Interpreting the ‘selection effect’ of biodiversity on ecosystem function
Bibliographic record
Abstract
Abstract Experimental ecosystems often function differently than expected under the null hypothesis that intra‐ and interspecific interactions are identical. Recent theory attributes this to the ‘selection effect’ (dominance by species with particular traits), and the ‘complementarity effect’ (niche differentiation and/or facilitative interactions). Using the Price Equation, I show that the ‘selection effect’ only partially reflects dominance by species with particular traits at the expense of other species, and therefore is only partially analogous to natural selection. I then derive a new, tripartite partition of the difference between observed and expected ecosystem function. The ‘dominance effect’ is analogous to natural selection. ‘Trait‐independent complementarity’ occurs when species function better than expected, independent of their traits and not at the expense of other species. ‘Trait‐dependent complementarity’ occurs when species with particular traits function better than expected, but not at the expense of other species. I illustrate the application of this new partition using experimental data.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".