Drivers of Flight Performance of California Condors ( Gymnogyps californianus )

Abstract

Flight behavior of soaring birds depends on a complex array of physiological, social, demographic, and environmental factors. California Condors (Gymnogyps californianus) rely on thermal and orographic updrafts to subsidize extended bouts of soaring flight, and their soaring flight performance is expected to vary in response to environmental variation and, potentially, with experience. We collected 6298 flight tracks described by high-frequency GPS telemetry data from five birds ranging in age from 1 to 19 yr old and followed over 32 d in summer 2016. Using these data, we tested the hypothesis that climb rate, an indicator of flight performance, would be related to the topographic and meteorological variables the bird experienced, and also to its age. Climb rate was greater when condors were flying in faster winds and during environmental conditions that were conducive to updraft development. However, we found no effect of age on climb rate. Although many of these relationships were expected based on flight theory, the lack of an effect of age was unexpected. Our work expands understanding of the relationship condors have with the environment, and it also suggests the potential for as-yet unexplored complexity to this relationship. As such, this study provides insight into avian flight behavior and, because flight performance influences bird behavior and exposure to anthropogenic risk, it has potential consequences for development of conservation management plans.

Full text

J. Raptor Res. 56(1):17–27
Ó2022 The Raptor Research Foundation, Inc.
DRIVERS OF FLIGHT PERFORMANCE OF CALIFORNIA CONDORS
(GYMNOGYPS CALIFORNIANUS)
SOPHIE R. BONNER
1
Department of Geography, University of Texas at Austin, Austin, TX 78712 USA
and
Department of Biological Sciences, REU Site in Raptor Research, and Raptor Research Center, Boise State University,
Boise, ID 83706 USA
SHARON A. POESSEL
US Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, ID 83706 USA
JOSEPH C. BRANDT AND MOLLY T. ASTELL
US Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, 2493 Portola Road, Ventura,
CA 93003 USA
JAMES R. BELTHOFF
Department of Biological Sciences, REU Site in Raptor Research, and Raptor Research Center, Boise State University,
Boise, ID 83706 USA
TODD E. KATZNER
US Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, ID 83706 USA
ABSTRACT.—Flight behavior of soaring birds depends on a complex array of physiological, social,
demographic, and environmental factors. California Condors (Gymnogyps californianus) rely on thermal
and orographic updrafts to subsidize extended bouts of soaring flight, and their soaring flight performance
is expected to vary in response to environmental variation and, potentially, with experience. We collected
6298 flight tracks described by high-frequency GPS telemetry data from five birds ranging in age from 1 to 19
yr old and followed over 32 d in summer 2016. Using these data, we tested the hypothesis that climb rate, an
indicator of flight performance, would be related to the topographic and meteorological variables the bird
experienced, and also to its age. Climb rate was greater when condors were flying in faster winds and during
environmental conditions that were conducive to updraft development. However, we found no effect of age
on climb rate. Although many of these relationships were expected based on flight theory, the lack of an
effect of age was unexpected. Our work expands understanding of the relationship condors have with the
environment, and it also suggests the potential for as-yet unexplored complexity tothis relationship. As such,
this study provides insight into avian flight behavior and, because flight performance influences bird
behavior and exposure to anthropogenic risk, it has potential consequences for development of
conservation management plans.
KEY WORDS:California Condor; Gymnogyps californianus; climb rate;experience;GPS-GSM telemetry;meteorology;
soaring;topography;updraft.
CONDICIONANTES DEL DESEMPE ˜
NO DEL VUELO EN GYMNOGYPS CALIFORNIANUS
RESUMEN.—El comportamiento de vuelo de las aves que planean depende de una compleja serie de factores
fisiolo´gicos, sociales, demogra´ficos y ambientales. El co´ndor Gymnogyps californianus depende de las
corrientes ascendentes te´rmicas y orogra´ ficas para sustentar los episodios prolongados de planeo, por lo que
1
Present address: Colorado State University, Department of Forest and Rangeland Stewardship, Fort Collins, CO
80523 USA; email address: sophie.bonner.eco@gmail.com
17

