Patterns of postglacial vegetation establishment clarified by lacustrine sedaDNA from Baffin Island, Arctic Canada
Bibliographic record
Abstract
The colonization of recently deglaciated landscapes by tundra vegetation during the early Holocene is an important case study for understanding possible rates and patterns of plant migration in a rapidly warming world. Fossil pollen in lake sediment has traditionally served as the primary tool for reconstructing paleovegetation and understanding postglacial biogeography. However, because pollen can be wind-transported long distances and, in some cases, reworked from older deposits on the landscape, pollen-based vegetation histories can sometimes obscure the true history of plant colonization. In contrast, lacustrine sedimentary ancient DNA (sedaDNA) is sourced locally and is less likely to be adequately preserved through reworking events, thus making it a more reliable proxy for determining the precise timing of plant colonization. Here, we present three sedaDNA records from Holocene lake sediment across southern Baffin Island, Arctic Canada, that clarify the timing of postglacial vegetation changes. In particular, DNA from the subarctic shrub Betula (dwarf birch) first appears thousands of years after deglaciation in all three lake catchments, suggesting delayed colonization despite its strong pollen signal in early postglacial sediments. While moderate levels of Alnus (alder) pollen characterize early to mid-Holocene lake sediments from the region, sedaDNA suggests that Alnus was likely not present in any of the three lake catchments during the Holocene. In addition, aquatic plant community changes indicated by sedaDNA faithfully reflect the timing of early Holocene warmth in the region, highlighting the potential utility of aquatic plant DNA as a qualitative temperature proxy. We suggest that ancient plant DNA in lake sediment provides key paleoecological information that is distinct from traditional proxy records, particularly during periods of relatively rapid ecological change like the early Holocene.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".