Spatial patterns of plant diversity below‐ground as revealed by DNA barcoding
Bibliographic record
Abstract
Our understanding of the spatial organization of root diversity in plant communities and of the mechanisms of community assembly has been limited by our ability to identify plants based on root tissue, especially in diverse communities. Here, we test the effectiveness of the plastid gene rbcL, a core plant DNA barcoding marker, for investigating spatial patterns of root diversity, and relate observed patterns to above-ground community structure. We collected 3800 root fragments from four randomly positioned, 1-m-deep soil profiles (two vertical transects per plot), located in an old-field community in southern Ontario, Canada, and extracted and sequenced DNA from 1531 subsampled fragments. We identified species by comparing sequences with a DNA barcode reference library developed previously for the local flora. Nearly 85% of sampled root fragments were successfully sequenced and identified as belonging to 29 plant species or species groups. Root abundance and species richness varied in horizontal space and were negatively correlated with soil depth. The relative abundance of taxa below-ground was correlated with their frequency above-ground (r = 0.73, P = 0.0001), but several species detected in root tissue were not observed in above-ground quadrats. Multivariate analyses indicated that diversity was highly structured below-ground, and associated with depth, root morphology, soil chemistry and soil texture, whereas little structure was evident above-ground. Furthermore, analyses of species co-occurrence indicates strong species segregation overall but random co-occurrence among confamilials. Our results provide insights into the role of environmental filtering and competitive interactions in the organization of plant diversity below-ground, and also demonstrate the utility of barcoding for the identification of plant roots.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".