Dual visible‐thermal camera approach facilitates drone surveys of colonial marshbirds
Bibliographic record
Abstract
Abstract Waterbirds are important indicators of wetland health, and understanding their status and trends is necessary for appropriate management and conservation. However, certain species are challenging to survey due to their sensitivity to disturbance and the difficulty of accessing their breeding habitats. This is especially true for colonially breeding marshbirds, for which a multi‐species survey protocol that maximizes accuracy of counts and minimizes disturbance does not exist. Small drone aircraft have shown promise for conducting accurate and low‐disturbance surveys of colonial waterbirds. However, complex marsh vegetation structures and the cryptic nature of some marshbird nests make them difficult to detect in aerial imagery. We used synchronous high‐resolution visible imagery (0.8–2 cm/pixel) and thermal‐infrared imagery (6–16 cm/pixel) captured from a drone to count nests of five species of marshbirds at eight colonies in Saskatchewan, Canada. We compared counts from the imagery to those obtained from traditional ground‐based surveys, generally considered the most accurate survey method for these species. The two types of imagery proved highly complementary, as heat signatures helped detect and confirm nests not easily spotted in the visible imagery, while the detailed visible imagery allowed species identification. For four species (Western Grebe Aechmophorus occidentalis , Franklin's Gull Leucophaeus pipixcan , Forster's Tern Sterna forsteri , and Black‐crowned Night‐Heron Nycticorax nycticorax ), drone‐based counts (range 74–1524 nests per colony) were within 5% of ground‐based counts (range 75–1582), while for smaller Black Tern Chlidonias niger colonies (range 3–7 nests), counts from the two methods were always within one nest of each other. Furthermore, an assessment of flight behavior before, during, and after drone surveys found no significant evidence of disturbance by the drone. Our drone‐borne dual visible‐thermal camera approach proved promising for surveying colonial marshbirds and could potentially be implemented in a range of situations where wildlife subjects are difficult to survey with visible or thermal imagery alone.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".