Contrasting the suitability of shade coffee agriculture and native forest as overwinter habitat for Canada Warbler (Cardellina canadensis) in the Colombian Andes.
Bibliographic record
Abstract
In the Neotropics, coffee production occurs on a large scale in some of the planet’s most biodiverse regions: tropical mountains. Coffee production systems involving shade trees are considered to have a lower impact on biodiversity than alternative sun coffee. To date, the majority of evidence for the value of shade coffee plantations has not taken into account the relative quality of this habitat compared to the native forests they replaced. We determined the suitability of shade coffee and forest as winter habitat for Canada Warbler (Cardellina canadensis) by comparing variation in the likelihood of capturing individuals, seasonal changes in body condition, and estimates of annual survival between the two habitats. We also determined the effect of the strong 2015-2016 El Niño event on survival. Males were relatively more likely to be captured in forest than females and this likelihood increased during drier years. Body condition change over the winter and apparent annual survival were very similar for individuals that used forest and coffee. However, condition and survival decreased in both habitats during the El Niño year. Apparent survival was also lower for individuals carrying a radiotag or geolocator. Our findings suggest that shade coffee with high canopy cover and height offers similar benefits to forest in terms of body condition and survival. Landscape conservation approaches, promoting diverse matrices of structurally complex shade coffee and forest might best ensure long-term survival in Neotropical migrants like Canada Warbler.
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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 | 0.001 | 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) | 0.001 | 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".