Spatial and Temporal Dynamics of Plant-Pollinator Interactions in Northern Norway

Spatial and temporal dynamics of plant-pollinator interactions in northern Norway

by Lisa Lunde Fagerli

Master thesis (60 credits)

Centre for Ecological and Evolutionary Synthesis Department of Biosciences Faculty of Mathematics and Natural Sciences

UNIVERSITY OF OSLO

May 2018

Spatial and temporal dynamics of plant-pollinator interactions in northern Norway

Lisa Lunde Fagerli

III

2018 Title: Spatial and temporal dynamics of plant-pollinator interactions in northern Norway Author: Lisa Lunde Fagerli http://www.duo.uio.no/ Print: Reprosentralen, Universitetet i Oslo

«Who was the blundering idiot who said that "fine words butter no parsnips?" Half the parsnips of society are served and rendered palatable with no other sauce.»

William Makepeace Thackeray - Vanity fair (p 169), 1848.

Anders, takk for planlegging, engasjement og kløkt. Takk for at du alltid finner plass og tid til meg, selv om det egentlig ikke finnes. Din støtte i form av «stå på»-kommentarer, optimisme og vitser er uten sidestykke. Anne, takk for grundige kommentarer, ærlighet og skarpsinn. Selv etter disse åra er det meg fortsatt en gåte hvordan du får det til. Og attpåtil med slik en moderlig varme og ro! Bård-Jørgen, takk for metodisk veiledning som aldri går på bekostning av biologien. Og takk for at du alltid står parat til å jule opp illsinte nordnorske kjerringer når jeg er trist og lei. Trond, takk for statistisk ekspertise og utfyllende svar uansett hvor dumme spørsmåla måtte være. Takk for at du har hatt genuin interesse i mine data, R-skript, analyser og ugjorte ideer. Mine to somre med feltarbeid på vakre Reinøya gikk ene og alene på grunn av all hjelpa jeg har fått fra fantastiske feltassistenter. Takk, Nellie, for et ihugga felt- og biologiengasjement. Din kunnskapshunger kan lede deg hvor enn du måtte ønske. Takk, Marius og Edgar, for alle timene dere talte fluer, natt som dag, uten å nøle. Takk, mamma, for at du viet en hel dag til å telle skrubbær og svelge fluer til det gikk rundt for deg. Ikke minst, takk for din kjærlighet og støtte! Takk, Oskar, for hjelp med døgnobservasjoner og et «biologisk eksperiment». De siste dagene på Reinøya ville ikke vært de samme uten ditt selskap med fisketurer, (fiske)middager og gitarspill. Takk for forløsende samtaler, et skarpt øye og varmt vennskap. Takk, Ingvild, for ditt muntre selskap på Reinøya, konferanser, bergenstur og KB. Takk for endeløs hjelp med feltarbeid, humlekunnskap, kommentarer og råd. Åra på Blindern ville ikke blitt de samme uten deilige og intelligente medstudenter – takk til alle dere. Silje, takk for latter, råd og designhjelp. Solveig, takk for utallige diskusjoner gjennom mastergraden og våre sene kollokviekvelder med kartongvin, trøffelpasta, samt en og annen Harry Potter-film på Svalbard. Malin, takk for at du er den som alltid vil henge, om det skulle være troll-i-eske eller Bygdømiddager. Magnus, takk for at du har vært min mest trofaste kollokviepartner helt fra starten av. Takk for at du er du! Jeg kan ikke se for meg somrene på Lunde uten mine faste roadtrippartnere som har fått meg helskinna opp, bedugget igjennom, men ned igjen med bena planta godt på jorda. Takk, Johan, Mansor, Ingrid og Martin. Henrik, en ekstra takk for pytagorasteoremer. Teo, en ekstra takk for ferier, empati, diskusjoner og vennskap. Til sist, takker jeg Nille for å være min trofaste og masete venn når jeg trenger det. Pappa, takk for ditt heroiske engasjement for Lunde. Takk for din støtte i oppgava og livet mitt. Takk til resten av familien og min hele slekt som har lagt grunnlag for denne oppgava.

ABSTRACT

The area north of the Arctic circle has long, cold winters and short summers with midnight sun. In the face of climate change, this area has warmed the most, resulting in a reduced spring snow cover and extended summer season. Plant-pollinator interactions is an important ecosystem function, but pollinators worldwide are declining. Warming of the Arctic will amplify in the future, which may lead to even longer summers or species invasions, events that could negatively affect native plants and pollinators. For two successive years, I studied the plant-pollinator system of a coastal subarctic region in northern Norway. The first year, I observed flower visitation throughout the summer and along an elevation gradient from sea level to the tree line, a proxy for ambient climatic conditions. I also estimated seed set of three focal plant species – Cornus suecica, Melampyrum sylvaticum and M. pratense. The second year, I performed a bagging experiment on M. sylvaticum and M. pratense and I observed flower visitation throughout the day during continuous light conditions. Elevation negatively affected seed set of C. suecica but did not affect flower visitation or seed set of M. sylvaticum and M. pratense, which indicate a robustness to changes in season length. However, my environmental recordings did not detect a climatic gradient along the elevation gradient. I showed that M. sylvaticum and M. pratense self-pollinate, perhaps as an adaptation to low pollinator availability. Flower visitation was affected by seasonal variation, probably due to differences in climate or the phenology of plants and insects. Last, I found that flower visitation followed a diurnal rhythm despite continuous light conditions, and independent of temperature, humidity or the presence of sun. I discuss that floral signals (e.g. scent emission) could control flower visitor rhythmicity, but that the plants in my study area do not exploit the opportunity of nocturnal pollination. With continued Arctic warming, plant-pollinator interactions could be at risk. However, my results from a coastal subarctic region indicate a robustness towards changes related to a warmer climate and longer summers.

Table of contents

1 Introduction ...... 1

2 Methods ...... 9

2.1 STUDY AREA...... 9 2.2 STUDY SPECIES ...... 10 2.2.1 Cornus suecica L...... 10 2.2.2 Melampyrum spp. L ...... 11 2.3 STUDY DESIGN ...... 12 2.3.1 Elevation gradient ...... 12 2.3.2 Bagging experiment ...... 14 2.3.3 Daily variation ...... 15 2.4 DATA COLLECTION ...... 16 2.4.1 Flower visitation data – elevation gradient ...... 16 2.4.2 Seed set – elevation gradient ...... 17 2.4.3 Seed set – bagging experiment ...... 20 2.4.4 Flower visitation data – daily variation ...... 20 2.5 STATISTICAL ANALYSES ...... 20 2.5.1 Spatial variation in flower visitation (H1.1) ...... 25 2.5.2 Spatial variation in seed set (H1.2-a-c) ...... 25

2.5.3 Pollinator limitation in Melampyrum spp. (HA2.1-a-b) ...... 26 2.5.4 Seasonal variation in flower visitation (H3.1) ...... 26 2.5.5 Daily variation in flower visitation (H3.2) ...... 26 3 Results...... 28

3.1 SPATIAL VARIATION ...... 28 3.1.1 Spatial variation in flower visitation ...... 28 3.1.2 Spatial variation in seed set...... 30 3.2 POLLINATOR LIMITATION IN Melampyrum spp...... 33 3.3 TEMPORAL VARIATION ...... 33 3.3.1 Seasonal variation in flower visitation ...... 33 3.3.2 Daily variation in flower visitation ...... 34 4 Discussion ...... 37

5 Future studies ...... 45

VII

REFERENCES ...... 48

APPENDICES...... 63

A. Weather data of Reinøya ...... 64

B. Plant species ...... 65

C. Covariates ...... 68

D. Bumblebee visits ...... 72

E. Nocturnal pollination ...... 73

VIII

1 Introduction

«I de siste dager har jeg tenkt og tenkt på Nordlandssommerens evige dag. Jeg sitter her og tenker på den og på en hytte som jeg bodde i og på skogen bak hytten og jeg gir meg til å skrive noe ned for å forkorte tiden og for min fornøielses skyld.»

Knut Hamsun - Pan (p 1), 1894.

Atmospheric temperatures have risen dramatically during the last century, a global warming that has been most intense for the last 35 years (Osborn and Jones 2014, Huang et al. 2017, GISTEMP 2018). As temperatures rise, we see changes in weather events and ecosystems around the world. The area north of the Arctic circle (hereafter: Arctic) suffers some of the most severe climatic changes. Here, the terrestrial surface temperature has increased at more than double the rates of lower latitudes (e.g. Serreze et al. 2009, Cowtan and Way 2014, Huang et al. 2017). Precipitation (Stocker et al. 2013, Vihma et al. 2016) and the number of extreme weather events (Hartmann et al. 2013) have increased. Furthermore, the spring snow cover has decreased by up to 11% in the Northern Hemisphere (Brown and Robinson 2011), decreasing more the further north you go (Déry and Brown 2007). This reduction advances the spring season and could impact the seasonal responses of organisms who live in the Arctic.

The Arctic is characterized by strong seasonality that influences all organisms living there. A dark, freezing winter restricts plant growth for half of the year and, with spring thaw out, life emerges from all corners of the land. The short summer is vital for the ecosystems’ primary productions and the nights are bright due to the midnight sun.

Global warming is expected to increase in the future (Collins et al. 2013, Frölicher and Paynter 2015, Yang et al. 2016). Model projections of future climatic changes in the Arctic are – to put it mildly – depressing. Here, temperatures will increase the most (Holland and Bitz 2003, Bekryaev et al. 2010, Hao et al. 2017) and, because warm air readily carries water vapour, precipitation will increase too (Chou and Lan 2012, Wu et al. 2015). There will also be more extreme weather events, in the form of warm and cold days, and

1 heavy precipitation (Saha et al. 2006, Sillmann et al. 2013). Furthermore, Arctic warming will be most profound during winter (Lu and Cai 2009, Kumar et al. 2010, Alexander et al. 2018) which leads to a continued decrease in spring snow cover (Brown and Mote 2009, Brutel-Vuilmet et al. 2013).

In this study, I have looked at how climate change may impact plant- pollinator interactions of a coastal subarctic region in northern Norway. I use the term subarctic to define the area north of the Arctic Circle (66°33’N) and south of the High Arctic (i.e. MJuT1 <10°C). Along the coast of northern Norway, the climate is more stable than in the rest of the subarctic. This is due to the warm North Atlantic Current, which creates relatively mild winters through a strong anticyclone (i.e. a large cluster of high-pressure winds). The anticyclone weakens during summer, bringing humid air, precipitation and strong winds (Fogg 1998). This has a cooling effect on the summer temperatures which, in comparison, may become extremely high in inland subarctic regions. As the effects of climate change will vary between different regions, it is important to consider the effects of climate change in coastal subarctic ecosystems.

Ecosystems provide an array of services that are of value to humans; from the plants’ creation of oxygen from carbon dioxide, to the recreational beauty of nature itself. These services and the stability of ecosystems are determined by biodiversity, or the variation of organisms in a community. Generally, biodiversity decreases with latitude (Hillebrand 2004) which implies that the subarctic is an area less diverse than regions at lower latitudes. Today, ecosystems suffer a global loss of biodiversity which may be caused, in part, by climate change (Dirzo and Raven 2003, Butchart et al. 2010). The best way to predict the impacts climate change could have on subarctic biodiversity would be to conduct observations over several years. However, such long-term studies do not exist for most subarctic regions. Space-for-time substitution is an approach that can be used to predict how species will respond to a different environment by observing their responses between sites with different climates. In other

1 MJuT = Mean July Temperatures. Regions where MJuT are ca. 10°C marks the Arctic tree line, or the southern limit of the High Arctic. 2 words, using spatial variation to hypothesize temporal trends. A drawback with this approach is that we assume the effects of climate to be equal across time and space. Still, using the method as an alternative to long-term observational studies is generally supported (Pickett 1989, Fukami and Wardle 2005, Blois et al. 2013). Another difficulty with predicting ecosystem changes, is to consider the complexity of communities. Certainly, species are directly affected by changes in climate, but the ecosystem is not a rigid collection of species. It consists of fluent communities where organisms interact. Interactions among species are influenced by climatic factors, even though they are often not mentioned in climate change assessments (Tylianakis et al. 2008, Gilman et al. 2010). To contemplate the qualities of species interactions in subarctic regions is a vital part of understanding ecosystem dynamics in the light of recent climate change.

The interaction between plants and their pollinators is an important and ubiquitous ecosystem service. Plants provide food to the pollinator, typically in the form of pollen or nectar. In turn, the pollinator is instrumental in the plants’ reproduction by moving pollen between flowers in a series of flower visits. If pollen is transferred to the stigma of a conspecific flower, successful pollination (i.e. fertilization) can occur. Morphology and behaviour of flower visitors differ so that some pollinate more effectively than others. For instance, bumblebees tend to visit conspecific flowers successively and pollen readily attaches to their body hairs. Plants attract pollinators with visual cues and by emitting floral scents. In the absence of effective pollinators, however, many plants can still produce seeds by adapting to self-pollination (Lloyd 1992, Kalisz et al. 2004).

In the Arctic, all flower visitors are insects, most notably flies (Kevan 1972, Elberling and Olesen 1999, Klein et al. 2008). Of these, muscid flies are the most important, probably due to some species with specialized flower-visiting behaviours (Pont 1993, Brown and McNeil 2009, Tiusanen et al. 2016). In addition, bumblebees are expected to be influential pollinators where they are present (Kevan 1973, Ranta and Lundberg 1981, Potapov et al. 2014, Ollerton 2017). Studies from the tundra and taiga indicate that many plants are dependent on insects to maximize their seed sets (Kevan 1972, Kevan et al. 1993,

Tiusanen et al. 2016). However, plants must also adapt to assure reproduction in years with high levels of stress. A plant’s pollination strategy is related to the predictability of pollinators in different environments (Moeller 2006). Self- pollination should be a selective advantage when pollinator availability is low or fluctuating (Baker 1955, Lloyd 1979), which could be the case in regions with seasonal climates like the Arctic. Yet, Moeller et al. (2017) did not find a higher proportion of self-pollinating plants with increased latitude.

During the Arctic summer, the midnight sun provides continuous light conditions; an opportunity for anyone who can adjust her innate diurnal pattern (i.e. active during day-time), like ptarmigans (Stokkan et al. 1986, Reierth and Stokkan 1998, Reierth et al. 1999) and reindeer (Stokkan et al. 2007, van Oort et al. 2007) do. It could be advantageous the plant-pollinator interaction would happen at all bright hours, i.e. the whole day during Arctic summers. Many plants in non-Arctic regions increase their seed set by adapting to both diurnal and nocturnal pollination (e.g. Stephenson and Thomas 1977, Haber and Franke 1982, Morse and Fritz 1983, Dar et al. 2006, Giménez‐Benavides et al. 2007, Amorim et al. 2013, Chapurlat et al. 2018). However, temperature, light intensity and quality is generally lower at night. The only study that has looked at flower visitation during the midnight sun found no activity (Bergman et al. 1996), although this was in an inland alpine region where nights can be very cold. Nevertheless, there is an urgent need for more observations to find out if plants and pollinators exploit the continuous light conditions of Arctic summers.

