Taxonomic sufficiency: The influence of taxonomic resolution on freshwater bioassessments using benthic macroinvertebrates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Changing the taxonomic scale of a biotic-assemblage dataset influences our ability to detect ecological patterns. In bioassessments, a test-site’s biological community is compared against a benchmark to indicate ecosystem condition, but the taxonomic resolution needed to judge impairment reliably is the subject of much scientific debate. This paper reviews taxonomic sufficiency for freshwater benthic-macroinvertebrate bioassessments. Three main issues are discussed: (1) the ecological significance of different taxonomic aggregations; (2) trade-offs involving taxonomic detail and information content versus money, time, expertise, and data quality; and (3) sampling- and analytical-method-specific factors affecting taxonomic sufficiency. Although Species should be the default taxonomic level for bioassessments, taxonomic sufficiency is chiefly determined by a study’s purpose, and pragmatism often dictates reduced detail. When a taxonomic-minimalism approach is necessary, a quantitative criterion for taxonomic sufficiency should be specified; this criterion should be based on an optimization of cost-benefit trade-offs associated with different taxonomic scales. Mixed-level aggregations, as well as morpho-species and ecological-trait classifications should be considered in this optimization process. Looking to the future, closer ties between taxonomists and bioassessment researchers would benefit both of their disciplines. Such coordination would provide the autoecological information and better diagnostic tools (such as keys and molecular methods) needed for biomonitoring, and better (and more widespread) biomonitoring would fuel taxonomy’s resurgence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.004 |
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 it