53

Dispersal strategies of male honeybees

This study was simultaneously the subject of the master thesis of Nicole Weyeneth.

SummaryMating distances and dispersal greatly influence the structure of animal populations. Dispersal again is driven by temporal variability in habitat, avoidance of inbreeding and kin-competition. These forces are counter-balanced by an increased mortality risk. In the western honeybee, Apis mellifera L., dispersal is further influenced by the dependency of single individuals on the colony. In lekking species as the honeybee, extensive mating flights are also thought to occur as a mechanism to avoid inbreeding. Although male honeybees (drones) are fully dependent on the colony, they are accepted by foreign colonies in great numbers. This behaviour, commonly referred to as drifting, decreased with distance and no drifting could be observed in distances above 150 m. However, the acceptance of drones by foreign colonies could facilitate a stepping-stone dispersal behaviour. We hypothesize, that drone dispersal may follow an overlapping island model, each island being defined by a colony and its maximal flight range. Drones are expected to show two dispersal strategies, a “sedentary”, where they fly to mating arenas within their island and return to their natal colony; and a “migratory”, where they disperse to other islands, by entering foreign colonies. Our model would predict a majority of drones following a sedentary strategy, while a small proportion would disperse through migration. We here quantify the mating distances of drones using a mark-recapture experiment covering an area of 20 km2, combined with genetic maternity analyses on 12 microsatellite loci. Of all marked drones (N=184), 48% were recaptured in hives within the study area. The results indicate that a majority of drones disperse only a short distance. A high proportion of recaptured drones (97%) returned to their natal apiary but not necessarily to their mother hive (55%). However, with the genetic analyses, few drones (3%) could also be identify that did not originate from the study area, suggesting that a small proportion of drones does indeed follow a migratory dispersal strategy. We suggest, that this low proportion of foreign drones reflects the risk for dispersal.

CHAPTER 3

54

IntroductionA central issue in population genetics is to identify the factors influencing the distribution of genetic variability. Traditional models assume that individuals mate randomly within homogenous populations, and that males and females have equal reproductive contributions and dispersal rates (e.g., Wright 1921, 1931, 1943; Weir and Cockerham 1984; Weir 1996). However, these assumptions are frequently violated in nature. In many species, populations are divided into discrete units formed by social groups (Sugg et al. 1996). Differences in social structure have a strong impact on mating behaviour and dispersal, which again accounts for the amount of gene flow among populations (Chesser 1991; Nunney 1993). The forces that are described to drive the evolution of dispersal are: 1) temporal habitat variability (Van Valen 1971); 2) inbreeding avoidance (Bengtsson 1978) and 3) kin competition (Hamilton 1964a, Hamilton 1964b; Hamilton & May 1977). They are counter-balanced by an increased mortality during or after dispersal (Rousset & Gandon 2002). One sex commonly disperses more than the other and it is argued that the sex with the higher local competition is more likely to evolve dispersal while philopatry will favour cooperative traits between members of the sedentary sex (Greenwood 1980).

Social Hymenoptera

In social organisms, patterns of dispersal and colonization are of special interest, because they can have important implication for the evolution of sociality (Wade and Breden 1981; Breden and Wade 1991; Ross and Keller 1995a). In social Hymenoptera, breeding systems with the production of large mating swarms are often interpreted as mechanisms to ensure outbreeding or prevent incestuous mating (Cole & Wiernasz 1997). Inbreeding effects are also reduced by male haploidy (Hedrick & Parker 1997) and often low levels of inbreeding have been detected in social insects (Cole & Wiernasz 1997). Surprisingly, detailed information on the social structure, breeding system, and dispersal of males and females is available for only very few social invertebrates (Ross and Keller 1995a; Shoemaker and Ross 1996; Goodisman and Ross 1998; Chapuisat and Keller 1999).

Honeybee mating system

The honeybee, Apis mellifera, has a highly polyandrous mating system which includes extensive mating flights and lekking at mating arenas, generally called drone-congregation-areas (DCAs) (Winston 1987). Non-resource-based rendezvous sites like DCAs have also been found in other insects and even in another social insect species (Ayasse et al. 2001; Cole & Wiernasz 1997; Höglund & Alatalo 1995). Usually, female choice is considered as one argument for the definition of a lek that can not be confirmed in the honeybee, as direct observation of the mating partners on a DCA is impossible

