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The global potential for wind power generation is vast, and the number of installations is increasing rapidly. We review case studies from around the world of the effects on raptors of wind-energy development. Collision mortality, displacement, and habitat loss have the potential to cause population-level effects, especially for species that are rare or endangered. The impact on raptors has much to do with their behavior, so careful siting of wind-energy developments to avoid areas suited to raptor breeding, foraging, or migration would reduce these effects. At established wind farms that already conflict with raptors, reduction of fatalities may be feasible by curtailment of turbines as raptors approach, and offset through mitigation of other human causes of mortality such as electrocution and poisoning, provided the relative effects can be quantified. Measurement of raptor mortality at wind farms is the subject of intense effort and study, especially where mitigation is required by law, with novel statistical approaches recently made available to improve the notoriously difficult-to-estimate mortality rates of rare and hard-to-detect species. Global standards for wind farm placement, monitoring, and effects mitigation would be a valuable contribution to raptor conservation worldwide.
VOL.52 NO.1MARCH 2018
J. Raptor Res. 52(1):1–18
!2018 The Raptor Research Foundation, Inc.
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ABSTRACT.—The global potential for wind power generation is vast, and the number of installations is
increasing rapidly. We review case studies from around the world of the effects on raptors of wind-energy
development. Collision mortality, displacement, and habitat loss have the potential to cause population-level
effects, especially for species that are rare or endangered. The impact on raptors has much to do with their
behavior, so careful siting of wind-energy developments to avoid areas suited to raptor breeding, foraging, or
migration would reduce these effects. At established wind farms that already conflict with raptors, reduction
of fatalities may be feasible by curtailment of turbines as raptors approach, and offset through mitigation of
other human causes of mortality such as electrocution and poisoning, provided the relative effects can be
quantified. Measurement of raptor mortality at wind farms is the subject of intense effort and study,
especially where mitigation is required by law, with novel statistical approaches recently made available to
improve the notoriously difficult-to-estimate mortality rates of rare and hard-to-detect species. Global
standards for wind farm placement, monitoring, and effects mitigation would be a valuable contribution to
raptor conservation worldwide.
KEY WORDS:avoidance;collision;displacement;energy;mitigation;mortality;raptor;renewable energy;wind farm;wind
IA E ´
RESUMEN.—El potencial global para la generacio´ n de energ´ıa eo´lica es enorme y las infraestructuras para su
generacio´n aumentan de manera acelerada. Revisamos casos de estudio de todo el mundo sobre los efectos
del desarrollo de la energ´ıa eo´lica en aves rapaces. La mortandad por colisiones, el desplazamiento y la
pe´rdida de ha´bitat tienen el potencial de causar efectos a nivel poblacional, especialmente en especies que
son raras o se encuentran en peligro. El impacto sobre las aves rapaces esta´ muy relacionado con su
comportamiento, por lo que el emplazamiento cuidadoso de proyectos de enerıa eo´lica que eviten a´ reas
adecuadas para cr´ıa, alimentacio´n o migracio´n de rapaces puede reducir dichos efectos. La reduccio´ n de
mortalidad en parques eo´licos ya establecidos y que presentan conflictos con aves rapaces, puede ser posible
mediante la reduccio´ n de la actividad de las turbinas en momentos de presencia de rapaces y la
compensacio´ n a trave´s de la mitigacio´n de otras causas humanas de mortalidad como electrocucio´n o
envenenamiento, en la medida que los efectos relativos puedan ser medidos. Cuantificar la mortalidad de
rapaces en parques eo´ licos es objeto de estudios intensos, especialmente en aquellos lugares donde la
mitigacio´n es requerida por ley, con aproximaciones estad´ısticas novedosas disponibles recientemente que
mejoran la estimacio´ n de las tasas de mortalidad, que son particularmente complicadas de estimar en
especies raras y de dif´ıcil deteccio´ n. El desarrollo de esta´ndares globales para la ubicacio´n, el seguimiento y la
mitigacio´n de los efectos producidos por los parques eo´licos sera´n una contribucio´ n valiosa para la
conservacio´ n de rapaces en todo el mundo.
[Traduccio´n del equipo editorial]
Wind-energy production worldwide has increased
rapidly in the last decade; wind power was the
leading source of new power generating capacity in
Europe and the United States in 2015 and the
second largest in China. Globally, a record 63
gigawatts (GW) of wind-energy production was
added in 2015 for a total of about 433 GW (REN21
2016). China currently leads the world with 145 GW
of installed capacity, about a third of the world’s total
wind power (Davidson et al. 2016), followed by the
United States (74 GW), Germany (45 GW), India (25
GW), Spain (23 GW), Italy (9 GW), and Japan (3
GW; REN21 2016). The potential for wind-power
generation globally is vast, potentially supplying .40
times the current worldwide consumption of elec-
tricity, and .5 times the total global use of energy in
all forms (Lu et al. 2009).
Attempts to measure and mitigate the effects of
wind turbines on wildlife have been an integral part
of wind-energy development. Raptors are among the
species known to be most strongly affected by wind
turbines, mostly through direct mortality and
secondarily through habitat alteration and loss. In
the United States, eagle mortality and mitigation
strategies have received most attention because of
eagles’ legal status under the Bald and Golden Eagle
Protection Act. The negative effects of wind turbines
on other raptor species are less well understood, and
2 VOL. 52, NO.1
corresponding mitigation responses less well devel-
This review of case studies illustrates the global
state of knowledge of the effects of wind-energy
development on raptors and is derived from nine
presentations at a symposium at the 2015 Raptor
Research Foundation annual conference. We begin
with an overview of raptor species affected by wind
farms worldwide. We introduce case studies of
effects of wind farms on raptors from Spain, Norway,
Canada, United States, and southern Africa, and
follow with an evaluation of the challenges of
measuring fatalities of raptors at wind farms and
how they may be overcome. We discuss conclusions
in common among the case studies, directions for
future research, and potential offset and mitigation
As the production of wind energy increases
worldwide, adverse effects of turbines and develop-
ment activities have been documented for many
avian groups, especially raptors. The risk of collisions
is highly variable and dependent upon a complex
interaction of site, season, and species-specific
factors (Marques et al. 2014). Of these factors,
foraging and territorial behaviors (Barrios and
Rodr´ıguez 2004, Hoover and Morrison 2005, Small-
wood et al. 2009), the interaction of wind and
topography (Barrios and Rodr´ıguez 2004, de Lucas
et al. 2008), and limitations in the degree to which
raptors perceive turbines as dangerous (Martin et al.
2012, May et al. 2015, Hunt and Watson 2016) are
thought to contribute to collisions of a number of
species worldwide. However, most factors associated
with collision risk are related to specific study areas
or relatively common species, making it difficult to
determine if those factors pose similar risk to
uncommon species or those found in other areas.
The search effort during post-construction fatality
monitoring is rarely sufficient to locate all individ-
uals killed by wind turbines, making it impossible to
conclude that rare species are not affected even if
none are found (Beston et al. 2015, Huso et al.
Bird species that share flight morphology are
more likely to forage similarly, and thus many of the
species killed regularly at turbines are taxonomically
related (Herrera-Alsina et al. 2013). For example,
Red-tailed Hawks (Buteo jamaicensis) and American
Kestrels (Falco sparverius) found at wind-energy
projects in the western U.S., especially the Altamont
Pass Wind Resource Area (hereafter Altamont;
Smallwood and Thelander 2008, ICF International
2015), make up the majority of known global
fatalities for Buteo hawks and small falcons, respec-
tively. Where other Buteo and Falco species, such as
Common Buzzards (Buteo buteo)inGermany
(Ho¨ tker et al. 2006), Eurasian Kestrel (Falco tinnun-
culus) in Europe (Ho¨ tker et al. 2006, Gr¨
unkorn et al.
2016), and the Nankeen Kestrel (Falco cenchroides)
and Brown Falcon (Falco berigora)inAustralia
(Smales 2015) interact with turbines, they likewise
appear to show the same high risk of collisions.
Where congeneric species exist together, the more
abundant species is more often found among
collision victims. Red-tailed Hawks composed the
largest percentage of raptors (22%, Johnson and
Erickson 2011) found during post-construction
fatality monitoring at wind-energy projects through-
out the Columbia Plateau Ecoregion (CPE) in
Oregon and Washington and nested in higher
densities (1.6 pairs/100 km
) compared with sym-
patric Buteo species such as Swainson’s Hawks (Buteo
swainsoni; 9% of fatalities, Johnson and Erickson
2011; 1.4 pairs/100 km
, Erickson et al. 2002).
However, in other areas of the CPE where the
density of Swainson’s Hawk pairs was greater (8.8
pairs/100 km
), they instead composed the majority
of reported raptor fatalities (45%) compared with
Red-tailed Hawks (8%, P. Kolar and M. Bechard
pers. comm.) that nested at half the density (4.4
pairs/100 km
, Kolar 2013). These observations
imply that nesting density is important in determin-
ing the probability of turbine collisions, but the
evidence of such a relationship from other studies
has been mixed (Marques et al. 2014). Predicting
collision rates based on abundance of raptors
through standardized point counts during pre-
construction surveys has been criticized for its poor
correlation, likely because collision risk also depends
on flight behavior, which can vary with differences in
topographic features between wind-energy project
sites (de Lucas et al. 2008, Ferrer et al. 2012).
Abundance may still be a useful indicator of
collisions, but may be best interpreted in relation
to the spatial distribution of breeding pairs at larger
spatial scales and when assessing fatality rates relative
to similar species within raptor groups rather than
predicting the number of fatalities at individual
wind-energy projects (Carrete et al. 2012).
In some specific cases, collision deaths have been
suspected or implicated in population-level effects.
The few known collisions of local White-tailed Hawks
MARCH 2018 3
(Buteo albicaudatus)atwind-energyprojectsin
southern Mexico have generated concern that the
area may become a local population sink for this
relatively common and nonmigratory species, espe-
cially in the light of planned increase in develop-
ment (Ledec et al. 2011). Likewise, prior to
repowering at the Altamont, the number of Burrow-
ing Owl (Athene cunicularia) fatalities at older-
generation turbines was reported to be similar to
the number of breeding pairs at the facility (Small-
wood et al. 2007). Collisions of Egyptian Vultures
(Neophron percnopterus) in Spain, where 80% of its
European breeding population is located, have
contributed to a local population decline (Carrete
et al. 2009). The population of Red Kites (Milvus
milvus) in Germany is predicted to decline due to
additional mortality from turbine collision (Belle-
baum et al. 2013). Gr¨
unkorn et al. (2016) also
predicted the Red Kite population in Germany
would decline, along with the widespread Common
Buzzard that nests in the region in high densities but
has not been considered in the planning process of
wind turbine construction.
Species from some raptor groups, such as some
kites, large falcons, and Accipiters, are infrequently
observed during raptor use surveys and just as
infrequently found as collision fatalities worldwide.
Others, such as harriers and New World vultures are
seldom found as collision fatalities, even when wind-
energy projects are constructed in areas of known
high population density (Erickson et al. 2002,
Ho¨ tker et al. 2006, Smallwood et al. 2009, Ferrer et
al. 2012, Herna´ndez-Pliego et al. 2015, Wilson et al.
2016). For some raptors, the number of collisions
also seems to vary across the species range or
between facilities. For example, in general Red Kite
fatalities are rarely found under turbines and kites
are assumed to utilize avoidance behaviors at wind-
energy projects (Whitfield and Madders 2006). Yet,
turbine collisions of Red Kites in Germany, where
half of the world’s breeding population occurs, are
reported to be the highest of any raptor species in
the area (Ho¨ tker et al. 2006). Older-generation wind
turbines at Altamont killed hundreds of Burrowing
Owls, Barn Owls (Tyto alba; 225) and Great Horned
Owls (Bubo virginianus; 71) over a 12-yr period
(Smallwood and Thelander 2008, ICF International
2015). In contrast, studies at wind-energy facilities in
Europe report fewer than ten fatalities of Eurasian
Eagle-Owls (Bubo bubo; Ho¨ tker et al. 2006, Ferrer et
al. 2012) and few owls of any species have been
documented as collision fatalities elsewhere in the
world. It is unclear whether these inconsistencies in
owl and kite fatality rates between geographic
regions result from differences in site-specific
factors, breeding densities, or behaviors that result
in habituation or avoidance of turbines.
As with any type of anthropogenic development,
construction of turbines results in some habitat
fragmentation and loss that can cause disturbance or
displacement of raptors, but these indirect effects
vary among published studies (Drewitt and Langston
2006, Madders and Whitfield 2006, Pearce-Higgins
et al. 2009, Garvin et al. 2011, May 2015). The
consequences of such effects likely depend upon the
extent of development and species-specific toleranc-
es to disturbance (May 2015). Dahl et al. (2012)
found that a combination of a high number of
turbine collisions by adult White-tailed Eagles
(Haliaeetus albicilla) and displacement led to vacan-
cies of previously used nesting areas close to
turbines. Conversely, Herna´ndez-Pliego et al.
