Kin recognition, multilevel selection and altruism in crop sustainability
Bibliographic record
Abstract
Summary Intraspecific competition among crop plants is undesirable. Less competitive crops are predicted to increase yield and decrease the need for added resources. Wild plants demonstrate the ability to recognize kin and potentially help their relatives by reducing their competitive behaviours, a form of altruism. Altruism can also evolve through multilevel selection. Are these processes relevant to sustainable agriculture? Crops do grow predictably with kin. However, their evolution is more strongly dictated by artificial selection (crop breeding), which incorporates individual and group selection, making multilevel selection more relevant than kin selection in favouring altruism. While current crop breeding protocols attempt to target the reduction of competitive traits, early mass selection may have the opposite effect. We predict that kin recognition itself is not relevant to crops, because of the consistently high relatedness within crop stands. Nonetheless, crops have shown cultivar and kin recognition. We argue that these responses cannot be assumed to demonstrate altruism, as current breeding practices offer little opportunity for kin selection. Synthesis . There is the opportunity to favour altruism through artificial breeding. Here we suggest how crop breeding protocols could be changed to favour cooperation by increasing group selection during early breeding.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".