Population Variability and Apparent Recent Decline of River Birds in the Indian Himalaya
Bibliographic record
Abstract
ABSTRACT Abundance estimates are critical to animal conservation in the tropics and sub‐tropics, but assessments for some species and ecosystems in these regions are poorly developed. Estimates are particularly scarce for subtropical mountain rivers where some river organisms reach their greatest global diversity while being at risk from global change. We addressed these issues along rivers in the western Indian Himalaya, focusing on 12 bird species with varying dependence on river production, distribution, abundance, and detectability. We estimated river bird abundance through repeat field counts across 5 years using N‐mixture models to correct for imperfect detection from sparse data over an altitudinal range of 330–3100 m. Estimated abundances were modeled against elevation, flow, and river width as covariates. Detection probabilities overall were greatest in flycatching insectivores connected closely to the river channel and lowest in two piscivorous kingfishers. Patterns of abundance also varied among groups particularly in relation to elevation, with river passerines mostly recorded at mid and higher elevations and piscivorous taxa recorded mostly below 1600 m a.s.l. Five species apparently declined in overall population size by 5%–10% across the 5‐year study, in three cases matching national scale trends recorded by citizen science platforms. Our results reveal the utility of open N ‐mixture models in assessing population trends of specialized river organisms in subtropical mountain environments where high‐resolution data are difficult to collect. The data also hint at possible threats to Himalayan rivers that could affect this globally unique community of river birds.
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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 | 0.000 |
| 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) | 0.000 | 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".