Drone‐based radiotelemetry and imagery systems provide an advantage over traditional techniques for estimating survival of dependent juveniles
Bibliographic record
Abstract
Abstract Traditional methods for estimating juvenile survival in wildlife populations often rely on marking and tracking individual young. However, traditional methods can introduce bias into survival estimates via the capture or handling process and are logistically challenging to implement. Alternatively, researchers studying avian and mammalian species that provide parental care can mark and monitor adults to estimate survival probability of the unmarked offspring. We implemented a field sampling approach using unoccupied aerial vehicles (UAVs, i.e., drones) to estimate detection probabilities and pre‐fledging survival of blue‐winged teal (Spatula discors) by marking and resighting adult females with broods (n = 27). We used a combination of 2 methods to detect marked females, drone‐based VHF radiotelemetry and multispectral imagery, to estimate brood detection and apparent survival using Cormack‐Jolly‐Seber models. We compared between the VHF drone and the camera drone to investigate the ability of the VHF drone to locate marked females and thermal cameras to detect broods. Detections using the camera drone were informed by prior location cues from the VHF drone, meaning that detections were not independent but instead relied on the cues provided by the VHF drone. We monitored marked females and their unmarked broods weekly during the summer of 2023 in Saskatchewan, Canada. Of marked females, 9 suffered complete brood loss (33%), 4 had unknown fates (15%), 14 had at least one duckling survive to 35 days (52%), and we observed that 120 of 230 ducklings survived. Detection probability for sampling with the VHF drone remained constant over time (mean = 0.920), whereas detection probability using the camera drone increased with each sampling occasion (mean = 0.756). Weekly brood survival probability increased with brood age and were similar between survey methods (VHF: mean = 0.907; camera: mean = 0.898). Capitalizing on the interdependence of unmarked juveniles and marked adults, and drone‐based methodology, our approach allowed us to effectively and efficiently estimate juvenile survival for waterfowl. Our approach is applicable to a range of other species as well, where providing more precise and efficient field sampling methods for monitoring vital rates and population dynamics can enhance ecological understanding and resultant management practices.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".