Browsing Defense in Tundra Dwarf Birches

Circumpolar variation in anti- browsing defense in tundra dwarf birches

Elin Lindén

Degree Thesis in Ecology 60 ECTS Master’s Level Report passed: 13 January 2017 Supervisor: Johan Olofsson & Mariska te Beest Abstract The shrub encroachment (shrubification) currently happening in tundra ecosystems can result in increased greenhouse gas emissions. Shrubification is in turn generally explained to be driven by increased temperature, but there is regional variation in shrub encroachment that cannot be solely explained by climate. Instead, herbivory is proposed as a key factor since browsing has been shown to regulate density of shrubs in the tundra. Furthermore, regional variation in anti-browsing defense, i.e. various deterrent and/or toxic compounds, has been hypothesized to control the herbivory pressure. Dwarf birches are present and often dominant throughout the low arctic. They can be divided into two functional groups based on their anti- browsing defense, i.e. resinous and non-resinous birches. This study investigated the variation in anti-browsing defense within and among different taxa of dwarf birches and the two functional groups. We also examined if these differences in anti-browsing defense affects the level of invertebrate damage in dwarf birch. We found that although there were clear differences in terpenes between resinous and non-resinous shrubs, neither functional groups nor taxa are sufficient to understand the circumpolar variation in defense compounds. Moreover, the variation in chemical anti-browsing defense had no clear effect on the level of invertebrate damage, indicating that many other factors than food quality regulate the abundance and importance of herbivores. This study does, for the first time, reveal the circumpolar variation in anti-browsing defense in dwarf birches, which will be vital for a mechanistic understanding of the greening of the arctic in the future.

Keywords Anti-browsing defense, dwarf birch, herbivory, plant secondary metabolites, tundra Table of contents

1 Introduction ...... 1 1.1 Background ...... 1 1.2 Herbivory impact ...... 1 1.3 Anti-browsing defense by plant secondary metabolites (PSMs) ...... 1 1.4 Defense in tundra dwarf birches ...... 2 1.5 Can variation in anti-browsing defense explain shrubification patterns? ...... 2 1.6 Aim and hypotheses ...... 3 2 Method and materials ...... 3 2.1 Study area and sampling ...... 3 2.1.1 Study area ...... 3 2.1.2 Sampling and sample selection ...... 4 2.2 Analyses...... 4 2.2.1 Targeted metabolite profiling ...... 4 2.2.2 Traditional condensed tannin analysis ...... 5 2.2.3 Plant traits ...... 5 2.3 Climate data ...... 5 2.4 Statistical analyses ...... 6 3 Results ...... 6 3.1 Circumpolar pattern in anti-herbivory defense in shrub birches ..... 6 3.1.1 General pattern ...... 6 3.2 Testing interspecific variation ...... 7 3.2.1 Secondary metabolites ...... 7 3.2.2 Other traits ...... 9 3.3 Invertebrate herbivory ...... 9 3.4 Traditional analytical methods, butanol acid assay ...... 10 4 Discussion and conclusions ...... 10 4.1 Large variation in anti-browsing defense ...... 10 4.2 No optimization of anti-browsing defense ...... 11 4.3 Herbivory ...... 12 4.4 Analyzing plant secondary metabolites ...... 12 4.5 Conclusions ...... 13 5 References ...... 15

1 Introduction

1.1 Background In arctic tundra ecosystems, there is an ongoing shrub encroachment (shrubification) believed to lead to a large number of negative effects on the ecosystem. Due to change in albedo (Sturm et al. 2005), nutrient cycling and carbon balance (Weintraub and Schimel 2005; Natali et al. 2011) an increase in shrub cover on the tundra can increase greenhouse gas emission resulting in a positive feedback loop between global warming and shrubification (Chapin et al. 2005; Swann et al. 2010; Zhang et al. 2013). Furthermore, there are concerns that an increased amount of shrubs would lead to diversity loss in the tundra plant community by invading and possibly outcompete other tundra vegetation types (Post and Pedersen 2008). Shrub encroachment in tundra ecosystems is generally explained by a close connection to global warming (Walker et al. 2006). That shrubs indeed do benefit from warming have been shown in numerous studies many of them including the genus Betula (Post and Pedersen 2008; Christie et al. 2015). There is however a strong regional variation in tundra shrub response to warming that cannot solely be explained by warming (Elmendorf et al. 2012; Xu et al. 2013). In order to display this, Myers-Smith et al. (2015) carried out a circumpolar study showing a greater sensitivity to summer temperatures in European tundra shrubs than in North American ones. The European shrubs also had a positive sensitivity, i.e. increased growth, while increased temperature rather had a negative or no effect on North American shrub growth. This pattern was, however, not completely homogeneous and exceptions were found in both regions.

1.2 Herbivory impact One factor that can contribute to explaining global shrubification patterns is herbivory (Olofsson et al. 2009; Myers-Smith et al. 2011; Christie et al. 2015). Large herbivores such as reindeer, sheep and muskoxen, especially in high densities, are known to prevent shrub expansion in many systems throughout the arctic by browsing (Olofsson et al. 2001, 2009, 2013; den Herder and Niemelä 2003; Post and Pedersen 2008; Eysteinsson 2009; Hofgaard et al. 2010; Speed et al. 2010, 2011). Also, smaller herbivores such as invertebrates are known to degrade tundra vegetation by severe defoliation during so called mass occurrences or outbreak years (Jepsen et al. 2008; Kaukonen et al. 2013). However, there are also examples of high densities of herbivores having transitory negative or even absent effect on growth and spread in dwarf shrubs (Crête and Doucet 1998; Tremblay et al. 2012). It thus seems like shrubs can respond differently to the same treatment, in this case browsing. This could be due to varying sensitivity to browsing or that herbivores differ in their preference towards the shrubs. Both these explanations could be connected to plant palatability or how well a plant is defended against herbivory.

1.3 Anti-browsing defense by plant secondary metabolites (PSMs) Plants have different ways of defending themselves against herbivores by reducing their palatability. Nutrient quality and lignin content are two examples, but nearly all woody plants also develop a separate anti-browsing defense containing chemical compounds (Kramer and Kozlowski 1979) with a direct deterrent effect on herbivores (Bryant et al. 1991). This chemical type of defense is generally composed of a variety of plant secondary metabolites (PSMs) and has been recognized for several decades (Fraenkel 1959). PSMs are compounds that rather than contributing to growth and development are specified for plants to survive in their environment. Triterpenes and phenolic compounds, such as tannins, are carbon based PSMs known to defend plants against herbivores and pathogens by decreasing palatability or even act as toxins towards enemies (Reichardt et al. 1984; Robbins 2001; Mclean et al. 2009; Forbey et al. 2011). Even within different PSM groups there is a wide diversity of compounds that can act as deterrents separately but are also shown to have interactive effects (Cates 1996; Gershenzon et al. 2012). How plants chemical anti-browsing defense is composed should therefore connect to the extent at which they are consumed.

1.4 Defense in tundra dwarf birches Dwarf birches are widely abundant in the tundra and can be divided into two functional groups based on their difference in secondary metabolite content: Resinous birches and non-resinous birches.

Resinous birches, such as B. glandulosa and B. nana ssp. exilis, are often considered to be the better defended ones. Their twigs (current annual growth) are densely covered with resin glands producing a resin rich in dammarane triterpenes as toxic papyriferic acid and 3-0- malyonylebetulafolientrioloxide I (Risenhoover et al. 1985; Reichardt et al. 1987; Mclean et al. 2009; Forbey et al. 2011). Deterred feeding in mammals has been shown in experimental studies where resin was added to otherwise palatable food (Bryant 1981; Reichardt et al. 1984; Williams et al. 1992). Also in the field, a variety of mammalian herbivores within the tundra biome have showed lower preference or even avoidance towards resinous birches (Reichardt et al. 1984; Risenhoover 1989; White and Lawler 2002; DeAngelis et al. 2015). Insects, as well as mammalian herbivores, have been recorded to have feeding preferences against resin birches (McLean and Jensen 1985). Insect herbivory, both the so-called background herbivory (herbivory at normal levels) and mass outbreaks, is expected to increase with increased temperature (Barrio et al. 2016; Birkemoe et al. 2016). Therefore, variation in anti-browsing defense should play a role also for the future pattern of insect herbivory. Although being well defended against herbivores, browsing of resinous birches does occur, most commonly in B. glandulosa (Crête and Doucet 1998; Tremblay et al. 2012; DeAngelis et al. 2015).

