A hydrogen isoscape for tracing the migration of herbivorous lepidopterans across the Afro‐Palearctic range

Abstract

RationaleMany insect species undertake multigenerational migrations in the Afro‐tropical and Palearctic ranges, and understanding their migratory connectivity remains challenging due to their small size, short life span and large population sizes. Hydrogen isotopes (δ2H) can be used to reconstruct the movement of dispersing or migrating insects, but applying δ2H for provenance requires a robust isotope baseline map (i.e. isoscape) for the Afro‐Palearctic.MethodsWe analyzed the δ2H in the wings (δ2Hwing) of 142 resident butterflies from 56 sites across the Afro‐Palearctic. The δ2Hwing values were compared to the predicted local growing‐season precipitation δ2H values (δ2HGSP) using a linear regression model to develop an insect wing δ2H isoscape. We used multivariate linear mixed models and high‐resolution and time‐specific remote sensing climate and environmental data to explore the controls of the residual δ2Hwing variability.ResultsA strong linear relationship was found between δ2Hwing and δ2HGSP values (r2 = 0.53). The resulting isoscape showed strong patterns across the Palearctic but limited variation and high uncertainty for the Afro‐tropics. Positive residuals of this relationship were correlated with dry conditions for the month preceding sampling whereas negative residuals were correlated with more wet days for the month preceding sampling. High intra‐site δ2Hwing variance was associated with lower relative humidity for the month preceding sampling and higher elevation.ConclusionThe δ2Hwing isoscape is applicable for tracing herbivorous lepidopteran insects that migrate across the Afro‐Palearctic range but has limited geolocation potential in the Afro‐tropics. The spatial analysis of uncertainty using high‐resolution climatic data demonstrated that many African regions with highly variable evaporation rates and relative humidity have δ2Hwing values that are less related to δ2HGSP values. Increasing geolocation precision will require new modeling approaches using more time‐specific environmental data and/or independent geolocation tools.