Effects of growing conditions and source habitat on plant traits and functional group definition
Bibliographic record
Abstract
Summary Plant functional groups are used to describe patterns of community organization. However, they are defined either by suites of correlated traits or by species groupings, and the responses of these two definitions to changing environmental conditions are unknown. We assessed 14 growth and morphological traits under low‐ and high‐resource conditions of 42 annual plant species from two source communities in Israel that differed in resource availability. As current theory predicts, plants growing in the high‐resource treatment were larger, had twofold greater relative growth rate (RGR) and thinner leaves, and allocated less biomass to roots than plants grown in the low‐resource treatment. Differences in these traits were less consistent between the two source communities. Instead, taxonomic groups (grasses, legumes and a group of other forbs), regardless of source, differed in most characteristics. Three general groups of species (functional groups) were identified in both resource treatments using cluster analysis on all 14 traits. In both resource treatments monocots were almost completely separated into one distinct cluster, regardless of source habitat, while the two other, mainly dicot, clusters were partially separated by habitat. However, the species composition and trait characterization of the dicot clusters differed strongly between treatments. Under low‐resource conditions the two dicot clusters were separated by size traits and seed mass, but under high‐resource conditions, they were separated by above‐ground size, morphology and RGR. Principal components analysis demonstrated inconsistency in relationships among traits and species groupings between treatments. The first two principal components emphasized different aspects of growth depending on the treatment; the third axis was defined by growth rates. As with the cluster analysis, plots of species scores revealed relatively little separation of species by habitat. The response of each species varies for different traits and with growing conditions. Variation may differ among species within a functional group, producing different definitions of functional groups under different experimental conditions. Because most functional group analyses are performed on data collected without manipulation of growing conditions, conclusions concerning the response of species or communities to changes in environmental conditions may be problematic.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".