Indirect effects of bear hunting: a review from Scandinavia
Bibliographic record
Abstract
Harvest by means of hunting is a commonly used tool in large carnivore management. To evaluate the effects of harvest on populations, managers usually focus on numerical or immediate direct demographic effects of harvest mortality on a population's size and growth. However, we suggest that managers should also give consideration to indirect and potential evolutionary effects of hunting (e.g., the consequences of a change in the age, sex, and social structure), and their effects on population growth rate. We define “indirect effects” as hunting-induced changes in a population, including human-induced selection, that result in an additive change to the population growth rate “lambda” beyond that due to the initial offtake from direct mortality. We considered 4 major sources of possible indirect effects from hunting of bears: (1) changes to a population's age and sex structure, (2) changes to a population's social structure, (3) changes in individual behavior, and (4) human-induced selection. We identified empirically supported, as well as expected, indirect effects of hunting based primarily on >30 years of research on the Scandinavian brown bear (Ursus arctos) population. We stress that some indirect effects have been documented (e.g., habitat use and daily activity patterns of bears change when hunting seasons start, and changes in male social structure induce sexually selected infanticide and reduce population growth). Other effects may be more difficult to document and quantify in wild bear populations (e.g., how a younger age structure in males may lead to decreased offspring survival). We suggest that managers of bear and other large carnivore populations adopt a precautionary approach and assume that indirect effects do exist, have a potential impact on population structure, and, ultimately, may have an effect on population growth that differs from that predicted by harvest models based on direct effects alone.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".