CHAPTER 3

55

by their fast flight speed. But as Höglund & Alatalo (1995) discussed, the aggregation for mating purposes only, without the involvement of resources of any kind, could be regarded as a strong enough argument in the favour of a lek without the necessity to have a confirmation for female choice. However, the high flight performance of honeybee mating partners and the strong competition of drones during mating flight (Koeniger et al. 2005) could be indicative of a indirect selection of drones by queens.Long-range mating flights are generally thought to be advantageous to avoid inbreeding which leads to sterile diploid males due to the haplo-diploid sex determination (Beye et al. 2003). The honeybee represents an extreme example of polyandry, with up to 45 matings per queen (Moritz 1996). However, in sharp contrast, honeybee males (drones) can mate only once, because they die immediately after mating (Ribbands 1953). This strong reproductive bias suggests that a sex-biased dispersal towards the more abundant sex, the drones, may occur. Moreover, drones cruising on leks are in strong competition for mating opportunities, because up to 25,000 males may aggregate (Page Jr. & Metcalf 1982). Finally, only one or very few virgin queens (only in case of multiple swarms, so-called after swarms) remain after swarming in the natal colony (Ribbands 1953). Thus, given a queen is lost, the colonies are often doomed because no diploid progeny and thus no emergency queen can be reared by the workers. This probably limits further selection for dispersal by queens. In conclusion, this implies that the evolution of dispersal behaviour is probably stronger for drones than for queens. However, since drones do not forage in the field their dispersal capacity is strictly limited by access to host colonies (Hrassnigg & Crailsheim 2005).It is well established that drones are commonly accepted by foreign colonies, a behaviour generally referred to as drifting. Drifting of drones has only been observed among hives on the same apiary and the amount of drifters decreases with increasing distance between hives (Currie & Jay 1991a). It has been shown that drifting can affect up to 90% of a colonies drone population (Currie & Jay 1991a, b; Neumann et al. 2000). It has widely been investigated, whether drifting is a random process or if it depends on factors affecting the preference for certain hives, i.e. the position of the hive to the sun or within the apiary, the queens state of a colony, the colour of the hives flight entrance, the age of the drifting drones or on kinship recognition (Currie & Jay 1988a, b, 1989, 1991a, b; Jensen et al. 2005; Moritz & Neumann 2004; Neumann et al. 2000). Current results suggest, that drifting is a non-directional, erroneous behaviour, arising out of the artificial colony density created by humans (Neumann et al. 2001). The ability of drones to be accepted by foreign hives could potentially multiply their dispersal distances by slipping into hives on foreign apiaries.While “short-range” drifting between apiaries is well established for drones and workers, “long-range” drifting of workers (hereafter termed dispersal) has less frequently and almost anecdotally been reported. Individuals of one colony joined a host colony in

CHAPTER 3

56

200 m (Fresnaye 1963), 600 m (Boylan-Pett et al. 1991), 800 m (Duranville et al. 1991; Mossadegh 1993) or 921 m (Neumann et al. 2001) distance and separated by patches of wood (Accorti 1991). These very rare dispersal movements performed by only a tiny fraction of honeybee workers (0.74 %) seem to be a biological mechanism fundamentally different from non-directional, erroneous drifting as it involves active finding of host colonies in several hundreds metres distance (Neumann et al. 2001). Dispersal of A. m. capensis workers was first reported by Onions (1912) who designated such workers as “invaders”, an often reported behavioural feature of the Cape honeybee (Hepburn et al., 1998). Dispersal of workers seems to be an active process in Cape honeybee workers, which is probably associated with host finding (Neumann et al. 2001) and it appears likely that drones may also actively search for distant host colonies in order to disperse.We therefore propose that drones are following an overlapping island model of dispersal where apiaries depict centres of islands. Island sizes can be defined by the maximal flight range around the apiary. Drone dispersal would be expected to follow strategies. One rather sedentary strategy, where drones stay within their natal island and return to their home apiary after a flight, or a migrant strategy where drones disperse to other islands (apiaries) during their flight. In this study it was tested whether drones change apiaries during a mark-recapture experiment including a maternity analysis over a defined area. We are thus able to distinguish between drones following a sedentary or a migratory dispersal strategy and the proportion of drones drifting within an apiary. Our results show no drifting among the apiaries within our study area, despite a high amount of drifters. Nevertheless a minor proportion of marked drones not originating from within the study area were found.

Material & Methods

Drone samples

Drones were captured at a DCA situated in north-western Switzerland (46°46’52.5’’/ 07°26’16.5’’), on two consecutive days in June during the mating season (Figure 1). The drones where caught in a funnel-shaped tulle-net with a volume of 0.12 m3 and lifted by a helium filled balloon. A wooden queen dummy was attached below the net to attract the drones to the trap (Gerig & Gerig 1986; Loper et al. 1992). An angling rod was used for directing the capture device from the ground. The thoraces of the sampled drones were marked with day specific colour (Apicolori). In the evening of day 2, flight entrances of all hives in all of the apiaries (Figure 1) were covered with a standard queen-exclusion-grid (Bienen Meier, Künten) in order to prevent drones from leaving the hive. Early in the morning all hives from nine apiaries were controlled

CHAPTER 3

57

and marked drones were collected (Figure 1, Table 1). The drones marked with both colours (n=1), were counted as recaptures for both days.

