Joint-species analysis reveals potential displacement of native fishes by non-native fishes within the Santa Ana River, California
Bibliographic record
Abstract
Accurate estimates of abundance are a cornerstone for resource managers to make effective decisions for fish conservation. However, multiple sampling methods often are required to sample fish communities and ignoring the detection process can create substantial bias in latent state parameter estimation (e.g., abundance, survival). We developed a joint-species N-mixture model that integrated snorkel, seining, and electrofishing surveys to estimate factors affecting native and non-native fish distributions in the Santa Ana River, California. We found through data integration that native Santa Ana sucker (Catostomus santaanae) and arroyo chub (Gila orcuttii) were most abundant in wide stream channels, and the abundance of both native fishes were negatively correlated with non-native largemouth bass (Micropterus salmoides). Our results highlight the power of integrating multiple data sets into a single analysis and incorporating among-species correlation into abundance modeling. Our results also highlight a pattern of native fish declines that coincide with an expanding largemouth bass population, a concern for the management of native aquatic communities within the Santa Ana River.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".