Determining colony densities in wild honeybee populations (Apis mellifera) with linked microsatellite DNA markers

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1 DOI /s ORIGINAL PAPER Determining colony densities in wild honeybee populations (Apis mellifera) with linked microsatellite DNA markers Robin F. A. Moritz Æ Vincent Dietemann Æ Robin Crewe Received: 23 February 2007 / Accepted: 5 March 2007 Ó Springer Science+Business Media B.V Abstract Estimating the population size of social bee colonies in the wild is often difficult because nests are highly cryptic. Because of the honeybee (Apis mellifera) mating behaviour, which is characterized by multiple mating of queens at drone congregation areas (DCA), it is possible to use genotypes of drones caught at these areas to infer the number of colonies in a given region. However, DCAs are difficult to locate and we assess the effectiveness of an alternative sampling technique to determine colony density based on inferring male genotypes from queen offspring. We compare these methods in the same population of wild honeybees, Apis mellifera scutellata. A set of linked microsatellite loci is used to decrease the frequency of recombination among marker loci and therefore increase the precision of the estimates. Estimates of population size obtained through sampling of queen offspring is significantly larger than that obtained by sampling drones at DCAs. This difference may be due to the more extensive flying range of queens compared with drones on mating flights. We estimate that the population size sampled through queen offspring is about double that sampled through drones. Keywords Apis mellifera scutellata Population size Genotyping Workers Drones R. F. A. Moritz (&) Institut für Biologie, Martin-Luther-Universität Halle-Wittenberg, Hoher Weg 4, Halle/Saale, Germany r.moritz@zoologie.uni-halle.de R. F. A. Moritz V. Dietemann R. Crewe Department of Zoology and Entomology, University of Pretoria, Pretoria 0002, South Africa Introduction The honeybee, Apis mellifera, is often considered to be a partially domesticated animal, kept by apiculturists for honey production and pollination. Indeed maintaining honeybee colonies in apiaries is both a rewarding business and a hobby for many beekeepers around the globe. Although, beekeeping is sometimes considered to be a threat to non-apis pollinator diversity, until today there are no reports on pollinator extinctions as a consequence of apiculture (Moritz et al. 2005). At first sight a paradox, the most important risk of apiculture for pollinator biodiversity is for honeybees themselves. Commercially bred strains of Apis mellifera kept by beekeepers and the introduction of queens of foreign subspecies have repeatedly been shown to introgress into endemic honeybee populations across Europe (de la Rua et al. 2001, 2003, 2006). Honeybees live most successfully in the wild either as feral colonies in the Americas and Australia, or as truly wild colonies in Africa and Europe (Moritz et al. 2007). Since concerns have emerged regarding the global pollinator crisis (Allen- Wardel et al. 1998; Buchmann and Nabhan 1996; Kearns et al. 1998; Kevan and Phillips 2001; Biesmeijer et al. 2006), the issue of pollinator densities in natural and agricultural habitats has raised new interest, expressing an urgent need to identify pollinator abundance (Ghazoul 2005). Densities of honeybee colonies are thus to be determined not only in beekeepers hives, but also in the wild. Measuring the density of wild and feral honeybee colonies is however a difficult task at best. Nests are extremely cryptic and only those in close proximity to human dwellings are usually readily found. In wild habitats or wilderness areas, one may easily detect honeybee workers foraging on nectar or pollen rewarding flowers, but this only allows for very poor estimates of colony densities.

