Pathogen prevalence and abundance in honey bee colonies involved in almond pollination
- First Online:
- 1.6k Downloads
Honey bees are important pollinators of agricultural crops. Since 2006, US beekeepers have experienced high annual honey bee colony losses, which may be attributed to multiple abiotic and biotic factors, including pathogens. However, the relative importance of these factors has not been fully elucidated. To identify the most prevalent pathogens and investigate the relationship between colony strength and health, we assessed pathogen occurrence, prevalence, and abundance in Western US honey bee colonies involved in almond pollination. The most prevalent pathogens were Black queen cell virus (BQCV), Lake Sinai virus 2 (LSV2), Sacbrood virus (SBV), Nosema ceranae, and trypanosomatids. Our results indicated that pathogen prevalence and abundance were associated with both sampling date and beekeeping operation, that prevalence was highest in honey bee samples obtained immediately after almond pollination, and that weak colonies had a greater mean pathogen prevalence than strong colonies.
Keywordshoney bee colony health honey bee viruses Black queen cell virus Lake Sinai virus almond pollination
Insects pollinate agricultural crops and plant species that enhance landscape biodiversity. The value of insect pollination worldwide is $153 billion annually, and honey bees (Apis mellifera) are the primary pollinators of US crops valued at over $17 billion per year (Calderone 2012; Gallai et al. 2009). A striking example of the role of honey bee pollinators in agriculture is California almond production, where ~80 % of the world’s almonds are produced (CAB 2014). Honey bees are essential to almond production, and over 60 % of the US commercially managed honey bee colonies are involved in almond pollination from late February to early March. In 2013–2014, approximately 1.6 million bee colonies participated in this single pollination event.
Honey bee-mediated pollination is important for agricultural production. Thus, high annual US honey bee colony losses (averaging 33 % since 2006) concern growers, beekeepers, scientists, and policy makers (Spleen et al. 2013; Steinhauer et al. 2014; van Engelsdorp et al. 2007, 2008, 2009, 2012). Although the total number of US commercially managed colonies has remained at around 2.5 million since the late 1990s, beekeepers must split colonies (i.e., make two colonies from one colony) more frequently in order to make up for higher annual losses (USDA NASS). Typically, the majority of colony losses occur during the winter months and thus coincide with the almond pollination season, though a recent US management survey indicated that 2014 summer losses were also significant (~27.4 %) (Steinhauer et al. 2015). Research to date suggests that multiple biotic and abiotic factors contribute to colony health and survival (e.g., viruses, mites, microbes, bee genetics, weather, forage quality and availability, management practices, and agrochemical exposure) (Calderone 2012; Cornman et al. 2012; Ellis et al. 2010; Gallai et al. 2009; Nazzi and Pennacchio 2014; Pettis and Delaplane 2010; van der Zee et al. 2012, 2014; van Engelsdorp et al. 2009, 2012). Although no single factor is responsible for colony losses or colony collapse disorder (CCD), honey bee samples from CCD-affected colonies had greater pathogen (e.g., viruses and Nosema) prevalence and abundance compared to unaffected colonies (Cornman et al. 2012; Cox-Foster et al. 2007; Johnson et al. 2009; Steinhauer et al. 2015; van Engelsdorp et al. 2009). In addition, several epidemiologic and temporal monitoring studies have partially attributed colony losses to pathogens (i.e., viruses, bacteria, fungi, trypanosomatids, and mites) (Chen et al. 2014; Cornman et al. 2012; Cox-Foster et al. 2007; Evans and Schwarz 2011; Genersch and Aubert 2010; Locke et al. 2014; McMenamin and Genersch 2015; Nazzi and Pennacchio 2014; Nielsen et al. 2008; Ravoet et al. 2013; Tentcheva et al. 2004; van Engelsdorp et al. 2009, 2012). While these studies are informative, the roles of pathogens in colony mortality and the relationships between colony strength and pathogen prevalence and abundance have not been fully elucidated.
