A new use of technology to solve an old problem: Estimating the population size of a burrow nesting seabird

Abstract

Estimating the population of burrow-nesting seabirds is a challenging task, as human presence in the colony creates disturbances and can damage burrows and occupants. Here, we present a novel method using aerial photographs taken with Unmanned Aerial Vehicles (UAVs) to estimate the population size of a burrow-nesting seabird, the Black-vented Shearwater (Puffinus opisthomelas), on Natividad Island, Mexico. Our results provide a census of burrows in the colony, with very low detection error (5.6%). This is greater accuracy compared to other methods based on extrapolating results from sample plots to total colony area. We then combined this burrow census with ground truth data on occupancy to estimate population size. We obtained a population estimate of 37,858 and 46,322 breeding pairs for 2016 and 2017 respectively. The proposed method provides a cost effective and repeatable approach for monitoring numbers of burrows occupied in a colony, thereby enabling easier and faster estimates of population trends. We suggest this method can be valid for other burrow-nesting species in habitats without dense vegetation cover.

Full text

RESEARCH ARTICLE
A new use of technology to solve an old
problem: Estimating the population size of a
burrow nesting seabird
Yuri V. Albores-Barajas
1,2
, Cecilia Soldatini
3
*, Alejandro Ramos-Rodrı
´guez
1,3
, Javier
E. Alcala-Santoyo
1,3
, Roberto Carmona
1
, Giacomo Dell’Omo
4
1Universidad Auto
´noma de Baja California Sur. La Paz, B.C.S., Mexico, 2Berta maris, La Goleta 330, La
Paz, B.C.S., Mexico, 3Centro de Investigacio
´n Cientı
´fica y de Educacio
´n Superior de Ensenada–Unidad La
Paz, Miraflores 334, La Paz, B.C.S. Mexico, 4Ornis italica, Piazza Crati 15, Rome, Italy
*csoldatini@cicese.mx
Abstract
Estimating the population of burrow-nesting seabirds is a challenging task, as human pres-
ence in the colony creates disturbances and can damage burrows and occupants. Here, we
present a novel method using aerial photographs taken with Unmanned Aerial Vehicles
(UAVs) to estimate the population size of a burrow-nesting seabird, the Black-vented Shear-
water (Puffinus opisthomelas), on Natividad Island, Mexico. Our results provide a census of
burrows in the colony, with very low detection error (5.6%). This is greater accuracy com-
pared to other methods based on extrapolating results from sample plots to total colony
area. We then combined this burrow census with ground truth data on occupancy to esti-
mate population size. We obtained a population estimate of 37,858 and 46,322 breeding
pairs for 2016 and 2017 respectively. The proposed method provides a cost effective and
repeatable approach for monitoring numbers of burrows occupied in a colony, thereby
enabling easier and faster estimates of population trends. We suggest this method can be
valid for other burrow-nesting species in habitats without dense vegetation cover.
Introduction
Seabirds are among the most threatened of bird taxa, with 42% of 365 species listed as threat-
ened on the IUCN Red List [1]. Accurate population sizes and trends are challenging given
they often breed in remote sites, are long-lived life-history and have vast at-sea distributions
(2,3,4). For burrowing petrels, estimating colony sizes is even more difficult than for surface-
nesting seabirds because it requires accurate identification of usually cryptic burrows and
nocturnal behavior, where nest occupancy requires ground truth [2,3]. Nonetheless, robust
colony and population estimates are critical for understanding trends and informing conserva-
tion management of the status of burrowing seabird populations. Improving the accuracy and
repeatability of burrow counting methods, plus lowering the cost and likelihood of disturbance
to colonies, remains important to improve population size estimates and understanding popu-
lation trends.
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 1 / 15
a1111111111
a1111111111
a1111111111
a1111111111
a1111111111
OPEN ACCESS
Citation: Albores-Barajas YV, Soldatini C, Ramos-
Rodrı
´guez A, Alcala-Santoyo JE, Carmona R,
Dell’Omo G (2018) A new use of technology to
solve an old problem: Estimating the population
size of a burrow nesting seabird. PLoS ONE 13(9):
e0202094. https://doi.org/10.1371/journal.
pone.0202094
Editor: Andre
´Chiaradia, Phillip Island Nature Parks,
AUSTRALIA
Received: September 18, 2017
Accepted: July 29, 2018
Published: September 14, 2018
Copyright: ©2018 Albores-Barajas 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. Burrow density information is
stored at: https://figshare.com/s/
39b18e04c8e3222e57d7 and https://figshare.com/
s/34c33bcd0a526a531e34.
Funding: This research was funded by project
CICESE 691111 and by Ornis italica to CS. The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.