2 Foraging workers of a colony can be swiftly recruited in large numbers to any rewarding food source (Seeley 1985) and foraging areas of different colonies may not be distributed evenly, biasing the density estimates. Even with modern molecular DNA tools, it is difficult to assign foraging honeybee workers to a specific colony in the field, because queens mated in the wild are highly polyandrous and the relatedness of nestmate workers is usually low (Schlüns et al. 2005a, b). Recently the power of using haploid males in biological and genetic analyses has been emphasized (Moritz et al. 2003; Koeniger 2005). Indeed, using haploid honeybee drones overcomes the polyandry problem of sampling workers in the field, allowing for the estimation of colony number (Baudry et al. 1998; Kraus et al. 2005a). Since queens transmit 50% of their genome to their sons, drones can be accurately assigned to their mother queens when genotyped with a sufficiently large number of variable microsatellite markers. Various analytical tools and algorithms (distance procedures (Kraus et al. 2005b), maximum likelihood techniques (Baudry et al. 1998) and Bayesian statistics (Wang 2004)) are available to group sampled drones into brothers or unrelated individuals. Drones from colonies spread over areas up to 70 km 2 aggregate for mating at so called drone congregation areas (DCAs) where they wait for queens circling in flight (Ruttner and Ruttner 1966, 1972). These DCAs therefore represent excellent sites for sampling drones from wild populations with little or no beekeeping infrastructure. At DCAs, drones can be readily caught with queen pheromone lures placed in a William s trap net attached to a weather balloon at a height of m (Williams 1987; Baudry et al. 1998). However, DCAs are not easy to find due to their small sizes and patchy occurrence (Ruttner and Ruttner 1972) and it often requires extensive searches and experience to locate them. In addition, weather conditions may prevent drone flight altogether (Ruttner and Ruttner 1966) rendering this sampling technique extremely weather dependent. Alternatively, virgin queens have been used in the past to conduct the drone sampling in a truly biological way (Kraus et al. 2003). Because A. mellifera queens mate with many males, a set of 10 virgin queens is usually mated by a sufficiently large number of males (about 120) to conduct a robust population genetic analysis based on the males genotypes. In this procedure, the worker offspring of the mated queens are genotyped and the genotypes of the male mates are inferred from the genotypes of the worker offspring. The disadvantage of this procedure lies in the high cost of the analyses because each inferred drone genotype requires the genotyping of many workers. In addition many loci need to be screened to ensure a sufficient resolution for identifying the genotypes of the drones mothers (Kraus et al. 2005b). Both drone sampling techniques at DCAs and via worker offspring thus have strengths and weaknesses and clearly it would be helpful to know whether the resulting population estimates of the different techniques are comparable. Indeed, one might expect substantial differences. Catching drones with a trap at a DCA only samples drones at this specific DCA. In contrast, virgin queens can fly out on repeated mating flights (e.g. Schlüns et al. 2005b), visiting different DCAs (Ruttner and Ruttner 1972), thus covering a much larger area. Genotype inference through their worker offspring might yield a very different drone sample. We present a comparison of both techniques in a wild population from the Kalahari Desert (South Africa), showing that population estimates obtained with both sampling strategies differ, as hypothesized, but are of the same order of magnitude. In addition, we use a set of linked microsatellite markers to enhance the precision of microsatellite DNA analyses in detecting the actual number of colonies in a population. Materials and methods Sampling A honeybee population in the Tswalu game reserve in the Kalahari Desert of South Africa was sampled in November 2005 and February There is no major apicultural activity within 70 km in this region of the Kalahari. One wild colony had been detected by the staff in the reserve near human dwellings. In addition, two swarms were caught with trap boxes. We sampled 20 workers per colony and kept them in 95% EtOH until further processing for DNA extraction. We were able to trap drones with a William s trap at two DCAs (near Gosa Lodge S, E) and near Motze Lodge, ( S, E). The traps were used as indicated by Williams (1987). Pheromone lures made of blackened cigarette filters were treated with about 10 queen equivalents of 9-oxodecenoic acid (2.5 mg) dissolved in dichloromethane. All the drones caught were immediately transferred into 95% EtOH until further processing for DNA extraction. Colony locations and DCAs were all within a radius of 3 km. Genotyping DNA was extracted from the hind leg of each individual based on routine procedures using Chelex (Walsh et al. 1991). Extracted DNA was amplified with standard PCR

