An Updated Review of Hypotheses Regarding Bat Attraction to Wind Turbines

Abstract

Patterns of bat activity and mortalities at wind energy facilities suggest that bats are attracted to wind turbines based on bat behavioral responses to wind turbines. For example, current monitoring efforts suggest that bat activity increases post-wind turbine construction, with bats making multiple passes near wind turbines. We separated the attraction hypothesis into five previously proposed explanations of bat interactions at or near wind turbines, including attraction based on noise, roost sites, foraging and water, mating behavior, and lights, and one new hypothesis regarding olfaction, and provide a state of the knowledge in 2022. Our review indicates that future research should prioritize attraction based on social behaviors, such as mating and scent-marking, as this aspect of the attraction hypothesis has many postulates and remains the most unclear. Relatively more data regarding attraction to wind turbines based on lighting and noise emission exist, and these data indicate that these are unlikely attractants. Analyzing attraction at the species-level should be prioritized because of differences in foraging, flight, and social behavior among bat species. Lastly, research assessing bat attraction at various scales, such as the turbine or facility scale, is lacking, which could provide important insights for both wind turbine siting decisions and bat mortality minimization strategies. Identifying the causes of bat interactions with wind turbines is critical for developing effective impact minimization strategies.

Activity of forest specialist bats decreases towards wind turbines at forest sites

“…Different responses may even be related to intraspecies variation across bat individuals, as was suggested by GPS tracking studies on N. noctula around wind turbines (Reusch et al, 2022; Roeleke et al, 2016). In contrast, our finding of open‐space foragers being attracted to wind turbines in late summer aligns with numerous previous studies suggesting an attraction effect of wind turbines on open‐space foragers, hypothesizing various, yet untested causes (Guest et al, 2022). Given the seasonality of the attraction, open‐space foragers possibly confuse forest turbines with tall trees, when searching for orientation points or stop‐over roosts during fall migration (Cryan et al, 2014; Jameson & Willis, 2014).…”

International assessment of priority environmental issues for land-based and offshore wind energy development

“…These include behaviors, such as avoidance and displacement, that can affect survival and reproduction if individuals alter their normal activity, use, or flight patterns to maneuver around the wind farm (May, 2015). Another type of behavioral response, attraction, can increase collision risk if individuals spend more time near the rotor-swept area (Cryan & Barclay, 2009; Guest et al, 2022; Kunz et al, 2007; Thaxter et al, 2019). Habitat alteration can also indirectly affect species if the development of a wind farm results in the loss of foraging, roosting, or mating resources (Watson et al, 2018).…”

A deep-learning based approach to detect and classify animals flying near wind turbines using thermal surveillance cameras and open-source software

“…There are many research questions yet to be answered regarding bat behavior at wind turbines, and the impetus for this work was to help facilitate the completion, dissemination, and scientific transparency of such work. For example, testing hypotheses of bat susceptibility to wind turbines (3, 20, 63) will be critical to understanding and advancing impact minimization solutions, and such research depends on obtaining observational data that are more temporally and behaviorally precise than existing information. We hope our findings open new opportunities for increasing the feasibility of such studies.…”

“…Analyzing image data with CNNs has been particularly effective in computer vision applications over the past 20 years (46)(47)(48). Machine learning and computer vision have been widely applied to wildlife research involving image-based surveillance [e.g., (49)(50)(51)(52)(53)(54)(55)(56)(57)(58)(59)(60)(61)(62)(63)] and more broadly in the biological sciences (44,53).…”