How Are Plants Responding to a Changing Climate?

Biodiversity, Conservation and Management 2008-2009 (MSc)

How are plants responding to a changing climate?

A case study of growth and allocation in Veronica alpina, Viola biflora, Veronica officinalis and Viola palustris in western Norway

September 2009

Tessa Bargmann

This work is submitted in partial fulfilment of the requirements of the degree of Master of Science University of Oxford, 2009

Candidate number: 264831

Table of Contents

List of Figures ...... iii

Abstract ...... iv

Acknowledgements ...... v

1. Introduction ...... 1

1.1 Climate change and alpine plants ...... 1

1.2 The importance of plant traits and allocation for understanding growth and survival ...... 2

1.3 Conservation significance ...... 5

1.4 Questions and Aims ...... 6

1.5 Focal species ...... 7

2. Methods ...... 11

2.1 Study sites ...... 11

2.2 Plant collection and Morphometrics ...... 12

2.3 Data Analysis ...... 14

3. Results ...... 15

3.1 Veronica alpina ...... 15

3.2 Viola biflora ...... 19

3.3 Veronica officinalis ...... 23

3.4 Viola palustris ...... 27

4. Discussion ...... 31

5. Conclusion ...... 36

6. References ...... 37

Appendix ...... 45

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List of Figures

Figure 1: Drawing of Viola biflora taken from CLOPLA (Klimešová & Klimeš, 1998) ...... 8

Figure 2: Drawing of Veronica alpina taken from CLOPLA (Klimešová & Klimeš, 1998) ...... 8

Figure 3: Drawing of Veronica officinalis taken from CLOPLA (Klimešová & Klimeš, 1998) ...... 9

Figure 4: Drawing of Viola palustris taken from CLOPLA (Klimešová & Klimeš, 1998) ...... 10

Figure 5: The SEEDCLIM climate grid in western Norway taken from SEEDCLIM protocol (2009).. 11

Figure 6 a-e: Traits of Veronica alpina plotted against the climate variables precipitation and temperature ...... 17

Figure 7 a-c: Allocations of Veronica alpina plotted against the climate variable temperature ..... 19

Figure 8 a-c: Traits of Viola biflora plotted against the climate variables precipitation and temperature ...... 20

Figure 9 a-d: Allocations of Viola biflora plotted against the climate variables precipitation and temperature ...... 22

Figure 10 a-c: Traits of Veronica officinalis plotted against the climate variables precipitation and temperature ...... 24

Figure 11 a-d: Allocation of Veronica officinalis plotted against the climate variables precipitation and temperature ...... 26

Figure 12 a-b: Traits of Viola palustris plotted against the climate variable precipitation ...... 27

Figure 13 a-e: Allocation of Viola palustris plotted against the climate variables precipitation and temperature ...... 29

iii

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Abstract

Climate change is increasingly affecting organisms around the world. Local extinctions and range changes have been documented over the years, calling for both wide ranging, and detailed studies of the tolerances and adaptations of these species. In this study, plant traits are used to identify these responses in terms of growth and allocation of two alpine plants, Veronica alpina and Viola biflora, as well as two generalists, Veronica officinalis and Viola palustris, which are considered within a climate grid in western Norway. This study shows that these four plant species shift their growth and allocation to different life functions in response to temperature and precipitation increase, and that there are consistencies in responses between species pairs when it comes to allocation. This understanding is then used to discuss the possible implications for the future conservation of alpine plants in the light of climate change.

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Acknowledgements

I owe my deepest thanks to Professor Vigdis Vandvik from the University of Bergen, without whose direction and support I could not have taken part in the SEEDCLIM project. I also want to thank Dr. Olav Skarpaas (University of Oslo), Dr. Kari Klanderud (Norwegian University of Life

Sciences), Eric Meineri and Joachim Spindelböck from the University of Bergen for their guidance and discussions. I want to thank Professor Katherine Willis and Professor Robert Whittaker for their early input and suggestions, and Shonil Bhagwat for both his help in the early stages of dissertation planning and for looking over and making useful comments on the first draft. Thank you also to Professor Deborah Goldberg and Emily Farrer from the University of Michigan for their insights. Finally, I want to say thank you to Keno Ferter, Simon Le Mellec and Daja Ferter for their help in the field.

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1. Introduction

1.1 Climate change and alpine plants

It is generally accepted by the scientific community that climate is changing at an accelerated rate, and that it is having a profound impact upon plants and animals alike (IPCC, 2007; Parmesan

& Yohe, 2003; Root et al., 2003; Walther et al., 2002; IPCC, 2001). The 2001 IPCC report warns that over the last 100 years the global temperature has increased by 0.6oC. However, it is not only the degree of warming during the last century that is alarming, but also the rate of warming, which is currently at its peak (IPCC, 2001). In addition to rising temperatures, changes in precipitation patterns have also been acknowledged. While it is accepted that global climate is changing, it is also important to point out that these changes are not globally uniform, and that some communities are affected more severely, while others remain more resilient during climatic changes (Walther et al., 2002). In addition, it is not only on a coarse scale that climatic impacts differ, but also on a finer scale, where smaller habitat niches exist for a given species.

As a result of a shifting climate the geographical ranges of many species have altered, as exemplified by numerous scientific studies (i.e. Moritz et al., 2008; Pauli et al., 2007; Lesica &

McCune, 2004; Molau & Alatalo, 1998). Increases in temperature and precipitation have placed additional pressures upon arctic and alpine communities in particular, because unlike their lowland counterparts, there is a limit to how far they can disperse upslope into colder and drier habitats (Roots, 1989). In addition, climate change is predicted to have the largest impact on the biodiversity of extreme environments like alpine and arctic biomes, where small changes in precipitation or temperature will have a proportionally larger effect (Sala et al., 2000). This means that these species face problems such as range contraction, and in the worst case, local extinction. As well as the general upslope and poleward shifts of various species (Holzinger et al.,

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2008; Lenoir et al., 2008; Parmesan, 2006; Klanderud & Birks, 2003), latitudinal range shifts have also been studied in the context of climate change (Colwell et al., 2008).

It is understood that climate is a crucial factor in determining where a species can exist. However, it is often insufficient to predict future distributions of a population solely on current distributions and bioclimatic envelope models, because there are other factors such as dispersal ability and biotic interactions that play a role (Pearson and Dawson, 2003). For instance, Molau and Alatalo

(1998) showed that responses to climate change differed between mosses, lichens and vascular plants, and caused a shift in the dominant vegetation type based on what the initial vascular plant cover was previously. It has also been shown that some species are more resilient in the face of climate change than expected, due to facilitation (e.g. Choler et al., 2001; Arft et al., 1999).

Clearly species differ in their responses to climate change, and what may make some species more sensitive than others are differences in climatic tolerances, that is to say their plasticity or their capacity for local adaptation. This calls for studies on how species performance varies within its range in response to climate. With more information on plant plasticity and ability to adapt, a deeper understanding of how plants respond to climatic change can be built up.

1.2 The importance of plant traits and allocation for understanding growth and survival

In order to predict how a species or group of species will behave in response to a change in the environment, it is necessary to understand how its traits respond to different environmental cues. McIntyre et al. define plant traits as characteristics that can be measured or observed and which we assume to be the plants’ response to a change in external conditions (1999). In other words, the use of traits enables us to elucidate the connection between an environmental cue and the subsequent behaviour of the plant, which in turn allows us to understand variation in growth and performance along climatic gradients (Barboni et al., 2004). In fact, scientists have

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been trying to elucidate the connections between external conditions such as climate, disturbance, biotic influences and their influence on plant traits and function for almost as long as the school of ecology exists (Díaz et al., 1998).

It is important to recognise that traits are not expressed independently of each other. Plants have to respond as a whole to selective pressures, rather than each trait responding individually, and it is therefore important to consider numerous traits together (Barboni et al., 2004). Studies have been conducted on how plant allocation changes when influenced by a shift in nutrient levels

(Berendse & Möller, 2009; van Wijk et al., 2003; Körner, 1989; Fitter & Setters, 1988); when impacted by different types of land use or environmental stress (Niu et al., 2009; Ursino, 2009); when influenced by varying levels of competition (Cahill, 2003); as well as due to climatic change

(Fan et al., 2009; Sebastià, 2007; Arft et al., 1999; Díaz et al., 1998).

A good knowledge of plant allocation is important to understand general evolutionary trends as well as how a single plant species or whole community may react to an environmental change.

However, the scientific community is not at a consensus when it comes to defining the rules of plant allocation (Niklas & Enquist, 2002). For example; it was previously hypothesized by Bloom et al. (1985) and others that the below ground structures of alpine species are more pronounced than they are in lowland species because they are found in more stressful, cold dominated habitats. Körner and Renhardt (1987) however, claim that high altitude species do not necessarily rely on a high underground biomass to survive. Instead, they hypothesized that alpine plants rely on either speedy and extensive leaf area development, or a large ratio of leaf area to plant dry weight. In addition to this, their 1987 study shows that the ratios of above and below ground biomass vary greatly between species at both high and low elevations. In another paper Körner

(1989) suggests that the leaves of alpine plants store more nutrients than their lowland counterparts, therefore avoiding the production of a larger root system.

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Over the years, research on plant allocation has accumulated, and scholars are beginning to come closer to being able to predict plant responses. Niklas and Enquist (2002) concluded in a paper that they would be able to predict root biomass fairly accurately based on above ground measurements. Li et al. (2008) also put forward that maximum plant height can be used to predict root to shoot ratios reliably. In their study on allocation in Plantago lanceolata, Berendse and Möller (2009) concluded that nutrient deprival had more of an effect on root to shoot ratios than light competition, and Cahill (2003) wrote that while below ground competition reduced plant growth, it did not increase root growth. There are also more species-specific studies such as

Fitter and Setters’ (1988), which address the topic of nutrients and reproductive allocation in six

Viola species.

While a lot of research has been done on plant traits, not so much has been done specifically with climate change and plant traits (Díaz et al., 1998; Barboni et al., 2004). However, there are some studies that have looked at this issue. Sebastià (2007) suggests that plant biomass production is more temperature limited than water limited in subalpine grasslands of the Pyrenees, and in the recent study by Fan et al. (2009), it was shown that a mean annual temperature increase resulted in a more dramatic decrease in total biomass than an annual precipitation decrease. However, while it is acknowledged that plant traits can be accurate predictors of plant response to environmental change (Barboni et al., 2004), very little is known about how plants respond to climate change (Arft et al., 1999). It is essential that more research is done on plant traits and allocation, because they are useful in both determining the limits of survival and in predicting the response of plant communities.

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1.3 Conservation significance

While the findings of studies based on plant traits are not always entirely conclusive, they are very helpful in that they can tell us something about the limits and tolerance of plant species. In turn, it is this tolerance that enables us to visualise how a species distribution may look under the influence of climatic change. Furthermore, allocation of reproduction due to climate change may tell us something about recruitment of potential migrant populations, which are not yet found in an area of interest (Ibáñez et al., 2008; Díaz et al., 1998).

The response of plants to different climatic conditions is species specific (Arft et al., 1999), and consequently, so are altitudinal range changes (Le Roux & McGeoch, 2008). Thus, a fine scale of analysis is needed in many cases because predictions about habitat loss tend to vary depending on the spatial resolution, geographical extent and elevation range used in predictive models

(Randin et al., 2009). From their study of two regions of the Swiss Alps, Randin et al. suggest that local scale data must be incorporated when predicting the adaptation and migration of plants, because Europe-wide predictions too often overestimate species loss. A fine scale analysis is also of merit when trying to identify refugial habitats, which although limited, may continue to exist for high altitude plants (Klanderud & Birks, 2003).

Effective conservation is not only a matter of science however. While it is important to identify which areas to protect and to predict how a species or group of species may behave, it is equally vital to advance climate change integrated conservation strategies, which will enable ecologists, biogeographers and conservation managers alike to participate. Again, for this to happen, action must be taken with attention to site specificity so that the best areas for conservation can be chosen (Hannah et al., 2002). Many scholars have also emphasized the need for increasing resistance and resilience of habitats by allowing gene pools to be diverse (Hobbs et al., 2006;

Hampe & Petit, 2005; Noss, 2001). Hobbs et al. (2006) urges conservationists to ‘future proof’

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systems, in other words, to think ahead, understand the system, and allow for organisms to respond to climate change without hindering their path. Hampe and Petit (2005) go further to emphasise the importance of rear edge populations as climatic niches as they may not disappear as quickly as it is estimated by bioclimatic envelope models. They also suggest the protection of as many local populations as possible and not just the core areas.

Clearly allowing for genetic diversity within populations is vital to any conservation strategy.

However the conservation implications are very different depending on whether a plant adaptation is genetic or plastic. If plant growth and allocation differs across a climate gradient and the response is genetically determined, it poses a much bigger threat to the species because each population needs to move and adapt independently. If the plant responses are due to their plasticity rather than genetic differentiation, then the threat to that species is less significant, because it can be preserved regardless of where it is found in the climate grid. While this study will not determine whether plant responses are plastic or genetic, it will accomplish the first step in this direction; to quantify the trends, and to determine if plant growth and allocation differs across a range with respect to climate factors.

1.4 Questions and Aims

Due to the need for generating more detailed and local information about different plants and to examine the effects of climate change on them, it is intuitive to assess their responses to climatic variables. By studying both growth and allocation patterns in relation to climatic variables of four plant species, I address four main questions that pertain to the conservation of arctic and alpine plants:

 Do plant species respond to climatic variability within their range in terms of growth and

allocation to different life functions?

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 Are there consistent patterns in these responses between climate specialists and

generalists?

 What implications does this have for future conservation efforts in the light of climate

change?

To answer these questions, I will look at the growth and allocation of two species of Veronica and two species of Viola, where one of each pair is a generalist and one is an alpine species. I will compare the same traits across all four of the species, enabling me to study if allocation is consistent between similar species, or between the alpine/generalist pair. This will then elucidate a little more how plants respond to climate, and what this can mean for conservation efforts.

1.5 Focal species

The plants chosen for this study are two species of Veronica and two species of Viola. Veronica alpina and Viola biflora are alpine species, while Veronica officinalis and Viola palustris are generalists. The rationale for choosing closely related plant species is that possible comparisons can be drawn between how alpine and generalist species do in different climate scenarios.

