A Tribute to Tributaries: Endangered Fish Distributions within Critical Habitat of the San Juan River, USA
Bibliographic record
Abstract
Abstract The extent of fish movements in contemporary river networks can depend on spatial position relative to attractive features that may provide food and refuge, such as tributary mouths, or restrictive features, such as barriers. From the San Juan River basin in New Mexico and Utah, we integrated remote summertime PIT tag detections at two tributary mouths (McElmo Creek and Chaco Wash) with main-stem recapture data from 2012 to 2015 to determine if two endangered species, Colorado Pikeminnow Ptychocheilus lucius and Razorback Sucker Xyrauchen texanus, differed in tributary use, demography, and seasonal ranges. Razorback Suckers were more abundantly detected (n = 266 individuals) and recaptured (n = 145) than were Colorado Pikeminnows (n = 96 detected; n = 55 recaptured). Despite the San Juan River flowing uninterrupted between the tributary mouths, individuals of each species separated into tributary-specific groups. Razorback Suckers had very similar sizes and ages at each tributary, reflecting a homogeneous augmented population. Colorado Pikeminnows separated into tributary-specific groups of either adult (Chaco Wash) or subadult (McElmo Creek) fish based on total length and age. Analyses suggested that fish size was a significant factor explaining the extent of seasonal ranges for both species. Although the ranges of both species exhibited a tributary effect, this effect was more significant in explaining Razorback Sucker ranges than Colorado Pikeminnow ranges, which were explained best by season. Understanding how mobile species interact with attractive and inhibitive river features can help managers identify potential sampling biases along with possible consequences of spatially structured populations. Managers should consider extending sampling upstream of barriers, installing additional permanent antenna arrays, and maintaining perennial flows in these tributaries.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".