Crowdsourced online data as evidence of absence of non-target attack from the century-old introduction of Istocheta aldrichi for biological control of Popillia japonica in North America
Bibliographic record
Abstract
Abstract The vast majority of historical biological control introductions have not resulted in documented negative effects on non-target species. However, in some cases, an absence of evidence of harm could be due to insufficient evidence of absence: That is, data specifically gathered to show that non-target species are not affected by the released biological control agent. The parasitoid fly Istocheta aldrichi (Mesnil) (Diptera: Tachinidae) was introduced to North America a century ago as a biological control agent targeting the invasive Japanese beetle, Popillia japonica Newman (Coleoptera: Scarabaeidae). Despite its longstanding and widespread establishment, the host specificity of I. aldrichi remains underexplored due to a lack of dedicated post-release monitoring. Leveraging crowdsourced data from iNaturalist.org, we investigated potential non-target parasitism among scarab beetles observed within the current geographic range of I. aldrichi . The taxonomic accuracy of iNaturalist identifications was evaluated and curated. Our analysis of > 21,000 observations of non-target scarabs photographed within the geographic range of I. aldrichi suggests that I. aldrichi is highly specific to P. japonica . Candidate parasitoid eggs resembling those of I. aldrichi were extremely rare on non-target species, representing less than 0.001% of all observations and not exceeding 1.3% of observations for any individual non-target species. These findings provide evidence that the incidence of non-target attacks by I. aldrichi is likely negligible, at least with respect to the scarab species commonly observed on iNaturalist. They also show the potential for crowdsourced data to complement traditional methods assessing whether non-target ecological impacts may have resulted from past biological control introductions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".