es de esperar que su desempe˜
no en vuelo var´
ıe en respuesta a la variacio´ n del ambiente y, potencialmente, a
la experiencia. Recopilamos 6298 trayectorias de vuelo registradas con telemetr´ıa GPS de alta frecuencia
provenientes de cinco aves con edades comprendidas entre 1 y 19 a ˜
nos, seguidas durante 32 d´ıas en el verano
de 2016. Con estos datos, evaluamos la hipo´tesis de que la tasa de ascenso, un indicador de desempe˜
no del
vuelo, estar´ıa relacionada con las variables topogra´ ficas y meteorolo´gicas que experimento´ el ave y tambie´n
con su edad. La tasa de ascenso fue mayor cuando los co´ ndores volaron con vientos ma´s ra´pidos y durante
condiciones ambientales propicias para el desarrollo de corrientes ascendentes. Sin embargo, no
encontramos ning´
un efecto de la edad sobre la tasa de ascenso. Aunque muchas de estas relaciones se
esperaban a partir de la teor´ıa del vuelo, la falta del efecto de la edad fue inesperado. Nuestro trabajo ampl´ıa
la comprensio´n de la relacio´ n que los co´ ndores tienen con el medio ambiente, y tambie´n sugiere una
complejidad potencial de esta relacio´n a´
un inexplorada. Como tal, este estudio proporciona informacio´n
sobre el comportamiento de vuelo de las aves y, dado que el rendimiento de vuelo influye en el
comportamiento de las aves y en la exposicio´n al riesgo antropoge´nico, tiene consecuencias potenciales para
el desarrollo de planes de manejo de conservacio´n.
[Traduccio´n del equipo editorial]
INTRODUCTION
Movements of animals are affected by internal and
external factors (Dodge et al. 2013, Martin et al.
2013). Characteristics such as age, experience, sex,
reproductive or social status (internal), and weather,
topography, land cover, and predators (external),
can alter the efficiency and energetic cost of
movement (Nathan et al. 2008). These factors
together can inhibit or enhance the speed of
movement of an animal across a landscape (Shepard
et al. 2013). They also can influence the path taken,
time in motion, the number of interruptions in
movement, and even the demographic consequenc-
es of movements (Nathan et al. 2008).
In the case of soaring animals, environmental
conditions conducive to the formation of atmo-
spheric updrafts are important influences on move-
ment (Shamoun-Baranes et al. 2006, Scacco et al.
2019). Soaring birds rely on thermal or orographic
updrafts to ascend and to move through the airspace
(Kerlinger 1989, Alerstam and Hedenstro¨m 1998,
Diehl 2013, Williams et al. 2020). Thermal updrafts
develop from uneven heating of the Earth’s surface
that produces vertical air movements, whereas
orographic updrafts form from wind deflecting
upward off topographic features (Kerlinger 1989,
Sage et al. 2019). The strength of updrafts is
influenced by multiple external meteorological
and topographic factors, all of which may affect
flight performance of soaring animals. Likewise,
flight performance is also influenced by experience
and age, often over many years (Mueller et al. 2013,
Sergio et al. 2014, Harel et al. 2016, Williams et al.
2020).
To better understand how environmental condi-
tions affect the movement of soaring animals, we
investigated flight performance, measured by climb
rate, of California Condors (Gymnogyps californianus;
hereafter condors). Condors are a large, obligate-
scavenging and -soaring species in western North
America. They are a suitable species for study of this
topic because of their conservation status, their
almost exclusive use of soaring during flight, and the
previous lack of knowledge of their flight perfor-
mance. The conservation status and consequent
intensive management focused on the species have
resulted in long-term monitoring and substantial
GPS tracking data that can be used to study flight
behavior. Also, as especially long-lived and large
flying animals, condors are expected to be influ-
enced by external factors in their environment, and
to learn and adjust their response over time. Given
these expectations, we used GPS data to test
predictions that, during soaring flight, (1) condors
would climb faster in conditions conducive to
thermal (i.e., strong solar generation and low wind
speed) and to orographic (i.e., rougher terrain and
high wind speed) updrafts, and (2) older, more
experienced condors would climb faster than
younger, less experienced condors.
METHODS
Study Area. We studied condors in Los Angeles,
Ventura, and Kern Counties in southern California,
USA (Fig. 1). Elevations in the region extend from
sea level to 3000 m above sea level (ASL). Land cover
ranges from shrub and scrub at low elevations to
barren rock at high elevations, and topography is
generally rugged. For more details on the study site,
see Poessel et al. (2018a, 2018b).
Trapping, Tagging, and Telemetry. Biologists
from the California Condor Recovery Program at
18 VOL. 56, NO.1
BONNER ET AL.

the US Fish and Wildlife Service (USFWS) trapped,
tagged, and released condors at Bitter Creek
National Wildlife Refuge (NWR) in Kern County
and at Hopper Mountain NWR in Ventura County.
Condors were outfitted with solar-powered Global
Positioning System-Global System for Mobile Com-
munications (GPS-GSM) patagial telemetry units
manufactured by Cellular Tracking Technologies,
LLC (Rio Grande, NJ, USA). These devices collected
GPS locations at intervals ranging from 1 hr to ,10
sec and sent them via the mobile phone network to a
server from which they were downloaded. The GPS
data included information on location (latitude and
longitude), date, time, altitude (altitude ASL, in m),
Figure 1. Map of GPS-recorded flight locations (gray dots) of five California Condors tracked in southern California,
2016, with names of counties and locations of the Bitter Creek and Hopper Mountain National Wildlife Refuges.
MARCH 2022 19
FLIGHT PERFORMANCE OF CALIFORNIA CONDORS

