Bibliographic record
Abstract
Abstract A number of metrics for quantifying the amount of functional redundancy in a community have been proposed over the years. Two of the most popular metrics are based on comparing a taxonomic diversity measure with a generalized form of the same measure that accounts for functional dissimilarities between taxa. These two metrics express redundancy as either an absolute or relative difference between the taxonomic diversity measure and its generalized form. Because they express the amount of redundancy in a community in terms of raw diversity values, both redundancy metrics are susceptible to the same issues that complicate the interpretation of most commonly used diversity indices. It is possible to overcome these issues by restating these two indices using a Hill numbers framework. As a growing number of authors have noted, these modified metrics provide a more intuitive quantitative definition of functional redundancy when used to rank communities. Beyond this intuitive definition, measuring redundancy in terms of Hill numbers allows researchers to control the influence of rare taxa on the output value, enabling ecologists to better predict how a community is expected to respond when exposed to an external perturbation that selectively eliminates rare or common taxa. Here I show that, of the two possible Hill number‐based redundancy metrics, the form based on a popular absolute redundancy metric is extremely sensitive to differences in taxonomic diversity and can provide a misleading picture of how much redundancy is present in a community. For this reason, I argue that Hill number‐based functional redundancy should be quantified using a relative metric that explicitly accounts for differences in effective taxonomic diversity. The proposed Hill number‐based relative redundancy measure is shown to provide a much more complete picture of the distribution of redundant taxa within a community, highlighting subtle patterns that are completely missed by the Hill number‐based absolute redundancy metric. I include open‐source R code for calculating the indices discussed here. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".