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A study of White-tailed Eagle Haliaeetus albicilla movements and mortality at a wind farm in Norway

Authors:
Torgeir Nygård at Norwegian Institute for Nature Research
Torgeir Nygård
Kjetil Bevanger at Norwegian Institute for Nature Research
Kjetil Bevanger
Espen Lie Dahl at Norwegian Institute for Nature Research
Espen Lie Dahl
Øystein Flagstad at Norwegian Institute for Nature Research
Øystein Flagstad
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Abstract and Figures

The wind power plant on the island of Smøla, western Norway, is currently the largest in Norway; it has 68 turbines with nominal capacity of 2–2.3 MW each, hub height of 70 m and rotor blade radius of 38–41 m,. It was constructed in two phases between 2001 and 2005. Approximately 60 White-tailed Eagle Haliaeetus albicilla territories are found in the whole Smøla archipelago. Before construction there were 13 Eagle pairs holding territories in the wind farm area and within 500 m of it, whereas in 2009 this was reduced to only five. Since 1996, baseline data on the White-tailed Eagle population size and reproduction have been collected. In a post-construction study, 50 fledglings were satellite-tagged during 2003–2009, of which 45 provided more than 80 000 GPS positions in total. In addition to the geographical location, data on altitude and flight speed were provided by the transmitters (Microwave Telemetry, Inc., Columbia, MD, USA). Juveniles of both sexes stayed within the Smøla archipelago during their first winter. Most individuals moved away from the area during spring in their second year (April–May). Females dispersed further than males, often more than 800 km during summer, generally to the north. There was a return movement to the natal area during the second autumn. The same pattern was repeated in the third and fourth years for females, while the males showed more philopatry (Bevanger et al., 2009). From August 2005 to May 2010, four of the satellite-tagged birds were killed by collisions with turbines, of 36 White-tailed Eagles in total, involving 20 adults, nine immatures and seven juveniles. April and May are the months with the highest collision frequencies, with 13 (c. 36%) and nine (c. 25%) of the known fatalities.
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Nygård'
et#al.'2010.'BOU#Proceedings#–#Climate#Change#and#Birds.
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http://www.bou.org.uk/bouproc‐net/ccb/nygard‐etal.pdf'
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©'2010'BOU'&'The'Author(s)'
1
This paper forms part of the proceedings from the BOU conference C l i m a t e Ch a n g e and B i r d s .
Other papers from these proceedings can be viewed at www.BOUPROC.net.
A study of White-tailed Eagle
Haliaeetus albicilla
movements
and mortality at a wind farm in Norway
Tor g eir N yg å rd, 1* Kj e til B ev a n ge r , 1 E s p en L ie D ah l , 1 Ø y s tei n Fl a gst a d,1
Arn e Fo l l es t a d, 1 P er n i lle L un d Ho e l,2 R oel M ay 1 & Ole Re i t an1
1Norwegian Institute for Nature Research, NO-7485 Trondheim, Norway
2
Norwegian Water Resources and Energy Directorate, NO-0301 Oslo, Norway
*Corresponding author.
Email: torgeir.nygard@nina.no
The wind power plant on the island of Smøla, western Norway, is currently the largest in Norway; it has 68
turbines with nominal capacity of 2–2.3 MW each, hub height of 70 m and rotor blade radius of 38–41 m,. It was
constructed in two phases between 2001 and 2005. Approximately 60 White-tailed Eagle Haliaeetus albicilla
territories are found in the whole Smøla archipelago. Before construction there were 13 Eagle pairs holding
territories in the wind farm area and within 500 m of it, whereas in 2009 this was reduced to only five. Since
1996, baseline data on the White-tailed Eagle population size and reproduction have been collected.
In a post-construction study, 50 fledglings were satellite-tagged during 2003–2009, of which 45 provided more
than 80 000 GPS positions in total. In addition to the geographical location, data on altitude and flight speed
were provided by the transmitters (Microwave Telemetry, Inc., Columbia, MD, USA). Juveniles of both sexes
stayed within the Smøla archipelago during their first winter. Most individuals moved away from the area during
spring in their second year (April–May). Females dispersed further than males, often more than 800 km during
summer, generally to the north. There was a return movement to the natal area during the second autumn. The
same pattern was repeated in the third and fourth years for females, while the males showed more philopatry
(Bevanger et al., 2009).