Non-resinous birches, on the other hand, are considered less defended with a defense dominated by condensed tannins (Julkunen-Tiitto 1996; Graglia et al. 2001). Rather than being toxic, condensed tannins can affect herbivores’ capacity to take up protein and/or have an oxidative effect (Julkunen-Tiito et al. 1996). Although being defended by less effective substances there are studies proposing that non-resinous birches invest more carbon in phenolic compounds (i.e. tannins and flavonoids) than resinous birches do (Graglia et al. 2001). Since resinous birches have triterpenes and not tannins as their main defense compound, this could mean that the dwarf birches invest their carbon in the best defense they have available. Nevertheless, it is said that the strongest vegetation response to herbivory is found within the non-resinous birch range (Fennoscandia, Iceland and Greenland), while the effect often is less or absent in all in areas dominated by resinous birches (Canada, Alaska, East Siberia) (Bryant et al. 2014).

The origin of these differences is not completely known, but there are theories proposing that B. nana in previously glaciated areas, like Fennoscandia, Iceland and Greenland, have a lower genetic diversity (Hewitt 1996; Alsos et al. 2002). This would be due to a more limited gene bank to start with in combination with difficulties for sexual reproduction because of the ice cover (Loveless et al. 1984; Bayer 1991; Bauert 1996; Taberlet 1998). In addition, natural selection driven by herbivores eating the less bad plants can have enhanced regional differentiation in anti-browsing defense (O'Reilly-Wapstra et al. 2012).

1.5 Can variation in anti-browsing defense explain shrubification patterns? It does seem like different kinds of birches differ in their chemical anti-browsing defense and therefore also in their sensibility to browsing. In fact, Bryant et al. (2014) have presented a hypothesis suggesting that the capacity of herbivores to suppress warming-induced increases in Betula shrubs can be decided by the nature of their defense, i.e. if they are resinous or non- resinous. Furthermore, they propose that not only herbivory but also anti-browsing defense should be included when modeling vegetation responses in the arctic as a response to a temperature increase. In order to develop viable predictions of the actual effect of anti- browsing defense on regional patterns in shrub response to global warming, more knowledge of the spatial variation in anti-browsing defense is needed and solid data are crucial.

1.6 Aim and hypotheses The aim of this study is to get an improved spatial resolution of the trait variation within tundra Betula shrubs, with a special focus on anti-browsing defense compounds. During one growth season, extensive sampling across the circumpolar tundra vegetation has been carried out in order to create a database to facilitate regional comparisons. This unique dataset is used to address the questions: 1) Focusing on plant chemical defense, what is the level of trait variation in shrub birches in tundra vegetation? and 2) Does intraspecific trait variation in dwarf birch affect circumpolar patterns in background insect herbivory in the arctic tundra via chemical defense?

To address these questions, the following hypotheses will be tested:

1. Anti-browsing defense composition in tundra dwarf birch is connected to what functional group, i.e. resinous and non-resinous birches, they belong to. 2. Anti-browsing defense composition in tundra dwarf birch is taxon dependent. 3. Tundra dwarf birch invests their carbon in order to optimize their main defense against herbivores, meaning that resinous birches are higher in triterpene content and non- resinous birches are higher in tannin content. 4. The composition of anti-browsing defense in tundra dwarf birch affects the level of insect herbivory. The better-defended resinous birches will be less damaged by insects.

2 Method and materials

2.1 Study area and sampling

2.1.1 Study area The study area covers circumpolar tundra vegetation in the Northern hemisphere at latitudes between 47.3 and 74.5 °N (figure 1).

Figure 1. The distribution of sampling locations of four dwarf birch taxa.

Figure 2. Betula makes 2 kinds of shoots, long shoots and short shoots. Long shoots elongate during the season, with younger and smaller leaves towards the top. Short shoots do not elongate and are made up of a cluster of usually 3-4 leaves that are all of the same age (Source: www.biolib.de).

2.1.2 Sampling and sample selection In the summer of 2014, sampling of both resinous and non-resinous dwarf birch (Betula nana ssp. nana, Betula nana ssp. exilis, Betula glandulosa and Betula pumila; Table 1) was carried out at 164 sites across 55 locations in tundra vegetation in the northern hemisphere. For each site (area of ~10 m radius), 10 individuals were chosen. For each individual, 10 long shoots and 50 short shoot leaves (figure 2) were sampled making a total of 100 long shoots and 500 additional leaves per sampling site. Sampled shoots were allowed to air dry. Some of the in total 164 samples were sampled in experimental sites where sampling was carried out both in control plots and in treatment plots (reindeer exclosures and thermokarsts). Also, some of the samples came from forest vegetation rather than tundra vegetation. In order to eliminate treatment effects and to isolate the study to tundra vegetation a subsample of 128 samples across 41 locations was selected, excluding all reindeer exclosures, thermokarsts, and forest sites.

2.2 Analyses For the chemical analyses of metabolites (both methods) and nitrogen and carbon, only short shoot leaves were used. This was to get as much phenological heterogeneity as possible between the samples. The dried leaves were ground using a ball mill.

2.2.1 Targeted metabolite profiling To quantify plant secondary metabolites connected to anti-browsing defense, a targeted liquid chromatography- mass spectrometry (LC/MS) analysis was carried out. LC/MS is an analytical chemistry technique combining physical separation methods of liquid chromatography and further detection by mass spectrometry (MS). In LC, liquid at high pressure is used to force samples through a column packed with particles chosen to separate out certain kinds of compounds. MS is used to further separate different metabolites from each other by ionizing them and sort the ions by their mass-to-charge ratio. Ten mg of dried leaves were ground and extracted according to (Gullberg et al. 2004). About 2 µl of the extracts were

4 injected into a onto an Acquity UPLC HSST3 column (2.1 x 50 mm, 1.8 µm C18) at 40 °C. The gradient elution was A (H2O, 0.1 % formic acid) and B (75/25 acetonitrile:2-propanol, 0.1 % formic acid) as follows: 0.1 - 10 % B over 2 minutes, B was increased to 99 % over 5 minutes and held at 99 % for 2 minutes; returning to 0.1 % for 0.3 – 0.9 minutes, the flow-rate being 0.5 mL min-1. The compounds were detected with an Agilent 6540 Q-TOF mass spectrometer equipped with an electrospray ion source operating in negative ion mode. This study focused on the secondary metabolite classes previously reported as being involved in plant anti- browsing defense as follows: triterpenes, condensed tannins, hydrolysable tannins, complex tannins, flavonoids and chlorogenic acid. So, a targeted MS approach was used based on the diagnostic fragments produced during LC/MS analysis of the predetermined class of metabolites. From the analysis, 112 metabolites were identified and grouped for the statistical analysis. Many of the metabolites within the classes are not cited in previous literature since they have never before been identified in Betula.

2.2.2 Traditional condensed tannin analysis To be able to compare the LC/MS technique to more traditional quantification methods, condensed tannins were analyzed colorimetrically using acid butanol assay (Porter 1986). Colorimetric assays induce color change of analyte using reagent whereupon quantification can be carried out measuring the absorbance of the tinted samples. Each sample was extracted twice in 70% acetone with 1% ascorbic acid. Twenty µl of the extracts were mixed with 800 µl reagent (95% butanol and 5% HCl) and 180 µl Milli-Q water. To induce color change reaction in tannins, the mixtures were heated in 95 °C for 50 minutes. Absorbance was measured using spectrophotometry and condensed tannin concentration was calculated using a standard curve (concentration range: 2 – 64 µg of procyanidin B/µl extract).

2.2.3 Plant traits Nitrogen and carbon isotopes: Ground leaf samples were sent to UC Davis Stable Isotope Facility, University of California, to be analyzed for d15N and d13C. Samples were analyzed using an elemental analyzer interfaces to a continuous flow isotope ratio mass spectrometer (IRMS). Also total nitrogen and total carbon content were quantified in this process.

