Pollinator Diversity As a Buffer for Pollinator Disease

POLLINATOR DIVERSITY AS A BUFFER FOR POLLINATOR DISEASE

Word count: 26,035

Tina Tuerlings Student number: 01306614

Promotors: Dr. Ivan Meeus, Prof. Dr. ir. Guy Smagghe Tutors: ir. Niels Piot, Matti Pisman

A dissertation submitted to Ghent University in partial fulfilment of the requirements for the degree of Master of Science in Bioscience Engineering: Cell and Gene Biotechnology

Academic year: 2017 – 2018

PREFACE

There are many people that played a part in making this thesis possible. First of all, and mostly, I would like to thank my tutor Niels Piot, for having a lot of patience and being a great mentor, both in the lab and with writing this thesis. From the beginning, when giving me lab training, until the end, when I was sometimes struggling with keeping an overview during writing, you were always happy to help as fast as you could. You fueled my passion for bees even more! Also a great appreciation for Ivan Meeus, who put a lot of time in guiding me through interpretation, analysis and formulation of the results, and for Matti Pisman, for helping me and giving lots of feedback. I also want to thank prof. Guy Smagghe for giving me the opportunity to do this thesis. Lastly, I would like to thank my friends and family for the eternal support, and always inform (and sometimes overwhelm) me about bee news and facts. Above all, I want to thank Jonas Vandicke for always being by my side, through all ups-and-downs of this process, and supporting me in everything. After a year of doing research on bees, I have learned an incredible amount about these little creatures. What will stay with me the most after this research is how complex the interaction of bees with the environment is, how much more we can learn about them but also how much we can learn fróm them.

“Human beings have fabricated the illusion that in the 21st century they have the technological prowess to be independent of nature. Bees underline the reality that we are more, not less, dependent on nature’s services in a world of close to 7 billion people” - Achim Steiner, Executive Director UN Environment Programme (UNEP)

TABLE OF CONTENTS Abstract ...... 1 Samenvatting ...... 2 Literature study ...... 3 1. Introduction ...... 3 2. Pollinator diversity ...... 5 Importance of pollinator diversity ...... 5 3. Pollinator pathogens ...... 7 3.1. Apidae pollinator pathogens ...... 7 3.2. Pathogens in bumble bees ...... 7 3.3. Transmission ...... 12 3.4. Multi-host pathogens and multi-pathogen hosts ...... 14 4. Biodiversity and disease ...... 17 Scope ...... 21 Material and methods ...... 22 1. Sampling ...... 22 Characterized locations ...... 22 Landscape analysis ...... 23 Species determination ...... 23 2. DNA extraction ...... 23 3. PCR ...... 24 4. qPCR ...... 25 5. Sequencing ...... 26 DNA purification ...... 27 6. Statistical analysis ...... 27 7. Diversity indices ...... 28 Results ...... 29 1. Introduction ...... 29 2. Sampling overview ...... 29 3. Pathogen screening ...... 29 3.1. Pathogen prevalence in species ...... 29 3.2. Pathogen prevalence in apple orchards ...... 32

3.3. Focusing on pathogen screening in Bombus sp...... 33 3.4. Genotyping ...... 34 3.5. Pathogen load ...... 35 4. Species diversity ...... 44 4.1. Four diversity indices ...... 44 4.2. Pathogen prevalence and diversity ...... 46 Discussion ...... 53 1. Species overview ...... 53 2. Pathogen prevalence ...... 54 3. Semi-natural habitat and effect of diversity ...... 55 Conclusion and future perspective ...... 58 References ...... 59 Addendum ...... 69 1. PCR master mixes...... 69 2. Sampling overview ...... 71 2.1. Species distribution ...... 71 2.2. Location distribution ...... 72

ABSTRACT Pathogens and their relationships with their hosts have been studied extensively, also in bees. The impact of diseases and the transmission of pathogens is a widely discussed topic, and the complex web of multi-host pathogens and multi-pathogen hosts has been acknowledged in many different disciplines. A lot of pathogens are able to infect multiple host species, but do not necessarily have the same virulence in all host species. Pathogens can have devastating effects in one species, and only subtle effects in the other. The interaction with the host partly determines pathogen transmission, the interactions with all host species together determine disease prevalence. In this way, the presence of a host species can influence the virulence in another host species, in a negative or positive way. The ‘decoy effect’ has been described in many host-pathogen relationships, i.e. the effect of increased host diversity resulting in a reduction of the pathogen prevalence by ‘decoying’ or distracting the pathogen to other host species. In insect pollinators, more specifically bees, this effect has not yet been investigated. Bee decline is a hot topic, and many causes have been assigned to this ecological and agricultural problem. Diseases have been recognized as a major driver, but still more research is needed to fully explain pathogen dynamics. Different host types are defined, and each of them are thought to have a special functionality within the multi-host system, with spreaders being hosts that have a high pathogen prevalence and infection load, while dead-end hosts do not spread the disease. The latter might be important for blocking further spread of the pathogen , while the former is a key-host for the pathogen to survive in the environment. In this thesis, the dynamics of pathogens in a multi-host network were assessed in order to explore the way host pollinator diversity influences pathogen prevalence and transmission. To investigate the effect of diversity, 8 apple orchards were sampled, between the tree rows, for a fixed amount of time. Because mostly bumble bees were sampled, the focus of this study was on the most important bumble bee pathogens, namely Apicystis bombi, Crithidia spp. and Nosema spp. Apicystis bombi was defined as a generalist, infecting different species and genera, while Crithidia spp. and Nosema spp. are specialists. Relationships with their hosts were determined by linking the pathogen prevalence of these three genera with the infection intensities in each host species. The genotypes were identified to be able to determine the exact pathogen-host relationships. Bombus pratorum was chosen as a focal species for a thorough analysis of the effect of diversity on pathogen prevalence, because of its abundant presence at all locations. Different models were tested, where each time pathogen prevalence was set as the response variable. All tested models indicated a positive effect of diversity on pathogen prevalence, but it was not always significant. Our results suggest that this bumble bee species acts as a spreader of A. bombi, cancelling out possible decoy effects of other host species. Some alterations to the experimental set up were proposed to be able to better assess the decoy effect in the future, but this thesis already gives a better understanding of possible multi-host-pathogen dynamics affecting disease spreading.

SAMENVATTING De interactie tussen pathogenen en hun gastheer wordt al lang bestudeerd, in vele organismen, waaronder ook pollinatoren. Het effect van ziektes en de verspreiding ervan is een belangrijk onderdeel van vele studies, en ook complexere interacties worden erkend, zoals pathogenen die meerdere gastheren hebben en omgekeerd. Ook is geweten dat pathogenen verschillende relaties kunnen hebben met verschillende gastheren, in de zin dat de gastheer een variërende virulentie kan ondergaan en de verspreiding ook anders verloopt. Op deze manier bepaalt de aanwezigheid van alle gastheersoorten samen hoe de ziekte zal beklijven. Het ‘lokaas effect’ of ‘decoy’ effect wordt gedefinieerd als het effect van diversiteit van de gastheer op ziekteprevalentie, waarbij een verhoogde diversiteit ervoor zorgt dat de ziekte zich minder goed kan verspreiden. In bijen werd dit effect nog niet bewezen. De achteruitgang van bijen is veelbesproken, door de directe invloed op de landbouw- en voedingssector, maar ook het ecologische aspect. Verschillende oorzaken zijn al blootgelegd, waaronder ook verhoogde ziekteverspreiding, maar hierover is er nog veel te ontdekken. In deze thesis werd daarom de focus gelegd op pathogeen dynamiek in een diversiteit van gastheren, waarbij het effect van diversiteit op pathogeen prevalentie werd nagegaan. Hiervoor werden bijen gevangen in appel boomgaarden op 8 verschillende locaties. De meerderheid van de gevangen bijen waren hommels, en de meeste hommels behoorden tot de soort Bombus pratorum. Daarom werd de focus vooral gelegd op deze hommelsoort en de 3 meest voorkomende microparasieten van hommels; Apicystis bombi, Crithidia spp. and Nosema spp. Van deze 3 genera werd de aanwezigheid, prevalentie en infectie-intensiteit bepaald, om zo een beeld te creëren van de impact van de ziektes op alle bijensoorten. Hieruit konden verschillende zaken opgemaakt worden. Apicystis bombi wordt gezien als een generalist, omdat deze pathogeen veel verschillende bijensoorten en -geslachten infecteert, terwijl Nosema spp. en Crithidia spp. meer specialisten zijn, die zich beperken tot een aantal soorten. Verschillende types van gastheren werden gedefinieerd op basis van de relatie tussen de prevalentie en intensiteit van elke pathogeen in elke gastheersoort. Deze verschillende types hebben elk een rol in het netwerk, waarbij er verspreiders zijn van ziektes en ook soorten die de verspreiding net tegengaan. Voor verdere analyse van het effect van diversiteit op de pathogeen prevalentie, werden verschillende modellen uitgetest, waarbij pathogeen prevalentie telkens de responsvariabele was. De pathogeen prevalentie van Bombus pratorum werd apart geanalyseerd, zodat er een betere vergelijking gemaakt kon worden tussen de verschillende locaties, aangezien deze soort op elke locatie aanwezig was, en meestal ook talrijk. Alle modellen suggereren dat een verhoogde bijendiversiteit ook een verhoogde pathogeen prevalentie teweegbrengt. Verschillende verklaringen werden onderzocht, en wat het meest opvalt is dat Bombus pratorum een verspreider lijkt te zijn van A. bombi, wat het ‘lokaas’ effect zou kunnen opheffen. Enkele aanpassingen aan de experimentele set-up werden voorgesteld, om dit diversiteitseffect beter te kunnen onderzoeken in de toekomst, maar deze thesis geeft alvast een beter inzicht in de mogelijke manieren dat de dynamiek van multi-gastheer pathogenen ziekteverspreiding kan beïnvloeden.

LITERATURE STUDY 1. Introduction Pollination, which is the vegetal way of fertilization, often has to be assisted by pollinators. Pollinators are mainly insects, but can also be birds and even bats. In this way, insects play a key role in the sexual reproduction of many plant species, including crops for human nutrition. About one third of our worldwide crops are dependent on pollination, and even 84% of the 264 agricultural crops in Europe are dependent on insects for pollination (Klein et al., 2007).

Of all pollinating insects, bees play the lead in the story of pollination. Honey bees, bumble bees and solitary bees all have an important contribution in pollinating plants. The European honey bee Apis mellifera is used as a commercial pollinator throughout the world, as well as bumble bees like Bombus terrestris. But also wild bees are needed to provide efficient pollination services (Kremen et al., 2002). Having a diverse pollinating network is important for sustaining the growing demand for food. It is becoming more and more clear that replacing wild bee services with commercial bees is not the solution (Klein et al., 2007). The combination of pollination by honey bees and wild bees is often complimentary, and is also often positively correlated with efficient pollination (Greenleaf & Kremen, 2006). At last, having a more diverse pollinator community also means having a better safety net when honey bee populations fluctuate (Neumann & Carreck, 2010).

Pollinator decline has been a hot topic throughout the world, since food security is threatened by this decline. There is a distinctly slower growth in yield of pollinator-dependent crops in comparison with pollinator-independent crops starting from the 1960s (Brittain et al., 2013). Honey bee colony losses have received a lot of attention the past years and even decades, but many populations of pollinators are declining, of which solitary bees and bumble bees are no exception. Bumble bee decline has been well documented, but there are no clear insights of wild bee decline. A quarter of the 68 European bumble bee species are endangered (Maebe et al., 2016) and in the UK, 8 out of 25 species of bumble bees have decreased since 1940, and a few species have gone extinct (Goulson et al., 2008). In North America, some species have decreased severely in number, such as Bombus terricola and Bombus occidentalis, and some are even believed to be extinct, like Bombus franklini (Dave Goulson et al., 2015a). Moreover, 11% of the wild bumble bee species worldwide are potentially under threat (Murray et al., 2013).

This decline cannot simply be explained by one phenomenon, it is more of a multifactorial problem. A major factor contributing to pollinator decline is the intensification of agriculture, which causes a decrease in flower biodiversity and habitats for bees. The active period of pollinators is overall longer than the flowering period of the crops they pollinate (Senapathi et al., 2015). Because of this, additional food resources will be required to sustain pollinator populations in agricultural areas when crops are not blooming. As these food resources are getting scarcer in homogeneous landscapes due to the loss of wild flowers, the pollinator community will suffer. Monotone landscapes also lead to habitat degradation and fragmentation (Williams et al., 2009).

The use of pesticides is another factor which contributes to bee decline. Broad spectrum insecticides can have a negative effect on non-target insects like pollinators. Pyrethroids, which are commonly used commercial household insecticides, have severe effects on honey bees, bumble bees and solitary bees (Gill et al, 2012). Also neonicotinoid insecticides, which are widely used, systemic agricultural insecticides, have been proven to have lethal and sublethal effects on bees (Van der Sluijs et al., 2013, Blacquière et al., 2012).

All the drivers mentioned above, which all have an anthropogenic origin, have a direct impact on bee decline, and can be seen in figure 1, which gives an overview of the most important bee decline drivers. Yet these drivers can also affect natural dynamics, causing an indirect effect on bee decline. One example is the increased competition between commercial pollinators and native bees through increased commercialization (Dave Goulson et al., 2015a). Another example, which has been well documented, is the disturbance of the host-pathogen equilibrium, increasing the impact of pathogens on the bee populations and making them a driver of bee decline with growing importance (Meeus et al., 2018). Stressors like exposure to pesticides or decreased availability of food resources can affect immune responses, making bees more susceptible to parasites (Park et al., 2015). These interactions can also be seen in figure 1. Another example is the link between intensification and the burden of pesticides, for example when limited resources cause bees to forage more on crops treated with pesticides.

Figure 1: An overview of the contribution and interactions of different drivers of bee decline. Red arrows show 4 major drivers of bee decline, while the blue arrows indicate interaction between the drivers. Neonicotinoids and pyrethroids are the best known pesticides that negatively affect the well-being of pollinators. (Dave Goulson et al., 2015) The effect of these different drivers on pollinators can be very diverse, as bee pollinators are very diverse, with different lifecycles, lifestyle, morphology and many more. Therefore the mentioned drivers will affect different bee species in a different extent. Certain species, which are more sensitive

4 and/or already struggling, might perish sooner compared to others, decreasing the diversity of the pollinator community. This loss of diversity can have a negative impact on pollination services (Blitzer et al., 2016). But changes in diversity can also have an impact on pathogen infection and transmission, which is already tackled in studies like (Dobson, 2004) and (Levine et al., 2017), and can also be applied to the pollinator community. In this way, in figure 1, a red arrow in the opposite direction between bees and pathogens is missing. The importance of pollinator diversity will be explained below (section 2), after which the most important pathogens in bees, and more specifically in bumble bees, will be described (section 3). Concepts like pathogen transmission and multi-host systems will be tackled, to finally address a possible relationship between biodiversity and disease development (section 4). 2. Pollinator diversity While pollinator abundance is important, pollinator biodiversity must also be ensured for the wellbeing of the pollinators and for efficient pollination. Honey bees are widely known pollinators, however more than 20 000 wild bee species are already identified, of which 90% is solitary, and about 250 are bumble bee species (Cameron et al., 2007, Goulson et al., 2008). Bees are part of the insect order Hymenoptera, which also contains wasps, ants and sawflies. Within this order, bees and sphecoid wasps are divided into the superfamily Apoidea. Apoidea can be subdivided into Spheciformes (sphecoid wasps) and Apiformes (bees). One large genus which is not part of this family is Andrena, belonging to the family Andrenidae, also called mining bees, and are ground-nesting bees. Apidae can generally be divided into the genera Apis (honey bees) and Bombus (bumble bees), but also other bees like stingless bees (Meliponidae), cuckoo bees (Nomadinae) and other less known groups (Michener, 2007). In Belgium, more than 380 bee species are known, of which about 330 are solitary bees and 30 bumble bee species (observed in 1990-1992, Rasmont et al., 1993 ). In this thesis, the focus is on bumble bees. Bumble bees, compared to other bees, can withstand colder weather better, which is why they are abundant in the northern temperate zone, especially high-elevation grassland habitats, but are found in a variety of habitats. Bumble bees are known for their hairy appearance, and bright color patterns. The 250 identified Bombus species are divided into 38 subgenera (Cameron et al., 2007). Bombus species like B. pascuorum, B. hortorum, B. pratorum and B. lapidarius are widespread in Belgium (Maebe et al., 2016).

Importance of pollinator diversity The impact of wild bees and bumble bees for crop pollination and ecosystem functioning was underestimated for a long time. A diverse community of bees, and not only one species such as honey bees, is important for efficient pollination (Blitzer et al., 2016). In a natural ecosystem, many different bee species pollinate a diverse variety of wild flowers. Different bee species are more specialized in pollinating different flowers and crops, depending on their own characteristics like size, tongue length, and flower architecture (such as flower size, depth and width, color, nectar composition). For instance, long-tongued pollinators will acquire nectar more efficiently from plants with long nectar spurs, compared to short-tongued pollinators (Fenster et al., 2004, Whittall & Hodges, 2007). Another example is the ability of bumble bees to do buzz pollination. This means that they move their flight

5 muscles without actually flying, which is used to keep them warm, and to shake loose pollen. This ability is used for pollination of tomatoes in greenhouses, where Bombus terrestris bumble bees replace the function of wind (Velthuis & van Doorn, 2006). Also many other bees use this technique to harvest pollen (Proenca, 2008), like the solitary bees of the genus Andrena (Cardinal, Buchmann, & Russell, 2018).

Because of these specializations, pollinators can complement each other in their services (Graystock, Goulson, & Hughes, 2015). When pollination of crops by wild bees is insufficient, honey bee pollination can act as a supplemented service, but not as a substitution. Wild bees can have a positive influence on pollination efficiency of honey bees (Garibaldi et al., 2014). Increased pollinator diversity can mean that crop flowers will have a greater chance of being visited. In apple orchards, wild bees could enhance fruit set compared to managed honey bees only, or could even completely substitute the pollination service of honey bees (Blitzer et al., 2016).

Not only complementarity but also interspecific interactions between different pollinators can increase efficiency. Honey bees seem to focus more on either female or male flowers of monoecious flowers, which makes the transfer of pollen from male to female flowers more difficult. For example, sunflowers are often planted in alternate rows of male and female cultivars, which means that bees need to change rows in order to successfully pollinate the flowers. Pollination is done more efficiently when other bees are interacting with the honey bees, because honey bees will move more from one row to another when encountering more wild bee species. Honey bees and wild bees also seem to be working together, for example when honey bees redistribute big lumps of pollen left on flowers by wild bees (Greenleaf & Kremen, 2006). In the study of Brittain et al., (2013), this phenomenon of functional synergy is tested in Californian almond orchards, and in a cage experiment with Osmia lignaria and Apis mellifera. Interactions between honey bees and wild bees can alter the behavior of honey bees, which are the main pollinators of almond orchards. As with the example of sunflowers, pollinator movement to compatible crops is important, since often pollen from the same variety do not set fruit. In this experiment, less direct interactions between honey bees and wild bees were observed, but alternative, indirect mechanisms like resource depletion and scent mark can make honey bees change rows. The former means that non-Apis bees might have depleted flowers before honey bees were able to visit them, making Apis mellifera increase their foraging range in the search of nectar and pollen. The latter are marks left by other bees on flowers, which a honey bee will try to avoid. The interaction of pollinators is not simple to describe, and is complex web rather than a linear correlation.

