasing day length. authors. Any queries (other than missing material) should be

directed to the New Phytologist Central Ofﬁce. Fig. S3 Transmission spectra of single bud scales of Aesculus hippocastanum, Fagus sylvatica and Picea abies.

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New Phytologist Supporting Information

Article title: Perception of photoperiod in individual buds of mature trees regulates leaf-out

Authors: Constantin M. Zohner ([email protected]), and Susanne S. Renner ([email protected])

The following Supporting Information is available for this article:

Fig. S1 Treatment of twigs with light tight bags, to ensure an 8 h photoperiod and translucent bags as control. From 1 Jan 2014 until day of leaf-out in the respective species, twigs were covered daily with bags from 17:00 h until 09:00 h

Aesculus hippocastanum

Fagus sylvatica

Fig. 4: Buds of Aesculus hippocastanum (3.4.14) and Fagus sylvatica (22.4.14) kept under 8h Fig.day- lengthS2 Bud (left) development and naturally ofincreasing Aesculus day hippocastanum-length (right). (on 3 April 2014) and Fagus sylvatica (on

22 April 2014) on mature trees kept under 8h day length (left) and naturally increasing day length (right). For photoperiod treatment, light-tight and translucent (control) bags were placed around the twigs every day at 17:00 h and removed the next morning at 09:00 h from 1 Jan 2014 until day of leaf-out in the respective species.

26 80 Picea abies Picea abies 70 A. hippocastanum Aesculus hippocastanum Fagus sylvatica Fagus sylvatica 60

20 Transmission of bud scale (%)

0 400 450 500 550 600 650 700 750 Wavelength (nm) Fig. S3 Transmission spectra of single bud scales of Aesculus hippocastanum, Fagus sylvatica and Picea abies.

Chapter 3

PHOTOPERIOD UNLIKELY TO CONSTRAIN CLIMATE CHANGE-DRIVEN SHIFTS IN LEAF-OUT PHENOLOGY IN NORTHERN WOODY PLANTS.

Zohner, C.M., B.M. Benito, J.-C. Svenning, and S.S. Renner

Nature Climate Change (in press)

30 Photoperiod unlikely to constrain climate change-driven shifts in leaf-out phenology in northern woody plants

Authors: Constantin M. Zohner1, Blas M. Benito2, Jens-Christian Svenning2, and Susanne S. Renner1

1Systematic Botany and Mycology, Department of Biology, Munich University (LMU), 80638 Munich, Germany 2Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, DK- 8000 Aarhus C, Denmark

Correspondence: Constantin M. Zohner, E-mail: [email protected]

Number of words: 1625 (text), 2778 (methods), 515 (legends)

Number of references: 30

Figures: 2; Tables: 1

31 The relative roles of temperature and day length in driving spring leaf unfolding are known for few species, limiting our ability to predict phenology under climate warming1,2. Using experimental data, we assess the importance of photoperiod as a leaf-out regulator in 173 woody species from throughout the Northern hemisphere, and we also infer the influence of winter duration, temperature seasonality, and inter-annual temperature variability. We combine results from climate- and light-controlled chambers with species’ native climate niches inferred from geo-referenced occurrences and range maps. Of the 173 species, only 35% relied on spring photoperiod as a leaf-out signal. Contrary to previous suggestions, these species come from lower latitudes, whereas species from high latitudes with long winters leafed out independent of photoperiod, supporting the idea that photoperiodism may slow or constrain poleward range expansion3. The strong effect of species’ geographic- climatic history on phenological strategies complicates the prediction of community-wide phenological change.

Understanding the environmental triggers of leaf out and leaf senescence is essential for forecasting the effects of climate change on temperate zone forest ecosystems2,3,4. Correlation analyses suggest that warmer springs are causing earlier leaf emergence, leading to an extended growing season5,6 and increased carbon uptake7. A continuing linear response to spring warming, however, is not expected because stimuli, such as photoperiod1,8-10 and chilling11-13, additionally trigger dormancy release. Photoperiod limitation refers to the idea that plant sensitivity to day length protects leaves against frost damage by guiding budburst into a safe time period1. Experiments have shown that day length-sensitive species react to spring temperatures only once day length increases10. Because day length will not change under climate warming, photosensitive species may be less responsive to warmer temperatures1,9,14,15. Experiments addressing the relative importance of photoperiod versus temperature for dormancy release have been carried out in about 40 species8-12, and among them a few species, most strikingly Fagus sylvatica, exhibited strong photoperiodism8-10,12,16-19. Results are often equivocal, perhaps in part reflecting experimental difficulties in adequately modifying day length when working with trees9,11,12,20,21.

32 Why species differ in their relative reliance on photoperiod and spring temperature as leaf-out signals is largely unknown. This prevents the development of mechanistic models for predicting spring phenology under climate warming. The need to understand spring phenology in its geographic-climatic context is highlighted by studies suggesting that phenological strategies in long-lived woody species have evolved as adaptations to the climate in a species’ native range22- 25. A common garden study of 495 woody species from different climates showed that species native to warmer climates flush later than species native to colder areas, but did not investigate whether this was due to different species relying on temperature or photoperiod25. If photoperiod indeed provides a safeguard against leafing out too early1,9, photoperiodism should be especially important (i) in regions with unpredictable frost events, i.e., high inter-annual variability in spring temperatures (here called ‘high temperature variability’ hypothesis)26 and (ii) in regions with oceanic climates in which temperature is a less reliable signal because the change between winter and spring temperatures is less pronounced (‘oceanic climate’ hypothesis)1. A third hypothesis is that photoperiodism mirrors species’ latitudinal occurrence because day-length seasonality increases towards the poles, and day length thus provides an especially strong signal at higher latitudes (‘high latitude’ hypothesis)3. Of these predicted correlates of photoperiod as a spring leaf-out signal, only the ‘oceanic climate’ hypothesis has been tested12, with no significant relationship found. We set out to (i) investigate the relative effect of photoperiod on leaf-out timing in species from different winter temperature regimes (‘high latitude’ hypothesis), temperature seasonality regimes (‘oceanic climate’ hypothesis), and between-year spring temperature variability (‘high temperature variability’ hypothesis) [Fig. 1a], and to (ii) test if photoperiod- sensitive species react less to spring temperatures than do photoperiod-insensitive species. We used 173 species (in 78 genera from 39 families) from the Northern Hemisphere grown in a mid- latitude (48°N) European Botanical Garden and modified the day length experienced by buds on twigs cut from these species at three different times and hence chilling levels (see Methods and Supplementary Fig. 1). To assign the species to their climate ranges, we queried geo-referenced occurrence data against climate grids for winter duration (Fig. 1b), temperature seasonality (T seasonality), and inter-annual spring temperature variability (T variability). In addition, each species was also assigned to its predominant Koeppen-Geiger climate type25. To achieve our second aim, we tested for correlations between species’ photoperiodism (as inferred from our

33 experiments on leaf-out in twigs under different light regimes) and their leaf-out behaviour in situ (as inferred from multi-annual leaf-out observations on intact trees; Fig. 2). With low chilling (twig-collection in December), 61 (35%) of the 173 species leafed out later under short day conditions than under long days, while the remaining 112 species did not react differently regardless of short and long days. Increased chilling reduced species’ sensitivity to photoperiod: Under intermediate chilling conditions (twig-collection in February), 16 (9%) of the 173 species showed delayed budburst under short days. Under long chilling conditions (twig- collection in March), only 4 (2%) species, namely Fagus crenata, F. orientalis, F. sylvatica, and Carya cordiformis, leafed out later under short days. Based on the current results, constraints on the climate-warming-driven advance of leaf-out15 likely will be twofold in photosensitive species: (i) reduced winter chilling per se will cause plants to require more forcing in the spring and (ii) reduced chilling additionally will cause higher photoperiod requirements. The latter constraint will become more significant, as springs will arrive ever earlier (i.e., at ever shorter photoperiods) in the future. Where do the species that rely on photoperiodism as a leaf-out trigger come from? Our data reject all three suggested correlates of photoperiodism (i.e., the ‘high latitude’, ‘high temperature variability’, and ‘oceanic climate’ hypotheses) and instead reveal that it is the species from shorter winters (i.e., lower, not higher latitudes) that rely on photoperiodism (P < 0.05; Table 1; Fig. 1). Of the 173 species, the 22 that come from regions with long winters (> 7 months with an average temperature below 5°C), such as alpine and subarctic regions are photoperiod- insensitive, while the 14 species with high photoperiod requirements are restricted to regions with shorter winters (not exceeding six months with an average temperature below 5°C; Fig. 1). In a hierarchical Bayesian model that controlled for possible effects of shared evolutionary history and species’ growth height, winter duration remained negatively correlated with species’ photoperiodism (Fig. 1a). Analyses that used the Koeppen-Geiger climate classification yielded the same results as analyses that used the climate grids, namely that most photoperiod-sensitive species are native to warm climates with mild winters (Supplementary Fig. 2). Why is there a negative correlation between species’ reliance on day length as a leaf-out signal and the winter duration in their native ranges? There are two possible mechanisms on how photoperiod perception in plants may interact with forcing requirements: (i) Either plants need to reach a fixed photoperiod threshold before they perceive forcing temperatures or (ii) forcing

34 requirements gradually decrease with increasing photoperiod. The first mechanism would require that plants from regions with long winters have higher photoperiod thresholds because in these areas days are already long (>14-h) when minimum temperatures cross the freezing threshold (see also Way & Montgomerey21: Fig. 1). The second mechanism would require that the relative use of photoperiod as a budburst regulator decreases towards regions with long winters because days in spring become long before the risk of encountering freezing temperatures has passed. Experimental results from Fagus sylvatica show a gradual response to photoperiod independent of the latitudinal origin of the experimental plants: Forcing requirements decrease with increasing day length up to about 16-h, with further increase of daylight having little additional effect8,10. This supports the second mechanism. The second mechanism is also supported by F. sylvatica leafing out earlier at regions with long winter duration than photo-insensitive species and therefore operating at a smaller ‘safety margin’ against late frosts27,28. The hypothesis that Northern woody species evolved photoperiod-independent leaf-out strategies because at high latitudes day length increase in spring occurs too early for frost to be safely avoided needs to be tested with further experiments addressing the physiological mechanisms of photoperiod perception in different taxonomic groups. That photosensitive species are restricted to regions with relatively short winters supports the idea that photoperiodism may slow or constrain poleward range expansion3. With a warming climate, however, the last day with night frost occurs ever earlier (in Germany, between 1955 – 2015, the last frost on average advanced by 2.6 days per decade; Supplementary Fig. 3), and photoperiod-sensitive species might do well at higher latitudes or elevations. The leaf-out dates showed that those species with high photoperiod requirements had lower between-year variance in leaf-out dates than species lacking photoperiodism. Accordingly, in photoperiod-sensitive species, accumulated thermal time until budburst showed greater variation among years than that of photoperiod-insensitive species (P < 0.01; Fig. 2). Leaf unfolding in species that rely on day length is thus less responsive to temperature increase, and in these species photoperiod will constrain phenological responses to climate warming, with possible consequences for carbon gain, the local survival of populations and community composition2,4. The extent to which species’ phenological strategies are influenced by their climatic histories highlights the need for a broader geographic sampling in global-change studies29.

35 Our results do not support previous ideas about phenological strategies in temperate woody species (the ‘high temperature variability’ hypothesis; the ‘oceanic climate’ hypothesis; the ‘high latitude’ hypothesis1,3,26). In regions with long winters, trees appear to rely on cues other than day length, such as winter chilling and spring warming. By contrast, in regions with short winters, many species – mostly from lineages with a warm-temperate or subtropical background, e.g., Fagus30 – additionally rely on photoperiodism. Therefore, only in regions with shorter winters, photoperiod may be expected to constrain climate change-driven shifts in the phenology of spring leaf unfolding.

Methods Twig cutting experiments We conducted twig-cutting experiments on 144 temperate woody species growing permanently outdoors without winter protection in the botanical garden of Munich to test for an effect of day length on dormancy release and subsequent leaf unfolding (see Supplementary Table 1 for species names). Twig cuttings have been shown to precisely mirror the phenology of donor trees because dormancy release is controlled at the bud level and not influenced by hormonal-signals from other parts of a tree, such as the stem or the roots10,31. In winter 2013/2014, c. 40 cm-long twigs were collected at three different dormancy stages (on 21 Dec, 10 Feb, and 21 Mar) for each species. After collection, we transferred the cut twigs to climate chambers and kept them under short (8 h) or long day (16 h) conditions. Temperatures in the climate chambers were held at 14°C during the night and 18°C during the day (see Supplementary Fig. 4 for a description of the temperature regime outside and in the climate chambers). Illuminance in the chambers was about 8 kLux (~100 µmol s-1 m-2). Relative air humidity was held between 40% and 60%. Immediately after cutting, we disinfected the twigs with sodium hypochlorite solution (200 ppm active chlorine), re-cut them a second time, and then placed them in 0.5 l glass bottles filled with 0.4 l cool tap water enriched with the broad-spectrum antibiotics gentamicin sulfate (40 microg/l; Sigma–Aldrich, Germany)9,10. We used 60 replicate twigs per species (10 twigs per treatment, 3x2 full factorial experiment) and monitored bud development every second day. For each treatment, we recorded the leaf-out dates of the first eight twigs that leafed out. A twig was scored as having leafed out when three buds had their leaves pushed out all the way to the petiole.

36 Flushing rate, i.e. the proportion of buds flushed over the total number of buds on the twigs, was not recorded. Treatment effects (long vs. short days at three different dormancy stages) on the response variable (accumulated degree days >0°C outside and in climate chamber from 21 Dec until leaf-out) were assessed in ANOVAs. We defined three categories to describe a species’ photoperiodism: none = No response to day length, low = sensitivity to day length during early dormancy, high = sensitivity to day length also during late dormancy. Species whose twigs when cut on 21 Dec (early dormancy stage) showed no statistical difference between 8-h and 16-h photoperiod treatments were categorized as having no photoperiod requirements. Species whose twigs when cut on 21 Dec leafed out significantly later when they were exposed to 8-h day length compared to 16 h days were categorized as having low photoperiod requirements. Species whose twigs when cut on 10 Feb (advanced dormancy stage) still leafed out later under short days (8 h) than under 16-h days were categorized as having high photoperiod requirements. When twigs were cut on 21 March, only three Fagus species and Carya cordiformis reacted differently to 8-h and 16-h photoperiods, and we categorized them as having high photoperiod requirements. In addition to the ANOVA assessment, a day length effect was only considered significant if the forcing requirements under 8-h day length were >50 degree days higher than under 16-h day length and if the additional forcing requirement was >10% larger than required under long days (see Supplementary Fig. 1 for species-specific treatment effects). Information on the photoperiod requirements of 29 additional species came from a previous study12 that used the same experimental approach to detect species’ photoperiod requirements, allowing us to apply the same definition of photoperiod categories to their data. This resulted in photoperiod data for a total of 173 woody species in 78 genera from 39 families.

In-situ leaf-out observations For 154 of the 173 species with information on photoperiod requirements (previous section), we have four years of observations of leaf-out dates, viz. 2012–2015, available from the Munich botanical garden. The 2012 and 2013 data come from our earlier study25, and the same individuals were monitored again in 2014 and 2015. A species’ leaf-out date was defined as the day when three branches on a plant had leaves pushed out all the way to the petiole. Thermal requirements of species were calculated as the sum of growing-degree days from 1 January until day of leaf-out using a base temperature of 0°C. Species names are given in Appendix Table S1.

37 To test if species with photoperiod requirements show lower variation in leaf-out and higher variation in thermal requirements among years than do photo-insensitive species, we applied difference-of-means tests (Fig. 2). Because vectors were not normally distributed we conducted Kruskal–Wallis H tests with a post-hoc Kruskalmc analysis (multiple comparison after Kruskal– Wallis)32.

Temporal occurrence of last frost events Weather data were downloaded from Deutscher Wetterdienst, Offenbach, Germany, via WebWerdis (https://werdis.dwd.de/werdis/ start_js_JSP.do) to gather information on the relative occurrence date and temporal shifts of the last frost (daily minimum temperature below 0°C). Information on the occurrence of the last frost from 1955 to 2015 for German locations differing in their winter duration is given in Supplementary Fig. 3. On average, across all stations, the last freezing event advanced by 2.6 days per decade.

Species ranges and climate characteristics To obtain species’ native distribution ranges, we extracted georeferenced locations from the Global Biodiversity Information Facility (GBIF; http://www.gbif.org/), using the dismo R- package33. Cleaning scripts in R were used to filter reliable locations and exclude species with unreliable records, using the following criteria: (i) only records from a species’ native continent were included; (ii) coordinate duplicates within a species were removed; (iii) records based on fossil material, germplasm, or literature were removed; (iv) records with a resolution >10 km were removed; and (v) only species with more than 30 georeferenced GBIF records within their native continent were included. After applying these filtering criteria, we were left with distribution data for 144 of the 173 species. We then derived species-specific climate ranges from querying georeferences against climate grids of three bioclimatic variables: T seasonality (BIO7; Temperature difference between warmest and coldest month), T variability (inter-annual spring T variability calculated as the standard deviation of March, April, and May average T from 1901 – 2013), and winter duration (defined as the numbers of months with an average T below 5°C). A grid file for the winter duration was based on global monthly weather data available at www.worldclim.org34, from which we calculated the number of months with an average temperature below 5°C for the

38 global land surface (see Fig. 1b). T seasonality was based on gridded information (2.5-arc minute spatial resolution data) about the annual temperature range derived from the WorldClim dataset (bioclim7)34. T variability was calculated as the standard deviation of spring (March, April, and May) average temperatures from 1901 to 2013 (see Supplementary Fig. 5). Data on monthly average temperatures during this period were available from the CRU database (5-arc minute spatial resolution data)35. For each bioclimatic variable we determined three species-specific measures: the upper and lower limits and the median which were obtained from the bioclimatic data covering a species range at the 0.95, 0.05, and 0.50 quantile, respectively. As an alternative approach that allowed us to infer the predominant climate of 171 of the 173 species, we used the Koeppen-Geiger system36. Information on species-specific Koeppen- Geiger climate types was available from our earlier study25 in which each species’ natural distribution was determined using information from range maps and range descriptions: http://linnaeus.nrm.se/flora/welcome.html and http://www.euforgen.org/distribution-maps/ for the European flora, http://plants.usda.gov/java/ and http://esp.cr.usgs.gov/data/little/ for North America, and http://www.efloras.org for Asia. As a proxy for a species’ native winter temperature regime, it was scored for the first Koeppen-Geiger letter (D-climate = coldest month average below -3°C, C-climate = coldest month average above -3°C). For species’ summer temperature, the third Koeppen-Geiger letter was used (a-climate = warmest month average above 22°C with at least four months averaging above 10°C; b-climate = warmest month average below 22 °C but with at least four months averaging above 10 °C, c-climate = warmest month average below 22°C with three or fewer months with mean temperatures above 10 °C). The second letter in the Koeppen system refers to precipitation regime and was disregarded in the analyses. Species were scored for the predominant conditions in their native range; for example, a species occurring in 40% Cfa, 30% Dfa, and 30% Dfb climates would be scored as “D” and “a”.

Data analysis The quantiles (0.05, 0.5, and 0.95) of each climate parameter (winter duration, T seasonality, and T variability) were highly correlated among each other (Pearson correlation, r > 0.5). To avoid multicollinearity in our models, we included only one quantile for each climate parameter. For each climate parameter, we kept the quantile that gave the best prediction of species-level variation in photoperiodism. We fitted univariate logistic regression models to our

39 data and, for each climate parameter, kept the variable with the lower Akaike information criterion (AIC), i.e., we kept the 0.95 quantile of winter duration, 0.95 quantile of T seasonality, and 0.5 quantile of T variability. We tested for multicollinearity among the retained predictor variables by using variance inflation factors (VIF). All VIF were smaller than 5, indicating sufficient independence of the predictor variables. ANOVA and ordinal logistic regression (OLR) were used to separately test for correlations among predictor variables and species-specific photoperiod sensitivity (see Table 1, Fig. 1c, and Supplementary Fig. 6). To examine the relative contribution of each climate variable to explain species-specific photoperiod sensitivity, we applied multivariate OLR, random forest37,38, and hierarchical Bayesian models. The hierarchical Bayesian models allowed us to control for phylogenetic signals in our data (Supplementary Fig. 7) using the Bayesian phylogenetic regression method39 (next section). We analysed correlations between species’ native climates as inferred from the Koeppen-Geiger system36 and their photoperiod requirements by applying contingency analyses (Fisher’s test) and hierarchical Bayesian models (next section).

Data analysis including the phylogenetic structure To account for possible effects of shared evolutionary history, we applied hierarchical Bayesian models. The phylogenetic signal in trait data was estimated using Pagel’s λ40, with the ‘phylosig’ function in the R package ‘phytools’ v0.2-141. The phylogenetic tree for our 173 target species came from Panchen et al.42 and was assembled using the program Phylomatic43 (Supplementary Fig. 7). Its topology reflects the APG III phylogeny44, with a few changes based on the Angiosperm Phylogeny Website45. We manually added about 10 missing species to the tree. Branch lengths of the PHYLOMATIC tree are adjusted to reflect divergence time estimates based on the fossil record46,47. Besides controlling for phylogenetic signal λ40 of traits, the hierarchical Bayesian approach allowed us to control for possible effects of growth height on species-level photoperiod requirements and climate ranges, by including species’ mature growth height as a fixed effect in the models. Mature growth height is a significant functional trait that is related to species’ growth phenology42 as well as climate ranges48. Slope parameters across traits are estimated simultaneously without concerns of multiple testing or P-value correction. To determine which climate parameter best explains species-level differentiation in photoperiodism, we treated species’ photoperiod requirements (ordinal data) as a dependent

40 variable. Three climate variables (species-specific maximum winter duration, 0.95 quantile; max. T seasonality; 0.95 quantile; and median T variability, 0.5 quantile) and species’ mature growth height were used as predictor variables (Table 1 and Supplementary Fig. 8). Regression components are of the form: ordered logit(photoperiodi) = βmax winter duration x max winter durationi

+ βmedian T variability x median T variabilityi

+ βmax T seasonality x max T seasonalityi

+ βgrowth height x growth heighti β refers to the estimated slopes of the respective variable. In an alternative model, we used species’ Koeppen winter and summer temperature types and mature growth height as predictor variables (Supplementary Fig. 9): ordered logit(photoperiodi) = βwinter temp x winter tempi

+ βsummer temp x summer tempi

+ βgrowth height x growth heighti These models do not statistically account for phylogenetic structure by allowing correlations to vary according to the phylogenetic signal λ, because λ estimation is not possible for ordinal (or logistic) models. To nevertheless account for data non-independence due to shared evolutionary history of species (see Supplementary Fig. 7), we inserted genus and family random intercept effects in the model. To examine relative effect sizes of predictor variables, we standardized all variables by subtracting their mean and dividing by 2 SD before analysis49. The resulting posterior distributions are a direct statement of the influence of each parameter on species-level differentiation in photoperiod requirements. The effective posterior means (EPM) for the relationships between winter duration, temperature seasonality, and spring temperature variability and species-specific photoperiodism are shown in Supplementary Fig. 8, and the EPMs for relationships between Koeppen-Geiger climates and photoperiod requirements are shown in Supplementary Fig. 9. The hierarchical Bayesian model strongly preferred winter duration to T seasonality and T variability as an explanatory variable for species’ photoperiodism. Likewise, the model using the Koeppen system preferred the Koeppen winter climate to the summer climate as a predictor of species’ photoperiodism. To validate these results, instead of treating photoperiodism as dependent variable, we tested two other models. The first compared the distribution of covariates

41 (max. winter duration, max. T seasonality, and median T variability) between the different photoperiod categories. Species’ values for max. winter duration, max. T seasonality, and median T variability can be treated as continuous characters, which allowed us to incorporate phylogenetic distance matrices to control for shared evolutionary history of species (Pagel’s λ values: max. winter duration = 0.40; max. temp. seasonality = 0.39; median temp. variability = 0.26; see inset Fig. 1a). This model included three dependent variables that were normally distributed with mean µ, variance σ2, and correlation structure Σ (Fig. 1a): 2 max winter durationi ~ N(µmax winter duration i, σ max winter duration, Σ) 2 median T variabilityi ~ N(µmedian T variability i, σ median T variability, Σ) 2 max T seasonalityi ~ N(µmax T seasonality i, σ max T seasonality, Σ) Regression components are of the form:

µmax winter duration i = α1 + βwinter dur x photoperiodismi + β1 x mature growth heighti

µmedian T variability i = α3 + βT variability x photoperiodismi + β2 x mature growth heighti

µmax T seasonality i = α2 + βT seasonality x photoperiodismi + β3 x mature growth heighti The other model, based on species’ Koeppen climate letters as outcome, included two binary dependent variables that capture whether species are native to regions with mild or cold winters (KW; Koeppen C or D climate) and warm or cold summers (KS; Koeppen a or b climate) [Supplementary Fig. 2]:

winter temp ~ Bernoulli(WTi)

summer temp ~ Bernoulli(STi) Regression components are of the form:

logit(WTi) = α1+ β1 x photoperiodismi + β3 x maximum growth heighti

logit(STi) = α2 + β2 x photoperiodismi + β4 x maximum growth heighti The term α refers to the intercept, β to the estimated slopes of the respective variable (photoperiodism and maximum growth height), and max winter duration, max temp seasonality, and median temp variability refer to species values of the respective climate parameters. The phylogenetic structure of the data was incorporated in the hierarchical Bayesian model using the Bayesian phylogenetic regression method of de Villemereuil et al.39, by converting the 173- species ultrametric phylogeny into a scaled (0–1) variance–covariance matrix (Σ), with covariances defined by shared branch lengths of species pairs, from the root to their most recent ancestor50. We additionally allowed correlations to vary according to the phylogenetic signal (λ)

42 of climate parameters, fitted as a multiple of the off-diagonal values of Σ39. Values of λ near 1 fit a Brownian motion model of evolution, while values near zero indicate phylogenetic independence. The phylogenetic variance–covariance matrix was calculated using the ‘vcv.phylo’ function of the ape library51. The resulting posterior distributions are a direct statement of the influence of spring photoperiodism on species-level differentiation in climate characteristics (i.e., species’ max. winter duration, median temp. variability, and max. temp. seasonality). Effective posterior means for the respective relationships are shown in Fig. 1a. To parameterize our models we used the JAGS52 implementation of Markov chain Monte Carlo methods, in the R package R2JAGS53. We ran three parallel MCMC chains for 20,000 iterations with a 5000-iteration burn-in and evaluated model convergence with the Gelman and Rubin54 statistic. Noninformative priors were specified for all parameter distributions, including normal priors for α and β coefficients (fixed effects; mean = 0; variance = 1000), uniform priors between 0 and 1 for λ coefficients, and gamma priors (rate = 1; shape = 1) for the precision of random effects of phylogenetic autocorrelation, based on de Villemereuil et al.39. In table 1 we summarize the statistical results. All statistical analyses relied on R 3.2.255.

References

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46 47. Smith, S.A., Beaulieu, J.M. & Donoghue, M.J. An uncorrelated relaxed-clock analysis suggests an earlier origin for flowering plants. Proc. Natl. Acad. Sci. U.S.A. 107, 5897–5902 (2010). 48. Stahl, U., Reu, B., Wirth, C. Predicting species’ range limits from functional traits for the tree flora of North America. Proc. Natl. Acad. Sci. U.S.A. 111, 13739–13744 (2014). 49. Gelman, A. & Hill, J. Data analysis using regression and multilevel/hierarchical models. Cambridge, UK: Cambridge University Press (2007). 50. Grafen, A. The phylogenetic regression. Philos. T. Roy. Soc. B. 326, 119–157 (1989). 51. Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. Bioinformatics 20, 289–290 (2004). 52. Plummer, M. JAGS: a program for analysis of Bayesian graphical models using Gibbs sampling. In: Hornik K, Leisch F, Zeileis A, eds. Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003). Vienna, Austria: Achim Zeileis (2003). 53. Su, Y.-S. & Yajima, M. R2jags: a package for running JAGS from R. R package version 0.04- 03 (2014). [WWW document] URL http://CRAN.R-project.org/ package=R2jags 54. Gelman, A., Rubin, D.B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992). 55. R Core Team. R: a language and environment for statistical computing. Vienna, Austria, 2015: R Foundation for Statistical Computing. [WWW document] URL http://www.R- project.org/. [Accessed 7 October 2015].

Acknowledgements We thank V. Sebald and M. Wenn for help with conducting the twig cutting experiments. B.M.B. acknowledges funding by Aarhus University and the Aarhus University Research Foundation under the AU IDEAS program (Centre for Biocultural History) and J.-C. S. support from by the European Research Council (ERC-2012-StG-310886-HISTFUNC). The study was part of the KLIMAGRAD project sponsored by the ‘Bayerisches Staatsministerium für Umwelt und Gesundheit’.

47 Author contributions C.M.Z. and S.S.R. designed the study. C.M.Z. conducted the experiments and leaf-out observations. C.M.Z. and B.M.B. performed the analyses. C.M.Z. and S.S.R. led the writing with inputs from the other authors.

Additional information Supplementary information is available in the online version of the paper. Reprints and permissions information is available online at www.nature.com/reprints. Correspondence and requests for materials should be addressed to C.M.Z.

Competing financial interests The authors declare no competing financial interests.

48 Figures and Tables

1.00 Pagel’s λ a 2" 0.50 0.75

0.50

0.25 3" 0.25 estimate Coefficient

0.00 λWinterwinter lengthdur. λT TV var. λTTS seasonality 4" 0.00

5" -0.25 Coefficient estimate Coefficient 6" estimate Coefficient -0.50

7" βwinterWinter duration length βT variability TV βT TSseasonality

0 8" C b 2 9" 4 6 10" 8 # months <5° months #

>9 11" c 12" 0.75

13" None 14" 0.50 15" 16" Low 0.25

17" Proportionof species

High

18" 19" 0.00 3 4 5 6 7 8 20" # months <5°C

21" 22"

23" Photoperiodism 24" Figure 1 | Relationship between species’ spring photoperiodism and the maximum 49 25" winter duration in their native ranges. a, Coefficient values (effective posterior means β 26" and 95% credible intervals) for the effect of spring photoperiodism on species’ maximum 27" winter duration, median T variability, and maximum T seasonality. Models control for 28" phylogenetic autocorrelation and species’ maximum growth height (see Supplementary 29" Methods for a detailed description of regression components). Values reflect standardized 30" data and can be interpreted as relative effect sizes. The inset shows fitted values of 31" phylogenetic signal (Pagel’s λ, mean and 95% CIs) for species’ maximum winter duration, 32" median T variability, and maximum T seasonality (dependent variables), respectively. b, 33" Winter duration calculated as the number of months with mean air temperature below 5°C. c, 34" Proportion of species with a given level of photoperiod sensitivity as a function of maximum Figure 1 | Relationship between species’ spring photoperiodism and the maximum winter duration in their native ranges. a, Coefficient values (effective posterior means β and 95% credible intervals) for the effect of spring photoperiodism on species’ maximum winter duration, median T variability, and maximum T seasonality. Models control for phylogenetic autocorrelation and species’ maximum growth height. See Supplementary Methods for a detailed description of regression components. Values reflect standardized data and can be interpreted as relative effect sizes. The inset shows fitted values of phylogenetic signal (Pagel’s λ, mean and 95% CIs) for species’ maximum winter duration, median T variability, and maximum T seasonality (dependent variables), respectively. b, Winter duration calculated as the number of months with mean air temperature below 5°C. c, Proportion of species with a given level of photoperiod sensitivity as a function of maximum winter duration (0.95 quantile) in a species’ native range (ordinal logistic regression model; P < 0.01; table 1). Colours as in panel b. Envelopes around each line show 95% confidence intervals. Boxplots for species’ maximum winter duration when they were grouped according to photoperiod requirements are shown below the graph. Photoperiod requirements: None = No sensitivity; Low = Sensitivity to day length during early dormancy; High = Sensitivity to day length also in late dormancy (see Supplementary Fig. 1).

* 35 * 12 outdates - 11 ** 30 **

10 25

annualvariation (SD) in leaf 8 - annualvariation (SD) in thermalrequirements - Inter Inter-annual variation (SD) in leaf-out dates leaf-out in (SD) variation Inter-annual 7 15 Inter 123 123 None (103) Low (36) High (15) requirements thermal in (SD) variation Inter-annual None (103) Low (36) High (15) Photoperiod requirement Photoperiod requirement Photoperiodism

Figure 2 | Photoperiod-dependent leaf-out strategies lead to low inter-annual variability in leaf-out dates (a) and high inter-annual variability in thermal time until budburst (b). For each species (n = 154) the SD in leaf-out dates and thermal requirements was calculated on the basis of leaf-out dates available from the Munich Botanical Garden from 2012 to 2015. We show the mean ± 95% confidence interval for each group. Thermal time was calculated as the sum of growing-degree days from 1 Jan until the day of leaf-out in the respective species using 0°C as base temperature. Asterisks above bars indicate which group differed significantly from the group of species with no photoperiod requirements (*P < 0.05, **P < 0.01).

51 Table 1 | Global relationships between species’ photoperiod requirements and duration of winter, inter-annual spring temperature variability (T variability), and T seasonality in their native range for 144 temperate woody species. Five comparative measures were used: the F value from univariate ANOVA, Akaike weights from bivariate regressions using ordinal logistic regression (OLR) models, parameter estimates and 95% confidence intervals (CI) based on multivariate OLR models, mean decrease in accuracy values (MDA) from random forest analysis, and coefficient values [effective posterior means (EPM) and 95% CIs] from a hierarchical Bayesian (HB) model controlling for phylogenetic autocorrelation and species’ maximum growth height. For each single climatic parameter we initially considered the upper limit (0.95-quantile), median (0.5 quantile), and lower limit (0.05-quantile) across each species' range and kept the variable that yielded the lower Akaike information criterion (AIC) according to OLR models (i.e. we kept the 0.95 quantile for winter duration and T seasonality, and the 0.5 quantile for T variability). Sample size: No photoperiod requirements = 88 species; Low = 42 species; High = 14 species. *P < 0.05, **P < 0.01.

ANOVA OLR Multiv. OLR Random forest HB model

F values WeightAIC Estimate ± CI MDA EPM ± CI

Winter duration F(1, 142) = 9.5** 0.90** -0.47 ± 0.28** 33.7 -1.1 ± 0.5 T variability F(1, 142) = 0.3 0.05 0.99 ± 1.17 22.9 -0.3 ± 0.5 T seasonality F(1, 142) = 1.9 0.05 0.00 ± 0.01 20.8 -0.2 ± 0.5

52 Supplementary Figures and Tables

Low None None None None 2000 Abies alba 2000 Abies homolepis 2000 Acer barbinerve 2000 Acer campestre 2000 Acer ginnala

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

NL NL NL 0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 Laube et al. 2014 Laube et al. 2014 This study This study This study

None None Low High Low 2000 Acer negundo 2000 Acer platanoides 2000 Acer pseudoplatanus 2000 Acer saccharum 2000 Acer tataricum

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

NL NL NL NL 0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 Laube et al. 2014 This study Laube et al. 2014 Laube et al. 2014 Laube et al. 2014

None High Low None Low 2000 Aesculus flava 2000 Aesculus hippocastanum 2000 Aesculus parviflora 2000 Alnus incana 2000 Alnus maximowiczii

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

None None None None Low 2000 Amelanchier alnifolia 2000 Amelanchier florida 2000 Amelanchier laevis 2000 Amorpha fruticosa 2000 Aronia melanocarpa

1500 1500 1500 1500 1500

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NL NL 0 0 0 0 0 Degree days days >0°C Degree C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study Laube et al. 2014 This study

None Low None None Low 2000 Berberis dielsiana 2000 Betula lenta 2000 Betula nana 2000 Betula pendula 2000 Betula populifolia

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0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study Laube et al. 2014 This study

None None None None None 2000 Buddleja albiflora 2000 Buddleja alternifolia 2000 Buddleja davidii 2000 Caragana pygmaea 2000 Carpinus betulus

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0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study 0 This study This study Figure S1 partial 53 None None High Low Low 2000 Carpinus laxiflora 2000 Carpinus monbeigiana 2000 Carya cordiformis 2000 Carya laciniosa 2000 Carya ovata

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0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

High None None Low Low 2000 Castanea sativa 2000 Cedrus libani 2000 Celtis caucasica 2000 Celtis laevigata 2000 Celtis occidentalis

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None None Low None High 2000 Cephalanthus occidentalis 2000 Cercidiphyllum japonicum 2000 Cercidiphyllum magnificum 2000 Cercis canadensis 2000 Cercis chinensis

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0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

High High Low None High 2000 Cladrastis lutea 2000 Cornus alba 2000 Cornus kousa 2000 Cornus mas 2000 Corylopsis sinensis

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Low Low High None None 2000 Corylopsis spicata 2000 Corylus avellana 2000 Corylus heterophylla 2000 Corylus sieboldiana 2000 Decaisnea fargesii

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None None None None None 2000 Deutzia gracilis 2000 Deutzia scabra 2000 Elaeagnus ebbingei 2000 Eleutherococcus senticosus 2000 Eleutherococcus setchuensis

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54 None Low None High High 2000 Eleutherococcus sieboldianus 2000 Euonymus europaeus 2000 Euonymus latifolius 2000 Fagus crenata 2000 Fagus engleriana

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High High None None Low 2000 Fagus orientalis 2000 Fagus sylvatica 2000 Forsythia ovata 2000 Forsythia suspensa 2000 Fraxinus chinensis

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None None None None None 2000 Hamamelis japonica 2000 Hamamelis vernalis 2000 Heptacodium miconioides 2000 Hibiscus syriacus 2000 Hydrangea arborescens

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Low None None None Low 2000 Hydrangea involucrata 2000 Hydrangea serrata 2000 Juglans ailantifolia 2000 Juglans cinerea 2000 Juglans regia

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None None None None None 2000 Larix decidua 2000 Larix gmelinii 2000 Larix kaempferi 2000 Ligustrum tschonoskii 2000 Liquidambar orientalis

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55 High Low None None None 2000 Liquidambar styraciflua 2000 Liriodendron tulipifera 2000 Lonicera alpigena 2000 Lonicera caerulea 2000 Lonicera maximowiczii

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None Low None None Low 2000 Populus koreana 2000 Populus tremula 2000 Prinsepia sinensis 2000 Prinsepia uniflora 2000 Prunus avium

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56 None None None Low None 2000 Pseudotsuga menziesii 2000 Ptelea trifoliata 2000 Pyrus elaeagnifolia 2000 Pyrus pyrifolia 2000 Pyrus ussuriensis

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None None None None None 2000 Rhamnus cathartica 2000 Rhododendron canadense 2000 Rhododendron dauricum 2000 Rhododendron mucronulatum 2000 Ribes alpinum

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None None None None None 2000 Salix gracilistyla 2000 Salix repens 2000 Sambucus nigra 2000 Sambucus pubens 2000 Sambucus tigranii

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1000 1000 1000 1000 1000

500 500 500 500 500

0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

Low None None None None 2000 Sinowilsonia henryi 2000 Sorbus aria 2000 Sorbus commixta 2000 Sorbus decora 2000 Spiraea canescens

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

Figure S1 continued

57 None None None Low None 2000 Spiraea chamaedryfolia 2000 Spiraea japonica 2000 Stachyurus praecox 2000 Stachyurus sinensis 2000 Symphoricarpos albus

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

NL 0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study Laube et al. 2014

None None None Low None 2000 Syringa josikaea 2000 Syringa reticulata 2000 Syringa villosa 2000 Syringa vulgaris 2000 Tilia dasystyla

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study Laube et al. 2014 This study

None None Low None None 2000 Tilia japonica 2000 Tilia platyphyllos 2000 Toona sinensis 2000 Ulmus amerciana 2000 Ulmus laevis

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

0 0 0 0 0 Degree days days >0°C Degree C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

Low Low Low None Low 2000 Viburnum betulifolium 2000 Viburnum buddleifolium 2000 Viburnum carlesii 2000 Viburnum opulus 2000 Viburnum plicatum

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

NL NL 0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

None None None None None 2000 2000 2000 2000 2000 Weigela coraeensis Weigela florida Weigela maximowiczii TreatmentSambucus legendpubens Sambucus tigranii 1500 1500 1500 1500 1500 Degree days until leaf-out under 8 h days 1000 1000 1000 1000 Degree days until1000 leaf-out under 16 h days 500 500 500 500NL No leaf-out under 8500 h days

0 0 0 0NL No leaf-out under 16 0h days C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study Low None None None None 2000 2000 2000 2000 2000 Sinowilsonia henryi Sorbus aria Sorbus commixta Sorbus decora Spiraea canescens 1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500

0 0 0 0 0 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 C1 C2 C3 This study This study This study This study This study

58 Figure S1 | Photoperiod requirements of 173 temperate woody species. The importance of photoperiod in regulating leaf-out (none, low, or high photoperiodism) was inferred from twig cutting experiments conducted in this study and in Laube et al.12. Graphs show forcing requirements (median growing degree days >0°C outdoors and in climate chamber ± SD) until leaf-out under short day length (8 h/d, black bars) and long day length (16 h/d, grey bars) at three different cutting dates (this study: C1 = 21 Dec 2013, C2 = 10 Feb 2014, C3 = 21 March 2014; Laube et al.12: C1 = 14 Dec 2011, C2 = 30 Jan 2012, C3 = 14 March 2012). NL indicates that no leaf-out occurred under 8-h (NL in black) or 16-h (NL in grey) day length. Some species leafed out before the last cutting date (C3), which is indicated by missing bars for the C3 treatment.

