Explaining variation in stream fish productivity with biotic and abiotic variables across wadeable rivers in eastern North America
Bibliographic record
Abstract
Abstract Biomass production is a key ecosystem process that provides insight into ecological processes such as growth, reproduction, mortality, and energy distribution. Previous studies have considered various fish response measures such as species richness, abundance, and/or biomass as response variables for river ecosystems or the productivity of particular species. However, few studies have investigated how total fish productivity of riverine systems is affected by environmental variables. Here, we identified important abiotic and biotic predictors of fish productivity in wadeable, temperate riverine systems. We investigated the relationships between total stream fish productivity and multiple abiotic and biotic variables in wadeable stream reaches across Ontario, Canada. Variance partitioning was used to evaluate the relative importance of the biotic, landscape, climatic, and geologic variables on total stream fish productivity. A modified bootstrap approach was used for the model‐selection process and to parameterise an empirical fish productivity model. We found that biotic predictors explained more variation in productivity relative to the abiotic variables. The best empirical model included day‐of‐year, growing degree days, latitude, salmonid presence/absence, species richness, and upstream catchment area. Our findings indicate that a combination of both biotic and abiotic variables can provide valuable insight into how ecological processes, such as fish productivity, differ across ecosystems. Species richness and differences in assemblage characteristics may be key determinants of the overall fish productivity in stream systems. Our model can estimate productivity from salmonid presence/absence data and total species richness, instead of fish abundance data, which require larger sampling efforts.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".