Regional trends in fish mean length at age: components of variance and the statistical power to detect trends
Bibliographic record
Abstract
We examined statewide time series (1940s–2002) of mean length at ages 2, 3, and 4 for seven fish species sampled from Michigan and Wisconsin inland lakes for temporal trends. We used a components of variance approach to examine how total variation in mean length at age was partitioned into lake-to-lake, coherent temporal, ephemeral temporal, trend, and residual variation. Using these estimated variance components, we simulated the effects of different variance structures on the power to detect trends in mean length at age. Of the 42 data sets examined, only four demonstrated significant regional (statewide) trends: age 4 largemouth bass (Micropterus salmoides) from Wisconsin lakes increased about 0.7 mm·year–1 in mean length at age, and ages 2, 3, and 4 walleye (Sander vitreus) from Wisconsin lakes decreased between 0.5 and 0.9 mm·year–1 in mean length at age. The structure of variation differed substantially among data sets, and these differences strongly affected the power to detect trends. Of particular note was that even modest levels of coherent temporal variation led to substantial decreases in power for detecting trends. To maximize trend detection capabilities, fisheries management agencies should consider variance structures prior to choosing indices for monitoring and realize that trend detection capabilities are species- and region-specific.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".