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Flight response to spatial and temporal correlates informs risk from wind turbinesto the California CondorAuthor(s): Sharon A. Poessel, Joseph Brandt, Laura Mendenhall, Melissa A. Braham, Michael J.Lanzone, Andrew J. McGann, and Todd E. KatznerSource: The Condor, 120(2):330-342.Published By: American Ornithological Societyhttps://doi.org/10.1650/CONDOR-17-100.1URL: http://www.bioone.org/doi/full/10.1650/CONDOR-17-100.1

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Volume 120, 2018, pp. 330–342DOI: 10.1650/CONDOR-17-100.1

RESEARCH ARTICLE

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

Sharon A. Poessel,1* Joseph Brandt,2 Laura Mendenhall,2a Melissa A. Braham,3 Michael J. Lanzone,4

Andrew J. McGann,4 and Todd E. Katzner1

1 Forest and Rangeland Ecosystem Science Center, U.S. Geological Survey, Boise, Idaho, USA2 Hopper Mountain National Wildlife Refuge Complex, U.S. Fish and Wildlife Service, Ventura, California, USA3 Department of Geology & Geography, West Virginia University, Morgantown, West Virginia, USA4 Cellular Tracking Technologies, Rio Grande, New Jersey, USAa Current address: U.S. Fish and Wildlife Service, McConnell Air Force Base, Kansas, USA* Corresponding author: spoessel@usgs.gov

Submitted May 23, 2017; Accepted January 16, 2018; Published April 11, 2018

ABSTRACTWind power is a fast-growing energy resource, but wind turbines can kill volant wildlife, and the flight behavior ofobligate soaring birds can place them at risk of collision with these structures. We analyzed altitudinal data from GPStelemetry of critically endangered California Condors (Gymnogyps californianus) to assess the circumstances under whichtheir flight behavior may place them at risk from collision with wind turbines. Condor flight behavior was stronglyinfluenced by topography and land cover, and birds flew at lower altitudes and closer to the rotor-swept zone of windturbines when over ridgelines and steep slopes and over forested and grassland cover types. Condor flight behavior wastemporally predictable, and birds flew lower and closer to the rotor-swept zone during early morning and evening hoursand during the winter months, when thermal updrafts were weakest. Although condors only occasionally flew ataltitudes that placed them in the rotor-swept zone of turbines, they regularly flew near or within wind resource areaspreferred by energy developers. Practitioners aiming to mitigate collision risk to this and other soaring bird species ofconservation concern can consider the manner in which flight behavior varies temporally and in response to areas ofhigh topographic relief and proximity to nocturnal roosting sites. By contrast, collision risk to large soaring birds fromturbines should be relatively lower over flatter and less rugged areas and in habitat used during daytime soaring.

Keywords: flight altitude, land cover, landform, Gymnogyps californianus, soaring, topographic roughness, windenergy development

La respuesta de vuelo a correlaciones espaciales y temporales revela el riesgo de las turbinas eolicas paraGymnogyps californianus

RESUMENLa energıa eolica es un recurso energetico en rapido crecimiento, pero las turbinas eolicas pueden matar a la faunasilvestre que vuela, y el comportamiento de vuelo de las aves que son planeadoras obligadas puede ponerlos en riesgode colision con estas estructuras. Analizamos datos altitudinales obtenidos con telemetrıa de GPS del Condor deCalifornia (Gymnogyps californianus), una especie en peligro crıtico, para evaluar las circunstancias bajo las cuales suscomportamientos de vuelo pueden ponerlos en riesgo de colision con las turbinas eolicas. El comportamiento devuelo del condor estuvo fuertemente influenciado por la topografıa y la cobertura del suelo, y las aves volaron amenores altitudes y mas cerca de la zona de barrido del rotor de las turbinas eolicas cuando estuvieron sobre los filos yen pendientes pronunciadas y sobre tipos de cobertura boscosa y de pastizal. El comportamiento de vuelo del condorfue temporalmente predecible, y las aves volaron mas bajo y mas cerca de la zona de barrido del rotor durante lamanana temprano y a la tarde y durante los meses de invierno, cuando las termicas ascendentes fueron mas debiles.Aunque los condores volaron solo ocasionalmente a altitudes en las que se ubicaron en la zona de barrido del rotor delas turbinas, sı volaron regularmente cerca o dentro de los recursos eolicos preferidos por los desarrollos de energıa.Los profesionales que deseen mitigar el riesgo de colision de esta y otras especies de aves planeadoras depreocupacion para la conservacion pueden considerar el modo en el que varıa el comportamiento de vuelotemporalmente y en respuesta a areas de alto relieve topografico y en proximidad a los dormideros nocturnos. Encontraste, el riesgo de colision de las aves planeadoras grandes con las turbinas deberıa ser relativamente mas bajo enlas areas mas planas y menos escarpadas y en habitats usados durante las horas de planeo diurno.

