On strategies of plant behaviour: Evolutionary games of habitat selection, defence, and foraging
Bibliographic record
Abstract
Background: Strategies of habitat selection, defence, and foraging depend critically on population density and the frequency of alternative strategies: they are evolutionary games. Although commonly modelled in studies of animal behaviour, they are less frequently used to provide insights into the behaviour of plants. A 'review' and analysis of how these universal strategies apply to plants should help motivate further development of plant evolutionary games. Questions: Should plants practise density-dependent habitat selection? Do games of plant defence depend on demography and habitat quality? How similar are games of competition for nutrients and light? Methods and models: Assessments of eco-evolutionary dynamics with computer simulations (habitat selection), evolutionary invasion analysis (defence), and G-functions (foraging). Results: Selection gradients for pre-emptive habitat selection are steeper than those for passive dispersal and yield an advantage that increases with population density. The evolutionarily stable defence level in a homogeneous environment is proportional to the ratio of survival by mature versus immature plants. In heterogeneous environments, investments in herbivore defence depend on habitat quality and are resolved by habitat selection. Games of competition for both nutrients and light predict Tragedies of the Commons in which size, density, and investment in tissue that does not directly contribute to fitness depend on the source of competition. Conclusion: Much of the dynamic feedback between the ecology of plants and their evolution can be understood with models of three universal processes: habitat selection, safety and defence against enemies, and foraging for nutrients and other resources. The specifics of models may differ among taxa, but not the underlying density and frequency dependence of their eco-evolutionary strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".