59 a ** * b 1.00 2

Koeppen_climate

0.75 1 Ca

Cb 0.50 Da 0

Db Koeppen climate Koeppen Coefficient estimate Coefficient 0.25 Dc-1 Coefficient estimate Coefficient

0.00 -2 KW KS NoneAB (112) Low (45) HighC (16) β1 β2 Photoperiod requirement Photoperiodism

Figure S2 | Species with photoperiod requirements are native to milder climates. a, For each photoperiod category we show the relative proportion of species’ Koeppen-Geiger temperature regimes (Ca = mild winter and hot summer periods, Cb = mild winter and warm summer periods, Da = cold winter and hot summer periods, Db = cold winter and warm summer periods, Dc = cold winter and cold summer periods). Asterisks above bars indicate which group differed significantly from the group containing species with high photoperiod requirements (*P < 0.05, **P < 0.01). Sample sizes are shown in brackets below the graph. b, Estimated coefficient values (effective posterior means and 95% credible intervals) for the effect of spring photoperiodism on species’ winter (β1) and summer (β2) temperature regime. Winter climate and summer temperature were included as binary variables of whether the species is native to (i) mild (Koeppen letter C) or cold winter climates (Koeppen letter D); and (ii) hot (Koeppen letter a) or colder summer climates (Koeppen letters b/c). Model controls for phylogenetic autocorrelation and species’ maximum growth height (see Supplementary Methods). Values reflect standardized data and can be interpreted as relative effect sizes.

60 Wendelstein

1919.06.15! June 2 R = 0.86; slope = -2.8 / decade

3030.05.15! May

2 1010.05.15! May R = 0.01; slope = 0.1 / decade Oberstdorf

2020.04.15! Apr

Munich

R2 = 0.82; slope = -3.7 / decade 31 Mar 31.03.15!

Helgoland

11 Mar 11.03.15!

2 R = 0.68; slope = -6.1 / decade 19 Feb

19.02.15!

1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2009 2011 2013 2015 1955! 1957! 1959! 1961! 1963! 1965! 1967! 1969! 1971! 1973! 1975! 1977! 1979! 1981! 1983! 1985! 1987! 1989! 1991! 1993! 1995! 1997! 1999! 2001! 2007 – – – – – – – – – – – – – – – – – – – - – – – –

1955 1957 1959 1961 1963 1965 1967 1969 1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 FigFigure S3 || LLastast frost frost events events between between 1955 1955 and and 2015 2015 at four at fourGerman German weather weather stations stations with a 15with-year a 15 moving-year moving window. window. Last Lastfrost frost events events were were defined defined as asthe the latest latest day day in in spring spring with a with a minimum temperature below 0°C. Data for Helgoland (40 m a.s.l.; 54°10’N, 07°53’E), minimum temperature below 0°C. Data for Helgoland (40 m a.s.l.; 54°10’N, 07°53’E), Munich Munich (501 m a.s.l.; 48°08’N, 11°31’E), Oberstdorf (806 m a.s.l.; 47°25’N, 10°17’E), and (501 m a.s.l.; 48°08’N, 11°31’E), Oberstdorf (806 m a.s.l.; 47°25’N, 10°17’E), and Wendelstein Wendelstein (1832 m a.s.l.; 47°42’N, 12°00’E). (1832 m a.s.l.; 47°42’N, 12°00’E).

61 20

15 C1 C2 C3

(°C)

Mean air temperature air Mean 0

-5 01.!Nov.!Nov 01.!Dez.! Dec 01.!Jan.! Jan 01.!Feb.! Feb 01.!März! Mar 01.!Apr.! Apr

FigureFigure S4 S 4| Mean| Mean airair temperaturetemperature during during the the stu studydy period period (Nov (Nov 2013 2013 – Apr – Apr 2014) 2014) outside (blackoutside line) (black and line) in climate and in climchambersate chambers (blue lines).(blue lines). C1 – C3:C1 – Daily C3: Daily mean m airean temperatureair in the climatetemperature chambers in the for climate different chambers chilling for treatments.different chilling C1: lowtreatments. chilling C1: = 38low c hillchilling days, = 38C2: intermediatechill days, C2: chilling intermediate = 72 chill chilling days, = C3:72 chill high days, chilling C3: high = 88 chilling chill days. = 88 Chillchill days. days Chillwere calculated asdays number were of calcul daysated with as anumber mean airof daystemperature with a mean <5°C air fromtemperature 1 November <5°C from until 1 start November of the respectiveuntil start climate of the respective chamber climate treatment chamber (C1, treatmentC2, C3). (C1, C2, C3).

0.00

0.45 0.90 1.35 1.80 2.25 deviation Standard 2.70

Figure S5S5 | |Inter Inter-annual-annual spring spring temperature temperature variability variability (T variability) (T variability). T variability. T variability was was calculatecalculatedd as as the the standard standard deviation deviation of meanof mean spring spring temperatures temperatures (March, (March, April, April,and May) and from May) from1901 to1901 2013. to Data2013. on Data monthly on monthly average temperaturesaverage temperatures during this during period thiswere period available were from available the

CRU database (5-arc minute spatial resolution data)35. 35 from the CRU database (5-arc minute spatial resolution data) .

a b

0.8

0.75

0.6

0.50

0.4 PredictedProb PredictedProb

0.25 0.2

Bio7 0.00 0.0 0.8 1.0 1.2 1.4 1.6 300 350 400 450 500 550 1.00 1.25 1.50 300 400 500 SD_Mean_Spring_0.5 Bio7_0.95 123 123 Photoperiod NonePhotoperiod (88) Low (42) High (14)

Photoperiodism T variability T seasonality

FigureFigure S6 | RelationshipsS6 | Relationships between between species’ species’ springspring photoperiodism photoperiodism and andthe between the between-year -year springspring temperature temperature variability variability (a) (a) and and temperature seasonality seasonality (b) in(b) their in their native native ranges. a, Probabilityranges of. a ,species Probability-specific of species photoperiod-specific photoperiod sensitivity sensitivity as a function as a functi of medianon of median spring T variability spring T variability in a species’ native range (0.5 quantile; P = 0.43; univariate GLM). b, in a species’ native range (0.5 quantile; P = 0.43; univariate GLM). b, Probability of species- Probability of species-specific photoperiod sensitivity as a function of maximum T specific photoperiod sensitivity as a function of maximum T seasonality in a species’ native seasonality in a species’ native range (0.95 quantile; P = 0.67; univariate GLM). Envelopes range (0.95 quantile; P = 0.67; univariate GLM). Envelopes around each line show 95% around each line show 95% confidence intervals. Boxplots for species’ median T variability confidenceand maximum intervals. T seasonality Boxplots whenfor species’ they were median grouped T according variability to photoperiodand maximum requirements T seasonality when arethey shown were below grouped the graph. according Photoperiod to photoperiod requirements: requirements None = No sensitivity; are shown Low below = the graph. PhotoperiodSensitivity requirements: to day length Noneduring =early No dormancy;sensitivity; High Low = Sensitivity = Sensitivity to day to length day lengthalso in lateduring early dormancy;dormancy High (see = SupplementarySensitivity to Fig.day 1).length also in late dormancy (see Supplementary Fig. 1).

Photoperiod length Winter ● Liriodendron tulipifera ● Decaisnea fargesii ● Berberis dielsiana ● Paeonia rockii

● Liquidambar styraciflua Ranunculales ● Liquidambar orientalis ● Sinowilsonia henryi ● Parrotiopsis jacquemontiana ● Parrotia persica ● Hamamelis vernalis ● Hamamelis japonica ● Corylopsis spicata Corylopsis sinensis ● Saxifragales ● Ribes glaciale ● Ribes divaricatum ● Ribes alpinum ● Cercidiphyllum magnificum ● Cercidiphyllum japonicum ● Stachyurus praecox ● Stachyurus chinensis ● Salix repens ● Salix gracilistyla ● Populus tremula ● Populus koreana ● Euonymus latifolius Malpighiales ● Euonymus europaeus ● Cercis chinensis ● Cercis canadensis ● Cladrastis lutea ● Robinia pseudoacacia ● Caragana pygmaea Fabales ● Amorpha fruticosa ● Rosa majalis ● Rosa hugonis ● Prunus tenella ● Prunus serrulata ● Prunus serotina ● Prunus padus ● Prunus cerasifera ● Prunus avium ● Prinsepia uniflora ● Prinsepia sinensis ● Oemleria cerasiformis ● Spiraea japonica ● Spiraea chamaedryfolia ● Spiraea canescens ● Sorbus decora ● Sorbus commixta ● Sorbus aria Pyrus ussuriensis

● Rosales ● Pyrus pyrifolia ● Pyrus elaeagnifolia ● Photinia villosa ● Aronia melanocarpa ● Amelanchier laevis ● Amelanchier florida ● Amelanchier alnifolia ● Rhamnus cathartica ● Rhamnus alpina ● Elaeagnus ebbingei ● Ulmus laevis ● Ulmus americana ● Celtis occidentalis ● Celtis laevigata ● Celtis caucasica ● Nothofagus antarctica ● Juglans regia ● Juglans cinerea ● Juglans ailanthifolia ● Carya ovata ● Carya laciniosa ● Carya cordiformis ● Corylus sieboldiana ● Corylus heterophylla ● Corylus avellana ● Ostrya virginiana ● Ostrya carpinifolia ● Carpinus monbeigiana ● Carpinus laxiflora ● Carpinus betulus ● Betula populifolia ● Betula pendula Fagales ● Betula nana ● Betula lenta ● Alnus maximowiczii ● Alnus incana ● Fagus sylvatica ● Fagus orientalis ● Fagus engleriana ● Fagus crenata ● Castanea sativa ● Quercus shumardii ● Quercus rubra ● Quercus robur ● Quercus bicolor ● Tilia platyphyllos ● Tilia japonica ● Tilia dasystyla

● Hibiscus syriacus Malvales ● Ptelea trifoliata ● Orixa japonica ● Toona sinensis ● Aesculus parviflora ● Aesculus hippocastanum ● Aesculus flava ● Acer tataricum ● Acer saccharum ● Acer pseudoplatanus ● Acer platanoides Sapindales ● Acer negundo ● Acer ginnala ● Acer campestre ● Acer barbinerve ● Hydrangea involucrata ● Hydrangea serrata ● Hydrangea arborescens ● Deutzia scabra ● Deutzia gracilis

● Cornus mas Cornales ● Cornus kousa ● Cornus alba ● Rhododendron mucronulatum ● Rhododendron dauricum Rhododendron canadense

● Ericales ● Viburnum plicatum ● Viburnum opulus ● Viburnum carlesii ● Viburnum buddleifolium ● Viburnum betulifolium ● Symphoricarpos albus ● Sambucus racemosa ● Sambucus pubens ● Sambucus nigra ● Weigela maximowiczii ● Weigela florida

● Weigela coraeensis Dipsacales ● Lonicera maximowiczii ● Lonicera caerulea ● Lonicera alpigena ● Heptacodium miconioides ● Eleutherococcus sieboldianus ● Eleutherococcus setchuenensis ● Eleutherococcus senticosus ● Cephalanthus occidentalis ● Syringa vulgaris ● Syringa villosa ● Syringa reticulata ● Syringa josikaea Photoperiod requirement Max. winter duration ● Ligustrum tschonoskii ● Fraxinus pennsylvanica ● Fraxinus ornus ● Fraxinus latifolia ● Fraxinus excelsior None > 6 months ● Fraxinus chinensis Lamiales ● Forsythia suspensa ● Forsythia ovata ● Buddleja davidii ● Buddleja alternifolia Low 5 – 6 months ● Buddleja albiflora ● Cedrus libani ● Abies homolepis ● Abies alba ● Picea abies High < 5 months ● Pinus wallichiana ● Pinus sylvestris ● Pinus strobus ● Pinus nigra

● Pseudotsuga menziesii Coniferales ● Larix kaempferi ● Larix gmelinii ● Larix decidua ● Metasequoia glyptostroboides ● Ginkgo biloba Fig.Figure Sx | PHYLOMATIC S7 | PHYLOMATIC tree modified tree from modified Panchen etfrom al. containing Panchen the et 173al.42 woody containing the 173 woody species for which photoperiod requirements were studied. Species’ photoperiod species for which photoperiod requirements were studied. Species’ photoperiod requirements requirements and their maximum winter duration (95th quantile for the number of months with and their maximum winter duration (0.95 quantile for the number of months with an average temperature below 5°C) are indicated by colored circles and squares, respectively.

-1 Coefficient estimate Coefficient

-2

WinterWinter durationlength T variability TV T seasonalityTS

Figure S8 | Effect of species’ climate parameters on variation in spring photoperiodism. Coefficient values (effective posterior means and 95% credible intervals) for relationships between species’ photoperiodism and their winter duration (0.95 quantile), inter-annual spring temperature variability (T variability; 0.5 quantile), and temperature seasonality (T seasonality; 0.95 quantile). Note that in this model, photoperiod is treated as dependent variable (ordinal logistic regression). Models account for phylogenetic structure in the data and species’ maximum growth height (see Supplementary Methods). Values reflect standardized data and can be interpreted as relative effect sizes. Sample sizes: N = 88 species (None), 42 (Low), 14 (High photoperiodism).

65 2

0 Coefficient estimate Coefficient -1

-2 Winterβwinter temperature temp Summer βsum temperaturemer temp

Figure S9 | The effect of winter and summer temperature regime on species-level variation in photoperiodism for 173 species using the Koeppen-Geiger climate classification. Coefficient values (effective posterior means and 95% credible intervals) for relationships between winter and summer climate and species’ photoperiodism. Winter climate was included as a binary variable capturing whether a species is native to mild (Koeppen letter C) or cold winter climates (Koeppen letter D). Summer climate was included as a binary variable capturing whether a species is native to hot (Koeppen letter a) or colder summer climates (Koeppen letters b/c). The dependent variable (species’ photoperiodism) was included as ordinal variable (no, low, high photoperiod requirements). To control for phylogenetic autocorrelation and a possible effect of species’ growth habit, the model includes random genus and family effects and a fixed effect of species’ maximum growth height (see Supplementary Methods). Values reflect standardized data and can be interpreted as relative effect sizes.

Table S1 | Photoperiod requirements, standard deviations in leaf-out dates / thermal requirements, maximum winter duration, predominant climate, and maximum growth height of 173 temperate woody species. The importance of photoperiod in regulating leaf-out (Photo) was inferred from twig cutting experiments conducted in this study and a previous study12. Species-specific standard deviations in leaf-out dates (SD DOY) or thermal requirements (SD GDD; growing degree days >0° from 1 Jan until leaf-out) were calculated on the basis of leaf-out dates available from the Munich Botanical Garden from 2012 to 2015. Climate refers to the predominant Koeppen-Geiger climate type in a species’ native range. Maximum winter duration (WD) refers to species’ 0.95 quantile for the number of months with an average temperature below 5°C in their native ranges. Height refers to the mature (maximum) recorded height of a species.

Genus Species Photo SD DOY SD GDD Climate WD Height Abies alba Low 5.56 42.35 Dfb 6 40 Abies homolepis None - - Cfa 6 25 Acer barbinerve None 13.49 11.29 Dwb 5 8 Acer campestre None 10.61 21.07 Cfb 5 20 Acer ginnala None 13.96 24.22 Dfa 6 15 Acer negundo None 11.46 14.15 Dfa 5 15 Acer platanoides None 11.41 38.97 Dfb 7 30 Acer pseudoplatanus Low - - Dfb 6 30 Acer saccharum High 10.34 9.17 Dfa 6 40 Acer tataricum Low - - Dfa 6 15 Aesculus flava None 8.83 19.91 Cfa 5 30 Aesculus hippocastanum High 10.37 33.61 Csa 6 30 Aesculus parviflora Low 17.46 39.03 Cfa 4 4 Alnus incana None 9.6 15.64 Dfb 8 20 Alnus maximowiczii Low 9.27 42.17 Dfa 8 9 Amelanchier alnifolia None 10.18 20.57 Dfb 8 4 Amelanchier florida None 9.11 14.41 Dfb - 4 Amelanchier laevis None 9.54 10.29 Dfb 7 8 Amorpha fruticosa None 5.1 21.67 Cfa 5 3 Aronia melanocarpa Low 12.01 22.49 Dfb 6 3 Berberis dielsiana None 14.72 10.89 Dwa - 2 Betula lenta Low 11.73 44.74 Dfa 5 25

67 Table S1 continued. Genus Species Photo SD DOY SD GDD Climate WD Height Betula nana None 11 11.24 Dfc 9 1 Betula pendula None 8.66 16.48 Dfb 7 30 Betula populifolia Low 9.18 15.06 Dfb 6 9 Buddleja albiflora None 13.03 31.09 BWk - 4 Buddleja alternifolia None 13.15 10.98 BWk 8 5 Buddleja davidii None - - Cwb 5 5 Caragana pygmaea None 14.08 25.66 Dwb - 0.5 Carpinus betulus None 12.01 15.05 Dfb 5 25 Carpinus laxiflora None 12.12 22.2 Cfa 5 30 Carpinus monbeigiana None 12.07 22.94 Cwb - 16 Carya cordiformis High 8.38 45.57 Cfa 5 35 Carya laciniosa Low 5.32 52.92 Dfa 4 30 Carya ovata Low 4.57 49.08 Dfa 5 27 Castanea sativa High 9.91 17.06 Cfb 5 30 Cedrus libani None 13.77 56.34 Csa 5 40 Celtis caucasica None - - Csa - 15 Celtis laevigata Low 5.68 21.43 Cfa 5 24 Celtis occidentalis Low 10.39 30.81 Dfa 5 24 Cephalanthus occidentalis None 4.19 73.28 Cfa 5 6 Cercidiphyllum japonicum None 11.76 26.9 Cfa 6 45 Cercidiphyllum magnificum Low 9.61 10.96 Dfa 8 10 Cercis canadensis None 6.38 29.72 Cfa 5 9 Cercis chinensis High 7.72 27.42 Cwa 4 3.5 Cladrastis lutea High 10.41 24.9 Cfa 5 15 Cornus alba High 11.79 10.51 Dwa - 3 Cornus kousa Low 9.54 11.38 Cfa 5 12 Cornus mas None 9.83 14.97 Cfb 5 5 Corylopsis sinensis High 9.98 9.97 Cfa 3 1.8 Corylopsis spicata Low 10.87 11.38 Cfa - 2.4 Corylus avellana Low 10.74 19.2 Cfb 6 8 Corylus heterophylla High 10.28 29.62 Cfa 6 7 Corylus sieboldiana None 12.12 31.29 Cfa 6 5 Decaisnea fargesii None 13.57 27.98 Cfa - 8 Deutzia gracilis None 13.99 20.6 Cfa 5 0.6 Deutzia scabra None 11.43 19.42 Cfa 4 4 Elaeagnus ebbingei None 11.32 18.87 - - 3 Eleutherococcus senticosus None 13.38 15.76 Dwb 7 2 Eleutherococcus setchuenensis None 11.32 15.46 Dwb - 4 Eleutherococcus sieboldianus None 11.00 22.99 Cfa 4 2

68 Table S1 continued. Genus Species Photo SD DOY SD GDD Climate WD Height Euonymus europaeus Low 11.73 16.88 Cfb 5 6 Euonymus latifolius None 9.2 16.95 Dfb 6 3 Fagus crenata High 11.7 42.67 Dfa 6 35 Fagus engleriana High 10.8 32.4 Cwa - 17 Fagus orientalis High - - Cfa 6 45 Fagus sylvatica High 6.85 30.26 Cfb 6 40 Forsythia ovata None 13.2 3.62 Dwa - 1.5 Forsythia suspensa None 12.14 11.13 Cfa 5 5 Fraxinus chinensis Low 11.62 44.04 Dwa 6 25 Fraxinus excelsior Low 6.78 60.31 Dfb 6 35 Fraxinus latifolia Low 5.51 43.32 Csb 5 25 Fraxinus ornus None 4.99 24.87 Cfa 5 25 Fraxinus pennsylvanica Low 3.87 52.37 Dfa 6 20 Ginkgo biloba High 10.23 43.61 Cfa 4 35 Hamamelis japonica None 12.12 22.54 Dfa 6 4 Hamamelis vernalis None 11.86 22.17 Dfa 4 4 Heptacodium miconioides None 12.69 14.23 Cfa - 8 Hibiscus syriacus None 10.42 39.63 Cfa 3 4 Hydrangea arborescens None 10.34 15.49 Dfa 5 3 Hydrangea involucrata Low 11.24 11.01 Cfa 5 1 Hydrangea serrata None 13.96 14.52 Dfb 6 1.2 Juglans ailanthifolia None 13.07 70.66 Dfa 6 20 Juglans cinerea None - - Dfa 6 24 Juglans regia Low 8.74 19.63 Cfb 5 25 Larix decidua None 12.39 6.61 Dfb 6 45 Larix gmelinii None 12.92 13.89 Dwb 8 30 Larix kaempferi None 12.01 12.6 Dfa 7 40 Ligustrum tschonoskii None 12.26 19.3 Cfa 6 3 Liquidambar orientalis None 9.11 16.37 Csa 40 Liquidambar styraciflua High 8.42 18.87 Cfa 3 35 Liriodendron tulipifera Low 13.67 20.31 Cfa 5 40 Lonicera alpigena None - - Dfc 7 2 Lonicera caerulea None 15.44 20.49 Dfc 8 1 Lonicera maximowiczii None 14.45 24.21 Dwb - 4 Metasequoia glyptostroboides None 12.28 23.49 Cwa 3 45 Nothofagus antarctica Low 14.24 26.78 Cfb 7 25 Oemleria cerasiformis None 15.5 42.43 Csb 5 5 Orixa japonica None 13.4 5.72 Cwa 4 3 Ostrya carpinifolia Low 9.31 14.49 Cfb 6 20

69 Table S1 continued. Genus Species Photo SD DOY SD GDD Climate WD Height Ostrya virginiana None 10.54 14.17 Dfa 6 18 Paeonia rockii None 13.77 18.67 Cwa - 3 Parrotia persica None 12.12 18.77 Csa 3 15 Parrotiopsis jacquemontiana None 9.56 12.52 Dwa - 6 Photinia villosa None 9.95 5.89 Cwa 5 15 Picea abies None 7.77 27.56 Dfb 8 55 Pinus nigra Low - - Cfa 5 40 Pinus strobus Low - - Dfb 6 50 Pinus sylvestris Low - - Dfb 8 30 Pinus wallichiana Low - - Dsb 7 40 Populus koreana None 14.45 10.64 Dfa - 15 Populus tremula Low - - Dfb 8 20 Prinsepia sinensis None 24.76 8.08 Dwb - 2 Prinsepia uniflora None 11.62 21.36 Dwa - 2 Prunus avium Low - - Cfb 6 25 Prunus cerasifera None 13.5 11.87 Dfa 7 15 Prunus padus None 12.29 12.27 Dfb 8 15 Prunus serotina None 12.44 9.03 Cfa 6 30 Prunus serrulata None 11.69 16.56 Cfa 5 12 Prunus tenella None 13.52 26.2 Dfb 6 1.5 Pseudotsuga menziesii None - - Csb 8 70 Ptelea trifoliata None - - Cfa 5 8 Pyrus elaeagnifolia None 12.71 32.72 Csa - 6 Pyrus pyrifolia Low 11.81 11.17 Cwa 5 15 Pyrus ussuriensis None 16.38 16.43 Dwa 7 15 Quercus bicolor Low 8.66 46.62 Dfa 5 25 Quercus robur Low 9.32 27.39 Cfb 6 40 Quercus rubra High 11.73 49.14 Dfa 6 35 Quercus shumardii Low 10.23 33.49 Cfa 3 35 Rhamnus alpina None 7.33 32.11 Dfb - 4 Rhamnus cathartica None 7.77 25.99 Cfb 6 6 Rhododendron canadense None 9.81 11.59 Dfb 7 1.2 Rhododendron dauricum None 12.87 17.44 Dwb 9 2 Rhododendron mucronulatum None 24.79 51.45 Dwb 6 2 Ribes alpinum None 12.5 7.93 Dfb 7 1.5 Ribes divaricatum Low 7.8 17.96 Dsb 6 3 Ribes glaciale None 5.51 11.3 Cwb 8 3 Robinia pseudoacacia None 6.65 29.42 Cfa 5 25 Rosa hugonis None 12.5 20.19 - - 2

70 Table S1 continued. Genus Species Photo SD DOY SD GDD Climate WD Height Rosa majalis None 13.5 10.5 Dfb 8 2 Salix gracilistyla None 13.52 8.14 Cfa 5 6 Salix repens None 8.81 17.79 Dfb 7 1 Sambucus nigra None 15.26 9.94 Dfb 6 6 Sambucus pubens None 13.33 11.98 Dfb 8 6 Sambucus racemosa None 13.38 3.84 Dfb 7 3 Sinowilsonia henryi Low 8.04 17.89 Cwa - 8 Sorbus aria None - - Cfb - 10 Sorbus commixta None 12.61 10.69 Dfb 6 10 Sorbus decora None 8.26 21.84 Dfb 8 10 Spiraea canescens None 13.5 11.87 Dwb 7 4 Spiraea chamaedryfolia None 13.53 19.07 Dfa 7 1.5 Spiraea japonica None 13.15 16.97 Cwa 6 1.8 Stachyurus chinensis Low 11.03 46.21 Cfb 5 4 Stachyurus praecox None 15.2 34.01 Cfa 5 1.5 Symphoricarpos albus None - - Csb 7 2 Syringa josikaea None 13.64 5.44 Dfb 7 4 Syringa reticulata None 11.9 13.26 Dwb 7 6 Syringa villosa None 14.01 14.08 Dwa - 4 Syringa vulgaris Low 12.48 4.83 Dfb 7 7 Tilia dasystyla None 9.95 28.13 Dfa - 30 Tilia japonica None 9.91 11.92 Cfa 6 20 Tilia platyphyllos None 9.43 16.91 Cfb 6 30 Toona sinensis Low 12.28 47.31 Cwa 2 25 Ulmus americana None 10.44 17.14 Dfa 6 30 Ulmus laevis None 8.96 37.07 Dfb 7 30 Viburnum betulifolium Low - - Cfa 6 3 Viburnum buddleifolium Low 14.18 22.55 Cfa - 5 Viburnum carlesii Low 13.15 13.53 Cfa 4 2 Viburnum opulus None 10.75 20.61 Dfb 7 5 Viburnum plicatum Low 11.81 15.87 Cfa 5 3 Weigela coraeensis None 14.55 26.27 Dfa 4 5 Weigela florida None 10.05 14.75 Dwa 6 2.5 Weigela maximowiczii None 13.45 11.55 Dfa 6 1.5

Chapter 4

SPRING PREDICTABILITY EXPLAINS DIFFERENT LEAF-OUT TIMES IN

THE NORTHERN HEMISPHERE WOODY FLORAS

Zohner, C.M., B.M. Benito, J.D. Fridley, J.-C. Svenning, and S.S. Renner

Nature (in review)

74 Title: Spring predictability explains different leaf-out strategies in the Northern Hemisphere woody floras

Authors: Constantin M. Zohner1*, Blas M. Benito2, Jason D. Fridley3, Jens-Christian Svenning2, and Susanne S. Renner1

Affiliations: 1Systematic Botany and Mycology, Department of Biology, Munich University (LMU), 80638 Munich, Germany. 2Section for Ecoinformatics and Biodiversity, Department of Bioscience, Aarhus University, Aarhus 8000 C, Denmark. 3Department of Biology, Syracuse University, Syracuse, NY 13244, USA.

*Correspondence to: E-mail: [email protected]

Words: 216 (abstract), 1901 (text), 2998 (methods), 570 (legends) References: 28 Figures: 3 Tables: 1

75 Abstract: Temperate zone trees and shrubs have species-specific requirements for winter duration (chilling) and spring warming that are thought to optimize carbon gain from leaf-out after significant frost risk has passed1,2. Climate-driven changes in bud break times should therefore depend on the historical frequency of frost occurrences in a given region. To date, however, regional differences in frost predictability have been largely ignored in phenology studies. We quantified continental-scale differences in spring temperature variability (STV) and species’ leaf-out cues using chilling experiments in 215 species and leaf-out monitoring in 1585 species from East Asia, Europe, and North America grown under common climate conditions. The results reveal that species from regions with high STV and unpredictable frosts have higher winter chilling requirements, and, when grown under the same conditions, leaf out later than related species from regions with lower STV. Since 1900, STV has been consistently higher in North America than in Europe and East Asia, and indeed experimentally long or short winter conditions differentially affected species from the three regions, with North American trees and shrubs requiring 84% more spring warming for bud break, European ones 49%, and East Asian ones only 1% when experiencing a short winter. Such strong continental-scale differences in phenological strategies underscore the need for considering regional climate histories in global change models.

Main text: Rising spring temperatures have advanced the onset of the growing season in many deciduous species3–5, affecting plant productivity and global carbon balance6–8. As shown by experiments and monitoring data, however, species differ greatly in the extent to which they rely on winter and spring temperatures to regulate leaf unfolding9–12. The two temperature signals interact, with species that need extended chilling unable to react to spring warming if winters are too short11–13. Hence, unfulfilled chilling requirements may halt the advance of spring leaf-out, as is already happening in seven European species analysed in this regard14. Previous work on the budbreak phenology of temperate species has largely ignored the potential contributions of local climate history (but see Lechowicz15), despite the fact that such histories will likely constrain the response of vegetation to ongoing climatic change.

76 Temperate woody plants face a trade-off between early carbon gain (early leaf expansion) and avoidance of frost damage (late leaf expansion)1. In regions with high spring temperature variability (STV) and unpredictable frosts, plants might have evolved ‘safe’ strategies and delay leaf unfolding until the risk of late frost damage has passed15. To test for possible regional differences in spring frost predictability we compiled STV throughout the Northern hemisphere, by computing a global map of the standard deviation of minimum spring temperatures over the past 100 years, using the Climatic Research Unit (CRU) time-series dataset16. Our analysis revealed marked continental-scale variation in STV, with peaks in eastern North America and northeastern Europe. STV was lowest in East Asia (EA). To test whether regional differences in STV have led to different phenological strategies of the woody floras of North America (NA), Europe (EU), and EA, we combined experimental and monitoring data for a representative set of species. Species’ winter chilling requirements were inferred from twig-cutting experiments in 215 species from 92 genera in 46 families from throughout the Northern Hemisphere. Leaf-out dates for 498 species (145 genera in 60 families) were collected over four years (2012 to 2015) in the Munich Botanical Garden, including the 215 species used in the experiments. We additionally analysed leaf-out dates from 1458 species (281 genera in 99 families) observed in 2012 at five other Northern Hemisphere gardens17. We first linked a species’ leaf-out behaviour to its biogeographic region (NA, EU, EA), and then tested for effects of STV on leaf-out dates and chilling requirements. Phenological traits in species from throughout the Northern Hemisphere are influenced by species’ shared evolutionary history17. We therefore constructed a phylogeny that included all 1593 species for which experimental and monitoring data were available (Extended data Fig. 1). To estimate the phylogenetic signal in leaf-out dates (Munich data) and chilling requirements, we constructed two further phylogenies based on DNA sequences for 374 and 180 species, respectively (Extended Data Figs. 2 and 3). There was a strong phylogenetic signal in leaf-out dates (Pagel’s λ = 0.81), and we therefore applied phylogenetic hierarchical Bayesian (HB) models to account for phylogenetic autocorrelation. Because trees tend to leaf-out later than shrubs and evergreen species later than deciduous species (see Panchen et al.17 and our Extended Data Fig. 4b), we also included growth habit and leaf persistence in our HB models. The results showed that these two life-history traits do not statistically effect chilling requirements (Extended Data Fig. 4a,c,d).

77 Leaf-out strategies differed strongly by continent, with EA species having much lower requirements for winter chilling than NA species, and EU species intermediate (Figs. 1 and 2). In our experiments, 57% of the 73 NA species had high chilling requirements, whereas only 30% of the 48 EU and 5% of the 94 EA species had high chilling requirements (Extended Data Fig. 5). Under short winter conditions (C1 treatment), the forcing requirements (degree days >0°C until budburst) of NA species increased by 84% (median degree days C1/C3 treatment = 792/430), those of EU species by 49% (568/392), and those of EA species by only 1% (360/355), compared to long winter conditions (Fig. 1). An ANCOVA that included chilling treatments (C1–C3), habit (shrubs vs. trees), and continent (NA, EU, and EA) as predictor variables for species’ forcing requirements revealed a significant (P <0.001) interaction between species’ chilling requirements and continent, i.e., chilling treatment had a greater effect on NA species than on EU and EA species (Fig. 1a, Extended Data Fig. 6, Extended Data Table 1). The effect of continent on chilling requirements remained significant when controlling for phylogenetic autocorrelation of phenological traits and when incorporating fixed effects for growth habit and leaf persistence in the HB model (Extended Data Fig. 5b). In line with this, in 12 (75%) of 16 families containing both NA and EA species, NA species had lower chilling requirements than EA species, while the opposite was true for only 2 (13%) of the 16 families (Extended Data Fig. 7a). Similarly, in 9 (53%) of 17 genera containing both NA and EA species, NA species had lower chilling requirements than EA species, while the opposite was only true for Fraxinus (Extended Data Fig. 7b). Results of the chilling experiment were unaffected by photoperiod treatment (Extended Data Fig. 8). The leaf-out data for 1585 species show that across all gardens (each with a different subset of species), NA species flushed 5±2 and 9±2 (mean ± SD) days later than EU and EA species, respectively (Fig. 2a). This continent effect had a similar magnitude in shrubs, trees, evergreens, and deciduous species (Fig. 2a and Extended Data Table 2). For all gardens, our HB models controlling for shared evolutionary history, growth habit and leaf persistence revealed a significant difference between NA and EA species (Fig. 2b and Extended Data Fig. 5c). Accordingly, in 13 (46%) of 28 families containing both NA and EA species, NA species leafed out later (> 5 days) than EA species, while the opposite was true for only 2 (7%) of the 28 families (Extended Data Fig. 9).

