Food web perspectives and methods for riverine fish conservation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Food web analyses offer useful insights into understanding how species interactions, trophic relationships, and energy flow underpin important demographic parameters of fish populations such as survival, growth, and reproduction. However, the vast amount of food web literature and the diversity of approaches can be a deterrent to fisheries practitioners engaged in on‐the‐ground research, monitoring, or restoration. Incorporation of food web perspectives into contemporary fisheries management and conservation is especially rare in riverine systems, where approaches often focus more on the influence of physical habitat and water temperature on fish populations. In this review, we first discuss the importance of food webs in the context of several common fisheries management issues, including assessing carrying capacity, evaluating the effects of habitat change, examining species introductions or extinctions, considering bioaccumulation of toxins, and predicting the effects of climate change and other anthropogenic stressors on riverine fishes. We then examine several relevant perspectives: basic food web description, metabolic models, trophic basis of production, mass‐abundance network approaches, ecological stoichiometry, and mathematical modeling. Finally, we highlight several existing and emerging methodologies including diet and prey surveys, eDNA, stable isotopes, fatty acids, and community and network analysis. Although our emphasis and most examples are focused on salmonids in riverine environments, the concepts are easily generalizable to other freshwater fish taxa and ecosystems. This article is categorized under: Water and Life > Nature of Freshwater Ecosystems Water and Life > Conservation, Management, and Awareness Water and Life > Methods
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it