Queen samples

For inference of the queen genotypes, area-wide samples were taken from all 56 colonies in the study area (Figure 1, Table 1). Queen wing tips, drone and worker brood were taken in all colonies and the queen was marked with a standard numbered queen plate on the thorax for individual identification. After the drone recapture, all queen locations were reconfirmed and all information about queen movements were collected as they could have changed location due to swarming activity. Additional samples were taken in two colonies, where the young queens were at an age where they could have contributed offspring to the sampled drone population at the DCA.

DNA analyses

Genomic DNA was extracted using a standard phenol–chloroform protocol or a modified MagneSil blue beads protocol (Promega Corporation, USA). In four colonies, queens tissue was obtained from wing tips (Châline et al. 2004). In 19 colonies, 20-30 worker pupae, and in the 35 remaining colonies a mix of 20 drone larvae was used to infer the genotypes of the queens (Moritz et al. 2003). The software COLONY 1.2 (Wang 2004) was used to infer a queens’ genotype from the samples of worker pupae. COLONY 1.2 was used to calculate the most probable mother genotype out of the genotypes of her offspring. To infer a queens’ genotype from drone larvae, the 20 drones were mixed together with a mixer (Polytron System PT 1200 CL, Kinematica AG) and then, as drones are haploid, analysed as one individual including all of the mother queens alleles (Moritz et al. 2003; Walsh et al. 1991). All samples were genotyped at 12 microsatellite loci (A007, A43, Ap33, Ap226, B24, Ap273, Ac306 Ap289, A28, Ap1, A29, A76) (Solignac et al. 2003). Polymerase chain reaction (PCR) was used to amplify the 12 microsatellite markers in 2 multiplex reactions. PCR was performed with a GeneAmp® PCR System 9700 (Applied Biosystems) in a 10 µl reaction volume containing 2 µl of DNA sample, 5 µl Multiplex PCR Kit (Quiagen), 2 µl of a primer mix and 1 µl ddH2O. The reaction profile for each mix was denatured at 96 °C for 15 min, followed by 32 cycles of denaturation at 94 °C for 30 sec, annealing at 60 °C for 90 sec and extension at 72 °C for 90 sec. An extension step at 72 °C for 10 min followed by 90 min at 24 °C was added at the end. The PCR product was then detected on an ABI Prism 3100 Genetic Analyser (Applied Biosystems) using the GeneScan-500 Liz internal size standard (Applied Biosystems). The genotypes were scored with the software GENEMAPPER v3.0 (Applied Biosystems). Drone and Queen genotypes with only one mismatch to a putative mother or son were repeated independently to ensure data quality.

CHAPTER 3

58

Maternity assignment

The software CERVUS 2.0 (Marshall et al. 1998) was used to conduct parentage analyses for all queen-drone pairwise comparisons. A queen was excluded as putative mother of a drone if one mismatch for at least one locus with the marked drone in question occurred.

Results

Mark-recapture

Drones were recaptured within 30 hives on six apiaries (Figure 1, Table 1). On day 1, 162 drones were recaptured within 27 hives from six apiaries (recapture rate 48%, Table 1) and 23 drones were recaptured within ten hives from three apiaries on day 2 (recapture rate 46 %, Table 1). One drone was marked on both days and was counted for each day separately. No marked drones were found in apiaries 7, 8 and 9. Apiary 9 is located right outside the study perimeter and was therefore excluded of the molecular analysis. No drones were recaptured outside the valley of the DCA (apiaries 7 to 9) (Figure 1, Table 1). The relationship between number of recaptures and distance followed a fat tailed distribution with a strong skew towards short distances (Figure 2).

Maternity assignment

Three to 46 alleles per locus were detected (mean: 13.5). The expected heterozygosity per locus ranged from 0.49 to 0.92. The total exclusionary power in the analysed data set was 99.15% for assignment of the first parent and 99.96% for the second parent, as calculated with the program CERVUS 2.0 (Marshall et al. 1998).A queen was excluded as a mother by 6.67 alleles on average (± 2.02 SD) for all 10’672 genotypic combinations of queen-drone pairs. For 178 out of 184 recaptured drones, only one queen could not be excluded as a putative mother each. In two of the remaining six drones, only one mismatch was found between a queen and the drone. The genotype of these two recaptured drones was confirmed by re-extraction and typing. For the remaining four drones, all analysed queens could be excluded as mothers by at least two genotypic mismatches. On day 1, 69 drones were recaptured in their mother hive, 87 drones were recaptured in their natal apiary but in a foreign hive, and 6 drones could not be assigned to any one queen within the study area (Table 2). All recaptured drones from day 2 (n=23) could be assigned to a queen of their apiary of recapture. Nine drones were recaptured in their natal hive and 14 drones in a foreign hive (Table 2).No significant difference was found in the number of drones recaptured in foreign hives or in their nata hives at neither of both days (day 1: Pearson Chi2 = 3.556, p = 0.0593; day 2: Pearson Chi2 = 1.087, p=0.297). Overall, a significant larger number

CHAPTER 3

59

of drones was recaptured in their natal apiary (n=174) than in foreign apiaries (n=6) (Pearson Chi2=161.78; p < 0.0001).

