Pathogen Webs in Collapsing Honey Bee Colonies
Publication Date
August 21, 2012
Journal
PLOS ONE
Authors
R. Scott Cornman, David R. Tarpy, Yanping Chen, Lacey Jeffreys, et al
Volume
7
Issue
8
Pages
e43562
DOI
http://doi.org/10.1371/journal.pone.0043562
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0043562
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/22927991
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3424165
Europe PMC
http://europepmc.org/abstract/MED/22927991
Web of Science
000307789700040
Scopus
84865172013
Mendeley
http://www.mendeley.com/research/pathogen-webs-collapsing-honey-bee-colonies
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Mendeley | Further Information

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CrossRef

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/309529", "https://ndownloader.figshare.com/files/309586", "https://ndownloader.figshare.com/files/309641", "https://ndownloader.figshare.com/files/309675", "https://ndownloader.figshare.com/files/309705"], "description"=>"<div><p>Recent losses in honey bee colonies are unusual in their severity, geographical distribution, and, in some cases, failure to present recognized characteristics of known disease. Domesticated honey bees face numerous pests and pathogens, tempting hypotheses that colony collapses arise from exposure to new or resurgent pathogens. Here we explore the incidence and abundance of currently known honey bee pathogens in colonies suffering from Colony Collapse Disorder (CCD), otherwise weak colonies, and strong colonies from across the United States. Although pathogen identities differed between the eastern and western United States, there was a greater incidence and abundance of pathogens in CCD colonies. Pathogen loads were highly covariant in CCD but not control hives, suggesting that CCD colonies rapidly become susceptible to a diverse set of pathogens, or that co-infections can act synergistically to produce the rapid depletion of workers that characterizes the disorder. We also tested workers from a CCD-free apiary to confirm that significant positive correlations among pathogen loads can develop at the level of individual bees and not merely as a secondary effect of CCD. This observation and other recent data highlight pathogen interactions as important components of bee disease. Finally, we used deep RNA sequencing to further characterize microbial diversity in CCD and non-CCD hives. We identified novel strains of the recently described Lake Sinai viruses (LSV) and found evidence of a shift in gut bacterial composition that may be a biomarker of CCD. The results are discussed with respect to host-parasite interactions and other environmental stressors of honey bees.</p> </div>", "links"=>[], "tags"=>["pathogen", "webs", "collapsing", "colonies"], "article_id"=>121067, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.s001", "https://dx.doi.org/10.1371/journal.pone.0043562.s002", "https://dx.doi.org/10.1371/journal.pone.0043562.s003", "https://dx.doi.org/10.1371/journal.pone.0043562.s004", "https://dx.doi.org/10.1371/journal.pone.0043562.s005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Pathogen_Webs_in_Collapsing_Honey_Bee_Colonies/121067", "title"=>"Pathogen Webs in Collapsing Honey Bee Colonies", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2012-08-21 00:17:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/589384"], "description"=>"<p>The thickness of lines is scaled to the Spearman’s rho correlation coefficient for each pair, the values of which are given in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone-0043562-t003\" target=\"_blank\">Table 3</a>.</p>", "links"=>[], "tags"=>["pairwise", "correlations", "pathogen", "abundance", "ccd", "non-ccd"], "article_id"=>259870, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Graphical_representation_of_pairwise_correlations_between_pathogen_abundance_in_CCD_and_non_CCD_colonies_/259870", "title"=>"Graphical representation of pairwise correlations between pathogen abundance in CCD and non-CCD colonies.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-21 02:44:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/589485"], "description"=>"<p>In all three panels, the horizontal axis is the number of reads mapping to each reference in the CCD− sample and the vertical axis is reads mapped in CCD+, adjusted for library size. The gray diagonal line in each panel demarcates equal representation in the two samples, and the axes are log10 scale. Only references with normalized read counts greater than 50 in each sample are displayed. <b>A.