Palabras clave: altitud de vuelo, cobertura del suelo, desarrollo de energıa eolica, forma del relieve, Gymnogypscalifornianus, planeo, rugosidad topografica

Q 2018 American Ornithological Society. ISSN 0010-5422, electronic ISSN 1938-5129Direct all requests to reproduce journal content to the AOS Publications Office at pubs@americanornithology.org

INTRODUCTION

Wind power is one of the fastest growing energy sources in

the world (http://windenergyfoundation.org/what-is-wind-

energy/). However, the development of wind energy does

not occur randomly, but instead occurs in global

renewable energy hotspots. One such hotspot is the

United States of America (USA), which is second globally

in total installed wind energy capacity (www.fi-powerweb.

com/Renewable-Energy.html), with 41 states currently

maintaining industrial-scale wind energy facilities (AWEA

2017).

Wind energy may have negative consequences for

wildlife, especially birds and bats. Wind energy–wildlife

conflicts can occur through collisions with turbines

(Arnett et al. 2008, Smallwood and Thelander 2008, Loss

et al. 2013, Zimmerling and Francis 2016), displacement

due to disturbance (Drewitt and Langston 2006, Madders

and Whitfield 2006), direct habitat loss (Drewitt and

Langston 2006, Zimmerling et al. 2013), or reduced

breeding success (Dahl et al. 2012). At one large wind

energy facility in California, USA, the adjusted bird

mortality rate estimate was 4.7 deaths per MW per year,

or 2,710 deaths per year (Smallwood and Thelander 2008).

On a national scale, an estimated 140,000–328,000 birds

are killed annually by turbine collisions (Loss et al. 2013).

Large soaring birds (e.g., storks, vultures, and eagles) in

particular are at high risk of demographic consequences

from fatalities at wind energy facilities due to their lowreproductive rates and their reliance on topographic and

weather conditions preferred by wind energy facility

developers (Madders and Whitfield 2006, Smallwood and

Thelander 2008, Chevallier et al. 2010, de Lucas et al.

2012). Furthermore, a number of environmental, temporal,

and behavioral factors may influence whether a soaring

bird will fly above or within the rotor-swept zone of

modern industrial horizontal-axis wind turbines (Barrios

and Rodrıguez 2004, Drewitt and Langston 2006, de Lucas

et al. 2008, 2012, Katzner et al. 2012, 2015).

A wide range of obligate soaring birds, including

Eurasian Griffons (Gyps fulvus; Barrios and Rodrıguez

2004, de Lucas et al. 2008), Egyptian Vultures (Neophron

percnopterus; Carrete et al. 2009, 2012), Cinereous

Vultures (Aegypius monachus; Vasilakis et al. 2016), and

Bearded Vultures (Gypaetus barbatus; Reid et al. 2015), are

considered at risk from or have suffered substantial

mortality from collisions with turbines. These birds likely

are at risk because their high wing loading and low

maneuverability increase their probability of flying at lower

altitudes and, thus, being struck by a rotating blade

(Barrios and Rodrıguez 2004, de Lucas et al. 2008, 2012).

The largest obligate soaring species in North America is

the critically endangered California Condor (Gymnogyps

californianus; hereafter, ‘‘condor’’). Once extirpated from

the wild and sustained by captive breeding, a wild but

mostly captive-hatched population of ~290 condors now

occupies parts of the southwestern USA and northwestern

Mexico (Snyder and Snyder 2000, Mee and Hall 2007,

USFWS 2017). This species is subject to many threats in

the wild, including lead poisoning, microtrash ingestion,

and exposure to certain organochlorines (reviewed by

Walters et al. 2010). Furthermore, California, in the center

of condor range and a biodiversity hotspot (Myers et al.

2000), is also ranked fourth among U.S. states in total

installed wind energy capacity (5,609 MW as of 2017;

AWEA 2017). Although no condor has yet been detected

as a casualty from collision with a wind turbine, as both

condor numbers and wind energy facilities have increased

in California, wildlife managers have expressed concern

that condors may be susceptible to mortality from wind

energy facilities (USFWS 2016, BLM 2016, http://www.fws.

gov/ventura/endangered/species/info/ca_condor-wind_

energy.html).

Because of their flight behavior and conservation

history, condors are a useful model species to understand

the risk that obligate soaring birds face from potential

wind energy development. As such, we evaluated patterns

in flight responses of these birds to spatial and temporal

variation in the topographic and vegetative environment

that they experience. We hypothesized that condors would

vary their flight altitude with spatially and temporally

specific responses to topography and land cover. We tested

this hypothesis by evaluating 2 predictions about their

flight behavior. First, we predicted that condors would

show an altitudinal response to the updraft potential of the

spatial landscape by flying higher over areas with greater

potential to generate thermal updraft (e.g., flat areas or

bare or sparsely vegetated ground where thermals are

produced). Second, we predicted that condors would show

an altitudinal response to the updraft potential of thetemporal landscape by flying highest at the times when

updraft capacity was greatest (i.e. midday, as opposed to

morning and evening, and summer, as opposed to winter,

both times when stronger thermals form). To place flight

altitude measurements in the context of potential collision

risk, we also evaluated condor flight behavior in relation to

distance to the wind resources preferred for wind energy

development and proportion of time spent within the

rotor-swept zone of turbines. Finally, we interpreted these

results more broadly, in the context of turbine-related risk

to other obligate soaring wildlife.

