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A colonial‑nesting seabird
shows no heart‑rate response
to drone‑based population surveys
Erica A. Geldart 1*, Andrew F. Barnas 1,2*, Christina A. D. Semeniuk 1,2,
H. Grant Gilchrist 3, Christopher M. Harris 2 & Oliver P. Love 2
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
eects that standard drone survey protocols may have on bird colonies. There is a particular gap in the
study of their inuence 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 aected them. To do so, we used heart‑rate recorders placed in nests to quantify their heart
rate in response to a quadcopter drone ying transects 30 m above the nesting colony. Eider heart
rate did not change from baseline (measured in the absence of drone survey ights) by a drone ying
at a xed altitude and varying horizontal distances from the bird. Our ndings 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.
Accurate, consistent, and economical surveys of wildlife populations continue to be a fundamental element
supporting wildlife science and management. Aerial imagery techniques have been traditionally used to detect
and count individuals to derive a range of demographic metrics including population size estimates1–9, spatial
distribution10,11, temporal and spatial dynamics of colony formation12, operational sex ratios13, nest survival
estimates14, and ne-scale foraging behaviours15–17. Traditional methods for censusing colonial-nesting bird
populations and nesting distributions have included ground-based and occupied aircra surveys, as well as
remote sensing techniques like satellite imagery18. Ground-based surveys gather data at smaller spatial scales to
monitor smaller or more cryptic species, but can be time consuming. Further, viewing nests from a great distance
can be dicult, therefore data collection on the ground may require close-proximity of researchers to individual
nests19. By contrast, occupied aerial surveys are designed to rapidly cover large areas, but can be logistically dif-
cult, expensive, and dangerous when conducted in remote areas20–22. Importantly, both investigator intrusions
and aircra activity can induce changes in nesting bird physiology and behaviour, suggesting disturbance eects
can be associated with both techniques23,24. Finally, while commercial satellite imagery is promising for wildlife
surveys due to broad spatial coverage and lack of investigator disturbance, such imagery is too low resolution
for identifying individuals and may be restrictive in polar regions due to cloud cover6,7,25.
There are now a number of emerging techniques used to survey wildlife, including the use of aerial
drones26. Drones can collect large quantities of data, be own in inaccessible locations5, are thought to mitigate
disturbance3,19 and can provide accurate, precise, and consistent population estimates4,19,27,28. Despite the appar-
ent advantages, the use of drone technology in the study of sensitive species such as colonial birds has raised
concerns about their disturbance eects to nesting birds19,29–32. Many of the studies that aim to quantify the eects
of drone disturbance on wildlife have been explicitly designed to use the drone as a disturbance stimulus to test
whether responses are proportional to the distance (e.g., lateral distance or survey altitude) between the stimulus
and the responder29–32, or whether dierent ight patterns (e.g., “target-oriented”, “lawn-mower”, and “hobby”33)
elicit dierent responses19. While preliminary investigations of drones as disturbance stimuli are informative for
understanding the behavioural responses of birds, these studies are oen designed and executed in ways that
may not necessarily reect standard survey methodologies that researchers would implement in the eld. In
such scenarios, the experimental response of birds to an approaching drone aircra may not be representative
of a response observed during a formal survey design34.
OPEN
1Great Lakes Institute for Environmental Research, University of Windsor, Windsor, ON, Canada. 2Department of
Integrative Biology, University of Windsor, Windsor, ON, Canada. 3National Wildlife Research Center, Environment
and Climate Change Canada, Ottawa, ON, Canada. *email: geldart@uwindsor.ca; andrew.f.barnas@gmail.com
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To date, research on the eects of drones on birds has focused primarily on behavioural alterations of colo-
nial-nesting individuals; e.g.,19,30,31,35, without considering the potential physiological responses that may occur
even when behavioural changes are not observed (e.g.,36). Breeding birds are oen highly attached to their nest
during the incubation period, and reluctant to leave to ensure their breeding success37. Consequently, studies
that measure behavioural responses such as nest abandonment as the sole indicator of stress may overlook the
possibility that physiological stress responses are occurring but are undetected (e.g., increases in stress hormones
or heart rate). us, studies that only examine behavioural responses may not fully capture the potential impact
that drones may have on their study subject(s)32,38. Of the few studies that have assessed a physiological metric
of stress38,39, only one has focused on birds32.