ground speed (knots), horizontal and vertical
dilution of precision (HDOP and VDOP, respective-
ly), and fix quality (2D or 3D). Exact age of all
condors was known because they all either hatched
in captivity or in the wild at monitored nests. For
additional details on trapping, tagging, and telem-
etry, see Poessel et al. (2018b).
Data Management. GPS telemetry data were
imported into a customized database and filtered
to remove locations for which diagnostic or altitudi-
nal data indicated errors (Poessel et al. 2018b). We
also excluded GPS data that were collected at night
(between 2000 H and 0430 H PST) or with fix
intervals .10 s.
We used a two-step process to identify segments of
the remaining data when birds were in continuous
upward flight (i.e., flight tracks). First, we defined a
flight track as a series of locations where the bird was
ascending (defined here as a change in altitude
between successive locations of .2 m, or for which
cumulative change in altitude over the track was .0
m). By defining continuous upward flight in this
way, we allowed an ascent phase to be interrupted by
short-term drops in altitude. Subsequently, we
removed short flight tracks with a duration ,30
sec as these may not be representative of general
flight performance. For each remaining flight track,
we then calculated the climb rate as the total change
in altitude divided by the total flight time, and we
report and analyze values in m/sec.
Data Associations. We used the Environmental-
Data Automated Track Annotation system (Env-
DATA; Dodge et al. 2013) in Movebank (http://
www.movebank.org; Kranstauber et al. 2011, Wikel-
ski and Kays 2016) to link each GPS location in a
flight track to the values of four meteorological
variables we believed were likely to influence updraft
strength and condor flight performance. Downward
shortwave radiation (DSR, W/m
2
), pressure at the
surface (Pa), and turbulent kinetic energy (TKE, J/
kg) all influence thermal updraft in a landscape
(Shamoun-Baranes et al. 2003, Chevallier et al. 2010,
Sapir et al. 2011). Wind speed (m/sec) positively
influences the strength of orographic updrafts and
negatively influences the strength of thermal up-
drafts (Pennycuick 1989, Chevallier et al. 2010). We
calculated wind speed from u-wind and v-wind
vectors that we downloaded from Movebank (Poes-
sel et al. 2018b). The spatial resolution of these
weather variables was 32 km
2
,theirtemporal
granularity was 3 hr, and data were linearly
interpolated to the location, time, and, in the case
of wind speed, the altitude of the bird.
We used ArcGIS v.10.6 (Esri, Redlands, CA, USA)
to link each GPS location in a flight track to three
topographic variables (US Geological Survey 2015):
terrain ruggedness index (TRI, 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), slope (in
percent), and aspect (Pierce et al. 2005, Piedallu and
Ge´gout 2013). Each of these variables influences
formation of updrafts and thus, a condor’s flight
performance (Reichmann 1978, Shamoun-Baranes
et al. 2003, Bohrer et al. 2012, Poessel et al. 2018a,
2018b, Duerr et al. 2019). Because aspect has a
circular distribution, we converted it into two linear
components: northness (cosine of aspect; ranging
from 1 [south] to 1 [north]) and eastness (sine of
aspect; ranging from 1 [west] to 1 [east]).
Finally, we linked each GPS location to flight
altitude above ground level (altitude AGL, in m)
because altitude can influence flight performance of
soaring birds (Poessel et al. 2018a). We obtained
altitude AGL by subtracting the ground elevation
below a condor’s flight location (determined using a
30-m resolution digital elevation model; US Geolog-
ical Survey 2015) from the altitude ASL of the
condor (as measured by the GPS). For each
meteorological, topographic, and flight altitude
variable, we averaged all values within a flight track
for analysis. Additionally, for each of the three
topographic variables, we calculated the standard
deviation of all values within a flight track to account
for variation within the flight track.
The coarse spatial and temporal resolutions of the
meteorological variables limit how accurately weath-
er data reflect microscale conditions that condors
may actually experience. Furthermore, additional
uncertainty occurs because Movebank linearly inter-
polates environmental variables to condor locations
and because the topographic data are averaged over
a30m
2
area, meaning that any drastic elevation
changes within that area may have been lost. Finally,
our dataset was limited in the number and age of
condors, which may have affected our analysis of the
effect of age on condor flight performance. In our
study, we attempted to interpret our outputs in the
context of these biases, as well as those induced by
spatial mismatch between datasets and by measure-
ment errors (Katzner and Arlettaz 2020, Pe´ron et al.
2020).
20 VOL. 56, NO.1
BONNER ET AL.

