Monitoring the effects of deer on plant abundance and diversity in old-growth coastal temperate rainforests, Haida Gwaii, British Columbia
Bibliographic record
Abstract
Overabundant deer populations are a major factor affecting forest ecosystem dynamics in many parts of North America. Sitka black-tailed deer (Odocoileus hemionus sitkensis) were introduced to Haida Gwaii, a remote island archipelago in British Columbia, in the late 19th century. The mild climate, abundant vegetation, absence of predators, low hunting pressure and lack of competing herbivores on Haida Gwaii\nallowed the deer to flourish and the population exploded. This long-term monitoring experiment studies the effects of Sitka black-tailed deer on the diversity, abundance and reproduction of understory vegetation. In this study 20 deer exclosures were monitored over a period of 12 years from 1997 to 2009 in old growth forests on Graham Island, the largest island of the Haida Gwaii archipelago. The results showed that protection from deer did not have an effect on species richness. However, deer are having a significant impact on the abundance of understory vegetation in the medium height stratum (0.5 m - 1.5 m) and they are shifting the community composition towards less palatable species. The plant species most affected by deer browsing were Bunchberry (Cornus unalaschkensis) and Fern-leaved goldthread (Coptis aspleniifolia), and these are important forage species for Sitka black-tailed deer in their natural habitat in Alaska. Deer are also having an impact on plant reproduction, as a significantly higher proportion of flowers and fruits were found inside the exclosures. More research is needed to understand the impacts of deer on understory vegetation and therefore this study provides practical recommendations for improving this long-term monitoring experiment as well as recommendations for future research.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".