Not only is pollinator diversity important for efficient pollination, depending on a single species for pollinating all crops worldwide can be a great risk. Using one pollinator species, the European honey bee Apis mellifera, creates a genetic uniformity. When a disease develops against this species, for example when a pathogen develops a specialized virulence against this pollinator, the whole species is threatened. This is already partly proven by the impact of colony collapse disorder (CCD) or the Varroa mite, causing severe declines in honey bees (Winfree, 2008, Goulson et al., 2015a).

3. Pollinator pathogens 3.1. Apidae pollinator pathogens A lot of microorganisms live within and on bees: bacteria, fungi, viruses, protozoa, etc. Many different pathogens are being studied in honey bees as well as bumble bees and solitary bees (Ravoet et al., 2014). While the term pathogen describes a biological agent that can cause a disease to its host, a parasite emphasizes the relationship of the organism with its host, and is adapted to the host’s life (Poulin & Morand, 2018). Here, pathogens will be used as a general term of all organisms infecting pollinators. A lot of research has been done on viruses. The best known virus families infecting the Apidae pollinator community are Dicistroviridae, such as Israeli Acute Paralysis Virus (IAPV), Acute Bee Paralysis Virus (ABPV), Kashmir Bee Virus (KBV) and Black Queen Cell Virus (BQCV), and Iflaviridae, such as Deformed Wing Virus (DWV), Sacbrood Virus (SBV) and Chronic Bee Paralysis Virus (CBPV) (Bailey & Woods, 1974, Bailey et al., 1963, Forfert et al., 2015, Bailey et al., 1983, Mcmahon et al., 2015). Non-viral pathogens can be divided into macroparasites and microparasites based upon their size. The former consists of mites (Acari), like Varroa destructor, Varroa jacobsoni, Locustacarus buchneri and Acarapis woodi (M C Otterstatter & Whidden, 2004), and nematodes like Sphaerularia bombi. The latter consists of protozoa and fungi. Examples of protozoa are euglenozoa like Crithidia bombi (Schwarz et al., 2015), or Apicomplexa of which Apicystis bombi is the best known. Microsporidia are obligate parasitary intracellular fungi, of which Nosema spp. are a great threat to Apidae pollinators. As in this thesis the focus is mainly on bumble bees, pathogens and the impact specifically associated with bumble bees are described below. 3.2. Pathogens in bumble bees Bumble bees are used as commercial pollinators, and are mass-produced and distributed throughout the world, mostly for greenhouses. But bumble bees are also essential wild pollinators in temperate ecosystems (Cameron et al., 2011). As mentioned in the introduction, bumble bees also suffer from great losses, especially in recent decades. Even though habitat loss is seen as the major driver of bumble bee decline, invasive and emergent parasites may be a big factor, now and in the future (Meeus et al., 2011). The commercialization of bumble bees is also one of the main suspects. Commercial bumble bees can threaten wild species through competition of resources, hybridization with local species, and by spreading and amplifying parasites (Whitehorn et al., 2013).

The lifecycle of bumble bees differs in many ways from other pollinators, and pathogens can affect bumble bees at different time points in its lifecycle, displaying differential virulence depending on the life stage of the host (Brown & Schmid-hempel, 2003). Most bumble bee colonies have a yearly cycle, which is shown in figure 2 (P. Schmid-Hempel, 2001). Only the bumble bee queen hibernates after mating in late summer (1), and emerges in spring, after which she forages for nectar and pollen, as food and to help her ovaries develop. Then she starts a new colony (2), by laying eggs after finding a good nesting place, and collects nectar to produce the heat needed to brood the eggs (Durrer & Schmid-Hempel, 1994). In mid-summer, after the colony has considerably increased in worker number (3 and 4), the queen produces only males (unfertilized eggs) and daughter queens (fertilized

7 eggs) (5). These can then mate (6) and the new queens enter diapause for a new hibernation period (Brown & Schmid-hempel, 2003 and figure 2).

Figure 2: A pathogen can intervene at different time points in a bumble bee colony life. It can affect hibernation survival (1), colony foundation (2), early colony growth (3), worker mortality (4), reproduction (5), and hibernation preparations (6).(P. Schmid- Hempel, 2001) Many different parasites infect bumble bees: viruses, nematodes such as Sphaerularia bombi, and mites such as the tracheal mite Locustacarus buchneri. The best known unicellular eukaryotes (also called Protozoa) infecting bumble bees are Crithidia bombi and Apicystis bombi, and the fungi Nosema spp. As this thesis focuses on these three pathogens they are described in more detail below.

Microparasites, like Crithidia bombi, Nosema spp. and Apicystis bombi, are a great threat to native bumble bee populations. Crithidia bombi is a trypanosome flagellate of the phylum Euglenozoa, while Apicystis bombi is a neogregarine (parasitic alveolate) of the phylum Apicomplexa, and Nosema spp. is a microsporidian (Plischuk et al., 2017)

Crithidia spp. Crithidia spp. is a genus of trypanosome microparasites. Trypanosomatidae are kinetoplastic protoza with one flagellum (see figure 3c). Kinetoplasts are structures of circular DNA with a high number of copies of the mitochondrial DNA (Shapiro & Englund, 1995). Some members of the Trypanosomatidae cause human diseases like the African sleeping sickness, Chagas disease and Leishmaniasis (Simpson et al., 2006). But Trypanosomatidae also cause many diseases in insects, mainly spread through feces, and develop in the intestinal tract where they pierce the epithelium with their flagellum (Boulanger et al., 2001). Trypanosomatids are known to infect honey bees, such as Crithidia mellificae, which was already described 50 years ago (Langridge & McGhee, 1967), and Lotmaria passim, only recently described (Schwarz et al., 2015). A similar story can be told about trypanosomatids in bumble bees (Ravoet et al., 2015).

Crithidia bombi was described 30 years ago (Lipa & Triggiani, 1988), while Crithidia expoeki was identified as a distinct lineage of C. bombi less than a decade ago (Schmid-Hempel & Tognazzo, 2010). At first, the effects of C. bombi were described as subtle. Effects are seen in workers carrying less pollen, lower colony growth, smaller ovaries and fat bodies and lower reproductive output, and a slightly increased mortality rate (Meeus et al., 2011, Imhoof & Schmid-Hempel, 1998, Colla et al., 2006). But C. bombi is known as a great threat to bumble bees, especially because of a high rate of horizontal transmission (Imhoof & Schmid-Hempel, 1999). A study by Brown et al. (2003) even suggests that Crithidia bombi castrates Bombus terrestris and strongly reduces fitness, especially during stressful times during the life cycle of its host.

Crithidia bombi was initially found in Bombus terrestris (Lipa & Triggiani, 1988), but is now known to infect many Bombus species, like B. impatiens (Colla et al., 2006), B. pratorum (Rutrecht & Brown, 2008), but also Andrena spp. like Andrena vaga and Osmia bicornis (Ravoet et al., 2014). C. bombi is unable to infect honey bees (Graystock et al., 2015). Crithidia bombi can infect up to 80% of foraging workers (Shykoff & Schmid-Hempel, 1991).

Figure 3 gives an overview of the effect of Crithidia bombi during the entire lifecycle of a bumble bee colony as shown in figure 2. Crithidia bombi’s life-cycle is simple, cells attach to the gut walls of bumble bees where it multiplies and eventually sheds infective propagules via the feces (Schmid-Hempel, 2001). In general, the survival of queens during hibernation is not affected by C. bombi (figure 2, 1). Effects mainly occur when workers are stressed, which can explain why C. bombi has the highest impact early in the season (P. Schmid-Hempel, 2001). The trypanosome seems to mainly affect emerging queens after hibernation (Rutrecht and Brown, 2008). Colony founding success (figure 2, 2) and early colony growth (figure 2, 3) can be reduced by infection, and workers can be affected by reduced foraging ability or even reducing lifespan (figure 2, 4). Reproduction can be delayed (figure 2, 5), and at last, the queen can be disturbed during hibernation preparations (figure 2, 6) (Schmid- Hempel, 2001, Sadd & Barribeau, 2013).

Figure 3: (a) shows the annual life cycle of Bombus terrestris infected with Crithidia bombi, (b) shows an example of Bombus terrestris, and (c) Transmission electronic microscope image of Crithidia bombi (Sadd & Barribeau, 2013)

Nosema sp. Microsporidians like Nosema spp. are obligatory intracellular parasites, with insects as a major host group. The spore stage is the infective stage, when the host takes up spores they infect cells in the gut. During the vegetative stage, the parasite spreads within the hosts, and new spores are released via feces (Otti & Schmid-Hempel, 2007). Parasites of Nosema spp. have been known as a disease of the adult honey bee, before it was also discovered in bumble bees. Parasites of the genus Nosema are known to be one of the most prevalent diseases in adult honey bee (Chen et al., 2008). One example is Nosema apis (Zander, 1909) which is a parasite developing in the intestines, and infection happens mainly through a fecal-oral passage (Van den Eijnde & Vette, 1993). Nosema ceranae (Fries et al., 1996) was first known to only infect the Asian honey bee Apis cerana, but spread through European honey bees worldwide. It was probably infecting honey bees long before identification, partly contributing to the big honey bee losses (Chen et al., 2008). It is probably more spread through an oral-oral passage (Smith, 2012). Nosema ceranae is now considered to be an emerging pathogen in bumble bees (Graystock et al., 2013). A third Nosema species threatening pollinators is the microsporidian Nosema bombi, which plays a big role in the decline of bumble bees (Otti & Schmid-Hempel, 2008). This obligate intracelllular parasite was identified and described more than a century ago by Fantham and Porter (1914). Since then, numerous studies have investigated the effects and impact on bumble bees, like Imhoof & Schmid-Hempel (1999) and Otti & Schmid-Hempel (2007) and a general finding is that the effects are variable, but severe. In a lab experiment of Imhoof & Schmid-Hempel (1999) with B. terrestris, colonies infected with N. bombi reproduced more than those free of infection, leading to a higher fitness, and queen production was unaffected. Both in the field and in laboratory experiments, the studies of Otti & Schmid-Hempel (2007 and 2008) found a destructive impact of infection on colonies, suggesting Nosema bombi to be a much greater danger to bumble bees than Crithidia bombi. In 10

Schmid-Hempel & Loosli (1998) genotype-genotype interactions were suggested, because infection and mortality rate differed between colonies and species, the latter suggesting host-specific interactions as well. Prevalence also varies much between field studies, from 2.4% and 12.4% in Argentina (Plischuk et al., 2017), to 53.2% of colonies reared from hibernated queens collected in spring in Switzerland (B. Imhoof & Schmid-Hempel, 1999), to only 9% of bumble bees captured from the field in Switzerland (Shykoff & Schmid-Hempel, 1991). P. Schmid-Hempel & Loosli (1998) suggest that the variability in prevalence could also be the result of variability of environmental conditions like temperature, or because of variable conditions of the host. Differences in prevalence can also be assigned to the experimental set up of the studies. Nosema bombi has been found in many Bombus species, like B. lapidarius and B. hypnorum, besides the well-known host B. terrestris (McIvor & Malone, 1995), but also B. pascuorum and B. hortorum (Shykoff & Schmid-Hempel, 1991). Nosema bombi cannot infect honey bees, while N. apis cannot infect bumble bees, and B. ceranae is capable of infecting both hosts (Graystock et al., 2015). A last Nosema species which has recently been identified in bees and especially many bumble bees, is Nosema thomsoni, known to be a parasite of moths (Wilson & Burke, 1971). Andrena vaga (Ravoet et al., 2014), 12 Bombus species from China (Li et al., 2012) and Andrena haemorrhoa (Schoonvaere et al., 2018) were already found positive for this pathogen. Information about infection in bees and impact is barely known. It is possible other Nosema species exist but are not identified yet. A microsporidian was found in B. pratorum, which resembled N. bombi, but had aberrant cytology, suggesting a new species (Larsson, 2011). Again, the impact on bumble bee colonies can be illustrated with the use of figure 2. In general, Nosema bombi infection of a colony happens mainly through an infected queen after hibernation (P. Schmid-Hempel & Loosli, 1998), but queen production itself is unaffected (B. Imhoof & Schmid- Hempel, 1999) (figure 2, 6). Nosema bombi doesn’t have a big impact on nest initiation and on laying eggs (figure 2, 2) (Otti & Schmid-Hempel, 2008), but it does affect brood (P. Schmid-Hempel & Loosli, 1998). Infected colonies might produce much less workers (figure 2, 3 and 4). Reproductive rate is mainly not affected, but males live half as long (figure 2, 5 and 6). Other studies suggest queens being prevented to mate by paralysis of the abdomen (figure 2, 5) (Rutrecht & Brown, 2009, Otti & Schmid- Hempel, 2007). Apicystis bombi The genus Apicystis belongs to the order Neogregarinorida and the phylum of Apicomplexa. Neogregarines are parasitic alveolates, having cortical sacs. Apicystis bombi was firstly recorded and described in Apis mellifera, Bombus terrestris and Bombus hortorum (J. J. Lipa & Triggiani, 1992, Lipa & Triggiani, 1996) and has now been detected in many Bombus species already (Maharramov et al., 2013), like B. pratorum (Rutrecht and Brown, 2008). It has also been found in bees from the genera Osmia, Heriades and Andrena. Apicystis bombi is still known to infect both bumble bees and honey bees (Graystock et al., 2015). Infected pollinators can be negatively influenced physically or socially, by disturbance and degradation of adipose tissue, or by disturbed communication (Plischuk et al., 2011).

Effects on bumble bee colonies can be described through figure 2, although the effect of the pathogen on the whole life cycle is not known yet. Bees get infected by ingesting oocysts, which then turn into sporozoites, and penetrate through the midgut wall to infect fat body cells (Lipa & Triggiani, 1996). Proliferation destroys the cells, causing high mortality especially in infected spring queens (figure 2, 1) (Maharramov et al., 2013), as has been noted in B. pratorum queens (Rutrecht & Brown, 2008, Murray et al., 2013). Infection reduces the chance of surviving hibernation because of the effect on body fat (Graystock et al., 2016), which means that less queens will be starting a colony (figure 2,2).

3.3. Transmission Efficient host transmission is a key factor for a parasite to thrive. Social insects often live in a high density colony, having low genetic variability, giving a higher risk of pathogen transmission (Folly, Koch, Stevenson, & Brown, 2017). Infection risk is generally even higher in bumble bees because bumble bee colonies are founded by a single queen (Fouks & Lattorff, 2011). Transmission routes depend on the polyethism (functional specialization) for social insects, which is age-dependent for pollinators such as bumble bees (Schmid-Hempel, 1993). Passing infection can happen venereal, through food or feces (Folly et al., 2017), or indirect transmission through a second ward. Transmission of parasites can be vertical or horizontal and can occur within or between populations.

Vertical transmission transfers the pathogen to next generations, and happens from queen to larvae and eggs (Ravoet et al., 2015, van Frankenhuyzen et al., 2007). Vertical transmission happens in viruses and in the fungus Nosema sp. (Ravoet et al., 2015, van Frankenhuyzen et al., 2007). Nosema bombi transmission is believed to be slow, because it probably occurs mainly when pollen or nectar contaminated with spores are fed to larvae, or via eggs (Van den Eijnde & Vette, 1993, Meeus et al., 2011, Murray et al., 2013, Rutrecht & Brown, 2009).

Horizontal transmission happens in the same generation, within or between colonies. This has been demonstrated for Crithidia bombi in the study of Durrer & Schmid-Hempel (1994), when healthy colonies from the lab were put into the field and became infected quickly, which can only be explained through horizontal transmission. In this way, C. bombi can be rapidly transmitted through horizontal adult-adult contact ( Meeus et al., 2011).

There are many ways of horizontal transmission. One of the most common ways is through shared use of flowers (Whitehorn et al., 2013), when infected pollinators deposit infective cells on the flower after pollination or via feces. Other pollinators pick up those cells by visiting the same flower. This was experimentally proven in the study of Durrer & Schmid-Hempel (1994) for C. bombi. Bumble bees even seem to avoid flowers with high doses of parasites, which is adaptive behavior to reduce uptake of pathogens (Fouks & Lattorff, 2011). It could be that bees taste the nectar before landing on the floral, and would detect chemical changes because of pathogen presence (McArt et al., 2014). Nosema bombi is less spread through shared use of flowers, explaining why it is probably spread less rapid than C. bombi (Murray et al., 2013). Transmission of pathogens via flowers can happen through pollen, by sticking to the bee cuticle, after which it can be rubbed off onto surfaces like nests

12 structures or other flowers, and in this way it can be further spread within colonies and between colonies or even species (Graystock et al., 2015).

Shared use of flowers is also the main route for pathogens to switch to different colonies, species or even genera (Durrer & Schmid-Hempel, 1994). Many pollinators are specialized in pollinating certain flowers, because flower size and architecture differs (explained in the chapter ‘Importance of pollinator diversity). Hence, it is known that bumble bee species have different foraging preferences (Ruiz-González et al., 2012). Also pathogen transmission can depend on flower type, since inflorescence complexity influences risk of infection (Durrer & Schmid-Hempel, 1994, Graystock et al., 2015). In this way, pathogen transmission, flower type and pollinator species are correlated.

Pathogen transmission to other colonies can also happen by inter- or intra-specific drifting. Intra- specific drifting is known in A. mellifera, and it is known of bumble bee queens (cuckoo bumble bees) to lay eggs in foreign nests of other bumble bee species, acting like social parasites (Manley et al., 2015).

An important driver contributing to pathogen transmission and using floral resources, is ‘pathogen spillover’, meaning the transmission of pathogens from commercial pollinators bred for crop pollination to wild pollinators (Colla et al., 2006b). While pollinating crops like tomatoes, bumble bees can escape to the environment and transmit pathogens to wild pollinators. Pathogens typically have a higher prevalence in commercial colonies because of higher densities, but can have a more devastating impact in native bumble bee populations (Colla et al., 2006, Graystock et al. , 2013, Otterstatter & Thomson, 2008). In South-America, introduction of Bombus terrestris has led to local extinction of native Bombus dahlbomii, suggesting spillover of pathogens from the commercial bumble bees to local species (Goulson et al., 2015). The effect of spillover depends on the speed of transmission and the degree of virulence in the new host population (Graystock et al., 2013, Meeus et al., 2011). Intraspecific transmission from commercial to wild Bombus sp. populations is frequently described, and seems to be more likely to happen, because of being phylogenetically closer (Ivan Meeus et al., 2011). But spillover can happen between different species or even genera. Honey bees can transmit pathogens to bumble bees, besides wild bees and other honey bees. For example, Nosema ceranae from commercial honey bees can spread to wild bumble bees (Fürst, McMahon, Osborne, Paxton, & Brown, 2014).

It is nearly impossible to rear bumble bee colonies free of disease, since honey bee collected pollen is used for feeding commercial bumble bee colonies. In this way, parasites can easily be transmitted from honey bee colonies to bumble bee colonies (Goulson et al., 2015a, Van den Eijnde & Vette, 1993). Besides, making sure that commercial pollinators are free of pathogens when transported is not the solution, since pathogens can rapidly amplify when they come into contact with high density bumble bees, after which pathogen spillover to wild populations can happen (Whitehorn et al., 2013). This concept is called ‘spillback’ (Graystock et al., 2016).

Direct contact as a way of transmission is also possible. Crithidia spp. is transmitted within colonies through contact networks, between workers but also between daughter queens, which can eventually lead to vertical transmission, to the next generation (B. Imhoof & Schmid-Hempel, 1999). Recent study suggests that C. bombi, and maybe many other pathogens, can be spread intra-colonial through larvae, without the larvae themselves being infected (Folly et al., 2017). This seems likely because workers have a lot of contact with brood during brood care.

