Data from: Phylogenetic relatedness, phenotypic similarity, and plant-soil feedbacks
Bibliographic record
Abstract
Plant–soil feedbacks contribute to species invasions, the maintenance of biodiversity and climate change impacts on terrestrial ecosystems. Despite their far-reaching importance, we lack a general understanding of the ecological and evolutionary determinants of plant–soil feedbacks. We conducted a large-scale plant–soil feedback experiment using 49 co-occurring plant species from southern Ontario, Canada, representing a wide phylogenetic range. We tested whether the effects of soil conditioning vary among these species and whether different focal species respond similarly to the same soil conditioning. Next, we investigated whether plant traits and soil feedbacks depend on phylogenetic similarity and which plant traits affect plant–soil feedbacks between pairs of plant species. Finally, we used our experimental results to test whether soil feedbacks affect co-occurrence of species in the field. We found evidence of both strong positive and negative soil feedbacks between pairs of plant species. Our soil-conditioning treatment explained nearly 20% of the variation in focal species performance. Phylogenetic relatedness and phenotypic similarity between plant species were unrelated to the strength of their soil feedback. However, numerous plant traits of the conditioning species influenced the strength of soil feedbacks on focal species, including specific leaf area and total above-ground productivity. Trait differences between species were also predictive of plant–soil feedbacks, though for some pairs of species, increased trait differences were associated with positive plant–soil feedbacks and for others, trait differences were associated with negative plant–soil feedbacks. Plant species co-occurrence in the field was related to their experimentally determined soil feedbacks but only for particular plant species. Synthesis. Our results illustrate how evolutionary history and phenotypic variation shape plant–soil feedbacks and highlight the need for trait-based studies. Due to the unique evolutionary history of individual traits and the variability in their importance across all possible interacting species, we show that indices of overall phenotypic and phylogenetic relatedness are poor predictors of plant–soil feedbacks at large phylogenetic scales. We conclude that a detailed trait-based approach can be used to predict plant–soil feedbacks, and laboratory measures of soil feedbacks can explain patterns of co-occurrence in nature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".