METHODS

Study Area, Focal Species, and Telemetry DataCollectionWe studied the flight behavior of condors north of Los

Angeles in southern California, USA, from the Pacific

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S. A. Poessel, J. Brandt, L. Mendenhall, et al. Flight response of California Condors 331

coast across mountains to deserts (Figure 1). Land cover in

the study area ranges from open grasslands and agricul-

tural fields to coniferous forests (http://gapanalysis.usgs.

gov/gaplandcover/data/download/). Elevations range from

sea level on the coast to .3,000 m above sea level (a.s.l.) in

the Sierra Nevada. The Tehachapi-Mojave Wind Resource

Area, one of the world’s largest concentrations of wind

energy facilities (http://www.tehachapiedc.com/about-

tehachapi/tehachapi-wind-industry), is located in the study

area in Kern County, between Tehachapi Pass and the

Mojave Desert (Figure 1).

U.S. Fish and Wildlife Service (USFWS) biologists

captured (sometimes repeatedly), attached telemetry

devices to, and released condors for this study from

FIGURE 1. Map showing densities of flight locations of 24 California Condors tracked in southern California, USA, 2013–2015. Darkershading indicates higher densities. Horizontal lines indicate areas of high-class winds (wind classes �3, with speeds .6.4 m s�1). Alsoshown are the locations of Hopper Mountain and Bitter Creek National Wildlife Refuges (NWR) and the Tehachapi-Mojave WindResource Area.

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

332 Flight response of California Condors S. A. Poessel, J. Brandt, L. Mendenhall, et al.

December 2013 to July 2015 in Hopper Mountain National

Wildlife Refuge (NWR) in Ventura County (34.4588N,

118.8518W) and Bitter Creek NWR in Kern County

(34.9288N, 119.3818W; Figure 1). Some captive-bred birds

in our study had telemetry devices attached when first

released into the wild. All birds were of known age, either

because they were raised in captivity and released into the

wild or because they were wild-hatched at monitored

nests. Condors were fitted with solar-powered Global

Positioning System–Global System for Mobile Communi-

cations (GPS–GSM) patagial telemetry units (Cellular

Tracking Technologies, Rio Grande, New Jersey, USA).

Telemetry units collected, at 30-s, 15-min, or 30-min

intervals, data on location, date, time, altitude a.s.l., ground

speed (knots), horizontal and vertical dilution of precision

(HDOP and VDOP), and fix quality (2D or 3D). We tested

GPS horizontal (x 6 SD¼ 19 6 18 m) and vertical (2 6 36

m) precision; these errors were normally distributed (S.

Poessel and T. Katzner personal observation).

Telemetry Data ProcessingTo calculate flight altitude above ground level (a.g.l.), we

first obtained the ground elevation a.s.l. at each GPS

location from an ~30-m resolution digital elevation model

(DEM; http://nationalmap.gov/3dep_prodserv.html). We

then subtracted the ground elevation value from the

altitude measurement collected by the telemetry unit. For

analysis purposes, we ignored the subset of data collected

at night because condors typically roost at night and donot engage in flight. We also subsampled higher-frequency

(i.e. 30-s) data to 15-min intervals by including only the

first location that occurred in a 15-min period. Analyzing

data collected at these less frequent intervals (i.e. 15 or 30

min) should have reduced the potential for autocorrelation

between successive GPS locations. We filtered the

remaining telemetry data to remove GPS locations for

which diagnostic or altitudinal data indicated errors. For

example, we removed locations with HDOP or VDOP �10(D’Eon and Delparte 2005; we also used our own data to

independently validate the VDOP threshold) and 2D fixes.

Fixes that are 2D can be inaccurate because they are

obtained from only 3 satellites (compared with �4satellites for 3D fixes), and they provide a coordinate

position but not altitude a.s.l. Likewise, we removed fixes

with altitudes a.g.l. lower than�50 m (Katzner et al. 2012)

and fixes with altitudes a.s.l. .4,000 m that were also

inconsistent with neighboring data points (for example, if

altitude at subsequent points was 800 m, 6,000 m, and 800

m, we interpreted the location with the largest altitude as

indicative of a GPS error). Finally, we removed any

locations collected by the telemetry units while the birds

were in captivity or in trapping pens.

Because our objective was to analyze flight altitudes of

condors relative to environmental correlates, we filtered

from our dataset all locations that we determined

represented a bird not in flight. We defined ‘‘flight

locations’’ as those GPS telemetry data with either (1) a

recorded speed �3 knots; or (2) a recorded speed ,3 knots

that also had an altitude a.g.l. �100 m. We focused all

subsequent analyses only on flight locations.

Potential error in defining flight locations can occur

from error in GPS measurements, in DEM values, and

from interactions between the two. Our definition of flight

locations was different from that recently developed by

others (Peron et al. 2017), deliberately conservative, and

almost certainly resulted in some valid flight points being

excluded. We took this approach for a number of reasons.

First, the goal of our study was to draw inference about

flight altitude, and, as such, we had to define points as

either flight or nonflight locations. Any definition will be

imperfect, and we surmised that our inference would be

stronger if we excluded a few flight locations from our data

than if we inappropriately included nonflight locations in

our data. Second, by removing from consideration any data

points with VDOP �10, we culled the data to be more

precise than they would have been otherwise; Peron et al.