Hormonal responses (e.g., circulating glucocorticoids) are a commonly used measure of physiological stress
in eld studies; e.g.,40,41, and can be reliable predictors of stress-responsiveness in birds42. However, the collec-
tion of blood samples requires the capture and handling of free-living birds which itself, has the potential to
interrupt incubation and chick-rearing behaviours of the focal bird and even those around them; e.g.,43. More
recently, metrics such as the measurement of heart-rate responses during stressful events has been used as real-
time physiological measures of an animal’s assessment of a stimulus in the eld44–48.
To the best of our knowledge, the only study to investigate avian heart-rate responses to drone ights was
conducted by Weimerskirch etal.32, which involved intentionally ying a drone over adult and chick King pen-
guins (Aptenodytes patagonicus) to examine their responses to drone passage and approaches using externally
mounted heart-rate biologgers. e authors found that both adult and chick heart rates increased in response
to drones at low altitude (2–50m above ground) surveys by the aircra. While these results demonstrate the
potential for approaching drones to cause physiological stress responses in birds, there is still a need to estimate
these responses during standardized transect surveys (i.e., species-specic drone methods that would actually
be used to collect imagery of nesting colonies).
We investigated changes in heart rate of incubating female Common eiders (Somateria mollissima, hereaer,
“eiders”) in response to colony survey ights by a small, quadrotor aerial drone. Since female eiders are highly
cryptic during incubation (Fig.1a) and nest densely within easily-disturbed colonies where ground-based surveys
are impractical49, drone-based surveys oer an ecient method to accurately survey population numbers and
densities, as long as they do not unduly disturb birds. Rather than using a drone to test at which specic height
or speed eiders responded physiologically to a drone, we instead collected data on birds equipped with heart-rate
monitors during an actual transect-based drone survey that was carried out to quantify nesting eiders. We pro-
ceeded with the hypothesis that incubating eiders would interpret the ight of a survey drone as an environmental
stressor, and that eiders would exhibit an increase in their heart rates during drone surveys as compared to their
resting, baseline heart rates measured in the absence of drone survey ights. Our overall goal was to quantify
whether aerial drone ights elicited a physiological response by nesting eider ducks, and assess whether drone
aerial surveys are a practical method for surveying colonial birds non-invasively.
Results
e two drone ights required to map the entire study area at 30m Above Ground Level (AGL) took 43min,
with an approximately 4-min-long gap in between ights to change batteries and SD cards. During drone ights,
no mammalian predators (e.g., Polar bears Ursus maritimus, or Arctic foxes Vulpes lagopus) were present on East
Bay Island. Nonetheless, Herring gulls Larus argentatus are common on East Bay Island (i.e., 25–30 breeding
pairs annually) and were therefore present during both ights. No focal eiders le their nest during drone ights
(i.e., displayed a ush response), and none of our focal nests were predated during the study period.
We collected 93 heart-rate samples from 11 focal eiders across the four drone distance categories. Mean date
of incubation (DOI) on June 29th was 7.6days (± 4.0 SD). For most individuals, we were able to collect 2 to 3
samples for each distance category, although three birds had categories where we were unable to collect a sample
of sucient audio quality (see TableS1 in Supplementary Materials).
Figure1. Photographs of (a) an incubating female Common eider Somateria mollissima (photo credit to Erica
Geldart) and (b) an articial-egg (white egg) in a Common eider nest (photo credit to Reyd Smith).
