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RESEARCH ARTICLE
Testing the Accuracy of Aerial Surveys for
Large Mammals: An Experiment with African
Savanna Elephants (
Loxodonta africana
)
Scott Schlossberg
1
*, Michael J. Chase
1
, Curtice R. Griffin
2
1Elephants Without Borders, PO Box 682, Kasane, Botswana, 2Department of Environmental
Conservation, University of Massachusetts Amherst, Amherst, Massachusetts, United States of America
*xanthocephalus@gmail.com
Abstract
Accurate counts of animals are critical for prioritizing conservation efforts. Past research,
however, suggests that observers on aerial surveys may fail to detect all individuals of the
target species present in the survey area. Such errors could bias population estimates low
and confound trend estimation. We used two approaches to assess the accuracy of aerial
surveys for African savanna elephants (Loxodonta africana) in northern Botswana. First,
we used double-observer sampling, in which two observers make observations on the
same herds, to estimate detectability of elephants and determine what variables affect it.
Second, we compared total counts, a complete survey of the entire study area, against
sample counts, in which only a portion of the study area is sampled. Total counts are often
considered a complete census, so comparing total counts against sample counts can help
to determine if sample counts are underestimating elephant numbers. We estimated that
observers detected only 76% ±SE of 2% of elephant herds and 87 ±1% of individual ele-
phants present in survey strips. Detectability increased strongly with elephant herd size.
Out of the four observers used in total, one observer had a lower detection probability than
the other three, and detectability was higher in the rear row of seats than the front. The hab-
itat immediately adjacent to animals also affected detectability, with detection more likely in
more open habitats. Total counts were not statistically distinguishable from sample counts.
Because, however, the double-observer samples revealed that observers missed 13% of
elephants, we conclude that total counts may be undercounting elephants as well. These
results suggest that elephant population estimates from both sample and total counts are
biased low. Because factors such as observer and habitat affected detectability of ele-
phants, comparisons of elephant populations across time or space may be confounded.
We encourage survey teams to incorporate detectability analysis in all aerial surveys for
mammals.
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 1 / 19
a11111
OPEN ACCESS
Citation: Schlossberg S, Chase MJ, Griffin CR
(2016) Testing the Accuracy of Aerial Surveys for
Large Mammals: An Experiment with African
Savanna Elephants (Loxodonta africana). PLoS
ONE 11(10): e0164904. doi:10.1371/journal.
pone.0164904
Editor: Alfred L. Roca, University of Illinois at
Urbana-Champaign, UNITED STATES
Received: April 8, 2016
Accepted: October 1, 2016
Published: October 18, 2016
Copyright: ©2016 Schlossberg et al. This is an
open access article distributed under the terms of
the Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: The study was funded by Paul G. Allen as
part of the Great Elephant Census. The funder had
no role in study design, data collection and
analysis, decision to publish, or preparation of the
manuscript.
Competing Interests: Authors Schlossberg and
Chase work for Elephants Without Borders, a non-
profit conservation and research organization.
Introduction
Worldwide, large mammals are under threat due to habitat loss and fragmentation, overhar-
vest, and human-wildlife conflict [1–4]. Because resources for conservation are limited, accu-
rate population estimates are needed to determine trends in mammal populations and to guide
interventions for maximum benefits [5]. For declining species, inaccuracy or bias in population
estimates is not just an academic issue but can actually hinder conservation by causing misallo-
cation of scarce resources [6]. In time series of population estimates, poor survey data can
introduce spurious trends or obscure real ones [7–9]. Thus, determining the accuracy of survey
methods for mammals is critical.
Because of their large geographic ranges, in open habitats, large mammals are typically
counted via aerial sur vey (e.g. [10]). An important but rarely acknowledged assumption of
standard aerial surveys is that all animals in the survey area are detected [5,11]. Past research,
however, suggests that aerial sur veys may underestimate numbers. Caughley [11] reported that
just 61% of large mammals were detected on aerial surveys, and subsequent studies have found
similar results [12–14]. Observers can miss animals on aerial surveys for a variety of reasons
including lack of skill or training, fatigue, inattention, dense vegetation, the demands of count-
ing multiple species,interference from the sun, and excessive speed or altitude in the survey
aircraft [15–18].
Aerial surveys for large mammals usually follow one of two sampling schemes: total counts
or sample counts [19]. Total counts involve counting all animals in the sur vey area by flying
along closely spaced transects. Sample counts survey a subset of the study area, usually 5–20%,
by flying widely spaced transects with narrow sur vey strips and then extrapolating to the larger
survey area. Though sample counts are asymptotically unbiased, the population estimate from
any individual count will deviate from the true value due to sampling error [20]. Thus, total
counts are often considered more precise and accurate than sample counts [21,22]. Nonethe-
less, almost no studies have tested the assumption that total counts are more accurate. Total
counts in open habitats typically have transect spacing of 1 km [23], which implies that observ-
ers must search 500 m on either side of the plane. Experiments have shown that the number of
animal detections per unit area surveyed decreases with strip width [11,15]. Given the wide
usage of total counts in aerial surveys, the possibility that total counts are undercounting ani-
mals needs to be tested.
