Large-scale experimental warming reduces soil faunal biodiversity through peatland drying
Bibliographic record
Abstract
Boreal peatlands are important ecosystems for carbon cycling because they store 1/3 of the world’s terrestrial carbon in only ∼3% of the global landmass. This high carbon storage capacity makes them a key potential mitigation strategy for increased carbon emissions induced by global climate warming. In high-carbon storage systems like peatlands, soil faunal communities are responsible for secondary decomposition of organic matter and nutrient cycling, which suggests they play an important role in the carbon cycle. Experiments have shown that warming can affect plant and microbial communities in ways that potentially shift peatlands from carbon sinks to sources. Although previous studies have found variable effects of climate change manipulations on soil communities, warming is expected to affect soil community composition mainly through reductions in moisture content, whereas elevated CO2 atmospheric concentrations are expected to only indirectly and weakly do so. In this study we used a large-scale peatland field-based experiment to test how soil microarthropod (oribatid and mesostigmatid mite, and collembolan species abundance, richness and community composition) respond to a range of experimental warming temperatures (between 0°C and +9°C) crossed with elevated CO2 conditions over 4 years in the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experiment. Here we found that warming significantly decreased surface peat moisture, which in turn decreased species microarthropod richness and abundance. Specifically, oribatid and mesostigmatid mite, collembolan, and overall microarthropod richness significantly decreased under lower moisture levels. Also, the abundance of microarthropods increased under higher moisture levels. Neither warming nor elevated [CO2] affected microarthropods when analysed together or separate, except for the richness of mesostigmatids that significantly increased under warming. At the community level, communities varied significantly over time (except collembolans), and moisture was an important driver explaining community species composition. While we expect that the cumulative and interactive effects of the SPRUCE experimental treatments on soil faunal biodiversity will continue to emerge, our results already suggest effects are becoming more observable over time. Taken together, the changes belowground indicate potential changes on carbon and nitrogen cycles, as microarthropods are important players of soil food webs.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".