Assessments over the last decades have led researchers to believe that we are facing a pollination crisis: from global to local scales, pollinators are declining in diversity and abundance (see Biesmeijer et al. 2006, Potts et al. 2010, Ollerton 2017). This is alarming as nearly 90% of all flowering plants are, more or less, dependent on pollinators to reproduce (Ollerton et al. 2011). The Arctic is expected to be more affected by climate change than anywhere else. In coastal High Arctic Greenland, muscid fly abundance has declined by 80% over 18 years in relation to increased summer temperatures (Loboda et al. 2017). Reports of bumblebee declines are also extensive (see Williams and Osborne 2009, Rasmont

4 et al. 2015), but it is not yet clear how the pollinator declines will affect plant- pollinator interactions in general.

The phenology of plants and their pollinators (i.e. plant flowering season and insect flight season), should be synchronized. Phenology could be determined by environmental cues, like temperature (e.g. vernalization1 (Amasino 2005) or, on insects, see Danks (2007)) or snow melt (e.g. Kudo 1991). Climate change could therefore alter phenology asymmetrically between plants and their pollinators, causing a phenological mismatch (i.e. no temporal overlap in phenologies) (Hegland et al. 2009). Climate-driven mismatches can negatively affect plant reproduction (Thomson 2010, Kudo and Ida 2013) or pollinator survival, if warming causes shorter flowering seasons (Høye et al. 2013). Yet, the importance of these mismatches compared to other impacts of climate change is poorly understood (Forrest 2015).

A meta-analysis by Parmesan and Yohe (2003) showed that many species have shifted their geographical ranges in response to global warming. As temperatures increase, species are forced to disperse to areas that fit their thermal limits. In the Northern Hemisphere, this usually means dispersing northwards. However, Kerr et al. (2015) showed that, concordant with warming, bumblebees have failed to shift their northern distributions. Yet, they have shifted their southern distributions, which means that bumblebees disappear in their southern regions, while they do not expand towards the north. This could have severe consequences for plants that are dependent on bumblebees for pollination. Tragically, climate-related local extinctions are extensive among plants and insects (Wiens 2016). Range shifts could also result in species invasions (i.e. species establishments in new habitats) and several studies predict massive invasions related to climate change (e.g. Peterson 2003, Ward and Masters 2007, Peterson et al. 2008). Because environmental factors often limit species’ establishment, climate change can alter a habitat to make it suitable to novel species. Recently, two bumblebee species – Bombus terrestris and B. lapidarius – have expanded their distributions northwards into subarctic Fennoscandia (Martinet et al. 2015). The introduction of new pollinators in a

1 Vernalization = flowering is only initiated if plants have been exposed to cold from a typical winter. 5 habitat can negatively affect native pollinators through interspecific competition on floral resources.

With this thesis, I have gained insight into spatial and temporal dynamics of the plant-pollinator system at a coastal subarctic site in northern Norway. I did this by observing flower visitation and estimating seed set of three focal plant species – Cornus suecica, Melampyrum sylvaticum and M. pratense. Observations of flower visitation and seed set can only describe a fraction of the plant-pollinator system in this area. However, observing specific responses in the field could give a better understanding as to how the system may respond to future climate change. By using two successive seasons of field observations, I approached the following three objectives and respective hypotheses:

OBJECTIVE I: SPATIAL VARIATION

For this part of my study, I observed flower visitation and estimated seed set at different elevations from the sea level to the tree line. The goal was to assess how climatic conditions affect the plant-pollinator system in coastal subarctic regions. The elevation gradient represents an environmental context for the residing species. For instance, spring snow melting happens later at high, compared to low, elevations (e.g. Blöschl and Kirnbauer 1992, Giorgi et al. 1997, Bell and Moore 1999). Similarly, snow arrives earlier at high elevations, which means that the summer, or snow-free, season will be shorter at high elevations. I also intended to find a climatic gradient along the elevation by recording temperature and humidity. The elevation gradient was supposed to be used as a proxy for ambient climatic conditions, following the space-for-time substitution approach.

SPATIAL VARIATION IN FLOWER VISITATION H1.1: Flower visitation frequency is affected by climatic conditions. I observed flower visitation and recorded environmental variables along the elevation gradient. The aim was to identify the most important variables affecting flower visitation in this area. I predicted flower visitation to decrease with increased elevation. If the elevation gradient represents a climatic gradient,

6 plants and pollinators living higher up are forced to adapt to a harsher environment. This will limit the amount of energy they can use on the plant- pollinator interaction.

SPATIAL VARIATION IN SEED SET H1.2-a: Seed set of C. suecica is affected by climatic conditions and/or flower visitation frequency. H1.2-b: Seed set of M. sylvaticum is affected by climatic conditions and/or flower visitation frequency. H1.2-c: Seed set of M. pratense is affected by climatic conditions and/or flower visitation frequency. I estimated seed set and observed flower visitation of the three focal species along the elevation gradient. The aim was to identify the importance of climate and flower visitation on the species’ seed production. I predicted the seed set to decrease with elevation and/or be directly linked to the observed flower visitation. Higher elevations may have shorter growing seasons and a harsher environment. Environmental stress and pollinator availability should affect the number of seeds an insect-pollinated plant manages to produce.

OBJECTIVE II: POLLINATOR LIMITATION IN Melampyrum spp.

H2.1-a: Melampyrum sylvaticum produces seeds in the absence of pollinators. H2.1-b: Melampyrum pratense produces seeds in the absence of pollinators. I conducted a bagging experiment to exclude potential pollinators of M. sylvaticum and M. pratense. The aim was to investigate their abilities to self- pollinate.

OBJECTIVE III: TEMPORAL VARIATION For this part of my study, I observed flower visitation at two temporal scales – within a season and within a day (i.e. 24h-periods). I wanted to find out how temporal variation affect the plant-pollinator system in a seasonal region with midnight sun.

SEASONAL VARIATION IN FLOWER VISITATION H3.1: Flower visitation frequency is affected by seasonal variation. I observed flower visitation and recorded environmental variables throughout the flowering season (along an elevation gradient). The aim was to investigate if seasonal variation was limiting flower visitation in this area. I predicted flower visitation to vary throughout the flowering season and seasonal variation to differ along the elevation gradient. Arctic climates are characterized with seasonal differences, where extremely cold or warm days make the environment variable and unpredictable. The plant-pollinator system in my study area should be adapted to seasonal fluctuations, which means being active when environmental conditions are favourable.

DAILY VARIATION IN FLOWER VISITATION H3.2: Flower visitation frequency is affected by daily variation. I observed flower visitation and recorded environmental variables throughout the day. The aim was to investigate if daily oscillation was affecting flower visitation in this area. The photoperiod during an Arctic summer lasts 24 hours, making it possible for pollinators to see flowers both at night and day. Still, earlier studies suggest that plants and pollinators do not exploit this (Lundberg 1980, Bergman et al. 1996, Stelzer and Chittka 2010). I predicted flower visitation to be affected by daily oscillation, which would mean that the interaction is rhythmic regardless of continuous light conditions or the recorded environmental variables.

2 Methods

«Essentially, all models are wrong, but some are useful.» George Box - Empirical model-building and response surfaces (p 424), 1987

2.1 STUDY AREA

Figure 2.1. Overlapping aerial photos of Tromsø, Karlsøy and Lyngen municipalities [1:150.000] (Kartverket 2016a). The study area is situated at the southern tip of Reinøya, marked in yellow.

My study was conducted at the southern tip of Reinøya, Troms County, northern Norway (69°50’21’’N, 19°27’54’’E) (Figure 2.1). Reinøya is an island with relatively steep elevation gradients and a low tree line (~320 m a.s.l.). The vegetation is dominated by alpine tundra, birch forests, heaths, peat bogs and cultural landscapes. The region has a coastal subarctic climate with mean annual temperatures of 3.6°C (MJuT: 11.8°C) and rainfall of 1032 mm (Appendix A). The

9 area is exposed to midnight sun from 19th May to 25th July (however, the civil twilight period1 is from 29th April to 14th August) (Thorsen 2018). The study area is located around the farm Lunde, historically part of a fishing-based rural community when the land was occupied by agricultural land and pastures. Today, most inhabitants have moved into more urban areas and most of the farms are left as lodges. Recently, novel species of geometrid moth larvae have defoliated birch forests in coastal subarctic Norway, including Reinøya (Jepsen et al. 2008, Jepsen et al. 2011).

2.2 STUDY SPECIES

2.2.1 Cornus suecica L.

Cornus suecica L. is an herbaceous perennial of the Cornaceae family, order Cornales (Byng et al. 2016). The species form rhizomes underground that can develop both vegetative and reproductive shoots. It is often found growing in dense tufts because one individual may give rise to many shoots. It typically grows in habitats with nutrient-poor soil and its distribution is circumpolar throughout the boreal forest belt (Taylor 1999).

Each reproductive shoot of C. suecica bears a single terminal inflorescence. This consists of 8-25 darkly coloured and highly reduced flowers that are clustered together. The flowers are protandrous and the anthers open simultaneously. Surrounding the inflorescence are four white bracts that serve as advertisment for potential pollinators, creating an illusion of the inflorescence being a single flower. According to Mosquin (1985), the flower has an “explosive [pollination] mechanism”; a projectile on each flower that catapults the pollen upwards, if stimulated by touch. Knowledge on the pollination biology of C. suecica is scarce, but it is generally believed that their main flower visitors are dipterans (Kevan et al. 1993, Taylor 1999, Meen et al. 2012). Considering these observations and the morphology of its inflorescence, C. suecica flowers are surely visited by insects. However, self-pollination could happen after the stigma

1 Civil twilight = the period where the sun will not go below 6° of the horizon. Data is from Tromsø (69°40’N). 10 becomes receptive, as the anthers are still full of pollen at that time and the flowers are clustered closely to each other (Taylor 1999).

2.2.2 Melampyrum spp. L

Melampyrum pratense L. and M. sylvaticum L. are herbaceous species in the Orobanchaceae family (Olmstead et al. 2001), order Lamiales (Byng et al. 2016). The species are entomophilous annuals and often grow in perennial-dominated forest communities with limited light availability (Dalrymple 2007, Průšová et al. 2013, Světlíková et al. 2018). Being generalist root hemiparasites, these plants manage to continuously establish in such stable environments by acquisition of additional water and nutrients through their host plant. The species are found on shallow soils (Tennant 2008, Mossberg and Stenberg 2016) and are distributed throughout Eurosiberia in temperate and subarctic regions. The fruit is a capsule and carries (0-)1-4 large seeds that resemble ant pupae and bear an elaiosome. They are dispersed passively or by ants (Dalrymple 2007, Heinken and Winkler 2009).

Melampyrum pratense and M. sylvaticum are morphologically quite similar, but do have some traits separating them. Both may be branched and typically bear yellow flowers. However, M. pratense is branched more frequently and the corolla often has a paler shade of yellow with a smaller entrance. Furthermore, the seeds of M. sylvaticum are heavier than those of M. pratense (Molau 1993), but the seed set is often lower (Mossberg and Stenberg 2016). Melampyrum pratense has occasionally been used in studies on plant-pollinator interactions (Jennersten and Kwak 1991, Kwak and Jennersten 1991, Molau 1993, Antonsen 1997, Totland et al. 2006), due to its reproductive biology and high abundance in various habitats.

The flowers of Melampyrum are arranged in pairs forming indeterminate spikes with sequential flowering. This means that as fruits emerge at the lower part of the inflorescence, flowers may still be receptive in the upper part. The individual flower is zygomorphic with a tubular bilabiate corolla, traits typically found within many genera of the Lamiales. Hairs with nodules at the corolla’s lower lip act as imitation anther markings, increasing the attractiveness of the

11 flower (Willmer 2011a). The hooded upper lip encloses the style and the lower lip the filaments. The lower lip is slightly longer than the upper and presents a landing platform for potential pollinators. An insect that lands there must push its body into the corolla to collect nectar. The stamens will then bend upwards (slightly behind the dorsally placed stigma) and over the insect, depositing pollen onto its notum. If the insect visits a conspecific flower, this pollen may be delivered to the flower’s exposed stigma. However, if outcrossing does not happen, there is a chance of delayed self-pollination because of the proximity between stigma and stamen in the flower (as has been shown for Pedicularis spp. (Sun et al. 2005)).

Tubular entomophilous flowers usually present nectar rewards to insects with long proboscides. In most studies recording flower visitation patterns of M. pratense, the predominant flower visitors have been bumblebees (Bombus spp.) (Jennersten and Kwak 1991, Antonsen 1997, Totland et al. 2006). This species has been shown to be both a pollen and nectar provider (Kwak and Jennersten 1991, Molau 1993). It is likely that the visitation patterns of M. sylvaticum are similar to those of M. pratense (Dalrymple 2007) but no data on nectar content or flower visitors currently exists for M. sylvaticum (but see Haug (2017). Neither species reproduce vegetatively nor do they hybridize, and Kwak and Jennersten (1991) showed that they can be pollen limited. Populations that can self-pollinate (Kwak and Jennersten 1991, Molau 1993, Crichton et al. 2016) might thus have an advantage if pollinator availability is low.

2.3 STUDY DESIGN

2.3.1 Elevation gradient

To study the spatial and seasonal variation of the plant-pollinator system at Reinøya, I selected twelve study plots at four elevations along an elevation gradient from sea level to the tree line (Figure 2.2). A plot was defined as being within a 5-m radius of a permanently marked centre. There were three replicates per elevation (12 plots1) which were situated approximately 100 m apart. Despite

1 4 elevations × 3 replicates = 12 study plots. 12 a short elevation gradient (ca. 20-280 m a.s.l.), the vegetation changed considerably for each elevation level and the tree line was low (ca. 320 m a.s.l.). The ascent was steep, and most plots were in a south-facing slope. The aim was to record flower visitation data mainly for the three focal species Cornus suecica, Melampyrum pratense and M. sylvaticum. I chose the study site because it had continuous distributions of C. suecica and M. pratense, as well as an extensive distribution of M. sylvaticum.

Figure 2.2. Aerial photo of southern Reinøy [1:10.000] showing the twelve plots in an elevation gradient from sea level to the tree line. Each orange line represents a 20 m elevation increase (Kartverket 2016b). Plot 1-3 ~ 20 m, 4-6 ~ 80 m, 7-9 ~ 180 m and 10-12 ~ 280 m a.s.l.

2.3.2 Bagging experiment

Figure 2.3. Mesh tent placed over shoots of Melampyrum sylvaticum and M. pratense on 16th June 2017.