Veronica alpina

The alpine speedwell (see Figure 1) is a clonal flowering plant found in the alpine regions of

Europe and Canada, with the highest densities found in Scandinavia and Iceland (GBIF, 2009). It is typically found scattered through the habitat where it is native, and has been found at a maximum altitude of 1160 meters and at a minimum altitude of 365 meters (Fitter & Peat, 1994).

Veronica alpina is usually between 5 and 15 centimetres in height, and is characterised by fine hairs on leaves and stems, as well as deep blue oval capsules or blue flowers (Lid & Lid, 1994). The below-ground stems are formed above ground as they are covered by litter or pulled under the soil as the roots contract, where they then act as storage organs for the plant (Klimešová &

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Klimeš, 1998). Veronica alpina is also said to

exist only in areas where the maximum

summer temperature is 25oC (Dahl, 1951).

Viola biflora

The arctic yellow violet, otherwise known as

two flower violet (See Figure 2), is found

primarily in the mountains of Europe and in

Alaska (GBIF, 2009), and it is usually found in Figure 2: Drawing of Veronica alpina taken from CLOPLA (Klimešová & Klimeš, 1998) wet grasslands (Klimešová & Klimeš, 1998).

Like Veronica alpina, Viola biflora’s below

ground stems also formed above ground

(Klimešová & Klimeš, 1998). The maximum

tolerable summer temperature for this

species is around 28oC (Dahl, 1951) and in

the Aurland area, which is near some of the

collection sites, it is not found above 1500

meters (Odland & Birks, 1999). Viola biflora is

characterised by its erect stem, bright yellow

flowers, ovoid capsules and roughly orbicular

or reniform leaves. They are sometimes

difficult to distinguish from Viola palustris Figure 1: Drawing of Viola biflora taken from CLOPLA (Klimešová & Klimeš, 1998) when they are not in flower, but unlike V. palustris, their leaves are always covered in fine hairs. The maximum height of Viola biflora is around 15 centimetres (Kleyer et al., 2008; Lid & Lid, 1994).

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Veronica officinalis

The common speedwell or gypsy weed (see Figure 3) is widely distributed throughout Europe and the eastern United States, and more sparsely distributed in the western United States (GBIF,

2009). It is characterised by horizontal creeping stems and toothed, regular leaves (Kleyer et al.,

2008), both of which are hairy (Lid & Lid, 1994). Viola officinalis can also be recognised by its violet or lavender leaves, or its green, heart shaped capsules. The Ecological Flora of the British

Isles Database cites a minimum altitude of 0 meters and a maximum altitude of 896 meters for V. officinalis (Fitter & Peat, 1994). It is often found in dense mats in dry grassland habitats

(Klimešová & Klimeš, 1998), and can reach a height of 30 (Lid & Lid, 1994), or 40 centimetres

(Kleyer et al., 2008). Veronica officinalis has specialised above ground stems which provide temporary connections between the mother and daughter plants in the first growing season, and which tend to decay leaving the daughter shoot on its own. These stems also occasionally act as temporary storage organs and have roots and leaves to provide food reserves (Klimešová &

Klimeš, 1998).

Figure 3: Drawing of Veronica officinalis taken from CLOPLA (Klimešová & Klimeš, 1998)

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Viola palustris

The marsh violet (see Figure 4) is widely distributed throughout Europe and can also be found in the western United States (GBIF, 2009). As its name suggests, Viola palustris usually grows in moist meadows and marshes, and it is found quite easily in the habitats where it is native (Fitter

& Peat, 1994). In less humid habitats however, it is either less abundant or entirely absent

(Brülheide, 2003). Its stems are horizontal, although not as markedly as those of Veronica officinalis, and as with Veronica officinalis, Viola palustris has short lived stems which are formed below ground (Klimešová & Klimeš, 1998). The Ecological Flora of the British Isles Database cites that V. palustris is found between 0 and 1219 metres in altitude (Fitter & Peat, 1994), and Lid &

Lid (1994) cite a maximum shoot height of about 10cm. As mentioned before, Viola palustris can be confused with Viola biflora when not in flower, but although the leaf shape is also typically reniform, V. palustris does not have hairs on its leaves or its stems. This fact is also helpful when these plants have produced capsules, because the capsules are also quite similar. When in flower, however, Viola palustris can be recognised by its violet petals.

Figure 4: Drawing of Viola palustris taken from CLOPLA (Klimešová & Klimeš, 1998)

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2. Methods

2.1 Study sites

The sites where the individuals were collected are the same as those used by the SEEDCLIM project which is a project run by the Ecological and Environmental Change Research Group of the

University of Bergen (see Figure 5). There are 12 sites located in south western Norway, at around

60oN and 7oE, which were chosen for their climatic attributes (i.e. temperature and precipitation); four are lowland sites, four are intermediate sites, and four are alpine. All sites are grassland habitats because this is the only habitat which can be found in the whole climate grid. They are also chosen for their similar land uses and similar geology in order to minimise variability. It is important to note that sites were chosen based on the presence of all the focal species, as well as high species diversity on a relatively small scale (SEEDCLIM protocol, 2009).

Figure 5: The SEEDCLIM climate grid in western Norway taken from SEEDCLIM protocol (2009)

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The table below (see Table 1) summarises the climatic attributes of each of the 12 sites in the

SEEDCLIM grid. I did not collect specimens at Veskre because the vegetation was not developed enough at the time I visited.

Table 1: The SEEDCLIM sites and their climatic attributes

Precipitation (mm per annum)

Temperature ~700 ~1100 ~2000 ~2700

Lowland (~11OC) Fauske Vikesland Arhelleren Øvstedal

Intermediate (~9OC) Ålrust Høgsete Rambæra Veskre1

Alpine (~6OC) Ulvehaugen Låvisdalen Gudmedalen Skjellingahaugen

2.2 Plant collection and Morphometrics

If it was present, I collected each of the four species described previously at each of the 11 sites.

All plants were collected, measured and pressed between the 9th and the 25th of July. Since the time of flowering varies greatly between years, and is typically very short (Thórhallsdóttir, 1998), the collection of specimens depended primarily on whether or not they were flowering at each site. Because of this, I visited the lowland and intermediate sites first, and the alpine sites last.

Special care was taken to remove the whole plant from the soil, leaving as much of the root system intact as possible. The method for collection was semi random, and was done by throwing out a 50 by 50 centimetre quadrat where the first of a species of interest was spotted. To avoid digging out only the large specimens, I removed all individuals from that quadrat from the soil, with further plots being added only if insufficient individuals were found in the first quadrat.

1 This site was left out because the vegetation was not ready at the time of the field visit.

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The traits I measured for each of the plant species were: shoot height, root or stolon length, number of leaves, width of biggest leaf, length of biggest leaf, number of flowers, number of buds, number of capsules, number of aborted capsules, inflorescence length. Additionally, I measured the plant height for Viola biflora and Viola palustris, and the inflorescence length for

Veronica alpina and Veronica officinalis. These measurements can be divided into three categories; reproductive parts, above ground productivity and below ground productivity. Having information about these traits can therefore tell us how much energy a plant invests in which type of allocation. For example, a plant under stress may invest more energy in above and below ground biomass production than in reproduction, or a plant growing in a habitat with little soil moisture may invest primarily in below ground growth to optimise water uptake. As mentioned before, this is interesting to this study because if allocation to different parts differs between lowland and alpine sites, it means that plants are somehow affected by climate change or are genetically different between sites due to climatic variables.

All measurements were taken with a clear plastic ruler. It is important to note that I measured shoot heights and root lengths according to what could be see above ground and what was below ground respectively. This is why measurements referring to root length also sometimes include stolons. Therefore the measurements shoot height and root/stolon length refer to above and below ground plant lengths in general. Shoot heights were taken from the base of the shoot to the base of the highest leaf, or to an inflorescence (in Veronicas with reproductive parts). Plant heights were taken from the base of the shoot to the base of the fruit or flower.

As all these plants are clonal, I made measurements for each shoot on each individual, and then I took means for each individual. This is how I have presented the data in this dissertation, and is the reason why I have number of shoots as a trait (see Appendices A-D for measurement data).

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2.3 Data Analysis

As a first step I constructed a correlation matrix between all measured traits, precipitation and temperature for each plant species, to determine which of the traits were correlated or linked to each other and to climate variables (see Appendices E-H for matrices). I then chose those the traits with the highest correlations to climatic variables and determined if the data was normally or not normally distributed by using the Kolmogorov-Smirnov test. I used this test to compare the data to a normal distribution, with the null hypothesis being that the data do not differ from a normal distribution. If the data was normally distributed, I used an ANOVA or a t-test, depending on the number of categories I had to compare. If the data was not normally distributed, I used the

Kruskal Wallis test or a Mann-Whitney U test. Both the one factor ANOVA and Kruskal-Wallis tests were then followed up by post hoc tests, Tukey post hoc and Mann-Whitney U respectively, to look for further patterns in the data. All P values in the results section were compared to a 95% confidence level and interpreted accordingly.

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3. Results

Individuals of Veronica alpina were found in all four alpine sites, and in the most western intermediate site, Rambæra. Viola biflora was also found in Rambæra and in all alpine sites except Skjellingahaugen, which is the westernmost alpine site. Veronica officinalis was found in all lowland and intermediate sites, as well as one alpine site, Gudmedalen. Finally, individuals of

Viola palustris were found in the two westernmost lowland sites, Arhelleren and Øvstedal, in two intermediate sites, Rambæra and Høgsete, as well as the two westernmost alpine sites,

Gudmedalen and Skjellingahaugen. This distribution shows a clear tendency of Viola palustris to grow in moist habitats, as the western sites are those with the highest precipitation per annum.

Although no individuals were collected from Veskre, the most western intermediate site, this site had all four species of interest. As expected from the described distributions, Veronica officinalis was often found in mats or was otherwise abundant when it was present at a site (see Appendix

I). In the case of Veronica alpina it was the opposite, as this species was typically more sparsely distributed at the sites. See Appendices J-M for examples of pressed individuals.

3.1 Veronica alpina

The correlation matrix for Veronica alpina shows that its traits are almost all positively correlated

(see Appendix E). The highest positive correlations are between shoot number and total number of leaves, and total number of leaves and total shoot height. The number of potential fruits is also correlated with both number of leaves and with total shoot height, and total shoot height and total root length are also fairly well correlated (see Appendix E for Pearson’s r). This simply reflects that on average, larger plants have more shoots, leaves, potential fruits and roots. There are no strong negative correlations between traits.

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Growth traits

The traits most correlated with precipitation were total shoot height (r = 0.24), mean leaf length

(r = -0.23) and mean leaf width over length (r = 0.23). Traits that were most correlated with temperature were total root length (r = -0.41) and mean leaf length (r = -0.24).

Figures 6a-c show that with increasing precipitation, Veronica alpina grows taller (6a; P= 0.048), produces longer (6b; P= 0.002) and wider (6c; P= 0.039) leaves. The second two figures (see

Figure d-e) show that as temperature increases, Veronica alpina tends to have shorter roots (6d;

P= 0.000) and leaves (6e; P= 0.020).

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a b

c d

Figure 6 a-e: Traits of Veronica alpina plotted against the climate variables precipitation and temperature

The one factor ANOVAs clarified that there is a statistically significant difference between shoot heights, leaf lengths and leaf width to length ratios at sites with different levels of precipitation.

In addition, a Tukey post hoc test (P = 0.028) specified that a significant difference in shoot heights is between Ulvehaugen (700mm of rain per annum) and Skjellingahaugen (2700mm of

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rain per annum). The result of Tukey test with a P value of 0.001 means that the significant difference in leaf length is between Ulvehaugen (700mm of rain per year) and Gudmedalen and

Rambæra (2000mm of rain per year). Finally, like with mean leaf length, the significant difference in the leaf width to length ratio is also between Ulvehaugen, and Gudmedalen and Rambæra (P =

0.023).

T-tests were used to prove that there is a significant difference between total root lengths and leaf lengths at sites with different temperatures. The implication is that root lengths tend to be smaller at the warmer intermediate site, Rambæra, than at the alpine sites. However, there is also a larger variation of root lengths at the alpine sites. The leaf lengths of Veronica alpina also get shorter at the intermediate site, but in this case, there is not much more variation in the alpine values when compared to the intermediate ones.

Allocation

The allocations most correlated with temperature were potential fruit number to total root length (r= 0.33), number of leaves to total root length (r= 0.34) and total shoot height to total root length (r= 0.38). No allocation ratios were correlated well with precipitation.

Figures 7b and 7c show that as the temperature increases between the intermediate site and the alpine sites, Veronica alpina increases its allocation of number of leaves to total root length (7b;

P= 0.002), as well as its allocation of shoot height to root length (7c; P= 0.002). Although Figure 7a visually suggests the same positive relationship for temperature and the allocation of fruits to root length, the Mann-Whitney U P value of 0.193 means that at a confidence level of 95%, there is no statistical difference in this allocation between the intermediate and the alpine sites.

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a b

Figure 7 a-c: Allocations of Veronica alpina plotted against the climate variable temperature

3.2 Viola biflora

The highest correlations between traits of Viola biflora were number of leaves with number of potential fruits (see Appendix F). Like with Veronica alpina, number of leaves and total shoot height are also fairly well correlated. Other well correlated traits of Viola biflora are number of potential fruits and total shoot height, total shoot height and leaf length and total root length and leaf length. This reflects that on average, larger plants also tend to have more leaves, more potential fruits, longer roots and longer leaves.

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Growth traits

Traits that were well correlated with precipitation were the number of leaves (r =0.23) and the number of potential fruits (r =0.31). The only trait that was fairly well correlated with temperature was mean leaf width to length (r =-0.36).