(2015) found no difference between pre- and post-
construction nest or colony abundances of Monta-
gu’s Harriers (Circus pygargus) in Spain. Kolar (2013)
found that the selection of nesting areas by Buteo
hawks in Oregon was not related to wind turbines.
However, Kolar and Bechard (2016) also found that
nest success and post-fledging survival of Ferrugi-
nous Hawks (Buteo regalis) in the same study area
were negatively affected by the density of wind
turbines within home ranges. Post-fledging survival
of Red-tailed Hawks and Swainson’s Hawks, the
species that made up most of the raptor fatalities at
wind-energy facilities in that study area (P. Kolar and
M. Bechard pers. comm.) and surrounding region
(Johnson and Erickson 2011), was also lower near
greater densities of turbines, but did not appear to
be affected to the same degree as that of Ferruginous
Hawks. These results suggest effects on reproduction
for these three species resulted from some combi-
nation of turbine collisions and indirect displace-
ment or disturbance effects associated with
operations and maintenance of the facilities and
The variable results of these studies underscore
the role of both local and regional factors that may
contribute to negative effects on raptor populations
at wind-energy projects. Understanding the site-
specific factors that influence collisions and dis-
placement, and the resulting population-level con-
sequences will help regional planners to better
integrate future wind-energy developments into the
4 VOL. 52, NO.1
landscape while avoiding or mitigating in areas
important for the long-term persistence of raptors.
Spain. The first published evaluation of the effects
of wind farms on bird populations in Spain was
conducted in Tarifa (Andalusia Province, southern
Spain) from July 1994 to September 1995 (de Lucas
et al. 2004). The area was chosen because of its
proximity to the Strait of Gibraltar, one of the most
important bird migration routes of the Palearctic.
Soaring birds in this study changed flight direction
when crossing the wind farm, increasing their
altitude and avoiding turbines. During the 14 mo
of the study period, researchers found only two
raptor carcasses, a number well below the average
found in studies of power lines using similar
methodology (Janss and Ferrer 1998). The results
supported the conclusion that mortality associated
with the wind farm was not an important factor, and
avian collisions with turbines were infrequent at this
To obtain a more detailed understanding of the
factors involved in influencing collision mortality of
birds at wind farms, de Lucas et al. (2008) carried
out a long-term study of avian fatalities at wind farms
in Spain between November 1993 and June 2003.
The results showed no relationship between density
(number of birds crossing the area) and mortality
rate of birds at the wind-farm scale. No indication of
a change in mortality rates across the study period
was found, suggesting that there were no long-term
temporal changes in birds’ reactions to those wind
farms, and implying that they did not habituate to
the presence of turbines.
The Griffon Vulture (Gyps fulvus) was the species
most affected by collision fatalities. However, colli-
sion mortality rates did not simply increase with
abundance. De Lucas et al. (2008) proposed that
differences in mortality were related to species-
specific flight behavior and morphology, weather,
and topography around the wind farm. In addition,
they found a skewed distribution of griffon fatalities
per turbine. Taller turbines at higher elevations
killed more vultures than did shorter turbines at
lower elevations (de Lucas et al. 2008). Likewise,
Carrete et al. (2009) found breeding pairs of
Egyptian Vulture tended to select roughly the same
areas as those preferred for wind turbine locations
and that the species’ population was decreasing
generally and at a faster rate in areas with wind
The effects of wind farms on Griffon Vulture
fecundity and mortality can be significant (Mart´ınez-
Abra´ın et al. 2012). Operational mitigation pro-
grams to manage these effects have been imple-
mented by selectively stopping turbines when
observers detect potential risk to birds (de Lucas et
al. 2012). In one project, turbines were stopped on
average for 6 h and 20 min each year. Under these
mitigation regimes, Griffon Vulture mortality rate
declined by 65%, with a reduction in total energy
production of the wind farms of only 0.07% per year.
The most relevant factor for predicting collision
risk to raptors has been generally assumed to be the
local density, usually measured as the number of
birds crossing the whole area of the future wind
farm. However, studies in Spain provide clear
evidence that the probability of bird collisions with
turbines also depends critically on species behavior
and topographical factors (Barrios and Rodr´ıguez
2004, de Lucas et al. 2008). Ferrer et al. (2012)
found no relationship between risk prediction from
pre-construction environmental impact assessment
studies (i.e., at the scale of the entire wind farm) and
the actual post-construction mortality of birds
recorded in wind farms located in southern Spain.
Relevant factors affecting the frequency of collisions
with turbine rotor blades, such as bird flight
behavior, topography, and wind speed and direc-
tion, were operating at the scale of the individual
turbine, and not at the entire wind-farm scale
(Ferrer et al. 2012).
Norway. The island of Smøla contains a 68-turbine
facility covering 18 km
of land and including 28 km
of roads. Before construction, White-tailed Eagles
bred at high density in and around the wind farm; in
total around 50 pairs were breeding on the island in
the period 2002–2005. A long-term time series on
population size and breeding status of the eagles at
Smøla from 1997 allowed the use of a before-after-
control-impact (BACI) design study. The study
demonstrated that this local population was affected
both by disturbance and collision mortality. Eagles
did not significantly change their flight behavior
when inside the wind farm, possibly explaining the
high collision mortality (Dahl et al. 2013). Breeding
success was lower in those territories that were close
to the wind farm, compared to those that were
farther away (Dahl et al. 2012). In addition to direct
mortality, there was displacement from the territo-
ries within the wind farm (May et al. 2013). Mortality
rates were higher for birds that had territories within
or close to the wind farm compared to those that
MARCH 2018 5
lived farther away (Dahl et al. 2012), and the
intrinsic growth potential of the population was
reduced by the wind-farm development (Dahl 2014).
The total population of White-tailed Eagles at Smøla
did not decrease, probably due to immigration of
birds from nearby islands, and the displacement of
breeding pairs to other sites in the surrounding area
(Dahl et al. 2012).
Post-construction monitoring at Smøla used
trained dogs to find collision fatalities. Because it is
an island with no mammalian ground predators,
Smøla has the advantage of long carcass persistence
rates, especially of large carcasses such as eagles.
There is little aerial bird activity on Smøla in winter,
so searches mainly focused on spring and early
summer (migration and breeding-season) and au-
tumn (migration). The search scheme was not
constant in all years, but was probably sufficient to
reveal the majority of the casualties. Starting in the
spring of 2014, researchers introduced a new search
scheme, involving weekly searches at painted tur-
bines and unpainted (control) turbines in a mitiga-
tion experiment, with some additional searches of all
turbines. During the study from 2005 to October
2016, 73 White-tailed Eagles were found dead under
or near turbines at the Smøla wind farm. More adult
birds were found killed than were birds of all other
age classes combined. This has major implications
for the population dynamics, because for long-lived
species with a low reproductive rate, adult survival
rate is the demographic parameter that has the
largest effect on population growth (Eberhardt
2002). Of other raptors, two juvenile Golden Eagles
(Aquila chrysaetos) were found killed, as well as four
Merlins (Falco columbarius), one Eurasian Kestrel
(Falco tinnunculus), and one juvenile Gyrfalcon
(Falco rusticolus). Most of the eagles were found
during spring. At that time of the year, there is much
interaction among the territorial eagles, including
fighting and chasing. This might reduce the birds’
awareness of moving rotor blades, making them
more susceptible to collisions (May et al. 2010b,
2011). The White-tailed Eagle is quite gregarious,
and that behavior may explain why the particular
turbine that killed most eagles at Smøla was one that
that was very close to a major roost site in a Sitka
spruce (Picea sitchensis) plantation.
Mitigation of turbine-induced mortality of birds at
wind farms has proven to be difficult, as mitigation
may involve sensory, aerodynamic, and habitat-
specific factors (May et al. 2015). During the
summer of 2014, four turbines at Smøla wind farm
had one rotor blade painted black in an effort to see
whether mortality could be reduced by increased
visibility to birds (as demonstrated in Hodos et al.
2001). In addition, the bases of 10 turbines were
painted black up to 10 m above ground during the
summers of 2014–2015. All searches for dead birds
were performed using trained dogs, in a radius of
100 m of the turbines. This research effort is
ongoing, but preliminary results suggest that mor-
tality of Willow Ptarmigan (Lagopus lagopus), the
species most frequently found dead under the
turbines (.180 fatalities), has been reduced follow-
ing these visual modifications (T. Nyg˚ard unpubl.
data). The development of a GIS-based micro-siting
tool analyzing topographic features that enhance
orographic and thermal updrafts, as well as an
operational shut-down model for birds, is underway
as part of the project. Other mitigation measures,
such as scaring devices (DTBird; May et al. 2010a)
and UV-lights have been tested at Smøla, but the
effectiveness of the latter is doubtful (Hunt et al.
Proper siting of wind farms is crucial to prevent
raptor casualties. Smøla is an example of develop-
ment that did not incorporate wildlife consider-
ations, as it was built in an important breeding area
for White-tailed Eagles. A plan that could reduce
bird casualties by repowering the Smøla wind farm
with fewer but larger turbines (up from 2–2.3 MW to
3–5 MW) has been proposed but not yet effected.
The plan also recommended more bird-friendly
placement of the new turbines based on vulnerabil-
ity maps that identified areas of low use by eagles.
Maps were created by plotting the flight paths of 73
satellite-tagged White-tailed Eagles, in combination
with direct observations of territorial eagles and
radar tracks from a MERLIN Aircraft Birdstrike
Avoidance Radare(DeTect, Inc., Panama City, FL
U.S.A.) placed centrally in the wind farm (Dahl et al.
Canadian Rocky Mountains. Wind-energy devel-
opment within the Hart Ranges of the Rocky
Mountains in British Columbia, Canada, overlaps
with a Golden Eagle migration corridor. Researchers
used a BACI study design to document Golden Eagle
flight behavior in response to wind turbines at this
ridgetop wind-energy development (Johnston et al.
2014). Golden Eagle flights were visually tracked
around a ridge containing 15 3-MW turbines during
three fall migration seasons, one pre-construction
(2009) and two post-construction (2010 and 2011).
Surveys were conducted by the same observer in all
6 VOL. 52, NO.1
years from three different observation points to
cover the entire ridge. Positions of eagles were
estimated in three dimensions as they migrated
within 2 km of an observation point. Estimated eagle
locations were then incorporated into GIS software
to ascertain flight heights above the ground for
eagles that flew within 100 m of the turbine string
(hereafter termed ‘‘ridgetop area’’). Of these flights,
eagles that were within 150 m of the ground were
identified as being within a ‘‘risk zone’’ (i.e., within
turbine height). Flights within the risk zone,
coupled with wind speeds above turbine cut-in
(activation) speed at nacelle height, were classified
as ‘‘higher-risk’’ movements.
Observers documented 1134 Golden Eagle pas-
sages: 327 during pre-construction (2009) and 807
post-construction (380 in 2010, 427 in 2011). The
proportion of observed eagles that crossed the
ridgetop where turbines were located, regardless of
flight height, were the same in pre-construction as in
post-construction (approximately 17%). However, a
smaller proportion of eagles crossed the ridgetop
area within the risk zone post-construction (1%)
compared to pre-construction (6%). In addition, a
substantially smaller proportion of higher-risk move-
ments within the risk zone were observed post-
construction (0.004%) compared to pre-construc-
tion (5%; Johnston et al. 2014). Golden Eagle flight
altitude was higher post-construction compared to
pre-construction, and a binomial model indicated
that the likelihood of an eagle crossing the ridgetop
within the risk zone was greater during pre-construc-
tion compared to post-construction. The model also
indicated that the likelihood of an eagle crossing the
ridgetop was greater under headwinds and tailwinds
compared to western crosswinds and decreased as
wind speed increased. However, higher-risk move-
ments within the risk zone did not occur under
tailwinds, which were generally weaker winds. In
headwinds however, higher-risk movements did
occur, although infrequently.
In conclusion, the proportions of eagles that flew
over the ridgetop area were consistent between pre-
and post-construction, yet during post-construction
these flights were at higher altitudes which reduced
the potential for collisions. This suggests that eagles
detect the turbines and increase their flight altitude
to avoid the structures during migration. However,
certain weather conditions, particularly headwinds
and potentially tailwinds, resulted in decreases in
flight altitude during ridge crossings. Should the
winds be sufficient to spin turbine blades during
such conditions, these circumstances may pose a
greater risk of collision mortality to migrating
Golden Eagles.
California, U.S.A. A dense resident population of
tree-nesting Golden Eagles breeds in the Diablo
Mountains just south of San Francisco Bay in
California. It is estimated that between 1000 and
2000 Golden Eagles have been killed at the nearby
Altamont Pass Wind Resource Area (Altamont) since
the completion of the facility in 1987 (Orloff and
Flannery 1992, Hunt 2002, Smallwood and The-
lander 2008).
