Trends in groundberry cover under climate change in the southern and central Yukon, 1997–2022
Bibliographic record
Abstract
Groundberries are an important component of the flora of the boreal forest and provide seasonally important food for many birds and mammals, as well as local people in northern Canada. Here, we ask whether there has been a change in the cover of groundberries in the Yukon boreal forest over the last two decades. We monitored five common species at undisturbed forest sites spaced 300 km apart. At our Kluane site, we monitored 710 fixed quadrats per year for 26 years (1997–2022), and at Mayo 500 quadrats per year for 18 years (2005–2022). The cover of four species, Arctostaphylos uva-ursi (L.) Spreng. (bearberry) , Arctostaphylos rubra (Rehder & E.H. Wilson) Fernald (red bearberry) , Empetrum nigrum L. (crowberry), and Geocaulon lividum (Richardson) Fernald (toadflax), declined annually by 0.2%–0.8% at both sites. In contrast, Vaccinium vitis-idaea L. (lingonberry) increased annually by 0.5% and 0.8%. We tested whether increases in summer temperature and rainfall were correlated with the observed changes but found no significant relationships. These boreal plants are changing in abundance, but we have limited data on the extent and speed of these changes. We recommend experiments to understand the cause(s) of these changes in groundberry productivity. Our study is a start in monitoring important berry species in this critical ecosystem of northern Canada.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".