Conservation Genomics of the Declining North American Bumblebee Bombus terricola Reveals Inbreeding and Selection on Immune Genes
Bibliographic record
Abstract
The yellow-banded bumblebee Bombus terricola was common in North America but has recently declined and is now on the IUCN Red List of threatened species. The causes of B. terricola’s decline are not well understood. Our objectives were to create a partial genome and then use this to estimate population data of conservation interest, and to determine whether genes showing signs of recent selection point to a specific cause of decline. Newer sequencing methods enable population genomics to be performed on non-model organisms without a published genome by creation of a contig set. We developed two large-scale genomic datasets to better understand the context and possible causes of B. terricola’s precipitous decline. First, we generated a draft partial genome (contig set) for B. terricola, sequenced using Pacific Biosciences RS II at an average depth of 35X. Second, we sequenced the individual genomes of 22 bumblebee gynes from Ontario and Quebec using Illumina HiSeq 2500, each at an average depth of 20, which were used to improve the PacBio genome calls and for population genetics analyses. The latter revealed that several samples had long runs of homozygosity, and individuals had high inbreeding coefficient F, consistent with low effective population size. Our data suggest that B. terricola’s effective population size has decreased orders of magnitude from pre-Holocene levels. We carried out tests of selection to identify genes that may have played a role in ameliorating environmental stressors underlying B. terricola’s decline. Several immune-related genes have signatures of recent positive selection, which is consistent with the pathogen-spillover hypothesis for B. terricola’s decline. The new B. terricola contig set can help solve the mystery of bumblebee decline by enabling functional genomics research to directly assess the health of pollinators and identifying the stressors causing declines.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".