This population has been monitored intermittent-
ly since 1994, beginning with a 7-yr investigation
involving radiotelemetry, nesting surveys, and de-
mographic analysis (Hunt et al. 1999, Hunt 2002,
Hunt and Hunt 2006). When these studies began,
the Altamont contained approximately 5400 tur-
bines on about 142 km
of open, hilly grassland. At
present, the facility is being repowered with fewer,
larger turbines that generate greater amounts of
power. The terrain is ideally suited to Golden Eagle
foraging upon abundant California ground squirrels
(Otospermophilus beecheyi). This situation is problem-
atic because the squirrels are commonly controlled
by ranchers outside the wind farm and, by virtue of
ongoing management policy, functionally protected
within it (Hunt and Watson 2016).
Research during 1994–2000 was designed to
estimate the trend of the Golden Eagle population
residing in the vicinity of the wind farm. The study
included airplane tracking of 257 radio-tagged
eagles of four life stages (juveniles, subadults,
floaters, and breeders) and a monitored sample of
58–69 territorial pairs. Radio-tagged eagles generally
remained year-round in the study area. Subadults
and floaters tended to aggregate in the wind farm in
areas where ground squirrels were abundant. To-
gether, although subadults and floaters represented
only 53% of the sample, they incurred 92% of the
blade-strike fatalities. However, not a single one of
the 101 eagles tagged as fledglings was killed by a
turbine during its entire first year of life on the wing,
from fledging to one year after fledging. The tagged
juveniles nonetheless frequently visited the wind
farm, in some months in proportions comparable to
those of subadults and floaters. A possible explana-
tion is that older eagles are killed while hunting, with
juveniles lacking the inclination and experience to
hunt effectively. Tagged breeders incurred few
turbine strikes because they tended to remain on
territory year round. When they did enter the wind
MARCH 2018 7
farm, however, they appeared as vulnerable to the
turbines as subadults and floaters.
Survival and reproductive rates, and their standard
errors, were estimated for each of the four life-stages
from telemetry data and territory monitoring soft-
ware. The potential population rate-of-change esti-
mate was consistent with both population stability
and decline (k¼approximately 1; Hunt et al. 2017).
This implies that the local breeding population was
not generating enough floaters to strongly buffer
itself against loss, and that any sustained increase in
human-related mortality might require immigration
to maintain the population. Continued monitoring
revealed that all the territories surveyed in 2000 were
still occupied in 2005, and almost all in 2013,
implying stability of the nesting population. Mean-
while, collision risk conditions at the wind farm are
expected to improve with a large-scale repowering
program currently in progress in which many of the
small turbines are being replaced with relatively few
large ones, with no overall increase in power
generation. New estimates of vital rates will be
needed to detect whether repowering delivers on
this expectation. All the other human-related
mortality agents present earlier are still operating,
and, apparently no new ones have been added.
There is another problem, however, that is showing
its influence, and that is the apparent effect of
drought on Golden Eagle reproduction. During the
course of the recent surveys in the extremely dry
years of 2013 and 2014, Golden Eagle nest success
was very much lower than in any previous year for
which there is information (Wiens et al. 2015). This
may be due to a response of prey populations to
reduction in primary productivity.
Southern Africa. The wind-energy industry is in its
infancy on the African continent and as a result
there are few published data on the effects of wind
turbines on raptors in the region. Colyn et al. (2014)
published the first recorded raptor mortality at a
South African wind farm (a Jackal Buzzard [Buteo
rufofuscus]). Preliminary results from South Africa,
based on 1 yr or 2 yr of post-construction monitoring
at eight wind farms, suggest that raptors account for
over one-third of carcasses found. Amur Falcon
(Falco amurensis), Jackal Buzzard, and Common
Kestrel (Falco tinnunculus) were the most frequently
reported raptor fatalities, possibly reflecting the
high abundance of these species at the wind farms
in the review. Verreaux’s Eagle (Aquila verreauxii),
Martial Eagle (Polemaetus bellicosus), Lanner Falcon
(Falco biarmicus), Lesser Kestrel (Falco naumanni),
Common Buzzard (Buteo buteo)andthenear-
endemic Black Harrier (Circus maurus) have also
been recorded as fatalities (S. Ralston-Paton pers.
comm.). Many of these species are of conservation
concern, either regionally or globally (Taylor et al.
2015, BirdLife International 2016).
Africa installed nearly 1 GW of wind energy in
2014, with most in South Africa, Egypt, and Morocco
(Fried et al. 2014) and a five-fold increase in energy
demand is expected over the next 25 yr (The World
Bank 2011, IRENA 2013, IEA 2015). A large number
of wind-energy developments are expected (BirdLife
International 2013, Nemaxwi 2013), with specific
plans to harness the renewable energy potential in
eastern and southern Africa in a ‘‘Clean Energy
Corridor.’’ In preparation for this expansion, re-
search has focused on predicting risk to inform wind
farm placement. Bearded Vultures (Gypaetus barba-
tus) and Cape Vultures (Gyps coprotheres) have been a
particular focus of research; proposed wind farms in
Lesotho and South Africa’s Maluti and Drakensberg
mountains (a transboundary World Heritage Site)
are expected to have negative consequences for
small local populations of Bearded Vulture (region-
ally Critically Endangered) and Cape Vulture (re-
gionally Endangered; Jenkins and Allan 2013,
BirdLife International 2013, Rushworth and Kr¨
2014, Reid et al. 2015). Literature published thus far
includes studies on spatial analyses of Bearded
Vulture movements to inform wind-farm placement
(Reid et al. 2015), Bearded Vulture population
viability analyses (Rushworth and Kr ¨
uger 2014),
flight behavior of Cape Vulture to influence turbine
placement (Pfeiffer et al. 2015, Pfeiffer 2016) and
investigation of radar to study bird movements
(Becker 2016).
Conservation organizations have also provided
spatial guidance for wind-farm developers and
decision-makers. Sensitivity maps have been pre-
pared for South Africa (Retief et al. 2012), and the
Red Sea and northern Rift Valley (BirdLife Interna-
tional 2014), and guidelines for impact assessment
and monitoring have also been produced (e.g.,
Jenkins et al. 2011, 2012, 2015, BirdLife Interna-
tional 2017). However, our understanding of site-
specific factors that influence the risk of raptor
collisions in Africa is still in its infancy, and
predictions of likely risk and species’ responses to
wind turbines need to be tested through further
research, monitoring, and data analysis.
In South Africa, most wind farms monitor their
effects on raptors and other birds either voluntarily,
8 VOL. 52, NO.1
or as a condition of their environmental authoriza-
tion. Best practice guidelines for impact assessment
and monitoring (Jenkins et al. 2011) are used to
guide the survey protocols. For example, carcass
surveys are generally conducted with a search
interval of 1–2 wk, with square or circular plots
searched in a radius around the turbine of 75% of
turbine height. To estimate fatality rates, surveys
include searcher efficiency and scavenger removal
trials. Monitoring reports are made available to
stakeholders either voluntarily or as a condition of
environmental authorization, or can be accessed
through the Promotion of Access to Information
Act. There is limited experience with operational
phase mitigation of wind farms in Africa, although
this is likely to change as wind farms become
operational for longer. Guidance on the use of
‘‘shutdown-on-demand’’ has been developed for
migrating soaring birds in the Rift Valley/Red Sea
Flyway (BirdLife International 2015).
Monitoring Raptor Fatalities at Wind-energy Facil-
ities, U.S.A. Post-construction fatality monitoring for
wind-energy projects presents significant challenges
due to the competing needs for precision and
affordability. Most wind-energy projects rely on
external financing for development and obtaining
this financing requires that the costs of develop-
ment, operations, and monitoring be balanced by
potential profitability. There is an implicit tradeoff
between economical approaches to development
and the need to accurately estimate the effects of
each wind-energy project on wildlife species. Recent
studies suggest population-level effects of wind-
energy development are generally small for most
avian species (Erickson et al. 2014, Loss et al. 2015),
but may be significant for some raptors (e.g., Carrete
et al. 2009, Dahl et al. 2012), and there is still much
to learn regarding these effects. Post-construction
fatality monitoring is therefore needed to improve
our understanding of these effects as well as for
regulatory purposes (i.e., permit compliance moni-
toring). Here, we review current practices within the
wind-energy industry to estimate raptor fatalities at
wind-energy projects and provide suggestions for
improved balance between precision and cost in
future fatality monitoring efforts.
Due to factors including, but not limited to, the
spatial scale of wind-energy projects and the tempo-
ral pattern of collisions with wind turbines (i.e.,
many collisions occur at night), it is not feasible to
produce a complete count of fatalities resulting from
collisions at a wind-energy project. Instead, efforts
have focused on estimating fatality rates from
observed counts adjusted by estimates of probability
of detection (Erickson et al. 1998, 2001, Drewitt and
Langston 2006, Arnett et al. 2007, 2008, Huso 2011,
Strickland et al. 2011). Estimates of fatality rates
must account for (1) the probability that a fatality is
detected if it is available for detection (searcher
efficiency), (2) the probability a fatality is available
for detection (i.e., persists from the time of a
collision to the next search; carcass persistence),
and (3) the proportion of carcasses falling into the
searched area. These sources of imperfect detection
are accounted for in common field study designs
that either measure searcher efficiency and carcass
persistence independently (Jain et al. 2007, Good et
al. 2011, Huso 2011, Korner-Nievergelt et al. 2011,
Warren-Hicks et al. 2013), or produce a combined
estimate of detectability (Erickson et al. 1998,
Shoenfeld 2004).
Extrapolation of fatality estimates produced by
statistical estimators is limited to the spatial extent of
the search area around the turbine, especially if the
area is relatively small. This is because the distribu-
tion of carcasses below the turbine is unknown.
Adjustment for the proportion of the carcass
distribution searched can be made using empirically
derived distributions from publicly available studies
or from within the same wind-energy project (e.g.,
the ratio or ‘‘road and pad’’ approach, Rabie et al.
2014). Additionally, models are available to predict
the proportion of the carcass distribution sampled
by a given field design by modeling the carcass fall
zone (Hull and Muir 2010, Huso and Dalthorp
2014). This allows researchers to adjust fatality
estimates for this potentially important source of
bias even in the absence of site-specific empirical
data. An additional source of bias in fatality estimates
arises from the variance among sampled turbines in
the number of fatalities detected. This source of bias
has a greater influence on less abundant species
groups, such as raptors, than with abundant species
groups like passerines, due to the smaller samples of
fatalities generally observed in the case of less
abundant species (Huso 2011).
Available information from post-construction
monitoring at wind-energy projects in North Amer-
ica suggests patterns of variation in searcher
efficiency and carcass persistence rates (Smallwood
2013 and references therein). These patterns appear
to be influenced by carcass size and species, location,
ground cover, and season. Development of site-
MARCH 2018 9
specific estimates of these sources of bias is therefore
standard industry practice in North America.
There are numerous statistical approaches that
extrapolate an annual fatality rate from a sample of
fatalities at a wind-energy project (Erickson et al.
1998, Johnson et al. 2003, Shoenfeld 2004, Huso
2011, Korner-Nievergelt et al. 2011, Etterson 2013,
Pe´ron et al. 2013, Warren-Hicks et al. 2013, Wolpert
2015). Each of the statistical estimators accounts for
sources of bias and adjusts the estimate to create a
relatively unbiased estimate of the true fatality rate at
a given project. However, most currently available
estimators of fatality rates do not produce accurate
and precise estimates when fatalities are rare ("5–10
fatalities per analysis period; M. Huso pers. comm.).
When the goal of fatality monitoring is the detection
of rare events, perhaps in association with compli-
ance monitoring for incidental take permits, differ-
ent analysis methods may be needed (Dalthorp and
Huso 2015).
Post-construction fatality monitoring studies at
wind-energy projects have typically used large,
square search areas around turbines with searchers
walking along transects spaced 3–10 m apart to
search for raptor carcasses (U.S.F.W.S. 2012). Bias
correction trials are usually conducted simulta-
neously with fatality searches. These trials involve
distributing test carcasses without the knowledge of
searchers to obtain estimates of detection probabil-
ity; test carcasses are then either removed after the
trial or left in place to monitor for removal (carcass
persistence) if combined bias trials are conducted
(e.g., Warren-Hicks et al. 2013).
