Understanding the paradox of deer persisting at high abundance in heavily browsed habitats
Bibliographic record
Abstract
In the context of remarkable increases in many deer populations observed in temperate forests, it is critical to better understand the processes sustaining abundant populations despite dramatic declines in the vegetation they feed on. When natural predation and hunting levels are too low to control deer populations, a resource‐driven negative feedback is expected. Such a feedback assumes that the remaining resources do not match the energy requirements of a current herbivore population, thereby limiting herbivore abundance. Here we take advantage of a well‐documented, long‐term study of abundant predator‐free populations of black‐tailed deer Odocoileus hemionus sitkensis on the Haida Gwaii archipelago (Canada) to assess the ability of a heavily browsed forest habitat to sustain abundant deer populations. For two deer populations, we: 1) compared the energy provided by various resource pools to the energy required by deer; and 2) identified what components of the environment contributed most to support them. Qualitatively, our results are robust to the many assumptions and uncertainties and identify the resources currently available that allow these abundant deer populations to meet their energy needs despite the apparent paucity in resources. Resources are provided by a flux of hardly visible plant tissue produced by perennial species highly tolerant of herbivory and able to survive via underground structures (e.g. rhizomes), and by subsidies provided by canopy trees or by plants in refuges (i.e. litterfall and seed bank). We discuss the possibility of a resource‐driven feedback that may ultimately occur in the long term as a result of changes in recruitment, productivity and fertility of plants. The possible lack of resource‐driven feedback in the short or medium term should be considered by managers when assessing the need for active deer population control in situations without carnivores or hunting.
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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 | 0.001 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".