Statistical Analysis. To test the effects of our
predictors on flight performance, we evaluated
multivariate relationships within the data with linear
mixed-effects models (LMMs) using the lme4 pack-
age (Bates et al. 2015) in R (R Core Team 2018).
First, we evaluated the potential for multicollinearity
among covariates by examining pairwise Pearson
correlations between weather, topographic, and
flight altitude variables. When two variables were
highly correlated (r0.60), we removed one
member of the pair. Mean slope, mean TRI, and
the standard deviation of TRI were all highly
correlated (Supplemental Material Table S1). The
standard deviation of TRI was also highly correlated
with the standard deviation of slope (Table S1).
Thus, we removed from consideration the mean
slope and the standard deviation of TRI. To meet
distributional assumptions of statistical tests, we
square-root-transformed the response variable
(climb rate of a flight track), and we rescaled
predictor variables by subtracting the mean of the
data and dividing by two standard deviations (Gel-
man 2008).
Next, we evaluated a global LMM that included
transformed climb rate as the response variable and
condor identification (ID) as a random effect.
Topographic predictors in the model included
means for northness, eastness, and TRI, and
standard deviations for northness, eastness, and
slope as fixed effects. Meteorological predictors
included DSR, TKE, wind speed, and pressure at
the surface as fixed effects. We also included
continuous predictors for age and altitude AGL as
fixed effects. We then used the dredge function
(MuMIn package in R; Barton 2015) to evaluate
performance of models based on all possible
combinations of variables (Doherty et al. 2012). We
used Akaike’s Information Criterion corrected for
small sample size (AIC
c
) to rank the models, and we
averaged the top-ranked sub-models (model weight
.0.01; Burnham and Anderson 2002). We used the
highest-ranked model to construct plots (effects
package in R; Fox 2003) illustrating the effects on
climb rate of each predictor variable. When plotted,
both climb rate and each predictor variable were
back-transformed to their original scales.
RESULTS
Condor Telemetry. Data were collected at 10-sec
intervals during the day only during the period
between 11 August 2016 and 11 September 2016
(Table 1). After filtering and conducting quality
control on the data, we retained 92,804 GPS
locations from five condors aged 1, 7, 8, 19, and 19
yr. These locations defined 6298 flight tracks, of
which there was a range of 1120–1484 per condor,
with a mean of 14.6 locations per track (range ¼
5107 locations per track) and a mean flight
duration of 88.9 sec per track (range: 30–669 sec
per track; Table 1). Average climb rates for each
Table 1. Summary statistics (post-filtering) describing data for five California Condors of different ages and their tracks of
ascending flight used in an analysis of flight performance in southern California, 2016. AGL is altitude above ground level,
DSR is downward shortwave radiation, TKE is turbulent kinetic energy, and TRI is terrain ruggedness index.
PARAMETER
CONDOR IDENTIFICATION NUMBER
#156 #161 #480 #507 #791
Age 19 19 8 7 1
Number of tracks 1239 1195 1484 1260 1120
AGL (m; ¯
x6SE) 320.5 67.43 269.2 67.29 284.6 66.33 330.9 66.03 271.8 68.59
Wind speed (m/sec; ¯
x6SE) 5.1 60.05 5.1 60.06 4.3 60.05 4.3 60.05 4.3 60.07
Pressure (Pa; ¯
x6SE) 89,973.2 622.60 90,270.9 624.35 90,191.7 622.45 90,261 622.45 91,112.3 649.07
DSR (W/m
2
;¯
x6SE) 820.4 64.72 823.9 64.98 830.6 64.93 827.8 64.12 788.5 65.43
TKE (J/kg; ¯
x6SE) 2.2 60.04 2.3 60.04 2.0 60.35 2.2 60.03 1.6 60.04
Northness (¯
x6SE) 0.11 60.02 0.10 60.02 0.03 60.02 0.07 60.01 0.09 60.02
Eastness (¯
x6SE) 0.01 60.02 0.04 60.02 0.02 60.02 0.02 60.01 0.09 60.02
TRI (¯
x6SE) 314.7 67.39 337.1 67.38 299.2 66.76 297.9 66.12 375.9 69.94
Slope (%; ¯
x6SE) 18.2 60.25 19.4 60.23 18.1 60.23 17.9 60.21 19.6 60.29
Climb rate (m/sec; ¯
x6SE) 1.1 60.02 1.1 60.02 1.3 60.02 1.1 60.02 1.1 60.02
Track duration (s; ¯
x6SE) 88.2 61.90 83.0 61.73 93.9 61.85 85.5 61.68 94.6 62.19
Days of flights 29 14 18 19 19
Time period 14 Aug–11 Sept 11–24 Aug 14–31 Aug 11–29 Aug 11–29 Aug
MARCH 2022 21
FLIGHT PERFORMANCE OF CALIFORNIA CONDORS

condor ranged from 1.1 60.02 to 1.3 60.02 m/sec
(¯
x6SE; Table 1).
Factors Affecting Climb Rate. Our top ranked
model, which had 77% of model weights, included
terms for mean altitude AGL, DSR, pressure, TKE,
windspeed,northness,TRI,andthestandard
deviations of eastness, northness, and slope (Table
2). The second-ranked model (19% of weights) was
similar to the top model, except it did not include a
term for TKE. Age was not included until the fourth-
ranked model.
Climb rate of condors was positively influenced
most heavily by altitude AGL and DSR (i.e., z-values
were high, confidence interval bands were narrow,
and both variables were included in each of the top
five sub-models; Tables 2, 3; Fig. 2a, 2b). Other
influential predictors with positive effects included
mean TRI (Table 3; Fig. 2c), the standard deviation
of eastness, the standard deviation of slope, wind
speed, and the standard deviation of northness
(Table 3; Fig. 3a–d). Climb rate was negatively
associated with mean northward aspects and increas-
ing pressure at the surface (Table 3; Fig. 3e, 3f). We
Table 2. Summary of selection process for the model set describing climb rate of California Condors as a function of age
and environmental effects. We used linear mixed effects models ranked by DAIC
c
(Akaike’s Information Criterion
corrected for small sample size), and we show the top five models, plus the model with all explanatory variables (8th-ranked
model). Explanatory variables in models included age, the means of altitude above ground level (AGL), downward
shortwave radiation (DSR), pressure at the surface (Pressure), turbulent kinetic energy (TKE), wind speed (WS), eastness
(East), northness (North), and terrain ruggedness index (TRI), and the standard deviations of northness (North SD),
eastness (East SD), and slope (Slope SD). Krefers to the number of parameters (including intercept and error terms) in a
model, adjusted R
2
is the percentage of explained deviance, and w
i
is the model weight.
MODEL KDAICc
ADJUSTED
R
2
w
i
AGL þDSR þPressure þTKE þWS þNorth þTRI þEast SD þNorth SD þSlope SD 13 0.00 0.551 0.77
AGL þDSR þPressure þWS þNorth þTRI þEast SD þNorth SD þSlope SD 12 2.81 0.549 0.19
AGL þDSR þPressure þTKE þWS þEast þNorth þTRI þEast SD þNorth SD þ
Slope SD
14 6.19 0.552 0.03
AGL þDSR þPressure þTKE þWS þNorth þTRI þEast SD þNorth SD þSlope
SD þAge
14 9.47 0.552 0.01
AGL þDSR þPressure þWS þEast þNorth þTRI þEast SD þNorth SD þSlope SD 13 11.43 0.549 0.00
AGL þDSR þPressure þTKE þWS þEast þNorth þTRI þEast SD þNorth SD þ
Slope SD þAge
15 15.59 0.553 0.00
Table 3. Model-averaged coefficients, standard errors, z-values, and 95% confidence intervals (CI) from the three best-
performing linear mixed models (with standardized predictor variables) explaining the environmental factors influencing
the climb rate (square-root transformed) of California Condors in southern California, 2016. SD is the standard deviation
of a variable.
PARAMETER COEFFICIENT SE Z-VALUE LOWER 95% CI UPPER 95% CI
Intercept 1.00 0.01 75.57 0.98 1.03
Altitude above ground level (AGL) 0.26 0.01 27.34 0.24 0.28
Downward shortwave radiation (DSR) 0.14 0.01 16.69 0.12 0.15
Terrain ruggedness index (TRI) 0.09 0.01 9.87 0.07 0.10
Eastness SD 0.09 0.01 9.19 0.07 0.11
Slope SD 0.09 0.01 9.05 0.07 0.11
Northness 0.06 0.01 7.52 0.08 0.05
Wind speed 0.07 0.01 7.19 0.05 0.09
Pressure at the surface 0.06 0.01 6.84 0.08 0.05
Northness SD 0.05 0.01 4.91 0.03 0.07
Turbulent kinetic energy (TKE) 0.02 0.01 1.73 0.00 0.05
Eastness 0.00 0.00 0.17 0.01 0.01
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BONNER ET AL.

