Long-term decline in a salt marsh hare population largely driven by bottom-up factors
Bibliographic record
Abstract
The widespread decline of the European brown hare (Lepus europaeus) in Europe has been attributed to both bottom-up and top-down factors, as well as climate change. Few studies have attempted to study the relative importance of these factors considered simultaneously. In this study we tested the hypotheses that hare population density is regulated by bottom-up (food), top-down (predation) or abiotic factors including tidal floods and climatic conditions. We related data on hare population density on a relatively isolated island to changes in surface area of suitable vs. unsuitable vegetation for forage, predator densities, flooding parameters and climatic variables. During the study period (1996–2012), hare numbers decreased from 580 to 219. Estimated population density was positively correlated with the cover of short, intermediate successional vegetation types and was negatively correlated with the cover of tall, late successional vegetation types. These findings corroborate results from earlier experimental studies that reported a strong aversion of hares to tall vegetation. Additionally, we found indications that raptor population density and unusually high floods also exerted negative effects on hare population density. We conclude that bottom-up factors (the availability of suitable forage) are the main regulators of the studied hare population. This suggests that the importance of bottom-up effects has been underestimated and could explain leporid population decline in areas that have experienced a similar increase in tall, unsuitable vegetation.
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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".