Estimates of caribou herd size using post-calving surveys in the Northwest Territories and Nunavut, Canada: A meta-analysis
Bibliographic record
Abstract
Post-calving surveys to estimate herd size of barren-ground caribou (Rangifer tarandus groenlandicus, R. t. granti, and R. t. caribou) have been used for caribou herds in Alaska, Yukon, Northwest Territories, Nunavut, and Québec/Labrador. The main field procedure uses relocation of collared caribou to locate aggregated groups of hundreds or thousands of caribou during times of high insect harassment that usually occur in July. These groups are then photographed to obtain a count of the caribou in the aggregated groups. Often some caribou are missed, and the count of caribou may be a negatively biased estimate of total herd size, unless a high proportion of the herd is found and photographed. To address this, some previous studies have used the Lincoln-Petersen estimator, which estimates the proportion of the herd counted based on the percentage of available collared caribou found during the survey. However, this estimator assumes equal probabilities of all groups of caribou being found, regardless of group size and the numbers of collared caribou in the group. These assumptions may not be valid, as larger groups are more likely to be found than smaller groups, particularly if there are several collared caribou present. This may lead to estimates that are biased low, along with an estimate of variance that may also be biased low. A two phase estimator developed by Rivest et al., in 1998 became available in R statistical software in 2012. We analyzed 20 data sets from post-calving surveys in the NWT and NU carried out between 2000 and 2015 using the Rivest estimator to explore working characteristics of this estimator. We compared the Rivest estimates with Lincoln-Petersen estimates and total counts on each survey. We considered factors that influence precision of the Rivest estimator with a focus on sampling factors such as the proportion of collars found, the number of collars available, and natural factors such as the degree of aggregation of caribou in each survey (as indexed by the negative binomial dispersion parameter). In general, the Rivest estimator displayed acceptable precision when high proportions of caribou groups with collars were detected and counted, collar numbers were sufficient, and aggregation was adequate. Notable exceptions occurred in years of lower aggregation which resulted in many small groups with 0 or few collared caribou, and in these cases herd estimates had large variances and low precision. Estimates from the Rivest estimator, Lincoln-Petersen estimator, and total counts converged when sampling effort was high, collar numbers relative to herd size were high, and caribou were well aggregated in a limited number of groups. In other cases, estimates of the Rivest estimator were generally higher than Lincoln-Petersen estimates, presumably due to negative bias with the Lincoln-Petersen estimator. We provide a set of working recommendations to optimize field sampling to ensure reliable estimates of herd size using post-calving methods.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.023 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".