3.4. Multi-host pathogens and multi-pathogen hosts Although it is not discussed very often, increasing knowledge about pathogens seems to prove that most interaction with hosts happens through multiple-parasite and multiple-host systems. In practice, interaction of pathogens with hosts is more like a web of multiple influences, rather than simple paired interactions. It is not always straightforward when to call an organism a parasite, and how the interaction between a host can be seen (Rigaud et al., 2010). Figure 4 illustrates how interactions between hosts and parasites can be addressed in general, which can also be applied to bumble bees and its parasites. Most studies look at single parasite and single host species interaction (a), such as studies of pathogen impact on the most widely known bumble bee Bombus terrestris (Folly et al., 2017). Multiple infections in one pollinator species have been studied quite often, like in B. terrestris (d) (Plischuk & Lange, 2009, Graystock et al., 2016), or infection of one parasite, A. bombi, in multiple bumble bee species (c) (Maharramov et al., 2013). Only a few have looked at multiple infections in multiple hosts at the same time (Rutrecht & Brown, 2009, Shykoff & Schmid-Hempel, 1991). The latter is the most representative, especially when different genotypes are also taken into account. Different genotypes of parasites can have different virulence; all depending on the interaction, and on the (geno)type of the host (Rigaud et al., 2010).

Figure 4: Different ways of a host-parasite association; Different host species interacting with many different parasites nematodes, microsporidia and fungi, which all can have different genotypes (shown by the different gray scales, but only shown in part (b) for simplicity). P means pathogen, H means host. (e) gives the most complete picture of a naturally occurring, realistic interaction web (Rigaud et al., 2010).

As figure 4 (d and e) illustrates, having multiple infections in one bee species can be possible. An example is the study of Rutrecht & Brown (2008), where 15% of queens of Bombus pratorum had multiple infections. Having more than one parasite infecting the same host can alter the individual virulence. A second infection can increase competition between the parasites, which can have opposite effects. The first parasite can also try to protect the host against a second infection, as a way of being exclusive, and this can lead to decreased virulence or even mutualism (Rigaud et al., 2010). For example, in a study of (Murray et al., 2013), A. bombi doesn’t infect bumble bees when already infected with Crithidia bombi or Nosema bombi. Also in the study of Rutrecht & Brown (2008), A. bombi did not overlap with other infections.

Pathogens infecting multiple host species (figure 4, c) often have a differential virulence in its host species. This can be because of difference in worker generations or life cycle, having an effect on infection intensity (Rutrecht & Brown, 2009). Multi-host pathogens need to make a compromise between specificity and virulence. They can relatively quickly adjust their virulence and specificity, especially compared to their hosts. In general, being specialized in one host, through co-evolution, means that they will have the highest virulence in that host (Ruiz-González et al., 2012). These pathogens will be more damaging to sympatric hosts, but will have decreased virulence in others (Meeus et al., 2011a, Imhoof & Schmid-Hempel, 1998). Sometimes specializing in one host species isn’t that simple, for example because of fluctuation of the host itself. That is why many pathogens are generalists in a multi-host system. Many parasites of bumble bees are able to infect many different Bombus species, having a different impact on each species, but can sometimes also infect other genera or families, besides bumble bees. In this way, many different kinds of host-types exist, which is explained and depicted in box 1 below.

Box 1: Host types

Figure 5: Four types of hosts infected by a pathogen, in a multi-host system. Black dots show the amount of infection (load), the smiley shows how severe the negative effects of infection are. A sad smiley means apparent negative effects, a neutral smiley means slightly negative effects, and a happy smiley means no negative effects. The impact of a pathogen can depend on how many and which hosts it infects, because different host types exist, undergoing differential virulence, shown in figure 5. A normal pathogen-host relationship is one where the pathogen multiplies inside the natural host (a), after which infective cells can be shed to infect other hosts. This is the case for pathogens like C. bombi infecting many bumble bee species like B. terrestris, where the host will undergo a negative impact. Sometimes a pathogen is able to infect the host but the host is not ideal for the pathogen, e.g. when a new pathogen is introduced into a local environment and is not adapted to the local hosts yet. The host will not undergo a high virulence but will also not spread the pathogen much further because the host environment is not ideal, thus acting as a pathogen sink or a dead-end host (b). When the pathogen cannot infect the host at all, the only reason the pathogen is detected in the host is because of an accidental pick-up. These incompatible hosts (c) can play a role in the transmission, by being a link in the complex interaction web of pollinators, pathogens and flowers, this as an external vector. Spreaders (d) are hosts that do not undergo a very destructive change due to infection, but have an important role in transmitting the pathogens to other species. This phenomenon is called ‘super-spreaders’ in the case of human diseases, when individuals spread pathogens much more than others (Yates et al., 2006). ‘Super- spreaders’ can here be seen as hosts that immensely amplify the disease. In the flower network explained in the chapter ‘Transmission’, this can be of great importance, redistributing infective cells to susceptible hosts, but also to incompatible hosts, which can then further spread the pathogen through other flowers or through direct contact.

The pathogens infecting bumble bees, described in the chapter ‘Pathogens in bumble bees’, are all capable of infecting many Bombus species. Nosema bombi for example, can be found in many bumble bee species. Recently, Nosema ceranae, a pathogen typically infecting the Asian honey bee, has been found infecting Western honey bee. Nosema ceranae can also infect bumble bees, as proven by (Graystock et al., 2013). But on genus level, their infection range is quite different. Nosema bombi is known to only infect bumble bees, while N. ceranae mainly infects honey bees but is an emergent pathogen to bumble bees (Graystock et al., 2015). Nosema thomsoni can also infect Andrena vaga (Graystock et al., 2015) and Andrena haemorrhoa (Schoonvaere et al., 2018). Crithidia bombi cannot infect honey bees (Graystock et al., 2015) but has been detected in Andrena vaga and Osmia bicornis (Ravoet et al., 2014). Apicystis bombi lastly, can infect many different genera, among which Bombus, Andrena, and Apis (Graystock et al., 2015). Because C. bombi has not been proven to actually infect other genera besides Bombus spp., and N. bombi is also known to only infect bumble bees, it can be said that these two pathogen species can be seen as more specialized in bumble bee species, while A. bombi are generalists, infecting many different species and genera. These different pathogen types and host types might play a role in the possible link between disease incidence and host diversity. 4. Biodiversity and disease

Whether there is a relation between biodiversity of pollinators and diseases or not, can be seen as a bigger question: do ecosystems degenerate as biodiversity losses increase? Changes in ecosystems because of growing demands for food, fresh water, and other sources for the growing human population, as well as climate change, can cause big shifts in number and diversity of species. These changes may result in higher risk of infections, because there are more species amplifying the pathogen, or there’s less competition of pathogens in reservoir hosts, or less heterogenic resistance against pathogens (Keesing et al., 2010). Biodiversity can buffer the influence of invading species, because of more filled niches in ecosystems, ensuring higher flexibility (Chapin III et al., 2000). As mentioned by Felicia Keesing et al. (2010) in several cases, the species most likely to be lost when diversity declines, are those more likely to reduce the transmission of pathogens. Indeed, low diversity communities are in general dominated by the most competent host species, which maximize transmission (Johnson & Thieltges, 2010). Weedy plants for example, which are fast-growing, can be more competent hosts for plant pathogens, and are also more abundant when plant diversity declines, which shows that species that persist in species-poor ecosystems are also more likely to carry high pathogen and vector burdens. A hypothesis for vertebrates is that when disturbances cause biodiversity loss, vertebrates will invest minimally in immunity, making them more susceptible for pathogens (Keesing et al., 2010).

For vector-borne pathogen transmission, the dilution effect has been described as the reduction of transmission because of a diverse potential of host species. Conditions for this are among other things vectors being generalists, and the most optimal hosts being the dominant species (Levine et al., 2017). For free-living parasites, the ‘decoy effect’ might be a more suitable term, referring to a ‘bait’ distracting the disease from the focal host. Increased species diversity linked with reduced disease

17 transmission has been acknowledged for more than 100 years. In a multi-host system, dead-end hosts and incompatible hosts, as also described for pollinator species in the previous section ‘Multi-host pathogens’, are two important host types playing a role in buffering disease (Levine et al., 2017). Before explaining different possible drivers of the decoy effect, it is important to note that increased diversity can also cause an amplification effect, especially when linked with density. A diverse community can implicate a thriving, dense community, where density and incompetent hosts can spread the disease, increasing pathogen abundance (Dobson, 2004, Levine et al., 2017). Another possible mechanism of amplification is the following. When a pathogen induces a high mortality rate in its hosts, it partly prevents its own transmission to another colony or to the next generation, as described for Nosema bombi (Otti & Schmid-Hempel, 2007). Also A. bombi, which is mainly transmitted through surviving queens, can prevent its own transmission when negatively affecting queen hibernation. (Graystock, Meeus, et al., 2016). But when a higher host species diversity is present, pathogen transmission can still happen through less compatible hosts, and in this way a higher diversity augments disease spreading.

In figure 6, a conceptual model is shown, summarizing the different scenarios leading to decreased transmission linked with a higher host diversity, which can be applied to pollinators and their pathogens. The concepts are explained for a system where a non-host species is added to a system with one host species. When applying the theory to pollinators, we keep in mind that pathogen transmission is mostly indirect for pollinators, and pollinators are frequently infected through contaminated sources on flowers. Direct contact between pollinators of different species or colonies is of less importance, especially for solitary bees which do not form colonies, and can only be defined for parasitic bees (Franks, 1987) or interspecific drifting (P. Schmid-Hempel, 1995). Also interactions like differential behavior of honey bees when encountering wild bees when pollinating sunflowers (Greenleaf & Kremen, 2006) cannot be seen as ‘direct’ contact.

In figure 6 (a), an additional species results in a reduced probability of transmission. The added non- host can reduce the pathogen load on flowers, by removing spores from the flowers and in this way reducing the infection potential. Also, introducing new pollinator species in a certain region will force the host species to forage on less flowers, because some flowers will be depleted and pollinators have a limiting foraging range. Resource depletion and thus foraging restriction because of an additional species has been recognized in a study of Brittain et al. (2013) which has been tackled in the chapter ‘Importance of pollinator diversity’. The host species can also be ‘pushed’ to other flowers, because different bee species might tend to avoid each other. Hence, the host species will visit the same flowers less frequently and might pick up less infective cells.

With a higher species richness, the availability of susceptible hosts might be lower than with lower richness, due to competition between host species (Johnson & Thieltges, 2010). Reducing the number of susceptible hosts (b) as a cause for reduce disease transmission is mainly applicable in pollinators through interspecific competition for limited floral resources. An example is the European honey bee invading local bumble bee habitats (Thomson, 2004) and in this way reducing the available food for

18 the present species, thereby reducing their population growth. Of course, like in this example, reducing the susceptible host population also has negative consequences for the ecosystem when this host species plays an important part in habitat maintenance.

Recovery augmentation (c) as such is a less evident explanation when applied to bees, because recovery of infection has not been described for the investigated pathogens. Recovery augmentation in general is also not well known. However, self-medication could be seen as a recovery mechanism for bees. Self-medication has been described as a way for bees to reduce infection by taking up metabolites from floral nectar. Secondary metabolites in floral nectar are a defense mechanism of plants against herbivores (Richardson et al., 2015), but have also been proven to reduce infection intensities in bees, for example nectar alkaloids reduce Crithidia bombi infection in bumble bees (Manson et al., 2010). This self-medication can be seen as a behavioral defense, because the pollinators are believed to increase foraging for these metabolites when infected with a parasite (Simone-Finstrom & Spivak, 2012). With this in mind, recovery augmentation might be a reasonable mechanism of reduced disease transmission when a non-host species is added. The added species might alter the host behavior in such a way that it will take up more antimicrobial metabolites from the nectar, although the underlying process is not straightforward. An added species could also have the opposite effect, depleting floral sources with antimicrobial nectar compounds, leaving less self- medication resources for the hosts that need it.

Different mechanisms can operate in concert, as Keesing et al. (2006) suggests. Part (a) can explain part (c), by reducing pathogen load on flowers, giving host pollinators more chance to recover from infection.

Lastly, a higher mortality of the infected hosts because of a more diverse community can decrease disease transmission (d). In the beginning of the chapter the opposite was stated, namely that incompatible hosts could further transmit pathogens, when otherwise the pathogen would prevent its own transmission in hosts because of a high mortality rate. But when you only look at two species of which only one is susceptible, then this strategy could work, because the incompatible hosts will only transmit the pathogen to other host species, accelerating mortality, and thus decreasing disease transmission while host abundance declines.

Figure 6: The conceptual model of the mechanisms by which diversity could reduce disease risk in a specialist host– pathogen system adjusted from Keesing et al. (2006). Here 4 different ways how an added non-host species can reduce pathogen transmission are showed. The filled circles mean that the individual is infected, unfilled mean uninfected and susceptible. The dashed lines mean home range. Black squares stand for an addition of a second species.

Of course, these four different approaches are not that simple in nature. Transmission depends on many more variables, such as polyethism (functional specialization) of pollinators, age-dependent behavior in bumble bees (Schmid-Hempel, 1993), multi-host character of the pathogen and the compatibility of the pollinators as a host (these last two are explained in the section ‘Multi-host pathogens’). Biodiversity has been recognized as an important factor in ecosystems. Relying on one species, and not on a diverse community, increases the risk of a devastating impact of pathogen infection. The more related the hosts, the easier to transmit the disease. Crithidia bombi is easier to transmit between relatives, such as full-sibs, than between unrelated individuals, in B. terrestris (P. Schmid- Hempel, 1995). Growing evidence shows that populations with lower genetic diversity had higher infection levels of Nosema bombi in North-America (Cameron et al., 2011). When keeping in mind the long-established and recognized link of species diversity and disease transmission in general, the different strategies can then also be applied to the pathogen-host interactions in pollinator. The idea of pollinator diversity buffering pollinator disease doesn’t seem too absurd, taking into account several possible exceptions and conditions.

SCOPE From current literature it is clear that the complex web of flowers, pollinators, and pathogens give rise to many different interpretations of the interactions between these three players. In this thesis we looked at different aspects of these interactions and tried to find a link between biodiversity of pollinators and disease prevalence. First of all, we assessed the amount of pollinator species found in 8 different apple orchards. Because mostly bumble bees were sampled, we mainly focused on Bombus spp. for further analysis. Measuring the diversity can be done in many different ways, so different measures were compared and the most suiting measure to represent the diversity of the pollinator community was chosen. All sampled specimens were screened for 3 genera of pathogens, and the resulting pathogen prevalence was linked with diversity measures at each location. To be able to make a good comparison, we analyzed the data of only one bumble bee species, Bombus pratorum, because this species was numerously present at all locations. We found significant effects of pollinator diversity on pathogen prevalence. A landscape parameter was included in the analysis to also consider the effect of flower diversity as a representation of pollinator diversity. Furthermore, pathogen loads were considered and linked with pathogen prevalence data, which gives more information about the host types discussed in box 1 (page 16).

MATERIAL AND METHODS 1. Sampling Characterized locations In May of 2017, 8 apple orchards were selected to collect wild bee specimens. Apple orchards were selected as sample sites as apple blossoms attract a variety of wild pollinators, maximizing the chance to find sufficient different species. Due to the cold weather in April 2017, apple bloom was delayed and the blossoms on one year old branches were still blooming. The sampling was done using standardized transects in between the rows of the apple trees. Each site was sampled for 1 hour by two persons and all Apidae pollinators that were encountered were caught using an insect net. The sampling was done on 2 consecutive sunny days in May, between 12:00 and 17:00, when bees are most active. Caught specimens were frozen and stored at -20°C.

Figure 7: Overview of sampling locations. The upper image displays an overview of the locations in Belgium, the lower image gives a more detailed overview of the 8 different locations. The letter codes refer to the initials of the farmers of the apple orchards.

Landscape analysis In a range of 1 km around each orchard, a landscape analysis was performed, and different land cover properties were defined. This was done through land cover data from Biological Valuation Map (BVM) of Flanders (De Saeger & Scheers, 2016). Different land area categories were defined and measured within a 1000 m radius, and two categories were used for this analysis. Semi-natural positive land area are semi-natural habitats which can provide food or nesting, while semi-natural neutral area does not provide food or nesting. Both the land area and percentage of land cover for these categories was calculated, for each location, to describe the landscape composition around the orchards. Species determination Species were determined in the context of another project, using the field guide of Falk & Lewington, (2017). Bees were visually determined using a light microscope. 2. DNA extraction To investigate the impact and virulence of pathogens on the pollinator community found in the apple orchards, all sampled specimens were screened for three genera of pathogens commonly found in bumble bees: Nosema spp., Crithidia spp. and Apicystis bombi. For this, all DNA was extracted from each specimen. For the protocol, the Invisorb® spin tissue mini kit (Stratec Biomedical AG, Birkenfeld, Germany) was used. Lysis buffer G, Proteinase S, binding buffer A, elution buffer and the spin filters were all supplied with the kit.

Table 1: Different volumes of RLT buffer used during the DNA extraction protocol, depending on the size of the pollinator Pollinator RLT buffer (µl) Bombus 800 Osmia 600 Other solitary bees 300 Apis mellifera 600

The abdomen of each bee was cut off and placed in a 2 mL tube. One 5 mm and two 3 mm stainless steel beads were added, together with RLT buffer with 1% β-mercapto-ethanol (volume dependent on size of pollinator, see table 1). The tubes were then put in a Qiagen TissueLyser II (Qiagen, Venlo, the Netherlands) to disrupt the cells, first 2 minutes at 30 Hz, then 2 minutes at 20 Hz. Samples were then centrifuged for 2 minutes at 2000 g. 200 µL of the fluid was transferred to a 1.5 mL tube, and 400 µL Lysis buffer G and 40 µl Proteinase S was added, after which the tubes were vortexed thoroughly. Next, samples were incubated for 1 hour at 52 °C and 400 rpm shaken. Then the samples were centrifuged for 2 minutes at 11 000 g, after which 200 µL supernatant was transferred in a new 1.5 mL tube, and 200 µL binding buffer A was added. The solution was immediately vortexed each time, for at least 10 seconds. Then this was transferred to a spin filter in a 2 mL tube and centrifuged for 2 minutes at 11 000 g after 1 minute of incubation. The filtrated was discarded, and then the filter was washed twice with 550 µl wash buffer and centrifuged for 1 minute at 11 000 g each time, and the filtrate was discarded. To remove the last wash buffer, the spin filter was centrifuged for 4 minutes at full speed. Then the filter was transferred to a new 1.5 mL tube supplied with the kit, and 200 µL of warm elution buffer at 52 °C was added and centrifuged at 11 000 g for 1 minute after 3 minutes 23 incubation time. DeNovix spectrophotometer (DeNovix, Wilmington, USA) was used to check if DNA concentrations were high enough. With a few exceptions, most concentrations were above 100 ng/µl. Samples were then stored at -80 °C. 3. PCR Standard PCR was used for the detection of protozoa, which is shown in table 3. Products used in the PCR master mix are shown in table 2. The different master mix components used for pathogen screening can be found in the addendum, in table A1, A2 and A3. Recombinant Taq DNA polymerase, PCR buffer, which contains 200 mM Tris-HCl and 500 mM KCl, and MgCl was used.