Today, survey protocols for many animals and even some plants incorporate corrections for
detectability (e.g. [24–26]), but for large mammals, detectability is often ignored. To improve
the reliability of data on mammal populations and aid in conservation efforts, we tested the
accuracy of aerial surveys for large mammals using African savanna elephants (Loxodonta afri-
cana) as a study species. We used double-observer aerial surveys to estimate detectability of ele-
phant herds and determine what variables affect it. We also compared total counts with sample
counts for the same survey areas to determine if total counts are more accurate. Our goals were
to determine the detectability of elephants and effects of covariates such as observer, habitat,
and flight speed on detectability. We also sought to provide suggestions for improving future
elephant surveys, and to learn whether or not elephant population estimates show any system-
atic bias.
Materials and Methods
Study area
We conducted aerial surveys for elephants in the Okavango Delta of northern Botswana. Our
study area consistedof five study areas or strata ranging from 236 to 545 km
2
in size (Fig 1).
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 2 / 19
Each stratum encompassed part or all of a concession used for wildlife viewing. On these sites,
elephants occur in a variety of habitats including marsh, grassland, savanna, woodland, and
shrubland. Human impact on these sites was minimal, though there were jeep tracks and safari
camps in some strata. Permission to fly surveys was granted by the Botswana Department of
Wildlife and National Parks.
Double-observer surveys
To estimate detectability, we used double-observer surveys in which two observers on the same
side of the aircraft independentlyand simultaneously counted elephants in the same survey
strip [27]. There were three possible outcomes for each herd observed: a) seen by the front
observer only, b) seen by the rear observeronly, or c) seen by both observers.By analyzing the
frequency of these outcomes, one can determinethe probability of detecting a herd and test
covariate effects on detectability (see below).
We flew double-observerflights in a GippsAero GA8 Airvan, which was ideal for our pur-
poses because of its large windows and four rows of two seats. During surveys, we had two sets
of paired observers,one pair on each side of the aircraft. The pilot and the front recordersat in
the front two seats. In the second row were two “front” observers, one on either side of the
Fig 1. Study areas for elephant surveys in Botswana. Stratum names are shown in italics. Thick gray lines
denote transects spaced 2 km apart used in sample countswith double-observer sampling. Transects used in
helicopter total counts were 1 km apart and followed both thick gray lines and thin gray lines. Shaded area in
lower figure indicates the Okavango Delta.
doi:10.1371/journal.pone.0164904.g001
Detectability of African Elephants on Aerial Surveys
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plane, and in the third row were two “rear” observers. The rear recorder sat in the fourth row.
Note that we refer to observers as “front” and “rear” throughout based on their relative posi-
tions; the observers were actually in the second and third rows of seats. One assumption of our
analysis is that front and rear observersmake observations independently, withoutcuing on
the other observer. To visually isolate front and rear observers,we hung an opaque cloth from
the cabin ceiling behind each front observer. Each crew member wore headphones connected
to an intercom system. The rear recorder and observers had an isolated intercom system so
that front and rear observers couldnot hear one another speak.The four observers rotated
positions between days so that each observer conducted at least one day of surveys in each seat.
Double-observer flights were flown using the sample transect method, a standard sampling
scheme for aerial surveys [19]. In each of the five strata, we established a setof parallel transects
spaced 2 km apart (Fig 1). The pilot flew along these transects sequentially, with the direction
of travel alternating between adjacent transects, and observers recorded animals in strips on
either side of the plane. Metal wands attached to the wing struts demarcated the strips, which
were calibrated on the ground to be 200 m wide at 91.4 m altitude. Because realized strip width
can vary with the observer’s head position, we placed tape on each window to ensure consistent
eye position. We flew a series of calibration flights to determine actual strip widths, following
the procedures of Frederick et al. [23].
Double-observer surveys took place from 28 July to 1 August 2014, during Botswana’s dr y
season. Deciduous plants were leafless at this time, allowing good viewing conditions. Also, ele-
phant movements are relatively restricted at this time [28]. We surveyed 1–2 strata per day.
The stratum surveyed on day 3 was surveyed again on day 5, when observers recorded only ele-
phant herds (see below).Surveys took place between 0900 and 1300 hrs. The pilot was
instructed to fly at 91 m above ground and 180 km/hr. We used a custom data logger to record
groundspeed as measured by a GPS receiverand altitude as measured by a laser altimeter.
Transects averaged 16.4 km in length (range: 2.4–31.3 km) and required a mean of 5.8 minutes
to survey.
The pilot navigated by GPS, and the front recorder monitored the GPS to determine when
transect start/end points were reached. The front recorder used hand gestures to signal the rear
recorder to tell the rear observers to start or stop counting. While on transects, observerscalled
out sightings by species and herd size,and the recorders obtaineda GPS fix for each herd and
recorded the sighting on a data sheet. We used digital audio recorders connected to the inter-
com systems to verify the written observations. The plane was equipped with four high-resolu-
tion digital SLR cameras, one for each observer, mounted in the windows and focused on the
survey strips. Observerswere instructed to use a remote shutter button to photograph each
herd sighted. We used photographs to reconcile observations between front and rear observers
and to correct herd-size estimates.
During the first four days of surveys, observers were instructed to count all medium and
large mammal species in the survey strips. We included species besides elephants because most
aerial surveys in Africa are multi-species surveys. To determine if searching for multiple species
affected elephant detections, on the fifth and final day of surveys, observers were instructed to
search for only elephants and no other species.
The four observers varied substantially in previous experience with aerial surveys. Observers
1 and 3 had ~1000 hrs of experienceconducting aerial surveys inAfrica. Observer 2 had ~150
hrs of previous experience. Observer 4 was highly experienced in identifying large mammals in
southern Africa but had no previous experience with aerial surveys. Before data collection
began, observer 4 received 5 hrs of airborne training in conducting surveys to increase his
familiarity with the procedures.
