Can Late Successional Understory Plants Survive High Intensity Crop Tree Release
Bibliographic record
Abstract
This study examines the impact of high intensity crop tree release (HICTR) on the understory communities of northern hardwood forests in Central Ontario. Specifically, it examines whether or not late successional understory story plants can survive in refugia produced by retained crop trees. Understanding the impacts of new silvicultural methods on understory communities is important for protecting the biodiversity of forest understories and for securing an appropriate understory environment for the regeneration of the next generation of commercially valuable trees. Cover data was collected from both the understory communities both within and without the refugia. Harvest was conducted in 2014 and data was collected in 2015 and 2016. These data were analyzed using a mixed linear effects model to compare community level statistics. Species indicator analysis was used to examine whether certain species were associated with the refugia or not. Late successional plants were found to produce significantly more cover in the refugia in 2015 but not in 2016; however, the refugia excluded early successional plants in both years. Diversity was greater in the open areas as compared to the refugia, likely due to the presence of both early and late successional plants. Two late successional plant species were associated with refugia: Trillium erectum and Viburnum alnifolium. Several species were associated with open areas including Rubus idaeus. While the data support the existence of a refugia effect, it is unclear whether this effect will persist. A duplicate of this experiment in ~10 years would address this question. In the intervening time caution should be taken in applying high intensity crop tree release. Precautionary measures could include only applying HICTR in small areas within larger cut blocks or by retaining clusters of trees dispersed throughout HICTR blocks.
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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 | 0.001 |
| 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 | 0.001 | 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".