Rhizosphere microbial response to predicted vegetation shifts and changes in rhizodeposition in boreal forest soils
Bibliographic record
Abstract
The boreal forest is the single largest terrestrial store of carbon on Earth. In Canada’s boreal forest, approximately 23% of these carbon stocks are found in forest floors and 40% within mineral soils. The rhizosphere, soil under the direct influence of plant roots, is a hotspot for microbial activity and plays an important role in soil carbon dynamics. Rhizodeposits, which contain labile carbon substrates, may result in increased decomposition of soil organic matter: a phenomenon known as priming. With climate change, vegetation shifts are expected in the boreal, and deciduous dominated stands will replace conifers. Increased atmospheric CO2 with climate change can indirectly affect plant photosynthesis, resulting in increased carbon allocation belowground and increased root biomass. I investigated how these potential vegetation shifts and changes in rhizodeposition could affect: (1) the composition and function of microbial communities, and (2) rhizosphere priming in mineral soils common to the boreal. To assess the impact of vegetation shifts on microbial communities, I collected rhizosphere samples from the forest floor and compared them to bulk forest floor. Samples were collected at the Ecosystem Management Emulating Natural Disturbance (EMEND) project in northern Alberta, Canada. Phospholipid fatty acid (PLFA) analysis was used to characterize microbial community composition and multiple substrate induced respiration (MSIR) to examine microbial community function. The natural abundance of carbon isotopes in individual PLFAs was used to examine carbon source utilization by microorganisms. I surveyed 17-year-old spruce clear cuts where aspen was naturally regenerating to investigate the effect of aspen replacing former spruce stands. These were compared to mature stands of aspen and spruce, and 17-year-old clear-cuts of aspen. The rhizosphere had a significantly higher proportion of fungi and a higher gram negative to gram positive bacteria ratio compared to bulk soil. Fungi and gram-negative bacteria biomarkers in the rhizosphere showed 13C depletion compared to bulk forest floor, indicating that rhizosphere microbes were accessing more recently fixed carbon than in bulk soil. Aspen trees exhibited greater influence over their rhizospheres than spruce trees in terms of community composition and function, and aspen rhizospheres showed the highest basal respiration. In less than two decades, aspen regeneration in former spruce stands shifted microbial communities towards aspen stands, with the rhizosphere responding more quickly than bulk forest floor. This study indicates that microbial communities of rhizosphere and bulk forest floor differ in the boreal, and that vegetation shifts have the potential to cause more immediate and profound changes in the rhizosphere. I investigated priming and microbial uptake of labile carbon in two mineral soils, a Luvisol and Brunisol, commonly found in Canada’s boreal. The Luvisol was collected from Cooking Lake Blackfoot Provincial Recreation Area and the Brunisol at the Woodbend Forest University of Alberta research site, both within 50 km of Edmonton, AB. I incubated A and B horizons from the two soils with 13C-labelled glucose as a model root exudate. Glucose was added at three rates relative to microbial biomass carbon: 0.125x, 1x, and 2x. Carbon isotope probing of PLFA biomarkers was used to assess which microbial groups were responsible for uptake and utilization of the added substrate carbon. At the end of the 65 day incubation, no differences in priming were observed between soil types, depth, or glucose treatments. However, in the first hours of the incubation I observed positive priming in B horizons and negative priming in A horizons. Results suggest that the magnitude and direction of priming are both strongly dependent on the timing of glucose addition and measurement. If labile carbon is added regularly, it appears that organic matter would be protected in topsoil but mineralized more quickly in subsoil. If labile carbon additions occur only periodically, our results suggest that organic matter mineralization would not be affected considerably in the long term. Further, I conclude that fungi are an important microbial group in uptaking and utilizing labile carbon added to soil.
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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 | 0.000 | 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 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".