Molecular Identification of Boreal Forest Roots: An Expansion of Techniques and Investigation of Limitations and Biases
Bibliographic record
Abstract
Plant identification is a fundamental ecological tool. While identifying flowers and leaves is relatively straightforward, identifying roots can be difficult. Here, I expand the use of fluorescent amplified fragment length polymorphisms (FAFLPs) as a tool to identify roots. Using this molecular tool, I examine possible limitations of identifying a large set of boreal plant species and compare the utility of FAFLPs to DNA barcoding. In addition, I address some challenges specific to belowground detection of roots, namely, the influence of species and root size class. To identify roots, fragment lengths of three non-coding cpDNA regions, the trnT-trnL intergenic spacer, trnL intron, and trnL-trnF intergenic spacer, were resolved using capillary electrophoresis for 194 plant species common to the Canadian boreal forest. To determine whether DNA sequencing increases successful identification of closely related species, Sanger sequencing of the trnL intron of a subset of 24 species across nine genera was compared to FAFLPs. FAFLPs produced unique size profiles for 74% of species using all three cpDNA regions. In contrast, only 27 species (14%) could be identified using the relatively conserved trnL intron alone. DNA sequencing did not increase detection success: eight (33%) species could be distinguished by sequences of the trnL region, nine (38%) by fragment lengths of the same region. Fifteen (63%) congeneric species could be distinguished by FAFLPs of all three regions. Fine roots yielded higher DNA concentrations as well as higher DNA purity than larger root classes. Fine roots of the grass species Poa pratensis, produced the highest yield and quality of DNA. This suggests that false-positives in belowground assays of roots may most likely to occur from fine roots of specific species. Overall, I found that molecular tools can be effective in identifying roots, but FAFLPs and DNA sequencing have strengths and limitations, and more assumptions of the methods presented here need to be tested before accurate multiplexing of roots from large species pools can occur.
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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 | 0.001 |
| 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".