</b> Read counts for 72 GenBank accessions that are representative of the major gut microbial phylotypes of the honey bee. The accessions are drawn from Fig. S1 of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone.0043562-Martinson1\" target=\"_blank\">[20]</a> and are listed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#s2\" target=\"_blank\">Materials and Methods</a>. Each accession is color-coded by taxonomy, following the phylotypes of <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone.0043562-Martinson1\" target=\"_blank\">[20]</a>: Alpha = the alpha-proteobacteria clusters Alpha1, Alpha2.1, and Alpha2.2; Beta = beta-proteobacteria cluster, Gamma = the gamma-proteobacteria clusters Gamma1 and Gamma2, Bifido. = <i>Bifidobacteria</i>, and Firm. = the firmicutes clusters Firm4 and Firm5. <b>B.</b> Read counts of contigs in <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone.0043562.s004\" target=\"_blank\">File S3</a></b> that were assigned to bacterial phyla using the Classifier tool <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone.0043562-Wang1\" target=\"_blank\">[27]</a>. All three actinobacteria contigs belonged to the genus <i>Bifidobacteria</i> based on high Classifier bootstrap support at the genus level (<b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone.0043562.s005\" target=\"_blank\">File S4</a></b>) and best BLAST match (<b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone.0043562.s004\" target=\"_blank\">File S3</a></b>). Other contigs are color-coded by phylum: Alpha = alpha-proteobacteria, Beta = beta-proteobacteria, Firm = firmicutes, and Gamma = gamma-proteobacteria. <b>C</b>. Read counts of all contigs with bacterial BLAST matches. A more diffuse but still bimodal distribution of relative change in read counts is apparent. The two contigs that show the greatest increase in CCD+ relative to other contigs (highlighted in green) both have best BLAST matches to the genus <i>Arsenophonus</i> with an expectation at least four orders of magnitude lower than the next closest taxon, but the maximum identity of these matches is only 90%.</p>", "links"=>[], "tags"=>["abundance", "bacterial", "taxa", "inferred", "illumina"], "article_id"=>259969, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Change_in_abundance_of_bacterial_taxa_inferred_from_mapping_of_Illumina_reads_/259969", "title"=>"Change in abundance of bacterial taxa inferred from mapping of Illumina reads.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-21 02:46:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/589569"], "description"=>"<p><b>A</b>. Phylogeny of the five longest 5′-aligning contigs with LSV1 and LSV2 (GenBank accessions HQ871931.1 and HQ888865.1) <b>B</b>. Phylogeny of the five longest 3′-aligning contigs with LSV1 and LSV2. The two trees have similar branch lengths and topologies, suggesting that a physical linkage between each 5′-aligning contig and a 3′-aligning contig.</p>", "links"=>[], "tags"=>["contigs", "sinai", "viruses"], "article_id"=>260054, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Phylogeny_of_contigs_related_to_the_Lake_Sinai_Viruses_LSV1_and_LSV2_/260054", "title"=>"Phylogeny of contigs related to the Lake Sinai Viruses (LSV1 and LSV2).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-21 00:00:54"}
  • {"files"=>["https://ndownloader.figshare.com/files/589648"], "description"=>"<p>Contigs and accessions are the same as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone-0043562-g003\" target=\"_blank\"><b>Figure 3</b></a>, with contigs aligning to the 5′ and 3′ regions, respectively, of LSV denoted as such. The frequency of mapped reads for each 5′ aligning contig is mirrored by that of a corresponding 3′ contig, suggesting physical linkage. Here read counts are normalized by contig length (reads per kilobase per million mapped reads, or RPKM) because the frequency of viral fragments of different lengths are being compared.</p>", "links"=>[], "tags"=>["abundance", "lsv", "strains"], "article_id"=>260140, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relative_abundance_of_LSV_strains_in_CCD_8722_and_CCD_samples_/260140", "title"=>"Relative abundance of LSV strains in CCD− and CCD+ samples.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-21 00:02:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/589777"], "description"=>"<p>Shading at each position indicates amino-acid similarity among at least 50% of the residues, based on the BLOSUM62 matrix. Alignment performed with ClustalW using default settings.</p>", "links"=>[], "tags"=>["alignment", "contigs", "blastx", "matches", "rdrp", "stoloniferum", "genbank", "accessions"], "article_id"=>260262, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sequence_alignment_of_three_contigs_with_BLASTX_matches_to_the_RDRP_of_Penicillium_stoloniferum_virus_S_GenBank_accessions_CAJ01909_1_and_AY156521_2_/260262", "title"=>"Sequence alignment of three contigs with BLASTX matches to the RDRP of <i>Penicillium stoloniferum virus S</i>, GenBank accessions CAJ01909.1 and AY156521.2.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2012-08-21 00:04:22"}