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We failed to detect a statistically signicant dierence in heart-rate response across all categorical treatment
levels (control, 0–150m, 151–300m, > 300m; F3,58 = 0.31, P = 0.82, Table1). We also failed to detect a statistically
signicant dierence in heart-rate response due to DOI (F1,58 = 0.01, P = 0.91). ese results suggest that incubat-
ing eiders within our study did not show a change in heart rate as a result of our drone-based population survey,
while accounting for DOI. Concordantly, least means data scale estimates of the mean and condence intervals
were similar for each distance category (Fig.2). Our random eects structure signicantly improved model t
(LRT: χ2(2) = 16.34, P < 0.0001), and covariance estimates for eider ID were higher than sample number (i.e.,
10-s heart-rate samples within each distance bin). is indicates that there was a higher proportion of variance
in heart rate attributed to individual eider, rather than sample number (i.e., intra-individual variance). Overall,
our full model, that included our distance categories and DOI as xed eects, received less support than the
intercept-only model (AICc = 21.40 vs. 4.18 respectively).
Discussion
e highly cryptic camouage of incubating female eiders makes them particularly dicult to survey. To detect
nesting eiders using aerial photography necessitated that aerial drones y 30m from the ground and y quickly
at approximately 10 m−s (Jagielski, Love and Semeniuk, unpubl. data); both requirements having the potential
to disturb nesting eiders. Despite this, results from our study indicated that these drone-based survey methods
did not impact nesting female eider heart rates and suggests that drone imagery can be used to survey nesting
eider ducks; a species known to be sensitive to ground-based surveys50. Our study therefore adds to the growing
body of literature which suggests that, if used appropriately, drones can be a suitable tool for surveying wildlife
Table 1. Model parameter estimates for eect of drone distance category (controlling for reproductive
investment; DOI – date of incubation) on heart rates of nesting Common eiders (Somateria mollissima).
Estimates obtained from 93 observations across 11 individual eiders. Distance categories are compared to
reference category that is the control period before drone ights occurred. ¥ Sample number nested within
Eider ID.
Model parameter Estimate ± SE
Fixed eects
Intercept 4.658 ± 0.13
0–150m −0.056 ± 0.07
151–300m 0.014 ± 0.06
> 300m −0.001 ± 0.06
DOI −0.002 ± 0.01
Covariance estimates
Eider ID 0.027 ± 0.02
Sample number¥0.0003 ± 0.005
Residual 0.047 ± 0.008
Figure2. Data scale model estimates for Common eider (Somateria mollissima) mean heart rates (bpm) in
each distance category (± 95% condence intervals). Dashed, coloured lines indicate mean and range of raw
heart-rate data for individual eiders. Note that points without range bars indicate only a single data point for that
individual within that category. Ranges jittered for clarity.
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populations while minimizing disturbance (e.g., Adélie penguins Pygoscelis adelia, Lesser black-backed gulls
Larus fuscus, and several species of penguin, albatross, and cormorant3,30,32). However, as the eiders in our study
had been previously exposed to practice ights and other drone projects on the island earlier in the season (see
“Methods”), it is possible that some degree of habituation occurred, masking any novel-stimulus response. How-
ever, we believe that sensitization to repeated ights is unlikely because of our lack of response despite our eiders
being exposed to previous ights. We acknowledge this logistical diculty, but contend that our ndings still hold
merit as an initial examination of heart-rate response to this emerging wildlife survey tool. In future studies, we
recommend researchers explore whether heart-rate responses vary over successive ights. Here we discuss our
ndings in relation to other relevant survey protocols used within and across breeding bird populations, as well
as implications of our ndings for the long-term monitoring of eider ducks. Note that the eect of recreational
drone use is beyond the scope of this study and the results should not be interpreted as such.
