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Distributions of host heterogeneity in susceptibility show signatures of pathogen geographic structure in an insect baculovirus

Fleming-Davies, A. E.; Shields, S.; Fletcher, J.; Recart, W.; Paez, D. J.

2026-06-19 ecology
10.64898/2026.06.18.732481 bioRxiv
Show abstract

Segregated variation between populations is a fundamental evolutionary process leading to parasite specialization, yet the resulting impacts on infection heterogeneity within populations are theoretically and empirically understudied. We asked whether the distribution of host susceptibility to infection within populations carries the signatures of geographic structure from pathogen local adaptation, maladaptation, or generalism in a nuclear polyhedrosis virus that infects the Gulf Fritillary butterfly Dione vanillae. For this virus, there is genetic support for two geographically distinct groups within San Diego County, based on whole genome sequencing of 16 virus isolates. Reciprocal laboratory infections showed evidence of two contrasting viral life history strategies: a generalist phenotype that consistently infected variable hosts and a specialist that performed slightly better in its local host population. As predicted by our theoretical model, the more consistent infection displayed by the generalist across populations corresponded to lower heterogeneity in susceptibility within populations, modeled as gamma distribution. Furthermore, the generalist phenotype was collected over a wider geographic range despite having a tenfold-lower mean infection rate than the specialist, suggesting that a strategy of more consistent infection provides key fitness advantages across diverse host populations. Intriguingly, when there is variation in host susceptibility, interpretations of pathogen local adaptation are dose-dependent. Measuring infectivity across multiple doses enables estimation of the whole distribution of susceptibility, which provides more reliable identification of pathogen specialization to its local host. Our work demonstrates how trait distributions and not only their mean values can carry quantifiable signatures of eco-evolutionary processes in interspecific interactions.

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