A simulation tool to scrutinise the behaviour of functional diversity metrics
Bibliographic record
Abstract
Abstract Many indices have been proposed to measure functional diversity and its four distinct dimensions: functional richness, evenness, divergence and redundancy. Identifying indices that reliably measure the functional diversity dimension(s) of interest requires careful testing of how each index responds to species' traits and abundance distributions. In the absence of a convenient simulation tool, tests with artificial data have to date explored only a limited number of scenarios or have altered trait and abundance distributions only indirectly based on principles of evolution and community assembly. We provide simul.comms , an R function that allows users to test the efficacy of functional diversity indices by easily creating artificial species communities with user‐specified abundance and trait distributions for continuous, ordinal and categorical traits. To illustrate the function's utility, we examine the performance of R , a recently published abundance‐sensitive index for functional redundancy. We use two approaches to designing simulation tests for this example analysis. The first uses simul.comms to create six separate sets of artificial communities to qualitatively assess how R responds to predictable changes in functional redundancy. The second uses simul.comms to independently alter seven community composition parameters, whose influence on R is then examined quantitatively via effect sizes in linear regression. Our analyses indicate that R broadly mirrors expected changes in functional redundancy and predictably responds to changes in community composition parameters. R appears, however, to primarily reflect trait distributions, responding minimally to variance in abundance and counter‐intuitively to abundance range. Further refinement of tools to measure functional redundancy may therefore be desirable. The R tool we provide should assist with refining functional diversity measures, a critical step towards improving our ability to understand and mitigate the impacts of biodiversity loss on ecosystem functioning. Because simul.comms simply produces two linked matrices, a species‐by‐traits matrix and a site‐by‐species abundance matrix, it may be equally valuable in exploring questions and analytical approaches in other areas of community ecology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".