Several different methods have been used to determine population sizes of burrowing sea-
birds [4–6]. For burrowing petrels, one of the more common methods is to measure the
perimeter of the colony and then extrapolate burrow densities from a sample of plot within the
colony [7,8]. Basic extrapolation methods have been improved by predictive habitat modeling
[9,10], reducing bias due to habitat characteristics, and accuracy of occupancy estimates
improved through the use of burrow scopes [11]. Methods based on extrapolation also pro-
duce a measure of variance for colony size estimates. Where variance is high, this leads to high
uncertainty in estimates, and lower power to detect trends over time, thereby reducing the
value of these population estimates for conservation management [12].
In recent years, various field methodologies, technologies, and mathematical models have
evolved, allowing researchers to detect, with increased accuracy, variation and changes in pop-
ulation size or demographic parameters, including distance sampling combined with burrow-
scoping [13], thermal cameras, and CO
2
detectors [12], and acoustic monitoring to estimate
population size [4]. In recent years, the use of unmanned aerial vehicles (UAVs) or drones, has
exponentially increased and are now regularly used for wildlife monitoring, with reduced dis-
turbance of sites and enhanced precision when compared to more conventional methods [14,
15] for counting surface-nesting seabirds [16] and waterfowl [17]. Advantages of UAVs
include archiving of images for future comparison and analysis, often less time in the field
once the method is established, and less time in the colony itself, thereby reducing costs and
disturbance to wildlife [18,19]. However, to our knowledge, no attempt has been made to
count burrows from aerial photography and census-burrowing seabirds.
We used a UAV-based method to estimate the population size of burrow-nesting seabird,
the Black-vented Shearwater (Puffinus opisthomelas), on Natividad Island, Mexico. The UAV
collected imagery, allowing a complete count of burrows in the colony, which we supple-
mented with on-the-ground estimates of burrow occupancy to determine colony population
size. We compared the results of our UAV approach and other population estimates methods
to highlight the applicability of using aerial-based methods, and potential application to other
species.
Methods and background
Ethics statement
All applicable national and institutional guidelines for care and use of animals were followed.
Fieldwork was carried out under permits SGPA/DGVS/00321/16 issued January 2016 and
extension SGPA/DGVS/003843/16, issued in April 2016 and renewal SGPA/DGVS/00404/17
issued in January 2017.
Study species and study site
Natividad Island (27˚51010@N, 115˚10022@W; Fig 1) is five km north of Punta Eugenia in the
State of Baja California Sur, Mexico. The island is 8.65 km
2
, arid, with little vegetation [5]. Pre-
vious surveys estimated that the island supports 75,000 breeding pairs of Black-vented Shear-
water, or 95% of the world’s population [7]. The Black-vented Shearwater is currently listed as
Near-threatened on the IUCN Red List [20] and was down-listed from Vulnerable after con-
servation actions to remove critical threats, including eradication of feral cats between 1997
and 2001 [21]. No population estimates have been conducted since removing feral cats in 2001
[22]. The species also lacks a Conservation Action Plan and long-term monitoring program
[1].
Black-vented Shearwaters are present on Natividad Island 10 months of the year, arriving
in December and departing in September [23]. They nest in areas without tree cover and dig
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 2 / 15
Competing interests: The authors have declared
that no competing interests exist.

burrows in sandy soil that are usually deeper than one metre, with nesting chambers usually
located 1–2 m below the surface. A small percentage of birds also nest in rocky crevices [23].
In 1998–99, the shearwater colony was estimated to occupy 250 ha on the southeastern part
of the island [7]. The area of the colony was estimated, using a handheld GPS (15m accuracy)
and mapping the perimeter on foot [7].
Nest counting by aerial survey
In 2016, we visited Natividad Island three times to update population estimates: 7–14 Febru-
ary, 19 April to 12 May, and 9 June to 3 July. We also visited the island on 11–14 July 2017 to
determine the effect of UAVs on ground nesting seabirds. We used a DJI Phantom 3 Standard
UAV (China), which was flown in all four visits. The UAV is 49 x 49 cm, with the props
mounted, and approximately 20 cm in height. It is white and, when in flight, it has two green
lights and two red/orange lights. The UAV weighs 1216 g. The noise produced by the UAV at
2 m distance is 60 dB [15]. Photographs were stored on a Class 10, 32 Gb miniSD card and
downloaded after we have completed each day’s flights.
The camera is the standard camera for the DJI Phantom 3 Standard, with a f/2.8 focal point,
exposure time: 1/2,000 (at least), and ISO = 100. Each photo is 4,000 x 3,000 pixels, with a reso-
lution of 72 DPI and 32 bits of color. The camera was supported on a gimbal to avoid vibration
effects and was pointed 90 degrees downwards, parallel to flat ground.
The first two field trips were used for calibration of the UAV method. Data collected from
the UAV during the June 2016 visit were used for the burrow census. As the number of bur-
rows was expected to remain constant within one breeding season (i.e., no more burrows are
dug after the laying has started), we did not compare the variation among the three trips.
Fig 1. Location of Natividad Island, northwestern Mexico. The Black-vented Shearwater colony on the island is in
dark grey.
https://doi.org/10.1371/journal.pone.0202094.g001
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 3 / 15

