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Enregistrement W7037968924

Forest Disturbance and Occupancy Patterns of Carnivores: Results of a Large-scale Field Study in Maine, USA

2021· article· en· W7037968924 sur OpenAlexaboutno aff

Notice bibliographique

RevueDigitalCommons (California Polytechnic State University) · 2021
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueForensic Entomology and Diptera Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésCarnivoreOccupancyHabitatWildlifeApex predatorDisturbance (geology)Abundance (ecology)Wildlife managementRange (aeronautics)Marten
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Understanding trends in the abundance and distribution of carnivores is important at global, regional and local scales due to their ecological role, their aesthetic and economic value, and the numerous threats to their populations. Carnivores in Maine range from the American black bear (Ursus americanus), to numerous native mesocarnivore species, such as American marten (Martes americana), fisher (Pekania pennanti), coyote (Canis latrans), red fox (Vulpes vulpes), bobcat (Lynx rufus), Canada lynx (Lynx canadensis) and to two small weasel species (Mustela erminea and Neogale frenata). Though smaller than their apex carnivore cousins, Mesocarnivores are essential components of ecosystems and have complex impacts on prey species and intraguild dynamics. However, these species can vary in how they respond to human disturbances, from direct declines due to unregulated harvest and habitat loss, and their ability to adapt to land-use change. Maine is a working landscape which provides habitat for diverse wildlife species coincident with extensive forest harvest industries, as well as tourism and recreation. The intensity, timing, and configuration of harvest activities all interact to modify the landscape, with cascading impacts on the distribution of many animals. Forest management practices have changed through time (Maine Forest Service 2003) with potentially unpredictable outcomes (e.g. Simons 2009). However, the extent to which carnivore species adapt to land use change is a key knowledge gap that needs to be addressed to ensure proper management and conservation going forward. I investigated these patterns by designing a natural experiment across the forested landscape of Maine, and by collecting detection data on multiple species at camera trapping survey stations deployed along a gradient of forest disturbance. My dissertation aims to collect broad-scale, relevant information for carnivore management and conservation, and assess the efficacy of motion-triggered trail cameras for long-term monitoring. My work is divided into four sections, reflected by the four chapters included in the dissertation. My first goal was to determine the optimal number and configuration of camera-trap transects, to balance between reasonable effort expended and high-quality data collection. I used multi-method occupancy analyses to compare between one, two or three camera units spaced either 100 m or 150 m apart. We found that a design with three cameras spaced 100 m apart increased detection probabilities up to five-fold over a single camera trap, and thus used this configuration for the duration of the following research. Once the survey unit was selected, I established a large-scale, multi-year camera trapping regimen across the northern two-thirds of Maine. Survey sites were selected in compliance with a natural experimental design, replicating across all combinations of a) forest disturbance intensity, b) latitude, and c) fur trapping harvest reports for key furbearing species. In the second chapter I present this study design in more detail, and use the resulting data to investigate the interspecies dynamics of marten and fisher, two species of interest to the state of Maine that co-exist in several geographic areas and partition habitat in distinct ways. Both species are sensitive to habitat change resulting from timber harvest, which was a more important factor in occupancy patterns than intraguild dynamics. In chapter three, I took advantage of the large data set I collected to provide a landscape scale understanding of long-tailed and short-tailed weasel distribution patterns in the face of habitat change. Both of these species are poorly studied, and may be in decline in North American. My results indicate that short-tailed weasel are widespread in Maine and do not appear limited by forest harvest practices, while long-tailed weasel are rarer and more apt to be present in southern Maine. Finally in chapter four I ran models incorporating multiple states for species occupancy, beyond mere present or absent, to understand the dynamics of black bears and of black bear reproduction across managed forests in Maine. I found that generally disturbance at a small scale was positively associated with both occupancy and probability of reproduction, while the availability of hardwood trees (an important food source for bears) was also positively linked to the probability of female bears being with cubs. In addition to meeting our stake holder needs for informed management guidelines, I hope that many of my findings will be directly relevant to the broader research community—as camera trapping equipment becomes more affordable, it will become feasible to both monitor and rigorously study wildlife populations in remote locations and under many scenarios of human land-use.

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.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,086
Score d'incertitude au seuil0,171

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,013
Tête enseignante GPT0,218
Écart entre enseignants0,205 · 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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2021
Routes d'admission1
Résumé présentoui

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