Hunting associations of American badgers (<i>Taxidea taxus</i>) and coyotes (<i>Canis latrans</i>) revealed by camera trapping
Bibliographic record
Abstract
Interspecies foraging associations occur in a wide variety of vertebrate taxa and are maintained through gains in foraging efficiency and (or) predator avoidance. Despite their advantages, foraging associations often are variable in space and time and benefits may not accrue equally to all participants. In mammals, interspecies associations between solitary mammalian carnivores are rare. Coyotes (Canis latrans Say, 1823) and American badgers (Taxidea taxus (Schreber, 1777)) occasionally form hunting associations in pursuit of ground squirrels (Spermophilus armatus Kennicott, 1863), yet spatiotemporal variation in this association may be substantial. Better documentation of coyote–badger interactions across space and time will improve our understanding of the environmental drivers of this relationship and its benefit to both species. We used a broad-scale camera trapping array to document coyote–badger hunting associations. Out of 46 detections of badgers, we found five instances of hunting associations with coyotes, all of which occurred in mid- to late summer when ground squirrels were most active. Given our high rate of detection, these interactions are likely common on our study area. Habitat characteristics of the regions where we document coyote–badger interactions may have increased the likelihood of hunting associations. Our study demonstrates the effectiveness of camera traps for documenting this association and suggests that the coyote–badger system may be ideal for studying drivers of spatiotemporal variation in foraging associations.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".