Linking ecosystem processes to consumer growth rates: gross primary productivity as a driver of freshwater fish somatic growth in a resource-limited river
Bibliographic record
Abstract
Individual growth can exert strong control on population dynamics but is constrained by resource acquisition rates. Difficulty in accurately quantifying resource availability over large spatial extents and at high temporal frequencies often limits attempts to understand the extent to which resources limit individual growth. Daily estimates of stream metabolism, including gross primary productivity (GPP), are increasingly available but have not, to our knowledge, been linked to fish growth. Here we examine how environmental variables such as GPP, water temperature, turbidity, and high-flow releases from a dam are linked to spatiotemporal variation in the growth of flannelmouth sucker ( Catostomus latipinnis) in the Colorado River within the Grand Canyon. We fit state-space growth models to 6 years of mark–recapture data collected in four river reaches spanning 300 river kilometers. Consistent with past research in this system, we find that all four environmental variables influence growth in length of a native primary consumer fish. GPP and temperature have a positive influence on growth, while turbidity and high-flow events have a negative influence. Water temperature is the dominant driver of spatiotemporal variation in growth, while the link between high-frequency GPP and fish growth is relatively novel. Fish growth is likely to be linked to stream metabolism in other systems where overall productivity, not the quality of primary producers, limits the food webs that support fish growth.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".