Table 2: General ingredients used in PCR mix. Units are given in a separate column. Components Stock Unit Final Unit concentration concentration Buffer 10x 1x MgCl 50 mM 1.5 mM dNTPs 10 mM 0.4 mM Taq polymerase 5 U/µl 1.25 reaction

Different universal primer sets (produced by Invitrogen, Merelbeke, Belgium) were used to check for the 3 pathogens: Apicystis bombi, Crithidia spp.and Nosema spp. Trypanosomatidae primers SEF and SER (slow-evolving SSU-rRNA sequences Forward and Reverse) (Meeus et al., 2010) were used to check for Crithidia positive samples. Neogregarine primers NeoF and NeoR (Meeus et al., 2010) were used to check for Apicystis bombi positive samples. A universal primer set for Nosema spp. was used. There was also a primer set added as an internal control for Apidae DNA presence. The different protocols used for the PCR reaction can be found in the addendum. In table 3, the PCR program is shown. The PCR products were visualized on a 1.5% agarose gel, and stained with ethidium bromide. For Apicystis bombi, a 260-bp-long 18S rDNA is generated, for Crithidia spp. this is 420 base pairs (bp), and the universal Nosema spp. primers generate a 250 bp band. The Apidae primer set amplifies a 130 bp fragment. For each PCR reaction, a no template control (nuclease free water) and a positive control were added.

Figure 8: Example of an agarose gel with some positive bands for Apicystis bombi at 260 bp, and Apidae fragments at 130 bp. At the borders, each time a ladder was loaded, showing bands with size 100 bp, 200 bp… and so on.

Table 3: PCR program used for amplification of protozoan DNA, but also used for Nosema spp. (a microsporidian) Protocol step Temperature (°C) Time (seconds) Initial denaturation 94 120

35 cycles of 94 – denaturation 30 amplification 56 – annealing 30 72 – extension 45 Final extension 72 180

4. qPCR QPCR was done with CFX96TM Real-Time PCR detection system (Bio-Rad, Hercules, CA). After all samples were screened for the 3 pathogens, qPCR was applied to the positive samples, together with the same primers used for screening through PCR. For each reaction 8 µl of the sample was added to 10 µl GoTaq® qPCR master mix (Promega, Madison, WI) and 1 µl (10 µM) of the forward, and 1 µl (10 µM) of the reverse primer. Each sample was run in duplicate or triplicate and for each primer set a standard curve was added. The standard curve of Crithidia spp. and Apicystis bombi was based on a plasmid dilution series, while the standard curve of Nosema spp. was prepared by making a dilution series of one of the positive samples. For Crithidia spp., the highest quantity in the standard curve was 5.13×104 plasmids, for Apicystis bombi it was 8000. Starting from the highest quantity, a 1/10 dilution series was made. For Apicystis bombi 3 interrun controls were used because multiple 96 well plates 25 were needed, and these were generated from a pool of 5 positive samples each. A no template control (nuclease free water) was added each time. For Crithidia spp. and Apicystis bombi, the same program was used as with PCR, while the program for Nosema spp. is slightly different, as shown in table 4. For the 116 positive samples of Apicystis bombi, three 96-well plates were used, and each time three interrun controls (IRCs) were used for interplate comparison. For each IRC, 20 µl of 5 positive samples were pooled. For Nosema spp., only the loads of samples positive for Nosema thomsoni were measured, with a primer developed in the lab (Forward sequence: GGGCGAAACTTGACCTAT, reverse sequence: CACTTGATTTGCCCTCCAAT). In total 34 samples of Nosema thomsoni, which were diluted in a 1/10 ratio, were measured in duplicate on one 96-well plate.

Table 4: qPCR program, giving the temperature and amount of time of each step. Screened pathogens Temperature (°C) Time (seconds) Crithidia spp. and 40 cycles 95 – initial denaturation 120 Apicystis bombi 94 – denaturation 30 56 – annealing 30 72 – extension 45 Melt curve 65 5 95 5 Nosema thomsoni. 40 cycles 94 – initial denaturation 120 94 – denaturation 30 60 – annealing 30 Melt curve 65 5 95 5

To obtain the pathogen loads, BioRad CFX Manager (Bio-Rad, Hercules, California, USA) was used. Relative quantities were determined by setting all loads relative to the sample with the lowest Cq value (quantitation cycle; cycle at which fluorescence can be detected). Samples with Cq values above 40 were not used for quantification. 5. Sequencing For the detection of pathogens, general primers were used to identify the pathogens on genus level. But different species of pathogens are known to infect the bee species sampled, so we expect to find different species here. To determine the pathogens to species level, amplified DNA was sent to LGC Genomics (Luckenwalde, Germany) for Sanger sequencing (The Ready2Run, 1-shot reaction with prepipetted primers). First, the appropriate DNA sequence was amplified through PCR. For Nosema spp., a nested PCR was done to obtain a PCR product ready for sequencing. The master mix ingredients can be found in the addendum, table A4. For the first PCR round, forward and reverse primers were used as described by Ghosh & Weiss (2009) (Forward: GTTGATTCTGCCTGACGT; Reverse: TTATGATCCTGCTAATGGTTC). For the second PCR amplification the following primers were used, ACCCATGCATGTTTTTGAAG as forward, and CAAAGAACAGGGACACATTCA as reverse primer. The preparations for the nested PCR were handled in a UV cabinet. PCR tubes, tips and 1.5 ml tubes were put in the UV cabinet for 45 minutes

26 before usage to avoid contamination. For Crithidia sp., ITS primers were used to amplify the DNA needed for sequencing (Ravoet, 2015). The PCR program in table 5 was used.

Table 5: PCR protocol used for the amplification of a DNA sequence needed for sequencing of Crithidia sp. and Apicystis bombi; For Nosema sp., 30 seconds instead of 1 minute of denature time was set for each cycle. Protocol steps Temperature (°C) Time (seconds) Initial denaturation 94 300

35 cycles of 94 – denaturation 60 amplification 50.7 – annealing 60 72 – extension 60 Final extension 72 600

DNA purification DNA was purified for direct DNA-sequencing before sending the PCR products to LGC genomics for direct DNA sequencing. The EXOSAP protocol was protocol was used. 10 µl of each PCR product was ExoFastAP purified by 15 minutes incubation at 37 °C with 1 unit/µl of Alkaline Phosphatase I (FastAP) (Fermentas, Germany) and 10 units/µl of Exonuclease I (EXO) (Fermentas, Germany), which cuts all ssDNA, and followed by enzyme inactivation at 80 °C for a further 15 minutes. After the EXOSAP, 10 µL sample was taken from this PCR product, and added to 4 µl of 5µM primer (forward) used for the PCR and send for sequencing. 6. Statistical analysis All statistical analyses were performed in R (version 3.5.0, R development Core Team, 2018). The effects of pollinator diversity and semi-natural landscape (the predictors) on pathogen prevalence (the response variable, which is absence/presence data) were tested with generalized linear models (GLMs). A GLM was fitted with as predictor the 8 different locations, and again pathogen absence/presence data as response variable. For all models, the distribution family was set as binomial. When the total infection data (absence/presence data of all 3 pathogen genera added up) was used as response variable, Poisson distribution was set. The coefficient estimates were given each time, together with standard errors and p-values, showing the accuracy and quality of the fitted model. Also residual degrees of freedom and sum of squares was added when possible, to acquire a full comparison and analysis. The Anova function as well as the AIC criterion were used to determine the quality of the model, in terms of explanation of variance. Also some additional tests were done, all performed at the default significance level of 5%. To test correlation between 2 variables (semi-natural percentage and diversity indices), a correlation test (stats package) was used. From the same package, a chi-squared test of independence was done for the prevalence data of 2 pathogen. Posthoc tests (from multcomp package) were done testing general linear hypotheses, to determine differential pathogen prevalence between species. AIC values were calculated (with lme4 package), to compare quality of the most complex model with simpler models.

7. Diversity indices Four different diversity indices were used to measure diversity at the 8 locations in a relative, comparative way. The 4 indices are calculated as shown in table 6. All 4 indices represent similar, yet distinct aspects of diversity. Species number, or richness, is simply the number of species in a sample. Shannon’s diversity index gives the uncertainty of predicting a species taken from the dataset, while the Simpson index gives the total probability that two entities are the same species. The Evenness index gives a measure of equal proportions of abundances (Hill, 1973).

Table 6: Formulas of the four diversity indices calculated for each location of sampling. Explanation of the formulas are given on the right. Diversity Index Formula Definition Richness R 푅 = ∑ 푛 Total number of species in the community, with n being species n.

푅 Shannon index H pi is the proportional abundance of species i 퐻 = − ∑ pilog(pi) 푖=1 b is the base of the logarithm R is richness (total number of species in dataset) 푅 Simpson index D pi is the proportional abundance of species i 2 퐷 = 1 − ∑ pi R is richness (total number of species in dataset) 푖=1 Evenness E 퐸 = H′/log(S) Shannon index divided by the logarithmic of the total number of species (richness), which is the maximum possible value of the Shannon index

RESULTS 1. Introduction The main focus of this research is host-parasite interaction, more specifically how the pathogen prevalence found in the sampled bees relates to the diversity of the bee community where the bees were sampled. Because mostly bumble bees were sampled, the focus was on 3 genera of protozoan parasites commonly found in bumble bees: i.e. Crithidia bombi, Nosema bombi and Apicystis bombi. C. bombi and N. bombi are known to mainly infect bumble bees, while A. bombi is a generalist (as discussed in Literature study, section ‘Multi-host pathogens’). Because of these different host ranges, 2 hypotheses can be tested. Specialist pathogens like C. bombi and N. bombi might undergo a dilution effect, meaning that the pathogens are diluted in a diverse pollinator community, because of a high prevalence of incompatible hosts. A second hypothesis is that A. bombi, as a generalist, multi-host pathogen, is buffered by the pollinator diversity through different mechanisms explained in the last section of the literature study ‘Biodiversity and disease’. Different aspects needed to be analyzed in order to arrive to the main question of biodiversity and disease. Universal primers were used to screen for the 3 genera of pathogens, then genotyping of the pathogens was done in order to investigate which species of the 3 pathogen genera considered were infecting the pollinators. Different diversity indices were calculated to measure species diversity on each location, and together with semi-natural percentage, calculated by a landscape analysis, generalized linear models were used to investigate the data. 2. Sampling overview At first, an overview of all bees sampled is given in the addendum section 2, to get acquainted with the diversity of pollinators in the apple orchards. In total, 425 bees and 25 different species were sampled over 8 different locations. 3. Pathogen screening 3.1. Pathogen prevalence in species About one third of the bees was infected by at least one of the 3 pathogens that we screened for, 15 bees had a double infection. Apicystis bombi had by far the highest prevalence, followed by Nosema spp. and Crithidia species. Absolute and relative numbers are given in table 7.

Table 7: Overview of the pathogen prevalence, in absolute and relative numbers, in all samples. Pathogen Apicystis bombi Crithidia spp. Nosema spp. Absolute number 116 13 54 Percent (%) 27.29 3.06 12.71

Apicystis bombi was found in 3 genera and 9 different species: Osmia bicornis, Apis mellifera, and 7 of the 8 bumble bee species. The highest prevalence was found in Bombus pratorum (50.35%), Bombus terrestris (36.36%), and Bombus hortorum (50%) as shown in figure 9. Note that the latter bumble bee species has only been sampled twice.

Crithidia sp. were only found in 2 Bombus species: B. hypnorum and B. lapidarius, with Crithidia sp. prevalence of 16.36% and 8.89%, respectively. Nosema sp. were found in 4 different genera and 9 different species, with a very high prevalence in Andrena haemorrhoa (11 out of 16 specimens), and Apis mellifera (11 out of 35 specimens), not taking into account Andrena carantonica because only 1 specimen was sampled. All prevalence data is shown in figure 10.

Figure 9: Percentage of A. bombi found in different species. Only the species with at least 1 positive sample is shown. Dark gray means Bombus sp. genus, light gray is Apis sp. and in middle gray is Osmia sp. An important note is that only 2 B. hortorum bumble bees were sampled, and only 3 B. jonellus bumble bees, so the prevalence found here might not be a good representation of the actual pathogen prevalence of these 3 bumble bee species in the 8 orchards.

Figure 10: Infection rate of Nosema spp. in pollinator species. Under the bar plots, the total number of bees sampled of each species is shown. Andrena carantonica was only sampled once, and was positive for Nosema spp., which explains the 100%. With a chi-squared test of independence, Apicystis bombi prevalence came out as dependent of host species (chi-squared = 82.088, residual degrees of freedom (df) = 24, p-value = 2.82×108). The same applies for Nosema spp. Two pairwise comparisons for Apicystis bombi were significant. First of all, Bombus pratorum and Bombus hypnorum have a significant difference in A. bombi prevalence, found by multiple comparisons through general linear hypotheses (df = 400, coefficient= 4.00, p-value = 0.010), and also Bombus pascuorum and Apis mellifera differ (coefficient = 1.20, p-value = 0.016). For Nosema spp., also prevalence was dependent on the host species (X-squared = 78.74, df = 24, p- value = 9.66×108). Andrena haemorrhoa has a significantly higher Nosema spp. prevalence, relative to 5 bee species, given in table 8. Because Nosema spp. has been mainly linked with honey bees (Nosema ceranae or Nosema apis) or with bumble bees (Nosema bombi), detecting such a high prevalence in a solitary bee species, especially compared to bumble bee species like the much sampled Bombus pratorum, is remarkable.

Table 8: Significant outcomes of linear hypotheses of pairwise comparisons of pathogen prevalence between pollinator species. Nosema spp. prevalence ratio Ratio value p-value 푂푠푚𝑖푎 푏𝑖푐표푟푛𝑖푠 -4.34 0.019

퐴푛푑푟푒푛푎 ℎ푎푒푚표푟푟ℎ표푎 퐵표푚푏푢푠 ℎ푦푝푛표푟푢푚 -2.89 0.0036

퐴푛푑푟푒푛푎 ℎ푎푒푚표푟푟ℎ표푎 퐵표푚푏푢푠 푙푎푝𝑖푑푎푟𝑖푢푠 -3.86 0.0025

퐴푛푑푟푒푛푎 ℎ푎푒푚표푟푟ℎ표푎 퐵표푚푏푢푠 푝푎푠푐푢표푟푢푚 -4.40 0.016

퐴푛푑푟푒푛푎 ℎ푎푒푚표푟푟ℎ표푎 퐵표푚푏푢푠 푝푟푎푡표푟푢푚 -2.78 0.00054

퐴푛푑푟푒푛푎 ℎ푎푒푚표푟푟ℎ표푎

3.2. Pathogen prevalence in apple orchards The overall pathogen prevalence on each location varies between about 20% and 70%, with location FB having the highest pathogen prevalence in general and also the highest A. bombi and Nosema sp. infection separately (figure 11). It is interesting to note that Crithidia sp. was not found here. On the location with the second highest infection rate of A. bombi, namely DW, Crithidia sp. were also absent. At all locations, bees were infected with A. bombi and Nosema spp., but not all locations had a Crithidia sp. infection. Strikingly we see that when A. bombi prevalence is high, Nosema spp. prevalence seems to be lower, for example when comparing location MG to location GR.

Figure 11: Bar plot of the percentage of pathogens in the bees sampled at each location. The percentage is calculated as a fraction of bees infected by the pathogen divided by the total amount of bees sampled at that location. Double infected bees were counted twice, which are 15 in total. When fitting a generalized linear model on the pathogen prevalence of all species with as a predictor the location, the Apicystis bombi prevalence is significantly higher on location DW (estimate β=1.7, standard error = 0.7, p=0.015) and FB (estimate β = 1.7, standard error = 0.7, p= 0.014), with F7 = 4.473. For the other pathogens, no significant effect was found. There’s also no significant effect of genus or species on pathogen prevalence. 3.3. Focusing on pathogen screening in Bombus sp. Reporting overall pathogen prevalence of 3 important bumble bee pathogens by using data of different genera of hosts can be misleading, because the pathogen prevalence in other genera might be completely different. Besides, 5 species of bumble bees were frequently sampled, while many solitary bees were barely sampled. When only focusing on those 5 most abundant bumble bee species, B. hypnorum, B. lapidarius, B. pratorum, B. terrestris, and B. pascuorum, a better comparison can be made, which is given in figure 12 and the numbers are given in table A5 in the addendum. B. pascuorum, B. pratorum and B. hypnorum were present at all locations, B. terrestris was present at 6 locations. B. pratorum was sampled at all locations with a minimum of 4 individuals. Bombus pratorum has the highest relative Apicystis bombi prevalence. Bombus hypnorum has the highest relative Crithidia sp. prevalence, and B. terrestris has the highest relative amount of infection by Nosema sp.

Figure 12: Comparative figure of the 3 pathogens in the 5 most common Bombus species. Each pathogen prevalence is plotted in a different subfigure. The percentages are calculated as being the proportion of the bee species infected by the pathogen. The percentage are also given inside of the bar plots in white, and the absolute count of specimens (both uninfected and infected), is given under the bar plots in black. If again a generalized linear model is fitted on this data, also here location DW (2.079 ± 0.73, p-value = 0.0045) and FB (0.75 ± 0.69, p value = 0.019), but also location GR (1.90 ± 0.71, p-value = 0.0078) have a significantly higher A. bombi prevalence compared to other locations. 3.4. Genotyping Nosema bombi is known to only infect bumble bees, so it could be that Nosema species found in the solitary bees is N. thomsoni, since both N. ceranae and N. apis are not known to infect solitary bees. Apis mellifera will likely be infected by N. ceranae or N. apis, because these two microsporidians typically infect honey bees. To check if these assumptions are right, all Nosema sp. infections were genotyped. The results of this genotyping are shown in a sunburst chart in figure 13. For Nosema spp., 5 of 54 samples were Nosema bombi, 4 were Nosema ceranae (all infected bees were honey bees), and 34 were Nosema thomsoni. The sequencing of 11 of the 54 samples positive for Nosema spp. failed. When not taking into account the failed samples, 14 out of 15 B. pratorum specimens were infected by N. thomsoni, and only 1 was infected by N. bombi. Also other bumble bees like B. hypnorum, B. lapidarius, B. terrestris and B. pascuorum were predominantly infected by N. thomsoni, and less by N. bombi. Nosema thomsoni also infected Osmia bicornis, Andrena carantonica, 8 out of 10 specimens of Andrena haemorrhoa, and 3 out of 7 specimens of Apis mellifera.

Nosema bombi was found in 2 specimens of A. haemorrhoa, and each time in one B. hypnorum, B. pratorum and B. terrestris. It is most likely that the ones found in A. haemorrhoa were not actually infecting the solitary bee. This suspicion is also partly confirmed by the vague bands on the agarose gel (addendum figure 32). The bands of N. thomsoni on the other hand, are much clearer, suggesting a real infection. These assumptions can also be investigated through the infection intensities, which will be discussed in the section ‘Pathogen load’ below.

