The salmonid and the subsurface: Hillslope storage capacity determines the quality and distribution of fish habitat
Bibliographic record
Abstract
Abstract Water in rivers is delivered via the critical zone (CZ)—the living skin of the Earth, extending from the top of the vegetation canopy through the soil and down to fresh bedrock and the bottom of significantly active groundwater. Consequently, the success of stream‐rearing salmonids depends on the structure and resulting water storage and release processes of this zone. Physical processes below the land surface (the subsurface component of the CZ) ultimately determine how landscapes “filter” climate to manifest ecologically significant streamflow and temperature regimes. Subsurface water storage capacity of the CZ has emerged as a key hydrologic variable that integrates many of these subsurface processes, helping to explain flow regimes and terrestrial plant community composition. Here, we investigate how subsurface storage controls flow, temperature, and energetic regimes that matter for salmonids. We illustrate the explanatory power of broadly applicable, storage‐based frameworks across a lithological gradient that spans the Eel River watershed of California. Study sites are climatically similar but differ in their geologies and consequent subsurface CZ structure that dictates water storage dynamics, leading to dramatically different hydrographs, temperature, and riparian regimes—with consequences for every aspect of salmonid life history. Lithological controls on the development of key subsurface CZ properties like storage capacity suggest a heretofore unexplored link between salmonids and geology, adding to a rich literature that highlights various fluvial and geomorphic influences on salmonid diversity and distribution. Rapidly advancing methods for estimating and observing subsurface water storage dynamics at large scales present new opportunities for more clearly identifying landscape features that constrain the distributions and abundances of organisms, including salmonids, at watershed scales.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".