An assessment of two taxonomic distinctness indices for detecting seaweed assemblage responses to environmental stress
Bibliographic record
Abstract
We tested the efficacy of two biodiversity indices, average taxonomic distinctness and variation in average taxonomic distinctness, for indicating environmental stress in seaweed assemblages from the Bay of Fundy, New Brunswick, Canada. These indices, which measure the average number of taxonomic levels between species in a sample, offer a potential panacea for biomonitoring because their calculation requires only a species list and a regional taxonomic hierarchy, they offer a statistical framework for testing whether assemblages deviate from an expected taxonomic breadth, and previous studies involving animal assemblages have demonstrated an independence from sampling effort. However, our results were not consistent with previously published studies or with our perception of site conditions. Specifically, putatively impacted sites scored above-average taxonomic distinctness values, while sites otherwise regarded as healthy were indicated as environmentally degraded. We also demonstrate that average taxonomic distinctness values can be negatively correlated with species richness, Shannon diversity and with functional diversity. Further, increasing the breadth of the regional species list to which specific sites were compared resulted in a more conservative test of impact. We recommend that a qualitative understanding of how specific biotic assemblages respond to stress is a necessary prerequisite to use the taxonomic distinctness indices for environmental stress assessments.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".