Going underground : patterns of fine-root and mycorrhizal fungal trait variation across a biogeographic gradient in western Canada
Bibliographic record
Abstract
Understanding fine-root adjustments to the environment and identifying factors that shape mycorrhizal fungal communities is a prerequisite for predicting the response and feedbacks of plants to global changes. As a consequence, trait-based plant ecology, which has mostly focused on above-ground traits, is increasingly placing the emphasis below-ground. To improve our functional understanding of fine roots, we first quantified root morphological, chemical and architectural trait variation in interior Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco) forests across a biogeographic gradient in Western Canada. We found substantial within-population root trait variation, which may enable acclimation of trees to future environmental conditions. Yet, we also identified moderate but consistent trait-environment linkages across populations of Douglas-fir. We provided evidence for decoupled variation in fine-root morphological and chemical traits. Our results highlight the existence of multiple axes of within-species fine-root adjustments that were consistent with a potential increase in fine-root acquisitive capacity with environmental limitations. Next, to better integrate mycorrhizal symbiosis into trait-based plant ecology, we combined trait measurements of fine roots and ectomycorrhizal fungi with next-generation sequencing. We found temperature, precipitation and soil C:N ratio affected ectomycorrhizal community similarities and exploration type abundance but had no effect on fungal richness and diversity. We did not provide evidence for a functional connection between root traits and fungal exploration types within Douglas-fir populations. Our study clarifies ectomycorrhizal taxonomic and functional responses to environmental factors but warrants further research to broaden root trait frameworks and evaluate the role of mycorrhizal fungi in mediating ecosystem responses to environmental changes. This line of inquiry will be particularly important to better manage existing forests and to ensure that well-adapted forest tree populations are regenerated in the future.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".