Our ndings suggest that drone surveys oer a less invasive alternative to ground- and occupied aircra-
based surveys, which have been associated with increases in heart rate by colonial-nesting seabirds (e.g., Adélie
penguins, Wandering albatrosses Diomedea exulans, Giant petrels Macronectes halli36,46,51,52). e only study (to
the best of our knowledge) that has directly compared avian responses to multiple censusing methods found that
drones caused less disturbance in terms of behavioural alterations, compared to investigators walking through
the colony making counts (e.g., Lesser black-backed gull3). However, a lack of behavioural response is not indica-
tive of no response, e.g.,36; thus additional studies that use heart rate as a physiological metric for disturbance to
directly compare animal responses across dierent censusing methods are needed.
We found that incubating eiders did not leave their nest, nor alter their heart rate, in response to a rotary
propeller drone ying over them as part of grid-pattern survey ights. Nonetheless, we caution that this lack of
heart-rate response at the tested altitude should not be considered repeatable in other species without further
testing. For example, the perceived level of threat generated by a drone can depend on both the attributes of the
drone and modes of operation33. Our study area is relatively small and can be mapped eciently with slower
moving rotary-wing drone models (Jagielski, Love, and Semeniuk, unpubl. data). Alternatively, xed-winged
aircras, which may be better suited to large-scale research on non-cryptic species, may resemble the silhouettes
of many avian predators, resulting in a higher perceived threat by bird species compared to quadcopter-style
drones33,53,54. e size of the drone is also known to inuence animal responses, with larger drones causing greater
reactivity than smaller ones33. Moreover, the noise level of a multirotor drone is limited when compared to xed-
wing and fuel-powered drone55. Whether a species can detect the sound emitted by a drone and at what specic
threshold drones cause acoustic disturbance depends on ambient noise levels, auditory capabilities of target
species, and drone altitude55,56. For example, the noise emitted by a multirotor drone of similar size class and at
the same altitude as in the current study was lost in the background noise and therefore caused no disturbance to
Chinstrap penguins (Pygoscelis antarcticus) during breeding55. Moreover, research supports the ‘distance hypoth-
esis’, which posits that animals display fewer disturbance responses to drone ights at greater altitudes29,32,33. As
such, disturbance impacts from the use of larger, noisier, or xed-wing drones in other study systems may be
mitigated by the comparatively higher survey altitudes typical of these models30. Research also indicates birds
are more likely to respond to drones exhibiting ight patterns similar to those used by avian predators such as
vertical ight patterns and target-oriented approach angles rather than horizontal ight patterns or tangential
angles33,54,57–59. Although our population-census work found no impacts on heart rate, future planned ights at
dierent altitudes and patterns may be required in future eider studies. As such, further testing of whether those
altered ight techniques impact physiological responses in eiders may be justied. Importantly, we recommend
these future studies be designed to examine disturbance eects in the context of actual survey protocols that
would be used for data collection in respective study systems instead of experimenting with drone-based methods
that would not be reproduced in standard drone-use protocols.
A bird’s perceived level of threat by a drone can also depend on characteristics of the birds themselves33.
Birds may respond dierently to disturbance depending on the investment ‘value’ of their current clutch. Birds
oen exhibit greater behavioural (e.g., ushing distance by several species of waterfowl60; return time by Com-
mon eiders49) and physiological responsiveness (e.g., greater modulation of heart rate by King penguins61) to
disturbance in their earlier stages of breeding than birds further along in their breeding, presumably because the
probability of eggs surviving until hatching increases and so does the expected benet of current reproduction
relative to the parent’s survival and future reproduction62. e response of eiders to drone ights in this study did
not dier in relation to their stage of incubation up until 17days. We note that our research did not evaluate the
response of eiders during the last stage of incubation, when body lipids are depleted (i.e., phase III at 23–26days
of fasting63). Because stress-induced heart-rate responses can be energetically costly for birds, e.g.,64, we might
expect the largest eect of incubation stage on eider responses to occur at the end of their incubation period
when eiders are most energetically limited. Given the timing of our study in relation to eider incubation stage
(see “Methods”), we cannot evaluate this possibility. As such, drone surveys of eiders occurring during the nal
phase of incubation and fasting should be studied, particularly if a project intends to carry out repeated ights
throughout the breeding period (e.g., colony formation mapping12). Next, individual variation in circulating
corticosterone may dierentially mediate physiological and behavioural responses to threats, e.g.,41,65, and may
therefore create inter-eider variation in sensitivity to drones. Finally, for nesting eiders in our study area, the
threat of avian predators comes primarily from herring gulls that commonly consume eider eggs and ducklings,
but pose little threat to adult eiders. However, other breeding populations of eiders or avian species that experi-
ence predation from avian predators of adults may perceive a greater threat to drones.
