The role of wetland vegetation and water connectivity in shaping waterbird populations under human disturbance
Bibliographic record
Abstract
• Under low human disturbance, rich abundance and diversity of waterbirds were observed. • Wetland vegetation coverage and water connectivity were the primary factors significantly impacting waterbird populations. • Wetland vegetation coverage had a more substantial influence on waterbirds in areas of low disturbance intensity. • The impact of water connectivity on waterbirds was complex under different intensities of human disturbance. The floodplain wetland of Poyang Lake, one of the largest stopover sites for overwintering waterbirds along the East Asian–Australasian Flyway, is experiencing rising human disturbance and rapid landscape changes. However, the impacts of these two factors on waterbird populations at three biological levels (i.e., the species, foraging guild, and community levels) remain poorly understood. Using multi-source remote sensing data and annual winter waterbird survey data (2013–2018), combined with statistical methods such as one-way ANOVA, generalized linear models, and redundancy analysis, we investigated landscape patterns influencing waterbird populations at three biological levels under human disturbance. Results indicated that most waterbird populations, excluding those specializing in invertebrate consumption, thrived greatly under low human disturbance intensity. Wetland vegetation and water connectivity played the most significant role among landscape metrics in shaping waterbird populations at three biological levels. The expanded wetland vegetation coverage strongly promoted waterbird populations, especially under low human disturbance intensity. Expanding main lakes with high water connectivity tended to suppress waterbird populations, while the newly formed shallow sub-lakes with limited connectivity promoted them. The role of water connectivity showed complexity across different intensities of human disturbance. Additionally, larger cropland patches benefited tuber- and seed-eating birds under low-moderate human disturbance, whereas built-up expansion harmed the waterbird community in highly disturbed areas. These findings offer useful insights for conserving overwintering waterbird populations and informing habitat management strategies in floodplain wetlands.
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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 | 0.000 | 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) | 0.001 | 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".