Introduction to the Special Issue: Beyond traits: integrating behaviour into plant ecology and biology
Bibliographic record
Abstract
The way that plants are conceptualized in the context of ecological understanding is changing. In one direction, a reductionist school is pulling plants apart into a list of measured 'traits', from which ecological function and outcomes of species interactions may be inferred. This special issue offers an alternative, and more holistic, view: that the ecological functions performed by a plant will be a consequence not only of their complement of traits but also of the ways in which their component parts are used in response to environmental and social conditions. This is the realm of behavioural ecology, a field that has greatly advanced our understanding of animal biology, ecology and evolution. Included in this special issue are 10 articles focussing not on the tried and true metaphor that plant growth is similar to animal movement, but instead on how application of principles from animal behaviour can improve our ability to understand plant biology and ecology. The goals are not to draw false parallels, nor to anthropomorphize plant biology, but instead to demonstrate how existing and robust theory based on fundamental principles can provide novel understanding for plants. Key to this approach is the recognition that behaviour and intelligence are not the same. Many organisms display complex behaviours despite a lack of cognition (as it is traditionally understood) or any hint of a nervous system. The applicability of behavioural concepts to plants is further enhanced with the realization that all organisms face the same harsh forces of natural selection in the context of finding resources, mates and coping with neighbours. As these ecological realities are often highly variable in space and time, it is not surprising that all organisms-even plants-exhibit complex behaviours to handle this variability. The articles included here address diverse topics in behavioural ecology, as applied to plants: general conceptual understanding, plant nutrient foraging, root-root interactions, and using and helping others. As a group, the articles in this special issue demonstrate how plant ecological understanding can be enhanced through incorporation of behavioural ideas and set the stage for future research in the emerging discipline of plant behavioural ecology.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.059 | 0.029 |
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".