Simple Rules for an Efficient Use of Geographic Information Systems in Molecular Ecology

Leempoel, Kevin; Duruz, Solange; Rochat, Estelle; Widmer, Ivo; Wengel, Pablo Orozco Ter; Joost, Stéphane

doi:10.3389/fevo.2017.00033

Cited by 26 publications

(28 citation statements)

References 46 publications

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“…Alternatively, problems with multicollinearity can be avoided by selecting one environmental variable as a representative of each correlated set (e.g., Trumbo et al, 2013 ). An overview of the use of GIS in landscape genomics studies is provided in Leempoel et al ( 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Navigating the Interface Between Landscape Genetics and Landscape Genomics

2018

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As next-generation sequencing data become increasingly available for non-model organisms, a shift has occurred in the focus of studies of the geographic distribution of genetic variation. Whereas landscape genetics studies primarily focus on testing the effects of landscape variables on gene flow and genetic population structure, landscape genomics studies focus on detecting candidate genes under selection that indicate possible local adaptation. Navigating the transition between landscape genomics and landscape genetics can be challenging. The number of molecular markers analyzed has shifted from what used to be a few dozen loci to thousands of loci and even full genomes. Although genome scale data can be separated into sets of neutral loci for analyses of gene flow and population structure and putative loci under selection for inference of local adaptation, there are inherent differences in the questions that are addressed in the two study frameworks. We discuss these differences and their implications for study design, marker choice and downstream analysis methods. Similar to the rapid proliferation of analysis methods in the early development of landscape genetics, new analytical methods for detection of selection in landscape genomics studies are burgeoning. We focus on genome scan methods for detection of selection, and in particular, outlier differentiation methods and genetic-environment association tests because they are the most widely used. Use of genome scan methods requires an understanding of the potential mismatches between the biology of a species and assumptions inherent in analytical methods used, which can lead to high false positive rates of detected loci under selection. Key to choosing appropriate genome scan methods is an understanding of the underlying demographic structure of study populations, and such data can be obtained using neutral loci from the generated genome-wide data or prior knowledge of a species' phylogeographic history. To this end, we summarize recent simulation studies that test the power and accuracy of genome scan methods under a variety of demographic scenarios and sampling designs. We conclude with a discussion of additional considerations for future method development, and a summary of methods that show promise for landscape genomics studies but are not yet widely used.

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Section: Methodsmentioning

confidence: 99%

Navigating the Interface Between Landscape Genetics and Landscape Genomics

2018

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“…The genetic boundaries of various plant and animal species can be geographically located (Leempoel et al, 2017), and a large number of honey bee subspecies can be separated using morphological and genetic characteristics (Meixner et al, 2013). Almost each country has regions for native bees and other regions GIS for beekeeping development for hybrid bees (native bees mixed with other bee subspecies).…”

Section: Gis and Honey Bee Morphology And Geneticsmentioning

confidence: 99%

Geographical Information System for Beekeeping Development

Abou‐Shaara

2019

Journal of Apicultural Science

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Geographical Information System (GIS) is a computer-based system that can perform analysis for various datasets in relation to specific geographical locations and has been widely applied in agriculture. So far, GIS has been used in relatively few studies related to honey bees and beekeeping, but this article aims to review those previously performed and to present trends to apply GIS in beekeeping and then to help and guide researchers to perform more studies utilizing it. GIS can clearly be utilized to select suitable locations for apiaries, to map bee plants, to study bee behavior, diseases and pests, and to predict the effects of climate change on honey bees beside other benefits for beekeeping. Many obstacles and problems facing beekeeping can be mapped and analyzed using GIS, and other techniques can be used with it to perform the required tasks as presented in this review.

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“…Spatially-explicit computer simulations with SPLATCHE2 require a 2D landscape/map, which, for various regions of the world, can be imported from a Geographical Information System (GIS) [ 63 ]. This map can be split into a grid of small areas (demes) with a given deme size.…”

Section: Simulation Of Genetic Gradientsmentioning

confidence: 99%

Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients

Branco

Arenas

2018

Genes

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Selecting among alternative scenarios of human evolution is nowadays a common methodology to investigate the history of our species. This strategy is usually based on computer simulations of genetic data under different evolutionary scenarios, followed by a fitting of the simulated data with the real data. A recent trend in the investigation of ancestral evolutionary processes of modern humans is the application of genetic gradients as a measure of fitting, since evolutionary processes such as range expansions, range contractions, and population admixture (among others) can lead to different genetic gradients. In addition, this strategy allows the analysis of the genetic causes of the observed genetic gradients. Here, we review recent findings on the selection among alternative scenarios of human evolution based on simulated genetic gradients, including pros and cons. First, we describe common methodologies to simulate genetic gradients and apply them to select among alternative scenarios of human evolution. Next, we review previous studies on the influence of range expansions, population admixture, last glacial period, and migration with long-distance dispersal on genetic gradients for some regions of the world. Finally, we discuss this analytical approach, including technical limitations, required improvements, and advice. Although here we focus on human evolution, this approach could be extended to study other species.

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Simple Rules for an Efficient Use of Geographic Information Systems in Molecular Ecology

Cited by 26 publications

References 46 publications

Navigating the Interface Between Landscape Genetics and Landscape Genomics

Navigating the Interface Between Landscape Genetics and Landscape Genomics

Geographical Information System for Beekeeping Development

Selecting among Alternative Scenarios of Human Evolution by Simulated Genetic Gradients

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