Agricultural land-use history increases non-native plant invasion in a southern Appalachian forest a century after abandonment
Bibliographic record
Abstract
Land-use history can play a significant role in shaping forest communities. We considered the effects of agricultural land-use legacies on the distribution of non-native invasive plants a century after abandonment in a watershed in western North Carolina, USA. Forest sites that were previously in cultivation and abandoned ca. 1905 were compared with nearby reference sites that were never cultivated. The most common invasive plants were Celastrus orbiculatus Thunb., Microstegium vimineum Trin., and Lonicera japonica Thunb. Formerly cultivated sites and plots positioned downslope from roads had the most invasives. Soil cation concentration and pH were positively correlated with invasive presence and abundance. Historic agricultural plots where the successional tree Liriodendron tulipifera L. was dominant had the highest soil cation concentrations and soil pH and the greatest abundance of invasive plants. Disentangling the cause–effect relationships between land-use history, the biotic community, and the abiotic template presents a challenge, but understanding the role of land-use legacies may provide important insights regarding the mechanisms underlying the establishment and spread of invasive plants in forest ecosystems. Our results suggest that land-use history at Bent Creek may be facilitating plant invasion indirectly by causing a shift in overstory community composition that in turn creates more suitable understory conditions for shade-tolerant invasive 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 | 0.001 | 0.001 |
| 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".