A multiscale analysis of factors influencing <i>Setophaga striata</i> (Blackpoll Warbler) occupancy and abundance during the nonbreeding season in eastern Colombia
Bibliographic record
Abstract
ABSTRACT Setophaga striata (Blackpoll Warbler), one of the few boreal-breeding migratory birds that migrates to the Orinoco and Amazon regions, is experiencing steep population declines. However, knowledge of the species’ distribution and habitat use during the nonbreeding season is limited. Here, we explore how stationary nonbreeding season (December to April) occupancy probabilities vary across regional and landscape scales, and with vegetation structure for S. striata in eastern Colombia. By running single-species, single-season hierarchical occupancy models and N-mixture models, we evaluated how regional (annual precipitation, nonbreeding season precipitation, driest month, elevation, and net primary productivity), landscape (habitat type and % forest in the landscape), and vegetation structure variables influenced occupancy by S. striata. In the regional analysis, occupancy was positively influenced by stationary nonbreeding precipitation and net primary productivity, and was predicted to peak at elevations between 500 and 1,000 m. At the landscape scale, occupancy and abundance were higher in agroforestry systems (in order of magnitude: shade-grown cacao, citrus plantations and silvopastures) than in forested habitats. In relation to vegetation structure, occupancy was negatively correlated with a principal component containing shrub density, canopy height, and canopy cover. Within the Orinoco region, a spatial prediction based on our results supported high occupancy rates in moister areas along and close to the Andean foothills and where the Orinoco grasslands transition into Amazonian forests. Our results highlight the importance of landscapes containing agroforestry systems as stationary nonbreeding habitats for S. striata in Colombia’s Orinoco region and the importance of promoting agroforestry systems, like shade-grown cacao, in areas with higher nonbreeding season precipitation and net primary productivity values in order to increase habitat availability for the species.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".