Ecological divergence and post-eclosion brain development shape visual performance during Heliconius speciation

# Ecological divergence and post-eclosion brain development shape visual performance during Heliconius speciationData & Analysis Repository (Zenodo) This repository contains datasets and analysis scripts used in a study of visual system divergence, developmental trajectories, and behavioral consequences in *Heliconius cydno*, *H. melpomene*, and their F1 hybrids. Data includes brain and eye morphology, visual acuity (optomotor assays), behavioral data (courtship, oviposition), and field ecology data (irradiance, habitat complexity).**DOI:** [Insert DOI here] ## Version history- v1.0 (2026-04-14): Initial release of data and analysis scripts accompanying the manuscript. ## AbstractSensory adaptation is increasingly recognized as a key driver of ecological speciation, but how visual system diverge is coordinated across development, and how this translates into behavioral differences, remains poorly understood. The butterfly *Heliconius cydno*, which inhabits closed-canopy forests, has larger eyes and greater investment in visual brain centers than its sympatric close-relative *H. melpomene*, which occupies more open forest-edge habitats, suggesting divergent ecological selection on the visual system. However, the behavioral consequences of these visual adaptations, their developmental trajectories, and whether they break down in hybrids is unknown. To address these questions, we combined ecological field data with behavioral assays and deep-learning-assisted segmentation of neuroanatomy. Visual acuity – the ability to resolve spatial detail – was higher in *H. cydno*, consistent with its greater ommatidia number, but also increased with age in both species despite no change in external eye morphology. These improvements coincided with the onset of male courtship and female oviposition, suggesting that early adult neurodevelopment shapes visual performance and may support the demands of reproduction. Brain morphology showed species-specific trajectories of post-eclosion optic lobe growth that broadly paralleled increases in acuity and were accompanied by ongoing neurogenesis in the adult optic lobes. While hybrids exhibited intermediate visual acuity, relationships among different components of the visual system were disrupted in hybrids. Together, these results show that ommatidia number alone cannot explain variation in visual acuity, and highlight how coordinated sensory evolution, and its breakdown in hybrids, may contribute to divergence during the early stages of speciation. ## ContentsThis Zenodo record contains:- **Data files:** 66 (CSV)- **R scripts:** 17- **README:** this file File names listed below match those deposited on Zenodo. Absolute file paths have been removed for reproducibility. ## Data and file overview Data and scripts are structured around the four main experimental components (Exp1-4) and their corresponding main and supplemental figures. ### 1) Experiment 1 & Figure 1: Ecology, habitat complexity, and irradiance**Scripts (`/Fig1_ecology_zenodo`):**- `Fig1_ecology_irradiance_zenodo.r`: Visualizes ecological and irradiance data. **Data (`/Exp1_Ecology_complexity_flightheight` & `/Fig1_ecology_zenodo`):**- `complexity_habitat_exp1.csv`: Habitat complexity measurements.- `recaptures_cc_mmc_exp1.csv` & `recaptures_cc_mmc1_exp1.csv`: Field recapture datasets.- `Lavega_irradiance_measuremets_fig1.csv`: Environmental irradiance data for different habitats. ### 2) Experiment 2 & Figure 2: Visual acuity, mating, and oviposition**Scripts (`/Exp2_matechoice_age`, `/Exp2_visualacuity_cc_mmc`, `/Exp2_oviposition_age`, `/Fig2...`, `/FigsS`):**- `Exp2_Age_preference.r`: Models for age-related mate preference.- `Exp2_Age_first_egg.R`: Models for age of first oviposition.