Specific Leaf Area (SLA): For each sample, 10 leaves were weighed and scanned. In some cases the leaves needed to be pressed before scanning. Leaf area was then calculated by analyzing the scanned images with the ROI manager in ImageJ software (Schneider et al. 2012). True Specific Leaf Area is measured as [fresh leaf area/dry mass], since the samples for this study were already dried leaf characteristics was measured as [dry leaf area/dry mass].

Gland count: for each sample, 10 twigs were defoliated, and a 15 mm segment starting 20 mm from the top of the twig was photographed with a digital camera. The diameter of the twig at the beginning and end of all segments were measured to calculate the area of which the count was carried out (approximated to a half of the total segment bark area). From the digital pictures all visible resin glands were counted. The number of glands was divided by the examined area to give gland/mm2, thereafter mean gland/mm2 was calculated for the 10 replicates.

Insect herbivory: In total, 100 leaves from both long and short shoots were examined for invertebrate damage. In some cases samples needed to be pressed to more accurately execute area estimates. Observed damage was noted as % of damaged area out of the total area of 100 leaves. Proportion (%) of leaves with at least one kind of invertebrate damage was also noted.

2.3 Climate data Climate data, such as annual precipitation, winter temperature (mean temperature of January) and summer temperature (mean temperature of July) were extracted from the WorldClim database (Hijmans et al. 2005). Interpolated grid data with a resolution of 30 arc-seconds (~1

5 km) were imported to the GIS software ArcMAP version 10.4.1. (ESRI 2011), and based on GPS co-ordinates from the sampling locations, climate data were extracted for each sample.

2.4 Statistical analyses For the statistical analyses, chemical compounds were classified into six different groups of compounds; triterpenes, condensed tannins, hydrolysable tannins, complex tannins, flavonoids, and chlorogenic acid. The compounds within each group were summed and all statistical analyses were carried out on group level. The two insect herbivory measurements were strongly correlated to each other (Spearman, rho=0.852, p<0.001) meaning that samples with high damage per leaf also had a higher number of damaged leaves. Since counts of damaged leaves are less sensitive to human errors than estimating damaged leaf area, damaged leaf count was chosen to represent insect herbivory in the statistical analyses.

Similarities and differences in the chemical anti-browsing defense throughout tundra vegetation in the Northern hemisphere were explored using nonmetric multidimensional scaling analysis (NMDS; Minchin 1987). The NMDS analysis was performed using the metaMDS function of the vegan package. Explanatory environmental variables where added to the NMDS using the envfit function in said package (Oksanen et al. 2015). The effect of taxa on invertebrate herbivory was tested using taxon-specific multiple regression analyses computed by lm models. Secondary metabolite groups, nitrogen and carbon content, and environmental variables were set as explanatory variables. The invertebrate herbivory data were found to not follow a normal distribution and was therefore loge-transformed to meet the criteria for analyses with parametric models. In order to compare results from the LC/MS analysis with the traditional butanol acid assay, Spearman’s rank correlation tests was carried out. All statistical analyses were performed using R software (version 3.0.2) (R Core Team 2016).

3 Results

3.1 Circumpolar pattern in anti-herbivory defense in shrub birches

3.1.1 General pattern The mean scores from the NMDS analysis (stress level=0.11) showed a gradient in triterpene to tannin content among the samples. That gradient seemed to be related to the first axis (NMDS1) with higher triterpene content at low NMDS1 values (figure 3). The second axis was mainly explained by a shift from condensed tannin to hydrolysable tannin content with more condensed tannins at high NMDS2 values (figure 3). The fist axis was somewhat explained by winter temperature and annual precipitation. Moreover, resin gland counts as well as SLA was higher, i.e. more glands and thinner leaves, at low NMDS1 values. Conversely, procyanidin content analyzed with traditional methods showed to be higher at high NMDS1 values. The traditional procyanidin measurements strangely enough fell out independently from the condensed tannins analyzed with the metabolomics method in the NMDS plot (see more in section 3.4). The second axis was explained by latitude, summer temperature and sampling date. Percentage of invertebrate damaged leaves fell out orthogonal to the triterpene-to-tannin gradient pointing towards no clear connection between level of herbivory and chemical defense, but rather an effect of environment and taxon. Annual precipitation and d15N was higher at high NMDS2 values (figure 3).

There was a differentiation between resinous birches (B. nana ssp. exilis and B. glandulosa) and non-resinous birches (B. nana ssp. nana and B. pumila) (figure 3) induced by a higher triterpene content in the resin birches. The difference was however continuous and better explained as a gradual change. Within the resin birches, the two species were not significantly different in anti-herbivory defense. For the non-resinous birches, B. pumila is clearly

Bp 0.6 Bp

Bp 0.4 Bp

Bg Bg Bg c_tan 0.2 invd_leafday ann_perc Bg c_acid Bg Bn BgBne Bn BgBg d15N NMDS2 Bne Bne Bn BgBneBgBg Bn Bn BnBn Bg BgBg Bne BnBnBnBn BneBgBneBneBne BnBnBnBnBn Bne Bne Bne BnBnBnBnBnBnBnBnBnBn glands Bne BneBne Bn BnBnBnBnBnBnBn temp_jan 0.0 BneBneBneBne Bn BnBnBnBn triter Bg BneBne Bn Bn Bn procyanidin Bne Bne compl_tanBnBnBnBnBnBnBnBn Bg Bn Bn BnBn Bg BneBne BnBn Bn Bn SLA Bn Bn Bn Bne Bn Bn Bne h_tanflav Bn Bn Bn Bne Bn -0.2 Bne Bn Bn

Bn latitude

-0.4

-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6

NMDS1

Figure 3. Non-Metric Dimensional Scaling ordination showing differences in chemical defense composition in samples of Betula glandulosa (orange), B. nana ssp. exilis (green), B. nana ssp. nana (blue) and B. pumila (black). Mean scores of the compared compound groups (triterpenes, condensed tannins, hydrolysable tannins, complex tannins, flavonoids and chlorogenic acid) are written out in black. Joint plots show environmental variables and plant traits in red. NMDS stress =0.11. separated from B. nana ssp. nana along NMDS2 (figure 3) showing a difference in tannin composition. No general difference in tannin composition between the two functional groups was however detected.

3.2 Testing interspecific variation

3.2.1 Secondary metabolites Both differences and similarities in composition of chemical anti-browsing defense were found for the four Betula taxa. Triterpenes were higher in the resinous B. glandulosa and B. nana ssp. exilis than in the non-resinous B. nana ssp. nana and B. pumila (Table 1; figure 4a). Both condensed and hydrolysable tannin content was at the same level for the non-resinous B. nana ssp. nana as for the two resinous taxa. (Fig 4b,c). For B. pumila mean condensed tannin content was at least 60% higher (Table 1; figure 4b) and hydrolysable tannin content at least 83% lower (Table 1; figure 4c) than in the other taxa. The most common defense compound in B. pumila was clearly condensed tannins while the other three taxa were highest in hydrolysable tannin content (figure 4b,c). Looking at the complex tannin content, it was higher in B. nana ssp. nana compared with the other three taxa and lower in B. pumila than in the two B. nana subspecies (Table 1; figure 4d). Also flavonoid content was higher in Betula nana

Table 1. One-way ANOVAs testing interspecific variation in plant secondary metabolites involved in anti-browsing defense in four dwarf birch taxa (B. glandulosa, B. nana ssp. nana, B nana ssp. exilis, B. pumila). Significant values are written in bold. Taxon Source of variation df F p Triterpene 3 230 <0.001 Condensed tannin 3 18.87 <0.001 Hydrolysable tannin 3 7.54 <0.001 Complex tannin 3 54.69 <0.001 Flavonoid 3 51.87 <0.001 Chlorogenic acid 3 1.43 0.237

Figure 4. Boxplots representing triterpene (a), condensed tannin (b), hydrolysable tannin (c), complex tannin (d) and flavonoid (e) content as well as specific leaf area (f), resin gland count on current year twigs (g) and stable isotope content as d15N (h) and d13C (i) in samples from four dwarf birch taxa (B. glandulosa, B. nana ssp. exilis, B. nana ssp. nana and B. pumila). The boxplots contain of median (thick line), inner quartile (box) and outer quartile (error bar). The inner and outer quartiles indicate the degree of dispersion within the samples, present outliers can be seen as circles.

ssp. nana while it was lower in B. pumila than in any other taxa (Table 1; figure 4e). There was, however, no difference in chlorogenic acid content between the four dwarf birch taxa (S2).

