Flight response to spatial and temporal correlates informs risk from wind turbines to the California Condor

It is likely that there will continue to be a substantial increase in the number of wind turbines as we aim to meet global energy demands through renewable sources. However, these structures can have adverse impacts on airborne wildlife, such as posing a potential collision risk with the turbine structure. A range of methods and technologies have been applied to the collection of bird flight parameters, such as height and speed, to improve the estimation of potential collision compared with traditional visual methods, but these are currently not applied in a consistent and systematic way. To this end, a systematic literature search was conducted to (1) examine the methods and technologies that can be used to provide bird flight data to assess the impact of wind energy developments and (2) provide an updated framework to guide how they might be most usefully applied within the impact assessment process. Four empirical measurement methods were found that improve the estimation of bird flight parameters: radar, telemetry, ornithodolite and LiDAR. These empirical sensor‐based tools were typically more often applied in academic peer‐reviewed papers than in report‐based environmental statements. Where sensor‐based tools have been used in the report‐based literature, their inconsistent application has resulted in an uncertain regulatory environment for practitioners. Our framework directly incorporates sensor‐based methods, together with their limitations and data requirements, from pre‐deployment of infrastructure to post‐consent monitoring of impacts. This revised approach will help improve the accuracy of estimation of bird flight parameters for ornithological assessment of wind energy. Sensor‐based tools may not be the most cost‐effective. However, a precedent has been set for wind energy development consent refusal based on ornithological impact assessment, and therefore the cost of collecting accurate and reliable flight data may be balanced favourably against the cost of development consent refusal.

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“…Poessel et al . 2018) or to restrict the data to within a certain range of the sensor (e.g. Welcker et al .…”

Section: Resultsmentioning

confidence: 99%

“…2015, Poessel et al . 2018), to remote‐sensing approaches, such as radar (Stumpf et al . 2011, Hulka et al .…”

mentioning

confidence: 99%

Methods to quantify avian airspace use in relation to wind energy development

Largey

Cook

Thaxter

et al. 2021

Ibis

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“…We removed data points assumed to be stationary, which had 2D speed less than the cut‐off and AGL <135.95 m (the sum of the maximum expected errors; i.e. within the range of zero AGL; Poessel et al., 2018; Schaub et al., 2020).…”

Section: Methodsmentioning

confidence: 99%

A predictive model for improving placement of wind turbines to minimise collision risk potential for a large soaring raptor

Murgatroyd

Bouten

Amar

2021

Journal of Applied Ecology

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With the rapid growth of wind energy developments world‐wide, it is critical that the negative impacts on wildlife are considered and mitigated. This includes minimising the number of large soaring raptors, which are killed when they collide with wind turbines. To reduce the likelihood of raptor collisions, turbines should be placed at locations which are least used by sensitive species. For resident or breeding species, this is often delineated crudely through the use of circular buffers centred on nest sites, which assume uniform habitat use around a nest site. Using GPS tracking data together with a digital elevation model we build and cross‐validate a simple generalisable model, to classify the spatial likelihood of wind turbine collisions for resident adult Verreaux's eagles in any landscape where there are known nests. We apply our methods to operational developments in South Africa to validate the model and demonstrate its ability in predicting actual collision mortalities. Our collision risk potential (CRP) model included the variables distance to nest, distance to conspecific nest, slope, distance to slope and elevation. Using our model, rather than a circular buffer, resulted in c. 4%–5% improvement in eagle protection while excluding development from the same amount (but not shape) of area. For an equal level of eagle protection, our model can make c. 20%–21% more area available for wind energy development compared to a circular buffer. Exploring collisions at operational wind farms in South Africa we show that our CRP model correctly predicted 79% of known collisions, while circular buffers (5.2 km radius) only captured 50% of collisions. Synthesis and applications. We show that by using predictive models to account for habitat use instead of simple buffers around a nest, a greater area of land can be made available for wind energy development without increased mortality risk to raptors. Our predictive model can be used to provide robust guidance on wind turbine placement in South Africa in a way which minimises the conflict between a vulnerable raptor species and the development of renewable energy.

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“…In fact, there are a suite of variables that correlate with flight altitude of soaring birds (e.g. see Poessel et al., in press) but are uncorrelated with each other. Use of these variables would be logically consistent for statistical modelling.…”

Section: From Flight Altitude To Risk Modellingmentioning

confidence: 99%

Improving estimation of flight altitude in wildlife telemetry studies

Poessel

Duerr²,

Hall

et al. 2018

Journal of Applied Ecology

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Altitude measurements from wildlife tracking devices, combined with elevation data, are commonly used to estimate the flight altitude of volant animals. However, these data often include measurement error. Understanding this error may improve estimation of flight altitude and benefit applied ecology. There are a number of different approaches that have been used to address this measurement error. These include filtering based on GPS data, filtering based on behaviour of the study species, and use of state‐space models to correct measurement error. The effectiveness of these approaches is highly variable. Recent studies have based inference of flight altitude on misunderstandings about avian natural history and technical or analytical tools. In this Commentary, we discuss these misunderstandings and suggest alternative strategies both to resolve some of these issues and to improve estimation of flight altitude. These strategies also can be applied to other measures derived from telemetry data. Synthesis and applications. Our Commentary is intended to clarify and improve upon some of the assumptions made when estimating flight altitude and, more broadly, when using GPS telemetry data. We also suggest best practices for identifying flight behaviour, addressing GPS error, and using flight altitudes to estimate collision risk with anthropogenic structures. Addressing the issues we describe would help improve estimates of flight altitude and advance understanding of the treatment of error in wildlife telemetry studies.

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Flight response to spatial and temporal correlates informs risk from wind turbines to the California Condor

Cited by 24 publications

References 35 publications

Methods to quantify avian airspace use in relation to wind energy development

Methods to quantify avian airspace use in relation to wind energy development

A predictive model for improving placement of wind turbines to minimise collision risk potential for a large soaring raptor

Improving estimation of flight altitude in wildlife telemetry studies

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