Sympatric finches differ in visitation patterns to watering holes: implications for site-focused bird counts
Bibliographic record
Abstract
Estimating trends in population size is critical for understanding population status and assessing the success of management interventions. Visual counts of birds as they congregate around predictable locations, such as waterholes, is a popular technique for estimating population size. Bird counts are used as a proxy for abundance, but how the relationship between counts and actual abundance varies over space and time is rarely assessed. Here, we demonstrate that colour banding and motion detection cameras provided a good method for monitoring finch visitation patterns across space and time. These methods were validated using three sympatric finch species, the abundance of which have been estimated from waterhole counts over many years. The study showed significant temporal inter-species variability in the proportion of birds visiting waterholes and the number of times the same individual returned to the same waterhole during the early morning. Bird visitation rates also varied between consecutive days, across adjacent waterholes and at different stages of the dry season. Our study suggests that spatiotemporal variation in individual behaviour may introduce substantial error into site-focused bird counts and we recommend considering this in census design.
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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 | 0.000 | 0.001 |
| 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".