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Record W4388639250 · doi:10.1080/00063657.2023.2264560

Habitat and other environmental correlates of the decline of breeding Whinchats <i>Saxicola rubetra</i> in the UK since the mid-1990s

2023· article· en· W4388639250 on OpenAlexaff
Andrew J. Stanbury, Robert W. Hawkes, Emma L. Teuten, Irena Tománková, David J. T. Douglas

Bibliographic record

VenueBird Study · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsDeer Lodge Centre
Fundersnot available
KeywordsPteridium aquilinumBrackenWoodlandEcologyHabitatTransectVegetation (pathology)Abundance (ecology)GeographyBiologyFern

Abstract

fetched live from OpenAlex

Capsule Declines in Whinchat Saxicola rubetra breeding abundance in the UK vary with habitat and other environmental conditions.Aims To test for associations between changes in Whinchat abundance between 1994 and 2018 and measures of habitat and environmental conditions on UK breeding sites.Methods Whinchat counts collected through a national monitoring scheme were tested at coarse 1 km2 square and finer 200 m transect scales against habitat data collected in 2017/2018, plus remotely sensed data to test for long-term change.Results At the 1 km2 square scale, mean change in Whinchat abundance was more negative where woodland occurred more frequently, and at more northerly latitudes. Rates of decline were lower where there was greater cover of Purple Moor-grass Molinia caerulea and non-bracken vegetation height was taller. At the finer 200 m transect scale, more closely resembling Whinchat territory size, rates of decline were greatest in areas dominated by human sites, woodland, and enclosed farmland, compared to unenclosed open semi-natural habitats; however, within the latter category, declines were lower in grass-dominated relative to heather-dominated habitats. Rates of decline were also lower closer to valley bottoms, with greater Bracken Pteridium aquilinum cover and at mid-elevations (300 m), and greatest where there was greater cover of bare ground and trees, and moderate cover of grasses (excluding Molinia and Lolium spp.). The strength of the finer-scale associations varied between dominant habitat types. Whinchat abundance was lower where a remotely sensed index of vegetation productivity (normalized difference vegetation index [NDVI]) was higher, and abundance change more negative where temporal increases in NDVI were greater.Conclusion Unenclosed semi-natural grassland showed the lowest rate of decline and offers the best opportunities to conserve Whinchats. Woodland expansion is likely to have a detrimental impact on breeding Whinchats. Future research should investigate how important habitat and environmental correlates influence key demographic rates affecting recruitment.

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)0.001
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 score0.003
Threshold uncertainty score0.198

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.229
Teacher spread0.213 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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