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Record W2103582922 · doi:10.1139/z02-237

Is dispersal distance of birds proportional to territory size?

2003· article· en· W2103582922 on OpenAlexvenueno aff
Jeff Bowman

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEcology and Vegetation Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiological dispersalBiologyRange (aeronautics)EcologyMammalSample size determinationStatisticsDemographyMathematicsPopulation

Abstract

fetched live from OpenAlex

Recent research has demonstrated that dispersal distance and the square root of home-range size covary proportionately across mammal species. I tested whether these findings could be generalized to another taxon. Breeding territories of some bird species are analogous to mammalian home ranges, so I tested whether dispersal distance and territory size in these birds covaried and were proportional. Variables were log 10 -transformed before analysis. When considered independently of body mass, median natal dispersal distance and breeding territory size were positively related (F [1,29] = 8.91, R 2 = 0.23, P = 0.005). Median dispersal distance was proportionally related to the square root of territory size by a multiple of 12. This relationship was especially strong for non-migrants (F [1,15] = 49.84, R 2 = 0.77, P = 3.87 × 10 –6 ). Maximum natal dispersal distance and breeding territory size also covaried when body size effects were removed, but this relationship was only significant when migrants were removed from the sample (F [1,24] = 5.66, R 2 = 0.19, P = 0.025). Maximum dispersal distance did not have a proportional relationship with territory size. This could result from sampling error or from real processes (e.g., relatively shorter dispersals by birds with large territories). The proportional relationship between median dispersal distance and territory size can be used as a cross-species scaling rule.

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 score0.176
Threshold uncertainty score0.999

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)0.0020.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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

Citations124
Published2003
Admission routes1
Has abstractyes

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