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 4 / 19
Total counts
We conducted total counts on the same five study sites used in the double-observer study.
Total counts were flown in a Robinson Raven helicopter with a crew of four: pilot, recorder,
and two rear-seat observers (1 and 2 from the double-observer study). We flew surveys with
the rear doors removed to facilitate viewing. As above, observers called out sightings of all
medium and large mammals seento the recorder who transcribed the data. The recorder also
made sightings opportunistically. We conducted total counts the week prior to the double-
observer counts, between 21 and 27 July 2014.
To ensure complete coverage of the study area, the helicopter flew along transects spaced 1
km apart rather than the 2 km used in the double-observer study. Researchers recommend
spacing of 1 km for total counts in open African habitats [23]. The total counts used the same
transects as the double-observer counts as well as an additionaltransect between each pair of
double-observer transects (Fig 1). No survey strip was delineated on total counts; observers
counted all wildlife in view. The helicopter deviated from the transects when obser vers needed
to obtain better views of herds. Each observer had a camera to photograph larger herds for
photo correction. Because, however, the helicopter could hover and circle as necessary to count
animals, photographs were unnecessary for most herds.
Double-observer analysis
Our unit of analysis for the double-observer analysis was the herd, as research has shown that
herd-forming animals tend to be detected as a group rather than individually [29]. Most ele-
phant herds observed were well-defined and clearly separated from one another. Where we
were uncertain about herd boundaries, we arbitrarily divided groups into separate herds where
they were separated from one another by the width of one photograph (approximately 500 m),
with no intervening elephants. Using photographs (available for 91% of herds observed), we
matched herd observationsbetween front and rear observers to determineif a herd had been
seen by one or both observers. For observations without photographs, we used the sexes (bull
vs. breeding herds), herd sizes, and times of the GPS fixes for the front and rear observers to
align sightings.
We also used the photographs to correct observers’ estimates of herd size. First, we corrected
each observer’s herd size estimates independently, ignoring any information from the other
observer on the same side of the plane. We only reduced herd size estimates below the original
estimate if a photograph clearly showed the entire herd. If individualanimals were potentially
left out of the photograph or obscured by vegetation, we did not lower a herd-size estimate. For
small herds, where the original herd size estimate was 6, we assumed that observers counted
accurately and did not adjust herd counts downward [30]. Otherwise, we adjusted herd counts
based on the number of individuals visible in the photographs.
After we independentlycorrected herd sizes for each observer, we reconciled the herd size
estimates for herds seen by both observers. If no photos were available for either observer, we
simply took the mean of the two estimates, rounding up for fractions.If photos were only avail-
able for only one observer, we used the photo-corrected estimate. If photos were available for
both observers, we attempted to combine the two photographs to determine the total number
of individuals present. Because photographs from each observer were usually taken at slightly
different times, the distinct viewing angles allowed us to determine which individuals were
missed by each observer for some herds. After accounting for each individual, we used the
count of all individuals based on both photographs as the herd size.
We modeled detectability with the closed-population recapture model developed by Hug-
gins [31,32]. This model uses maximum likelihood to estimate the probability of detection by
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 5 / 19
conditioning on the unknown number of herds missed by both observers. The Huggins model
allows detectability to be parameterized as a logistic function with continuous or categorical
covariates for individual herds. The basicequation for the detectabilityfunction was
pi¼ ð1þeXiβÞ1, where p
i
is the detection probability for herd i,X
i
is a row vector of covari-
ate information for herd i, and βis a column vector of coefficients to be estimated. We imple-
mented models using Program MARK [33].
Based on previous research, we identified several variables that could affect detectability of
elephants (Table 1). We used a two-stage information-theoretic process to determine which
variables were supported by the data [34,35]. In the first stage, we pre-screened all variables
against a constant-only model. Most of the pre-screened variables could be modeled with a sin-
gle parameter plus an intercept, but some, such as the model with distinct detection probabili-
ties for each observer, had multiple parameters. Variables that were pre-screened fell into seven
categories (Table 1). For position in the plane, we tested 1) front versus rear row because visi-
bility may differ by row, 2) an effect of the rear-left seat because that window was slightly
smaller than the other observers’ windows,and 3) all four seats individually [17]. Models for
observer effects included a model with unique parameters for each of the four observers and a
model for each observer in which that obser ver had a different detection probability than the
other three. We used this formulation because we expected observer 4, with no previous experi-
ence, to have less ability to detect elephants than the other three observers [16]. We modeled
four different types of fatigue: within transects (time since start of transect), within days (time
Table 1. Covariates screened for effects on detectability in double-observer aerial surveys of ele-
phants in northern Botswana.
