A lack of native congeners may limit colonization of introduced conifers by indigenous insects in Europe
Bibliographic record
Abstract
We compared the recruitment of phytophagous arthropod pests onto exotic conifers introduced in Europe without any congeners with that of exotic conifers that have native congeners. In 130 years of extensive plantation forestry in Europe, Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) recruited only 87 arthropod species, i.e., only 33.9% of the number of associated arthropod species in its native range (257 spp.). Exotic species of Cupressaceae without indigenous congeners also recruited only a portion (3.4% to 57.9%) of the arthropod fauna observed in their native range. In both cases, the majority of the recruited species were polyphagous, i.e., that they can feed on plants of different families of conifers and (or) angiosperms. In contrast, exotic conifers with native congeners recruited most of the insects colonizing the native congeneric conifers. Differences in arthropod recruitment were observed according to both guild and feeding habit, with the externally feeding herbivores being dominant. Typically, the damage caused by native insects that had been recruited by exotic conifers without congeners was limited, whereas the damage caused by native insects that had been recruited by exotic conifers with congeners often led to severe outbreaks at the time the shift between hosts occurred. However, when a highly specialized exotic insect was introduced along with the host, the invasive insect tended to occupy the entire niche, causing more damage than in the original range, in the absence of natural enemies and indigenous competitors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".