78 To test our hypothesis that the observed continental-scale differences reflect species’ adaptation to STV, we inferred the native climate conditions of 1137 species for which both leaf- out dates and experimental data were available, by querying over a million geo-referenced records from the Global Biodiversity Information Facility (GBIF) against climate grids for STV, mean annual temperature (MAT), and temperature seasonality (TS). We used MAT to test our expectation that species from cold climates are adapted to lower energy/temperature resources and therefore leaf-out earlier than species from more southern locations when grown together in a common garden5 and TS to test for possible phenological differences between species from continental and oceanic climates18,19. To test for associations between species’ leaf-out strategies and climate factors, we applied spatial and HB models (Fig. 3c,d). For the HB models, we determined the climate optimum for each species by calculating its 0.5 quantile (median) for the respective climate variable. As expected under the hypothesis, species from areas with high STV had late bud break and high chilling requirements. In a partial correlation analysis that controlled for effects of MAT, STV was positively correlated with chilling requirements and leaf-out dates (partial r2 = 0.35 and 0.20, respectively, see Fig. 3c,d). Recursive partitioning analyses yielded similar results: of the 91 species from regions with high STV (>1.4), 50% had high chilling requirements, while only 9% of the 92 species from low STV had such requirements (Fig. 3b). The mean leaf-out date (day of the year; DOY) of the 97 tree species from regions with high STV (>1.2) was DOY 111, while the mean leaf-out date of 78 trees from regions with low STV was DOY 104—on average 7 days earlier. Similarly, in shrubs, the 158 species from regions with lower STV on average leafed out 7 days earlier than the 44 species from regions with high STV (DOY 95 and 102, resp.; Extended Data Fig. 10a). For both chilling requirements and leaf-out dates, the effect of STV remained significant when controlling for phylogenetic (HB models) and spatial autocorrelation (SAR models; Fig. 3c and Table 1). The effect of STV on leaf-out dates was consistent across all locations for which we had leaf-out data, i.e., in four gardens species from high STV leafed later than species from low STV (Extended Data Fig. 10b). We also asked whether MAT and TS might explain the dissimilar leaf-out strategies among North American, European, and East Asian species. In accordance with earlier studies5,20, there was a positive association between MAT and leaf-out dates (Table 1, inset Fig. 3c, and Extended Data Fig. 10b). This, however, does not explain the observed early leaf-out of East

79 Asian species; on average these species experience warmer MAT than European and North American species (as shown in Extended Data Fig. 11). With respect to chilling requirements, MAT had little predictive power (Table 1 and inset Fig. 3c), and the continent effect on leaf-out strategies also remained significant when controlling for MAT in HB models (Extended Data Fig. 5b,c). Another possible explanation for the continental-scale differences in leaf-out phenology could be that modern-day North America, and especially its eastern part from which most (86%) of our 419 American species originate, has a high TS (Extended Data Fig. 11). However, TS had little effect on both leaf-out dates and chilling requirements (Fig. 3, Extended Data Fig. 10b, and Table 1). This leaves STV as the best explanation for the different flushing strategies and suggests that leaf-out phenology in the modern North American woody flora is the result of high interannual fluctuations in spring temperatures that have selected for conservative growth strategies. The west coast of North America, especially at low elevations, experiences less STV than does the eastern part (Fig. 3a). Hence, our STV hypothesis predicts that species restricted to western North America should have more opportunistic (earlier) leaf-out strategies. To test this, we contrasted the leaf-out dates of western North American species against eastern North American, European, and East Asian species. The results matched our prediction. On average, the leaf-out dates of western North American species preceded those of eastern North American species by 12 days (Extended Data Fig. 12a and Extended Data Table 3). In phylogenetic HB models, western North American species leafed out significantly earlier than eastern ones and did not differ from the leaf out times of European and East Asian species (Extended Data Figure 12b). Previous work has emphasized the importance of latitudinal variation in phenological strategies5; this is the first study to report longitudinal differences in the leaf-out strategies of woody floras of the Northern Hemisphere. The finding that species from East Asia require significantly less chilling before leaf out than their North American relatives suggests that these continents’ forests will react differently to continuing climate warming: earlier leaf-out in North American trees and shrubs will be constrained by unmet chilling requirements as winters get warmer, whereas East Asian woody species, lacking such winter requirements, may opportunistically benefit from increased carbon gain and nutrient uptake6,7,21. Hence, with continuing climate warming, the conservative growth strategies in many North American species

80 might have adverse consequences for them and cause greater openness to invasion by pre-adapted exotics. This may help explain the invasive capacities of introduced Asian and European woody species in eastern North America22–25. Surprisingly little is known so far about long-term changes in spring frost damage (but see Augspurger26) or hail frequency27,28, but our results underscore the need for considering regional climate histories and their evolutionary effects on species pools in global change models.

Methods

Phenological monitoring and experiments Multi-annual observational data on leaf-out Observations and experiments were carried out between January 2012 and June 2015 in the botanical garden of Munich. Leaf-out dates of 498 woody species (from 840 individuals; on average two individuals per species were monitored) growing permanently outdoors without winter protection in the botanical garden of Munich were monitored in spring 2014 and 2015 and combined with leaf-out data for 2012 and 2013 for the same species available from our earlier study5. As in Zohner and Renner5, a plants’ leaf-out date was defined as the day when at least three branches on that plant had leaves pushed out all the way to the petiole. To obtain our response variable (species leaf-out date), we first calculated the mean of all individual flushing dates for the respective species and year (2012–2015) and then calculated the average over the four years. Twig cutting experiments (next section) were conducted on 144 of the 498 species (listed in Table S4). To cross validate our results obtained from the Munich leaf-out data, we used leaf-out data from 1487 species observed at five Northern hemisphere gardens available from Panchen et al.17 (Fig. 2, Extended Data Table 2).

Twig cutting experiments to test the effects of chilling on leaf-out To study the relative importance of chilling in a broad range of temperate woody species, we carried out twig cutting experiments under controlled conditions, which can be used as adequate proxies for inferring phenological responses of adult trees to climatic changes13,29. Twig-cutting

81 experiments were newly conducted on 144 of the 498 temperate woody species for which we had leaf-out data (see Extended Data Table 4 and Extended Data Fig. 13 for species selection). Data from the same type of experiments for 71 further species are available from the literature and were later added (see below). To investigate species-specific chilling requirements we implemented a climate chamber experiment with three chilling treatments. In winter 2013/2014, c. 40 cm-long twigs were collected three times for each species (10 replicate twigs per species and collection). Twigs were cut on 21 Dec (referred to as short chilling treatment ‘C1’), 10 Feb (intermediate chilling treatment ‘C2’), and 21 March (long chilling treatment ‘C3’) [Extended Data Table 5]. Temperatures in the climate chambers ranged from 18°C during the day to 14°C at night. We standardized photoperiod throughout the experiment by applying a constant day length of 16 h. To test for a possible effect of short-day conditions we also ran the experiment under a day length of 8 h (see Extended Data Fig. 8). Immediately after cutting, we cleaned twigs with sodium hypochlorite solution (200ppm active chlorine) and placed them in water bottles enriched with the broad-spectrum antibiotics gentamicin sulfate (40 microg/l; Sigma–Aldrich, Germany)13,30. Water was changed twice a week, and twigs were trimmed weekly by about 2 cm. Bud development was monitored every third day. The leaf-out dates of the first 8 twigs that leafed out were recorded, and a twig was scored as having leafed out when three buds had their leaves pushed out all the way to the petiole.

Assignment of species to chilling categories Results of our own twig cutting experiments were used to categorize the 144 species in terms of their chilling requirements. We therefore assessed the effects of the treatments on the forcing requirements of species (sum of growing degree days [GDD] from 21 Dec until budburst using 0°C as base temperature). Climate data outside and in the climate chambers were obtained from Hobo data loggers (Onset Computer Corp., Bourne, MA, USA). If the median forcing requirements under C1 treatment (collection date = 21 Dec; see Extended Data Table 5) were less than 75 GDDs higher than under C3 (Collection date = 21 March), a species was assigned to the category no chilling requirements. If the difference was higher than 75 GDDs, a species was scored as intermediate chilling. If the forcing requirements under C2 (Collection date = 10 Feb) were more than 75 GDDs higher than under C3, a species was scored as high chilling. Information on the chilling requirements of 71 additional species

82 came from studies, which used the same experiment to detect species’ chilling requirements11,12, and we applied the same definition for chilling categories to their data. This resulted in chilling data for a total of 215 species (Extended Data Table 4 and Extended Data Fig. 13).

Continental effect on phenological traits To obtain information on the native distribution area for our 1593 species, we used floristic information available from the USDA PLANTS database31, eflora32,33, http://linnaeus.nrm.se/flora/welcome.html, and http://www.euforgen.org/distribution-maps/ and grouped species according to their main geographic region: North America (NA), South America (SA), Europe (EU), West Asia (WA) and East Asia (EA). The Ural Mountains were defined as the right border of Europe; Europe and Asia were separated by the Turgai Sea throughout the Paleocene and into the Eocene34. Species that do not occur in one of the defined regions were excluded from analysis. To detect a possible continent effect on species-level chilling requirements, we tested for differential effects of chilling treatments among species from NA, EU, and EA using ANCOVA (Fig. 1a and Extended Data Table 1). SA and WA were not included in the analysis because of the few species available from these regions (chilling data for 1 SA, and 5 WA species; see Extended Data Table 4). We included chilling treatments (C1–C3), growth habit (shrubs vs. trees), and continent (NA, EU, and AS) as predictor variables of species’ forcing requirements (GDD >0°C until leaf-out) and found a highly significant (P <0.001) interaction between species’ chilling requirements and continent, i.e., chilling treatment had a greater effect on NA than on EU and EA species (Fig. 1, Extended Data Table 1). Extended data Fig. 6 shows the results when using days to leaf-out after collection instead of GDDs as response variable. Extended Data Fig. 8 compares the results obtained when exposing twigs to long-day (16-h) and short-day (8-h) conditions in the greenhouse. To detect effects of biogeographic origin on species-specific leaf-out dates, for each garden, we contrasted the leaf-out dates of NA, EU, and EA species against each other, when using all available species or including only certain functional categories, i.e., trees, shrubs, deciduous, and evergreen species (Fig. 2). Contrasts with sample sizes below 20 species per continent are not shown (grey fields in heat maps). For a summary of leaf-out dates in NA, EA, and EU species monitored at six gardens see Fig. 2a and Extended Data Table 2.

83 To further validate the results we applied a hierarchical Bayesian (HB) approach, which accounted for the phylogenetic structure in our data and allowed us to control for the effect of growth habit (trees vs. shrubs), leaf persistence (evergreens vs. deciduous species; see Panchen et al.17 and our Extended Data Fig. 4) and modern climate association (see Zohner & Renner5 and our Fig. 3 and Extended Data Fig. 10) on species-specific leaf-out strategies; for explanation of the HB model see section on “Trait analysis using the Phylogenetic Comparative Method in a HB model”. To additionally test if the biogeographic differences in leaf-out strategies are consistent within different phylogenetic clades, we analysed continental-scale differences in leaf- out strategies and chilling requirements on the genus and family level (Extended Data Figs. 7 and 9).

Species ranges and climate characteristics We obtained species’ native distribution ranges, by extracting species location data from the Global Biodiversity Information Facility (GBIF; http://www.gbif.org/) using the gbif function of the dismo R-package35. To exclude unreliable records and reduce spatial clustering cleaning scripts in R were applied using the following criteria: (i) Only records from a species’ native continent were included; (ii) coordinate duplicates at a resolution of 2.5-arc minutes were removed; (iii) records based on fossil material, germplasm, or literature were removed; and (iv) records with a resolution >10 km were removed. After filtering only species with more than 30 records within their native continent were included, resulting in data for 1137 species (1,411,996 presence records), of which we had leaf-out data for 1130 species and chilling information for 183 species. To estimate the climatic range of each species, georeferenced locations were queried against grid files for mean annual temperature (MAT), temperature seasonality (TS), and inter- annual spring temperature variability (STV). MAT and TS were based on gridded information (2.5-arc minute spatial resolution data) from the Worldclim dataset (BIO 1 and BIO 7)36,37. STV was calculated as the standard deviation of mean minimum temperatures from March until May over the past 100 years (1901 – 2013). Gridded data on monthly minimum temperatures during this period were available from the Climatic Research Unit (CRU) time-series dataset16 (version 3.00 with a spatial resolution of 5-arc minutes38). For each species, we determined the climate optimum by calculating its 0.5 quantile for the respective climate variable.

84 Relationships between climate parameters and species-specific leaf-out times and chilling categories We tested for multicollinearity of our predictor variables by using a variance inflation factor (VIF) analysis, implemented in the R function “vif”, from the package “HH”39. All VIF were smaller than 5 (threshold recommended by Heiberger39), indicating sufficient independence among predictor variables. We then ran random forest models (randomForest R library)40,41, applied a hierarchical Bayesian approach (see section on “Trait analysis using the Phylogenetic Comparative Method in a HB model”) to allow for phylogenetic autocorrelation in our dependent variables, and applied Simultaneous autoregressive (SAR) models controlling for spatial autocorrelation in the residuals (see section on “Spatial regression between leaf-out strategies and bioclimatic parameters”; Table 1). For analysis of leaf-out times we included only gardens with more than 200 species for which both leaf-out and climate data was available, i.e., the Arnold Arboretum, the Berlin Botanical Garden, the Munich Botanical Garden, and the Morton Arboretum (see Extended Data Fig. 10b). To study the set of ecological conditions determining species’ chilling requirements and leaf-out dates, we carried out recursive partitioning analyses (R library “rpart”42; Fig. 3b and Extended Data Fig. 10a). We allowed three climate variables (MAT, TS, and STV), growth habit (trees vs. shrubs), and leaf persistence (evergreens vs. deciduous species) as potential split points and set the minimum node size to 30 (minimum number of species contained in each terminal node).

Validation: the eastern – western North American contrast To further validate our conclusion that conservative growth phenologies are more abundant in regions with high STV, we examined contrasts between species restricted to eastern North America and western North America. Western North America is characterised by lower STV (Fig 3a) and we therefore expected species from there to display earlier leaf-out than eastern North American species. Because there was a high bias in coniferous species in our western-eastern North American comparison (25% conifers in western and only 4% conifers in eastern North America) we excluded them in the analysis of mean leaf-out dates (see Extended Data Fig. 12a and Extended Data Table 3). In a HB model we included conifers but controlled for this bias by including a gymnosperm effect (Extended Data Fig. 12b).

85 Trait analysis using the Phylogenetic Comparative Method in a HB model Generating an ultrametric phylogenetic tree To estimate the phylogenetic signal in species-level leaf-out dates and chilling requirements we created a phylogenetic tree for our 498 target species and used Pagel’s λ43 and Blomberg’s K44, with the ‘phylosig’ function in the R package ‘phytools’ v0.2-145. To build the tree we used MEGAPTERA46 and BEAST47. We gathered sequence information for four plastid genes (atpB, matK, ndhF, and rbcL) and included all species for which at least one of the four genes was available from GenBank (atpB: 107 species available, matK: 353 species, ndhF: 145 species, and rbcL: 264 species). This resulted in a concatenated matrix of 377 species and a total length of 6395 bp. We performed divergence time estimation under a strict clock model of molecular substitution accumulation, the GTR+G substitution model, and the Yule process as tree prior, implemented in BEAST (v1.8.0)47. To calibrate our tree we set the crown age of angiosperms to 185 Ma48; since absolute ages are not used in this study, we did not run our analyses with alternative calibrations. The phylogeny is presented as Extended Data Fig. 2. A reduced phylogeny of 180 species illustrating the phylogenetic signal of species’ chilling requirements is shown in Extended Data Fig. 3. The initial tree used to account for shared evolutionary history when testing for associations between leaf-out dates and biogeographic/climate parameters came from Panchen et al.17 and had been assembled using the program Phylomatic49 (Extended Data Fig. 1). Its topology reflects the APG III50 phylogeny, with a few changes based on the Angiosperm Phylogeny Website51. We manually added missing species, which led to a total of 1630 species included in the tree. Branch lengths of the PHYLOMATIC tree are adjusted to reflect divergence time estimates based on the fossil record48,52.

Analysis of phenological characters (leaf-out dates and chilling requirements) We applied a hierarchical Bayesian (HB) approach (see Fridley & Cradock53) for testing effects of continental origin (NA, EU, EA; Fig. 2b and Extended Data Figs. 5b,c and 12b) and climate parameters (Fig. 3c,d and Extended Data Fig. 10b) on species-level differentiation in spring leaf- out dates and chilling requirements. This approach allows estimating species-level differences in leaf-out phenology while controlling for phylogenetic signal λ43 of phenological traits. In addition it allowed us to test for effects of continental origin (NA, EU, and EA) on species’ leaf-out dates

86 and chilling requirements while controlling for (i) species’ life history strategy by including growth habit (shrubs vs. trees) and leaf persistence (evergreen vs. deciduous species; see Fig. 2b) and (ii) species’ modern climate association by including variables reflecting species’ native climate conditions (MAT; see Extended Data Fig. 5b,c) in the model. Slope parameters across traits are estimated simultaneously without concerns of multiple testing or P-value correction. To incorporate phylogenetic autocorrelation across all relationships a common correlation matrix (Σ) based on shared branch lengths in the PHYLOMATIC tree was incorporated in the model54. The resulting posterior distributions of the relationships between biogeographic/climate parameters and phenological traits are a direct statement of the influence of each parameter on species-level differentiation in chilling requirements and leaf-out dates. To examine relative effect sizes of climate variables on species-specific leaf-out times and chilling requirements, we standardized all climate variables by subtracting their mean and dividing by 2 SD before analysis55. When using leaf-out times (continuous character) as response variable (Pagel’s λ value of leaf-out dates = 0.81; see Extended Data Fig. 2), the phylogenetic structure of the data was incorporated in the HB model using the Bayesian phylogenetic regression method of de Villemereuil et al.54, by converting the 1630-species ultrametric phylogeny into a scaled (0–1) variance–covariance matrix (Σ), with covariances defined by shared branch lengths of species pairs, from the root to their most recent ancestor56. We additionally allowed correlations to vary according to the phylogenetic signal (λ) of flushing dates, fitted as a multiple of the off-diagonal values of Σ54. The phylogenetic variance–covariance matrix was calculated using the ‘vcv.phylo’ function of the ape library57. When using chilling requirements (ordinal data) as response variable we accounted for phylogenetic structure in our data by incorporating genus and family random effects in the model because λ estimation is not possible for ordinal (or logistic) models. We parameterized our models using the JAGS58 implementation of Markov chain Monte Carlo methods in the R2JAGS R-package59. We ran three parallel MCMC chains for 20,000 iterations after a 5,000-iteration burn-in, and evaluated model convergence with the Gelman and Rubin60 statistic. We specified non-informative priors for all parameter distributions, including normal priors for fixed effect α and β coefficients (mean = 0; variance = 1000), uniform priors between 0 and 1 for λ coefficients, and gamma priors (rate = 1; shape = 1) for the precision of random effects of phylogenetic autocorrelation53,54.

Spatial regression between leaf-out strategies and bioclimatic parameters To determine if between-region differences in leaf-out strategies (leaf-out dates and chilling requirements) are attributable to between-region differences in STV we carried out a spatial regression analysis. We only included cells occupied by at least five species with existing phenological data. For each cell, the mean trait value was calculated and used for subsequent analyses. (For the calculation of mean chilling requirements in each cell, the chilling categories were treated as numerical characters: no chilling requirements = 0, intermediate = 1, high = 2.) We then aggregated all response and predictor variables to a spatial resolution of 2.5° x 2.5°; initially, the resolution of climate grids and species distribution data was 2.5-arc minutes (~0.05°). Next, we regressed the aggregated response variable against aggregated predictor variables. As a first step, we applied partial regression analysis (to remove the covariate effects of MAT) and multiple ordinary least squares regression (OLS) between each response and all predictor variables. In the OLS models there was considerable spatial autocorrelation in the residuals (Moran’s I test for leaf-out dates: I = 0.38, P <0.001; Moran’s I test for chilling requirements: I = 0.30, P <0.001), potentially biasing significance tests and parameter estimates61. To remove the autocorrelation we applied simultaneous autoregressive (SAR) models62,63 using the R-package spdep64,65. We used a spatial weights matrix with neighbourhoods defined as cells within 3,000 km of the focal cell. For all response variables the SAR models effectively removed autocorrelation from the residuals (Moran’s I test for leaf-out dates: I = 0.001, P = 0.52; Moran’s I test for chilling requirements: I = 0.001, P = 0.43). See Table 1 for parameter estimates and P-values inferred from the OLS and SAR models. Next, we examined all subsets of the full SAR models and selected the model with the lowest AIC score

(for parameter estimates of the reduced models see SARreduced in Table 1). As an additional statistical measure to evaluate the SAR models we calculated Akaike weights for all predictor variables by comparing AIC scores of models containing the focal variable with models omitting the focal variable (see WeightAIC in Table 1).

All statistical analyses relied on R66.

88 References

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93 Acknowledgments: The study was part of the KLIMAGRAD project sponsored by the ‘Bayerisches Staatsministerium für Umwelt und Gesundheit’. We thank V. Sebald and M. Wenn for help with conducting the twig cutting experiments. We thank C. Polgar, A. Gallinat, and R.B. Primack for sharing their experimental data on chilling requirements. J.-C.S. acknowledges support by the European Research Council (ERC-2012-StG-310886-HISTFUNC). B.M.B. was supported by Aarhus University and Aarhus University Research Foundation under the AU IDEAS program (via Center for Biocultural History).

Author contributions C.M.Z. and S.S.R. designed the study. C.M.Z. conducted the experiments and leaf- out observations. C.M.Z. and B.M.B. performed the analyses. C.M.Z. and S.S.R. led the writing with inputs from the other authors.

94 Figures

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685 hemisphere woody plants. a, Inter-annual spring temperature variabilityL (STV) calculated as 852 0.5

HemisphereProbability 0.4 woody0.4 plants. a0.4, Inter-annual0.4 spring temperature0.4 variability (STV) calculated as 8

6 686 SD of minimum temperatures between March and May from 1901 to 2013. b, Recursive4 0.2 0.2 0.2 0.2 0.2 1.0 2 cient estimate 853 f 0 SD of minimum temperatures between March and May from 1901 to 2013. b, RecursiveCoe 2 ST V MAT TS 687 partitioning tree for the relationship between climate parameters and species-specific chillingSTV 688854 requirements in temperate woody species. STV, mean annual temperature (MAT),1.0 0.5 temperature0.0 0.5 1.0 partitioningn = 30 tree forn =the 32 relationship n = 30 betweenn = 30climate parameters n = 61 and species-specificSTV | ochillingthers 689855 seasonality (TS), growth habit, and leaf persistence were evaluated as potential split points. 690856 requirementsNumberFigure 3 of | The inspecies temperate effe ctcontained of springwoody in temperature species.each terminal STV variability, nodemean shown annual on leafbelow temperature-out graphs. strategies (MAT), c,d, inThe Northern temperature relationship 691857 betweenhemisphere global woody STV plants and proportional. a, Inter-annual mean variability chilling requirementsof minimum temperature (c) and mean in springMunich (STV) leaf- out seasonality (TS), growth habit, and leaf persistence were evaluated as potential split points. 692858 datescalculated (d) within as SD 2.5°of the × minimum2.5° regions temperature as shown between by partial M-archregression and May plots from after 1901 controlling to 2013. bfor, 693859 NumberMATRecursive (seeof species partitioningtable 1).contained Insets tree showfor in theeach e stimatedrelationship termina coefficientl node between shown valuesthree below climate (means graphs. parameters and c95%,d, The credibleand relationship species intervals)- 694860 fromspecific phylogenetic chilling requirements hierarchical in modelstemperate for woody relationships species. between STV, m eanthree annual climate temperatu variablesre (STV, between global STV and proportional mean chilling requirements (c) and mean Munich leaf-out 695861 MAT,(MAT), and temperature TS) and species’ seasonality chilling (TS) ,requirements growth habit, (upperand leaf panel, persistence N = 183 were species) evaluated and asMunich 696862 timesleafpotential (-dout) within dates split points.(lower2.5° × Number2.5°panel regions, N of = species36 as6 shownspecies). contained by Values partial in each reflect-regression terminal standardized plotsnode shownafter data controlling below and can graphs. be for c, 697863 interpretedThe relationship as relative between effect STV sizes. and proportionalTo account formean phylogenetic chilling requirements autocorrelation, (upper we panel) inserted and MAT (see Table 1). Insets show estimated coefficient values (means and 95% credible intervals) 698864 genusmean Munichand family leaf -randomout dates effects (lower for panel) ordinal at a chilling global sca categoriele withins or2.5° incorporated × 2.5° regions the asphylogenetic shown 865 by partial-regression plots after controlling for MAT (see table 1). Insets show estimated 699 fromstructure phylogenetic using the hierarchical Bayesian phylogeneticBayesian models regres forsion relationships method for between leaf-out threedates .climate variables 866 coefficient values (means and 95% credible intervals) for relationships between three climate 700 867 variables (STV, MAT, and TS) and species’ chilling requirements (upper panel, N = 178 species) 701 (STV, MAT, and TS) and species’ (c) chilling requirements (N = 183 species) and (d) Munich 868 and Munich leaf-out dates (lower panel, N = 365 species). Values reflect standardized data and 702869 leaf can-out be dates interpreted (N = 366 as relative species). effect Values sizes. reflect To account standardized for phylogenetic data and autocorrelation,can be interpreted we as 703870 inserted genus and family random effects for ordinal chilling categories or incorporated the 704 relative effect sizes. 705 706 707 708 709

98 Table 1 | Relationships between climate variables and global patterns of leaf-out times and chilling requirements. MAT, mean annual temperature; TS, temperature seasonality; STV, spring temperature variability. Five comparative measures were used: the coefficient of 653 determinationTable 1 | Relationships from bivariate between partial climate regression variables (partial and globalr2), standardized patterns of regression leaf-out times coefficients and 654 chilling requirements. MAT, mean annual temperature; TS, temperature seasonality; STV, 655 fromspring multivariate temperature ordinaryvariability. least Five-squares comparative regression measures (OLS), were standardized used: the coefficient regression of coefficients 2 656 fromdetermination simultaneous from bivariateautoregressive partial modelsregression (SAR), (partial Akaike r ), standardized weights based regression on SAR coefficients models, mean 657 from multivariate ordinary least-squares regression (OLS), standardized regression coefficients 658 decreasefrom simultaneous in accuracy autoregressive values (MDA) models from (SAR), random Akaike forest weights analysis, based and on coefficient SAR models, estimates mean 659 (decreaseeffective in posterior accuracy means values and(MDA) 95% from credible random intervals) forest analysis, from a hierarchicaland coefficient Bayesian estimates (HB) model 660 (effective posterior means and 95% credible intervals) from a hierarchical Bayesian (HB) model 661 controlling forfor phylogeneticphylogenetic autocorrelation. autocorrelation. 662 2 partial r OLS SAR SARreduced WeightAIC MDA HB Leaf-out times (Munich, N = 366 species) MAT 0.19*** 0.43*** 0.37*** 0.39*** 1.00 40.2 6.3 ± 1.3 TS 0.01 -0.01 -0.08 0.34 23.0 2.8 ± 1.2 STV 0.20*** 0.51*** 0.36*** 0.33*** 1.00 42.9 5.2 ± 1.2 Chilling (N = 183 species) MAT 0.07*** 0.22*** 0.06 0.49 14.5 1.1 ± 1.1 TS 0.01* -0.37*** -0.18** -0.22*** 0.97 39.0 1.2 ± 1.0 STV 0.35*** 0.70*** 0.28*** 0.29*** 0.99 85.0 2.3 ± 0.9 663 664 665

99 Extended data

Physocarpus_opulifolius

Physocarpus_bracteatus

Physocarpus_amurensis

Physocarpus_capitatus

Potentilla_phyllocalyx

Dasiphora_floribunda

Potentilla_tridentata

Potentilla_fruticosa

Prunus_pennsylvanica

Fallugia_paradoxa

Prunus_tomentosa Neillia_ribesioides

Prunus_subhirtella Prunus_maximowiczii Prunus_virginiana

Prunus_munsoniana

Neillia_thibetica

Rosa_acicularis

Prunus_serrulata Prunus_laurocerasus Neillia_sinensis Prunus_sargentii

Prunus_prostrata Prunus_serotina Prunus_spinosa Prunus_nipponica Prunus_ramburii

Prunus_tenella

Prunus_salicina Rosa_albertii

Neillia_affinis

Prunus_maritima Prunus_pumila Prunus_ssiori Neillia_uekii Prunus_mahaleb Prunus_allegheniensis Prunus_glandulosa Prunus_hortulana Prunus_padus Prunus_maackii Prunus_mume Rhodotypos_scandens Prunus_cyclamina Prunus_angustifolia Prunus_brigantinaPrunus_cerasifera Prunus_alabamensis Neviusia_alabamensisMaddenia_hypoleuca Prunus_armeniaca Exochorda_tianschanica Prunus_grayana Prunus_lanata Prunus_incana Oemleria_cerasiformis Prunus_incisa Amygdalus_triloba Chamaebatiaria_millefolium Exochorda_serratifolia

Prunus_apetala Prunus_avium Exochorda_racemosa Prinsepia_sinensisPrinsepia_uniflora