- `Exp2_Optomotor_analysis_cc_mmc.R`: Analysis of visual acuity (optomotor response) in *cydno* and *melpomene*.- `Fig2_density_zenodo.r`: Plotting script for visual acuity densities.- `FigS2_mating_behaviours_zednodo.r`: Supplemental plotting for mating behaviors.- `FigS3_oviposition_survival_zednodo.r`: Supplemental plotting for oviposition and survival. **Data (`/Exp2_visualacuity_cc_mmc`, `/Exp2_matechoice_age`, `/Exp2_oviposition_age`):**- `Optomotor_cc_mmc_exp2.csv`: Raw optomotor response data (*cydno* & *melpomene*).- `Egg_date_exp2.csv`: Oviposition tracking data.- `first_courtship_exp2.csv`, `Preference_Age_results_exp2.csv`, `species_avg.csv`: Age-related mate choice and courtship data.- **Statistical Outputs:** Multiple tables covering mixed-effects models, EMMs (estimated marginal means), and likelihood ratio tests (e.g., `acuity_cc_mmc_tbl.csv`, `acuity_cc_emm_converted.csv`, `lrt_mating_table.csv`, `lrt_courtship_table.csv`). ### 3) Experiment 3 & Figure 3: Species comparison (Brain and Eye Morphology)**Scripts (`/Exp3_eye_species`, `/Exp3_brain_species`, `/Fig3...`, `/FigsS`):**- `Exp3_Eye_analysis_species.r`: Analytical pipeline for eye morphology.- `Exp3_Brain_volumes_cc_mmc.r`: Analytical pipeline for neuropil volumes.- `Fig3_combined_zenodo.r`: Plotting script for combined morphological results.- `FigS4_p_eye_males_zednodo.r`: Supplemental plotting for male eye morphology.- `FigS5_p_eye_females_zednodo.r`: Supplemental plotting for female eye morphology. **Data (`/Exp3_eye_species`, `/Exp3_brain_species`):**- `Colombia_cc_mmc_complete_exp3.csv`: Master dataset containing brain and eye measurements.- `eye_cc_mmc_summary.csv`: Summary statistics of eye parameters.- **Statistical Outputs:** ANOVA tables and SMATR major axis analysis outputs for both brains and eyes (e.g., `anova_table_corneal_area.csv`, `anova_table_cc_mmc_brains.csv`, `smatr_area_count_tibia_summary_species.csv`, `smatr_brain_table_cc_mmc.csv`). ### 4) Experiment 4 & Figure 4: F1 Hybrid comparison (Acuity, Brain, and Eye)**Scripts (`/Exp4_eye_f1`, `/Exp4_brain_f1`, `/Exp4_visual_acuity_f1`, `/Fig4_f1`, `/FigsS`):**- `Exp4_Eye_analysis_f1.r`: Eye morphology pipeline for F1s.- `Exp4_Brains_volumes_cc_mmc_f1.r`: Brain morphology pipeline for F1s.- `Exp4_Optomotor_analysis_hybrids.r`: Modeling visual acuity in F1 hybrids.- `Fig4_f1_zenodo.r`: Plotting script for F1 main figure.- `FigS9_p_acuity_f1_zednodo.r`: Supplemental plotting for F1 visual acuity. **Data (`/Exp4_eye_f1`, `/Exp4_brain_f1`, `/Exp4_visual_acuity_f1`):**- `Colombia_f1_complete_exp4.csv`: Master dataset for F1 hybrids (present in corresponding subdirectories for eye, brain, and acuity).- `Panama_cc_mmc_eyes_f1.csv` & `Panama_cc_mmc_brains_f1.csv`: Hybrid measurements from Panama populations.- `Optomotor_cc_f1_mmc_age10.csv`: Optomotor response data for F1 hybrids.- `eye_f1_summary.csv`, `neuropil_f1_summary_table.csv`, `brain_f1_overall_summary.csv`: Summary statistics for hybrid morphology.- **Statistical Outputs:** Comprehensive ANOVA, post-hoc pairwise comparisons, SMATR, and LOO comparison tables for F1 traits (e.g., `posthoc_count_f1_interaction_pairwise.csv`, `anova_table_F1_LRT.csv`, `loo_comparison_f1_eye_tibia.csv`). --- ## Variable descriptions (data dictionaries)*(Note: These descriptions outline the core master files used across the scripts. Derived files like `anova_table`, `posthoc_`, and `emm_converted` contain standard statistical output columns (df, p-value, F-ratio, emmean, SE, etc.).)* ### `Colombia_cc_mmc_complete_exp3.csv` & `Colombia_f1_complete_exp4.csv`Master morphological datasets containing body, eye, and brain measurements.- **ID** (factor): Individual identifier.- **Species** (factor): Species or hybrid cross identity (e.g., *cydno*, *melpomene*, F1).- **Sex** (factor): `female`, `male`.