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Modelling collective motion and obstacle avoidance to assess avian collision risk with wind turbines

Croft, Simon (2014) Modelling collective motion and obstacle avoidance to assess avian collision risk with wind turbines. PhD thesis, University of York.

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Abstract

Hazardous obstacles are a prominent feature of all natural environments and moving animals must demonstrate a robust avoidance response in order to prevent collisions. Whilst the study of collective motion has yielded many models for simulating animal movements, comparatively few have considered interactions with such obstacles. This thesis outlines a framework for incorporating obstacles into existing models of collective motion and uses these models to explore the impact of social interactions on collision risk. The findings presented show that in the case of obstacle avoidance, where navigational information can be contradictory, the collective decisions of homogeneous groups often results in increased collision risk due to contradictory information between individuals. The introduction of heterogeneous social networks, which gives preference to particular individuals, acts as a natural mechanism by which these conflicted decisions may be averted, thereby facilitating coherent avoidance manoeuvres. However, this comes at the cost of cohesion, and groups must balance staying together against the benefits of more effective decision making. The insights provided by models are applied to assess avian collision risk with wind turbines. This is an increasingly important ecological problem and has received wide attention. The difficulties in obtaining accurate empirical data at the individual level require that accurate and robust modelling solutions are developed. The models presented in this thesis provide a powerful tool in which collision risk can be assessed taking into account site- and species-specific factors. The key observation is that both social factors such as flock size, and spatial factors such as array design, significantly affect avoidance rates and consequently collision risk. Therefore the established methods of risk assessment, which assume a general avoidance rate and apply this to each individual independently, are argued to be inadequate.

Item Type: Thesis (PhD)
Related URLs:
Keywords: individual-based model; group navigation; obstacle avoidance; social network; bird; wind turbine; collision risk
Academic Units: The University of York > Biology (York)
Identification Number/EthosID: uk.bl.ethos.680604
Depositing User: Mr Simon Croft
Date Deposited: 29 Feb 2016 11:59
Last Modified: 08 Sep 2016 13:33
URI: http://etheses.whiterose.ac.uk/id/eprint/11940

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