Table 2. One-way ANOVAs testing interspecific variation in plant traits in four dwarf birch taxa (B. glandulosa, B. nana ssp. nana, B. nana ssp. exilis, B. pumila). Significant values are written in bold.

Source of variation: Species df F p Carbon 3 0.18 0.911 Nitrogen 3 2.42 0.069 d13C 3 8.14 <0.001 d15N 3 7.68 <0.001 SLA 3 8.67 <0.001 Gland count 3 232 <0.001 Invertebrate damage 3 3.18 0.027

3.2.2 Other traits There was no statistical difference in carbon or nitrogen content between the taxa (S3a,b). Specific Leaf Area was higher in B. nana ssp. exilis than B. nana ssp. nana, meaning that the resinous sub-species has thinner leaves than the non-resinous one (Table 2; figure 4f). Resin gland count was significantly higher in B. glandulosa and B. nana ssp. exilis than in B. nana ssp. nana and B. pumila (Table 2; figure 4g). B. nana ssp. exilis was lower in d15N than the non-resinous birches but did not differ from B. glandulosa (Table 2; figure 4h) in this trait. B. nana ssp. exilis and B. nana ssp. nana was lower in d13C than B. glandulosa. Additionally, d13C was lower in B. nana ssp. exilis than in B. pumila (Table 2; figure 4i)

3.3 Invertebrate herbivory Statistical difference was found between the four Betula taxa in number of invertebrate- damaged leaves (Table 2), but the difference did, however, not survive the post hoc test. However graphical examinations implies a difference between species (figure 5). When further investigating the cause of variation in invertebrate-damaged leaves among the taxa, no clear trend was found. Higher invertebrate damage in B. nana ssp. nana was explained to 33% by lower nitrogen, flavonoid and complex tannin content, as well as higher summer temperature

Figure 5. Boxplot representing number of invertebrate damaged leaves out of 100 leaves in samples from four dwarf birch taxa (B. glandulosa, B. nana ssp. exilis, B. nana ssp. nana and B. pumila). The boxplots contain of median (thick line), inner quartile (box) and outer quartile (error bar). The inner and outer quartiles indicate the degree of dispersion within the samples, present outliers can be seen as circles.

Table 3. Multiple linear regression model describing the effect of plant chemistry and environmental variables on invertebrate damage in three dwarf taxa (B. glandulosa, B. nana ssp. exilis, B. nana ssp. nana).

Betula glandulosa Betula nana ssp. exilis Betula nana ssp. nana

β t p β t p β t p

Nitrogen 0.81 3.58 <0.01 -0.445 -3.66 <0.001 Condensed 0.91 4.46 <0.001 tannin Complex -0.26 -2.15 <0.01 tannin Triterpene 0.96 4.16 <0.01 -0.72 -3.21 <0.01 Flavonoid -0.36 -3.10 0.035 Summer 0.23 2.06 0.04 temp. Annual prec. 0.90 4.60 <0.001 0.36 3.06 <0.01

Total model: F (3,13)=10.01 (2,16)=7.31 (5,59)=5.87 R2 0.698 0.477 0.332 p <0.01 <0.01 <0.001 and annual precipitation (Table 3). For B. nana ssp. exilis 48% of the variation could be explained by a higher nitrogen and lower triterpene content (Table 3). Moreover, an increase in invertebrate damage in B. glandulosa could be explained to 69.8% by higher triterpene and condensed tannin content and higher annual precipitation (Table 3).

3.4 Traditional analytical methods, butanol acid assay In the NMDS analysis, the traditional procyanidin measurements showed to be independent from the condensed tannins analyzed with the metabolomics method (figure 3). A correlation test confirmed this independency (rho=0.091, p=0.31). When further comparing the measurements from the butanol acid assay to all separate metabolites analyzed with the LC/MS, they were correlated with many of the condensed tannins (rho=0.224-0.519) but also uncorrelated or even negatively correlated with several of them (S1). Correlations were also found to compounds from other metabolite groups such as hydrolysable tannins and flavonoids (S1).

4 Discussion and conclusions

In this study we wanted to study the circumpolar variation in anti-browsing defense in tundra dwarf birches. The results show a difference in anti-browsing defense between resinous and non-resinous dwarf birches in terms of triterpene content but not in terms of tannin content. Large variations in anti-browsing defense within the two functional groups, but also within species and subspecies of the investigated tundra dwarf birches, were also found. No clear connection was, however, found between chemical anti-browsing defense and insect herbivory damage. Lastly, the comparison of the traditional analytical methods and our LC/MS analysis showed poor correlations for many of the compounds that are said to be quantified by the traditional method.

4.1 Large variation in anti-browsing defense The first hypothesis of this study was that anti-browsing defense composition is connected to if a birch is resinous or non-resinous. This connects to the hypothesis of Bryant et al. (2014)

10 meaning that the response of herbivory on warming-induced shrub increase in tundra regions could be explained by dominance of resinous versus non-resinous birches. For their hypothesis to be true there must be some kind of homogeneity within the two functional groups when it comes to anti-browsing defense, i.e. my first hypothesis must be true. The results from this study however show that it is not this straightforward. The resinous birches do indeed have more triterpenes than non-resinous birches, but there are no unanimous differences in tannin content between the two groups. Also, the variation within the two functional groups is large. The two non-resinous birches B. pumila and B. nana ssp. nana are for example differentiated in anti-browsing defense composition. Not only do they differ in condensed tannin and hydrolysable tannin content, B. pumila also contains far less complex tannins and flavonoids compared to B. nana ssp. nana. Even though the two resinous birch species do not differentiate from each other there is still a significant variation in anti-browsing defense within the functional group, most of it connected to differences in tannin content. It is thus not completely straightforward to define the nature of the anti-browsing defense in dwarf birch vegetation by functional group. Estimating regional response of Arctic birch shrubs with increased temperature based on if resinous or non-resinous birches are dominating would therefore cause large errors.

Moreover, the second hypothesis, that anti-browsing defense should be taxon dependent, is proven wrong since there is generally large variations in defense composition also within all the studied birch taxa. The NMDS analysis show scattered results for all four taxa, and when looking at the content of the secondary metabolite groups on taxon level, the range is generally large. Even generalizing vegetation response to herbivory on taxon level might therefore lead to misinterpretations, at least when focusing on anti-herbivory defense.

The origin of the large variation in anti-browsing defense even on taxon level is to be further investigated. There are studies showing that differences soil nutrient resources affects the production of at least phenolic defense compounds, where an increase in plant available nitrogen results in a decrease in chemical defense (Keinanen et al. 1999; Stark et al. 2015). Moreover, the variation in genetic diversity throughout the Arctic with generally lower genetic diversity in previously glaciated areas (Hewitt 1996; Alsos 2002) should not be neglected. More studies are needed in order to fully understand the genetic diversity within the taxon and to examine if anti-browsing defense diversity can be connected to genetic diversity. Furthermore, this study did not investigate any spatial trends in the variation of anti-browsing defense. There is still a possibility that anti-browsing defense can affect shrubification just not at a large regional scale and not with the regards to functional group or taxa. Apart from anti-browsing defense there are however other factor that could affect if variabilities in shrub expansion such as nutrient and carbon resources and hydrological conditions. These factors should also be taken into further consideration in future studies.

4.2 No optimization of anti-browsing defense The third hypothesis of this study, that tundra dwarf birch invests their carbon in order to optimize their main defense against herbivores, has been suggested before. Graglia et al. (2001) found higher concentrations of phenolic compounds in non-resinous B. nana from Abisko compared to resinous B. nana from Toolik. In contrast to this, and to hypothesis 3 of this study, the results do not reveal any clear optimization of main chemical defense. B. nana ssp. nana does not differ much from the resinous birches when it comes to how much condensed or hydrolysable tannins they contain. In other words, just because tannins are the major secondary metabolite involved in non-resinous birch defense they do not necessarily have to contain more of those compounds. A defense optimization could be found in B. pumila; they seem to be almost exclusively defended by condensed tannins. Although, the B. pumila population is relatively small and isolated and does not contribute a lot to large-scale regional patterns, it does provide important information of the complexity of anti-browsing defense in Betula shrubs.

