Determining the influence of migratory stopover timing and movement on plant seed dispersal by a short-distance migratory songbird, the American robin, Turdus migratorius
Bibliographic record
Abstract
Animals disperse approximately 50% of seeds worldwide and could assist plants in shifting their distribution in the face of climate change. However, seed dispersal by animals is understudied particularly in temperate areas. American robins (Turdus migratorius) are migratory thrushes which have a frugivorous diet in the fall and may be important seed dispersers. Nineteen American robins were fitted with GPS tracking units at Assiniboine Park, Winnipeg, Manitoba during their fall migration in 2022. The GPS units provided live location data for the birds every 6 hours. This data was used to investigate the seed dispersal potential of American robins during their fall migration by examining the time spent and distance travelled while at stopover, as well as the use of urban versus rural habitats. Fecal samples collected at capture were used to identify the seeds of plant species these robins had consumed and therefore may disperse. I found that individual robins spent 15 to 33 days at fall migratory stopovers (an average of 26.64 days ± 4.95) and travelled extensively at stopovers (29-320 km; average 101.68 km ± 84.95) before continuing migration. Birds made use of both urban and rural habitats while at stopovers. I also found that fecal samples contained both native and non-native seed types, with Malus sp. making up most (62.71%) of the seeds in the samples. Overall, my results show that American robins have the potential to be important dispersers of native and non-native seeds across both rural and urban habitats, due to the large amount of time and distance travelled at fall migratory stopovers. Future studies should evaluate the viability and recruitment of seeds that have been dispersed by robins and examine the movements of American robins during fall stopovers originating from both urban and rural 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 | 0.001 | 0.002 |
| 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".