  • {"files"=>["https://ndownloader.figshare.com/files/589850"], "description"=>"<p>The expected log2 differential is taken as the log2 of fold change in CCD colonies from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#pone-0043562-t002\" target=\"_blank\">Table 2</a>. For read counts, differential abundance = log2 (CCD+ reads) – log2 (CCD− reads) – log2 (total CCD+ reads/total CCD− reads). N/A indicates that the log2 difference in abundance could not be calculated because no reads matched in CCD−.</p>", "links"=>[], "tags"=>["rna", "abundances", "illumina", "reads", "compared", "qpcr"], "article_id"=>260335, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t008"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_RNA_target_abundances_in_Illumina_sequence_reads_compared_with_qPCR_results_/260335", "title"=>"Comparison of RNA target abundances in Illumina sequence reads compared with qPCR results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:05:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/589893"], "description"=>"<p>These samples tested whether pathogen covariation occurred at the level of individual <i>Nosema</i>-exposed bees, outside of a CCD context. N equals the number of co-infected bees upon which the correlation is calculated for each pathogen pair, out of a total of 77 bees tested. CBPV = Chronic bee paralysis virus; ABPV = acute bee paralysis virus; DWV = deformed wing virus; SBV = sacbrood virus; BQCV = black queen cell virus; IAPV = Israeli acute paralysis virus; NC = <i>Nosema ceranae</i>; NA = <i>Nosema apis</i>. Asterisk indicates a significant comparison after Bonferroni correction for multiple tests.</p>", "links"=>[], "tags"=>["coefficients", "pathogen", "abundance", "randomly", "sampled", "bees", "colonies", "observable", "characteristics"], "article_id"=>260377, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Pearson_correlation_coefficients_of_pathogen_abundance_within_randomly_sampled_individual_worker_bees_from_colonies_with_an_observable_Nosema_infection_but_no_characteristics_of_CCD_/260377", "title"=>"Pearson correlation coefficients of pathogen abundance within randomly sampled individual worker bees from colonies with an observable <i>Nosema</i> infection but no characteristics of CCD.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:06:17"}
  • {"files"=>["https://ndownloader.figshare.com/files/589932"], "description"=>"<p>Match criteria are the conditions under which an aligned read is considered a valid mapping. For Bowtie, this column shows the number of mismatches (the parameter ‘V’) and whether the match was required to be the best match in the reference database. For Mega BLAST, the minimum expectation of the match is shown.</p>", "links"=>[], "tags"=>["illumina", "sequencing", "reads"], "article_id"=>260422, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Assignment_of_Illumina_sequencing_reads_to_bins_/260422", "title"=>"Assignment of Illumina sequencing reads to bins.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:07:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/589965"], "description"=>"*<p>Best BLAST match was to ribosomal sequence of another insect species but contig is presumed to derive from <i>A. mellifera.</i></p>**<p>Includes the trypanosome genera <i>Leishmania</i> and <i>Leptomonas.</i></p>", "links"=>[], "tags"=>["contigs", "assembled", "sequencing", "cdna", "libraries", "derived", "pooled", "samples"], "article_id"=>260451, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Best_BLAST_match_of_contigs_assembled_from_deep_sequencing_of_the_CCD_and_CCD_8722_cDNA_libraries_derived_from_pooled_colony_samples_of_total_RNA_/260451", "title"=>"Best BLAST match of contigs assembled from deep sequencing of the CCD+ and CCD− cDNA libraries derived from pooled colony samples of total RNA.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:07:31"}