Category Model Description K
Observer All 4 observers Separate parameter for each observer 4
Obs. 1 Observer 1 vs. observers 2, 3, and 4 2
Obs. 2 Observer 2 vs. observers 1, 3, and 4 2
Obs. 3 Observer 3 vs. observers 1, 2, and 4 2
Obs. 4 Observer 4 vs. observers 1, 2, and 3 2
Position in plane Side Left vs. right side 2
Rear-left Rear-left seat vs. all others 2
Row Front vs. rear rows 2
Each position distinct Separate parameter for each position 4
Fatigue Across days Linear effect of day number 2
Within day Linear effect of time since start of day 2
Within transect Linear effect of time since start of transect 2
(Within day) *(within
transect)
Interaction between time of day and time since start of
transect
2
Herd size Herd size Linear effect of herd size 2
All species vs.
elephants only
Elephants only Parameter for day when only elephants were counted
vs. days counting all species
2
Flight parameters Speed Linear effect of ground speed 2
Height Linear effect of height above ground 2
Speed *height Speed by height interaction 2
Direction Aircraft heading: north, south, east, or west 4
Sun Sun elevation Linear effect (degrees) 2
Relative azimuth Sun azimuth relative to observer 2
Elevation *relative
azimuth
Interaction between azimuth and elevation 2
K, number of parameters. All models were linear on a logistic scale.
doi:10.1371/journal.pone.0164904.t001
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 6 / 19
since beginningof day’s surveys), across days (number of days since day 1), and an interaction
between the within-transect and within-day effects, testing if within-transect fatigue increased
as the day progressed [18]. Because the final day’s sample included only elephants, we tested
effects of counting elephants versus all species. Past research has shown that flight parameters
can affect detectability, so we tested groundspeed, altitude, and an interaction between the two
[15]. Because larger groups have been more detectable in past studies, we tested a linear effect
of herd size [17]. Finally, we tested whether or not the azimuth of the sun and its height above
the horizon affected detectability [18].
We tested each variable in Table 1 against the constant-only model by comparing values of
Akaike’s information criterion corrected for small sample size (AIC
c
) [36]. Variables with a
lower AIC
c
than the constant-only model passed the initial screening. In the second stage, we
generated models with all possible additive combinations of variables that passed the first
stage. We excluded models with redundant parameters, such as a model with parameters for
front vs. rear position and parameters for each of the four seats. Our primary goal was to deter-
mine which variables affected elephant detectability, not to choose a single best model. Thus,
we ranked the final models by AIC
c
and then used model-averaging on the top 90% of models
by weight to make inferences about variables. We considered variables supported if they had
weight of evidence (sum of Akaike weights) >0.5 and strongly supported if they also had
model-averaged parameters with 85% confidence intervals (CI) that did not include 0 [37].
We next attempted to estimate the proportions of herds and individualspresent in the sam-
ple strips that were detected by each observer (not to be confused with the probability of detect-
ing a single herd, which we refer to as “detectability” or “detection probability”). This required
us to estimate the number of herds and individuals missed by observers. We used the model-
averaged parameter estimates to calculate detectability for each observer and herd size. We
then estimated the total number of herds in the strip for each observerand herd size as m
i
=n
i
/
p
i
, where n
i
is the number of herds of size iobserved, p
i
is the detectability for herd size i, and
m
i
is the corrected number of herds present [31]. The estimated proportion of herds detected
by an observer was ∑
i
n
i
/∑
i
m
i
. We used the delta method to estimate the SE of this quantity.
We used similar calculations to determine the overall proportion of individual elephants pres-
ent detected by each observer. Multiplying the counts of herds, mand n, by itransforms them
to counts of individuals. Thus, the proportion of individualsdetected was ∑
i
n
i
i/∑
i
m
i
i. Again,
we use the delta method to estimate the SE of the proportion of individuals detected.
To determine effects of habitat on detectability, we developed a simple scheme to classify habi-
tat around elephant herds (Table 2). These categories were meant to be heuristic rather than a
detailed description of habitats. Vegetation in the Okavango Delta is highly heterogeneous and
patchy on multiple scales. Thus, we chose to focus on only the habitat type immediately around
each elephant, defined as a radius of one body length from each animal. For herds where individual
Table 2. Categories used to classify habitat around elephants and number of herds observed in each
category on double-observer aerial surveys.
Category Description Number of herds
water open water 4
bare ground no vegetation 4
low grass leaving elephant legs at least partially exposed 51
open shrub woody plants up to height of adult; canopy cover <50% 117
open tree woody plants taller than adult; canopy cover <50% 55
tall grass completely covering the legs or taller 12
closed shrub woody plants up to height of adult; canopy cover >50% 27
closed tree woody plants taller than adult; canopy cover >50% 8
doi:10.1371/journal.pone.0164904.t002
Detectability of African Elephants on Aerial Surveys
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elephants were found in different habitat types, we used the modal type for the herd. In cases of
ties, we used the habitat that we considered more open a priori (closer to the top of Table 2).
Because some habitat categories had too few observations for analysis, we eliminated or
merged categories with <10 observations. We combined “closed tree” with “closed shrub,” as
both had >50% woody cover. We also combined “bare ground” with “low grass,” as both had
no cover obscuringthe view of the elephant’s body. We excluded “water” from the analysis
because there was no analogous category with which to combine it.
Some elephant herds were not photographed, and the Hugginsmodel does not allow for miss-
ing data, so we could not analyze habitat effects along with other covariates. Instead, we applied
the results from the above analysis of elephant detectability done with the full dataset. We used
AIC
c
to compare 1) a “null” model including only the supported variables from the two-stage
analysis described above and 2) a model including those variables and the five modified habitat
categories. We treated “low grass/bare ground” as the reference category in the analysis.
Total and sample count analysis
As an additional way of determining the accuracy of elephant sur veys, we compared popula-
tion estimates from the helicopter total counts with those form the double-observersample
counts. To analyze the total count data, we first corrected herd-size estimates with photo-
graphs. Because the helicopter was able to circle herds, photos were taken for only 22% of the
659 herds obser ved. For those herds, we followed the same rules in photographic correction as
we did for the initial corrections in the double-observerstudy. To estimate population sizes for
total counts, we simply summed the corrected herd sizes for each stratum. Total counts do not
have an associated variance.