3 techniques (Kraus et al. 2005b). We used a set of seven tightly linked microsatellite loci for genotyping, 750 kbp within the flanking regions of the thelytoky locus on chromosome 13 (Lattorff et al. 2005, 2007). The use of closely linked loci greatly reduces the non-detection error (the probability of not identifying a mother queen, because two genotypes are identical by chance) because not only the occurrence of a given allele but the complete allele combination at all tested loci must be identical. In addition, each queen produces two drone genotypes with little recombination allowing an easy identification of the drones mothers. We compared the drone and worker derived population genetic parameters for population sub differentiation based on the allele frequencies using Wright s estimation procedure for the fixation index F st (Wright 1921). The total number of drone genotypes in the population contributing to the drone pool was estimated by correcting the number of observed genotypes for the finite sample size according to the procedure of Cornuet and Aries (1980). We used a resampling jackknife method (over drones) in order to improve the estimate of number of drone genotypes and to obtain a variance for this value. Results Worker samples We genotyped 20 workers per colony (n = 3) and determined both queen and siring drone genotypes by Mendelian inference. We identified 12, 18 and 19 drone genotypes for each colony respectively, which had sired the 60 genotyped workers. This resulted in an average of observed matings per queen and an estimated average mating frequency of 45 matings per queen after correction for the finite sample size. Some drones that mated with different queens had the same genotype, thus originating from the same mother. In total, we could identify 39 different genotypes among the 49 father drones. Figure 1 shows the number of drones detected per genotype and the corresponding genotype frequencies inferred from the worker samples. Correcting this value (39 genotypes out of 49 sampled drones) for the number of not sampled genotypes due to the finite sample ( non-sampling error ) and jackknifing the sample over drones, we obtain a total estimate of ± 0.82 genotypes (mean ± s.e.) that contributed to the male gene pool. Because drones are haploid and receive only one set of alleles from the queen, we estimate the number of mother colonies as 47.2 ± This is a most conservative estimate by far and does not consider that we most certainly sampled colonies which only contributed single drones in our sample. Because any two singleton genotypes will be grouped with one common mother genotype, we underestimate the number of drone producing colonies. Drone samples We genotyped 49 drones caught on the DCA and could discriminate 33 genotypes in this sample. After correcting for sample size and after jackknifing the sample this results in an estimate of ± 0.33 genotypes contributing to the gene pool. Again, using a most conservative estimate, we obtain ± 0.17 colonies contributing to the male pool on the DCA. The estimate of the colony number derived from the workers (47.2) is significantly lager that that obtained from the drone sample (t-test, P < 0.001). Population differentiation We computed classical population genetic estimators for population sub differentiation based on the allele frequencies derived from the observed drone genotypes in the drone sample and from the queen and drone genotypes inferred from the worker sample (Table 1). We estimated a small F st = 0.04 ± 0.034, which was not significantly larger than zero (t-test: t = 1.17, P > 0.05) indicating that the estimation for the genetic composition of the target population were very similar for both sampling techniques. Discussion We sampled workers and drones in the same season (summer 2005/2006), while it is clear that the worker samples reflect the drone population of previous years depending on the age of the sampled queens. Indeed, queens can store the semen of their mates for many years and we could not control for the age of wild queens. In spite of this uncertainty, the overall population genetic analyses based on both the worker and the drone sample showed little population sub differentiation with an F st near and not significantly different from zero. This suggests that we sampled the same genetic population and that potential temporal dynamics in allele composition did not interfere with our analyses. Although, the numbers of colonies inferred from the two approaches are of the same order of magnitude, they were significantly different with 47.2 and 26.5 colonies estimated for the worker and the drone sample respectively. Using the average flight distance of 900 m for European honeybee drones (Taylor and Rowell 1987) to the DCA rather then the maximum possible distance, the majority of drones will be recruited to a DCA from colonies within a

