Calibration of visually estimated distances to migrating
seabirds with radar measurements
Marı́a Mateos,1,2,4 Gonzalo M. Arroyo,1,2 Alejandro Rodrı́guez,3 David Cuenca,1
and Andrés de la Cruz2
1
Biology Department, Faculty of Marine and Environmental Sciences, University of Cádiz, Av. Republica Saharaui, s/n,
11510 Puerto Real, Cádiz, Spain
2
Fundación Migres, Complejo Huerta Grande, Ctra. N-340 Km 96.7, 11390 Pelayo, Algeciras, Cádiz, Spain
3
Department of Conservation Biology, Estación Biológica de Doñana, CSIC, Américo Vespucio s/n, Isla de la Cartuja,
41092 Sevilla, Spain
ABSTRACT. Censusing seabirds from coastal areas requires reliable estimates of bird numbers and the distances
of the birds from the coastline. Logistical constraints make visual estimation of distances the only feasible method
in many studies. We tested the accuracy of visually estimated offshore distances of six migratory seabird species in
the Strait of Gibraltar using simultaneous measurements obtained by radar. Most birds (91%) were detected within
3 km of the coast and we truncated our calibration at this distance. We found a strong correlation between radar
and visual estimates (R 2 = 0.83, P < 0.0001). The magnitude of errors in visual estimates was moderate and ranged
from 0.08 to 0.20 for different distances and observers. Among the factors potentially affecting the accuracy of
visual estimates of distance to seabird in our study were observer identity, bird species, bird behavior, and weather;
the most parsimonious model in our study included observer identity as the only predictor, and no model with more
than one predictor had a smaller Akaike’s information criterion value. Radar can be used to help train observers and
to reduce biases in visual estimates of distances by means of calibration. When no other methods are available to
accurately measure distances to seabirds, visual estimates of distances, recorded by experienced observers and once
calibrated with radar (or other ground-truthing methods), may be acceptable for different species under a wide
range of environmental conditions.
RESUMEN. Utilización de medidas de radar para la calibración de distancias a aves marinas
migratorias estimadas visualmente
Censar aves marinas desde áreas costeras requiere estimaciones fiables del nú mero de aves y de la distancia de
estas a costa. Restricciones logı́sticas hacen que a veces las estimaciones visuales de las distancias sean el ú nico
método factible para llevar a cabo estos estudios. Pusimos a prueba, en el Estrecho de Gibraltar, la exactitud de
las estimaciones visuales desde costa, utilizando seis especies de aves marinas, concurrentemente, con medidas
obtenidas por radar. La mayorı́a de las aves (91%) fueron detectadas dentro de un área de 3 km de la costa y
ajustamos nuestra calibración a esta distancia. Encontramos una fuerte correlación entre las estimaciones visuales y
las del radar (R 2 = 0.83, P < 0.0001). La magnitud de los errores visuales al estimar las distancias fue moderada
y variaron de 0.08 a 0.20 para diferentes distancias y observadores. Entre los factores que afectaron a la exactitud
de las estimaciones visuales, se encuentran el observador, la especie de ave, la conducta exhibida por el ave y las
condiciones meteoroló gicas. El modelo con mayor parsimonia incluyó la identidad del observador como el ú nico
vaticinador y ningún otro modelo, con más de un vaticinador, tuvo un valor de AIC menor. El radar puede ser
utilizado para entrenar al personal y reduce el sesgo en las estimaciones visuales mediante la calibración. Cuando
no hay disponible otros métodos para medir con exactitud la distancia a que se mueven las aves marinas, el uso de
observadores experimentados y una calibración con radar (u otro método), pueden ser aceptables para trabajar con
diferentes especies bajo una amplia gama de condiciones ambientales.
Key words: coastal migratory routes, distance calibration, distance underestimation, seabird abundance, seaclutter, Strait of Gibraltar
Distance sampling is a widespread method
for estimating animal abundance (Buckland
et al. 2001, 2004), and may be useful for
estimating densities of marine birds (Komdeur
4
Corresponding author. Email: maria.mateos@
uca.es
et al. 1992, Camphuysen et al. 2004). Along
seabird migratory pathways or frequently used
flight paths, such as between foraging areas
and breeding colonies, surveys from stationary
observation platforms are required to census
birds (Camphuysen et al. 2004). These counts
may yield abundance estimates with high accuracy provided that distances to birds can be
measured without biases (Buckland et al. 2004).
Different procedures have been used to obtain
those distances, including visual estimates, optical range finders (Heinemann 1981), binocular
reticules (Yen et al. 2004), radars (Petersen et al.
