A Chromosome-Level Reference Genome for the Black-Legged Kittiwake (<i>Rissa tridactyla</i>), a Declining Circumpolar Seabird
Bibliographic record
Abstract
Amidst the current biodiversity crisis, the availability of genomic resources for declining species can provide important insights into the factors driving population decline. In the early 1990s, the black-legged kittiwake (Rissa tridactyla), a pelagic gull widely distributed across the arctic, subarctic, and temperate zones, suffered a steep population decline following an abrupt warming of sea surface temperature across its distribution range and is currently listed as Vulnerable by the International Union for the Conservation of Nature. Kittiwakes have long been the focus for field studies of physiology, ecology, and ecotoxicology and are primary indicators of fluctuating ecological conditions in arctic and subarctic marine ecosystems. We present a high-quality chromosome-level reference genome and annotation for the black-legged kittiwake using a combination of Pacific Biosciences HiFi sequencing, Bionano optical maps, Hi-C reads, and RNA-Seq data. The final assembly spans 1.35 Gb across 32 chromosomes, with a scaffold N50 of 88.21 Mb and a BUSCO completeness of 97.4%. This genome assembly substantially improves the quality of a previous draft genome, showing an approximately 5× increase in contiguity and a more complete annotation. Using this new chromosome-level reference genome and three more chromosome-level assemblies of Charadriiformes, we uncover several lineage-specific chromosome fusions and fissions, but find no shared rearrangements, suggesting that interchromosomal rearrangements have been commonplace throughout the diversification of Charadriiformes. This new high-quality genome assembly will enable population genomic, transcriptomic, and phenotype-genotype association studies in a widely studied sentinel species, which may provide important insights into the impacts of global change on marine systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".