Functional responses of understory plants to natural disturbance‐based management in eastern and western Canada
Bibliographic record
Abstract
Natural disturbance-based management (NDBM) is hypothesized to maintain managed forest ecosystem integrity by reducing differences between natural and managed forests. The effectiveness of this approach often entails local comparisons of species composition or diversity for a variety of biota from managed and unmanaged forests. Understory vegetation is regularly the focus of such comparison because of its importance in nutrient cycling, forest regeneration, and for wildlife. However, larger scale comparisons between regions with distinct species assemblages may require a trait-based approach to better understand understory responses to disturbance. We compared the long-term effects of retention harvesting on understory vegetation in two large experimental study sites located in eastern and western regions of the Canadian boreal forest. These sites included the Sylviculture en Aménagement Forestier Ecosystémique (SAFE) experiment and the Ecosystem Management Emulating Natural Disturbance (EMEND) experiment, located in the eastern and western regions of Canada, respectively. EMEND and SAFE share common boreal understory species but have distinct tree communities, soils, and climate. Both experiments were designed to evaluate how increasing tree retention after harvest affects biodiversity. Here, we examined taxonomic richness, functional diversity, and functional composition (using community trait mean values) of understory plant communities, and also examine intraspecific trait variability (ITV) for five species common and abundant in both experiments. We observed the limited impacts of retention level on richness, functional diversity, and functional composition of understory plants 20 years postharvest. However, ITV of leaf morphological traits varied between retention levels within each experiment, depending on the species identity. Common species had different functional responses to retention level, showing species-specific reactions to environmental variation. Our result suggests that understory plant communities in the boreal forest achieve resilience to disturbance both in terms of interspecific and intraspecific functional trait diversity. Such diversity may be key to maintaining understory biodiversity in the face of future disturbances and environmental change. Our results reveal the significance of ITV in plant communities for understanding responses to forest harvesting and the importance of choosing appropriate traits when studying species responses to the environment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".