Research eorts on drone technology and best practices for wildlife and ecological science are still develop-
ing as a methodology, so eorts must be made to minimize disturbance to focal wildlife66. For example, human
disturbance can produce long-term eects on colonial breeding bird individuals and populations23,24. Incubation
alone has a high energetic demand for eiders67,68, so any further energy loss from drone disturbance during the
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incubation period may have negative reproductive tness consequences. Findings from the current study suggest
that eiders do not endure tness consequences associated with a heart-rate response from a drone ying over
them during a transect survey design. Research that monitors both behavioural and physiological responses of
wildlife during comprehensive drone-based survey methods will help improve population survey results, as well
as evaluate the impacts of drone use for wildlife studies, and ultimately help to minimize disturbance to wildlife69.
Methods
Study species and area. We conducted our study on incubating female eiders nesting on East Bay (Miti-
vik) Island, Nunavut, Canada (64° 02′ N, 81° 47′ W) in June of 2019. East Bay Island is a small (approximately
800×400m), low-lying, and predominantly rocky island (Fig.3) that supports the largest known Common eider
breeding colony in the Canadian Arctic (Inuit Nunangat)70. Breeding and demographic studies on this colony
have been conducted continuously since 199750,71–73. Traditionally, breeding colony size and density estimates
have been derived via multiple methods; namely, trained observers in xed observation blinds have conducted
point counts of female eiders nesting in long-term study plots and scaled up to the area of the island, as well
as hatched eider nests at the end of the breeding season (Love etal. pers. comm.). However, although these
estimates have been robust, they are inherently associated with a degree of uncertainty. Moreover, over the
past 10years, factors such as large-scale mortality from novel disease outbreaks (e.g., avian cholera74–76) and an
increasing rate of Polar bear nest predation77,78 has made quantifying accurate estimates of colony size and den-
sity increasingly dicult and dangerous to collect in this manner. As such, we have begun to turn to aerial drone
surveys to determine whether the dynamic breeding colony can be censused safely and more accurately than via
previous methods. No animals were handled for the current study.
Heart‑rate monitoring. On June 24th, 25th and 28th, we deployed heart-rate monitoring equipment dur-
ing laying/early incubation in active eider nests (n = 11) for another set of projects (Geldart etal. in review).
Study nests were located in areas of low nesting densities to limit researcher-induced disturbance in denser
portions of the colony. Study nests were each located an average (± SD) of 302 ± 162m (range: 11–560m) apart
from each other. Each nest was equipped with an articial-egg heart-rate monitor. Our heart-rate monitors were
adapted from Giese etal.79, who found that resting heart rate of Adélie penguins recorded using articial eggs
were indistinguishable from those recorded using electrocardiogram units attached externally to the penguins
(Fig.4; see Geldart etal. in review for more details). Heart-rate monitors (Fig.4a) consisted of a 3D-printed
plastic eider egg (sub-elliptical, 2.9 in. long × 1.9 in. at the widest point, Fig.4b) equipped with two Electret
Figure3. Map of study area, (a) Nunavut, Canada, (b) Southampton Island, Nunavut, Canada, (c) East Bay,
Nunavut, Canada and (d) East Bay Island, Nunavut, Canada. Canadian Provinces and Territories map layers
provided by ESRI online, accessed May 30, 2018. Map layer of East Bay Island created using ArcMap v10.6.1
(Esri, Redlands, CA, USA).
