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Record 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 on OpenAlexaboutno aff

Bibliographic record

VenueJournal of the Arkansas Academy of Science · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
Fundersnot available
KeywordsWaterfowlOverwinteringHabitatAnasAnatidaeAnnual cycle
DOInot available

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.551
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.246
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

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