There is a clear need for post-construction fatality
monitoring methodology that is powerful enough to
produce reliable estimates of avian fatality rates as
well as to detect rare events (e.g., fatalities of raptors
or threatened and endangered species), yet is also
economical enough to be used regularly at wind-
energy projects over long time frames. The devel-
opment of such a methodology will likely be
facilitated by the forthcoming availability of fatality
data from multiple wind-energy facilities via the
American Wind Wildlife Information Center (AWWI
2015). Use of these data may enable development of
robust, empirical distributions for carcasses around
turbines that can be used to accurately extrapolate
from small search areas. The development of new
tools such as the Evidence of Absence estimator
(Dalthorp et al. 2014, Huso et al. 2015) also provides
the means for designing fatality monitoring pro-
grams around a priori power analysis to ensure that
goals of the monitoring are met. This is the case for
fatality rate estimation or compliance monitoring.
Integration of cost-effective monitoring protocols,
meta-analyses of data, and emerging analytical tools
will improve our ability to estimate and appropriately
mitigate raptor fatalities at wind-energy projects.
Mitigating for Raptor Fatalities at Wind-energy
Facilities, U.S.A. The U.S. Fish and Wildlife Service
has begun issuing Incidental Take Permits (ITPs) to
wind-power developers for take under the Endan-
gered Species Act and the Bald and Golden Eagle
Protection Act. Once a take limit is set and
minimization and mitigation approaches agreed
upon, conditions of the permit often stipulate
additional actions necessary if the permitted take
limit is exceeded. Accurately collecting and inter-
preting data to provide evidence that take is within
permitted limits presents challenges. To date,
monitoring of wind-energy facilities has been mostly
carried out by the industry with the objective of
estimating general bird and bat fatality rates, not to
address compliance with take limits for an individual
protected species. Current statistical approaches can
usually provide adequate estimates when observed
counts are fairly large, even when detection proba-
bility is very low. But when the target population is
small, as might be expected for endangered species
or species with low population densities, the
likelihood of finding no carcasses may be high, yet
observing no carcasses cannot necessarily be inter-
preted to mean zero or even low numbers of
fatalities (Huso et al. 2015).
Huso et al. (2015) describe an approach based on
Bayes’ theorem that uses information about the
search process and estimated detection probabilities
to provide posterior probabilities of the actual
mortality. Software to carry out the extensive
calculations required by this estimator has been
developed simultaneously by Dalthorp et al. (2014)
and Korner-Nievergelt et al. (2015) to give managers
tools for designing monitoring programs to provide
evidence of industry compliance with ITPs.
Dalthorp and Huso (2015) have developed a
statistical framework for inferring when observed
carcass counts are inconsistent with permitted take
levels either in the short-term (3-yr running average)
or the long-term (life of project), and define
decision-points (triggers) for initiating adaptive
management actions (AMAs) when estimated take
rates exceed permitted levels, as well as for rescind-
ing previous AMAs when warranted by low take rates.
Dalthorp and Huso (2015) evaluate the consequenc-
10 VOL. 52, NO.1
es of choices for certain parameters in terms of
species conservation and cost of operations. The
purpose is not to define optimal parameters but to
provide critical information to guide decision-
making in the management of ITPs.
The process of trying to minimize risk of collision
can be performed pre-construction, through im-
proved siting of turbines and the avoidance of prey-
rich areas, or post-construction. Approaches used to
minimize turbine collision post-construction include
temporary turbine shut-down upon approach by
eagles or endangered species, such as California
Condors (Gymnogyps californianus). Human observ-
ers may be employed to watch for these species in
wind farms where they are frequent; other methods
of detection being tested include radar, digital
image recognition, and radiotelemetry on resident
birds (R. Watson unpubl. data). Mitigation of
collision fatalities includes mortality offsets from
other known causes, such as retrofitting power lines
to reduce electrocution, carcass removal from roads
to reduce vehicle collisions, and abatement of lead
poisoning among avian scavengers that consume the
remains of hunter-harvested game shot with lead-
based ammunition.
Wind-energy development is progressing because
of environmental and economic motives including
reduced greenhouse gas emission, improved air
quality and public health, reduced water consump-
tion, and market benefits such as savings in the costs
of electricity, power systems, and other energy
sources, and creation of jobs (U.S.D.O.E. 2015).
Wind-energy developments can be detrimental to
birds of prey. Even low numbers of anthropogenic
fatalities of certain species, especially raptors, can be
additive with other causes of mortality and signifi-
cant to their populations. For example, anthropo-
genic factors were responsible for about 56% of
satellite-tagged Golden Eagle mortality in the United
States, and reduced annual survival by an estimated
10% (U.S.F.W.S. 2016). Poisoning and shooting
were leading causes of fatality, followed by electro-
cution and collision (U.S.F.W.S. 2016). Many large
raptors including vultures are vulnerable to small
increases in mortality, due to their longevity and low
reproductive rates. They are also often susceptible to
collisions with turbine blades, potentially jeopardiz-
ing the existence of local or regional populations
(Drewitt and Langston 2006, Madders and Whitfield
2006, de Lucas et al. 2008, Carrete et al. 2009, Dahl
et al. 2012, Mart´ınez-Abra´ın et al. 2012). Beyond
direct effects of wind turbines, collisions with
associated infrastructure such as power lines and
guy wires as well as potential displacement and loss
of habitat may also influence avian populations
(Erickson et al. 2001, 2005). Evidence from some of
the sites we reviewed suggests that careful siting and
continued research on optimizing coexistence can
minimize or even eliminate negative effects on
raptors. With the potential for vast expansion of
wind energy across the globe, our review reveals
some important considerations for siting and
questions for further research.
Population-level Effects. Although most studies
have focused on measuring mortality rates at wind
farms, researchers at Altamont focused on the local
Golden Eagle population around the turbines to
detect population effects, rather than inferring
population effects from killed birds. Occupied
territories remained stable over a 13-yr period
despite averaging around 60 turbine-related eagle
fatalities per year, suggesting that local recruitment
may be buffered by a more robust metapopulation.
Recent research supports this conclusion (Katzner et
al. 2017). Evidence indicates that the population
balance seen over past decades might be upset by
reduction in productivity caused by drought-related
factors related to climate change (Wiens et al. 2015).
Population studies should therefore be long-term
and consistent.
Studies in Spain found inconclusive evidence of
population effects from collision mortality among
many birds, and no evidence of mortality being a
function of bird density. Yet, for Griffon Vultures in
Spain, turbine collisions were found to have a
significant effect on fecundity and survival, with
likely population effects because they too, like
Golden Eagles, are large, slow to mature, long-lived,
and slow to reproduce. Elsewhere there is some
evidence of population-level effects on White-tailed
Hawks, Red Kites, Common Buzzards, Burrowing
Owls, and Egyptian Vultures, and some evidence of
indirect population effects from habitat loss. Results
from studies of indirect effects have been mixed
because the effect may vary depending on the extent
of wind-energy development and differences among
species in their tolerance to disturbance; cause and
effect are also notoriously difficult to demonstrate in
observational studies. In addition to measuring
mortality and its causes, including collision with
turbines, understanding the effect of wind-energy
development on a raptor population requires an
MARCH 2018 11
understanding of nest-site occupancy, productivity,
immigration, emigration, and movements of raptors
through the area using either a BACI study design or
a strategy in which spatially separate but similar sites
are studied simultaneously for comparison of
population and behavior parameters between areas
with and without turbines.
For endangered species, a few deaths may have a
large effect on a small remnant population. In South
Africa, the choice of the Maluti and Drakensberg
Mountains for a new wind farm was considered
dangerous because that site is thought to be in the
top 1% of most sensitive sites for endangered Cape
Vultures and Bearded Vultures. Endangered species
are typically rare and therefore difficult to detect
when they are killed by collision with turbines,
necessitating better methods for estimating mortal-
ity rates and population effects, such as those
described by Huso et al. (2015). Where endangered
species such as California Condors are satellite-
tagged for other research purposes, this method
might also provide early warning of approach to
turbines, allowing for shut-down to avoid collision.
Role of Behavior in Collision Risk. Collision
mortality in wind farms has much to do with raptor
behavior. Among Golden Eagles at Altamont, more
subadults were killed than adults or juveniles,
possibly because juveniles relied more on scavenging
than older age classes, and were therefore less likely
to forage and hunt ground squirrels among tur-
bines. Adults, on the other hand, were less likely to
leave their territories to enter the neighboring
turbine area. This finding contrasts with that of a
study of White-tailed Eagles in Norway, where
turbines were placed in nesting areas and where
territorial battles in spring and gregarious roosting
were thought likely explanations of high adult
mortality from wind turbine collisions. Studies in
Spain indicated evidence of behavioral avoidance of
turbines by some raptors, as also shown for Golden
Eagles in Canada. Conversely, large soaring Griffon
Vultures were susceptible to collision mortality in
Spain, most likely as a function of their soaring flight
behavior and related morphology, as well as weather
and topographic factors, as also suggested for
Golden Eagles in Canada. Among raptors other
than eagles and vultures, there is also a pattern of
collision mortality among species with similar flight
styles, with Buteo hawks, Burrowing Owls, and Red
Kites suffering higher fatality rates where dense
nesting populations overlap with wind-energy pro-
jects. Kestrel mortality is also high, but large falcons,
Accipiters, and New World vultures have relatively
low mortality rates from turbine collision. Kites,
harriers, and owls are also not frequently found as
collision victims in most areas, but they are not
immune to these effects, as evidenced by the
number of collision fatalities of Black Harriers in
South Africa, Red Kites in Germany, and various owl
species at Altamont, California.
In Spain, predictive environmental impact assess-
ments based on bird density as an index of collision
probability were not useful in terms of the param-
eters measured. Rather, flight behavior related to
turbine-specific characteristics was more likely to
predict bird collision, especially of soaring birds, and
therefore more useful in making local site adjust-
ments prior to construction. In Africa, the develop-
ment of avifaunal sensitivity maps, which include a
layer for predictable flight corridors based on
topography and bird flight paths tracked by telem-
etry, offers a useful tool.
Informed Turbine Siting and Risk Minimization.
Size and location of specific turbines were related to
raptor collisions in Spain, with taller and higher-
elevation turbines more likely to kill soaring birds
than shorter turbines located at lower elevations.
That taller turbines killed more raptors in Spain
contrasts with findings in the U.S., where repowering
with fewer, taller, slower-moving turbines at Alta-
mont seems to have reduced collision fatalities
compared to the original installation of short, fast-
rotating turbines. This suggests that elevation and
topography may be more important factors affecting
raptor collisions than turbine size and rotation
speed in the study from Spain.
In Spain, post-construction mitigation by shutting
turbines down was effective in reducing mortality by
65% while only reducing energy production by
0.07%. Researchers derived the same conclusion for
White-tailed Eagles in Norway, where siting turbines
close to a communal roost was the cause of much
mortality that could easily have been avoided with
some care. Repowering with larger turbines is
planned and may reduce mortality of White-tailed
Eagles. In southern Africa, pre-construction assess-
ment would inform mitigation measures during
planning, and post-construction mortality surveys,
coupled with measures like turbine curtailment
found to be successful in Spain, could mitigate
mortality where turbines are already sited in sensitive
locations, provided government authorities attend
to the findings. Engaging with stakeholders, includ-
ing government agencies, non-governmental orga-
12 VOL. 52, NO.1
nizations, power companies, development agencies
and other financial backers, is essential to ensure
that wind-power development occurs responsibly.
Turbine-associated Fatalities and Raptor Conser-
vation. Efforts to mitigate wind farm impacts on
raptors by reducing other unrelated human-caused
mortality agents, like electrocution (Lehman et al.
2007), lead exposure from hunters’ spent ammuni-
tion (Golden et al. 2016), poisoning, and trade
(Ogada et al. 2015), could have great benefits for
survival of large raptor species. However, measuring
the relative benefit of these mitigation strategies
depends, in part, on reliable estimates of mortality
from each source. Reliable post-construction mor-
tality monitoring at wind farms is notoriously
difficult and the subject of intense effort to improve
reliability of results. Reliable mortality estimates are
particularly important for measuring compliance
with take permits issued in the U.S.A. by the
U.S.F.W.S. under the Bald and Golden Eagle
Protection Act to allow wind farms to unintentionally
kill these species without risk of prosecution. With
strong regulation and diligent oversight, authorities
can use these permits to leverage the benefits of
mitigation measures by wind farms.
In conclusion, wind farms have the potential to
have important population-level effects on some
raptor species, especially large soaring raptors that
are long-lived, reach maturity at an older age, and
have low reproductive rates. Where such species
may be affected by wind-farm development, pre-
construction analysis of their flight patterns and
behaviors in the proposed site should be used to
inform turbine siting to avoid frequent flight paths
of soaring birds. Avoiding areas of high prey density
can dramatically reduce collision mortality, as
would repowering with fewer, larger turbines.
There is less information about other raptors, and
certain groups of species seem to be more at risk of
collision than others, especially large Buteos and
small falcons such as kestrels. The problem of
estimating mortality among rare species is being
tackled statistically to provide critical information
to guide decision-making in the management of
incidental take permits in the U.S., but the same
methods may prove useful in estimating mortality
rates of small, difficult-to-find species that have
been largely ignored thus far. The large variability
of experience between countries suggests the need
for global standards in wind-farm placement,
monitoring, and impact mitigation.
