Comparing functional diversity in traits and demography of <scp>C</scp>entral <scp>E</scp>uropean vegetation
Bibliographic record
Abstract
Abstract Question A major obstacle to understanding non‐random patterns in plant traits (over‐dispersion or under‐dispersion) has been our limited knowledge of trait–demography relationships for large sets of species. Here, we suggest that some of the needed data on demographic processes can be gathered from growth records on plants in botanical gardens. We examine within‐community patterns in demographic responses determined from such growth records, and ask whether they are different from patterns in plant traits. Location Czech Republic. Methods We assembled data on seed and vegetative reproduction for ca. 1000 Central European species from the Botanical Garden of Charles University in Prague. We used these data as estimates of potential vegetative and seed reproduction of individual species under favourable conditions. We linked these data with co‐occurrence data from the Czech National Phytosociological Database and with data on major species traits. We examined dispersion of both species traits and garden reproduction using randomization tests on the data set as a whole and on the data stratified using EUNIS classification into seven or 32 habitat types. Results The patterns found for species traits and for garden reproduction are similar, with strong under‐dispersion for the data set as a whole and diminishing under‐dispersion in subsets of the data. Under‐dispersion was much stronger for traits than for garden reproduction. No over‐dispersion was detected in either trait or garden reproduction data. Conclusions The major source of the pattern in the data is environmental filtering. Stronger filtering for traits indicates that the linkage between environment and traits is much tighter than that between environment and demography. In ecologically homogeneous communities, reproduction parameters are closer than trait values to a random distribution, indicating that co‐existence of species are not limited by either similarities or differences in their demography. These findings show that trait dispersion need not be directly related to species demography and, more generally, that correct identification of trait–demography relationships is necessary for better understanding patterns of trait dispersion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".