The influence of native shrub density on bird communities in the southern drylands of California, USA
Bibliographic record
Abstract
Interactions between key landscape features in desert ecosystems such as shrubs and other foundation plants can influence avian community assembly. Bird species often use resources and microhabitats provided by these shrubs for food, perching sites, and as thermal refuges. Citizen science data are broadly accessible and can be used to examine fine-scale avian distribution. Coupling this accessible data with key factors, such as native shrubs, can be used for conservation practices. eBird data offers the opportunity to examine avian communities across regional ecological gradients. Using eBird, we tested the hypothesis that shrub density and relative differences in aridity among sites within this region shape the structure of bird communities throughout Central California drylands. Shrub density positively influenced the observation rates of avian communities sampled. Decreasing aridity increased the positive associations of birds with shrubs. Citizen science data such as eBird offers promise for testing predictions at fine spatial scales, and further research can explore availability and reporting of data for other regions - particularly in drylands subject to substantial pressures from climate change globally. Simple landscape features in drylands, such as native shrub density and cover, offer a viable path forward for avian community conservation and potential habitat restoration in drylands in the face of a changing climate and increasing desertification.
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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 | 0.001 |
| 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".