However, occupancy was monitored during all three visits to account for within-season vari-
ability. We selected the highest occupancy figure of the three trips to determine the population
size. Non-breeders were considered in the population estimate.
To estimate detection error between aerial surveys and on-ground counts, we visited the
colony and counted burrows in 20 circular plots (4.37 m radius, area 60 m
2
) chosen randomly
in the colony. Concurrently, we collected aerial photographs of these same plots with the UAV
at a height of 30 m, and burrows identified later were counted. Each circular calibration plot
was marked on the ground with a flag in the center and a metric strip marking the diameter to
aid comparison between methods. Comparisons were then made between ground and aerial
burrow counts to determine the detection error. Two different persons counted either the bur-
rows from the aerial photograph or the plots on the ground, without exchanging information.
To establish a complete count of burrows in the colony, aerial photographs of the entire col-
ony were taken between 25 and 60 m above ground level. Altitude variation was largely an arti-
fact of navigating in the hilly terrain. Given the high resolution of the photographs (average
post-processing–see below 27,500 x 27,500 pixels, 96 DPI, and 32 bits of color), this height dif-
ference did not appear to affect detection probability, as we were able to zoom in considerably
before image resolution limited burrow detection. The center point of each photograph was
approximately 10–15 m apart, with each photograph overlapped at least 25%. The UAV was
flown at a speed of 2 m s
–1
and a photograph was taken every five seconds. Each photograph
contained metadata (30 variables, including GPS location, height, precision estimates, time,
date, and photo settings) that were used by the imaging software to locate and arrange all pho-
tos. Photographs were taken between 11 AM and 3 PM local time to reduce shadows, which
could be mistaken for burrows (Fig 2). During the June–July visit, we used the app “Litchi”
(https://flylitchi.com/) to establish the flight path and avoid excessive overlap and large areas
without burrows on the edge of the colony. Each flight was programmed to last for 13 minutes
and fly a total distance of 3100 m on parallel paths of 300 m long and 20 m apart; each flight
covered between 9 and 12 hectares. Weather permitting, the UAV was flown daily. When
wind was over 40 km/h, we did not fly the UAV.
We used the photogrammetry software, Agisoft Photoscan Professional [24], to generate
orthomosaics from the original photos [25,26]. The resulting photographs were exported as
tagged image file format (tiff) files and were added to QGIS software QGIS Development
Team [27]. In QGIS, we added an 11.16 x 11.16 m grid (124.6 m
2
) to use as a reference point
when counting the burrows. The grid size is the minimum set by QGIS. We found the opti-
mum scale before image resolution limited burrow detection to be 1:40. Subsequently, all bur-
rows were counted and marked on QGIS. We created a layer on QGIS and marked each
burrow with a point over the burrow entrance. Two people carried out the counting indepen-
dently over a period of 20 working days (8 hr per day). When in doubt, the counters consulted
each other to reach a consensus on whether it was a burrow or not. To estimate burrow den-
sity, we divided the number of burrows per 124.6 m
2
grid square.
Occupancy estimate
Burrow occupancy was estimated using ground-based methods. We used 25 quadrants of 20
m x 20 m randomly selected. Each corner was marked with wooden stakes and locations
recorded with GPS. We separated the quadrats in three categories, based on burrow density—
low, medium, and high, following Keitt et al. [7] for comparisons. High density areas
had 0.15 burrows per m
2
(>18 burrows per 124.6 m
2
quadrat), medium density areas had
between 0.10 and 0.15 burrows per m
2
(between 12 and 18 burrows per quadrat), and low den-
sity had <0.10 burrows per m
2
(<12 burrows per quadrat). Burrows were monitored in April,
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 4 / 15

May, and June 2016, and June 2017, and occupancy was estimated using toothpicks placed at
the entrance of the burrows. We monitored 1,342 burrows with toothpicks. The toothpicks
were deployed late in the evening, when ravens (Corvus corax) and gulls (Larus occidentalis)
were known to reduce or stop their activity; therefore, they would not interfere with tooth-
picks. The toothpicks were recovered early in the morning, before ravens and gulls became
active. If the toothpick was down, we assumed the burrow to be occupied [7,28]. The tooth-
picks were deployed for only one night per field trip. We deployed the toothpicks three times
per quadrat in 2016 and once per quadrat in 2017.
Burrow scopes were used to detect whether there were signs of reproduction, “real occu-
pancy”, and to discriminate from nests visited randomly or used by non-breeders, “apparent
occupancy”. This method was used to calibrate our occupancy estimates, using the toothpick
method. Twenty burrows were inspected inside four of the 20 x 20 m quadrats, for a total of 80
burrows. We used a burrow scope with a SHARP CMOS sensor, 6 white LEDs, 640 x 480 reso-
lution and a 7 mm lens. The burrow scope was connected to a smart phone and photographs
taken were stored on the internal memory of the phone.
We could not choose our sample plots randomly because the burrow scope limited our
selection to burrows that had shorter and straighter access tunnels (<1 m) dug in solid soil.
The burrows were repeatedly inspected in May and June to record activity histories for each
Fig 2. Sample image of the images taken by the drone. This photo was taken at 47.6 m. Red circles indicate some of the burrows present in the image.
https://doi.org/10.1371/journal.pone.0202094.g002
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 5 / 15

nest. We had a similar number of burrows in each of the burrow density groups—low (27),
medium (27), and high (26).
We used multi-event capture-recapture models to estimate the proportion of breeders and
non-breeders and the probability of detecting evidence of reproduction. For species that have
asynchronous laying, such as the Black-vented Shearwater, capture-mark-recapture (CMR) was
used to estimate the breeding population size in a given year [29]. Occupancy models were
applied to the subset of 80 burrows of data, using E-SURGE, a general software for multi-event
capture-recapture models [30,31]. Nests were sampled on discrete occasions, and they may be
occupied or empty. The detection of a nest as occupied has an intrinsic uncertainty on the status
of the occupant, which can be a breeder or a non-breeder (prospector). Consequently, we con-
sidered this uncertainty in the assessment of state “breeder”. We then used a model with multi-
ple states and uncertainty to determine the proportion of breeders. We considered four states:
unoccupied, occupied with no evidence of reproduction, occupied with evidence of reproduc-
tion (egg or chick), plus the default state death (necessary for processing data in E-SURGE).
There are three categories of events: species undetected (0), detected alone (1), or detected with
a chick (2). The non-breeder state represented the model uncertainty [30]. The proportion of
non-breeders obtained was subsequently used for estimating the total population breeding in
2016. To estimate the total number of chicks, we applied the probability of finding a young in
an occupied nest to the total number of occupied nests obtained with the toothpick method.
Disturbance and costs
The use of UAVs over a colony can cause stress and evasion behavior by birds [15,32] depend-
ing on the speed, distance, and noise of the UAVs. On Isla Natividad, the Western Gull (Larus
occidentalis) nest near the Black-vented Shearwater colony and would be disturbed by the
UAV. Common Ravens (Corvus corax) and Osprey (Pandion haliaetus) also fly over the colony
during the day. Ravens frequently land in the burrow areas and walk among burrow entrances
to prey upon individuals close to the entrance. The gull colony is dispersed throughout the
island, with no preferred nesting areas. The population is estimated of over 15,000 individuals
(unpubl. data). To determine the effect of the UAV on Western Gulls, we flew the UAV in
three randomly selected areas directly over the gull colony in July 2017. We counted the initial
number of gulls present in the area and recorded the behavior of the gulls when the UAV flew
at elevations of 60 m, 50 m, 40 m and 30 m in a 200 m line transect, with flight time between
100 and 130 seconds. We recorded the time when the first individuals were flushed, the time
to return to the nest, the number of reacting birds, and the kind of reaction: alarm (when the
individual stopped doing its previous activity) and/or mobbing behavior. All flights were done
between 1100 and 1500 hours, the same time the UAV was flown to collect images for burrow
counts. During the disturbance study, there was one UAV operator and one independent
observer of the gull behavior. The observer was no closer than 30 m from the nearest nest, and
gull behavior was not noticeably influenced by the observer. We concentrated on behavioral
and not on physiological responses [33].
To make a qualitative comparison between the cost of the UAV method and a traditional
population assessment, we budgeted a survey on Isla Natividad from our headquarters in La
Paz, Mexico, in days or number of pieces of equipment, including traveling, lodging, days/per-
son, and field material costs. We estimated the cost of a survey, using on-ground only methods
and assuming three survey trips to the island, which included mapping 181 sample plots of 60
m2 (the same number used for previous population estimates), counting all burrows within
each plot, and measuring burrow occupancy in half of these, plus measuring the perimeter of
the island.
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 6 / 15

