Modeling and mapping fish abundance across wadeable streams of Illinois, USA, based on landscape-level environmental variables
Bibliographic record
Abstract
To effectively conserve and restore stream ecosystems, we need to better understand the distribution and abundance of individual fish species in relation to natural environments and anthropological stressors. In this study, we modeled the abundance of 97 fish species in small wadeable streams of Illinois, USA, based on random forests regression and landscape-level environmental variables. Model R2 values for intermediately common species were higher than for common species, but highly variable among rare ones. Models for 50 species reached R2 of 0.2–0.70 and were tested with a separate set of samples and applied to unsampled wadeable reaches to show the population hotspots of each species across the state. Furthermore, we evaluated the importance of individual environmental variables to a given fish species as well as the directional responses of each species to top 10 key predictors. Climate and land use were the best predictors for most species, followed by topography, geology, and soil permeability. Spatial connection of a stream also was associated with a large number of species. These findings improved our understanding of the relationships between fish species and landscape environments. The distribution maps could guide resource management, restoration, and monitoring of stream fish assemblages.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".