DiscussionOur data show that honeybee drones can move to foreign apiaries after visiting a DCA. This suggests that honeybee drones can follow a migratory dispersal strategy by using distant colonies as stepping-stones. Further, our data give strong support to earlier studies showing a high proportion of foreign drones in some colonies .

Drifting

It is well established that foreign drones are readily accepted within honeybee colonies, which is confirmed by the high amount of drifters (58%) in our study. The amount of drifters may impact on the fitness of its host colony, due to the limit of drone carrying capacity of colonies (Rinderer et al. 1985). Thus, differences in both the drifting of drones and acceptance of drones by host colonies between European and African subspecies seems to result in male reproductive parasitism (Rinderer et al. 1985). Natural selection should therefore favour colonies rejecting foreign drones and on the other hand increase the fitness of colonies that produce large numbers of successfully dispersing drones. This may result in some stable equilibrium between drifting of drones and rejection of drones by workers. The higher amount of drifters in queenless colonies could be due to a higher acceptance of drones as colonies might increase their fitness by accepting foreign drones to decrease the amount of inbreeding in a future colony generation (Currie & Jay 1988b). This could be one factor balancing the acceptance and contribution of drifters.Besides factors involved in reproduction, the acceptance of foreign drones has little if any impact on colony performance (e.g. honey yield and colony size) and infestations with parasites (Neumann et al. 2000). Moreover, it is more costly to rear drones with worker-prepared royal food than to refuel them after mating flight with honey that is most abundant (Hrassnigg & Crailsheim 2005). Finally, the system involved in recognition of drones, seems different from that applying to workers (Moritz & Neumann 2004) and the threshold for workers to accept foreign drones seems to be low. Indeed, foreign workers may rob the colonies, leading to a reduced acceptance threshold (Reeve & Nonacs 1992) during periods of nectar dearth (Downs & Ratnieks 2000).In conclusion, the very high acceptance of foreign drones suggests that costs of hosting male sexuals are usually very low for the colonies (Neumann et al. 2000). This general high acceptance of foreign drones in honeybee colonies sets a fundamental stage for long range dispersal by minimizing the risk for a drone of being rejected by any distant host.

CHAPTER 3

60

Dispersal

It is unlikely that the observed proportion of dispersed drones can be explained by mutations. Mutation rates for average sized microsatellites have been estimated to range between 10-3 and 10-4 haplotypic changes per generation (Whittaker et al. 2003). For our allele sample of 2206 observed alleles, the expected number of mutant alleles would range between 0.022 and 0.0022 which is significantly lower than the total number of observed alleles (N=18) due to which the drones could be excluded as sons (Chi2= 17.9, df=1, p<0.0001). This clearly suggests that the dispersed drones must either have originated from a feral colony or from colonies outside of the study area.The fact that drones rely on a colony for their survival greatly limits their dispersal capacity. Indeed, our results show that 97% of all recaptured drones were found in their natal apiary. Moreover, the dispersal distribution of drones is strongly skewed towards short distance dispersers as it has been shown for many vertebrates and birds (Frankham et al. 2002; Koenig et al. 1996; Murray Jr. 1967). This is in line with results of Koeniger et al. (2005b) that a higher proportion of drones prefers a close DCA. This probably reflects the high cost of mating flights for drones that counter-balances the evolution of long dispersal distances.Migratory dispersal is more risky than a sedentary strategy, as not finding a colony in time and finding it but being rejected, both results in death. Drones returning from DCAs may simply follow other drones to different apiaries (van Praagh personal communications), and instead of actively searching for colonies, this might simply be an orientation error at the DCA when drones follow others to foreign apiaries. Because of this potentially high cost of dispersing, it should be evolutionary rewarding to maintain migration. Therefore it is much more likely that drones, once they migrate, do not stay within their natal “island” but move further, where the increased fitness of their offspring through outcrossing balances the increased mortality risk. Outcrossing would result in a decreased proportion of diploid sterile male offspring in the following generation or an increased intra-colony variability which has many beneficial effects on the colony level, like enhanced foraging behaviour, stable development temperatures or increased resistance to pathogens (Oldroyd et al. 1992).

