Using vertebrate prey capture locations to identify cover type selection patterns of nocturnally foraging Burrowing Owls
Bibliographic record
Abstract
Studies of habitat selection often measure an animal's use of space via radiotelemetry or GPS-based technologies. Such data tend to be analyzed using a resource selection function, despite the fact that the actual resources acquired are typically not recorded. Without explicit proof of resource use, conclusions from RSF models are based on assumptions regarding an animal's behavior and the resources gained. Conservation initiatives are often based on space-use models, and could be detrimental to the target species if these assumptions are incorrect. We used GPS dataloggers and digital video recorders to determine precise locations where nocturnally foraging Burrowing Owls acquired food resources (vertebrate prey). We compared land cover type selection patterns using a presence-only resource selection function (RSF) to a model that incorporated prey capture locations (CRSF). We also compared net prey returns in each cover type to better measure reward relative to foraging effort. The RSF method did not reflect prey capture patterns and cover-type rankings from this model were quite different from models that used only locations where prey was known to have been obtained. Burrowing Owls successfully foraged across all cover types; however, return vs. effort models indicate that different cover types were of higher quality than those identified using resource selection functions. Conclusions about the type of resources acquired should not be made from RSF-style models without evidence that the actual resource of interest was acquired. Conservation efforts based on RSF models alone may be ineffective or detrimental to the target species if the limiting resource and where it is acquired are not properly identified.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".