With increasing precipitation, individuals of Viola biflora have a higher number of potential fruits

(see Figure 8b; P= 0.020) on average. In Figure 8a however, while there appears to be a positive correlation between precipitation and number of leaves, a Kruskal-Wallis test showed that the number of leaves per plant is not significantly different between sites with different precipitation levels (P= 0.083). The final plot in this set (see Figure 8c) shows that mean leaf width to length tends to decrease with rising temperature (P= 0.001).

a b

Figure 8 a-c: Traits of Viola biflora plotted against the climate variables precipitation and temperature

Candidate number: 264831

Despite the result of the Kruskal-Wallis test, a Mann-Whitney U post hoc test shows that there is a statistical difference in the number of leaves between the site with 700mm of rain per annum

(Ulvehaugen) as compared to sites with 2000mm of rain per annum (Rambæra and Gudmedalen;

P = 0.035). A Mann-Whitney post hoc test also revealed that a significant difference in number of potential fruits can be found between the site with 700mm of rain per annum and sites with

2000mm of rain per annum (P = 0.007).

Allocation

Allocations most correlated with precipitation are potential fruits to number of leaves (r = 0.24), potential fruits to root length (r = 0.29) and potential fruits to total shoot height (r = 0.30). Total shoot height to total root length is the only allocation that is fairly well correlated with temperature for Viola biflora (r = -0.25).

Figures 9a-c show that with increasing precipitation, individuals of Viola biflora tend to increase their allocation of potential fruits to number of leaves (9a; P= 0.038), total root length (9b; P=

0.021) and total shoot height (9c; P= 0.018). The last plot (see Figure 9d) shows that as temperature increases, individuals of Viola biflora increase their allocation of shoot height to root length (P= 0.022).

Candidate number: 264831

a b

c d

Figure 9 a-d: Allocations of Viola biflora plotted against the climate variables precipitation and temperature

The Kruskal-Wallis tests showed that there is a statistically significant difference in allocation of potential fruits to leaves, potential fruits to root length and potential fruits to shoot length between sites with different levels of precipitation. In addition, a Mann-Whitney U post hoc test places significant differences in allocation of potential fruits to leaves between Ulvehaugen

(700mm) and Gudmedalen and Rambæra (2000mm), with a P value of 0.019. Further Mann-

Whitney U post hoc tests showed that the significant difference in allocation of potential fruits to root length (P= 0.006) and potential fruits to shoot height (P= 0.005) is also between Ulvehaugen

(700mm of rainfall per annum) and Gudmedalen and Rambæra (2000mm of rainfall per annum).

Candidate number: 264831

3.3 Veronica officinalis

The correlation matrix for Veronica officinalis (see Appendix G) shows that the number of shoots, leaves, potential fruits, total shoot height and total root length are all very well correlated with each other. The total root length and length of leaf are also well correlated. This means that on average, larger specimens of Veronica officinalis also have more shoots, fruits, leaves, leaf lengths and longer roots.

Growth traits

The traits most correlated with precipitation are number of shoots (r =0.2) and total number of leaves (r =0.23). Leaf length is the only trait that is fairly well correlated with temperature (r

=0.22).

Figure 10a-b below shows that with increasing precipitation, individuals of Veronica officinalis tend to produce more shoots (10a; P= 0.000) and leaves (10b; P= 0.000). Veronica officinalis also produces longer leaves as temperature increases (Figure 10c; P= 0.022)

Candidate number: 264831

a b

Figure 10 a-c: Traits of Veronica officinalis plotted against the climate variables precipitation and temperature

The Kruskal-Wallis tests showed that there is a statistically significant difference in the number of shoots and the number of leaves at sites with different precipitation levels. The Mann-Whitney U post hoc tests clarify that the most significant difference in the number of shoots is found between Fauske and Ålrust (700mm of rain per annum) and Arhelleren, Rambæra and

Gudmedalen (2000mm of rain per annum), with a P value of 0.000. There are also statistically significant differences between sites of 700mm of rain per annum and 1100mm of rain per annum (P= 0.001) and between sites of 700mm of rain per annum and 2700mm of rain per annum (P= 0.021). As for the number of leaves, Mann-Whitney U post hoc tests clarified that there are statistically significant differences in leaf number between sites of all different precipitation levels. The most significant difference is found between sites of 700mm of rain per

Candidate number: 264831

annum and 2000mm of rain per annum (P= 0.000). The P values of 0.001, between sites with

700mm of rain per year and 2000mm of rain per year, 0.003, between 700mm of rain per year and 1100mm of rain per year, and 0.018, between 1100mm of rain per year and 2000mm of rain per year, all confirm significant differences in number of leaves between sites.

After the one factor ANOVA confirmed statistical difference in leaf length between sites with different temperatures, I could use a Tukey post hoc test to specify that the significant difference is between sites with temperatures of 6oC and 11oC (P= 0.033), that is to say between alpine and lowland sites.

Allocation

The only allocation that is correlated fairly well with precipitation is the allocation of number of leaves to total shoot height (r =0.2). Allocations that correlate best with temperature are potential fruits to total root length (r =-0.21), total number of leaves to total root length (r =-0.39) and total number of leaves to total shoot height (r =-0.37).

With increasing precipitation, Veronica officinalis roughly increases its allocation of number of leaves to total shoot height (see Figure 11a; P= 0.000), although this is not as visually apparent from the box plot. With increasing temperature, Veronica officinalis decreases its allocation of number of potential fruits to root length (see Figure 11b; P= 0.024), number of leaves to root length (see Figure 11c; P= 0.000) and number of leaves to shoot height (see Figure 11d; P= 0.000).

Candidate number: 264831

a b

c d

Figure 11 a-d: Allocation of Veronica officinalis plotted against the climate variables precipitation and temperature

The Kruskal-Wallis tests all confirmed that there is a statistically significant difference in the allocations between sites with differing temperatures and precipitations alike. A Mann-Whitney U test also showed that there are statistically significant differences in allocation of number of leaves to shoot height between sites of all different precipitation levels, with P values of 0.000 between all sites except sites with 700mm of rain per annum and 2700mm of rain per annum, where P= 0.046. A further set of Mann-Whitney U post hoc tests specified that the most significant difference in allocation of potential fruits to total root length is between alpine sites

(6oC) and lowland sites (11oC), where P is 0.017. However, the difference in allocation is also statistically significant between intermediate sites (9oC) and lowland sites (11oC), as the P value was 0.043. The final set of Mann-Whitney U post hoc tests suggested that the largest difference

Candidate number: 264831

in allocation of leaves to root length is between alpine sites and lowland sites (P= 0.000), but there is also a statistically significant difference in allocation of number of leaves to root length between alpine and intermediate (P= 0.002), as well as intermediate and lowland sites (P= 0.001).

3.4 Viola palustris

Apart from potential fruits, leaf length and leaf width to length ratio, all measured traits for Viola palustris, were positively correlated (see Appendix H). This simply means that on average, larger individuals of Viola palustris also tend to have more shoot, leaves and longer roots.

Growth traits

The traits most correlated with precipitation were total shoot height (r =-0.28) and total root length (r =-0.31). No traits were well correlated with temperature.

Figure 12a-b shows that with increasing precipitation, individuals of Viola palustris are smaller

(12a; P= 0.011) and have shorter roots (12b; P= 0.000).

a b

Figure 12 a-b: Traits of Viola palustris plotted against the climate variable precipitation

Candidate number: 264831

The Kruskal-Wallis tests showed that there is a statistically significant difference in shoot height and root length between sites with different levels of precipitation, and the Mann-Whitney U post hoc test clarified that the differences in shoot height are most significant between Høgsete

(1100mm of rain per annum) and Øvstedal and Skjellingahaugen (2700mm of rain per annum), where the P value is 0.004. There is also a statistically significant difference in shoot heights between Arhelleren, Rambæra and Gudmedalen (2000mm of rain per annum) and the two sites with 2700mm of rain per year (P= 0.043). The second set of Mann-Whitney U tests confirmed the most significant difference in root lengths to be between sites with 1100mm of rain per year and

2000mm of rain per year, where the P value was 0.000. However, there are also a differences in root length between sites with 1100mm of rain per year and 2700mm of rain per year (P= 0.004), and between sites with 2000mm of rain per year and 2700mm of rain per year (P= 0.017).

Allocation

The only allocation correlated well with precipitation is total number of leaves to total shoot height (r =0.39). Allocations which are well correlated with temperature are number of potential fruits to total root length (r =-0.25), number of potential fruits to total shoot height (r =-0.23), total number of leaves to total shoot height (r =-0.23) and total shoot height to total root length (r

=0.24).

Figure 13a shows that as precipitation increases, individuals of Viola palustris increase their allocation of total number of leaves to total shoot height (P= 0.000). Figure 13d and e show that as temperature increases, individuals of Viola palustris decrease their allocation of leaves to shoot height (13d; P= 0.047), and increase their allocation of shoot height to root length (13e; P=

0.012). When assessed visually, Figures 13b and c suggest that the allocation of fruits to shoot height and root length decrease as temperature increases, however the use of a Kruskal-Wallis

Candidate number: 264831

test clarifies that there is no statistically significant difference in allocation of potential fruits to root length (P= 0.074) or shoot height (P= 0.083) between sites with different temperatures.

a b

c d

Figure 13 a-e: Allocation of Viola palustris plotted against the climate variables precipitation and temperature

Following the Kruskal-Wallis test, the Mann-Whitney U post hoc tests confirmed a statistically significant difference in allocation of number of leaves to shoot height between the site with

Candidate number: 264831

1100mm of rain per annum (Høgsete) and 2700mm of rain per annum (Øvstedal and

Skjellingahaugen, P= 0.000), as well as between sites with 2000mm of rain per annum

(Arhelleren, Rambæra and Gudmedalen) and 2700mm of rain per annum (P= 0.001). There is a also a significant difference in allocation between sites with 1100mm of rain per annum and

2000mm of rain per annum (P= 0.048).

Despite the results of the Kruskal-Wallis tests for allocation of potential fruits to shoot height an root length, a Mann-Whitney U post hoc test suggested that there is a significant difference in allocation of potential fruits to root length between alpine sites (6oC) and lowland sites (11oC, P=

0.024). A second post hoc test also found a significant difference in the allocation of number of fruits to shoot length between alpine sites and lowland sites (P= 0.026).

Mann-Whitney U tests also confirmed the significant difference in number of leaves to shoot height to be between alpine and lowland sites (P= 0.033), as well as between alpine and intermediate sites (P= 0.035). Finally, the significant difference in the allocation of total shoot height to total root length was found to be between alpine and lowland sites (P= 0.003), as well as between intermediate and lowland sites (P= 0.044).

Candidate number: 264831

4. Discussion

Growth traits, allocation and consistency

The results show that all four species in this study respond within the climate grid in terms of growth and allocation to different life functions, meaning that there must be some kind of genetic or plastic difference in species between sites. The most dominant type of response in terms of growth traits appears to be a change in above ground growth. Veronica officinalis is the strongest example, as the number of shoots and leaves increase with increasing precipitation and the leaf length increases with increasing temperature. Neither its roots nor its reproductive parts seem to be affected by changing climate variables. Leaf length and width are also traits that are often affected by both temperature and precipitation. Veronica alpina produces longer and wider leaves with increasing precipitation, and Veronica alpina produces longer leaves, while Viola biflora produces leaves with a smaller leaf width to length ratio with increasing temperature. The shoot height also becomes longer with increasing precipitation for Veronica alpina, and shorter for Viola palustris. For Veronica alpina the underground production is reduced in the site where temperatures are higher, which is the intermediate site Rambæra. For Viola palustris, on the other hand, it is higher precipitation levels that reduce its root growth. The final trait group, the reproductive parts, do not seem to be as responsive to climate variation, as only Viola biflora responds to increasing precipitation levels with more potential fruits.

Although species responded to climate variables within the grid, the types of traits that responded were not the same for species pairs. In terms of growth traits alone, no two groups of plants responded to a single climate variable in the same way. For example, while Veronica alpina responded to increasing precipitation with an increase in shoot height, leaf length and leaf width to length ratio, Veronica officinalis responded with an increase in shoot number and leaf number.

Similarly, Viola biflora responded to a precipitation increase with an increase in potential fruit

Candidate number: 264831

number, and Viola palustris responded with a decrease in shoot height and root length. The comparison is the same even when looking at the species pairs by specialisation. Neither the alpines, nor the generalists responded to either of the climate variables in the same way.

The situation is different when considering allocation. Here the most dominant type of response to temperature and precipitation in terms of allocation appears to be the allocation of number of leaves to shoot height or root length, as all species except Viola biflora increase or decrease these allocations with respect to one or both climate variables. This is especially true for the Veronicas;

Veronica alpina responded to an increase in temperature by increasing its leaf number to root length allocation, and Veronica officinalis responded by decreasing its leaf number to root length allocation. The increase in allocation of shoot height to root length in response to temperature increase was also common to all species except Veronica officinalis. When looking at species pairs by specialisation, the two alpine species both increased their shoot height to root length allocations as temperature increases. The two generalists on the other hand, both increased their allocation of number of leaves to shoot height as precipitation increased, and decreased it as temperature increased.

It is not possible to directly compare these results with those of other studies due to the fact that previous studies tend to use biomass to compare growth and allocation. However, I can say that the Körner and Renhardt study in 1987 more closely reflects the underground production of these species than the Bloom et al. (1985) hypothesis. Veronica alpina and Viola palustris have much less extensive underground systems than the two generalist species. In addition, Körner and

Renhardt suggested that rather than spending energy on below ground production, alpine plants rely on more extensive leaf area development. This remark is interesting with respect to my data because both alpine species change their leaf length and/ or leaf width to length ratios as the climate variables changed. The leaves of the generalists were not as affected; Veronica officinalis

Candidate number: 264831

only had longer leaves at sites with higher temperature, and the size of the leaves of Viola palustris were similar in all sites regardless of the climate conditions. Both Fan et al. (2009) and

Sebastià (2007) hypothesise that biomass production is more reduced or limited by temperature increase than by precipitation decrease. While my data does not include biomass, I can say that this is not necessarily the case for all species. Viola palustris is a good example, because none of its growth traits differ between sites with different temperatures, but both shoot height and root length is smaller at sites with higher precipitation. Moreover, both alpine species experience reductions in leaf length or width with increasing temperature, and Veronica alpina increases its leaf length and width with increasing precipitation. While this data adds to the evidence that plant responses are species specific (Arft et al., 1999), the commonalities in responses between both species that are closely related and species which have the same specialisations mean that some generalisations can be made for the purpose of conservation.

