Applicability and Interpretation of Fish Indices of Biotic Integrity (IBI) for Bioassessment in the Upper Midwest
Bibliographic record
Abstract
Abstract Multiple fish-based indices of biotic integrity (IBIs) and biological condition gradient models have been developed and validated to assess ecological integrity in the Laurentian Great Lakes region. We evaluated the applicability and effectiveness of using fish community indices for assessing site integrity in central Great Lakes streams, which have diverse temperature regimes and can be classified as warmwater, coolwater, or coldwater. Sites with different thermal regimes require different assessment tools to ensure comparability. Streams in the Big Manistee River watershed, Michigan, are near thermal thresholds for classification as coolwater or coldwater. We evaluated two coolwater and three coldwater indices developed for Upper Midwest streams. Output from coolwater indices were not correlated with coldwater index outputs and did not discriminate among the stream systems we evaluated. In monitoring temporal patterns over time (2002–2010), we found that coldwater indices showed similar patterns and agreed in relative scoring of sites from high to low. The three coldwater indices also similarly discriminated among stream systems; however, when the coldwater indices were used for specific site assessments, they produced differential results. Depending on which index was applied, a single site could be classified into three different levels of quality. This highlights the importance of index selection for management actions. An understanding of the factors that drive the indices and an understanding of reference conditions are imperative for effective use of fish-based IBIs in the Upper Midwest. Received July 15, 2014; accepted December 12, 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".