Does species‐level resolution matter? Taxonomic sufficiency in terrestrial arthropod biodiversity studies
Bibliographic record
Abstract
Abstract Taxonomic sufficiency, or the suitability of substituting higher level taxonomic designations as response variables in community ecology analyses, is important in biodiversity studies from practical and fundamental perspectives. While there are many studies of taxonomic sufficiency in aquatic systems, there are few studies with terrestrial arthropods that examine the effects of taxonomic resolution on the interpretation of multivariate community data. We analysed data sets from three major arthropod orders ( A raneae, C oleoptera, and L epidoptera) using multivariate methods to determine whether altering the level of taxonomic resolution (species, genus, or family) affected patterns in community composition and beta diversity under various forest disturbance treatments. Overall patterns of community composition and beta diversity did not differ across taxonomic levels; however, patterns in group structure and significance of treatment effects were often stronger at species and/or genus level. The similarity between the outcomes of multivariate analyses at different levels of taxonomic resolution was related to within‐group taxonomic ratios; results were less consistent across levels of taxonomic resolution in groups with higher taxonomic ratios (i.e. more species per genus). We conclude that higher levels of taxonomic resolution will be sufficient for detecting the impacts of disturbance in lineages of terrestrial arthropods with higher levels of phylogenetic constraint, although this does not negate the necessity and importance of species‐level identifications in situations with sufficient resources and where study questions demand alpha taxonomy.
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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 | 0.088 | 0.226 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".