From August 2005 to May 2010, four of the satellite-tagged birds were killed by collisions with turbines, of 36
White-tailed Eagles in total, involving 20 adults, nine immatures and seven juveniles. April and May are the
months with the highest collision frequencies, with 13 (c. 36%) and nine (c. 25%) of the known fatalities.
Risk assessments were performed based on GPS positions during the different months of the year and the age of
the birds. The transmitters were programmed to transmit their positions at different intervals. Long time
intervals (up to 24 h) were used during winter, 3–6 h during spring and autumn, and 1–3 h during summer. An
analysis of moves showed that the birds changed positions on average 15 times per day, using a 100-m difference
between positions as an indicator of movement. Every change in position was considered to involve a collision
risk when the birds were at Smøla and its archipelago. Moves when they were elsewhere were not considered.
Monthly 95 and 50% utilization distributions (UDs) (Worton 1989) were produced using the positions from the
Smøla archipelago only, with all birds in each month and age-class pooled. The expected number of moves by
each bird was estimated by weighing the number of obtained positions by a factor equal to 15 divided by the pre-
programmed number of positions taken for each transmitter and month. The total number of expected moves
(Me) was then obtained by summing over all birds for each calendar year and month. Kernel UD (95 and 50%)
areas by calendar year and month (Ak95 and Ak50) were produced via ArcView 3.3, by using cross-validation and
Nygård'
et#al.'2010.'BOU#Proceedings#–#Climate#Change#and#Birds.
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http://www.bou.org.uk/bouproc‐net/ccb/nygard‐etal.pdf'
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the default smoothing factors. The total rotor-swept area (RSA) (At95 and At50) of turbines that overlapped each
kernel area was found by multiplying the number of turbines by the RSA (r², where r is the radius of the rotor-
blades). The probability of each position being within an RSA was then calculated as At95/Ak95 and At50/Ak50 for
each calendar year and month. The expected number of positions within an RSA was then calculated as Me*0.95
At/Ak and Me*0.5 At/Ak for the 95 and 50% kernel areas, respectively, for each calendar year and month.
Based on information from 34 birds for which the altitude was known (birds in their nests), a standard deviation
of altitude of 7.8 m was found. This was considered sufficient to produce an estimate of the fraction of the
flights within rotor height. Using only the data from positions when the birds were assumed to be flying (speed
> 0), we found that on average 24% of the flights in the wind farm were within rotor height. Calculations with
and without this figure as an adjustment factor were used. Figure 1 shows the expected number of positions
within an RSA per calendar year and month based on 95 and 50% UDs, and the number of actual kills of tagged
birds was registered.
Figure 1. The expected number of positions of satellite-tagged young White-tailed Eagles within the rotor-swept area at
Smøla wind farm by calendar year and month. Calculations were based on 50% kernel UDs (unadjusted for altitude = solid line,
adjusted = open dashed line) and 95% kernel UDs adjusted for altitude (= densely dashed line). Actual recorded kills of tagged
birds are shown as black dots. Note that the number of birds with working tags is decreasing with age, so the graph does not
indicate individual risk rate over time.
The method seems to be able to correctly identify the periods of the year and age-classes associated with the
greatest hazard rate, judged from the recorded casualties. Calculations based on the unadjusted 50% UDs seemed
to be the best predictor. It is worth noting that no avoidance rate was assumed, and that no adults were included.
Nygård'
et#al.'2010.'BOU#Proceedings#–#Climate#Change#and#Birds.
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http://www.bou.org.uk/bouproc‐net/ccb/nygard‐etal.pdf'
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The widely used Band method for collision risk assessment (Band et al. 2007) is often used in conjunction with
an avoidance factor based on observations and recorded kills. We did not attempt to calculate an avoidance rate,
as sufficient observational data from the field were not available. Furthermore, the number of moves per day was
based on estimations, and any movement may involve a combination of circling and directional flight at different
altitudes, and could involve risks connected with several of the 68 turbines. One should also bear in mind that
the studied birds had an affinity to the area, being their natal place. Thus, our findings are probably only typical
to juvenile White-tailed Eagles relatively close to their natal area; nevertheless, they are relevant for large parts of
the Norwegian coast. Displacement was probably negligible or small in our case, as all birds were born within or
close to the wind farm. On this basis, we suggest that the proposed avoidance rate proposed for Golden Eagles
Aquila chrysaetos (c. 99%) at other wind farms (Whitfield 2009) is not applicable to White-tailed Eagles in
connection with wind farms close to their breeding-sites. We are currently developing other risk assessment
methods based on GPS position data using the method of ‘Brownian bridges’ (May and Nygård, 2009) and by
using ground-truthed bird radar tracks.
