The relative contributions of seed bank, seed rain, and understory vegetation dynamics to the reorganization of <i>Tsuga canadensis</i> forests after loss due to logging or simulated attack by <i>Adelges tsugae</i>
Bibliographic record
Abstract
Profound changes are occurring in forests as native insects, nonnative insects, or pathogens irrupt on foundation tree species; comprehensive models of vegetation responses are needed to predict future forest composition. We experimentally simulated hemlock woolly adelgid ( Adelges tsugae Annand) infestation (by girdling trees) and preemptive logging of eastern hemlock ( Tsuga canadensis (L.) Carrière) and compared vegetation dynamics in replicate 90 m × 90 m treatment plots and intact hemlock stands from 2004 to 2010. Using Chao–Sørensen abundance-based similarity indices, we assessed compositional similarities of trees, shrubs, forbs, and graminoids among the seed bank, seed rain, and standing vegetation over time and among treatments. Post-treatment seed rain, similar among treatments, closely reflected canopy tree composition. Species richness of the seed bank was similar in 2004 and 2010. Standing vegetation in the hemlock controls remained dissimilar from the seed bank, reflecting suppressed germination. Recruits from the seed rain and seed bank dominated standing vegetation in the logged treatment, whereas regeneration of vegetation from the seed bank and seed rain was slowed due to shading by dying hemlocks in the girdled treatment. Our approach uniquely integrates multiple regeneration components through time and provides a method for predicting forest dynamics following loss of foundation tree species.
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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 | 0.000 | 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) | 0.000 | 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".