Results
Burrow counts by aerial survey
We flew two to six flights each day with the UAV on most days, depending on wind speed. We
covered a total of 300 hectares with aerial photographs, resulting in more than 40,000 photo-
graphs. These photographs produced 30 overlapping orthomosaic images extending approxi-
mately 150 m beyond the edge of the colony. Within these orthomosaics, the entire colony was
represented by 11,963 quadrats of 124.6 m
2
. From the quadrats with at least one burrow, we
estimated the area occupied by the colony to be 148 ha (Figs 3and 4), with 11,315 quadrats
having low density (<0.10 burrows/m
2
), 565 having medium density (between 0.10 and 0.15
burrows/m
2
), and 83 having high density (>0.15 burrows/m
2
).
We estimated detection error of the aerial counts of –5.6%, meaning the aerial counts
missed 5.6 burrows for every 100, after comparing aerial and ground-based counts within the
60 m
2
circular calibration plots. The corrected total from the aerial photographs was 56,395
burrows. The area within the colony perimeter was 261 ha, of which 148 ha contained bur-
rows, an increase of 10 ha from the previous estimate that used the perimeter method to esti-
mate colony size [7].
From the grid superimposed on the aerial photographs (11.16 x 11.16 m), we estimated that
burrow densities vary from an average of 0.07 (±0.016 SD, n = 9 quadrats) burrows m
–2
in low
density clusters, 0.12 (±0.016 SD, n = 9 quadrats) burrows m
–2
in medium density clusters,
and 0.18 (±0.020 SD, n = 7 quadrats) burrows m
–2
in high density clusters (Table 1).
Occupancy estimate
We compared our toothpick method for estimating occupancy against burrow scope results of
true occupancy to determine error in the toothpick method for detecting actual occupancy.
The toothpick method resulted in an overestimate of 22% in May (when there were only eggs
in the nests) and an underestimate of 16% in June when eggs or chicks were in the nests. Based
on burrow depths and soil characteristics, only a small portion of nests could be inspected
with the burrow scope, potentially biasing our occupancy estimates. Consequently, for occu-
pancy, we used only the toothpick method.
The average occupancy, based on the toothpick method and calibrated with the burrow scope,
was variable in low density zones with 65% (±26% SD) of burrows occupied, and less variable in
medium and high-density zones, with 78% (±7% SD) and 77% (±10% SD) of burrows occupied,
respectively. In June 2016, following an apparent widespread breeding failure, we observed a
decrease of occupancy, compared to earlier in the year, resulting in estimates of: 37% (±16% SD)
in low-density clusters, 56% (±17% SD) in medium density clusters, and 37% (±12% SD) in high-
density clusters. The occupancy rate in 2016 is similar to the 1997 estimates, but much higher
than in 1998 [5], see Table 2). Estimates for 2017 were higher than 1997, 1998 and 2016.
When applying multi-state occupancy models [30] during the reproductive period, we
assumed that nests were most probably occupied by breeders (87.5%), but in the population,
there is a small portion of non-breeders visiting the nests (12.5%) in both years. From the
same models, we estimated the probability of detecting a breeder as 40% and the probability of
detecting a non-breeder, inspecting a nest during the early breeding phase at 7%.
Population estimates
The Natividad Black-vented Shearwater population was 37,858 (±8,510 SD) breeding pairs in
2016, and 46,322 (±4931 SD) in 2017. When adding non-breeders, the estimated number of
individuals visiting the colony was 84,802 in 2016 and 103,761 in 2017.
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 7 / 15

