Ecological effects of the non-native <i>Pinus nigra</i> on sand dune communities
Bibliographic record
Abstract
Owing to their successional nature, sand dunes provide an opportunity to examine the effects of non-native species introduced into multiple habitats. We investigated the biotic and abiotic effects of non-native Pinus nigra in four habitats on the dunes of the eastern shore of Lake Michigan. The 26 000 pines were planted in foredunes, forest edges, wetpannes, and inland blowouts as a stabilization measure in 19561972, and in 1995 the surviving trees ranged in stand density from 2741176 trees per hectare. Pinus nigra stands were associated with reduced cover of dune vegetation except in forest edges, and with depressed species richness only in wetpanne sites. Higher densities of woody stems occurred in P. nigra stands at the edge of native forest than in sites lacking P. nigra, suggesting that pines accelerate succession to a woody community. Pinus nigra stands were associated with lower light levels than native stands of comparable or greater stand densities (Pinus banksiana in wetpannes and Populus deltoides in foredunes). In addition, P. nigra sites were drier than P. banksiana sites in wetpannes. The non-native pines may have modified the four dune habitats and appear to be functionally different from stands of native trees.Key words: functional equivalency, non-native species, Pinus nigra, plant invasion, sand dunes.
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 | 0.000 | 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".