To see if M. sylvaticum and M. pratense had the ability to self-pollinate, I conducted a bagging experiment to exclude potential pollinators of the species. On the 16th June 2017, I established six ~2.08 × 2.08 × 0.5 m mesh tents1 (mesh size: 1.2 x 1.4 mm) over patches of Melampyrum shoots (Figure 2.3). The mesh was made of fiberglass, a plastic that has been used earlier in pollinator exclusion experiments (Kauffeld and Williams 1972, Whitney 1984, Keys et al. 1995). The tents were lifted with an iron rod and fastened with nails and stones to prevent ground-dwelling insects to enter. Three tents were set up in each of two areas (Plot 1 and 2; Figure 2.2). Unfortunately, there were no controls, neither for the physical effects of the mesh tents, nor an experimental control (i.e. Melampyrum shoots with no exclusion treatment). Because of the latter, I could not test if there

1 2.2 × 2.2 m squares were cut out and lifted 0.5 m above ground. Not considering construction errors or elasticity, and following the Pythagorean theorem (a2 + b2 = c2), the area of the tent floors was 2.08 × 2.08 m. 14 was a significant difference in Melampyrum seed set inside, as compared to outside, of the mesh tents. Sadly, only two tents were eligible for assessment by the end of the experiment. One was destroyed due to harsh weather, while all three tents at Plot 2 had to be removed due to a rabid landowner.

2.3.3 Daily variation

Figure 2.4. Aerial photo of Lunde [1:1500] showing Plot N and S, situated ~60 m apart (Norkart AS 2018). Plot 1-3 (Figure 2.2) were situated across the road at the top left corner.

To study daily variation of the plant-pollinator system at Reinøya, I selected two plots in a cultural landscape close to the sea (Plot N and S; Figure 2.4). The area was situated beneath a mountain that hid the sun during night, overshadowing Plot N first. The plots were situated in what used to be part of an agricultural field until the 1970s and were placed ~60 m apart. Despite the proximity, the vegetation cover and plant species composition differed between the plots. Generally, Plot N had taller vegetation dominated by Deschampsia cespitosa,

Allium schoenoprasum ssp. sibiricum, Geum rivale and Geranium sylvaticum. Plot S was dominated by Avenella flexuosa, Achillea millefolium, Alchemilla sp. and Geranium sylvaticum.

2.4 DATA COLLECTION

2.4.1 Flower visitation data – elevation gradient

For 14 days between 29th June and 25th July 2016, three field workers recorded flower visitation along the elevation gradient (Plot 1-12; Figure 2.2). The data was collected during 10-min long sampling events by observing flowers in a plot area and recording flower visits to them. Each day in the field, I tried to collect data in all twelve plots. No data was collected when it was raining, as I expected to find little (or no) insect activity on flowers at that time. Prior to each sampling event, the following variables were recorded: plant species in bloom, the number of open flowers of each species, date, time and environmental variables (see below for details). Inflorescences of Cornus suecica and Asteraceae species were counted as single open flowers. All flowering plants were identified to species or genus level following Lid (2013). A flower visit was defined as an insect performing an activity on a flower with the possibility that it would either receive pollen from the androecium or deliver pollen to the gynoecium. The set of flowers chosen to be observed during a sampling event was determined by the field worker following three criteria: (1) it had to include more than one open flower of each species chosen, (2) all flowers had to be close enough, so they could be observed simultaneously, and (3) flowers of either Melampyrum sylvaticum or M. pratense, and if possible also C. suecica, had to be included. The set of flowers varied for each sampling event within each plot according to both plant species composition and number of flowers. Flower visitors were identified (if possible) to ‘functional pollinator groups’, which varied in taxonomical levels. These were based on a priori information on their flower-visiting habits. The functional groups identified were: (1) ‘bumblebees’ – genus Bombus, (2) ‘flies’– order Diptera (no Syrphidae observed), (3) ‘ants’ – family Formicidae, (4) ‘Micropterigidae’ (mandibulate

16 archaic moths) – a family in order Lepidoptera, (5) ‘others’ (beetles – order Coleoptera, or unidentified). The environmental variables temperature (°C) and relative air humidity (%) were measured with a handheld weather recorder (WeatherHawk: SM-28 Skymaster). In the field, the weather recorder was carried alfresco to stabilize the device. Additionally, the weather was described according to the presence of sun (‘Sun’, ‘Some sun’ or ‘No sun’).

2.4.2 Seed set – elevation gradient

Figure 2.5. Field worker counting number of vegetative and reproductive shoots of Cornus suecica within a defined area in Plot 3 on 27th July 2016

On 27th July 2016, seed set of C. suecica was estimated in all twelve plots along the elevation gradient. Seed set was estimated by counting the proportion of flowers developing into a drupe within a reproductive shoot1. In each plot, the first patch of C. suecica with reproductive shoots that was approached, was assigned and defined by encapsulating it with a rope (Figure 2.5). The patch would vary in size because most C. suecica shoots were vegetative (and I was interested in finding reproductive shoots). This technique for assigning patches of C. suecica was useful in plots with few reproductive shoots but represents a potential bias to the sampling procedure. Replicated samples with a standardized measure, for instance a quadrat, could prove useful in future studies with the same implications.

Inside the defined patch, I counted the number of vegetative and reproductive shoots (Table 2.1). For each reproductive shoot inside the patch, I counted the number of drupes and flowers with no sign of fruit development.

Table 2.1. The number of vegetative and reproductive shoots of Cornus suecica counted 27th July 2016 at twelve plots (Figure 2.4)a.

Plot Vegetative shoots Reproductive shoots

1 130 59

2 154 70

3 271 62

4 326 44

5 210 30

6 259 35

7 221 11

8 109 26

9 168 15

10 235 2

11 219 32

1 As each drupe bears only one seed, the number of drupes equals the number of seeds. 18

12 150 19

a Lines separate the rows according to elevation (four levels from sea level to the tree line; Figure 2.2).

From 20th to 27th July 2016, seed set of M. sylvaticum and M. pratense were estimated along the elevation gradient by sampling fruits and counting the number of seeds in them. Initially, I wanted to sample 30 fruits from each species twice in all plots. However, as the number of available fruits from the two species varied considerably between the plots, I ended up sampling 30 fruits in total at each plot. Furthermore, fruits were sampled twice only at Plot 1-3 (i.e. closest to sea level; Table 2.2) due to a limited number of plants in the other plots.

Table 2.2. The number of sampled fruits for Melampyrum sylvaticum and M. pratense species in twelve study plots from sea level to the tree linea.

Plot M. sylvaticum M. pratense Date fruitsb fruits

1 20 10 20.07.2016

1 20 10 27.07.2016

2 20 10 20.07.2016

2 20 10 27.07.2016

3 20 10 20.07.2016

3 20 10 27.07.2016

4 0 30 20.07.2016

5 0 30 20.07.2016

6 15 15 20.07.2016

7 10 20 23.07.2016

8 20 10 23.07.2016

9 0 30 23.07.2016

10 0 30 23.07.2016

11 0 30 23.07.2016

12 0 30 23.07.2016

a Lines separate the rows according to elevation (four levels from sea level to the tree line; Figure 2.2). Fruits were sampled twice at Plot 1-3. b Melampyrum sylvaticum was not present in all plots.

2.4.3 Seed set – bagging experiment

On 14th August 2017, seed set of bagged M. sylvaticum and M. pratense (Figure 2.3) were estimated by collecting all fruits in the tents and counting the seeds of each fruit. From the first tent, 21 fruits of M. sylvaticum and 26 fruits of M. pratense were collected. From the second tent, only two M. pratense plants had produced five fruits.

2.4.4 Flower visitation data – daily variation

From 29th July to 13th August 2017, four field workers collected flower visitation data over four 24-h periods. When working, the field workers attempted to sample each hour of the day, in the end resulting in 77 hourly observation periods. For each hour, data was collected in both plots (Figure 2.4), with the same sampling event procedure as the previous year (specified in section 2.4.1), but with minor alterations: a) there were no criteria on which plant species were included during a sampling event, b) non-micropterigidian ‘Lepidoptera’ and Syrphidae were observed, and treated as two additional functional groups of pollinators, and c) bumblebees were identified to species level following Ødegaard et al. (2015).

2.5 STATISTICAL ANALYSES

All calculations, diagrams and statistical analyses were produced in the R programming environment, version 3.4.3 (R core team 2017). Hypotheses of Objective II (‘Bagging experiment’) were investigated with null hypothesis testing. To investigate the hypotheses of Objective I and III (‘Spatial and Temporal patterns’), I chose, however, to use a model selection procedure.

Instead of focusing on the falsification of a null hypothesis, I wished to identify which variables were explaining the variation in my response variables (e.g. number of flower visits). Model selection finds the model that is expected to explain future observations, given my data and included variables, i.e. the best approximating model (Buckland et al. 1997, Anderson et al. 2000, Cubedo and Oller 2002, Aho et al. 2014). It does not evaluate which model is correct, as a model essentially cannot be true (Popper 1959, Wit et al. 2012). The data for Objective I and III were analysed using Generalized Linear Mixed Models (GLMMs, using ‘glmer’ function in lme4 package version 1.1-14 (Bates et al. 2014)). I chose to use Generalized Linear Models (GLMs) because they handle non-normal error distributions. This is relevant for my analyses as the response variables were counts and assumed to follow a Poisson distribution

(Zuur et al. 2009). The model parameters were estimated using loge-link because the expected value deriving from a Poisson distribution must be positive (Bolker et al. 2009, Zuur et al. 2009). Therefore, the presented model outcomes are given on the natural logarithmic scale. I chose to use Mixed Models – i.e. adding more complexity to the ‘standard’ GLM – because it allows me to interpret predictor variables as fixed or random effects (Henderson Jr 1982, McLean et al. 1991). Fixed effects are included to calculate a variable’s exact effect on the response variable, for instance including temperature because I assume it to directly impact flower visitation. The meaning of random effects, however, is to calculate the variation among units, like the replicated plots in my study (Figure 2.2, 2.4). This variation can affect the response variable but including it as a random effect deals with this potential dependency. I did not standardize the response variables – i.e. divide the counts by the exposure (e.g. number of visits / number of flowers) – because the exposure could vary for each observation. To account for this variation in the analyses, I defined the exposures as offset variables (Reitan and Nielsen 2016). This means that while the input values are, for instance, ‘number of visits’ and ‘number of flowers’, the modelled response is nonetheless the number of visits per flower (i.e. flower visitation frequency). I used a stepwise model selection method (Reitan 2017) that starts with a list of covariates that I identify as ecologically relevant to explain the variation

21 in the response variable. The procedure then tries all covariates in a collection of models and finds the best model based on a Bayesian Information Criterion (BIC), i.e. the one with the lowest BIC value1 (Schwarz 1978, Wit et al. 2012). Information criterion-based approaches are founded on the principle of parsimony and thus favours the model that is “as simple as possible, but not simpler” (Aho et al. 2014). Because I wished to try many covariates, I chose to use BIC because it is conservative, i.e. it penalizes the number of parameters (k) based on the number of observations (n) (Schwarz 1978, Johnson and Omland 2004). After finding the best model, I quantified the relative contributions of each predictor variable in the model, by using the squared standardized regression coefficients (Afifi et al. 2003).

An overview of all the investigated hypotheses is listed in Table 2.3. I have included some of the necessary information to translate the biological hypotheses to mathematical models: the response variable, statistical method and the parameters of interest, i.e. covariates that are of concern to the hypothesis approached with model selection. Support is given to these hypotheses, if at least one of their parameters of interest is included in the best model. Conversely, if a best model does not include a parameter of interest, I will discuss which other biological processes (not included as parameters) might be causing the variation in the observations.

1 BIC (M) = _ 2 ln L(θ) + k ln (n), where M = model, θ = the parameter set values that maximizes the likelihood function of the model, L(θ), k = the number of parameters estimated by the model and n = the number of data points in the observed data. 22

Table 2.3. An overview of all hypotheses investigated, the response variables, statistical methods and parameters of interest.

Hypothesis Response variable Statistical Parameters of interestb a method

OBJECTIVE I SPATIAL VARIATION

SPATIAL VARIATION IN FLOWER VISITATION

H1.1. Flower visitation frequency is affected by climatic The number of flower visits per GLMM Elevation, Elevation x «Environmental conditions. 10 min variables»

H1.2-a. Seed set of Cornus suecica is affected by climatic Number of drupes per GLMM Elevation, Visitation frequency conditions and/or flower visitation frequency. reproductive shoot

H1.2-b. Seed set of Melampyrum sylvaticum is affected by Number of seeds per fruit GLMM Elevation, Visitation frequency climatic conditions and/or flower visitation frequency.

H1.2-c. Seed set of Melampyrum pratense is affected by Number of seeds per fruit GLMM Elevation, Visitation frequency climatic conditions and/or flower visitation frequency.

OBJECTIVE II POLLINATOR LIMITATION IN Melampyrum spp.

HA2.1-a. Melampyrum sylvaticum produces seeds in the Number of seeds per fruit t-test absence of pollinators.

HA2.1-b. Melampyrum pratense produces seeds in the Number of seeds per fruit t-test absence of pollinators.

OBJECTIVE III TEMPORAL VARIATION

SEASONAL VARIATION IN FLOWER VISITATION

H3.1. Flower visitation frequency is affected by seasonal The number of flower visits per GLMM DOY, DOY2, DOY x Elevation variation. 10 min

H3.2. Flower visitation frequency is affected by daily The number of flower visits per GLMM Daily oscillation, daily oscillation x variation. 10 min «Environmental variables»

a GLMMs were investigated with a model selection function (Section 2.5), and t-tests with null hypothesis testing. b Covariates (Appendix C Table C1, C2) tested with a model selection method that returns the best model based on a BIC criterion. - ‘Elevation’ includes both the categorical and continuous predictors. ‘Elevation’ is only the continuous predictor. - DOY = Day of year. - «Environmental variables» includes temperature (°C), relative air humidity (%) and sun presence (‘Sun’, ‘Some sun’ or ‘No sun’). - Daily oscillation includes six sine and cosine oscillations of different frequencies based on a continuous linear variable of each minute of the day.

2.5.1 Spatial variation in flower visitation (H1.1)

Twenty-six covariates were tried with the model selection procedure (explained in Section 2.5), including fixed effects, quadratic terms, statistical interactions and random effects (Appendix C Table C.1). The parameters of interest to this hypothesis were the categorical and continuous predictors of the numerical variable ‘Elevation’, as well as their possible interaction with ‘Temperature’, ‘Humidity’ and ‘Sun presence’. I included a factor variable with a unique level for every data point (‘Unexplained variation’ w/429 levels) as a random effect to account for possible overdispersion (Harrison 2014). The aim was to use the space-for-time substitution approach, where elevation from sea level to the tree line would act as a proxy for climatic conditions. Prior to the analysis, observations including plant species only found at one height, or with visitation frequencies equal to zero, were excluded (see Appendix B Table B.1). The entire system was dominated by flies and preliminary model outputs showed no clear differences between specific insect groups. Therefore, the total number of flower visits (by all insects) was used as a response. The number of flowers observed was included as an offset variable in the model.

