Evaluation Of Aerial Strip-Transect Surveys For Estimating Average Use By Waterfowl Wintering At Sequoyah National Wildlife Refuge, Oklahoma, 2012-2014.
Bibliographic record
Abstract
In 2012, we evaluated aerial transects (strip transects) for surveying wintering waterfowl at Sequoyah NWR and the landscape immediately surrounding the refuge. In surveying the landscape around the refuge, we attempted to capture the area used by waterfowl making daily commute flights to forage off refuge (Johnson et al. 2014). In total, we surveyed 26,895 hectares, with the 8,464.8 hectare refuge comprising 31% of the survey area. The survey consisted of 33 transects that ranged in length from 1.3 km to 12.0 km (Figure 1); on-refuge survey consisted of 26 transects that ranged in length from 0.6 km to 7.3 km. Our purpose was to examine the feasibility of aerial surveys and to develop waterfowl density and population estimates for local biological planning efforts, such as developing population estimates linked to energetic carrying capacity models (Reinecke and Loesch 1996, Michot 1997).Between February 2012 and February 2014, eleven surveys were conducted; two in the 2011-2012 non-breeding period, five in the 2012-2013 non-breeding period and 4 in the 2013-2014 non-breeding period. Due to low numbers of waterfowl observations off-refuge, we discontinued off-refuge surveys in the 2013-2014 winter period. Flight procedures (height above ground, air speed, etc.) followed the Standard Operating Procedures (SOP; Schmidt et al. 2014), and attempts were made to identify waterfowl observed to species (Bowman 2014).Peak on-refuge population estimates varied by guild and year (Tables 2A-E). Peak estimates were 31,871.7 ± 13,085.7 (SE) for Mallards in mid-January 2013; 3,848.6 ± 1,206.5 (SE) for Gadwall in late-November 2012; 3,722.7 ± 2,101.3 (SE) for all other species of dabbling ducks combined but excluding Mallards and Gadwall in February 2012; 314.7 ± 132.5 (SE) for all species of diving ducks combined in January 2014; and 32,935.1 ± 13,049.9 (SE) for Lesser Snow Geese and Canada Geese combined in mid-January 2013. Precision for individual on-refuge survey estimates was generally poor (CV > 20%). We had >1 on-refuge survey per month in all years combined for the months of December (N = 2), January (N = 4), and February (N = 3). The mean on-refuge population estimate for Mallards was 11,200 ± 3,626 (SE) in December, 18,266 ± 2,326 (SE) in January, and 18,678 ± 3,177 (SE) in February. For the same months respectively, mean population estimates for Gadwall were 1,468 ± 452 (SE), 1,034 ± 105 (SE), and 577 ± 115 (SE) and for Lesser Snow Geese and Canada Geese combined were 17,486 ± 5,507 (SE), 23,131 ± 2,454 (SE) and 15,177 ± 3,134 (SE). The precision of average monthly on-refuge estimates was considerably better than for individual survey periods. Precision for average on-refuge estimates for November and January were < 20% for Mallards, Gadwall and Geese; precision of average on-refuge estimates for December was around 30% for Mallards, Gadwall, and Geese (Table 3). Despite insufficient precision for individual survey estimates, average monthly on-refuge population estimates had suitable precision (CVs <20%) with as few as three surveys per month (Table 3). Waterfowl populations are known to widely fluctuate over time (NAWMP 2012). As such, individual population estimates lack both precision and the ability to reflect the range of waterfowl numbers and are not appropriate for use in biological planning efforts. Rather, average estimates for a given month or period taken over >1 year should be used if suitably precise (i.e., CV <20%) and can incorporate the full range of waterfowl population numbers for the area and time of interest. Aerial surveys of waterfowl wintering at SNWR should be repeated in the future to determine if average use by wintering waterfowl has changed over time and if the energetic requirements of local populations are being met by the resources available within the landscape.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".