A dragonfly (<i>δ</i><sup>2</sup>H) isoscape for North America: a new tool for determining natal origins of migratory aquatic emergent insects
Bibliographic record
Abstract
Summary 1. Tracking insect migration at continental scales is intractable using exogenous markers because of tiny body size and high improbability of recapture. Naturally occurring endogenous isotopic markers, such as tissue δ2H and δ18O, are a means of assigning origins to both vertebrate and invertebrate populations, but the success depends upon derivation of a robust algorithm linking measured tissue isotope values with large‐scale geospatial isotopic patterns (isoscapes) in the terrestrial hydrosphere. 2. We derived a North American dragonfly wing δ2H and δ18O isoscape from known‐origin dragonflies of three species (Aeshna interrupta, Aeshna umbrosa and Pachydiplax longipennis) obtained across North America. A strong relationship (r2 = 0·75) was found between wing δ2H and hydrologic geospatial δ2H patterns, and between wing δ2H and δ18O (r2 = 0·92). The strong coupling between emergent insect tissue and hydrologic spatial patterning suggested that this dragonfly isoscape may be applicable to other aquatic emergent migratory insects in North America and elsewhere. 3. As a proof of concept, we used the wing isoscape algorithm to map the probability of natal origin of Common Green Darners (Anax junius) migrating through southern Texas. Results showed that these Texan dragonflies were a mix of local and far‐distant migrant (e.g. northern United States) individuals. We suggest that this isoscape algorithm opens new opportunities to quantify the migration and natal origins of dragonflies and other aquatic emergent insects where conventional methods have failed.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".