Data and code supporting the manuscript: Soil and microbial responses to wild ungulate trampling depend more on ecosystem type than trampling severity
Bibliographic record
Abstract
1. Physical trampling is a ubiquitous activity of walking vertebrates, but is poorly understood as a mechanism impacting biogeochemical cycling in soil. Lack of detailed knowledge of soil abiotic-biotic interactions underlying trampling effects, and the primary sources of heterogeneity in these effects, limits our ability to predict ecosystem-level consequences of ongoing population changes in large animals.2. We conducted a summer field study of moose trampling effects on soil properties and nitrogen cycling using natural moose trails in boreal forest and heath barren ecosystems on the island of Newfoundland, Canada. We tested the extent to which trampling effects on soil abiotic properties, microbes, and nitrogen cycling depend on local trampling severity, and/or ecosystem type. Using structural equation models, we further tested whether trampling effects on soil nitrogen mineralization occurred via indirect interaction chains mediated by soil abiotic and microbial properties. Finally, we tested whether trampling modifies plot-scale controls on net nitrogen mineralization.3. Trampling effects on soil environment, substrate, and microbial properties depended on ecosystem type, but rarely on trampling severity. Trampling effects were of consistently greater magnitude in heath than forest. Further, trampling effects on organic matter content, pH, moisture, microbial abundance, and microbial community composition were qualitatively different between forest and heath ecosystems. Trampling severity increased the magnitude of some trampling effects in heath, but did not impact the direction of the effects, and did not significantly impact any trampling effects in forest.4. Trampling indirect effects on soil microbes were ecosystem-dependent but did not alter net N mineralization rates. In forest, trampling did not shift N mineralization rates via any indirect interaction chains, but modified relationships between N mineralization and other soil physical and microbial properties. In heath, multiple opposing interaction chains resulted in no net shift in N mineralization rates, and most relationships between diverse soil properties and N mineralization were not modified by trampling.5. Overall, our results disentangle complex abiotic-biotic interactions underlying megafauna effects on ecosystem functioning. These insights pave the way for better integration of animal consumptive and non-consumptive mechanisms into biogeochemical models, as well as prediction of trampling effects over landscapes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 teacher head, 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".