2006), and laser range finders (Ransom and
Pinchak 2003). Characteristics of the surveyed
populations, environmental conditions, and the
accuracy required will dictate which methods
should be used (e.g., Bibby et al. 1992). Estimating distance by eye has been criticized, but could
be the only option under some circumstances
(Bibby et al. 1992), and nearly all seabird census
work, whether using shore-based or shipboard
observers, relies on visually estimated distances.
However, no assessment of the accuracy with
which observers estimate these distances exists.
Previous attempts to measure error in distance
estimates to animals at sea, and the possible
effects of factors such as weather on the accuracy
of visual estimates of distance, have focused on
cetaceans (Schweder 1997, Baird and Burkhart
2000, Williams et al. 2007). Because detection
conditions and methods for estimating distances
to cetaceans differ from those used for seabirds,
calibrating distance estimates to birds at sea
may require specific studies as well. However,
to our knowledge, no study has assessed errors
in distance estimates to seabirds or has examined
the factors that may influence these errors.
Different types of error may arise from visual
estimation of distances, with biased distance
estimates posing the main problem because they
are difficult to detect and will transfer errors to
the estimates of bird abundance. Such errors
can be corrected either in the field through
testing procedures and calibration (Marques
2004, Williams et al. 2007) or through statistical techniques employed during data analysis
(Chen 1998, Marques 2004). Posthoc analytical
approaches are less robust than field-testing
programs (Marques 2004, Marques et al. 2006,
Williams et al. 2007). Calibration of distance
estimates to seabirds is therefore needed to improve estimates of bird abundance. Calibration
will be further enhanced if errors in distance
estimates could be predicted from factors such
as bird size, bird behavior, weather conditions,
or observer ability.
We recorded and compared visual and radar
estimates of distances to flying seabirds along a
migratory pathway. Our specific aims were to:
(1) test the accuracy of visually estimated off-
shore distances to seabirds by means of radar
measurements, and (2) determine the effect of
bird size, bird behavior, weather, and observer
skill on the accuracy of visual estimates.
METHODS
Our study was conducted from 5 March to
20 April and 16 October to 18 November 2006
at the Strait of Gibraltar (Fig. 1). This channel is
14 km wide at its narrowest point and is the only
connection between the Atlantic Ocean and the
Mediterranean Sea.
Fieldwork was carried out at Tarifa Island
(southwest Spain), the southernmost point of
the north coast of the Strait of Gibraltar (Fig. 1).
From this point, simultaneous observations of
migrating seabirds were collected by an observer
and a radar operator. The radar antenna and
observers were at the same vantage point. Observers scanned the sea with and without the aid
of binoculars (Victory FL 10 × 42 T; Zeiss,
Oberkochen, Germany) and telescopes (Zeiss
20–60 × 85). Each seabird flock detected was
identified to species level and its distance from
the coast was estimated by the observer who
then communicated the position of the flock
and its flight direction to the radar operator.
If the target was found on the screen, distance
was also measured simultaneously by an Sband surveillance radar (FR-2137SBB, Furuno,
Nishinomiya, Japan; peak power output 30 kW,
variable pulse 0.07- 1.0 jis, transmitting at
3050 MHz) located on a platform 10 m above
sea level. Distance estimates were made when
targets crossed an imaginary line perpendicular
to the coast at the observation point.
At the beginning of each observation period,
the radar operator adjusted the radar parameters
(range, gain, and sea and rain filter) to optimize
the radar picture. We combined both the range
function and the off-center function, placing
the origin (antenna position) in the lower part
of the screen, to reach a scanning radius of
either up to 4.5 km (short range) or 9.1 km
(long range), with scanning radius alternated
with each observation period. Gain, the receptor
sensitivity to the energy reflected, was adjusted
from 45 to 100, where 100 corresponded to the
maximum sensitivity. Sensitivity was between
51 and 70 75% of the time, between 45 and
50 10% of the time, and between 71 and 100
15% of the time. The sea clutter filter reduced
Fig. 1. Details of the African and Spanish coastline in the area of Tarifa. Note the characteristic narrow
funnel-shape of the Strait of Gibraltar. Tarifa Island is the most efficient location for surveying seabirds the
Strait of Gibraltar from the coast (Programa Migres 2009).
the clutter produced by waves (0 = off; 100 =
maximum). However, using this filter may result
in the loss of weak echoes. The best setting was
achieved when the size of the spots produced by
sea clutter on the radar screen was minimized
while weak echoes unequivocally produced by
flying birds remained visible. During our study,
the sea clutter filter was set at values ranging
from 0 to 40, with the filter set between 11 and
30 75% of the time. The rain filter (range = 0–
100) reduced the clutter produced by the rain,
and was always set at values <30 (and was set at
zero 90% of time).