(2017) did no culling of VDOP. We considered using a

stricter VDOP threshold. However, cursory evaluation of

our data suggested that .90% of GPS locations considered

in our analysis had VDOP ,5 and .97% of locations had

VDOP ,7. Thus, a stricter threshold would have been

unlikely to have affected the patterns that our models

detected in the data. Finally, we did not filter our data by

time of day, as did Peron et al. (2017). Our data showed

that condors spent much of their time on the ground

during the middle of the day, and so filtering based on time

of day would have resulted in many ground locations being

included erroneously as flight locations.

External Data LinkagesWe used ArcGIS 10.3 (ESRI, Redlands, California, USA) to

link each flight location to 4 types of spatial data. To

understand how condor flight behavior responded to

topography, we associated flight locations with measure-

ments of a topographic position index (TPI; Jenness et al.

2013) and a terrain ruggedness index (TRI; Riley et al.

1999) in the cell (~30-m resolution) directly below each

flight location. The TPI, estimated with Topography Tools

for ArcGIS (Dilts 2015), is a measurement of slope position

that reflects the difference between the elevation in a cell

and the average elevation of its neighboring cells. We

classified TPI measurements into 6 landform categories

(valleys, lower slopes, gentle slopes, steep slopes, upper

slopes, and ridges) based on the value and sign of the

measurement and the slope of the cell (graphical

representations of each of these categories are illustrated

by Jenness et al. 2013). The TRI, estimated with Geo-

morphometry & Gradient Metrics Tools (Evans et al.

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

S. A. Poessel, J. Brandt, L. Mendenhall, et al. Flight response of California Condors 333

2014), reflects landscape roughness, calculated as the

square root of the sum of the squared differences between

the elevation in a cell and the elevation of its neighboring

cells (Riley et al. 1999). Larger values of TRI indicate

rougher terrain (Figure 2). We calculated both metrics

from the same DEM as above (http://nationalmap.gov/

3dep_prodserv.html). To understand how condor flight

behavior responded to variation in cover, we associated

flight locations with 30-m land cover data from the 11-

category National Vegetation Classification System Class

of the National Gap Analysis Program (http://gapanalysis.

usgs.gov/gaplandcover/data/download/) in the cell directly

below each flight location.

Finally, to understand the relationship between condor

flight behavior and wind resources used in commercial

wind energy development, we measured the distance from

each flight location to the nearest commercially valuable

winds. The dataset that we used for this analysis (NREL

2015) classifies wind at 50 m a.g.l. based on its speed and

power. Of the 7 wind classes, areas with a class of �3 (i.e.

high-class winds, with speeds .6.4 m s�1) are considered

to be suitable for wind energy development and thus

commercially valuable (NREL 2015).

Statistical AnalysesTo understand condor flight responses to topographic,

vegetative, and temporal variation in updraft potential (i.e.

to test our 2 predictions), we employed a 2-step approach.

First, we graphically evaluated patterns in our raw data by

plotting condor flight altitudes a.g.l. against environmental

and temporal variables and the rotor-swept zone of

modern horizontal-axis wind turbines (~50–150 m a.g.l.,

indicated by light gray shading and a wind turbine graphic

on the plots; http://www.fws.gov/radar/windpower/; Fig-

ures 3–6). The values on these plots were based on

averaged flight altitudes for each bird in our study. We

included on the plots the percentage of locations, averaged

FIGURE 2. Photograph of the landscape used by CaliforniaCondors tracked in southern California, USA, 2013–2015.Average values of the terrain ruggedness index (TRI) are shownfor 3 different areas.

FIGURE 3. Boxplots of altitude above ground level (a.g.l.) in each of 6 landform types for flight locations of 24 California Condorstracked in southern California, USA, 2013–2015. Values are based on flight altitudes averaged for each of the 24 birds within eachlandform type. The top of the box represents the third quartile, the bottom of the box represents the first quartile, the line inside thebox represents the median, and the top and bottom whiskers represent the maximum and minimum averaged values. Averagepercentages of locations in each landform type that were �150 m in altitude a.g.l. (i.e. within or below the rotor-swept zone ofindustrial-scale wind turbines) are shown above each boxplot.

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

334 Flight response of California Condors S. A. Poessel, J. Brandt, L. Mendenhall, et al.

by bird and within each category, that were �150 m in

flight altitude (i.e. within or below the rotor-swept zone of

industrial-scale wind turbines). We also used plots to

illustrate potential interactions of month and hour with

each of the 3 spatial data types (TPI, TRI, and land cover).

When we observed an apparent pattern in these plots of

interactions, we included that interaction in statistical

models (below).

Second, we evaluated multivariate relationships within

the data with linear mixed-effects models using the lme4

package (Bates et al. 2015) in R (R Core Team 2015), in

which flight altitude was the response variable. We used a

cube root transformation of the response variable because

it allowed us to most closely meet the distributional

assumptions of our statistical tests (e.g., independence of

the observations and normality of the residuals). We first

tested for multicollinearity in our predictor variables by

computing variance inflation factors (VIFs). A VIF

represents the proportion of variance in one predictor

explained by all other predictors in the model, and a VIF

.3 may indicate a correlated variable (Zuur et al. 2010).

