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Predicting Migratory Corridors of White Storks, Ciconia ciconia, to Enhance Sustainable Wind Energy Planning: A Data-Driven Agent-Based Model

1
Department of Geoinformatics (Z_GIS), University of Salzburg, Schillerstraße 30, 5020 Salzburg, Austria
2
Max Planck Institute for Ornithology, Vogelwarte Radolfzell, Schlossalee 2, 78315 Radolfzell, Germany
3
School of Technology, Environments and Design, Discipline of Geography and Spatial Sciences, University of Tasmania, Churchill Avenue, Hobart, Tasmania 7001, Australia
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(5), 1470; https://doi.org/10.3390/su10051470
Received: 6 March 2018 / Revised: 1 May 2018 / Accepted: 5 May 2018 / Published: 8 May 2018
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Abstract

White storks (Ciconia ciconia) are birds that make annual long-distance migration flights from their breeding grounds in the Northern Hemisphere to the south of Africa. These trips take place in the winter season, when the temperatures in the North fall and food supply drops. White storks, because of their large size, depend on the wind, thermals, and orographic characteristics of the environment in order to minimize their energy expenditure during flight. In particular, the birds adopt a soaring behavior in landscapes where the thermal uplift and orographic updrafts are conducive. By attaining suitable soaring heights, the birds then use the wind characteristics to glide for hundreds of kilometers. It is therefore expected that white storks would prefer landscapes that are characterized by suitable wind and thermal characteristics, which promote the soaring and gliding behaviors. However, these same landscapes are also potential sites for large-scale wind energy generation. In this study, we used the observed data of the white stork movement trajectories to specify a data-driven agent-based model, which simulates flight behavior of the white storks in a dynamic environment. The data on the wind characteristics and thermal uplift are dynamically changed on a daily basis so as to mimic the scenarios that the observed birds experienced during flight. The flight corridors that emerge from the simulated flights are then combined with the predicted surface on the wind energy potential, in order to highlight the potential risk of collision between the migratory white storks and hypothetical wind farms in the locations that are suitable for wind energy developments. This work provides methods that can be adopted to assess the overlap between wind energy potential and migratory corridors of the migration of birds. This can contribute to achieving sustainable trade-offs between wind energy development and conservation of wildlife and, hence, handling the issues of human–wildlife conflicts. View Full-Text
Keywords: agent-based models; collision risk; data-driven models; sustainability; wind energy agent-based models; collision risk; data-driven models; sustainability; wind energy
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Oloo, F.; Safi, K.; Aryal, J. Predicting Migratory Corridors of White Storks, Ciconia ciconia, to Enhance Sustainable Wind Energy Planning: A Data-Driven Agent-Based Model. Sustainability 2018, 10, 1470.

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