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Record W2492622172 · doi:10.1002/jwmg.21124

Cross‐seasonal effects on waterfowl productivity: Implications under climate change

2016· article· en· W2492622172 on OpenAlexaboutno aff
Erik E. Osnas, Qing Zhao, Michael C. Runge, G. Scott Boomer

Bibliographic record

VenueJournal of Wildlife Management · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsnot available
FundersU.S. Fish and Wildlife Service
KeywordsWaterfowlAnasProductivityPopulationEnvironmental scienceEcologyGeographyBiologyHabitatDemography

Abstract

fetched live from OpenAlex

ABSTRACT Previous efforts to relate winter‐ground precipitation to subsequent reproductive success as measured by the ratio of juveniles to adults in the autumn failed to account for increased vulnerability of juvenile ducks to hunting and uncertainty in the estimated age ratio. Neglecting increased juvenile vulnerability will positively bias the mean productivity estimate, and neglecting increased vulnerability and estimation uncertainty will positively bias the year‐to‐year variance in productivity because raw age ratios are the product of sampling variation, the year‐specific vulnerability, and year‐specific reproductive success. Therefore, we estimated the effects of cumulative winter precipitation in the California Central Valley and the Mississippi Alluvial Valley on pintail (Anas acuta) and mallard (Anas platyrhnchos) reproduction, respectively, using hierarchical Bayesian methods to correct for sampling bias in productivity estimates and observation error in covariates. We applied the model to a hunter‐collected parts survey implemented by the United States Fish and Wildlife Service and band recoveries reported to the United States Geological Survey Bird Banding Laboratory using data from 1961 to 2013. We compared our results to previous estimates that used simple linear regression on uncorrected age ratios from a smaller subset of years in pintail (1961–1985). Like previous analyses, we found large and consistent effects of population size and wetland conditions in prairie Canada on mallard productivity, and large effects of population size and mean latitude of the observed breeding population on pintail productivity. Unlike previous analyses, we report a large amount of uncertainty in the estimated effects of wintering‐ground precipitation on pintail and mallard productivity, with considerable uncertainty in the sign of the estimated main effect, although the posterior medians of precipitation effects were consistent with past studies. We found more consistent estimates in the sign of an interaction effect between population size and precipitation, suggesting that wintering‐ground precipitation has a larger effect in years of high population size, especially for pintail. When we used the estimated effects in a population model to derive a sustainable harvest and population size projection (i.e., a yield curve), there was considerable uncertainty in the effect of increased or decreased wintering‐ground precipitation on sustainable harvest potential and population size. These results suggest that the mechanism of cross‐seasonal effects between winter habitat and reproduction in ducks occurs through a reduction in the strength of density dependence in years of above‐average wintering‐ground precipitation. We suggest additional investigation of the underlying mechanisms and that habitat managers and decision‐makers consider the level of uncertainty in these estimates when attempting to integrate habitat management and harvest management decisions. Collection of annual data on the status of wintering‐ground habitat in a rigorous sampling framework would likely be the most direct way to improve understanding of mechanisms and inform management. © 2016 The Wildlife Society.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2016
Admission routes1
Has abstractyes

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