The VIFs of our predictor variables were between 1 and 2

(maximum ¼ 1.5 for the month variable; Supplemental

Material Table S1). Thus, the fixed effects in our models

included sex, age (in years) of the bird at the time of

telemetry data collection, hour of the day and month of the

year as categorical variables, TPI category, TRI value, and

land cover category. We included interactions only for

hour*TRI and month*TRI. Random effects included

individual bird and year. For modeling purposes only, we

rescaled the TRI variable (the only continuous variable) by

subtracting the mean and dividing by 2 times the standard

deviation, because interpretation of model coefficients is

sensitive to the scale of the predictor variables (Gelman

2008).

We ran a global model with all fixed effects and the 2

interaction terms, and then used the dredge function in the

MuMIn package (Barton 2015) in R (R Core Team 2015) to

run all possible submodel combinations (n¼ 208; Grueber

et al. 2011, Doherty et al. 2012). We used Akaike’s

information criterion (AIC) to rank the models and

selected the models with the most support from the data

based on Akaike model weights � 0.01 (Burnham and

Anderson 2002, Anderson 2008). Because our models did

not contain multicollinearity among the predictor vari-

ables (Cade 2015), we then averaged the supported models

(using the zero method; Burnham and Anderson 2002) and

calculated variable importance factors for each fixed effect.

Finally, to place flight responses in the context of

collision risk, we linked flight altitude data to commercial

wind energy development potential by plotting distances

from condor flight locations to high-class winds (Katzner

et al. 2012). We categorized condor locations into 49

equally sized distance intervals of 500 m, ranging from 0 m

to 24,500 m, and we computed skewness and kurtosis of

this frequency distribution and compared it with a normal

distribution using a Kolmogorov-Smirnov test in R (R

Core Team 2015). We then calculated the average

percentage of locations within each distance category that

were �150 m in flight altitude.

RESULTS

Condor TelemetryWe tagged and released 24 condors (14 females, 10 males)

ranging in age from 1 to 33 yr old (x 6 SD¼ 9.4 6 8.4 yr).

For this study, we considered GPS locations collected from

December 2013 to November 2015. During that period, we

tracked condors for a mean of 368 6 152 days (range ¼103–564 days), and the telemetry units collected 534,631

locations. We removed 35% of these locations by

subsampling 30-s data, and, after subsampling the data

and removing poor-quality and captive locations, 312,635

FIGURE 4. Scatterplots of mean (6 SE) altitude above groundlevel in relation to the terrain ruggedness index (TRI) for flightlocations of 24 California Condors tracked in southern California,USA, 2013–2015. Plots are by (A) hour of the day (PacificStandard Time [PST]) and (B) month. Numbers of condorstracked ranged from 8 to 24 in each hour and from 18 to 24 ineach month (see Figure 6 for details).

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

S. A. Poessel, J. Brandt, L. Mendenhall, et al. Flight response of California Condors 335

locations remained for analysis. Of these, 102,950 locations

(33%) met our definition of ‘‘flight locations’’ (x¼ 4,290 6

2,166 locations per condor; range ¼ 905–8,845 locations),

5% of which were locations collected at 30-min intervals.

Condors flew at an average altitude a.g.l. of 295 6 41 m

(Supplemental Material Figure S1), and 24% of flight

locations were below 100 m a.g.l. (range =�50 m to 3,593

m), which was our threshold for determining whether

birds were in flight. Condors flew within 8 km of the

Tehachapi-Mojave Wind Resource Area (Figure 1).

Flight Altitude Responses of Condors: UnivariateComparisonsCondor flight altitude responded to variation in the

updraft potential of the topographic landscape. On

average, condors flew at the highest altitudes a.g.l. (~500m) when over gentle slopes (i.e. flat areas) and the lowest

altitudes (~200 m) when over ridges (i.e. hilltops; Figure

3). Flight altitudes of condors generally decreased with

terrain ruggedness (Figure 4). In most cases, regardless of

where condors were flying, the majority of their average

flight altitudes were above the rotor-swept zone of most

industrial-scale wind turbines (Figures 3 and 4).

Condor flight altitude also responded, but less strongly,

to variation in the updraft potential of the vegetative

landscape. In the 7 land cover types that condors used (see

Supplemental Material Figure S2 for details), they

generally flew highest (~500–600 m) over agricultural

vegetation and open water (Figure 5). Again, in most cases,

regardless of land cover type, the majority of average

condor flight altitudes were above the rotor-swept zone of

industrial-scale wind turbines (Figure 5).

Condors appeared to display an altitudinal response to

daily and seasonal variation in the updraft potential of the

temporal landscape. They tended to fly higher (~330 m)

during the middle of the day, when thermals were

strongest, compared with the mornings and evenings,

when they flew lower (~100–150 m; Figure 6A). The only

times of day when condors regularly flew within the rotor-

swept zone of industrial-scale wind turbines were early in

the morning and late in the day (Figure 6A). Likewise,

condor flight altitudes were, on average, higher during the

warmer months of the year (March–October; ~280–345m) than during the cooler months (November–February;

~230–245 m; Figure 6B), when thermals were relatively

less available. Regardless of month, average flight altitudes

were above the rotor-swept zone of industrial-scale wind

turbines (Figure 6B).

