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Record W4249863465 · doi:10.1093/condor/106.4.896

Autumn Dispersal and Winter Residency do not Confer Reproductive Advantages on Female Spruce Grouse

2004· article· en· W4249863465 on OpenAlexaff
Daniel M. Keppie

Bibliographic record

VenueOrnithological Applications · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicRangeland and Wildlife Management
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBiological dispersalGeographyJuvenileNest (protein structural motif)ImmigrationEcologyGrouseRange (aeronautics)Reproductive successSpring (device)BiologyForestryDemographyHabitatPopulationArchaeology

Abstract

fetched live from OpenAlex

Abstract Many, but not all, juvenile Spruce Grouse (Falcipennis canadensis) disperse from their natal range in autumn. In spring shortly before breeding, some of these autumn dispersers will disperse a second time from their winter range, whereas others make their first dispersal from natal range. I postulated that dispersing first in autumn provides greater experience on a potential breeding area than immigrating to the breeding area in spring. I predicted that autumn-immigrant females would show a higher percentage of females nesting, would nest earlier, and would produce more juveniles into late summer than would immigrants in spring. Data were available for females on three areas widely spaced across their geographic range. Immigrants contributed most to production. Parameters did not vary greatly among study areas. Combining all areas, 68% of autumn immigrants and 69% of spring immigrants were known to nest, mean hatch dates relative to the annual median differed by less than 1 day, and spring immigrants produced more juveniles surviving into late summer than did autumn immigrants (1.1 versus 0.7 juveniles per female). Hence, there is no evidence yet that autumn dispersal directly confers a reproductive advantage for female Spruce Grouse. Results underscore a perplexing question: if site-specific conditions stimulate winter residents to emigrate, why do the spring immigrants that replace them fare so well? La Dispersión Otoñal y la Residencia Invernal no Confieren una Ventaja Reproductiva a las Hembras de Falcipennis canadensis Resumen. Una gran parte de los individuos juveniles de Falcipennis canadensis se dispersan desde sus sitios natales en el otoño. En la primavera, justo antes del apareo, algunos de estos individuos dispersados en el otoño se dispersan nuevamente desde sus sitios de invernada mientras que otros se dispersan por primera vez desde sus sitios natales. Mi postulado fue que la dispersión inicial de otoño confiere mayor experiencia en cuanto al área potencial de apareo en comparación con aquellos que emigran al área de reproducción en la primavera. Mi predicción fue que las hembras que emigran en otoño representarían un mayor porcentaje de las hembras anidando, anidarían más temprano y producirían más individuos juveniles a finales del verano que aquellas que emigran en la primavera. Conté con datos disponibles para hembras en tres áreas ampliamente espaciadas a lo largo de su rango geográfico de distribución. Los inmigrantes contribuyeron más a la producción. Los parámetros no presentaron mayor variación entre las áreas de estudio. Combinando todas las áreas, el 68% de los inmigrates otoñales y el 69% de los inmigrantes primaverales anidaron. La diferencia entre las fechas promedio de nacimiento y la mediana anual fue menos de 1 día y los inmigrantes primaverales produjeron más individuos juveniles que sobrevivieron hasta finales del verano que los inmigrantes otoñales (1.1 versus 0.7 individuos por hembra). Por lo tanto, aún no existe evidencia de que la dispersión otoñal le confiere una ventaja reproductiva a las hembras de F. canadensis. Estos resultados revelan una pregunta fascinante: si las condiciones particulares del sitio estimulan a los residentes invernales a emigrar, ¿por qué los inmigrantes primaverales que los reemplazan se desenvuelven tan bien?

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 score0.095
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)0.0010.001

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 head0.258
Teacher spread0.242 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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