Food for fish: Challenges and opportunities for quantifying foodscapes in river networks
Bibliographic record
Abstract
Abstract Riverine fishes face many challenges including habitat degradation and climate change, which alter the productivity of the riverscapes in which fish live, reproduce, and feed. Understanding the watershed portfolio of foraging and growth opportunities that sustain productive and resilient fish populations is important for prioritizing conservation and restoration. However, the spatiotemporal distribution and availability of fish food are poorly understood relative to other factors such as abiotic habitat quantity and quality (e.g., water temperature). In this paper, we build on the concept of “foodscapes,” and describe three components of food for fish, including abundance, accessibility, and quality. We then discuss methodological advances to help address three key questions: (1) Why is food availability hard to estimate? (2) What are the consequences of uncertainty in food availability estimates? and (3) What approaches are available or emerging for quantifying food available to fish? To address the first question, we characterize data acquisition and analytical challenges; for the second, we demonstrate the importance of evaluating and communicating potential consequences of uncertainty; and for the third, we posit opportunities for future work. Collectively, we highlight the need for greater appreciation of the role food plays in stream fish conservation, especially given its critical influence on responses to warming temperatures. This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Water and Life > Conservation, Management, and Awareness Water and Life > Methods
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".