Vocal behaviour of Song and Swamp sparrows upon arrival on shared breeding grounds
Bibliographic record
Abstract
Closely related species interact often, typically competitively, aggressively, and asymmetrically, with a consistent dominance hierarchy among species. Competition for resources appears to be a rate-limiting step in diversification, but beyond this, we know little about the ecological role of aggression in facilitating or constraining the coexistence of species due to the difficulty in observing natural interactions. To examine how closely related species in a dominance hierarchy aggressively interact, and how those interactions may facilitate coexistence, we documented vocalizations of Song (Melospiza melodia) and Swamp (M. georgiana) sparrows during natural and simulated territory settlement to answer the question: How does vocal behaviour of a dominant species change when first faced with a subordinate competitor on shared breeding territory? Though sample sizes were too low for statistical testing, we saw slightly increased rates of “Swamp Sparrow-like” songs sung by Song Sparrows in relation to Swamp Sparrow presence, as well as trill syllable lengths of Song Sparrow songs approaching average lengths of Swamp Sparrow trill syllables. These trends may suggest syllable sharing or vocal shifts in Song Sparrows as a response to Swamp Sparrow competitors – this vocal convergence may be beneficial in mediating conflicts over shared resources. We also provide novel descriptions of Swamp Sparrow behaviour during settlement on territories overlapping Song Sparrows. Further descriptions of vocal interactions can inform how closely related species interact aggressively, and will contribute to our understanding of how aggression might relate to coexistence on shared territories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".