Environmental Microbiology · 2015 · 132 citations · 91 references
Life SciencesEcologyMicrobiologyMarine EcologyMarine MicrobiologyMicrobial EcologyAnimal Microbial EcologyAquatic Microbial EcologyAquatic Animal MicrobiomesBiotic StressCrustacean MicrobiologyEnvironmental Effects On MicrobiotaEstablishment Success MetricsEstablishment Success MonitoringMarine MicrobiomesShellfish Microbiology
Microbiota can have positive and negative effects on hosts depending on the environmental conditions. Therefore, it is important to decipher host-microbiota-environment interactions, especially under natural conditions exerting (a)biotic stress. Here, we assess the relative importance of microbiota in different tissues of Pacific oyster for its successful establishment in a new environment. We transplanted oysters from the Southern to the Northern Wadden Sea and controlled for the effects of resident microbiota by administering antibiotics to half of the oysters. We then followed survival and composition of haemolymph, mantle, gill and gut microbiota in local and translocated oysters over 5 days. High mortality was recorded only in non-antibiotic-treated translocated oysters, where high titres of active Vibrio sp. in solid tissues indicated systemic infections. Network analyses revealed the highest connectivity and a link to seawater communities in the haemolymph microbiota. Since antibiotics decreased modularity and increased connectivity of the haemolymph-based networks, we propose that community destabilization in non-treated translocated oysters could be attributed to interactions between resident and external microbiota, which in turn facilitated passage of vibrios into solid tissues and invoked disease. These interactions of haemolymph microbiota with the external and internal environment may thus represent an important component of oyster fitness.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Fitting Linear Mixed-Effects Models Using <b>lme4</b>
Douglas M. Bates, Martin Mächler, Benjamin M. Bolker et al. · Journal of Statistical Software · 2015 · 85.6K citations · Full text