Limitations of the study

One obvious limitation of this study is that one of the twelve sites, Veskre, is not included in the data. One place where this may be visible is in Figure 6b, which depicts the relationship between precipitation and leaf length for Veronica alpina. In this box plot, it appears that leaf length increases between sites with precipitations of 700mm, 1100mm and 2000mm per annum, but slightly decreases again for sites with 2700mm of rain per annum. This seemingly bimodal response could in fact be due to the absence of Veskre, which would have provided further data for the 2700mm precipitation category.

There are also additional factors which could not be taken into consideration in this study. It is clear that the locations where the plant individuals were collected were not identical in nutrient level, or in their proximity to rocks and so forth. It is in the nature of the methods that where a particular species was especially abundant (e.g. officinalis), all individuals were collected in the

Candidate number: 264831

same quadrat. Conversely, where a species was less abundant (e.g. alpina), the collected individuals were taken from quadrats far apart from each other. This means that the species collected in the same quadrat may not have been representative of the population in that site.

Other factors such as competition (Cahill, 2003), and the possibility that allocation of an individual is dependent on what plants grew in the same spot before (Molau & Alatalo, 1998) were also not covered, as they extended beyond the scope and resources of this research. For additional research on this topic, it might also be particularly useful to take biomass measurements, so that allocations and growth could be compared further against each other and with other studies.

Implications for future conservation efforts in the light of climate change

This study has shown that plant growth and allocation is a complex issue, especially so when coupled with climate variation. However, it was possible to tease out the types of responses that could be expected from Veronica alpina, Viola biflora, Veronica officinalis and Viola palustris as a result of climate change. It is important to note that while species of the same specialisation shared some of the same allocation shifts, the closely related species did as well. This means that, based on these species, it is difficult to predict how alpine species as a group may react to a change in climate. However, this type of fine scale analysis can help to understand where populations of these species or similar species can exist as their environments change.

As discussed before, the study of growth and allocation is just the first step to understanding plant responses. What has to be determined at this point is whether these responses are due to a genetic difference between plant populations, or due to adaptive capacities. If there are genetic differences between the populations, this means that each population has to adapt and survive independently, creating yet a bigger challenge for conservation. Both genetic tests and transplant experiments can be helpful to elucidate this issue, and will surely provide a more solid foundation for predicting plant response.

Candidate number: 264831

Determining genetic or plastic response is vital to conservation because it is in this way that conservationists can focus their attentions on the populations most at risk. The rising emphasis on the need to increase resilience of habitats by diversifying gene pools means that much of the future concentration of conservation may be focussed on species that are genetically diverse

(Hobbs et al., 2006; Hampe & Petit, 2005; Noss, 2001). This ‘future proofing’ coined by Hobbs et al. challenges conservationists to think ahead, and rather than attempting to conserve species in a site because they exist there historically, it may be wiser to conserve genetically diverse rear edge populations, as suggested by Hampe and Petit (2005). The suggestion that conservation of many local populations rather than isolated core areas might also be a more forward-thinking strategy (Hampe & Petit, 2005) as genetically diverse populations tend to be healthy populations, which allow for plasticity and resilience during climate change. In the case of alpine plants in

Norway and elsewhere, it may also be astute to consider protecting climatic refugia and a wide range of environmental gradients as suggested by Noss (2001). Furthermore, Sala et al. (2000) suggest that conservation management should be specific to a region in order to adequately combine factors to which local species are the most sensitive. A study such as this one can help to provide information about the sensitivities of the species of interest, and adjust conservation areas accordingly.

Candidate number: 264831

5. Conclusion

This study has shown that these four plant species shift their growth and allocation to different life functions in response to temperature and precipitation increase, and that there are consistencies in responses between species pairs when it comes to allocation. The alpine species,

Veronica alpina and Viola biflora both responded to temperature increase by increasing their allocation of shoot height to root length, while the generalists both increased their allocations of leaves to shoot height with increasing precipitation and decreased it with increasing temperature.

The growth traits most commonly affected by an increase in one of the climate variables were above ground traits, while the allocations most commonly affected were leaves to shoot height and shoot height to root length. A detailed knowledge of the response of plant traits such as those in this study can help determine future adaptations, tolerances and ranges of species, and thus allow conservationists to plan reserve networks that will be more resilient to climate change.

To further this study, more species responses should be explored, and it would be necessary to determine if species responses are due to tolerances of the plant, or due to genetic variability. In combination, this information could be used to guide much more effective reserve choices for alpine communities in the future.

Candidate number: 264831

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Candidate number: 264831

Appendix

Measurements by Species and Site

Legend: S# = number of shoots, TL = number of leaves, TFL = number of flowers, TB = number of buds, TF = number of fruits, TAF = number of aborted fruits, PF = number of potential fruits, TS = total shoot height, TR = total root/ stolon length, L(l) = leaf length, L(w/l) = leaf width/ length

Appendix A. Veronica alpina trait measurements

Rambæra (V. alpina)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 2 28 0 0 6 0 6 19.00 1.10 1.10 0.55 0.21 5.45 0.32 1.47 25.45 17.27 4 38 0 0 3 0 3 20.00 0.30 0.78 0.48 0.08 10.00 0.15 1.90 126.67 66.67 5 51 0 0 4 10 14 30.90 1.30 1.04 0.46 0.27 10.77 0.45 1.65 39.23 23.77 4 51 0 0 27 0 27 35.80 3.50 0.95 0.52 0.53 7.71 0.75 1.42 14.57 10.23 1 8 0 0 6 0 6 11.80 0.50 1.30 0.62 0.75 12.00 0.51 0.68 16.00 23.60 3 19 1 0 1 0 2 11.70 0.60 0.77 0.49 0.11 3.33 0.17 1.62 31.67 19.50 1 14 0 0 0 0 0 6.00 0.80 1.20 0.50 0.00 0.00 0.00 2.33 17.50 7.50 1 4 0 0 6 0 6 10.20 3.00 1.30 0.38 1.50 2.00 0.59 0.39 1.33 3.40 1 8 0 0 2 0 2 10.80 0.50 0.90 0.44 0.25 4.00 0.19 0.74 16.00 21.60 1 16 0 0 7 0 7 15.50 2.50 1.50 0.40 0.44 2.80 0.45 1.03 6.40 6.20 1 5 0 0 0 0 0 4.70 2.00 1.50 0.53 0.00 0.00 0.00 1.06 2.50 2.35 1 2 0 0 2 0 2 9.80 1.00 1.00 0.50 1.00 2.00 0.20 0.20 2.00 9.80 1 9 0 0 3 0 3 10.00 2.00 1.50 0.40 0.33 1.50 0.30 0.90 4.50 5.00 2 18 0 0 5 0 5 15.70 2.00 1.00 0.50 0.28 2.50 0.32 1.15 9.00 7.85 45

Candidate number: 264831

1 11 0 0 0 1 1 9.20 2.70 1.20 0.50 0.09 0.37 0.11 1.20 4.07 3.41 1 10 0 0 0 0 0 1.70 1.00 0.50 0.40 0.00 0.00 0.00 5.88 10.00 1.70 2 12 0 0 0 0 0 2.70 5.10 0.75 0.35 0.00 0.00 0.00 4.44 2.35 0.53 1 10 0 0 0 0 0 1.00 0.50 0.50 0.40 0.00 0.00 0.00 10.00 20.00 2.00 1 8 0 0 0 0 0 1.20 0.40 0.70 0.43 0.00 0.00 0.00 6.67 20.00 3.00 1 10 0 0 0 0 0 2.50 0.60 1.20 0.33 0.00 0.00 0.00 4.00 16.67 4.17 1 10 0 0 0 0 0 1.10 0.20 0.70 0.29 0.00 0.00 0.00 9.09 50.00 5.50

Skjellingahaugen (V. alpina)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 2 18 0 0 6 0 6 23.8 7.5 1.35 0.48 0.33 0.80 0.25 0.76 2.40 3.17 2 12 0 0 6 0 6 10.8 3.4 1.1 0.50 0.50 1.76 0.56 1.11 3.53 3.18 2 16 0 0 0 0 0 8.3 2.9 1.1 0.41 0.00 0.00 0.00 1.93 5.52 2.86 1 10 0 0 7 0 7 12.2 9 1.8 0.44 0.70 0.78 0.57 0.82 1.11 1.36 1 13 0 0 0 0 0 5.2 0.8 1.2 0.42 0.00 0.00 0.00 2.50 16.25 6.50 1 11 0 0 5 0 5 15.6 2.6 1.6 0.50 0.45 1.92 0.32 0.71 4.23 6.00 2 28 0 0 7 0 7 13.6 7.4 1.15 0.48 0.25 0.95 0.51 2.06 3.78 1.84 2 20 0 0 5 0 5 16.2 8 1.3 0.54 0.25 0.63 0.31 1.23 2.50 2.03 2 19 2 0 2 0 4 11.3 3.3 1.1 0.50 0.21 1.21 0.35 1.68 5.76 3.42 1 8 4 0 4 0 8 16.1 7 1.5 0.60 1.00 1.14 0.50 0.50 1.14 2.30 3 20 0 0 4 0 4 7.7 0.6 0.6 0.50 0.20 6.67 0.52 2.60 33.33 12.83 2 20 0 0 2 0 2 19.7 1.6 0.9 0.55 0.10 1.25 0.10 1.02 12.50 12.31 4 33 8 0 7 0 15 36.8 8.5 1.18 0.51 0.45 1.76 0.41 0.90 3.88 4.33 3 31 0 0 12 0 12 19.8 4.8 1.37 0.50 0.39 2.50 0.61 1.57 6.46 4.13 4 46 0 0 9 0 9 42.6 17.6 1.15 0.49 0.20 0.51 0.21 1.08 2.61 2.42 2 28 1 0 0 0 1 21.7 11.5 0.95 0.60 0.04 0.09 0.05 1.29 2.43 1.89 46

Candidate number: 264831

1 11 4 0 2 0 6 10 5.1 1.4 0.43 0.55 1.18 0.60 1.10 2.16 1.96 3 30 1 0 5 0 6 29 4.3 1.07 0.53 0.20 1.40 0.21 1.03 6.98 6.74 2 29 0 0 16 0 16 22 4 1.35 0.40 0.55 4.00 0.73 1.32 7.25 5.50 1 12 0 0 0 0 0 7.6 5 0.8 0.50 0.00 0.00 0.00 1.58 2.40 1.52 1 8 0 0 3 0 3 8.2 1.4 0.8 0.50 0.38 2.14 0.37 0.98 5.71 5.86 2 34 0 0 10 0 10 21.7 6 1.15 0.52 0.29 1.67 0.46 1.57 5.67 3.62

Gudmedalen (V. alpina)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 17 0 0 7 0 7 19.2 1.4 1.2 0.67 0.41 5.00 0.36 0.89 12.14 13.71 2 20 0 0 0 0 0 4.6 0.3 0.75 0.50 0.00 0.00 0.00 4.35 66.67 15.33 1 8 0 0 0 0 0 0.6 1.5 0.6 0.33 0.00 0.00 0.00 13.33 5.33 0.40 1 22 0 0 8 0 8 19.2 2.3 1.4 0.57 0.36 3.48 0.42 1.15 9.57 8.35 2 30 0 0 4 0 4 19.1 4.7 1.1 0.55 0.13 0.85 0.21 1.57 6.38 4.06 2 25 0 0 0 0 0 18.4 7.5 1 0.50 0.00 0.00 0.00 1.36 3.33 2.45 2 22 0 0 2 0 2 20.5 15.9 1.15 0.57 0.09 0.13 0.10 1.07 1.38 1.29 1 9 0 0 2 0 2 12.1 3.6 0.8 0.75 0.22 0.56 0.17 0.74 2.50 3.36 1 14 0 0 0 0 0 12.5 5.6 1.1 0.45 0.00 0.00 0.00 1.12 2.50 2.23 1 14 0 2 2 0 4 15 10.7 1.2 0.58 0.29 0.37 0.27 0.93 1.31 1.40 1 14 0 0 0 0 0 8.5 3.6 1.2 0.42 0.00 0.00 0.00 1.65 3.89 2.36 2 24 0 0 7 0 7 22 7.2 1.2 0.55 0.29 0.97 0.32 1.09 3.33 3.06 4 39 0 0 4 0 4 17 5.6 0.625 0.53 0.10 0.71 0.24 2.29 6.96 3.04 2 36 0 0 0 0 0 20.6 3.1 0.95 0.62 0.00 0.00 0.00 1.75 11.61 6.65 2 24 0 1 4 0 5 23.3 3.8 1.05 0.62 0.21 1.32 0.21 1.03 6.32 6.13

Candidate number: 264831

Låvisdalen (V. alpina)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 13 0 4 0 0 4 6.2 2.7 1.2 0.50 0.31 1.48 0.65 2.10 4.81 2.30 1 16 0 4 0 0 4 9 7.3 1.4 0.57 0.25 0.55 0.44 1.78 2.19 1.23 1 16 0 2 0 0 2 5 2 1.4 0.50 0.13 1.00 0.40 3.20 8.00 2.50 5 45 1 3 0 0 4 22.9 14.4 1.06 0.43 0.09 0.28 0.17 1.97 3.13 1.59 2 30 0 2 0 0 2 8.8 5.4 1.25 0.40 0.07 0.37 0.23 3.41 5.56 1.63 2 22 0 2 0 0 2 8 8.5 1.1 0.54 0.09 0.24 0.25 2.75 2.59 0.94 3 35 0 4 0 0 4 14.9 7.4 1.03 0.51 0.11 0.54 0.27 2.35 4.73 2.01 1 10 0 2 0 0 2 5.5 5 1.4 0.43 0.20 0.40 0.36 1.82 2.00 1.10 1 20 7 0 0 0 7 16 2.8 1.4 0.50 0.35 2.50 0.44 1.25 7.14 5.71 2 23 5 0 0 0 5 21.9 5.3 1.3 0.58 0.22 0.94 0.23 1.05 4.34 4.13 4 71 24 0 0 0 24 51.5 8.8 1.28 0.43 0.34 2.73 0.47 1.38 8.07 5.85 1 18 5 0 0 0 5 12.7 1 1.4 0.50 0.28 5.00 0.39 1.42 18.00 12.70 5 46 0 9 0 0 9 25.7 8 0.9 0.53 0.20 1.13 0.35 1.79 5.75 3.21 1 13 4 1 0 0 5 9.9 1.5 1.3 0.62 0.38 3.33 0.51 1.31 8.67 6.60 2 20 0 3 0 0 3 11.5 3 1.05 0.43 0.15 1.00 0.26 1.74 6.67 3.83 1 8 0 2 0 0 2 7.5 4 1.4 0.50 0.25 0.50 0.27 1.07 2.00 1.88 1 13 0 2 0 0 2 5.3 5.7 1.1 0.36 0.15 0.35 0.38 2.45 2.28 0.93 2 18 0 0 0 0 0 2 0.6 0.65 0.45 0.00 0.00 0.00 9.00 30.00 3.33 1 11 0 0 0 0 0 3.5 3 1.3 0.46 0.00 0.00 0.00 3.14 3.67 1.17

