Fish recruitment on floodplains: the roles of patterns of flooding and life history characteristics
Bibliographic record
Abstract
Floodplain inundation in rivers is thought to enhance fish recruitment by providing a suitable spawning environment and abundant food and habitat for larvae. Although this model has not previously been tested in Australian rivers, it is often extrapolated to fishes of the Murray-Darling Basin. Fortnightly sampling of larvae and juveniles was conducted in the unregulated Ovens River floodplain during springsummer of 1999 (non-flood year) and 2000 (flood year). The only species that increased in larval abundance during or shortly after flooding was an introduced species, common carp (Cyprinus carpio). Additionally, the peak abundance of larvae on the floodplain occurred during a rapidly declining hydrograph under low flow conditions in isolated billabongs and anabranches. The low use of the inundated floodplain for recruitment contradicts previous models. We propose a model of the optimum environmental conditions required for use of the inundated floodplain for fish recruitment. The model suggests that the notion of the flood pulse alone controlling fish recruitment is too simplistic to describe all strategies within a system. Rather, the life history adaptations in the fauna of the system and aspects of the hydrological regime such as duration and timing of inundation will control the response of a river's fish fauna to flooding.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".