Using intra‐individual variation in shrub architecture to explain population cover
Bibliographic record
Abstract
Plant architecture is related to the performance of long‐lived plants; its role in promoting species coexistence and in successional patterns is now widely recognized. However, because plant architecture involves branching processes, it is highly variable at the intra‐specific level. In this paper, we address two questions: what is the best way to describe plant architecture to obtain meaningful information for explaining population cover: at the whole‐plant level, or at the level of its unitary constituent parts? Further, are there architectural designs related to populations’ success? We evaluated the relative impact of ontogeny and whole‐plant traits on the cover achieved by the populations of five shrub species developing on 25 abandoned farmlands in southwestern Québec (Canada). We compared four ways of analyzing plant architecture: 1–2) using morphological traits described at the scale of a module (an elementary architectural unit made up of all the different types of shoots), with or without taking into account the ontogeny of the whole organism, 3) using the rate of changes during ontogeny as traits, and 4) using whole‐plant traits describing branching processes at a scale larger than modules. We then used variation partitioning to discriminate the actual effects of these traits on percent cover of the species from hidden effects due to plant ontogenesis and population spatial structure. Our results suggest that the predominant variables that effectively describe population cover vary from one species to another. At the same time, whole‐plant architectural traits and the rate of change of morphological traits during ontogeny both have an important effect on population cover. These findings suggest that acknowledging the developmental pattern of woody species can clarify the impact of intra‐specific trait variation on population cover.
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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 | 0.000 |
| 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 | 0.000 |
| 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".