Modelling population dynamics of woodland caribou in Ontario and Quebec, Canada, using the logistic equation with uncertainty analysis
Bibliographic record
Abstract
The purpose of this study is to introduce the model CARIBOUMOD, which simulates the dynamics of woodland caribou ( Rangifer tarandus caribou ) populations in Ontario and Quebec, Canada. The model, based on the Verhulst-Pearl population dynamics equation, relies on input population parameters commonly collected across many woodland caribou populations in Canada (e.g., calf-female ratios, proportion of females and mortality rates) and has the option to account for variability in population parameters using a Monte Carlo algorithm. Population projections over 50 years were performed for 18 woodland caribou populations in Ontario and Quebec. The most recently published demographic data were used to initialize input parameters of the model. Three different carrying capacity levels were tested to demonstrate the sensitivity of the model to the density-dependent term. If the recruitment and mortality rates were to remain similar over time, 15 of the 18 populations would be expected to decline, one would remain stable, and two would increase. For 13 populations, carrying capacity had a negligible effect on simulated population dynamics. However, carrying capacity had a non-negligible effect on the rate of increase for the two increasing populations, and on the rate of decline for the three populations where initial population densities were more than 20% of the lowest carrying capacity level tested. The population dynamics model can be used to simulate long-term patterns of population development based on current knowledge and determine whether population dynamics can be predicted using data from current population monitoring protocols. For some local populations, additional surveys of demographic data are desirable to better characterize the long-term population trajectories. The ability to simulate populations using alternative input parameters, including recruitment, mortality and extraction (harvest) rates, was integrated in the developed software (CARIBOUMOD), allowing it to facilitate the analysis of the effects of management scenarios and the development of conservation policies.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".