Habitat overlap among bobcats (<i>Lynx rufus</i>), coyotes (<i>Canis latrans</i>), and Wild Turkeys (<i>Meleagris gallopavo</i>) in an agricultural landscape
Bibliographic record
Abstract
Wild Turkey (Meleagris gallopavo Linnaeus, 1758) populations have grown considerably in the Midwestern U.S. alongside mesocarnivores, such as coyotes (Canis latrans Say, 1823) and bobcats (Lynx rufus (Schreber, 1777)). However, few studies have assessed habitat overlap between mesocarnivores and turkeys with a goal to understand potential impacts of mesocarnivores on turkeys. We captured and radiomarked bobcats, coyotes, and Wild Turkey hens in southern Illinois during 2011–2013 in an agricultural landscape and created single-species resource selection and overlap models. Wild Turkeys and bobcats demonstrated concentrated use in forested areas, whereas coyote use was highest in agricultural areas. We documented Wild Turkey nests (n = 107) and hen mortalities (n = 28), which were used to model the effect of bobcat, coyote, and Wild Turkey habitat use on turkey nest success and mortality. Increased coyote use was associated with higher nest success and increased turkey use was associated with higher probability of mortality. These findings suggest that top predators, such as coyotes, may be important and beneficial for ground-nesting avian species. With coyotes acting as the top predator throughout much of the Midwest, they are likely reducing densities of other important turkey nest predator species, thereby increasing nest success.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".