Riparian forests of <scp>S</scp>outhwest <scp>E</scp>urope: are functional trait and species composition assemblages constrained by environment?
Bibliographic record
Abstract
Abstract Questions What are the species and functional trait composition of riparian forests in near‐natural Southwest European rivers? Are functional trait and species assemblages constrained by environment? Location Near‐natural riparian habitats throughout mainland Portugal, Southwest Europe. Methods We collected data on riparian woody abundances and environmental variables from 175 river locations. Twenty‐eight key functional traits were assigned to the surveyed species. Hierarchical clustering and indicator species analysis were used to derive compositional and functional groups of sites. We used Mantel tests to relate species and trait abundances and environmental gradients, and identified sets of relevant variables at diverse spatial scales. Then, four Dispersion indicators were developed to assess the extent to which the groups are constrained by the environment. These measures were tested for significance using iteration procedures. Results Clustering revealed four compositional groups of sites: Alder woods, Ash woods, Tree–heath shrublands and Semi‐arid shrublands; and three functional groups: Mixed riparian forests, Shrublands with fleshy fruits and Stress‐tolerant shrublands. These groups were primarily defined by broad‐scale gradients of climate, elevation and river hierarchy. The most dispersed groups had broad trait composition (e.g. Mixed riparian forests) and were floristically diverse (Alder woods). In general, the compositional groups were more closely related to environmental gradients than the functional groups. Conclusions Assemblages with very specific functional traits or with homogeneous species composition were largely constrained to the most extreme environmental conditions in the study area. Future research to assist environmental management should be directed to the role of fine‐scale environmental determinants of riparian assemblages, especially those related to flow variability and water stress.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".