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Record W2012116470 · doi:10.2193/2006-376

The Effects of Seasonal Flooding on Seed Availability for Spring Migrating Waterfowl

2007· article· en· W2012116470 on OpenAlexaff
ANDREW K. GREER, Bruce D. Dugger, DAVE A. GRABER, Mark J. Petrie

Bibliographic record

VenueJournal of Wildlife Management · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsDucks Unlimited Canada
Fundersnot available
KeywordsWaterfowlSpring (device)Flooding (psychology)Biomass (ecology)WetlandEnvironmental scienceAgronomyBiologyFisheryEcologyHabitat

Abstract

fetched live from OpenAlex

Abstract: We hypothesized the seed biomass available to migrating waterfowl would be higher in spring‐ versus fall‐flooded wetlands. To test this hypothesis we conducted an experiment using 5 pairs of wetland impoundments in northern Missouri, USA (2000‐2002). We strategically assigned one impoundment of each pair to either a fall or spring treatment group. We estimated seed biomass in fall and in spring by clipping seed heads and collecting soil cores at 20 random locations within each impoundment. We placed exclosures near each fall sample site in spring‐flooded impoundments to estimate seed loss from granivorous birds and rodents. Despite similar biomass in fall between treatments ( P = 0.64), overwinter seed loss was greater in fall‐flooded (79%; 1,324 ± 195 kg/ha) than in spring‐flooded (31%; 653 6 130 kg/ha) impoundments ( P = 0.009). Considering barnyard grass or millet ( Echinochloa spp.) only, seed loss was higher in fall‐flooded than in spring‐flooded impoundments ( P = 0.05). Spring biomass estimates were similar inside versus outside exclosures (P = 0.63) indicating loss to granivorous birds and rodents was limited. Our results suggest that fall flooding reduces seed availability for spring migrating waterfowl. We recommend spring flooding be used in areas where impoundment water levels can be manipulated to increase seed availability for spring migrating waterfowl.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.200

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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.

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

Citations43
Published2007
Admission routes1
Has abstractyes

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