Flight distances

The high proportion of short flight distances observed in our data, could be due to a sampling bias. Drones nearly always return with an empty energy supply from their mating flights that last 27 min on average (Duay et al. 2002; Howell & Usinger 1933; Hrassnigg & Crailsheim 2005; Page Jr. & Peng 2001; Ruttner 1966). Capturing drones during these flights and withholding them for about 10 min for marking might make them exhaust energy supplies needed for their flight back to the hive, as confinement puts a major stress on drones (Page Jr. & Peng 2001). Therefore, drones at the last

CHAPTER 3

61

third of their flight duration could have used up energy stores during marking that were crucial for their flight home. It would be expected that the proportion of drones not returning is positively correlated with the distance to their apiary. This might at least partially explain the low recapture rate of 48%. It might also explain, why dispersed drones were only found in apiaries (3, 4, 5) close to the DCA.Because of the high cost of migration, the proportion of drones following a migratory dispersal strategy would be expected to be much smaller than the proportion of drones returning to their natal apiary. This is in agreement with our results, where only 3% were dispersers to all colonies within the study area. These drones have moved at least 3.5 km, which is the minimum distance from the closest apiary outside our study area to the DCA and further to the closest apiary where dispersed drones were found. Still this displays a minimum distance but it cannot be excluded that much larger distances have been covered by these dispersed drones as flight distances of up to 15 km have been reported for drones (Ruttner 1959). Anyhow, the occurrence of foreign drones shows that their maximal flight range can be increased by dispersal to foreign apiaries. Many factors could influence the dispersal behaviour and strategy of drones. For example it has been shown that drones infected by the ectoparasitic mite Varroa destructor during their developmental stage display a much shorter flight ability (Duay et al. 2002). This would greatly shorten the time available to find a new host colony and thus increase the risk of mortality. Drones of colonies with high infestation levels of V. destructor would therefore either be expected to show a lower proportion of migration, low dispersal distances or a higher mortality during flight. Kin-competition mechanisms on the other hand would be expected to increase dispersal distances of drones (Hamilton 1964a, b) and drones have been observed to display a highly competitive behaviour in flight speed and navigation abilities on DCAs (Koeniger et al. 2005a). It has also been suggested, that dispersal is affected by the drone density within an area (Böttcher 1975). Therefore, in areas with high drone occurrence more drones would be expected to follow a migratory dispersal strategy.In conclusion, long-range dispersal of drones may result in an increase in gene flow with a high potential to greatly influence population structure of honeybees.

CHAPTER 3

62

AcknowledgementsWe thank the many volunteers and beekeepers for their help and support during the sampling. This project was funded by the Swiss Bee Research Centre , the Swiss Federal Office for Agriculture (SFOA), the “Verband Schweizerischer Bienenzüchtervereine” (VSBV) and the “Verein deutschschweizerischer und rätoromanischer Bienenfreunde” (VDRB).

ReferencesAccorti, M. (1991) Indagine preliminare sulla deriva delle api. Apicoltura 7, 1-15.Ayasse, M, Paxton, R.J., Tengo, J. (2001) Mating Behavior. and Chemical Communication in the Order

Hymenoptera. Annual Review of Entomology 46, 31-78.Bengtsson, B.O. (1978) Avoiding inbreeding: at what cost? Journal of Theoretical Biology 73, 439-

444.Böttcher, F.K. (1975) Beiträge zur Kenntnis des Paarungsfluges der Honigbiene. Apidologie 6, 233-

281.Boylan-Pett, W., Ramsdell, D.C., Hoopinggarner, R.A, Hancock, JF (1991) Honeybee foraging

behaviour., in-hive survival of infectious, pollen-borne blueberry leaf mottle virus and transmission of the virus in high bush blueberry. Pythopathology 81, 1407-1412.

Châline, N., Ratnieks, F.L.W., Raine, N.E., Badcock, N.S., Burke, T. (2004) Non-lethal sampling of honey bee, Apis mellifera, DNA using wing tips. Apidologie 35, 311-318.

Cole, B.J., Wiernasz, D.C. (1997) Inbreeding in a lek-mating ant species, “Pogonomyrmex occidentalis”. Behavioral Ecology and Sociobiology 40, 79-86.

Currie, R.W. (1987) The biology and behaviour of drones. Bee World 68, 129-143.Currie, R.W., Jay, S.C. (1988a) Factors affecting the acceptance of foreign drones into honey bee (Apis

mellifera L.) colonies. Apidologie 19, 231-240.Currie, R.W., Jay, S.C. (1988b) The Influence of a colony’s queen state, time of year, and drifting

behaviour, on the acceptance and longevity of adult drone honeybees (Apis mellifera L.). Journal of Apicultural Research 27, 219-226.