Although there is not necessarily a difference in phenolic compound content in tundra dwarf birches, when including triterpenes there is a quantitative difference in carbon based secondary metabolites. Furthermore, as Graglia et al. (2001) proposed there is no difference in carbon content between the taxa. Resinous birches therefore seem to invest more of its carbon, in total, in secondary metabolites than non-resinous birches. This gets even further support in the SLA data. Resinous birches that invest more carbon for secondary metabolites have a higher SLA, i.e. thinner leaves, whereas the less chemically defended Betula nana ssp. nana has lower SLA. Leaf structure can also affect plant palatability. For example, higher lignin content is considered to reduce plant palatability. Even though this study cannot confirm that this is where the ‘left over’ carbon ends up it is definitely a possibility. This result does, however, indicate a trade-off between investing in chemical defense or leaf structure.

4.3 Herbivory We hypothesized that variations in anti-browsing defense would lead to variations in insect herbivory damage where better-defended taxa would show less invertebrate damage. Despite differences in anti-browsing defense among the taxa, no substantial difference was found in background insect herbivory. Herbivores, both invertebrates and mammalians, can find ways of coping with low quality food and there are examples of herbivore counter-adaptions and offenses towards plant anti-browsing defense. Feeding choices (like keeping a mixed diet), enzymatic metabolism of defense compounds and sequestration of such compounds (Karban and Agrawal 2002; Sorensen and Dearing 2006) are some examples. Another factor that obviously affects the level of herbivory damage is herbivore density. For example, our study only included background herbivory. Thus, we cannot say if there are any connections between what areas that are more or less affected during mass outbreaks and spatial variation in anti- browsing defense. When it comes to larger herbivores, such as reindeer, densities can be affected by weather events diminishing their food supply. For example, in 2006 and 2013 so called rain on snow events on the Yamal Peninsula led to massive winter mortalities in the reindeer population in the region (Forbes et al. 2016).

That no effect of anti-browsing defense was found on invertebrate damage does however not mean that anti-browsing defense has no effect on herbivores or herbivory levels at all. Without chemical defense the shrubs would most certainly not survive, if not eaten by invertebrates or larger herbivores, they would not be able to cope with fungi or other pathogens (Kramer and Kozlowski 1979). Moreover, the fact that anti-browsing defense has evolved in such a variety despite its high cost for the plant is strong support for its necessity. The result does, however, indicate that other factors than just food quality regulate both the abundance and importance of herbivores.

4.4 Analyzing plant secondary metabolites Our results from the LC/MS analysis show that both Betula nana sub-species and Betula glandulosa have more hydrolysable tannins than condensed tannins. This does not mean that hydrolysable tannins should be considered as the major secondary metabolite in the non- resinous birch anti-browsing defense. Condensed tannins can still be the more effective of the two tannins when it comes to defense against browsing. It does however tell us something about the importance of incorporating new and better methods of analyzing ecological samples in order to improve our knowledge. Previous literature state that the major secondary metabolite involved in the defense in non-resinous birches is condensed tannins (Julkunen- Tiito et al. 1996). In the study by Graglia et al. (2001), condensed tannins were shown to be the most abundant phenolic compound in both resinous and non-resinous Betula nana. Using butanol acid assay for analyzing condensed tannins and HPLC (high performance light chromatography) methods previously described (Julkunen-Tiitto 1989; Julkunen-Tiitto et al. 1996) for analyzing hydrolysable tannins they measured the condensed tannin content to be at least 100 times higher than the hydrolysable tannins content. In our study, the comparison of the LC/MS analysis and the traditional butanol acid assay showed that the results from the two methods were more or less decoupled from each other. There was no correlation at all between

12 the procyanidin content measured with the butanol acid assay and the summed condensed tannins from the LC/MS. When comparing each secondary metabolite to the procyanidin content from the butanol acid assay several condensed tannins were not at all correlated whereas some were correlated. Also, secondary metabolites from other classes, such as flavonoids, which are precursors (building blocks) to condensed tannins, were at least as correlated as some of the condensed tannins. There is, in other words, a big uncertainty in what the traditional quantification methods can tell us about plant anti-browsing defense.

Incorporating the LC/MS metabolomics technique has not only resulted in more exact quantifications of secondary compounds, it has also contributed to more detailed data including identification of many secondary metabolites not previously found in Betula. Moreover, this technique allows us to analyze all kinds of compounds with the same method giving us the opportunity to compare relative differences between analytes. This technique definitely opens up new doors for future phytochemical studies by providing high-resolution data.

4.5 Conclusions For the first time, this study unveils the circumpolar variation in anti-browsing defense in dwarf birches. This information will be of great help for understanding what the variation might mean to the shrubification in the Arctic. It can be concluded that neither functional group nor taxon can provide a valid interpretation of the fate of shrub vegetation with increased temperature when looking at anti-browsing defense. More studies including genetic diversity, herbivore density and possible spatial effects of variation in anti-browsing defense are, however, necessary in order to examine further factors regulating the impact of herbivores on vegetation and vice versa.

Acknowledgements First and foremost I want to thank Johan Olofsson and Mariska te Beest for trusting me with this project. You have given me a lot of freedom to take my own decisions but also given me both scientific and moral support when I needed it. I also want to thank Ilka Abreu and Thomas Moritz at the Umeå Plant Science Center for a nice collaboration. A special thanks to Ilka for carrying out the metabolomics analyses and for guiding me in the butanol acid assay. Lastly I really must express my warmest gratitude to my wonderful friends. Thanks for cheering me on, driving me, comforting me, letting me spend time in my “thesis-cave” and for occasionally dragging me out of it. A special thanks to Cecilia Ribbefors, Madelene Fridell, Helena Dahlberg, Petter Johansson and Emily Pickering Pedersen.

