Insect invasion success depends on taxon and trophic group
Bibliographic record
Abstract
The majority of non-native animal species globally are insects, though some insect species are more successful invaders than others. These differences can be attributed, in part, to differences among dominant trophic groups. Previous analyses indicate that insect herbivores are generally over-represented among non-native species while other groups, such as predators, are under-represented. Here we explore how invasion success varies among insect taxa and trophic groups. We quantify over-representation in species grouped by taxon (order or family) and larval trophic group (herbivore, detritivore, predator, parasite, brood carer); over- and under-representation is computed by comparing proportional representation of groups among non-native species in 12 world regions with total numbers in these same groups globally. Although herbivores are generally the most successful group among non-natives, we found their invasion success to vary among their taxonomic groups: herbivores are over-represented among Hemiptera, Diptera, Thysanoptera and Hymenoptera, but under-represented among Lepidoptera and Orthoptera; similar patterns are seen at the family-level within orders. Even after accounting for trophic group, some orders were still over-represented. Within trophic groups, this pattern appeared strongest for herbivores, where predominantly parthenogenetic families belonging to the Hemiptera and Thysanoptera were over-represented in non-native assemblages, while families in the Lepidoptera and Polyneoptera were under-represented. Over time (1850s to 2000s), fractions of non-native species numbers in certain orders and trophic groups have varied, such as among parasites, where a considerable turnover took place from mostly bird-lice (Psocodea) in the 19th century to parasitic wasps (Hymenoptera) in the 20th century. It is thus likely that factors other than trophic group, such as associations with invasion pathways (e.g., plants, wood packaging), cause the observed differences in the over-representation of families belonging to different orders.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".