Our plots suggested that condor flight altitude respond-

ed to interacting effects of topographic and temporal

variation in updraft potential at both daily and seasonal

scales (Figure 4). Average flight altitude of condors was

lowest and most variable over rougher terrain early in the

morning and late in the afternoon and highest and least

variable over smoother terrain near midday (Figure 4A and

Supplemental Material Figures S3–S5). Likewise, condors

flew lowest over rougher areas during the winter months

(November–February) and highest over smoother areas

FIGURE 5. Boxplots of altitude above ground level (a.g.l.) in each of 7 land cover types for flight locations of 24 California Condorstracked in southern California, USA, 2013–2015. Values are based on flight altitudes averaged for each of the birds within each landcover type. The top of the box represents the third quartile, the bottom of the box represents the first quartile, the line inside the boxrepresents the median, and the top and bottom whiskers represent the maximum and minimum averaged values. Numbers of condorstracked are shown in parentheses below each land cover type. Average percentages of locations in each land cover type that were�150 m in altitude a.g.l. (i.e. within or below the rotor-swept zone of industrial-scale wind turbines) are shown above each boxplot.

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

336 Flight response of California Condors S. A. Poessel, J. Brandt, L. Mendenhall, et al.

during the late summer months (August and September;

Figure 4B and Supplemental Material Figure S3). We also

observed interactions between temporal variables (i.e. hour

and month) and ground elevation (Supplemental Material

Figure S6), but not between other spatial or temporal

variables that we evaluated (Supplemental Material Figures

S2 and S7–S9).

Flight Altitude Responses of Condors: MultivariateComparisonsWe statistically evaluated the patterns elucidated by our

plots with 208 linear mixed-effects models (as described

in the Methods). Only 2 of these models were supported

by the data (Akaike model weights of 79% and 20%; Table

1); both included terms for hour, month, TPI, TRI, and

land cover (Table 1). The only difference between the 2

models was that the less well-supported model included

the term for sex. Neither model included bird age or

interaction terms.

The general patterns that we observed in the non-

inferential univariate plots were replicated in these

inferential statistics. Higher flight altitudes were associated

with flatter and smoother terrain, agricultural vegetation

and open water land cover, and the specific times of day

(late morning to early afternoon) and year (spring,

summer, and fall) when thermal potential was highest

(Supplemental Material Table S2). Additionally, male

condors tended to fly higher than females (Supplemental

Material Table S2). However, the sex effect was weak, so we

do not further discuss or interpret the differences in flight

behavior between the sexes.

Condors and Wind EnergyAlthough the majority of condor flight locations were

above the rotor-swept zone of modern horizontal-axis

wind turbines (150 m a.g.l.), 37% of all flight locations

were below that threshold. Condors also tended to fly

closer than expected to high-class winds (D ¼ 0.72, P ,

0.001; kurtosis¼ 31.22; skewness¼ 4.58). In total, almost

28% of all flight locations were within areas of high-class

winds and 58% of locations (averaged by bird) were

within 500 m of high-class winds (Figure 7). Of the

locations within 500 m of high-class winds, 39% were

�150 m a.g.l. (Figure 7).

DISCUSSION

Our analyses demonstrate strong spatially and temporally

explicit flight responses by an obligate soaring species to

variation in the updraft potential of the topographic and

FIGURE 6. Mean (6 SE) altitude above ground level (a.g.l.) forflight locations of 24 California Condors tracked in southernCalifornia, USA, 2013–2015. Plots are by (A) hour of the day(Pacific Standard Time [PST]) and (B) month. In (A), n ¼ 24condors for each hour except 04:00 (hour 4), when n ¼ 8, 18:00(hour 18), when n¼ 23, and 19:00 (hour 19), when n¼ 17. Hour20 is not shown because we recorded only 1 flight location from1 condor during this hour. In (B), numbers of condors tracked areshown in parentheses below each month. Average percentagesof locations in each hour and month that were �150 m inaltitude a.g.l. (i.e. within or below the rotor-swept zone ofindustrial-scale wind turbines) are shown above each point.

TABLE 1. Top 2 models in model selection explaining influenceson altitude above ground level for flight locations of 24California Condors tracked in southern California, USA, 2013–2015. K refers to the number of parameters (including interceptand error terms) in a model, and �2lnL is the maximized log-likelihood. AIC is Akaike’s information criterion, and DAIC is thedistance in AIC from the top model. Explanatory variables inmodels included the hour of the day (Hour), month of the year(Month), topographic position index (TPI), terrain ruggednessindex (TRI), type of land cover (Land cover), and the sex oftracked condors (Sex).

Model K DAICModelweight �2lnL

Hour, Month, TPI, TRI,Land cover

42 0.0 a 0.789 425,062

Sex, Hour, Month, TPI,TRI, Land cover

43 2.7 0.205 425,063

a The AIC value of the top model was 425,146.

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

S. A. Poessel, J. Brandt, L. Mendenhall, et al. Flight response of California Condors 337

vegetative landscape. These novel patterns support our

initial hypothesis in an unexpectedly strong manner and

aid in understanding the mechanisms that determine

soaring flight behavior. When combined with the infor-

mation on behavior in and around commercially valuable

winds, our results provide important insights into the

context in which condors and other soaring species may

encounter collision risk from wind turbines. Importantly,

inference from this study about risk to condors from wind

energy extends only to flying birds, and not to birds on the

ground, where they may encounter other risks.