The majority of honey bee-infecting pathogens are RNA viruses, including Acute bee paralysis virus, Black queen cell virus, Israeli acute paralysis virus, Kashmir bee virus, Deformed wing virus, Sacbrood virus, Chronic bee paralysis virus (reviewed in (Brutscher et al. 2015; Chen and Siede 2007; de Miranda et al. 2013; Evans and Schwarz 2011; Genersch and Aubert 2010; McMenamin and Genersch 2015), and the Lake Sinai viruses (Cornman et al. 2012; Daughenbaugh et al. 2015; Granberg et al. 2013; Ravoet et al. 2013, 2015; Runckel et al. 2011). Longitudinal monitoring of colony health and pathogen prevalence and abundance is critical to determining the role of pathogens in colony losses, yet there have been very few studies of this nature performed in the USA.
To better understand the role of pathogens on honey bee colony health and mortality, we monitored colony health (using colony size as a proxy), pathogen prevalence, and pathogen abundance, before, during, and after the 2013–2014 almond pollination season (Delaplane and van der Steen 2013; OSU 2011). We utilized polymerase chain reaction (PCR) to test for 16 common honey bee pathogens. The majority of these pathogens were viruses including Lake Sinai virus 1 (LSV1), LSV2, LSV3, LSV4, LSV5, Black queen cell virus (BQCV), Deformed wing virus (DWV), Sacbrood virus (SBV), Acute bee paralysis virus (ABPV), Chronic bee paralysis virus (CBPV), Israeli acute paralysis virus (IAPV), and Kashmir bee virus (KBV). In addition, we tested for bacterial pathogens (i.e., Paenibacillus larvae and Melissococcus plutonius) and eukaryotic parasites including the microsporidia Nosema spp. and trypanosomatids (i.e., Crithidia mellificae SF/Lotmaria passim). Herein, we present data from an observational cohort study at the colony level that provides current information regarding pathogen prevalence and abundance in Western US commercially managed honey bee colonies involved in almond pollination. Enhanced understanding of the dynamics of pathogenic infections in bee colonies across different geographies and in independent beekeeping operations will inform ongoing and future epidemiologic studies aimed at investigating the potential relationships between colony health and pathogen occurrence, prevalence, and abundance on a larger scale (van Engelsdorp et al. 2013). Understanding the role of pathogens and other factors on honey bee health is critical to the development of pollinator management and conservation strategies that limit annual bee colony mortality (Gallant et al. 2014).
2 Materials and methods
Additional details regarding methods are available in the Supplemental Information (Online Resource 1).
2.1 Longitudinal monitoring and sampling of commercially managed honey bee colonies
2.2 Honey bee samples
Five female bees from each sample were used for RNA extraction, cDNA synthesis, pathogen-specific PCR, and qPCR. The objective for pathogen screening was to identify the most prevalent pathogens associated with honey bees sampled from individual colonies at each sampling event. Based on empirical data, literature values, and practical sample handling considerations, we assayed five bees per colony per sampling event. The following equation from Pirk et al. (2013), N = ln(1 − D) / ln(1 − P) (N = sample size, ln = natural logarithm, D = probability of detection, P = proportion of infected bees), predicts that with a sample size of five bees, pathogenic infections affecting 45 % or more of the individuals within a colony would be detected with 95 % probability (Pirk et al. 2013); this sample size has been proven sufficient for the pathogen-specific PCR detection of highly prevalent pathogens (Daughenbaugh et al. 2015; Runckel et al. 2011).
2.3 RNA isolation
Bee samples were homogenized in water using beads (3 mm) and a TissueLyzer (Qiagen) at 30 Hz for 2 min. Samples were centrifuged for 12 min at 12,000×g at 4 °C to pellet debris, and RNA from supernatants was extracted using TRIzol reagent (Life Technologies) according to the manufacturer’s instructions (Runckel et al. 2011).
