%0 Journal Article %A Kevin Leempoel %A Solange Duruz %A Estelle Rochat %A Ivo Widmer %A Pablo Orozco-terWengel %A Stéphane Joost %T Simple rules for an efficient use of Geographic Information Systems in molecular ecology %D 2017 %R 10.1101/113225 %J bioRxiv %P 113225 %X Geographic Information Systems (GIS) are becoming increasingly popular in the context of molecular ecology and conservation biology thanks to their display options efficiency, flexibility and management of geodata. Indeed, spatial data for wildlife and livestock species is becoming a trend with many researchers publishing genomic data that is specifically suitable for landscape studies. GIS uniquely reveal the possibility to overlay genetic information with environmental data and, as such, allow us to locate and analyze genetic boundaries of various plant and animal species or to study gene-environment associations (GEA). This means that, using GIS, we can potentially identify the genetic bases of species adaptation to particular geographic conditions or to climate change. However, many biologists are not familiar with the use of GIS and underlying concepts and thus experience difficulties in finding relevant information and instructions on how to use them. In this paper, we illustrate the power of free and open source GIS approaches and provide essential information for their successful application in molecular ecology. First, we introduce key concepts related to GIS than are too often overlooked in the literature, for example coordinate systems, GPS accuracy and scale. We then provide an overview of the most employed open-source GIS-related software, file formats and refer to major environmental databases. We also reconsider sampling strategies as high costs of Next Generation Sequencing (NGS) data currently diminish the number of samples that can be sequenced per location. Thereafter, we detail methods of data exploration and spatial statistics suited for the analysis of large genetic datasets. Finally, we provide suggestions to properly edit maps and to make them as comprehensive as possible, either manually or trough programming languages.Rasterregular grids of pixels that describe continuous phenomena, retaining information such as color (for aerial images), elevation, temperature.Vectorpoints, lines or polygons whose nodes are defined by geographical coordinates and describe discrete phenomenon such as borders, rivers, catchment areas. Vectors are usually stored in Shapefiles (.shp and associated files).DatumThe datum defines the 3-dimensional sphere used to approximate the earth. It provides a frame of reference to measure coordinates in both geographic and projected coordinate systems.Geographic Coordinate SystemA GCS give the coordinates (i.e. latitude and longitude) of a point as measured from the angles to the center of a defined sphere and meridian.Projected Coordinate systemA PCS is a projection of the sphere on a flat, two-dimensional surface. Its coordinates (X and Y) are thus consistent and equally spaced.DEMDigital Elevation Models are grids of elevation data. Each pixel of that grid is spaced at regular horizontal intervals and contains one value of elevation.GrainThe grain is the size of a pixel, the smallest unit on a grid. A small grain corresponds to a high spatial resolution.ExtentThe extent is the size of the study area. %U https://www.biorxiv.org/content/biorxiv/early/2017/03/02/113225.full.pdf