Complex Life Cycles Shape the Functional Biogeography of European Dragonflies
Bibliographic record
Abstract
ABSTRACT Aim To investigate how trait correlations between life stages associated with complex life cycles (aquatic nymph and terrestrial adult) shape the functional diversity and trait–environment relationships of European dragonflies (Odonata: Anisoptera). Location European mainland. Time Period Pre‐1990 and post‐1990. Major Taxa Studied Dragonflies (Odonata: Anisoptera). Methods Based on functional traits linked to dispersal and microhabitat preference, we use trait hypervolumes and structural equation modelling to estimate spatial and temporal trait correlations between terrestrial (adult) and aquatic (nymphal) life stages, and potential complex trait–environment relationships across life stages. Results Adult and nymphal functional diversity were positively correlated and trait variation between life stages did show reciprocal causality. Cross‐lagged correlations showed that historical nymphal traits most strongly impacted present nymphal and adult diversity, suggesting that functional diversity patterns are influenced by carryover effects and differential selection pressures on nymphs relative to adults. Between the two life stages, we find both parallel and contrasting patterns between direct and indirect trait–environment relationships. The effect of mean annual temperature on adult trait diversity is largely driven by its positive correlation with nymphal traits. Positive nymphal trait correlations with habitat availability and topography are reducing the direct negative effects these variables have on adult trait diversity. Main Conclusions We show that constraints inherent to complex life cycles significantly influence functional diversity patterns in European dragonflies, creating indirect trait–environment relationships across life stages. Spatial patterns in functional diversity were determined by both life stages, not just adults or nymphs, via a combination of independent and interactive trait–environment relationships. These findings challenge conventional functional biogeography models focused solely on direct environmental filtering. Consequently, integrating reciprocal trait relationships enhances causal claims when predicting functional biodiversity responses to environmental changes.
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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 | 0.000 |
| 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) | 0.001 | 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".