Conspecific attraction during establishment of Least Flycatcher clusters
Bibliographic record
Abstract
ABSTRACT Some birds exhibit clustered breeding in which all-purpose territories are densely packed, leaving intervening but apparently suitable habitat unoccupied. Clustering could be ecologically driven by material resource patterns or socially driven by social or sexual benefits. Least Flycatchers (Empidonax minimus) breed in clusters in forests over much of North America. In 2003, we mapped all Least Flycatcher clusters along 18.7 km of secondary roads in central Ontario. In May 2004, we broadcast recorded territorial song in five areas not used by Least Flycatchers in 2003, but in the same study area. During settlement, we found Least Flycatchers in the established clusters, in three of five treatment sites, and in one nontreatment site. However, no pairs were noted at the treatment sites, and no males ultimately remained. One male did, however, defend a territory at a treatment site for 6 d. Despite limited success at attracting Least Flycatchers to new locations, manipulating settlement using social cues could be a useful management tool for some species. Algunas aves muestran agregaciones reproductivas en donde el territorio de todo propósito, está densamente conglomerado, aparentemente, dejando hábitat adecuado sin ocupar. Las agregaciones pueden ser ecológicamente dirigidas en el sentido de usar mejores recursos materiales o socialmente dirigidas en el sentido de beneficios sociales o sexuales. El papamoscas Empidonax minimus se congrega para reproducirse en bosques, virtualmente a todo lo largo de Norte América. En el 2003, marcamos en mapas todas las agregaciones de estas aves que se encontraron a lo largo de 18.7 km de caminos secundarios en la parte central de Ontario, Canadá. En mayo de 2004 transmitimos canciones territoriales que habíamos grabado en cinco áreas que no habían sido utilizados por los papamoscas durante el 2003, pero contenidas dentro de la misma área de estudio. Durante el asentamiento, encontramos papamoscas establecidos en agregaciones, en tres de las cinco localidades tratadas (totalizando 10 días), y en una localidad no-tratada. Sin embargo, no se encontraron parejas en las áreas tratadas y no se quedaron en la localidad machos cortejando. Una sola ave defendió un territorio en una de las áreas tratadas por seis días. No obstante al éxito limitado en atraer aves para formar grupos, utilizando pistas sociales, este pudiera ser de utilidad como herramienta de manejo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".