2.5.2 Spatial variation in seed set (H1.2-a-c)

The parameters of interest for the hypotheses were the continuous predictor of ‘Elevation’ and ‘Visitation frequency’ which were tried with the model selection procedure (explained in Section 2.5). The latter was estimated from data that was collected from flower visitation observations (subsection 2.4.1) as average flower visits per flower per 10 min per plot. A factor variable specific for each plot was included as a random effect (‘Plot number’). Additionally, for the analysis on C. suecica seed set (H1.2-a), I included a fixed effect accounting for the proportion of reproductive shoots at a site (‘Site fertility’1). I included a factor variable unique for every data point as a random effect to account for possible

1 ‘Site fertility’ = number of reproductive shoots / number of reproductive and vegetative shoots. 25 overdispersion (Harrison 2014). This represented each reproductive shoot that was counted and was equal to the number of rows in the dataset (see Table 2.1; ‘Unexplained variation’ w/443 levels). In the model explaining the seed set of C. suecica, the total number of flowers before fruit development in a reproductive shoot was used as an offset variable1.

2.5.3 Pollinator limitation in Melampyrum spp. (HA2.1-a-b)

To test if the seed set of M. sylvaticum or M. pratense from the bagging experiment (subsection 2.4.3) was significantly larger than zero, I used simple (one-sided) t-tests. These are simple null hypothesis tests that are appropriate for experiments (Whitlock and Schluter 2009).

2.5.4 Seasonal variation in flower visitation (H3.1)

I used the same dataset and covariates list as explained in subsection 2.5.1. However, the parameters of interest to this hypothesis were the continuous predictor of the numerical variable ‘Day of year’, its quadratic term and its interaction with ‘Elevation’.

2.5.5 Daily variation in flower visitation (H3.2)

Thirty covariates were tried with the model selection procedure (explained in Section 2.5), including fixed effects, quadratic terms, statistical interactions and random effects (Appendix C Table C.2). The parameters of interest to this hypothesis were variables of ‘Daily oscillation’ and their interactions with ‘Temperature’, ‘Humidity’, ‘Sun presence’ and ‘Day of year’. ‘Daily oscillation’ was represented by six oscillatory transformations of a continuous linear variable of each minute of the day (1 - 1440)2. The transformations were done by using cosine (cos) and sine (sin) functions of different frequencies. Single-frequency functions (‘cos 1’ and ‘sin 1’) were the principal variables to describe daily oscillation. Double- (‘cos 2’ and ‘sin 2’) and triple- (‘cos 3’ and ‘sin 3’) frequency functions were added to test if a more detailed description of daily oscillation could better

1 Offset = log (‘Number of drupes’ + ‘Number of flowers with no sign of fruit development’) 2 Daily oscillation (sin x ∨ cos x) = (2 × pi × sin ∨ cos × ‘Minute of day’ × x)/1440, where x = the frequency of the function 26 describe the daily variation in flower visitation by adjusting the single-frequency functions. I included a factor variable with a unique level for every data point (‘Unexplained variation’ w/397 levels) as a random effect to account for possible overdispersion (Harrison 2014). Before the analysis, observations including the plant species Achillea millefolium were excluded as these presented severe outliers to the dataset. The total number of flower visits (by all insects) was used as a response variable. The number of flowers observed was used as an offset variable.

3 Results

«(…) the stupider one is, the closer one is to reality. The stupider one is, the clearer one is. Stupidity is brief and artless, while intelligence wriggles and hides itself. Intelligence is a knave, but stupidity is honest and straightforward.»

Fyodor Dostoyevsky – The brothers Karamazov (p 259), 1879

3.1 SPATIAL VARIATION

3.1.1 Spatial variation in flower visitation

Flower visitation data was recorded along the elevation gradient in 176 sampling events, recording 818 visits (0.2 visits per flower per 10 min). Of them 87.4% were by flies (no Syrphidae recorded), 3.4% by bumblebees, 3.3% by ants, 2.1% by micropterigids and 3.8% by other insects (beetles or unidentified). Twenty-three plant species were observed (Appendix B Table B.1). Ranunculus acris received most visits (203; 0.74 visits per flower per 10 min), while the three focal plants Melampyrum sylvaticum, M. pratense and Cornus suecica had relatively low average visitation frequencies (0.022, 0.019 and 0.055 visits per flower per 10 min, respectively). Visits to almost all species were dominated by flies, but more than half of the visits to C. suecica were by ants (20%), micropterigids (17.5%) or other insects (13.6%). The mean temperature for all recordings was 17.9ºC (Q1-Q31: 15.1-20.2ºC) and for relative air humidity 62.5% (57.5-67.4%). I did not find that the elevation gradient represented a true gradient in neither temperature nor humidity (Table 3.1). Therefore, elevation could not be used as a proxy for climatic conditions following the space-for-time substitution approach.

Table 3.1. Mean flower visitation frequencies, temperature (ºC) and relative air humidity (%) per 10 min along an elevation gradient from sea level to the tree line (Figure 2.2) from 29th June to 25th July 2016.

Elevation Visitation Temperature (ºC) Humidity (m a.s.l.) frequency (%)

1 Q1 = 1st quartile, Q3 = 3rd quartile. 28

20 0.205 17.7 64.5

80 0.159 18.1 63.0

180 0.238 18.3 61.4

280 0.194 17.6 60.1

Hypothesis 1.1 – “Flower visitation frequency is affected by climatic conditions” – was not supported as none of the elevation variables were included in the best model explaining flower visitation. The model included the fixed effect ‘Plant species’. The different plant species varied strongly in visitation frequencies (Table 3.2).

Table. 3.2. Generalized linear mixed model outputa of observed flower visitation frequency per 10 min along an elevation gradient from sea level to the tree line (Figure 2.2) from 29th June to 25th July 2016b.

95% confidence limitsc

Fixed effect Estimate SE Lower Upper

Intercept -22.3 2.65 -24.9 -19.7

Day of yeard 0.0959 0.0138 0.0821 0.110

Minute of dayd -0.0178 0.000711 -0.0185 -0.0170

Alchemilla sp. -1.07 0.675 -1.74 -0.394

Bistorta vivipara 0.949 0.873 0.0763 1.82

Campanula rotundifolia 2.24 0.791 1.45 3.03

Dactylorhiza sp. -0.233 0.698 -0.930 0.465

Geranium sylvaticum 2.20 0.377 1.8179 2.5721

Hieracium sp. 1.18 0.734 0.448 1.92

Melampyrum pratense -2.13 0.355 -2.48 -1.77

Melampyrum sylvaticum -1.95 0.379 -2.33 -1.57

Othilia secunda 1.03 0.697 0.333 1.73

Pinguicula vulgaris -0.645 1.31 -1.95 0.664

Ranunculus acris 2.57 0.350 2.22 2.92

Rhinanthus minor -0.758 0.718 -1.48 -0.0399

Solidago virgaurea 1.36 0.592 0.770 1.95

Trientalis europaea 0.340 0.571 -0.232 0.911

a Flower visit per 10 min ~ Day of year + Minute of day + Plant species + Unexplained variation + offset (log (‘Total number of flowers’ × ‘Sampling event length’)). - ‘Plant species’ is fitted as a factor variable with 15 levels and Cornus suecica as a reference. - ‘Unexplained variation’ is fitted as a random effect. b The best model using a BIC criterion when testing a list of covariates (Appendix C Table C.1). Values are on a natural logarithmic scale. c 95% confidence limits = Estimate + SE (standard error). d Fitted as continuous predictors.

Table 3.3. Variance contribution (%) of covariates in a generalized linear mixed model of flower visitation frequency per 10 min for any plant species from 29th June to 25th July 2016 (Table 3.2).

Covariate Variance contribution

Unexplained variation 71.1%

Day of year 26.2%

Minute of day 2.7%

3.1.2 Spatial variation in seed set

Mean Cornus suecica seed set (the proportion of flowers to develop into a drupe) was 0.157. Along the elevation gradient, seed set was highest at the lowest elevation (20 m a.s.l.; 0.216) and then gradually decreased (Figure 3.1). Hypothesis 1.2-a – “Seed set of C. suecica is affected by climatic conditions and/or flower visitation frequency” – was supported as the best model explaining variation in seed set included negative effects of ‘Elevation’ and ‘Visitation frequency’ (Table 3.4). The model also included a positive effect of ‘Site fertility’. ‘Site fertility’ and ‘Elevation’ had the strongest effects, contributing to >80% of the variation in seed set (Table 3.5).

Figure 3.1. Boxplot showing Cornus suecica seed set (proportion of flowers developed into drupes) at four elevations on 27th July 2016. Scattered points are showing the variation in the dataset, not exact values.

Table 3.4. Generalized linear mixed model outputa of Cornus suecica seed setb in an elevation gradient from sea level to the tree line (Figure 2.2) on 27th July 2016.

95% confidence limitsc

Fixed effect Estimate SE Lower Upper

Intercept -2.01 0.130 -2.14 -1.88

Elevationd -0.00259 0.000652 -0.00324 -0.00194

Site fertility 1.47 0.269 1.45 1.50

Visitation -2.00 0.747 -2.75 -1.26 frequency

a Number of drupes per reproductive shoot ~ Elevation + Site fertility + Visitation frequency + offset (log (‘Number of flowers’ + ‘Number of drupes’)). b The best model using a BIC criterion when testing a list of covariates (see subsection 2.5.2). Values are on a natural logarithmic scale. c 95% confidence limits = Estimate + SE (standard error). d Fitted as a continuous predictor.

Table 3.5. Variance contribution (%) of covariates in a generalized linear mixed model of Cornus suecica seed set on 27th July 2016 (Table 3.5).

Covariate Variance contribution (%)

Site fertility 44.3%

Elevation 36.8%

Visitation frequency 18.9%

Mean M. sylvaticum seed set (number of seeds per fruit) was 2.45 (n = 165 fruits) and for M. pratense 2.53 (n = 285 fruits). Seed set of both species was highest at 180 m a.sl. (Table 3.6). Neither hypotheses H1.2-b nor -c – “Seed sets of M. sylvaticum/M. pratense is affected by climatic conditions and/or flower visitation frequency” – were supported as ‘Elevation’ or ‘Visitation frequency’ was not included in the best models explaining variation in seed set.

Table 3.6. Mean seed seta of Melampyrum pratense and M. sylvaticum, and the number of fruits collected along an elevation gradient from sea level to the tree line (Figure 2.2) from 20th to 27th July 2016.

M. pratense M. sylvaticum

Elevation Seed set Fruits Seed set Fruits collected (m a.s.l.) collected (n) (n)

20 2.45 60 2.43 120

80 2.37 75 1.88 15

180 2.8 60 2.37 30

280 2.57 90 _ 0

a Seed set was estimated by counting the number of seeds in a fruit (max. four).

3.2 POLLINATOR LIMITATION IN Melampyrum spp.

H02.1-b – “M. sylvaticum does not produce seeds in the absence of pollinators” – was rejected as the mean number of M. sylvaticum seeds per fruit (1.67, n = 21 fruits) from the bagging experiment was significantly larger than zero (one- tailed t(164) = 36.1, p = <0.01).

H02.1-c – “M. pratense does not produce seeds in the absence of pollinators” – was rejected as the mean number of Melampyrum pratense seeds per fruit (2.81, n = 31 fruits) from the bagging experiment was significantly larger than zero (one- tailed t(284) = 42.8, p = <0.01).

3.3 TEMPORAL VARIATION

3.3.1 Seasonal variation in flower visitation

Figure 3.2. Flower visitation frequency (the number of flower visits per flower per 10 min) in the elevation gradient from 29th June to 25th July 2016. The green line shows a LOESS smoother fitted on the data with shaded confidence interval (for method see Wickham 2009).

Hypothesis 3.1 – “Flower visitation frequency is affected by seasonal variation” – was supported as ‘Day of year’ was included in the best model explaining flower

33 visitation throughout a flowering season (Table 3.2). The model also included another temporal variable, the fixed effect ‘Minute of day’. ‘Day of year’ contributed to more than a quarter of the variation in the model (Table 3.3). Flower visitation frequency gradually increased throughout the season (Figure 3.2).

3.3.2 Daily variation in flower visitation

Daily flower visitation data was recorded in 170 sampling events, recording 2612 visits (0.44 visits per flower per 10 min). Of them, 79.1% were done by flies (only 0.2% hoverflies), 15.5% by bumblebees, 4.1% by ants and 1.3% by others (lepidopterans, or unidentified). Ten plant species were recorded (Appendix B Table B.2). Solidago virgaurea received most visits (1035; 0.436 visits per flower per 10 min). All species received more than half of their visits by flies, except for Geum rivale that was visited almost exclusively by bumblebees (98% of visits). Five bumblebee species were recorded (Appendix D). The average temperature for all recordings was 14.5ºC (Q1-Q3: 11.9-17.0ºC) and relative air humidity 73.6% (66.8-81.3%).

Hypothesis 3.2 – “Flower visitation frequency is affected by daily variation” – was supported as the best model explaining variation in flower visitation throughout the day included ‘Daily oscillation’ (Table 3.7). Although ‘Day of year, ‘Sun presence’ and ‘Humidity’ were also included in the model, more than half of the variation was explained by ‘Daily oscillation’ alone (Table 3.8). Estimating from this model, the highest flower visitation frequency is expected at 11.50 a.m. (Figure 3.3). The expected frequency when there is sun (1.840) is about twice as high as when there is no sun (0.916). Nocturnal flower visitation was never observed in the focal plots, but occasionally on an ornamental plant in their vicinity (Appendix E).

Table 3.7. Generalized linear mixed model outputa of daily flower visitation frequency per 10 min from 29th July to 13th August 2017b.

95% confidence limitsc

Fixed Estimate SE Lower Upper effect

Intercept 33.8 2.79 31.0 36.6

Humidity -0.0245 0.00877 -0.333 -0.0157

Sun -0.699 0.215 -0.914 -0.484 presence (some sun)

Sun -0.569 0.189 -0.758 -0.380 presence (no sun)

Day of year -0.154 0.0131 -0.167 -0.141

Daily -2.22 0.166 -2.39 -2.06 oscillation (cos 1)

Daily -0.451 0.116 -0.568 -0.335 oscillation (sin 1)

Daily -0.463 0.118 -0.581 -0.346 oscillation (cos 2)

Daily -0.227 0.119 -0.346 -0.109 oscillation (sin 2)

a Flower visit per 10 min ~ Humidity + Day of year + Sun presence + Daily oscillation (cos 1) + …(sin 1) + …(cos 2) + …(sin 2) + Unexplained variation + offset (log (‘Total number of flowers’)). - ‘Sun presence’ is fitted as a factor variable with three levels and ‘Sun’ as a reference level. - ‘Unexplained variation’ is fitted as a random effect. - ‘Day of year’ is fitted as a continuous predictor. b The best model using a BIC criterion when testing a list of covariates (Appendix C Table C2). Values are on a natural logarithmic scale. c 95% confidence limits = Estimate + SE (standard error).