Kerria_japonica Sorbaria_tomentosaExochorda_giraldii Exochorda_albertii

Rosa_amblyotis Rosa_arkansana Rosa_arvensis Rosa_bella Rosa_blanda Rosa_britzensis Rosa_californica Rosa_canina Rosa_carolina Rosa_caroliniana Rosa_centifolia Rosa_cinnamomea Rosa_caesia Rosa_corymbifera Rosa_davurica Rosa_dumalis Sorbaria_sorbifolia Rosa_eglanteria Rosa_gallica Rosa_alba Sorbaria_stellipila Rosa_glauca Rosa_gymnocarpa Rosa_henryi Rosa_hugonis Rosa_iberica Rosa_inodora Rosa_jakutica Rosa_luciae Rosa_macrocarpa Sorbaria_kirilowii Rosa_macrophylla Rosa_majalis Rosa_micrantha Sorbaria_arborea Rosa_mollis Rosa_moyesii Spiraea_virginiana Rosa_multiflora Rosa_nitida Rosa_nitidula Spiraea_sargentiana Rosa_nutkana Spiraea_tomentosa Rosa_omeiensis Rosa_oxyodon Spiraea_wilsonii Rosa_palustris Rosa_pendulina Rosa_pisocarpa Spiraea_trilobata Rosa_prattii Spiraea_pubescens Rosa_primula Spiraea_veitchii Rosa_pteragonis Spiraea_salicifolia Rosa_pulverulenta Rosa_roxburghii Spiraea_myrtilloides Spiraea_prunifolia Rosa_rubiginosa Rosa_rugosa Spiraea_nipponica Rosa_scabriuscala Rosa_schrenkiana Rosa_serafinii Spiraea_longigemmis Rosa_sericea Rosa_setigera Rosa_sherardii Spiraea_mollifoliaSpiraea_nervosa Rosa_spinosissima Rosa_stylosa Rosa_sweginzowii Rosa_tomentella Rosa_transmorrisonensis Rosa_villosa Spiraea_martinii Rosa_virginiana Rosa_wichuraiana Spiraea_fritschiana Spiraea_media Rosa_willmottiae Spiraea_japonicaSpiraea_latifolia Rosa_woodsii Spiraea_chamaedryfolia Rosa_xanthina Rosa_zalana Spiraea_fastigiata Rubus_biflorus Spiraea_henryii Rubus_cockburnianus Spiraea_douglasii Rubus_corchorifolius Spiraea_conspicua Rubus_crataegifolius Rubus_idaeus Rubus_lasiostylus Spiraea_cantoniensis Spiraea_crenata Rubus_mesogaeus Spiraea_chinensis Rubus_phoenicolasius Rubus_allegheniensis Spiraea_trichocarpa Spiraea_inflexa Rubus_parvifolius Rubus_thibetanus Sibiraea_laevigata Spiraea_miyabei Stephanandra_chinensis Stephanandra_incisa Stephanandra_tanakae Spiraea_lenneana Coriaria_japonica Adenocarpus_complicatus Spiraea_canescensSpiraea_confertaSpiraea_pumila Albizia_julibrissin Gleditsia_aquatica Gleditsia_caspica Spiraea_betulifolia Gleditsia_ferox Spiraea_arguta Gleditsia_japonica Holodiscus_discolor Spiraea_billiardii Sorbus_tamamschjanae Gleditsia_macracantha Gleditsia_macrocarpa Gleditsia_sinensis Gleditsia_triacanthos Sorbus_wilsoniana Gymnocladus_chinensis Gymnocladus_dioicus Sorbus_pohuashanensisSorbus_rufoferruginea Spiraea_bella Amorpha_canescens Amorpha_fruticosa Sorbus_wilfordii Spiraea_alba Amorpha_nana Sorbus_sitchensis Sorbus_yuana Amorpha_ouachitensis Sorbus_scopulina Caragana_arborescens Caragana_aurantiaca Caragana_boisii Caragana_brevispina Caragana_decorticans Sorbus_koheneana Caragana_franchetiana Sorbus_hupehensis Caragana_frutex Sorbus_prattii Sorbus_latifolia Caragana_microphylla Caragana_pygmaea Sorbus_hemsleyi Caragana_rosea Sorbus_domestica Colutea_buhsei Colutea_orientalis Colutea_persica Sorbus_commixtaSorbus_forestii Wisteria_floribunda Sorbus_aucuparia Wisteria_frutescens Sorbus_decora Wisteria_sinensis Wisteria_venusta Hippocrepis_emurus Pyrus_ussuriensis Robinia_kelseyi Sorbus_alnifolia Robinia_pseudoacacia Pyrus_phaeocarpa Robinia_viscosa Sorbus_aria Indigofera_amblyantha Pyrus_elaeagnifoliaPyrus_pyrifolia Indigofera_decora Indigofera_kirilowii Pyrus_nipponica Pueraria_lobata Cytisus_scoparius Pyrus_nivalis Cytisus_supinus Pyrus_bretschneideriPyrus_communis Cytisus_villosus Pyrus_amygdaliformis Pyrus_cossonii Lembotropis_nigricans Pyrus_caucasica Pyrus_calleryana Genista_tinctoria Stranvaesia_davidianaPyracantha_coccinea Spartium_junceum Maackia_amurensis Pyrus_betulifolia Photinia_beauverdiana Maackia_floribunda Sophora_davidii Cladrastis_lutea Adenorachis_arbutifolia Cladrastis_platycarpa Aronia_melanocarpa Styphnolobium_japonica Photinia_villosa Cercis_canadensis Aronia_prunifolia Cercis_chinensis Cercis_gigantea Cercis_griffithii Malus_tschonoskiiCydonia_oblonga Cercis_racemosa Malus_toringoides Cercis_siliquastrum Cytisophyllum_sessilifolium Lespedeza_bicolor Laburnum_alpinum Malus_spectabilisMalus_sylvestris Laburnum_anagyroides Malus_sikkimensisMalus_toringo Petteria_ramentacea Celastrus_angulatus Celastrus_orbiculatus Malus_sieversii Celastrus_rosthornianus Malus_sieboldii Celastrus_scandens Malus_sargentii Euonymus_alatus Euonymus_bungeanus Malus_robusta Euonymus_carnosus Malus_prunifolia Euonymus_europaeus Malus_pumila Euonymus_fimbriatus Malus_mandshuricaMalus_platycarpa Euonymus_grandiflorus Malus_orientalisMalus_prattii Euonymus_hamiltonianus Euonymus_japonicus Euonymus_fortunei Malus_kansuensis Euonymus_lanceifolius Malus_malifolia Euonymus_latifolius Malus_zumi Euonymus_maackii Malus_hupehensis Euonymus_macropterus Malus_honanensis Malus_ioensis Euonymus_nanus Euonymus_oresbius Euonymus_oxyphyllus Malus_halliana Euonymus_pauciflorus Euonymus_phellomanus Malus_floribundaMalus_hartwigii Euonymus_prezwalskii Malus_bretschneideriMalus_fusca Malus_coronaria Euonymus_sachalinensis Euonymus_sieboldianus Malus_brevipes Euonymus_verrucosus Cotoneaster_tomentosus Euonymus_yedoensis Cotoneaster_zabelii Tripterygium_wilfordii Cotoneaster_submultiflorusCotoneaster_wardiiMalus_argenteaMalus_baccata Hypericum_buckleyi Cotoneaster_tenuipes Hypericum_frondosum Cotoneaster_taoensis Hypericum_kalmianum Cotoneaster_splendens Andrachne_colchica Cotoneaster_scandinavicusCotoneaster_sikangensis Flueggea_suffruticosa Cotoneaster_simonsii Idesia_polycarpa Poliothyrsis_sinensis Cotoneaster_salicifolius Populus_alba Populus_beijingensis Populus_bolleana Cotoneaster_racemiflorusCotoneaster_reticulatus Populus_canadensis Cotoneaster_procumbensCotoneaster_roseus Populus_candicans Populus_deltoides Cotoneaster_perpusillus Populus_koreana Cotoneaster_nummularius Populus_maximowiczii Cotoneaster_obscurus Populus_nigra Populus_tomentosa Cotoneaster_watereri Populus_tremula Cotoneaster_moupinensisCotoneaster_multiflorus Populus_tremuloides Populus_trichocarpa Cotoneaster_microphyllusCotoneaster_mongolicusCotoneaster_niger Populus_ussuriensis Cotoneaster_melanocarpus Chosenia_arbutifolia Salix_aegyptica Salix_alba Salix_apoda Cotoneaster_horizontalisCotoneaster_lucidus Salix_aurita Cotoneaster_hebephyllusCotoneaster_insignis Salix_babylonica Salix_bebbiana Cotoneaster_harrysmithii Salix_candida Salix_caprea Cotoneaster_glabratus Salix_caspica Salix_daphnoides Cotoneaster_franchetiiCotoneaster_frigidus Salix_discolor Cotoneaster_foveolatus Salix_glabra Cotoneaster_falconeri Salix_glauca Cotoneaster_apiculatus Salix_gracilistyla Cotoneaster_divaricatus Salix_humilis Cotoneaster_atropurpureusCotoneaster_dielsianus Salix_integra Salix_lapponum Cotoneaster_dammeri Salix_magnifica Salix_matsudona Cotoneaster_armenus Cotoneaster_ambiguus Salix_miyabeana Cotoneaster_adpressus Salix_myrsinifolia Cotoneaster_acutifolius Salix_nigra Cotoneaster_acuminatus Salix_pentandra Salix_petiolaris Chaenomeles_speciosa Salix_pierotti Chaenomeles_sinensis Salix_purpurea Chaenomeles_cathayensisChaenomeles_japonica Salix_repens Salix_rorida Salix_rosmarinifolia Mespilus_germanica Salix_schwerinii Salix_triandra Crataegus_wilsonii Salix_udensis Salix_viminalis Crataegus_vulsa Salix_waldsteiniana Crataegus_succulentaCrataegus_unifloraCrataegus_viridis Lagerstroemia_indica Crataegus_submollis Lagerstroemia_limii Crataegus_songorica Stachyurus_chinensis Crataegus_pseudoambiguaCrataegus_sanguinea Stachyurus_praecox Euscaphis_japonica Crataegus_punctata Staphylea_bumalda Staphylea_colchica Crataegus_persimilis Staphylea_holocarpa Crataegus_pringlei Staphylea_pinnata Crataegus_pratensis Staphylea_trifolia Crataegus_praecoqua Crataegus_poliophylla Staphylea_trifoliata Tapiscia_sinensis Crataegus_phaenopyrumCrataegus_pinnatifida Ampelopsis_megalophylla Crataegus_pennsylvanica Parthenocissus_henryana Parthenocissus_inserta Crataegus_pedicellata Parthenocissus_quinquefolia Crataegus_paucispina Vitis_aestivalis Crataegus_oxyacantha Vitis_betulifolia Vitis_coignetiae Crataegus_monogyna Vitis_piasezkii Crataegus_nitida Crataegus_maximowiczii Vitis_riparia Crataegus_macrosperma Vitis_vinifera Crataegus_mollis Cercidiphyllum_japonicum Crataegus_macauleya Cercidiphyllum_magnificum Ribes_alpinum Crataegus_laurentianaCrataegus_lucorum Ribes_americanum Crataegus_lanuginosa Ribes_aureum Ribes_cynosbati Crataegus_laevigata Ribes_diacanthum Crataegus_korolkowii Ribes_divaricatum Ribes_fasciculatum Crataegus_kansuensisCrataegus_keepii Ribes_glaciale Ribes_himalense Crataegus_holmesiana Crataegus_irrasa Ribes_maximowiczii Ribes_odoratum Crataegus_heldreichii Ribes_sachalinense Crataegus_harbisoniiCrataegus_hillii Ribes_sanguineum Ribes_succirubrum Crataegus_flavida Ribes_triste Crataegus_fastosa Ribes_uva-crispa Crataegus_erythropodaCrataegus_exclusa Corylopsis_gotoana Corylopsis_glabrescens Crataegus_douglasii Corylopsis_glandulifera Crataegus_cuneiformisCrataegus_dahurica Corylopsis_sinensis Corylopsis_spicata Crataegus_crus-galli Fothergilla_gardenii Crataegus_compta Fothergilla_major Crataegus_compacta Hamamelis_japonica Hamamelis_mollis Crataegus_coccinoidesCrataegus_coleae Crataegus_chrysocarpa Hamamelis_intermedia Hamamelis_ovalis Crataegus_brainerdii Crataegus_almaatensis Hamamelis_vernalis Amelanchier_stolonifera Hamamelis_virginiana Amelanchier_nantucketensis Disanthus_cercidifolius Amelanchier_ovalis Parrotia_persica Parrotia_subaequalis Parrotiopsis_jacquemontiana Amelanchier_laevis Amelanchier_pumila Sinowilsonia_henryi Amelanchier_interior Liquidambar_acalycina Liquidambar_orientalis Amelanchier_canadensisAmelanchier_florida Liquidambar_styraciflua Amelanchier_arborea Paeonia_ludlowii Amelanchier_alnifolia Paeonia_ostii Paeonia_rockii Paeonia_suffruticosa Paliurus_spinachristiZiziphus_jujuba Fallopia_aubertii Tamarix_chinensis Hovenia_dulcis Buxus_microphylla Rhamnus_pentapomicaRhamnus_purshianaRhamnus_utilis Buxus_sempervirens Buxus_sinica Sarcococca_humilis Rhamnus_macrocarpaRhamnus_pallasii Platanus_acerifolia Rhamnus_frangula Platanus_occidentalis Rhamnus_davurica Platanus_hispanica Rhamnus_cathartica Platanus_orientalis Trochodendron_aralioides Rhamnus_alaternusRhamnus_alpina Berberis_actinacantha Berchemia_scandens Berberis_aetnensis Shepherdia_argentea Berberis_mentorensis Hippophae_rhamnoides Berberis_aggregata Elaeagnus_umbellata Berberis_amurensis Elaeagnus_ebbingei Berberis_angulosa Berberis_asiatica Elaeagnus_pungens Elaeagnus_multiflora Berberis_canadensis Berberis_ottawensis Elaeagnus_lanceolataElaeagnus_mollis Berberis_pekinensis Elaeagnus_angustifolia Berberis_candidula Berberis_chinensis Berberis_chitria Zelkova_sinica Zelkova_schneiderianaZelkova_serrata Berberis_concinna Berberis_crataegina Zelkova_abelicea Berberis_dictyophylla Ulmus_thomasii Berberis_dielsiana Berberis_francisci-ferdinandi Ulmus_rubra Berberis_hispanica Ulmus_pumila Ulmus_parvifolia Berberis_jaeschkeana Berberis_jamesiana Ulmus_macrocarpaUlmus_minor Berberis_julianae Berberis_koehneana Ulmus_lamellosa Berberis_koreana Ulmus_laevis Berberis_mandhurica Ulmus_laciniata Berberis_nummularia Ulmus_glabra Berberis_poiretii Ulmus_davidiana Berberis_sibirica Ulmus_crassifolia Berberis_sphaerocarpa Ulmus_chenmoui Ulmus_castaneifolia Berberis_thibetica Berberis_thunbergii Ulmus_americana Berberis_tischleri Pteroceltis_tatarinowiiUlmus_alata Berberis_vernae Berberis_virescens Hemiptelea_davidii Berberis_virgetorum Celtis_tournefortii Berberis_vulgaris Celtis_tenuifolia Berberis_wilsonae Celtis_spinosa Mahonia_aquifolium Celtis_sinensis Mahonia_repens Celtis_occidentalis Clematis_alpina Celtis_laevigata Clematis_apiifolia Celtis_caucasica Clematis_columbiana Celtis_jessoensis Clematis_hexapetala Celtis_iguanaea Clematis_japonica Celtis_choseniana Clematis_ligusticifolia Celtis_bungeana Clematis_macropetala Celtis_australis Clematis_maximowicziana Boehmeria_biloba Clematis_montana Clematis_obscura Morus_rubra Morus_bombycis Clematis_orientalis Clematis_vitalba Morus_australis Clematis_viticella Maclura_pomiferaMorus_alba Xanthorhiza_simplicissima Cudrania_tricuspidata Cocculus_trilobus Broussonetia_papyrifera Menispermum_canadense Broussonetia_kazinoki Menispermum_dauricum Akebia_quinata Humulus_lupulus Nothofagus_antarctica Akebia_trifoliata Myrica_pennsylvanica Decaisnea_fargesii Euptelea_pleiosperma Myrica_heterophylla Euptelea_polyandra Myrica_gale Asimina_triloba Pterocarya_rhoifolia Liriodendron_chinensis Platycarya_strobilacea Liriodendron_tulipifera Juglans_regia Magnolia_acuminata Juglans_nigra Magnolia_amoena Juglans_microcarpa Magnolia_bohuashensis Juglans_mandshurica Magnolia_cylindrica Juglans_major Magnolia_denudata Juglans_macrocarpa Magnolia_fraseri Juglans_cinerea Magnolia_grandiflora Juglans_cathayensis Magnolia_kobus Juglans_ailanthifolia Magnolia_liliiflora Cyclocarya_paliurus Magnolia_macrophylla Carya_texana Magnolia_salicifolia Carya_ovata Magnolia_sargentiana Carya_ovalis Magnolia_sieboldii Carya_myristiciformis Magnolia_stellata Carya_laciniosa Magnolia_tripetala Carya_illinoinensis Magnolia_virginiana Magnolia_zenii Carya_glabra Carya_cordiformis Calycanthus_chinensis Corylus_sieboldiana Calycanthus_floridus Corylus_jaquemontii Calycanthus_sinensis Corylus_heterophylla Chimonanthus_praecox Chimonanthus_yunnanensis Corylus_ferox Sinocalycanthus_chinensis Corylus_fargesii Lindera_benzoin Corylus_cornuta Lindera_erythrocarpa Corylus_colurna Lindera_neesiana Corylus_chinensis Lindera_obtusiloba Corylus_avellana Lindera_praecox Corylus_americana Lindera_umbellata Ostrya_virginiana Persea_borbonia Ostrya_rehderiana Sassafras_albidum Ostrya_japonica Aristolochia_contorta Ostrya_carpinifolia Aristolochia_macrophylla Carpinus_turczaninowii Aristolochia_manshuriensis Carpinus_tschonoskii Aristolochia_tomentosa Carpinus_orientalis Schisandra_chinensis Carpinus_monbeigiana Ephedra_intermedia Carpinus_laxiflora Ephedra_minuta Carpinus_japonica Ginkgo_biloba Carpinus_fargesiana Juniperus_chinensis Carpinus_coreana Juniperus_communis Carpinus_cordata Juniperus_horizontalis Carpinus_caroliniana Juniperus_pinchotii Carpinus_betulus Juniperus_procumbens Betula_utilis Juniperus_rigida Betula_uber Juniperus_sabina Betula_turkestanica Juniperus_squamata Betula_tatewakiana Juniperus_virginiana Betula_schmidtii Cupressus_arizonica Betula_raddeana Thuja_occidentalis Betula_pumila Thuja_plicata Platycladus_orientalis Betula_pubescens Betula_procurva Chamaecyparis_obtusa Chamaecyparis_pisifera Betula_populifolia Chamaecyparis_thyoides Betula_platyphylla Metasequoia_glyptostroboides Betula_pendula Sequoiadendron_giganteum Betula_papyrifera Betula_nigra Taiwania_cryptomeroides Betula_nana Cryptomeria_japonica Taxodium_distichum Betula_maximowicziana Microbiota_decussata Betula_mandshurica Taxus_baccata Betula_luminifera Taxus_cuspidata Betula_litwinowii Taxus_floridana Betula_lenta Taxus_mairei Betula_jacquemontii Torreya_grandis Betula_insignis Torreya_nucifera Betula_humilis Cephalotaxus_diveri Betula_grossa Cephalotaxus_fortunei Betula_globispicata Cephalotaxus_harringtonia Betula_fruticosa Cephalotaxus_koreanea Betula_ermanii Cephalotaxus_sinensis Betula_davurica Sciadopitys_verticillata Betula_dahurica Larix_decidua Betula_costata Larix_gmelinii Betula_costa Larix_kaempferi Betula_chinensis Larix_laricina Larix_occidentalis Betula_chichibuensis Betula_celtiberica Larix_principisrupprechtii Betula_caerulea Larix_sibirica Pseudotsuga_menziesii Betula_alleghaniensis Pinus_aristata Betula_albosinensisAlnus_viridis Pinus_armandii Pinus_banksiana Alnus_sieboldiana Alnus_serrulata Pinus_bungeana Pinus_cembra Alnus_maximowiczii Pinus_contorta Alnus_mandshurica Pinus_densiflora Alnus_kamschatica Alnus_japonica Pinus_flexilis Alnus_incana Pinus_henryi Alnus_hirsuta Pinus_jeffreyi Pinus_koraiensis Alnus_glutinosa Alnus_firma Pinus_lambertiana Pinus_monticola Fagus_sylvatica Pinus_mugo Fagus_orientalis Fagus_lucida Pinus_nigra Pinus_parviflora Fagus_japonica Pinus_peuce Fagus_grandifolia Pinus_ponderosa Fagus_engleriana Pinus_pumila Fagus_crenata Pinus_pungens Castanea_sativa Pinus_resinosa Castanea_pumila Pinus_rigida Castanea_mollisima Pinus_sibirica Castanea_dentata Pinus_strobus Castanea_crenata Pinus_sylvestris Quercus_virginiana Pinus_tabuliformis Quercus_velutina Pinus_taiwanensis Quercus_variabilis Pinus_thunbergeri Pinus_virginiana Quercus_shumardii Quercus_serrata Pinus_wallichiana Quercus_salicina Picea_abies Quercus_rubra Picea_alba Picea_asperata Quercus_rotundifoliaQuercus_robur Picea_crassifolia Picea_engelmanii Quercus_pyrenaica Picea_glauca Quercus_pubescensQuercus_prinus Picea_glehnii Picea_jenuensis Quercus_phellos Picea_jezoensis Quercus_petraea Picea_koraiensis Quercus_palustris Picea_koyamai Picea_likiangensis Quercus_muehlenbergiiQuercus_mongolica Picea_meyerii Quercus_michauxii Picea_obovata Picea_omorika Quercus_marilandica Quercus_macrolepis Picea_orientalis Picea_pungens Quercus_macrocarpaQuercus_lyrata Picea_sitchensis Quercus_libani Picea_torano Quercus_laevis Picea_wilsonii Quercus_imbricaria Tsuga_canadensis Quercus_ilicifolia Tsuga_caroliniana Tsuga_chinensis Quercus_hartwissiana Tsuga_diversifolia Quercus_glanduliferaQuercus_frainetto Abies_alba Quercus_falcata Abies_balsamea Quercus_faginea Abies_cephalonica Quercus_fabri Abies_concolor Abies_fabri Quercus_ellipsoidalisQuercus_dentata Abies_fargesii Abies_firma Quercus_coccineaQuercus_cerris Abies_holophylla Abies_homolepis Quercus_bicolor Abies_koreana Quercus_castaneifolia Abies_lasiocarpa Quercus_betulina Abies_nephrolepis Quercus_arkansanaQuercus_aliena Abies_pinsapo Quercus_alba Abies_sachalinensis Abies_sibirica Ilex_verticillata Abies_cilicica Quercus_accutissima Abies_veitchii Ilex_serrata Pseudolarix_amabilis Ilex_shennongjiaensisIlex_rugosa Cedrus_atlantica Ilex_pernyi Cedrus_deodora Ilex_opaca Cedrus_libani Ilex_pedunculosa Bignonia_capreolata Campsis_grandifolia Ilex_mucronata Ilex_montana Campsis_radicans Catalpa_bignonioides Catalpa_bungei Ilex_maximowiczianaIlex_macrocarpaIlex_glabra Catalpa_fargesii Ilex_fargesii Catalpa_ovata Ilex_decidua Catalpa_speciosa Ilex_collina Elsholtzia_stauntonii Lavandula_angustifolia Ilex_ciliospinosa Rostrinucula_dependens Ilex_aquifolium Buddleja_albiflora Ilex_amelanchier Buddleja_alternifolia Buddleja_davidii Dirca_palustris Buddleja_japonica Wikstroemia_trichotoma Buddleja_lindleyana Dirca_occidentalis Paulownia_tomentosa Daphne_tangutica Callicarpa_americana Callicarpa_bodinieri Daphne_mezereumDaphne_laureola Daphne_giraldii Callicarpa_cathayana Callicarpa_dichotoma Daphne_genkwa Callicarpa_japonica Daphne_cneorum Callicarpa_kwantungensis Tilia_tomentosa Callicarpa_mollis Tilia_platyphyllos Caryopteris_clandonensis Tilia_paucicostata Clerodendrum_trichotomum Tilia_mongolica Vitex_negundo Tilia_mandshurica Vitex_rotundifolia Tilia_laetevirensTilia_kiusiana Abeliophyllum_distichum Tilia_japonica Chionanthus_retusus Tilia_insularis Chionanthus_virginicus Fontanesia_fortunei Forestiera_acuminata Tilia_heterophylla Tilia_dasystylaTilia_cordata Forestiera_neomexicana Forsythia_girrauldiana Tilia_begonifolia Forsythia_japonica Tilia_amurensis Forsythia_mandshurica Tilia_americana Forsythia_ovata Forsythia_suspensa Forsythia_viridissima Malvaviscus_arboreusHibiscus_syriacusGrewia_biloba Fraxinus_americana Fraxinus_angustifolia Firmiana_simplex Fraxinus_baroniana Cistus_laurifolius Fraxinus_bungeana Fraxinus_chinensis Picrasma_quassioidesLeitneria_floridana Fraxinus_crimensis Ptelea_trifoliata Fraxinus_excelsior Ailanthus_altissima Fraxinus_insularis Poncirus_trifoliata Fraxinus_latifolia Fraxinus_longicuspis Fraxinus_mandshurica Orixa_japonica Fraxinus_nigra Phellodendron_piriforme Fraxinus_ornus Phellodendron_chinense Fraxinus_pennsylvanica Phellodendron_amurense Fraxinus_potamophila Fraxinus_quadrangulata Zanthoxylum_simulans Fraxinus_sieboldiana Fraxinus_syriaca Zanthoxylum_bungeanumZanthoxylum_piperitum Fraxinus_tomentosa Fraxinus_xanthoxyloides Tetradium_ruticarpumTetradium_daniellii Zanthoxylum_americanumSkimmia_japonicaMelia_azedarach Jasminum_fruticans Toona_sinensis Jasminum_keskyi Jasminum_nudiflorum Cedrela_sinensis Ligustrum_acutissimum Ligustrum_compactum Ligustrum_ibota Ligustrum_japonicum Xanthoceras_sorbifoliumSapindus_drummondiiAesculus_wangii Ligustrum_massalongianum Ligustrum_obtusifolium Koelreuteria_paniculata Aesculus_turbinataAesculus_pavia Ligustrum_ovalifolium Aesculus_sylvatica Ligustrum_quihoui Ligustrum_salicinum Aesculus_parviflora Ligustrum_tschonoskii Aesculus_glabraAesculus_flava Ligustrum_vulgare Osmanthus_americanus Syringa_chinensis Aesculus_hippocastanum Aesculus_chinensis Syringa_josikaea Acer_wuyuanenseAcer_velutinum Syringa_meyeri Syringa_oblata Syringa_patula Acer_tschonoskii Acer_ukurunduenseAcer_truncatum Syringa_komarowii Acer_triflorum Syringa_persica Syringa_robusta Acer_tataricum Syringa_amurensis Acer_tegmentosum Syringa_pubescens Syringa_pekinensis Acer_sterculiaceumAcer_stenolobum Syringa_reticulata Acer_spicatumAcer_sinense Syringa_sweginzowii Syringa_villosa Acer_stachyophyllum Syringa_vulgaris Syringa_tomentella Acer_sieboldianum Lycium_barbarum Acer_saccharum Lycium_chinense Acer_shirasawanumAcer_rufinerve Periploca_graeca Acer_saccharinumAcer_rubrum Trachelospermum_asiaticum Adina_rubella Cephalanthus_occidentalis Gardenia_jasminoides Leptodermis_oblonga Acer_platanoides Acer_opalus Ehretia_ovalifolia Acer_pseudoplatanusAcer_palmatum Eucommia_ulmoides Aucuba_japonica Acer_pseudosieboldianumAcer_pensylvanicum Bupleurum_spinosum Acer_nigrum Acer_oliverianumAcer_olivaceum Aralia_elata Aralia_spinosa Acer_negundoAcer_mono Hedera_colchica Hedera_helix Acer_miyabei Kalopanax_septemlobus Eleutherococcus_henryi Eleutherococcus_senticosus Eleutherococcus_seoulensis Acer_monspessulanumAcer_micranthum Eleutherococcus_sessiliflorus Eleutherococcus_setchuenensis Acer_longipes Eleutherococcus_sieboldianus Tetrapanax_papyrifer Acer_mandshuricum Acer_henryi Baccharis_halimifolia Acer_macrophyllumAcer_japonicum Acer_maximowiczianumAcer_hyrcanum Myripnois_dioica Abelia_chinensis Acer_griseum Abelia_mosanensis Acer_heldreichiiAcer_ginnala Abelia_zanderi Abelia_biflora Diervilla_lonicera Acer_flabellatum Diervilla_rivularis Acer_erianthum Diervilla_sessilifolia Acer_davidii Dipelta_floribunda Acer_elegantulum Dipelta_yunnanensis Acer_diabolicum Heptacodium_miconioides Kolkwitzia_amabilis Lonicera_alpigena Acer_cissifoliumAcer_caudatum Lonicera_bracteolaris Acer_crataegifolium Lonicera_caerulea Lonicera_canadensis Lonicera_caprifolium Acer_caudatifolium Acer_caesium Lonicera_chaetocarpa Acer_carpinifoliumAcer_campestre Lonicera_chrysantha Lonicera_conjugialis Acer_cappadocicum Lonicera_demissa Acer_argutum Acer_barbinerveRhus_typhina Lonicera_etrusca Lonicera_ferdinandi Acer_buergerianum Rhus_trilobataRhus_glabra Lonicera_floribunda Lonicera_fragrantissima Rhus_coriaria Lonicera_gracilipes Lonicera_graebneri Rhus_copallina Lonicera_gynochlamydea Rhus_chinensis Lonicera_henryi Lonicera_hirsuta Rhus_aromatica Lonicera_iberica Lonicera_japonica Lonicera_korolkowii Lonicera_notha Pistacia_chinensis Pistacia_terebinthus Lonicera_pekinensis Cotinus_obovatus Lonicera_purpusii Lonicera_salicifolia Cotinus_coggygria Lonicera_standishii Lonicera_lanata Lonicera_maackii Lonicera_maximowiczii Lonicera_morrowii Philadelphus_triflorus Lonicera_myrtillus Lonicera_orientalis Lonicera_elisae Philadelphus_zhejiangensisPhiladelphus_tenuifoliusPhiladelphus_subcanus Lonicera_periclymenum Lonicera_purpurascens Philadelphus_schrenkiiPhiladelphus_satsumi Lonicera_pyrenaica Philadelphus_sericanthus Lonicera_quinquelocularis Lonicera_ramosissima Lonicera_reticulata Philadelphus_lewisii Lonicera_ruprechtiana Philadelphus_satsumanus Lonicera_sempervirens Philadelphus_pubescens Lonicera_sovetkiniae Lonicera_subsessilis Philadelphus_pekinensis Philadelphus_purpurascensPhiladelphus_insignis Lonicera_tangutica Philadelphus_keteleerei Lonicera_tatarica Lonicera_webbiana Philadelphus_inodorusPhiladelphus_incanus Lonicera_xylosteum Weigela_coraeensis Philadelphus_edsonii Philadelphus_hirsustus Weigela_decora Jamesia_americana Weigela_florida Philadelphus_delavayiHydrangea_radiata Weigela_hortensis Weigela_japonica Weigela_maximowiczii Weigela_praecox Philadelphus_coronarius Weigela_subsessilis Hydrangea_quercifolia Sambucus_canadensis Hydrangea_paniculata Sambucus_nigra Hydrangea_serrata Sambucus_pubens Sambucus_racemosa Hydrangea_cinerea Sambucus_tigranii Hydrangea_macrophyllaHydrangea_involucrata Symphoricarpos_albus Symphoricarpos_hesperius Hydrangea_heteromalla Deutzia_scabra Symphoricarpos_occidentalis Deutzia_rubens Symphoricarpos_rotundifolius Hydrangea_anomala Symphoricarpos_orbiculatus Symphoricarpos_oreophilus Viburnum_acerifolium Hydrangea_arborescens Deutzia_mollis Viburnum_betulifolium Hydrangea_acuminataDeutzia_parviflora Viburnum_bitchiuense Hydrangea_bretschneideri Deutzia_rosea Viburnum_bracteatum Deutzia_schneideriana Viburnum_buddleifolium Deutzia_myriantha Viburnum_burejaeticum Viburnum_carlesii Deutzia_ningpoensis Viburnum_cassinoides Viburnum_bodnantense Deutzia_grandifloraDeutzia_malifloraDeutzia_gracilis Viburnum_dentatum Viburnum_dilatatum Viburnum_erubescens Deutzia_glabrata Viburnum_erosum Deutzia_discolor Viburnum_farreri Deutzia_carnea Viburnum_flavescens Deutzia_coreana Viburnum_hupehense Viburnum_koreanum Deutzia_elegantissima Viburnum_lanatoides Viburnum_lantana Deutzia_corymbosa Viburnum_lentago Nyssa_sylvatica Viburnum_lobophyllum Nyssa_aquatica Viburnum_melanocarpum Deutzia_amurensis Viburnum_molle Cornus_walteri Viburnum_mongolicum Decumaria_sinensis Viburnum_nudum Decumaria_barbara Cornus_sericea Viburnum_opulus Viburnum_plicatum Cornus_rugosa Viburnum_prunifolium Viburnum_rafinesqueanum Davidia_involucrata Viburnum_recognitum Cornus_pumila Cornus_mas Viburnum_rhytidophyllum Viburnum_rudifolium Cornus_sanguinea Viburnum_rufidulum Viburnum_sargentii Cornus_racemosa Viburnum_schensianum Viburnum_setigerum Viburnum_sieboldii Cornus_kousa Viburnum_trilobum Viburnum_utile Viburnum_veitchii Cornus_poliophylla Viburnum_wrightii Cornus_officinalis Cornus_florida Itea_virginica Nemopanthes_mucronata Cornus_paucinervis Cornus_foeminaCornus_ebulos Helwingia_japonica Actinidia_arguta Actinidia_chinensis Actinidia_coriacea Actinidia_eriantha Cornus_alba Actinidia_kolomikta Actinidia_macrosperma Actinidia_polygama Cornus_macrophylla Clethra_acuminata Clethra_alnifolia Clethra_barbinervis Clethra_fargesii Cornus_australis Clethra_monostachya Cyrilla_racemiflora Andromeda_glaucophylla Andromeda_polifolia Arctostaphylos_uvaursi Calluna_vulgaris Cornus_amomum Chamaedaphne_calyculata Cornus_drummondiiCornus_controversa Corema_conradii Empetrum_nigrum Enkianthus_campanulatus Enkianthus_chinesis Enkianthus_deflexus Enkianthus_perulatus Epigaea_repens Cornus_alternifolia Erica_carnea Erica_darleyensis Erica_spiculifolia Erica_vagans Gaultheria_procumbens Gaylussacia_brachycera Stewartia_sinensis Kalmia_angustifolia Kalmia_buxifolia Stewartia_rostrata Kalmia_cuneata Kalmia_latifolia Leiophyllum_buxifolium Cornus_bretschneideri Leucothoe_axillaris Diospyros_kaki Leucothoe_fontanesiana Leucothoe_racemosa Leucothoe_recurva Lyonia_ligustrina Ledum_palustre Rhododendron_alabamense Rhododendron_arborescens Rhododendron_atlanticum Rhododendron_auriculatum Rhododendron_austrinum Rhododendron_calendulaceum Rhododendron_brachycarpum

Rhododendron_campanulatum

Camellia_japonica Diospyros_lotus Styrax_obassia

Franklinia_alatamaha Styrax_japonicusStyrax_confusus Stewartia_monadelpha Styrax_americana Halesia_diptera Symplocos_paniculataDiospyros_virginiana Halesia_carolina Ternstroemia_gymnantheraStewartia_pseudocamellia Sinojackia_sinensis Halesia_tetraptera Diospyros_cathayensis Sinojackia_xylocarpa

Pterostyrax_hispidus Pieris_japonica

Pieris_floribunda Zenobia_pulverulenta Vaccinium_pallidum Vaccinium_oldhamii Vaccinium_vitis-idaea

Pyxidanthera_barbulata Vaccinium_uliginosum Vaccinium_stamineum

Vaccinium_myrtilloides

Vaccinium_macrocarpon Vaccinium_corymbosum

Rhododendron_vaseyii Oxydendrum_arboreum Vaccinium_angustifolium Vaccinium_arctostaphylos

Rhododendron_minus Rhododendron_yedoense Rhododendron_searsiae Rhododendron_viscosum

Rhododendron_smirnowii Rhododendron_luteum

Rhododendron_obtusum Rhododendron_sichotense Rhododendron_makinoi

Rhododendron_fargesii

Rhododendron_fortunei

Rhododendron_prunifolium Rhododendron_maximum Rhododendron_occidentale Rhododendron_ledebourii Rhododendron_dauricum Rhododendron_praevernum Rhododendron_kaempferi

Rhododendron_micranthus

Rhododendron_japonicum

Rhododendron_prinophyllum Rhododendron_canadense

Rhododendron_catawbiense Rhododendron_schlippenbachii Rhododendron_kiyosumense

Rhododendron_mucronulatum

Rhododendron_degronianum

Rhododendron_micranthemum Rhododendron_periclymenoides

Rhododendron_caroliniuanum

Rhododendron_greenlandieum

Rhododendron_hippophaeoides Rhododendron_cumberlandense 17 Extended Data Figure 1 | PHYLOMATIC tree4.0 modified from Panchen et al. containing 1630 species.

100 Leaf-out date Lamiales Gentianales Ericales 24 Feb 31 March 5 May Dipsacales

Cornales

Viburnum burejaeticum

Fraxinus longicuspis

Fraxinus americana

Eucommia ulmoides Buddleja alternifolia

Viburnum prunifolium

Viburnum lentago

Viburnum carlesii Buddleja davidii Catalpa fargesii Viburnum setigerum Viburnum plicatum Viburnum betulifolium Viburnum nudum Viburnum erubescens Viburnum dentatum Crossosomatales Viburnum farreri Viburnum buddleifolium Viburnum molle Symphoricarpos rotundifolius Viburnum lantana Sambucus racemosa Viburnum opulus les Symphoricarpos oreophilus Sambucus pubens

Sambucus nigra Weigela praecox

Fraxinus tomentosa Fraxinus latifolia Lonicera periclymenum Fraxinus ornus Fraxinus chinensis Fraxinus angustifolia Fraxinus excelsior Malva Jasminum nudiflorum Syringa oblata Weigela florida Syringa vulgaris Ligustrum vulgare Lonicera tangutica Syringa reticulata Syringa amurensis Syringa patula Syringa pubescens Syringa villosa Forsythia ovata Lonicera xylosteum Forsythia suspensa Lonicera maximowicziiLonicera maackii Abeliophyllum distichum Heptacodium miconioides Cephalanthus occidentalis Lonicera caerulea Periploca graeca Diospyros virginiana Stewartia monadelpha Eleutherococcus senticosusLonicera webbiana Stewartia pseudocamellia Eleutherococcus sieboldianus Enkianthus campanulatus Rhododendron schlippenbachii Abelia mosanensis Rhododendron mucronulatum Rhododendron canadense Kolkwitzia amabilis Rhododendron yedoense Kalopanax septemlobus Vaccinium uliginosum Vaccinium angustifolium Cornus florida Cornus kousa Staphylea colchica Cornus officinalis Cornus mas Staphylea pinnata Cornus walteri StachyurusStaphylea chinensis trifoliata Cornus sanguinea Cornus bretschneideri Stachyurus praecox Cornus alba Cornus sericea Cornus racemosa Daphne laureola Cornus foemina Ranunculales Hibiscus syriacus Cornus controversa Tilia platyphyllos Cornus alternifolia Phellodendron amurense Nyssa sylvatica Toona sinensis Davidia involucrata Orixa japonica Deutzia gracilis Deutzia corymbosa ZanthoxylumTetradium simulans daniellii Deutzia scabra Hydrangea arborescens Hydrangea paniculata Ptelea trifoliata PicrasmaPoncirus quassioides trifoliata Hydrangea heteromalla Hydrangea involucrata Aesculus parviflora Hydrangea anomala Hydrangea serrata Sapindales Aesculus hippocastanumAesculus glabra Hydrangea macrophylla Philadelphus pubescens Aesculus flava Philadelphus lewisii Acer saccharinum Philadelphus coronarius Philadelphus subcanus Acer shirasawanumAcer rubrum Philadelphus pekinensis Philadelphus satsumi Acer palmatum Philadelphus sericanthus Menispermum dauricum Acer negundo Acer diabolicum Clematis ligusticifolia Berberis koreana Acer tataricum Acer tegmentosum Berberis actinacantha Gymnosperms Berberis concinna Acer saccharum Berberis vernae Acer pensylvanicumAcer griseum Berberis amurensis Berberis vulgaris Acer crataegifolium Berberis thunbergii Akebia quinata Acer davidii Acer campestre Decaisnea fargesii Acer platanoides Ginkgo biloba Tsuga canadensis Acer henryi Abies alba Acer barbinerve Euonymus macropterus Cedrus libani Euonymus oxyphyllus Pinus banksiana Pinus ponderosa Euonymus latifolius Pinus armandii Euonymus fortunei Picea abies Celastrales Euonymus verrucosus Picea asperata

Euonymus alatus Picea omorika Euonymus europaeus Picea glehnii Euonymus hamiltonianus Larix gmelinii Idesia polycarpa Larix decidua Larix kaempferi Salix gracilistyla Thuja plicata Salix magnifica Juniperus communis Salix caprea Juniperus sabina Salix repens Metasequoia glyptostroboides Salix rosmarinifolia Sequoiadendron giganteum Salix alba Taxus baccata Populus koreana Chimonanthus praecox Flueggea suffruticosa Calycanthus floridus Lindera benzoin Malpgighiales Rhamnus frangula Rhamnus cathartica Sassafras albidum Aristolochia macrophylla Broussonetia papyrifera Magnolia acuminata Celtis occidentalis Magnolia sieboldii Celtis laevigata Magnolia tripetala Ulmus laevis Magnolia kobus Ulmus americana Magnolia zenii Ulmus glabra Magnolia salicifolia Zelkova serrata Magnolia denudata Magnolia macrophylla Zelkova schneideriana Liriodendron tulipifera Elaeagnus pungens Schisandra chinensis Elaeagnus umbellata Asimina triloba Elaeagnus ebbingei Corylopsis sinensis Hippophae rhamnoides Corylopsis spicata Shepherdia argentea Parrotiopsis jacquemontiana Prunus serrulata Hamamelis japonica Magnoliids Prunus subhirtella Hamamelis vernalis Prunus padus Hamamelis mollis Prunus virginiana Hamamelis virginiana Prunus serotina Parrotia persica Prunus grayana Sinowilsonia henryi Cercidiphyllum japonicum Prunus angustifolia Prunus tenella Cercidiphyllum magnificum Liquidambar styraciflua Prunus cerasifera Liquidambar orientalis Prunus spinosa Ribes uva Malus sieboldii Ribes himalense Malus halliana Ribes alpinum−crispa Ribes glaciale Malus hupehensisMalus prattii Paeonia rockii Gleditsia japonica Malus floribunda Gleditsia sinensis Malus spectabilis Gleditsia caspica Malus prunifolia Malus baccata Gymnocladus dioicus Malus fusca Amorpha fruticosa Caragana aurantiaca Malus orientalis Caragana pygmaea Malus ioensis Robinia pseudoacacia Sorbus decora Spartium junceum Petteria ramentacea Sorbus aucuparia Cytisophyllum sessilifolium Cydonia oblonga Cladrastis lutea Cercis chinensis Pyracantha coccinea Cercis canadensis Nothofagus antarctica Crataegus monogyna Fagus orientalis Crataegus laevigata galli Saxifragales − Fagus sylvatica CrataegusCrataegus persimilis mollis Fagus crenata Fagus engleriana Castanea sativa Crataegus submollis Quercus cerris Quercus variabilis CrataegusCrataegus chrysocarpa crusPhotinia villosa Quercus mongolica Mespilus germanica Quercus robur Quercus macrocarpa Quercus alba Amelanchier alnifolia Quercus bicolor Amelanchier laevis Quercus rubra Juglans microcarpa Juglans mandshurica Juglans ailanthifolia Juglans regia Juglans nigra ChaenomelesChaenomeles cathayensis speciosaPyrus calleryanaPyrus pyrifolia Aronia melanocarpa Carya cordiformis Chaenomeles japonica Carya laciniosa Carya ovata Pyrus ussuriensis Carpinus laxiflora Carpinus monbeigiana Carpinus betulus Pyrus elaeagnifolia Ostrya virginiana 0 Ostrya carpinifolia Cotoneaster niger Corylus americana Rosales Corylus colurna Corylus sieboldiana Corylus heterophylla Cotoneaster acutifolius Corylus avellana Betula populifolia Betula lenta Betula ermanii Betula maximowicziana Betula dahurica Betula nana Cotoneaster foveolatus Betula pendula Alnus firma Cotoneaster franchetii Alnus glutinosa Alnus incana Rubus parvifolius Rubus allegheniensis Rosa acicularis Rosa spinosissima Cotoneaster multiflorus Cotoneaster divaricatus Cotoneaster dielsianus

Cotoneaster acuminatus Fabales CotoneasterCotoneaster horizontalis adpressus Cotoneaster simonsii Rosa alba Prinsepia uniflora

Neillia thibetica Prinsepia sinensis Kerria japonica Sorbaria arborea Cotoneaster moupinensis Oemleria cerasiformis Rosa blanda Exochorda racemosa Sorbaria sorbifolia Rosa sericea

Rosa rugosa Cotoneaster harrysmithii Spiraea miyabei

Spiraea salicifolia Rosa carolina

Rosa multiflora Rosa centifolia

Rosa willmottiae

Spiraea trichocarpa

Spiraea chamaedryfolia

24 Feb

10" Fagales 5 May 11" Extended Data Figure 2 | Phylogeny of 374 woody temperate species with mean leaf-out 12" dates observed in the Munich Botanical Garden (between 2012 and 2015) indicated by 13" Extended Data Figure 2 | Phylogeny of 374 woody temperate species with average leaf- 14"coloursout dates and in the Munich outermost (2012 to bars. 2015) Pagel’s indicated λ =by 0.81, colou Prs

19"

101 20"

