Relative Contributions of Sampling Error in Initial Population Size and Vital Rates to Outcomes of Population Viability Analysis
Bibliographic record
Abstract
Abstract: We evaluated the relative contributions of sampling error (randomly chosen standard errors applied as 0–30% of parameter estimates) in initial population size and vital rates (survival and reproduction) to the outcome of a simulated population viability analysis for grizzly bears ( Ursus arctos ). Error in initial population size accounted for the largest source of variation (model II analysis of variance, F 25,5 = 10.8, p = 0.00001) in simulation outcomes, explaining 60.5% of the variance. In contrast, error in vital rates contributed little to simulation outcomes ( F 25,5 = 0.61, p = 0.70), accounting for only 2.4% of model variation. Reduced global variation in vital rates, as a result of independent random sampling of annual deviates for each parameter, likely contributed to the results. Errors in estimates of initial population size, if ignored in PVA, have the potential to leave managers with estimates of population persistence that are of little value for making management decisions.
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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 | 0.002 |
| 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".