5 References

Alsos, I. G., Engelsskjøn, T., Brochmann, C. 2002. Conservation genetics and population history of Betula nana, Vaccinium uliginosum and Campanula rotundifolia in Arctic archipelago of Svalbard. Arctic, Anarctic and Alpine Research, 34: 408-418. Barrio, I. C., Bueno, C. G. & Hik, D. S. 2016. Warming the tundra: reciprocal responses of invertebrate herbivores and plants. Oikos, 125: 20-28. Bauert, M. R. 1996. Genetic diversity and ecotypic differentiation in arctic and alpine populations of Polygonum viviparum. Arctic and alpine research, 28: 190-195. Bayer, R. J. 1991. Patterns of colonal diversity in geographically mariginal populations of Annetaria rosea (Asteraceae, Inuleae) from sub-arctic Alaska and Yukon territory. Botanical gazette, 152: 486-493. Birkemoe, T., Bergmann, S., Hasle, T. E. & Klanderud, K. 2016. Experimental warming increases herbivory by leaf-chewing insects in an alpine plant community. Ecology and Evolution, 6: 6955-6962. Bryant, J. P. 1981. Phytochemical Deterrence of Snowshoe Hare Browsing by Adventitious Shoots of 4 Alaskan Trees. Science, 213: 889-890. Bryant, J. P., Joly, K., Chapin, F. S., Deangelis, D. L. & Kielland, K. 2014. Can antibrowsing defense regulate the spread of woody vegetation in arctic tundra? Ecography, 37: 204-211. Bryant, J. P., Provenza, F. D., Pastor, J., Reichardt, P. B., Clausen, T. P. & Dutoit, J. T. 1991. Interactions between Woody-Plants and Browsing Mammals Mediated by Secondary Metabolites. Annual Review of Ecology and Systematics, 22: 431-446. Cates, R. G. 1996. The role of mixtures and variation in the production of terpenoids in conifer-insect-pathogen interactions. Phytochemical Diversity and Redundancy in Ecological Interactions, 30: 179-216. Chapin, F. S., Sturm, M., Serreze, M. C., Mcfadden, J. P., Key, J. R., Lloyd, A. H., Mcguire, A. D., Rupp, T. S., Lynch, A. H., Schimel, J. P., Beringer, J., Chapman, W. L., Epstein, H. E., Euskirchen, E. S., Hinzman, L. D., Jia, G., Ping, C. L., Tape, K. D., Thompson, C. D. C., Walker, D. A. & Welker, J. M. 2005. Role of land-surface changes in Arctic summer warming. Science, 310: 657-660. Christie, K. S., Bryant, J. P., Gough, L., Ravolainen, V. T., Ruess, R. W. & Tape, K. D. 2015. The Role of Vertebrate Herbivores in Regulating Shrub Expansion in the Arctic: A Synthesis. Bioscience, 65: 1123-1133. Crete, M. & Doucet, G. J. 1998. Persistent suppression in dwarf birth after release from heavy summer browsing by caribou. Arctic and Alpine Research, 30: 126-132. Deangelis, D. L., Bryant, J. P., Liu, R. S., Gourley, S. A., Krebs, C. J. & Reichardt, P. B. 2015. A plant toxin mediated mechanism for the lag in snowshoe hare population recovery following cyclic declines. Oikos, 124: 796-805. Den Herder, M., Niemelä, P. 2003. effects of reindeer on the re-establishment of Betula pubescens subsp. czerepanovii and Salix phylicifolia in a subarctic meadow. Rangifer, 23: 3-12. Elmendorf, S. C., Henry, G. H. R., Hollister, R. D., Bjork, R. G., Boulanger-Lapointe, N., Cooper, E. J., Cornelissen, J. H. C., Day, T. A., Dorrepaal, E., Elumeeva, T. G., Gill, M., Gould, W. A., Harte, J., Hik, D. S., Hofgaard, A., Johnson, D. R., Johnstone, J. F., Jonsdottir, I. S., Jorgenson, J. C., Klanderud, K., Klein, J. A., Koh, S., Kudo, G., Lara, M., Levesque, E., Magnusson, B., May, J. L., Mercado-Diaz, J. A., Michelsen, A., Molau, U., Myers-Smith, I. H., Oberbauer, S. F., Onipchenko, V. G., Rixen, C., Schmidt, N. M., Shaver, G. R., Spasojevic, M. J., Porhallsdottir, P. E., Tolvanen, A., Troxler, T., Tweedie, C. E., Villareal, S., Wahren, C. H., Walker, X., Webber, P. J., Welker, J. M. & Wipf, S. 2012. Plot-scale evidence of tundra vegetation change and links to recent summer warming. Nature Climate Change, 2: 453-457. Esri 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.

Eysteinsson, T. 2009. Forestry in a treeless land. Iceland forest service. Midvangi 2-4, IS- 700, Egilstadir, Iceland. Forbes, B. C., Kumpula, T., Meschtyb, N., Laptander, R., Macias-Fauria, M., Zetterberg, P., Verdonen, M., Skarin, A., Kim, K-L., Boisvert, L.N., Stroeve, A.B. 2016. Sea ice, rain- on-snow and tundra reindeer nomadism in Arctic Russia. Biology Letters, 12. Forbey, J. S., Pu, X. Z., Xu, D., Kielland, K. & Bryant, J. 2011. Inhibition of Snowshoe Hare Succinate Dehydrogenase Activity as a Mechanism of Deterrence for Papyriferic Acid in Birch. Journal of Chemical Ecology, 37: 1285-1293. Fraenkel, G. S. 1959. The raison d'etre of secondary plant substances. Science, 129: 1499- 1470. Gershenzon, J., Fontana, A., Meike, B., Wittstock, U., Degenhardt, J. 2012. Mixtures of plant secondary metabolites: metabolic origins and ecological benefits. In: GLENN R. IASON, M. D. A. S. E. H. (ed.) The ecology of plant secondary metabolites: From genes to global processes. New York: Cambridge Univeristy Press. Graglia, E., Julkunen-Tiitto, R., Shaver, G. R., Schmidt, I. K., Jonasson, S. & Michelsen, A. 2001. Environmental control and intersite variations of phenolics in Betula nana in tundra ecosystems. New Phytologist, 151: 227-236. Gullberg, J., Jonsson, P., Nordstrom, A., Sjostrom, M. & Moritz, T. 2004. Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Analytical Biochemistry, 331: 283-295. Hewitt, G. M. 1996. Some genetic consequences of ice ages, and their role in divergence and speciation. Biological Journal of the Linnean Society, 58: 247-276. Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, 25: 1965-1978. Hofgaard, A., Lokken, J. O., Dalen, L. & Hytteborn, H. 2010. Comparing warming and grazing effects on birch growth in an alpine environment - a 10-year experiment. Plant Ecology & Diversity, 3: 19-27. Jepsen, J. U., Hagen, S. B., Ims, R. A. & Yoccoz, N. G. 2008. Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: evidence of a recent outbreak range expansion. Journal of Animal Ecology, 77: 257-264. Julkunen-Tiitto, R. 1989. Distribution of certain phenolics in Salix species (Salicaceae), Joensuu: University of Joensuu. Julkunen-Tiitto, R. R., M.; Bryant, J.; Sorsa, S.; Keinänen, M.; Sikanen, H. 1996. Chemical diversity of several Betulaceae species: comparison of phenolics and terpenoids in northern stems. Trees, 11: 16-22. Karban, R., Agrawal, A. A. 2002. Herbivore offense. Annual Review of Ecology and Systematics, 33: 641-664. Kaukonen, M., Ruotsalainen, A. L., Wali, P. R., Mannisto, M. K., Setala, H., Saravesi, K., Huusko, K. & Markkola, A. 2013. Moth herbivory enhances resource turnover in subarctic mountain birch forests? Ecology, 94: 267-272. Keinanen, M., Julkunen-Tiitto, R., Mutikainen, P., Walls, M., Ovaska, J. & Vapaavuori, E. 1999. Trade-offs in phenolic metabolism of silver birch: Effects of fertilization, defoliation, and genotype. Ecology, 80: 1970-1986. Kramer, P. J., Kozlowski, T.T. 1979. Physiology of woody plants. , New York: Academic press. Loveless, M. D. a. H., J. L. 1984. Ecological determinants of genetic structure in plant populations. Annual Review of Ecology and Systematics, 15: 65-95. Mclean, S., Richards, S. M., Cover, S. L., Brandon, S., Davies, N. W., Bryant, J. P. & Clausen, T. P. 2009. Papyriferic Acid, An Antifeedant Triterpene From Birch Trees, Inhibits Succinate Dehydrogenase From Liver Mitochondria. Journal of Chemical Ecology, 35: 1252-1261. Mclean, S., Jensen, S.J. 1985. Food plant selection by insect herbivores in Alascan arctic tundra: the role of plant life form. Oikos, 44: 211-221.