Impact of the UAV on other birds
Flights were conducted after Western Gulls chicks had already hatched in three sample areas
(a, b, c) of the colony with 90, 75, and 98 nesting adult individuals, respectively. We saw little
evidence of behavioral reaction to the UAV flying at 50 m above ground and no evidence of
reaction at 60 m. Behavioral reactions in response to lower altitude flights consisted of alarm
calls and increased vigilance, observed in 1 to 5 of 263 birds. Between 0 and 2 birds flushed
in response (Table 3). Based on anecdotal observations, we saw no response from ravens or
osprey to the UAV.
Cost comparison
We estimated that our UAV-based survey of burrowing seabirds represented a 68% savings in
man-hours over traditional ground counts, even when accounting for the additional time
required for UAV image processing (double the research assistant time; Table 4). For example,
we estimated that a team of five people would require 17 days to cover the 181 circular survey
plots (1.09 ha). The UAV covered 12–13 hectares in 13 minutes. We estimated that a
Fig 3. Map of the black-vented shearwater colony on Isla Natividad. The red line is roughly the perimeter of the colony recorded in 1998. Line is for comparison
purposes only. The colored grid are the locations of 11x11 m squares with at least one burrow. The shoreline of the island was obtained from CONANP (http://sig.
conanp.gob.mx/website/pagsig/info_shape.htm).
https://doi.org/10.1371/journal.pone.0202094.g003
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 8 / 15

traditional ground survey would require a team to spend an additional 35 days in the field.
While the UAV survey was more expensive (cost of the UAV), future additional UAV-based
surveys would represent an even greater savings, since no purchase is necessary.
Discussion
Accurate population estimates and the ability to easily track trends in populations are key to
effective management of threatened species. Here we describe the first use of UAVs to capture
Fig 4. A close up of the density grid obtained from the aerial photographs and projecting them on a 11x11m grid. Many of the areas without burrows are rocky
soils, without the possibility to host burrows. These small gaps in the middle (tan cells) might have led to the overestimation in 1998–99.
https://doi.org/10.1371/journal.pone.0202094.g004
Table 1. Comparison of conventional, ground-based surveys (1997/98) [7] and a UAV-based aerial photography survey (this study) of Black-vented Shearwaters on
Isla Natividad.
1997/98 2016
Low High TOTAL low medium high TOTAL
mean burrows / sq m ±SD 0.031 ±0.04 0.08 ±0.07 0.07 ±0.01 0.12 ±0.01 0.18 ±0.02
Coefficient of Variation 127% 84% 23% 13% 11%
Area (ha) 191 65 256 140 7 1 148
Burrows per density 60,303 54,152 114,455 46,135 8,407 1,853 56,395
https://doi.org/10.1371/journal.pone.0202094.t001
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 9 / 15

images to facilitate an accurate and repeatable count of seabird burrows on a breeding colony,
which are difficult to monitor. The advantages of this approach include: (a) a complete burrow
census and thus greater power in detecting changes over time, (b) archivable, as images are
available for future reference, (c) data can be collected without damaging burrows, i.e., only
limited walking is needed in the colony, (d) data are reproducible and relatively inexpensive to
collect.
Nest count by aerial survey
Our nest census showed 56,395 burrows, which combined with an occupancy estimate, pro-
vided an estimate of 84,802 individuals in 2016. These results are not directly comparable to
the Keitt et al. [7] estimate of 1997/1998 because of they used different methods. The main dif-
ference was that the aerial approach allowed us to exclude areas of zero burrow density. Such
exclusion was not possible in the ground-based method of Keitt et al. [7]. Multiplying the
Table 2. Comparison of the conventional survey method used in 1997/98 and the UAV method used in 2016.
Colony size, burrow number, and density estimate, as determined by two methods
Source 1997/98 [7] 2016 (this study)
Colony size method walking perimeter aerial photograph of entire colony area
Colony area area (km2) 2.56 1.48 (walking perimeter 2.61)
Burrow count
/estimate
method 181 plots of 4.37m radius (60 m2), to determine no. burrows
per sq km in LBD areas (n = 88), and HBD (93). Mean
density then extrapolated to determine total burrow count
Counted all burrows in photographs. Split total colony area into
11,963 x 124m2 quadrats based on photos and counts of >0.15
b/m2 = high burrow density (n = 83), >0.10 &
<0.15 = medium burrow density (n = 565), and <0.10 = low
burrow density (n = 11315). Excluded plots with zero density,
which essentially is a 4
th
category.
area surveyed
(ha)
1.086 148
% colony
surveyed
0.424% 100%
No. burrows 114,455 56,395
Lower bound
estimate
86,935 no error estimate because number is total count of colony
Occupancy Method Using same 181 plots described above, burrow scope of all
burrows
Surveying 25 x 400 m2 quadrats (9 in LBD, 9 in MBD, 6 in
HBD), using tooth-picking determine status and burrowscope
to calibrate.
Occupancy
(year)
66.9% (1997), 19.6% (1998) 67.1% (2016), 82% (2017)
Population Size
Estimate (breeding
pairs)
76,570 (1997), 22,433 (1998) 37,858 (2016), 44,235 (2017)
https://doi.org/10.1371/journal.pone.0202094.t002
Table 3. Results of the disturbance experiment of three UAV flights in the post-hatching period of the Western
Gull. Flights were done at four altitudes. The total number of individuals in the areas were counted before the flights
started (N = 263). The number of gulls remained constant during the trials, with no individuals coming into or leaving
the colony.
UAV flight height % of individuals showing reaction
Reacted, but stayed on the nest Flushed
30 m 2±5.5 0.1±1.2
40 m 2±4.1 0.6±1.2
50 m 0.3±0.6 0
60 m 0 0
https://doi.org/10.1371/journal.pone.0202094.t003
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 10 / 15