2.4 Reverse transcription/cDNA synthesis
cDNA synthesis reactions were performed by incubating 1,000–2,000 ng total RNA, Moloney murine leukemia virus (M-MLV) reverse transcriptase (Promega), and 500 ng random hexamer primers (IDT) for 1 h at 37 °C, according to the manufacturer’s instructions (Runckel et al. 2011).
2.5 Polymerase chain reaction (PCR)
PCR was performed according to standard methods using the primers listed in Supplemental Table S1 (Runckel et al. 2011). In brief, 1 μL cDNA template was combined with 10 pmol of each forward and reverse primer and amplified with ChoiceTaq polymerase (Denville) according to the manufacturer’s instructions using the following cycling conditions: 95 °C for 5 min; 35 cycles of 95 °C for 30 s, 57 °C for 30 s, and 72 °C for 30 s, followed by final elongation at 72 °C for 4 min. The PCR products were visualized by gel electrophoresis/fluorescence imaging.
2.6 Quantitative PCR (qPCR)
Quantitative PCR was used to analyze the relative abundance of the most prevalent pathogens, which were all RNA viruses, in select samples to investigate the relationship between virus abundance and honey bee colony health. Five hundred nanograms of RNA from each of these samples was reverse transcribed with M-MLV as described above. All qPCR reactions were performed in triplicate with a CFX Connect Real Time instrument (BioRad); reaction conditions and equations for determining the relative abundance based on standard curves are provided in supplemental methods (Online Resource 1).
2.6.1 Statistical analysis of PCR
For this study, we use “pathogen prevalence” to refer to the total number of pathogens detected by PCR out of a target list of 16. Though our interest was in the relationship between strength rating and pathogen prevalence, graphical analyses indicated that there were likely relationships between pathogen prevalence and sampling time as well as between strength and sampling time. Thus, we used a Poisson log-linear regression model and accounted for an interaction between sample date (time period), beekeeping operation, colony strength, and pathogen prevalence. Observations with average strength rating were not included in some analyses to simplify the inferences between strong (S) and weak (W). The natural logarithm (ln) of the pathogen prevalence data was used in comparisons between each beekeeping operation and time period combination (Pirk et al. 2013). For the model, we used beekeeping operation 1, before almond pollination (time period 1), and weak colonies as the base level.
where yi = the total abundance/prevalence for the ith observation i = 1, 2, …, 180.
Operation2i = 1 if observation i came from beekeeping operation 2 and 0 otherwise.
Operation3i = 1 if observation i came from beekeeping operation 3 and 0 otherwise.
Period 2i = 1 if observation i was taken during and 0 otherwise.
Period 3i = 1 if observation i was taken after pollination and 0 otherwise.
Period 4i = 1 if observation i was taken in the second after pollination sampling time and 0 otherwise.
Ai = 1 if observation i was average (colony strength) and 0 otherwise.
Si = 1 if observation i was strong (colony strength) and 0 otherwise.
2.6.2 Statistical analysis of qPCR
3.1 Honey bee colony monitoring and pathogen diagnostics
Commercially managed colonies from three Montana-based beekeeping operations were monitored before, during, and after the almond pollination season (i.e., October 2013 to June 2014). Colony population size was utilized as a proxy for colony health and monitored at each sampling event. At the onset of the study, each beekeeping operation selected weak, average, and strong honey bee colonies that were monitored throughout the study. These colonies were located in Montana before and after almond pollination and in California during the almond pollination season (February 2014) (Figure 1). Honey bee colony strength, pathogen prevalence, and abundance were monitored throughout the study.
