Losses in understory diversity over three decades in an old-growth cool-temperate forest in Michigan, USA
Bibliographic record
Abstract
Diversity in temperate forests is concentrated in the understory, but understory dynamics are poorly understood for old-growth forests. We use repeated measurements of more than eight hundred 1 m2plots over three decades to assess patterns of understory diversity in old-growth mesic and wet forests in northern Michigan, USA. We ask whether diversity changes systematically over time and whether dynamics are related to spatial scale. We find, for all habitats, significant understory diversity loss at square-metre scales but not at coarser scales. Total herbaceous cover, however, remained constant or increased in total and for nearly all frequent species, and no species were lost overall. We explore hypotheses about diversity regulation by exploring correlations with habitat, canopy composition, and properties of understory species. Nonindigenous plants are rare at the study site, earthworm invasion is not apparent, and deer browse is not intense. Diversity changes may be related to ecological guild membership. We suggest that the general loss of fine-scale diversity is driven by either changing canopy composition or competitive dynamics within the understory community. Management for diversity maintenance in temperate forests must address understory communities; if herbaceous diversity is scale dependent and unstable over decadal time frames, management approaches need to account for factors driving changes.
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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 | 0.000 |
| 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".