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Some wind farms have implemented automated camera-based monitoring systems, e.g., IdentiFlight to mitigate the impact of wind turbines on protected birds. These systems have promoted the collection of large amounts of unique data that can be used to describe flight behavior in a novel way. The aim of this study was to evaluate how this unique data can be used to create a robust quantitative behavioral analysis, that can be used to identify risk-prone flight behavior and avoidance behavior and thereby used to assess collision risk in the future. This was achieved through a case study at a wind farm on the Swedish island Gotland, where golden eagles (Aquila chrysaetos), white-tailed eagles (Haliaeetus albicilla), and red kites (Milvus milvus), were chosen as the bird species. These three species are generally rare breeds in Europe and have also been shown to be particularly vulnerable to collisions with wind turbines. The results demonstrate that data from the IdentiFlight system can be used to identify risk-prone flight behaviors, e.g., tortuous flight and foraging behavior. Moreover, it was found that these flight behaviors were affected by both weather conditions, but also their distance to the nearest wind turbine. This data can, thus, be used to evaluate collision risk and avoidance behavior. This study presents a promising framework for future research, demonstrating how data from camera-based monitoring systems can be utilized to quantitatively describe risk-prone behavior and thereby assess collision risk and avoidance behavior.

Behavioral instability has recently been introduced as a new quantitative method for investigating the personality of animals through the use of behavioral reaction norms (Linder et al. 2020). Previous methods used in the field are unable to produce comparable quantitative results and lack a protocol ensuring systematic data sampling (Shyne 2006, Rose et al. 2017, Linder et al. 2020). The methods used in the past have primarily been used to estimate the percentage of time spent on various behaviors (Altmann 1974). These traditional methods are deeply dependent on the ethogram used, since the percentage of time spent on one behavior always will be dependent on the amount of time spent on other behaviors. Hence, reducing the reproducibility of results and making comparisons between studies difficult (Linder et al. 2020). Furthermore, the methods previously used do not account for the predictability of an individual (the probability that a given behavior occurs). The predictability of an individual is an important factor when assessing its personality and the novel approach using behavioral instability takes the predictability of the individual into account when assessing their personality. This is enabled by analyzing the variables: median, variance, asymmetry index and kurtosis of the time spent on all occurrences of an individual’s various behaviors (Linder et al. 2020). The asymmetry index (skewness) is a measure of the asymmetry of a distribution, i.e. how much a distribution is tilted due to a high frequency of extreme values in one direction (Mann 2016). The kurtosis is a measure of the relative distance from the center of the distribution to the tails, i.e. how spread the values are around the center of the distribution (Balanda and MacGillivray 1988). Linder et al. (2020) have showed the application of behavioral instability in the investigation of behavioral reaction norms in a case study investigating the behavior of two captive polar bears at Aalborg Zoo. Thus, showing how the estimation of the variables: median, variance, asymmetry index and kurtosis can be used for assessing the effectiveness of enrichment manipulation. The results presented in Linder et al. (2020) show an intuitive visual representation of the individuals’ reaction norms where the variables describing behavioral instability are plotted as a response to an environmental stimulus. Our aim is to demonstrate a novel way of visually representing this type of data as a three-dimensional plot, exemplified with the data collected and presented in Linder et al. (2020). First, the variables of behavioral instability were plotted simultaneously for different behaviors demonstrating the effect of the environmental stimuli and secondly, three different behaviors were plotted against each other showing the individuals’ responses to the environmental stimuli for each of the variables, respectively. This novel way of illustrating an individual’s reaction norms, enables the immediate identification of changes in an individual’s predictability for a given behavior. This is exemplified in the figures presented below.