Table 3.8. Variance contribution (%) of covariates in a generalized linear mixed model (Table 3.7) of daily flower visitation frequency per 10 min from 29th July to 13th August 2017.

Covariate Variance contribution

Daily oscillationa 58.1%

Unexplained variation 19.8%

Day of season 16.4%

Sun presence 4.1%

Humidity 1.5%

a The summed variance contribution of all single- and double-frequency daily oscillation variables.

Figure 3.3. Expected flower visitation frequency (number of flower visits per flower per 10 min) throughout the day. Estimated from a generalized linear mixed model (Table 3.7) at 29th July 2017 with mean relative air humidity (73.1%). Orange line = sun, grey line = no sun.

4 Discussion

«Truth is not always in a well. In fact, as regards the more important knowledge, I do believe that she is invariably superficial. The depth lies in the valley where we seek her, and not upon the mountain-tops where she is found.» Edgar Allan Poe – Murders in the rue morgue (p 16), 1841.

In this study, I looked at spatial variation of the plant-pollinator system along an elevation gradient from sea level to the tree line (Objective I). By far, the most frequent flower visitors for the whole system were non-syrphid flies, contributing to 87.5% of all flower visits. Unexpectedly, the elevation gradient did not represent a climatic gradient, at least not during the sampled period (29th June to 25th July 2016); I documented no systematic change in temperature and/or humidity with increasing elevation. Neither flower visitation (H1.1) nor seed set of Melampyrum sylvaticum (H1.2-b) nor M. pratense (H1.2-c) were affected by elevation. However, seed set of Cornus suecica decreased with elevation, giving support to H1.2-a. This represents a partial confirmation that C. suecica seed set shows spatial variability at a scale from sea level to the tree line, which in theory could be linked to climatic conditions. The reason why I did not find temperature or humidity to vary along the elevation gradient could be the relatively mild coastal subarctic climate in the study area. As the elevation gradient was rather short, climatic differences could be small. However, my temperature and humidity data are based on a handheld weather recorder that was difficult to stabilize in the field. Moreover, I only measured temperature and humidity in situ. By doing this, I implicitly assume that the weather prior to the sampling event, and at other locations than the study plots, does not affect flower visitation. The same technique and device was used by Nielsen et al. (2017), but then as an addition to stationary loggers that recorded temperature every hour throughout the day. They found that the logger temperatures were better at explaining honeybee and bumblebee visitation. This suggests that bumblebees are dependent on temperature at other locations and times than when they are visiting flowers. This could be the case for flies as well, as they are poikilothermic, i.e. their internal temperatures largely depend on their surroundings. The temperature at other locations and times than during a flower visit therefore presents constraints, or benefits, to a fly.

Flower visitation was not affected by elevation (H1.1). If different elevations have similar climates (as suggested by my temperature and humidity recordings), then the plant-pollinator system at sea level might be like the one observed at the tree line. However, temperature and humidity are not the only abiotic factors affecting the plant-pollinator interaction. The melting of snow in spring generally happens earlier at low elevations, while snow emerges later in the autumn (e.g. Peterson 2003, Ward and Masters 2007, Peterson et al. 2008). For this reason, the summer season should be longer at sea level and shorter further up, and flower visitation seems not to be affected by season length. The system appears robust to challenges related to shorter winters, as is expected in the future (Brown and Mote 2009, Brutel-Vuilmet et al. 2013) The seed set of C. suecica did not only decrease with elevation (H1.2-a), but also increased with ‘Site fertility’, i.e. the proportion of reproductive shoots. This variable is probably an indicator of how well the species is doing at a site because there is a cost in producing reproductive shoots. The species should produce more inflorescences at sites where the plants are less limited by environmental stress. Therefore, my result suggests that C. suecica seed set decreased with elevation due to environmental stress, perhaps of a shorter growing season further up (as discussed above). Interestingly, C. suecica seed set was negatively influenced by flower visitation frequency. While flower visitation is often beneficial for plants (by leading to successful pollination), this might not always be the case. A large proportion of the flower visits to C. suecica were from micropterigidans (17.5%), who are pollen-eaters, or ants (20%). Ants are considered ineffective pollinators (Puterbaugh 1998, Willmer 2011b), for several reasons: They move over short distances (except for alate individuals, which were not observed in this study) and pollen do not adhere readily to their hairless bodies. Several studies (e.g. Hull and Beattie 1988, Gómez and Zamora 1992, Galen and Butchart 2003) have found reduced pollen viability following ant visitation, probably due to antibiotic secretions from the ant thorax. As flower visitation in general was not beneficial for C. suecica, seeds must have been produced either by a few specialized pollinators, or by self-pollination. Even though C. suecica flowers are protandrous, self-pollination can occur within an inflorescence. First, because pollen remains on the anthers after the stigma

38 becomes receptive, and second, because the flowers are clustered closely together (Taylor 1999). However, it is important to note that the sample size from which I estimated C. suecica seed set, could be lower than what it appears. Although I sampled many shoots of C. suecica, I only sampled from a single patch within each plot. As an individual often grows many shoots in dense tufts, there is a possibility that each patch (i.e. a sample) could represent only one individual. Thus, my results could be biased because of variation between individuals, and not represent a true sample of C. suecica populations in the study area. Seed set of M. sylvaticum (H1.2-b) and M. pratense (H1.2-c) were not affected by elevation or flower visitation. Although I expected the species to be pollinated by bumblebees, they were visited almost exclusively by flies1. However, as flower visitation did not affect seed set, my results suggest that M. sylvaticum and M. pratense are not well pollinated by flies. This is supported when considering their morphologies; Melampyrum flowers have an elongated corolla, and most flies have short proboscides. Yet, I did not identify flies to species level, or record pollination efficiency. I can therefore not know if there are any specialized fly pollinators of Melampyrum in the study area, as has been shown for Arctic Dryas (Tiusanen et al. 2016).

If M. sylvaticum and M. pratense were not pollinated by the observed flower visitors, they must have been self-pollinating. I investigated this by conducting a bagging experiment (Objective II) which confirmed that M. sylvaticum and M. pratense self-pollinate (HA2.1-a and -b, respectively). Melampyrum sylvaticum and M. pratense have several requirements to retain their populations. They are annual hemiparasites, which mean that they need to find a suitable host and reproduce during one growing season. Indeed, Moeller et al. (2017) found that self-pollination is more common for annual plants. Living in the Arctic can present additional challenges, because climate varies a lot between years, or throughout the summer. Moreover, M. sylvaticum and M. pratense cannot reproduce vegetatively and they do not hybridize. Kwak and Jennersten (1991) found M. pratense to be pollen limited – i.e. a higher seed set in hand-pollinated,

1 Except for three bumblebee visits (by one individual) to M. pratense flowers during only one sample event. 39 compared to bagged, flowers. Yet, ~20% of the bagged flowers developed into fruits. Similarly, Crichton et al. (2016) found M. sylvaticum to be highly inbreeding in Great Britain where it is endangered. If environmental stress is high, the ability to self-pollinate can be advantageous (Baker 1955). My results show that M. sylvaticum and M. pratense can sustain seed production, even when pollinator availability is low. However, as I lacked an experimental control and several replicates were destroyed, these results should be interpreted with caution.

In this study, I also looked at temporal varation of the plant-pollinator system in a coastal subarctic region (Objective III). As expected, flower visitation was affected by seasonal and daily variation, giving support to hypotheses H3.1 and H3.2, respectively. This confirms that flower visitation in my study area shows temporal variability at two different scales – within a season and within a day. Flower visitation was affected by seasonal variation (H3.1) and gradually increased throughout the season (Figure 3.2). Arctic climates are seasonal, and the weather conditions during some periods or days could be limiting for the plant-pollinator interaction, thus contributing to variation in flower visitation. However, seasonal variation in flower visitation did not differ along the elevation gradient. If the elevation gradient represents climatic differences (e.g. shorter summers with increased elevation), then seasonal variation did not represent climatic variation. If so, seasonal variation could be attributed to phenological differences between plants or pollinators. For instance, a flower visitor species that is highly active, but has a short flight season, could contribute to much variation in flower visitation. However, as I did not find a climatic gradient along the elevation, or record species’ phenologies, I can only speculate on the reasons why seasonal variation affect flower visitation in my study area. I found that flower visitation was affected by daily variation, independent of temperature, humidity and the presence of sun (H3.2). The most frequent flower visitors during my observations were non-syrphid flies (79% of visits) but bumblebees were also common (15.5% of visits). Despite the midnight sun, flower visitation was strictly diurnal: it started at about 5 a.m., peaked at noon, and ended before 10 p.m. (Figure 3.3). I recorded no flower visits in the focal plots

40 during night, even though it was light, and the flowers were open. The Arctic summer is short with unpredictable weather conditions that could be challenging for plant-pollinator interactions. The midnight sun offers an opportunity for organisms to be active during both day- and night-time, thereby lengthening the summer season. Foraging at night can be a benefit to pollinators as it could reduce the risk of interspecific competition or predation. My result regarding diurnal flower visitation is supported by the other studies that have looked at pollinator activity during the midnight sun (Lundberg 1980, Bergman et al. 1996, Stelzer and Chittka 2010). In my best model, ‘Daily oscillation’ during continuous light conditions explained most variation in flower visitation (58.1%). Therefore, flower visitor rhythmicity must be controlled by a rhythmic signal that I did not measure. While I only recorded the presence of sun, foraging rhythms could be controlled by the intensity (Bergman et al. 1996) or quality (Krüll 1976) of light. For instance, UV radiation levels drop drastically during Arctic summer nights and synchronize bumblebee foraging rhythms (Chittka et al. 2013). It is also possible that floral signals (e.g. scent emission) can initiate foraging at night, as for the most common bumblebee in my study area (Bombus pascuorum; Appendix E). If floral signals control flower visitor rhythms, the diurnal pattern I observed in flower visitation could be due to adaptations of the plants in the area. Continuous light conditions can be beneficial to the reproductive success of insect-pollinated plants if they are pollinated both during day and night; many populations adjust to both diurnal and nocturnal pollination according to their environments (e.g. Morse and Fritz 1983, Chapurlat et al. 2018). Bloch et al. (2017) suggested that scent emission by day-pollinated plants is regulated by the light through light-dark cycles, like seen in Trifolium repens (Jakobsen and Olsen 1994). However, in my study area, where the plant species had typical day- pollinated flowers (including T. repens), there was not a regular light-dark cycle (due to the midnight sun). If the plants can emit scent during night, and the flower visitors can respond to this, it is intriguing that they do not do so. Adaptations to both diurnal and nocturnal pollination is often seen in plants that are pollen-limited (Morse and Fritz 1983, Dar et al. 2006, Amorim et al. 2013). Even though the Arctic summer is short and unpredictable, the meadow I

41 observed was swarming with life at daytime. At noon, when it was sunny, (nearly) two visits were expected to each flower per ten minutes (Figure 3.3). One reason why plants in my study area do not adapt to nocturnal pollination could be because they receive sufficient visits (to optimize seed production) during day.

The Arctic is warming at a fast rate, and this trend is expected to amplify in the future (Holland and Bitz 2003, Bekryaev et al. 2010). Recently, some studies have tried to predict how spring snow cover will change in a situation of increased temperature and precipitation (e.g. Brown and Mote 2009, Brutel-Vuilmet et al. 2013). They find that the duration of snow cover is extremely sensitive to climate change, and that this sensitivity is greatest in coastal regions. If the observed warming continues, native species could face intense changes in length of the winter, or summer, season. However, my results indicate a robustness to this in a coastal subarctic region; flower visitation (H1.1) and seed production of M. sylvaticum (H1.2-b) and M. pratense (H1.2-c) do not vary along an elevation gradient, presumably representing differences in season length. I found that flower visitation was affected by seasonal variation (H3.1) which may represent phenological differences between plants and pollinators. Climate change could alter phenologies of plants and pollinators differently, potentially causing a phenological mismatch between interacting species (Hegland et al. 2009). In response to increased temperatures, phenologies of plants have already advanced (see Bertin 2008). The temporal overlap between the flowering and pollinator season has shrunk markedly in coastal High Arctic Greenland (Schmidt et al. 2016), even though the cause of this is not well understood. Although phenological changes will vary largely between habitats and species (Høye et al. 2014, Forrest 2016), a review by Forrest (2015) assert that few mismatches have been observed, and none are from long-term studies. More importantly, they do not seem to increase in frequency with climate change (Forrest 2015). In response to global warming, Parmesan and Yohe (2003) found that species have shifted their distributional ranges 6.1 km per decade northwards on average. These range shifts may lead to massive species invasions if they manage to outcompete native species or find available niches (Peterson 2003, Ward and

Masters 2007, Peterson et al. 2008). Indeed, there are reported expansions of novel bumblebee species into the Arctic (Martinet et al. 2015). In my study area, I found that the plant-pollinator system is diurnal, despite continuous light conditions (H3.2). However, two species of bumblebees did forage at night on a non-native ornamental plant that seems to be adapted to nocturnal pollination (Appendix E). If other plants or pollinators with the same pollination strategy invade the Arctic, they may have competitive advantages to native species. However, the expected warming of the Arctic does not necessarily lead to species invasion by outcompeting natives. Deutsch et al. (2008) found that warming may increase growth of terrestrial ectotherms living at high latitudes, because they have broad thermal tolerances. This apparent robustness of native insects in the Arctic is concordant with my results on flower visitation along the elevation gradient. Moreover, the plants in my study area may gain sufficient visits during day or, if they do not, self-pollinate (as shown for M. sylvaticum and M. pratense). In the face of new climatic regimes, plant-pollinator systems in coastal subarctic regions could adapt and respond well.

