Do Management Actions to Restore Rare Habitat Benefit Native Fish Conservation? Distribution of Juvenile Native Fish Among Shoreline Habitats of the Colorado River
Bibliographic record
Abstract
Abstract Many management actions in aquatic ecosystems are directed at restoring or improving specific habitats to benefit fish populations. In the Grand Canyon reach of the Colorado River, experimental flow operations as part of the Glen Canyon Dam Adaptive Management Program have been designed to restore sandbars and associated backwater habitats. Backwaters can have warmer water temperatures than other habitats, and native fish, including the federally endangered humpback chub Gila cypha, are frequently observed in backwaters, leading to a common perception that this habitat is critical for juvenile native fish conservation. However, it is unknown how fish densities in backwaters compare with that in other habitats or what proportion of juvenile fish populations reside in backwaters. Here, we develop and fit multi‐species hierarchical models to estimate habitat‐specific abundances and densities of juvenile humpback chub, bluehead sucker Catostomus discobolus, flannelmouth sucker Catostomus latipinnis and speckled dace Rhinichthys osculus in a portion of the Colorado River. Densities of all four native fish were greatest in backwater habitats in 2009 and 2010. However, backwaters are rare and ephemeral habitats, so they contain only a small portion of the overall population. For example, the total abundance of juvenile humpback chub in this study was much higher in talus than in backwater habitats. Moreover, when we extrapolated relative densities based on estimates of backwater prevalence directly after a controlled flood, the majority of juvenile humpback chub were still found outside of backwaters. This suggests that the role of controlled floods in influencing native fish population trends may be limited in this section of the Colorado River. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".