To compare total counts with sample counts, we treated the double-observer flights as sam-
ple counts. Typical sample counts have just one observer on each side of the plane. Because,
however, we had two observers on each side of the airplane, we were able to compute three
different sample estimates for each stratum: one for front observers’ sightings, one for rear
observers’ sightings, and one for combined sightingsof front and rear observers.This was
advantageous because detection probabilities differed between the front and rear rows, so we
could compare population estimates for each row with the total count. We computed popula-
tion estimates for both rows of seats combined by including all herds sighted by at least one
observer. We did this because combining the front and rear observations leads to a sample-
count estimate that comes close to controlling for detectability. The detection probability for
front and rear observers combined is 1 – (1 – p
front
)(1 – p
rear
), where pis the detection probabil-
ity for a position. If p
front
and p
rear
are >0.8, the combined probability should be >0.94, which
allowed us to compare the total count against a sample count with high detectability.
For front, rear, and all observers, we used Jolly’s [38] estimator for unequal transects to esti-
mate population sizes and their variances for each stratum. Accordingly, elephant density in a
stratum is the total number of elephants counted divided by the area sampled. To compute the
area sampled, we used position-specific strip widths corrected for height above ground level by
transect [19]. For the stratum surveyed on day 3 and day 5 (Vumbra; see Fig 1), we computed
separate population estimates for each day. For each stratum, we compared the sample esti-
mates with the total count for that stratum with one-sample z-tests.
Results
Double-observer sampling
Over five days of double-observer sampling, we recorded 308 elephant herds, averaging 5.5 ani-
mals per herd (range: 1–69). Individual observers recorded between 119 and 135 herds each.
Detectability of African Elephants on Aerial Surveys
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Of the 22 variables that we initially screened for effects on detectability (Table 1), we
retained 5 for further testing: front vs. rear row, seat position (4 separate estimates), rear-left
seat vs. other seats, observer 2 vs. the other three observers, and herd size (S1 Table). We tested
those five variables in all possible additive combinations to create a final set of 16 models.
There was considerablemodel-selection uncertainty in the finalmodel set, as the top model
had weight = 0.50 (S2 Table). Model averaging indicated strong support for effects of herd size,
front vs. rear seat, and observer 2 on detectability (Table 3). Observer 2 had the third most
experience of the observersin our study.
Herd size was the best-supported variable in the final models (weight = 1.0, Table 3); detec-
tion probability increased with elephant herd size (Fig 2A). Estimated detectability was 0.65 for
a single elephant and increased to near 1 for herds of >25 elephants. Because herd size was so
important, we plotted all other covariates in combination with it. A differencein detection
probabilities between the front and rear seats had strong support, with detectability lower in
the front seats (Fig 2B;Table 3). We also found strong support for observer 2 being less able to
detect elephants than the other three observers (Fig 3;Table 3). For herds of >25 elephants,
however, estimated detection probabilities approached 1 for all four observers. We found little
support for separate detection probabilities for each seat in the plane and no support for a sepa-
rate detection probability for the rear-left seat (Table 3).
Using model-averaged parameters, we estimated that obser vers detected a mean of 76% ±
SE of 2% of all herds and 87 ± 1% of all individuals present in the survey strips (Fig 4). Though
our models showed strong support for observer 2 having a lower detection probability than the
other three observers, this observer’s estimated proportions of herds and individuals detected
were not significantly different from the other observers’ (herds: all |z|0.80, all P0.42;
individuals: all |z|1.06, all P0.29).
For the 274 elephant herds with vegetation data, a model including five habitat categories,
herd size, observer 2, and front vs. rear seat had lower AIC
c
than a model with just herd size,
observer 2, and front vs. rear seat (ΔAIC
c
= 2.17). In the model with habitat, the parameters for
closed tree/shrub, open shrub, and open tree all had 85% CI that did not include 0, indicating
that detectability in those habitats is lower than in grass/bare ground (Table 4;Fig 5). The
parameter estimate for tall grass was negative but had 85% CI overlapping 0 (Table 4). As with
other covariates, differences in detectability between habitat categories were apparent only at
low herd sizes; the model predicted that nearly all herds of >25 individuals would be detected
in all habitat types (Fig 5).
Table 3. Model-averaged parameter estimates for variables that passed the initial screening in the double-observer experiment.
Model Variable Estimate SE 85% CL Sum of model weights
Herd size Herd size 0.18 0.04 0.12–0.24 1.00
Front vs. rear Rear row 0.83 0.20 0.50–1.16 0.78
Observer 2 Observer 2 -0.41 0.23 -0.78 –-0.03 0.63
Individual seat Rear-left seat 0.63 0.28 0.17–1.08 0.22
Front-right seat -0.03 0.29 -0.50–0.44 0.22
Rear-right seat 1.04 0.35 0.46–1.62 0.22
Rear-left seat Rear-left seat n/a 0.00
CL, confidence limits. The model-averaged estimate for rear-left seat is not shown because the variable was not present in any of the top 90% of models by
weight. Variables in italics had strong support from the data.
doi:10.1371/journal.pone.0164904.t003
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Total counts and sample counts
We found no significant differences between total counts and sample counts for any stratum or
combination of rows (front, rear, or both) on the sample counts (all |z|<1.75, all P>0.08; Fig
6). When we summed population estimates over all five strata, sample-count estimates were
6% greater than total-count estimates for front observers, 20% greater for rear observers, and
28% greater for both observers combined. These differences, however, were not significant (all
|z|<1.69, all P>0.09; Fig 6).
