Phylogenetic and functional mechanisms of direct and indirect interactions among alien and native plants
Bibliographic record
Abstract
Summary Disentangling direct and indirect interactions among alien and native plants is essential to understanding the success of alien plants in multispecies communities, but studies have only focused on direct pairwise interactions. Moreover, it is also essential to explore phylogenetic and functional mechanisms driving these interactions. In a greenhouse experiment, we selected nine groups of alien and native plant species from the herbaceous flora of Germany to disentangle their direct and indirect interactions. Each group had an alien (A) that is common or rare in Germany (i.e. non‐native range), two natives that are phylogenetically closely related (Nclose) and distantly related (Ndist) to A respectively, and a distantly related “target” native (T). We grew the four species of each group alone, and in two‐species and three‐species combinations. Specifically, we tested whether competition is greater between A and Nclose than between A and Ndist, whether presence of Nclose rather than Ndist indirectly alleviates competition of A on T, and whether these interaction patterns depend on commonness of A. Moreover, we tested how intensity of these interactions is explained by phylogenetic distance, functional traits (height, seed mass, SLA, leaf size, specific root length, leaf area ratio, root length ratio (root length/plant mass), shoot weight ratio) and traits‐based functional distance. We found A had stronger competition on Nclose than on Ndist. In turn, A was more suppressed by Nclose than by Ndist, but this was only true for rare rather than common A. The presence of Ndist rather than Nclose indirectly reduced competition of A on T. The intensity of these interactions was not explained by phylogenetic or functional distance, but by some of the functional traits. Specifically, a plant experienced stronger competition when it was shorter and had smaller leaves and lower shoot weight ratio, and when its neighbours were taller, had greater SLA, leaf area ratio and shoot weight ratio, and had a lower root length ratio. Synthesis. Functional traits can help explain competitive interactions. While direct competition tended to be stronger between more closely related alien and native plants, this did not indirectly facilitate other co‐occurring native plants.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".