POPULATION MONITORING OF WHITE-TAILED DEER IN RHODE ISLAND
Bibliographic record
Abstract
Wildlife conservation and management occurs across the world through many different mechanisms and underlying principles. North America has developed a unique and successful process coined the North American Model of Wildlife Conservation. A key outcome of this model is that wildlife science informs management decisions, which are made by government officials in the public’s trust. If a species undergoes some form of legal take, managers are often required to ensure it is done responsibly with empirical evidence and consideration of ecological and societal objectives. Recent research suggests that 60% of wildlife management systems in Canada and the United States were not using science to guide their decisions, as they contained fewer than half of what they referred to as four “fundamental hallmarks of science”: measurable objectives, evidence, transparency, and independent review. We borrow from their framework and expand on it by evaluating whether white-tailed deer (Odocoileus virginianus) management in the northeastern United States includes the essential elements of a structured decision-making process. Our aim is to evaluate the regional management of a species that receives considerable focus to better understand whether the ideals of the North American Model of Wildlife Conservation are being implemented by way of a logical, transparent, and science-based decision-making process. Of the 11 states evaluated, seven had published a white-tailed deer management plan. Of these seven, we found that the “hallmarks” and most structured decision-making components were present, and the information collected was being used to inform decisions. Our findings indicate four main ways white-tailed deer management may be improved in the northeast United States: 1) states without a management plan should develop one, 2) states should incorporate an external review process, 3) states could consider alternative actions for each measurable objective and their consequences, and 4) states need to consider tradeoffs among multiple and possibly conflicting objectives. Our recommendations should lead to increased management transparency and build public support.\nAdditionally, a key principle of The North America model of Wildlife Conservation is that science is the proper tool for discharging wildlife policy. Using science to understand population abundances and dynamics is especially critical in managing harvested wildlife. Tracking population changes allows resource managers to adapt regulations to ensure populations are maintained. In Rhode Island, USA white-tailed deer (Odocoileus virginianus) are annually harvested, but there is no systematic annual population estimation to track changes, which may put the population and forest ecosystem at risk. Our objective was to evaluate the utility of statistical population reconstruction (SPR) to monitor white-tailed deer in Rhode Island by estimating annual deer abundance, harvest probabilities, and recruitment for males and females, separately. To do so, we used age-at-harvest data collected from hunter harvested deer from state operated check stations (2011-2020) and online/phone reporting, hunter effort derived from annually reported deer harvest, and natural mortality probabilities from the literature. Without a reliable measure of reporting rate, we considered three possible reporting rates (25%, 50%, and 75%). As not all deer reported were aged, we used random forest models to predict the age of 19,277 deer reported via mail- in/online/phone using age, weight, sex and antler beam measurements of deer checked by staff. The out-of-sample prediction accuracy was between 85-99% with most over 90%. We estimated male abundance with a 75% reporting rate to range from a low of 9,503 (SE, 1,291) in 2017 to a high of 15,767 (SE, 2,183) in 2011, with the most current estimate at 10,054 (SE, 1,325) in 2020. Using a 50% reporting rate, male abundances were higher, ranging from a low of 13,730 (SE, 1,753) in 2017 to a high of 22,271 (SE, 2,912) in 2011, with the most current estimate at 14,031 (SE, 1,745) in 2020. Using a 25% reporting rate, male abundances were the lowest, ranging from a low of 9,310 (SE, 362) in 2015 to a high of 10,766 (SE, 369) in 2019, with the most current estimate at 10,525 (SE, 362) in 2020. Depending on the reporting rate, the male population between 2011-2020 was estimated to be either slightly increasing or decreasing. The SPR failed to produce realistic estimates for females with estimated harvest probabilities near or at zero, which inflated abundance estimates to unreasonable values (>1 million). Overall, SPR appears to be a useful methodology for monitoring deer populations in Rhode Island. However, to rely on it as part of management policy will require several improvements over the current implementation. Foremost, it is recommended that hunter effort, reporting rate and survival probability are determined in Rhode Island via additional research, such as hunter surveys and survival studies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".