From diaspore germination to climatic niche: Does microhabitat regulate the geographic distribution of peatland plants?
Bibliographic record
Abstract
Microhabitats bridge macroclimatic conditions and local plant establishment, yet the fine-scale processes shaping species’ responses remain poorly understood. Peatland hummock-hollow microtopography, with its distinct hydrologic conditions, provides an ideal model to test this. We examined how hummocks and hollows drive species differentiation across regeneration, geographic, and climatic niches, comparing tracheophytes and bryophytes. We compiled a database of 41 peatland species and experimentally evaluated their regeneration niches using dormancy-breaking and temperature treatments. At the regeneration scale, bryophytes consistently exhibited wider regeneration niches than tracheophytes. Driven by microhabitat heterogeneity, tracheophytes evolved divergent regeneration strategies. Hollow species exhibited higher germination and broader regeneration niches following dormancy release. Their strict reliance on overwintering cues aligns with a risk-avoiding “best-bet” strategy, concentrating mass germination during continuous, stable moisture to maximize recruitment. Conversely, hummock species—facing higher evaporation and moisture fluctuations—employed a risk-minimizing conservative strategy. Higher germination thresholds and stringent responses to external cues prevented recruitment during unstable wet windows, mitigating drought mortality risks. Unexpectedly, local microhabitat type did not significantly constrain species’ macro-geographic ranges or climatic niche medians. Instead, macroclimatic adaptation diverged fundamentally between taxa. Tracheophyte climatic optima were predicted by niche breadth, reflecting broad tolerance to seasonal hydrological extremes. In contrast, bryophyte climatic medians were strictly constrained by niche symmetry, highlighting a reliance on distributional stability. We conclude that peatland microhabitats do not directly dictate macro-distributions but act as strong environmental filters during early regeneration in plants.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".