WILD TURKEY HARVEST TRENDS ACROSS THE MIDWEST IN THE 21ST CENTURY
Bibliographic record
Abstract
Abstract: The perception that wild turkey (Meleagris gallopavo; hereafter, turkey) populations across the midwestern United States and Canada (Midwest) are declining is a growing concern among natural resource agencies. However, there have been no attempts to assess population trends over large extents across their range in the Midwest. This gap in knowledge makes regional coordination of turkey management efforts among natural resource agencies challenging because there is a limited basis for comparing trends across space and time. To address this, we used turkey harvest data from 11 states and 1 province (hereafter, states) to evaluate harvest trends through time and space across the Midwest. We used negative binomial mixed models in the catch‐per‐unit‐effort (CPUE) framework to evaluate trends in harvest. We used piecewise regression and model selection approaches to identify years when harvest trends shifted. Our harvest data sets varied considerably in length (4–49 years), and magnitude (maximum spring male [gobbler] harvest/year ranged from 1,630 to 60,548 turkeys). Our results indicated trends in harvest typically stabilized or decreased in recent years. Eight states with increasing turkey harvest prior to the 2000s showed stabilized harvest trends, whereas 2 states with stable harvests prior to the 2000s shifted to decreasing harvest trends. Trends in harvest increased in Indiana and Ohio after 2000. Variability in harvest through time at a local level sometimes conflicted with state‐level (hereafter, includes province of Ontario) trends and demonstrated the need to describe harvest at multiple scales. Under strict but probably unmet assumptions, trends in harvest can be used to index trends in abundance and, as such, our results provide evidence for a general stabilization of turkey populations in most states across the Midwest. This stabilization likely is mediated by reductions in number of turkeys harvested as a result of decreased hunting effort (i.e., fewer days of hunting). However, collection of information on hunting effort is not universally practiced, which complicates treatment of raw harvest counts as abundance indices because spatial—temporal changes in abundance are statistically confounded with changes in effort. We recommend natural resource agencies consider developing protocols for collecting hunter effort data, as this information provides a more complete understanding of the nature of harvest dynamics and could provide more useful indices of turkey abundance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".