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condenser microphones (PUI Audio model AOM-5024L-HD-R) for stereo recording. e microphones were
each soldered to the bare end of a shielded cable assembly (approx. 72 in. long) with a stereo plug (3.5mm) at
the opposite end. e primary microphone was situated at the end of a 3D-printed plastic funnel for amplied
sound while the secondary one was pressed against an opening on the surface of the egg to increase the likeli-
hood of recording heart sound when the eider shied positions on the egg (i.e., o the primary microphone).
We weighted the bottom half of the egg to ensure the egg maintained a xed orientation in the nest, with the
microphones always facing toward the eiders’ brood patch. Once equipment was assembled, each half of the
3D-printed egg was fastened together with glue and covered by a balloon membrane for waterproong. Both
microphones were wired to a recorder (Tascam DR-05X equipped with a 128GB microSD card) by plugging
the stereo plug into the recorder’s stereo mini jack. e recorder was also attached to an external assembled
battery pack (with 24 AA Lithium Ion batteries) to allow for 11–12days of continuous recording. e recorder
and battery pack were situated within a weatherproof camouaged storage box (11.4 in. long, 5.4 in. wide, 7.1
in. high, Fig.4c) located approximately one meter outside the nest and the box and cable were secured with the
surrounding terrain. e rst-laid egg from each study nest was collected and used to estimate incubation stage
(see Estimating incubation stage below). is rst-laid egg was replaced with the articial egg (Fig.1b). Previous
research has suggested that using articial-egg heart-rate monitors had no eects on incubation behaviour79 or
nest survival47, thus it was assumed that incubating the articial eggs would not aect the eiders dierently from
incubating their natural eggs. In the current study, no nest abandonment occurred aer aer nest equipment
deployment andbirds returned to their nest approximately 71min (i.e., average, 5s to 7h range) aer equipment
was deployed on their nest.
Estimating incubation stage. e rst-laid eggs collected from each study nest were immediately can-
dled to assess the stage of embryo development and hence to estimate the DOI for each focal hen80. ese
methods provided estimates of the number of days the rst-laid eggs had been incubated on the day heart-rate
monitoring equipment was deployed, and therefore the degree of incubation investment females had made up to
June 29th (i.e., the day of drone survey ights). DOI was measured as a means to control for potential changes in
heart rate with increased investment in the clutch61.
Drone surveys. We conducted drone survey ights on June 29th using a small, quadrotor DJI Phantom 4
Pro drone. However, prior to these ights on June 29th, several practice ights and other drone projects had
taken place on the island. us, the ights investigated on the 29th were not the only ights to which eiders had
been exposed, but these were the earliest ights available to which we could pair heart-rate recorders to. Flights
were planned as semi-autonomous parallel line transects at 30m AGL. Survey altitude and ight paths were
chosen to produce a georeferenced orthomosaic of suciently high image resolution to estimate the number of
nesting eiders on East Bay Island. Due to battery life constraints, two survey ights were required to map the
entire 24-hectare island at 30m AGL. Briey, the drone was launched from the East Bay Island research com-
pound, and the drone automatically moved towards the start of its line transect, where it then moved along a
straight-line ight path collecting imagery. Following completion of each transect, the drone moved to the next
Figure4. Schematics of (a) an articial-egg heart-rate monitor, (b) the interior of the articial-egg and (c) the
interior of the airtight camouage storage box.
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transect start position and continued with this “lawn-mower” ight pattern33 until the end of the ight. e two
drone ights conducted on June 29th took place from 8:52:37–9:13:12 and 9:17:28–9:35:59.
During surveys, the drone’s position (latitude and longitude) was logged several times each second with an
associated Unix timestamp, stored onboard the drone’s SD card which were downloaded following ight comple-
tion. We used this time and position data for the drone to estimate distance of the drone to each focal eider nest
throughout each ight. We paired this time and location data of the drone with the eider heart-rate monitors to
estimate heart rate of eiders for dierent distances to the drone during surveys (see Heart-rate quantication).
