GENES IN FLIGHT: POPULATION GENOMICS OF ADAPTIVE DIVERSITY IN HELICONIUS ERATO

Neotropical biodiversity hotspots such as the Amazon and the Atlantic Forest concentrate extraordinary species richness and endemism, but are experiencing rapid habitat loss and climate change. These processes alter not only species distributions but also the underlying genetic diversity and adaptive potential of populations, with largely unknown consequences for long‑term persistence. Heliconius butterflies, and particularly Heliconius erato, are an outstanding model to address these questions: they are widespread, ecologically specialized on Passiflora host plants, chemically defended via cyanogenic glucosides, and supported by high‑quality genomic resources and extensive ecological knowledge. Building as a fork and deepening of the CNPq project “Biotic and abiotic adaptive response toward future global warming scenarios in Neotropical hotspots” (CNPq #444740/2024‑4), this PhD project will use population genomics to dissect patterns and drivers of adaptive diversity in H. erato across Neotropical forests. Using whole‑genome resequencing data from 80 individuals sampled in two contrasting Amazonian localities, complemented by existing datasets from the broader CNPq network, the project will (i) quantify genome‑wide diversity, population structure and demographic history; (ii) identify loci and genes under selection using FSTST outlier tests and genotype–environment association methods; and (iii) test how variation in cyanogenic glucoside profiles and host‑plant communities correlates with genetic differentiation and spatial patterns. State‑of‑the‑art pipelines (FastQC/MultiQC, fastp, BWA‑MEM2, GATK/bcftools, SnpEff/VEP, PLINK and related tools) will be implemented on the LaBIS/CBMEG high‑performance cluster, with all scripts version‑controlled and released via the LaBIS GitHub, and all sequence data deposited in ENA. By integrating genomic, chemical‑ecological and environmental data, this project will generate a high‑resolution picture of how H. erato populations respond to landscape heterogeneity and environmental change. More broadly, it will evaluate the potential of insect molecular data as sensitive bioindicators of forest integrity, connectivity and adaptive capacity in some of Brazil’s most threatened biomes. The expected outcomes include robust population‑genomic baselines for H. erato, identification of candidate adaptive regions and pathways, and conservation‑relevant insights into which populations and regions may be most vulnerable—or most crucial as reservoirs of adaptive genetic variation—under future climate scenarios.