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Robert R. Fitak

Researcher at University of Central Florida

Publications -  44
Citations -  927

Robert R. Fitak is an academic researcher from University of Central Florida. The author has contributed to research in topics: Population & Magnetoreception. The author has an hindex of 13, co-authored 40 publications receiving 654 citations. Previous affiliations of Robert R. Fitak include University of Arizona & Duke University.

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Abiotic factors shape microbial diversity in Sonoran Desert soils

TL;DR: The results of the replicate sampling approach show that microbial communities are shaped primarily by soil characteristics associated with geographic locations, while rhizosphere associations are secondary factors, and highlight the importance of culture-independent approaches in surveying microbial communities of extreme environments.
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Prospects and challenges for the conservation of farm animal genomic resources, 2015-2025

TL;DR: There is still a large gap between the current state-of-the-art in the use of tools to characterize genomic resources and its application to many non-commercial and local breeds, hampering the consistent utilization of genetic and genomic data as indicators of genetic erosion and diversity.
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The de novo genome assembly and annotation of a female domestic dromedary of North African origin

TL;DR: The de novo assembly of a female dromedary genome provides a useful resource of genomic variation for future studies of the camel's adaptations to arid environments and economically important traits, and suggests that draft genome assemblies constructed with only two differently sized sequencing libraries can be comparable to those sequenced using additional library sizes.
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Bringing the analysis of animal orientation data full circle: model-based approaches with maximum likelihood.

TL;DR: A new R package called CircMLE is developed in R to implement the maximum likelihood analysis of circular data and provides a convenient interface that facilitates the use of model-based approaches in animal orientation studies.
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OptM: estimating the optimal number of migration edges on population trees using Treemix.

TL;DR: In this paper, an optimal value of m can be inferred from the second-order rate of change in likelihood (Δm) across incremental values of m. This method has been implemented in a freely available R package called "OptM" and as a web application (https://rfitak.shinyapps.io/OptM/) to interface directly with the output files of Treemix.