A topographic moisture index explains understory vegetation response to retention harvesting
Bibliographic record
Abstract
To inform biodiversity conservation efforts in managed forest landscapes, we explore if a topographic moisture index (depth-to-water, based on remotely-sensed (LIDAR) data) can provide insight into responses of understory vegetation to retention harvesting in the boreal mixedwood forests of northwestern Alberta, Canada. Sample plots were placed along the depth-to-water moisture gradient in three forest types: coniferous, mixedwood, and deciduous (broadleaf), and in four retention harvesting treatments: unharvested (control), 50% (dispersed green-tree) retention, 20% retention, and clearcut (2% retention). Understory diversity, abundance, and composition were assessed 15 years after harvest. Harvesting affected the relationships between understory variables and the depth-to-water index, with the effects differing between forest types. Coniferous stands showed the most dramatic responses to harvesting, in that most relationships between understory attributes and the depth-to-water index changed due to harvesting. For instance, harvested coniferous stands had higher diversity on wetter sites, rather than on drier sites as was seen in the unharvested stands. In mixedwood stands only the relationship between composition and depth-to-water was affected by harvesting. Broadleaf stands were intermediate; abundance and composition showed a significant depth-to-water by harvesting treatment interaction. Abundance and depth-to-water relationships were weaker in harvested, as compared to unharvested, broadleaf stands. Within each forest type, the effects of harvesting also varied along the depth-to-water gradient. In coniferous and mixedwood forest types, wetter sites were most sensitive to harvesting while in broadleaf stands drier sites were more sensitive. Our study shows that the depth-to-water index can be used to better understand and predict the response of understory vegetation to harvesting and can be useful for guiding the placement of retention.
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 | 0.001 |
| 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".