Data were collected under a wide range of
meteorological conditions, and visual observations were made by three experienced observers.
All observers had censused seabirds for more
than 4 years at the study site, were familiar with
the bird species and environmental conditions
occurring in the study area, and had worked
with the radar for 4 months before the onset of
this study.
We restricted our analyses to the six most
abundant seabirds, including Northern Gannets
(Morus bassanus), Cory’s Shearwaters (Calonec-
tris diomedea), Balearic Shearwaters (Puffinus
mauretanicus), Atlantic Puffins (Fratercula arctica), Razorbills (Alca torda), and Great Skuas
(Catharacta skua). Due to the difficulty of identification and the frequent occurrence of mixed
flocks, Atlantic Puffins and Razorbills were considered together and referred to as auks. For each
sighting, observers recorded species, flock size,
flight direction, and estimated distance.
Each hour, we recorded the speed and direction of wind at the observation site. After
examining the frequency distribution of wind
directions during 2006 at the Tarifa meteorological station, wind directions were categorized as either east or west because winds came
from these directions 94% of time. Easterly
winds (51.2% of records) had a mean direction
(±1 SD) of 79.8 ± 22.2◦ , and westerly winds
(42.8% of records) a mean direction of 271.0 ±
39.8◦ . All flying birds were observed heading
either east or west. Flight directions were categorized as either with a headwind or tailwind
(Spear and Ainley 1997). Visibility was assigned
to one of three categories based on how clouds,
haze, and light levels affected visibility of the
African coast located 14 km away: (1) good, with
the African coast clearly visible, (2) moderate,
with only the silhouette of the coast visible, and
(3) bad, with the African coast not visible, but
the sea surface visible at a distance of about 7 km.
No observations were made when boats, which
typically follow a route along the center of the
strait, could not be seen.
Although radar records were used to calibrate
visual estimates, radar measurements are not
free from error. The technical specifications of
our radar establish an inherent measurement
error of 1%, with a maximum error of 30 m.
Furthermore, birds appear on a radar screen
as a two-dimensional echo instead of a onedimensional point, especially when in flocks. We
measured the distance to the center of the echo.
Thus, there might be a systematic error in these
radar distances because the size of an echo on the
screen could vary with the distance to the target
due to the level of energy reflected. To test the
magnitude of this error, we randomly selected a
sample of 100 targets. We measured the echo size
on the north–south axis on the radar screen with
the aid of a geographical information system
(ArcView 3.2.; Environmental Systems Research
Institute 1999). We finally examined whether
echo size varied with distance to the target as
measured by radar.
Statistical analyses.
The error of the
visual estimate of distance was calculated as
Error = |radar estimate – visual estimate|/radar
estimate. Only birds or flocks that were detected
visually and had distances estimated with radar
were included in the analysis. We did not assume
that the relationship between visual estimates of
distance, or their error, and the corresponding
radar estimates were linear. Therefore, we analyzed this relationship with generalized additive
models that may fit nonlinear, smooth functions
(Hastie and Tibshirani 1990).
Second, we examined whether observer identity, bird species, bird behavior (flock size and
flight direction relative to wind direction), and
meteorological conditions (wind speed and visibility) influenced the accuracy of visual estimates
of distance. We used visually estimated distances
as the dependent variable and included the
distance estimated by radar as a covariate so that
the effect of predictors was assumed to explain
measurement errors. We built one model for
each predictor and compared their fit to the data
with the Akaike’s information criterion (AIC).
We then examined whether the fit improved
when two or more predictors were combined
in the same model. We selected the most parsimonious model as that having the lowest AIC
value (Burnham and Anderson 2002).
Analyses were performed with the package
“gam” in R 2.6.2 (R Development Core Team
2008). We built models assuming a gamma distribution, using the log-link function and thin
plate regression splines as smooths functions.
We allowed for a maximum of three degrees of
freedom for the smooth functions. Values are
reported as means ± 1 SD.
RESULTS
Observers made 1173 sightings that were
subsequently located by radar, including 402
Northern Gannets, 375 Cory’s Shearwaters, 265
Balearic Shearwaters, 117 auks, and 14 Great
Skuas. Overall, 42.4% of sightings were single
birds and the rest were flocks.
