Occurrence patterns of wild turkeys altered by wild pigs
Bibliographic record
Abstract
Abstract Eastern wild turkeys ( Meleagris gallopavo silvestris ) are an important game bird in the United States, particularly in the Southeast. The introduction of wild pigs ( Sus scrofa ) can negatively affect native wildlife, habitat quality, and ecosystem functions. To explore the potential effects that pig presence, through habitat degradation and interspecific competition, may have on wild turkeys, we evaluated changes in occupancy of co‐occurring wild pigs and wild turkeys.We deployed camera‐monitored bait stations on 3 wildlife management areas in Arkansas during January–April 2017–2019 and collected >680,000 images to determine turkey and wild pig occurrence. We evaluated presence/absence of turkeys and wild pigs in camera trap images using Timelapse2 image analyzer software® and then used program MARK to create 2‐species occupancy models and determine the effect of wild pig presence and various landscape covariates on the occupancy and detectability of wild turkeys. The occupancy rate of wild pigs was 50.4% (95% CI = 46.9%–54.0%) and included positive relationships with the percent cover of deciduous forest and the number of wildlife openings within 500 m. The occupancy rate of turkeys increased from 45.5% (95% CI = 39.3%–51.6%) when wild pigs were not present to 59.4% (95% CI = 45.2%–73.5%, 95%) when wild pigs were present, indicating a tendency of the 2 species to select for similar environmental conditions (Species Interaction Factor = 1.13). Detectability of turkeys decreased when wild pigs occupied a site at any point during the season, regardless of whether or not the wild pigs were detected during the same trapping occasion. The decrease in detectability suggests a possible short‐term displacement of turkeys by wild pigs. This displacement can have indirect effects through alteration of breeding behaviors and altered habitat use patterns, and these indirect effects represent an important topic for research moving forward.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".