To feed or not to feed? Evidence of the intended and unintended effects of feeding wild ungulates
Bibliographic record
Abstract
ABSTRACT Ungulate populations are important natural resources, associated with both costs and benefits. Conflicts have arisen between stakeholders who benefit from high ungulate numbers and those faced with the costs. Supplementary or diversionary feeding may potentially mitigate conflicts while maintaining harvest yields but can have conservation implications. We quantified the empirical evidence for whether the intended effects, and hence management goals, of feeding are met. We also examined whether any potential unintended consequences of feeding occur and under what conditions. We found clear evidence that supplementary feeding enhanced reproduction and population growth under certain conditions. By contrast, we found limited evidence of the effectiveness of diversionary feeding to protect crops, forestry, and natural habitats, with positive effects often undermined by increases in ungulate density. However, the use of diversionary feeding to reduce traffic collisions seems promising but requires further investigation. The unintended effects of feeding are typically complex, involving changes to demography, behavior, and vegetation with consequent cascading effects on other trophic levels, as well as exacerbated risks of disease transmission. Increased ungulate density is the primary driver behind these unintended effects, the consequences of which tend to increase with longevity of feeding and affect a range of stakeholders. We urge managers to take seriously the risks as well as the economic and ethical issues before deciding to feed ungulates. © 2014 The Wildlife Society.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".