We made 3.4% of observations during calm
conditions (wind speed < 0.5 m s−1 ), 44.9%
during periods with easterly winds (range = 0.8–
15.8 m s−1 , mean = 3.9 ± 2.8 m s−1 ), and 51.7%
during periods with westerly winds (range =
0.6–9.4 m s−1 , mean = 3.6 ± 2.4 m s−1 ).
Visibility was good for 28.1% of the observations, moderate for 45.5%, and bad for
26.5%.
Radar estimates of distance to bird sightings ranged from 183 to 6913 m (mean =
1559 ± 1009 m), and visually estimated distances ranged from 100 to 7000 m (mean
1438 ± 978 m). Most contacts (91%) were
within 3000 m of the coast, as estimated by
radar, and, for further analysis, we truncated our
data at that distance.
The estimated size of echoes varied from 9 to
33 m (mean = 17.9 ± 7.8 m, N = 100 flocks)
for distances to the targets from 105 to 2950 m.
Echo size on the radar screen did not vary with
distance to the target (R 2 = 0.004, P = 0.65).
The error in distance estimates attributed to the
size of the echo was small, always <0.04, and,
because most targets passed within 500 m, this
error was often <0.02.
Visual estimates of distance were correlated
with the corresponding radar estimates (R 2 =
0.83, F = 1135, P < 0.0001, N = 1068;
Figs. 2A and B). The absolute mean difference
between visual and radar estimates of distance
Fig. 2. Relationship between radar estimates of distance to flying seabirds and corresponding visual estimates
in the range 0–3 km. The broken line indicates an unbiased relationship. (A) Data recorded by observer 1,
the most accurate observer. (B) Data recorded by observer 3, the least accurate observer.
increased with increasing distance, with differences of 141 m, 200 m, and 327 m for
distances 0–1, 1–2, and 2–3 km, respectively.
The error, however, decreased with distance,
with error values of 0.20, 0.15, and 0.13 for
the three distance intervals, respectively (overall
mean error = 0.17 ± 0.15).
Among the factors potentially affecting the accuracy of visual estimates of distance to seabird,
the most parsimonious model included observer
identity as the only predictor (Table 1), and
no model with two or more predictors had
a smaller AIC value. All observers tended to
underestimate distances, and underestimation
was higher at greater distances (>2 km; Figs. 2A
and B). Errors in estimating distance were less
for observer 1 (mean error = 0.13) than for
observers 2 and 3 (mean error = 0.18 and 0.20,
respectively) for all distances up to 3 km. Error
differences between observers were exacerbated
at greater distances. For the 2–3 km distance
interval, mean errors were 0.08, 0.19, and 0.20
for observers 1, 2, and 3, respectively.
DISCUSSION
We found that observers could estimate distances with reasonable accuracy, as reflected by
Table 1. Generalized additive models examining
the effect of four types of predictors, namely visual
observer, bird species, bird behavior (flock size and
flight direction), and weather conditions (wind speed
and visibility) on visual estimates of distances to
seabirds. Distances estimated with radar were fitted as
a covariate in all models, taking the form of a spline
with 3 df.
Predictors
AICa AAICb
Covariate only 14,785
91
2
Visual observer 14,694
0
3
Bird species
14,780
86
4
Flock size
14,781
87
5
Flight direction 14,784
90
6
Wind speed
14,781
87
7
Visibility
14,780
86
a
Akaike’s information criterion.
b
Akaike differences.
c
Akaike weights.
Model
w ic
1.7 10−20
1
2.1 10−19
1.3 10−19
2.9 10−20
1.3 10−19
2.1 10−19
the significant correlation between radar and
visual estimates. Furthermore, the error of visual estimates was relatively low (between 0.08
and 0.20, depending on the range of distances
and observer) and similar to errors reported
for visual estimates of distances to cetaceans
(Williams et al. 2007). The required level of
accuracy in distance estimates varies among specific ecological applications. However, Marques
(2007) suggested that errors less than 10% are
needed before applying estimates to distance
sampling methodology. Errors in distance estimates produce errors of a similar magnitude in
density estimates, that is, distance consistently
overestimated by 10% underestimates densities by 9%, and distances underestimated by
10% produce densities overestimated by 11%
(Buckland et al. 2001, 2004). Errors of visual
estimates of distance in our study were on
average higher than recommended for a reliable
use of distance sampling methods, and may
translate into unacceptably biased estimates of
population size. However, a number of authors
have suggested that such errors can be corrected
through calibration, specifically by using regression equations obtained from the comparison of
visual estimates of distances to seabirds with a
ground-truthing method (Chen 1998, Williams
et al. 2007).
