Island biogeography theory and the assembly patterns of native versus non‐native forest insects
Bibliographic record
Abstract
Forest tree diversity helps shape the assembly of herbivorous insect communities in forests worldwide, and Island biogeography theory explains the role of tree range area as a driver of variation in native insect richness per tree species. However, it is unclear if these biogeographical principles apply to non‐native insect herbivores. We created an extensive list of tree–insect associations for tree species native to Palearctic Europe bioregion, and investigated how both native and non‐native insect herbivore species richness per tree may be influenced by various factors including the range area of the tree and the tree type (conifer or angiosperm). Models were further subsetted to compare drivers for the richness of different feeding guilds (folivores, sap feeders or wood borers) and host breadth (specialist or generalist). Tree range area had a positive effect on native, but not non‐native, insect species richness per tree, while non‐native richness was affected by host type and had a positive correlation with native herbivore richness on the same tree species. Host breadth grouping did not influence the difference in the effect of tree range on native versus non‐native insect richness, but native folivores had a stronger relationship with range distribution area compared to the other groups. We conclude that the assembly of non‐native herbivorous insect species on trees occurs through fundamentally different mechanisms than the co‐evolutionary processes proposed by the framework of Island biogeography theory.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".