Using Larval Fish Community Structure to Guide Long-Term Monitoring of Fish Spawning Activity
Bibliographic record
Abstract
Abstract Larval fishes provide a direct indication of spawning activity and may therefore be useful for long-term monitoring efforts in relation to spawning habitat restoration. However, larval fish sampling can be time intensive and costly. We sought to understand the spatial and temporal structure of larval fish communities in the St. Clair–Detroit River system, Michigan–Ontario, to determine whether targeted larval fish sampling can be made more efficient for long-term monitoring. We found that larval fish communities were highly nested, with lower river segments and late-spring samples containing the highest genus richness of larval fish. We created four sampling scenarios for each river system: (1) using all available data, (2) limiting temporal sampling to late spring, (3) limiting spatial sampling to lower river segments only, and (4) limiting both spatial and temporal sampling. By limiting the spatial extent of sampling to lower river sites and/or limiting the temporal extent to the late-spring period, we found that effort could be reduced by more than 50% while maintaining over 75% of the observed and estimated total genus richness. Similarly, limiting the sampling effort to lower river sites and/or the late-spring period maintained between 65% and 93% of the observed richness of lithophilic-spawning genera and invasive genera. In general, community composition remained consistent among sampling scenarios. Targeted sampling offers a lower-cost alternative to exhaustive spatial and temporal sampling and may be more readily incorporated into long-term monitoring. Received August 14, 2014; accepted November 28, 2014
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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 | 0.001 |
| 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) | 0.001 | 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".