three density estimates in our study and the area of the colony, the estimate of 114,000 to
108,000 burrows is similar to Keitt et al. [7] (Table 2).
It is notable that the colony area, when using the method of including all the area within the
bounds of the colony (Fig 3), is similar in our study (Table 2) and the Keitt et al. [7] study. The
100 ha difference in area reported in our study, corresponding in a reduction in area of 42.8%
from that of Keitt et al. [7], is primarily due to the exclusion of survey plots with zero burrows
within the bounds of the colony.
Unmanned aerial vehicles have been used to estimate population of ground-nesting sea-
birds [16]. For Lesser Frigatebirds (Fregata minor), Crested Terns (Thalasseus bergii), and
Royal Penguins (Eudyptes schlegeli), the estimation error was ±10% [16]. However, the meth-
ods have been improved and now have a much lower detection error [34], comparable to the
5.6% detection error we obtained in our study.
Occupancy estimate
We found that the toothpick method underestimated Black-vented Shearwater burrow occu-
pancy, compared to actual occupancy, as determined with the burrow scope. This is likely
because of the Black-vented Shearwater nest- attending behaviour, where parents leave the
nest unattended for two or more days or remain inside for several days. Toothpicks would not
be knocked down on those survey days, and nests would be classified as unoccupied. We chose
to use the toothpick data in the population estimate because it provided a larger and more vari-
able sample of nests, including many types of soil, vegetation cover, and different areas within
the colony. Burrow scope inspection was limited only to suitable nests in terms of soil type and
length of the nest chamber.
Table 4. Comparison of survey costs, in terms of time spent traveling to or on the island for personnel, lodging, meals, transportation, and materials, between the
two survey methods: the traditional nest counting for sample units and UAV. The traditional method estimates are for a highly accuracy survey, comparable to the
UAV method. The duration of the working days was eight hours each day.
Category Conventional survey UAV survey difference (conventional-UAV)
Personnel units Days or number total units Days or number total
senior researcher (field work) 1 50 50 1 15 15 35
senior researcher (data analysis) 1 5 5 1 5 5 0
research assistant (field work) 4 50 200 1 15 15 185
research assistant (data analysis) 1 30 30 2 30 60 -30
Lodging
on island 5 50 250 2 15 30 220
traveling to the island 15 2 30 2 2 4 26
Meals
on island 5 50 250 2 15 30 220
traveling to the island 15 2 30 2 2 4 26
Transportation
Gas tanks 3 3 9 1 3 3 6
Field material
Burrow scopes 1 5 5 1 2 2 3
UAV Phantom 3 Standard 0 1 1 1 1
software Litchi licence 0 1 1 1 1
GPS 3 1 3 1 1 1 2
metric tapes + sticks and signs 3 3 3 1 1 1 2
desktop computer 1 1 1 1 1 1 0
https://doi.org/10.1371/journal.pone.0202094.t004
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 11 / 15

We included a non-breeder state in our models [30] to account more accurately for the pro-
portion of the population that skip a breeding year, which may be an artifact of individual lower
body condition or unfavorable environmental conditions, such as a post-El Niño year [35,36].
From the E-Surge modelling exercise, we estimated that 12% of the population in the colony
were non-breeders, a percentage we expect would be lower in years with favorable environmen-
tal conditions that increase the likelihood of reproduction. We also estimated 44% of the active
burrows were occupied by a chick in June 2016, giving us an estimate of the species productivity
under post-El Niño conditions. June is generally late in the normal breeding schedule [23]. A
similar delay was described by Keitt and colleagues after the El Niño event of 1998 [7].
Occupancy rates can change dramatically under El Niño conditions, compared to other
years [7]. Thus, estimating the bird population from one year of survey during an El Nino is
challenging. The numbers from 1997, and 2016 should be considered a minimum population
estimate as the surveys were done during unfavourable conditions. We suggest it is more use-
ful to include a measure of occupancy with burrow counts, rather than only counting physical
burrows, because this will produce a more biologically relevant value to compare over time.
Furthermore, there is always a proportion of burrows that are not occupied and could lead to
an overestimation.
Cost comparison and disturbance effects
The time spent in the field to carry out the UAV survey is much less than the time spent in a
traditional survey (Table 4) and will be directly reflected in the funding needed to complete
the survey. Also, by spending less time in the colony area and without walking among the bur-
rows, trampling will be much lower. However, the costs of the traditional method can be lower
if instead of covering the whole colony area, only randomly selected plots are used but is signif-
icantly lower in the accuracy [37].
We suspected that gulls would assess the UAVs as a potential predation risk, making subse-
quent decisions about fight or flight [38]. We observed little to no change in Western Gull
behavior to UAV flights. No individuals abandoned their nesting attempts; the few individuals
that flushed from nests returned immediately after the UAV passed.
Similar behaviour has been observed in other surface nesters when approached by a UAV,
in some cases passing as close as 4 m from the individual, without causing the bird to flush
[15]. While we did not monitor physiological stress responses; such responses can occur, even
in the absence of behavioural responses [39], such as increased heart rate and production of
stress hormones [32,40].
The UAV method could also be applied to populations of other burrowing seabird species,
especially those that breed in sandy soil free of heavy vegetation, such as the Peruvian Diving-
petrel (Pelecanoides garnotii) or the African penguin (Spheniscus demersus). For colonies
where part of the area is obscured by vegetation, a subsample of an accessible part of a colony
could be monitored to detect changes. For future research, we suggest determing the best
times for taking photographs. We decided to take them between 11 AM and 3 PM to have azi-
muthal light and reduced shadows. However, this may not be the best approach, as slope or lat-
itude may influence the resulting photographs. To monitor changes in colony population over
time, the method we are proposing is less accurate than a census of the whole colony, but it
offers a much cheaper option with less likelihood of disturbance.
Acknowledgments
We thank Coop. Buzos y Pescadores of Natividad Island and Comunidad y Biodiversidad A.C.
for their support in field activities. Brad Keitt and Nick D. Holmes greatly improved the MS.
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 12 / 15

