References
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References 1. Renewable Energy Road Map. Renewable energies in the 21st century: building a more sustainable future. Communication from the Commission to the European Council and the European Parliament. Brussels, 10 January 2007. 2. Band, W. (2000). Wind farms and birds: calculating a theoretical collision risk assuming no avoidance behaviour. Scottish Natural Heritage Report . 3. Desholm, M. (2006) Wind farm related mortality among avian migrants – a remote sensing study and model analysis. PhD thesis. Dept. of Wildlife Ecology and Biodiversity, NERI, and Dept. of Population Biology, University of Copenhagen. National Environmental Research Institute, Denmark. 4. Couzin, I. D., Krause, J., James, R., Ruxton, G. D., Franks, N. R. (2002). Collective Memory and Spatial Sorting in Animal Groups. Journal of Theoretical Biology, 218(1), 1-11. 5. Codling, E. A., Pitchford, J. W., Simpson, S. D. (2007). Group navigation and the 'many wrongs principle' in models of animal movement. Ecology 88: 1864-1870. 6. Bode, N. W. F., Franks, D. W., Wood, A. J. (2011) Limited interactions in flocks: relating model simulations to empirical data. J. R. Soc. Interface 8: 301-304. 7. Cavagna, A., Giardina, I., Orlandi, A., Parisi, G., Procaccini, A., Viale, M. & Zdravkovic, V. (2008) The STARFLAG handbook on collective animal behaviour: 1. Empirical methods. Animal Behaviour, Vol. 76(1): 217-236. Original background image source: http://stanleykemp.blogspot.com/2011/01/yet-another-waste-of-money.html Figure 1 bird images: Angry Birds, developed by Rovio Mobile Ltd, published by Clickgamer Media and Chillingo. Acknowledgments We would like to thank the members of YCCSA and the University of York Biology Graphics Unit for their help and support. This research is funded by FERA and the University of York. Group navigation and wind farm collision avoidance in flocks Simon Croft, Richard Budgey, Jon Pitchford, Jamie Wood Contact Address: York Centre for Complex Systems Analysis, University of York, Heslington, York Telephone: +441904 325352 Email: [email protected] Introduction The UK has committed to a substantial shift towards renewable energy to comply with EU targets [1], a move which will treble the existing number of wind farms. The impact of these changes on wildlife is unknown. Birds may be affected many different ways including habitat loss, displacement due to disturbance, and collisions [2]. Of these, collision is potentially the most difficult to prevent, and accurate predictions of the number of impacts is required. Current models (e.g. [2]) can be inadequate in determining the frequency of bird strikes; collision avoidance is included as a fixed multiplier based on limited field data, errors can be compounded [3], and group interactions are ignored. The focus of this research is to develop a model incorporating group motion to more accurately predict bird strikes across various ecological and engineering scenarios. By taking a more holistic view of obstacle avoidance, more reliable predictions can be achieved across a wide number of species. Modelling Group Navigation Group movement can be accurately simulated through a hierarchy of interactions of individuals with neighbours [4, 5, 6], as outlined in Figure 1. Each individual is assumed to have a limited interaction range. Neighbours within this range influence the orientation of the individual depending on their proximity according to hierarchical rules. Here, there are two rules: Asocial: If the nearest neighbour (N*) is within the collision radius then avoidance takes priority; the individual moves directly away from N*. Social: If N* lies outside the collision radius then the individual moves based on the sum of influences from each neighbour in the interaction range. Reaction to obstacles within the interaction range is the same as for neighbours within that range; movement is parallel to the visible sides of the obstacle in the direction closest to the current orientation. If the obstacle lies within the collision range then the individual moves directly away from the obstacle. Figure 2 uses the model to compare social group navigation with purely asocial navigation in the absence of obstacles, showing that it is not always beneficial to navigate as part of a group. The graphical output of this model in the case where obstacles are included is shown below in figure 3. Further Work The model described above will be used on a number of different environmental and engineering scenarios to determine the likely number of collisions. Initial field work at a local landmark will be conducted to establish a framework and methodology for accurate recreation of bird flight trajectories using similar stereoscopic recording techniques and analysis to those outlined in the STARFLAG project which employed this technology to track flocks of Starlings [7]. These flight paths will then be used to measure the success of our model and to identify key parameters in the collision avoidance strategy. How’s my science? Any comments or questions please contact me via email. Figure 2. Plots of average time taken over 100 iterative runs of the simulation for all individuals to reach the target zone, against (i) group size (population); (ii) orientation variance; (iii) sensing variance; (iv) radius of orientation interaction, R O , (where the radius of group cohesion is given by R G = 1.5 R O ). The null model (Asocial) is defined as collision avoidance only with no other group interaction. Figure 1. Illustration of the effects of group interactions on the preferred direction of an individual; In all plots, Z is the individual of interest; θ 0 is the target direction; γ is the influence due to the neighbours within the green zone; R C is the radius of collision avoidance; R O is the radius of orientation interaction; and R G is the radius of group cohesion [5]. Figure 3. Graphical output from the current model at both global and local scale showing a group of individuals moving using social navigation. The left hand snapshot is taken as the group approaches the obstacle. The right hand snapshot is taken as the group reassembles after negotiating the obstacle. Figure 4. A flock of geese flying toward York Millennium Bridge (chosen field work site) on their daily route to feeding grounds.
