Limitations of ecomorphological analysis in explaining macrohabitat segregation in a songbird guild
Bibliographic record
Abstract
It is not uncommon for different species within a guild to be non-randomly segregated within a landscape through the occupation of different preferred macrohabitats. The explanation most amenable with ecological theory is that different guild members are adapted for exploiting the different opportunities offered by such macrohabitats. Morphological characters are possible adaptations reflecting habitat preferences. We investigated morphological traits in a guild of small foliage-gleaning, insectivorous birds breeding in central Ontario, Canada (4 families; 23 species). We measured 27 skeletal features and compared 2 types of differences: those between conspecific sexes (that by necessity occupy the same macrohabitat) and those between different guild members (that tend to occupy different macrohabitats). We predicted that if macrohabitat differences are reflected in different morphologies, the differences between conspecific sexes would be less than the differences between species, at least after correcting for size. We used (a) principal components analysis (PCA), (b) distance matrices derived from PCA scores, and (c) Mantel tests. Although conspecific male and female morphologies were correlated, nearest neighbours in morphological space were frequently non-conspecifics. Accordingly, because morphological differences between similar species that tend to occupy different macrohabitats are often smaller than morphological differences between conspecific sexes, our findings indicate that skeletal morphology provides no basis for explaining patterns of within-guild macrohabitat segregation.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".