In conclusion, I have gained insight into spatial and temporal dynamics of the plant-pollinator system in a coastal subarctic region in northern Norway and I discuss how it may respond to future climate change. As far as I know, this is the first study to observe and report flower visitors to Melampyrum sylvaticum. Overall, non-syrphid flies were the dominant flower visitors in the area, while bumblebees were common visitors of specific plant species. I found that seed set of M. sylvaticum and M. pratense, and flower visitation were not affected by elevation, which may indicate a robustness to differences in season length. However, my recordings did not detect a true climatic gradient along the elevation gradient. Melampyrum sylvaticum and M. pratense were also found to self-pollinate, which could be a response to pollinator limitation as their seed set were not affected by flower visitation. I found a negative relationship between flower visitation and C. suecica seed set, indicating that their main flower visitors did not contribute to pollination. I did not test if C. suecica can self- pollinate, but its seed set decreased with elevation, suggesting that this species suffered from increased environmental stress at higher elevations. For the whole

43 system, I found that flower visitation was affected by seasonal variation, which may imply that there are climatic or phenological differences between plants and pollinators throughout a season. My last, and perhaps most interesting, finding was that flower visitation followed a diurnal pattern despite continuous light conditions. I showed that the rhythmicity of the plant-pollinator interaction must be controlled by a different signal than temperature, humidity or the presence of sun. I suggested that flower visitor rhythmicity could be controlled by floral signals (e.g. scent emission), though this was not measured. If so, the plants in the study area do not exploit the opportunity of receiving additional flower visits at night, perhaps due to sufficient visits during the day. If the warming that is observed in the Arctic continues, plants and pollinators may face shorter winters and species invasions. However, my results indicate that the plant-pollinator system in coastal subarctic regions are robust to these challenges.

5 Future studies

My main aim in this study was to gain insight into spatial and temporal dynamics of the plant-pollinator system in my study area. However, I only recorded flower visitation and, for three plant species, estimated seed set. In truth, this will only describe a fraction of the plant-pollinator system in the area. First, flower visitation does not necessarily equal successful pollination. Second, seed set alone does not necessarily describe plant reproductive success, not even if the seed set reflects insect-mediated fertilization. Future studies in the region should try to record successful pollination and assess whether it is linked directly to plant reproductive success. One way to do this is to observe flower visitation of tagged plants at specific times, bag the flowers when they are not being observed, and later estimate fruit and seed set of the plants1. Flower visitation in the study area was dominated by non-syrphid flies, which I treated as one ‘functional pollinator group’. By doing this, I implicitly assume that all species within this group are equally effective as pollinators. However, this is a misconception as we know that there are non-syrphid fly species with specialized flower-visiting behaviors (Pont 1993, Brown and McNeil 2009, Tiusanen et al. 2016). If this is the case in my study area, my results and interpretations regarding flower visitation, and how flower visitors may respond to future climate change, could be misleading. For instance, this would be the case if a specialized, and highly important, fly pollinator decreases in abundance with elevation while all other fly species do not. In the future, it is crucial that we identify flower visitors to species level and assess their relative contributions as pollinators. As I found temporal variation to affect flower visitation at two different scales – within a season and a day –, there is a chance that annual variation also affects flower visitation in my study area. Long-term studies are needed to properly assess how plant-pollinator systems in coastal subarctic areas respond to future climate change.

1 Fruit set (flower:fruit ratio) × seed set (seed:ovule ratio) is an estimate of relative reproductive success sensu Molau (1993). 45

My environmental data was based on in situ recordings of temperature and humidity, which implicitly assumes that these variables at other times and locations are not important for flower visitation. It could be beneficial to use a different method, as well, for instance stationary loggers that measure temperature and humidity throughout the whole day. Furthermore, the elevation gradient in my study was rather short (20-280 m a.s.l.) due to a low tree line. Similar studies could find it useful to include more environmental variables, for instance time of snow melt, to get a more detailed description of the climate along an elevation gradient. Melampyrum sylvaticum or M. pratense seed set was not affected by elevation. However, I did not estimate total fruit set of the plants, because the fruits emerge sequentially over a time span that was too long to monitor that summer. Future studies on Melampyrum should account for this, if they wish to assess the species’ reproductive success. Moreover, seed quality (e.g. seed weight) could be a better indicator of seed production in M. sylvaticum and M. pratense because they produce only 1-4 large seeds per fruit. I also found that both species self-pollinate by conducting a bagging experiment. However, the experiment was prone to several biases as there was no experimental control and four (out of six) mesh tents were destroyed. Furthermore, M. sylvaticum and M. pratense seed set was not affected by flower visitation, and they were (unexpectedly) visited predominantly by flies. More research, including bagging and hand-pollination experiments, is needed to assess if seed set in these species are limited by a lack of effective pollinators. Future studies on Melampyrum pollination or reproductive biology could hopefully benefit from my experiences and suggestions. Cornus suecica seed set decreased with elevation and flower visitation, but at each plot, I only estimated seed set from a single patch of shoots. As a patch could represent only one individual of C. suecica, the results may be biased due to differences among genetic individuals. To sample several patches at each plot, for instance within a quadrate, could be advantageous in similar studies.

I found that flower visitation followed a diurnal rhythm during the Arctic summer. However, I do not know if this rhythmicity is mediated by the flower

46 visitors, the plants, or the plant-pollinator interaction itself (i.e. the synchronization between plant and pollinator rhythmicity). I recommend future studies to test native pollinator’s foraging activity or plant’s floral signals (e.g. scent emission or nectar production) under constant conditions in the laboratory. It is urgent to assess the variables that control these rhythms or, if they are circadian, the zeitgebers that synchronize them. The field work I conducted was at the end of the summer, during the civil twilight period (technically not midnight sun). At this time, daily variation in light intensity and quality should be higher than during midnight sun. Chittka et al. (2013) found UV radiation to synchronize Bombus terrestris foraging rhythms. Floral scent could be another variable controlling, or synchronizing, flower visitor foraging rhythms, as I saw for B. pascuorum (Appendix E). Intriguingly, the only nocturnal visitation I observed was at an ornamental plant that probably is adapted to nocturnal pollination. Future studies on flower visitation during Arctic summer nights are recommended to look at typical night-pollinated plants that lives in the Arctic, e.g. Platanthera bifolia (Lid 2013, Mossberg and Stenberg 2016).

REFERENCES

Afifi, A., S. May, and V. A. Clark. 2003. Computer-aided multivariate analysis (p 90). CRC Press.

Aho, K., D. Derryberry, and T. Peterson. 2014. Model selection for ecologists: the worldviews of AIC and BIC. Ecology 95:631-636.

Alexander, M. A., J. D. Scott, K. D. Friedland, K. E. Mills, J. A. Nye, A. J. Pershing, and A. C. Thomas. 2018. Projected sea surface temperatures over the 21st century: Changes in the mean, variability and extremes for large marine ecosystem regions of Northern Oceans. Elem Sci Anth 6.

Amasino, R. M. 2005. Vernalization and flowering time. Current opinion in biotechnology 16:154-158.

Amorim, F., L. Galetto, and M. Sazima. 2013. Beyond the pollination syndrome: nectar ecology and the role of diurnal and nocturnal pollinators in the reproductive success of Inga sessilis (Fabaceae). Plant biology 15:317-327.

Anderson, D. R., K. P. Burnham, and W. L. Thompson. 2000. Null hypothesis testing: problems, prevalence, and an alternative. The journal of wildlife management 64:912-923.

Antonsen, H. 1997. Effects of spatial parameters on the interaction between Melampyrum pratense and bumblebees (Master's Thesis). Intitute of Biology, University of Oslo.

Baker, H. G. 1955. Self-compatibility and establishment after 'long-distance' dispersal. Evolution 9:347-349.

Bates, D., M. Mächler, B. Bolker, and S. Walker. 2014. Fitting linear mixed-effects models using lme4. arXiv preprint arXiv: 1406.5823.

Bekryaev, R. V., I. V. Polyakov, and V. A. Alexeev. 2010. Role of polar amplification in long-term surface air temperature variations and modern Arctic warming. Journal of Climate 23:3888-3906.

Bell, V., and R. Moore. 1999. An elevation‐dependent snowmelt model for upland Britain. Hydrological Processes 13:1887-1903.

Bergman, P., U. Molau, and B. Holmgren. 1996. Micrometeorological impacts on insect activity and plant reproductive success in an alpine environment, Swedish Lapland. Arctic and Alpine Research 28:196-202.

Bertin, R. I. 2008. Plant phenology and distribution in relation to recent climate change. The Journal of the Torrey Botanical Society 135:126-146.

Biesmeijer, J. C., S. P. Roberts, M. Reemer, R. Ohlemüller, M. Edwards, T. Peeters, A. Schaffers, S. G. Potts, R. Kleukers, and C. Thomas. 2006. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313:351-354.

Bloch, G., N. Bar-Shai, Y. Cytter, and R. Green. 2017. Time is honey: circadian clocks of bees and flowers and how their interactions may influence ecological communities. Phil. Trans. R. Soc. B 372:20160256.

Blois, J. L., J. W. Williams, M. C. Fitzpatrick, S. T. Jackson, and S. Ferrier. 2013. Space can substitute for time in predicting climate-change effects on biodiversity. Proceedings of the National Academy of Sciences 110:9374-9379.

Blöschl, G., and R. Kirnbauer. 1992. An analysis of snow cover patterns in a small alpine catchment. Hydrological Processes 6:99-109.

Bolker, B. M., M. E. Brooks, C. J. Clark, S. W. Geange, J. R. Poulsen, M. H. H. Stevens, and J.-S. S. White. 2009. Generalized linear mixed models: a practical guide for ecology and evolution. Trends in ecology & evolution 24:127-135.

Brown, A. O., and J. N. McNeil. 2009. Pollination ecology of the high latitude, dioecious cloudberry (Rubus chamaemorus; Rosaceae). American journal of botany 96:1096-1107.

Brown, R., and D. Robinson. 2011. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. The Cryosphere 5:219-229.

Brown, R. D., and P. W. Mote. 2009. The response of Northern Hemisphere snow cover to a changing climate. Journal of Climate 22:2124-2145.

Brutel-Vuilmet, C., M. Ménégoz, and G. Krinner. 2013. An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. The Cryosphere 7:67.

Buckland, S. T., K. P. Burnham, and N. H. Augustin. 1997. Model selection: an integral part of inference. Biometrics 53:603-618.

Butchart, S. H., M. Walpole, B. Collen, A. Van Strien, J. P. Scharlemann, R. E. Almond, J. E. Baillie, B. Bomhard, C. Brown, and J. Bruno. 2010. Global biodiversity: indicators of recent declines. Science:1187512.

Byng, J. W., M. W. Chase, M. J. Christenhusz, M. F. Fay, W. S. Judd, D. J. Mabberley, A. N. Sennikov, D. E. Soltis, P. S. Soltis, and P. F. Stevens. 2016. An update of the Angiosperm Phylogeny Group classification for the orders and families of flowering plants: APG IV. Botanical Journal of the Linnean Society 181:1-20.

Chapurlat, E., J. Anderson, J. Ågren, M. Friberg, and N. Sletvold. 2018. Diel pattern of floral scent emission matches the relative importance of diurnal and nocturnal pollinators in populations of Gymnadenia conopsea. Annals of botany 121:711-721.

Chittka, L., R. J. Stelzer, and R. Stanewsky. 2013. Daily changes in ultraviolet light levels can synchronize the circadian clock of bumblebees (Bombus terrestris). Chronobiology international 30:434-442.

Chou, C., and C.-W. Lan. 2012. Changes in the annual range of precipitation under global warming. Journal of Climate 25:222-235.

Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W. Gutowski, T. Johns, and G. Krinner. 2013. Chapter 12 - Long-term climate change: projections, commitments and irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

Cowtan, K., and R. G. Way. 2014. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Quarterly Journal of the Royal Meteorological Society 140:1935-1944.

Crichton, R. J., S. E. Dalrymple, S. J. Woodin, and P. M. Hollingsworth. 2016. Conservation genetics of the annual hemiparasitic plant Melampyrum sylvaticum (Orobanchaceae) in the UK and Scandinavia. Conservation Genetics 17:547-556.

Cubedo, M., and J. Oller. 2002. Hypothesis testing: a model selection approach. Journal of statistical planning and inference 108:3-21.

Dalrymple, S. E. 2007. Biological Flora of the British Isles: Melampyrum sylvaticum L. Journal of ecology 95:583-597.

Danks, H. 2007. The elements of seasonal adaptations in insects. The Canadian Entomologist 139:1-44.

Dar, S., M. d. C. Arizmendi, and A. Valiente-Banuet. 2006. Diurnal and nocturnal pollination of Marginatocereus marginatus (Pachycereeae: Cactaceae) in Central Mexico. Annals of botany 97:423-427.

Déry, S. J., and R. D. Brown. 2007. Recent Northern Hemisphere snow cover extent trends and implications for the snow‐albedo feedback. Geophysical Research Letters 34.

Deutsch, C. A., J. J. Tewksbury, R. B. Huey, K. S. Sheldon, C. K. Ghalambor, D. C. Haak, and P. R. Martin. 2008. Impacts of climate warming on terrestrial ectotherms across latitude. Proceedings of the National Academy of Sciences 105:6668-6672.

Dirzo, R., and P. H. Raven. 2003. Global state of biodiversity and loss. Annual Review of Environment and Resources 28:137-167.

Elberling, H., and J. M. Olesen. 1999. The structure of a high latitude plant‐flower visitor system: the dominance of flies. Ecography 22:314-323.

Fogg, G. E. 1998. The biology of polar habitats (p 15). Oxford University Press, USA.

Forrest, J. R. 2015. Plant–pollinator interactions and phenological change: what can we learn about climate impacts from experiments and observations? Oikos 124:4-13.

Forrest, J. R. 2016. Complex responses of insect phenology to climate change. Current opinion in insect science 17:49-54.

Frölicher, T. L., and D. J. Paynter. 2015. Extending the relationship between global warming and cumulative carbon emissions to multi-millennial timescales. Environmental Research Letters 10:075002.

Fukami, T., and D. A. Wardle. 2005. Long-term ecological dynamics: reciprocal insights from natural and anthropogenic gradients. Proceedings of the Royal Society of London B: Biological Sciences 272:2105-2115.

Galen, C., and B. Butchart. 2003. Ants in your plants: effects of nectar‐thieves on pollen fertility and seed‐siring capacity in the alpine wildflower, Polemonium viscosum. Oikos 101:521-528.

Gilman, S. E., M. C. Urban, J. Tewksbury, G. W. Gilchrist, and R. D. Holt. 2010. A framework for community interactions under climate change. Trends in ecology & evolution 25:325-331.

Giménez‐Benavides, L., S. Dötterl, A. Jürgens, A. Escudero, and J. M. Iriondo. 2007. Generalist diurnal pollination provides greater fitness in a plant with nocturnal pollination syndrome: assessing the effects of a Silene–Hadena interaction. Oikos 116:1461-1472.

Giorgi, F., J. W. Hurrell, M. R. Marinucci, and M. Beniston. 1997. Elevation dependency of the surface climate change signal: a model study. Journal of Climate 10:288-296.

GISTEMP, T. 2018. GISS Surface Temperature Analysis (GISTEMP). NASA Goddard Institute for Space Studies. Data accessed 2018-03-12 at https://data.giss.nasa.gov/gistemp/.

Gómez, J., and R. Zamora. 1992. Pollination by ants: consequences of the quantitative effects on a mutualistic system. Oecologia 91:410-418.