did not find a strong effect of TKE or mean eastward
aspects on climb rate (i.e., the confidence intervals
for the averaged effect estimates of these variables
overlapped 0; Table 3).
DISCUSSION
Flight performance of the condors we monitored
was influenced by meteorological and topographic
factors, but not by age of the bird. Although based
on a small sample size, these observations are
consistent with our expectation that the vertical
climb rate of condors would be faster in conditions
conducive to thermal and orographic updraft
generation. That said, the lack of relevance of age-
related variation in flight performance was unex-
pected.
Condors rely on thermal and orographic updrafts
to achieve energy-efficient soaring flight (Poessel et
al. 2018b). Data from this study provide the first
descriptions of flight performance of condors and,
despite the small number of birds, they give insight
into the general trends underpinning drivers of
condor flight behavior. Thermal formation is linked
to both solar radiation and atmospheric instability,
the latter of which is higher when low pressure
systems are present (Hertenstein 2005). One way
condors use these updrafts is to rapidly gain altitude.
Figure 2. Plots of the back-transformed, model-fitted values of climb rate and (a) altitude above ground level, (b)
downward shortwave radiation, and (c) mean terrain ruggedness index for five California Condors tracked in southern
California in 2016. Gray bands represent 95% confidence intervals. Black rug plots on the x-axis show the distribution of
data.
MARCH 2022 23
FLIGHT PERFORMANCE OF CALIFORNIA CONDORS

Figure 3. Plots of the back-transformed, model-fitted values of climb rate and (a) standard deviation of eastness, (b)
standard deviation of slope, (c) wind speed, (d) standard deviation of northness, (e) mean northness, and (f) pressure at
the surface for five California Condors tracked in southern California in 2016. Gray bands represent 95% confidence
intervals. Black rug plots on the x-axis show the distribution of data.
24 VOL. 56, NO.1
BONNER ET AL.

Furthermore, thermals bring birds high in the air
where air density and resistance are lower, reducing
the amount of effort it takes to soar (Buffo et al.
1972, Williams et al. 2020). Thus, it is not surprising
that altitude AGL was a significant predictor of climb
rate. In contrast, thermals tend to be broken down
by the higher winds that create orographic updrafts
(Murgatroyd et al. 2018). As such, wind speed has
opposing effects on the two updraft types. The
strong positive effect of this variable that we
observed indicates that faster winds during oro-
graphic updrafts have a stronger influence on climb
rate than do slower winds during thermal updrafts.
These factors combined illustrate the complex ways
that both thermal and orographic updrafts are used
by condors to enhance flight performance.
Flight behavior of soaring birds also is strongly
linked to topography (Katzner et al. 2012, Poessel et
al. 2018a, Scacco et al. 2019, Sur et al. 2019). In the
northern hemisphere, south-facing slopes generally
receive higher solar radiation, and thus are more
conducive to thermal generation, than north-facing
slopes (Buffo et al. 1972). In fact, the climb rates we
measured were positively associated with TRI and
southward aspects, and with variation in slope and
aspect. Although classifying behavioral modes was
not a research objective, these data indicate that
condors were likely using orographic updrafts when
flying over rougher terrain with varying slopes. This
supposition is further supported because our data
collection occurred at a time of the year when winds
in southern California blow perpendicular to east-
facing slopes (e.g., the east-west Santa Ana winds).
Contrary to expectations, we did not detect an
effect of age on flight performance. Improvement in
flight performance may be greatest in the first year
of life but can extend well beyond that (e.g., Mueller
et al. 2013), sometimes into a bird’s second decade
of life (Sergio et al. 2014). These patterns suggest
that from the perspective of learning and flight
performance, if we had used the usual system of
breaking age into two categories (juvenile and
adult), it likely would have been simplistic, as some
birds learn continuously over time. The fact that we
did not detect an age effect in this study may be a
result of our small sample size or of the importance
of rapid learning of flight by condors. Future studies
of this topic may wish to focus especially on
comparisons between birds of multiple age classes,
including those ,1 yr old, when flight skills are still
developing and wing shape is most different.
The limited temporal scope of our study simplified
our analysis by allowing us to ignore seasonal
variation in weather. However, despite this advan-
tage, there are also costs to this limited scope. In
particular, the data we considered were all collected
during a time period with high thermal strength and
a relatively constant solar declination angle. Data
collected in winter, when the sun is lower in the sky
and weather patterns are different, may tell a
different story about the relationship of flight
performance and environmental variation.
Understanding flight performance of obligate-
soaring birds under different demographic and
environmental conditions has important implica-
tions for understanding individual fitness, ecology,
and behavior. Movement behavior also interacts with
risk to condors and other soaring birds from wind
turbines (e.g., Miller et al. 2014, Reid et al. 2015,
Poessel et al. 2018a), and wind energy generation is
rapidly expanding within California (US Energy
Information Administration 2020). Flight perfor-
mance of condors may influence this risk because
some of the factors that influence flight perfor-
mance (e.g., altitude AGL, landscapes, DSR, and
wind speed) may also be associated with higher or
lower risk. Our work here expands understanding of
the relationship condors have with the environment
and it also suggests the potential for as-yet unex-
plored complexity to this relationship.
SUPPLEMENTAL MATERIAL (available online). Table
S1: Pearson correlation (r) matrix for meteorolog-
ical, topographic, and flight altitude variables
associated with California Condor soaring flight in
southern California, 2016.
ACKNOWLEDGMENTS
Funding was provided by the National Science Founda-
tion REU Site Award (DBI: 1852133 to JRB), Boise State
University Raptor Research Center, College of Arts and
Sciences, Department of Biological Sciences, and Boise
State University Division of Research and Economic
Development. Field research on condors was supported
by the California Department of Fish and Wildlife (CDFW
agreements P1182024 and P148006), the Bureau of Land
Management (US BLM contract L11PX02237), the Na-
tional Fish and Wildlife Foundation, and the authors’
institutions. This study was carried out in strict accordance
with the recommendations in the Guidelines to the Use of
Wild Birds in Research of the Ornithological Council. No
animal care committee reviews research conducted under
endangered species recovery permits; therefore, condor
field program permits were reviewed and approved by the
US Fish and Wildlife Service Permit Coordinator, the US
Fish and Wildlife Service California Condor Coordinator,
and the US Fish and Wildlife Service Region 8 Endangered
MARCH 2022 25
FLIGHT PERFORMANCE OF CALIFORNIA CONDORS

