Response of a floodplain fish community to river-floodplain connectivity: natural versus managed reconnection
Bibliographic record
Abstract
To restore lateral connectivity in highly regulated river-floodplain systems, it has become necessary to implement localized, “managed” connection flows, made possible using floodplain irrigation infrastructure. These managed flows contrast with “natural”, large-scale, overbank flood pulses. We compared the effects of a managed and a natural connection event on (i) the composition of the large-bodied fish community and (ii) the structure of an endangered catfish population of a large floodplain lake. The change in community composition following the managed connection was not greater than that exhibited between seasons or years during disconnection. By contrast, the change in fish community structure following the natural connection was much larger than that attributed to background, within- and between-year variability during disconnection. Catfish population structure only changed significantly following the natural flood. While the natural flood increased various population rates of native fishes, it also increased those of non-native carp, a pest species. To have a positive influence on native biodiversity, environmental flows may need to be delivered to floodplains in a way that simulates the properties of natural flood pulses. A challenge, however, will be managing river-floodplain connectivity to benefit native more than non-native species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".