For additional technical specications and details on ight operations, see the Drone Reporting Protocol in
Supplementary Materials81.
Heart‑rate quantication. To quantify eider responses to our drone ight survey, we made opportun-
istic use of the heart-rate recording data. We reviewed heart-rate recordings of eiders using the sound analysis
soware Audacity (Version 2.3.2)82. We synchronized drone GPS and heart-rate monitors to the nearest minute
from the clock of an iPad (Apple Inc.). We estimated baseline (i.e., resting) heart rate of eiders by collecting
heart-rate samples approximately 2h before drone ights commenced. We collected up to three 10-s samples
from each eider at approximately 7:00am, 7:15am, and 7:30am on June 29th. No researchers were present on
the ground within the eider colony during the study period, but instead were all working within the research
compound (approx. 12m from the nearest eider nest and not within view of the incubating eiders). For measures
of heart rate during the drone ights, the lateral distance of the drone from each nest was separated into three
distance categories (0–150m, 151–300m, and greater than 300m). We chose these large distance categories
due to the relatively fast ight speed of the drone (approximately 10 m-s). To accommodate any dierences in
temporal resolution between the drone and the heart-rate monitors, we identied time periods where the drone
was within a given distance category for ≥ 60s, and collected 10-s samples of heart rate from the approximate
center of that time period48. For example, if a drone was within 0–150m of the focal eider nest from 08:55:30
to 08:56:30, we attempted to nd a clean audio sample from the heart-rate recorder at the approximate 08:56:00
mark in Audacity. To maximize our sample size, we sometimes shied our sampling time several seconds to
obtain unambiguous heart-rate measures. Unclear recordings may result from the bird moving on their nest,
which has been associated with increased heart rate83, thus avoiding unclear recordings of heart rate may skew
our data. However, discarding ambiguous measures of heart rate (as done in previous studies using articial-egg
heart-rate monitors47,48) would have the same eect and reduce sample size. For each focal nest, we collected
up to three heart-rate samples in each distance bin along with the control period before thedrone ight started.
However, not all focal nests had clean audio les for each distance bin, resulting in dierent sample sizes for
each bird in each bin (see Results). We extracted all the samples as wav les and heartbeat sounds were counted
aurally at least twice to avoid measurement error and therefore increase accuracy of the counts. Finally, we
extrapolated each 10-s sample to beats-per-minute for statistical analysis of heart rate.
To conrm eider presence on their nest during drone ights we combined the heart-rate recording data with
simultaneous video footage recorded from Browning trail cameras (model: BTC-5HDPX, set to motion-trigger
activation) placed approximately one meter from each focal nest for another project (Geldart etal. in review).
Statistical methods. To estimate changes in eider heart rate (bpm) during drone surveys, we constructed
linear mixed models using PROC GLIMMIX in SAS Studio v3.884. We modeled eider heart rate as a function
of the xed eect for distance category (categorical with 4 levels: control period before ight, 0–150m, 151–
300m, > 300m), as well as a xed eect for estimated DOI on June 29th (continuous, range 3–17). We included
heart-rate sample number as a nested random eect for each distance category within eider ID. Since sample
numbers for each distance category were ordered by time of day (e.g., sample 1 comes before sample 2, etc.),
this nested random eect structure should partially account for the increased exposure of eiders to the drone
throughout the sampling period. We tested whether this random eect structure signicantly improved model
t compared to the xed eect only model using a likelihood ratio test. We log-transformed eider heart rate
to facilitate the assumption of a Gaussian distribution, assumed a variance-components covariance structure,
and an identity link function. Note that we intentionally did not include an autoregressive covariance structure
here as (1) exploratory autocorrelation function plots did not reveal any obvious signs of temporal autocorrela-
tion, and (2) individual samples for each eider were not equally spaced in time due to the opportunistic nature
of heart-rate data collection, which violates a main assumption of the autoregressive covariance structure. We
assessed model t by examining Studentized and Conditional Pearson’s residual plots and comparison of AICc
scores between our full model and an intercept-only model85,86.