Species Division. The use of GPS transmitters was autho-
rized as a recovery action under section 10(a)(1)(A) of the
Endangered Species Act with a permit issued to the Hopper
Mountain NWR Complex (#TE-108507 HMNWR-0). This
work also was authorized by the state of California under a
Memorandum of Understanding between managers of the
Hopper Mountain NWR Complex and the California
Department of Fish and Wildlife under sections 650 and
670.7, Title 14, California Code of Regulations. We thank
the many people at the US Fish and Wildlife Service who
assisted with condor monitoring, trapping, and telemetry.
We also thank A. Punzalan for her review of the manuscript.
SRB, SAP, TEK, and JRB designed the study; JCB and MTA
handled and telemetered condors; SRB performed the
analysis with supervision from SAP and TEK; SRB, SAP, and
TEK led the writing; and all authors contributed to revising
and improving the manuscript. The findings and conclu-
sions in this article are those of the authors and do not
necessarily represent the views of the US Fish and Wildlife
Service. Any use of trade, product, or firm names is for
descriptive purposes only and does not imply endorsement
by the US Government.
LITERATURE CITED
Alerstam, T., and A. Hedenstro¨m (1998). Optimal migra-
tion. Journal of Avian Biology 29:339–340.
Barton, K. (2015). MuMIn: Multi-model inference. R
package version 1.15.1. http://CRAN.R-project.org/
package¼MuMIn.
Bates, D., M. M¨
achler, B. Bolker, and S. Walker (2015).
Fitting linear mixed-effects models using lme4. Journal
of Statistical Software 67:1–48.
Bohrer, G., D. Brandes, J. T. Mandel, K. L. Bildstein, T. A.
Miller, M. Lanzone, T. E. Katzner, C. Maisonneuve, and
J. A. Tremblay (2012). Estimating updraft velocity
components over large spatial scales: Contrasting
migration strategies of Golden Eagles and Turkey
Vultures. Ecology Letters 15:96–103.
Buffo, J., L. J. Fritschen, and J. L. Murphy (1972). Direct
solar radiation on various slopes from 0 to 60 degrees
north latitude. US Department of Agriculture Forest
Service, Portland, OR, USA.
Burnham, K. P., and D. R. Anderson (2002). Model
Selection and Multimodel Inference: A Practical Infor-
mation-Theoretic Approach, Second Ed. Springer, New
York, NY, USA.
Chevallier, D., Y. Handrich, J. Y. Georges, F. Baillon, P.
Brossault, A. Aurouet, Y. Le Maho, and S. Massemin
(2010). Influence of weather conditions on the flight of
migrating black storks. Proceedings of the Royal Society
B: Biological Sciences 277:2755–2764.
Diehl, R. H. (2013). The airspace is habitat. Trends in
Ecology & Evolution 28:377–379.
Dodge, S., G. Bohrer, R. Weinzierl, S. C. Davidson, R. Kays,
D. Douglas, S. Cruz, J. Han, D. Brandes, and M. Wikelski
(2013). The environmental-data automated track an-
notation (Env-DATA) system: Linking animal tracks
with environmental data. Movement Ecology 1:3.
https://doi.org/10.1186/2051-3933-1-3.
Doherty, P. F., G. C. White, and K. P. Burnham (2012).
Comparison of model building and selection strategies.
Journal of Ornithology 152:S317–S323.
Duerr, A. E., T. A. Miller, L. Dunn, D. A. Bell, P. H. Bloom,
R. N. Fisher, J. A. Tracey, and T. E. Katzner (2019).
Topographic drivers of flight altitude over large spatial
and temporal scales. The Auk 136:1–11.
Fox, J. (2003). Effect displays in R for generalized linear
models. Journal of Statistical Software 8:1–27.
Gelman, A. (2008). Scaling regression inputs by dividing by
two standard deviations. Statistics in Medicine 27:2865–
2873.
Harel, R., N. Horvitz, and R. Nathan (2016). Adult vultures
outperform juveniles in challenging thermal soaring
conditions. Scientific Reports 6:27865.
Hertenstein, R. (2005). Thermals. Bob Wander’s Gliding
Mentor Series, Soaring Books & Supplies, Minneapolis,
MN, USA.
Katzner, T. E., and R. Arlettaz (2020). Evaluating contri-
butions of recent tracking-based animal movement
ecology to conservation management. Frontiers in
Ecology and Evolution 7:519. https://doi.org/10.
3389/fevo.2019.00519.
Katzner, T. E., D. Brandes, T. Miller, M. Lanzone, C.
Maisonneuve, J. A. Tremblay, R. Mulvihill, and G. T.
Merovich, Jr. (2012). Topography drives migratory
flight altitude of Golden Eagles: Implications for on-
shore wind energy development. Journal of Applied
Ecology 49:1178–1186.
Kerlinger, P. (1989). Flight Strategies of Migrating Hawks.
University of Chicago Press, Chicago, IL, USA.
Kranstauber, B., A. Cameron, R. Weinzerl, T. Fountain, S.
Tilak, M. Wikelski, and R. Kays (2011). The Movebank
data model for animal tracking. Environmental Model-
ling & Software 26:834–835.
Martin, J., B. van Moorter, E. Revilla, P. Blanchard, S. Dray,
P. Y. Quenette, D. Allaine´, and J. E. Swenson (2013).
Reciprocal modulation of internal and external factors
determines individual movements. Journal of Animal
Ecology 82:290–300.
Miller, T. A., R. P. Brooks, M. Lanzone, D. Brandes, J.
Cooper, K. O’Malley, C. Maisonneuve, J. Tremblay, A.
Duerr, and T. E. Katzner (2014). Assessing risk to birds
from industrial wind energy development via paired
resource selection models. Conservation Biology
28:745–755.
Mueller, T., R. B. O’Hara, S. J. Converse, R. P. Urbanek,
and W. F. Fagan (2013). Social learning of migratory
performance. Science 341:999–1002.
Murgatroyd, M., T. Photopoulou, L. G. Underhill, W.
Bouten, and A. Amar (2018). Where eagles soar: Fine
resolution tracking reveals the spatiotemporal use of
differential soaring modes in a large raptor. Ecology
and Evolution 8:6788–6799.
Nathan, R., W. M. Getz, E. Revilla, M. Holyoak, R. Kadmon,
D. Saltz, and P. E. Smouse (2008). A movement ecology
paradigm for unifying organismal movement research.
26 VOL. 56, NO.1
BONNER ET AL.