Candidate number: 264831

Ulvehaugen (V. alpina)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 2 22 0 0 9 0 9 16.6 5.3 1.35 0.44 0.41 1.70 0.54 1.33 4.15 3.13 1 8 0 0 0 0 0 4 0.5 1.4 0.43 0.00 0.00 0.00 2.00 16.00 8.00 2 15 2 0 3 0 5 13 7 1.2 0.55 0.33 0.71 0.38 1.15 2.14 1.86 1 17 4 0 0 0 4 9 4.1 1.5 0.40 0.24 0.98 0.44 1.89 4.15 2.20 1 17 4 2 0 0 6 5 3.9 1.5 0.53 0.35 1.54 1.20 3.40 4.36 1.28 1 12 1 0 2 0 3 10.5 5.5 1.4 0.43 0.25 0.55 0.29 1.14 2.18 1.91 2 44 7 0 5 0 12 15.5 5.8 1.25 0.44 0.27 2.07 0.77 2.84 7.59 2.67 1 8 1 2 3 0 6 7.5 1.7 1.5 0.47 0.75 3.53 0.80 1.07 4.71 4.41 1 7 0 0 0 0 0 4 3 1.4 0.29 0.00 0.00 0.00 1.75 2.33 1.33 1 13 1 4 0 0 5 8.4 1 1.3 0.46 0.38 5.00 0.60 1.55 13.00 8.40 1 8 0 0 0 0 0 1.6 2 1 0.40 0.00 0.00 0.00 5.00 4.00 0.80 1 10 0 0 0 0 0 3.5 0.5 1.5 0.40 0.00 0.00 0.00 2.86 20.00 7.00 2 27 10 0 0 0 10 20.5 3.4 1.45 0.46 0.37 2.94 0.49 1.32 7.94 6.03 2 15 0 0 0 0 0 6.4 2.4 1.1 0.45 0.00 0.00 0.00 2.34 6.25 2.67 1 16 0 0 0 0 0 6.4 7.4 1.4 0.43 0.00 0.00 0.00 2.50 2.16 0.86 2 19 0 0 0 0 0 2 1.1 0.7 0.43 0.00 0.00 0.00 9.50 17.27 1.82 1 6 0 0 0 0 0 1.2 5 0.8 0.25 0.00 0.00 0.00 5.00 1.20 0.24 3 19 5 0 4 0 9 26.1 8.1 2 0.48 0.47 1.11 0.34 0.73 2.35 3.22

Candidate number: 264831

Appendix B. Viola biflora trait measurements

Rambæra (V. biflora)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 7 0 0 4 0 4 7 1 1.2 1.75 0.57 4.00 0.57 1.00 7.00 7.00 1 7 0 0 5 1 6 3.8 1.5 1.1 1.73 0.86 4.00 1.58 1.84 4.67 2.53 1 1 0 0 0 0 0 1.5 0.5 0.6 1.67 0.00 0.00 0.00 0.67 2.00 3.00 1 4 0 0 2 0 2 5.5 1.5 1.6 1.63 0.50 1.33 0.36 0.73 2.67 3.67 1 2 0 0 0 0 0 6.2 2.2 1.8 1.83 0.00 0.00 0.00 0.32 0.91 2.82 1 1 0 0 0 0 0 3 0.7 0.9 1.44 0.00 0.00 0.00 0.33 1.43 4.29 1 1 0 0 0 0 0 2.3 0.5 0.5 1.80 0.00 0.00 0.00 0.43 2.00 4.60 1 5 0 0 4 0 4 6 1.6 1.3 1.38 0.80 2.50 0.67 0.83 3.13 3.75 1 8 0 1 2 0 3 3.8 2 1.1 1.55 0.38 1.50 0.79 2.11 4.00 1.90 1 2 0 0 3 0 3 4 2 1.1 1.55 1.50 1.50 0.75 0.50 1.00 2.00 1 6 0 3 5 0 8 4 1 0.8 1.50 1.33 8.00 2.00 1.50 6.00 4.00 1 4 0 0 2 0 2 4.2 1.2 1.1 1.73 0.50 1.67 0.48 0.95 3.33 3.50 1 4 0 3 3 0 6 4.6 1.5 1 1.80 1.50 4.00 1.30 0.87 2.67 3.07 1 2 0 0 0 0 0 2.5 0.7 0.9 1.44 0.00 0.00 0.00 0.80 2.86 3.57 1 1 0 0 0 0 0 1.2 1 0.5 1.60 0.00 0.00 0.00 0.83 1.00 1.20 1 1 0 0 0 0 0 0.9 0.3 0.2 1.50 0.00 0.00 0.00 1.11 3.33 3.00 1 3 0 1 1 0 2 2.3 0.8 1 1.30 0.67 2.50 0.87 1.30 3.75 2.88 1 4 0 0 2 0 2 3.5 2 1.1 1.64 0.50 1.00 0.57 1.14 2.00 1.75 1 2 0 0 0 0 0 4 1.5 1.3 1.85 0.00 0.00 0.00 0.50 1.33 2.67 1 5 0 0 2 0 2 3.2 1.2 1 1.60 0.40 1.67 0.63 1.56 4.17 2.67 1 1 0 0 0 0 0 2.7 0.5 0.8 1.63 0.00 0.00 0.00 0.37 2.00 5.40 1 1 0 0 0 0 0 4.2 1.2 1.2 1.67 0.00 0.00 0.00 0.24 0.83 3.50 1 6 0 0 1 0 1 4 1.8 1.1 1.82 0.17 0.56 0.25 1.50 3.33 2.22 50

Candidate number: 264831

Gudmedalen (V. biflora)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 3 0 0 0 0 0 3.6 0.3 1.1 1.91 0.00 0.00 0.00 0.83 10.00 12.00 1 5 0 2 2 0 4 4 0.5 1 1.70 0.80 8.00 1.00 1.25 10.00 8.00 1 3 0 0 0 0 0 3 0.5 1.1 1.91 0.00 0.00 0.00 1.00 6.00 6.00 1 3 0 0 0 0 0 4.5 1 1.2 1.92 0.00 0.00 0.00 0.67 3.00 4.50 1 12 1 1 3 0 5 8 2.2 1.5 2.13 0.42 2.27 0.63 1.50 5.45 3.64 1 2 0 1 1 0 2 5.4 1.1 0.9 1.44 1.00 1.82 0.37 0.37 1.82 4.91 1 2 0 0 0 0 0 2.5 0.5 0.6 2.00 0.00 0.00 0.00 0.80 4.00 5.00 1 7 0 2 1 0 3 6.2 1.2 1.3 1.92 0.43 2.50 0.48 1.13 5.83 5.17 1 2 0 0 0 0 0 4.3 0.7 1.4 1.93 0.00 0.00 0.00 0.47 2.86 6.14 1 1 0 0 0 0 0 4.6 0.5 0.9 1.89 0.00 0.00 0.00 0.22 2.00 9.20 1 7 2 2 2 0 6 5.5 2.5 1.3 2.15 0.86 2.40 1.09 1.27 2.80 2.20 2 18 4 4 4 0 12 9.6 1.4 1.15 2.01 0.67 8.57 1.25 1.88 12.86 6.86 1 4 0 0 0 0 0 4.6 1.5 1.4 2.00 0.00 0.00 0.00 0.87 2.67 3.07 1 3 0 0 0 0 0 2.4 1 1.1 1.36 0.00 0.00 0.00 1.25 3.00 2.40 1 2 0 0 0 0 0 2.4 0.5 0.8 2.13 0.00 0.00 0.00 0.83 4.00 4.80 1 5 0 4 2 0 6 5.1 0.5 1.1 2.18 1.20 12.00 1.18 0.98 10.00 10.20 1 3 0 0 0 0 0 2.5 0.5 0.9 2.00 0.00 0.00 0.00 1.20 6.00 5.00 2 6 0 4 0 0 4 5.9 1 1.7 1.65 0.67 4.00 0.68 1.02 6.00 5.90 1 6 0 2 3 0 5 5 1 1.1 1.91 0.83 5.00 1.00 1.20 6.00 5.00 1 1 0 0 0 0 0 1.4 0.5 0.6 1.33 0.00 0.00 0.00 0.71 2.00 2.80 1 1 0 0 0 0 0 1.5 0.2 0.4 1.75 0.00 0.00 0.00 0.67 5.00 7.50

Candidate number: 264831

Låvisdalen (V. biflora)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 6 1 2 0 0 3 6.3 1.5 1.9 1.58 0.50 2.00 0.48 0.95 4.00 4.20 1 4 1 0 0 0 1 3.5 0.9 1.1 1.64 0.25 1.11 0.29 1.14 4.44 3.89 1 8 3 1 0 0 4 3.8 1 1.2 1.83 0.50 4.00 1.05 2.11 8.00 3.80 1 2 0 0 0 0 0 5.3 0.5 1.5 1.73 0.00 0.00 0.00 0.38 4.00 10.60 1 4 1 1 0 0 2 2.8 1 1.1 1.91 0.50 2.00 0.71 1.43 4.00 2.80 1 7 1 2 0 0 3 4 1.1 1.4 1.93 0.43 2.73 0.75 1.75 6.36 3.64 1 8 1 2 0 0 3 2.8 1.1 0.9 3.00 0.38 2.73 1.07 2.86 7.27 2.55 1 2 0 0 0 0 0 2.4 0.7 1.3 1.69 0.00 0.00 0.00 0.83 2.86 3.43 1 3 1 1 0 0 2 3.4 1.3 1.1 1.64 0.67 1.54 0.59 0.88 2.31 2.62 1 2 0 0 0 0 0 4.4 0.5 1.2 1.75 0.00 0.00 0.00 0.45 4.00 8.80 1 6 2 0 0 0 2 3.2 0.5 1 1.70 0.33 4.00 0.63 1.88 12.00 6.40 1 1 0 0 0 0 0 5.9 0.5 1.1 1.73 0.00 0.00 0.00 0.17 2.00 11.80 1 3 0 0 0 0 0 2.5 1.2 1.3 1.62 0.00 0.00 0.00 1.20 2.50 2.08 1 5 0 1 2 0 3 4.4 1.5 1.4 1.93 0.60 2.00 0.68 1.14 3.33 2.93 1 1 0 0 0 0 0 3.7 0.3 0.8 1.75 0.00 0.00 0.00 0.27 3.33 12.33 1 2 0 0 0 0 0 1 0.5 0.5 1.80 0.00 0.00 0.00 2.00 4.00 2.00 1 2 0 0 0 0 0 4 1 1.3 1.69 0.00 0.00 0.00 0.50 2.00 4.00 1 2 0 0 0 0 0 3 0.2 0.9 1.89 0.00 0.00 0.00 0.67 10.00 15.00 1 4 0 0 0 0 0 4 1 1.4 2.00 0.00 0.00 0.00 1.00 4.00 4.00 1 2 0 0 0 0 0 6.2 1.1 1.3 1.62 0.00 0.00 0.00 0.32 1.82 5.64 1 2 0 0 0 0 0 2.1 0.3 0.7 2.14 0.00 0.00 0.00 0.95 6.67 7.00 1 1 0 0 0 0 0 3.5 0.7 1 1.80 0.00 0.00 0.00 0.29 1.43 5.00

Candidate number: 264831

Ulvehaugen (V. biflora)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 2 0 2 1 0 3 9.1 1.1 1.3 1.46 1.50 2.73 0.33 0.22 1.82 8.27 1 1 0 0 0 0 0 3.8 1 1 1.80 0.00 0.00 0.00 0.26 1.00 3.80 1 2 0 0 0 0 0 4.4 1.8 1.8 1.72 0.00 0.00 0.00 0.45 1.11 2.44 1 2 0 0 0 0 0 3.2 2.4 1.1 2.18 0.00 0.00 0.00 0.63 0.83 1.33 1 2 0 0 0 0 0 3.4 1 1 2.10 0.00 0.00 0.00 0.59 2.00 3.40 1 6 0 0 1 0 1 4.6 1.1 1.5 1.67 0.17 0.91 0.22 1.30 5.45 4.18 1 1 0 0 0 0 0 2.1 1.2 0.9 1.67 0.00 0.00 0.00 0.48 0.83 1.75 1 1 0 0 0 0 0 0.8 0.2 0.2 2.00 0.00 0.00 0.00 1.25 5.00 4.00 1 4 0 0 0 0 0 3.8 1.4 1 1.80 0.00 0.00 0.00 1.05 2.86 2.71 1 4 0 2 1 0 3 4 2 1.7 1.82 0.75 1.50 0.75 1.00 2.00 2.00 1 2 0 0 0 0 0 3.9 0.5 0.7 1.71 0.00 0.00 0.00 0.51 4.00 7.80 1 3 0 0 0 0 0 4.2 1 1.2 1.83 0.00 0.00 0.00 0.71 3.00 4.20 1 1 0 0 0 0 0 2.3 0.9 0.9 2.00 0.00 0.00 0.00 0.43 1.11 2.56 1 4 0 0 0 0 0 3.5 1 1.1 2.00 0.00 0.00 0.00 1.14 4.00 3.50 1 1 0 0 0 0 0 2.9 1 0.9 1.56 0.00 0.00 0.00 0.34 1.00 2.90 1 2 0 0 0 0 0 2.2 1.5 0.7 1.86 0.00 0.00 0.00 0.91 1.33 1.47 1 2 0 0 0 0 0 2.2 2.5 1.1 1.55 0.00 0.00 0.00 0.91 0.80 0.88 1 2 0 0 0 0 0 3.7 1.1 0.9 1.89 0.00 0.00 0.00 0.54 1.82 3.36 1 2 0 0 0 0 0 2.1 0.5 0.7 1.86 0.00 0.00 0.00 0.95 4.00 4.20 1 2 0 0 0 0 0 2 1.5 0.9 1.78 0.00 0.00 0.00 1.00 1.33 1.33 1 2 0 0 0 0 0 1.5 1.1 1 1.50 0.00 0.00 0.00 1.33 1.82 1.36 1 2 0 0 0 0 0 2.7 0.7 0.9 1.44 0.00 0.00 0.00 0.74 2.86 3.86 1 3 0 1 3 0 4 6.1 2 0.8 2.75 1.33 2.00 0.66 0.49 1.50 3.05

