Legacies of forest harvesting on plant diversity and plant community composition in temperate deciduous forest
Bibliographic record
Abstract
Abstract Aims To conserve forest natural heritage, sustainable forest harvesting requires the recovery of plant diversity and ecosystem functions following management. There is a need to clarify the temporal dynamics of plant diversity following harvesting, for both even‐aged and uneven‐aged silvicultural systems. To achieve this goal, the temporal dynamics of plant diversity in the herb layer were measured in unmanaged forests (control) and along a chronosequence (<5 years, 15 years and 30 years after harvesting) for even‐aged and uneven‐aged managed forests. Location Hardwood forest of southern Quebec, Canada. Methods Plant diversity, plant community composition and ecosystem functioning were investigated using metrics exploring richness, evenness and disparity diversity components, and included two scales of diversity partitioning (alpha and beta). Shrub–canopy layer, forest tree species composition and structure, and total forest basal area were also measured. Results In both uneven‐aged and even‐aged managed forest stands, we found: (a) a substantial decrease in mean plant phylogenetic diversity compared with unmanaged forest, even 30 years after harvesting (i.e., decrease of 16% and 22%, respectively); and (b) lowest plant alpha‐diversity in the herb layer 15 years after harvesting. Modification of community composition based upon dissimilarity (beta‐diversity) metrics demonstrated more numerous effects of even‐aged management than uneven‐aged management. For forest composition and structure, plant community and plant traits, dissimilarity relative to the unmanaged control was highest 5 years after even‐aged management. Trait‐based communities were more similar to unmanaged forest at the intermediate levels of forest density (i.e., ~20 m2/ha) that were found 5 years after uneven‐aged management. Conclusions Forest management clearly affected diversity, community composition and ecosystem functions along the chronosequence, highlighting the strongest effects of more intensive management (i.e., even‐aged) and the need to improve the sustainability of forest management.
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".