Anthropogenically-modified soil increases the performance of non-native plants in a subarctic ecosystem
Bibliographic record
Abstract
Waste dumps contain human-modified soils that differ substantially from soils in natural areas. Such soils can create a suitable environment for weedy non-native species, so that waste dumps can act as epicentres for further dispersal. In the subarctic town of Churchill, Manitoba, Canada, multiple sites have been anthropogenically disturbed by the input of manure, agricultural waste and garden waste. Large populations of non-native plants often dominate these anthropogenically-altered sites, while nearby undisturbed areas with natural soil remain free of non-native species. When soil from these dumps is moved to other areas for construction, road repair or other purposes, these non-natives can travel with it and potentially establish new populations. In this study, we conducted soil addition experiments to investigate whether human-modified soil provide an ameliorated environment for non-native species when they are moved together into native-dominated subarctic ecosystems. We found that non-native species were able to germinate and survive in soils translocated from dumpsites into previously uninvaded areas in tundra or boreal forest. In addition, we found that deeper translocated soil tended to further increase the growth of non-native species. These results indicate that transported dumpsite soil creates an improved environment for non-native plants temporarily. However, survival decreased over time, suggesting that the ameliorated below-ground associated conditions were not sufficient to allow persistence in natural environments. As the climate continues to warm, anthropogenic soil movement may increase future risk of spread into currently inhospitable habitats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".