David Will reviewed the manuscript. We acknowledge the Vizcaino Biosphere Reserve for its
proactivity towards this project. This research was funded by project CICESE 691111 and by
Ornis italica. Fieldwork was carried out under permit SGPA/DGVS/00321/16.
Author Contributions
Conceptualization: Yuri V. Albores-Barajas, Cecilia Soldatini, Giacomo Dell’Omo.
Formal analysis: Yuri V. Albores-Barajas, Cecilia Soldatini, Alejandro Ramos-Rodrı
´guez,
Roberto Carmona.
Funding acquisition: Cecilia Soldatini, Giacomo Dell’Omo.
Investigation: Yuri V. Albores-Barajas, Cecilia Soldatini, Javier E. Alcala-Santoyo.
Methodology: Yuri V. Albores-Barajas, Cecilia Soldatini, Alejandro Ramos-Rodrı
´guez, Gia-
como Dell’Omo.
Project administration: Cecilia Soldatini.
Resources: Giacomo Dell’Omo.
Software: Alejandro Ramos-Rodrı
´guez.
Supervision: Yuri V. Albores-Barajas, Cecilia Soldatini.
Validation: Javier E. Alcala-Santoyo.
Visualization: Giacomo Dell’Omo.
Writing – original draft: Yuri V. Albores-Barajas, Cecilia Soldatini.
Writing – review & editing: Yuri V. Albores-Barajas, Cecilia Soldatini.
References
1. Croxall JP, Butchart SHM, Lascelles B, Stattersfield AJ, Sullivan B, Symes A, et al. Seabird conserva-
tion status, threats and priority actions: a global assessment. Bird Conservation International. 2012; 22
(1):1–34. https://doi.org/10.1017/s0959270912000020 PubMed PMID: WOS:000301298900001.
2. Brooke ML. Albatrosses and petrels across the world. Oxford: Oxford University Press; 2004.
3. Schumann N, Dann P, Hoskins AJ, Arnould JP. Optimizing survey effort for burrow-nesting seabirds.
Journal of Field Ornithology. 2013; 84(1):69–85.
4. Oppel S, Hervias S, Oliveira N, Pipa T, Silva C, Geraldes P, et al. Estimating population size of a noctur-
nal burrow-nesting seabird using acoustic monitoring habitat mapping. Nature Conservation. 2014;
7:1–13.
5. Keitt BS, Wilcox C, Tershy BR, Croll DA, Donlan CJ. The effect of feral cats on the population viability of
black-vented shearwater (Puffinus opisthomelas) on Natividad Island, Mexico. Animal Conservation.
2002; 5:217–23. PubMed PMID: 83.
6. Sutherland DR, Dann P. Improving the accuracy of population size estimates for burrow-nesting sea-
birds. Ibis. 2012; 154(3):488–98. https://doi.org/10.1111/j.1474-919X.2012.01234.x PubMed PMID:
WOS:000310738400007.
7. Keitt BS, Tershy BR, Croll DA. Breeding biology and conservation of the Black-vented Shearwater Puffi-
nus opisthomelas. Ibis. 2003; 145(4):673–80.
8. Bailey EP. Breeding bird distribution and abundance in the Barren Islands, Alaska. The Murrelet. 1976;
57(1):2–12.
9. Rayner MJ, Clout MN, Stamp RK, Imber MJ, Brunton DH, Hauber ME. Predictive habitat modelling for
the population census of a burrowing seabird: A study of the endangered Cook’s petrel. Biological Con-
servation. 2007; 138(1–2):235–47. https://doi.org/10.1016/j.biocon.2007.04.021 |ISSN 0006-3207
PubMed PMID: WOS:000247864200021.
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 13 / 15

