Modelling golden eagle habitat selection and flight activity in their home ranges for safer wind farm planning

Tikkanen, Hannu; Rytkönen, Seppo; Karlin, Olli-Pekka; Ollila, Tuomo; Pakanen, Veli‐Matti; Tuohimaa, Heikki; Orell, Markku

doi:10.1016/j.eiar.2018.04.006

Cited by 29 publications

(30 citation statements)

References 46 publications

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“…They make the simplistic assumption that the higher the abundance of individuals of a given species is at a particular site, the higher their susceptibility for collision with wind energy structures installed at that particular site [31,32]. This assumption has been readily challenged by many researchers, since their findings show that the pre-construction bird abundances and the observed numbers of carcasses as a measure of the post-construction bird collisions through detections are not closely related [12,33,34]. The German State Bird Conservancies have also additionally developed recommendations in terms of the distances of wind turbines to such important bird areas as well as to the breeding sites of different species of birds [35].…”

Section: Introductionmentioning

confidence: 99%

Predicting strike susceptibility and collision patterns of the common buzzard at wind turbine structures in the federal state of Brandenburg, Germany

et al. 2020

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With the increase in wind turbines, bird collisions have developed as a potential hazard. In the federal state of Brandenburg, Germany, despite the ongoing mitigation efforts of increasing the distances of wind turbines from the breeding areas of the more severely affected populations of red kites (Milvus milvus), the additional detrimental influences on the buzzard populations (Buteo buteo) have added to the challenges for wind power expansion. Using data on the regional distribution of the buzzards, along with their carcass detections around the wind turbines (WTs), we aimed to better understand their collision distribution patterns in relation to their habitat use patterns to predict their exposure to collision risk using boosted regression trees (BRTs). Additionally, we integrated the developed collision potential map with the regional density map of buzzards to identify areas of increased strike susceptibility in turbine installations. Our study showed that the buzzard collisions were primarily concentrated at the turbines situated at sensitive distances from the edges of watercourses (>1000 metres), as well as those along the edges of grasslands (>750 metres), in the green open areas around/areas with minimal settlements (750 metres-1750 metres), and along the edges of bushlands (>1500 metres), together explaining 58% of the variance in their collision distribution. Conclusively, our study is applicable to conservation because it demonstrates the identification of potential collision areas along with the causes of the collisions, in addition to demonstrating the benefits of incorporating a species collision dataset as a proxy for species presence into species distribution models to make informed management decisions to eventually combat biodiversity loss.

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Section: Introductionmentioning

confidence: 99%

Predicting strike susceptibility and collision patterns of the common buzzard at wind turbine structures in the federal state of Brandenburg, Germany

et al. 2020

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“…The distance to the nest was included in all models because it is known to be important in describing space use by all breeding raptors, which demonstrate central‐place foraging by typically returning to a nest (Newton, 1979). Slope was the next most important variable in our CRP model, likely due to the orographic lift which is generated along hillsides and is a primary determinant of how large territorial raptors use their surrounding landscape (Bosch et al., 2010; Murgatroyd et al., 2018; Reid et al., 2015; Tikkanen et al., 2018; Watson et al., 2014). In addition, CRP was also higher at closer distances to any slope, reflecting the habitat preference of these areas.…”

Section: Discussionmentioning

confidence: 99%

“…Predictive space‐use models are able to overcome many of these issues if they are generalisable to areas where no tracking data have been collected. For raptors, a few predictive models have been devised to determine collision risk (Mcleod et al., 2002; Reid et al., 2015; Tikkanen et al., 2018), however, their generalisability and model performance are rarely explored, and we are un‐aware of any study that directly compares the efficacy of describing raptor space use between predictive models and circular buffers (but see: Watson et al., 2014).…”

Section: Introductionmentioning

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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“…Flight height datasets are often reduced to a single summary metric, the mean flight height and its variation with environmental and individual covariates [29,[46][47][48][49]. This decision is mostly based on the ease of implementing spreadsheets, linear models, moving averages, or spline models.…”

Section: The Mean Flight Height Is Not Sufficient To Describe the Distribution Of Flight Heightsmentioning

confidence: 99%

The challenges of estimating the distribution of flight heights from telemetry or altimetry data

et al. 2020

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Background Global positioning systems (GPS) and altimeters are increasingly used to monitor vertical space use by aerial species, a key aspect of their ecological niche, that we need to know to manage our own use of the airspace, and to protect those species. However, there are various sources of error in flight height data (“height” above ground, as opposed to “altitude” above a reference like the sea level). First the altitude is measured with a vertical error from the devices themselves. Then there is error in the ground elevation below the tracked animals, which translates into error in flight height computed as the difference between altitude and ground elevation. Finally, there is error in the horizontal position of the animals, which translates into error in the predicted ground elevation below the animals. We used controlled field trials, simulations, and the reanalysis of raptor case studies with state-space models to illustrate the effect of improper error management. Results Errors of a magnitude of 20 m appear in benign conditions for barometric altimeters and GPS vertical positioning (expected to be larger in more challenging context). These errors distort the shape of the distribution of flight heights, inflate the variance in flight height, bias behavioural state assignments, correlations with environmental covariates, and airspace management recommendations. Improper data filters such as removing all negative flight height records introduce several biases in the remaining dataset, and preclude the opportunity to leverage unambiguous errors to help with model fitting. Analyses that ignore the variance around the mean flight height, e.g., those based on linear models of flight height, and those that ignore the variance inflation caused by telemetry errors, lead to incorrect inferences. Conclusion The state-space modelling framework, now in widespread use by ecologists and increasingly often automatically implemented within on-board GPS data processing algorithms, makes it possible to fit flight models directly to the output of GPS devices, with minimal data pre-selection, and to analyse the full distribution of flight heights, not just the mean. In addition to basic research about aerial niches, behaviour quantification, and environmental interactions, we highlight the applied relevance of our recommendations for airspace management and the conservation of aerial wildlife.

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Modelling golden eagle habitat selection and flight activity in their home ranges for safer wind farm planning

Cited by 29 publications

References 46 publications

Predicting strike susceptibility and collision patterns of the common buzzard at wind turbine structures in the federal state of Brandenburg, Germany

Predicting strike susceptibility and collision patterns of the common buzzard at wind turbine structures in the federal state of Brandenburg, Germany

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

The challenges of estimating the distribution of flight heights from telemetry or altimetry data

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