Influence of trampling-induced microtopography on growth of the soil crust bryophyte<i>Ceratodon purpureus</i>in Jasper National Park
Bibliographic record
Abstract
The growth of the moss Ceratodon purpureus (Hedw.) Brid. is enhanced by microtopography created by ungulates on silt-rich dune soils. Ungulates such as elk (Cervus elaphus) are important structural modifiers of the soil on the shore of Jasper Lake, Jasper National Park, Canada. Ungulates increase microtopography at the soil surface by creating hoof prints and small raised mounds from kicked-up soil. Experiments with artificial microtopography revealed that C. purpureus grew faster (1) inside artificial hoof prints, (2) in the shade of soil mounds and plastic barriers, and (3) on north-sloping soil, probably owing to provision of shelter from desiccation. A laboratory study of soil drying rates in the presence and absence of shelters supported this trend. Furthermore, patterns of moss height in naturally occurring hoof prints indicated that the response of moss to this microrelief is scale-dependent, with the strongest response occurring at the finest scale investigated. Finally, at larger spatial scales, moss cover did not decline with increasing hoof print density until 25% of the ground was covered by hoof prints. The incidental creation of microhabitat by ungulates seems to buffer C. purpureus from the negative crushing effects of trampling.Key words: Ceratodon purpureus, ecosystem engineering, facilitation, microtopography, soil crust, trampling.
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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) | 0.001 | 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".