4 A) frequency of genotype (%) B) frequency of genotype (%) number of drones per genotype number of drones per genotype Fig. 1 Relative frequency of drone genotypes observed (black bar) and the estimated frequency of non-sampled genotypes (white bar). (A) Estimate from the worker sample originating from three colonies, (B) estimate from the drone sample caught on the DCA range of 2.5 km 2. Similar results were observed in African honeybees, where drones could be attracted with a pheromone lure in a 1,000 m radius around an apiary in the South African Highveld near Pretoria (Tribe 1982). The colony number estimate from the drone sample thus results in a density of 10.6 colonies per square kilometer. The higher number of colonies estimated through the worker sample is expected because queens are known to fly out for multiple mating flights to different DCAs (Ruttner and Ruttner 1972) and to more distant DCAs than drones (Koeniger et al. 2005). Depending on the number of mating flights, queens will thus sample multiple DCAs increasing the sampling area compared with the drone sample obtained at a single DCA. Based on our results we expect the sampling area to be almost twice as large using the queen offspring technique in contrast to the drone trapping technique. In spite of the additional problem of multiple mating flights to different DCAs, the queen Table 1 Allele frequencies of the tested loci in the worker and drone sample HB 004 worker drone HB 005 worker drone HB 007 worker drone HB 010 worker drone HB 015 worker drone HB 017 worker drone SV 240 worker drone

5 offspring technique yielded reliable estimates under absolutely controlled conditions of closed populations on mating islands (Kraus et al. 2005). Since the technique also works in the wild, it appears that both sampling drones on DCAs and the analyses of worker offspring of a few colonies are suitable for the evaluation of colony densities in wild and feral honeybee populations. The drone trapping technique may however be superior when it comes to relatively precise density estimates. Acknowledgements We thank Mike Allsopp and the Oppenheimer foundation for allowing us to sample colonies in the Tswalu Game Reserve. We are grateful to Eckart Stolle and Rhian L. M. Moritz for helping with the sampling and to Petra Leibe for assistance with sampling and genotyping. Financial support was provided by the Bundesministerium für Bildung und Forschung (RMC, RFAM), the National Research Foundation of South Africa (RMC) and the EC framework 6 Integrated Project ALARM (Assessing Large scale environmental Risks with tested Methods GOCE-CT ). References Allen-Wardell G, Bernhardt P, Bitner R, Burquez A, Buchmann S, Cane J, Cox PA, Dalton V, Feinsinger P, Ingram M, Inouye D, Jones CE, Kennedy K, Kevan P, Koopowitz H, Medellin R, Medellin-Morales S, Nabhan GP (1998) The potential consequences of pollinator declines on the conservation of biodiversity and stability of food crop yields. Conserv Biol 12:8 17 Baudry E, Solignac M, Garnery L, Gries M, Cornuet J-M, Koeniger N (1998) Relatedness among honeybees (Apis mellifera) of a drone congregation area. 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Z Bienenforsch 8: Ruttner F, Ruttner H (1972) Untersuchungen über die Flugaktivität und das Paarungsverhalten der Drohnen. 5. Drohnensammelplätze und Paarungsdistanz. Apidologie 3: Schlüns H, Moritz RFA, Lattorff HMG, Koeniger G (2005a) Paternity skew in seven species of honeybees (Hymenoptera: Apidae: Apis). Apidologie 36: Schlüns H, Moritz RFA, Neumann P, Kryger P, Koeniger G (2005b) Multiple nuptial flights, sperm transfer and the evolution of extreme polyandry in honeybee queens. Anim Behav 70: Seeley TD (1985) Honeybee ecology. Princeton University Press, Princeton Taylor OR, Rowell GA (1987) Drone abundance, queen flight distance and the neutral mating model for the honey bee, Apis mellifera. In Needham GR, Page RE, Delfinado-Baker M, Bowman CE (eds) Africanized bees and bee mites. Ellis Hoorwood, Chichester, Tribe J (1982) Drone mating assemblies. 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