Observer identity affected the fit between visually estimated distances and distance measured
by radar. As also reported in other studies (Baird
and Burkhart 2000, Buckland et al. 2001), the
three observers in our study tended to underestimate distances. Underestimation increased at
greater distances, which was also observed in
flagging experiments (Camp 2007). Moreover,
we detected an observer-specific effect of underestimation that produced a systematic error that
should be corrected before using data to estimate abundance (Buckland et al. 2001). In our
study, the most parsimonious model (model 2,
Table 1) may be used as a calibration model
to correct the only source of bias we found
(Mateos 2009). Differences in accuracy between
observers may not be attributed to their degree of
training or experience, which was similar among
the three observers, but to variation in visual
acuity or sharpness, perception of distance, or
other aspects of their sensory abilities. Less experienced observers would likely have produced
larger errors. Previous studies of the effect of
training on the accuracy of distance estimation
on land (Gibson and Bergman 1954, Gibson
et al. 1955) and at sea (Oien and Schweder 1992,
Schweder 1996, 1997) have clearly shown that
training improves estimation of distance. Baird
and Burkhart (2000) found a similar effect of
experience when censusing whales.
Calibration experiments involving the use of
radar or other ground-truthing methods can be
also useful for training observers and reducing
differences in distance estimates. The accuracy
of visual estimates might also be improved by
specific training programs, where observers are
told the distances measured with the radar after
they estimate the distance to the target. Such
training would help observers to better estimate
distances (Camp 2007).
For investigators without access to radarestimated distances, other methods are available
for training observers when measuring distances
(e.g., using buoys or floats placed at known
distances from the observation point) and we
encourage the implementation of training programs prior to field work (either with radar
or other methods) for improving the accuracy
of distance estimates. Furthermore, our results
suggest that, when possible, selecting observers
whose inherent ability to estimate distance is
best among all potential candidates is advisable.
Our results may be also useful for determining
strip width when data are taken in the field only
by distance intervals (Buckland et al. 2001). For
example, in our case, because the highest error
value was 0.2 within 3 km of the coast, trying
to establish strip widths narrower than 600 m
would not make sense.
Distance estimates in our study were not
species-dependent, even for species that varied
considerably in size (e.g., 398 g and 549 mm
wing span for Atlantic Puffins and 3010 g and
1850 mm wing span for Northern Gannets) and
flight behavior (e.g., fast-flapping auks, intermediate flap-gliding Northern Gannets and glideflapping Balearic Shearwaters, and dynamicsoaring Cory’s Shearwaters). Similarly, the accuracy of distances estimated visually was not
affected by weather conditions, flight direction,
or wind direction. Consequently, we suggest
that when no other method is practical, visual
estimates could be reliably used for a wide range
of seabird species and field conditions, provided
that observers are sufficiently trained.
Measuring distance with devices such as
telemeters and radar in the field is not free from
error and the possible consequences of this error
on the estimation of bird abundance are rarely
considered. Particularly when using radar, the
signal on the radar screen appears as a spot that
occupies a space generally larger than a bird or
flock in the field. Nevertheless, the magnitude of
this error in our study was low (generally <2%),
and did not substantially affect the validity of
distance measurements.
To statistically address measurement errors,
many observations are needed (Williams et al.
2007). On the other hand, collecting sufficient
ground-truthing data to calibrate visually estimated distances could be costly (Chen and
Cowling 2001). However, using radar, we found
it possible to obtain a considerable amount of
independent data, suitable for calibration, with
relatively little effort, and large samples permit
more reliable calibration than smaller samples.
We conclude that, when no other methods
are available to accurately measure distances in
seabird studies, surveys by experienced observers
can provide reasonably accurate distance estimates under a wide range of environmental
conditions and for different species. Our conclusions could be applicable to at-sea surveys for
seabirds when observers are on a moving ship, as
long as landmarks located at known distances,
measured with radar, are available. This might
be the case for studies from boats close to the
coast or where ship traffic is relatively common.
Finally, the ability of radar to detect low-flying
seabirds is limited when waves cause sea-clutter,
but radar can be used to calibrate visual observations that, in turn, can be obtained under
conditions when radar cannot be operated.
ACKNOWLEDGMENTS
This study was conducted within a collaboration agreement between the Migres Foundation and the University
of Cádiz. The radar facility was supplied by Ceowind
Capital Energy Off Shore Company. M. Mateos was supported by the Junta de Andalucı́a with a FPU fellowship.
We thank the Migres Foundation technical staff for their
help with the fieldwork and B. Bruderer for extensive
comments on early drafts. The manuscript was greatly
improved thanks to the suggestions of A.E. Burger, one
anonymous reviewer, and the editor.
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