How important is nectar in shaping spatial variation in the abundance of temperate breeding hummingbirds?
Bibliographic record
Abstract
Abstract Aim Our aim was to test the degree to which nectar production predicts hummingbird abundances at large spatial scales compared with other large‐scale environmental variables. Location Arizona, Colorado, New Mexico and Utah, USA. Methods We surveyed nectar producing flowers at 67 sites in the summer of 2008 and converted flower densities to nectar production using data obtained from the literature. We derived a model of nectar production and used this to create a nectar production map for the study region. We then tested the degree to which nectar production predicted the abundance and occupancy of black‐chinned (Archilochus alexandri) and broad‐tailed (Selasphorus platycercus) hummingbirds with zero‐inflated Poisson regression. Abundance data were taken from the North American Breeding Bird Survey. We compared the predictions made from nectar production to those made from temperature, precipitation, growing degree‐days, elevation and primary productivity. Results We found that black‐chinned hummingbird abundance was best predicted by the abundance of conspecifics in a surrounding 20‐km neighbourhood as opposed to any of the environmental variables. Their occupancy varied independently of any underlying spatial or environmental variation. Broad‐tailed hummingbird abundance was best predicted by average temperature. Nectar was a weak predictor of both species' abundance and occupancy. Main conclusions Of the variables we measured, no single one is a key predictor of spatial variation of hummingbird abundance. It is possible that breeding abundances respond to local habitat characteristics that do not correlate strongly with large‐scale environmental variability. Within the breeding season, hummingbird abundance and occupancy may depend on factors unrelated to nectar production and hummingbirds may not disperse to track spatial variation in nectar production.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".