Candidate number: 264831

Appendix C. Veronica officinalis trait measurements

Øvstedal (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 4 115 0 0 0 0 0 29.3 54.3 2.4 0.48 0.00 0.00 0.00 3.92 2.12 0.54 1 9 0 0 0 0 0 5.2 5.5 2.4 0.58 0.00 0.00 0.00 1.73 1.64 0.95 2 19 0 0 0 0 0 2.5 6 1.4 0.54 0.00 0.00 0.00 7.60 3.17 0.42 1 10 0 0 0 0 0 3.5 15.5 1.5 0.53 0.00 0.00 0.00 2.86 0.65 0.23 3 66 21 3 23 0 47 42.6 31 2.1 0.47 0.71 1.52 1.10 1.55 2.13 1.37 2 28 0 0 0 0 0 8.8 5.8 1.4 0.57 0.00 0.00 0.00 3.18 4.83 1.52 1 17 0 0 0 0 0 2.5 11.2 1.9 0.53 0.00 0.00 0.00 6.80 1.52 0.22 1 11 2 0 0 0 2 5.4 17.2 2.3 0.57 0.18 0.12 0.37 2.04 0.64 0.31 1 16 0 0 0 0 0 1.8 1.1 1.4 0.57 0.00 0.00 0.00 8.89 14.55 1.64 1 20 11 5 0 0 16 9.9 8.8 2.7 0.44 0.80 1.82 1.62 2.02 2.27 1.13 1 6 0 0 0 0 0 2.8 9.5 1.8 0.39 0.00 0.00 0.00 2.14 0.63 0.29 3 29 0 0 0 0 0 11.5 14.8 1.5 0.42 0.00 0.00 0.00 2.52 1.96 0.78 1 12 0 0 0 0 0 1.4 3.2 1.3 0.46 0.00 0.00 0.00 8.57 3.75 0.44 1 22 0 0 0 0 0 6.3 8.1 2 0.50 0.00 0.00 0.00 3.49 2.72 0.78 1 15 0 0 0 0 0 2.3 2 1.3 0.46 0.00 0.00 0.00 6.52 7.50 1.15 2 28 0 0 0 0 0 4.4 6.8 1.3 0.50 0.00 0.00 0.00 6.36 4.12 0.65 2 14 0 0 0 0 0 2.9 2.5 1.6 0.47 0.00 0.00 0.00 4.83 5.60 1.16 3 53 0 0 0 0 0 6 12.8 1.3 0.48 0.00 0.00 0.00 8.83 4.14 0.47 3 61 0 0 0 0 0 6.5 11.5 1.3 0.47 0.00 0.00 0.00 9.38 5.30 0.57 3 39 0 0 0 0 0 5.3 11.8 1.5 0.47 0.00 0.00 0.00 7.36 3.31 0.45

Candidate number: 264831

Arhelleren (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 7 100 12 1 21 2 36 73.4 36.5 1.8 0.50 0.36 0.99 0.49 1.36 2.74 2.01 2 25 0 0 0 0 0 21.5 26 2.2 0.53 0.00 0.00 0.00 1.16 0.96 0.83 1 10 0 0 0 0 0 8 22 1.6 0.50 0.00 0.00 0.00 1.25 0.45 0.36 2 18 3 0 7 0 10 15.4 23 1.6 0.58 0.56 0.43 0.65 1.17 0.78 0.67 1 10 0 0 0 0 0 8.2 19 1.8 0.56 0.00 0.00 0.00 1.22 0.53 0.43 1 6 0 0 0 0 0 4.5 13.5 1.8 0.44 0.00 0.00 0.00 1.33 0.44 0.33 2 10 0 0 0 0 0 9.7 20.4 2.1 0.49 0.00 0.00 0.00 1.03 0.49 0.48 2 36 0 0 0 0 0 18.9 19.8 2 0.56 0.00 0.00 0.00 1.90 1.82 0.95 1 35 11 4 0 0 15 10 27.8 2.9 0.34 0.43 0.54 1.50 3.50 1.26 0.36 10 173 25 11 7 0 43 77.3 74.6 2 0.42 0.25 0.58 0.56 2.24 2.32 1.04 2 35 0 0 0 0 0 10.7 18.3 2.1 0.42 0.00 0.00 0.00 3.27 1.91 0.58 4 68 22 5 0 0 27 25.5 16.6 1.9 0.39 0.40 1.63 1.06 2.67 4.10 1.54 3 75 0 0 0 0 0 27.7 35.1 2.1 0.44 0.00 0.00 0.00 2.71 2.14 0.79 1 10 0 0 0 0 0 6.2 12.2 1.6 0.38 0.00 0.00 0.00 1.61 0.82 0.51 2 31 0 0 0 0 0 10.3 5.6 1.9 0.42 0.00 0.00 0.00 3.01 5.54 1.84 1 8 0 0 0 0 0 4 6.5 1.7 0.41 0.00 0.00 0.00 2.00 1.23 0.62 7 113 25 11 0 0 36 52.2 36.5 1.7 0.45 0.32 0.99 0.69 2.16 3.10 1.43 3 67 0 0 0 0 0 17.8 11.8 1.5 0.41 0.00 0.00 0.00 3.76 5.68 1.51

Candidate number: 264831

Vikesland (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 3 22 4 0 10 1 15 23 21 1.8 0.45 0.68 0.71 0.65 0.96 1.05 1.10 2 17 0 1 18 0 19 22 21 2.4 0.45 1.12 0.90 0.86 0.77 0.81 1.05 1 8 0 0 0 0 0 6.5 5 1.5 0.53 0.00 0.00 0.00 1.23 1.60 1.30 4 42 5 7 32 0 44 63.8 56.7 2.4 0.47 1.05 0.78 0.69 0.66 0.74 1.13 3 20 3 4 16 0 23 38.7 37.5 2.2 0.45 1.15 0.61 0.59 0.52 0.53 1.03 5 41 3 2 24 3 29 59.3 48 2.1 0.42 0.71 0.60 0.49 0.69 0.85 1.24 2 18 2 3 21 5 26 33.2 32.5 2.7 0.41 1.44 0.80 0.78 0.54 0.55 1.02 1 11 0 0 0 0 0 9 13.5 2.2 0.45 0.00 0.00 0.00 1.22 0.81 0.67 1 10 1 0 13 1 15 18.2 8 2.8 0.39 1.50 1.88 0.82 0.55 1.25 2.28 3 22 1 1 3 1 6 24.6 7 1.5 0.56 0.27 0.86 0.24 0.89 3.14 3.51 3 26 0 5 24 2 31 45.5 24.5 1.8 0.47 1.19 1.27 0.68 0.57 1.06 1.86 2 18 2 4 15 0 21 32 22 2.4 0.46 1.17 0.95 0.66 0.56 0.82 1.45 1 2 0 0 9 3 12 12 11 2 0.40 6.00 1.09 1.00 0.17 0.18 1.09 2 5 1 1 4 6 12 22 21.5 1.9 0.50 2.40 0.56 0.55 0.23 0.23 1.02 1 8 2 2 6 1 11 17 12.5 1.9 0.53 1.38 0.88 0.65 0.47 0.64 1.36 2 16 0 0 0 0 0 13 19.5 1.8 0.46 0.00 0.00 0.00 1.23 0.82 0.67 1 10 3 0 0 3 6 11 18 2 0.40 0.60 0.33 0.55 0.91 0.56 0.61 1 6 0 0 0 0 0 4 5 1 0.50 0.00 0.00 0.00 1.50 1.20 0.80 1 3 0 0 0 0 0 4.5 0.5 1.1 0.45 0.00 0.00 0.00 0.67 6.00 9.00 1 6 0 0 0 0 0 11.5 8 1.8 0.44 0.00 0.00 0.00 0.52 0.75 1.44

Candidate number: 264831

Fauske (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 4 0 0 0 0 0 6.6 9.3 2 0.50 0.00 0.00 0.00 0.61 0.43 0.71 1 8 3 0 4 0 7 8.6 4 1.7 0.41 0.88 1.75 0.81 0.93 2.00 2.15 1 7 0 0 0 0 0 3 4.5 1.3 0.54 0.00 0.00 0.00 2.33 1.56 0.67 3 14 0 2 13 0 15 15.7 10.5 1.3 0.43 1.07 1.43 0.96 0.89 1.33 1.50 2 7 0 0 0 0 0 2.3 4.4 1.4 0.48 0.00 0.00 0.00 3.04 1.59 0.52 1 10 0 0 0 0 0 3.2 3.5 1.4 0.57 0.00 0.00 0.00 3.13 2.86 0.91 1 8 0 0 0 0 0 2.5 2.5 1.4 0.43 0.00 0.00 0.00 3.20 3.20 1.00 2 18 0 0 0 0 0 8.5 9.5 1.9 0.39 0.00 0.00 0.00 2.12 1.89 0.89 1 8 4 0 6 0 10 8.5 8.5 1.6 0.44 1.25 1.18 1.18 0.94 0.94 1.00 1 8 0 0 0 0 0 7.5 11 1.7 0.41 0.00 0.00 0.00 1.07 0.73 0.68 1 12 0 0 0 0 0 4.5 7 1.6 0.44 0.00 0.00 0.00 2.67 1.71 0.64 1 10 0 0 0 0 0 5.5 6 2 0.45 0.00 0.00 0.00 1.82 1.67 0.92 1 7 0 0 0 0 0 5 6 1.5 0.40 0.00 0.00 0.00 1.40 1.17 0.83 1 7 0 0 0 0 0 4.5 9 1.7 0.47 0.00 0.00 0.00 1.56 0.78 0.50 2 10 0 0 0 0 0 11.5 6.5 1.1 0.43 0.00 0.00 0.00 0.87 1.54 1.77 1 8 0 0 0 0 0 2.5 2 1.5 0.47 0.00 0.00 0.00 3.20 4.00 1.25 1 8 0 0 0 0 0 3 2.5 1.2 0.58 0.00 0.00 0.00 2.67 3.20 1.20 1 5 0 0 0 0 0 2.5 2 1 0.70 0.00 0.00 0.00 2.00 2.50 1.25 2 10 0 0 0 0 0 1.2 1 0.8 0.48 0.00 0.00 0.00 8.33 10.00 1.20 1 10 0 0 0 0 0 2.8 1 1.3 0.46 0.00 0.00 0.00 3.57 10.00 2.80 1 7 0 0 0 0 0 1.5 1 1 0.40 0.00 0.00 0.00 4.67 7.00 1.50 1 6 0 0 0 0 0 1.8 1 0.8 0.38 0.00 0.00 0.00 3.33 6.00 1.80 1 6 0 0 0 0 0 1.5 0.5 0.7 0.43 0.00 0.00 0.00 4.00 12.00 3.00

Candidate number: 264831

Rambæra (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 24 410 94 37 0 2 133 93.3 60.3 1.3 0.52 0.32 2.21 1.43 4.39 6.80 1.55 10 140 15 25 0 0 40 45.3 25.3 1.2 0.51 0.29 1.58 0.88 3.09 5.53 1.79 4 50 5 4 0 0 9 8.5 12.2 1 0.56 0.18 0.74 1.06 5.88 4.10 0.70 2 37 16 1 0 0 17 18.2 1.8 1.1 0.50 0.46 9.44 0.93 2.03 20.56 10.11 3 24 9 2 0 0 11 8.1 15.5 1 0.51 0.46 0.71 1.36 2.96 1.55 0.52 1 19 0 0 0 0 0 1.3 4.5 0.8 0.50 0.00 0.00 0.00 14.62 4.22 0.29 1 11 0 0 0 0 0 0.5 3.5 0.6 0.50 0.00 0.00 0.00 22.00 3.14 0.14 1 17 0 0 0 0 0 4.5 2.2 1.5 0.47 0.00 0.00 0.00 3.78 7.73 2.05 1 16 7 3 0 0 10 5.7 11 1.3 0.54 0.63 0.91 1.75 2.81 1.45 0.52 1 15 3 6 0 0 9 5 7.5 1.6 0.56 0.60 1.20 1.80 3.00 2.00 0.67 14 182 25 34 0 0 59 51.6 19.6 1.2 0.52 0.32 3.01 1.14 3.53 9.29 2.63 1 17 0 0 0 0 0 5.5 3.6 1.5 0.53 0.00 0.00 0.00 3.09 4.72 1.53 16 200 75 20 0 0 95 84.5 42.2 1.4 0.53 0.48 2.25 1.12 2.37 4.74 2.00 7 71 15 20 0 0 35 32.2 10 1.4 0.51 0.49 3.50 1.09 2.20 7.10 3.22 2 24 8 4 0 0 12 8.6 20.5 1.3 0.50 0.50 0.59 1.40 2.79 1.17 0.42 2 32 7 1 0 0 8 11.5 20 1.3 0.49 0.25 0.40 0.70 2.78 1.60 0.58