3.1.1 Pathogen detection
To identify the pathogens associated with the honey bee samples collected over the course of this study, we utilized PCR to test for a suite of 16 common pathogens. The majority of these pathogens were viruses including Lake Sinai virus 1 (LSV1), LSV2, LSV3, LSV4, LSV5, Black queen cell virus (BQCV), DWV, SBV, ABPV, CBPV, IAPV, and KBV. In addition, we tested for bacterial pathogens (i.e., P. larvae and M. plutonius) and eukaryotic parasites including the microsporidia Nosema spp. and trypanosomatids (i.e., C. mellificae SF / L. passim). In order to identify the most prevalent pathogens associated with each sample (i.e., affecting 45 % or more of the individuals within a colony), five bees per sample were utilized for pathogen analyses (Daughenbaugh et al. 2015; Pirk et al. 2013). Pathogen-specific PCR was performed to identify the pathogens associated with each sample (Supplemental Tables S1 and S2 and Supplemental Figures S1 and S2). In this work, we use “pathogen occurrence” to indicate the frequency of detecting a specific pathogen as a percentage of the total number of positive tests, “pathogen prevalence” to indicate the number of different pathogens detected in a sample, and “total pathogen abundance” to describe the total number of pathogen genome copies in select samples.
Estimated multiplicative difference in pathogen prevalence from strong to weak colonies in designated sampling period.
Estimated multiplicative difference
Lower 95 %
Upper 95 %
After 2 (June)
3.1.2 Pathogen abundance
Estimated multiplicative differences in the pathogen abundance between beekeeping operations, at different sampling time periods, and between colonies of different strengths; natural log scale.
Operation 2 vs. operation 1
Operation 3 vs. operation 1
During (February) vs. before (October–December)
After (March–April) vs. before (October–December)
After2 (June) vs. before (October–December)
Average vs. weak
Strong vs. weak
3.1.3 Colony mortality
Percentage of honey bee colony mortality by beekeeping operation and time period.
Honey bees play an important role as pollinators of numerous agricultural crops. The majority of commercially managed honey bee colonies in the US are transported throughout the year to meet the pollination demands of crops including California almond production. Commercial beekeepers in the US and some parts of Europe have experienced high annual average losses, though the factors most responsible for these losses are not well understood. The objectives of this study were to identify the pathogens currently circulating in Western US-based commercial honey bee colonies involved in almond pollination and to examine the relationship between colony strength, pathogen prevalence, and abundance. Pathogen incidence and abundance have been associated with CCD-affected colonies and colony losses in the US (Chen et al. 2014; Cornman et al. 2012; Cox-Foster et al. 2007; Daughenbaugh et al. 2015; Li et al. 2013; van Engelsdorp et al. 2009), Canada (van der Zee et al. 2012), Austria (Berényi et al. 2006), Denmark (Nielson et al. 2008), Luxembourg (Clermont et al. 2014) and Belgium (Ravoet et al. 2013). Therefore, we hypothesized that weak colonies would harbor a greater number of pathogens, as well as have greater loads of these pathogens. Surprisingly, there have been few honey bee colony monitoring studies in the US; therefore, the data presented herein provide an important examination of colony health and pathogen prevalence and abundance in Western US-based honey bee colonies involved in almond pollination.
There are multiple biotic and abiotic factors that contribute to honey bee colony health and survival. The focus for this study was pathogen prevalence and abundance, since several studies indicate that pathogens affect colony health. Specifically, one US-based study that compared multiple variables in CCD and non-CCD-affected colonies determined that weak or dead colonies were more likely to neighbor other weak or dead colonies and that CCD-affected samples had a higher pathogen incidence than controls, whereas pesticide levels were comparable (van Engelsdorp et al. 2009). Similarly, metagenomic analysis of those samples suggested that IAPV abundance was related to colony health (Cox-Foster et al. 2007), though later studies indicated that pathogen number, DWV, and a combination of other pathogens were more positively correlated with CCD-affected colonies (i.e., ABPV, Nosema apis, KBV, LSV1, and LSV2) (Chen and Evans 2007; Johnson et al. 2009; Cornman et al. 2012). More recently, a study that involved ten colonies determined that IAPV was more abundant in colonies with less food stores and less brood/developing bees (Chen et al. 2014). In addition, data from a small-scale monitoring project that also involved only ten colonies suggested that LSV2, LSV1, BQCV, and N. ceranae were more abundant in weak colonies, as compared to strong colonies (Daughenbaugh et al. 2015). Though the exact cause(s) of CCD and colony loss remain unknown, these US-based studies suggest that pathogens are an important factor.