Discussion
Overall, we estimate that observers misseda mean of 24% of elephant herds present in the sur-
vey strips. B ecause, however, larger herds tended to have high detectability, the estimated pro-
portion of individuals missed was lower at 13%. This suggests that our population estimates
based on sample counts were approximately 13% below the actual values. Population estimates
from sample counts were not statistically distinguishable from those based on total counts.
Estimates for sample counts, however, tended to be equal to or larger than those from total
counts. Because observers missed approximately 13% of elephants on sample counts, the
Fig 2. Model-averaged effects of (A) herd size and (B) front vs. rear observer on detectability of
elephants. Shading indicates ±1 SE. Estimates in (A) are averaged across front and rear seats. All
estimates are averaged across the four observers.
doi:10.1371/journal.pone.0164904.g002
Detectability of African Elephants on Aerial Surveys
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similarity between sample and total counts suggests that total counts are biased low as well.
These findings are consistent with past research indicating that observers on aerial surveys
miss some large animals [11]. Even animals as large as elephants are not all detected.
Because of their large size and tendency to occur in herds, elephants should be among the
most detectable species on aerial surveys. Consequently, our estimated 87% detection rate for
elephants may well represent an upper limit for land mammals and suggests that other large
and medium mammal species are even harder to detect. Thus, aerial surveys for mammals
should assume that their results are biased low by at least 10–15% and possibly more. More
research is needed to determine the amount of undercounting occurring for other species and
the factors affecting their detectability.
For conservation, undercounting is important to recognize because imperfect detectability
can induce spurious trends in time series [7,9]. For elephants, we can envision at least three
realistic scenarios where changes in detectability may confound trend estimation. As megaher-
bivores, elephants change vegetation by reducing woody cover and increasing grass and herba-
ceous growth [39]. Hypothetically, as elephants reduce woody cover over time, their
detectability would steadily increase and bias trend estimates upward. Likewise, where elephant
populations have declineddue to poaching, woody plant density may increase,thereby reduc-
ing detectability over time and biasing trend estimates downward. In some areas, intensive
poaching of elephants has led to the formation of large aggregations, as animals appear tojoin
Fig 3. Model-averaged effect of observer on detectability of elephants for selected herd sizes. Error bars indicate ±1 SE.
Estimates are averaged over front and rear seats.
doi:10.1371/journal.pone.0164904.g003
Detectability of African Elephants on Aerial Surveys
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together for safety [40]. This could lead to increasing detectability as populations are declining,
again confounding trend estimation.
Concerns about deterministic changes in detectability are not merely hypothetical. In a
recent study of antelope, a population decreasecoincided with a decrease in herd size that likely
reduced detectability [41]. Similarly, Hochachka & Fiedler [42] reported that the probability of
resighting birds declined over time, leading to overestimation of population declines. With ele-
phant populations declining across Africa [43,44], biased trend estimates could hinder conser-
vation and lead to misallocation of resources. Thus, assessing elephant detectability and
correcting counts for vegetation, observers, herd size, and other factors should become a stan-
dard part of survey protocols.
Fig 4. Number of elephant herds observed and estimated number missed (+ 1 SE) by herd size for each
observer. Numbers in text are the estimated proportion of herds and individuals present in the survey strips that were
observed on surveys (±1 SE).
doi:10.1371/journal.pone.0164904.g004
Table 4. Parameter estimates for habitat effects on detectability in the double-observer experiment.
Parameter Estimate SE 85% CL
Intercept (low grass/bare ground) 0.98 0.37 0.38–1.59
Closed tree/shrub -1.54 0.51 -2.37 –-0.70
Open shrub -0.87 0.41 -1.54 –-0.20
Open tree -0.94 0.45 -1.68 –-0.20
Tall grass -0.71 0.69 -1.84–0.43
doi:10.1371/journal.pone.0164904.t004
Detectability of African Elephants on Aerial Surveys
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Fig 5. Predicted detectability of elephants by habitat type around elephant herds (±1 SE). Values are
averaged over the four observers and the front and rear seats. For readability, “tall grass” and “open tree” are not
shown; detectability for those categories was very similar to “open shrub.”
doi:10.1371/journal.pone.0164904.g005
Fig 6. Elephant population estimates (±1 SE) by stratum for sample and total counts. Sample-count estimates are presented for rear observers, front
observers, and both rows combined on double-observer flights. The rightmost graph sums estimates from all strata but does not include the second double-
observer survey of Vumbra when only elephants were counted.
doi:10.1371/journal.pone.0164904.g006
Detectability of African Elephants on Aerial Surveys
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Factors affecting detectability of elephants
Herd size was, by far, the most important variable affecting detectability in the double-observer
study. Detectability, averaged across observers and rows, increased from 65% for a lone indi-
vidual to nearly 100% for herds of >25 elephants. Herd size also influenced other covariates;
effects of observer, habitat, and position in the plane were only apparent for smaller herd sizes.
Large herds appear to be highly detectable regardless of other mitigating factors.
Our finding is consistent with past studies reporting that larger groups are more detectable
in herd- or flock-forminganimals [17,18,30,45]. Presumably, this is because larger herds offer
observers more opportunities to detect at least one animal when scanning the survey strip.
