Kin discrimination causes plastic responses in floral and clonal allocation
Bibliographic record
Abstract
The composition of a plant’s neighbourhood shapes its competitive interactions. Neighbours may be related individuals due to limited seed dispersal or clonal growth, so that the ability to recognize and respond to the presence of kin is beneficial. Here, we ask whether plants plastically adjust their floral and clonal allocation in response to their neighbour’s identity. In a species that reproduces both sexually and clonally, we test the following predictions in response to neighbouring kin: (i) a reduction in floral display will occur to minimize costly floral structures and pollinator competition, as well as to mitigate inbreeding; and (ii) a decrease in clonality will occur to minimize resource competition and overcrowding among kin. We grew focal individuals of Mimulus guttatus (syn. Erythranthe guttata ) surrounded by neighbours of varying relatedness (non-kin, outcross siblings or self siblings) and measured a suite of vegetative, floral and clonal traits. Consistent with our predictions, focal plants reduced floral and clonal allocation in the presence of kin. Moreover, focal plants increased their floral and clonal allocation when surrounded by non-kin neighbours that were high-performing. Together, we demonstrate a clear and predictable response to kin, which has general implications for the structure and function of plant neighbourhoods.
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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 | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".