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Gabidulin, A. R.

Publications and source records attributed to Gabidulin, A. R..

2 recordsLinked to original sources

A test for microbiome-mediated rescue via host phenotypic plasticity in Daphnia

Phenotypic plasticity is a primary mechanism by which organismal phenotypes shift in response to the environment. Host-associated microbiomes often exhibit considerable shifts in response to environmental variation and these shifts could facilitate host phenotypic plasticity, adaptation, or rescue populations from extinction. However, it is unclear how much shifts in microbiome composition contribute to host phenotypic plasticity, limiting our knowledge of the underlying mechanisms of plasticity and, ultimately, the fate of populations inhabiting changing environments. In this study, we examined phenotypic responses and microbiome composition in 20 genetically distinct Daphnia magna clones exposed to non-toxic and toxic diets containing Microcystis, a cosmopolitan cyanobacteria and common stressor for Daphnia. Daphnia exhibited significant plasticity in survival, reproduction, and population growth rates in response to Microcystis exposure. However, the effects of Microcystis exposure on the Daphnia microbiome were limited, with the primary effect being differences in abundance observed across five bacterial families. Moreover, there was no significant correlation between the magnitude of microbiome shifts and host phenotypic plasticity. Our results suggest that microbiome composition played a negligible role in driving host phenotypic plasticity or microbiome-mediated rescue. One sentence summaryDaphnia exhibits considerable plasticity in individual and population-level responses to a cosmopolitan stressor, yet shifts in microbiome composition are not correlated with the magnitude of this plasticity.

ecology↗

MLDAAPP: Machine Learning Data Acquisition for Assessing Population Phenotypes

Collecting phenotypic data from many individuals is critical to numerous biological disciplines. Yet, organismal phenotypic or trait data are still often collected manually, limiting the scale of data collection, precluding reproducible workflows, and creating the potential for human bias. Computer vision could largely ameliorate these issues, but currently available packages only operate with specific inputs and hence are not scalable or accessible for many biologists. We present Machine Learning Data Acquisition for Assessing Population Phenotypes (MLDAAPP), a package of tools for collecting phenotypic data from groups of individuals. We demonstrate that MLDAAPP is both accurate and uniquely effective at measuring phenotypes in challenging conditions - particularly images and videos of varying quality derived from both lab and field environments. Employing MLDAAPP solves key issues of reproducibility, increases both the scale and scope of data generation, and reduces the potential for human bias.

evolutionary biology↗