Multiscale effects of flow regime and habitat and their interaction on fish assemblage structure in eastern Australia
Bibliographic record
Abstract
We examine the multiscale influence of environmental and hydrological features of the riverine landscape on spatial and temporal variation in fish assemblages in eastern Australia. Multiresponse artificial neural network models provided accurate predictions of fish assemblages in the Mary River based on species presence–absence data (mean Bray–Curtis similarity between predicted and observed composition = 84%) but were less accurate when based on species relative abundance or biomass (mean similarity = 62% and 59%, respectively). Landscape- and local-scale habitat variables (e.g., catchment area and riparian canopy cover) and characteristics of the long-term flow regime (e.g., variability and predictability of flows) were more important predictors of fish assemblages than variables describing the short-term history of hydrological events. The relative importance of these variables was broadly similar for predicting species occurrence, relative abundance, or biomass. The transferability of the Mary River predictive models to the nearby Albert River was high for species presence–absence (i.e., closer match between predicted and observed data) compared with species abundances or biomass. This suggests that the same landscape-scale features are important determinants of distribution of individual species in both rivers but that interactions between landscape, hydrology, and local habitat features that collectively determine abundance and biomass may differ.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".