Currie, R.W., Jay, S.C. (1989) Sun position and a colony’s queen state as possible factors influencing the orientation and drift of drone honeybees (Apis mellifera L.). Journal of Apicultural Research 28, 142-149.

Currie, R.W., Jay, S.C. (1991a) Drifting behaviour of drone honey bees (Apis mellifera L.) in commercial apiaries. Journal of Apicultural Research 30, 61-68.

Currie, R.W., Jay, S.C. (1991b) The influence of a colony’s queen state on the drifting of drone honeybees (Apis mellifera L.). Apidologie 22, 183-195.

Downs, S.G. & Ratnieks, F.L.W. (2000) Adaptive shifts in honey bee (Apis mellifera L.) guarding behavior support predictions of the acceptance threshold model. Behavioural Ecology 11, 326-333.

Duay, P., De Jong, D., Engels, W. (2002) Decreased flight performance and sperm production in drones of the honey bee (Apis mellifera) slightly infested by Varroa destructor mites during pupal development. Genetic Molecular Research 1, 227-232.

Duranville, C., Miniggio, C., Arnold, G., Borneck, R. (1991) Dérive des butineuses et réinfestation des colonies d’abeilles par Varroa jacobsoni. Santé Abeil 125, 228-235.

Frankham, R, Ballou, J.D., Briscoe, D.A. (2002) Introduction to Conservation Genetics, Cambridge University Press edn., Cambridge.

Fresnaye, J. (1963) Les erreurs d’orientation des abeilles (dérive) dans la rucher moderne. Ann Abeil 6, 185-200.

CHAPTER 3

63

Gerig, L., Gerig, C. (1986) Drohnen mit Varroamilben auf dem Hochzeitsflug. Schweizerische Bienen-Zeitung 9, 405-407.

Greenwood, P.J. (1980) Mating systems, philopatry and dispersal in birds and mammals. Animal Behaviour 28, 1140-1162.

Hamilton, W.D. (1964a) The genetical evolution of social behaviour. I. Journal of Theoretical Biology 7, 1-16.

Hamilton, W.D. (1964b) The genetical evolution of social behaviour. II. Journal of Theoretical Biology 7, 17-52.

Hamilton, W.D,. May, R.M. (1977) Dispersal in stable habitats. Nature 269, 578-581.Hedrick, P.W., Parker, J.D. (1997) Evolutionary genetics and genetic variation of haplodiploids and X-

linked genes. Annual Review of Ecology and Systematics 28, 55-83.Hepburn, H.R., Radloff, S.E., Fuchs, S. (1998) Population structure and the interface between Apis

mellifera capensis and Apis mellifera scutellata. Apidologie 29, 333-346.Höglund, J., Alatalo, R.V. (1995) Leks, Princeton University Press edn., Princeton (N.J.).Howell, D.E., Usinger, R.L. (1933) Observations on the flight and length of life of drone bees. Annals

of the entomological society of America 26, 239.Hrassnigg, N., Crailsheim, K. (2005) Differences in drone and worker physiology in honeybees (Apis

mellifera). Apidologie 36, 255-277.Jensen, A.B., Palmer, K.A., Boomsma, J.J., Pedersen, B.V. (2005) Varying degrees of “Apis mellifera

ligustica” introgression in protected populations of the black honeybee, “Apis mellifera mellifera”, in northwest Europe. Molecular Ecology 14, 93-106.

Jones J.C., Myerscough, M.R., Graham, S., Oldroyd ,B.P,. (2004) Honey Bee Nest Thermoregulation: Diversity Promotes Stability. Science 305, 402-404.

Koenig, W.D., Van Vuren, D., Hooge, P.N. (1996) Detectability, philopatry, and the distribution of dispersal distances in vertebrates. Trends in Ecology & Evolution 11, 514-517.

Koeniger, N., Koeniger, G., Gries, M., Tingek, S. (2005a) Drone competition at drone congregation areas in four Apis species. Apidologie 36, 211-221.

Koeniger, N., Koeniger, G., Pechhacker, H. (2005b) The nearer the better? Drones (Apis mellifera) prefer nearer drone congregation areas. Insectes Sociaux 52, 31-35.

