Environmental genomics of Late Pleistocene black bears and giant short-faced bears
Bibliographic record
Abstract
Analysis of ancient environmental DNA (eDNA) has revolutionized our ability to describe biological communities in space and time, 1–3 by allowing for parallel sequencing of DNA from all trophic levels. 4–8 However, because environmental samples contain sparse and fragmented data from multiple individuals, and often contain closely related species, 9 the field of ancient eDNA has so far been limited to organellar genomes in its contribution to population and phylogenetic studies. 5 , 6 , 10 , 11 This is in contrast to data from fossils 12 , 13 where full-genome studies are routine, despite these being rare and their destruction for sequencing undesirable. 14–16 Here, we report the retrieval of three low-coverage (0.03×) environmental genomes from American black bear ( Ursus americanus ) and a 0.04× environmental genome of the extinct giant short-faced bear ( Arctodus simus ) from cave sediment samples from northern Mexico dated to 16–14 thousand calibrated years before present (cal kyr BP), which we contextualize with a new high-coverage (26×) and two lower-coverage giant short-faced bear genomes obtained from fossils recovered from Yukon Territory, Canada, which date to ∼22–50 cal kyr BP. We show that the Late Pleistocene black bear population in Mexico is ancestrally related to the present-day Eastern American black bear population, and that the extinct giant short-faced bears present in Mexico were deeply divergent from the earlier Beringian population. Our findings demonstrate the ability to separately analyze genomic-scale DNA sequences of closely related species co-preserved in environmental samples, which brings the use of ancient eDNA into the era of population genomics and phylogenetics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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 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".