Can management regulate the population size of wild reindeer (<i>Rangifer tarandus</i>) through harvest?
Bibliographic record
Abstract
We analyzed a 51-year time series of harvest data from a small population of wild mountain reindeer ( Rangifer tarandus (L., 1758)) in southern Norway and examined the relative role of biological and management related processes as drivers of its population dynamics. The population is monitored annually to obtain information on population size and structure, and since 1980, managers have attempted to stabilize the population at about 1.1 reindeer/km2. The harvest increased at a higher rate than the population size and was thus probably sufficient to not only limit but also regulate population size. Phase plot analyses showed that the population has varied around a density attractor of about 1.0 reindeer/km2since 1980 and is therefore close to the targeted population size of 1.1 reindeer/km2. However, the annual harvest explained only 49% of the variation in population growth rate (λ) in a linear regression model, despite relatively low variation in population productivity (proportion of calves). Between 1999 and 2006, the population in Forolhogna declined by almost 50% before recovering to its previous size. We suggest that both imprecise population estimates and high harvest effectiveness at reduced population densities contributed to this decline. As such, this study points to some of the obstacles managers are facing when trying to stabilize productive ungulate populations even when they live in closed populations and in stable, predator-free environments.
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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 | 0.001 |
| 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 | 0.000 | 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".