Cost-effective Sampling Design Applied to Large-scale Monitoring of Boreal Birds
Bibliographic record
Abstract
Despite their important roles in biodiversity conservation, large-scale ecological monitoring programs are scarce, in large part due to the difficulty of achieving an effective design under fiscal constraints. Using long-term avian monitoring in the boreal forest of Alberta, Canada as an example, we present a methodology that uses power analysis, statistical modeling, and partial derivatives to identify cost-effective sampling strategies for ecological monitoring programs. Empirical parameter estimates were used in simulations that estimated the power of sampling designs to detect trend in a variety of species' populations and community metrics. The ability to detect trend with increased sample effort depended on the monitoring target's variability and how effort was allocated to sampling parameters. Power estimates were used to develop nonlinear models of the relationship between sample effort and power. A cost model was also developed, and partial derivatives of the power and cost models were evaluated to identify two cost-effective avian sampling strategies. For decreasing sample error, sampling multiple plots at a site is preferable to multiple within-year visits to the site, and many sites should be sampled relatively infrequently rather than sampling few sites frequently, although the importance of frequent sampling increases for variable targets. We end by stressing the need for long-term, spatially extensive data for additional taxa, and by introducing optimal design as an alternative to power analysis for the evaluation of ecological monitoring program designs.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".