Loper, G.M. (1992) What do we really know about drone flight behaviour? Bee World 73, 198-203.Loper, G.M., Wolf, W.W., Taylor, J.O.R. (1992) Honey bee drone flyways and congregation areas-radar

observations. Journal of the Kansas Entomological Society 65, 223-230.Marshall, T.C., Slate, J., Kruuk, L.E.B., Pemberton J.M. (1998) Statistical confidence for likelihood-

based paternity inference in natural populations. Molecular Ecology 7, 639-655.Moritz, R.F.A., Neumann, P. (2004) Differences in nestmate recognition for drones and workers in the

honeybee, Apis mellifera (L.). Animal Behaviour 67, 681-688.Moritz, R.F.A., Scharpenberg, H., Lattorff, H.M.G., Neumann P. (2003) A technical note for using

microsatellite DNA analyses in haploid male DNA pools of social Hymenoptera. Insectes sociaux 50, 398-400.

Moritz, R.F.A. (1996) Competition for Royalty in Bees. Nature 384, 1Mossadegh, M.S. (1993) The biology of the tracheal mite Acarapis woodi (Acari: Tarsonemidae) and

role of drone honey bees in intercolony spread of the mite, in North Carolina. Scientific Journal of Agriculture 16, 14-22.

Murray, Jr. B.G (1967) Dispersal in vertebrates. Ecology 48, 975-9.78.Neumann, P., Moritz, R.F.A., Mautz, D (2000) Colony evaluation is not affected by drifting of drone

and worker honeybees (Apis mellifera L.) at a performance testing apiary. Apidologie 31, 67-79.Neumann, P., Radloff, S.E., Moritz, R.F.A., Hepburn, H.R., Reece, S.L (2001) Social parasitism by

honeybee workers (Apis mellifera capensis Escholtz): host finding and resistance of hybrid host colonies. Behavioural Ecology 12, 419-428.

Oldroyd, B., Rinderer, T., Buco, S. (1992) Intra-colonial foraging specialism by honey bees (“Apis mellifera”) (Hymenoptera:Apidae). Behavioral Ecology and Sociobiology 30, 291-295.

CHAPTER 3

64

Onions, G.W. (1912) South African “fertile worker bees”. Agricultural Journal of the Union of South Africa 1, 720-728.

Page Jr., R.E. & Metcalf, R.A. (1982) Multiple mating, sperm utilization, and social evolution. American Naturalist 119(2): 263-281.

Page Jr., R.E., Peng, C.Y. (2001) Aging and development in social insects with emphasis on the honey bee, Apis mellifera L. Experimental Gerontology 36, 1773-1775.

Reeve, H.K., Nonacs, P (1992) Social Contracts in Wasp Societies. Nature 359, 823-825.Ribbands, C.R. (1953) The behaviour and social life of honeybees, Bee Research Association Limited

edn., London.Rinderer, T.E., Hellmich, R.L., Danka, R.G., Collins A.M. (1985) Male reproductive parasitism: A,

factor in the Africanization of European honeybee populations. Science 228, 1119-1121.Rousset, F., Gandon, S. (2002) Evolution of the distribution of dispersal distance under distance-

dependent cost of dispersal. Journal of Evolutionary Biology 15, 515-523.Ruttner, F. (1959) Drohnenflugweite und Drohnendichte. Deutsche Bienenwirtschaft, 42-47.Ruttner, F. (1966) The life and flight activity of drones. Bee World 47, 93-100.Solignac, M., Vautrin, D., Loiseau, A., et al. (2003) Five hundred and fifty microsatellite markers for the

study of the honeybee (Apis mellifera L.) genome. Molecular Ecology 3, 307-311.Van Valen, L. (1971) Group Selection and the Evolution of Dispersal. Evolution 25, 591-598.Walsh, P.S., Metzger, D.A., Higuchi, R. (1991) Chelex 100 as medium for simple extraction of DNA for

PCR-based typing from forensic material. Biotechniques 10, 506-513.Wang, J. (2004) Sibship Reconstruction From Genetic Data With Typing Errors. Genetics 166, 1963-

1979.Whittaker, J.C., Harbord, R.M., Boxall, N., et al. (2003) Likelihood-Based Estimation of Microsatellite

Mutation Rates. Genetics 164, 781-787.Winston, M.L. (1987) The Biology of the Honey Bee, Cambridge, London.

CHAPTER 3

65

Tables & Figures

Table 1. Results of the capture-mark-recapture experiment showing the number of marked drones recaptured in nine apiaries (DW: white marked drones; DY: yellow marked drones; DWY: white/yellow marked drones; DTOTAL: sum of all marked drones; DCA: drone-congregation-areas).

No of marked drones

DTOTAL DW DY DWYAltitude

(m)Captures at DCA 389 339 49 1 815

ApiaryDistance to

DCA (m)No of hives

Hives with recaptures

DTOTAL DW DY DWYAltitude

(m)1 1010 8 3 6 6 0 0 9502 310 8 4 4 4 0 0 8963 750 8 8 150 131 18 1 8274 1000 10 6 9 7 2 0 8355 1250 7 6 9 9 0 0 8046 3000 10 3 6 4 2 0 8437 4000 1 0 0 0 0 0 8128 4260 8 0 0 0 0 0 7799 4350 14 0 0 0 0 0 785

Total 74 30 184 161 22 1

Table 2. Results of the maternity analyses over all hives per marking day.

