Microhabitat segregation, foraging behavior, and elevation enable coexistence of new world warblers (Aves: Parulidae) in agroforestry systems
Bibliographic record
Abstract
Abstract Understanding how closely related species partition their habitat and the mechanisms that facilitate their coexistence is central to advancing community ecology. When ranges of several species overlap (i.e., they are sympatric), those that use the same food resources should differ in other niche dimensions due to niche complementarity or resource partitioning. In bird communities, this primarily occurs through differences in foraging behavior, diet specialization or composition, and habitat use. In this paper, we evaluated how six species of new world warblers (Canada warbler Cardellina canadensis , Tennessee warbler Leiothlypis peregrina , Bay-breasted warbler Setophaga castanea , Cerulean warbler Setophaga cerulea , Blackburnian warbler Setophaga fusca , and Tropical parula Setophaga pitiayumi ) segregate their ecological niches across an elevational gradient of agroforestry systems in the western Andes of Colombia. We found evidence of microhabitat (vertical forest stratum use, foraging height and substrate) and elevational segregation for the six warbler species, suggesting patterns of multidimensional niche partitioning. High levels of niche overlap among microhabitat variables could indicate that interspecific interactions are key for structuring this co-occurring parulid community. In particular, the warblers exhibited a clear distributional pattern across the elevational gradient. Our analysis of multiple niche dimensions (i.e., elevational and microhabitat parameters) revealed differential patterns of habitat use that can suggest niche partitioning in ecologically similar species.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".