Recruitment sources and dispersal of an invasive fish in a large river system as revealed by otolith chemistry analysis
Bibliographic record
Abstract
The contributions of nursery habitats to recruitment of non-native common carp (Cyprinus carpio) were estimated via analysis of water and otolith 87 Sr/ 86 Sr and otolith trace element concentrations (Mg:Ca, Mn:Ca, Sr:Ca, Ba:Ca) over 3 years in the Lachlan River, Australia. Water samples and otoliths of postlarval carp were analyzed to characterize 87 Sr/ 86 Sr and multi-elemental signatures of nursery habitats. Considerable temporal variation occurred in both water 87 Sr/ 86 Sr and otolith multi-elemental signatures, which limited our ability to directly match water and otolith 87 Sr/ 86 Sr in nurseries of the lower catchment. However, spatial variation in multi-elemental signatures was sufficient to allow accurate classification of nurseries within years. Assignment analysis of young-of-year fish suggested that several wetland and floodplain systems made significant contributions to young-of-year recruitment in the lower catchment. These contributions were strongly influenced by river flows and water management. Nurseries contributed fewer recruits to the main channel as distance from the nursery increased. Fish from the upper catchment originated from local sources, and there was no evidence of mixing of recruits between the upper and lower catchments. We conclude that identification of recruitment “hotspots” via otolith chemical analysis can assist in developing strategies to control invasive fishes in large river networks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".