Flight Responses to Variation in Updraft Potential

Although spatial patterns in flight altitude were consistent

with findings from previous work on other species (e.g.,

Katzner et al. 2012, Reid et al. 2015), the diel and seasonal

cycles that we observed were unexpectedly strong. The

altitudinal response that condors displayed to the updraft

potential of the spatial and temporal landscape that they

encountered illustrates the mechanisms that determine

soaring flight behavior. Because large vultures are heavy

and have high wing loading, they are unable to use flapping

flight for long periods of time (Bildstein et al. 2009). Thus,

when not flying to roost or feed, their flight altitude

depends largely on the spatial and temporal patterns in the

strength of updrafts, which in turn depend on the

interaction of solar radiation and wind with time of day,

topography, and land cover (Hertenstein 2005). Therefore,

we are not surprised that these obligate soaring birds flew

highest over less rough, more open areas around midday

and during summer, when thermals are strongest. By

contrast, lower-altitude soaring, consistent with the use of

orographic updrafts, was observed in rougher terrain and

when thermals tend to be weakest in the early morning

and evening hours and during winter.

The requirement of using updrafts creates multiple

tradeoffs for soaring species. For example, large birds must

simultaneously fly high and fast enough to avoid

grounding (Horvitz et al. 2014), but also low enough to

find food and to approach foraging or roosting opportu-

nities in suitable habitats. When combined with their

tendency to sometimes roost in unvegetated areas (i.e.

cliffs), this may explain why condors flew at relatively low

altitudes during the limited amount of time that they were

over bare or sparsely vegetated ground (0.13% of locations;

Figure 5). By contrast, condors flew higher than expected

over open water (generally small lakes with a low potential

to generate thermals; 0.15% of locations; Figure 5), likely

because water does not provide food for condors and

because of the negative consequences of grounding in

water.

Other aspects of biology almost certainly interact with

updraft potential to influence condor flight behavior. The

models with interactions that we built were penalized (and

thus not well supported) because of the large numbers of

parameters that they contained. Nevertheless, our plots of

FIGURE 7. Distance from high-class winds (wind classes �3, with speeds .6.4 m s�1) from which 24 California Condors tracked insouthern California, USA, 2013–2015, flew. Values shown are average (6 SE) frequencies in each distance category; categories aregrouped into equal 500-m intervals. Labels on the x-axis are the midpoint of each category and sample size per category. The figureindicates that the vast majority of condor flight locations were close to high-class winds. Average percentages of locations in eachcategory that were �150 m in altitude above ground level (i.e. within or below the rotor-swept zone of industrial-scale windturbines) are shown above each bar. Only distances up to 15,250 m are shown because n � 10 at greater distances.

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

338 Flight response of California Condors S. A. Poessel, J. Brandt, L. Mendenhall, et al.

interactions (Figure 4 and Supplemental Material Figures

S3 and S6) clearly demonstrate novel interacting effects of

spatial and temporal parameters on flight altitude. These

data show that in the early morning and late afternoon

condors flew lower and over rougher and higher-elevation

terrain (Figure 4 and Supplemental Material Figures S3

and S6). Flight altitude, terrain roughness, and elevation

were also more variable at these times (Supplemental

Material Figures S4–S6). These patterns are consistent

with condors using a diversity of nighttime roosting sites

in the mountains, but generally foraging during daytime

over less rough, lower landscapes (Snyder and Snyder

2000).

Condors flew lower over rougher and lower-elevation

terrain during winter, suggesting the use of orographic

updrafts (Figure 4 and Supplemental Material Figures S3

and S6). These seasonal fluctuations in behavior may be

linked not only to variation in updrafts, but also to

historical shifts in local food availability due to hunting and

ranching cycles (Meretsky and Snyder 1992, Snyder and

Snyder 2000). Although reintroduced condors are provi-

sioned with carcasses (USFWS 2016), historically consis-

tent seasonal foraging patterns likely persist because

condors also utilize natural sources of food throughouttheir range (Meretsky and Snyder 1992, Snyder and Snyder

2000).

Soaring Flight Responses and Wind EnergyThe consistency in flight responses that we observed to

variation in the spatial and temporal landscape illustrates

the collision risk that condors may face from wind

turbines. Condors regularly flew near classes of winds

required by energy developers, and for 39% of the time that

these birds were near such winds, they were flying within

the rotor-swept zone of wind turbines (Figure 7). This

indicates that the flight behavior of condors makes them

vulnerable to collisions with turbines.

The flight altitudes of these heavily wing-loaded birds

responded strongly to the presence of thermal updrafts.

During the daytime and in summer, condors generally flew

at higher altitudes, indicating the use of well-developed

thermals and placing them far above the rotor-swept zone

of most wind turbines. However, these soaring specialists

also used lower-altitude orographic updrafts, especially in

areas with high topographic relief. Our results suggest that,

although they can use these orographic updrafts at any

time, they used them most frequently (1) in mornings and

evenings, presumably when closer to their roost sites, and

(2) during winter, when thermals were weakest. Hence,

these orographic updrafts should generate the greatest

potential for wind energy–wildlife conflicts.