Candidate number: 264831

Høgsete (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 3 31 12 0 24 0 36 32.8 18 1.8 0.50 1.16 2.00 1.10 0.95 1.72 1.82 2 30 0 0 0 0 0 19.5 7.3 1.8 0.50 0.00 0.00 0.00 1.54 4.11 2.67 1 11 0 0 0 0 0 6 5 1.5 0.40 0.00 0.00 0.00 1.83 2.20 1.20 1 14 0 0 0 0 0 5 6.5 1.9 0.53 0.00 0.00 0.00 2.80 2.15 0.77 2 12 1 1 9 0 11 12.8 8.3 1.9 0.47 0.92 1.33 0.86 0.94 1.45 1.54 5 84 0 0 0 0 0 25.5 44.5 1.7 0.50 0.00 0.00 0.00 3.29 1.89 0.57 4 108 2 1 16 0 19 34.5 43.5 2.3 0.54 0.18 0.44 0.55 3.13 2.48 0.79 6 144 1 2 23 0 26 67.9 94.2 2.4 0.49 0.18 0.28 0.38 2.12 1.53 0.72 1 29 0 0 0 0 0 2.8 1.8 1.4 0.36 0.00 0.00 0.00 10.36 16.11 1.56 3 34 3 2 3 0 8 25.8 20.8 1.9 0.53 0.24 0.38 0.31 1.32 1.63 1.24 10 144 14 5 47 0 66 88.5 99.5 2.1 0.52 0.46 0.66 0.75 1.63 1.45 0.89 7 142 12 1 55 0 68 92 119.5 2.7 0.52 0.48 0.57 0.74 1.54 1.19 0.77 1 22 4 0 10 2 16 16 17 2.7 0.48 0.73 0.94 1.00 1.38 1.29 0.94 1 38 0 0 0 0 0 9 16 2.3 0.52 0.00 0.00 0.00 4.22 2.38 0.56 1 16 0 0 0 0 0 11.5 11.5 2.2 0.55 0.00 0.00 0.00 1.39 1.39 1.00 1 11 0 0 0 0 0 8 13.5 2.3 0.52 0.00 0.00 0.00 1.38 0.81 0.59 1 20 0 0 0 0 0 7 8 2 0.50 0.00 0.00 0.00 2.86 2.50 0.88 6 89 5 6 29 3 43 65 56 2.2 0.53 0.48 0.77 0.66 1.37 1.59 1.16

Candidate number: 264831

Ålrust (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 10 0 0 0 0 0 3.5 7.5 1.5 0.53 0.00 0.00 0.00 2.86 1.33 0.47 1 15 0 0 8 6 14 9 10 2.1 0.48 0.93 1.40 1.56 1.67 1.50 0.90 1 10 0 0 0 0 0 2.5 1 1.4 0.29 0.00 0.00 0.00 4.00 10.00 2.50 1 14 0 0 0 10 10 5 7.5 1.7 0.53 0.71 1.33 2.00 2.80 1.87 0.67 1 20 0 3 3 2 8 11.5 3.5 1.6 0.63 0.40 2.29 0.70 1.74 5.71 3.29 1 15 0 0 0 0 0 3 9 1.7 0.47 0.00 0.00 0.00 5.00 1.67 0.33 3 33 0 0 0 0 0 17.5 4.5 1.3 0.51 0.00 0.00 0.00 1.89 7.33 3.89 1 30 0 0 0 0 0 4.5 6.5 2 0.45 0.00 0.00 0.00 6.67 4.62 0.69 3 31 0 0 0 0 0 4.5 1.5 1.2 0.35 0.00 0.00 0.00 6.89 20.67 3.00 3 34 0 0 0 0 0 8.5 16 1.4 0.50 0.00 0.00 0.00 4.00 2.13 0.53 1 16 1 0 15 0 16 9.5 13 2 0.40 1.00 1.23 1.68 1.68 1.23 0.73 2 24 0 2 6 0 8 17 12 1.5 0.71 0.33 0.67 0.47 1.41 2.00 1.42 3 68 0 0 0 0 0 14.5 17.7 1.9 0.57 0.00 0.00 0.00 4.69 3.84 0.82 1 9 3 1 8 0 12 7 19 1.7 0.47 1.33 0.63 1.71 1.29 0.47 0.37 1 37 2 2 0 3 7 7 2 1.3 0.54 0.19 3.50 1.00 5.29 18.50 3.50 1 18 0 0 0 0 0 3 1.5 1.2 0.5 0.00 0.00 0.00 6.00 12.00 2.00 1 10 0 0 0 0 0 2 2.5 1.2 0.42 0.00 0.00 0.00 5.00 4.00 0.80 1 9 0 0 0 0 0 1.5 1.5 1 0.50 0.00 0.00 0.00 6.00 6.00 1.00 1 8 0 0 0 0 0 0.8 1 0.5 0.40 0.00 0.00 0.00 10.00 8.00 0.80 1 25 0 0 0 0 0 3.5 3 1.4 0.57 0.00 0.00 0.00 7.14 8.33 1.17 1 8 0 0 0 0 0 1 1.5 1.1 0.45 0.00 0.00 0.00 8.00 5.33 0.67 1 11 0 0 0 0 0 0.5 0.5 0.5 0.60 0.00 0.00 0.00 22.00 22.00 1.00

Candidate number: 264831

Gudmedalen (V. officinalis)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 17 0 0 0 0 0 1.3 2 1.4 0.50 0.00 0.00 0.00 13.08 8.50 0.65 2 24 0 0 0 0 0 2.3 3.7 0.85 0.59 0.00 0.00 0.00 10.43 6.49 0.62 3 33 0 0 0 0 0 4 3.2 1.2 0.57 0.00 0.00 0.00 8.25 10.31 1.25 2 13 0 0 0 0 0 0.7 1.4 1.1 0.47 0.00 0.00 0.00 18.57 9.29 0.50 1 37 0 8 0 0 8 3.5 3.8 1.6 0.44 0.22 2.11 2.29 10.57 9.74 0.92 2 25 0 0 0 0 0 2.3 3 1.2 0.51 0.00 0.00 0.00 10.87 8.33 0.77 8 87 0 7 0 0 7 28.6 5.2 1.5 0.52 0.08 1.35 0.24 3.04 16.73 5.50 13 140 0 87 0 12 99 123.2 45.3 2 0.55 0.71 2.19 0.80 1.14 3.09 2.72 5 59 0 22 12 16 50 32.9 12.8 1.8 0.47 0.85 3.91 1.52 1.79 4.61 2.57 1 8 0 0 0 0 0 0.5 0.8 0.8 0.38 0.00 0.00 0.00 16.00 10.00 0.63 7 57 0 18 0 13 31 38.5 34 1.6 0.50 0.54 0.91 0.81 1.48 1.68 1.13 12 152 0 24 0 0 24 73 28.7 1.8 0.49 0.16 0.84 0.33 2.08 5.30 2.54 6 73 5 5 8 2 20 31.2 14 1.7 0.49 0.27 1.43 0.64 2.34 5.21 2.23 3 31 0 8 0 0 8 13 6.8 1.7 0.50 0.26 1.18 0.62 2.38 4.56 1.91 3 33 0 2 4 2 8 12.9 7.4 1.3 0.57 0.24 1.08 0.62 2.56 4.46 1.74 1 17 0 0 0 0 0 1.2 1 0.9 0.44 0.00 0.00 0.00 14.17 17.00 1.20 1 19 0 0 0 0 0 3.4 4 1.4 0.36 0.00 0.00 0.00 5.59 4.75 0.85 4 69 0 8 3 1 12 20.1 10 1.6 0.55 0.17 1.20 0.60 3.43 6.90 2.01

Candidate number: 264831

Appendix D. Viola palustris trait measurements

Øvstedal (V. palustris)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 1 0 0 0 0 0 2 18.2 2.1 1.24 0.00 0.00 0.00 0.50 0.05 0.11 2 6 0 0 0 0 0 5.8 16.6 1.7 1.27 0.00 0.00 0.00 1.03 0.36 0.35 5 9 0 0 1 0 1 18.8 71.6 1.7 1.37 0.11 0.01 0.05 0.48 0.13 0.26 1 2 0 0 0 0 0 2.5 6.2 1.5 1.40 0.00 0.00 0.00 0.80 0.32 0.40 1 3 0 0 0 0 0 3.5 7.1 1.6 1.50 0.00 0.00 0.00 0.86 0.42 0.49 1 2 0 0 0 0 0 7.8 6 2.4 1.21 0.00 0.00 0.00 0.26 0.33 1.30 1 6 0 0 0 0 0 6 7.5 2.5 1.40 0.00 0.00 0.00 1.00 0.80 0.80 2 5 0 0 0 0 0 8.4 23.1 1.9 1.42 0.00 0.00 0.00 0.60 0.22 0.36 1 2 0 0 0 0 0 1.9 4.6 1 1.70 0.00 0.00 0.00 1.05 0.43 0.41 1 3 0 0 1 0 1 2.8 13.5 2 1.20 0.33 0.07 0.36 1.07 0.22 0.21 1 3 0 0 0 0 0 5.1 5.3 1.5 1.40 0.00 0.00 0.00 0.59 0.57 0.96 1 3 0 0 0 0 0 3.6 5.2 1.8 1.39 0.00 0.00 0.00 0.83 0.58 0.69 1 1 0 0 0 0 0 2.2 2.5 0.6 2.00 0.00 0.00 0.00 0.45 0.40 0.88 1 3 0 0 0 0 0 3.8 4 1.8 1.44 0.00 0.00 0.00 0.79 0.75 0.95 1 1 0 0 0 0 0 4.8 7.3 1.4 1.50 0.00 0.00 0.00 0.21 0.14 0.66 1 3 0 0 0 0 0 2 4 1.6 1.31 0.00 0.00 0.00 1.50 0.75 0.50 1 3 0 0 0 0 0 1.8 4.5 1.2 1.42 0.00 0.00 0.00 1.67 0.67 0.40

Candidate number: 264831

Arhelleren (V. palustris)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 2 0 0 0 0 0 2.2 2.6 1.3 1.46 0 0 0 0.91 0.77 0.85 1 2 0 0 0 0 0 2 4.5 1.7 1.29 0 0 0 1.00 0.44 0.44 1 3 0 0 0 0 0 1.3 6 1.6 1.38 0 0 0 2.31 0.50 0.22 1 1 0 0 0 0 0 2.3 2.7 1.5 1.20 0 0 0 0.43 0.37 0.85 2 4 0 0 0 0 0 4.2 8.8 1.1 1.47 0 0 0 0.95 0.45 0.48 1 3 0 0 0 0 0 2.8 4 2.2 1.27 0 0 0 1.07 0.75 0.70 1 2 0 0 0 0 0 3.9 3 1.4 1.43 0 0 0 0.51 0.67 1.30 1 2 0 0 0 0 0 5.4 2.8 1.9 1.37 0 0 0 0.37 0.71 1.93 1 3 0 0 0 0 0 2 6.3 1.6 1.31 0 0 0 1.50 0.48 0.32 1 1 0 0 0 0 0 4.3 3.3 1.2 1.83 0 0 0 0.23 0.30 1.30 1 1 0 0 0 0 0 3.8 3.2 1.4 1.29 0 0 0 0.26 0.31 1.19 1 2 0 0 0 0 0 1.8 2.7 1.2 1.58 0 0 0 1.11 0.74 0.67 2 4 0 0 0 0 0 8 11.5 1.4 1.49 0 0 0 0.50 0.35 0.70 1 3 0 0 0 0 0 4.9 14.5 1.7 1.59 0 0 0 0.61 0.21 0.34 2 4 0 0 0 0 0 12.6 13.7 2 1.25 0 0 0 0.32 0.29 0.92 1 2 0 0 0 0 0 5.5 4.2 1.6 1.50 0 0 0 0.36 0.48 1.31 1 2 0 0 0 0 0 6.2 4.4 1.6 1.38 0 0 0 0.32 0.45 1.41 2 3 0 0 0 0 0 9.2 7.3 1.2 1.47 0 0 0 0.33 0.41 1.26 1 2 0 0 0 0 0 4.6 3.6 1.9 1.16 0 0 0 0.43 0.56 1.28 3 5 0 0 0 0 0 11.3 5.6 1.3 1.4 0 0 0 0.44 0.89 2.02

Candidate number: 264831

Rambæra (V. palustris)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 3 7 2 0 2 0 4 8.6 25.9 1.4 1.33 0.57 0.15 0.47 0.81 0.27 0.33 1 4 0 0 0 0 0 3.3 5 2.4 1.21 0.00 0.00 0.00 1.21 0.80 0.66 2 4 0 0 0 0 0 5.6 12.2 1.2 1.41 0.00 0.00 0.00 0.71 0.33 0.46 2 4 0 0 0 0 0 5.6 11 1.05 1.52 0.00 0.00 0.00 0.71 0.36 0.51 1 4 0 0 0 0 0 8.6 3.3 1.5 1.33 0.00 0.00 0.00 0.47 1.21 2.61 1 3 0 0 0 0 0 1.9 5 1.5 1.40 0.00 0.00 0.00 1.58 0.60 0.38 1 2 0 0 0 0 0 1 1 1 1.40 0.00 0.00 0.00 2.00 2.00 1.00 1 2 0 0 0 0 0 2.8 2.5 0.8 1.38 0.00 0.00 0.00 0.71 0.80 1.12 1 1 0 0 0 0 0 3.3 7 1.2 1.42 0.00 0.00 0.00 0.30 0.14 0.47 1 3 0 0 0 0 0 3.2 2 1.5 1.20 0.00 0.00 0.00 0.94 1.50 1.60 1 2 0 0 0 0 0 1.5 0.5 1 1.50 0.00 0.00 0.00 1.33 4.00 3.00 1 2 0 0 0 0 0 1.2 2.5 0.9 1.44 0.00 0.00 0.00 1.67 0.80 0.48 1 3 0 0 0 0 0 1 0.6 0.4 1.00 0.00 0.00 0.00 3.00 5.00 1.67 1 2 0 1 0 0 1 1.6 9.1 1.4 1.21 0.50 0.11 0.63 1.25 0.22 0.18 1 2 0 0 0 0 0 2 4.5 1.1 1.45 0.00 0.00 0.00 1.00 0.44 0.44 2 2 0 0 0 0 0 4 14 1.8 1.17 0.00 0.00 0.00 0.50 0.14 0.29 1 3 0 0 0 0 0 3.5 3.5 1.5 1.33 0.00 0.00 0.00 0.86 0.86 1.00 2 5 0 1 0 0 1 3.5 14.5 1.2 1.54 0.20 0.07 0.29 1.43 0.34 0.24 1 1 0 0 0 0 0 3.8 3.3 0.9 1.56 0.00 0.00 0.00 0.26 0.30 1.15 1 1 0 0 0 0 0 3.3 3 1.2 1.33 0.00 0.00 0.00 0.30 0.33 1.10 1 1 0 0 0 0 0 4 5.5 1.3 1.54 0.00 0.00 0.00 0.25 0.18 0.73

