Average functional distinctness as a measure of the composition of assemblages
Bibliographic record
Abstract
Abstract Somerfield, P. J., Clarke, K. R., Warwick, R. M., and Dulvy, N. K. 2008. Average functional distinctness as a measure of the composition of assemblages. – ICES Journal of Marine Science, 65: 1462–1468. Indices are used to quantify change in the environment by reducing aspects of environmental complexity to numbers. Biodiversity indices are typically calculated using the numbers of species and their relative abundances. A recent advance has been the development of additional measures of diversity, such as phylogenetic diversity, based on relationships between organisms. The emerging paradigms of the importance of biodiversity to ecosystem services and the ecosystem approach to fishery management could be well served by the development of indicators of ecosystem functioning. We discuss how relatedness measures may be adapted to quantify aspects of community structure of relevance to ecosystem functioning, by combining information on species’ occurrence, life history, and ecological traits. We present an index that reflects average functional distinctness within assemblages. We illustrate the approach using North Sea fish. Results reveal that average functional distinctness is not independent of taxonomic distinctness. This is expected, but the weakness of the relationship suggests that both indices may prove useful, because they are not constrained to convey the same information about samples. Both indices are shown to be weakly related to species richness, which was not expected. This is a consequence of differences in the frequencies of occurrence among species.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".