Statement of author contributions: Richard Watson
conceived the idea of a global raptors and wind-power
symposium with a joint article authored by invited
symposium delegates. Watson wrote the introduction and
discussion, and Todd Katzner and Richard Watson
compiled and edited the manuscript. Listed in order of
contributions to the paper, symposium presenters included
co-authors as follows: Patrick Kolar and Marc Bechard
wrote the overview; Miguel Ferrer, Virginia Morandini, and
Manuela de Lucas wrote the case from Spain; Torgeir
Nyg˚ard, Espen Lie Dahl, Kjetil Bevanger, Roel May, Ole
Reitan, and B˚ard Gunnar Stokke wrote the case from
Norway; Naira Johnston, James Bradley, and Ken Otter
wrote the case from Canada; W. Grainger Hunt, J. David
Wiens, Patrick S. Kolar, Teresa L. Hunt, Daniel Driscoll,
and Ronald E. Jackman wrote the case from California;
Hanneline Smit-Robinson and Samantha Ralston-Paton
wrote the case from southern Africa; Christopher J. Farmer
and Thomas Snetsinger wrote on monitoring raptor
fatalities; and Manuela Huso and Dan Dalthorp wrote on
mitigating raptor fatalities. Any use of trade, product, or
firm names is for descriptive purposes only and does not
imply endorsement by the U.S. government.
ican Wind Wildlife information center. https://awwi.
org/resources/tools (last accessed 16 July 2016).
Patterns of bat fatalities at wind energy facilities in
North America. Journal of Wildlife Management 72:61–78.
STRICKLAND,AND R. THRESHER. 2007. Impacts of wind
energy facilities on wildlife and wildlife habitat. Wildlife
Society Technical Review 072. The Wildlife Society,
Bethesda, MD U.S.A.
IGUEZ. 2004. Behavioural and
environmental correlates of soaring-bird mortality at
on-shore wind turbines. Journal of Applied Ecology 41:72–
BECKER, F. 2016. Optimising the use of visual and radar
observations for the mitigation of wind energy related
impacts on Cape Vultures (Gyps coprotheres) in the
Eastern Cape Province. M.S. thesis, Stellenbosch Uni-
versity, Rhodes, South Africa.
MAMMEN. 2013. Wind turbine fatalities approach a level
of concern in a raptor population. Journal for Nature
Conservation 21:394–400.
Insufficient sampling to identify species affected by
turbine collisions. Journal of Wildlife Management 79:513–
MARCH 2018 13
BIRDLIFE INTERNATIONAL. 2013. Wind farm in Lesotho could
cause the local extinction of vultures. http://www.
could-cause-the-local-extinctionof-vultures-2/ (last ac-
cessed 11 January 2015).
———. 2014. Migratory Soaring Birds Project sensitivity
en/sensitivity-map (last accessed 22 February 2017).
———. 2015. Review and guidance on use of ‘‘shutdown-
on-demand’’ for wind turbines to conserve migrating
soaring birds in the Rift Valley/Red Sea Flyway.
Regional Flyway Facility, Amman, Jordan. http://
pdf (last accessed 22 February 2017).
———. 2016. Datazone.
datazone/ species/ (last accessed 25 July 2016).
———. 2017. Migratory Soaring Birds Project. Wind energy
guidance v.1., developers and consultants. http://
20logo%20PR.pdf (last accessed 22 February 2017).
´ZAR. 2009. Large scale risk-assessment of
wind-farms on population viability of a globally endan-
gered long-lived raptor. Biological Conservation
———, ———, ———, ———, F. MONTOYA,AND J.A.
´ZAR. 2012. Mortality at wind-farms is positively
related to large-scale distribution and aggregation in
Griffon Vultures. Biological Conservation 145:102–108.
recorded raptor mortality at a South African wind farm.
Gabar 24:95–99.
DAHL, E.L. 2014. Population dynamics in White-tailed Eagle
at an on-shore wind farm area in coastal Norway. Ph.D.
dissertation, Norwegian University of Science and
Technology, Trondheim, Norway.
———, K. BEVANGER, T. NYG ˚
STOKKE. 2012. Reduced breeding success in White-tailed
Eagles at Smøla windfarm, western Norway, is caused by
mortality and displacement. Biological Conservation
RØSKAFT,AND B.G. STOKKE. 2013. White-tailed Eagles
(Haliaeetus albicilla) at the Smøla wind-power plant,
central Norway, lack behavioral flight responses to wind
turbines. Wildlife Society Bulletin 37:66–74.
———, ———, T. NYG ˚
ARD, J. ˚
2015. Repowering Smøla wind-power plant: an assess-
ment of avian conflicts. NINA Rapport. Norwegian
Institute for Nature Research, Trondheim, Norway.
DALTHORP, D.H. AND M.M.P. HUSO. 2015. A framework for
decision points to trigger adaptive management actions
in long-term incidental take permits. U.S.G.S. open-file
report. DOI:10.3133/ofr20151227 (last accessed 30
December 2015).
———, ———, D. DAIL,AND J. KENYON. 2014. Evidence of
absence software. U.S. Geological Survey, Corvallis, OR
U.S.A. (last accessed
30 December 2015).
KARPLUS. 2016. Modelling the potential for wind energy
integration on China’s coal-heavy electricity grid. Nature
Energy 1:16086. doi:10.1038/nenergy.2016.86 (last ac-
cessed 17 November 2017).
2012. Griffon Vulture mortality at wind farms in
southern Spain: distribution of fatalities and active
mitigation measures. Biological Conservation 147:184–
———, G.F.E. JANSS,AND M. FERRER. 2004. The effects of a
wind farm on birds in a migration point: the Strait of
Gibraltar. Biodiversity and Conservation 13:395–407.
———, ———, D.P. WHITFIELD,AND M. FERRER. 2008.
Collision fatality of raptors in wind farms does not
depend on raptor abundance. Journal of Applied Ecology
DREWITT, A.L. AND R.H.W. LANGSTON. 2006. Assessing the
impacts of wind farms on birds. Ibis 148:29–42.
EBERHARDT, L.L. 2002. A paradigm for population analysis
of long-lived vertebrates. Ecology 83:2841–2852.
JR., K.J. SERNKA,AND R.E. GOOD. 2001. Avian collisions
with wind turbines: a summary of existing studies and
comparisons to other sources of avian collision mortal-
ity in the United States. National Wind Coordinating
Committee, Washington, DC U.S.A.
———, ———, AND D.P.J. YOUNG. 2005. A summary and
comparison of bird mortality from anthropogenic
causes with an emphasis on collisions. U.S.D.A. Forest
Service, Gen. Tech. Rep. PSW-GTR-191, Albany, CA
BOURASSA, K. BAY,AND K. SERNKA. 2002. Synthesis and
comparison of baseline avian and bat use, raptor
nesting and mortality information from proposed and
existing wind developments. Report for Bonneville
Power Administration, Portland, OR U.S.A.
1998. Examples of statistical methods to assess risks of
impacts to birds from wind plants. Pages 172–182 in
Proceedings of the National Avian-Wind Power Plan-
ning Meeting III, San Diego, CA. Prepared for the Avian
Subcommittee of the National Wind Coordinating
Committee by LGL, Limited, King City, Ontario,
GEHRING. 2014. A comprehensive analysis of small-
passerine fatalities from collision with turbines at wind
energy facilities. PLoS ONE 9:e107491. doi:10.1371/
journal.pone.0107491 (last accessed 17 November
14 VOL. 52, NO.1
ETTERSON, M.A. 2013. Hidden Markov models for estimat-
ing animal mortality from anthropogenic hazards.
Ecological Applications 23:1915–1925.
relationship between risk assessment studies and
recorded mortality in wind farms. Journal of Applied
Ecology 49:38–46.
Global wind report: market update 2014. Global
Wind Energy Council, Brussels, Belgium. http://
global-wind-report-2014-annual-market-update/ (last
accessed 10 January 2016).
2011. Response of raptors to a windfarm. Journal of
Applied Ecololgy 48:199–209.
GOLDEN, N.H., S.E. WARNER,AND M.J. COFFEY. 2016. A review
and assessment of spent lead ammunition and its
exposure and effects to scavenging birds in the United
States. Reviews of Environmental Contamination and
Toxicology 237:123–191.
K. BAY,AND C. FRITCHMAN. 2011. Bat monitoring studies
at the Fowler Ridge Wind Energy Facility, Benton
County, Indiana. Report prepared for Fowler Ridge
Wind Farm by Western EcoSystems Technology, Inc.
Cheyenne, WY U.S.A.
pdf (last accessed September 2016).
AND S. WEITEKAMP. 2016. Prognosis and assessment of
bird collision risks at wind turbines in northern
Germany (PROGRESS). Final report commissioned by
the Federal Ministry for Economic Affairs and Energy in
the framework of the energy research programme of
the federal government. Reference number FKZ
0325300A-D. Husum, Germany.
FERRER. 2015. Effects of wind farms on Montagu’s
Harrier (Circus pygargus) in southern Spain. Biological
Conservation 191:452–458.
AND H.T. ARITA. 2013. Bird communities and wind
farms: a phylogenetic and morphological approach.
Biodiversity and Conservation 22:2821–2836.
Reduction of motion smear to reduce avian collisions
with wind turbines. Pages 88–105 in Proceedings of the
National Avian-Wind Power Planning Meeting IV.
Prepared for the National Wind Coordinating Commit-
tee by RESOLVE Inc., Washington, DC and Carmel, CA
HOOVER, S.L. AND M.L. MORRISON. 2005. Behavior of Red-
tailed Hawks in a wind turbine development. Journal of
Wildlife Management 69:150–159.
¨STER. 2006. Impacts
on biodiversity of exploitation of renewable energy
sources: the example of birds and bats. Facts, gaps in
knowledge, demands for further research, and ornitho-
logical guidelines for the development of renewable
energy exploitation. Michael-Otto-Institut im NABU,
Bergenhusen, Germany.
HULL, C.L. AND S. MUIR. 2010. Search areas for monitoring
bird and bats carcasses at wind farms using a Monte-
Carlo model. Australasian Journal of Environmental
Management 17:77–87.
HUNT, W.G. 2002. Golden Eagles in a perilous landscape—
predicting the effects of mitigation for wind turbine
blade-strike mortality. Report to California Energy
Commission under contract P500-02-043F. Public In-
terest Energy Research, California Energy Commission,
Sacramento, CA U.S.A.
reports/2002-11-04_500-02-043F.PDF (last accessed 26
September 2017).
——— AND T.L. HUNT. 2006. The trend of Golden Eagle
territory occupancy in the vicinity of the Altamont Pass
Wind Resource Area: 2005 Survey. CEC-500-2006-056.
Public Interest Energy Research, California Energy
Commission, Sacramento, CA U.S.A. http://www.
CEC-500-2006-056.PDF (last accessed 26 September
———, R.E. JACKMAN, T.L. BROWN,AND L. CULP. 1999. A
population study of Golden Eagles in the Altamont Pass
Wind Resource Area: population trend analysis 1994–
1997. Report to National Renewable Energy Laboratory,
Subcontracts XAT-5-15174-01, XAT-6-16459-01 to the
Predatory Bird Research Group, University of Califor-
nia, Santa Cruz, CA U.S.A.
fy99osti/26092.pdf (last accessed 26 September 2017).
———, C.J.W. MCCLURE,AND T.D. ALLISON. 2015. Do
raptors react to ultraviolet light? Journal of Raptor
Research 49:342–343.
——— AND J.W. WATSON. 2016. Addressing the factors that
juxtapose raptors and wind turbines. Journal of Raptor
Research 49:342–343.
DRISCOLL,AND R.E. JACKMAN. 2017. Quantifying the
demographic cost of human-related mortality to a
raptor population. PLoS ONE 12:e0172232.
10.1371/journal.pone.0172232 (last accessed 17 No-
vember 2017).
HUSO, M. 2011. An estimator of wildlife fatality from
observed carcasses. Environmetrics 22:318–329.
——— AND D. DALTHORP. 2014. Accounting for unsearched
areas in estimating wind turbine-caused fatality. Journal
of Wildlife Management 78:347–358.
———, ———, D. DAIL,AND L. MADSEN. 2015. Estimating
turbine-caused bird and bat fatality when zero carcasses
are observed. Ecological Applications 25:1213–1225.
ICF INTERNATIONAL. 2015. Final report Altamont Pass Wind
Resource Area bird fatality study, monitoring years 2005–
MARCH 2018 15
2013. Prepared for Alameda County Community Devel-
opment Agency, Hayward, CA U.S.A. https://www.
(last accessed 6 February 2017).
energy outlook electricity access database 2015.