Haber, W. A., and G. Franke. 1982. Pollination of Leuhea (Tiliaceae) in Costa Rican deciduous forest. Ecology 63:1740-1750.

Hao, M., J. Huang, Y. Luo, X. Chen, Y. Lin, Z. Zhao, and Y. Xu. 2017. Narrowing the surface temperature range in CMIP5 simulations over the Arctic. Theoretical and Applied Climatology 132:1073-1088.

Harrison, X. A. 2014. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ 2:e616.

Hartmann, D., A. Klein Tank, M. Rusticucci, L. Alexander, S. Brönnimann, Y. Charabi, F. Dentener, E. Dlugokencky, D. Easterling, and A. Kaplan. 2013. Chapter 2 - Observations: Atmosphere and Surface In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

Haug, S. 2017. Flower visitation and seed set in Melampyrum; effects of elevation and Rubus idaeus (Master's Thesis), Institute of Biology, University of Oslo.

Hegland, S. J., A. Nielsen, A. Lázaro, A. L. Bjerknes, and Ø. Totland. 2009. How does climate warming affect plant‐pollinator interactions? Ecology letters 12:184- 195.

Heinken, T., and E. Winkler. 2009. Non-random dispersal by ants: long-term field data versus model predictions of population spread of a forest herb. Perspectives in plant ecology, evolution and systematics 11:1-15.

Henderson Jr, C. R. 1982. Analysis of covariance in the mixed model: higher-level, nonhomogeneous, and random regressions. Biometrics 38:623-640.

Hillebrand, H. 2004. On the generality of the latitudinal diversity gradient. The American Naturalist 163:192-211.

Holland, M. M., and C. M. Bitz. 2003. Polar amplification of climate change in coupled models. Climate Dynamics 21:221-232.

Huang, J., X. Zhang, Q. Zhang, Y. Lin, M. Hao, Y. Luo, Z. Zhao, Y. Yao, X. Chen, and L. Wang. 2017. Recently amplified arctic warming has contributed to a continual global warming trend. Nature Climate Change 7:875-879.

Hull, D. A., and A. J. Beattie. 1988. Adverse effects on pollen exposed to Atta texana and other North American ants: implications for ant pollination. Oecologia 75:153-155.

Høye, T. T., A. Eskildsen, R. R. Hansen, J. J. Bowden, N. M. Schmidt, and W. D. Kissling. 2014. Phenology of high-arctic butterflies and their floral resources: species-specific responses to climate change. Current Zoology 60:243-251.

Høye, T. T., E. Post, N. M. Schmidt, K. Trøjelsgaard, and M. C. Forchhammer. 2013. Shorter flowering seasons and declining abundance of flower visitors in a warmer Arctic. Nature Climate Change 3:759.

Jakobsen, H. B., and C. E. Olsen. 1994. Influence of climatic factors on emission of flower volatiles in situ. Planta 192:365-371.

Jennersten, O., and M. M. Kwak. 1991. Competition for bumblebee visitation between Melampyrum pratense and Viscaria vulgaris with healthy and Ustilago-infected flowers. Oecologia 86:88-98.

Jepsen, J. U., S. B. Hagen, R. A. Ims, and N. G. Yoccoz. 2008. Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: evidence of a recent outbreak range expansion. Journal of Animal Ecology 77:257-264.

Jepsen, J. U., L. Kapari, S. B. Hagen, T. Schott, O. P. L. Vindstad, A. C. Nilssen, and R. A. Ims. 2011. Rapid northwards expansion of a forest insect pest attributed to spring phenology matching with sub‐Arctic birch. Global Change Biology 17:2071-2083.

Johnson, J. B., and K. S. Omland. 2004. Model selection in ecology and evolution. Trends in ecology & evolution 19:101-108.

Kalisz, S., D. W. Vogler, and K. M. Hanley. 2004. Context-dependent autonomous self-fertilization yields reproductive assurance and mixed mating. Nature 430:884-887.

Kartverket. 2016a. 1. Tromsø, Lyngen and Karlsøy Kommune, 1:150000. Background map (overlapping aerial photos). Accessed 2018-01-15 at www.norgeskart.no. Statens Kartverk.

Kartverket. 2016b. 2. Southern Reinøy. 1:10000. Aerial photo. Accessed 2018-01-15 at www.norgeskart.no. Project name: Tromsø 2016. Statens Kartverk.

Kauffeld, N., and P. Williams. 1972. Honey bees as pollinators of pickling cucumbers in Wisconsin. American bee journal 112:252-254.

Kerr, J. T., A. Pindar, P. Galpern, L. Packer, S. G. Potts, S. M. Roberts, P. Rasmont, O. Schweiger, S. R. Colla, and L. L. Richardson. 2015. Climate change impacts on bumblebees converge across continents. Science 349:177-180.

Kevan, P., E. Tikhmenev, and M. Usui. 1993. Insects and plants in the pollination ecology of the boreal zone. Ecological Research 8:247-267.

Kevan, P. G. 1972. Insect pollination of high arctic flowers. The Journal of Ecology 60:831-847.

Kevan, P. G. 1973. Flowers, insects, and pollination ecology in the Canadian High Arctic. Polar Record 16:667-674.

Keys, R. N., S. L. Buchmann, and S. E. Smith. 1995. Pollination effectiveness and pollination efficiency of insects foraging Prosopis velutina in south-eastern Arizona. Journal of Applied Ecology 32:519-527.

Klein, D. R., H. H. Bruun, R. Lundgren, and M. Philipp. 2008. Climate change influences on species interrelationships and distributions in high-Arctic Greenland. Advances in Ecological Research 40:81-100.

Krüll, F. 1976. Zeitgebers for animals in the continuous daylight of high arctic summer. Oecologia 24:149-157.

Kudo, G. 1991. Effects of snow-free period on the phenology of alpine plants inhabiting snow patches. Arctic and Alpine Research 23:436-443.

Kudo, G., and T. Y. Ida. 2013. Early onset of spring increases the phenological mismatch between plants and pollinators. Ecology 94:2311-2320.

Kumar, A., J. Perlwitz, J. Eischeid, X. Quan, T. Xu, T. Zhang, M. Hoerling, B. Jha, and W. Wang. 2010. Contribution of sea ice loss to Arctic amplification. Geophysical Research Letters 37.

Kwak, M. M., and O. Jennersten. 1991. Bumblebee visitation and seedset in Melampyrum pratense and Viscaria vulgaris: heterospecific pollen and pollen limitation. Oecologia 86:99-104.

Lid, J. L., D T. 2013. Norsk flora (4th volume). Det norske samlaget, Oslo.

Lloyd, D. G. 1979. Some reproductive factors affecting the selection of self- fertilization in plants. The American Naturalist 113:67-79.

Lloyd, D. G. 1992. Self-and cross-fertilization in plants. II. The selection of self- fertilization. International Journal of Plant Sciences 153:370-380.

Loboda, S., J. Savage, C. M. Buddle, N. M. Schmidt, and T. T. Høye. 2017. Declining diversity and abundance of High Arctic fly assemblages over two decades of rapid climate warming. Ecography 41:265-277.

Lu, J., and M. Cai. 2009. Seasonality of polar surface warming amplification in climate simulations. Geophysical Research Letters 36.

Lundberg, H. 1980. Effects of weather on foraging‐flights of bumblebees (Hymenoptera, Apidae) in a subalpine/alpine area. Ecography 3:104-110.

Martinet, B., P. Rasmont, B. Cederberg, D. Evrard, F. Ødegaard, J. Paukkunen, and T. Lecocq. 2015. Forward to the north: two Euro-Mediterranean bumblebee species now cross the Arctic Circle. Pages 303-309 in Annales de la Société entomologique de France (NS). Taylor & Francis.

McLean, R. A., W. L. Sanders, and W. W. Stroup. 1991. A unified approach to mixed linear models. The American Statistician 45:54-64.

Meen, E., A. Nielsen, and M. Ohlson. 2012. Forest stand modelling as a tool to predict performance of the understory herb Cornus suecica. Silva Fennica 46:479- 499.

Moeller, D. A. 2006. Geographic structure of pollinator communities, reproductive assurance, and the evolution of self‐pollination. Ecology 87:1510-1522.

Moeller, D. A., R. D. Briscoe Runquist, A. M. Moe, M. A. Geber, C. Goodwillie, P. O. Cheptou, C. G. Eckert, E. Elle, M. O. Johnston, and S. Kalisz. 2017. Global biogeography of mating system variation in seed plants. Ecology letters 20:375-384.

Molau, U. 1993. Phenology and reproductive ecology in six subalpine species of Rhinanthoideae (Scrophulariaceae). Op. Bot:7-17.

Morse, D. H., and R. S. Fritz. 1983. Contributions of diurnal and nocturnal insects to the pollination of common milkweed (Asclepias syriaca L.) in a pollen- limited system. Oecologia 60:190-197.

Mosquin, T. 1985. The explosive pollination mechanism in the pop flower, Chamaepericlymenum (Cornaceae). Can. Field-Nat 99:1-5.

Mossberg, B., and L. Stenberg. 2016. Store nordiske flora (3rd volume). Gyldendal Norsk Forlag AS, Oslo.

Nielsen, A., T. Reitan, A. W. Rinvoll, and A. K. Brysting. 2017. Effects of competition and climate on a crop pollinator community. Agriculture, Ecosystems & Environment 246:253-260.

Norkart AS. 2018. Lunde, southern Reinøy. 1:1500. Aerial photo. Geovekst og kommunene. NASA, Meti. Mapbox. OpenStreetMap.

Ollerton, J. 2017. Pollinator diversity: distribution, ecological function, and conservation. Annual Review of Ecology, Evolution, and Systematics 48:353- 376.

Ollerton, J., R. Winfree, and S. Tarrant. 2011. How many flowering plants are pollinated by animals? Oikos 120:321-326.

Olmstead, R. G., C. W. Pamphilis, A. D. Wolfe, N. D. Young, W. J. Elisons, and P. A. Reeves. 2001. Disintegration of the Scrophulariaceae. American journal of botany 88:348-361.

Osborn, T., and P. Jones. 2014. The CRUTEM4 land-surface air temperature data set: construction, previous versions and dissemination via Google Earth. Earth System Science Data 6:61-68.

Parmesan, C., and G. Yohe. 2003. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37.

Peterson, A. T. 2003. Predicting the geography of species’ invasions via ecological niche modeling. The quarterly review of biology 78:419-433.

Peterson, A. T., A. Stewart, K. I. Mohamed, and M. B. Araújo. 2008. Shifting global invasive potential of European plants with climate change. PLoS one 3:e2441.

Pickett, S. T. 1989. Space-for-time substitution as an alternative to long-term studies (pp 110-135). In: Long-term Studies in Ecology. Springer, New York.

Pont, A. 1993. Observations on anthophilous Muscidae and other Diptera (Insecta) in Abisko National Park, Sweden. Journal of Natural History 27:631-643.

Popper, K. 1959. The logic of scientific discovery. Hutchinson Education, London.

Potapov, G., Y. S. Kolosova, and M. Y. Gofarov. 2014. Zonal distribution of bumblebee species (Hymenoptera, Apidae) in the North of European Russia. Entomological review 94:79-85.

Potts, S. G., J. C. Biesmeijer, C. Kremen, P. Neumann, O. Schweiger, and W. E. Kunin. 2010. Global pollinator declines: trends, impacts and drivers. Trends in ecology & evolution 25:345-353.

Průšová, M., J. Lepš, M. Štech, and J. Těšitel. 2013. Growth, survival and generative reproduction in a population of a widespread annual hemiparasite Melampyrum pratense. Biologia 68:65-73.

Puterbaugh, M. N. 1998. The roles of ants as flower visitors: experimental analysis in three alpine plant species. Oikos 83:36-46.

R core team. 2017. R: A language and environment for statistical computing (version 3.4.3). R Foundation for Statistical Computing, Vienna, Austria.

Ranta, E., and H. Lundberg. 1981. Resource utilization by bumblebee queens, workers and males in a subarctic area. Ecography 4:145-154.

Rasmont, P., M. Franzén, T. Lecocq, A. Harpke, S. P. Roberts, J. C. Biesmeijer, L. Castro, B. Cederberg, L. Dvorak, and Ú. Fitzpatrick. 2015. Climatic risk and distribution atlas of European bumblebees. BioRisk 10:1-236.

Reierth, E., and K.-A. Stokkan. 1998. Activity rhythm in High Arctic Svalbard ptarmigan (Lagopus mutus hyperboreus). Canadian journal of zoology 76:2031-2039.

Reierth, E., T. J. Van’t Hof, and K.-A. Stokkan. 1999. Seasonal and daily variations in plasma melatonin in the high-arctic Svalbard ptarmigan (Lagopus mutus hyperboreus). Journal of biological rhythms 14:314-319.

Reitan, T. 2017. BIC model selection function (R script). Accessed 2018-01-09 at http://folk.uio.no/trondr/R/glmm_bic_search.R.

Reitan, T., and A. Nielsen. 2016. Do not divide count data with count data; A story from pollination ecology with implications beyond. PLoS one 11:e0149129.

Saha, S., A. Rinke, and K. Dethloff. 2006. Future winter extreme temperature and precipitation events in the Arctic. Geophysical Research Letters 33.

Schmidt, N. M., J. B. Mosbacher, P. S. Nielsen, C. Rasmussen, T. T. Høye, and T. Roslin. 2016. An ecological function in crisis? The temporal overlap between plant flowering and pollinator function shrinks as the Arctic warms. Ecography 39:1250-1252.

Schwarz, G. 1978. Estimating the dimension of a model. The annals of statistics 6:461-464.

Serreze, M., A. Barrett, J. Stroeve, D. Kindig, and M. Holland. 2009. The emergence of surface-based Arctic amplification. The Cryosphere 3:11-19.

Sillmann, J., V. Kharin, F. Zwiers, X. Zhang, and D. Bronaugh. 2013. Climate extremes indices in the CMIP5 multimodel ensemble: Part 2. Future climate projections. Journal of Geophysical Research: Atmospheres 118:2473-2493.

Stelzer, R. J., and L. Chittka. 2010. Bumblebee foraging rhythms under the midnight sun measured with radiofrequency identification. BMC biology 8:93.

Stephenson, A. G., and W. W. Thomas. 1977. Diurnal and nocturnal pollination of Catalpa speciosa (Bignoniaceae). Systematic Botany 2:191-198.

Stocker, T., D. Qin, G. Plattner, M. Tignor, S. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P. Midgley. 2013. IPCC, 2013: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (p 203; Table 2.10). Cambridge University Press, Cambridge, UK.

Stokkan, K. A., A. Mortensen, and A. S. Blix. 1986. Food intake, feeding rhythm, and body mass regulation in Svalbard rock ptarmigan. American Journal of Physiology-Regulatory, Integrative and Comparative Physiology 251:264- R267.

