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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.
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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
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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 [46]. 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
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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
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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
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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
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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
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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).
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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
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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.
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UAVs and burrowing seabirds
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... • Local, national and regional administrative regulations can affect possibility of data acquisition (Chabot et al. 2015, Nowak et al. 2019). • Upfront cost of training and purchasing a drone makes surveys more expensive than field observations (e.g., using notebook, pencil and binoculars) for one-off or a small number of surveys; and multiple flights may be required to cover larger areas, increasing survey time (Albores- Barajas et al. 2018). (Goebel et al. 2015). ...
... This should include details of the landscape to decide whether the objects of interest will be visible in drone images. Seabird nests may be camouflaged, hidden by vegetation and rocky outcrops, or be underground, with small entrance holes hard to see from the air (Albores- Barajas et al. 2018, Dickens et al. 2021. It should also document features that could affect site access (e.g., tidal forecasts for island surveys) or be a hazard to drone flight, namely physical obstructions (e.g., pylons, buildings), restricted areas in the vicinity (e.g., classified airspace and military operations), habitation and recreational activities, public access, and environmental regulations, as well as phone numbers to contact Air Traffic Control (ATC) at nearby aerodromes/airports if the pilot loses control of the drone. ...
... or ESRI Drone2Map (https://www.esri.com/en-us/arcgis/products/arcgis-drone2map/overview) (e.g., Rush et al. 2018, Albores-Barajas et al. 2018). This is difficult for surveys of vertical seabird colonies, as the software is designed to create a georeferenced image in the horizontal plane (i.e., a map), rather than the vertical plane. ...
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Aerial drones are increasingly being used as tools for ecological research and wildlife monitoring in hard-to-access study systems, such as in studies of colonial-nesting birds. Despite their many advantages over traditional survey methods, there remains concerns about possible disturbance effects that standard drone survey protocols may have on bird colonies. There is a particular gap in the study of their influence on physiological measures of stress. We measured heart rates of incubating female common eider ducks (Somateria mollissima) to determine whether our drone-based population survey affected them. To do so, we used heart-rate recorders placed in nests to quantify their heart rate in response to a quadcopter drone flying transects 30 m above the nesting colony. Eider heart rate did not change from baseline (measured in the absence of drone survey flights) by a drone flying at a fixed altitude and varying horizontal distances from the bird. Our findings suggest that carefully planned drone-based surveys of focal species have the potential to be carried out without causing physiological impacts among colonial-nesting eiders.
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Drones or unoccupied aerial vehicles are rapidly being used for a spectrum of applications, including replacing traditional occupied aircraft as a means of approaching wildlife from the air. Though less intrusive to wildlife than occupied aircraft, drones can still cause varying levels of disturbance. Policies and protocols to guide lowest-impact drone flights are most likely to succeed if considerations are derived from knowledge from scientific literature. This study examines trends in the scientific literature on using drones to approach wildlife between 2000 and 2020, specifically in relation to the publication types, scientific journals that works are published in, purposes of drone flights reported, taxa studied, and locations of studies. From 223 publications, we observed a large increase in relevant scientific literature, the majority of which were peer-reviewed articles published across 86 scientific journals. The largest proportion of peer-reviewed research articles related to aquatic mammals or aquatic birds and the use or trial of drone flights for conducting population surveys, animal detection, or investigations of animal responses to drone flights. The largest proportion of articles were studies conducted in North America and Australia. Since animal responses to drone flights vary among taxa, populations, and geographic locations, we encourage further growth in the volume of relevant scientific literature needed to inform policies and protocols for specific taxa and (or) locations, particularly where knowledge gaps exist.
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Knowing how many individuals are in a wildlife population allows informed management decisions to be made. Ecologists are increasingly using technologies, such as remotely piloted aircraft (RPA; commonly known as “drones,” unmanned aerial systems or unmanned aerial vehicles), for wildlife monitoring applications. Although RPA are widely touted as a cost‐effective way to collect high‐quality wildlife population data, the validity of these claims is unclear. Using life‐sized, replica seabird colonies containing a known number of fake birds, we assessed the accuracy of RPA‐facilitated wildlife population monitoring compared to the traditional ground‐based counting method. The task for both approaches was to count the number of fake birds in each of 10 replica seabird colonies. We show that RPA‐derived data are, on average, between 43% and 96% more accurate than the traditional ground‐based data collection method. We also demonstrate that counts from this remotely sensed imagery can be semi‐automated with a high degree of accuracy. The increased accuracy and increased precision of RPA‐derived wildlife monitoring data provides greater statistical power to detect fine‐scale population fluctuations allowing for more informed and proactive ecological management.
