Bibliographic record
Abstract
Neighbouring plants can interact strongly, competing for resources including light, water, animal mutualists, and local germination sites. From an evolutionary perspective, this implies that a plant’s best resource acquisition strategy will usually depend on the traits of its neighbours, and for plants in particular, neighbours are often genealogical relatives. Here, I use a combination of theory and experiments to expose some important consequences of social interactions among plants. The first model analyzes selection on traits used to attract pollinators, showing that competitive interactions (in the absence of local relatedness) can select for exaggerated secondary sexual characters. To complement this model, I performed experiments that confirm the mechanisms by which adaptive pollinator foraging naturally leads to interactions among plants. The observed foraging behaviour (of bumble bees) also provides unique evidence for ‘Bayesian foraging’, a sophisticated type of resource assessment that depends on prior experience in a particular environment. A second model considers how selection on the sex allocation of cosexual, animal-dispersed plants leads to competition and cooperation over local germination sites, sometimes leading to the origin of gender dimorphism. The model reveals novel ecological contexts in which disruptive selection on sex allocation can arise, and in general, illustrates how selection for cooperation can facilitate or inhibit evolutionary diversification. In the models considered here, cooperation is indiscriminant, but plants might also assess the relatedness of neighbours and cooperate with kin over non-kin. In the final chapter, I present experimental evidence that is consistent with preferential cooperation over soil resources among sibling plants. This study is the first to link a potentially cooperative resource allocation strategy with an increase in the mean fitness of related plants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".