Does flood rhythm drive ecosystem responses in tropical riverscapes?
Bibliographic record
Abstract
Biotic communities are shaped by adaptations from generations of exposure to selective pressures by recurrent and often infrequent events. In large rivers, floods can act as significant agents of change, causing considerable physical and biotic disturbance while often enhancing productivity and diversity. We show that the relative balance between these seemingly divergent outcomes can be explained by the rhythmicity, or predictability of the timing and magnitude, of flood events. By analyzing biological data for large rivers that span a gradient of rhythmicity in the Neotropics and tropical Australia, we find that systems with rhythmic annual floods have higher-fish species richness, more stable avian populations, and elevated rates of riparian forest production compared with those with arrhythmic flood pulses. Intensification of the hydrological cycle driven by climate change, coupled with reductions in runoff due to water extractions for human use and altered discharge from impoundments, is expected to alter the hydrologic rhythmicity of floodplain rivers with significant consequences for both biodiversity and productivity.
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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) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".