MétaCan
Menu
Retour à la cohorte
Enregistrement W7062083085

SPATIAL ECOLOGY OF ROCKY MOUNTAIN ELK (<i>CERVUS CANADENSIS NELSONI</i>) COWS IN SOUTHEASTERN KENTUCKY

2022· article· en· W7062083085 sur OpenAlexaboutno aff

Notice bibliographique

RevueUKnowledge (University of Kentucky) · 2022
Typearticle
Langueen
DomaineEngineering
ThématiqueParticle accelerators and beam dynamics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPopulationHabitatVegetation (pathology)ExclosureEctotherm
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The elk (Cervus canadensis) was extirpated from its range in eastern North America by the end of the 1800s, prompting several U.S. states and Canadian provinces to begin translocation programs with the goal of reestablishing elk populations. While eastern elk managers have relied on information from western herds to guide population and habitat management, there is a need for region-specific research on the spatial ecology and habitat associations of translocated elk given the stark differences in landscape, climate, predator communities, and harvest regimes across the continent. While the Kentucky elk reintroduction is one of the best documented programs to date, including a wealth of scientific study, a deeper understanding of how elk relate to their environment will be required for the future considering changes in landscape and population dynamics.\nWe studied the spatial ecology of female elk across the Kentucky Elk Restoration Zone (KERZ), focusing our inquiry on several aspects of reproduction and behavior via GPS telemetry data. First, we analyzed movement, space, and habitat use patterns during the calving season to predict parturition and early neonatal survival. We trained a random forest model to classify focal time windows as parturition or non-parturition, using birth events confirmed by expulsion of vaginal implant transmitters (VITs). We then applied the model to pregnant females for which parturition and one week calf survival status was unknown. Using a probability-based decision rule, our model was highly accurate (89.5%) at correctly classifying elk with known status. When applied to unknown individuals, we found that less than half (48.6%) were predicted to give birth and rear their calf to a week old, compared to 85.1% of known-status females, suggesting that fetal mortality and early calf loss may contribute more strongly to reproductive success in this population than previously thought.\nSecond, we investigated calving habitat selection by comparing landscape characteristics from confirmed parturition sites to random, “available” locations within elk home ranges. Specifically, we sampled landscape covariates (reflecting vegetation, topography, and human footprint) and both used and available locations and modeled the relative intensity of use with resource selection functions (RSFs) implemented with generalized linear mixed-effects models (GLMMs). We sampled covariates at several spatial grains to capture scale-dependent selection patterns. We found that females selected birth sites with intermediate levels of canopy cover on gentle topography at fine grains, with lower vegetation greenness, higher topographic positions, higher edge densities, and intermediate levels of patch interspersion at coarser grains. This suggests that female elk make multi-scale decisions during parturition to minimize predation risk to their neonate near the birth site while maximizing forage availability in the surrounding area.\nFinally, we characterized the behavioral variation present in Kentucky elk by fitting GLMM RSFs including random slopes to female elk data collected during four biological seasons. We used the random slopes, representing group-level selection coefficients, to assess the effects of landscape composition and configuration on variation in habitat selection (i.e., testing for functional responses) with regression splines. We also used k-means clustering to identify general behavioral tactics within seasons, accounting for multidimensional correlation between behaviors (i.e., behavioral syndromes). We found the highest variability in elk responses to open areas, primary/secondary roads, and successional forests, despite strong population-level selection/avoidance of these covariates. While elk exhibited clear functional responses to availability and configuration of these and other covariates, showing the partial context dependence of habitat selection in this population, clustering analysis assigned groups to major behavioral tactics which better predicted intensity of use over global and functional models.\nOur overall results demonstrate both population- and individual-level patterns of space and landscape use in Kentucky elk. We provide a framework for identifying successful reproduction remotely, without the use of VITs, which can assist in informing population models that may be limited by sample size and/or ignoring the potential influence of fetal mortality on calf recruitment. We also illustrate population wide patterns of parturition site selection that can be useful in delineating suitable calving habitat and targeting management actions such as the introduction of disturbance. Lastly, the extensive variability in female habitat selection across the annual cycle can help explain shifts in elk behavior and landscapes in the KERZ change, highlighting how flexible individuals in the population are to changing and novel landscapes.

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 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)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,489
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
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 Opus0,008
Tête enseignante GPT0,177
Écart entre enseignants0,169 · 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 tête enseignante, pas un consensus.

Devis d'étudeSimulation ou modélisation
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
Publié2022
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueUKnowledge (University of Kentucky)Même sujetParticle accelerators and beam dynamicsTravaux en français237 207