Overcoming Biases in Opportunistic Citizen Science for Studying Life History Traits of an Invasive Leaf-Mining Tree Insect Pest
Bibliographic record
Abstract
The aim of this study was to determine whether opportunistic citizen science can support the detection of life history traits in invasive insects. Using the invasive leaf-mining micromoth Macrosaccus robiniella (Clemens 1859) (Lepidoptera: Gracillariidae) as a model species, we analyzed data from iNaturalist submitted by citizen scientists to assess the variability in its leaf mines on its native host, Robinia pseudoacacia L., 1753 (Fabaceae), across both the moth’s invaded (Europe, North America–Eastern United States) and native range (North America–Southern and Western Unites States, Eastern Canada). We examined 86,489 photographs collected over the past 20 years to compare the occurrence and proportions of different M. robiniella leaf mine types between invaded and native ranges using three search variants: (I) M. robiniella, (II) all endophagous invasive insects associated with R. pseudoacacia, and (III) the host plant itself. The first two datasets revealed differences in the ratio of leaf mine types between Europe and North America (when analyzed separately for native and invaded areas), whereas the third dataset showed no significant differences in either the presence or proportion of mine types between invaded and native ranges. Leaf mine types atypical of M. robiniella, which resemble damage caused by other invasive insects such as Parectopa robiniella Clemens, 1863 (Lepidoptera: Gracillariidae) and Obolodiplosis robiniae (Haldeman, 1847) (Diptera: Cecidomyiidae)—also associated with R. pseudoacacia—have been observed in Europe for at least a decade. Our main conclusion is that, when investigating the life history traits of invasive herbivorous insects, focusing data collection on the host plant rather than on the insect species alone can reduce biases associated with opportunistic citizen science and help reveal true ecological patterns.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".