Performance of Two Southern California Benthic Community Condition Indices Using Species Abundance and Presence-Only Data: Relevance to DNA Barcoding
Bibliographic record
Abstract
DNA barcoding, as it is currently employed, enhances use of marine benthic macrofauna as environmental condition indicators by improving the speed and accuracy of the underlying taxonomic identifications. The next generation of barcoding applications, processing bulk environmental samples, will likely only provide presence information. However, macrofauna indices presently used to interpret these data are based on species abundances. To assess the importance of this difference, we evaluated the performance of the Southern California Benthic Response Index (BRI) and the AZTI Marine Biotic Index (AMBI) when species abundance data were removed from their calculation. Presence only versions of these two indices were created by eliminating abundance weighting while preserving species identity. Associations between the presence and abundance BRI, and the presence and abundance AMBI were highly significant, with correlation coefficients of 0.99 and 0.81, respectively. The presence versions validated almost equally to the abundance-based indices when applied to the spatial and the temporal monitoring data used to validate the original indices. Simulations in which taxa were systematically removed from calculation of the indices were also conducted to assess how large the barcode library must be for the indices to be effective. Correlation between the BRI-P and BRI remained above 0.9 with only 370 species in the library and reducing the number of species to 450 had almost no effect on correlation between the presence and abundance versions of the AMBI.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".