Modelling available habitat versus available energy flux: do PHABSIM applications that neglect prey abundance underestimate optimal flows for juvenile salmonids?
Bibliographic record
Abstract
Common applications of models to predict the response of fish habitat to altered stream flow (such as the Physical Habitat Simulation Model; PHABSIM) assume that fish abundance is directly related to the area of suitable habitat for limiting life stages and usually ignore flow effects on prey abundance. However, if prey availability is flow sensitive, then fish production may be more closely related to the total flux of available prey than to habitat area. We compared instream flow predictions from PHABSIM to predictions of optimal energy flux to drift-feeding juvenile coho salmon ( Oncorhynchus kisutch ) estimated using a drift-foraging bioenergetics model. Flux of available energy to juvenile coho salmon declined much more rapidly with decreasing flow than suitable habitat area estimated using PHABSIM, so that, relative to the bioenergetic model, predictions from PHABSIM systematically overestimated productive capacity at very low flows (i.e., underestimated the negative consequences of simulated water withdrawal). Applications of habitat suitability based models like PHABSIM may systematically overestimate low-flow productive capacity for species that prefer low velocities (e.g., pools) but are dependent on energy fluxes generated in higher velocity habitats (e.g., riffles).
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".