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Biogeographical regions (bioregions) reveal how different sets of species are spatially grouped and therefore are important units for conservation, historical biogeography, ecology, and evolution. Several methods have been developed to identify bioregions based on species distribution data rather than expert opinion. One approach successfully applies network theory to simplify and highlight the underlying structure in species distributions. However, this method lacks tools for simple and efficient analysis. Here, we present Infomap Bioregions, an interactive web application that inputs species distribution data and generates bioregion maps. Species distributions may be provided as georeferenced point occurrences or range maps, and can be of local, regional, or global scale. The application uses a novel adaptive resolution method to make best use of often incomplete species distribution data. The results can be downloaded as vector graphics, shapefiles, or in table format. We validate the tool by processing large data sets of publicly available species distribution data of the world's amphibians using species ranges, and mammals using point occurrences. We then calculate the fit between the inferred bioregions and WWF ecoregions. As examples of applications, researchers can reconstruct ancestral ranges in historical biogeography or identify indicator species for targeted conservation. ; Swedish Research Council ; European Research Council under the European Union's Seventh Framework Programme ; Wallenberg Academy Fellowship ; Sao Paulo Research Foundation ; Umea Univ, Dept Phys, Integrated Sci Lab, SE-90187 Umea, Sweden ; Univ Gothenburg, Dept Biol & Environm Sci, POB 461, SE-40530 Gothenburg, Sweden ; Univ Fed Sao Paulo, BR-09972270 Diadema, Brazil ; Univ Sao Paulo, Museum Zool, BR-04263000 Sao Paulo, Brazil ; Gothenburg Bot Garden, Carl Skottsbergs Gata 22A, S-41319 Gothenburg, Sweden ; Univ Fed Sao Paulo, BR-09972270 Diadema, Brazil ; Swedish Research Council: B0569601 ; Swedish Research Council: 2012-3729 ; European Research Council under the European Union's Seventh Framework Programme: 331024 ; FAPESP: 2013/04170-8 ; FAPESP: 2014/18837-7 ; Web of Science

This folder contains the data and analysis scripts necessary to reproduce the example analysis presented in Zizka et al (2021) Bio-Dem, a tool to explore the relationship between biodiversity data availability and socio-political conditions in time and space.

Aim: Geo-referenced species occurrence records are a prime example of biodiversity data and a cornerstone of biodiversity research. Yet, their availability depends on non-biological factors, including the political framework in the region of collection. Here we present Bio-Dem (www.bio-dem.surge.sh), an open-source software to explore the relationship between the availability of species occurrence records and the political framework in countries worldwide and through time. Location: Global. Taxon: Any. Methods: Bio-Dem accesses the number of occurrence records available from countries worldwide from the Global Biodiversity Information Facility (www.gbif.org) and socio-political information from these countries from the Varieties of Democracy database (www.v-dem.net) as well as information on colonial history from the Issue Correlates of War Project. Results: Bio-Dem is available as a free graphical user interface web application at www.bio-dem.surge.sh. Through three interactive graphs, Bio-Dem enables users to explore the relationship between species occurrence records and countries' regime type, freedom of movement, freedom of association, gross domestic product, education length, political corruption, armed conflict and colonial history. We describe possible links of these specific political indicators with the collection and mobilization of biodiversity data. Illustrative examples and video tutorials explaining the use of Bio-Dem are available at www.bio-dem.surge.sh. Main conclusions: Bio-Dem provides a user-friendly way to explore the link between socio-political variables and the availability of species occurrence records in user-selected taxa and geographic regions, and to generate hypotheses on their connection. We envision Bio-Dem as a useful tool for teachers communicating the caveats of available biodiversity data, for biodiversity scientists exploring potential spatial and temporal bias in their data, and for social scientists exploring the impact of political systems on scientific data collection and biodiversity conservation.