Stokkan, K. A., B. E. Van Oort, N. J. Tyler, and A. S. Loudon. 2007. Adaptations for life in the Arctic: evidence that melatonin rhythms in reindeer are not driven by a circadian oscillator but remain acutely sensitive to environmental photoperiod. Journal of pineal research 43:289-293.

Sun, S.-G., Y.-H. Guo, R. Gituru, and S.-Q. Huang. 2005. Corolla wilting facilitates delayed autonomous self-pollination in Pedicularis dunniana (Orobanchaceae). Plant Systematics and Evolution 251:229-237.

Světlíková, P., T. Hájek, and J. Těšitel. 2018. A hemiparasite in the forest understorey: photosynthetic performance and carbon balance of Melampyrum pratense. Plant biology 20:50-58.

Taylor, K. 1999. Cornus suecica L. (Chamaepericlymenum suecicum (L.) Ascherson & Graebner). Journal of ecology 87:1068-1077.

Tennant, D. 2008. Small cow-wheat Melampyrum sylvaticum L.; Scrophulariaceae in England. Watsonia 27:23-36.

Thomson, J. D. 2010. Flowering phenology, fruiting success and progressive deterioration of pollination in an early-flowering geophyte. Philosophical Transactions of the Royal Society B: Biological Sciences 365:3187-3199.

Thorsen, S. 2018. Time and Date AS 1995-2018 | Sun & Moon | Sunrise and Sunset | Tromsø. Accessed 2018-01-29 at https://www.timeanddate.com/- sun/norway/tromso.

Tiusanen, M., P. D. Hebert, N. M. Schmidt, and T. Roslin. 2016. One fly to rule them all—muscid flies are the key pollinators in the Arctic. Proc. R. Soc. B 283:20161271.

Totland, Ø., A. Nielsen, A.-L. Bjerknes, and M. Ohlson. 2006. Effects of an exotic plant and habitat disturbance on pollinator visitation and reproduction in a boreal forest herb. Am. J. Bot. 93:868-873.

Tylianakis, J. M., R. K. Didham, J. Bascompte, and D. A. Wardle. 2008. Global change and species interactions in terrestrial ecosystems. Ecology letters 11:1351-1363. van Oort, B. E., N. J. Tyler, M. P. Gerkema, L. Folkow, and K.-A. Stokkan. 2007. Where clocks are redundant: weak circadian mechanisms in reindeer living under polar photic conditions. Naturwissenschaften 94:183-194.

Vihma, T., J. Screen, M. Tjernström, B. Newton, X. Zhang, V. Popova, C. Deser, M. Holland, and T. Prowse. 2016. The atmospheric role in the Arctic water cycle: A review on processes, past and future changes, and their impacts. Journal of Geophysical Research: Biogeosciences 121:586-620.

Ward, N. L., and G. J. Masters. 2007. Linking climate change and species invasion: an illustration using insect herbivores. Global Change Biology 13:1605-1615.

Whitlock, M. C., and D. Schluter. 2009. Chapter 12: Comparing two means. In:. The analysis of biological data. Roberts and Company Publishers, Colorado, USA.

Whitney, G. G. 1984. The reproductive biology of raspberries and plant-pollinator community structure. American journal of botany 71:887-894.

Wickham, H. 2009. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York.

Wiens, J. J. 2016. Climate-related local extinctions are already widespread among plant and animal species. PLoS biology 14:e2001104.

Williams, P. H., and J. L. Osborne. 2009. Bumblebee vulnerability and conservation world-wide. Apidologie 40:367-387.

Willmer, P. 2011a. 1. Pollination and floral ecology (p 529). Princeton University Press.

Willmer, P. 2011b. 2. Pollination and floral ecology (p 298). Princeton University Press.

Wit, E., E. v. d. Heuvel, and J. W. Romeijn. 2012. ‘All models are wrong...’: an introduction to model uncertainty. Statistica Neerlandica 66:217-236.

Wu, W. Y., C. W. Lan, M. H. Lo, J. T. Reager, and J. S. Famiglietti. 2015. Increases in the annual range of soil water storage at northern middle and high latitudes under global warming. Geophysical Research Letters 42:3903-3910.

Yang, S., W. Dong, J. Chou, J. Feng, Z. Wei, Y. Guo, X. Wen, T. Wei, D. Tian, and X. Zhu. 2016. Global warming projections using the human-earth system model BNU-HESM1.0. Science Bulletin 61:1833-1838.

Zuur, A., E. Ieno, N. Walker, A. Saveliev, and G. Smith. 2009. Mixed effects models and extensions in ecology. New York, NY: Spring Science and Business Media.

Ødegaard, F., A. Staverløkk, J. Gjershaug, A. Mjelde, and R. Bengtson. 2015. Humler i Norge. Kjennetegn, utbredelse og levesett. Norsk institutt for naturforskning (NINA), Trondheim, Norway.

APPENDICES

A. Weather data of Reinøya p 64 B. Plant species p 65 C. Covariates p 68 D. Bumblebee visits p 72 E. Nocturnal pollination p 73

A. Weather data of Reinøya

Precipitation (mm) and temperature (°C) measures were estimated from data given by seven weather station (Table A1) situated in proximity to Reinøya. The data was retrieved from The Norwegian Meteorological Institute. Mean annual precipitation is only estimated from two stations (Table A1), due to missing values at the other stations.

Table A.1. An overview over the weather stations from where data was used to estimate temperature and precipitation measurements for Reinøya.

Station name m a.s.l. Latitude (°N’) Longitude (°E’) Municipalitya

Tromsøb 100 69.65 18.94 Tromsø

Tromsø – Holt 20 69.65 18.91 Tromsø

Tromsø – Langnesb 8 65.68 18.91 Tromsø

Kvaløysletta 63 69.70 18.88 Tromsø

Fakken 57 70.10 20.11 Karlsøy

Torsvåg fyr 21 70.25 19.50 Karlsøy

Arnøya – Trolltinden 850 70.08 20.43 Skjervøy a All municipalities are situated in Troms County, Norway. b Stations from where precipitation data were used.

B. Plant species

Table B.1. An overview of all the plant species observed in the elevation gradient (Plot 1-12; Figure 2.2) during field work from 29th June and 25th July 2016a.

Plant species Elevation Visitation Flower Proportions (%)c (plot number) frequencyb visits

Black Bumble- Others flies bees

Achillea 20 m a.s.l. 0.500 7 100 0 0 millefolium (2)

Alchemilla sp. 20 - 280 m a.s.l. 0.116 12 16.7 0 83.3 (1, 5, 8, 11)

Andromeda 80, 280 m a.s.l. 0 0 - - - polifolia (5, 10-11)

Bartsia alpina 280 m a.s.l. 0 0 - - - (12)

Bistorta vivipara 20 - 80, 280 m a.s.l 0.400 5 100 0 0 (1, 4, 10)

Campanula 20 - 180 m a.s.l. 1.392 22 100 0 0 rotundifolia (3, 5, 9)

Cornus suecica 20 - 280 m a.s.l. 0.055 80 48.8 0 51.2 (1-12)

Dactylorhiza sp. 20 - 80, 280 m a.s.l. 0.173 22 100 0 0 (3, 5, 11)

Euphrasia sp. 20 m a.s.l. 0 0 - - - (1)

Geranium 20 - 280 m a.s.l. 0.671 130 88.5 6.9 4.6 sylvaticum (1-5, 7-8, 10-12)

Geum rivale 80 m a.s.l. 0.167 2 0 100 0 (4)

Hieracium sp. 80 - 280 m a.s.l. 0.861 12 100 0 0 (7, 9, 12)

Melampyrum 20 - 280 m a.s.l. 0.019 84 91.7 3.6 4.7 pratense (1-12)

Melampyrum 20 - 180 m a.s.l. 0.022 65 93.8 0 6.2 sylvaticum (1-3, 6-8)

Othilia secunda 80 - 280 m a.s.l. 0.482 32 84.4 15.6 0 (5, 7, 9, 12)

Pinguicula 20 - 280 m a.s.l. 0.014 1 100 0 0 vulgaris (3, 4, 8, 11)

Potentilla erecta 20 m a.s.l. 1.000 17 100 0 0 (3)

Ranunculus acris 20 - 280 m a.s.l. 0.740 203 99.5 0 0.5 (1-5, 7-8, 10-12)

Rhinanthus minor 20 - 80 m a.s.l. 0.062 10 100 0 0 (2,4)

Rumex acetosa 180 m a.s.l. 0.050 4 75 0 25 (8)

Solidago 20, 180 m a.s.l. 0.800 94 81.9 9.6 8.5 virgaurea (2-3, 8-9)

Trientalis 20 - 280 m a.s.l. 0.060 16 100 0 0 europaea (2-3, 5, 7-8, 11-12)

Vaccinium vitis- 180 m a.s.l. 0 0 - - - idaea (9) a Species in grey colour were excluded from the GLMM analyses because they were: never visited, only present at one height or sampled only once. b Flower visits per 10 min per flower. c Numbers in bold indicate that an insect groups contributed to more than 5% of the flower visits.

Table B.2. Visitation data of all plant species recorded from 29th July to 13th August 2017, and the proportions (%) of different flower visitorsa.

Plant Visitation Flower Proportions (%)c species frequencyb visits

Black Bumble- Ants Others flies bees

Achillea 2.04 210 98.6 0 0 1.4 millefolium

Allium 0.941 250 98 1.2 0 0 schoenoprasum

Comarum 1.14 226 87.2 12.8 0 0 palustre

Campanula 0.949 295 97.6 2 0 0.3 rotundifolia

Geranium 0.464 421 97.6 1.2 0.7 0.5 sylvaticum

Galeopsis 0.130 10 60 40 0 0 tetrahit

Geum rivale 0.182 147 2 98 0 0

Ranunculus 1.63 123 97.6 0 0 2.4 acris

Solidago 0.436 1035 68 19.5 9.9 2.6 virgaurea

Trifolium repens 0.528 105 89.5 9.5 1 0

a Achillea millefolium was excluded from the GLMM analyses because of outliers. b Flower visits per 10 min per flower. c Numbers in bold indicate that an insect group contributed to more than 5% of the flower visits.

C. Covariates

Table C.1. A list of covariates that were included in the model selection function to find the best GLMM of number of flower visits recorded from 29th June to 25th July 2016 (H1.1 and 3.1; Table 2.3).

Covariate Description

FIXED EFFECTS

Spatial Elevation Categorical predictor w/four levels

variables (20, 80, 180, 280 m a.s.l.)

Continuous predictor (20 to 280 m a.s.l.)

Temporal Day of year (DOY) Continuous predictor (181 to 207).

variables 1 = 1st January 2016.

Minute of day (MOD) Continuous predictor (590 to 1311).

1 = 00.01 A.M.

Environmental Temperature (°C)

variables Continuous variables measured with a handheld weather recorder (WeatherHawk: (Relative air) Humidity SM-28 Skymaster). (%)

Sun presence Factor variable w/three levels

(‘Sun’, ‘Some sun’, ‘No sun’).

Other Plant species Factor variable w/15 levels (all species written in black in Table B1). variables

Quadratic terms Temperature2 Tests for non-linear effects of temperature (°C).

Humidity2 Tests for non-linear effects of humidity (%).

Day of year2 Tests for non-linear effects of DOY.

Statistical Both predictors of ‘Elevation’ were tested for interactions with ‘Temperature’, ‘Humidity’, interactions ‘Sun presence’, ‘DOY’ and ‘Plant species’.

RANDOM EFFECTS

Spatial

variables Plot number Categorical predictor w/12 levels (1-12).

Temporal

variables Day of year Categorical predictor of DOY w/14 levels.

Other

variables Field worker Factor variable w/3 levels (ID of each field worker).

Sampling event Factor variable w/176 levels (unique ID for each sampling event).

Unexplained variation Factor variable w/429 levels (unique ID for each data point) to account for possible overdispersion.

Table C.2. A list of covariates that were included in the model selection function to find the best GLMM of number of flower visits recorded from 29th July to 13th August 2017 (H3.2 and 3.3; Table 2.3).

Covariate Description

FIXED EFFECTS

Temporal Daily oscillation… Continuous variables.

variables Oscillatory transformations from a linear daily covariate.

... (sin 1) - Single frequency sine function.

... (cos 1) - Single frequency cosine function.

... (sin 2) - Double frequency sine function.

... (cos 2) - Double frequency cosine function.

... (sin 3) - Triple frequency sine function.

... (cos 3) - Triple frequency cosine function.

Day of year (DOY) Continuous predictor (210 to 225).

1 = 1st January 2016.

Temperature (°C) Continuous variables measured with a handheld weather recorder (WeatherHawk: (Relative air) Humidity Environmental SM-28 Skymaster). (%) variables

Sun presence Factor variable w/three levels

(‘Sun’, ‘Some sun’, ‘No sun’).

Other Plant species Factor variable w/9 levels (all species written in black in Table B.2). variables

Quadratic terms Temperature2 Tests for non-linear effects of temperature (°C).

Humidity2 Tests for non-linear effects of relative air humidity (%).

Statistical Daily oscillation (cos 1 + sine 1) was tested for interactions with ‘Temperature’, interactions ‘Humidity’, ‘Sun presence’, DOY and ‘Plant species’.

RANDOM EFFECTS

Spatial Plot number Categorical predictor w/2 levels (N, S). variables

Other variables Field worker Factor variable w/5 levels (ID of each field worker)

Sampling event Factor variable w/170 levels (unique ID for each sampling event).

Unexplained variation Factor variable w/397 levels (unique ID for each data point) to account for possible overdispersion.

D. Bumblebee visits

Table D.1. Flower visitation matrix between bumblebees (columns) and plants (rows) at Reinøya, northern Norway from 29th July to 13th August 2017. Numbers indicate visits.

Bombus

Plant species

m m

ascuoru ratorum

onellus ucorum

p p j l soroeenensis

Solidago virgaurea 141 12 17 28 4

Geum rivale 91 53 0 0 0

Comarum palustre 0 9 20 0 0

Trifolium repens 7 0 0 3 0

Galeopsis tetrahit 2 2 0 0 0

Campanula rotundifolia 0 0 0 0 6

Geranium sylvaticum 5 0 0 0 0

Allium schoenoprasum 3 0 0 0 0

E. Nocturnal pollination

During field work between 29th July and 13th August 2017, a field assistant and I observed two species of bumblebees visiting an ornamental woody plant close to Plot N and S during night (Figure E.1b). The plant was probably of the species Syringa josikaea (family Oleaceae). It had typical features to suggest nocturnal pollination: strong smell during night, numerous inflorescences and the individual flower was light in colour and elongated (Figure E.1a). The most frequent visitor was Bombus pascuorum, but B. hortorum was also observed.

Figure E.1 a) Syringa josikaea in the garden of Lunde farm at Reinøya, b) Bombus pascuorum visiting Syringa josikaea in the middle of the night.