Figure 13: Sunburst chart showing the results of Sanger sequencing of Nosema spp.-positive samples. The inner circle is showing the genus subdivision, the middle circle showing the species subdivision, and the outer circle showing the Nosema spp. found in each bee species. The pollinator genera are also given in the legend below the sunburst. Osmia bicornis, which is the smallest blue section of the sunburst, was only infected by N. thomsoni. The white section mean that these samples failed to identify the genotype All Crithidia sp. samples were identified as Crithidai bombi, meaning that 9 out of 55 Bombus hypnorum were infected with C. bombi, and 4 out of 45 B. lapidarius were infected with C. bombi. 3.5. Pathogen load Relative pathogen loads We looked at the infection loads of Apicystis bombi, Crithidia bombi and Nosema spp. Infection loads were calculated relatively taking the lowest infection load (lowest signal) as a reference (set to 1). For Apicystis bombi, 80 of the 116 samples were successfully quantified. The other 36 samples failed, as they did not amplify. These samples were not included in the analysis. All positive samples were

35 represented in a boxplot with infection loads for each species (figure 14). Since only 1 sample of Bombus hortorum, Bombus hypnorum and Bombus jonellus was positive for A. bombi, no boxplots could be made, nonetheless these values are included in figure 14 to enable comparison between these infections and infections in other species. Note that logarithmic values of the loads were used to be able to make a better visual comparison. Apis mellifera has the highest median infection load for A. bombi, when the single measures of pathogen load of B. hortorum, B. hypnorum and B. jonellus are not taken into account. Osmia bicornis follows closely. Of the bumble bees, B. terrestris has the highest median of A. bombi infection load. The outliers of the data give a different message, with B. pratorum and B. pascuorum having the highest pathogen load of A. bombi, followed by A. mellifera and B. jonellus. But strong conclusions cannot be made from this, because many species have only one or a few measurements. For this reason, and because the data of infection load of Apicystis bombi is too variable, it was not possible to fit a model or a distribution on the data. But with a Kruskal-Wallis test for multiple comparisons, the loads between different species could be compared, on a 5% significance level. Results suggest that O. bicornis differs in load from B. pascuorum, B. pratorum and B. lapidarius. The outliers of B. pascuorum and B. pratorum are clearly ignored through statistics, but these few divergently high values can give an important underlying message. A high pathogen load is interpreted as a high virulence. In this way, the higher the pathogen load, the more severe the impact on the host. The extremely high outliers can suggest that many pollinators undergoing such a high virulence were already dead, or these few extreme high loads are simply not frequently occurring. This will be further discussed when pathogen load is compared to pathogen prevalence.

Figure 14: Boxplots of the logarithmic base 10 of the infection loads of Apicystis bombi. The actual infection loads range from an order of magnitude of 100 to 108 times the lowest amount found. The different data points are plotted on top of the boxplots in blue. Dark grey boxplots show Bombus spp., Apis mellifera is in light grey and Osmia spp. is a shade in between. The absolute positive individuals are given below the boxplots (N=…) The C. bombi infection loads of B. hypnorum and B. lapidarius are quite similar (figure 15). What stood out during the qPCR analysis is that the loads were much higher than the average loads observed in the lab. Compared to A. bombi, the loads of C. bombi are much less variable, with pathogen loads being maximum 5.22 times higher than the lowest load.

Figure 15: Boxplots of the logarithmic base 10 of the infection loads of Crithidia bombi. The different data points are plotted on top of the boxplots in blue. The absolute positive individuals are given below the boxplots (N=…) Nosema thomsoni infection loads are given in figure 16. Andrena haemorrhoa has the highest median infection, while the highest outlier was found in Apis mellifera. Of the bumble bees, loads are quite similar. B. pratorum, which has the highest N. thomsoni prevalence, has the lowest median infection load. Because most species do not have a lot of data for N. thomsoni, no strong conclusions can be made from this plot, but a more interpretable plot will be given below, when also accounting for prevalence.

Figure 16: Boxplots of the logarithmic base 10 of the infection loads of Nosema thomsoni. The actual infection loads range from an order of magnitude of 100 to 103 times the lowest amount found. Important to know is that all samples were diluted in a 1/10 ratio. The different data points are plotted on top of the boxplots in blue. Dark grey boxplots show Bombus spp., Apis mellifera is in light grey and Osmia spp. is a shade in between. The genus Andrena is shown in black. The absolute positive individuals are given below the boxplots (N=…) Pathogen prevalence as a function of pathogen load Pathogen prevalence data as a function of pathogen load in each species could provide a lot of information about functionality and impact of parasites. Parasites can have many different kinds of effects on their hosts. Apicystis bombi A. bombi is considered as a generalist and a multi-host parasite, as mentioned before, because of its ability to infect many different species and even genera, having a differential virulence in different species. The link between prevalence data and infection intensities might provide a better insight into the impact of this pathogen on its host. Therefore the average pathogen load of A. bombi is plotted against the prevalence data of each pollinator species, see figure 17. The error bars of the calculated averages are also given, as well as the number of samples for each species whose infection load was able to be quantified. Note that the error bars of A. mellifera, B. pratorum and B. pascuorum are very large, and that the error bars of B. terrestris, O. bicornis and B. lapidarius are not visible due to the scale of the plot. For this reason, all values are given below in table 9. Standard errors of B. hortorum, B. hypnorum and B. jonellus could not be calculated because for each of them, only a single sample was positive for A. bombi (also, B. jonellus and B. hortorum were only sampled thrice and twice respectively). Besides the high variability of pathogen load within each host species, also the average loads between species vary greatly. This can be seen by the large values of the standard errors, often even being larger than the average load itself. The plot is divided with two red lines, showing the average prevalence and the average of all averaged pathogen loads.

Table 9: Values of average load of A. bombi with its standard errors, also used in figure 14. The standard errors of 3 Bombus species could not be calculated because of only having a single measurement, these are shown as ‘NA’. The prevalence of A. bombi is given, as well as the number of samples that were successfully quantified (which does not necessarily match with the total amount of samples positive for A. bombi, as some samples failed during the qPCR). Also the total amount of specimens from each species are given, in the last column. Species Average load Standard Prevalence # of quantified Total # of errors samples samples Bombus lapidarius 63.25 × 10² 13.48 × 103 24.44 10 45

Bombus hortorum 71.11 × 10³ NA 50.00 1 2

Bombus terrestris 11.19 × 104 47.91 × 103 36.36 8 22

Bombus hypnorum 11.73 × 104 NA 1.82 1 55

Osmia bicornis 43.58 × 104 22.52 × 104 30.56 11 36

Bombus jonellus 54.80 × 105 NA 33.33 1 3

Bombus pratorum 21.60 × 106 85.97 × 106 50.35 37 141

Bombus pascuorum 21.76× 106 56.64 × 106 23.68 8 38

Apis mellifera 50.05 × 106 27.85 × 106 8.57 3 35

Figure 17 can be interpreted using box 1 about host types (page 16 in literature). Two main situations can be distinguished. Pollinators are either true hosts, or incompatible (non-true) hosts that picked up the pathogen by accident. Non-true hosts are expected to have a low prevalence and also a low infection load, because pathogens that are not capable of infecting a pollinator will also not multiply or thrive inside of the host. These hosts would be present in the bottom-left corner of the plot. Particularly Bombus hypnorum meets these conditions, especially because only 1 out of 55 species in total (also see table 8) was found positive for A. bombi, and the pathogen load is also relatively low compared to other average pathogen loads found. Although, having a pathogen load which is 104 times higher than the lowest load, is still quite high. In this way, the plot is misleading, because a lot of points seem to be close to zero, but actually have values of at least 103. Therefore, a second figure (figure 18) shows all data points except for A. mellifera, and no error bars. In this figure, the same conclusions can be made, because the average of all averaged loads is still much higher than the average of B. hypnorum. Hosts with a low infection load but a high prevalence of A. bombi (upper left corner) are most probably dead-end hosts, experiencing a low virulence of the pathogen. This mainly applies to B. hortorum and B. terrestris, these bumble bee species could be important in the prevention of disease prevalence,

40 because the non-ideal host environment reduces pathogen load. An important reminder is that only 2 specimens of B. hortorum were sampled, so it is still possible that with a higher sample size of this bumble bee species, pathogen prevalence might be totally different, also possibly suggesting a different host type. Bee species with a high pathogen load, in the order of 107, are B. pratorum, B. pascuorum and A. mellifera. Because of these high loads, these three species also have the largest error bars. Especially B. pratorum seems to have variable loads, ranging from an order of 100 to 109. Still, the average is quite high, with 102 times higher than the species categorized as dead-end hosts. This bumble bee species also has the highest prevalence. Hosts with these characteristics might be important spreaders of the disease, because unlike dead-end hosts, the host environment seems to be ideal for the pathogen to multiply, and the host can cope quite good with the infection. Because the pathogen is present in many specimens of the species, the probability that one of the many infective cells in the infected bees could spread to others could be much higher. The difference with the high loads in the species B. pascuorum and A. mellifera, is that the prevalence is much lower, which could suggest a different type of host, namely a normal, true host which undergoes a high virulence. The big difference with the spreaders described above is that here, because of low prevalence, the disease cannot be spread as much. The very high pathogen load and low prevalence in A. mellifera even suggests that A. bombi has such a high virulence that many honey bees infected by the pathogen are already deceased. Pathogens preventing its own transmission by inducing a high mortality in its host can play an important role in disease spreading, especially linked with species diversity. Bee species which can be less obviously divided into these host types are B. jonellus, and O. bicornis and B. lapidarius. Even though the position of B. lapidarius is below the horizontal red line indicating the average prevalence, a pathogen prevalence of 24.44% is still fairly high, which suggests that this bumble bee species is a true host, and not incompatible like the figure suggests. Bombus jonellus and O. bicornis are less convincing dead-end hosts, because the pathogen prevalence is lower, meaning they will play a less important part in buffering the disease.

Figure 17: Average load plotted against prevalence (%) data of A. bombi infected pollinator species. The number of samples that were quantified are shown in blue next to the data. The red dotted lines indicate the average of the data, both for the average pathogen loads, and the prevalence data.

Figure 18: Average load plotted against prevalence (%) data of A. bombi infected pollinator species. Here, A. mellifera data is left out, as well as error bars. The number of samples that were quantified are shown in blue next to the data. The red dotted lines indicate the average of the data, both for the average pathogen loads, and the prevalence data.

Nosema thomsoni For Nosema thomsoni, the same theory can be applied when again the average load is plotted against prevalence data in figure 19. Here, again A. mellifera can be considered a true host undergoing a severe virulence, explaining the high relative load and low prevalence. It can again be noted that A. mellifera has a variable pathogen load. There is not a convincing dead-end host present, because A. haemorrhoa, the only bee species with a fairly high N. thomsoni prevalence, has a high average load, which is 101 to 102 times higher than all the others except A. mellifera. This solitary bee might be better defined as a spreader, with its distinctly high N. thomsoni prevalence. The rest of the species are considered incompatible hosts in the plot, with a low load and prevalence, which could be true because in fact, N. thomsoni has not proven to be virulent yet. These incompatible hosts could play in important role in the ‘dilution effect’, meaning that specialists pathogens will be more diluted in a diverse pollinator network, because more incompatible hosts are present.

Figure 19. Average load plotted against prevalence (%) data of N. thomsoni infected pollinator species. The number of samples that were quantified are shown in blue next to the data. The red dotted lines indicate the average of the data, both for the average pathogen loads, and the prevalence data. The plot was not made for C. bombi, as only 2 Bombus species were infected with C. bombi, and their pathogen load was similar (as seen in figure 15 and discussed above the figure).

4. Species diversity 4.1. Four diversity indices Differences Measuring species diversity means measuring how many species are present, whether or not taking into account the total amount of sampled individuals. A higher diversity is the same as having lower proportional abundances on average for the same sample size. Calculating diversity by a numerical index provides a measure of comparison. The indices calculated here are all relative. Different diversity indices were calculated as a mathematical measurement of the species diversity at the different locations. Many different indices exist, with most of them having a strong relation, but they are often not comparable (Morris et al., 2014). Species number (richness), equity in relative species abundance (evenness), Shannon’s diversity index and Simpson’s diversity index were calculated for 8 locations. Species richness, which is the number of species in a sample, doesn’t give much information about community composition, and doesn’t account for the difference in relative abundance between samples (Hill, 1973). The Shannon-Weaver formula, which calculates the uncertainty of predicting an individual’s species identity when randomly taken from a dataset, does account for their relative abundance (Dickman, 1968). The Simpson index sums the probability that two entities from the dataset are the same type, for all types in the dataset, which comes down to the same as the weighted arithmetic mean of the proportional abundances of the different types of interests. At last, the Evenness index shows how equal a community is in numbers. When proportional abundances of species are similar, the evenness index will be high. It gives a comparison between the diversity as measured by richness, and another measurement, like the Shannon index. The different formulas are given in the chapter ‘Material and methods’, in table 6. A comparison of the different diversity indices is given in figure 20, and the values of the indices are again given in table A6 in the addendum. Richness differences are much more distinctive than the other indices. Location GR and MG seem to have the highest Shannon index.

Figure 20: Overview of the four calculated indices of pollinator diversity at each location, shown in different colours. Colour legend is given on the right.

Pollinator diversity and natural habitat Before choosing a diversity index for further analysis, a correlation between the diversity of the sampled pollinators, and the percentage of semi-natural landscape was calculated. Semi-natural landscape is a summation of neutral and positive semi-natural landscape percentage, with neutral meaning that the land does not provide food or nesting, and positive meaning provides food and/or nesting. It is the most ideal foraging habitat for bees, because a more natural environment logically also has a more extensive and diverse flower community. In this way, semi-natural area can be seen as a measurement for the flower diversity. Here, the amount of semi-natural landscape was considered in a 1 km range of the sampling. The most obvious outcome here would be to see that within the same size of buffer, a higher percentage of semi-natural landscape would attract a more diverse pollinator community, indicated by a higher index value. From here on, the total percentage of semi-natural will be referred to as SN%. Figure 21 shows a plot of the correlation between SN% and the 4 diversity indices. Species richness seems to have a negative correlation with SN%, the other diversity indices are quite constant. This is also confirmed in a correlation test, which tested significant for all diversity indices. Correlation values and p-values are given in table 10. It seems most logical that with an increasing percentage of semi- natural habitat, the pollinator community has more resources to prosper, and diversity indices will indicate this by increasing in value. This is confirmed for all diversity indices except for richness.

Figure 21: Correlation plot of SN% of landscape, and the 4 diversity indices.

Table 10: Correlation values between the diversity indices and SN% (percentage of semi-natural habitat). Estimated coefficients are given in the second column, p-values are given in the last column. Diversity index Correlation with SN% P-value Richness -0.80 <0.010 Evenness +0.52 <0.010 Shannon +0.20 <0.010 Simpson +0.20 <0.010

For further analysis, the Shannon-Weaver index was chosen as a diversity measure. The index increases when Richness and Evenness increase, which gives a simple and complete summary of diversity. Values are between 1.50 and 3.50. A disadvantage is that when communities differ greatly in richness, this will give a weaker comparison, but in our analysis, richness was similar across locations, or at least in the same order of magnitude. 4.2. Pathogen prevalence and diversity A. General prevalence Now that a positive correlation has been found between pollinator diversity (now defined as the Shannon index) and percentage of semi-natural area within 1 km of the sampling site, the next step is to check if pollinator diversity has an effect on pathogen prevalence. We first look at the prevalence of each parasite separately, because virulence and functionality of each parasite can be different. When pathogen prevalence data of all pollinators is accounted for, then no significant effect was found of species diversity on each location, for all 3 pathogens separately as can be found in table 11, 46 since all p-values are higher than the 5% significance level. When testing for total infection rate, i.e. prevalence data of the 3 pathogens added up, also no significant relation was found for pollinator diversity (table 11). This doesn’t necessarily mean that we already can conclude that pollinator diversity does not have any effect on pathogen prevalence. Many factors could be the cause of this outcome, like unequal sampling sizes, and sampling different bee species in each location. A better comparison could be made when pathogen prevalence of a pollinator species, which is sampled frequently at each site, is used to examine the effect of diversity calculated on each location.

Table 11: Parameters of the results of model fitting. Estimated coefficients, standard errors and p-values were taken from the summary of the results of fitting a generalized linear model, and the sum of squares and the F-value were taken from the summary of fitting an analysis of variance model (anova), to determine how much the different groups (e.g. species) differ from each other. A high F-value means that the variation between sample means is much higher than the variation within the samples of each group (pollinator species). Response Estimated coefficient of Standard p-value Sum of Df F-value variable diversity error squares A. bombi 0.06 0.21 0.77 0.017 423 0.087 prevalence Nosema spp. 0.47 0.31 0.13 0.26 423 2.38 prevalence Crithidia spp. 0.43 0.60 0.47 0.015 423 0.51 prevalence Total infection 0.17 0.15 0.24 0.59 423 1.88 rate

B. Bombus pratorum prevalence As could be seen in figure 30, Bombus pratorum is present on all locations, at least 4 times, and sometimes more than 20 times on one location. In this way, B. pratorum can be used as an indicator species to investigate the impact of pollinator diversity on pathogen prevalence. Bombus pratorum has been sampled 141 times in total, and has a high A. bombi prevalence (50.35%) and a mild Nosema spp. prevalence (12.06%). Crithidia bombi has not been detected in B. pratorum, therefore we will only look at prevalence data of Nosema spp. and A. bombi. A subset of the data for B. pratorum was made, and a generalized linear model was fitted on the data, with A. bombi and Nosema spp. prevalence as response variables, and each time diversity as a predictor. This time, the Shannon index as a measure for diversity had a significant effect on both A. bombi and Nosema spp. prevalence, as well as the total infection rate. However, as can be seen in table 12, and in figure 22 and 23, estimated coefficients are positive, suggesting that diversity has a positive impact on pathogen prevalence. Especially for Nosema spp. this seems to be the case, even suggesting a more exponential relation in figure 23, and the data in table 12 suggests that when the Shannon index increases by one unit, then Nosema spp. prevalence will increase more than twice as much.

Figure 22: Visualisation of the fitted generalized linear model with response variable being A. bombi prevalence, and predictors being Shannon index as a diversity measure

Figure 23: Visualisation of the fitted generalized linear model with response variable being Nosema spp. prevalence, and predictors being Shannon index as a diversity measure

Table 12: Parameters of the results of model fitting. Estimated coefficients, standard errors and p-values were taken from the summary of the results of fitting a generalized linear model, and the sum of squares and the F-value were taken from the summary of fitting an analysis of variance model (anova), to determine how much the different groups (e.g. species) differ from each other. A high F-value means that the variation between sample means is much higher than the variation within the samples of each group (pollinator species). Response Estimated Standard p-value Sum of Df F-value variable coefficient of error squares diversity A. bombi 0.82 0.24 0.012 1.64 139 6.78 prevalence Nosema spp. 2.31 0.79 0.0034 1.15 139 11.60 prevalence Total infection 0.64 0.22 0.0033 5.54 139 18.54 rate

C. Also considering semi-natural percentage With all previous literature in mind, it seems quite naive to try to explain the effect of diversity on pathogen prevalence by one simple diversity measure. Many different factors can have an effect on the prevalence, flower specificity and its shared use. Because of the high collinearity between diversity of pollinators and diversity of flowers (indirectly described by semi-natural percentage, as seen in figure 21, it seems like a good, but also a bad idea to include a measure of flower diversity in the model. Although it is always better to not include predictors that partly explain each other because it can make the model unnecessarily complicated, this time flower diversity could explain more of the effect of diversity on pathogen prevalence. Semi-natural percentage (SN%), which here is seen as an indirect indication of flower diversity (as described in the section ‘Pollinator diversity and natural habitat’), might be able to describe more of the pollinator diversity, besides the Shannon index. One of the reasons of this could be that the sampled bees are not a good representation of the diversity on each location, and in this way, the measure of SN% could provide more information about diversity, in an indirect way. For this reason, a multivariate generalized linear model was fitted (table 13), and afterwards also 2 univariate generalized linear models were fitted (table 14). The multivariate model has pathogen prevalence as a response variable. Again, the general prevalence had a positive relation with the predictors, this time being both the Shannon index and SN%. The question now is if the model with both predictors is a better model than the previous one, when only the Shannon index was used. The Akaike information criterion (AIC) was calculated as an estimation of the quality of the model, when each time one predictor was dropped from the model AIC values suggest that the model with both predictors is still the best, having the lowest value for the full model, compared to the model with only Shannon-index as a predictor or only SN% as a predictor.

