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The incidence of bacterial endosymbionts in terrestrial arthropods.


ABSTRACT: Intracellular endosymbiotic bacteria are found in many terrestrial arthropods and have a profound influence on host biology. A basic question about these symbionts is why they infect the hosts that they do, but estimating symbiont incidence (the proportion of potential host species that are actually infected) is complicated by dynamic or low prevalence infections. We develop a maximum-likelihood approach to estimating incidence, and testing hypotheses about its variation. We apply our method to a database of screens for bacterial symbionts, containing more than 3600 distinct arthropod species and more than 150 000 individual arthropods. After accounting for sampling bias, we estimate that 52% (CIs: 48-57) of arthropod species are infected with Wolbachia, 24% (CIs: 20-42) with Rickettsia and 13% (CIs: 13-55) with Cardinium. We then show that these differences stem from the significantly reduced incidence of Rickettsia and Cardinium in most hexapod orders, which might be explained by evolutionary differences in the arthropod immune response. Finally, we test the prediction that symbiont incidence should be higher in speciose host clades. But while some groups do show a trend for more infection in species-rich families, the correlations are generally weak and inconsistent. These results argue against a major role for parasitic symbionts in driving arthropod diversification.

SUBMITTER: Weinert LA 

PROVIDER: S-EPMC4424649 | biostudies-literature | 2015 May

REPOSITORIES: biostudies-literature

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The incidence of bacterial endosymbionts in terrestrial arthropods.

Weinert Lucy A LA   Araujo-Jnr Eli V EV   Ahmed Muhammad Z MZ   Welch John J JJ  

Proceedings. Biological sciences 20150501 1807


Intracellular endosymbiotic bacteria are found in many terrestrial arthropods and have a profound influence on host biology. A basic question about these symbionts is why they infect the hosts that they do, but estimating symbiont incidence (the proportion of potential host species that are actually infected) is complicated by dynamic or low prevalence infections. We develop a maximum-likelihood approach to estimating incidence, and testing hypotheses about its variation. We apply our method to  ...[more]

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