International Energy Agency, Paris, France. http://
energydevelopment/energyaccessdatabase (last ac-
cessed 10 January 2016).
Working together to build an east and southern African
clean energy corridor. International Renewable Energy
Agency, Abu Dhabi, United Arab Emirates. https://
(last accessed 10 January 2016).
Annual report for the Maple Ridge Wind Power Project
post-construction bird and bat fatality study—2006.
Annual report prepared for PPM Energy and Horizon
Energy, Curry and Kerlinger LLC, Cape May Point, NJ
JANSS, G.F.E. AND M. FERRER. 1998. Rate of bird collision
with power lines: effects of conductor-marking and
static wire-marking. Journal of Field Ornithology 69:8–17.
JENKINS, A.R. AND D. ALLAN. 2013. An ill wind blows over the
roof of Africa. African Birdlife 1:52–56.
H.A. SMIT. 2011. Best practice guidelines for avian
monitoring and impact mitigation at proposed wind
energy development sites in southern Africa. BirdLife
South Africa and Endangered Wildlife Trust, Johannes-
burg, South Africa.
———, ———, ———, J.A. HARRISON, M. DIAMOND,AND
H.A. SMIT. 2012. Best practice guidelines for avian
monitoring and impact mitigation at proposed wind
energy development sites in southern Africa. BirdLife
South Africa and Endangered Wildlife Trust, Johannes-
burg, South Africa.
———, ———, ———, ———, ———, H.A. SMIT-ROBIN-
SON,AND S. RALSTON. 2015. Birds and wind-energy best-
practice guidelines: best practice guidelines for avian
monitoring and impact mitigation at proposed wind
energy development sites in southern Africa, Third Ed.
The Endangered Wildlife Trust and BirdLife South
Africa, Johannesburg, South Africa.
JOHNSON, G.D. AND W.P. ERICKSON. 2011. Avian and bat
cumulative impacts associated with wind energy devel-
opment in the Columbia Plateau ecoregion of eastern
Washington and Oregon. Technical Report prepared
for Klickitat County Planning Dept by WEST, Inc.,
Cheyenne, WY U.S.A. (last ac-
cessed 22 July 2015).
SHEPHERD,AND S.A. SARAPPO. 2003. Mortality of bats at a
large-scale wind power development at Buffalo Ridge,
Minnesota. American Midland Naturalist 150:332–342.
Increased flight altitudes among migrating Golden
Eagles suggest turbine avoidance at a Rocky Mountain
wind installation. PLoS ONE 9:e93030.
1371/journal.pone.0093030 (last accessed 17 Novem-
ber 2017).
continental-scale consequences of local wind-energy
generation. Conservation Biology 31:406–415.
KOLAR, P.S. 2013. Impacts of wind energy development on
breeding Buteo hawks in the Columbia Plateau
ecoregion. MS thesis, Boise State University, Boise, ID
——— AND M.J. BECHARD. 2016. Wind energy, nest success,
and post-fledging survival of Buteo hawks. Journal of
Wildlife Management 80:1242–1255.
estimation from carcass searches using the R-package
carcass – a tutorial. Wildlife Biology 21:30–43.
BRINKMANN,AND B. HELLRIEGEL. 2011. A new method to
determine bird and bat fatality at wind energy turbines
from carcass searches. Wildlife Biology 17:350–363.
LEDEC, G.C., K.W. RAPP,AND R.G. AIELLO. 2011. Greening
the wind: environmental and social considerations for
wind power development. A World Bank Study. World
Bank, Washington, DC U.S.A.
state of the art in raptor electrocution research: a global
review. Biological Conservation 136:159–174.
LOSS, S.R., T. WILL,AND P. MARRA. 2015. Direct mortality of
birds from anthropogenic causes. Annual Review of
Ecology, Evolution, and Systematics 46:99–120.
potential for wind-generated electricity. Proceedings of the
National Academy of Sciences 106:10933–10938.
MADDERS, M. AND D.P. WHITFIELD. 2006. Upland raptors and
the assessment of wind farm impacts. Ibis 148:43–56.
NO. 2014. Understanding bird collisions at wind farms:
an updated review on the causes and possible mitigation
strategies. Biological Conservation 179:40–52.
fields, foraging and collision vulnerability in Gyps
vultures. Ibis 154:626–631.
M. SURROCA,AND D. ORO. 2012. Effects of wind farms
and food scarcity on a large scavenging bird species
16 VOL. 52, NO.1
following an epidemic of bovine spongiform encepha-
lopathy. Journal of Applied Ecology 49:109–117.
MAY, R.F. 2015. A unifying framework for the underlying
mechanisms of avian avoidance of wind turbines.
Biological Conservation 190:179–187.
ARD. 2010a.
Evaluation of the DTBird video-system at the Smøla
wind-power plant. NINA Report 910:27. Norwegian
Institute for Nature Research, Trondheim, Norway.
ARD. 2010b. Collision risk in
White-tailed Eagles. NINA Report 639. Norwegian
Institute for Nature Research, Trondheim, Norway.
———, T. NYG ˚
Habitat utilization in White-tailed Eagles (Haliaeetus
albicilla) and the displacement impact of the Smøla
wind-power plant. Wildlife Society Bulletin 37:75–83.
———, ———, ———, O. REITAN,AND K. BEVANGER. 2011.
Collision risk in White-tailed Eagles. Modelling kernel-
based collision risk using satellite telemetry data in
Smøla wind-power plant. NINA Report 692. Norwegian
Institute for Nature Research, Trondheim, Norway.
ARD. 2015. Mitigating wind-turbine induced avian
mortality: sensory, aerodynamic and cognitive con-
straints and options. Renewable and Sustainable Energy
Reviews 42:170–181.
NEMAXWI, H. 2013. Eskom Sere wind farm tobe constructed
by Siemens South Africa. http://www.discoversiemens
by-siemens-south-africa/ (last accessed 11 January
JEM,AND A.R.E. SINCLAIR. 2015. Another continental
vulture crisis: Africa’s vultures collapsing toward extinc-
tion. Conservation Letters 9:89–97.
ORLOFF, S. AND A. FLANNERY. 1992. Wind turbine effects on
avian activity, habitat use and mortality in Altamont Pass
and Solano County Wind Resource Areas, 1989–91.
California Energy Commission, Sacramento, CA U.S.A.
BAINBRIDGE,AND R. BULLMAN. 2009. The distribution of
breeding birds around upland wind farms. Journal of
Applied Ecology 46:1323–1331.
PETERS,AND D.S. MIZRAHI. 2013. Estimation of bird and
bat mortality at wind-power farms with superpopulation
models. Journal of Applied Ecology 50:902–911.
PFEIFFER, M.B. 2016. Ecology and conservation of the Cape
Vulture in the Eastern Cape Province, South Africa.
PhD dissertation, University of KwaZulu-Natal, South
———, J.A. VENTER,AND C.T. DOWNS. 2015. Foraging range
and habitat use by Cape Vulture Gyps coprotheres from
the Msikaba colony, Eastern Cape province, South
Africa. Koedoe 57:1–11.
J. ROPPE. 2014. A flexible modeling approach to ‘road
and pad’ correction factors for bats in post-construction
monitoring projects. Poster presented at the National
Wind Coordinating Collaborative, Wind-Wildlife Re-
search Meeting X, Denver, CO U.S.A. https://www.
Rabie.pdf (last accessed 16 September 2017).
REID, T., S. KR¨
Using spatial analyses of Bearded Vulture movements in
southern Africa to inform wind turbine placement.
Journal of Applied Ecology 52:881–892.
(REN21). 2016. Renewables 2016 global status report.
REN21 Secretariat, Paris, France.
JENKINS,AND M. BROOKS.2012.Avianwindfarm
sensitivity map for South Africa. Johannesburg, South
UGER. 2014. Wind farms threaten
southern Africa’s cliff-nesting vultures. Ostrich 85:1–11.
SHOENFELD, P.S. 2004. Suggestions regarding avian mortal-
ity extrapolation: West Virginia Highlands Conservancy,
Davis, WV U.S.A.
(last accessed 15 November 2016).
SMALES, I. 2015. Fauna collisions with wind turbines: effects
and impacts, individuals and populations. What are we
trying to assess? Pages 23–40 in C. Hull, E. Bennett, E.
Stark, I. Smales, J. Lau, and M. Venosta [EDS.], Wind
and wildlife. Springer, Dordrecht, The Netherlands.
SMALLWOOD, K.S. 2013. Comparing bird and bat fatality-rate
estimates from North American wind-energy projects.
Wildlife Society Bulletin 37:19–33.
———, L. RUGGE,AND M.L. MORRISON. 2009. Influence of
behavior on bird mortality in wind energy develop-
ments: the Altamont Pass Wind Resource Area, Cal-
ifornia. Journal of Wildlife Management 73:1082–1098.
——— AND C.G. THELANDER. 2008. Bird mortality in the
Altamont Pass Wind Resource Area, California. Journal
of Wildlife Management 72:215–223.
———, ———, M.L. MORRISON,AND L.M. RUGGE. 2007.
Burrowing Owl mortality in the Altamont Pass Wind
Resource Area. Journal of Wildlife Management 71:1513–
AND W. WARREN-HICKS. 2011. Comprehensive guide to
studying wind energy/wildlife interactions. Prepared
for the National Wind Coordinating Collaborative.
Washington, DC U.S.A.
Eskom Red Data Book of birds of South Africa, Lesotho
and Swaziland. BirdLife South Africa, Johannesburg,
South Africa.
MARCH 2018 17
THE WORLD BANK. 2011. Fact sheet: The World Bank and
energy in Africa.
8VI6E7MRU0 (last accessed 10 January 2016).
vision: a new era for wind power in the United States.
U.S. Department of Energy, Washington, DC U.S.A.
(last accessed 16 July 2016).
based wind energy guidelines. OMB Control No. 1018-
0148. Washington, DC U.S.A.
———. 2016. Bald and Golden Eagles: population demo-
graphics and estimation of sustainable take in the
United States, 2016 update. Division of Migratory Bird
Management, Washington, DC U.S.A.
L. TRAN. 2013. Improving methods for estimating fatality
of birds and bats at wind energy facilities. California
Wind Energy Association publication CEC-500-2012-086,
California Energy Commission, Sacramento, CA U.S.A.
CEC-500-2012-086/CEC-500-2012-086.pdf (last accessed
16 September 2017).
WHITFIELD, D.P. AND M. MADDERS. 2006. Deriving collision
avoidance rates for Red Kites Milvus milvus. Natural
Research Information Note 3. Natural Research Ltd,
Banchory, U.K.
HUNT. 2015. Estimation of occupancy, breeding success,
and predicted abundance of Golden Eagles (Aquila
chrysaetos) in the Diablo Range, California, 2014. U.S.
Geological Survey open-file report 2015-1039, Corvallis,
(last accessed 16 September 2017).
O’HALLORAN. 2016. Hen Harrier Circus cyaneus popula-
tion trends in relation to wind farms. Bird Study 64:20–29.
WOLPERT, R.L. 2015. ACME: A partially periodic estimator
of avian and chiropteran mortality at wind turbines.
Cornell University Library, Ithaca, NY U.S.A. http:// (last accessed 16 September
Received 18 November 2016; accepted 17 March 2017
18 VOL. 52, NO.1
... Raptors may be injured or killed by collisions with wind turbines [45][46][47][48], and rates of mortality at commercial wind facilities may be underestimated due to lack of rigorous monitoring and reporting [17]. To reduce the risk of population-level impacts to golden eagles (Aquila chrysaetos) in the western Great Plains, we mapped wind development ...
... Raptors may be injured or killed by collisions with wind turbines [45][46][47][48], and rates of mortality at commercial wind facilities may be underestimated due to lack of rigorous monitoring and reporting [17]. To reduce the risk of population-level impacts to golden eagles (Aquila chrysaetos) in the western Great Plains, we mapped wind development avoidance areas corresponding to the highest modeled golden eagle densities in ecoregions assessed by the Western Golden Eagle Team (top 2 of 7 area-adjusted frequency quantiles; Figure 2B). ...
Full-text available
To help avoid the most catastrophic effects of climate change, society needs to achieve net-zero greenhouse gas emissions by mid-century. Wind energy provides a clean, renewable source of electricity; however, improperly sited wind facilities pose known threats to wildlife populations and contribute to degradation of natural habitats. To support a rapid transition to low-carbon energy while protecting imperiled species, we identified potential low-impact areas for wind development in a 19-state region of the central U.S. by excluding areas with known wildlife sensitivities. By combining maps of sensitive habitats and species with wind speed and land use information, we demonstrate that there is significant potential to develop wind energy in the region while avoiding significant negative impacts to wildlife. These low-impact areas have the potential to yield between 930 and 1550 GW of name-plate wind capacity. This is equivalent to 8–13 times current U.S. installed wind capacity. Our analysis demonstrates that ambitious low-carbon energy goals are achievable while minimizing risks to wildlife.