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Drones and unmanned aerial vehicles are increasingly used in research on wildlife. Their wide applications can also give interesting insights into habitat use and population distribution. However, the disturbance they might be responsible for, on species and especially in protected areas has yet to be investigated. We assessed and compared the behavioural response of 11 southern seabird species at the Crozet Islands, Southern Indian Ocean, to drone approaches at specific altitudes. We first show that the behavioural response differed between species depending on the altitude of the drone approach. At 50 m of altitude, only one of the studied species showed a detectable reaction, whereas at 10 m, most species showed strong behavioural postures of stress. Adult penguins breeding in large colonies, and some albatross species showed little behavioural response even when the drone was as close as 3 m, whereas other species such as giant petrels or cormorants appeared highly sensitive to drone approaches. Among King Penguins, although incubating adults showed little signs of behavioural stress, non-breeding adults and fledglings in crèches exhibited strong behavioural responses to the drone approach. Monitoring heart rate allowed us to investigate the link between behavioural and physiological response to that specific potential stressor in king penguins. Whereas we confirmed the expected link between physiological and behavioural response in chicks, breeding adults showed no behavioural sign of stress but had a significant increase in heart rate, the relative increase being higher than in chicks. All together these results have important implications for the conservation of species and should be helpful for future legislations on the use of drones.
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Tropical deforestation continues to be a major driver of biodiversity loss and greenhouse gas emissions. Remote sensing technology is increasingly used to assess changes in forest cover, species distributions and carbon stocks. However, satellite and airborne sensors can be prohibitively costly and inaccessible for researchers in developing countries. Here, we describe the development and use of an inexpensive (<$2,000) unmanned aerial vehicle for surveying and mapping forests and biodiversity (referred to as ‘Conservation Drone’ hereafter). Our prototype drone is able to fly pre-programmed missions autonomously for a total flight time of ~25 minutes and over a distance of ~15 km. Non-technical operators can program each mission by defining waypoints along a flight path using an open-source software. This drone can record videos at up to 1080 pixel resolution (high definition), and acquire aerial photographs of <10 cm pixel resolution. Aerial photographs can be stitched together to produce real-time geo-referenced land use/cover maps of surveyed areas. We evaluate the performance of this prototype Conservation Drone based on a series of test flights in Aras Napal, Sumatra, Indonesia. We discuss the further development of Conservation Drone 2.0, which will have a bigger payload and longer range. Initial tests suggest a flight time of ~50 minutes and a range of ~25 km. Finally, we highlight the potential of this system for environmental and conservation applications, which include near real-time mapping of local land cover, monitoring of illegal forest activities, and surveying of large animal species.
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Population size assessments for nocturnal burrow-nesting seabirds are logistically challenging because these species are active in colonies only during darkness and often nest on remote islands where manual inspections of breeding burrows are not feasible. Many seabird species are highly vocal, and recent technological innovations now make it possible to record and quantify vocal activity in seabird colonies. Here we test the hypothesis that remotely recorded vocal activity in Cory’s shearwater (Calonectris borealis) breeding colonies in the North Atlantic increases with nest density, and combined this relationship with cliff habitat mapping to estimate the population size of Cory’s shearwaters on the island of Corvo (Azores). We deployed acoustic recording devices in 9 Cory’s shearwater colonies of known size to establish a relationship between vocal activity and local nest density (slope = 1.07, R2 = 0.86, p < 0.001). We used this relationship to predict the nest density in various cliff habitat types and produced a habitat map of breeding cliffs to extrapolate nest density around the island of Corvo. The mean predicted nest density on Corvo ranged from 6.6 (2.1–16.2) to 27.8 (19.5–36.4) nests/ha. Extrapolation of habitat-specific nest densities across the cliff area of Corvo resulted in an estimate of 6326 Cory’s shearwater nests (95% confidence interval: 3735–10,524). This population size estimate is similar to previous assessments, but is too imprecise to detect moderate changes in population size over time. While estimating absolute population size from acoustic recordings may not be sufficiently precise, the strong positive relationship that we found between local nest density and recorded calling rate indicates that passive acoustic monitoring may be useful to document relative changes in seabird populations over time.
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Robust population estimates are needed for conservation management of burrowing petrel populations. Estimates of population size for burrowing petrels are often obtained by extrapolation of burrow surveys to a population-or island-wide scale. However, extrapolation will also extrapolate bias or error, giving rise to potentially large error bounds reflecting imprecise estimates of population size. This hinders species risk assessment and limits the ability to detect trends in population size over time. We review methods for estimating the breeding population size of burrowing petrels by extrapolation from surveys, focusing in particular on the error associated with population estimates of the larger Procellaria petrels. Sources of error in extrapolation of survey data are divided into five key areas: (1) uncertainty of burrow contents (2) timing, (3) availability bias, (4) burrow detection probability, and (5) observer bias. The review highlights that there is no single-best method for minimising error levels in population estimates. Rather, the most accurate and precise studies are those designed according to the specifics of the study resources, species and site, and we discuss a range of the factors that are important to consider.
