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POPULATION MONITORING OF WHITE-TAILED DEER IN RHODE ISLAND

2022· article· en· W7014626880 sur OpenAlex

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Notice bibliographique

RevueJournal of Media Literacy Education · 2022
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueConservation, Ecology, Wildlife Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWildlifeWildlife managementWildlife conservationGovernment (linguistics)PopulationNorth American Model of Wildlife ConservationProcess (computing)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,018
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0020,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,010
Tête enseignante GPT0,278
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle