Spawning phenology and habitat use in a Great Plains, USA, stream fish assemblage: an occupancy estimation approach
Bibliographic record
Abstract
Knowledge of basic life-history attributes, paired with unbiased estimates of species distribution, is critical for the effective conservation of sensitive fish species. We quantified the spawning phenology, habitat use, and detectability for larvae of an assemblage of threatened Great Plains, USA, stream fishes using new occupancy estimation methods. Spawning by six Great Plains fish species occurred from April through July, and was likely initiated by changes in water temperature and photoperiod. Habitat size and type were important factors influencing the occupancy of larvae in spawning habitats. Detectability of larvae differed among species and over time, and was influenced by habitat depth and fish size. Our models indicated that multiple samples from individual habitats within a season are needed to adequately detect and predict occupancy by stream fish larvae. Conservation efforts for imperiled Great Plains fish assemblages should focus on sustaining flows that maintain a sufficient density and size of habitats needed for successful spawning and recruitment. The occupancy estimation and modeling methods employed here will be useful in developing comprehensive, unbiased programs to monitor the reproductive success of Great Plains stream fishes.
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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 | 0.001 | 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".