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Kimberly R. Andrews

Researcher at University of Idaho

Publications -  50
Citations -  2886

Kimberly R. Andrews is an academic researcher from University of Idaho. The author has contributed to research in topics: Population & Biological dispersal. The author has an hindex of 20, co-authored 45 publications receiving 2341 citations. Previous affiliations of Kimberly R. Andrews include Idaho State University & Durham University.

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Harnessing the power of RADseq for ecological and evolutionary genomics

TL;DR: This Review provides a comprehensive discussion of RADseq methods to aid researchers in choosing among the many different approaches and avoiding erroneous scientific conclusions from RADseq data, a problem that has plagued other genetic marker types in the past.
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ezRAD: a simplified method for genomic genotyping in non-model organisms

TL;DR: The simplicity of ezRAD makes it particularly useful for the discovery of single nucleotide polymorphisms and targeted amplicon sequencing in natural populations of non-model organisms that have been historically understudied because of lack of genomic information.
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Comparative phylogeography of the ocean planet.

TL;DR: Comparison of comparative phylogeography from tropical seas that host the highest marine biodiversity, temperate seas with high productivity but volatile coastlines, migratory marine fauna, and plankton that are the most abundant eukaryotes on earth are provided.
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Defining Boundaries for Ecosystem-Based Management: A Multispecies Case Study of Marine Connectivity across the Hawaiian Archipelago.

TL;DR: It is argued that this multispecies approach to determine concordant patterns of connectivity is an objective and logical way in which to define the minimum number of management units and that EBM in the Hawaiian Archipelago requires at least five spatially managed regions.
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Unbroken: RADseq remains a powerful tool for understanding the genetics of adaptation in natural populations

TL;DR: It is strongly argued that RADseq remains a powerful and efficient approach that provides sufficient marker density for studying selection in many natural populations and that researchers should consider a wide range of trade‐offs among genomic techniques.