Sympatry leads to reduced body condition in chickadees that occasionally hybridize
Bibliographic record
Abstract
Both abiotic and biotic drivers influence species distributions. Abiotic drivers such as climate have received considerable attention, even though biotic drivers such as hybridization often interact with abiotic drivers. We sought to explore the (1) costs of co-occurrence for ecologically similar species that hybridize and (2) associations between ecological factors and condition to understand how abiotic and biotic factors influence species distributions. For two closely related and ecologically similar songbirds, black-capped and mountain chickadees, we characterized body condition, as a proxy for fitness, using a 1358-individual range-wide dataset. We compared body condition in sympatry and allopatry with several abiotic and biotic factors using species-specific generalized linear mixed models. We generated genomic data for a subset of 217 individuals to determine the extent of hybridization-driven admixture in our dataset. Within this data subset, we found that ~11% of the chickadees had hybrid ancestry, and all hybrid individuals had typical black-capped chickadee plumage. In the full dataset, we found that birds of both species, independent of demographic and abiotic factors, had significantly lower body condition when occurring in sympatry than birds in allopatry. This could be driven by either the inclusion of cryptic, likely poor condition, hybrids in our full dataset, competitive interactions in sympatry, or range edge effects. We are currently unable to discriminate between these mechanisms. Our findings have implications for mountain chickadees in particular, which will encounter more black-capped chickadees as black-capped chickadee ranges shift upslope and could lead to local declines in mountain chickadee populations.
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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 | 0.001 |
| 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 | 0.000 | 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 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".