Studies on Smøla have shown that White-tailed Eagles seem to use the air space inside and outside the wind farm
area similarly (Hoel, 2009). Several observers have noted that White-tailed Eagles at Smøla often circle close to
and around turbines, possibly induced by the extra wind energy created by the turbulence. The satellite-tagged
victims were either killed in the first autumn (two in September) or in the following spring (two in April). The
first autumn incidents may be influenced by lack of agility and experience, their naivety making them more prone
to collisions. The incidents during spring in their second calendar year coincide with an overall greater turbine-
related mortality rate during spring of all age-classes, possibly caused by increased territorial activity and good
thermal conditions.
A Kaplan–Meier survival analysis showed that the additional mortality caused by the wind farm at Smøla was c.
10%, reducing the cumulative survival through their third year of life from 0.84 to 0.74. A full population model
including adults is now under way, involving the use of DNA analysis of moulted feathers from nesting pairs to
estimate adult turnover rates.
References
Band, W., Madders, M. & Whitfield, D.P. 2007. Developing field and analytical methods to assess avian collision risk at
wind farms. In: de Lucas, M., Janss, G.F.E. & Ferrer, M. (eds) Birds and Wind Farms. Risk Assessment and Mitigation: 259–
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Bevanger, K., Berntsen, F., Clausen, S., Dahl, E.L., Flagstad, Ø., Follestad, A., Halley, D., Hanssen, F., Hoel, P.L., Johnsen,
L., Kvaløy, P., May, R., Nygård, T., Pedersen, H.C., Reitan, O., Steinheim, Y. & Vang, R. 2009. Pre- and post-
construction studies of conflicts between birds and wind turbines in coastal Norway (BirdWind). Progress Report 2009.
In NINA Report. 505. Trondheim: Norsk institutt for naturforskning.
Hoel, P.L. 2009. Do wind power developments affect the behaviour of White Tailed Sea Eagles on Smøla? Master of science thesis,
Department of Biotechnology, Norwegian University of Science and Technology, Trondheim.
May, R. & Nygård, T. 2009. Spatial assessment of white-tailed sea eagle collision risk at the onshore wind-power plant on
the island of Smøla. In: 2nd European Congress of Conservation Biology. Conservation biology and beyond: from science to practice.
Czech University of Life Sciences, Prague.
http://www.eccb2009.org/uploads/book_of_abstracts_errata.pdf
Whitfield, D.P. 2009. Collision avoidance of Golden Eagles at wind farms under the ‘Band’ collision risk model. Banchory, UK: Natural
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Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70: 164–168.
CITATION:
Nygård, T., Bevanger, K., Dahl, E.L., Flagsted, Ø
., Follestad, A., Hoel, P.H., May, R. & Reitan, O.
Nygård'
et#al.'2010.'BOU#Proceedings#–#Climate#Change#and#Birds.