Minchin, P. R. 1987. An Evaluation of the Relative Robustness of Techniques for Ecological Ordination. Vegetatio, 69: 89-107. Myers-Smith, I. H., Elmendorf, S. C., Beck, P. S. A., Wilmking, M., Hallinger, M., Blok, D., Tape, K. D., Rayback, S. A., Macias-Fauria, M., Forbes, B. C., Speed, J. D. M., Boulanger-Lapointe, N., Rixen, C., Levesque, E., Schmidt, N. M., Baittinger, C., Trant, A. J., Hermanutz, L., Collier, L. S., Dawes, M. A., Lantz, T. C., Weijers, S., Jorgensen, R. H., Buchwal, A., Buras, A., Naito, A. T., Ravolainen, V., Schaepman-Strub, G., Wheeler, J. A., Wipf, S., Guay, K. C., Hik, D. S. & Vellend, M. 2015. Climate sensitivity of shrub growth across the tundra biome. Nature Climate Change, 5: 887-+. Myers-Smith, I. H., Forbes, B. C., Wilmking, M., Hallinger, M., Lantz, T., Blok, D., Tape, K. D., Macias-Fauria, M., Sass-Klaassen, U., Levesque, E., Boudreau, S., Ropars, P., Hermanutz, L., Trant, A., Collier, L. S., Weijers, S., Rozema, J., Rayback, S. A., Schmidt, N. M., Schaepman-Strub, G., Wipf, S., Rixen, C., Menard, C. B., Venn, S., Goetz, S., Andreu-Hayles, L., Elmendorf, S., Ravolainen, V., Welker, J., Grogan, P., Epstein, H. E. & Hik, D. S. 2011. Shrub expansion in tundra ecosystems: dynamics, impacts and research priorities. Environmental Research Letters, 6. Natali, S. M., Schuur, E. a. G., Trucco, C., Pries, C. E. H., Crummer, K. G. & Lopez, A. F. B. 2011. Effects of experimental warming of air, soil and permafrost on carbon balance in Alaskan tundra. Global Change Biology, 17: 1394-1407. O'reilly-Wapstra, J. M., McArthur, C., Potts, B.M. 2012. Natural selection for anti-herbivore plant secondary metabolites: a Eucaluptys system. In: GLENN R. IASON, M. D. A. S. E. H. (ed.) The ecology of plant secondary metabolites: From genes to global processes. New York: Cambridge university press. Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R, O'hara, R.G. 2015. Vegan: Community ecology package. R package version 2.3.0 http://CRAN.R- project.org/package=vegan. Olofsson, J., Kitti, H., Rautiainen, P., Stark, S. & Oksanen, L. 2001. Effects of summer grazing by reindeer on composition of vegetation, productivity and nitrogen cycling. Ecography, 24: 13-24. Olofsson, J., Oksanen, L., Callaghan, T., Hulme, P. E., Oksanen, T. & Suominen, O. 2009. Herbivores inhibit climate-driven shrub expansion on the tundra. Global Change Biology, 15: 2681-2693. Olofsson, J., Te Beest, M. & Ericson, L. 2013. Complex biotic interactions drive long-term vegetation dynamics in a subarctic ecosystem. Philosophical Transactions of the Royal Society B-Biological Sciences, 368. Porter, L. J., Hirstich, L. N., Chan, B.G. 1986. The conversion of procyanidins and prodelphinidins to cyanidin and delphinidin. Phytochemistry, 25: 223-230. Post, E. & Pedersen, C. 2008. Opposing plant community responses to warming with and without herbivores. Proceedings of the National Academy of Sciences of the United States of America, 105: 12353-12358. Reichardt, P. B., Bryant, J. P., Clausen, T. P. & Wieland, G. D. 1984. Defense of Winter- Dormant Alaska Paper Birch against Snowshoe Hares. Oecologia, 65: 58-69. Reichardt, P. B., Green, T. P. & Chang, S. M. 1987. 3-O-Malonylbetulafolientriol Oxide-I from Betula-Nana Subsp Exilis. Phytochemistry, 26: 855-856. Risenhoover, K. L. 1989. Composition and Quality of Moose Winter Diets in Interior Alaska. Journal of Wildlife Management, 53: 568-577. Risenhoover, K. L., Renecker, L. A. & Morgantini, L. E. 1985. Effects of Secondary Metabolites from Balsam to Poplar and Paper Birch on Cellulose Digestion. Journal of Range Management, 38: 370-371. Robbins, C. T. 2001. Wildlife feeding and nutrition. , San Diego: Academic press. Schneider, C. a. R., W. S. & Eliceiri, K. W. 2012. NIH Image to ImageJ: 25 years of image analysis. Nature methods, 9: 671-675. Sorensen, J. S. a. D., M. D. 2006. Efflux transporters as a novel herbivore countermechanism to plant chemical defenses. Journal och Chemical ecology, 32: 1181-1196. Speed, J. D. M., Austrheim, G., Hester, A. J. & Mysterud, A. 2010. Experimental evidence for herbivore limitation of the treeline. Ecology, 91: 3414-3420.

Speed, J. D. M., Austrheim, G., Hester, A. J. & Mysterud, A. 2011. Growth limitation of mountain birch caused by sheep browsing at the altitudinal treeline. Forest Ecology and Management, 261: 1344-1352. Stark, S., Vaisanen, M., Ylanne, H., Julkunen-Tiitto, R. & Martz, F. 2015. Decreased phenolic defence in dwarf birch (Betula nana) after warming in subarctic tundra. Polar Biology, 38: 1993-2005. Sturm, M., Schimel, J., Michaelson, G., Welker, J. M., Oberbauer, S. F., Liston, G. E., Fahnestock, J. & Romanovsky, V. E. 2005. Winter biological processes could help convert arctic tundra to shrubland. Bioscience, 55: 17-26. Swann, A. L., Fung, I. Y., Levis, S., Bonan, G. B. & Doney, S. C. 2010. Changes in Arctic vegetation amplify high-latitude warming through the greenhouse effect. Proceedings of the National Academy of Sciences of the United States of America, 107: 1295-1300. Taberlet, P. 1998. Biodiversity at the intraspecific level: the comparative phylogeographic approach. Journal och Biotechnology, 64: 91-100. Team, R. C. 2016. R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna. Austria. ISBN 3-900051-07-0. URL http://www.R- project.org/. Tremblay, B., Levesque, E. & Boudreau, S. 2012. Recent expansion of erect shrubs in the Low Arctic: evidence from Eastern Nunavik. Environmental Research Letters, 7. Walker, M. D., Wahren, C. H., Hollister, R. D., Henry, G. H. R., Ahlquist, L. E., Alatalo, J. M., Bret-Harte, M. S., Calef, M. P., Callaghan, T. V., Carroll, A. B., Epstein, H. E., Jonsdottir, I. S., Klein, J. A., Magnusson, B., Molau, U., Oberbauer, S. F., Rewa, S. P., Robinson, C. H., Shaver, G. R., Suding, K. N., Thompson, C. C., Tolvanen, A., Totland, O., Turner, P. L., Tweedie, C. E., Webber, P. J. & Wookey, P. A. 2006. Plant community responses to experimental warming across the tundra biome. Proceedings of the National Academy of Sciences of the United States of America, 103: 1342-1346. Weintraub, M. N. & Schimel, J. P. 2005. Nitrogen cycling and the spread of shrubs control changes in the carbon balance of Arctic Tundra ecosystems (vol 55, pg 408, 2005). Bioscience, 55: 551-551. White, R. G. & Lawler, J. P. 2002. Can methane suppression during digestion of woody and leafy browse compensate for energy costs of detoxification of plant secondary compounds? A test with muskoxen fed willows and birch. Comparative Biochemistry and Physiology a-Molecular and Integrative Physiology, 133: 849-859. Williams, D. E., Sinclair, A. R. E. & Andersen, R. J. 1992. Triterpene Constituents of the Dwarf Birch, Betula-Glandulosa. Phytochemistry, 31: 2321-2324. Xu, L., Myneni, R. B., Chapin, F. S., Callaghan, T. V., Pinzon, J. E., Tucker, C. J., Zhu, Z., Bi, J., Ciais, P., Tommervik, H., Euskirchen, E. S., Forbes, B. C., Piao, S. L., Anderson, B. T., Ganguly, S., Nemani, R. R., Goetz, S. J., Beck, P. S. A., Bunn, A. G., Cao, C. & Stroeve, J. C. 2013. Temperature and vegetation seasonality diminishment over northern lands. Nature Climate Change, 3: 581-586. Zhang, W. X., Miller, P. A., Smith, B., Wania, R., Koenigk, T. & Doscher, R. 2013. Tundra shrubification and tree-line advance amplify arctic climate warming: results from an individual-based dynamic vegetation model. Environmental Research Letters, 8.

S1: Table of analyzed compounds and results from Spearman’s rank correlations testing for correlation between each compound and quantifications of condensed tannins performed with traditional butanol acid assay. All significant positive correlations are marked out in bold text.