Because bull herds (
x= 1.5 elephants in this study) tend to be smaller than breeding groups (
x
= 8.1 elephants), numbers of bulls are likely underestimated on many surveys, and sex ratio
estimates may be biased towards females as well. Consequently, changes in sex ratios or herd
sizes over time could affect the accuracy of trend estimates.
We were surprised to find a difference in detectability between front and rear observers,
which has not been reported previously. In the Airvan, the front observers were immediately
behind the wing struts, which may have interfered with observers’ ability to look forward for
herds. Rear obser vers were ~0.6 m further back, so their forward view may have been less
obstructed. In most aerial surveys, the observers sit in the second row of seats, immediately
behind the wing struts. Our findings offer the interesting possibility that having observers sit
further backin the aircraft may leadto more detections.This result, however, may be specific
to the Airvan we used, as Koneff et al. [17] found that effects of seating position on detectability
were aircraft-specific. Thus, effects of seating position on detectability should be tested in the
specific plane models used by surveyors.
Another surprising result of the double-observer study was that the four observers missed
similar proportions of elephants despite widely varying levels of experience. We found strong
support for a lower detection probability for observer 2,but the estimated proportionof herds
detected was just 2 percentage points lower for this observer than for the other three, and the
difference was not significant (Figs 3and 4). Observer 2 had ~150 hrs of previous survey expe-
rience. Observer 4, whose only previous experience with aerial surveys was 5 hrs of pre-study
training, had detectability indistinguishable from two observers with ~1,000 hrs of previous
experience. Despite these findings, we hesitate to claim that survey experience is unimportant.
Studies of other species also reported significant observer effects on aerial surveys [15,17].
Observer experience may be less important for elephants than other species, but additional
studies are needed to confirm this result.
The habitat immediately adjacent to elephants had a strong effect on their detections.
Detectability was highestin bare ground/low grass, lowest in closed-canopy shrubs or trees,
and intermediate in open trees, open shrubs, and tall grass. These findings echo a previous
study reporting that detectability on aerial surveys is inversely proportional to vegetation den-
sity [13]. Despite these results, habitat is little discussed when results of aerial surveys for ele-
phants or other mammals are reported. Perhaps this is because habitat cannot be controlled on
surveys. Some ecologists suggest that raw counts, uncorrected for detectability, can be used as
an index of abundance so long as detectability is roughly equal across surveys [46,47]. For fac-
tors such as observer or seat position, differences in detectability between sites or surveys may
be minimal if the same observers, aircraft, and seating arrangements are used. Habitat, how-
ever, may have more insidious effects. Our data indicate that the detectability of elephants will
be very different in areas with high and low woody cover. Because sample transects are usually
placed systematically but use a random starting point, the actual vegetation sampled in a stra-
tum may vary from survey to survey. In an area with heterogeneous vegetation, like the
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 14 / 19
Okavango Delta, random variation in the habitats sampled can introduce statistical noise to
population estimates due to differing detection probabilities. Likewise, seasonal changes in veg-
etation cover (e.g. wet vs. dry season) could substantially alter the detectability of elephants.
Thus, comparing raw population estimates across time or space could lead to erroneous con-
clusions about habitat quality or population status if habitat is not considered [8].
To what extent can we generalize from our double-observer study to other elephant surveys?
The ultimate goal of studies like ours is to generate correction factors to control for detectabil-
ity and obtain unbiased estimates of population size. Unfortunately, survey-specific factors and
the dearth of otherstudies examining detectabilityof elephants or other large mammals make
applying our findings to other surveys problematic. Each type of aircraft may produce different
effects of seating based on window sizes and wing strut placement [17]. Likewise, habitats will
vary regionally, so that the categories used in our study may not apply universally. We did not
find effects of height above ground or fatiguein our study, but other studies have, suggesting
that these variables cannot be ignored based on our findings alone [15,18,30]. Our transects
were relatively short (
x= 16.4 km), and there was little variation in flight altitude during sur-
veys (range = 88.7–98.1 m). Thus, our study may have had little power to detect effects of these
variables. More generally, the factors that affect detectability on aerial surveys may be too vari-
able or study-specific to apply universal corrections.Because detectability is likely to be variable
across studies or evenover time for the same study areas, comparisons of uncorrectedpopula-
tion estimates may be highly misleading in some cases [8,9]. Thus, detectability analysis should
be a part of each large-scaleaerial survey. Ideally, each survey would producea raw population
estimate and an estimate correctedfor detectability based on its own accuracyassessment.
One caveat of our analysis is that the Huggins model is useful for identifyingcovariates on
detectability but has limitations for estimating the number of herds missed and correcting
counts [48]. The model conditions on herds missed by both observers, but for missed herds,
covariate values such as herd size and habitat are unknown. Thus, the model bases the esti-
mated distributionof covariates for missed herds on that of observedherds. In reality, herds
missed and herds observed should have different distributions of values for covariates affecting
detectability [49]. For instance, missed elephant herds should be smaller on average than those
observed (Fig 5). Bayesian data-augmentation models can be used to model unobserved covari-
ate values [49,50]. For the top model in S2 Table, the Huggins model predicted that 21.6 ± 6.8
elephant herds were missed by both observers (unpublished results). A Bayesian data augmen-
tation model with the same variables predicted 9.0 ± 3.7 herds missed. When one’s goal is to
correct herd size estimates rather than simply to estimate detectability, data augmentation may
be preferable because it explicitly models the distribution of missed herds [50,51]. We plan to
explore specific methods for using double-observer counts to correct population estimates in a
future publication.
