Design considerations for breeding waterfowl surveys
Bibliographic record
Abstract
Abstract Each year, surveys are conducted across North America to provide crucial information that guides sustainable waterfowl population management. The design of surveys requires careful evaluation to produce accurate and precise waterfowl population estimates. We assessed the impact of different survey design considerations on bias and precision of breeding waterfowl population estimates in the highly‐modified landscape of Iowa's Prairie Pothole Region (PPR). We used breeding pair counts of wood duck ( Aix sponsa ), blue‐winged teal ( Spatula discors ), and mallard ( Anas platyrhynchos ) collected during aerial surveys in the Iowa PPR from 2016 to 2018 to evaluate the influence of survey replication (1, 2, and 4 visits within a year) on precision of breeding pair estimates. Next, we simulated breeding abundance estimates for each species for a single year using 100 sample draws from 3 wetland sampling strategies and compared simulated estimates from the different sampling strategies to predicted abundance estimates. Breeding pair estimates for all species were less biased and more precise using data from 4 surveys ( = 0.06) than estimates using data from a single survey ( = 0.09) and 2 surveys ( = 0.07). For all species, breeding abundance estimates from the Neyman (i.e., optimal) sampling strategy were similar to simulated abundances but were less precise ( = 0.53) than estimates from both the equal (i.e., stratified uniform; = 0.31) and proportional (i.e., probability proportional to size; = 0.27) sampling strategies. We recommend that future waterfowl surveys include at least 2 visits to wetlands per season and employ the Neyman wetland sampling strategy to minimize bias in breeding pair abundance estimates.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.003 | 0.001 |
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".