... . Predictive modelling, based on previous knowledge about the species' behaviour in specific environmental contexts, is considered instrumental when it comes to informing investors about the forecasted impact of a particular wind farm on a species of concern. Different modelling studies have shown that topography is one of the primary correlates of increased risk of collision for soaring birdsDe Lucas et al., 2008;Ferrer et al., 2012;Gove et al., 2013;Watson et al., 2018) due to the soaring opportunities it provides(Sage et al., 2019;Shepard, Williamson and Windsor, 2016). Yet, topography has been only rarely considered as an environmental correlate in models predicting fatality rates (De Lucas,Ferrer and Janss, 2012;Smallwood, Neher and Bell, 2009) or flight behaviour(Aurbach et al., 2018;Becciu et al., 2019;Katzner et al., 2012;Scacco et al., 2019).We have recently highlighted the role of static topographic features in predicting soaring behaviour and energy expenditure of the white stork Ciconia ciconia, an obligate soaring bird species. ...
... Yet, topography has been only rarely considered as an environmental correlate in models predicting fatality rates (De Lucas,Ferrer and Janss, 2012;Smallwood, Neher and Bell, 2009) or flight behaviour(Aurbach et al., 2018;Becciu et al., 2019;Katzner et al., 2012;Scacco et al., 2019).We have recently highlighted the role of static topographic features in predicting soaring behaviour and energy expenditure of the white stork Ciconia ciconia, an obligate soaring bird species. In that study we focused on one single species, like most other studies proposing predictive models of habitat use and collision risk, which are often driven by the urgency of assessing the potential impact on a species of concern(Barrios and Rodríguez, 2004;Marques et al., 2014;Smallwood, Rugge and Morrison, 2009;Watson et al., 2018). Such focus has clear advantages for the targeted species. ...
... WPPs equipped with blade turbines are becoming one of the major factors limiting the number of large birds of prey, causing death in nesting territories and on flyways, negatively affecting their behavior and habitat usage (Orloff, 1992;Meek et al., 1993;Winkelman, 1995;Ogden, 1996;Hunt et al., 1998;Osborn et al., 1998;Erickson et al., 2001;Langston, Pullan, 2003;Fontán et al., 2003;2012a;2012b;Desholm, 2006;Drewitt, Langston, 2006;Smallwood, Thelander, 2008;Carrete et al., 2009;Farfán et al., 2009;Bellebaum et al., 2012;Martinez-Abrain et al., 2012;Marques et al., 2014;Hunt et al., 2017;Koppel, 2017). The problem of bird of prey mortality on WPPs is aggravated by their relatively low density, and most of them have long lifespans with a low birthrate that makes them especially vulnerable to excess mortality (Kikuchi, 2008;Watson et al., 2018). WPPs that kill birds of prey at the "bottlenecks" of their flyways might influence not only local populations but the whole complex of populations continental scale, as happened to Golden Eagle (Aquila chrysaetos) in the USA . ...
... All measurements, except for azimuth, were taken in Albers coni-2012; Martinez-Abrain et al., 2012;Marques et al., 2014;Hunt et al., 2017;Koppel, 2017). Проблема гибели хищных птиц на ВЭС усугубляется тем, что эти виды встречаются с относительно низкой плотностью, и большинство из них долгожители с низкой продуктивностью, что делает их особенно уязвимыми к избыточной смертности (Kikuchi, 2008;Watson et al., 2018). ВЭС, уничтожающие хищных птиц в местах их концентрации на пролёте, могут оказывать влияние не только на локальные популяции, но и на целый комплекс популяций в континентальном масштабе, как это показано на примере беркута (Aquila chrysaetos) в США . ...
Full-text available
On the basis of data obtained from ARGOS/GPS and GPS/GSM tracking of 34 eagles (4 Steppe Eagles (Aquila nipalensis) from Central KZ, 1 Steppe Eagle from Southern Ural region, 22 Steppe Eagles, 5 Eastern Imperial Eagles (Aquila heliaca) from the ASR and 2 Greater Spotted Eagles (Aquila clanga) from the from the Altai-Sayan Ecoregion), we have defined the main flyways, terms, and other parameters of migration of eagles through Eastern Kazakhstan. We have outlined the borders of the migration corridor and estimate the number of migrants passing through it. The study highlights the importance of the Karatau ridge for eagles from the vast territories of Russia and Kazakhstan. But we are also concerned about the development of wind farms with horizontal-axis wind turbines that expose ultimate danger for raptors in the Karatau migration corridor. One of them already exists – the Zhanatas Wind-Power Station. Here we calculated the possible negative impact on the eagle population from the existing and projected wind farms of the Karatau ridge and give our recommendations for neutralizing the damage from the development of the electric power industry in Karatau.
... Mitigation of turbine-induced mortality of birds at wind farms has proven to be difficult (May et al. 2015), and post-construction mitigation measures like painting rotor blades to increase visibility, scaring devices and UV-lights have been tested at Smøla, but the effectiveness of these measures is doubtful (Watson et al. 2018 and references therein). Proper situating of wind farms, such as avoiding placement in important breeding area for White-tailed Eagles, is crucial to prevent raptor casualties (Watson et al. 2018). A plan that could reduce bird casualties by repowering the Smøla wind farm with fewer, but larger turbines has been proposed, but not yet effected (Watson et al. 2018). ...
... Proper situating of wind farms, such as avoiding placement in important breeding area for White-tailed Eagles, is crucial to prevent raptor casualties (Watson et al. 2018). A plan that could reduce bird casualties by repowering the Smøla wind farm with fewer, but larger turbines has been proposed, but not yet effected (Watson et al. 2018). ...
... Thus, potentially increasing energy expenditure can affect breeding success and survival (Madsen et al. 2014; Peschko et al. 2020). Even low numbers of anthropogenic induced fatalities of certain species can be additive to other causes of mortality and signi cantly reduce their population (Watson 2018). Moreover, turbines may disturb the foraging and breeding of waterfowl (Drewitt and Langston 2006) resulting in habitat loss (Larsen and Madsen 2000). ...
Ruddy-headed goose Chloephaga rubidiceps has a migratory population that overwinters mainly in the Pampas region, Argentina, and breeds in Southern Patagonia. This population has decreased considerably, with less than 800 individuals remaining to date. We conducted the first assessment on the influence of environmental and anthropogenic impact (wind farms and high voltage networks) variables on Ruddy-headed goose migration pathways across the Patagonian coast by applying kernel density analyses and statistical procedures on satellite tracking data obtained from six Ruddy-headed geese during their migration pathways between 2015 and 2018. Five core distribution areas were identified during migration. During autumn migration, core areas were associated with high primary productivity and low elevation areas, while during spring migration they were located in the proximity of watercourses and waterbodies. We found that around 30% of the grid cells included in the high-density areas were located in the influence area of high voltage networks during both migrations. While 33% of the grid cells included in the high-density areas were in the influence zone of wind farms during autumn migration; while this applied to only 13% during spring migration. We highlight areas of high risk along the distributional range of the species where large-scale patterns of collision mortality are likely to occur and mitigation measures should be prioritized. We suggest proactive measures that could mitigate future collisions with energy infrastructure because, given their threatened status, a few deaths may have a large effect on the small remnant population.
... As an example of how this approach could be used to understand ecological impacts of development of renewable energy, consider diurnal birds of prey. Raptors are negatively affected by renewables via fatalities, especially collision with wind turbines, and by habitat loss, at both wind and solar energy facilities (Watson et al., 2018;Kosciuch et al., 2020;Diffendorfer et al., 2021). Effects of climate change are less dramatic but equally important, for example acting through shifts in range and phenology (Paprocki et al., 2015;Therrien et al., 2017). ...
... Considerable efforts have been devoted to quantifying and monitoring the number of bird casualties produced by wind energy facilities, as well as to predicting collision risk (e.g. [3][4][5][6][7]). However, it is critical to go one step further and contextualize these numbers to understand the repercussions of this additional mortality on bird populations, and investigate whether these impacts are sustainable over time [8]. ...
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As the demand for carbon-neutral energy sources increases, so does the need to understand the impacts that these technologies have on the environment. Here, we assess the potential consequences of additional mortality on an Endangered raptor recently exposed to wind farms for the first time, the Black Harrier Circus maurus , one of the world’s rarest harriers. We conduct a population viability assessment using a Bayesian model integrating life-history information and annual reporting rates from detection/non-detection surveys from the South African Bird Atlas Project. Our model estimates a global population of approximately 1300 birds currently declining at 2.3% per year, and one that could collapse in under 100 years, if an average of three to five adult birds are killed annually. This level of mortality may soon exist, given the current rate of fatalities and the number of wind farms planned within the species’ distribution. In addition, we find that the population is sensitive to changes in climate. Our results highlight the critical need for appropriate placement, and adaptive management of wind farms and other infrastructure causing harrier mortality. We also show how detection/non-detection data may be used to infer population dynamics and viability, when population counts are unavailable.
... This is particularly concerning as they have a late sexual maturity and a low reproductive rate. Hence, even a slight increase in their mortality rates can exert strong negative impacts on their population dynamics [20][21][22][23]. Wind energy facilities are often erected in regions where landforms and climate generate favourable conditions to support the soaring flight of vultures [24,25], either via thermal or orographic updraughts. ...
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Deployment of wind energy is proposed as a mechanism to reduce greenhouse gas emissions. Yet, wind energy and large birds, notably soaring raptors, both depend on suitable wind conditions. Conflicts in airspace use may thus arise due to the risks of collisions of birds with the blades of wind turbines. Using locations of GPS-tagged bearded vultures, a rare scavenging raptor reintroduced into the Alps, we built a spatially explicit model to predict potential areas of conflict with future wind turbine deployments in the Swiss Alps. We modelled the probability of bearded vultures flying within or below the rotor-swept zone of wind turbines as a function of wind and environmental conditions, including food supply. Seventy-four per cent of the GPS positions were collected below 200 m above ground level, i.e. where collisions could occur if wind turbines were present. Flight activity at potential risk of collision is concentrated on south-exposed mountainsides, especially in areas where ibex carcasses have a high occurrence probability, with critical areas covering vast expanses throughout the Swiss Alps. Our model provides a spatially explicit decision tool that will guide authorities and energy companies for planning the deployment of wind farms in a proactive manner to reduce risk to emblematic Alpine wildlife.
... During the last decade, many modeling and data-collection efforts have been undertaken to understand raptor movements as a function of atmospheric, topographic, and anthropogenic factors at varying spatiotemporal scales (Brandes and Ombalski, 2004;Eisaguirre et al., 2019;Nielson et al., 2016;Tack et al., 2020;Watson, 2018;Tikkanen et al., 2018;Thelander et al., 2003;Poessel et al., 2018a;Pirotta et al., 2018;Lanzone et al., 2012;Poessel et al., 2018b;Hanssen et al., 2020;Péron et al., 2020;Dennhardt et al., 2015). At the regional scale, species distribution models or dynamic occupancy models can aid wind energy developers in narrowing down prospective wind energy sites and can help regulators determine potential levels of mitigation (Tapia et al., 2007;Nielson et al., 2014Nielson et al., , 2016Tack et al., 2020). ...
Rapid expansion of wind energy development across the world has highlighted the need to better understand turbine-caused avian mortality. The risk to golden eagles (Aquila chrysaetos) is of particular concern due to their small population size and conservation status. Golden eagles subsidize their flight in part by soaring in orographic updrafts, which can place them in conflict with wind turbines utilizing the same low-altitude wind resource. Understanding the behavior of soaring raptors in varying atmospheric conditions can therefore be relevant to predicting and mitigating their risk of collision. We present a predictive movement model that simulates individual paths of golden eagles during directional flight (such as migration) that is subsidized by orographic updraft. We modeled eagles in a 50 km by 50 km study area in Wyoming containing three wind power plants with documented golden eagle collisions with turbines. The movement model is applicable to any region where ground elevation is known at turbine scale (<50 m) and wind conditions are known at facility scale (<3 km). For a given set of atmospheric conditions, the model simulates movements of thousands of orographic soaring eagles to produce a density map quantifying the relative probability of eagle presence. We validated the simulated tracks with GPS telemetry data showing four directional tracks made by golden eagles transiting through the area in 2019 and 2020. For each eagle track, validation was performed using the ratio of the model-simulated eagle presence likelihood with uniform eagle presence and the presence computed using directed random-walk movements. We found that the predictive performance of the model was significantly better (likelihood ratio >1) for low-altitude movements than high-altitude movements that can involve thermal-soaring. We employed the model to produce seasonal presence maps for migrating golden eagles. We found significant turbine-level variations in eagle presence between northerly and southerly migration routes through the study area. Overall, the proposed model offers a generalizable, probabilistic, and predictive tool to assist wind energy developers, ecologists, wildlife managers, and industry consultants in estimating the potential for conflict between soaring birds and wind turbines, thereby reducing the need for site-specific data on golden eagle movements.
