The tiny mayfly in the room: implications of size-dependent invertebrate taxonomic identification for biomonitoring data properties
Bibliographic record
Abstract
The appropriate level of taxonomic identification, taxonomic sufficiency, for biomonitoring purposes continues to be controversial. Taxonomic sufficiency, however, fails to address the bias created by size-dependent taxonomic identification, which can result in coarse-resolution identification for immature specimens lacking distinguishing characteristics. Our study provides a direct test for this potential systematic bias in biomonitoring data by examining two morphological traits: body size and shape of key organisms (Ephemeroptera, Plecoptera, Trichoptera and Odonata) collected from standard aquatic biomonitoring samples. Direct measurement of body size and a geometric morphometric description of body shape provide consistent, quantitative variables to describe the composition of specimens identified at different levels of taxonomic resolution (genus or family). Corroborating our expectations, we observed evidence of systematic size bias in family-level identifications. Specimens that could only reliably be identified to the family level were significantly smaller than specimens identified to the genus level. Qualitative comparisons of shape variation between specimens demonstrated a high degree of variation in specimens identified only at the family level and support the conclusion that specimens identified at the family level possess multiple constituent taxa (genera or species). Thus, size-dependent taxonomy can have negative consequences for the accurate determination of biodiversity and may invalidate common biomonitoring metrics. Improvements to biomonitoring protocols through technological advances, including DNA-based taxonomy to augment specimen identification, should effectively remove the size-bias problem in the long term. In the short-term, recognizing instances of size bias, the degree to which it may impact bioassessment and exploring methods for remediation, including traits-based assessments, can enhance data quality and inferences derived from biomonitoring studies.
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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 | 0.022 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".