Proceedings of the National Academy of Sciences of the
United States of America 105:19052–19059.
Pennycuick, C. J. (1989). Bird Flight Performance: A
Practical Calculation Manual. Oxford University Press,
Oxford, UK.
Pe´ron, G., J. M. Calabrese, O. Duriez, C. H. Fleming, R.
Garc´ıa-Jime´nez, A. Johnston, S. A. Lambertucci, K. Safi,
and E. L. C. Shepard (2020). The challenges of
estimating the distribution of flight heights from
telemetry or altimetry data. Animal Biotelemetry 8:5.
https://doi.org/10.1186/s40317-020-00194-z.
Piedallu, C., and J.-C. Ge´gout (2013). Efficient assessment
of topographic solar radiation to improve plant
distribution models. Agricultural and Forest Meteorol-
ogy 148:1696–1706.
Pierce, K. B., Jr., T. Lookingbill, and D. Urban (2005). A
simple method for estimating potential relative radia-
tion (PRR) for a landscape-scale vegetation analysis.
Landscape Ecology 20:137–147.
Poessel, S. A., J. Brandt, L. Mendenhall, M. A. Braham, M. J.
Lanzone, A. J. McGann, and T. E. Katzner (2018a).
Flight response to spatial and temporal correlates
informs risk from wind turbines to the California
Condor. The Condor 120:330–342.
Poessel, S. A., J. Brandt, T. A. Miller, and T. E. Katzner
(2018b). Meteorological and environmental variables
affect flight behavior and decision-making of an
obligate soaring bird, the California Condor Gymnogyps
californianus. Ibis 160:36–53.
R Core Team (2018). R: A language and environment for
statistical computing. R Foundation for Statistical
Computing, Vienna, Austria. https://www.R-project.
org.
Reichmann, H. (1978). Cross-country Soaring. Thomson
Publications, Santa Monica, CA, USA.
Reid, T., S. Kr¨
uger, D. P. Whitfield, and A. Amar (2015).
Using spatial analyses of Bearded Vulture movements in
southern Africa to inform wind turbine placement.
Journal of Applied Ecology 52:881–892.
Riley, S. J., S. D. DeGloria, and R. Elliot (1999). A terrain
ruggedness index that quantifies topographic hetero-
geneity. Intermountain Journal of Sciences 5:23–27.
Sage, E., W. Bouten, B. Hoekstra, K. C. J. Camphuysen, and
J. Shamoun-Baranes (2019). Orographic lift shapes
flight routes of gulls in virtually flat landscapes.
Scientific Reports 9:9659.
Sapir, N., N. Horvitz, M. Wikelski, R. Avissar, Y. Mahrer, and
R. Nathan (2011). Migration by soaring or flapping:
Numerical atmospheric simulations reveal that turbu-
lence kinetic energy dictates bee-eater flight mode.
Proceedings of the Royal Society B 278:3380–3386.
Scacco, M., A. Flack, O. Duriez, M. Wikelski, and K. Safi
(2019). Static landscape features predict uplift locations
for soaring birds across Europe. Royal Society Open
Science 5:181440. https://doi.org/10.1098/rsos.
181440.
Sergio, F., A. Tanferna, R. De Stephanis, L. L. Jime´nez, J.
Blas, G. Tavecchia, D. Preatoni, and F. Hiraldo (2014).
Individual improvements and selective mortality shape
lifelong migratory performance. Nature 515:410–413.
Shamoun-Baranes, J., Y. Leshem, Y. Yom-Tov, and O.
Liechti (2003). Differential use of thermal convection
by soaring birds over central Israel. The Condor
105:208–218.
Shamoun-Baranes, J., E. Van Loon, D. Alon, P. Alpert, Y.
Yom-Tov, and Y. Leshem (2006). Is there a connection
between weather at departure sites, onset of migration
and timing of soaring-bird autumn migration in Israel?
Global Ecology and Biogeography 15:541–552.
Shepard, E. L. C., R. P. Wilson, W. G. Rees, E. Grundy, S. A.
Lambertucci, and S. B. Vosper (2013). Energy land-
scapes shape animal movement ecology. American
Naturalist 182:298–312.
Sur, M., A. E. Duerr, D. A. Bell, R. N. Fisher, J. A. Tracey,
P. H. Bloom, T. A. Miller, and T. E. Katzner (2019).
Relevance of individual and environmental drivers of
movement of Golden Eagles. Ibis 162:381–399.
US Energy Information Administration (2020). Wind
explained: Where wind power is harnessed. https://
www.eia.gov/energyexplained/wind/where-wind-
power-is-harnessed.php.
US Geological Survey (2015). The National Map, 3D
Elevation Program website. http://nationalmap.gov/
3dep_prodserv.html.
Wikelski, M., and R. Kays (2016). Movebank: Archive,
analysis and sharing of animal movement data. Hosted
by the Max Planck Institute for Ornithology. http://
www.movebank.org.
Williams, H. J., E. L. C. Shepard, M. D. Holton, P. A. E.
Alarco´n, R. P. Wilson, and S. A. Lambertucci (2020).
Physical limits of flight performance in the heaviest
soaring bird. Proceedings of the National Academy of
Sciences of the United States of America 117:17884–
17890.
Received 31 July 2020; accepted 17 May 2021
Associate Editor: Pascual Lo´pez-Lo´ pez
MARCH 2022 27
FLIGHT PERFORMANCE OF CALIFORNIA CONDORS

