Boreal Forest Multifunctionality Is Promoted by Low Soil Organic Matter Content and High Regional Bacterial Biodiversity in Northeastern Canada
Bibliographic record
Abstract
Boreal forests provide important ecosystem services, most notably being the mitigation of increasing atmospheric CO2 emissions. Microbial biodiversity, particularly the local diversity of fungi, has been shown to promote multiple functions of the boreal forests of Northeastern China. However, this microbial biodiversity-multifunctionality relationship has yet to be explored in Northeastern Canada, where historical environment have shaped a different regional pool of microbial diversity. This study focuses on the relationship between the soil microbiome and ecosystem multifunctionality, as well as the influence of pH and redox potential (Eh) on the regulation of such relationship. Structural equation modelling (SEM) was used to explore the different causal relationships existing in the studied ecosystems. In a managed part of the Canadian boreal forest, 156 forest polygons were sampled to (1) estimate the α- and β-diversity of fungal and bacterial communities and (2) measure 12 ecosystem functions mainly related to soil nutrient storage and cycling. Both bacteria and fungi influenced ecosystem multifunctionality, but on their own respective functions. Bacterial β-diversity was the most important factor increasing primary productivity and soil microbial biomass, while reducing soil emitted atmospheric CO2. Environmental characteristics, particularly low levels of organic matter in soil, were shown to have the strongest positive impact on boreal ecosystem multifunctionality. Overall, our results were consistent with those obtained in Northeastern China; however, some differences need to be further explored especially considering the history of forest management in Northeastern Canada.
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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 | 0.000 |
| 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".