Longitudinal monitoring studies are required to understand the key factors affecting honey bee colony losses. These studies help to establish a baseline for typical pathogen incidence and abundance in honey bee colonies throughout the year. The most prevalent pathogens detected in this study were BQCV, LSV2, SBV, N. ceranae, and C. mellificae / L. passim. Lake Sinai viruses (i.e., LSV2, LSV1, LSV3, and LSV4) accounted for a large proportion of the positive tests. One of the first US-based longitudinal monitoring studies involved 20 colonies managed by a single large-scale commercial beekeeping operation (Runckel et al. 2011). This study indicated that pathogen status within healthy colonies is dynamic and seasonal, and it and other studies have indicated that several samples, obtained at specified time intervals, provide a more accurate depiction of colony health than a single time point. Longitudinal sampling and analysis revealed increased incidence of particular viruses in most colonies within short time periods, resulted in the discovery of the Lake Sinai viruses, and facilitated the isolation and sequencing of a honey bee-infecting trypanosomatid (originally designated C. mellificae, strain SF but recently re-classified and re-named L. passim (Runckel et al. 2011, 2014; Schwarz et al. 2015). However, monitored honey bee colonies remained healthy throughout the study, so it did not provide the sample cohort required to associate pathogens with health status and disease. Similarities between the results of the 2009–2010 sample cohort (Runckel et al. 2011) and the 2013–2014 sample cohort described herein include high prevalence of LSVs, particularly LSV2 during almond pollination, low occurrence of DWV, IAPV, and KBV, and frequent detection of BQCV and SBV. In general, pathogen prevalence was largest in the samples obtained after almond pollination and lowest in samples obtained during almond pollination for all operations; this observation may stem from the seasonality of pathogenic infections (Chen and Siede 2007; Evans and Schwarz 2011; Runckel et al. 2011).
US-wide surveys, including those performed by the Bee Informed Partnership, provide an important overview of the status of colonies and pathogens (reviewed in Steinhauer et al. 2015). However, these surveys rely on voluntary sample submission over the course of the year and are not sufficiently controlled to investigate subtle relationships between colony health and particular biotic or abiotic factors. Monitoring projects outside of the US are informative and serve as models for future studies. The German Bee Monitoring project is an excellent example of a longitudinal monitoring study that assessed the role of management practices, pathogens, pesticides, and environmental factors, in periodic high winter losses of honey bee colonies over a period of 4 years (Genersch et al. 2010; Gisder et al. 2010). That study identified several factors that were significantly related to honey bee colony loss including high levels of Varroa destructor mites, queen age, DWV and ABPV infection, and weak colony strength rating in autumn (Genersch et al. 2010). Likewise, the prevalence and seasonal variation of virus infections and mite infestation was assessed in a 1-year country-wide study in France that involved 360 honey bee colonies from 36 apiaries sampled in the spring, summer, and autumn (Tentcheva et al. 2004). PCR was used to assess the incidence of six bee viruses; DWV was the most prevalent followed by BQCV and SBV, and the detection frequency varied over the course of the year (Tentcheva et al. 2004). Recently, the distributions of ABPV, BQCV, CBPV, DWV, SBV across all districts of Croatia were examined to provide an epidemiological baseline (Gajger et al. 2014). DWV was the most readily detected virus, whereas KBV and IAPV were not detected (Gajger et al. 2014). Several studies indicate that the combination of DWV infection and V. destructor mite infestation negatively affect colony health (reviewed in Nazzi et al. 2012). DWV prevalence was very low in our study, thus its relationship with colony health could not be assessed.
