Range Dynamics of Barren-Ground Caribou Implied by Historical Population Cycles and Logistic Growth
Bibliographic record
Abstract
We examined logistic range dynamics of three cyclic subpopulations of migratory barren-ground caribou in northern Canada (Qamanirjuaq, Bathurst, and George River). We used time series census data from each subpopulation cycle to project numbers (Nt), calculate subpopulation annual finite rates of population increase (λt), and estimate the corresponding time series of range condition or carrying capacity (Kt) using an algebraically rearranged version of the discrete logistic growth equation. Range condition varied regularly over each cycle, presumably due to seasonal overgrazing and range recovery dynamics. Maximum and minimum annual rates of increase and decline for Qamanirjuaq caribou were 1.196 and 0.836. In contrast, maximum annual subpopulation growth rates for the Bathurst and George River herds were greater than intrinsically possible, indicating that immigration was a component of the irruption period of their recoveries. Subpopulation numbers for Qamanirjuaq, Bathurst, and George River barren-ground caribou subpopulations closely tracked carrying capacity throughout their cycles, with mean lag times of 3.95 (SE = 0.15), 3.65 (SE = 0.18), and 3.39 (SE = 0.19) years, respectively. Other factors appear to be of relatively minor or transitory importance to population growth for barren-ground caribou if barren-ground caribou are truly a logistic growth species. Range recovery and population increase did not occur until caribou numbers declined to a recovery threshold number (Qamanirjuaq = 41,971; Bathurst = 18,265; George River = 3141). Predator management and restrictive harvest practices during the low portion of the caribou cycle may unintentionally extend the time required for caribou to decline below the grazing threshold, and thus prolong the period of scarcity. Immigration from adjacent subpopulations played a role in the acceleration of the irruption period in the Bathurst and George River subpopulations, but not the Qamanirjuaq subpopulation. Once the subpopulation range begins to recover, the rapid recovery of subpopulation numbers suggests that other density-dependent and density-independent factors are of relatively minor importance compared to range condition. Continuation of barren-ground caribou cycles at historical levels is likely if habitat conservation measures are adopted so that annual migration patterns are not disrupted, summer and winter range remain undisturbed, and the natural decline of caribou to the threshold for range recovery is not artificially extended.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".