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Extraterritorial Movements of a Forest Songbird in a Fragmented Landscape

2001· article· en· W2152067463 on OpenAlexaff
Darren Norris, Bridget J. M. Stutchbury

Bibliographic record

VenueConservation Biology · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsQueen's UniversityYork University
Fundersnot available
KeywordsSongbirdGeographyForest fragmentationFragmentation (computing)EcologyForestryHabitatBiology

Abstract

fetched live from OpenAlex

Abstract: Forest isolation resulting from fragmentation is thought to impede the movement of forest songbirds. Because of the difficulty of tracking birds continuously, however, few data exist documenting the influence of isolation and landscape features on avian movements. During the breeding season, male Hooded Warblers ( Wilsonia citrina ) leave their small (<2.5 ha), isolated forest patches to travel between forest fragments. We documented a total of 106 forays ( n = 20 males) and found that individuals traveled up to 2.5 km away from their resident forest patch, primarily to solicit covert extra‐pair copulations. Forays occurred despite the absence of forested corridors connecting fragments; even when corridors were present, males most often chose to fly directly across open fields. Resident patch size and distance to forests visited were not correlated with the frequency of forays. The maximum distance males flew over open fields did not exceed 465 m, and longer distances likely inhibit males from traveling outside their woodlots. If territorial establishment depends on the availability of extra‐pair partners, then higher degrees of isolation between forests could explain why some species avoid settling in extremely fragmented landscapes. Conservation efforts should limit isolation between forest stands, thereby preserving the ability of animals to move within fragmented landscapes during the breeding season.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.998

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)0.0030.000

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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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.

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

Citations130
Published2001
Admission routes1
Has abstractyes

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