Inferring origins of migrating insects using isoscapes: a case study using the true armyworm, <i>Mythimna unipuncta</i> , in North America
Bibliographic record
Abstract
1. Many important insect pests undertake seasonal migrations at continental scales in response to changes in resource quality and availability. The frequency and timing of these events could be influenced by the impact of climate change on the suitability of the different sites exploited throughout the year, yet, in many cases, little is known about the origin of seasonal populations, as tracking insect movements is extremely challenging due to their small body size. 2. The use of stable isotope measurements in insect tissues combined with the development of tissue‐specific ‘isoscapes’ of modelled geographic isotope patterns presents a potentially valuable but rarely used approach for obtaining such information on important pest species. In this paper it is illustrated how stable hydrogen isotope analyses (δ 2 H) in wing chitin of the true armyworm ( Mythimna ( Psuedaletia ) unipuncta Haworth), a seasonal migrant, clearly delineated between 2016 spring immigrants and later locally produced moths in southern Ontario, Canada. 3. It is shown that adult moths captured in early fall in Texas were immigrants from farther north, the first direct confirmation of a southward return migration of this species. Stable carbon isotope (δ 13 C) measurements indicated that spring immigrants in Ontario and autumn immigrants in Texas were from exclusively C3 biomes. Stable nitrogen isotope (δ 15 N) measurements also provided information on the probability of individuals coming from agriculturally intensive (i.e. higher δ 15 N) sites. Finally, several recommendations are provided regarding future research that could improve the Bayesian assignment models and thus improve assignment accuracy.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".