10. Scott D, Moller H, FletcHer D, Newman J, Aryal J, Bragg C, etal. Predictive habitat modellingto esti-
mate petrel breeding colony sizes: sooty shearwaters (Puffinus griseus) and mottled petrels (Ptero-
droma inexpectata) on Whenua Hou Island. New Zealand Journal of Zoology. 2009; 36(3):291–306.
11. Whitehead AL, Lyver POB, Jones CJ, Bellingham PJ, MacLeod CJ, Coleman M, et al. Establishing
accurate baseline estimates of breeding populations of a burrowing seabird, the grey-faced petrel (Pter-
odroma macroptera gouldi) in New Zealand. Biological Conservation. 2014; 169:109–16.
12. Parker GC, Rexer-Huber K. Guidelines for designing burrowing petrel surveys to improve population
estimate precision.: Agreement on the Conservation of Albatrosses and Petrels; 2016 [updated 18/07/
2017; cited 2018 21/03/2018]. http://www.acap.aq/en/resources/acap-conservation-guidelines ].
13. Lawton K, Robertson G, Kirkwood R, Valencia J, Schlatter R, Smith D. An estimate of population sizes
of burrowing seabirds at the Diego Ramirez archipelago, Chile, using distance sampling and burrow-
scoping. Polar Biology. 2006; 29(3):229–38. PubMed PMID: 515.
14. Ratcliffe N, Guihen D, Robst J, Crofts S, Stanworth A, Enderlein P. A protocol for the aerical survey of
penguin colonies using UAVs. Journal of Unmanned Vehicle Systems. 2015; 3:95–101.
15. Vas E, Lescroe
¨l A, Duriez O, Boguszewski G, Gre
´millet D. Approaching birds with drones: first experi-
ments and ethical guidelines. Biology Letters. 2015; 11(2):20140754. https://doi.org/10.1098/rsbl.2014.
0754 PMID: 25652220
16. Hodgson JC, Baylis SM, Mott R, Herrod A, Clarke RH. Precision wildlife monitoring using unmanned
aerial vehicles. Scientific Reports. 2016; 6:22574. https://doi.org/10.1038/srep22574 http://www.nature.
com/articles/srep22574#supplementary-information. PMID: 26986721
17. McEvoy JF, Hall GP, McDonald PG. Evaluation of unmanned aerial vehicle shape, flight path and cam-
era type for waterfowl surveys: disturbance effects and species recognition. Peerj. 2016; 4:e1831.
https://doi.org/10.7717/peerj.1831 PubMed PMID: WOS:000372579000009. PMID: 27020132
18. Koh L, Wich S. Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical
Conservation Science. 2012; 5(2):121–32.
19. Christie KS, Gilbert SL, Brown CL, Hatfield M, Hanson L. Unmanned aircraft systems in wildlife
research: current and future applications of a transformative technology. Frontiers in Ecology and the
Environment. 2016; 14(5):241–51. https://doi.org/10.1002/fee.1281
20. Birdlife-International. Oceanodroma leucorhoa: www.iucnredlist.org; 2016 [26 February 2017].
21. Keitt B, Tershy B. Cat eradication significantly decreases shearwater mortality. Animal Conservation.
2003; 6(4):307–8.
22. Wood B, Tershy B, Hermosillo MA, Donlan CJ, Sanchez Pacheco JA, Keitt B, et al. Removing cats from
the islands in Northwestern Mexico. In: Veitch CR, Clout M, editors. Turning the tide: the eradication of
invasive species. Gland, Switzerland: World Conservation Union; 2002.
23. Keitt B, Tershy B, Croll D. Black-vented Shearwater (Puffinus opistomelas). In: Poole A, Gill F, editors.
Philadelphia, PA: The Birds of North America, Inc.; 2000. p. 1–16.
24. Edition APP. St. Petersburg, Russia: Agisoft; 2016.
25. Themistocleous K, Ioannides M, Agapiou A, Hadjimitsis D, editors. The methodology of documenting
cultural heritage sites using photogrammetry, UAV, and 3D printing techniques: the case study of Asi-
nou Church in Cyprus. Third International Conference on Remote Sensing and Geoinformation of the
Environment; 2015: International Society for Optics and Photonics.
26. Boike J. Documentation of orthomosaic processing: Bremerhaven, Pangaea 2015.
27. QGIS Development Team T. QGIS Geographic Information System, Open Source Geospatial Founda-
tion Project. 2014.
28. Buxton RT, Jones IL. An experimental study of cosial attraction in two species of storm-petrel by acous-
tic and olfactory cues. Condor. 2012; 114(4):733–43. https://doi.org/10.1525/cond.2012.110091
PubMed PMID: WOS:000311882700007.
29. Williams KA, Frederick PC, Nichols JD. Use of the superpopulation approach to estimate breeding pop-
ulation size: an example in asynchronously breeding birds. Ecology. 2011; 92:821–8. PMID: 21661545
30. Gimenez O, Blanc L, Besnard A, Pradel R, Doherty PF, Marboutin E, et al. Fitting occupancy models
with E-SURGE: hidden Markov modelling of presence–absence data. Methods in Ecology and Evolu-
tion. 2014; 5(6):592–7.
31. Choquet R. E-SURGE. 1.8.5 ed. Montpellier, France: Centre d’Ecologie Fonctionnelle et Evolutive
(CEFE), UMR 5175; 2011.
32. Weimerskirch H, Prudor A, Schull Q. Flights of drones over sub-Antarctic seabirds sow species- and
status-specific behavioural and physiological responses. Polar Biology. 2017. https://doi.org/10.1007/
s00300-017-2164-6
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 14 / 15

33. Holmes ND, Giese M, Kriwoken LK. Testing the minimum approach distance guidelines for incubating
Royal penguins Eudyptes schlegeli. Biological Conservation. 2005; 126:339–50.
34. Hodgson JC, Mott R, Baylis SM, Pham TT, Wotherspoon SJ, Kilpatrick AD, et al. Drones count wildlife
more accurately and precisely than humans. Methods in Ecology and Evolution. 2018. https://doi.org/
10.1111/2041-210X.12974.
35. Cubaynes S, Doherty PF Jr., Schreiber EA, Gimenez O. To breed or not to breed: a seabird’s response
to extreme climatic events. Biology Letters. 2011; 7(2):303–6. https://doi.org/10.1098/rsbl.2010.0778
PubMed PMID: WOS:000288244100039. PMID: 20943677
36. Soldatini C, Albores-Barajas YV, Massa B, Gimenez O. Forecasting ocean warming impacts on seabird
demography: a case study on the European storm petrel. Marine Ecology Progress Series. 2016;
552:255–69.
37. Gutzwiller KJ. Refining the use of point counts for winter studies of individual species. Wilson Bulletin.
1993; 105(4):612–27. PubMed PMID: WOS:A1993MP63200006.
38. Frid A, Dill L. Human-caused disturbance stimuli as a form of predation risk. Conservation Ecology.
2002; 6(1):art-11. PubMed PMID: 72.
39. Gill JA, Norris K, Sutherland WJ. Why behavioural responses may not reflect the population conse-
quences of human disturbance? Biological Conservation. 2001; 97:265–8. http://dx.doi.org/10.1016/
S0006-3207(00)00002-1. PubMed PMID: 302.
40. Nimon AJ, Schroter RC, Oxenham RKC. Artificial eggs: Measuring heart rate and effects of disturbance
in nesting penguins. Physiology & Behavior. 1996; 60(3):1019–22. http://dx.doi.org/10.1016/0031-9384
(96)00079-0. PubMed PMID: 95.
UAVs and burrowing seabirds
PLOS ONE | https://doi.org/10.1371/journal.pone.0202094 September 14, 2018 15 / 15