Carpinus_betulus Carpinus_laxiflora Carpinus_monbeigiana 21" Ostrya_carpinifolia Ostrya_virginiana Corylus_heterophylla Corylus_sieboldiana Corylus_avellana Corylus_americana Alnus_incana Alnus_serrulata Betula_papyrifera 22" Betula_lenta Betula_populifolia Betula_nana Betula_pendula Carya_ovata Carya_cordiformis Chilling requirements Carya_glabra Carya_laciniosa Fagales 23" Juglans_regia Juglans_cinerea Comptonia_peregrina Myrica_pensylvanica High Quercus_alba Quercus_bicolor Castanea_sativa Quercus_robur Quercus_rubra 24" Intermediate Fagus_crenata Fagus_engleriana Fagus_orientalis Fagus_sylvatica Fagus_grandifolia Nothofagus_antarctica None Sorbus_aria Sorbus_decora 25" Photinia_villosa Aronia_melanocarpa Amelanchier_laevis Amelanchier_alnifolia Pyrus_pyrifolia Pyrus_ussuriensis Pyrus_elaeagnifolia Prunus_avium 26" Prunus_serrulata Prunus_cerasifera Prunus_tenella Prunus_padus Prunus_serotina Spiraea_chamaedryfolia Rosales Prinsepia_uniflora Prinsepia_sinensis Oemleria_cerasiformis 27" Rosa_multiflora Celtis_laevigata Celtis_occidentalis Ulmus_americana Ulmus_laevis Rhamnus_frangula Rhamnus_cathartica Rhamnus_alpina 28" Elaeagnus_ebbingei Elaeagnus_umbellata Caragana_pygmaea Robinia_pseudoacacia Amorpha_fruticosa Cladrastis_lutea Cercis_chinensis Fabales Cercis_canadensis Populus_tremula 29" Populus_grandidentata Populus_koreana Salix_repens Malpighiales Salix_gracilistyla Euonymus_latifolius Euonymus_alatus Euonymus_europaeus Celastrus_orbiculatus Celastrales 30" Acer_saccharinum Acer_rubrum Acer_barbinerve Acer_campestre Acer_pseudoplatanus Acer_negundo Aesculus_flava Aesculus_hippocastanum Sapindales Aesculus_parviflora 31" Ptelea_trifoliata Orixa_japonica Toona_sinensis Rhus_typhina Hibiscus_syriacus Tilia_platyphyllos Malvales Stachyurus_chinensis Stachyurus_praecox 32" Hamamelis_japonica Hamamelis_vernalis Crossosomatales Hamamelis_virginiana Parrotia_persica Sinowilsonia_henryi Corylopsis_spicata Corylopsis_sinensis Cercidiphyllum_magnificum Cercidiphyllum_japonicum Saxifragales 33" Liquidambar_styraciflua Liquidambar_orientalis Ribes_glaciale Ribes_alpinum Paeonia_rockii Fraxinus_pennsylvanica Fraxinus_americana Fraxinus_latifolia 34" Fraxinus_ornus Fraxinus_chinensis Fraxinus_excelsior Syringa_reticulata Syringa_villosa Syringa_vulgaris Lamiales Ligustrum_compactum Forsythia_ovata Forsythia_suspensa 35" Buddleja_alternifolia Buddleja_davidii Buddleja_albiflora Cephalanthus_occidentalis Sambucus_racemosa Sambucus_nigra Eleutherococcus_senticosus Eleutherococcus_sieboldianus 36" Viburnum_carlesii Viburnum_plicatum Viburnum_opulus Viburnum_betulifolium Viburnum_buddleifolium Lonicera_maackii Lonicera_caerulea Dipsacales Lonicera_maximowiczii Symphoricarpos_albus 37" Heptacodium_miconioides Weigela_florida Hydrangea_arborescens Hydrangea_involucrata Hydrangea_serrata Deutzia_gracilis Deutzia_scabra Nyssa_sylvatica Cornales 38" Cornus_alba Cornus_amomum Cornus_mas Cornus_kousa Kalmia_angustifolia Kalmia_latifolia Rhododendron_mucronulatum Rhododendron_canadense Gaylussacia_baccata 39" Vaccinium_angustifolium Vaccinium_corymbosum Ericales Clethra_alnifolia Berberis_vulgaris Berberis_thunbergii Decaisnea_fargesii Ranunculales Smilax_rotundifolia Lindera_benzoin 40" Sassafras_albidum Liriodendron_tulipifera Magnoliids Pinus_sylvestris Pinus_nigra Pinus_strobus Pinus_wallichiana Picea_abies Larix_decidua Larix_kaempferi Coniferales 41" Larix_gmelinii Pseudotsuga_menziesii Abies_homolepis Abies_alba Cedrus_libani Metasequoia_glyptostroboides 42" Ginkgo_biloba 200 mya 100 mya 0 43" Extended Data Figure 3 | Phylogeny of 180 woody temperate species with their chilling Extended Data Figure 3 | Phylogeny of 180 woody temperate species with their chilling 44" requirements indicated by the colours. The tree was built with MEGAPTERA (46) and requirements indicated by the colours. 45" BEAST (47) and is based on sequence information from four plastid loci (atpB, matK, ndhF, 46" rbcL). 102 154" Extended Data Figure 9 | Days to leaf-out for North American (NA), European (EU), 155" and eastern Asian (EA) species under three different chilling levels. a, Median days until 156" leaf-out in a climate chamber ± 95% CI under different chilling levels for NA (N = 72 157" species), EU (N = 48), and EA (N = 88) species. b–d, Leaf-out probability curves for NA, 158" EU, and EA species calculated as the number of days required in climate chamber until 159" budburst under different chilling treatments: (b) long chilling, (c) intermediate chilling, and 160" (d) short chilling. Dashed lines indicate median forcing requirements for NA, EU, and EA 161" species. 162" 163" 164" 165" a Chilling requirements b Leaf-out times 4 0.4 ●

2 0.2 ● ● 0 0.0 ● −0.2 Coefficient estimate Coefficient estimate −2

−0.4 −4 Growth habit Leaf persistence Growth habit Leaf persistence Variable Variable 166" c d Growth habit: Shrubs Trees GDD (>0°C) GDD Days to leaf-out to Days 0 10 20 30 40 50

167" 300 500 700 900 0.5Long 1.0 (C3) 1.5 Intermediate 2.0 (C2) 2.5 Short 3.0 (C1) 3.5 0.5Long 1.0 (C3) 1.5 Intermediate 2.0 (C2) 2.5 Short 3.0 (C1) 3.5 168" Chilling Chilling Chilling Chilling 169" Extended Data Figure 4 | The effect of growth habit and leaf persistence on species’ leaf-out 170" strategies.Extended a Data, b, Coefficient Figure x | valuesThe effect (effective of growth posterior habit means and leaf and persistence95% credible on intervals) species’ forleaf the- 171" effectout strategies. of growth habita, Coefficient (shrubs vs. values trees) (effective and leaf posteriorpersistence means (deciduous and 95% vs. credible evergreen intervals) species) on 172" speciesfor differences-specific (ina )chilling chilling requirements requirements between and (b) leaftrees-out and dates shrubs (Munich (growth data). habit) Chilling and data: 108 173" shrubsevergreen and 107and trees,deciduous 202 deciduous species (leaf and persistence) 13 evergreen. Sample species; sizes: Leaf Growth-out data: habit: 295 108 shrubs shrubs and 203 174" trees,and 107470 trees,deciduous leaf persistence: and 28 evergreen 202 deciduous species. Toand account 13 evergreen for phylogenetic species. b, autocorrelation,Coefficient values we inserted genus and family random effects for ordinal chilling categories or incorporated the phylogenetic structure using the Bayesian phylogenetic regression method for leaf-out dates. Values reflect standardized data and can be interpreted as relative effect sizes. c, d, Median forcing requirements (accumulated degree days >0°C outdoors and in a climate chamber) ± 95% CI (c) and median days until leaf-out in a climate chamber ± 95% CI (d) under different chilling levels for trees (red curve, N = 107 species) and shrubs (blue curve, N = 108 species).

103 0.6 47"

Chilling requirements Group 0.4 0.6 ANone

BIntermediate

0.6 CHigh Group 0.2 0.4 47" A

a Chilling requirements Group B 0.4 0.6 a ANone C

Chilling requirements per continent (%) continent per requirements Chilling 0.2 BIntermediate 0.0 CHigh Group 0.2 AM 0.4 EU AS A

Chilling requirements per continent (%) continent per requirements Chilling C 0.0(%) continent per requirements Chilling 0.2 48" 0.0 49" AMNAAM (72) EU EUEU (48) AS EAAS (88) 67"

50" (%) continent per requirements Chilling 0.0 68" 48" AM EU AS 51" Extended64" 49 " Data Figure 4N A| Effect(72) of continental EU (48) origin on EA species(88) -specific chilling 50" 69" b52 " requirements65" and leaf-out times. a, Number of species with available data shown in 51"b Extended Data Figure 4 | Effect of continentalChilling origin on species-specific chilling

70" 53 " brackets66" 52. " Seerequirements Fig. 4S11 and for leaf species-out times-specific. a, Number results of species of twigwith available-cutting data experiment shown in 71" 54 " 53" brackets. See Fig. S11 for species-specific results of twig-cutting experiment 67" 2 54" 72" 55 " 68" 55 " 0 73" 56 " 56" 69" 57" 74" 57 " a -2a ChillingChilling

4 70" 58 " estimate Coefficient

58" 59" 4 75" estimate Coefficient 2 59" 71" 60 " -4 76" 61" 2 0 60" NA EU AS 62" -2 Location 72" 0 estimate Coefficient 77" 61 " 63" estimate Coefficient c -4 Leaf-out 64" 10 NA EU AS 62" 73" -2 Location

65" estimate Coefficient 78" b Leaf-out 8 10

63" estimate Coefficient 66" 8 -4 6 79" 64 " 74" 67 " 6 NA4 EU AS 68" 4 Location 65" 2 80" 75" 69 "b 0 Leaf-out 10 2 -2 -4 66" 70" 8 0 Coefficient estimate Coefficient -6 71" 6 81" 76" estimate Coefficient 67" -2 -8 4 -10 NA EU AS 68" 72" Location 82" 77" 2 -4 NA EU EA 0

69" 73" estimate Coefficient -6 83" -2 70" 78" 74 " -4 estimate Coefficient -8 71" estimate Coefficient -6 -10

75" Extended estimate Coefficient Data Figure 5 | Contrasting leaf-out strategies in North America (NA), Europe 79" -8 NA EU AS 84" -10 NA Location EU EA 72" NA EU AS 80" NA Location EU EA 85" 73 " 81" Extended Data Figure 5 | Contrasting leaf-out strategies in North America (NA), Europe 74" 86" 82" 75" Extended83" (EU), Data Figure and 5East | Contrasting Asia (EA). leaf-out astrategies, Effect in ofNorth continental America (NA), origin Europe on species-specific chilling categories. 87" Extended Data Figure 5 | Contrasting leaf-out strategies in North America (NA), Europe 84" NumberExtended Data of speciesFigure 4 | withEffect availableof continental d ataorigin shown on species in -brackets.specific chilling b, Estimated coefficient values 88" (EU),85 "andrequirements eastern Asiaand leaf (E-outA )times estimated. a, Number from of species phylogenetic with available datamodels shown. in a , Estimated coefficient (effective posterior means and 95% credible intervals) from phylogenetic models for differences 89" values86" (effectivebrackets. See posterior Fig. S11 for means species -andspecific 95% results credible of twig-cutting intervals) experiment for differences in chilling 87" in chilling requirements between NA, EU, and EA species (N = 66 NA, 43 EU, and 68 EA 90" requirements between NA, EU, and EA species (N = 66 NA, 43 EU, and 68 EA species). 88" species).Extended Data The Figure model 5 | Contrasting includes leafgenus-out strategies and family in North random America effects (NA), Europe to account for shared evolutionary 91" Model89" includes(EU), and genus eastern andAsia (familyEA) estimated random from effects phylogenetic to account models. a for, Estimated shared coefficient evolutionary history of 90" historyvalues (effective of species. posterior cmeans, Estimated and 95% credible coefficient intervals) values for differences including in chilling phylogenetic autocorrelation for 92" species. b, Estimated coefficient values including phylogenetic autocorrelation for differences differences in Munich leaf-out times between NA, EU, and EA species (N = 100 ENA, 74 EU, 93" in Munich leaf-out times between NA, EU, and EA species (N = 100 ENA, 74 EU, and 173 and 173 EA species). To control for species’ life history strategy and native climate (mean annual 94" EA species). To control for species’ life history strategy and native climate (mean annual temperature), both models (b + c) include fixed effects for growth habit (shrubs vs. trees), leaf 95" temperature), both models (a + b) include fixed effects for growth habit (shrubs vs. trees), persistence (evergreens vs. deciduous species), and species’ 0.5 quantiles for mean annual 96" leaf persistence (evergreens vs. deciduous species), and species’ 0.5 quantiles for mean temperature in their native ranges. Values reflect standardized data and can be interpreted as 97" annual temperature in their native ranges. Values reflect standardized data and can be relative effect sizes. 98" interpreted as relative effect sizes. Species from NA leaf out later and have higher chilling

104 142" Values reflect standardized data and can be interpreted as relative effect sizes. 143" 144" 145" 146" 147" 148" 149" 150" 151"

50 Aa NA

EU 40

t AS

u o - 30 af e l

t 20 s y

D 10

0 !"# L$"on!g (C3$") #Inter%"med! iate%" (C#2) S&"ho!rt (C1&"#) Chilling 1 Bb 0.8 0.6

0.4

0.2 Long chilling (C3)

1 t Cc s ur

b 0.8 d u

b 0.6 f o

y t 0.4 ili b a

b 0.2 Intermediate ro chilling (C2) P 1 Dd 0.8

0.6

0.4

0.2 Short chilling (C1) 0 0 20 40 60 80 152" Days until budburst 153" Extended Data Figure 6 | Days to leaf-out for North American (NA), European (EU), and East Asian (EA) species under three different chilling levels. a, Median days until leaf-out in a climate chamber ± 95% CI under different chilling levels for NA (N = 72 species), EU (N = 48), and EA (N = 88) species. b–d, Leaf-out probability curves for NA, EU, and EA species calculated as the number of days required until budburst under different chilling treatments: (b) long chilling, (c) intermediate chilling, and (d) short chilling. Dashed lines indicate median forcing requirements for NA, EU, and EA species.

105 214" 215" 216" a b c Adoxaceae Abies

Acer Altingiaceae Aesculus 1.5 Alnus 16 13 16 Anacardiaceae Amelanchier Amorpha 1.0 Araliaceae Aronia Berberis Berberidaceae Betula Buddleja 0.5 Betulaceae Caragana Carpinus Cannabaceae Carya 0.0 Castanea Caprifoliaceae Celastrus Celastraceae Celtis −0.5 Cephalanthus NA NA EU Cercidiphyllaceae Cercidiphyllum 10 Cercis vs. vs. vs. Clethraceae Cladrastis20 −1.0 Clethra EA EU EA Coniferales Comptonia Cornus −1.5 Cornaceae Corylopsis per family Mean chilling difference Corylus family per difference chilling Mean 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Cupressaceae Decaisnea Continent contrast Deutzia Elaeagnaceae Elaeagnus Eleutherococcus Ericaceae Euonymus Fagus Ericaeae Forsythia d Fraxinus Fabaceae Gaylussacia Ginkgo 1.5 Fagaceae chillingHamamelis chilling 17 19 24 Heptacodium3.0 Ginkgoaceae20 Hibiscus 3.01.0 Hydrangea Grossulariaceae Juglans40 2.5Kalmia 2.5 Hamamelidaceae Larix 0.5 Ligustrum Hydrangeaceae Lindera Liquidambar2.0 2.00.0 Juglandaceae Liriodendron Lonicera family_num Lardizabalaceae 1.5 Genus_num Malus 1.5 Metasequoia −0.5 NA NA Lauraceae Myrica EU Nyssa vs. vs. vs. Magnoliaceae 1.0 Orixa 1.0−1.0 Ostrya EA EU EA Malvaceae Paeonia n chilling difference per genus per difference chilling n Parrotiopsis Meliaceae Photinia −1.5 Picea per family Mean chilling difference

Mea 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Myricaceae30 Pinus Populus Continent contrast Oleaceae Prinsepia60 Prunus

Paeoniceae Ptelea High Pyrus Pinaceae Quercus

Rhamnus

Rhamnaceae Rhododendron Chilling requirements Rhus Rosaceae Ribes Robinia Intermediate Rubiaceae Rosa Salix Rutaceae Sambucus Sassafras Salicaceae Sinowilsonia Smilax Salicaeae Sorbus Sapindaceae40 Spiraea Stachyurus Scrophulariaceae Symphoricarpos Syringa80 Tilia

Smilacaceae Low Toona Ulmus Stachyuraceae Vaccinium Ulmaceae Viburnum Vitis No Vitaceae Weigela species 217" "" 0.5 NA1.0 1.5 EU2.0 2.5 EA3.0 3.5 0.5NA1.01.5 EU2.02.5 3.0EA3.5 218" continent_num continent_num

219" ExtendedExtended Data Data Figure Figure | Family 7 | Family- and genus- and- levelgenus differences-level differences in chilling in requirements chilling requirements 220" betweenbetween North North America America (NA), (NA), easte Eastrn Asia Asia (EA), (EA), and andEurope Europe (EU). (EU).a, b Heat a, b,maps Heat maps showing 221" showingmean chilling mean chilling requirements requirements per familyper family (a) ( anda) and genus genus ( b(b) )for for NA,ENA, EU, EU ,and and EAEA species. c, d, Mean 222" species.within -c,family d, Mean (c )within and within-family- genus(c) and (withind) differences-genus (d) indifferences chilling requirementsin chilling (± confidence 223" requirementsintervals) between (± confidence NA and intervals) EA species betwee n(left NA bar), and EA NA species and EU (left (middle), bar), NA and EUEU and EA species 224" (middle), and EU and EA species (right). Note that for the calculation the chilling categories (right). Note that for the calculation the chilling categories were treated as numerical characters (no chilling requirements = 0, intermediate = 1, high = 2). Numbers of within-family / within- genus contrasts are shown above bars.

106 Short days (8-h) Long days (16-h)

NA 900

EU EA 0°C 700 GDD (>0°C) GDD GDD (>0°C) 500 egreedays > D 300 500 700 900 226" 300 0.5Long 1.0 (C3) 1.5 Intermediate 2.0 (C2) 2.5 Short 3.0 (C1) 3.5 0.5 Long1.0 (C3) 1.5 Intermediate2.0 (C2)2.5 Short3.0 (C1)3.5 227" Extended Data FigureChilling 8 | Region-specific responses to reducedChilling chilling are not sensitive to 228" Extended Data Figure x | Region-specific responses to reduced chilling are not sensitive photoperiod treatment. Contrasting responses of North American (NA), European (EU), and 229" to photoperiod treatment. Contrasting responses of North American (NA), European (EU), 230" andEast East Asian Asian (EA) (EA) species species to to experimentally experimentally reduced reduced winterwinter chillingchilling underunder shortshor t(left (left panel) panel) and 231" andlong long day day conditions conditions (right (right panel). panel). We We show show the the median median forcingforcing requiremrequirementsents (accumulated degree 232" (accumulateddays >0°C outdoors degree days and >0°Cin a climate outdoors chamber) and in a climate± 95% CIchamber) until leaf ± 95%-out CIunder until three leaf -differentout 233" underchilling three levels different for NA chilling (N = levels 49 species), for NA EU(N =(N 49 = species), 34), and EU EA ( N(N = = 34 78)), and species, EA (N when = 78 )twigs were 234" species,exposed when to 8h twigs (left were panel) exposed or 16 hto day 8h (leftlength panel) (right or panel)16 h day in lengththe climate (right chamber.panel) in the 235" climate chamber. 236" 237" 238" 239" 240" 241"

242" 243" 244" 245" 246" 247" 248" 249" 250" 251"

107 15 201" 543" 10 544" 202" NA - EA 5 NA - EU EU - EA 545" Adoxaceae 0 546" 203" Anacardiaceae 547" -5 Coefficient estimate Coefficient 204" Berberidaceae 548" -10 -15 Betulaceae Anacardiaceae 5 10 15 205" Bignoniaceae Location Aquifoliaceae Caprifoliaceae Aristolochiaceae 206" Celastraceae Berberidaceae 207" Cornaceae Betulaceae Leaf Cupressaceae Bignoniaceae

Caprifoliaceae- 208" Ericaceae leafout ENA out difference 10 Cornaceae Fabaceae 10 leafout Cupressaceae

209" Leaf Fagaceae 20 20 Ericaceae - 210" Grossulariaceae outdifference (days) Fabaceae 10

Hamamelidaceae Fagaceae- 211" 10 EA

Hydrangeaceae 0 Hamamelidaceae 212" Juglandaceae Hydrangeaceae family_num -10 0 Lauraceae Juglandaceae 20 Lauraceae 213" Magnoliaceae -20 family_num

-10 Magnoliaceae Malvaceae 214" No Moraceae Moraceae shared 20 species -20 Oleaceae 215" Oleaceae Pinaceae 216" Pinaceae Ranunculaceae Ranunculaceae Rosaceae Rosaceae Rutaceae 217" 54930" Salicaceae Rutaceae 550" 5 10 15 218" Sapindaceae Salicaceae 551" garden_num Saxifragaceae Sapindaceae 552" 219" Styracaceae Saxifragaceae 553" Tilaceae 220" Ulmaceae 554" Ulmaceae Vitaceae 221" 30 Vitaceae Adoxaceae

15 5 10AA Berlin Mort. Mun. Ott. USNA15 222" 543" 10 garden_num (days) 223" 544 " 224" 545" 5 0 225" 546" out difference out 547- " -5 226" estimate Coefficient 548" -10 227" Mean leaf Mean -15 26 23 15 15 8 4 13 13 10 10 5 14 14 11 15

Anacardiaceae

228" 5 10 15 AA AA AA

rton Locationrton rton Aquifoliaceae nich nich nich erlin erlin erlin SNA SNA SNA ttawa ttawa ttawa B B B U U U Mo Mo Mo O O O 229" Mu Mu Mu Aristolochiaceae 230" Extended Data Figure 9 | Family-level differences in the timing of spring leafBerberidaceae unfolding 231" Betulaceae between North America (NA), East Asia (EA), and Europe (EU). a, Heat maps showing 232" BignoniaceaeLeaf

within-family differences in leaf out times monitored at six gardens (see Fig. 2)Caprifoliaceae between- NA and 233" ENA out difference Cornaceae EA species10 (left panel), NA and EU (middle panel), and EUleafout and EA species. Each contrast Cupressaceae contains at least two species per continent. b, Mean within-family differences in leaf-out dates (± 20 Ericaceae confidence intervals) between NA and EA species (left panel), NA and EU (middleFabaceae panel), and 10

Fagaceae- EU and EA species. Number of within-family contrasts available for each garden is shownEA above

0 Hamamelidaceae garden names. Hydrangeaceae

family_num -10 Juglandaceae 20 -20 Lauraceae 108 Magnoliaceae

Moraceae

Oleaceae

Pinaceae

Ranunculaceae

Rosaceae

Rutaceae 54930" Salicaceae 550" 5 10 15 Sapindaceae 551" garden_num Saxifragaceae 552" Styracaceae 553" Tilaceae 554" Ulmaceae

Vitaceae

Adoxaceae

AA Berlin Mort. Mun. Ott. USNA 94" Growth habit a Growth habit Shrub Tree Shrub Tree STV STV STV STV

< 1.4 ≥ 1.4 < 1.2 ≥ 1.2

MAT MAT MAT MAT

< 9.7°C ≥ 9.7°C < 13.2°C ≥ 13.2°C

140 < 13.2°C ≥ 13.2°C 140

120 120 100 100 out (DOY) out - 80 80 Leaf 60 N = 60 88 44 41 37 96 60 94" 8 8 8 8 8 8 8 8 b 6 6 6 6 6 6 6 6 4 4 4 4 4 4 4 4 2 2 2 2 2 2 2 2 0 0 0 0 0 0 0 0 2 2 2 2 2 2 2 2 4 4 4 4 4 4 4 4

Coefficient estimate 6 6 6 6 Coefficient estimate 6 6 6 6 88 8 8 8 8 8 8 STVSTV WTWT TSTS STVSTV WTWT TSTS STVSTV WTWT TS TS STV STV WT WT TS TS AAAA BeBerlinrlin MoMortonrton MunichMunich 9595"" 9696"" Extended Extended DataData Figure Figure 10 10 | The | The effect effect of climate of climate on species on species-specific-specific leaf-out leaf times.-out a,times . a, 97" ExtendedRecursive partitioning Data Figure tree for 10 the | relationshipThe effect between of climate climate on parameters species and-specific species- specificleaf-out times. a, 97" Recursive partitioning tree for the relationship between climate parameters and species- 98" Recursiveleaf-out dates partitioning (day-of-year, DOY)tree for observed the relationship in Munich in 366between temperate three woody climate species. parameters Sprig and species- 98" specifictemperature leaf variability-out dates (STV), (day-of mean-year, annual DOY temperature) observed (MAT), in Munich temperature in 366 temperateseasonality woody (TS), 99" specificgrowth habit, leaf and-out leaf times persistence (Munich were data)evaluated in 366as potential temperate split points. woody Number species. of species Sprig temperature 99" species. Sprig temperature variability (STV), mean annual temperature (MAT), temperature contained in each terminal node shown below boxplots. b, Coefficient values (effective posterior variability (STV), mean annual temperature (MAT), temperature seasonality (TS), growth 100100"" seasonalitymeans and 95% (TS), credible growth intervals) habit, and for theleaf relationship persistence between were evaluated three climate as potentialparameters split and points.leaf- out times monitored at four gardens. Sample sizes: Arnold Arboretum (AA), 822 species; Berlin 101101"" Numberhabit, and of speciesleaf persistence contained inwere each evaluated terminal node as potential shown below split boxplots points.. Numberb, Coefficient of species Botanical Garden (Berlin), 627 species; Morton Arboretum (Morton), 354 species; Munich 102102"" valuescontainedBotanical (effective Garden in each (Munich),posterior terminal 366means species.node and shown 95%Models credible below include intervals) graphs.phylogenetic b,for Coefficient autocorrelationthe relationship values and between fixed (effective tree and evergreen effects. Values reflect standardized data and can be interpreted as relative 103103"" threposteriore climate means parameters and 95% and credibleleaf-out times intervals) monitored for theat four relationship gardens. Sample between sizes: three Arnold climate effect sizes. 104" Arboretum (AA), 822 species; Berlin Botanical Garden (Berlin), 627 species; Morton 104" parameters and leaf-out times monitored at four gardens. Sample sizes: Arnold Arboretum 105" Arboretum (Morton), 354 species; Munich Botanical Garden (Munich), 366 species. Models 105" (AA),109 822 species; Berlin Botanical Garden (Berlin), 627 species; Morton Arboretum 106" include phylogenetic autocorrelation and fixed tree and evergreen effects. Values reflect 106" (Morton), 354 species; Munich Botanical Garden (Munich), 366 species. Models include 107" standardized data and can be interpreted as relative effect sizes. 107" phylogenetic autocorrelation and fixed tree and evergreen effects. Values reflect standardized

108" data and can be interpreted as relative effect sizes. 45 2.0 20 1.8 40 15 1.6 35 1.4 10 TS (°C) MAT (°C) MAT 1.2 30 5 STV (standard deviation) 1.0 25 0.8 0

America EU EA America EU EA America EU EA

Continent Continent Continent

Extended Data Figure 11 | The temperature regimes experienced by species native to North America (NA, N = 431), Europe (EU, n= 237), or East Asia (EA, n = 929). Boxplots show 50% quantiles of species’ spring temperature variability (STV), mean annual temperature (MAT), and temperature seasonality (TS). STV as standard deviation of minimum spring temperatures from 1901 to 2013, MAT in °C (BIO1), TS as temperature difference between the warmest and coldest month in °C (BIO7).

110 AA Berlin AA Berlin AA Berlin a 18/211 23/126 18/148 23/95 18/610 23/401 L

Al1l 1 1 e -12.0 - 12.4 - 6.7 -8.0 -6.3 -4.8 10 a f -

fill fill fill o u 2 2 2 t Trees 10 10 5

10 d i 5 5 5 ff e r y y y 3 3 3 Shrubs -12.9 - 6.2 0-3.3 -0.2 0 - 5.7 1.5 0 0 e n

c -5 -5 -5 e

Deciduous4 4 4 -5 ( -10.6 -12.5 -10-3.0 -6.0 -10-3.7 -3.3 -10 d

a ys

-10 ) Evergreen5 5 5

0.5 1.0 1.5 2.0 2.50.5 1.0 1.5 2.0 2.50.5 1.0 1.5 2.0 2.5 b 16 x x x WNA - ENA WNA - EU WNA - EAAS

12 8 4 0 -4 -8 Coefficient estimate Coefficient -12 -16 61" 62" ExtendedExtended Data Data Figure Figure 12 7 | Leaf--outout contrastscontrasts between between western western North North American American (WNA) (WNA) and Fig. S6. Leaf-out contrasts between Western North American (WNA) and Eastern North 63" easternand eastern North North American American (ENA), (ENA), European European (EU), and(EU), East and Asian Asian (EA) (AS) species. species. a, aHeat, Heat maps American (ENA), European (EU), and Asian (AS) species. (A) Heat maps for the difference 64" formaps thein for speciesdifference the- leveldifference in leaf species-out indates species-level between leaf-level -WNAout leaf dates and-out ENA between dates species between WNA (left panel) and WNA ,ENA WNA and species and ENA EU (left species panel), (left 65" WNApanel),species and WNA EU (middle speciesand panel) EU (middle species, and WNA panel),(middle and AS and panel), species WNA (rightand and WNApanel) EA species monitoredand AS (right speciesat two panel) gardens (right monitored panel) at two 66" gardensmonitoredwhen when all at species, twoall species, gardens or only orshrubs when only / deciduousallshrubs species, / deciduousspecies or only were species shrubsincluded. w/ Sampledeciduousere included. sizes speciesfor Sampleeach were sizes included. for each continent at the respective garden are shown below garden names. Contrasts with sample 67" continentSample sizes at the for respective each continent garden atare the shown respective below garden garden arenames. shown Contrasts below gardenwith sample names. sizes sizes below 10 species per continent (trees and evergreen species) are not shown (grey fields below 10 species per continent (trees and evergreen species) are not shown (grey fields in heat 68" Contrastsin heat withmap) .sampleLeaf-out sizesdates wbelowere observed 10 species in 2012 per and continent came from Panchen(trees and et al. evergreen 2014 (see species) are map). Leaf-out dates were observed in 2012 and came from Panchen et al.17 (see Extended Data 69" not shownTable Sx (grey). AA: fields Arnold in Arboretum, heat map). Boston, Leaf MA,-out USA; dates Berlin: were Botanic observed Garden in 2012and Botanical and came from Table 3). AA: Arnold Arboretum, Boston, MA, USA; Berlin: Botanic Garden and Botanical 70" PanchenMuseum et al.Berlin (2014-Dahlem,) (see Berlin, Table Germany. S3). AA:(B) ArnoldCoefficient Arboretum, values (effective Boston, posterior MA, means USA; Berlin: Museum[EPM Berlin] and 95%-Dahlem, credible Berlin, intervals Germany.) for differences b, Coefficient in leaf-out dates values between (effective WNA andposterior ENA means and 71" Botanic Garden and Botanical Museum Berlin-Dahlem, Berlin, Germany. b, Coefficient 95% species,credible WNA intervals) and EU for species, differences and WNA in and leaf AS-out species dates. Models between include WNA phylogenetic and ENA species, WNA 72" values (effective posterior means and 95% credible intervals) for differences in leaf-out dates and EUautocorrelation species, and and WNA fixed tree, and evergreen, EA species. and gymnospermModels include effects. phylogenetic Values reflect autocorrelation and 73" betweenstandardized WNA dataand and ENA can species,be interpreted WNA as relative and EU effect species, sizes. and WNA and AS species. Models fixed tree, evergreen, and gymnosperm effects. Values reflect standardized data and can be 74" include phylogenetic autocorrelation and fixed tree, evergreen, and gymnosperm effects. interpreted as relative effect sizes. 75" Values reflect standardized data and can be interpreted as relative effect sizes.

76"

77" 78" 79" 80" 81" 82" 111 83" 84" Intermediate Intermediate None High None 2000 2000 2000 2000 2000 Abies alba Abies homolepis Acer barbinerve Acer campestre Acer ginnala

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

●

● ● 500 500 500 500 500 ● ● ● NL ● ● ● ● ● ● ● ● 0 0 0 0 0 Laube et al. 2014 Laube et al. 2014 This study This study This study

Intermediate Intermediate High High High 2000 2000 2000 2000 2000 Acer negundo Acer platanoides Acer pseudoplatanus Acer rubrum Acer saccharinum

1500 1500 1500 1500 1500

NL NL ● 1000 1000 1000 1000 1000

●

● ● ● ● ● 500 500 500 500 NL NL ● 500 ●

● ● 0 0 0 0 0 Laube et al. 2014 This study Laube et al. 2014 Polgar 2014 Polgar 2014

High Intermediate High Intermediate High 2000 2000 2000 2000 2000 Acer saccharum Acer tataricum Aesculus flava Aesculus hippocastanum Aesculus parviflora

1500 1500 1500 ● 1500 1500

● 1000 1000 1000 1000 1000

NL ● ● ● ● ● ● 500 500 500 500 ● 500 ● ● ● ● ● 0 0 0 0 0 Laube et al. 2014 Laube et al. 2014 This study This study This study

Intermediate None High Intermediate Intermediate 2000 2000 2000 2000 2000 Alnus incana Alnus maximowiczii Alnus serrulata Amelanchier alnifolia Amelanchier florida

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ● ● ● ● NL NL ● ● 500 ● 500 500 500 ● 500 ● ● ●

0 0 0 0 0 This study This study Polgar 2014 This study This study

Intermediate None High None None 2000 2000 2000 2000 2000 Amelanchier laevis Amorpha fruticosa Aronia arbutifolia Aronia melanocarpa Berberis dielsiana

1500 1500 1500 1500 1500

● 1000 1000 1000 1000 1000

●

● ● 500 ● ● 500 500 500 500 ● ● ● ● ● ● ● ● ● 0 0 0 0 0 This study Laube et al. 2014 Polgar 2014 This study This study

Intermediate None High High High 2000 2000 2000 2000 2000 Berberis thunbergii Berberis vulgaris Betula lenta Betula nana Betula papyrifera

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ● ● ● ● ● 500 500 ● 500 500 500 ● ● ● ● ● ● ●

0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 Polgar 2014 Polgar 2014 This study This study Polgar 2014 132" 133" FigureExtended S11 partialData Figure 13 partial 134" 135" 112 136" IntermediateIntermediate None NoneNone NoneNone NoneNone 20002000 20002000 20002000 20002000 20002000 BetulaBetula pendulapendula BetulaBetula populifoliapopulifolia BuddlejaBuddleja albifloraalbiflora BuddlejaBuddleja alternifoliaalternifolia BuddlejaBuddleja davidiidavidii

15001500 15001500 15001500 15001500 15001500

10001000 10001000 10001000 10001000 10001000

500500 500500 ● 500500 500500 500500 ●● ● ●● ●● ● ●● ●● ● ● ● ●● ●● ●● ●● ●● ● 00 00 00 00 00 LaubeLaube etet al.al. 20142014 ThisThis studystudy ThisThis study study ThisThis study study ThisThis study study

None IntermediateIntermediate IntermediateIntermediate NoneNone HighHigh 20002000 20002000 20002000 20002000 20002000 CaraganaCaragana pygmaeapygmaea CarpinusCarpinus betulusbetulus CarpinusCarpinus laxifloralaxiflora CarpinusCarpinus monbeigianamonbeigiana CaryaCarya cordiformiscordiformis

15001500 15001500 15001500 15001500 15001500

10001000 10001000 10001000 10001000 10001000 ●● ●●

●● ●●

●● ●● ●● 500 500 ●● 500 ● 500 ●● 500 500 500 ●● 500 ● 500 500 ●●

●● ●● ●● 00 00 00 00 00 ThisThis studystudy ThisThis studystudy ThisThis study study ThisThis study study ThisThis study study

High High HighHigh NoneNone IntermediateIntermediate 20002000 20002000 20002000 20002000 20002000 CaryaCarya glabraglabra CaryaCarya laciniosalaciniosa CaryaCarya ovataovata CastaneaCastanea sativasativa CedrusCedrus libanilibani ●●

●● 15001500 15001500 ●● 15001500 15001500 15001500

●● 10001000 10001000 10001000 10001000 10001000

●● ●● ●● ●● ●● ●● ●● ●● 500500 NL NL ●● 500500 500500 500500 500500

00 00 00 00 00 PolgarPolgar 20142014 ThisThis studystudy ThisThis study study ThisThis study study ThisThis study study

IntermediateIntermediate IntermediateIntermediate HighHigh HighHigh IntermediateIntermediate 20002000 20002000 20002000 20002000 20002000 CelastrusCelastrus orbiculatusorbiculatus CeltisCeltis caucasicacaucasica CeltisCeltis laevigatalaevigata CeltisCeltis occidentalisoccidentalis CephalanthusCephalanthus occidentalisoccidentalis

15001500 15001500 15001500 15001500 15001500

10001000 10001000 10001000 10001000 ●● 10001000 ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● ●● 500500 500500 ●● 500500 500500 500500

00 00 00 00 00 PolgarPolgar 20142014 ThisThis studystudy ThisThis study study ThisThis study study ThisThis study study

IntermediateIntermediate High HighHigh NoneNone HighHigh 20002000 20002000 20002000 20002000 20002000 CercidiphyllumCercidiphyllum japonicumjaponicum CercidiphyllumCercidiphyllum magnificummagnificum CercisCercis canadensiscanadensis CercisCercis chinensischinensis CladrastisCladrastis lutealutea

15001500 15001500 15001500 15001500 15001500

10001000 10001000 10001000 10001000 10001000 ●● ●●

●● ●● ●● ●● ●● ●● ● ●● ● ●● 500500 ●● ●● 500500 ●● 500500 500500 500500 ●●

00 00 00 00 00 ThisThis studystudy ThisThis studystudy ThisThis study study ThisThis study study ThisThis study study

High High IntermediateIntermediate IntermediateIntermediate NoneNone 20002000 20002000 20002000 20002000 20002000 ClethraClethra alnifoliaalnifolia ComptoniaComptonia peregrinaperegrina CornusCornus albaalba CornusCornus amomumamomum CornusCornus kousakousa

15001500 15001500 15001500 15001500 15001500

10001000 10001000 10001000 10001000 10001000 NL ●● ●● ●● NL ●● ●● ●● ●● ●● ●● 500500 ●● 500500 ●● 500500 ●● 500500 ●● 500500

00 C1 C2 C3 0 0 C1 C2 C3 0 0 C1 C2 C3 0 0 C1 C2 C3 0 0 C1 C2 C3 137" Polgar Polgar 20142014 PolgarPolgar 20142014 ThisThis study study PolgarPolgar 2014 2014 ThisThis study study 138" FigureExtended S11 continuedData Figure 13 continued 139" 140" 113 141" None None None Intermediate None 2000 2000 2000 2000 2000 Cornus mas Corylopsis sinensis Corylopsis spicata Corylus americana Corylus avellana

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

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● ● 500 500 ● ● 500 ● 500 500 ● ● ● ● ● ● ● ● ● 0 0 0 0 0 Laube et al. 2014 This study This study Polgar 2014 This study

Intermediate Intermediate Intermediate None None 2000 2000 2000 2000 2000 Corylus heterophylla Corylus sieboldiana Decaisnea fargesii Deutzia gracilis Deutzia scabra

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

●

● ●

500 500 500 ● 500 500 ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 This study This study This study This study This study

None None None None None 2000 2000 2000 2000 2000 Elaeagnus umbellata Elaeagnus ebbingei Eleutherococcus senticosus Eleutherococcus setchuensis Eleutherococcus sieboldianus

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 ● 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 Polgar 2014 This study This study This study This study