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Wind energy production has expanded as an alternative to carbon emitting fossil fuels, but is causing impacts on wildlife that need to be addressed. Soaring birds show concerning rates of collision with turbine rotor blades and losses of critical habitat. However, how these birds interact with wind turbines is poorly understood. We analyzed high-frequency GPS tracking data of 126 black kites (Milvus migrans) moving near wind turbines to identify behavioural mechanisms of turbine avoidance and their interaction with environmental variables. Birds flying within 1000 m from turbines and below the height of rotor blades were less likely to be oriented towards turbines than expected by chance, this pattern being more striking at distances less than 750 m. Within the range of 750 m, birds showed stronger avoidance when pushed by the wind in the direction of the turbines. Birds flying above the turbines did not change flight directions with turbine proximity. Sex and age of birds, uplift conditions and turbine height, showed no effect on flight directions although these factors have been pointed as important drivers of turbine collision by soaring birds. Our findings suggest that migrating black kites recognize the presence of wind turbines and behave in a way to avoid then. This may explain why this species presents lower collision rates with wind turbines than other soaring birds. Future studies should clarify if turbine avoidance behaviour is common to other soaring birds, particularly those that are facing high fatality rates due to collision.
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Capsule: The data presented here demonstrate a considerable spatial overlap between wind farms and the breeding distribution of Hen Harriers in Ireland, but evidence for a negative impact of wind farms on their population is weak. Aims: To assess the extent of the overlap between wind farms and breeding Hen Harriers and to investigate their potential impact on Hen Harrier population trends. Methods: Data on Hen Harrier breeding distribution in 10 km × 10 km survey squares from national surveys were used in conjunction with information on the location of wind farms to examine whether, and to what extent, changes in Hen Harrier distribution and abundance between 2000 and 2010 were related to wind energy development. Results: Of the 69 survey squares holding Hen Harriers during the 2010 breeding season, 28% also overlapped with one or more wind farms. Data from 36 of the squares with breeding Hen Harriers during the 2000 survey revealed a marginally non-significant negative relationship between wind farm presence and change in the number of breeding pairs between 2000 and 2010. Conclusions: A considerable overlap exists between Hen Harrier breeding distribution and the location of wind farms in Ireland, particularly in areas between 200 and 400 m above sea level. The presence of wind farms is negatively related to Hen Harrier population trends in squares surveyed in 2000 and 2010, but this relationship is not statistically significant, and may not be causal. This is the first study to assess the influence of wind energy development on Hen Harriers at such a large geographic and population scale.
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Expanding the use of wind energy for electricity generation forms an integral part of China’s efforts to address degraded air quality and climate change. However, the integration of wind energy into China’s coal-heavy electricity system presents significant challenges owing to wind’s variability and the grid’s system-wide inflexibilities. Here we develop a model to predict how much wind energy can be generated and integrated into China’s electricity mix, and estimate a potential production of 2.6 petawatt-hours (PWh) per year in 2030. Although this represents 26% of total projected electricity demand, it is only 10% of the total estimated physical potential of wind resources in the country. Increasing the operational flexibility of China’s coal fleet would allow wind to deliver nearly three-quarters of China’s target of producing 20% of primary energy from non-fossil sources by 2030.
Technical Report
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The U.S. Fish and Wildlife Service (USFWS) has begun to issue incidental take permits (ITPs) to wind power companies to allow limited take of bird and bat species that are protected under the Endangered Species Act, the Bald and Golden Eagle Protection Act, or the Migratory Bird Treaty Act (Huso and others, 2015). Expected take rates are determined using scientifically based collision-risk models and knowledge about the ecology of the population of interest. ITPs often include mitigation requirements to compensate for estimated take and further describe (1) adaptive management actions (AMAs) that may be required to reduce take rates if permitted rate is exceeded, or (2) additional compensatory mitigation to offset take that exceeds permitted levels. Confirming the accuracy of predicted take and providing evidence that permitted take levels have not been exceeded can be challenging because carcasses may be detected with probability much less than 1, and often no carcasses are observed. When detection probability is high, finding 0 carcasses can be interpreted as evidence that none (or few) were actually killed. As the probability of observing an individual decreases, the likelihood of missing carcasses increases, making it unclear how to interpret having observed 0 (or few) carcasses. In a practical sense, the consequences of incorrect inference can be significant: overestimating take could result in costly and unjustified mitigation, whereas underestimating could result in unanticipated declines in species populations already at risk. Huso and others (2015) propose an approach using Bayes’ Theorem to construct a posterior distribution of potential take given the observed count and the estimated probability of detecting a carcass. Dalthorp and others (2014) published Evidence of Absence (EoA) software and associated user guide to calculate the posterior distribution of take. These seminal publications originally addressed inferential limits regarding potential take at an individual site in any single year. Subsequent discussions with users led to the idea that these concepts could be expanded and applied over a series of years (the duration of an ITP) and field data could be used to accurately infer whether actual take levels are consistent with, higher than or lower than permitted levels. In response, we have developed a statistical framework for signaling when take levels are inconsistent with permitted levels, both on a short-term (3- year running average) and long-term (cumulative total take) basis. This document examines the accuracy and precision of these “triggers” and their sensitivity to input parameters, including estimated detection probability (𝑔), level of assurance desired (1 − 𝛼), effectiveness of AMAs (𝜌), etc. We present a statistical framework for defining triggers for AMAs when take rates exceed permitted levels, as well as triggers for rescinding previous AMAs when 2 warranted by low take rates. The triggers (with associated AMAs) are evaluated in terms of the consequences (conservation benefits and operations costs) of various choices about trigger parameters (including permitted take limit, credibility levels, AMA options, and monitoring requirements) against a number of ecological backdrops (including actual take levels and effectiveness of AMAs). The report is strictly statistical and does not make specific recommendations about management or regulatory parameters. Instead, a range of scenarios that span a wide range of possibilities is considered. The purpose is to provide a framework for defining triggers for AMAs and guide decision-making in the management of ITPs with a quantitative consideration of potential consequences. In the following discussions, an average take rate of 𝜏 per year is permitted over the course of 𝑛 years of operation, and a total take of Τ = 𝑛𝑛 is allowed over the course of the permit. The methods used to set τ are beyond the scope of this document; we start from the premise that τ has been externally established. Estimated cumulative take is tracked through the years, and when estimated take exceeds the total permitted take (Τ), a long-term trigger fires and full-avoidance AMAs are implemented to avoid further take. In addition, a moving-average take rate is tracked through the years, and when the average take rate has risen clearly above the permitted level (τ), a short-term trigger fires providing a check against excessive take over the span of a few years and signaling that the long-term take limit is likely to be exceeded unless conditions change. Finally, a reversion trigger may be defined to signal when fatality rates are low enough so that previously implemented AMAs may be reversed without serious risk that future fatality rates will exceed permitted levels. A program of incremental AMAs may be developed to respond to the firing of the short-term trigger. Such a strategy may involve (1) AMAs to reduce the actual fatality rate (𝜆) incrementally each time the short-term trigger is fired to keep the fatality rate in line with the expected rate (𝜏) in future years; (2) an intensification of monitoring to increase precision of estimates; or (3) adjustment of 𝜏 to align permitted with actual take rates. AMAs that reduce the fatality rate or increase precision are likely to reduce the chances that the short-term trigger will fire in future years if the short-term trigger remains the same. In addition, after the short-term trigger has fired some predetermined number of times, with or without implementation of incremental AMAs, it may be desirable to implement a full-avoidance AMA to avoid further take (as with the long-term trigger).
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Understanding and reversing the widespread population declines of birds require estimating the magnitude of all mortality sources. Numerous anthropogenic mortality sources directly kill birds. Cause-specific annual mortality in the United States varies from billions (cat predation) to hundreds of millions (building and automobile collisions), tens of millions (power line collisions), millions (power line electrocutions, communication tower collisions), and hundreds of thousands (wind turbine collisions). However, great uncertainty exists about the independent and cumulative impacts of this mortality on avian populations. To facilitate this understanding, additional research is needed to estimate mortality for individual bird species and affected populations, to sample mortality throughout the annual cycle to inform full life-cycle population models, and to develop models that clarify the degree to which multiple mortality sources are additive or compensatory. We review sources of direct anthropogenic mortality in relation to the fundamental ecological objective of disentangling how mortality sources affect animal populations.
Current knowledge about bird and bat collisions with wind turbines in Australia is limited by a lack of consistent monitoring methods and of publicly available information where data have been collected. An overview of information that is available for mortalities and for collision modelling is provided and it suggests that frequency of collisions is generally low and unlikely to have significant impacts on population of many species. The perceptions and paradigms within which wind turbine collisions are considered are compared with aviation fauna collisions in Australia. Assessment by approval authorities of potential and actual bird and bat collisions have generally not been well focused on whether the levels of mortality involved influence viability of populations of species of concern. This is despite important regulatory policy that is clearly intended to ensure this approach. There is a great deal of potential to improve our understanding of bird and bat collisions with turbines and recommendations are made to ensure that assessments of collision rates are focused on determining whether they have impacts on populations of threatened taxa.
Renewable energy production is expanding rapidly despite mostly unknown environmental effects on wildlife and habitats. We used genetic and stable isotope data collected from Golden Eagles (Aquila chrysaetos) killed at the Altamont Pass Wind Resource Area (APWRA) in California in demographic models to test hypotheses about the geographic extent and demographic consequences of fatalities caused by renewable energy facilities. Geospatial analyses of δ(2) H values obtained from feathers showed that ≥25% of these APWRA-killed eagles were recent immigrants to the population, most from long distances away (>100 km). Data from nuclear genes indicated this subset of immigrant eagles was genetically similar to birds identified as locals from the δ(2) H data. Demographic models implied that in the face of this mortality, the apparent stability of the local Golden Eagle population was maintained by continental-scale immigration. These analyses demonstrate that ecosystem management decisions concerning the effects of local-scale renewable energy can have continental-scale consequences.
There are multiple sources of lead in the environment. However, scientific evidence points to spent lead ammunition as the most frequent cause of lead exposure and poisoning in scavenging birds in the United States. Despite the ban on its use for waterfowl hunting, lead ammunition is still widely used for other hunting and shooting activities. Therefore, it can remain on the landscape in carcasses not retrieved and in discarded offal piles. Carcasses and gut piles can be attractive food sources to scavenging birds that can ingest bullet fragments or shot while feeding. Scavenging birds may be particularly vulnerable to exposure and effects of lead due to their foraging strategies and food preferences, physiological processes that facilitate the absorption of lead, and demographic traits. Numerous lines of evidence support ammunition as the source of exposure in the majority of lead poisoned scavenging birds and include the recovery of ingested lead fragments or shot from exposed birds, observations of birds feeding on contaminated carcasses, isotopic signatures of lead in tissue that match that found in ammunition, patterns of mortality coincident with hunting seasons, and the lack of abundant evidence for other lead sources. Lead can be replaced in ammunition by alternative metals that are currently available and present limited environmental threats.
Many wind-power facilities in the United States have established effective monitoring programs to determine turbine-caused fatality rates of birds and bats, but estimating the number of fatalities of rare species poses special difficulties. The loss of even small numbers of individuals may adversely affect fragile populations, but typically, few (if any) carcasses are observed during monitoring. If monitoring design results in only a small proportion of carcasses detected, then finding zero carcasses may give little assurance that the number of actual fatalities is small. Fatality monitoring at wind-power facilities commonly involves conducting experiments to estimate the probability (g) an individual will be observed, accounting for the possibilities that it falls in an unsearched area, is scavenged prior to detection, or remains undetected even when present. When g < 1, the total carcass count (X ) underestimates the total number of fatalities (M). Total counts can be 0 when M is small or when Mis large and g<1. Distinguishing these two cases is critical when estimating fatality of a rare species. Observing no individuals during searches may erroneously be interpreted as evidence of absence. We present an approach that uses Bayes' theorem to construct a posterior distribution for M, i.e., P(MjX, g), reflecting the observed carcass count and previously estimated g. From this distribution, we calculate two values important to conservation: the probability that M is below a predetermined limit and the upper bound (M∗) of the 100(1 - α)% credible interval for M. We investigate the dependence of M∗on a, g, and the prior distribution of M, asking what value of g is required to attain a desired M∗for a given a. We found that when g < ∼0.15, M∗ was clearly influenced by the mean and variance of g and the choice of prior distribution for M, but the influence of these factors is minimal when g . ∼0.45. Further, we develop extensions for temporal replication that can inform prior distributions of M and methods for combining information across several areas or time periods. We apply the method to data collected at a wind-power facility where scheduled searches yielded X = 0 raptor carcasses.