An ideal free distribution explains the root production of plants that do not engage in a tragedy of the commons game
Bibliographic record
Abstract
Summary Below‐ground competition is intense and may dramatically reduce plant performance. However, there is still no consensus on the best strategies plants should use to maximize competitive ability in soil. Some suggest plants should grow roots according to nutrient availability, while others suggest plants should grow roots taking into account both nutrients and neighbours. Unambiguously, testing between these two alternative hypotheses has been challenging. This manuscript had three objectives. First, we presented a model of root growth under competition that is based on an ideal free distribution ( IFD ). Secondly, we develop the concept of the best response curve as a tool for clearer experimental tests of plant responses to neighbours. Thirdly, we test these ideas by growing fast cycling Brassica rapa either alone or with neighbours to examine this species' root growth strategy both alone and under competition. We hypothesize that those plants with no direct response to neighbours should grow roots according to an IFD . This means that if plants produce x roots in a soil volume of quality R, they should produce x / n roots in a soil volume of quality R / n . The experimental data were consistent with this prediction. Growing plants with neighbours was statistically identical to growing them with half as many nutrients. Thus, the only effect of neighbours was to reduce nutrient availability. This model provides an alternative to previous game theoretic models and suggests an experimental protocol based on the concept of the best response curve. We hope that testing the game theoretic model with a clear alternative model will guide experiment and debate. Synthesis . The data in the literature are mixed, with species sometimes responding to nutrients only, and sometimes responding interactively to both nutrients and neighbours. At present, we lack a general understanding of the causes or consequences of this diversity of strategies. We suggest that a greater understanding of trade‐offs among traits that are important for other biotic interactions (above‐ground competition, enemy defence, mutualisms) will lead to a greater understanding of why some species over‐proliferate roots when in competition but other species do not.
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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 | 0.000 |
| 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".