Candidate number: 264831

Høgsete (V. palustris)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 2 0 0 0 0 0 2.8 11.5 1.8 1.33 0.00 0.00 0.00 0.71 0.17 0.24 1 2 0 0 0 0 0 2.5 11.3 1.5 1.40 0.00 0.00 0.00 0.80 0.18 0.22 1 2 0 0 0 0 0 3.5 16.7 1.4 1.43 0.00 0.00 0.00 0.57 0.12 0.21 3 3 0 0 0 0 0 6 36.6 1.4 1.49 0.00 0.00 0.00 0.50 0.08 0.16 3 4 0 0 0 0 0 12.4 51.5 1.9 1.36 0.00 0.00 0.00 0.32 0.08 0.24 1 2 0 0 0 0 0 3 15 1.9 1.37 0.00 0.00 0.00 0.67 0.13 0.20 1 2 0 0 0 0 0 3 5.5 1.6 1.44 0.00 0.00 0.00 0.67 0.36 0.55 6 10 0 0 3 0 3 25.7 170.8 2 1.58 0.30 0.02 0.12 0.39 0.06 0.15 1 1 0 0 1 0 1 3.8 15 2.1 1.48 1.00 0.07 0.26 0.26 0.07 0.25 1 1 0 0 0 0 0 3.9 6 1.5 1.40 0.00 0.00 0.00 0.26 0.17 0.65 1 1 0 0 0 0 0 3.1 4.2 0.8 1.75 0.00 0.00 0.00 0.32 0.24 0.74 3 3 0 0 0 0 0 11.7 52.5 1.6 1.58 0.00 0.00 0.00 0.26 0.06 0.22 1 2 0 0 0 0 0 4.2 33.5 1.8 1.56 0.00 0.00 0.00 0.48 0.06 0.13 4 5 0 0 0 0 0 15 81.7 1.5 1.42 0.00 0.00 0.00 0.33 0.06 0.18

Candidate number: 264831

Gudmedalen (V. palustris)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 1 2 0 0 0 0 0 3.4 8 1.8 1.50 0.00 0.00 0.00 0.59 0.25 0.43 1 2 0 0 0 0 0 2.5 5.5 1.6 1.63 0.00 0.00 0.00 0.80 0.36 0.45 1 3 0 1 0 0 1 1.6 8.6 1.6 1.69 0.33 0.12 0.63 1.88 0.35 0.19 1 1 0 0 0 0 0 3 7 1.5 1.47 0.00 0.00 0.00 0.33 0.14 0.43 1 2 0 0 0 0 0 6.8 6 2.1 1.52 0.00 0.00 0.00 0.29 0.33 1.13 1 3 0 0 0 0 0 4.1 5.5 2.1 1.38 0.00 0.00 0.00 0.73 0.55 0.75 1 2 0 0 0 0 0 2.9 6.5 1.8 1.50 0.00 0.00 0.00 0.69 0.31 0.45 1 2 0 0 0 0 0 4.1 6.5 2.1 1.48 0.00 0.00 0.00 0.49 0.31 0.63 1 1 0 0 0 0 0 6.3 6 1.6 1.38 0.00 0.00 0.00 0.16 0.17 1.05 3 4 0 0 0 0 0 10.5 6.3 1.3 1.59 0.00 0.00 0.00 0.38 0.63 1.67 1 1 0 0 0 0 0 2.5 4.5 1.4 1.64 0.00 0.00 0.00 0.40 0.22 0.56 1 2 0 0 0 0 0 2.3 8.1 1.4 1.64 0.00 0.00 0.00 0.87 0.25 0.28 3 4 0 0 0 0 0 11.9 11 1.6 1.49 0.00 0.00 0.00 0.34 0.36 1.08 1 2 0 0 0 0 0 2.3 1.4 0.8 1.38 0.00 0.00 0.00 0.87 1.43 1.64 1 1 0 0 0 0 0 4.2 8.5 1.6 1.50 0.00 0.00 0.00 0.24 0.12 0.49 1 2 0 0 0 0 0 3 6 1.4 1.50 0.00 0.00 0.00 0.67 0.33 0.50 3 6 0 0 1 0 1 15.7 27.7 2.1 1.72 0.17 0.04 0.06 0.38 0.22 0.57

Candidate number: 264831

Skjellingahaugen (V. palustris)

S# TL TFL TB TF TAF PF TS TR L(l) L(w/l) PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR 2 6 0 1 0 0 1 4.3 11 1.3 1.56 0.17 0.09 0.23 1.40 0.55 0.39 2 6 0 0 0 0 0 2.4 11.9 1.15 1.58 0.00 0.00 0.00 2.50 0.50 0.20 1 2 0 0 0 0 0 2.3 11.8 1.5 1.60 0.00 0.00 0.00 0.87 0.17 0.19 3 4 0 0 0 0 0 7 16.2 1.43 1.44 0.00 0.00 0.00 0.57 0.25 0.43 2 6 0 0 0 0 0 2.7 10.2 1.25 1.40 0.00 0.00 0.00 2.22 0.59 0.26 1 2 0 0 0 0 0 2.3 14.8 1.6 1.38 0.00 0.00 0.00 0.87 0.14 0.16 3 7 0 1 1 0 2 5 18.7 1.67 1.45 0.29 0.11 0.40 1.40 0.37 0.27 1 3 0 0 0 0 0 1.6 5.1 1.4 1.43 0.00 0.00 0.00 1.88 0.59 0.31 1 3 0 1 0 0 1 2.6 5.2 1.5 1.47 0.33 0.19 0.38 1.15 0.58 0.50 1 2 0 0 0 0 0 1.1 3 1 1.50 0.00 0.00 0.00 1.82 0.67 0.37 1 1 0 0 1 0 1 1.4 7 1.4 1.57 1.00 0.14 0.71 0.71 0.14 0.20 1 2 0 0 0 0 0 1.5 4 1.3 1.62 0.00 0.00 0.00 1.33 0.50 0.38 2 6 0 0 1 0 1 2.8 8.8 1.2 1.46 0.17 0.11 0.36 2.14 0.68 0.32 1 2 0 0 0 0 0 1.9 4 1 1.60 0.00 0.00 0.00 1.05 0.50 0.48 3 6 0 0 0 0 0 3.4 28 1.27 1.32 0.00 0.00 0.00 1.76 0.21 0.12 1 2 0 0 0 0 0 1.8 2.5 1.1 1.45 0.00 0.00 0.00 1.11 0.80 0.72 1 3 0 0 0 0 0 1.5 3.8 1.2 1.67 0.00 0.00 0.00 2.00 0.79 0.39 1 2 0 0 0 0 0 3.2 6.6 1.4 1.57 0.00 0.00 0.00 0.63 0.30 0.48 1 1 0 0 0 0 0 1.1 2.5 0.7 1.71 0.00 0.00 0.00 0.91 0.40 0.44 2 5 0 0 1 0 1 2.8 12.2 1.15 1.48 0.20 0.08 0.36 1.79 0.41 0.23 1 2 0 0 0 0 0 2.3 4.6 1.5 1.40 0.00 0.00 0.00 0.87 0.43 0.50 1 2 0 0 0 0 0 1.6 13 1.3 1.38 0.00 0.00 0.00 1.25 0.15 0.12 2 4 0 1 1 0 2 2.1 18.9 1.2 1.54 0.50 0.11 0.95 1.90 0.21 0.11

Candidate number: 264831

Correlation Matrices

Legend: S# = number of shoots, TL = total number of leaves, PF = number of potential fruits, TS = total shoot height, TR = total root/ stolon length, x l = mean length of leaf, x w/l = mean width of leaf divided by mean length of leaf, Pr = precipitation, Tp = temperature

Appendix E. Correlation matrix for Veronica alpina

S# TL PF TS TR l w/l PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR S# 1 TL 0.84 1 PF 0.52 0.66 1 TS 0.70 0.80 0.77 1 TR 0.39 0.42 0.25 0.52 1 l -0.23 -0.10 0.28 0.16 0.21 1 w/l 0.13 0.19 0.19 0.38 0.18 0.06 1 PF/TL -0.05 -0.07 0.51 0.27 0.04 0.42 0.20 1 PF/TR 0.29 0.28 0.50 0.34 -0.29 0.02 0.23 0.39 1 PF/TS 0.11 0.23 0.69 0.31 0.10 0.44 0.22 0.68 0.47 1 TL/TS -0.12 -0.15 -0.35 -0.49 -0.29 -0.55 -0.47 -0.45 -0.27 -0.38 1 TL/TR 0.25 0.17 -0.07 -0.04 -0.42 -0.36 -0.07 -0.20 0.46 -0.15 0.24 1 TS/TR 0.27 0.20 0.10 0.18 -0.37 -0.17 0.14 0.06 0.74 0.02 -0.14 0.84 1 Pr 0.10 0.04 0.10 0.24 0.06 -0.23 0.23 0.16 0.10 -0.03 -0.16 0.07 0.13 Tp -0.05 -0.15 -0.04 -0.14 -0.41 -0.24 -0.18 0.10 0.33 -0.10 0.12 0.34 0.38

Candidate number: 264831

Appendix F. Correlation matrix for Viola biflora

S# TL PF TS TR x l x w/l PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR S# 1 TL 0.49 1 PF 0.45 0.81 1 TS 0.36 0.56 0.58 1 TR 0.04 0.36 0.34 0.38 1 l 0.17 0.37 0.23 0.64 0.51 1 w/l 0.03 0.25 0.14 0.12 0.09 -0.02 1 PF/TL 0.14 0.43 0.80 0.50 0.35 0.20 0.02 1 PF/TR 0.35 0.65 0.87 0.45 0.09 0.16 0.11 0.73 1 PF/TS 0.23 0.69 0.92 0.38 0.31 0.19 0.11 0.83 0.85 1 TL/TS 0.16 0.70 0.48 -0.09 0.14 -0.01 0.23 0.22 0.43 0.57 1 TL/TR 0.34 0.62 0.49 0.20 -0.34 0.00 0.25 0.21 0.62 0.45 0.57 1 TS/TR 0.11 -0.04 0.01 0.29 -0.59 -0.02 0.07 -0.03 0.16 -0.08 -0.28 0.51 1 Pr 0.15 0.23 0.31 0.14 -0.04 -0.02 -0.16 0.24 0.29 0.30 0.10 0.20 0.08 Tp -0.09 -0.01 0.14 -0.04 0.18 -0.10 -0.36 0.21 0.07 0.23 0.01 -0.19 -0.25

Candidate number: 264831

Appendix G. Correlation matrix for Veronica officinalis

S# TL PF TS TR x l w/l PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR S# 1 TL 0.94 1 PF 0.86 0.81 1 TS 0.84 0.80 0.89 1 TR 0.60 0.66 0.67 0.83 1 l 0.05 0.12 0.23 0.36 0.50 1 w/l 0.13 0.12 0.08 0.09 0.05 -0.10 1 PF/TL 0.06 -0.01 0.29 0.22 0.17 0.31 -0.12 1 PF/TR 0.34 0.31 0.49 0.35 0.10 0.06 0.08 0.32 1 PF/TS 0.30 0.29 0.54 0.34 0.26 0.25 0.02 0.52 0.62 1 TL/TS -0.12 -0.07 -0.23 -0.32 -0.30 -0.56 0.02 -0.30 -0.20 -0.25 1 TL/TR 0.06 0.07 -0.07 -0.15 -0.32 -0.53 -0.01 -0.24 0.25 -0.12 0.57 1 TS/TR 0.18 0.12 0.15 0.16 -0.13 -0.17 0.02 0.04 0.60 0.09 -0.17 0.50 1 Pr 0.20 0.23 0.10 0.08 0.07 0.04 0.09 -0.13 0.07 0.04 0.20 0.02 -0.09 Tp -0.19 -0.16 -0.12 -0.10 0.05 0.22 -0.17 0.08 -0.21 -0.18 -0.37 -0.39 -0.13

Candidate number: 264831

Appendix H. Correlation matrix for Viola palustris

S# TL PF TS TR x l w/l PF/TL PF/TR PF/TS TL/TS TL/TR TS/TR S# 1 TL 0.79 1 PF 0.48 0.54 1 TS 0.83 0.61 0.33 1 TR 0.80 0.59 0.47 0.76 1 l 0.08 0.17 0.08 0.34 0.24 1 w/l 0.04 -0.08 0.05 0.02 0.05 -0.32 1 PF/TL 0.15 0.16 0.71 0.04 0.17 0.10 0.05 1 PF/TR 0.15 0.28 0.76 -0.04 0.07 0.00 0.05 0.80 1 PF/TS 0.13 0.22 0.73 -0.07 0.08 -0.02 0.06 0.83 0.90 1 TL/TS -0.09 0.28 0.13 -0.45 -0.17 -0.33 -0.11 0.08 0.25 0.27 1 TL/TR -0.18 0.00 -0.12 -0.21 -0.25 -0.35 -0.26 -0.14 -0.09 -0.11 0.46 1 TS/TR -0.17 -0.17 -0.25 0.09 -0.32 -0.14 -0.14 -0.25 -0.25 -0.27 -0.22 0.60 1 Pr -0.14 0.14 0.03 -0.28 -0.31 -0.13 0.04 0.02 0.17 0.16 0.39 0.07 -0.05 Tp -0.05 -0.02 -0.14 0.14 0.03 0.16 -0.31 -0.15 -0.25 -0.23 -0.23 0.06 0.24

Candidate number: 264831

Appendix I. Photo of a dense mat of Veronica officinalis at the alpine site Gudmedalen

Appendix J. Photo of a pressed specimen of Veronica alpina

Candidate number: 264831

Appendix K. Photo of a pressed specimen of Viola biflora

Appendix L. Photo of a pressed specimen of Veronica officinalis

Candidate number: 264831

Appendix M. Photo of a pressed specimen of Viola palustris