Intermediate Intermediate High High High 2000 2000 2000 2000 2000 Euonymus alatus Euonymus europaeus Euonymus latifolius Fagus crenata Fagus engleriana

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ● ● NL ● ● ● 500 ● 500 ● 500 500 500 ● ● ● ● ●

0 0 0 0 0 Polgar 2014 This study This study This study This study

High High High None None 2000 2000 2000 2000 2000 Fagus grandifolia Fagus orientalis Fagus sylvatica Forsythia ovata Forsythia suspensa

1500 1500 1500 1500 1500

● NL NL ● 1000 1000 1000 1000 1000 ●

●

● 500 500 ● 500 500 500 ● ● ● ● ● ● ● 0 0 0 0 0 Polgar 2014 This study This study This study This study

Intermediate Intermediate None Intermediate High 2000 2000 2000 2000 2000 Fraxinus americana Fraxinus chinensis Fraxinus excelsior Fraxinus latifolia ● Fraxinus ornus

1500 1500 1500 1500 1500 ●

1000 1000 1000 1000 1000 ● ● ● ● NL ● ●

● ● 500 500 ● 500 ● 500 500 ● ●

0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 142" Polgar 2014 Laube et al. 2014 Laube et al. 2014 This study This study 143" FigureExtended S11 continuedData Figure 13 continued 144" 145" 114 146" None High Intermediate Intermediate High 2000 2000 2000 2000 2000 Fraxinus pensylvanica Gaylussacia baccata Ginkgo biloba Hamamelis japonica Hamamelis vernalis

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ● ● ● ●

● ● ● ● 500 ● 500 ● 500 500 500 ● ● ● ● ●

0 0 0 0 0 Laube et al. 2014 Polgar 2014 This study This study This study

High None None Intermediate Intermediate 2000 2000 2000 2000 2000 Hamamelis viginiana Heptacodium miconioides Hibiscus syriacus Hydrangea arborescens Hydrangea involucrata

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ● ● ● ● ●

500 ● 500 500 ● 500 500 ● ● ● ● ● ● ● ● 0 0 0 0 0 Polgar 2014 This study This study This study This study

Intermediate Intermediate Intermediate Intermediate Intermediate 2000 2000 2000 2000 2000 Hydrangea serrata Juglans ailantifolia Juglans cinerea Juglans regia Kalmia angustifolia

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ● ● ● ● 500 500 500 ● 500 ● 500 NL ● ● ● ● ● ●

0 0 0 0 0 This study Laube et al. 2014 Laube et al. 2014 Laube et al. 2014 Polgar 2014

High None Intermediate Intermediate None 2000 2000 2000 2000 2000 Kalmia latifolia Larix decidua Larix gmelinii Larix kaempferi Ligustrum compactum

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 NL NL ●

●

500 500 500 500 ● 500 ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 Polgar 2014 Laube et al. 2014 This study This study Polgar 2014

None None High None Intermediate 2000 2000 2000 2000 2000 Ligustrum ibota Ligustrum tschonoskii Lindera benzoin Liquidambar orientalis Liquidambar styraciflua

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 NL NL ● ●

● ● 500 500 500 500 ● 500 ● ● ● ● ● ● ●

● 0 0 0 0 0 Polgar 2014 This study Polgar 2014 This study This study

Intermediate None None None None 2000 2000 2000 2000 2000 Liriodendron tulipifera Lonicera alpigena Lonicera caerulea Lonicera maackii Lonicera maximowiczii

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● 500 500 500 500 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 147" This study This study This study Polgar 2014 This study 148" FigureExtended S11 continuedData Figure 13 continued 149" 150" 151" 115 None Intermediate None High Intermediate 2000 2000 2000 2000 2000 Lonicera subsesillis Malus domestica Metasequoia glyptostroboides Myrica pensylvanica Nothofagus antarctica

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ●

● ● 500 500 500 ● 500 500 ● ● ● ● ● ● ● ● ● 0 0 0 0 0 Polgar 2014 Polgar 2014 This study Polgar 2014 This study

High None Intermediate None High 2000 2000 2000 2000 2000 Nyssa sylvatica Oemleria cerasiformis Orixa japonica Ostrya carpinifolia Ostrya virginiana

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ●

NL NL ●

● ● ● ● 500 500 500 500 ● 500 ● ● ● ● ● ●

0 0 0 0 0 Polgar 2014 This study This study This study This study

None None None None Intermediate 2000 2000 2000 2000 2000 Paeonia rockii Parrotia persica Parrotiopsis jaquemontiana Photinia villosa Picea abies

1500 1500 1500 1500 1500

● 1000 1000 1000 1000 1000 ● ●

500 500 ● 500 ● ● 500 ● 500 ● ● ● ● ● ● ● ● 0 0 0 0 0 This study This study This study This study This study

None None Intermediate None High 2000 2000 2000 2000 2000 Pinus nigra Pinus strobus Pinus sylvestris Pinus wallichiana Populus grandidentata

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

NL NL ●

500 ● ● 500 500 ● 500 500 ● ● ● ● ● ● ● ● ● 0 0 0 0 0 Laube et al. 2014 Laube et al. 2014 Laube et al. 2014 Laube et al. 2014 Polgar 2014

None High None None Intermediate 2000 2000 2000 2000 2000 Populus koreana Populus tremula Prinsepia sinensis Prinsepia uniflora Prunus avium

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 ● 500 500 500 ● ● ● ● ● ● ● ● ● ● ● 0 0 0 ● ● ● 0 0 This study Laube et al. 2014 This study This study Laube et al. 2014

None None Intermediate Intermediate None 2000 2000 2000 2000 2000 Prunus cerasifera Prunus padus Prunus serotina Prunus serrulata Prunus tenella

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ● 500 500 500 500 ● 500 ● ● ● ● ● ● ● ● ● ● ●

0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 152" This study This study Laube et al. 2014 This study This study 153" FigureExtended S11 continuedData Figure 13 continued 154" 155" 156" 116 157" Intermediate None Intermediate None None 2000 2000 2000 2000 2000 Pseudotsuga menziesii Ptelea trifoliata Pyrus elaeagnifolia Pyrus pyrifolia Pyrus ussuriensis

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

● ● ● ● ● 500 500 500 500 ● ● 500 ● ● ● ● ● ● ● ● 0 0 0 0 0 Laube et al. 2014 This study This study This study This study

High High Intermediate High Intermediate 2000 2000 2000 2000 2000 Quercus alba Quercus bicolor Quercus robur Quercus rubra Quercus shumardii

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ●

NL NL ● ● NL ● ● ● ● 500 500 ● 500 ● 500 500 ● ● ●

0 0 0 0 0 Polgar 2014 Laube et al. 2014 This study Laube et al. 2014 This study

High Intermediate High None None 2000 2000 2000 2000 2000 Rhamnus alpina Rhamnus cathartica Rhamnus frangula Rhododendron canadense Rhododendron dauricum

1500 1500 1500 1500 1500

1000 ● 1000 1000 1000 1000 ●

● ● ● ● ● ● ● ● 500 ● 500 500 ● 500 ● 500 ● ●

0 0 0 0 0 This study This study Polgar 2014 This study This study

None High Intermediate Intermediate None 2000 2000 2000 2000 2000 Rhododendron mucronulatum Rhus typhina Ribes alpinum Ribes divaricatum Ribes glaciale

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ●

● ●

500 500 ● 500 500 500 ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 This study Polgar 2014 This study This study This study

Intermediate None None Intermediate None 2000 2000 2000 2000 2000 Robinia pseudoacacia Rosa hugonis Rosa majalis Rosa multiflora Salix gracilistyla

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

500 500 500 500 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 Laube et al. 2014 This study This study Polgar 2014 This study

High None Intermediate Intermediate High 2000 2000 2000 2000 2000 Salix repens Sambucus nigra Sambucus pubens Sambucus tigranii Sassafras albidum

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 NL NL ● ●

● 500 500 500 ● 500 ● 500 ● ● ● ● ● ● ● ●

0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 158" This study This study This study This study Polgar 2014 159" FigureExtended S11 continuedData Figure 13 continued 160" 161" 162" 117 163" None High High Intermediate Intermediate 2000 2000 2000 2000 2000 Sinowilsonia henryi Smilax rotundifolia Sorbus aria Sorbus commixta Sorbus decora

1500 1500 1500 1500 1500

● 1000 1000 NL NL ● 1000 1000 1000

● ●

● ● 500 ● 500 500 500 ● 500 ● ● ● ● ●

0 0 0 0 0 This study Polgar 2014 This study This study This study

None None None High None 2000 2000 2000 2000 2000 Spiraea canescens Spiraea chamaedryfolia Spiraea japonica Spiraea latifolia Stachyurus praecox

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

●

500 500 500 500 ● 500 ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 This study This study This study Polgar 2014 This study

Intermediate Intermediate None None None 2000 2000 2000 2000 2000 Stachyurus sinensis Symphoricarpos albus Syringa josikaea Syringa reticulata Syringa villosa

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000

●

● ●

500 500 ● 500 500 500 ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 This study Laube et al. 2014 This study This study This study

None High None Intermediate None 2000 2000 2000 2000 2000 Syringa vulgaris Tilia dasystyla Tilia japonica Tilia platyphyllos Toona sinensis

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ●

● ● ● ● ● ● ● 500 500 500 500 ● 500 ● ● ●

● ● ● 0 0 0 0 0 Laube et al. 2014 This study This study This study This study

High High Intermediate High High 2000 2000 2000 2000 2000 Ulmus amerciana Ulmus laevis Vaccinium angustifolium Vaccinium corymbosum Vaccinium pallidum

1500 1500 1500 1500 1500

1000 1000 ● 1000 1000 1000

● ● ● ● ● ● ● ● 500 ● 500 500 ● ● 500 500 ● ● ● 0 0 0 0 0 This study This study Polgar 2014 Polgar 2014 Polgar 2014

Intermediate High None Intermediate Intermediate 2000 2000 2000 2000 2000 Viburnum betulifolium Viburnum buddleifolium Viburnum carlesii Viburnum opulus Viburnum plicatum

1500 1500 1500 1500 1500

1000 1000 1000 1000 1000 ●

● ● ● ● ● 500 ● ● 500 NL ● 500 500 500 ● ● ● ● ●

0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 164" This study This study This study This study This study 165" FigureExtended S11 continuedData Figure 13 continued 166" 167" 168" 118 169" High High None None Intermediate 2000 High 2000 High 2000 None 2000 None 2000 Intermediate 2000 Viburnum recognitum 2000 Vitis aestivalis 2000 Weigela coraeensis 2000 Weigela florida 2000 Weigela maximowiczii Viburnum recognitum Vitis aestivalis Weigela coraeensis Weigela florida Weigela maximowiczii

1500 1500 1500 15001500 15001500

1000 1000 1000 10001000 10001000 ● ●

● ● ● ● ● ● ● ● ● ● 500 500 ● 500 500 500 500 ● 500 ● 500 ● 500 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 0 0 0 0 170" 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 0 C1 C2 C3 Polgar 2014 Polgar 2014 This study This study This study 171" Polgar 2014 Polgar 2014 This study This study This study None None None High None 2000 None 2000 None 2000 None 2000 High 2000 None 2000 Spiraea canescens 2000 Spiraea chamaedryfolia 2000 Spiraea japonica 2000 Spiraea latifolia 2000 Stachyurus praecox 172" FigExtendedure SSpiraea11 canescens |Data Chilling Figure requirements 13Spiraea | chamaedryfoliaChilling of requirements 215 temperateSpiraea japonica of woody215 temperate species.Spiraea latifolia Thewoody impo species.rtanceStachyurus praecoxTheof 1500 1500 1500 15001500 15001500 173" chillingimportance for subsequent of chilling bud for developmentsubsequent bud (none, development intermediate (none,, or intermediate, high chilling orrequirements high chilling) 1000 1000 1000 10001000 10001000 11 ● 174" wasrequirements) inferred from was twig inferred cutting from experiments twig cutting conducted experiments in Zohner conducted● & Renner in this 2016, study, Laube Laube et etal. al., , 500 500 500 500 ● 500 500 500 500 500 ● 500 ● ● ● ● ● ● ● ● ● 12 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 175" andand Polgar Polgar et et al. al 2014. (see (see label label below● below each each graph). graph). Graphs Graphs show show forcing forcing requirements requirements (median 0 0 0 00 00 This study This study This study Polgar 2014 This study 176" (mediangrowingThis study growing degree degreedays > 0°CdaysThis study outdoors >0°C outdoors and inThis climate andstudy in climatechamber chamber ±Polgar SD) 2014 until ± SD leaf) until-out leafThisin study 215-out woody in Intermediate Intermediate None None None 2000 Intermediate 2000 Intermediate 2000 None 2000 None 2000 None 2000 Stachyurus sinensis 2000 Symphoricarpos albus 2000 Syringa josikaea 2000 Syringa reticulata 2000 Syringa villosa 177" 215species woodyStachyurus at speciesthree sinensis different at three Symphoricarposcutting different albus dates cutting (this datesstudy:Syringa josikaea(Zohner C1 = 21 & Dec Renner 2013,Syringa reticulata 2016: C2 = C1 10 =Feb 21 2014,DecSyringa villosa C3 = 21 1500 1500 11 1500 15001500 15001500 178" 2013,March C2 2014; = 10 FebLaube 2014, et al C3. := C1 21 = March 14 Dec 2014; 2011, Laube C2 = et30 al. Jan 2014: 2012, C1 C3 = =14 14 Dec March 2011, 2012; C2 =Polg 30 ar et 1000 1000 1000 10001000 10001000 12 ● al. ; C1● = 9 Jan 2013, C2 = 17 Feb 2013, C3 = 22 March 2013). Lines in dark blue: species ● ● 179" Jan 2012, C3● = 14● March 2012; Polgar et al. 2014; C1 = 9 Jan 2013, C2 = 17 Feb 2013, C3 = 500 500 ● 500 500 500 500 500 ● 500 ● 500 ● 500 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● assigned to the category high ● chilling● requirements; blue: species assigned to the category 180" 22 March0 2013). Lines 0in dark blue: species0 assigned to the category00 high chilling00 intermediateThis study chilling requirements;LaubeLaube etet al.al. 20142014 light blue:ThisThis studystudy species assignedThisThis study studyto the category noThisThis studychilling study 181" requirements;None blue: species assignedHigh to the categoryNone intermediateIntermediateIntermediate chilling requirements;NoneNone light 2000 2000 2000 20002000 20002000 Syringa vulgaris Tilia dasystyladasystyla TiliaTilia japonicajaponica TiliaTilia platyphyllos platyphyllos ToonaToona sinensis sinensis 182" blue:requirements. species assigned NL indicates to the categorythat no leaf no- outchilling occurred require at thements repective. NL indicates cutting thatdate. no Some leaf -speciesout 1500 1500 1500 15001500 15001500 leafed out before the last cutting date (C3), and in this case we show the degree days required 183" occurred1000 at the repective1000 cutting date. Some1000 species leafed 10001000out before the last 1000cutting1000 date ● ●

● ● ● ● until leaf-out in the field for the same individuals. ● ● ● ● ● ● ● 184" (C3),500 and in this case we500 show the degree500 days required● until500 leaf-out● in the● field500 for● the same 500 500 ● 500 ● ● 500 ● 500 ● ● ●

● ● ● ● ● ● 185" individuals.0 0 0 00 00 Laube et al. 2014 ThisThis studystudy ThisThis studystudy ThisThis study study ThisThis study study

186" High High IntermediateIntermediate HighHigh HighHigh 2000 2000 2000 20002000 20002000 Ulmus amerciana Ulmus laevis VacciniumVaccinium angustifoliumangustifolium VacciniumVaccinium corymbosum corymbosum VacciniumVaccinium pallidum pallidum

187" 1500 1500 1500 15001500 15001500

● 188" 1000 1000 ● 1000 10001000 10001000 ● ●● ● ● ●● ● ●● ● ●● ● ● ● 500 ● 500 500 ●● ●● 500500 500500 189" ●● ● ●●

190" 0 0 0 00 00 This study ThisThis studystudy PolgarPolgar 20142014 PolgarPolgar 2014 2014 PolgarPolgar 2014 2014

Intermediate High None IntermediateIntermediate IntermediateIntermediate 191" 2000 2000 2000 20002000 20002000 Viburnum betulifolium Viburnum buddleifolium ViburnumViburnum carlesiicarlesii ViburnumViburnum opulus opulus ViburnumViburnum plicatum plicatum

192" 1500 1500 1500 15001500 15001500

●● 193" 1000 1000 1000 10001000 10001000

●● ● ● ● ●● ● ●● 500 ● ● 500 ●● 500 500500 500500 ●● ●● ● 194" ●● ●● ● 0 0 0 00 00 195" This study ThisThis studystudy ThisThis studystudy ThisThis study study ThisThis study study

196" 197" 198" 199" 119 200" Extended Data Table 1 | ANCOVA for the relationship between forcing requirements and continent (North America, Europe, East Asia), chilling treatment (C1 – C3), and habit (shrub, tree) [see Figs. 1 and S10]. *P < 0.05, **P < 0.01, ***P < 0.001.

N = 208 species F-value Continent F(2) = 86.4*** Chilling F(1) = 65.8*** Habit F(1) = 23.1*** Continent x Chilling F(2) = 12.4*** Continent x habit F(2) = 0.4 Chilling x habit F(1) = 1.4

120 211" Extended Data Table 2 | Mean leaf-out dates monitored at six gardens of species restricted

212" to North America (NA), Europe (EU), and Eastern Asia (EA), when all species or only trees / Extended Data Table 2 | Leaf-out times of North American (NA), European (EU) and East 213" shrubs / deciduous / evergreen species were included. 95% confidence intervals and sample Asian (EA) species. Mean leaf-out dates monitored at six gardens of species restricted to NA, 214" EU,sizes and shown EA, whenin brackets, all species respectively. or only AAtrees: Arnold / shrubs Arboretum, / deciduous Boston, / evergreen MA, USA; species Berlin were: 215" included.Botanical 95% Garden confidence and Botanical intervals Museum and sample Berlin -sizesDahlem, shown Berlin, in brackets, Germany; respectively. Morton: Morton AA: Arnold Arboretum, Boston, MA, USA; Berlin: Botanical Garden and Botanical Museum Berlin-Dahlem, 216" Arboretum, Lisle, IL, USA; Munich: Munich Botanical Garden, Munich, Germany; Ottawa: Berlin, Germany; Morton: Morton Arboretum, Lisle, IL, USA; Munich: Munich Botanical 217" Ottawa Arboretum, Ottawa, Canada; USNA: US National Arboretum, Washington, DC and Garden, Munich, Germany; Ottawa: Ottawa Arboretum, Ottawa, Canada; USNA: US National 218" Arboretum,Beltsville, MD,Washington, USA. DC and Beltsville, MD, USA.

AA Berlin Morton Munich Ottawa USNA NA All 106.1 106.8 92.5 105.5 126.7 89.6 (1.6) (2.4) (2.2) (2.2) (2.7) (3.9) (291) (190) (137) (106) (90) (22) Trees 109.9 113.1 94.8 110.1 128.4 92.8 (1.9) (2.7) (2.9) (2.6) (2.8) (4.7) (166) (106) (96) (63) (73) (12) Shrubs 101.3 98.8 87.1 98.7 119.6 85.8 (2.8) (3.6) (2.5) (2.9) (7.2) (6.0) (125) (84) (41) (43) (17) (10) Deciduous 103.0 105.4 91.1 104.7 125.0 89.7 (1.4) (2.3) (2.0) (2.1) (2.7) (4.1) (249) (175) (125) (101) (78) (21) Evergreen 125.2 126.5 106.8 121.8 141.7 89.0 (5.2) (11.4) (11.5) 3.9 (3.1) (-) (42) (14) (12) (5) (11) (1) EU All 99.8 102.6 92.2 98.5 122.0 91.9 (3.1) (3.3) (3.6) (2.8) (8.1) (5.2) (148) (95) (38) (85) (21) (10) Trees 106.0 109.9 95.7 106.0 126.6 93.5 (4.4) (4.3) (4.4) (2.9) (7.3) (5.9) (46) (51) (26) (38) (17) (8) Shrubs 93.4 94.0 84.5 92.4 102.2 85.5 (3.6) (4.0) (3.6) (3.7) (21.7) (6.8) (45) (44) (12) (47) (4) (2) Deciduous 96.1 100.6 90.5 97.5 115.6 91.9 (2.7) (3.5) (3.6) (2.8) (8.5) (5.2) (74) (81) (32) (78) (16) (10) Evergreen 115.8 114.0 101.0 110.3 142.2 - (8.2) (8.6) (10.6) (10.0) (3.5) (-) (17) (14) (6) (7) (5) (0) EA All 99.4 99.4 86.4 94.4 116.0 82.4 (1.2) (1.7) (1.5) 1.4 (4.4) (1.7) (610) (401) (206) (295) (57) (139) Trees 103.7 107.0 89.4 103.3 116.9 83.9 (1.7) (2.2) (2.4) (2.0) (4.8) (2.2) (283) (193) (115) (100) (48) (81) Shrubs 95.8 92.3 82.6 89.8 111.0 80.3 (1.7) (2.1) (1.1) (1.6) (11.4) (2.7) (327) (208) (91) (195) (9) (58) Deciduous 96.8 97.9 85.0 94.0 114.2 81.6 (1.2) (1.6) (1.2) (1.4) (4.4) (1.7) (525) (367) (190) (279) (53) (132) Evergreen 116.4 115.3 103.5 100.2 140.2 97.0

121 Extended Data Table 3 | Contrasting leaf-out times of eastern and western North American species. Mean leaf-out dates monitored at two gardens of species restricted to western North America and eastern North America, when all species or only trees / shrubs / deciduous / evergreen species were included. 95% confidence intervals and sample sizes shown in brackets, respectively. AA: Arnold Arboretum, Boston, MA, USA; Berlin: Botanical Garden and Botanical Museum Berlin-Dahlem, Berlin, Germany.

AA Berlin All 93.1 94.6 (5.5) (6.1) (18) (23) Trees 103.5 97.8 (7.7) (12.7) (4) (5) Western Shrubs 90.1 93.8 North (5.9) (7.1) America (14) (18) Deciduous 93.1 94.6 (5.5) (6.1) (18) (23) Evergreen - - (-) (-) (0) (0) All 105.1 107.0 (1.7) (2.6) (211) (126) Trees 106.8 112.8 (1.7) (2.5) (115) (70) Eastern Shrubs 103.0 100.0 North (3.1) (4.4) America (96) (56) Deciduous 103.7 107.1 (1.5) (2.7) (194) (124) Evergreen 121.2 105.5 (9.5) (48.1) (17) (2)

122 Extended Data Table 4 | Results of twig cutting experiments to study the relative

233" importanceExtended Data of Table chilling 4 | Results in 215 oftemperate twig cutting woody experiments species. to Continent: study the relative continent a species is native 234" toimporta (NA =nce North of chilling America, in 215 SA temperate = South woodyAmerica, species. EU Continent:= Europe, cont EAinent = East a species Asia, isWA = West 235" Asia).native toChilling: (NA = North classification America, SA of =species South America, according EU to = chillingEurope, ErequirementsA = East Asia ,(see WA Methods= for 236" classificationWest Asia). Chilling: rules). classification Species for of which species the according results ofto chillingthe twig requirements cutting experiments (see come from 237" Methods for classification rules). Species for which the results of the twig cutting experiments previous studies are indicated by superscripts: (1) data from Laube et al.11 and (2) data from 238" come from previous studies are indicated by superscripts: (1) data from Laube et al.11 and (2) Polgar et al.12. C1 / C2 / C3: median forcing requirements (growing degree days >0°C outdoors 239" data from Polgar et al.12. C1 / C2 / C3: median forcing requirements (growing degree days 240" and>0°C in outdoors climate and chamber) in climate until chamber) leaf-out until for leaf the-out three for thedifferent three different chilling chilling treatments C1 (short 241" chilling),treatments C2C1 (short(intermediate chilling), chillinC2 (intermediateg), and C3 chilling), (long chilling). and C3 (long NL: chilling). no leaf -NL:out nowithin leaf- study period. 242" Forout withingraphic study representation period. For graphic see Extended represent ationData see Fig. Extended 13. Data Fig. 13. Genus Species Continent Chilling C1 / C2 / C3 Abies alba EU Intermediate1 510 / 300 / 375 Abies homolepis EA Intermediate1 NL / 360 / 375 Acer barbinerve EA None 307 / 272 / 309 Acer campestre EU High 757 / 476 / 377 Acer ginnala EA None 368 / 393 / 429 Acer negundo NA Intermediate1 610 / 210 / 200 Acer platanoides EU Intermediate 809 / 527 / 483 Acer pseudoplatanus EU High1 665 / 480 / 340 Acer rubrum NA High2 NL / NL / 430 Acer saccharinum NA High2 NL / NL / 1046 Acer saccharum NA High1 NL / 690 / 360 Acer tataricum EA Intermediate1 340 / 240 / 250 Aesculus flava NA High 1421 / 1132 / 622 Aesculus hippocastanum EU Intermediate 587 / 527 / 498 Aesculus parviflora NA High 737 / 604 / 514 Alnus incana EU Intermediate 700 / 511 / 454 Alnus maximowiczii EA None 700 / 665 / 638 Alnus serrulata NA High2 NL / NL / 540 Amelanchier alnifolia NA Intermediate 632 / 445 / 392 Amelanchier florida NA Intermediate 586 / 445 / 377 Amelanchier laevis NA Intermediate 1240 / 509 / 482 Amorpha fruticosa NA None1 350 / 550 / 525 Aronia arbutifolia NA High 660 / 402 / 298 Aronia melanocarpa NA None 327 / 314 / 368 Berberis dielsiana EA None 188 / 285 / 306 Berberis thunbergii EA Intermediate2 374 / 314 / 276 Berberis vulgaris EU None2 352 / 402 / 452 Betula lenta NA High 737 / 618 / 498 Betula nana EU High 719 / 716 / 604 Betula papyrifera NA High2 660 / 446 / 298 Betula pendula EU Intermediate1 300 / 210 / 190 Betula populifolia NA None 368 / 391 / 412 Buddleja albiflora EA None 150 / 225 / 300 Buddleja alternifolia EA None 150 / 299 / 355 Buddleja davidii EA None 155 / 300 / 354 Caragana pygmaea EA None 111 / 225 / 207 Carpinus betulus EU Intermediate 719 / 435 / 407 Carpinus laxiflora EA Intermediate 527 / 376 / 424 Carpinus monbeigiana EA None 527 / 450 / 549 Carya cordiformis NA High 1059 / 1040 / 718 Carya glabra NA High2 NL / NL / 452 Carya laciniosa NA High 1455 / 1203 / 709 Carya ovata NA High 1793 / 1552 / 777 Castanea sativa EU None 606 / 556 / 567 Cedrus libani WA Intermediate 574 / 558 / 498 Celastrus orbiculatus EA Intermediate2 682 / 534 / 584 Celtis caucasica EU Intermediate 820 / 492 / 443 Celtis laevigata NA High 898 / 781 / 509 Celtis occidentalis NA High 779 / 922 / 622 Cephalanthus occidentalis NA Intermediate 700 / 604 / 624 Cercidiphyllum japonicum EA Intermediate 645 / 423 / 420 243" " "

123 244" Extended Data Table 4. Continued Genus Species Continent Chilling C1 / C2 / C3 Cercidiphyllum magnificum EA High 617 / 445 / 344 Cercis canadensis NA High 827 / 699 / 638 Cercis chinensis EA None 488 / 558 / 704 Cladrastis lutea NA High 941 / 683 / 585 Clethra alnifolia NA High2 NL / 688 / 430 Comptonia peregrina NA High2 NL / 534 / 430 Cornus alba EA Intermediate 606 / 481 / 409 Cornus amomum NA Intermediate2 682 / 490 /430 Cornus kousa EA None 448 / 452 / 498 Cornus mas EU None1 280 / 230 / 230 Corylopsis sinensis EA None 448 / 393 / 459 Corylopsis spicata EA None 547 / 466 / 496 Corylus americana NA Intermediate2 748 / 402 / 386 Corylus avellana EU None 254 / 190 / 194 Corylus heterophylla EA Intermediate 725 / 398 / 358 Corylus sieboldiana EA Intermediate 601 / 319 / 328 Decaisnea fargesii EA Intermediate 568 / 393 / 420 Deutzia gracilis EA None 167 / 209 / 262 Deutzia scabra EA None 345 / 429 / 358 Elaeagnus ebbingei EA None 141 / 146 / 198 Elaeagnus umbellata EA None 352 / 314 / 298 Eleutherococcus senticosus EA None 300 / 302 / 297 Eleutherococcus setchuenenis EA None 408 / 393 / 420 Eleutherococcus sieboldianus EA None 316 / 256 / 328 Euonymus alatus EA Intermediate2 682 / 380 / 430 Euonymus europaeus EU Intermediate 468 / 452 / 381 Euonymus latifolius EU High NL / 525 / 377 Fagus crenata EA High 663 / 607 / 377 Fagus engleriana EA High 663 / 492 / 377 Fagus grandifolia NA High2 NL / NL / 1024 Fagus orientalis EU High 1079 / 766 / 508 Fagus sylvatica EU High 900 / 570 / 330 Forsythia ovata EA None 227 / 316 / 371 Forsythia suspensa EA None 330 / 256 / 297 Fraxinus americana NA Intermediate2 NL / 754 / 826 Fraxinus chinensis EA Intermediate1 490 / 450 / 390 Fraxinus excelsior EU None1 510 / 400 / 450 Fraxinus latifolia NA Intermediate 896 / 850 / 782 Fraxinus ornus EU High 1887 / 1381 / 689 Fraxinus pennsylvanica NA None1 360 / 375 / 430 Gaylussacia baccata NA High2 814 / 842 / 430 Ginkgo biloba EA Intermediate 809 / 604 / 585 Hamamelis japonica EA Intermediate 617 / 382 / 377 Hamamelis vernalis NA High 976 / 509 / 344 Hamamelis virginiana NA High2 792 / 842 / 430 Heptacodium miconioides EA None 227 / 316 / 345 Hibiscus syriacus EA None 347 / 452 / 622 Hydrangea arborescens NA Intermediate 709 / 350 / 377 Hydrangea involucrata EA Intermediate 601 / 382 / 344 Hydrangea serrata EA Intermediate 488 / 301 / 394 Juglans ailantifolia EA Intermediate1 625 / 400 / 335 Juglans cinerea NA Intermediate1 624 / 440 / 390 Juglans regia EU Intermediate1 580 / 550 /426 Kalmia angustifolia NA Intermediate2 NL / 424 / 584 Kalmia latifolia NA High2 NL / NL / 826 Larix decidua EU None1 250 / 200 / 190 Larix gmelinii EA Intermediate 267 / 209 / 176 Larix kaempferi EA Intermediate 663 / 461 / 392 Ligustrum compactum EA None2 352 / 314 / 298 Ligustrum ibota EA None2 352 / 314 / 298 Ligustrum tschonoskii EA None 130 / 242 / 317 Lindera benzoin NA High2 NL / NL / 900 Liquidambar orientalis WA None 247 / 393 / 498 Liquidambar styraciflua NA Intermediate 881 / 604 / 567 Liriodendron tulipifera NA Intermediate 737 / 332 / 317 Lonicera alpigena EU None 267 / 257 / 238 Lonicera caerulea EU None 111 / 151 / 151 Lonicera maackii EA None2 352 / 314 / 298 Lonicera maximowiczii EA None 130 / 183 / 177 Lonicera subsessilis EA None2 352 / 292 / 298 Malus domestica - Intermediate2 374 / 314 / 276 Metasequoia glyptostroboides EA None 307 / 362 / 408 Myrica pensylvanica NA High2 660 / 600 / 452 245"

124 246" Extended Data Table 4. Continued Genus Species Continent Chilling C1 / C2 / C3 Nothofagus antarctica SA Intermediate 587 / 452 / 409 Nyssa sylvatica NA High2 NL / NL / 730 Oemleria cerasiformis NA None 327 / 301 / 317 Orixa japonica EA Intermediate 606 / 527 / 498 Ostrya carpinifolia EU None 327 / 332 / 440 Ostrya virginiana NA High 867 / 525 / 297 Paeonia rockii EA None 347 / 347 / 358 Parrotia persica WA None 468 / 289 / 394 Parrotiopsis jacquemontiana EA None 468 / 362 / 454 Photinia villosa EA None 347 / 378 / 468 Picea abies EU Intermediate 1226 / 885 / 908 Pinus nigra EU None1 490 / 300 / 515 Pinus strobus NA None1 350 / 225 / 300 Pinus sylvestris EU Intermediate1 510 / 295 / 325 Pinus wallichiana EA None1 300 / 225 / 260 Populus grandidentata NA High2 NL / NL / 804 Populus koreana EA None 287 / 287 / 336 Populus tremula EU High1 460 / 400 / 305 Prinsepia sinensis EA None 73 / 74 / 74 Prinsepia uniflora EA None 141 / 162 / 161 Prunus avium EU Intermediate1 365 / 230 / 195 Prunus cerasifera WA None 227 / 301 / 317 Prunus padus EU None 347 / 332 / 336 Prunus serotina NA Intermediate1 460 / 200 / 195 Prunus serrulata EA Intermediate 508 / 466 / 420 Prunus tenella EU None 307 / 393 / 394 Pseudotsuga menziesii NA Intermediate1 510 / 355 / 360 Ptelea trifoliata NA None 663 / 542 / 622 Pyrus elaeagnifolia EU Intermediate 601 / 335 / 344 Pyrus pyrifolia EA None 287 / 423 / 420 Pyrus ussuriensis EA None 267 / 209 / 237 Quercus alba NA High2 NL / NL / 584 Quercus bicolor NA High1 510 / 440 / 310 Quercus robur EU Intermediate 509 / 430 / 374 Quercus rubra NA High1 NL / 540 / 340 Quercus shumardii NA Intermediate 985 / 604 / 549 Rhamnus alpina EU High 1040 / 623 / 427 Rhamnus cathartica EU Intermediate 694 / 461 / 474 Rhamnus frangula EU High2 814 / 688 / 430 Rhododendron canadense NA None 428 / 496 / 604 Rhododendron dauricum EA None 448 / 496 / 423 Rhododendron mucronulatum EA None 141 / 194 / 198 Rhus typhina NA High2 814 / 600 / 430 Ribes alpinum EU Intermediate 345 / 162 / 95 Ribes divaricatum NA Intermediate 663 / 256 / 237 Ribes glaciale EA None 167 / 162 / 147 Robinia pseudoacacia NA Intermediate1 375 / 225 / 290 Rosa hugonis EA None 224 / 209 / 237 Rosa majalis EU None 195 / 209 / 266 Rosa multiflora EA Intermediate2 374 / 314 / 276 Salix gracilistyla EA None 307 / 287 / 317 Salix repens EU High 820 / 557 / 377 Sambucus nigra EU None 327 / 242 / 293 Sambucus pubens NA Intermediate 450 / 209 / 286 Sambucus tigranii EU Intermediate 450 / 225 / 286 Sassafras albidum NA High2 NL / NL / 980 Sinowilsonia henryi EA None 428 / 393 / 498 Smilax rotundifolia NA High2 NL / NL / 1024 Sorbus aria EU High 1087 / 638 / 509 Sorbus commixta EA Intermediate 420 / 302 / 311 Sorbus decora NA Intermediate 632 / 382 / 410 Spiraea canescens EA None 300 / 225 / 276 Spiraea chamaedryfolia EA None 330 / 194 / 297 Spiraea japonica EA None 360 / 303 / 344 Spiraea latifolia NA High2 792 / 446 / 298 Stachyurus chinensis EA Intermediate 773 / 573 / 585 Stachyurus praecox EA None 307 / 301 / 394 Symphoricarpos albus NA Intermediate1 415 / 190 / 190 Syringa josikaea EU None 287 / 378 / 293 Syringa reticulata EA None 227 / 287 / 370 Syringa villosa EA None 227 / 227 / 293 Syringa vulgaris EU None1 190 / 150 / 190 Tilia dasystyla WA High 836 / 509 / 392 247"

125 248" Extended Data Table 4. Continued Genus Species Continent Chilling C1 / C2 / C3 Tilia japonica EA None 388 / 409 / 468 Tilia platyphyllos EU Intermediate 587 / 437 / 468 Toona sinensis EA None 488 / 635 / 585 Ulmus americana NA High 617 / 461 / 328 Ulmus laevis EU High 1040 / 735 / 377 Vaccinium angustifolium NA Intermediate2 792 / 446 / 452 Vaccinium corymbosum NA High2 682 / 490 / 276 Vaccinium pallidum NA High2 814 / 754 / 584 Viburnum betulifolium EA Intermediate 587 / 481 / 459 Viburnum buddleifolium EA High NL / 466 / 317 Viburnum carlesii EA None 347 / 301 / 327 Viburnum opulus EU Intermediate 663 / 496 / 514 Viburnum plicatum EA Intermediate 1059 / 466 / 498 Viburnum recognitum NA High2 616 / 556 / 386 Vitis aestivalis NA High2 814 / 534 / 430 Weigela coraeensis EA None 375 / 287 / 328 Weigela florida EA None 327 / 316 / 526 Weigela maximowiczii EA Intermediate 632 / 382 / 377

249"

250"

251"

252" Extended Data Table 5 | Experimental setup of the twig cutting experiment addressing 253" species’ chilling requirements. C1, C2, C3 = Different collection dates of twigs resulting in different levels of chilling. Chill days were calculated as days with a mean air temperature below 254" 5°C between 1 November and the respective collection date. 255"

256" C1 C2 C3

257" Start of experiment 21 Dec 2013 11 Feb 2014 21 March 2014 258" (Collection date) 259"

260" Chilling status Low Intermediate High 261"

262" Chill days (below 5°C) from 1 38 72 88 263" November until collection date

264"

265"

126

Chapter 5

DISTRIBUTION RANGES AND SPRING PHENOLOGY EXPLAIN LATE

FROST SENSITIVITY IN 170 WOODY PLANTS FROM THE NORTHERN

HEMISPHERE.