The results from this cohort study indicate that the relationship between pathogen incidence and abundance is complex and dependent upon beekeeping operation and sampling time. The data presented herein are similar to other studies in that they do not rule out a relationship between colony strength and pathogen prevalence. Large variability in this data set suggests that future studies should include larger sample sizes, involve more beekeeping operations, and incorporate standardized management practices. Colony mortality in this study was lower than the average losses calculated from recent US national survey data (14.8 % vs. 33 % respectively), though the reason(s) for low colony mortality remain unknown. Future studies that closely monitor additional factors, such as foraging success and diversity via pollen traps, agrochemical exposure via chemical analysis, and mite abundance, in conjunction with colony health and pathogen prevalence and abundance will help elucidate the factors that most correlate with colony health and survival.
This work was supported in part by Montana Department of Agriculture Specialty Crop Block Grant Program (1450687), National Institutes of Health IDeA Program COBRE grant GM110732, National Science Foundation EPSCoR NSF-IIA-1443108, the Montana State University Agricultural Experiment Station, and Project Apis m. Ian Cavigli, Madison Martin, and Emma Garcia received support from the Howard Hughes Medical Institute Undergraduate Scholars Program (52006931), Michael Lerch and Katie Banner were supported by the National Institute of General Medical Sciences of the National Institutes of Health (P20GM103474), and Laura M. Brutscher is supported by a Honey Bee Biology Fellowship funded by Project Apis m. The Flenniken laboratory is also supported by USDA-NIFA (2015-67014-23284), the National Honey Board, and the Montana State Beekeepers Association. We would like to thank members of the Flenniken laboratory (Elisa Boyd and William Glenny) for reviewing this manuscript prior to publication, Dr. Megan D. Higgs (Department of Mathematical Sciences, Montana State University) for assistance with statistical analyses, and members of the Montana State Beekeepers Association for honey bee samples.
MLF conceived and designed experiments. IC, KFD, MM, and EG performed experiments. IC, ML, KB, and MLF analyzed the data. MLF, IC, ML, KB, LMB, and EG wrote the paper.
Conflict of interest
The authors declare that they have no competing interests.
This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.
- Bates, D., Maechler, M., Bolker, B., Walker, S. (2015) Package “lme4.”Google Scholar
- California ABO Almond Almanac 2014Google Scholar
- Chen, Y., Evans, J.D. (2007) Historical presence of Israeli acute paralysis virus in the United States. Am Bee J 147, 1027–1028Google Scholar
- Chen, Y.P., Siede, R. (2007) Honey bee viruses. In: Advances in virus research. Elsevier, pp 33–80Google Scholar
- Core Team R (2014) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
- de Miranda, J.R., Bailey, L., Ball, B.V. (2013) Standard methods for virus research in Apis mellifera. J. Apic. Res. 52(4). doi:10.3896/IBRA.220.127.116.11
- Delaplane, K.S., van der Steen, J. (2013) Standard methods for estimating strength parameters of Apis mellifera colonies. J Apic Res 52(1). doi:10.3896/IBRA.1.52.1.03
- Granberg, F., Vicente-Rubiano, M., Rubio-Guerri, C., Karlsson, O.E., Kukielka, D., Belák, S., Sánchez-Vizcaíno, J.M. (2013) Metagenomic detection of viral pathogens in Spanish honeybees: co-infection by aphid lethal paralysis, Israel acute paralysis and Lake Sinai viruses. PLoS ONE 8, e57459. doi:10.1371/journal.pone.0057459 PubMedCentralCrossRefPubMedGoogle Scholar
