Climate-induced phenological shifts in a Batesian mimicry complex
Bibliographic record
Abstract
Climate-induced changes in spatial and temporal occurrence of species, as well as species traits such as body size, each have the potential to decouple symbiotic relationships. Past work has focused primarily on direct interactions, particularly those between predators and prey and between plants and pollinators, but studies have rarely demonstrated significant fitness costs to the interacting, coevolving organisms. Here, we demonstrate that changing phenological synchrony in the latter part of the 20th century has different fitness outcomes for the actors within a Batesian mimicry complex, where predators learn to differentiate harmful "model" organisms (stinging Hymenoptera) from harmless "mimics" (hoverflies, Diptera: Syrphidae). We define the mimetic relationships between 2,352 pairs of stinging Hymenoptera and their Syrphidae mimics based on a large-scale citizen science project and demonstrate that there is no relationship between the phenological shifts of models and their mimics. Using computer game-based experiments, we confirm that the fitness of models, mimics, and predators differs among phenological scenarios, creating a phenologically antagonistic system. Finally, we show that climate change is increasing the proportion of mimetic interactions in which models occur first and reducing mimic-first and random patterns of occurrence, potentially leading to complex fitness costs and benefits across all three actors. Our results provide strong evidence for an overlooked example of fitness consequences from changing phenological synchrony.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".