Table 13: Parameters of a fitted GLM model with as response variable multivariate data of pathogen prevalence, and as predictors the Shannon index and the SN%, only applied on the data of B. pratorum. Model Predictor(s) Estimate p-value Predictor dropped AIC from model pathogens ~ Shannon + SN% Shannon 3.18 0.0080 SN% 285.05 SN% 2.41 0.038 Shannon 297.30 None 282.64

Also the univariate models suggest positive effects of the 2 predictors on A. bombi and Nosema spp. prevalence. All parameters can be found below in table 14, and the fitted models are also plotted in figure 24 and 25. Positive effects of diversity on pathogen prevalence are found here as well, but not all predictors are significant in both models. This time, Nosema spp. prevalence seems to be more linear than the simpler model with only diversity as a predictor, although the variability not explained by the model still seems to increase exponentially with increasing predictor values. The model for A. bombi is similar to the one without SN% as a predictor.

Figure 24: Visualization of the fitted model with response variable being A. bombi prevalence, and predictors being Shannon index as a diversity measure, and semi-natural percentage as a measure of natural habitat, indirectly measuring flower and pollinator diversity. The grey area shows the variability not explained by the model.

Figure 25: Visualization of the fitted model with response variable being Nosema spp. prevalence, and predictors being Shannon index as a diversity measure, and semi-natural percentage as a measure of natural habitat, indirectly measuring flower and pollinator diversity. The grey area shows the variability not explained by the model. Again the AIC values were calculated. The AIC value of the full model is lower than the other two values given in table 14. So the full model is better than the model we checked before, with only Shannon index as a predictor. The same can be said about the model with Nosema spp. as a response variable, because the AIC value of the full model is the lowest.

Table 14: parameters of a generalized linear model in R. The model is shown in the first column, parameters of the predictors are given in columns 2 to 5. Columns 6 and 7 show the AIC values of simpler models when each time one predictor is dropped. Model Predictors Estimate Standard p-value Predictor dropped AIC error from model A. bombi ~ Shannon + SN% Shannon 0.61 0.34 0.078 SN% 192.81

SN% 0.054 0.025 0.029 Shannon 191.10 none 189.92 Nosema spp. ~ Shannon + Shannon 4.10 1.65 0.013 SN% 94.49 SN% SN% 0.13 0.077 0.090 Shannon 106.20 none 92.56

All models suggest that A. bombi and Nosema spp. prevalence in B. pratorum is positively influenced by pollinator diversity. This was already nuanced because B. pratorum might be a spreader of A. bombi, and an incompatible host of Nosema thomsoni. Because B. pratorum is so numerously present, its dynamics could influence a possible decoy effect.

A final approach is to make a subset of the data, only for the species positive for the pathogen, so all other species that do not participate in the pathogen-host dynamics are left out. Apicystis bombi was found in 9 different species, of which 7 are bumble bee species. Some of them were defined as spreaders, some as dead-end hosts, and some as incompatible hosts. All of them together might represent a total effect of diversity on pathogen prevalence. A model with predictors SN% and Shannon index was fitted on A. bombi prevalence of the subset, and this time, the estimated coefficients are again positive, albeit not significant (table 15). Many exceptions, conditions and nuanced can be made, which will be discussed in the chapter ‘Discussion’, but one possible explanation can be analyzed already. Bombus pratorum, as an omnipresent species in the sampling and thus also possibly in the apple orchards, acts as a spreader for A. bombi, cancelling out possible decoy effects of other host species. The positive effect of this species on A. bombi prevalence can be checked by fitting a generalized linear model on A. bombi prevalence, linked with proportional abundance of B. pratorum on each location. And indeed, a significant positive relation was found (coefficient estimate = 3.0986 ± 1.0308, p-value= 0.00265), implying that this bumble bee species might be augmenting the disease.

Table 15: Generalized linear model fitted to the subset data of all species positive for Apicystis bombi, with values of the parameters.

Model Predictors Estimate Standard Df p-value error A. bombi ~ Shannon + SN% Shannon 0.071 0.23 374 0.76

SN% 0.023 0.016 374 0.15

DISCUSSION 1. Species overview What was immediately clear from the sampling is that bumble bees were present in large numbers. Besides Bombus species, we found many other species commonly found in Europe, like bees from the genus Andrena, Osmia, Bombus, Apis, etc. (Michener, 2007). Fruit cultivation depends greatly on a diverse community for pollination services, and fruit set decreases when intensive cultivation increases in the surroundings (Eeraerts et al., 2017). Bumble bees are a major pollinator for apple trees, but also honey bees and solitary bees help in the cross pollination of the trees. A large amount of Bombus pratorum was sampled, which was partly expected through literature, being one of the most widespread bumble bees in Europe next to B. pascuorum, B. lapidarius and B. terrestris (Michener, 2007). The reason for B. pratorum to be sampled the most could be because the queens start their colony quite early, so by May, the colony already has a great size. On the other hand, it is know that B. pratorum colonies have small sized colonies (Rutrecht & Brown, 2008), so this probably means that many colonies exist in the neighborhood of the orchards. Of the solitary bees, Osmia bicornis was sampled the most. This agrees with previously mentioned abundance, with Osmia bicornis being the most abundant species of all 11 Osmia spp. in Britain (Raw, 1972), and similar findings in Europe (Michener, 2007). Adults are known to be active during the months of May and June (Raw, 1972). Diversity of the pollinator community was measured through the Shannon diversity index. Other diversity measures exist, but we chose the Shannon index as a good representation, which is also explained in the chapter ‘Results’, section 4. In general, we concluded that the species richness, a number often given in research as a diversity measure, is not always a good interpretation of diversity. Richness only gives the number of species sampled in each group, regardless of differential sample sizes. When linking pollinator species with disease spreading, it is important to acknowledge that pathogen transmission is influenced by both the number of host species and the amount of hosts. The richness index only takes into account the amount of different species present. The Shannon index on the other hand also accounts for species abundance, because it measures the uncertainty of predicting the identity of a certain species. The more diverse the community, the more uncertain which species you will sample from the data. However a compound index like Shannon might not clearly explain external effects on biodiversity, as Magurran & Dornelas, (2010) suggest, because it is not easy to link this measure to sample sizes and species numbers. Interpretation is complex because of the combined indices; species richness and evenness. Besides, a small difference in value does not immediately indicate if the diversity differs greatly or not. However, a big advantage of the Shannon index is that many studies use this index as a diversity measure, and it exists already for a long time (Magurran & Dornelas, 2010). In a comparative study of Morris et al. (2014), the Shannon index described the most relationships between organisms, which is a trait useful for this research. Pollinator species act as a community in its local habitat, indirectly affected by each other and by their pathogens. Simpson is very similar to Shannon, while Evenness is simpler, being a measurement of equality of species proportion in each group. Evenness is also less suitable when related to disease transmission, because host-pathogen relations are more complex. For example, multi-host relations,

53 where one host species might have a different effect of and on disease prevalence than another, could also affect species proportion importance. Shannon index values vary mostly between 1.5 and 3.5 (Magurran, 2004), but in our dataset, the highest index value is not much more than 2, and the lowest is around 0.7 (table A6). In general, it is difficult to compare these results with data from literature, because the sample sizes and sample time differ greatly and other diversity measures besides richness are usually not given. Even though the Shannon index is believed to be quite low in this study, a similar species richness was found in the study of Eeraerts et al. (2017), where 12 different species of bees were found of a total of 349 pollinators, which are quite comparable numbers, with a side note that most of the sampled species were A. mellifera. Comparing with a study in the Netherlands, where they also sampled in apple orchards, a similar but slightly lower amount of bee species was found (19 species compared to 24 in our study). 2. Pathogen prevalence One third of the sampled bees were infected with one of the three pathogens we screened for. This is quite high, because for many bee species, the foraging season wasn’t halfway through yet at the moment of sampling, so prevalence might be even higher at the end of the season. During sampling at the end of the blooming period, all bumble bee species were having worker bees, and B. pratorum and B. hypnorum also had male bees and queens, showing that they were at the end of their colony cycle already. These last two species had the highest A. bombi prevalence and C. bombi prevalence, respectively. The sampled solitary bees were mostly species active early in the season, from around February/March until May/June, which means that May is already quite late in their cycle. Also, many other pathogens, like viruses, were not screened for, so bees where none of the 3 investigated pathogens was detected, could still be infected by others. The prevalence data of each pathogen varies, with Apicystis bombi found twice as much as Nosema spp., and the latter found four times more than Crithidia bombi. Crithidia bombi was only found in 2 species: Bombus hypnorum and Bombus lapidarius. This is surprising, because C. bombi is known to be a common parasite in many Bombus species, and affects bumble bee queens during early colony establishment after hibernation (Plischuk et al., 2017). So even though the pathogen prevalence was rather low, the infection load, meaning the amount of C. bombi cells found in each bee, was very high. In a study of Whitehorn et al. (2013), B. lapidarius had the highest Crithidia spp. loads, while B. pratorum suffered the highest rate of infection at the end of the season. It is possible that later in the season, more species would be infected, and a higher prevalence would have been found than before summer. Apicystis bombi was found in 116 of the 425 bees, giving an overall prevalence of 27.29%, and a bumble bee prevalence of 33.22%. Apicystis bombi is known to infect many Bombus species, but also Apis mellifera. In a study of Ravoet et al. (2013), 40.8% of the honey bees were infected by A. bombi, while here only 8.6% of honey bees were infected. The sampling was done much later than in our study, in July. Although honey bees, unlike bumble bees, have a perennial life cycle, and queens can live up to 3 years, honey bees in the temperate climate region have a bounded foraging season, similar to most bumble bees. A higher infection rate thus seems logic in the previously mentioned study,

54 since the sampling was done much later in the season, and the disease might have been much more spread by then. B. pratorum has a notably higher prevalence than others, with 50.35% of all specimens infected by A. bombi, which is the highest infection rate. Comparing this to the study of Rutrecht & Brown (2008), both the spring queens (3.8%) and summer workers (5.5%) of B. pratorum had a low A. bombi prevalence. Summer workers were sampled between mid-May and end of June. The colonies were founded in nesting boxes, starting with queens taken from the field. As said before, B. pratorum is an early nesting bee. Because colonies are founded earlier, they will have a considerate size already by the beginning of May, and in this way diseases can already be spread more between the individuals within a colony compared to other bumble bee species. Bombus pratorum colonies are known to be quite small in size (Falk & Lewington, 2009), which can also make them more susceptible because they do not have much reserves to recover fast. Bumble bee colonies which are large in size, in range and in abundance, generally have larger parasite loads (Williams & Osborne, 2009). This does agree with the high pathogen load found in B. terrestris, since this bumble bee species is known to have large colony sizes. Bombus pascuorum at last has a considerable infection rate of A. bombi, but a low prevalence of Crithidia bombi and Nosema spp. Bombus pascuorum queens are known to be one of the last of the commonly found bumble bee species to emerge, and their colonies develop slowly (Falk & Lewington, 2009), which can explain prevalence of the latter two pathogens in a similar way as B. pratorum. Apis mellifera had a low pathogen prevalence but a high load for both Nosema spp. and A. bombi, which could be the result of the dense colonies in beehives, quickly amplifying disease, and in this way even having such mortal effects that the pathogen prevents its own transmission. Almost all Nosema spp. infections were identified as Nosema thomsoni, which is remarkable. Even though not much data is present about Nosema thomsoni infection, A. haemorrhoa has been proven to be infected most likely with N. thomsoni recently (Schoonvaere et al., 2018). Nosema thomsoni is a parasite of moths (Wilson and Burke, 1971) but there is not yet any confirmation that it is a real parasite of bumble bees (Li et al., 2012). Figure 19 suggests that all bumble bees where this Nosema species was found, are incompatible hosts, because of low prevalence and low loads, indicating that this pathogen was picked up by accident. 3. Semi-natural habitat and effect of diversity A general decline of pollinator richness with increasing distance from natural habitat was noticed in a review of Ricketts et al. (2008). An illustration of this can be seen in figure 26. Pollinator richness is dependent on resource availability, like forage sites, hibernation and nesting habitats. If less of these niches are available, more and more species will decline, because bees can differ greatly in their preferences for habitats. Some bumble bees often nest in substrates which are more available in modified habitats, like ground cavities. In this way, they have an advantage over other native social bees, which prefer more natural habitats. Bombus hypnorum is known to be positively affected by human influence on landscapes (Williams & Osborne, 2009). Solitary bees are also able to nest in disturbed areas, nesting in the ground or hollow stems. Solitary bees often have shorter flight seasons comparing to social bees, which could mean that they need less (diverse) floral resources, and crop

55 flowers may suffice (Ricketts et al., 2008). So although different species use different kind of landscapes as a habitat, all of them will thrive when having access to a diverse habitat. Having more semi-natural landscape also means having a more diverse flower community, this in turn will attract a more diverse pollinator network. This was also proven in this study, as can be seen in figure 21, where all diversity indices except species richness show a positive relationship with semi-natural area. Also, when populations are smaller because of habitat loss or patches of (semi-)natural habitat (as also depicted in figure 26), it could mean a higher chance for inbreeding and genetic drift, which could lead to increased susceptibility. The higher the habitat fragmentation and the more intensive land use, the more this will affect pollination services. In our study, the percentage of semi-natural landscape varied between 14% and 35%. To compare, Swiss farms on average have 22% semi-natural areas on organic farms and 13 percent on conventional farms, found by a comparative study of Schader et al. (2008).

Figure 26: An indicative figure showing the relationship between pollinator richness and abundance, and semi-natural habitats. The latter correlates with the amount of land use intensification for agricultural landscapes. The more intensification, the more semi-natural habitats fragment, the more pollinators will suffer from it (Steffan-Dewenter & Westphal, 2008). For the reasons mentioned above, semi-natural percentage as a measure of natural habitat was added to the fitted generalized linear models as an additional interpretation of pollinator diversity. All models suggest a positive influence of diversity on pathogen prevalence, suggesting that a more diverse pollinator community increases disease abundance. First, all specimens were taken into account, but the high variability of the data, especially the fact that many species were only sampled a few times on a few locations, did not provide a good comparison of diversity. Since Bombus pratorum was sampled on all locations, and also mostly in high numbers, a subset of this bumble bee species was made. In this way, the prevalence data of B. pratorum only was taken into account, which was then linked with the diversity of pollinator species of each location. Again, diversity and semi-natural habitat seem to increase pathogen prevalence.

It is important to make some nuances. First of all, diversity values may not correctly represent the diversity of the area. Likewise, the pathogen prevalence in B. pratorum only may not represent overall trends of pathogen infection and transmission in pollinators in the area of the orchards. Sampling was done for a short amount of time and with only two people, giving a total sample size of 425 species, which might not represent the actual diversity of the pollinator community. However because sampling was done in the same way for all locations, it is a good comparative measure. Sampling was also only done between rows of apple trees in the orchards. Certain species playing a part in the community might have been missed because they forage mainly on other parts of the habitat, while maybe still playing an important role in disease spreading. In the study of Reemer & Kleijn (2010) in the Netherlands, there was a much higher species diversity at dikes next to the orchards compared to in between the tree rows. Likewise, the amount of bee species sampled in our study might be lower than the actual amount of species in the pollinator community sharing floral resources. Albeit, the fitted model still gives a convincing positive effect of diversity on pathogen prevalence. When the Shannon index increases by one unit, then Nosema spp. prevalence will increase more than twice as much. Maybe this can be explained by the theory that Nosema spp. are not really known as multi-host pathogens, suggesting that the ‘decoy effect’ explained in literature section ‘Biodiversity and disease’ cannot be applied to these pathogens. A possible ‘dilution effect’ described for specialist pathogens, meaning that in a more diverse community with more incompatible hosts, the pathogen is diluted, was not found here. Also, most Nosema spp. (62.96%) were identified as N. thomsoni, which had a low infection load and prevalence in B. pratorum, as shown in figure 19. This implies a incompatibility of the pathogen to infect this bumble bee species. Most other bee species were considered impotent to either increase the disease (by being a spreader) or buffer (by being a dead- end host, or perhaps a host with high mortality), so the positive effect found might be contributed to Andrena haemorrhoa. This mining bee has a 50% N. thomsoni prevalence, and a relatively high infection load, suggesting it is a spreader of the pathogen, just like B. pratorum might be an important spreader of A. bombi. For A. bombi, different assumptions can be made, because this pathogen is known as a generalist, being able to infect many different species and genera. Also here, as can be seen in figure 22, a positive effect of diversity on pathogen prevalence is noted, although less prominent compared to Nosema spp. Bombus pratorum, contributing to one third of the data, has the highest Apicystis bombi infection, and also the second highest Nosema spp. infection. Furthermore, for A. bombi prevalence, figure 17 relating infection load to prevalence suggested that B. pratorum was a spreader of the disease, because of a high infection load and prevalence. Because of the high abundance of B. pratorum in the sampling, it also implies a high abundance in the local ecosystem of the apple orchards. The abundance of B. pratorum on each location could be a confounding variable, giving a disturbed image of the effect of diversity on disease prevalence. Indeed, a significant positive effect was found, indicating that B. pratorum increases A. bombi prevalence in all species. Bombus terrestris and Bombus hortorum, which both were suggested to be dead-end hosts of Apicystis bombi, were sampled 22 and 2 times, respectively. Maybe when more of these species are present, A. bombi prevalence might have been more negatively affected. These results prove that many different interactions in the complex web of pathogens, pollinators and their habitats are present, and that the presence of particular host types, and correct measurements of diversity are important for the decoy effect to be fully functional. 57

CONCLUSION AND FUTURE PERSPECTIVE Our results managed to identify areas in which the study design could be optimized, to be able to better assess the effect of pollinator diversity on pathogen prevalence in the future. Here we defined host types by means of their pathogen prevalence and infection intensities. Host types are important to fully understand the effect of the pathogens in their hosts. For future research, it is recommended to first fully acknowledge the functionalities of the hosts by acquiring sampling on a large scale and performing lab infection experiments. Bombus pratorum was chosen as the indicator species for pathogen prevalence, but has been defined as a potential spreader of Apicystis bombi. A positive relation was found between A. bombi prevalence and B. pratorum abundance, showing a potential interference of this bumble bee species with a potential underlying decoy effect of diversity in the pollinator community. Since A. bombi is defined as a generalist pathogen, it might be better to focus only on this pathogen in the future when trying to prove the decoy effect, but still use the fully sampled pollinator diversity to assess how this pathogen is influenced by the different host types. Here, sample sizes were different on each location, but sampling time was the same. This allows for an extra measurement of pollinator density on each location. Choosing a constant sample size instead could also means that diversity indices like species richness can be used and interpreted. Richness is an index that is often used in papers, especially when the sample sizes are equal, so then the measurements can be more easily compared and interpreted. Expanding the sampling area will also give more information about diversity, as here it is highly possible some important pollinator species contributing to diversity effects were missed. Pathogens often build up in intensity and prevalence throughout the foraging season. Sampling only once in the foraging season gives a static view of the impact of pathogens, especially when sampling all bee species present, because many species are at different time points in their life cycle. Virulence effects might be better assessed when different sample time points are used throughout the season. Besides all these possible improvements, this study gives some new insights to pathogen-host dynamics. Dividing pollinator species into host types can be the key to understand the effect of bee diversity on disease spreading. Here, Bombus pratorum was chosen as a focus species to assess the diversity effect, but in the future, many different bee species can be examined, and a total picture might give a clear overview of the combined, net effect of diversity.