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How’s my science? Any comments or questions please contact me via email. Group navigation and wind farm collision avoidance in flocks Simon Croft, Richard Budgey, Jon Pitchford, Jamie Wood - PowerPoint PPT Presentation
Transcript of References
References1. Renewable Energy Road Map. Renewable energies in the 21st century: building a more sustainable future. Communication from the Commission
to the European Council and the European Parliament. Brussels, 10 January 2007.2. Band, W. (2000). Wind farms and birds: calculating a theoretical collision risk assuming no avoidance behaviour. Scottish Natural Heritage Report.3. Desholm, M. (2006) Wind farm related mortality among avian migrants – a remote sensing study and model analysis. PhD thesis. Dept. of Wildlife
Ecology and Biodiversity, NERI, and Dept. of Population Biology, University of Copenhagen. National Environmental Research Institute, Denmark.4. Couzin, I. D., Krause, J., James, R., Ruxton, G. D., Franks, N. R. (2002). Collective Memory and Spatial Sorting in Animal Groups. Journal of
Theoretical Biology, 218(1), 1-11.5. Codling, E. A., Pitchford, J. W., Simpson, S. D. (2007). Group navigation and the 'many wrongs principle' in models of animal movement. Ecology
88: 1864-1870.6. Bode, N. W. F., Franks, D. W., Wood, A. J. (2011) Limited interactions in flocks: relating model simulations to empirical data. J. R. Soc. Interface 8:
301-304.7. Cavagna, A., Giardina, I., Orlandi, A., Parisi, G., Procaccini, A., Viale, M. & Zdravkovic, V. (2008) The STARFLAG handbook on collective animal
behaviour: 1. Empirical methods. Animal Behaviour, Vol. 76(1): 217-236.
Original background image source: http://stanleykemp.blogspot.com/2011/01/yet-another-waste-of-money.htmlFigure 1 bird images: Angry Birds, developed by Rovio Mobile Ltd, published by Clickgamer Media and Chillingo.
AcknowledgmentsWe would like to thank the members of YCCSA and the University of York Biology Graphics Unit for their help and support. This research is funded by FERA and the University of York.
Group navigation and wind farm collision avoidance in flocksSimon Croft, Richard Budgey, Jon Pitchford, Jamie Wood
Contact Address: York Centre for Complex Systems Analysis, University of York, Heslington, York Telephone: +441904 325352 Email: [email protected]
Introduction
The UK has committed to a substantial shift towards renewable energy to comply with EU targets [1], a move which will treble the existing number of wind farms. The impact of these changes on wildlife is unknown. Birds may be affected many different ways including habitat loss, displacement due to disturbance, and collisions [2]. Of these, collision is potentially the most difficult to prevent, and accurate predictions of the number of impacts is required.
Current models (e.g. [2]) can be inadequate in determining the frequency of bird strikes; collision avoidance is included as a fixed multiplier based on limited field data, errors can be compounded [3], and group interactions are ignored. The focus of this research is to develop a model incorporating group motion to more accurately predict bird strikes across various ecological and engineering scenarios. By taking a more holistic view of obstacle avoidance, more reliable predictions can be achieved across a wide number of species.
Modelling Group Navigation
Group movement can be accurately simulated through a hierarchy of interactions of individuals with neighbours [4, 5, 6], as outlined in Figure 1. Each individual is assumed to have a limited interaction range. Neighbours within this range influence the orientation of the individual depending on their proximity according to hierarchical rules. Here, there are two rules:
Asocial: If the nearest neighbour (N*) is within the collision radius then avoidance takes priority; the individual moves directly away from N*.
Social: If N* lies outside the collision radius then the individual moves based on the sum of influences from each neighbour in the interaction range.
Reaction to obstacles within the interaction range is the same as for neighbours within that range; movement is parallel to the visible sides of the obstacle in the direction closest to the current orientation. If the obstacle lies within the collision range then the individual moves directly away from the obstacle.
Figure 2 uses the model to compare social group navigation with purely asocial navigation in the absence of obstacles, showing that it is not always beneficial to navigate as part of a group. The graphical output of this model in the case where obstacles are included is shown below in figure 3.
Further Work
The model described above will be used on a number of different environmental and engineering scenarios to determine the likely number of collisions.
Initial field work at a local landmark will be conducted to establish a framework and methodology for accurate recreation of bird flight trajectories using similar stereoscopic recording techniques and analysis to those outlined in the STARFLAG project which employed this technology to track flocks of Starlings [7].
These flight paths will then be used to measure the success of our model and to identify key parameters in the collision avoidance strategy.
How’s my science? Any comments or questions please contact me via email.
Figure 2. Plots of average time taken over 100 iterative runs of the simulation for all individuals to reach the target zone, against (i) group size (population); (ii) orientation variance; (iii) sensing variance; (iv) radius of orientation interaction, RO, (where the radius of group cohesion is given by RG = 1.5 RO). The null model (Asocial) is defined as collision avoidance only with no other group interaction.
Figure 1. Illustration of the effects of group interactions on the preferred direction of an individual; In all plots, Z is the individual of interest; θ0 is the target direction; γ is the influence due to the neighbours within the green zone; RC is the radius of collision avoidance; RO is the radius of orientation interaction; and RG is the radius of group cohesion [5].
Figure 3. Graphical output from the current model at both global and local scale showing a group of individuals moving using social navigation. The left hand snapshot is taken as the group approaches the obstacle. The right hand snapshot is taken as the group reassembles after negotiating the obstacle.
Figure 4. A flock of geese flying toward York Millennium Bridge (chosen field work site) on their daily route to feeding grounds.
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