  • {"files"=>["https://ndownloader.figshare.com/files/590010"], "description"=>"<p>Taxonomy was estimated by the RDP Classifier tool for all 303 contigs with bacterial best BLAST matches. Only the 67 contigs with a minimum 80% bootstrap support at the order level are included here. N/A = not applicable, due to a low number of mapped reads.</p>", "links"=>[], "tags"=>["cdna", "contigs", "homology", "16s", "ribosomal", "bacterial"], "article_id"=>260500, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Taxonomic_distribution_of_cDNA_contigs_with_homology_to_16S_ribosomal_sequence_by_bacterial_order_/260500", "title"=>"Taxonomic distribution of cDNA contigs with homology to 16S ribosomal sequence, by bacterial order.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:08:20"}
  • {"files"=>["https://ndownloader.figshare.com/files/590048"], "description"=>"<p>Values are the number of colony samples (n = 61 for CCD and n = 63 for non-CCD) in which the pathogen was detected at any level. The likelihood ratio chi-square test of contingency was used to compute the probability of equal pathogen incidence in CCD and non-CCD colonies. ABPV = acute bee paralysis virus; DWV = deformed wing virus; SBV = sacbrood virus; BQCV = black queen cell virus; IAPV = Israeli acute paralysis virus; KBV = Kashmir bee virus; NC = <i>Nosema ceranae</i>; NA = <i>Nosema apis</i>.</p>", "links"=>[], "tags"=>["pathogen", "incidence"], "article_id"=>260542, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Honey_bee_pathogen_incidence_by_colony_status_/260542", "title"=>"Honey-bee pathogen incidence by colony status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:09:02"}
  • {"files"=>["https://ndownloader.figshare.com/files/590090"], "description"=>"<p>The difference in mean ΔC<sub>T</sub> values (normalized threshold cycle in qPCR reactions) was compared by ANOVA for two non-independent contrasts: all CCD colonies (n = 61) versus all non-CCD colonies (n = 63), and weak (n = 15) versus strong (n = 29) colonies in non-CCD apiaries. Weak colonies had six or fewer frames of bees and strong colonies had seven or more frames (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0043562#s2\" target=\"_blank\">Materials and Methods</a>). Non-CCD colonies include both sympatric and allopatric colonies, which were combined for increased statistical power. ΔΔ is the mean Δof the non-CCD population minus the mean Δvalue of the CCD population. Thus, positive numbers represent a decrease in mean threshold cycle and an increase in pathogen abundance. Fold change between categories is calculated as (1+ primer efficiency) <sup>ΔΔC</sup><sub>T</sub>. SE = standard error of population mean ΔC<sub>T</sub>; P-value = probability of equal mean ΔC<sub>T</sub> by ANOVA (i.e., that the true ΔΔC<sub>T</sub> = 0); ABPV = acute bee paralysis virus; DWV = deformed wing virus; SBV = sacbrood virus; BQCV = black queen cell virus; IAPV = Israeli acute paralysis virus; KBV = Kashmir bee virus.</p>", "links"=>[], "tags"=>["honey-bee", "pathogen", "abundance"], "article_id"=>260576, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Contrasts_in_honey_bee_pathogen_abundance_by_colony_status_/260576", "title"=>"Contrasts in honey-bee pathogen abundance by colony status.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:09:36"}
  • {"files"=>["https://ndownloader.figshare.com/files/590129"], "description"=>"<p>The number of colonies for each category is shown in parentheses. Sympatric non-CCD colonies are those that occurred in the same apiaries as CCD colonies, whereas non-sympatric colonies were sampled from different locations or in different years, or both, and thus were far removed from any diagnosed cases of CCD. The distinction is made because it was not possible to follow non-CCD colonies after sampling to determine if any subsequently experienced CCD. ABPV = acute bee paralysis virus; DWV = deformed wing virus; SBV = sacbrood virus; BQCV = black queen cell virus; IAPV = Israeli acute paralysis virus; KBV = Kashmir bee virus. Asterisk indicates a significant comparison after Bonferroni correction for multiple tests.</p>", "links"=>[], "tags"=>["pathogen", "abundance"], "article_id"=>260620, "categories"=>["Genetics", "Microbiology"], "users"=>["R. Scott Cornman", "David R. Tarpy", "Yanping Chen", "Lacey Jeffreys", "Dawn Lopez", "Jeffery S. Pettis", "Dennis vanEngelsdorp", "Jay D. Evans"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0043562.t003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlations_of_pathogen_abundance_within_different_colony_types_/260620", "title"=>"Correlations of pathogen abundance within different colony types.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2012-08-21 00:10:20"}

PMC Usage Stats | Further Information

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Relative Metric

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