Estimating waterfowl breeding pair and brood densities using distance sampling with uncrewed aerial systems
Bibliographic record
Abstract
Abstract Wildlife management relies on effective methods to estimate population indices, which are crucial for understanding the status and trends of wildlife populations and the factors that influence their persistence. Detection of wildlife during surveys is rarely perfect, and therefore, biologists often need to account for observation bias. Biologists have conducted surveys of waterfowl since the 1950s using a combination of aerial (fixed‐wing aircraft) and ground‐based methods to account for imperfect detection, where some fraction of individuals are missed by the aerial observer. If ground‐based observers detect all individuals in a subset of the total area surveyed by the aerial crew, then a visual correction factor can be used to adjust counts from the aerial observers. However, the assumption that ground observers are detecting every individual is unlikely and can potentially lead to biased population estimates. We conducted breeding waterfowl surveys in Wisconsin during 2022 and 2023. We collected observations of duck pairs and broods using uncrewed aerial systems (UAS) equipped with thermal cameras, as these tools have been found to improve detection of many wildlife species. We flew UAS surveys along transects and employed distance sampling to estimate duck densities while accounting for imperfect detection. This can be accomplished by recording the distance at which birds are detected and fitting a detection function to the data, which models detection probability and excludes the need for two independent observers. We detected 773 pairs and 573 broods during both years of our study. Our average density estimates of pairs (6.76 pairs/km 2 ) and broods (1.32 broods/km 2 ) were comparable to other estimates within our region. Additionally, the characteristics of our detection functions, including the expected decline in detection with distance, signified that our top distance sampling models fit the data well, which is necessary for reliable density estimation. Practical implication . Performing a distance sampling analysis using UAS equipped with thermal cameras can be used as a method to monitor breeding waterfowl and produce reliable population metrics. Furthermore, this approach has potential for surveying waterfowl in other regions or during different stages of the life cycle and should be considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".