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Winter bird distribution in abiotic and habitat structural gradients: A case study with mediterranean montane oakwoods

2006· article· en· W2175622421 on OpenAlexvenueno aff
Luis M. Carrascal, Leticia Díaz

Bibliographic record

VenueEcoscience · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
FundersComunidad de Madrid
KeywordsSpecies richnessEcologyAbiotic componentHabitatGuildAltitude (triangle)GeographyUndergrowthBreeding bird surveyMicroclimateBiology

Abstract

fetched live from OpenAlex

The influence of habitat structure and abiotic factors on winter bird distribution was studied at the within-habitat level in the montane Pyrenean oakwoods of central Spain. Abiotic factors associated with thermal stress were estimated based on altitude and solar radiation received by woodlands (calculated by the steepness and orientation of the terrain). This paper demonstrates the great importance of abiotic factors in influencing bird distribution. Several bird community parameters related to density and species richness decreased with altitude, while they increased with radiation incidence of oakwood plots (i.e., birds avoided northern orientations where solar radiation is minimal in winter). The most important habitat structure variables related to bird distribution were the density of young and mature oaks. A thick undergrowth of thin oaks negatively influenced total bird abundance and species richness and the number of species of the ground searchers guild. Conversely, oak maturity played a positive role on total bird density and species richness and on the number of species of tree canopy gleaners and trunk foragers. Bird density and species richness were better explained by tree regression models considering complex interactions between variables than by general linear regression analyses. To enhance winter survival and habitat suitability for birds, forest management in these mediterranean endemic oakwoods should preserve the most mature forests at lower altitudes exposed to the south.

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)0.000
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 score0.207
Threshold uncertainty score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.224
Teacher spread0.218 · 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

Citations22
Published2006
Admission routes1
Has abstractyes

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