The principles that underpin soaring flight are not

unique to condors (Pennycuick 1972, 1998), and these new

results can inform an understanding of the collision risk

from wind energy development to other obligate soaring

birds. Although no similar analysis of temporal patterns in

flight altitude has been conducted for other species, the

logic underlying our interpretation of these data likely

applies similarly to a suite of obligate soaring birds

globally, many of which are species of conservation

concern and are threatened by wind energy (for example,

but not limited to, Cinereous Vultures, Eurasian Griffons,

Bearded Vultures, Golden Eagles (Aquila chrysaetos),

White-tailed Eagles (Haliaeetus albicilla), and Black

Storks (Ciconia nigra) in Europe, Asia, and Africa, and

Andean Condors (Vultur gryphus) in South America). The

collision risk to these species should also be highest from

turbines placed in areas with high topographic relief or

near nocturnal roosting sites. Many of these species have

high wing loading and low maneuverability, resulting in

high susceptibility to collision with tall moving structures

such as wind turbines (reviewed by Smith and Dwyer

2016). Furthermore, aerial foragers (e.g., Golden Eagles)

that forage near turbines may be at high risk of mortality ifthey focus more on prey than on turbine blades (Small-

wood et al. 2009, Smith and Dwyer 2016). However, unlike

condors, some of these species migrate, and comparisons

with telemetry (Katzner et al. 2012) and observational

(Johnston et al. 2014) studies of migration of soaring birds

suggest that collision risk from wind turbines may be

different during migration than during the more local

movements that we studied here.

Practitioners and wind energy developers can incorpo-

rate the new knowledge gained from this study to place

turbines in areas where collision risk to obligate soaring

birds is lowest. Turbines placed in areas of high

topographic relief, where orographic winds are strongest,

are most likely to interact with local obligate soaring birds.

These turbines are also those at which collision risk may

be most effectively lowered with careful siting decisions.

For example, requiring new wind turbines to be placed a

certain distance (e.g., 100 m) away from a ridgeline with

strong orographic updrafts might reduce collision risk to

soaring birds.

Another option for wind energy developers is to

monitor birds with geofence alert systems incorporated

into telemetry units (Sheppard et al. 2015). These systems

are designed to increase the fix frequency of the telemetry

unit and to send a short message service alert to a user

group when a bird crosses a virtual boundary near a wind

energy facility. Upon receiving the alert, wind facility

operators can implement mitigative actions, such as

curtailing turbine operations. Several telemetry units with

geofence alert systems were deployed on condors in

southern California after our study. However, because the

effectiveness of this measure relies upon placing telemetry

units on a large percentage of the studied population, this

technology may be cost-effective only for small popula-

The Condor: Ornithological Applications 120:330–342, Q 2018 American Ornithological Society

S. A. Poessel, J. Brandt, L. Mendenhall, et al. Flight response of California Condors 339

tions of high-risk species, or those already intensively

monitored.

Postconstruction management of risk to condors and

other species has predominantly focused on operational

mitigation. Thus, the information from seasonally specific

temporal cycles that we have documented here may be a

useful tool for managers making operational mitigation

decisions in areas with existing turbines. More complex

risk models, especially ones that incorporate variation in

weather and the specific flight behaviors of local species,

may be additionally useful for decision-making about

micrositing and mitigation to reduce the potential for wind

energy–wildlife conflicts.

ACKNOWLEDGMENTS

We thank A. Fesnock, A. Blackford, K. Martin, S. Kirkland,and others for their support of this research. The findings andconclusions in this article are those of the authors and do notnecessarily represent the views of the U.S. Fish and WildlifeService. Any use of trade, product, or firm names is fordescriptive purposes only and does not imply endorsement bythe U.S. Government.Funding statement: This work was supported by theCalifornia Department of Fish and Wildlife (CDFW agree-ments P1182024 and P148006), the Bureau of Land Manage-ment (U.S. BLM contract L11PX02237), the National Fish andWildlife Foundation, and the authors’ institutions. None ofthe funders had any input into the content of the manuscriptnor required their approval of the manuscript beforesubmission or publication.Ethics statement: The condor is a federally listed endangeredspecies, and extreme care was taken during all capture andhandling procedures to minimize stress and disturbance. Thisstudy was carried out in strict accordance with therecommendations in the Guidelines to the Use of Wild Birdsin Research of the Ornithological Council. No animal carecommittee reviews research conducted under endangeredspecies recovery permits; therefore, condor field programpermits were reviewed and approved by the USFWS PermitCoordinator, California Condor Coordinator, and Region 8Endangered Species Division. The use of GPS transmitterswas authorized as a recovery action under section 10(a)(1)(A)with a permit issued to the Hopper Mountain NWR Complex(#TE-108507 HMNWR-0). In addition, this work wasauthorized in the state of California under a separateMemorandum of Understanding between managers of theHopper Mountain NWR Complex and the CaliforniaDepartment of Fish and Game under sections 650 and670.7, Title 14, California Code of Regulations.Author contributions: S.A.P. and T.E.K. designed the study;J.B. and L.M. handled and attached telemetry units tocondors; M.A.B. and L.M. organized telemetry data andmaintained the telemetry database; M.J.L. and A.J.M. designedthe telemetry units; S.A.P. analyzed the data; S.A.P. and T.E.K.led the writing; and all authors contributed to revising andimproving the manuscript.

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