Changes in Plant Community Composition, Structure, and Function in Response to Permafrost Thaw
Bibliographic record
Abstract
Air temperature is increasing at three or more times the global average in high latitudes, causing widespread permafrost thaw across the boreal biome. Since the boreal biome stores 30-40% of global terrestrial carbon (C), of which about 30-45% is found in permafrost soils, this temperature increase could cause a large efflux of C to the atmosphere. Climate warming and permafrost thaw are also expected to alter plant community composition and productivity and, given the link between plant functional traits and ecosystem C fluxes, may alter overall ecosystem function. Across the boreal biome of western Canada, we know surprisingly little about warming-induced changes in plant functional traits, thus narrowing the ability to understand and model warming-induced changes on ecosystem function. I aimed to address this knowledge gap through three main objectives: 1. exploring understory plant community composition and community-level trait variation across four boreal peatland sites spanning 15° of latitude; 2. understanding the implications of permafrost thaw-induced environmental changes on vascular plant community composition and community-level traits along gradients in aboveground tree biomass and active layer; 3. determining how understory plant community composition and community-level traits mediate ecosystem C fluxes following increased nutrient availability at depth and whether responses differ with canopy density. I found that the large-scale climatic gradient had a small influence on community composition and community-level traits compared to within-site environmental gradients. In addition, a thicker active (seasonally thawed) layer increased community-level traits. Similarly, nutrient increases in shallow soil (~20 cm) increased community-level traits of vascular plants and forest floor C fluxes, but only when canopy was sufficiently open. Importantly, variation in community-level traits found throughout my dissertation was generally explained by species turnover. Thus, my research suggests that local increases in active layer thickness, and nutrient and light availability will drive changes in plant community composition toward species that enhance community-level productivity, thereby enhancing plant-mediated C uptake of peatland sites. However, this increased plant productivity is unlikely to account for C loss with continued warming and permafrost thaw in the long term due to concomitant changes in fluxes attributable to soil microbial activity.
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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 | 0.000 |
| 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".