- Li, J.L., Cornman, R.S., Evans, J.D., Pettis, J.S., Zhao, Y., Murphy, C., Peng, W.J., Wu, J., Hamilton, M., Boncristiani, H.F., Zhou, L., Hammond, J., Chen, Y.P. (2013) Systemic spread and propagation of a plant-pathogenic virus in European honeybees, Apis mellifera. MBio 5, e00898–13. doi:10.1128/mBio.00898-13 CrossRefGoogle Scholar
- Nazzi, F., Brown, S.P., Annoscia, D., Del Piccolo, F., Di Prisco GVarricchio, P., Vedova, G.D., Cattonaro, F., Caprio, E., Pennacchio, F. (2012) Synergistic parasite-pathogen interactions mediated by host immunity can drive the collapse of honeybee colonies. PLoS Pathog . doi:10.1371/journal.ppat.1002735 PubMedCentralPubMedGoogle Scholar
- Oregon State University. Extension Service, Sagili RR, Burgett DM, Washington State University. Extension, University of Idaho. Extension, Agriculture USDO, Extension PNC (2011) Evaluating honey bee colonies for pollination: a guide for commercial growers and beekeepers, 1–8Google Scholar
- Pirk, C., de Miranda, J.R., Kramer, M. (2013) Statistical guidelines for Apis mellifera research. J Apic Res 52(4). doi:10.3896/IBRA.18.104.22.168
- 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, e72443. doi:10.1371/journal.pone.0072443 PubMedCentralCrossRefPubMedGoogle Scholar
- Runckel, C., Flenniken, M.L., Engel, J.C., Ruby, J.G., Ganem, D., Andino, R., DeRisi, J.L. (2011) Temporal analysis of the honey bee microbiome reveals four novel viruses and seasonal prevalence of known viruses, Nosema, and Crithidia. PLoS ONE 6, e20656. doi:10.1371/journal.pone.0020656 PubMedCentralCrossRefPubMedGoogle Scholar
- 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. J Eukaryot Microbiol . doi:10.1111/jeu.12209 PubMedGoogle Scholar
- Spleen, A.M., Lengerich, E.J., Rennich, K., Caron, D., Rose, R., et al. (2013) A national survey of managed honey bee 2011–12 winter colony losses in the United States: results from the Bee Informed Partnership. J Apic Res 52(2). doi:10.3896/IBRA.1.52.2.07
- Steinhauer, N.A., Rennich, K., Lee, K., Pettis, J., Tarpy, D.R., et al. (2015) Colony loss 2014–2015: preliminary results. In: beeinformed.org. http://beeinformed.org/results/colony-loss-2014-2015-preliminary-results/. Accessed 26 May 2015
- Tentcheva, D., Gauthier, L., Zappulla, N., Dainat, B., Cousserans, F., Colin, M.E., Bergoin, M. (2004) Prevalence and seasonal variations of six bee viruses in Apis mellifera L. and Varroa destructor mite populations in France. Appl Environ Microbiol 70, 7185–7191PubMedCentralCrossRefPubMedGoogle Scholar
- van der Zee, R., Brodschneider, R., Brusbardis, V., Charrière, J.D., Chlebo, R., et al. (2014) Results of international standardised beekeeper surveys of colony losses for winter 2012–2013: analysis of winter loss rates and mixed effects modelling of risk factors for winter loss. J Apic Res 53(1), 19–34CrossRefGoogle Scholar
- van Engelsdorp, D., Underwood, R., Caron, D., Hayes, J.J. (2007) An estimate of managed colony losses in the winter of 2006–2007: a report commissioned by the apiary inspectors of America. Am Bee J 147, 599–603Google Scholar
- van Engelsdorp, D., Evans, J.D., Saegerman, C., Mullin, C., Haubruge, E., Nguyen, B.K., Frazier, M., Frazier, J., Cox-Foster, D., Chen, Y., Underwood, R., Tarpy, D.R., Pettis, J.S. (2009) Colony collapse disorder: a descriptive study. PLoS ONE 4, e6481. doi:10.1371/journal.pone.0006481 CrossRefGoogle Scholar
- van Engelsdorp, D., Lengerich, E., Spleen, A., Dainat, B., Cresswell, J., et al. (2013) Standard epidemiological methods to understand and improve Apis mellifera health. J Apic Res 52(4). doi:10.3896/IBRA.22.214.171.124