Compound Group rho p Chlorogenic acid Chlorogenic acid 0.157 0.045 Catechin-(4-alpha-8)-epigalloCatechin Complex tannin (P) 0.365 <0.001 Catechin-(4-alpha-8)-epigalloCatechin2 Complex tannin (P) 0.492 <0.001 Catechin-(4-alpha-8)-epigalloCatechin3 Complex tannin (P) -0.412 <0.001 EpigalloCatechin-8-C-ascorbyl-3-O-gallate Complex tannin (P) 0.4198 <0.001 EpigalloCatechin-derived Complex tannin (P) -0.290 <0.001 (+)Catechin-(4-alpha-8)-(+)Catechin Condensed tannin 0.367 <0.001 (Proanthocyanidin B1) (P) (+)Catechin-(4-alpha-8)-(+)Catechin Condensed tannin 0.102 0.196 (Proanthocyanidin B1)5 (P) (+)Catechin-(4-alpha-8)-(+)Catechin Condensed tannin 0.241 0.002 (Proanthocyanidin B1)6 (P) (+)Catechin-(4-alpha-8)-(+)Catechin Condensed tannin -0.186 0.018 (Proanthocyanidin B1)8 (P) (+)Catechin-(4-alpha-8)-(+)Catechin Condensed tannin 0.014 0.857 (Proanthocyanidin B1)11 (P) (+)Catechin-(4-alpha-8)-(+)Catechin Condensed tannin 0.075 0.341 (Proanthocyanidin B1)12 (P) Catechin_derived Condensed tannin -0.486 <0.001 Catechin_derived_2 Condensed tannin -0.345 <0.001 EpiCatechin(4-beta-8)]3-epiCatechin Condensed tannin 0.034 0.666 (-)-EpigalloCatechin-3-O-p-coumaroate Condensed tannin 0.224 0.004 (P) (-)EpiCatechin-(4-beta-6)-(-)epiCatechin- Condensed tannin 0.367 <0.001 (4-beta-8)-(-)epiCatechin (P) Prodelphinidin B4 Condensed tannin 0.519 <0.001 Prodelphinidin B44 Condensed tannin 0.383 <0.001 Prodelphinidin B47 Condensed tannin -0.068 0.39 Prodelphinidin B49 Condensed tannin -0.407 <0.001 Prodelphinidin B410 Condensed tannin -0.156 0.047 Acacetin Flavonoid -0.122 0.121 Acactin19 Flavonoid 0.173 0.027 Apigenin Flavonoid 0.194 0.013 Aromadendrin-glucoside Flavonoid -0.051 0.517 Aromadendrin-glucoside_2 Flavonoid -0.133 0.09 Aromadendrin-glucoside13 Flavonoid -0.179 0.022 Aromadendrin-glucoside14 Flavonoid 0.289 <0.001 Catechin-7-O-xyloside Flavonoid 0.034 0.664 Chrysoeriol Flavonoid -0.238 0.002 Chrysoeriol_derived Flavonoid 0.259 <0.001 Chrysoeriol17 Flavonoid 0.119 0.13 Isorhamnetin Flavonoid -0.156 0.047 Isorhamnetin16 Flavonoid 0.025 0.749 Kaempferol Flavonoid -0.116 0.142 Kaempferol_like Flavonoid 0.323 <0.001 Kaempferol-3-O-glucuronide Flavonoid -0.285 <0.001 Ladanein Flavonoid -0.113 0.151 Ladanein18 Flavonoid 0.007 0.934

Ladanein20 Flavonoid 0.308 <0.001 Myricetin Flavonoid 0.401 <0.001 Myricetin-3-O-galactoside Flavonoid 0.056 0.477 Myricetin-3-O-rhamnoside OR Quercetin- Flavonoid -0.121 0.125 rham Myricetin-3-O-rhamnoside OR Quercetin- Flavonoid 0.445 <0.001 rham15 Naringenin Flavonoid -0.123 0.119 Pentahydroxyflavone trimethyl ether Flavonoid -0.142 0.070 Pentahydroxyflavone trimethyl ether21 Flavonoid 0.319 <0.001 Quercetin Flavonoid -0.07 0.375 Quercetin-3-arabinoside Flavonoid 0.088 0.263 Quercetin-3-arabinoside_? Flavonoid -0.096 0.274 (Galloyl-HHDP-glucose) Corilagin Hydrolysable tannin 0.227 0.004 (Galloyl-HHDP-glucose) Corilagin25 Hydrolysable tannin 0.123 0.119 1.2.3.4.6-penta-O-galloylglucose Hydrolysable tannin 0.149 0.058 1.2.6-tri-O-galloylglucose Hydrolysable tannin -0.155 0.048 1-beta-O-GalloylPedunculagin Hydrolysable tannin 0.211 0.007 1-beta-O-GalloylPedunculagin27 Hydrolysable tannin 0.386 <0.001 Casuarin Hydrolysable tannin 0.390 <0.001 Casuarin26 Hydrolysable tannin 0.197 0.012 Casuarin_derived Hydrolysable tannin 0.049 0.536 Digalloylglucose 1 Hydrolysable tannin -0.057 0.47 Digalloylglucose 2 Hydrolysable tannin -0.052 0.509 Digalloylglucose 3 Hydrolysable tannin -0.124 0.116 Digalloylglucose 123 Hydrolysable tannin -0.017 0.831 ellagic acid-2-O-beta-D-glucopyranoside Hydrolysable tannin -0.047 0.548 Gallic acid_conjugated Hydrolysable tannin -0.267 <0.001 Pedunculagin Hydrolysable tannin 0.002 0.984 Pedunculagin22 Hydrolysable tannin 0.094 0.23 Pedunculagin24 Hydrolysable tannin 0.068 0.387 Tellimagrandin I Hydrolysable tannin 0.28 <0.001 Tetragalloylglucose 1 Hydrolysable tannin -0.143 0.068 Deacetylpapyriferic acid_2 Triterpene -0.360 <0.001 Deacetylpapyriferic acid_230 Triterpene -0.412 <0.001 Deacetylpapyriferic acid_231 Triterpene -0.331 <0.001 BFTO Triterpene -0.32 <0.001 BFTO (m/z 646.392) Triterpene -0.379 <0.001 BFTO_conjugated Triterpene -0.281 <0.001 BFTO_conjugated (m/z 1043.66) Triterpene 0.341 <0.001 BFTO_conjugated (m/z 653.364) Triterpene -0.281 <0.001 BFTO_conjugated_629 Triterpene -0.191 0.145 BFTO28 Triterpene -0.324 <0.001 BFTO29 Triterpene -0.342 <0.001 compound 4_1 Triterpene -0.228 0.003 compound5 Triterpene 0.089 0.258 compound5_1 Triterpene 0.095 0.225 compound5_1 (m/z 1171.69) Triterpene -0.35 <0.001 Compund6 Triterpene -0.247 0.001 Compund7 Triterpene 0.171 0.029 Compund8 Triterpene 0.192 0.014 demalonyl PA Triterpene -0.384 <0.001 demalonyl PA_2 Triterpene -0.273 <0.001

demalonyl PA_conjugated (m/z741.405) Triterpene -0.358 <0.001 Dihydroxy demalonyl PA_1 Triterpene -0.37 <0.001 Dihydroxy demalonyl PA_2 Triterpene -0.353 <0.001 Dihydroxy demalonyl PA_3 Triterpene -0.353 <0.001 Hydroxy demalonyl PA_1 Triterpene -0.355 <0.001 Hydroxy demalonyl PA_2 Triterpene -0.287 <0.001 Hydroxy demalonyl PA (m/z 735.306) Triterpene -0.346 <0.001 Hydroxy demalonyl PA (?) Triterpene -0.336 <0.001 methyl papyriferate Triterpene 0.015 0.061 Papyriferic acid Triterpene -0.324 <0.001 Papyriferic acid (m/z 1075.74) Triterpene -0.323 <0.001 Papyriferic acid_2 Triterpene -0.346 <0.001 Papyriferic acid32 Triterpene -0.324 <0.001 papyriferic acid_conjugated (m/Z 561.378) Triterpene 0.022 0.783 papyriferic acid_conjugated (m/z 757.345) Triterpene -0.366 <0.001

S2: Chlorogenic acid

S2. Boxplot representing chlorogenic acid content in samples from four dwarf birch taxa (B. glandulosa, B. nana ssp. exilis, B. nana ssp. nana and B. pumila).

S3 Carbon and nitrogen

S3. Boxplots representing carbon (a) and nitrogen (b) content in samples from four dwarf birch taxa (B. glandulosa, B. nana ssp. exilis, B. nana ssp. nana and B. pumila).

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