Projecting the futures of plant traits across habitats in Central Europe
Bibliographic record
Abstract
ABSTRACT Many plant traits covary with environmental gradients, reflecting shifts in adaptive strategies under changing conditions and thus providing information about potential consequences of future environmental change for vegetation and ecosystem functioning. Despite extensive efforts to map trait–environment relationships, the evidence remains heterogeneous and often conflicting, partially because of insufficient consideration of distinct trait syndromes for certain growth forms and habitats. Moreover, it is unclear whether traits of non-native and native plant taxa respond similarly to environmental gradients, limiting our ability to assess the consequences of future plant invasions. Here, using comprehensive data for Germany and the Czech Republic and a Bayesian multilevel modeling framework, we assessed relationships between three major plant traits (maximum height, H max ; specific leaf area, SLA ; and seed mass, SM ) and environmental factors (7 climate variables and percentage of urban land cover) for native and non-native woody and herbaceous plant assemblages across six broad habitat types. We projected the trait change in these assemblages under future environmental change scenarios until 2081–2100 and quantified the change in trait difference between native and non-native plants. Our models depicted multiple trait–environment relationships, with several important differences attributed to biogeographical status and woodiness within and across habitat types. The overall magnitude of trait change is projected to be greater for non-native than native taxa and to increase under more extreme scenarios. Native woody plant assemblages may generally experience an increase across all three traits, whereas woody non-natives may decline in H max and increase in SLA and SM . Herbaceous H max is expected to increase and SLA to decrease in most habitats. The obtained trait projections highlight the conditions under which non-native plants may prevail over natives and vice versa and can serve as a starting point for projecting future changes in ecosystem functions and services.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".