Day 1 Day 2 Total

Natal drones 69 9 78 42%Drifters 87 14 101 55%Dispersers 6 0 6 3%Total 162 23 185 100%

CHAPTER 3

66

1133 m

823m

921 m

900 m

783 m

1 km

8

7

9

6 5 4 3

1 2

Figure 1. Map of the study area in NW-Switzerland, showing the locations of the DCA (black circle) and apiaries (white squares, see table 1). A small mountain pass is indicated by a broken line. The grey area displays forest. Altitudes are indicated as metres above sea level (m).

68

Final conclusions

By using molecular techniques, the results confirm the existence of an A. m. mellifera population in some areas in eastern Switzerland, which might be the last remaining population of the alpine ecotype “Nigra”.

Hybridization

Although still significantly differentiated, the native A. m. mellifera population is probably endangered by introductions of foreign subspecies, because increased hybridization levels have been shown to occur in areas of sympatry (Chapter 1, Chapter 2). Although in isolated breeding populations, on average only 6% of all individuals were identified as intermediate hybrids, a considerable amount of introgression was detected, originating from successive backcrossing (Chapter 1). Since hybrids are present within the assumed pure breeding populations, it shows that artificial selection criteria for hybridization are either insufficient and/or inconsequently applied. As crossbreeding results in a heterosis effect on the colony level (Ruttner 1968), the high performance of hybrid colonies (e.g. honey yields) might be too intriguing for the amateur breeders to exclude the colony from further reproduction. This might result in an artificial increase in hybrid fitness if the colony is not only remaining in the population, but moreover queens are preferentially raised from these hybrid colonies. Further support for such a scenario is given by the reduced fitness of hybrid drones compared to queens (Chapter 2), because no considerable amount of introgression would be expected to occur with hybrid drones suffering a reduced fitness. This strongly suggest, that introgression within the isolated breeding stocks is mainly due to the artificial rearing of queens from hybrid colonies and subsequent backcrossing with pure drones. This would plausibly explain the rather high proportion of backcrosses and the only few first generation hybrids.

Dispersal

Besides the high proportion of drifted individuals joining closely neighbouring colonies, a small proportion of dispersing drones, originating from apiaries outside the study area, was found. Such dispersing drones considerably could extend their maximum mating flight range by entering foreign host colonies (Chapter 3). This result is in agreement with the reduced proportion of cross matings observed on non-isolated mating apiaries compared to their surrounding population (Chapter 2). It further explains the occurrence of rare hybridization events on isolated mating apiaries (Chapter 1). Although the distance of these mating apiaries gives some indication about the migration abilities of drones, the data did not allow for an estimation of migration distances. Still, the dispersal distribution of sedentary drones showed that most of

Final Conclusions

69

them fly to very close mating arenas, which could reflect a high cost of mating flights for drones (Chapter 3).

Conservation management

The results suggest, that the conservation of honeybee subspecies is most efficient by the maintenance of preservation areas, as the persistence of pure populations is crucial for the survival of species (Frankham et al. 2002). Besides, mating apiaries can be maintained as small population fragments, but high attention should be laid on a high amount of unidirectional gene flow from pure populations to these small fragments.Criteria for the identification of hybrid individuals needs to be improved to preserve pure populations. The molecular tools and analytic methods applied in this study offer reliable criteria to detect against individual hybrids and could improve the management of conservation areas and population.

ReferencesFrankham, R., Ballou, J. D., et al. (2002). Introduction to Conservation Genetics. Cambridge, Cambridge

University PressRuttner, F. (1968). „Methods of breeding honeybees: intra-racial selection or inter-racial hybrids?“ Bee

World 49: 66-72.

71

Curriculum Vitae

Personal details

Name Gabriele Soland

Date of birth 13th December 1969

Citizenship Lausanne VD, Freienstein-Teufen ZH, Reinach AG

Nationality Swiss

Marital status married to Reto Soland

Education

2003-2006 PhD thesis with Prof. Dr. Laurent Excoffier and Dr. Gerald Heckel , Zoological Institute, University of Bern on the “Genetic differentiation and hybridizaton in the honeybees (Apis mellifera L.) in Switzerland.”

2002-2003 Master thesis with PD Dr. C. Largiadèr, Zoological Institute, University of Bern on “Postglacial-colonization history of the Lake Geneva basin by the bullhead Cottus gobio L.”

1998-2002 Undergraduate studies in biology at the University of Bern

1999 Matura degree, type E (economics) at theWirtschaftsgymnasium Biel