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Unmanned aircraft systems (UAS) - also called unmanned aerial vehicles (UAVs) or drones - are an emerging tool that may provide a safer, more cost-effective, and quieter alternative to traditional research methods. We review examples where UAS have been used to document wildlife abundance, behavior, and habitat, and illustrate the strengths and weaknesses of this technology with two case studies. We summarize research on behavioral responses of wildlife to UAS, and discuss the need to understand how recreational and commercial applications of this technology could disturb certain species. Currently, the widespread implementation of UAS by scientists is limited by flight range, regulatory frameworks, and a lack of validation. UAS are most effective when used to examine smaller areas close to their launch sites, whereas manned aircraft are recommended for surveying greater distances. The growing demand for UAS in research and industry is driving rapid regulatory and technological progress, which in turn will make them more accessible and effective as analytical tools.
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Bottom-up climatic forcing has been shown to be influential for a variety of marine taxa, but evidence in seabird populations is scarce. Seasonal variation in environmental conditions can have indirect effect on subsequent reproduction, which, given the longevity and single-brooding of seabirds, may affect population dynamics. Our study focuses on linking the effect of oceanographic conditions (from 1991 to 2013) to the fecundity and consequently population growth rate of the Mediterranean subspecies of the European storm petrel (Hydrobates pelagicus melitensis). In this study, we examined 23 years of more than 5,100 mark-recaptures and modelled the probability of skipping reproduction as a function of oceanographic variables using capture-recapture models. We demonstrate that a decrease in sea surface temperature in the pre-breeding period negatively influences skipping propensity, and therefore hypothesize that this behaviour would have significant influence on population abundance over time. For this reason, we analysed population growth as a function of skipping probability as affected by oceanographic conditions. We used stochastic demographic models to forecast the fate of the population, and evaluated contrasted environmental condition scenarios. As a result, we found that a decrease in frequency of cold winter events would probably reduce skipping propensity, with a positive effect on the population as a whole.
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The use of unmanned aerial vehicles (UAVs) for ecological research has grown rapidly in recent years, but few studies have assessed the disturbance impacts of these tools on focal subjects, particularly when observing easily disturbed species such as waterfowl. In this study we assessed the level of disturbance that a range of UAV shapes and sizes had on free-living, non-breeding waterfowl surveyed in two sites in eastern Australia between March and May 2015, as well as the capability of airborne digital imaging systems to provide adequate resolution for unambiguous species identification of these taxa. We found little or no obvious disturbance effects on wild, mixed-species flocks of waterfowl when UAVs were flown at least 60m above the water level (fixed wing models) or 40m above individuals (multirotor models). Disturbance in the form of swimming away from the UAV through to leaving the water surface and flying away from the UAV was visible at lower altitudes and when fixed-wing UAVs either approached subjects directly or rapidly changed altitude and/or direction near animals. Using tangential approach flight paths that did not cause disturbance, commercially available onboard optical equipment was able to capture images of sufficient quality to identify waterfowl and even much smaller taxa such as swallows. Our results show that with proper planning of take-off and landing sites, flight paths and careful UAV model selection, UAVs can provide an excellent tool for accurately surveying wild waterfowl populations and provide archival data with fewer logistical issues than traditional methods such as manned aerial surveys.
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Unmanned aerial vehicles (UAVs) represent a new frontier in environmental research. Their use has the potential to revolutionise the field if they prove capable of improving data quality or the ease with which data are collected beyond traditional methods. We apply UAV technology to wildlife monitoring in tropical and polar environments and demonstrate that UAV-derived counts of colony nesting birds are an order of magnitude more precise than traditional ground counts. The increased count precision afforded by UAVs, along with their ability to survey hard-to-reach populations and places, will likely drive many wildlife monitoring projects that rely on population counts to transition from traditional methods to UAV technology. Careful consideration will be required to ensure the coherence of historic data sets with new UAV-derived data and we propose a method for determining the number of duplicated (concurrent UAV and ground counts) sampling points needed to achieve data compatibility.
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As the affordability, reliability and ease-of-use of Unmanned Aerial Vehicles (UAV) advances, the use of aerial surveying for cultural heritage purposes becomes a popular choice, yielding an unprecedented volume of high-resolution, geo-tagged image-sets of historical sites from above. As well, recent developments in photogrammetry technology provide a simple and cost-effective method of generating relatively accurate 3D models from 2D images. These techniques provide a set of new tools for archaeologists and cultural heritage experts to capture, store, process, share, visualise and annotate 3D models in the field. This paper focuses on the methodology used to document the cultural heritage site of Asinou Church in Cyprus using various state of the art techniques, such as UAV, photogrammetry and 3D printing. Hundreds of images of the Asinou Church were taken by a UAV with an attached high resolution, low cost camera. These photographic images were then used to create a digital 3D model and a 3D printer was used to create a physical model of the church. Such a methodology provides archaeologists and cultural heritage experts a simple and cost-effective method of generating relatively accurate 3D models from 2D images of cultural heritage sites.