The interplay between hunting rate, hunting selectivity, and reproductive strategies shapes population dynamics of a large carnivore
Bibliographic record
Abstract
Abstract Harvest, through its intensity and regulation, often results in selection on female reproductive traits. Changes in female traits can have demographic consequences, as they are fundamental in shaping population dynamics. It is thus imperative to understand and quantify the demographic consequences of changes in female reproductive traits to better understand and anticipate population trajectories under different harvest intensities and regulations. Here, using a dynamic, frequency‐dependent, population model of the intensively hunted brown bear ( Ursus arctos ) population in Sweden, we quantify and compare population responses to changes in four reproductive traits susceptible to harvest‐induced selection: litter size, weaning age, age at first reproduction, and annual probability to reproduce. We did so for different hunting quotas and under four possible hunting regulations: (i) no individuals are protected, (ii) mothers but not dependent offspring are protected, (iii) mothers and dependent offspring of the year (cubs) are protected, and (iv) entire family groups are protected (i.e., mothers and dependent offspring of any age). We found that population growth rate declines sharply with increasing hunting quotas. Increases in litter size and the probability to reproduce have the greatest potential to affect population growth rate. Population growth rate increases the most when mothers are protected. Adding protection on offspring (of any age), however, reduces the availability of bears for hunting, which feeds back to increase hunting pressure on the nonprotected categories of individuals, leading to reduced population growth. Finally, we found that changes in reproductive traits can dampen population declines at very high hunting quotas, but only when protecting mothers. Our results illustrate that changes in female reproductive traits may have context‐dependent consequences for demography. Thus, to predict population consequences of harvest‐induced selection in wild populations, it is critical to integrate both hunting intensity and regulation, especially if hunting selectivity targets female reproductive strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".