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http://www.bou.org.uk/bouproc‐net/ccb/nygard‐etal.pdf'
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©'2010'BOU'&'The'Author(s)'
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2010. A study of White-tailed Eagle movements and mortality at a wind farm in Norway. BOU Proceedings –
Climate Change and Birds. http://www.bou.org.uk/bouproc-net/ccb/nygard-etal.pdf
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Thesis
  • Nov 2014
1. In response to climate change, renewable energy projects including wind energy are rapidly expanding in Scotland. Evaluating impacts of new wind farm proposals often incorporates bird collision risk modelling, which relies on accurate flight data. Among other factors, birds’ altitudes are crucial for risk estimation. Using conventional means of height data collection (i.e. visual vantage point survey), errors in height estimation are unquantifiable. Here I present a novel methodology to accurately estimate a bird’s location and height in the landscape; I test it for accuracy and apply it in practice to estimate flight height of hen harriers in Perthshire. 2. The newly developed flight triangulation methodology incorporated two observers watching the same bird simultaneously through telescopes. An electronic compass mounted on each scope recorded and logged bearing and pitch angles. Accuracy of the methodology was tested using both stationary and moving objects. Hen harriers were observed using this methodology at three localities in Scotland, including the Griffin wind farm. XY co-ordinates and height of bird flight locations were calculated using trigonometry; positions were overlaid on Ordnance Survey maps to account for landscape features. I tested for possible influences of month, flight type, sex and site on the proportion of time spent in the rotor-swept area (RSA). 3. Flight triangulation methodology was most accurate in location estimation at short to intermediate distances and when observers’ sightlines were wider than 20° from each other. Height estimation accuracy was generally good, with some variation according to the accuracy of XY location in combination with distance from observer. 4. The proportion of time spent by hen harriers in the RSA was significantly affected by month and flight type. The highest proportion of time in RSA occurred during April and June, and during soaring and display flights. In general hen harriers spent more time in the RSA than expected. 5. The methodology presented here: provides accurate height estimation of birds accounting for landscape; is applicable to any wind farm due to the recording of continuous height data rather than assigning into specific height bands; and could also be applied to further analysis, such as avoidance behaviour or habitat use. This methodology could complement or replace currently used vantage point surveys, and could assist the wind farm consenting process by providing more precise collision risk modelling.
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Results and Analysis of Eagle Studies from the Bluff Point and Studland Bay Wind Farms 2002–2012
Chapter
  • Oct 2015
The Tasmanian wedge-tailed eagle (WTE, Aquila audax fleayi) and the white-bellied sea-eagle (WBSE, Haliaeetus leucogaster) are present on the Bluff Point (37 Vestas V66 turbines) and Studland Bay (25 Vestas V90 turbines) Wind Farms in north-west Tasmania, Australia. These species have been intensively studied since the commencement of operations in 2002 and 2007, respectively, as part of compliance monitoring. Monitoring has included documenting collisions with turbines, breeding success surveys, and movement and behaviour studies. Additional investigations (outside regulatory requirements) have also been conducted, including targeted studies and trials of collision mitigation techniques. Both species of eagle have continued to use the sites during construction and operation of the wind farms. The average collision rates for WTE were 1.54 and 0.95 per year, and for WBSE 0.36 and 0 per year at Bluff Point and Studland Bay, respectively (calculated up to October 2012). These are below maximum rates estimated in collision risk modeling which formed part of the information for the assessment of the wind farms. The collision rate for WTE was constant across years, although there was some evidence the rate could be declining at Studland Bay. Analyses could not be conducted on WBSE due to small sample sizes. Seasonal and other temporal patterns were tested for in the collision data, but all evidence supported the theory that the strikes were independent and random in time, with no support found for some proposed theories about why eagles collide with turbines. A spatial analysis of collisions was not possible, again due to small sample sizes. Eagles continued to breed at the sites, with at least the same level of success as nests outside the wind farms. The observational studies provided useful data about how eagles interacted with turbines at these sites. These data were used to calculate turbine avoidance rates and to assess how rates changed with development of the wind farm and when turbines were operational or not.
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Utilization Probability Map for Migrating Bald Eagles in Northeastern North America: A Tool for Siting Wind Energy Facilities and Other Flight Hazards
Article
Full-text available
Collisions with anthropogenic structures are a significant and well documented source of mortality for avian species worldwide. The bald eagle (Haliaeetus leucocephalus) is known to be vulnerable to collision with wind turbines and federal wind energy guidelines include an eagle risk assessment for new projects. To address the need for risk assessment, in this study, we 1) identified areas of northeastern North America utilized by migrating bald eagles, and 2) compared these with high wind-potential areas to identify potential risk of bald eagle collision with wind turbines. We captured and marked 17 resident and migrant bald eagles in the northern Chesapeake Bay between August 2007 and May 2009. We produced utilization distribution (UD) surfaces for 132 individual migration tracks using a dynamic Brownian bridge movement model and combined these to create a population wide UD surface with a 1 km cell size. We found eagle migration movements were concentrated within two main corridors along the Appalachian Mountains and the Atlantic Coast. Of the 3,123 wind turbines ≥100 m in height in the study area, 38% were located in UD 20, and 31% in UD 40. In the United States portion of the study area, commercially viable wind power classes overlapped with only 2% of the UD category 20 (i.e., the areas of highest use by migrating eagles) and 4% of UD category 40. This is encouraging because it suggests that wind energy development can still occur in the study area at sites that are most viable from a wind power perspective and are unlikely to cause significant mortality of migrating eagles. In siting new turbines, wind energy developers should avoid the high-use migration corridors (UD categories 20 & 40) and focus new wind energy projects on lower-risk areas (UD categories 60-100).
