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Record W4379538318 · doi:10.5751/ace-02464-180121

Evaluating the effects of Natural Resources Conservation Service project implementation on the disturbance-dependent avian community with implications for Blue-winged Warblers

2023· article· en· W4379538318 on OpenAlexvenueno aff
Lincoln R. Oliver, Richard Bailey, Kyle R. Aldinger, Petra Wood, Christopher M. Lituma

Bibliographic record

VenueAvian Conservation and Ecology · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsnot available
FundersNatural Resources Conservation ServiceNational Institute of Food and AgricultureWest Virginia University
KeywordsWarblerEcologySpecies richnessShrublandOccupancyHabitatGeographyForbGrasslandSongbirdBiology

Abstract

fetched live from OpenAlex

The Blue-winged Warbler (Vermivora cyanoptera) is a songbird that breeds in eastern deciduous forests of North America. The species is declining, partially due to declines in forest disturbances. According to the umbrella species concept, management actions implemented to benefit other critically declining disturbance-dependent species like the Cerulean (Setophaga cerulea) and Golden-winged (Vermivora chrysoptera) warblers should positively affect Blue-winged Warbler site occupancy and species richness of shrubland and grassland birds. Similarly, determining if the umbrella concept is supported by relating species richness of disturbance-dependent avian guilds would support continued funding for species-specific conservation and management. Our goal was to evaluate if Natural Resources Conservation Service (NRCS) projects in West Virginia implemented for Cerulean and Golden-winged warblers also positively affected Blue-winged Warbler site occupancy and the disturbance-dependent avian community. We hypothesized that Blue-winged Warbler single-season occupancy and species richness for shrubland and grassland bird species would be greater on treated sites than on untreated sites. We also included other vegetation variables (i.e., percent cover of grasses, forbs, etc.) and spatial variables (i.e., elevation (m), ecoregion, etc.) that could affect Blue-winged Warbler site occupancy. We conducted point count surveys at 341 total locations distributed among 20 private properties managed for Golden-winged Warblers (n = 147); 19 private properties managed for Cerulean Warblers (n = 197); and two properties managed for both species during 2019–2020. Treatments included a variety of management practices (i.e., brush management) following specific guidelines to improve Cerulean and Golden-winged warbler habitat. We identified and defined untreated sites as either pre-treatment sites with planned management that had not yet occurred, or as reference sites, which were outside of treatment areas and representative of pre-treatment vegetation structure conditions. Contrary to our hypotheses, treated points had lower Blue-winged Warbler site occupancy than untreated points by 34–44% depending on ecoregion (Central Appalachians, Ridge and Valley, Western Allegheny Plateau), and shrubland and grassland avian guild richness were not different at untreated and treated locations. Thus, NRCS conservation project implementation for Cerulean and Golden-winged warblers did not meaningfully affect Blue-winged Warbler site occupancy or associated shrubland and grassland bird avian richness. We detected Blue-winged Warblers across the range of elevations surveyed (244–917 m), suggesting that their breeding distribution is continuing to expand into higher elevations in the Central Appalachians. Additionally, Blue-winged Warbler site occupancy was positively correlated with shrubland conditions within 100 m of survey points and decreased with increasing basal area within 100 m of survey points. Thus, management that increases the amount of shrubland in the Central Appalachians has potential as a conservation action to benefit Blue-winged Warbler site occupancy.

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)0.005
metaresearch head score (Gemma)0.011
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 score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.011
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)0.0010.000

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 head0.050
GPT teacher head0.321
Teacher spread0.271 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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