Identifying drivers of tropical riverine larval fish abundance and diversity
Bibliographic record
Abstract
Several hypotheses and conceptual models propose to explain mechanisms mediating riverine fish abundance, but few empirical studies to date have explored their utility in tropical systems. This study assesses key components of previous fish recruitment models by exploring spatiotemporal variation in larval fish assemblages in response to predicted key drivers in a tropical Australian river catchment. Data on larval fish composition and abundance, alongside hydrological, hydraulic, habitat and food variables, were collected monthly to bimonthly over one year at eight sites. Variables which best predicted larval fish abundance and diversity were determined with Boosted Regression Trees. The most commonly important predictors were microfauna abundance, structural habitat complexity and temperature, with high values of each predicting high larval fish abundance and diversity. Maximum larval diversity occurred when discharge was highest because several wet-season spawning taxa occurred alongside aseasonally spawning taxa. These findings support previous generic fish recruitment models, demonstrating the utility of their inclusion in the recent Riverine Recruitment Synthesis Model and the applicability of this model for describing processes important for tropical riverine fish recruitment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".