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

• Main Authors: Kevin Leempoel, Solange Duruz, Estelle Rochat, Ivo Widmer, Pablo Orozco-terWengel, Stéphane Joost
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2017-04-01
• Series: Frontiers in Ecology and Evolution
• Subjects: Geographic Information Systems; spatial analysis; landscape genetics; gene-environment associations; open-source software; geographic map
• Online Access: http://journal.frontiersin.org/article/10.3389/fevo.2017.00033/full

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 that 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.