We back transformed model least-square means and 95% condence intervals of the mean to the original
data scale (heart rate bpm). Prior to model construction in SAS, all data manipulation was done with packages
dplyr87, lubridate88, and geosphere89, while plots were constructed with ggplot290 in R Studio v3.6.291. For all
statistical signicance tests, we used α = 0.05.
Permit statement. Data collection and monitoring of eider nests were authorized by Migratory Bird
Sanctuary Permit, Canadian Wildlife Service MM-NR-2019-NU-013, Migratory Bird Scientic Permit, Cana-
dian Wildlife Service SC-NR-2019-NU-006, Water Licence, Nunavut Water Board 8WLC-PCE1920, Wildlife
Research Permit, NU – Department of Environment – Wildlife Division, Land Use Permit, Indigenous Aairs
and Northern Development Canada, Animal Care Permit, Environment and Climate Change Canada 19GG26,
EC PN 18 026, Animal Care Permit, University of Windsor: AUPP – Reproductive Strategies of Arctic-Breeding
Common Eiders 19–11.
Content courtesy of Springer Nature, terms of use apply. Rights reserved
8
Vol:.(1234567890)
Scientic Reports | (2022) 12:18804 | https://doi.org/10.1038/s41598-022-22492-7
www.nature.com/scientificreports/
Drone operations in this study were performed in accordance with the rules of the Canadian Aviation Regu-
lations and Nunavut Wildlife Research Permit WL-2019-027, and the pilot obtained a Drone Pilot Certicate
from Transport Canada (Issued: 2019-02-28, certicate number: PC1905952549, Transport Canada Account
Number: TC1905980118). e DJI Phantom 4 Pro was registered with Transport Canada on 2019-05-23 to
Christopher Harris.
Data availability
e datasets used and/or analysed during the current study available from the corresponding author on reason-
able request.
Received: 23 June 2022; Accepted: 14 October 2022
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Acknowledgements
is research was supported by grants and logistics provided by the Canadian Wildlife Service (Iqaluit Oce
CWS); Wildlife Research Division of Environment and Climate Change Canada (ECCC); Nunavut Arctic Col-
lege; Polar Continental Shelf Project (PCSP); Northern Scientic Training Program (NSTP); ArcticNet; and
Natural Sciences and Engineering Research Council of Canada (NSERC). We thank Patrick Jagielski for their
previous drone census testing in 2018, which led us to the technique chosen to census the eider colony in 2019.
We thank Bronwyn Harkness and Holly Hennin for coordinating the 2019 East Bay Island eld season. We
appreciate Lincoln Savi for ying the drone, and Reyd Smith, Brandan Norman, Bronwyn Harkness, Russell
Turner, Christophe Boyer, Ariel Lenske, Brian Smith, Lenny Emiktaut, Sarah Neima, and Willow English for
additional assistance with East Bay Island eldwork. We particularly thank Josiah Nakoolak, Jupie Angootealuk,
Mark Eetuk for additional research assistance and eld guidance. Dr. Evan Richardson provided the photo of
the drone used in this study for the creation of FigureS1, for which we are grateful. We wish to thank Dr. Brian
Darby for statistical advice during the analysis stage of this manuscript.
Author contributions
C.A.D.S., O.P.L., E.A.G., A.F.B., and C.H. conceived the ideas and designed methodology, C.H. designed and
constructed the heart-rate monitors, E.A.G. collected the data, A.F.B. analysed the data, E.A.G. and A.F.B. wrote
the manuscript. All authors contributed critically to the dras and gave nal approval for submission to Scientic
Reports.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 022- 22492-7.
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