Tropical deforestation alters hummingbird movement patterns
Bibliographic record
Abstract
Reduced pollination success, as a function of habitat loss and fragmentation, appears to be a global phenomenon. Disruption of pollinator movement is one hypothesis put forward to explain this pattern in pollen limitation. However, the small size of pollinators makes them very difficult to track; thus, knowledge of their movements is largely speculative. Using tiny radio transmitters (0.25 g), we translocated a generalist tropical 'trap-lining' hummingbird, the green hermit (Phaethornis guy), across agricultural and forested landscapes to test the hypothesis that movement is influenced by patterns of deforestation. Although, we found no difference in homing times between landscape types, return paths were on average 459+/-144 m (+/-s.e.) more direct in forested than agricultural landscapes. In addition, movement paths in agricultural landscapes contained 36+/-4 per cent more forest than the most direct route. Our findings suggest that this species can circumvent agricultural matrix to move among forest patches. Nevertheless, it is clear that movement of even a highly mobile species is strongly influenced by landscape disturbance. Maintaining landscape connectivity with forest corridors may be important for enhancing movement, and thus in facilitating pollen transfer.
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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 | 0.000 | 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) | 0.001 | 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".