Muffler, L., C. Beierkuhnlein, G. Aas, A. Jentsch, A.H. Schweiger, C.M. Zohner, J. Kreyling

Global Ecology and Biogeography doi: 10.1111/geb.12466 (2016)

127

128 Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2016)

RESEARCH Distribution ranges and spring PAPER phenology explain late frost sensitivity in 170 woody plants from the Northern Hemisphere Lena Muffler1*, Carl Beierkuhnlein2, Gregor Aas3, Anke Jentsch4, Andreas H. Schweiger2, Constantin Zohner5 and Juergen Kreyling1

1Experimental Plant Ecology, Institute of ABSTRACT Botany and Landscape Ecology, Greifswald Aim Cold events determine the distributional range limits of woody species. University, Soldmannstraße 15, Greifswald, 17487, Germany, 2Biogeography, Bayreuth Despite global warming, the magnitude of late frost events in boreal and temperate regions is not expected to change. Hence, the risk for late spring Center of Ecology and Environmental Research (BayCEER), University of frost damage of woody species may increase with an earlier onset of the Bayreuth, Universitaetsstraße 30, Bayreuth, growing season. Here, we investigated biogeographical, phenological and 95440, Germany, 3Ecological-Botanical phylogenetic effects on late frost sensitivity. Gardens, University of Bayreuth, Location Ecological-Botanical Gardens Bayreuth, Germany (49855 45 N, Universitaetsstraße 30, Bayreuth, 95440, 0 00 4 Germany, Disturbance Ecology, BayCEER, 1183501000 E). University of Bayreuth, Universitaetsstraße Methods We inspected 170 woody species in the Ecological-Botanical Gardens 5 30, Bayreuth, 95440, Germany, Systematic from across the entire Northern Hemisphere for frost damage after an extreme Botany and Mycology, Department of late frost event in May 2011 (air temperature 24.3 8C after leaf unfolding of all Biology, University of Munich (LMU), species). Distribution range characteristics, climatic parameters of place of Menzinger Straße 67, Munich, 80638, origin and phenological strategy were linked to sensitivity to the late frost event. Germany Results The northern distribution limit and the range in continentality across the distributional ranges correlated negatively with a taxon’s late frost sensitivity 2 2 (pseudo-R 5 0.42, pseudo-R 5 0.33, respectively). Sensitivity to the late frost event was well explained by the climatic conditions within species’ native ranges (boosted regression trees; receiver operating characteristic 0.737). Average (1950–2000) May minimum temperature in species’ native ranges was the main explanatory variable

of late frost sensitivity (51.7% of explained variance). Phylogenetic relatedness explained additional variance in sensitivity to the late frost event. Sensitivity to the late frost event further correlates well withspeciesphenologicalstrategy.Frost- tolerant species flushed on average 2 weeks earlier than frost-sensitive species. Main conclusions Range characteristics and the prevalent climatic parameters across species native ranges are strongly related to their susceptibility to late spring frost damage. Further, more late frost-sensitive species unfolded their leaves later than more tolerant species and late frost tolerance is phylogenetically conserved. Thus, late frost sensitivity may challenge natural

and human-assisted migration of woody species under global warming. *Correspondence: Lena Muffler, Experimental Plant Ecology, Institute of Botany and Keywords Landscape Ecology, Greifswald University, Assisted colonization, assisted migration, common garden experiment, dis- Soldmannstraße 15, Greifswald 17487, Germany. E-mail: lena.muffler@uni- tribution limit, extreme events, frost damage, leaf-out, leaf unfolding, spring greifswald.de freeze.

VC 2016129 John Wiley & Sons Ltd DOI: 10.1111/geb.12466 http://wileyonlinelibrary.com/journal/geb 1 L. Muffler et al.

INTRODUCTION Sudden late frost events can affect large areas and can cause widespread damage (Gu et al., 2008; Hufkens et al., Due to climate warming, extreme cold events are generally 2012; Kreyling et al., 2012a; Lenz et al., 2013). A strong late expected to occur less frequently (IPCC, 2012), but their frost event in spring 2007 caused severe damage to woody magnitude is likely to persist (Kodra et al., 2011). Further species and crops across the eastern United States, and led to decrease in wintertime sea ice in the Barents–Kara seas could the loss of young foliage, shoots and fruits as well as to wide- even increase the likelihood of extreme cold events in Europe spread necrosis and desiccation of leaves (Gu et al., 2008). (Petoukhov & Semenov, 2010). Such extreme cold events can Another large-scale cold event during the early vegetation cause considerable damage to plants with significant ecologi- period occurred in May 2011, where large parts of Germany cal and also economic consequences (Gu et al., 2008; Jalili experienced an extreme late frost event. This frost event led et al., 2010). to frost damage such as severe leaf damage and a shortened In Central Europe, the start of the growing season has vegetation period, and meant that additional investment of advanced over the last decades (Menzel & Fabian, 1999; resources in second leaf-out across species was necessary for Badeck et al., 2004). Over three decades, leaf-out has started recovery (Kreyling et al., 2012a). 6 days earlier (Menzel & Fabian, 1999). However, extreme Minimum temperatures in winter are assumed to limit the cold events (spring frosts) after an earlier onset of the grow- native ranges of woody species (Sakai & Weiser, 1973). Like- ing season are increasing the risk of frost damage in the tem- wise, cold tolerance of tree species is closely related to the cli- perate zone (Inouye, 2008; Martin et al., 2010; Hufkens et al., mate of their native ranges, with a study focusing solely on 2012; Augspurger, 2013). cold tolerance over winter and before bud burst (Kreyling Trees are less able to cope with rapid climate changes com- et al., 2015) finding the strongest correlations in late winter pared with other plant functional types due to their conserv- and early spring. In general, it has long been acknowledged ative dispersal strategies and their longevity (Petit & Hampe, that late frost events pose a risk for woody species in temper- 2006). An important factor limiting adaptation to global ate regions (Gayer, 1882; Ellenberg, 1963). change in temperate tree species might be late frost sensitiv- However, for a long time there were no studies quantifying ity (Kollas et al., 2014). Knowledge on the role of late spring the effect of late frost events on the distribution ranges of frost sensitivity in controlling range limits is therefore essen- woody species. Just recently, Kollas et al. (2014) pointed out tial for understanding current and future natural and that it is not the absolute minimum temperature in winter human-assisted range shifts. that controls the native range limits but rather the low- In general, the probability of frost damage differs between temperature extremes during bud burst in springtime, which tree species and is modified by their phenological phase is the phenological stage where woody plants respond most (Augspurger, 2009). Directly after bud burst, temperate sensitively to sudden freezing events. This is in line with woody plants respond sensitively to frost events starting Lenz et al. (2013), who found that freezing temperatures in around 23 to 258C (Sakai & Larcher, 1987; Inouye, 2008; spring might be one of the main driving factors for range Martin et al., 2010; Kreyling et al., 2012b; Lenz et al., 2013). limits due to the selective pressure controlling the beginning Recent studies suggest that the susceptibility of species to late of the growing season. Given this potentially strong effect of frosts is influenced by their phenological strategy, i.e. the late frost sensitivity on distribution ranges, the increased risk leaves of early flushing species tend to withstand lower tem- of late frost damage (Inouye, 2008; Martin et al., 2010; Hufk- peratures than species with a late spring phenology (Lenz ens et al., 2012; Augspurger, 2013) opposes the poleward and et al., 2013; Vitasse et al., 2014a). Species with a longer dor- upward range shifts expected with global warming (Parmesan mancy period avoid late frost damage at the price of a et al., 1999; Lenoir et al., 2008). However, studies quantifying shorter growing season (Lockhart, 1983; Leinonen & the effect of distributional and underlying climatic character- H €anninen, 2002; Basler & Korner,€ 2012). In contrast, species istics of species native ranges on late frost sensitivity across a with a short dormancy period should profit from a pro- large spatial scale and multiple species are missing. longed vegetation period, but have to invest more in frost Here, we tested if late frost sensitivity of woody species resistance mechanisms. can be explained by the climatic conditions in their native In addition, drought tolerance of plant species can modify distributional ranges, in particular spring minimum tempera- the impact of frost events due to the physiologically compa- ture. In particular we hypothesized that woody species whose rable mechanisms aimed at preventing dehydration of cells native ranges are characterized by low temperatures (spring, (Bl odner€ et al., 2005; Beck et al., 2007). Similar to drought winter, annual) and low amounts of precipitation (summer, stress, frost leads to dehydration of plant tissues and cells by growing season, annual), are well adapted to late frost events. crystallization of water (Sakai & Larcher, 1987). Hence, the In addition, we tested if frost sensitivity is related to the water balance across a species’ native range can have an order in which species leaf out each year. We expected early impact on its late frost sensitivity due to cross-stress toler- leafing species to develop high frost resistance, while pheno- ance between drought and frost (Walter et al., 2012). In con- logically late species should afford lower frost resistance to sequence, differing drought tolerance between woody species their leaves. Finally, we checked if phylogeny (members of is likely to also be reflected in varying late frost sensitivity. certain genera) contributed additional power for explaining

2130 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Late frost sensitivity of woody species

25 became clearly visible over the following days. On 16 May we 20 checked the new foliage and new needles of adult plant indi-

15 viduals of 170 woody species in the EBG (with heights 10 between 1 and 15 m) – one to ten individuals per species – 5 for visible frost damage (0 5 no frost damage, 1 5 at least

Temperature (°C) Temperature 0 one individual showing frost damage measured by leaf −5 browning as an indicator). Apr 06 Apr 16 Apr 26 May 06 May 16 Species distributional characteristics and underlying Date of Year 2011 climatic conditions

Figure 1 Air temperature (hourly means at 12m) from 1 April For each species we obtained the native distribution range to 16 May 2011 showing the warm April preceding the late frost from various sources (data are given in Appendix S1 in Sup- event on 4 and 5 May at the Ecological-Botanical Gardens of the porting Information). Based on the species distribution ranges, University of Bayreuth, Germany. Data courtesy of Th. Foken, Department of Micrometeorology, University of Bayreuth. for each species we calculated the following distributional characteristics: southernmost occurrence, latitudinal and longi- the sensitivity to a late spring frost event. For this, we tudinal distribution centroid as well as northernmost occurrence. For the climatic characterization of the distribution inspected 170 adult and established woody species growing in the Ecological-Botanical Gardens (EBG) of the University ranges, the current climatic conditions (averages over the time ofBayreuth for damage after one extreme frost event (air period 1950–2000) with a spatial resolution of 10 arcmin (obtained from WorldClim, http://worldclim.org; Hijmans temperature below 24.38C). This late frost event occurred et al., 2005), were intersected with the native ranges. Conti- naturally in May 2011 after the start of the growing season nentality was chosen as a further parameter because a strong (Kreyling et al., 2012a). We then tested if the observed frost continental climate within a species’ native range might lead damage could be explained by distributional and underlying to a higher frost tolerance due to required protection against climatic characteristics of these species native ranges. cold winters, a higher risk of extreme late frost events and METHODS drought during summer (Czajkowski & Bolte, 2006). Conti- nentality within a species’ distribution range was quantified by Ecological-Botanical Gardens Bayreuth and the late using a simplified continentality index (high values equal high frost event in May 2011 continentality; Iwanow, 1959 in Hogewind & Bissolli, 2011): The EBG of the University of Bayreuth, Germany 2603annual temperature range (498 55 45 N, 11835 10 E, 16 ha) is located at an elevation continentality5 : 0 00 0 00 latitude of 355 to 370 m a.s.l. The local climate represents a transition between oceanic and continental influences, with a long- Spatial information about the annual temperature range was term mean annual temperature of 8.2 8C and mean annual derived from Bioclim variable 7 (BIO7) from the WorldClim precipitation of 724 mm (Foken, 2007). As the EBG was dataset, which is calculated as the difference between the founded in 1978, all tree specimens are of comparable age maximum temperature of the warmest month (BIO5) and and have reached tree size with considerable growth in the minimum temperature of the coldest month (BIO6). height. Thus, the EBG offers an implicit common garden set- Based on this gridded information about the annual temper- ting to study late frost sensitivity of even-aged woody plant ature range and the latitude of the corresponding grid cells species. we calculated minimum, mean and maximum continentality Late frost events, i.e. frost events after the end of winter in as well as the range of continentality (maximum – mini- spring or summer, occur occasionally. Such frost events can mum) for the distribution range of each species. All spatial appear after bud burst of trees. The late frost event in May analyses were conducted with the GIS software ARCGIS 10 2011 was the most extreme since the start of temperature (ESRI 2011, Redlands, CA, USA). recording on the site in 1997 and at the nearest station of To test the influence of phylogenetic relatedness on the the German Weather Service in 1961 (distance about 10 km; sensitivity to the late frost event, we pooled species-specific the second coldest event in 1976 reached 23.7 8C). Tempera- distributional characteristics for the 69 different genera under tures dropped to 210 8C close to the surface (15 cm) and investigation. For all genera we calculated: northernmost and 24.3 8C at a height of 2 m on the early morning of 4 May southernmost occurrence, maximum and minimum conti- (meteorological station at the EBG, coordinates as above; nentality, the average range of continentality (average of the data courtesy of Th. Foken, Department of Micrometeorol- species-specific ranges) as well as the average and variation ogy, University of Bayreuth) (Fig. 1). This frost event hap- (standard deviation) of species latitudinal and longitudinal pened after an extraordinarily warm April during which all distribution centroids. Of the 69 genera, only those genera studied species had started greening (Fig. 1). Bud burst was (16 genera, 105 species) with more than three species were completed when the late frost event took place. Frost damage included in the genera-specific analyses (Appendix S1).

Global131 Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 3 L. Muffler et al.

Table 1 Climatic parameters and their univariate (generalized linear model, GLM) and multivariate (boosted regression tree, BRT) relationship with the sensitivity of 170 woody species to the late frost event in May 2011 in the Ecological-Botanical Gardens, Bayreuth.

Climatic parameter Aggregation across species range PGLM Expl. var.BRT May minimum temperature Mean <0.001 51.7% Temperature annual range Standard deviation <0.001 14.4% Annual precipitation sum Standard deviation 0.157 Mean warmest month temperature Standard deviation 0.025 18.7% Sum of monthly precipitation (May–September) Maximum 0.007 15.1% De Martonne aridity index Standard deviation 0.555

The current climatic conditions (averages over the period 1950–2000) at 10-arcmin spatial resolution from WorldClim (http://worldclim.org; Hij- mans et al., 2005) were used for the analyses. Each single climatic parameter was assessed as the maximum (0.95 quantile), the mean, the minimum (0.05 quantile) and the standard deviation over all grid cells occupied by each respective species. After excluding collinearity (see Methods), six candidate climatic parameters were kept for further statistics in the stated aggregation across each species range. PGLM provides their univariate P-value according to a binomial GLM. Expl. var.BRT provides the explained variance of those parameters, which showed significant univariate relations to late frost damage (PGLM <0.001) and have thus been used in the binomial BRT model (ROC 5 0.737).

To understand the underlying climatic processes that shape Zohner & Renner, 2014 for methodological details). The the general relationships between late frost sensitivity and sampling included a broad range of woody species from the distributional characteristics, we analysed climatic parameters Northern Hemisphere. Individuals grown in the garden are of the species distribution ranges at the species level. Here, mostly wild collections that are acclimated, but not evolutio- we initially considered nine climatic parameters. The six narily adapted. Hence, their leaf-out times reflect native parameters which have been used for further analyses are phenological strategies. For analysis, the mean of a species’ shown in Table 1. Three climatic parameters (annual mean leaf-out date (from 2012 to 2015) was used. Leaf-out was temperature, minimum temperature of the coldest quarter, defined as the day when three to four branches of a plant and precipitation of the warmest quarter) were removed due unfolded leaves and pushed out all the way to the petiole. to autocorrelation with the six remaining parameters (see below). For each of the climatic parameters we considered Statistical analysis the maximum (0.95 quantile), mean, minimum (0.05 quan- The effects of species distributional characteristics on late tile) and standard deviation across each species’ native range frost tolerance (at species as well as genus level) were tested (resulting in 36 parameters). Minima and maxima were used by simple and mixed generalized linear models based on a to take extreme values into account. Extreme values might quasi-binomial distribution. To estimate goodness of fit for characterize the absolute limits of species occurrences more the generalized linear models, we calculated a pseudo-R2 precisely than mean conditions (Zimmermann et al., 2009). according to Nagelkerke (1991) using the NagelkerkeR2()- Standard deviations were chosen to characterize the spatial function of the fmsb-R-package (version 0.5.1). heterogeneity across species ranges and to investigate the The influence of the climate within a species’ native range potential impact of climatic variability. Such variability can on sensitivity to the late frost event was quantified by be expected to lead to more conservative phenology, with boosted regression trees (BRT) (Elith et al., 2008). Before fit- strategies to avoid spring frost risk (e.g. later onset of leaf ting BRTs, a reduction in dimensionality was applied by unfolding at the price of a shorter growing season) and removing autocorrelated parameters. Candidate climate higher investment in protection (Wang et al., 2014). Dehy- parameters were tested for collinearity with each other using dration tolerance of plants plays an important role not only Spearman’s nonparametric correlation. Where pairs of varia- during drought but also during frost events (Sakai & Larcher, bles were highly correlated (q > 0.7), a univariate binomial 1987; Blodner€ et al., 2005; Beck et al., 2007). Therefore, pre- generalized additive model (GAM) was fitted to the data cipitation of the warmest quarter, sum of monthly precipita- using each highly correlated variable. In order to obtain less tion from May to September and the aridity index according correlated variables and a final minimal model, the variable to De Martonne (1926) were considered in addition to tem- within each pair that yielded the higher Akaike information perature and annual precipitation parameters [aridity index- criterion (AIC) value was omitted. For the six resulting cli- 5 mean annual precipitation sum (mm)/(mean annual mate parameters, we ran univariate binomial generalized lin- temperature (8C) 1 10)]. ear models (GLMs) which resulted in four climate parameters that were significantly related to sensitivity to the Species leaf-out strategies late frost event: (1) mean over the species’ range of the mini- Data on leaf-out dates for 110 of the 170 species were avail- mum temperature in May, (2) standard deviation over the able from observational studies on woody species conducted species’ range of the mean temperature of the warmest in the Munich Botanical Garden from 2012 to 2015 (see month, (3) maximum over the species’ range of the sum of

4132 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Late frost sensitivity of woody species

a) b) Castanea Magnolia Quercus Fraxinus Magnolia Castanea Quercus Fraxinus

) 1.0 − Rhododendron Rhododendron 0.8 Chamaecyparis Chamaecyparis

0.6 Abies Abies

0.4 Carpinus Amelanchier Carpinus Amelanchier

Picea Larix Sorbus Picea Sorbus Larix Betula Betula 0.2 Acer Acer Pinus Pinus

Portion of frost damage ( damage frost of Portion Alnus Alnus 0 35 40 45 50 55 40 45 50 55 60 65 70

Mean latitudinal distribution (°) Northernmost occurrence (°)

Figure 2 Relation between the probability of late frost damage for 16 Northern Hemisphere genera of 105 woody plant species and (a) the mean latitudinal distribution (species-specific latitudinal distribution centroid averaged for each genus, P 5 0.005, pseudo-R2 5 0.59) and (b) the northernmost occurrence (maximum latitudinal occurrence for each genus, P 5 0.022, pseudo-R2 5 0.42). The probability of late frost damage is depicted as the portion of species within each genus with visible late frost damage during May 2011. Filled symbols refer to coniferous species and open symbols to broad-leaved species. precipitation from May to September, and (4) standard devi- RESULTS ation over the species’ range of the annual temperature range The probability of leaf damage of the observed 170 woody (Table 1). Only these four significantly explaining climatic plant species and 16 genera due to the studied late frost parameters (P < 0.05) were considered in the subsequent event significantly decreased with increasing latitudinal distri- BRT models. bution centre (at species level, P 5 0.005, pseudo-R2 5 0.15, Binomial BRTs were fitted according to Elith et al. (2008) Appendix S2; at genus level P 5 0.005, pseudo-R2 5 0.59, Fig. with the selection of the final model being based on minimal 2a). This pattern was consistent for broad-leaved as well as estimated cross-validated deviance. This was obtained by set- coniferous genera as the effect of leaf morphology on late ting the tree complexity to 5, the learning rate to 0.001 and frost sensitivity was not significant in a generalized linear the bag fraction to 0.9. The cross-validated receiver operating mixed effect model (P 5 0.09). The same positive effect on characteristic (ROC) score was used to express the correla- sensitivity to the late frost event was found for the northern- tion between climate within a species’ native range and late most occurrence (species level, P 5 0.001, pseudo-R2 5 0.12; frost damage. For each climatic parameter, its relative impor- genus level, P 5 0.022, pseudo-R2 5 0.42; Fig. 2b), again with tance for explained variance was provided. no significant difference between broad-leaved and coniferous In addition to the climatic parameters of species native genera (P 5 0.17), but not for the southernmost occurrence ranges the role of phylogenetic relatedness on late frost toler- (species level, P 5 0.13, pseudo-R2 5 0.02; genus level, ance was tested with ANOVA analyses paired with post hoc P 5 0.48, pseudo-R2 5 0.04). multiple comparison tests. To omit statistical biases caused Besides the significant effects detected for the geographical by small sample sizes we focused on a comparison of seven ranges (distribution centre and northernmost occurrence), genera for which at least five species were investigated (Abies, phylogenetic relatedness showed a strong effect on the frost Acer , Betula, Fraxinus, Pinus, Quercus, Rhododendron; see tolerance of the investigated species. Species-specific frost tol- Appendices S1 & S2 for detailed information). Differences in erance was significantly better explained when including the geographical distribution of these genera were tested by ‘genus’ as an additional explanatory variable besides the dis- using Tukey honestly significant difference tests for multiple tributional variables (pseudo-R2 5 0.15 vs. 0.86 for latitudinal comparisons. Differences in late frost sensitivity were tested distribution centre and pseudo-R2 5 0.12 vs. 0.87). For pairwise by using Wilcoxon rank sum tests for independent instance, observed frost damage differed significantly between samples because of the binomial character of the tested vari- the genera Quercus (frost damage in all observed species) and able. The level of significance was adjusted for these multiple Pinus (no frost damage in any observed species), despite their tests by applying the Bonferroni–Holm correction. largely overlapping geographical distribution ranges (Appen- All statistical analyses were executed with the software R 3.0.2 dices S2 & S3). Likewise, Pinus and Acer (frost damage in (R Development Core Team, 2013) and the additional packages only 2 out of 14 species) differed significantly from Fraxinus mgcv v.1.7-26, gbm v.2.1, sciplot v.1.1-0, and popbio v.2.4. (frost damage in all observed species) and Rhododendron

Global133 Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 5 L. Muffler et al.

(frost damage in four out of five observed species) despite ing species being less susceptible to late frost events. These the distributional characteristics (latitudinal distribution patterns were consistent for broad-leaved and coniferous centre, northernmost as well as southernmost occurrence) species. not differing significantly among these genera in our dataset Notably, many of the woody species studied here grew out- (Appendix S3). side their native range. Thus, climate, community composi- Longitude had no significant effect on sensitivity to the tion, photoperiod and soil conditions may not be at their late frost event, neither the mean nor the variation of the preferred values. Still, frost sensitivity as well as phenological longitudinal centroids (P 5 0.42 and P 5 0.10, respectively). strategy of leaf-out can be assumed to be rather conservative The same was true for maximum and minimum continental- traits so that our results bear implications beyond the single ity experienced by a species over its range within each genus study site. We did observe clear geographical patterns by only ( P 5 0.20 and P 5 0.30, respectively). Also species-specific considering the natural species distributions without further mean continentality averaged for each genus showed no sig- information on the precise origin of the studied ecotypes. To nificant effect on sensitivity to the late frost event (P 5 0.23). address this limitation, not just the mean of the climatic However, the probability of late frost damage significantly parameters within the native ranges but also the maxima, decreased with increasing species-specific range of continen- minima and the standard deviation have been used to char- 2 tality averaged for each genus (P 5 0.045, pseudo-R 5 0.33). acterize the distribution ranges. Hence, extreme values and This means that the wider the range of continentality experi- spatial heterogeneity across species ranges are taken into enced by the species of a certain genus in their distribution account. ranges, the lower was the probability of late frost damage Species sets in botanical gardens represent a subjective within this genus. sample of species able to tolerate the conditions at the spe- The species-specific probability of being damaged by the cific garden. Despite this obvious bias, our results indicate late frost event was well explained by the climatic conditions that sensitivity to the studied late frost event could be signifi- within the native distribution ranges (BRT cross-validated cantly better explained by including genus as an additional ROC score 5 0.737). The probability of frost damage was explanatory factor besides the distributional variables. Fur- best explained by the mean over the species’ range of the ther, the observed frost damage differed significantly between May minimum temperature (51.7%) followed by the stand- genera, even if the distributional characteristics did not due ard deviation over the species’ range of the mean tempera- to the given subset of species within the genera. Hence, our ture of the warmest month (18.7%), the maximum over the study hints at phylogenetic relatedness having strong effects species’ range of the sum of precipitation from May to Sep- on the late frost tolerance, i.e. phylogenetic conservatism of tember (15.1%) and by the standard deviation over the spe- late spring frost tolerance. cies’ range of the annual temperature range (14.4%) (Table Up to now, more attention has been paid to the role of 1). The probability of being damaged by the late frost event extreme cold events in winter and winter frost sensitivity as increased with increasing mean over the species’ range of the limiting factors for the ranges of tree species (Sakai & Weiser, May minimum temperature (P < 0.001), with decreasing 1973; Jalili et al., 2010; Kreyling et al., 2015). ‘Winter hardi- standard deviation over the species’ range of the mean tem- ness zones’ have been classified, reflecting distribution pat- perature of the warmest month (P 5 0.025), with increasing terns related to the extreme minimum temperatures in tree maximum over the species’ range of the sum of precipitation species ranges (Roloff & B€artels, 2006; Daly et al., 2012). from May to September (P 5 0.007), and with decreasing However, Lenz et al. (2013) and Kollas et al. (2014) found standard deviation over the species’ range of the annual tem- extreme frost events during bud burst in spring rather than perature range (P < 0.001) (Fig. 3). minimum winter temperature to be the factor that was most Species leaf-out dates (mean for 2012 to 2015) were highly limiting for species distribution. Focusing on the underlying correlated with sensitivity to the late frost event (P < 0.001) climatic drivers, the probability of frost damage in our study (Fig. 4). On average, the leaf-out dates of frost-resistant spe- was well explained by the climatic characteristics of species cies preceded those of frost-sensitive species by 10 days. native ranges (BRT ROC 5 0.737). Concerning specific climate parameters, late frost sensitivity was most strongly DISCUSSION related to the May minimum temperature within the native The sensitivity of the 170 woody species studied to the late range (> 50% of explained variance), which is at the begin- frost event in May 2011 was found to be significantly related ning of the growing season of most species considered. Spe- to species distributions (latitude of species distributional cies with higher May minimum temperatures in their native centres, northern range limit). Furthermore, genera with range responded more sensitively to this particular late frost wider ranges in their latitudinal distribution and in continen- event. This tight link across 170 species from all over the tality turned out to be less vulnerable to the late frost event Northern Hemisphere supports the conclusion of Lenz et al. in May 2011. In addition to the biogeographical patterns, we (2013) and Kollas et al. (2014) that late frost sensitivity is an found that the phenological strategy of species was highly important consideration in projections of range shifts of adapted to sensitivity to the late frost event, with early leaf- woody species in the face of climate change.

6134 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Late frost sensitivity of woody species

1.0 1.0

0.8 0.8

0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0

−50 5101520 010203040506070 Mean of the Minimum Temperature in May (°C) SD of the Mean Warmest Month Temperature (°C)

1.0 1.0

0.8 0.8

damage frost of Probability 0.6 0.6

0.4 0.4

0.2 0.2

0.0 0.0

0500100015002000 024681012

Maximum of the Sum of Precipitation (mm) SD of the Temperature annual range (°C)

Figure 3 The univariate probability of species-specific late frost damage (no damage 5 0, damage 5 1) in relation to climatic characteristics of the species ranges of 170 woody species (only those parameters with P < 0.05 in univariate generalized linear models are shown; see Table 1).

In addition to species differences in sensitivity to the late leafed-out as much as 10 days earlier than susceptible species. frost event, phenological adaptation to climatic conditions in This demonstrates that species finely adjust the time of leaf the native range of woody species could play an important appearance – the most freezing-sensitive phenological phase role with regard to their response to late frost events. The – to their susceptibility to late frosts. By using a broad range timing of bud burst, which is a sensitive phase in the pheno- of woody temperate species from various climates, our study logical cycle, is crucial for the risk of frost damage in respect thereby confirms similar patterns found for smaller and more to cold events in the temperate latitudes during the spring regional subsets of species (Lenz et al., 2013; Vitasse et al., (Sakai & Larcher, 1987; Inouye, 2008; Martin et al., 2010; 2014a). Kreyling et al., 2012b; Augspurger, 2013; Vitasse et al., According to Lenz et al. (2013) freezing tolerance within 2014b). By investigating the leaf-out strategies of a broad species differs among phenological stages. Here, the strongest range of taxonomically distinct temperate woody species in changes in frost sensitivity occurred before bud burst and relation to their sensitivity to the late frost event, we found there were none or only slight changes in both possible direc- that leaf unfolding dates were highly related to the frost sen- tions after leaf unfolding, depending on the individual spe- sitivity of the leaves: species resistant to the late frost event cies. Thus, a possible caveat of our approach is that not all

Global135 Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 7 L. Muffler et al.

promoted development. Savage & Cavender-Bares (2013) found that northern species within the family Salicaceae were 1.0 more strongly constrained by photoperiod as a cue for bud burst than southern species. The role of photoperiodic con- 0.8 straints on spring phenology indicated by their example clearly requires further attention, as it is of great importance 0.6 for understanding the impacts of climate change on species migrations. 0.4 In our study, high spatial (and ecological) heterogeneity within a species’ natural range was found to be linked to reduced observed damage as a response to the spring frost 0.2 event. For a species as a whole, a high spatial heterogeneity of annual air temperature and climatic continentality within Probability of0.0 frost damage its range necessitates, among other things, protection against cold winters, extreme late frost events and summer drought 60 70 80 90 100 110 120 (Czajkowski & Bolte, 2006). Moreover, species that tolerate high heterogeneity in terms of the warmest mean monthly Leaf-out date (DOY) temperature also need to be adapted to drought, as high temperatures during summer are likely be connected to a Figure 4 The univariate probability of species-specific late frost higher evapotranspirational demand and hence can cause damage (no damage 5 0, damage 5 1) in relation to the leaf-out strategy of 110 woody species (P < 0.001; univariate generalized drought stress (Dai et al., 2004). Likewise, low precipitation during the growing season (the linear model). Species leaf-out dates (recorded as day of the year, DOY) are the average dates from 2012 to 2015 observed in maximum, i.e. 95% quantile, of the sum of monthly precipi- the Munich Botanical Garden. tation from May to September across the species’ native range) in the native range reduced the probability of being species were at exactly the same phenological stage when damaged by the late frost event. Thus, water shortage experi- they were exposed to the freezing event. However, bud burst enced during evolution can play a role with regard to late and leaf unfolding were completed in all studied species frost sensitivity. This, again, can potentially be explained by when they were hit by the late frost. Therefore, it is unlikely cross-stress tolerance in the face of drought or frost (Walter that contrasting phenological stages at the time of the frost et al., 2012). Plant species that are adapted to drought are event would be responsible for the observed pattern of early often also adapted to frost-induced water stress via physio- leafing species being more frost tolerant. Unfortunately, we logical responses, such as accumulation of non-structural car- lack phenological data for the 2011 study year, but infer spe- bohydrates, to protect phenological, morphological or cies phenological strategies from leaf-out data collected from physiological traits (Inouye, 2000; Beck et al., 2007). 2012 to 2015 (including the warmest recorded spring in In conclusion, the individual sensitivity of the 170 woody Bavaria in 2014). The order of species-level leaf-out dates species observed to a late frost event after leaf unfolding can was found to be highly conserved over time (Panchen et al., be explained by the species’ natural latitudinal range, the 2014; Zohner & Renner, 2014) and therefore our leaf-out spectrum of continentality and by specific climatic condi- data can be assumed to reflect also the sequence of leaf tions, in particular the mean minimum temperature in May unfolding in the year 2011. across the species’ distribution. Which phenological safety mechanisms do frost-sensitive Thus, late frost sensitivity appears to be a factor control- species use to avoid precocious bud development? Current ling species’ distribution limits and is an important consider- studies suggest that chilling requirements are the main driv- ation in projections of range shifts of tree species or in ers to limit advanced budburst (Laube et al., 2014). Accord- concepts of assisted migration. Furthermore, we reveal in this ing to Fu et al. (2015), reduced chilling over winter study that late frost sensitivity appears to be synchronized potentially leads to an increased heat requirement in spring with the species’ phenological strategy. and consequently to a delayed tracking of climate warming Implications and outlook in spring phenology. However, the impact of photoperiod as well as temperature cues on phenology have to be kept in Species are expected to respond to global warming with mind, especially in times of global warming (Korner€ & Bas- upward or poleward shifts of their distribution limits (Par- ler, 2010; Basler & Korner,€ 2012): late successional tree spe- mesan et al., 1999; Lenoir et al., 2008). In particular, tree cies have been observed to be photoperiod sensitive. species are found to lag behind the rapidity of warming, a Photoperiodic control of phenology can limit the phenologi- fact commonly explained by their conservative dispersal strat- cal responses of late successional species to warming, particu- egies and long regeneration cycles (Petit & Hampe, 2006). larly when a warm spring temperature would suggest Therefore, assisted migration is discussed as an option to

8136 Global Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd Late frost sensitivity of woody species support the adaptation of forest stands to future climate con- REFERENCES ditions. However, the success of actively shifted populations Augspurger, C.K. (2009) Spring 2007 warmth and frost: phe- might be jeopardized by extreme winter frost (Jalili et al. nology, damage and refoliation in a temperate deciduous 2010) and late frost events (our results, but see also Kollas forest. Functional Ecology, 23, 1031–1039. et al., 2014), as these appear to be two of the most important Augspurger, C.K. (2013) Reconstructing patterns of tempera- factors controlling species native range limits. ture, phenology, and frost damage over 124 years: spring Even though the late frost event in May 2011 was extreme damage risk is increasing. Ecology, 94, 41–50. 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tial patterns of tree species. Proceedings of the National Academy of Sciences USA, 106, 19723–19728. Editor: Thomas Hickler

Global139 Ecology and Biogeography, VC 2016 John Wiley & Sons Ltd 11 SUPPORTING INFORMATION Appendix S1: 170 species were inspected for frost damage (1 = if at least one individual showed frost damage measured by leaf browning as indicator, 0 = no damage) in the Ecological Botanical Gardens in Bayreuth after a late frost event in May 2011. Source shows the reference of the species’ native ranges (1 = Bioversity International (2013) European Forest Genetic Resources Programme. Distribution maps. http://www.euforgen.org/distribution_maps.html, 2 = U.S. Department of the Interior & U.S. Geological Survey (2013) Digital representations of tree species range maps from „Atlas of United States Trees“ by Elbert L. Little, Jr. http://esp.cr.usgs.gov/data/little/., 3 = Horikawa, Y. (1976) Atlas of the Japanese Flora II: An Introduction to Plant Sociology of East

Asia, Gakken Co. Ltd., Tokyo., 4 = Ying, T.-S., Chen, M.-L. & Chang, H.-C. (2003) Atlas of the

Gymnosperms of China, China Science and Technology Press, Beijing., 5 = Jalas, J. & Suominen,

J. (1973) Atlas florae Europaeae. Distribution of vascular plants in Europe: Gymnospermae

(Pinaceae to Ephedraceae), Committee for Mapping the Flora of Europe, Helsinki., 6 = Meusel, H.,

Jäger, E. & Weinert, E. (1992) Vergleichende Chorologie der zentraleuropäischen Flora - Karten,

VEB Gustav Fischer Verlag, Jena., 7 = Little, E.L. (1971) Atlas of United States Trees. Conifers and Important Hardwoods, United States Government Printing Office, Washington D.C., 8 = Little, E.L. (1977) Atlas of United States Trees. Minor Eastern Hardwoods, United States Government Printing Office, Washington D.C., 9 = Little, E.L. (1976) Atlas of United States Trees. Minor Western Hardwoods Washington, United States Government Printing Office, Washington D.C., 10 = Interactive agricultural ecological atlas of Russia and neighboring countries (2009) Economic

plants and their diseases, pests and weeds. http://www.agroatlas.ru/en/content/related/).