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Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies
Article
  • Feb 1989
In this paper kernel methods for the nonparametric estimation of the utilization distribution from a random sample of locational observations made on an animal in its home range are described. They are of flexible form, thus can be used where simple parametric models are found to be inappropriate or difficult to specify. Two examples are given to illustrate the fixed and adaptive kernel approaches in data analysis and to compare the methods. Various choices for the smoothing parameter used in kernel methods are discussed. Since kernel methods give alternative approaches to the Anderson (1982) Fourier transform methods, some comparisons are made.
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Developing field and analytical methods to assess avian collision risk at wind farms
  • Jan 2007
  • 259-275
  • W Band
  • M Madders
  • D P Whitfield
Band, W., Madders, M. & Whitfield, D.P. 2007. Developing field and analytical methods to assess avian collision risk at wind farms. In: de Lucas, M., Janss, G.F.E. & Ferrer, M. (eds) Birds and Wind Farms. Risk Assessment and Mitigation: 259-275. Madrid: Servicios Informativos Ambientales/Quercus.
Do wind power developments affect the behaviour of White Tailed Sea Eagles on Smøla? Master of science thesis
  • Jan 2009
  • P L Hoel
Hoel, P.L. 2009. Do wind power developments affect the behaviour of White Tailed Sea Eagles on Smøla? Master of science thesis, Department of Biotechnology, Norwegian University of Science and Technology, Trondheim.
Pre-and postconstruction studies of conflicts between birds and wind turbines in coastal Norway (BirdWind)
  • Jan 2009
  • K Bevanger
  • F Berntsen
  • S Clausen
  • E L Dahl
  • Ø Flagstad
  • A Follestad
  • D Halley
  • F Hanssen
  • P L Hoel
  • L Johnsen
  • P Kvaløy
  • R May
  • T Nygård
  • H C Pedersen
  • O Reitan
  • Y Steinheim
  • R Vang
Bevanger, K., Berntsen, F., Clausen, S., Dahl, E.L., Flagstad, Ø., Follestad, A., Halley, D., Hanssen, F., Hoel, P.L., Johnsen, L., Kvaløy, P., May, R., Nygård, T., Pedersen, H.C., Reitan, O., Steinheim, Y. & Vang, R. 2009. Pre-and postconstruction studies of conflicts between birds and wind turbines in coastal Norway (BirdWind). Progress Report 2009. In NINA Report. 505. Trondheim: Norsk institutt for naturforskning.
Spatial assessment of white-tailed sea eagle collision risk at the onshore wind-power plant on the island of Smøla
  • Jan 2009
  • R May
  • T Nygård
May, R. & Nygård, T. 2009. Spatial assessment of white-tailed sea eagle collision risk at the onshore wind-power plant on the island of Smøla. In: 2nd European Congress of Conservation Biology. Conservation biology and beyond: from science to practice. Czech University of Life Sciences, Prague. http://www.eccb2009.org/uploads/book_of_abstracts_errata.pdf
Kernel methods for estimating the utilization distribution in home-range studies
  • Jan 1989
  • ECOLOGY
  • 164-168
  • B J Worton
  • T Nygård
  • K Bevanger
  • E L Dahl
  • Ø Flagsted
  • A Follestad
  • P H Hoel
  • R May
  • O Reitan
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range studies. Ecology 70: 164-168. CITATION: Nygård, T., Bevanger, K., Dahl, E.L., Flagsted, Ø., Follestad, A., Hoel, P.H., May, R. & Reitan, O.