Total counts and sample counts
Before conducting this study, we hypothesized that total counts would produce higher popula-
tion estimates than sample counts. We expected that improved visibility from the helicopter
and its ability to hover and circle would allow observersto count elephants that were unavail-
able on the sample transects due to vegetation or other elephants blocking them from view. In
reality, we found that total-count estimates were not significantly different from the sample
counts. Sample-count estimates for front observers were 6% greater than total-count estimates,
yet the Huggins model estimatedthat front observersmissed 18% of elephants. These results
suggest that total counts underestimated elephant populations roughlyas much as sample
counts, if not more.
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 15 / 19
Total counts generally do not have a formal sampling strip. Because, however, our total
count transects were 1 km apart, observers had to search500 m on each side of the helicopter
to cover the entire stratum. Research on aerial surveys has shown that animal detections per
unit area decrease rapidly as strip width increases, with detectability greatest for strips of 100
m [11,15]. For a 500-m wide strip, Caughley [11] found that elephant detections may be
reduced by ~60%. This raises the possibility that total counts may simply require too wide a
survey strip for observers to locate all herds and, therefore, may underestimate populations. In
South Africa, a total count by helicoptermissed a mean of 8% of elephants in a fencedpopula-
tion [52]. Likewise, Norton-Griffiths [19] found that total counts of African buffalo (Syncerus
caffer) were 11% lower than the known population size. These findings support our conclusion
that total counts may produce population estimates that are biased low. Total counts do not
have sampling error, but our results indicate that total counts for elephants may have errordue
to missed herds.
One caveat with our comparison of sample and total counts is that surveys in the same
stratum were separated by 4–8 days (
x= 5.6 days). During this time, elephants could have
moved in or out of strata, potentially affecting our population estimates. Still, we have no rea-
son to believe that elephant movements during the intervening time were directional. The
study took place in the middle of the dry season, which runs from May to October. In
Botswana large-scale migrations occur at the beginning and end of the dry seasons [28,53].
Also, elephant movements in Botswana are more restricted during the dry season than at
other times of year [28]. Thus, our expectation is that random movements of elephants into
and out of each stratum should have been in balance and relatively infrequent. We cannot,
however, rule out the possibility that small changes in the actual elephant population affected
our results.
Conclusions
Analyses and study designs that account for detectability are now de rigueur in a variety of dis-
ciplines within ecology and conservation [29,48]. Our results show that even species as large as
elephants are subject to detectability issues. In the future, we suggest that aerial surveys for
large mammals incorporate some type of detectability analysis into their methods wherever
possible. Even if only a single aircraft and two observers are available, both observers can sit on
the same side of the plane and conduct double-observertrials on some strata. If sufficient
observers are available, one could argue that all aerial surveys should be conducted as double-
observer studies. If researchers lack the expertise needed to analyze double-observer data with
Huggins or data-augmentation models, the relatively simple estimator of Magnusson et al. [54]
can be used. An even simpler approximation would be to use the combined observations of
front and rear observers. The resulting population estimates, without any analysis of detection
probabilities, will be less biased than those from a single observer. Additionally, the assumption
that total counts are a complete census of a population and do not needto account for sam-
pling error needs to be questioned. Our findings suggest that total counts may, like sample
counts, be biased low, but a larger number of total and sample counts is needed to confirm this
result. One usefulapproach would be to conducta double-observer study withina total-count
framework (i.e., unlimited strip width). Elephants are likely to be among the most detectable of
all mammals, so other mammal species may have more serious detectability issues. Because
population estimates from aerial surveys for elephants are biased low and vary due to study-
specific factors such as observers and habitat, caution should be taken when interpreting the
results of surveys and analyzing trends.
Detectability of African Elephants on Aerial Surveys
PLOS ONE | DOI:10.1371/journal.pone.0164904 October 18, 2016 16 / 19
Supporting Information
S1 Data. Zip file with raw data used in double-observeranalyses and in the comparison of
helicopter total counts with sample counts.
(ZIP)
S1 Table. Results from pre-screening of variables predicting elephant detectability in dou-
ble-observer aerial surveys of African elephants. See Table 1 in text for descriptions of mod-
els. Models in bold had lower AIC
c
than a constant-only model.
(DOCX)
S2 Table. Results from final set of models predicting elephant detectability in double-
observer aerial surveys of elephants.
(DOCX)
Acknowledgments
We are thankful to the staff at Vulcan Inc.: Dave Stewart, Dune Ives and Lauren Kickham. We
appreciate the support provided by Jody Allen and Ferina Keshavjee. This study was endorsed
by the Botswana Government and we thank theDepartment ofWildlife and National Parks for
allowing us to fly these surveys. Honorable Minister Khama, Michael Flyman, Cyril Taolo,
Amo Keitsile, Elford Seonyatseng, Robert Sutcliffe, Tempe Adams, Kelly Landen, Marcus Han-
cock and the staff of Abu Camp and Wilderness Safaris are acknowledged for their assistance.
Author Contributions
Conceptualization:MJC.
Data curation: SS.
Formal analysis: SS.
Funding acquisition: MJC.
Investigation: MJC CRG SS.
Methodology: MJC CRG SS.
Resources: MJC.
Validation: SS.
Visualization: SS.
Writing – original draft: SS.
Writing – review & editing: MJC CRG.
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