Using Land-Based Surveys to Assess Sea Duck Abundance and Behavior in Nearshore Waters of Southern New England, USA
Bibliographic record
Abstract
Nearshore waters provide very important habitat for sea ducks (Tribe Mergini) during migration and winter, but gathering information on sea duck use of shallow nearshore waters is challenging because traditional aerial and boat-based surveys are expensive, are usually conducted infrequently, and are often not feasible near the coast. The objective of this study was to use land-based surveys to characterize spatiotemporal variation in the abundance and behavior (e.g., foraging, flying) of Common Eider (Somateria mollissima) and scoters (Melanitta spp.) in nearshore waters of southern New England. Surveys (60–120 min per survey, n = 1,044 surveys) were conducted throughout the day from February 2009 to July 2010 to assess diurnal and seasonal variation in sea duck behavior and spatial distribution at nine sites in southern Rhode Island. The density of sea ducks resting or foraging on the water exhibited little diurnal variation, whereas flight activity dramatically increased nearer to sunrise. Sea duck densities and passage rates (individuals/km2/hr) peaked during migration periods from October through November and February through April, although there were important seasonal differences between sites. For example, the highest densities of Common Eider during fall were in a protected estuary, whereas abundance of scoters during fall was greater at a coastal headland. The relative activity of Common Eider on the water and in flight was similar among sites, whereas scoters exhibited highly variable activity among sites, particularly during winter and spring. The spatiotemporal patterns in abundance and behavior of sea ducks in nearshore waters that we detected using land-based surveys provides essential, complementary information to that available from other types of waterfowl and seabird surveys in southern New England.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".