REFERENCES

Bailey, L., Ball, B. V., & Perry, J. N. (1983). Association of viruses with two protozoal pathogens of the honey bee. Annals of Applied Biology, 103(1), 13–20. https://doi.org/10.1111/j.1744- 7348.1983.tb02735.x Bailey, L., Gibbs, A. J., & Woods, R. D. (1963). Two viruses from adult honey bees (Apis mellifera Linnaeus). Virology, 21(3), 390–395. https://doi.org/10.1016/0042-6822(63)90200-9 Bailey, L., & Woods, R. D. (1974). Three previously undescribed viruses from the honey bee. Journal of General Virology, 25(2), 175–186. https://doi.org/10.1099/0022-1317-25-2-175 Blacquière, T., Smagghe, G., Van Gestel, C. A. M., & Mommaerts, V. (2012). Neonicotinoids in bees: A review on concentrations, side-effects and risk assessment. Ecotoxicology, 21(4), 973–992. https://doi.org/10.1007/s10646-012-0863-x Blitzer, E. J., Gibbs, J., Park, M. G., & Danforth, B. N. (2016). Agriculture , Ecosystems and Environment Pollination services for apple are dependent on diverse wild bee communities. “Agriculture, Ecosystems and Environment,” 221, 1–7. https://doi.org/10.1016/j.agee.2016.01.004 Boulanger, N., Ehret-Sabatier, L., Brun, R., Zachary, D., Bulet, P., & Imler, J.-L. (2001). Immune response of Drosophila melanogaster to infection with the flagellate parasite Crithidia spp. Insect Biochemistry and Molecular Biology, 31(2), 129–137. https://doi.org/10.1016/S0965- 1748(00)00096-5 Brittain, C., Williams, N., Kremen, C., Klein, A., & B, P. R. S. (2013). Synergistic effects of non- Apis bees and honey bees for pollination services Synergistic effects of non- Apis bees and honey bees for pollination services, (January). https://doi.org/http://dx.doi.org/10.1098/rspb.2012.2767 Brown, M. J. F., Schmid-hempel, R., & Schmid-hempel, P. (2003). Strong context-dependent virulence in a host – parasite system : reconciling genetic evidence with theory. Journal of Animal Ecology, 72, 994–1002. Cameron, S. A., Hines, H. M., & Williams, P. H. (2007). A comprehensive phylogeny of the bumble bees ( Bombus ). Biological Journal of the Linnean Society, 91, 161–188. Cameron, S. A., Lozier, J. D., Strange, J. P., Koch, J. B., Cordes, N., Solter, L. F., & Griswold, T. L. (2011). Patterns of widespread decline in North American bumble bees. Proceedings of the National Academy of Sciences, 108(2), 662–667. https://doi.org/10.1073/pnas.1014743108 Cardinal, S., Buchmann, S. L., & Russell, A. L. (2018). The evolution of floral sonication, a pollen foraging behavior used by bees (Anthophila). Evolution, 72(3), 590–600. https://doi.org/10.1111/evo.13446 Chapin III, F. S., Zavaleta, E. S., Eviner, V. T., Naylor, R. L., Vitousek, P. M., Reynolds, H. L., … Díaz, S. (2000). Consequences of changing biodiversity. Nature, 405(6783), 234–242.

https://doi.org/10.1038/35012241 Chen, Y., Evans, J. D., Smith, I. B., & Pettis, J. S. (2008). Nosema ceranae is a long-present and wide-spread microsporidian infection of the European honey bee (Apis mellifera) in the United States. Journal of Invertebrate Pathology, 97(2), 186–188. https://doi.org/10.1016/j.jip.2007.07.010 Colla, S. R., Otterstatter, M. C., Gegear, R. J., & Thomson, J. D. (2006). Plight of the bumble bee: Pathogen spillover from commercial to wild populations. Biological Conservation, 129(4), 461–467. https://doi.org/10.1016/j.biocon.2005.11.013 De Saeger, S., & Scheers, K. (2016). The INBO Biological Valuation Map | Research Institute for Nature and Forest (INBO). Retrieved August 13, 2018, from https://www.inbo.be/en/inbo- biological-valuation-map Dickman, M. (1968). Some Indices of Diversity. Ecology, 6(6), 1191–1193. Dobson, A. (2004). Population Dynamics of Pathogens with Multiple Host Species. The American Naturalist, 164(S5), S64–S78. https://doi.org/10.1086/424681 Durrer, S., & Schmid-Hempel, P. (1994). Shared Use of Flowers Leads to Horizontal Pathogen Transmission. Proceedings of the Royal Society B: Biological Sciences, 258(1353), 299–302. https://doi.org/10.1098/rspb.1994.0176 Eeraerts, M., Meeus, I., Van Den Berge, S., & Smagghe, G. (2017). Landscapes with high intensive fruit cultivation reduce wild pollinator services to sweet cherry. Agriculture, Ecosystems and Environment, 239, 342–348. https://doi.org/10.1016/j.agee.2017.01.031 Falk, S. J., & Lewington, R. (2009). Field guide to the bees of Great Britain and Ireland (1st ed.). Devon: British Wildlife Publishing. Fantham, H. B., & Porter, A. (1914). The Morphology, Biology and Economic Importance of Nosema Bombi , n.sp. , Parasitic in Various Humble Bees ( Bombus spp. ). Annals of Tropical Medicine & Parasitology, 8(3), 623–638. https://doi.org/10.1080/00034983.1914.11687667 Fenster, C. B., Armbruster, W. S., Wilson, P., Dudash, M. R., Thomson, J. D., Specialization, P., … Thomson, J. D. (2004). Pollination Syndromes and Floral Specialization. Annual Review of Ecology, Evolution, and Systematics, 35(1), 375–403. https://doi.org/10.1146/annurev.ecolsys.34.011802.132347 Folly, A. J., Koch, H., Stevenson, P. C., & Brown, M. J. F. (2017). Larvae act as a transient transmission hub for the prevalent bumblebee parasite Crithidia bombi. Journal of Invertebrate Pathology, 148(June), 81–85. https://doi.org/10.1016/j.jip.2017.06.001 Forfert, N., Natsopoulou, M. E., Frey, E., Rosenkranz, P., Paxton, R. J., Moritz, R. F. A., … Gunn, A. (2015). Parasites and Pathogens of the Honeybee (Apis mellifera) and Their Influence on Inter-Colonial Transmission. PLOS ONE, 10(10), e0140337. https://doi.org/10.1371/journal.pone.0140337 Fouks, B., & Lattorff, H. M. G. (2011). Recognition and avoidance of contaminated flowers by

foraging bumblebees (Bombus terrestris). PLoS ONE, 6(10). https://doi.org/10.1371/journal.pone.0026328 Franks, N. R. (1987). The parasitic strategies of a cuckoo bee. Trends in Ecology & Evolution, 2(11), 324–326. https://doi.org/10.1016/0169-5347(87)90106-6 Fries, I., Feng, F., da Silva, A., Slemenda, S. B., & Pieniazek, N. J. (1996). Nosema ceranae n. sp. (Microspora, Nosematidae), morphological and molecular characterization of a microsporidian parasite of the Asian honey bee Apis cerana (Hymenoptera, Apidae). European Journal of Protistology, 32(3), 356–365. https://doi.org/10.1016/S0932- 4739(96)80059-9 Fürst, M. A., McMahon, D. P., Osborne, J. L., Paxton, R. J., & Brown, M. J. F. F. (2014). Disease associations between honeybees and bumblebees as a threat to wild pollinators. Nature, 506(7488), 364–366. https://doi.org/10.1038/nature12977 Garibaldi, L. A., Steffan-dewenter, I., Winfree, R., Aizen, M. A., Bommarco, R., Cunningham, S. A., … Carvalheiro, L. G. (2014). Honey Bee Abundance. Science, 339(May), 1608–1611. https://doi.org/10.1126/science.1230200 Ghosh, K., & Weiss, L. M. (2009). Molecular Diagnostic Tests for Microsporidia. Interdisciplinary Perspectives on Infectious Diseases, 2009, 1–13. https://doi.org/10.1155/2009/926521 Gill, R. J., Ramos-Rodriguez, O., & Raine, N. E. (2012). Combined pesticide exposure severely affects individual-and colony-level traits in bees. Nature, 491(7422), 105–108. https://doi.org/10.1038/nature11585 Goulson, D., Lye, G. C., & Darvill, B. (2008). Decline and Conservation of Bumble Bees. Annual Review of Entomology, 53(1), 191–208. https://doi.org/10.1146/annurev.ento.53.103106.093454 Goulson, D., Nicholls, E., Botías, C., & Rotheray, E. L. (2015). Bee declines driven by combined Stress from parasites, pesticides, and lack of flowers. Science, 347(6229), 1255957. https://doi.org/10.1126/science.1255957 Graystock, P., Blane, E. J., Mcfrederick, Q. S., Goulson, D., & Hughes, W. O. H. (2016). Do managed bees drive parasite spread and emergence in wild bees ? International Journal for Parasitology: Parasites and Wildlife, 5(1), 64–75. https://doi.org/10.1016/j.ijppaw.2015.10.001 Graystock, P., Goulson, D., & Hughes, W. O. H. (2015). Parasites in bloom: flowers aid dispersal and transmission of pollinator parasites within and between bee species. Proceedings. Biological Sciences / The Royal Society, 282(1813), 20151371. https://doi.org/10.1098/rspb.2015.1371 Graystock, P., Meeus, I., Smagghe, G., Goulson, D., & Hughes, W. O. H. (2016). The effects of single and mixed infections of Apicystis bombi and deformed wing virus in Bombus terrestris. Parasitology, 143(3), 358–365. https://doi.org/10.1017/S0031182015001614

Graystock, P., Yates, K., Darvill, B., Goulson, D., & Hughes, W. O. H. (2013). Emerging dangers: Deadly effects of an emergent parasite in a new pollinator host. Journal of Invertebrate Pathology, 114(2), 114–119. https://doi.org/10.1016/j.jip.2013.06.005 Graystock, P., Yates, K., Evison, S. E. F., Darvill, B., Goulson, D., & Hughes, W. O. H. (2013). The Trojan hives: pollinator pathogens, imported and distributed in bumblebee colonies. Journal of Applied Ecology, 50(5), n/a-n/a. https://doi.org/10.1111/1365-2664.12134 Greenleaf, S. S., & Kremen, C. (2006). Wild bees enhance honey bees’ pollination of hybrid sunflower. Proceedings of the National Academy of Sciences of the United States of America, 103(37), 13890–13895. https://doi.org/10.1073/pnas.0600929103 Hill, M. O. (1973). Diversity and Evenness : A Unifying Notation and Its Consequences. Ecology, 54(2), 427–432. https://doi.org/10.2307/1934352 Imhoof, B., & Schmid-Hempel, P. (1998). Patterns of Local Adaptation of a Protozoan Parasite to Its Bumblebee host. Nordic Society Oikos, 82(1), 59–65. Imhoof, B., & Schmid-Hempel, P. (1999). Colony success of the bumble bee, Bombus terrestris , in relation to infections by two protozoan parasites, Crithidia bombi and Nosema bombi. Insectes Sociaux, 46(3), 233–238. https://doi.org/10.1007/s000400050139 Johnson, P. T. J., & Thieltges, D. W. (2010). Diversity, decoys and the dilution effect: how ecological communities affect disease risk. Journal of Experimental Biology, 213(6), 961–970. https://doi.org/10.1242/jeb.037721 Keesing, F., Belden, L. K., Daszak, P., Dobson, A., Harvell, C. D., Holt, R. D., … Ostfeld, R. S. (2010). Impacts of biodiversity on the emergence and transmission of infectious diseases. Nature, 468(7324), 647–652. https://doi.org/10.1038/nature09575 Keesing, F., Holt, R. D., & Ostfeld, R. S. (2006). Effects of species diversity on disease risk. Ecology Letters, 9(4), 485–498. https://doi.org/10.1111/j.1461-0248.2006.00885.x Klein, A.-M., Vaissière, B. E., Cane, J. H., Steffan-dewenter, I., Cunningham, S. A., Kremen, C., … Tscharntke, T. (2007). Importance of pollinators in changing landscapes for world crops. Proceedings. Biological Sciences / The Royal Society, 274(1608), 303–313. Kremen, C., Williams, N. M., & Thorp, R. W. (2002). Crop pollination from native bees at risk from agricultural intensification. Proceedings of the National Academy of Sciences of the United States of America, 99(26), 16812–16816. https://doi.org/10.1073/pnas.262413599 Langridge, D. F., & McGhee, R. B. (1967). Crithidia mellificae n. sp. an Acidophilic Trypanosomatid of the Honey Bee Apis mellifera. The Journal of Protozoology, 14(3), 485–487. https://doi.org/10.1111/j.1550-7408.1967.tb02033.x Larsson, J. I. R. (2011). Cytological variation and pathogenicity of the bumble bee parasite Nosema bombi ( Microspora , Nosematidae ). Journal of Invertebrate Pathology, 94(2007), 1–11. https://doi.org/10.1016/j.jip.2006.07.006 Levine, R. S., Hedeen, D. L., Hedeen, M. W., Hamer, G. L., Mead, D. G., & Kitron, U. D. (2017). Avian

species diversity and transmission of West Nile virus in Atlanta , Georgia. Parasites & Vectors, 10–62. https://doi.org/10.1186/s13071-017-1999-6 Li, J., Chen, W., Wu, J., Peng, W., An, J., Schmid-Hempel, P., & Schmid-Hempel, R. (2012). Diversity of nosema associated with bumblebees (Bombus spp.) from China. International Journal for Parasitology, 42(1), 49–61. https://doi.org/10.1016/j.ijpara.2011.10.005 Lipa, J. J., & Triggiani, O. (1988). Crithidia bombi sp. n. a flagellated parasite of a bumble bee Bombus terrestris L. Acta Protozoologica, 27(3–4). Retrieved from http://yadda.icm.edu.pl/yadda/element/bwmeta1.element.agro-a4509174-bc5e-4708- 9bac-126c83084cc0 Lipa, J. J., & Triggiani, O. (1992). A Newly Recorded Neogregarine (Protozoa, Apicomplexa), Parasite in Honey Bees (Apis mellifera) and Bumble Bees (Bombus spp). Apidologie, 23(6), 533–536. https://doi.org/10.1051/apido:19920605 Lipa, J. J., & Triggiani, O. (1996). Apicystis gen nov and Apicystis bombi (Liu, Macfarlane {&} Pengelly) comb nov (Protozoa: Neogregarinida), a cosmopolitan parasite of Bombus and Apis (Hymenoptera: Apidae). Apidologie, 27(1), 29–34. https://doi.org/10.1051/apido:19960104 Lipa, J., & Triggiani, O. (1996). Apicystis gen nov and Apicystis bombi ( Liu , Macfarlane & Pengelly ) comb nov ( Protozoa : Neogregarinida ), a cosmopolitan parasite of Bombus and Apis ( Hymenoptera : Apidae ). Apidologie, 27(1), 29–34. Maebe, K., Meeus, I., Vray, S., Claeys, T., Dekoninck, W., Boevé, J. L., … Smagghe, G. (2016). A century of temporal stability of genetic diversity in wild bumblebees. Scientific Reports, 6(December). https://doi.org/10.1038/srep38289 Magurran, A. E. (2004). Measuring biological diversity. Oxford: Blackwell Publishing Inc. Magurran, A. E., & Dornelas, M. (2010). Biological diversity in a changing world. Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences, 365(1558), 3593– 3597. https://doi.org/10.1098/rstb.2010.0296 Maharramov, J., Meeus, I., Maebe, K., Arbetman, M., Morales, C., Graystock, P., … Smagghe, G. (2013). Genetic variability of the neogregarine Apicystis bombi, an etiological agent of an emergent bumblebee disease. PLoS ONE, 8(12), 6–13. https://doi.org/10.1371/journal.pone.0081475 Manley, R., Boots, M., & Wilfert, L. (2015). Emerging viral disease risk to pollinating insects: ecological, evolutionary and anthropogenic factors. The Journal of Applied Ecology, 52(2), 331–340. https://doi.org/10.1111/1365-2664.12385 Manson, J. S., Otterstatter, M. C., & Thomson, J. D. (2010). Consumption of a nectar alkaloid reduces pathogen load in bumble bees. Oecologia, 162(1), 81–89. https://doi.org/10.1007/s00442-009-1431-9 McArt, S. H., Koch, H., Irwin, R. E., & Adler, L. S. (2014). Arranging the bouquet of disease: floral traits and the transmission of plant and animal pathogens. Ecology Letters, 17(5), 624–636.

https://doi.org/10.1111/ele.12257 McIvor, C. A., & Malone, L. A. (1995). Nosema bombi a microsporidian pathogen of the bumble bee bombus terrestris (L.). New Zealand Journal of Zoology, 22(1), 25–31. https://doi.org/10.1080/03014223.1995.9518020 Mcmahon, D. P., Fürst, M. A., Caspar, J., Theodorou, P., Brown, M. J. F., & Paxton, R. J. (2015). A sting in the spit: Widespread cross-infection of multiple RNA viruses across wild and managed bees. Journal of Animal Ecology, 84(3), 615–624. https://doi.org/10.1111/1365- 2656.12345 Meeus, I., Brown, M. J. F., De Graaf, D. C., & Smagghe, G. (2011). Effects of Invasive Parasites on Bumble Bee Declines. Conservation Biology, 25(4), 662–671. https://doi.org/10.1111/j.1523- 1739.2011.01707.x Meeus, I., De Graaf, D. C., Jans, K., & Smagghe, G. (2010). Multiplex PCR detection of slowly- evolving trypanosomatids and neogregarines in bumblebees using broad-range primers. Journal of Applied Microbiology, 109(1), 107–115. https://doi.org/10.1111/j.1365- 2672.2009.04635.x Meeus, I., Pisman, M., Smagghe, G., & Piot, N. (2018). Interaction effects of different drivers of wild bee decline and their influence on host–pathogen dynamics. Current Opinion in Insect Science, 26, 136–141. https://doi.org/10.1016/j.cois.2018.02.007 Michener, C. D. (2007). The bees of the world (2nd ed.). Baltimore: The John Hopkins University Press. Morris, E. K., Caruso, T., Buscot, F., Fischer, M., Hancock, C., Maier, T. S., … Rillig, M. C. (2014). Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecology and Evolution, 4(18), 3514–3524. https://doi.org/10.1002/ece3.1155 Murray, T. E., Coffey, M. F., Kehoe, E., & Horgan, F. G. (2013). Pathogen prevalence in commercially reared bumble bees and evidence of spillover in conspecific populations. Biological Conservation, 159, 269–276. https://doi.org/10.1016/j.biocon.2012.10.021 Neumann, P., & Carreck, N. L. (2010). Honey bee colony losses. Journal of Apicultural Research, 49(1), 1–6. https://doi.org/10.3896/IBRA.1.49.1.01 Otterstatter, M. C., & Thomson, J. D. (2008). Does pathogen spillover from commercially reared bumble bees threaten wild pollinators? PLoS ONE, 3(7). https://doi.org/10.1371/journal.pone.0002771 Otterstatter, M. C., & Whidden, T. L. (2004). Patterns of parasitism by tracheal mites (Locustacarus buchneri) in natural bumble bee populations. Apidologie, 35(4), 351–357. https://doi.org/10.1051/apido:2004024 Otti, O., & Schmid-Hempel, P. (2007). Nosema bombi: A pollinator parasite with detrimental fitness effects. Journal of Invertebrate Pathology, 96(2), 118–124.

