Macroecology in the age of Big Data – Where to go from here?

• DOI: 10.1111/jbi.13633
• Publication Date: 2019-07-18T02:16:55Z
• Authors: Rafael O. Wüest; Niklaus E. Zimmer...; Damaris Zurell; Jake M. Alexander; Susanne A. Fritz; Christian Hof; Holger Kreft; Signe Normand; Juliano Sarmento ...; Eniko Szekely; Wilfried Thuiller; Martin Wikelski; Dirk Nikolaus Karger
• Keywords: 0106 biological sciences; range dynamics; wallacean shortfall; [SDE.MCG]Environmental Sciences/Global Changes; 610; 01 natural sciences; diversity; remote sensing; competitors; conference overview; patterns; [SDE.ES]Environmental Sciences/Environment and Society; Macroecology; climate; biogeography; biodiversity; Biogeography; Conference overview; Data science; Linnean shortfall; Machine learning; Macroecology; Remote sensing; Space-borne ecology; Wallacean shortfall; info:eu-repo/classification/ddc/570; Linnean shortfall; space-borne ecology; [SDE.ES]Environmental Sciences/Environmental and Society; Wallacean shortfall; niche; machine learning; species distributions; Biogeography; 13. Climate action; [SDE]Environmental Sciences; macroecology; data science; predictions; [SDE.BE]Environmental Sciences/Biodiversity and Ecology; linnean shortfall

AbstractRecent years have seen an exponential increase in the amount of data available in all sciences and application domains. Macroecology is part of this “Big Data” trend, with a strong rise in the volume of data that we are using for our research. Here, we summarize the most recent developments in macroecology in the age of Big Data that were presented at the 2018 annual meeting of the Specialist Group Macroecology of the Ecological Society of Germany, Austria and Switzerland (GfÖ). Supported by computational advances, macroecology has been a rapidly developing field over recent years. Our meeting highlighted important avenues for further progress in terms of standardized data collection, data integration, method development and process integration. In particular, we focus on (a) important data gaps and new initiatives to close them, for example through space‐ and airborne sensors, (b) how various data sources and types can be integrated, (c) how uncertainty can be assessed in data‐driven analyses and (d) how Big Data and machine learning approaches have opened new ways of investigating processes rather than simply describing patterns. We discuss how Big Data opens up new opportunities, but also poses new challenges to macroecological research. In the future, it will be essential to carefully assess data quality, the reproducibility of data compilation and analytical methods, and the communication of uncertainties. Major progress in the field will depend on the definition of data standards and workflows for macroecology, such that scientific quality and integrity are guaranteed, and collaboration in research projects is made easier.