To dendrogram or not? Consensus methods show that is the question needed to move functional diversity metrics forward
Bibliographic record
Abstract
Functional diversity indices have become important tools for measuring variation in species characteristics that are relevant for ecosystem services. A frequently used dendrogram-based method for measuring functional diversity, ‘FD’, was shown to be sensitive to methodological choices in its calculation, and consensus methods have been suggested as an improvement. The objective of this study was to determine whether consensus methods can be used to reduce sensitivity when measuring FD. To calculate FD, a distance measure and a clustering method must be chosen. Using data from three natural communities, this study demonstrates that consensus methods were unable to resolve even simple choices of distance measure (Euclidean and cosine) and clustering method (UPGMA, complete and single linkage). Overall, there was low consensus, ranging from 41–45%, across choices inherent in functional diversity. Further, regardless of how FD was measured, or how many species were removed from the community, FD closely mirrored species richness. Future research on the impact of methodological choices, including choices inherent in producing a dendrogram and the statistical complications they produce, are needed to move functional diversity metrics forward.
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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 | 0.031 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".