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Enregistrement W7045536292

Body Mass and Body Condition Variation of Mallards (Anas platyrhynchos) Within and Among Winters Within the Lower Mississippi Alluvial Valley

2021· article· en· W7045536292 sur OpenAlexaboutno aff

Notice bibliographique

RevueJournal of the Arkansas Academy of Science · 2021
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueAvian ecology and behavior
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésWaterfowlOverwinteringHabitatAnasAnatidaeAnnual cycle
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Most North American waterfowl overwinter in southern North America before migrating back to breeding grounds in the northern US and Canada. These species face the challenge of needing to maintain or increase their body mass during an environmentally difficult winter period. Successful body mass maintenance during the winter period has major ramifications not only for their winter survival but for their fitness across the entire year. Recent research in Europe and the western United States suggests that the body mass of mallards (Anas platyrhynchos) has increased from the late 1960s to early 2000s. However, the factors responsible for increases in mallard body mass remain unknown. Because research has shown that mallard body mass and condition is directly proportional to energy acquired across the landscape, conservation agencies attempt to provide high-energy habitat such as woody wetlands, herbaceous wetlands, and open water areas for waterfowl to feed, rest, and complete other important life-cycle activities. Additionally, managers have tried to increase the amount of flooded agricultural grain across the landscape, as crops like rice can provide waterfowl with a source of high-energy food, especially in important overwintering waterfowl areas such as the Lower Mississippi Alluvial Valley (LMAV). However, long-term trends in mallard body mass, as well as the relationship between body condition of mallards and landscape composition has yet to be assessed in the LMAV. To assess mallard body mass over time in the LMAV, we collected measurements from hunter-harvested mallards across the LMAV of Arkansas and Mississippi during duck hunting seasons from 1979-2021. We measured body mass, wing length, and aged and sexed each bird. We then developed four age-sex linear mixed effects models (LMM) analyzing changes in body mass across years. We also analyzed body mass within a winter period across the day of duck season, as well as in relation to cumulative rainfall, river flooding, and a weather severity index (WSI). We determined that mallard body mass has increased within the LMAV from 1979-2021. Within years, body mass generally decreased over the course of the hunting season. Mallard body mass generally increased when rainfall and river flooding increased. However, there was generally no relationship with mallard body mass and WSI. Using Arkansas mallard measurements from duck hunting seasons 2019-2020 and 2020-2021, we calculated body condition indices (BCI) for each bird using the residuals from a mass by wing length regression for each age-sex class. We then used an LMM to analyze changes in mallard BCI in relation to landscape variables known to influence mallard body mass or BCI within a 30-km radius of each harvest site. Landscape variables included proportion of water cover, rice, soybeans, woody wetlands, herbaceous wetlands, open water areas, and areas of human disturbance. We found that mallards with high BCI came from areas with higher proportions of water cover, woody wetlands, and open water. However, mallards with lower BCI came from areas with higher proportions of herbaceous wetlands and human disturbance. We suggest managers restore, protect, and increase food resource availability in wetlands including bottomland hardwood forests.

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)0,002
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,551
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,003
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,009
Tête enseignante GPT0,246
Écart entre enseignants0,237 · 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'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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