- **Age_days** (numeric): Age of the individual in days post-eclosion.- **tibia_length** (numeric): Proxy for overall body size.- **corneal_area** (numeric): Measured surface area of the eye.- **facet_count** (integer): Number of ommatidia.- **Neuropil volumes** (numeric): Typically log10-transformed volumes for brain regions (e.g., `ME` for medulla, `LAM` for lamina, `LOB` for lobula). ### `Optomotor_cc_mmc_exp2.csv` & `Optomotor_cc_f1_mmc_age10.csv`Optomotor assay results measuring visual acuity.- **ID** (factor): Individual identifier.- **Species** (factor): Taxon label.- **Sex** (factor): `female`, `male`.- **Age** (numeric): Age at the time of testing.- **Spatial_frequency** (numeric): Cycles per degree (cpd) of the rotating stimulus.- **Response** (binary/integer): Whether the butterfly tracked the stimulus (1) or not (0).- **Acuity_threshold** (numeric): Calculated visual acuity limit. ### `Lavega_irradiance_measuremets_fig1.csv`Environmental light availability in field sites.- **Location / Habitat** (factor): E.g., closed-canopy, forest-edge.- **Time** (POSIXct): Time of measurement.- **Wavelength** (numeric): Spectrum wavelength (nm).- **Irradiance** (numeric): Measured light intensity. ## Reproducibility notes- Analyses were performed in R (see “R Environment” section below).- Scripts are designed to run using relative paths. Download the entire repository and set your working directory to the root folder before executing the scripts. - Ensure that the folder structure (e.g., `/Fig1_ecology_zenodo`, `/Exp2_matechoice_age`) is maintained exactly as extracted from the Zenodo archive. ## R Environment (for reproducibility)Analyses were conducted in R with the following environment:- R version: 4.5.1 (2025-06-13)- Platform: aarch64-apple-darwin20- OS: macOSAttached packages (version numbers) include:`lme4`, `glmmTMB`, `emmeans`, `car`, `smatr`, `ggplot2`, `tidyverse`, `dplyr`, `DHARMa`, `multcompView`, `readr`. *(Full package namespace list is available via sessionInfo()).* ## How to run the analyses1. Download and unzip the Zenodo record to a local folder.2. Open R/RStudio and set your working directory to the unzipped root folder.3. Open the corresponding `.r` script for the analysis you wish to reproduce.4. Run the code sequentially. Scripts rely on local relative paths. Avoid using absolute file paths. ## Contributors and contact**Authors:**- José Borrero^1^, Amaia Alcalde-Anton^2^, Leo Laborieux^1,4^, Daniel Shane Wright^1^, Daniela Lozano-Urrego^1,3^, Geraldine Rueda-Muñoz^1,3^, Carolina Pardo-Diaz^3^, Camilo Salazar^3^, Stephen H. Montgomery^2^ & Richard M. Merrill^1^ **Affiliations:**1. Department of Evolutionary Biology, LMU Munich2. School of Biological Sciences, University of Bristol3. Biology Program, Faculty of Natural Sciences, Universidad del Rosario, Bogotá4. Present address: Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, MI, USA. **Corresponding Contacts:**- José Borrero ([e-mail])- Stephen H. Montgomery ([e-mail])- Richard M. Merrill ([e-mail]) ## FundingResearch Funded by ERC Starter Grant 851040 to R.M.M, a SWBio DTP Studentship (BB/M009122/1) to A.A.A, and a NERC IRF (NE/N014936/1) to S.H.M. Leica Stellaris 5 confocal microscope used in this study was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – Project number 495215303. ## Dates and locations of data collection- Field sampling, behavioral assays, and ecological measurements were collected at standard field sites (e.g., La Vega / Panama / Colombia) as detailed in the manuscript.- Brain morphology segmentation and laboratory measurements were conducted at LMU Munich. ## LicenseThis Zenodo record is released under the license selected on Zenodo for the deposition (typically CC-BY 4.0). If you reuse these data, please cite the Zenodo DOI above and the associated publication.