Biology subjects

Gabidulin, A. R.

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

4 recordsLinked to original sources

bioRxiv2025

Microbiome evolution plays a secondary role in host rapid adaptation

Understanding how populations adapt to environmental change is a central goal in evolutionary biology. Microbiomes have been proposed as a source of heritable variation that is central to rapid adaptation in hosts, yet empirical evidence supporting this remains limited, particularly in naturalistic settings. We combined a field evolution experiment in Drosophila melanogaster exposed to an insecticide with microbiome manipulations to disentangle the contributions of host standing genetic variation and microbiome evolution to adaptation. Within three generations, independent populations rapidly and repeatedly evolved increased survivorship, a defining feature of resistance evolution. Adaptive changes in sub-lethal traits such as reproductive output, stress tolerance, and body size occurred with a delayed response following the evolution of resistance. Core microbiome taxa declined following insecticide exposure, and resistant populations evolved to house lower microbial abundances. Axenic rearing and microbiome transplant experiments demonstrated that adaptation via host standing genetic variation was the mechanism for resistance evolution. Microbiome evolution played a secondary and cryptic role in host adaptation by masking slowed development rates that evolved in resistant populations. Together, these results reinforce the primacy of adaptation occurring through selection on host standing genetic variation while also demonstrating the contributions of microbiome evolution in host adaptation. SignificanceIdentifying the mechanisms that allow organisms to adapt to environmental stress is a foundational goal in biology. Using field experimental evolution and microbiome manipulations in Drosophila melanogaster, we directly tested the relative contributions of host genomic evolution and microbiome evolution to adaptation. We found that adaptation to environmental stress occurred rapidly and repeatedly, driven primarily by selection on host standing genetic variation, with microbiome evolution acting as a secondary contributor. These findings reinforce the importance of host genetic variation in rapid adaptation and demonstrate that microbiome evolution can contribute to host evolutionary trajectories in a cryptic manner.

evolutionary biology↗

bioRxiv2025

Adaptation and trade-offs with insecticide resistance in overwintering Drosophila

Winter is a formidable challenge for ectotherms that inhabit temperate climates. Prior work has demonstrated that multivoltine organisms can evolve rapidly in response to temporal changes within a growing season, a process termed adaptive tracking. However, the mechanisms by which winter conditions drive rapid adaptation, particularly when combined with strong anthropogenic stressors, remain poorly understood. Here we use replicate populations of Drosophila melanogaster in a field experiment to test i) whether winter conditions drive rapid adaptation and ii) for trade-offs between insecticide resistance and overwintering survival. Following a longitudinal field experiment spanning summer and fall investigating the evolution of insecticide resistance, we tracked subsequent evolution during an overwintering period. We detected repeated evolutionary shifts indicative of adaptation to winter conditions in multiple traits, including body size and fecundity. Additionally, populations that had evolved insecticide resistance during the growing season had reduced survival and showed patterns of lower resistance following the winter period, suggestive of a trade-off between overwintering success and insecticide resistance. These rapid evolutionary responses and potential trade-offs provide important context for understanding overwintering performance in temperate insects, with implications for pest management and ecosystem services.

evolutionary biology↗

bioRxiv2024

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↗

bioRxiv2023

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↗