Influence of hard mast, harvest framework, and other factors on black bear harvest
Bibliographic record
Abstract
The American black bear (Ursus americanus) is a species of least concern and inhabits a major portion of North America, including 41 states in the United States and all 13 Canadian provinces. Thirty of those states provide an annual bear season, contributing to a diverse hunting experience for thousands of outdoor enthusiasts. Each state has a unique set of harvest regulations and methods to harvest bears based on tradition and the optimization of this natural resource. Some states only allow for coordinated bear drives, stalking, and still hunting while others allow hunters to use bait and/or dogs. Hard mast makes up a major part of a bear’s diet during the autumn harvest season and can influence the relative vulnerability of different sex and age classes bears. Natural food abundance, harvest framework, bear population size, weather, hunter effort, and general hunting regulations can influence the total number of bears harvested and overall harvest composition. \n \nIn Chapter 1, I conducted a literature review on black bear diet, acorn production and sampling methods, black bear harvest frameworks and harvest vulnerabilities, cub production, and some other factors that may influence black bear harvest. In Chapter 2, I determined a means of standardizing acorn production from various visual survey methods. The conversion equations generated in Chapter 2 were then used in Chapter 3 where I gathered 20 years of data on acorn production, black bear harvest, and hunting regulation from California, Minnesota, North Carolina, Pennsylvania, and West Virginia to investigate how different factors influence black bear harvest demographics with an emphasis on hard mast production and harvest frameworks. This study provided a large spatial and temporal scale, including 212,992 harvested bears. To gain inference at a smaller spatial scale, in Chapter 4, I gathered data on over 20 county-level covariates in Wisconsin, including weather, public land access, road densities, hard mast availability, hunter effort, and harvest framework data to further investigate their influence on total harvest, including 7,688 harvested bears, during the 2020 and 2021 bear hunting seasons. These two studies investigated the effects that different factors may have on bear harvest at the landscape, state, and county levels. \n \nI found that hard mast production had a negative relationship with total harvest, median bear age, and the harvested sex ratio of females. Our results suggest that bait was the most effective framework when targeting adult, female bears when acorn production was low while using dogs was more effective when adult, female bears were targeted in high acorn production years. Hunters that used bait harvested younger bears overall with a bias toward males. Using dogs likely gave hunters more opportunity to encounter and judge individual bears and was less affected by fluctuations in natural food abundance than bait hunters. Higher bear populations supported higher harvest through an increase in hunter opportunity and tag issuance. The number of days hunted had a positive relationship with total harvest and the number of tags issued was more important than the length of the season in relation to variation in total harvest results. \n \nThe results from this research provide a more detailed understanding of the influence that various factors have on black bear harvest demographics and harvest vulnerability at different spatial scales. The knowledge and understanding gained from this research may help to better inform management decisions and provide insight into strategies that could be used to achieve management goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".