A Global Model of Predicted Peregrine Falcon (Falco peregrinus) Distribution with Open Source GIS Code and 104 Open Access Layers for use by the global public

Other literature type English OPEN
Sriram, Sumithra ; Huettmann, Falk (2017)

Peregrine falcons (<i>Falco peregrinus</i>) are among the fastest members of the animal kingdom, and they are probably the most widely distributed raptors in the world; their migrations and habitats range from the tundra, mountains and some deserts to the tropics, coastal zones and urban habitats. Habitat loss, conversion, contamination, pesticides and other anthropogenic pressures are all known factors that have an adverse effect on these species. However, while peregrine falcons were removed from the list of endangered species due to rebounding populations linked with the DDT ban in many nations of the world, no accurate global distribution models have ever been developed for good conservation practice and in an open access data framework. <br><br> Here we used the best-available open access peregrine falcon data from the Global Biodiversity Information Facility (GBIF.org) to obtain the first publicly available global distribution model for peregrine falcons. For that purpose, we compiled over a hundred high resolution global GIS layers (1&thinsp;km pixel size) that incorporated various variables such as biological, climatic, and socio-economic predictors allowing to analysis habitat relationships in a holistic fashion and to build a generalizable model. These value-added layers have also been made available by us for the global public, free of charge, for further use and consumption in any modeling effort wanted (<a href="https://scholarworks.alaska.edu/handle/11122/7151" target ="_blank">https://scholarworks.alaska.edu/handle/11122/7151</a>). We created data extraction explicit in space and time also with an open source python script tool as well as with ArcGIS (via the GUI) on a PC. The obtained data cube (global, 1&thinsp;km pixel, 104 GIS layers) was "mined" with the Salford Predictive Modeler (SPM) software suite, which offers one of the best platforms for data mining, to build the prediction model for robust inference. We found that peregrine falcons are widely urbanized occurring in coastal areas and also associated with riparian zones. This is the first model ever obtained using 104 predictors on a 1&thinsp;km scale predicting the potential ecological niche of falcons around the world. While our model might show uncertainty for parts of Siberia, Russia, it has an assessed global accuracy of over 95&thinsp;% and hence provides the currently best possible public available global prediction model for peregrine falcons, based on all available empirical data. Overlaid with the national parks of the world we found that most peregrine hotspots are actually located outside of protected areas warranting more protection efforts while global change unfolds. Finally, a nationwide assessment of the presence points taken from GBIF allows for insight as to the many signatory nations that are still in violation of the open access data sharing requirement set by the Convention of Biological Diversity (CBD) and the Budapest and Berlin Declaration.
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