Host phylogeny and elevation predict infection by avian haemosporidians in a diverse New Guinean bird community
Bibliographic record
Abstract
Abstract Aim The biotic interactions hypothesis predicts that interactions between species are stronger at lower elevations compared to higher elevations. We tested whether this prediction is met by examining elevational patterns in avian haemosporidian infections in New Guinean birds. Location YUS Conservation Area, Papua New Guinea. Taxon Birds and haemosporidia. Methods We collected blood samples from 2268 individuals of 66 bird species along a reef‐to‐ridgetop tropical elevational gradient covering ~3000 m of elevation. We used molecular methods to screen samples for haemosporidian parasites, and sequenced 70% of positive infections to determine the identity of parasite genera. We fit a phylogenetic generalized mixed‐effects model to test whether infection prevalence was higher at low elevations, as predicted by the biotic interactions hypothesis. Finally, we tested for phylogenetic signal in parasite prevalence and in host elevational distribution using Pagel's λ. Results Overall parasite prevalence was 45%. Infection prevalence decreased with elevation, though prevalence remained above ~30% even for high‐elevation communities between 2000 and 3000 m. We found strong phylogenetic signal in infection prevalence, but no phylogenetic signal in host elevational distribution. Nearly all sequenced infections (97%) were of Haemoproteus. Main Conclusions Our results are consistent with predictions arising from the biotic interactions hypothesis. However, phylogenetic relationships among hosts are a much better predictor of infection prevalence than is elevational position: closely related bird species tend to have similar infection prevalence regardless of the elevations where they live.
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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 | 0.000 | 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) | 0.002 | 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".