Species Number of individuals Frost damage Source Abies alba Mill. 5 1 1 Abies balsamea (L.) Mill. 1 0 2 Abies cephalonica Loud. 8 1 5 Abies chensiensis Tiegh. 1 0 4 Abies cilicica (Ant. & Kotschy) Carr. 5 1 6 Abies concolor (Gord. & Glend.) Lindl. ex Hildebr. 3 0 2 Abies delavayi Franch. 3 1 4 Abies equi-trojani (Asch. & Sint. ex Boiss.) Coode & Cullen 1 1 6 Abies grandis (Dougl. ex D.Don) Lindl. 2 0 2 Abies holophylla Maxim. 2 1 4 Abies lasiocarpa (Hook.) Nutt. 6 0 2 Abies nordmanniana (Stev.) Spach 4 1 6 Abies procera Rehd. 2 0 2 Abies sibirica Ledeb. 1 1 4 Abies veitchii Lindl. 5 0 3 Acer campestre L. 1 0 1 Acer cappadocicum Gled. 1 0 6 Acer circinatum Pursh 2 0 2 Acer heldreichii Orph. ex Boiss. 2 1 6 Acer monspessulanum L. 2 0 6 Acer negundo L. 4 0 2 Acer platanoides L. 1 0 6 Acer pseudoplatanus L. 1 0 1 Acer rubrum L. 6 0 2 Acer saccharum Marshall 1 0 2

140 Acer semenovii Regel & Herder 1 0 6 Acer tataricum L. 4 0 6 Acer turkestanicum Pax 1 0 6 Acer velutinum Boiss. 2 1 6 Actinidia kolomikta (Rupr. & Maxim.) Maxim. 1 1 10 Alnus incana (L.) Moench 1 0 6 Alnus maximowiczii Callier 1 0 3 Alnus tenuifolia Nutt. 1 0 2 Amelanchier alnifolia (Nutt.) Nutt. ex M.Roem. 2 0 2 Amelanchier asiatica (Sieb. & Zucc.) Endl. ex Walp. 1 1 3 Amelanchier ovalis Medik. 2 0 10 Aralia cordata Thunb. 1 1 3 Aralia elata (Miq.) Seem. 3 1 3 Berberis amurensis Rupr. 1 0 6 Berberis vulgaris L. 1 0 6 Betula alleghaniensis Britton 1 0 2 Betula fruticosa Pall. 5 0 6 Betula humilis Schrank 1 1 6 Betula occidentalis Hook. 1 0 2 Betula papyrifera Marshall 1 0 2 Betula pubescens Ehrh. 1 0 6 Buxus sempervirens L. 1 0 6 Callicarpa japonica Thunb. 2 1 3 Carpinus betulus L. 1 0 6 Carpinus orientalis Mill. 3 0 6 Carpinus turczaninovii Hance 1 1 3 Castanea crenata Sieb. & Zucc. 1 1 6 Castanea dentata (Marshall) Borkh. 1 1 2 Castanea sativa Mill. 3 1 1 Celtis australis L. 1 1 6 Cercis canadensis L. 2 1 8 Chamaecyparis lawsoniana (A.Murr.) Parl. 2 1 2 Chamaecyparis obtusa (Sieb. & Zucc.) Endl. 1 1 3 Chamaecyparis thyoides (L.) Britton, Sterns & Poggenb. 1 0 2 Chionanthus virginicus L. 3 1 2 Cladrastis kentukea (Dum.Cours.) Rudd 1 1 2 Clethra barbinervis Sieb. & Zucc. 1 1 3 Cornus mas L. 1 0 6 Cornus occidentalis (Torr. & A.Gray) Coville 1 0 2 Corylus avellana L. 1 0 6 Corylus colurna L. 1 0 6 Cotinus coggygria Scop. 6 1 6 Crataegus douglasii Lindl. 1 1 8 Diospyros virginiana L. 2 1 2 Euonymus europaeus L. 1 0 6 Euonymus verrucosus Scop. 1 1 6 Fagus orientalis Lipsky 1 1 1 Fagus sylvatica L. 1 1 1 Fraxinus americana L. 3 1 2 Fraxinus angustifolia Vahl 3 1 6 Fraxinus excelsior L. 5 1 1 Fraxinus latifolia Benth. 1 1 2 Fraxinus ornus L. 5 1 6 Fraxinus pennsylvanica Marshall 1 1 2 Fraxinus quadrangulata Michx. 1 1 2 Fraxinus syriaca Boiss. 1 1 6 Ginkgo biloba L. 3 1 4 Gleditsia triacanthos L. 3 1 2 Halesia carolina L. 1 0 2 Hamamelis virginiana L. 2 0 2 Hippophae rhamnoides L. 3 0 6 Ilex aquifolium L. 2 1 6 Juglans ailantifolia Carr. 1 1 3 Juglans nigra L. 1 1 2 Kalmia latifolia L. 1 1 2 Larix decidua Mill. 3 1 1 Larix laricina (Du Roi) K.Koch 1 0 7 Larix occidentalis Nutt. 1 0 2 141 Larix sukaczewii Dylis 3 0 6 Ledum palustre L. 4 1 6 Lindera obtusiloba Blume 1 1 3 Liriodendron tulipifera L. 2 1 2 Lonicera nigra L. 2 0 6 Magnolia kobus DC. 2 1 3 Magnolia tripetala (L.) L. 1 1 2 Magnolia virginiana L. 1 1 2 Metasequoia glyptostroboides Hu & Cheng 1 1 4 Morus australis Poir. 1 1 3 Morus rubra L. 1 1 2 Nyssa sylvatica Marshall 2 1 2 Ostrya virginiana (Mill.) K.Koch 2 1 2 Oxydendrum arboreum (L.) DC. 1 1 2 Photinia villosa (Thunb.) DC. 1 1 3 Picea jezoensis (Sieb. & Zucc.) Carr. 2 0 4 & 3 Picea orientalis (L.) Link 2 0 6 Picea pungens Engelm. 2 0 2 Picea smithiana (Wall.) Boiss. 4 1 4 Pinus cembra L. 2 0 1 Pinus contorta Dougl. ex Loud. 1 0 2 Pinus jeffreyi Balf. 1 0 2 Pinus mugo Turra 5 0 6 Pinus nigra J.F.Arnold 3 0 1 Pinus ponderosa Dougl. ex P. & C.Lawson 7 0 2 Pinus resinosa Aiton 1 0 2 Pinus sylvestris L. 1 0 6 Pinus wallichiana A.B.Jacks. 2 1 4 Pinus washoensis Mason & Stockw. 1 0 2 Populus trichocarpa Torr. & A.Gray ex Hook. 1 0 2 Pseudotsuga menziesii (Mirb.) Franco 4 0 2 Pterocarya rhoifolia Sieb. & Zucc. 3 1 3 Quercus acutissima Carruth. 1 1 3 Quercus alba L. 1 1 2 Quercus bicolor Willd. 2 1 2 Quercus cerris L. 2 1 6 Quercus dentata Thunb. 2 1 3 Quercus falcata Michx. 6 1 2 Quercus lobata Née 1 1 2 Quercus macrocarpa Michx. 1 1 2 Quercus michauxii Nutt. 1 1 2 Quercus muehlenbergii Engelm. 1 1 2 Quercus palustris Münchh. 1 1 2 Quercus prinus L. 1 1 2 Quercus pubescens Willd. 6 1 6 Quercus rubra L. 1 1 2 Quercus serrata Thunb. 1 1 3 Quercus velutina Lam. 1 1 2 Rhamnus alpina L. 1 0 6 Rhamnus cathartica L. 1 0 6 Rhododendron catawbiense Michx. 3 0 2 Rhododendron ferrugineum L. 3 1 6 Rhododendron japonicum (A.Gray) Sur. 10 1 3 Rhododendron luteum Sweet 10 1 6 Rhododendron ponticum L. 3 1 6 Rhododendron smirnowii Trautv. 3 1 6 Rhus glabra L 1 1 2 Ribes alpinum L. 1 1 6 Rubus phoenicolasius Maxim. 1 0 3 Sambucus canadensis L. 1 0 2 Sciadopitys verticillata (Thunb.) Sieb. & Zucc. 3 0 3 Sequoia sempervirens (D.Don) Endl. 1 1 2 Sequoiadendron giganteum (Lindl.) J.Buchh. 4 0 2 Sorbus alnifolia (Sieb. & Zucc.) K.Koch 1 0 3 Sorbus decora (Sarg.) C.K.Schneid. 1 0 9 Sorbus matsumurana (Makino) Koehne 3 0 3 Sorbus sibirica Hedl. 1 1 6 Staphylea trifolia L. 1 0 2 142 Taxus baccata L. 1 1 6 Taxus canadensis Marshall 1 1 6 Thuja plicata Donn ex D.Don 2 0 2 Thuja standishii (Gordon) Carr. 1 0 3 Tilia dasystyla Steven 1 1 6 Tsuga caroliniana Engelm. 2 0 2 Viburnum lantana L. 1 0 6 Viburnum lentago L. 1 0 2 Zelkova serrata (Thunb.) Makino 2 1 3

143 Latitudinal distribution (°) 20 30 40 50 60 70

Abies alba Abies balsamea frost damage Abies cephalonica Abies cilicica no frost damage Abies concolor Abies equi−trojani Abies grandis Abies lasiocarpa Abies nordmanniana Abies procera Acer campestre Acer cappadocicum Acer circinatum Acer heldreichii Acer monspessulanum Acer negundo Acer platanoides Acer pseudoplatanus Acer rubrum Acer saccharum Acer semenovii Acer tataricum Acer turkestanicum Acer velutinum Alnus incana Alnus tenuifolius Amelanchier alnifolia Amelanchier ovalis Beberis amurensis Berberis vulgaris Betula alleghaniensis Betula fruticosa Betula humilis Betula occidentalis Betula papyrifera Betula pubescens Buxus sempervirens Carpinus betulus Carpinus orientalis Castanea crenata Castanea dentata Castanea sativa Celtis australis Cercis canadensis Chamaecyparis lawsoniana Chamaecyparis thyoides Chionanthus virginicus Cladrastis kentukea Cornus mas Cornus occidentalis Larix decidua Corylus avellana Larix laricina Corylus colurna Larix occidentalis Cotinus coggygria Larix sukaczewii Crataegus douglasii Ledum palustre Diospyros virginiana Liniodendron tulipifera Euonymus europaea Lonicera nigra Euonymus verrucosa Magnolia tripetala Fagus orientalis Magnolia virginiana Fagus sylvatica Morus rubra Fraxinus americana Nyssa sylvatica Fraxinus angustifolia Ostrya virginiana Fraxinus excelsior Oxydendrum arboreum Fraxinus latifolia Picea orientalis Fraxinus ornus Picea pungens Fraxinus pennsylvanica Pinus cembra Fraxinus quadrangulata Pinus contorta Fraxinus syriaca Pinus jeffreyi Gleditsia triacanthos Pinus mugo Halesia carolina Pinus nigra Hamamelis virginiana Pinus ponderosa Hippophae rhamnoides Illex aquifolium Pinus resinosa Juglans nigra Pinus sylvestris Kalmia latifolia Pinus washoensis Populus trichocarpa Pseudotsuga menziesii Quercus alba Quercus bicolor Quercus cerris Quercus falcata Quercus lobata Quercus macrocarpa Quercus michauxii Quercus muehlenbergii Quercus palustris Quercus prinus Quercus pubescens Quercus rubra Quercus velutina Rhamnus alpina Rhamnus cathartica Rhododendron catawbiense Rhododendron ferrugineum Rhododendron luteum Rhododendron ponticum Rhododendron smirnowii Rhus glabra Ribes alpinum Sambucus canadensis Sequoia sempervirens Sequoiadendron giganteum Sorbus decora Sorbus sibirica Staphylea trifolia Taxus baccata Taxus canadensis Thuja plicata Tilia dasystyla Tsuga caroliniana Viburnum lantana Viburnum lentago

Appendix S2: Distributional characteristics and late frost sensitivity of woody species investigated in this study. Shown is the distributional centre (points) as well as the southernmost and northernmost occurrence of each investigated species. Species-specific information about frost damage is indicated by point color (black: frost damage was detected; white: no frost damage was detected). Genera are separated by grey horizontal lines. Without species where just single occurrence points could be found in the literature. 144 Appendix S3: Differences in late frost sensitivity and geographical distributions between the tree genera investigated in this study. Bold printed p-values depict significant differences. Differences in frost damage were tested by using pairwise Wilcoxon Rank Sum test in combination with Bonferroni-Holm correction of the level of significance. Differences in geographical distributions are tested by Tukey HSD tests for multiple comparison tests. Only genera with at least 5 species were included (for detailed information see Appendix S1 and Appendix S2)

Wilcox-test Tukey HSD Tukey HSD Tukey HSD Frost damage Centr Lat Max Lat Min Lat Genus p p p p Quercus-Pinus 5.54E-06 0.0286465 0.3716428 0.2054556 Quercus-Acer 1.26E-05 0.1696174 0.6970641 0.3285181 Pinus-Fraxinus 8.00E-05 0.4715257 0.9666129 0.534572 Fraxinus-Acer 0.0001732 0.9220777 0.9998628 0.7360812 Quercus-Betula 0.000238145 0.0000005 0.0000398 0.028745

Fraxinus-Betula 0.002481524 0.0000988 0.0034449 0.1222686 Rhododendron-Pinus 0.002935353 0.7690877 0.5107114 0.9492052 Quercus-Abies 0.005516333 0.1629635 0.9867465 0.0229243

Rhododendron-Acer 0.009772931 0.9916618 0.7872328 0.7422515 Pinus-Abies 0.01867785 0.988373 0.8641217 0.9867807 Fraxinus-Abies 0.0257534 0.8735566 0.9999595 0.1386153 Rhododendron-Betula 0.05777957 0.0013111 0.0005748 1 Acer-Abies 0.06866261 0.9999843 0.9930786 0.8083061 Rhododendron-Quercus 0.136641 0.910126 0.9999755 0.0474949 Betula-Abies 0.2200314 0.0022374 0.0008253 0.9996843 Rhododendron-Fraxinus 0.2683816 0.9999902 0.943574 0.1613587 Pinus-Acer 0.2730348 0.9427577 0.9920683 0.9986046 Pinus-Betula 0.2763029 0.0209967 0.0318206 0.9374512 Rhododendron-Abies 0.3133992 0.9781886 0.9798797 0.9997301 Betula-Acer 0.9469029 0.0005165 0.0023826 0.688668 Quercus-Fraxinus 1 0.934884 0.9484493 0.9997928

145

146

Chapter 6

GENERAL DISCUSSION

147

148 6.1 Do temperate woody species use the photoperiod in spring as a cue for leaf-out? Determining the environmental cues that trigger the timing of plant growth and development is of vital importance for climate change research (Körner & Basler, 2010; Richardson et al., 2013; Way & Montgomerey, 2015). While temperature-dependent processes should be affected by global warming, processes that are mediated by day length should not change in the future because photoperiod will not change with climate warming. The topic is complex because phenological events can be triggered by a combination of different external cues. This is the case for the timing of leaf unfolding in temperate woody species: cues from chilling, warming, and photoperiod interact, leading to a situation in which leaf emergence is the result of multiple environmental forces operating at different times during dormancy (Sanz-Perez et al., 2009; Caffarra & Donnelly, 2011; Cooke et al., 2012; Laube et al., 2014a; Polgar et al., 2014; Zohner & Renner, 2014). Hence, to forecast future responses, we need to broaden our understanding of the physiological and molecular mechanisms of leaf unfolding. A main goal of my doctoral research was to answer the following questions about photoperiod-control of leaf unfolding: (i) Where (in which tissues and organs) do plants perceive photoperiod signals and how? (ii) When during dormancy are photoperiod signals perceived? (iii) Which species rely on photoperiod to time leaf-out? And finally, what is the evolutionary advantage of using photoperiod to trigger dormancy release? The first two questions were addressed in Chapter 2 of this thesis, questions three and four in Chapter 3. Since “photoreceptors and the clock system are, in principal, found in all plant cells” (Cooke et al., 2012, p. 1715), there are many ways how photoperiod could trigger leaf unfolding. To answer where (and how) photoperiod is perceived I conducted in situ bagging experiments, in which branches of trees were kept under short day conditions, while the remaining parts of the same trees experienced the natural day length increase during spring (Chapter 2). Principally, light signals could be perceived by all parts of the tree exposed to solar radiation (leaving the root system as the most unlikely organ to be involved in light perception), with two mechanisms for how light signals could be perceived, either systemic or local. A systemic response predicts that buds kept under constant short days will not differ in their reaction from uncovered buds of the same tree because (hormonal or other) signaling processes should lead to a uniform reaction. Alternatively, there might be localized responses only in certain parts of a tree. The latter was the case in my experiments: in Fagus sylvatica, buds kept under constant 8-h days leafed out 41 days

149 later than buds on the same branch exposed to natural photoperiod, ruling out a systemic response and instead suggesting that each bud autonomously perceives and reacts to day length. Additional experiments in which I exposed buds to different light spectra revealed that it is the leaf primordial cells inside the dead bud scales that react to far-red light to activate the signal cascade ultimately inducing leaf unfolding (Figs. 3 and 4 in Chapter 2). Another question I aimed to answer was when photoperiod signals are perceived during the dormant winter period. Related to this is the question if photoperiod signals at some threshold value induce an irreversible reaction or if instead a continuous long-day signal is required that can be interrupted, then causing a slower or arrested reaction? Using a combination of in situ bagging and twig cutting experiments, I found that buds of F. sylvatica perceive photoperiod signals only in the late phase of dormancy, while long days experienced concurrent with cold air temperatures do not affect dormancy. Therefore it can be concluded that, in photosensitive species, long-days do not per se cause an irreversible reaction, but are required concurrent with spring warming to allow for bud burst. Which particular day-length is required to allow for bud development and in which way does photoperiod interact with chilling and warming temperatures? According to Vitasse and Basler (2013), there are two possibilities: (i) Either a fixed photoperiod threshold has to be met before buds are able to respond to warming signals or (ii) warming requirements continuously decrease with increasing day-length. To test this I exposed dormant twigs of F. sylvatica to different photoperiods (8-h, 12-h, and 16-h light per day) and found strong support for the second mode of action, i.e., the longer the days, the less warming was required to induce leaf unfolding (see Fig. 3 in Chapter 2). Having answered where and when photoperiod perception takes place in trees, I focused on inter-specific variability in photoperiodism and the underlying adaptive mechanisms fostering it. According to Körner and Basler (2010), long-lived species, especially those from regions with unpredictable temperature regimes (oceanic climates), might rely on photoperiod signals to time their leaf unfolding. In addition, species from higher latitudes might be more sensitive to photoperiod, first, because the annual variation in photoperiod increases with latitude, and second, because of its hypothesized function to act as an insurance against being misguided by unpredictable spring temperatures (Körner, 2006; Saikkonen et al., 2012). Prior to my work a single study on 36 temperate woody species had addressed Körner’s hypotheses and had failed to

150 find any significant correlation between species’ native climates or successional strategies and their relative use of photoperiod, instead suggesting that most temperate species have evolved a photoperiod-independent leaf-out strategy (Laube et al., 2014a). I conducted photoperiod experiments on another 144 species, which together with the results obtained by Laube et al. (2014a) now provides information on 173 species (in 78 genera from 39 families) from throughout the Northern Hemisphere. The results contradict the view that photoperiodism (reliance on day-length increase in spring) is especially pronounced in species from high-latitude regions with unpredictable weather systems. Instead, they show that it is the species from more Southern regions with relatively short winter periods that use day-length signals to time leaf unfolding (Chapter 3). Why is the relative importance of photoperiod as a bud burst signal decreasing towards high-latitude regions with long winters? As I show in Chapter 2 for F. sylvatica (also Heide 1993a), photoperiod signals interact with warming requirements, such that longer days continuously reduce the amount of warming required for budburst. The period in spring when days are getting long, however, is not changing with latitude (the spring equinox around which day length is maximally increasing occurs on 21/22 March all over the World). Therefore it should be riskiest for a population to rely on photoperiod in regions with long winters because day-length is increasing too early for guiding leaf-out into a frost-free period, probably explaining why especially genera with a subtropical history, such as Fagus, show photoperiodism, whereas genera with a mainly Northern distribution history such as Betula leaf- out independent of photoperiod. These results suggest that photoperiod will not constrain leaf-out phenology in northern woody plants with continuously warmer springs. Much more likely is a constraint coming from chilling requirements and spring frost risks (Chapter 4 and 6.2; below).

6.2 Geographic variation in leaf-out strategies The following section of my general discussion focuses on the adaptive mechanisms leading to geographic variation in winter chilling and spring warming requirements. For a discussion of geographic variation in photoperiod requirements see Chapters 3 and paragraph 6.1 above. Theoretically it should benefit deciduous plants to delay leaf unfolding until tissue- damaging frosts have passed. On the other hand, delaying leaf-out should decrease an individual’s fitness because it would forego the opportunity to use available energy resources for

151 carbon fixation. Leaf unfolding in locally adapted genotypes should therefore occur as soon as possible after the last occurrence of damagingly low temperatures for the respective genotypes. A study on five European tree species reported exactly such a convergence of leaf-out towards the time of minimum risk of freezing damage (Lenz et al., 2016). In all five species, and irrespective of climatic conditions (leaf-out was monitored at different altitudes and over multiple years), species leafed out soon after the probability to encounter freezing damage had approached zero, with the specific times differing among species because of differences in freezing resistance of emerging leaves. Given the stochasticity of spring temperatures, how do species know when the probabilistically safe period has arrived? My results suggest that the timing of budburst depends on the interplay between chilling and spring warming requirements (Laube et al., 2014a; Polgar et al., 2014; Chapter 4). With increasing winter duration, forcing requirements decrease towards a minimum value, allowing species to track the progression of the winter season and to ‘predict’ its probable end. On the basis of these findings it can be argued that regional differences in leaf-out strategies of temperate woody plants reflect the different late frost probabilities that plants have experienced (during evolutionary times) in their native ranges. To test this, experimental and observational data for a broad range of temperate woody species are required, allowing for inferring species-specific chilling and spring warming requirements. In addition, global climate data are needed to compute regional frost probabilities. The calculation of such maps, however, is difficult because, to infer when the risk of a plant to experience frost damage has passed, information on a species’ specific frost sensitivity is required, emphasizing the need of further studies addressing frost sensitivity in leaves (e.g., Chapter 5). In Chapter 4 of this dissertation, I worked around this problem by calculating a global map of inter-annual spring temperature variability (see Fig. 3a in Chapter 4) as a proxy for late frost probability. The map shows that especially the eastern part of North America has a highly variable spring climate, matching my experimental results that species from this region have high chilling and spring warming requirements, resulting in late leaf-out compared to species from regions with low spring temperature variability (such as East Asia) when grown in a common garden.

152 6.3 Phenology and invasive success Geographic variation in the timing of leaf unfolding should influence species’ invasion success. With the ever-warmer winter and spring conditions that are expected under climate change, opportunistic phenological strategies might enable species to take advantage of rising air temperatures by extending their growing season. Regions, such as eastern North America, with many species with conservative leaf-out strategies – due to historically high temperature variability favoring conservative growth strategies (see Chapters 4 and 6.2) – might be especially vulnerable to invasions by species with opportunistic leaf-out strategies. By contrast, regions in which opportunistic strategies were already favored in the past, such as Eastern Asia, might be less prone to invasions. Four studies have addressed possible links between phenological strategy and invasive success. They have found that, by adjusting flowering times (Willis et al., 2010; Wolkovich et al., 2013), leaf-out times (Polgar et al., 2014), or leaf senescence times (Fridley, 2012), eastern North American invasives are better able to track climatic changes than are natives. While focusing on invasive/native comparisons, these studies also point towards a biogeographic pattern, with East Asian species phenologically behaving more opportunistic than their North American brethren. While I did not focus on invasive/non-invasive contrasts per se, my results (Chapters 4 and 6.2) show that there are, in fact, large differences in the leaf-out strategies among continental woody floras, which might well explain the above discussed asymmetric invasion pattern in the Northern hemisphere. Most shrubs and trees invading eastern North America are from East Asia, and invasions of East Asian species in North America are much more common than vice versa (Fridley, 2008, 2013). Because their opportunistic leaf-out strategies allow them to make use of soil and light resources in spring, in a way occupying a “vacant niche” (Elton, 1958; Wolkovich & Cleland 2011, 2014), species from East Asia should have a competitive advantage over species from North America. Effects of current climate change are likely to further separate the “phenological niches” between native and invasive species. This can be predicted from the lack of chilling requirements revealed in Chapter 4 for East Asian ‘candidate invaders’. These species will linearly track climate warming while warmer winters will constrain temperature tracking, especially in highly chilling-sensitive North American species (see Fig. 1 in Chapter 4).

153 6.4 Herbarium phenology Analyzing long-term phenological data (i) allows to predict species’ responses to climate warming and (ii) to draw conclusions about constraints that photoperiod and chilling requirements may place on temperature-driven phenological changes. Data on the flowering times of hundreds of species in North America and Europe are now available from long-term observations (Fitter et al., 1995; Bradley et al., 1999; Fitter & Fitter, 2002, Miller-Rushing & Primack, 2008; Amano et al., 2010; Dunnell & Travers, 2011, Iler et al., 2013; Mazer et al., 2013) or herbarium specimens (Borchert, 1996; Primack et al., 2004; Lavoie and Lachance, 2006). However, prior to my work, herbarium data had never been used for assessing long-term leaf-out times, because botanists normally collect fertile plants (with flowers or fruits) and the potential of herbarium material for judging leaf-out times was therefore not seen or at least underestimated. Numerous species, such as Acer platanoides, Carpinus betulus and Fagus sylvatica, however, flush and flower simultaneously, and for these, herbarium records can be used to provide data on spring flushing times (Fig. 1). The Munich herbarium with 3 million specimens is among the World’s largest, and already in my M.Sc. thesis (Zohner & Renner, 2014) I used label data on specimen collecting times to create long-term series of local leaf-out times (as far back as 140 years) for native woody species (Acer platanoides, Carpinus betulus, and Fagus sylvatica, see Fig. 2). Using this approach, I have since obtained data for 17 additional species, with the results showing species-specific climate tracking correlated with species’ photoperiod and chilling requirements (Zohner & Renner 2014; Zohner & Renner, unpublished data gathered for a DFG funding application). The utility of herbarium specimens for inferring data on budburst times (at least in species that flower and leaf out simultaneously) suggests further studies, using what I call the ‘herbarium approach’. The approach opens up the possibility to (i) investigate species-specific and even within-species phenological variation along latitudinal gradients (ecotypic phenological differentiation) and (ii) to compare the inter-annual variation in budburst dates between photoperiod-sensitive and insensitive species to discover the limitations day-length dependency sets to climate-change induced phenological shifts. Knowing the extent to which photoperiod- sensitive species are able to track temperature might give important ecological implications for future warming scenarios, because day-length independent species are thought to gain a

154 competitive advantage over photoperiod-sensitive species due to a greater potential to lengthen the growing season (Körner & Basler 2010).

Fig. 1. The spring phenology of Acer platanoides as seen in herbarium records.

The herbarium method also opens up the possibility to study phenological evolution: by comparing phenological behavior along latitudinal gradients it is possible to assess how long- lived plant genotypes respond to climatic niches. Evolution requires heritable traits that differ among individuals and eventually populations. Documenting population-level traits in natural history collections is often difficult because collecting (i.e., sampling) is not always sufficiently dense for addressing micro-evolutionary questions. Historic collections sometimes allow observing trait change directly, for example, over 100-, 50- or 25-year periods. Among the plant traits in which changes over time and space (in different populations) has been inferred from herbarium collections are leaf width (Guerin et al., 2012), leaf-out times (Zohner & Renner, 2014; Everill et al., 2014), and flowering times (Borchert, 1986, 1996; Primack et al., 2004; Lavoie & Lachance, 2006; Miller-Rushing et al., 2006; Panchen et al., 2012; Calinger et al., 2013). Typically, this type of study involves plotting the days of the year when flowering or flushing specimens of a particular species were collected over time or, alternatively, against the accumulated chill days or spring warming days of the respective years. The method has been used since the mid-1980s and has been tested against actual observations in the field for the same species also studied in the herbarium (Borchert, 1986, 1996; Robbirt et al., 2011; Davis et al., 2015). With the except of the earliest such studies (Borchert, 1986, 1996), all authors have related earlier flowering or leaf-out times today compared to those in the past (back to 130 years; Zohner

155 & Renner, 2014) to the earlier arrival of spring in the North American and European continent from where the respective herbarium material came, using linear regression. Based on this work, species-specific phenological behavior obtained from sufficiently large numbers of herbarium specimens is similar to behavior observed in field studies. The herbarium method is unique in containing a time and space component. Hence, it allows for studying how plant genotypes have adapted to climatic niches by comparing the phenological behavior (over time) between different populations. In combination, all this means that herbarium specimens represent a great resource for a targeted enlargement of our understanding of species’ phenological behavior. So far, leaf- out phenology has been studied in a tiny proportion of the World’s woody species, and many potentially important factors are just beginning to be analyzed (see Chapter 5).

6.5 Future research questions 6.5.1 Intraspecific variability of leaf-out phenology studied along latitudinal gradients Analyses of intraspecific phenological variation in naturally wide-ranging species’ along N-S gradients will allow assessing how long-lived plant genotypes respond to changing climatic niches (Olson et al., 2013). Up to now, few studies have applied this idea (but see Borchert et al., 2005), most likely because gathering phenological data along wide-ranging latitudinal gradients is very time consuming. To tackle this problem I am planning to combine two approaches that will allow for fast and inexpensive generation of multi-species phenological data covering Northern to Southern Europe: (i) the use of herbarium specimens to obtain the timing of leaf unfolding and (ii) the use of the twig-cutting method to study regional effects on species’ phenological cueing mechanisms (photoperiod, chilling, and spring warming). I have already selected about 20 widespread tree species from seven families that are well represented in herbaria and whose leaf-out I plant to study (i) along latitude, (ii) over time, and (iii) between species. This will give me long time series for local leaf-out times to study the degree of site- specificity in the magnitude and direction of the responses to global climate warming. In summer 2014, I visited the herbaria of Aarhus, Copenhagen, and Stockholm to gather phenological data for Northern Europe, and I now have in hand photos of about 3000 specimens from the 20 species, all geo-referenced, at the stage of leaf-out as defined with consistent criteria. Phenological data for Southern Germany is available for the same species from my earlier work

156 (Zohner & Renner, 2014). To enlarge the sampling, especially for Southern locations, I am planning to visit the herbaria of Berlin, Florence, Istanbul, and Paris. To study and compare leaf-out dates, specimens collected over large areas need to be geo-referenced and related to the local climate and photoperiodic conditions. For example, a specimen sampled on 25 April may represent a very early-flushing event at the northern limit of the area, but a mid- or late-flushing case at the southern limit. Consequently, each date has to be adjusted according to the prevailing climate conditions occurring at a plant’s sampling location. This will allow comparing climate tracking in temperature-sensitive and photoperiod-sensitive species. Because day-length changes consistently each year, plants using these signals to time their leaf unfolding can be expected to show low variation from year to year. Preliminary evidence supports this (see Fig. 2 in which temperature sensitivity is compared between photosensitive Fagus sylvatica and insensitive Acer platanoides and Carpinus betulus).

Fig. 2. Using herbarium specimens to study the influence of spring air temperature on leaf-out date in three common European tree species modified from Zohner and Renner (2014). (A) Fagus sylvatica, showing a leaf-out advance of only 2.3 days per each 1°C increase in spring temperature. (B) Acer platanoides, showing a leaf-out advance of 3.2 days/°C. (C) Carpinus betulus, showing a leaf-out advance of 5.4 days/°C.

In addition, because the fastest increase in day length occurs at the same time all over the Northern Hemisphere, photosensitive species are expected to show low spatial variation

157 (along their latitudinal distribution gradient) compared to photo-insensitive species. Up to now, no study has investigated this, however, because phenology is usually monitored locally so as to reduce confounding effects from genetic or phenotypic geographic variation within species. To test the twin hypotheses of low inter-annual variation and predictable spatial variation along latitude (with spring day length change become steeper further north) in photosensitive species, I will compare their phenological responses to that of species that do not rely on photoperiod. The expectation is that photoperiod-sensitive plants should be less affected by spring warming at all sites and will show lower between-site variation. Because the temporal occurrence of photoperiod and temperature signals changes with latitude, I expect change in species’ strategies with latitude. Using herbarium data, I can infer species’ order of leaf-out at different latitudes. By combining this information with climate information, I will also be able to answer the question if the Northern distribution limit of certain species is constrained by the probabilities of young leaves to suffer frost damage (Chapter 5 provides examples of spring frost damage and how it relates to species’ native ranges). The pilot data show that leaf unfolding in Acer platanoides on average occurs 2.4 days later per each degree increase in Northern latitude (Fig. 3).

150

140

130 out (DOY) − Leaf

120

110

50 55 60 Latitude Northern latitude Fig. 3. Mean leaf-out dates (day-of-the-year, DOY) of Acer platanoides as a function of latitude. Information on leaf-out came from label information on 80 herbarium specimens photographed in the herbaria of Aarhus, Copenhagen, Munich, and Stockholm. R2 = 0.59, P < 0.001, Slope = 2.4 days / degree latitude.

158 A shortcoming of the herbarium method is that it does not offer direct insights into the environmental (photoperiod and temperature) cues triggering leaf unfolding. To address the question how species’ relative requirements of external leaf-out cues change along latitude, greenhouse experiments are necessary because they allow disentangling the effects of local temperature and day length. I am planning to study intraspecific differentiation of photoperiod and chilling requirements in Fagus sylvatica and other species featuring similar distribution by collecting twigs from 40°N to 60°N (complete latitudinal range of F. sylvatica). Therefore twigs of the same individuals (in Botanical Gardens or private gardens of colleagues) will be collected twice during the winter period (to allow different chilling treatments), brought to Munich and kept under different light conditions to test for latitudinal differentiation in photoperiod requirements. The question I want to answer is if Northern populations are less sensitive to photoperiod signals because day length increases occur too early for late frosts to be safely avoided (see Chapter 3 for a species-level analysis of photoperiod sensitivity).

6.5.2 Leaf senescence and vegetation periods In 2014 and 2015, I gathered leaf-senescence dates for the 450 species that have already been monitored for leaf-out in the Munich Botanical Garden. Sandra Petrone Mendoza, a M.Sc. student in the lab of Susanne Renner whom I co-advised, conducted the 2014 observations (see Petrone Mendoza, 2014). The data in hand allow for studying the adaptive mechanisms leading to interspecific differentiation in the timing of leaf senescence. One expectation is that warm- adapted species lose their leaves earlier than species originating from colder climates because they have higher temperature thresholds necessary to induce senescence (Zohner & Renner, 2014). The opposite expectation arises under a photoperiod-driven senescence scenario: species from cold climates should rely on high photoperiod thresholds because in their native ranges days are still long when temperatures begin to drop. Therefore, when grown together in the Munich Botanical Garden, the senescence times of species from cold regions should precede those of species from more southern climates because the critical short-day threshold to induce senescence is met earlier in Northern species. As shown in Chapter 4, species-specific frost resistance in combination with regional frost probabilities should also have major influences on leaf-fall times. Hence, one can expect conservative growth strategies (in this case early leaf senescence) in species from regions with high inter-annual temperature fluctuations and frost

159 risk. Preliminary analyses support this hypothesis: North American species on average senesced their leaves two weeks earlier than East Asian species, with European species intermediate (see Petrone Mendoza, 2014). Combining data on senescence and leaf-out times will also allow studying the duration of the vegetation period in a global species sample. This might help forecasting future phenological transitions, such as changes in the growing season length, in the floristically changed communities expected from climate warming.

6.5.3 The molecular basis of dormancy release Understanding the molecular mechanisms underlying plant’s responses to environmental forces is essential for predicting plant behavior under global climate change. However, the genetic mechanisms underlying the transition from winter dormancy to photosynthetic activity in temperate woody species are poorly understood. In a review about dormancy in temperate trees, Cooke et al. (2012, p. 1708) state that “[...] dormancy is remarkably difficult to quantify in buds, and we do not yet have any validated molecular or non-destructive physiological markers to demarcate bud dormancy.” A major issue when dealing with temperate tree species is that laboratory research on developmental processes in tall plant species is difficult, restricting studies to young trees. In addition, genomic data are still scarce. Hence, most developmental molecular studies are focusing on annual model plants that do not undergo dormancy (but see Böhlenius et al., 2006; Ibanez et al., 2010). In Populus tremula x tremuloides, Ibanez et al. (2010) showed that the expression of clock genes (e.g., LHY and TOC1) changes as bud burst progresses, suggesting that genes involved in photoperiodism might be adequate indicators for developmental stages during dormancy and subsequent bud development. The CONSTANS (CO) and FLOWERING LOCUS T (FT) genes function downstream of the circadian clock system. The FT gene has been shown in Populus to control growth cessation and bud set in autumn induced by short days (Böhlenius et al., 2006). Down-regulation of FT in Populus resulted in early bud set under short days as well as under long days; while overexpression of CO or FT resulted in continuing growth even under short days, suggesting that FT acts as an inhibitor of growth cessation (Olsen, 2010). Different members of the family of FT-like genes have different roles in dormancy-related processes, and

160 further gene characterization and functional studies are needed to detect their specific roles (Hsu et al., 2011; Olsen, 2010). A first step towards understanding the molecular nature of the transition between winter dormancy and spring development would be the quantification of a plant’s state of dormancy. Therefore, marker genes (that are up-regulated during dormancy or dormancy release) have to be established by performing gene expression analyses in buds. These analyses will give deeper insights in the molecular mechanisms behind budburst and help to describe the complex process of leaf-out more precisely rather than the simple noting of budburst dates. I am planning to use some of the genes described above and analyze their transcription rates via qRT-PCR. By studying the expression of such genes during bud development within trees for which the photoperiod response has been experimentally studied, one would link molecular with experimental data allowing for studying how gene expression controls bud phenology. In photoperiod-sensitive species one would assume circadian-clock genes to be up-regulated during dormancy release in buds, while day-length independent species should lack such a response. A review of the molecular mechanisms suggests that PRR5 is a candidate marker gene for depth of bud dormancy in Populus (Cooke et al., 2012). A pilot study carried out by Sandra Petrone, the M.Sc. student whom I co-advised, on five temperate tree species (Aesculus hippocastanum, Fagus crenata, F. orientalis, F. sylvatica, and Populus tremula), tested for RNA isolation out of leaf primordial tissue in buds, and established two candidate genes (CO and FT) to quantify their expression during dormancy. Sufficient amounts of RNA from leaf buds could be obtained for all selected species. Therefore, leaf bud collection and grinding protocol as described in Petrone Mendoza (2014) can be used for further studying the expression profiles of other woody, non-model species. The quality of the PCR sequences, however, was poor, possibly due to amplification of more than one gene of the same family. To obtain clean sequences, it will be necessary to clone the PCR products to make sure that only one copy is amplified for each species by designing specific primers. In future studies, the expression levels of the selected genes will be studied via qRT-PCR using the isolated RNA obtained. This pilot study represents a first step towards establishing a standard method for RNA isolation of leaf buds within non-model, woody species, with the ultimate goal of studying the molecular basis of dormancy in a broad range of temperate woody species.

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170 ACKNOLEDGEMENTS

I am grateful to Susanne S. Renner for her unequalled support, availability, and trust in my work. I thank M. Silber for help on uncountable organizational issues. I thank J.-C. Svenning and B. Benito for their hospitality and guidance during my time in Aarhus. I thank my colleagues and friends A. Rockinger, G. Chomicki, and L. Chen for sharing their time, humor, and knowledge with me. I thank Hans ‘Schmidl’ for taking care of my experiment plants. C. Bolle, A. Gröger, A. Wiegert, T. Heller, and E. Schmidbauer are thanked for their support with the experiments carried out in the Munich Botanical Garden. I thank all past and present researchers of the institute for the enjoyable working atmosphere and their many great suggestions on my work: S. Abrahamczyk, C. Bräuchler, G. Chomicki, N. Cusimano, A. Fleischman, M. Gottschling, A. Gröger, D. März, A. Patova, S. Petrone, O. Pérez, A. Rockinger, A. Scheunert, M. Smolka, A. Sousa dos Santos, J.-C. Villarreal, and M. Wecker. Finally, I thank my family for their constant support––Mausi, Xavi, Uta, Walther, Magic, Nanni. Mausi is thanked for sacrificing innumerable hours, listening to and reflecting on my ideas concerning this dissertation.

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