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Understanding the patterns and processes underlying the uneven distribution of biodiversity across space and time constitutes a major scientific challenge in evolutionary biology. With rapidly accumulating species occurrence data, there is an increasing ne ed for making the process of coding species into operational units for biogeographic and evolutionary analyses faster, automated, transparent and reproducible. Here we present SpeciesGeoCoder, a free software package written in Python and R, that allows fo r easy coding of species into user-defined areas. These areas may be of any size and be purely geographical (i.e., polygons) such as political units, conservation areas, biomes, islands, biodiversity hotspots, and areas of endemism, but may also include al titudinal ranges. This flexibility allows scoring species into complex categories, such as those encountered in topographically and ecologically heterogeneous landscapes. In addition, SpeciesGeoCoder can be used to facilitate sorting and cleaning of occurr ence data. The various outputs of SpeciesGeoCoder include quantitative biodiversity statistics, global and local distribution maps, and NEXUS files that can be directly used in many phylogeny-based applications for ancestral state reconstruction, investiga tions on biome evolution, and diversification rate analyses. Our simulations indicate that even datasets containing hundreds of millions of records can be analysed in relatively short time using a regular desktop computer. We exemplify the use of our progr am through two contrasting examples: i) inferring historical dispersal of birds across the Isthmus of Panama, separating lowland vs. montane species and optimising the results onto a species-level, dated phylogeny; and ii) exploring seasonal variations in the occurrence of 10 GPS-tracked individuals of moose (Alces alces) over one year in northern Sweden. These analyses show that SpeciesGeoCoder allows an easy, flexible and fast categorisation of species distribution data for various ...

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.