https://doi.org/10.1016/j.jip.2007.03.016 Otti, O., & Schmid-Hempel, P. (2008). A field experiment on the effect of Nosema bombi in colonies of the bumblebee Bombus terrestris. Ecological Entomology, 33(5), 577–582. https://doi.org/10.1111/j.1365-2311.2008.00998.x Park, M. G., Blitzer, E. J., Gibbs, J., Losey, J. E., & Danforth, B. N. (2015). Negative effects of pesticides on wild bee communities can be buffered by landscape context. Proceedings of the Royal Society B: Biological Sciences, 282(1809), 20150299. https://doi.org/10.1098/rspb.2015.0299 Paul, H. W., Juliet, L. O., Williams, P. H., Osborne, J. L., Paul, H. W., & Juliet, L. O. (2009). Bumblebee vulnerability and conservation world-wide. Apidologie, 40(3), 367–387. https://doi.org/10.1051/apido/2009025 Plischuk, S., Antúnez, K., Haramboure, M., Minardi, G. M., & Lange, C. E. (2017). Long-term prevalence of the protists Crithidia bombi and Apicystis bombi and detection of the microsporidium Nosema bombi in invasive bumble bees. Environmental Microbiology Reports, 9(2), 169–173. https://doi.org/10.1111/1758-2229.12520 Plischuk, S., & Lange, C. E. (2009). Invasive Bombus terrestris (Hymenoptera: Apidae) parasitized by a flagellate (Euglenozoa: Kinetoplastea) and a neogregarine (Apicomplexa: Neogregarinorida). Journal of Invertebrate Pathology, 102(3), 263–265. https://doi.org/10.1016/j.jip.2009.08.005 Plischuk, S., Meeus, I., Smagghe, G., & Lange, C. E. (2011). Apicystis bombi (Apicomplexa: Neogregarinorida) parasitizing Apis mellifera and Bombus terrestris (Hymenoptera: Apidae) in Argentina. Environmental Microbiology Reports, 3(5), 565–568. https://doi.org/10.1111/j.1758-2229.2011.00261.x Poulin, R., & Morand, S. (2018). The Diversity of Parasites. The Quarterly Review of Biology, 75(3), 277–293. Proenca, C. E. B. (2008). Buzz Pollination - Older and More Widespread Than We Think ? Cambridge University, 8(1), 115–120. Rasmont, P., Leclercq, J., Jacob-Remacle, A., Pauly, A., & Gaspar, C. (1993). The faunistic drift of Apoidea in Belgium. Bees for Pollination, (January 2014), 65–87. Ravoet, J., & De Graaf, D. (2015). Exploring the involvement of honey bee pathogens in colony collapses from epidemiological data , pathogen genotyping and host immune responses . Jorgen Ravoet. University of Ghent. Ravoet, J., De Smet, L., Meeus, I., Smagghe, G., Wenseleers, T., & de Graaf, D. C. (2014). Widespread occurrence of honey bee pathogens in solitary bees. Journal of Invertebrate Pathology, 122, 55–58. https://doi.org/10.1016/j.jip.2014.08.007 Ravoet, J., De Smet, L., Wenseleers, T., & de Graaf, D. C. (2015). Vertical transmission of honey bee viruses in a Belgian queen breeding program. BMC Veterinary Research, 11(1), 61.

https://doi.org/10.1186/s12917-015-0386-9 Ravoet, J., Maharramov, J., Meeus, I., De Smet, L., Wenseleers, T., Smagghe, G., & de Graaf, D. C. (2013). Comprehensive bee pathogen screening in Belgium reveals Crithidia mellificae as a new contributory factor to winter mortality. PloS One, 8(8), e72443. https://doi.org/10.1371/journal.pone.0072443 Ravoet, J., Schwarz, R. S., Descamps, T., Yañez, O., Ozge, C., Martin-hernandez, R., … Graaf, D. C. De. (2015). Differential diagnosis of the honey bee trypanosomatids Crithidia mellificae and Lotmaria passim. Journal of Invertebrate Pathology, 130, 21–27. https://doi.org/10.1016/j.jip.2015.06.007 Raw, A. (1972). The Biology Of the solitary bee Osmia rufa(L.)(Megachilidae). Transactions of the Royal Entomological Society of London, 3(4), 213–229. Reemer, M., & Kleijn, D. (2010). Wilde bestuivers in appel - en perenboomgaarden in de Betuwe. Leiden. Richardson, L. L., Adler, L. S., Leonard, A. S., Andicoechea, J., Regan, K. H., Anthony, W. E., … Irwin, R. E. (2015). Secondary metabolites in floral nectar reduce parasite infections in bumblebees. Proceedings. Biological Sciences, 282(1803), 2014–2471. https://doi.org/10.1098/rspb.2014.2471 Ricketts, T. H., Regetz, J., Steffan-Dewenter, I., Cunningham, S. A., Kremen, C., Bogdanski, A., … Viana, B. F. (2008). Landscape effects on crop pollination services: Are there general patterns? Ecology Letters, 11(5), 499–515. https://doi.org/10.1111/j.1461- 0248.2008.01157.x Rigaud, T., Perrot-Minnot, M.-J., & Brown, M. J. F. (2010). Parasite and host assemblages: embracing the reality will improve our knowledge of parasite transmission and virulence. Proceedings of the Royal Society B: Biological Sciences, 277(1701), 3693–3702. https://doi.org/10.1098/rspb.2010.1163 Ruiz-González, M. X., Bryden, J., Moret, Y., Reber-Funk, C., Schmid-Hempel, P., & Brown, M. J. F. (2012). Dynamic transmission, host quality, and population structure in a multihost parasite of bumblebees. Evolution, 66(10), 3053–3066. https://doi.org/10.1111/j.1558- 5646.2012.01655.x Rutrecht, S. T., & Brown, M. J. F. (2009). Differential virulence in a multiple-host parasite of bumble bees: Resolving the paradox of parasite survival? Oikos, 118(6), 941–949. https://doi.org/10.1111/j.1600-0706.2009.17392.x Rutrecht, S. T., & Brown, M. J. F. F. (2008). The life-history impact and implications of multiple parasites for bumble bee queens. International Journal for Parasitology, 38(7), 799–808. https://doi.org/10.1016/j.ijpara.2007.11.004 Sadd, B. M., & Barribeau, S. M. (2013). Heterogeneity in infection outcome: Lessons from a bumblebee-trypanosome system. Parasite Immunology, 35(11), 339–349. https://doi.org/10.1111/pim.12043

Schader, C., Pfiffner, L., Schlatter, C., & Stolze, M. (2008). Umsetzung von Ökomassnahmen auf Bio- und ÖLN-betrieben. AgrarForschung, 15(10), 506–511. Schmid-Hempel, P. (1995). Parasites and Their Social Hosts. Trends in Parasitology, 33(6), 453– 462. https://doi.org/10.1016/j.pt.2017.01.003 Schmid-Hempel, P. (2001). On the evolutionary ecology of host-parasite interactions: Addressing the question with regard to bumblebees and their parasites. Naturwissenschaften, 88(4), 147–158. https://doi.org/10.1007/s001140100222 Schmid-Hempel, P., & Loosli, R. (1998). A contribution to the knowledge of Nosema infections in bumble bees, Bombus spp. Apidologie, 29(6), 525–535. https://doi.org/10.1051/apido:19980605 Schmid-Hempel, P., & Schmid-Hempel, R. (1993). Transmission of a pathogen in Bombus terrestris, with a note on division of labour in social insects. Behavioral Ecology and Sociobiology, 33(5), 319–327. https://doi.org/10.1007/BF00172930 Schmid-Hempel, R., & Tognazzo, M. (2010). Molecular divergence defines two distinct lineages of crithidia bombi (Trypanosomatidae), parasites of bumblebees. Journal of Eukaryotic Microbiology, 57(4), 337–345. https://doi.org/10.1111/j.1550-7408.2010.00480.x Schoonvaere, K., Smagghe, G., Francis, F., & de Graaf, D. C. (2018). Study of the metatranscriptome of eight social and solitary wild bee species reveals novel viruses and bee parasites. Frontiers in Microbiology, 9(FEB), 1–12. https://doi.org/10.3389/fmicb.2018.00177 Schwarz, R. S., Bauchan, G. R., Murphy, C. A., Ravoet, J., de Graaf, D. C., & Evans, J. D. (2015). Characterization of Two Species of Trypanosomatidae from the Honey Bee Apis mellifera: Crithidia mellificae Langridge and McGhee, and Lotmaria passim n. gen., n. sp. The Journal of Eukaryotic Microbiology, 62(5), 567–583. https://doi.org/10.1111/jeu.12209 Senapathi, D., Biesmeijer, J. C., Breeze, T. D., Kleijn, D., Potts, S. G., & Carvalheiro, L. G. (2015). Pollinator conservation - The difference between managing for pollination services and preserving pollinator diversity. Current Opinion in Insect Science, 12, 93–101. https://doi.org/10.1016/j.cois.2015.11.002 Shapiro, A. T., & Englund, P. T. (1995). The Structure and Replication of Kinetoplast DNA. Annual Review of Microbiology, 49, 117–143. Shykoff, J. A., & Schmid-Hempel, P. (1991). Incidence and effects of four parasites in natural populations of bumble bees in Switzerland. Apidologie, 22(2), 117–125. https://doi.org/10.1051/apido:19910204 Simone-Finstrom, M. D., & Spivak, M. (2012). Increased Resin Collection after Parasite Challenge: A Case of Self-Medication in Honey Bees? PLoS ONE, 7(3), e34601. https://doi.org/10.1371/journal.pone.0034601 Simpson, A. G. B., Stevens, J. R., & Lukeš, J. (2006). The evolution and diversity of kinetoplastid

flagellates. Trends in Parasitology, 22(4), 168–174. https://doi.org/10.1016/j.pt.2006.02.006 Smith, M. L. (2012). The honey bee parasite nosema ceranae: Transmissible via food exchange? PLoS ONE, 7(8), 1–6. https://doi.org/10.1371/journal.pone.0043319 Thomson, D. (2004). Competitive interactions between the invasive European honey bee and native bumble bees. Ecology, 85(2), 458–470. https://doi.org/10.1890/02-0626 Van den Eijnde, J., & Vette, N. (1993). Nosema infection in honeybees (Apis mellifera L.) and bumblebees (Bombus terrestris L.). Proc. Exper & Appl. Entomol., 4, 205–208. Van der Sluijs, J. P., Simon-Delso, N., Goulson, D., Maxim, L., Bonmatin, J. M., & Belzunces, L. P. (2013). Neonicotinoids, bee disorders and the sustainability of pollinator services. Current Opinion in Environmental Sustainability, 5(3–4), 293–305. https://doi.org/10.1016/j.cosust.2013.05.007 van Frankenhuyzen, K., Nystrom, C., & Liu, Y. (2007). Vertical transmission of Nosema fumiferanae (Microsporidia: Nosematidae) and consequences for distribution, post-diapause emergence and dispersal of second-instar larvae of the spruce budworm, Choristoneura fumiferana (Clem.) (Lepidoptera: Tortricidae). Journal of Invertebrate Pathology, 96(2), 173–182. https://doi.org/10.1016/j.jip.2007.03.017 Velthuis, H. H. W., & van Doorn, A. (2006). A century of advances in bumblebee domestication and the economic and environmental aspects of its commercialization for pollination. Apidologie, 37(4), 421–451. https://doi.org/10.1051/apido:2006019 Whitehorn, P. R., Tinsley, M. C., Brown, M. J. F., & Goulson, D. (2013). Investigating the impact of deploying commercial Bombus terrestris for crop pollination on pathogen dynamics in wild bumble bees. Journal of Apicultural Research, 52(3), 149–157. https://doi.org/10.3896/IBRA.1.52.3.06 Whittall, J. B., & Hodges, S. A. (2007). Pollinator shifts drive increasingly long nectar spurs in columbine flowers. Nature, 447(7145), 706–709. https://doi.org/10.1038/nature05857 Williams, P. H., & Osborne, J. L. (2009). Bumblebee vulnerability and conservation world-wide. Apidologie, 40(3), 367–387. https://doi.org/10.1051/apido/2009025 Wilson, G. G., & Burke, J. M. (1971). Nosema thomsoni n. sp., a microsporidian from Choristoneura conflictana (Lepidoptera: Tortricidae). Canadian Journal of Zoology, 49(5), 786–788. https://doi.org/10.1139/z71-118 Winfree, R. (2008). Pollinator-Dependent Crops: An Increasingly Risky Business. Current Biology, 18(20), 968–969. https://doi.org/10.1016/j.cub.2008.09.010 Yates, A., Antia, R., & Regoes, R. R. (2006). How do pathogen evolution and host heterogeneity interact in disease emergence? Proceedings of the Royal Society B: Biological Sciences, 273(1605), 3075–3083. https://doi.org/10.1098/rspb.2006.3681 Zander, E. (1909). Tierische Parasiten als Krankenheitserreger bei der Biene. Münchener Bienenzeitung, 31, 196–204.

ADDENDUM 1. PCR master mixes Table A1: Apicystis bombi PCR master mix. The first column gives the different reagents in the master mix, the second column gives the initial concentration, the third column gives the final concentration in the master mix by adding the volume given in the last column. Reagent Stock concentration Final concentration Volume (μl)

Buffer 10 x 1 x 2.5 MgCl 50 mM 1.5 mM 0.75 dNTPs 10 mM 0.4 mM 1 NeoF 10 µM 0.5 µM 1.25 NeoR 10 µM 0.5 µM 1.25 ApidaeF 10 µM 0.1 µM 0.25 ApidaeR 10 µM 0.2 µM 0.5 Taq polymerase 5 U/µl 1.25 reaction 0.25 ddH2O 16.25 Sum 24 DNA template 1 Sum 25

Table A2: Crithidia sp. PCR master mix. The first column gives the different reagents in the master mix, the second column gives the initial concentration, the third column gives the final concentration in the master mix by adding the volume given in the last column. Reagent Stock concentration Final concentration Volume (μl)

Buffer 10 x 1 X 2.5 MgCl 50 mM 1.5 mM 0.75 dNTPs 10 mM 0.4 mM 1 SEF 10 µM 0.5 µM 1.25 SER 10 µM 0.5 µM 1.25 ApidaeF 10 µM 0.1 µM 0.25 ApidaeR 10 µM 0.2 µM 0.5 Taq polymerase 5 U/µl 1.25 reaction 0.25 ddH2O 16.25 Sum 24 DNA template 1 Sum 25

Table A3: Nosema sp. PCR master mix. The first column gives the different reagents in the master mix, the second column tives the initial concentration, the third column gives the final concentration in the master mix by adding the volume given in the last column. This time Apidae primers were not used because from previous PCRs, it was proven that all extractions succeeded. Reagent Stock concentration Final concentration Volume (μl)

Buffer 10 x 1 X 2.5 MgCl 50 mM 1.5 mM 0.75 dNTPs 10 mM 0.4 mM 1 NosF 10 µM 0.5 µM 1.25 NosR 10 µM 0.5 µM 1.25 Taq polymerase 5 U/µl 1.25 reaction 0.25 ddH2O 17 Sum 24 DNA template 1 Sum 25

Table A4: Master mix for nested PCR, for Nosema sp. genotyping. For the first PCR, Weiss primers were used, for the second PCR, Nested primers were used. Reagent Stock concentration Final concentration Volume (μl)

Buffer 10 X 1 x 2.5 MgCl 50 mM 1.5 mM 0.75 dNTPs 10 mM 0.2 mM 0.5 WeissF/NestedF 10 µM 0.5 µM 1.25 WeissR/NestedR 10 µM 0.5 µM 1.25 Taq polymerase 5 U/µl 1.25 reaction 0.25

ddH2O 17.5 Sum 24 DNA template 1 Sum 25

2. Sampling overview 2.1. Species distribution Eight different genera were found, of which Bombus spp. was the most sampled genus with 307 specimens. 35 specimens were honey bees, of which 5 were pooled bees from commercial beehives at 2 apple orchards (three at location FB and two at location LV), and the rest were solitary bees, mainly of the genus Osmia and Andrena. An overview of the number of specimens in each genus is displayed in figure 27.

Figure 27. Overview of the genera sampled in May 2017, and the amount of specimens of each genus. The count data for each genus is also indicated on top of each bar. Bombus pratorum is the most prevalent among the bumble bees and also among all sampled specimens, while Osmia bicornis is the most prevalent among the solitary bees. A lot of solitary bees were only sampled once, such as many Andrena species (A. bicolor, A. carantonica, A. chrysosceles, A. clarkella) and Lasioglossum species. In figure 28, a more detailed overview of the prevalence data of Bombus species is given. Here we see that B. pratorum and B. hypnorum are the two most prevalent bumble bees. B. hortorum, B. jonellus and B. campestris were only sampled twice, thrice and once, respectively. All of these bumble bee species are commonly found in Belgium.

Figure 28: Bar plots of the count data of all Bombus spp. sampled, with the counts indicated on top of each bar. 2.2. Location distribution Genus level When looking at the genus distribution across the different locations (figure 29), we see that at almost all locations mostly Bombus species were sampled. On average, 53.13 ± 19.24 bees were sampled on each location. In general, at most of the locations, about the same ratio of genera was sampled, without major deviations. On average, Bombus spp. prevalence was 73.61% ± 9.38, Apis mellifera prevalence was 7.53% ± 4.72, and prevalence of solitary bees was 18.86% ± 9.18.

Figure 29: Overview of distribution of genera across the 8 locations. The orchards are shown as codes referring to the farmer’s name. The dot size is proportional to the count size, as shown in the legend on the right hand side. Species level In figure 30, a summarized overview is given for each sampled species at the different locations. B. pratorum is clearly sampled much more than any other species. This species is also one of the most widespread bumble bees in Europe next to B. pascuorum, B. lapidarius and B. terrestris (Michener, 2007). These last 3 were also sampled often.

Figure 30: Overview of all sampled species on each location. Colours stand for the different genera, while the size of the dots represent the sample size at each location. Both colours and point size are also explained in the legend on the right.

Bombus spp. level When focusing on Bombus species in figure 31, it is clear that 3 species were sampled on all locations; B. hypnorum, B. pratorum and B. pascuorum, and also B. terrestris and B. lapidarius were sampled quite often. Because a very high amount of B. pratorum was sampled on all locations, we focused on this species later on for pathogen prevalence, performing linear regression analysis on the predictors of diversity and semi-natural percentage.

Figure 31: overview of distribution of Bombus spp. across the 8 locations. Dot size represents sample size.

Table A5: Occurrence of the most common Bombus species, and the relative pathogen prevalence. The first column gives the absolute counts, while the second gives the relative abundance of that species. Bombus species Absolute count frequency (%) A. bombi% Crithidia spp. % Nosema spp. % B. hypnorum 55 12.9 1.8 16.4 10.9 B. lapidarius 45 10.5 24.4 8.9 4.4 B. pratorum 141 33.2 50.4 0.0 12.1 B. terrestris 22 5.2 36.4 0.0 18.2 B. pascuorum 38 9.0 26.7 0.0 2.6

Table A6: Overview of the values of the diversity indices calculated for each location where the pollinators were sampled. Location Richness Shannon Evenness Simpson DB 6.00 1.16 0.65 0.55 DW 8.00 1.50 0.72 0.68 EG 13.00 0.67 0.26 0.48 FB 6.00 1.47 0.82 0.72 GR 13.00 2.08 0.81 0.83 KW 9.00 1.86 0.84 0.81 LV 13.00 1.75 0.68 0.72 MG 13.00 2.14 0.83 0.84

Figure 32: Gel of PCR results with universal primers. The vague bands on the first lanes, marked with 2 red circles, are DNA samples from Andrena haemorrhoa specimens, which are positive for Nosema bombi. The vague bands at 250 bp indicate a low pathogen load of Nosema bombi and thus also that this pathogen does not infect A. haemorrhoa.