The effect of flight efficiency on gap‐crossing ability in Amazonian forest birds
Bibliographic record
Abstract
Abstract The ability to move across the landscape is a fundamental property of species that can determine their chances of persistence in fragmented landscapes and in rapidly changing environments. Despite its importance, empirical evidence showing the effect of movement capacity on patterns of movement across fragmented landscapes is limited. In this study, we examine the role of flight efficiency on the likelihood of crossing a man‐made habitat gap. We used data from the Biological Dynamics of Forest Fragments Projects on recaptures of banded birds in an Amazonian forest bisected by a road. For a total of 45 species, we estimated flight efficiency using the hand‐wing index (a proxy for the wing's aspect ratio) and used it as a predictor of the probability of road crossing in phylogenetic binomial regression models. We found that flight efficiency was a strong predictor of road‐crossing probability: species with high hand‐wing indices crossed the road more frequently than those with low hand‐wing indices. In contrast, other characteristics such as body mass, diet, flocking behavior, and foraging stratum did not show significant associations with road‐crossing probability. Our results suggest that proxies of flight efficiency such as the hand‐wing index can be powerful tools for predicting the vulnerability of bird species to forest fragmentation. Abstract in Spanish is available with online material
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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 | 0.001 | 0.003 |
| 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".