SUPPLEMENTAL MATERIAL
DRIVERS OF FLIGHT PERFORMANCE OF CALIFORNIA CONDORS (GYMNOGYPS
CALIFORNIANUS)
SOPHIE R. BONNER
Department of Geography, University of Texas at Austin, Austin, TX 78712 USA
and
Department of Biological Sciences, REU Site in Raptor Research, and Raptor Research Center,
Boise State University, Boise, ID 83706 USA
SHARON A. POESSEL
US Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, ID 83706 USA
JOSEPH C. BRANDT AND MOLLY T. ASTELL
US Fish and Wildlife Service, Hopper Mountain National Wildlife Refuge Complex, 2493
Portola Road, Ventura, CA 93003 USA
JAMES R. BELTHOFF
Department of Biological Sciences, REU Site in Raptor Research, and Raptor Research Center,
Boise State University, Boise, ID 83706 USA
TODD E. KATZNER
US Geological Survey, Forest and Rangeland Ecosystem Science Center, Boise, ID 83706 USA

Table S1. Pearson correlation (r) matrix for meteorological, topographic, and flight altitude variables associated with California
Condor soaring flight in southern California, 2016. Bold values indicate variables with a correlation >0.60. Meteorological variables
are wind speed at flight altitude (WS), downward shortwave radiation (DSR), turbulent kinetic energy (TKE), and pressure at the
surface (Pressure). Topographic variables are slope, terrain ruggedness index (TRI), and northness (North) and eastness (East) of the
slope. Flight altitude is altitude above ground level (AGL). Standard deviation for topographic variables is represented by the suffix
“SD”.
DSR
TKE
Pressure
Slope
TRI
North
East
AGL
Slope
SD
TRI
SD
North
SD
East
SD
WS
0.08
0.26
−0.28
−0.03
0.00
0.06
−0.02
0.29
0.02
0.05
0.06
0.07
DSR
0.10
−0.03
−0.09
−0.08
−0.08
0.02
0.18
0.10
0.05
0.13
0.12
TKE
−0.21
−0.09
−0.08
0.00
−0.20
−0.07
−0.05
−0.06
−0.01
−0.01
Pressure
0.09
0.08
−0.18
−0.12
−0.21
0.07
0.05
0.01
−0.09
Slope
0.93
0.00
−0.03
−0.18
0.32
0.63
−0.02
−0.09
TRI
0.02
−0.05
−0.13
0.25
0.65
−0.06
−0.14
North
0.15
−0.06
−0.11
−0.06
−0.12
0.05
East
0.09
0.00
−0.06
0.01
0.04
AGL
0.12
0.05
0.24
0.22
Slope SD
0.77
0.49
0.42
TRI SD
0.29
0.19
North SD
0.39