Drone-based photogrammetry assessments of body size and body condition of Antillean manatees

Abstract

Assessments of individual animal health alerts to early signs of population level effects in wildlife but often rely on logistically complex wild animal captures, hindering our understanding of the wellbeing of populations in regions with limited resources. Here, we tested photogrammetry methods using small aerial drones for accurate morphometric measurements of Antillean manatee (Trichechus manatus manatus) body size and body condition. We flew drones to collect aerial imagery of captive manatees in Quintana Roo, Mexico and compared manatee body size measurements from scaled aerial imagery with physically measured body sizes. To assess optimal altitude for imaging, body size measurements acquired with an out-of-the-box drone were compared to measurements from the same drone model equipped with a LiDAR for precision altimetry flown at three altitudes (30 m, 50 m, 70 m). The accuracy of body size measures was similar for all drone models but improved with the addition of LiDAR. Difference in body size estimates between manual and drone-based measurements indicate a correction factor may be needed to account for disparities. We then used body size measurements to develop a body condition index for Antillean manatees. Our findings highlight the strength of low-cost aerial drones for morphometric measurements and assessments of manatee body condition.

Full text

Vol.:(0123456789)
1 3
Mammalian Biology
https://doi.org/10.1007/s42991-022-00228-4
ORIGINAL ARTICLE
Drone‑based photogrammetry assessments ofbody size andbody
condition ofAntillean manatees
EricAngelRamos1 · SarahLandeo‑Yauri1 · NatalyCastelblanco‑Martínez1,2,3 · MariaRenéeArreola4·
AdamH.Quade5· GuillaumeRieucau5
Received: 12 October 2020 / Accepted: 5 January 2022
© The Author(s) under exclusive licence to Deutsche Gesellschaft für Säugetierkunde 2022
Abstract
Assessments of individual animal health alerts to early signs of population level effects in wildlife but often rely on logisti-
cally complex wild animal captures, hindering our understanding of the wellbeing of populations in regions with limited
resources. Here, we tested photogrammetry methods using small aerial drones for accurate morphometric measurements of
Antillean manatee (Trichechus manatus manatus) body size and body condition. We flew drones to collect aerial imagery
of captive manatees in Quintana Roo, Mexico and compared manatee body size measurements from scaled aerial imagery
with physically measured body sizes. To assess optimal altitude for imaging, body size measurements acquired with an
out-of-the-box drone were compared to measurements from the same drone model equipped with a LiDAR for precision
altimetry flown at three altitudes (30m, 50m, 70m). The accuracy of body size measures was similar for all drone models
but improved with the addition of LiDAR. Difference in body size estimates between manual and drone-based measurements
indicate a correction factor may be needed to account for disparities. We then used body size measurements to develop a
body condition index for Antillean manatees. Our findings highlight the strength of low-cost aerial drones for morphometric
measurements and assessments of manatee body condition.
Keywords Body condition· Health indices· Photogrammetry· Trichechus manatus· Unmanned aerial vehicles
Introduction
Wild animal health is often evaluated through assessment of
their body condition, i.e., the accumulation of energy stores
in the form of higher lipid content relative to lean tissue
(e.g., McKinney etal. 2014; Norkaew etal. 2018; Christian-
sen etal. 2020a, b). Individuals in good body condition can
have higher survivability, reproductive success, and better
ability to cope with environmental stressors compared to
animals in poor condition, all of which confer fitness benefits
(Stevenson and Woods 2006; Clutton-Brock and Sheldon
2010). Poor body condition and population health in wildlife
have been associated with major losses of available habi-
tat impacting animal reproduction and survival (Preen and
Marsh 1995; Harwood etal. 2000). Body condition assess-
ments in wild populations help to determine the baseline
health status of individuals, detect animals in poor health,
and estimate survival and mortality rates. Additionally, they
can provide insights into the environmental and anthropo-
genic causes and the consequences of these effects within
and across populations (Stamper and Bonde 2012).
Handling editors: Stephen C.Y. Chan and Leszek Karczmarski.
This article is a contribution to the special issue on “Individual
Identification and Photographic Techniques in Mammalian
Ecological and Behavioural Research – Part 1: Methods and
Concepts”—Editors: Leszek Karczmarski, Stephen C.Y. Chan,
Daniel I. Rubenstein, Scott Y.S. Chui and Elissa Z. Cameron.
* Guillaume Rieucau
grieucau@lumcon.edu
1 Fundación Internacional para la Naturaleza y la
Sustentabilidad, Chetumal, QuintanaRoo, Mexico
2 Consejo Nacional de Ciencia y Tecnología, Chetumal,
QuintanaRoo, Mexico
3 División de Desarrollo Sustentable, Universidad de Quintana
Roo, Chetumal, QuintanaRoo, Mexico
4 Dolphin Discovery Group, Cancún, QuintanaRoo, Mexico
5 Louisiana Universities Marine Consortium, Chauvin, LA,
USA

E.A.Ramos et al.
1 3
Body condition indices typically include a variety of
insitu and laboratory-based measures of morphology, blood
chemistry, and physiological condition linked to animal
health (Stevenson and Woods Jr 2006; Morfeld etal. 2016;
Booth etal. 2020). These assessments can be logistically
complex in free-ranging animals, particularly in marine
mammals where most species are fully aquatic (e.g., ceta-
ceans, sirenians) and are impractical to capture and measure
insitu. Collecting physiological data including blood chem-
istry and morphometrics on wild marine megafauna often
relies on the direct capture and release of free-ranging ani-
mals (Wells and Scott 1990; Lanyon etal. 2010; Bonde etal.
2012) or on stranded animals (Larrat and Lair 2021). Live
capture and release methods for wild animals are expensive
and complicated undertakings, requiring teams of trained
experts in animal handling and veterinarians to safely cap-
ture animals (e.g., Alves-Stanley etal. 2010; Bonde etal.
2012; Sulzner etal. 2012). Collecting data to develop robust
condition indices for evaluating the health of wild marine
mammals, and, therefore, assessing their body condition is
a pressing challenge.
Body size is an important characteristic of animals asso-
ciated with their reproduction, energetics, and survival
(Speakman 2005).Photogrammetry (i.e.,measurements
throughtheuse of photographs) provides a non-invasive
alternative for remote acquisition of morphometric meas-
urements of animals(Booth etal. 2020), and has been suc-
cessfully used to estimatethe age, sex, body size and mass in
several terrestrial and aquatic mammals (Karczmarski etal.
2022a, b). In marine mammals, photogrammetry techniques
have been applied broadly for measuring body size and esti-
mating body condition of several species of baleen whales
(Best and Rüther 1992; Pettis etal. 2004; Christiansen etal.
2016; Johnston etal. 2022), dolphins (Perryman and Lynn
1993; Fearnbach etal. 2018), pinnipeds (Goebel etal. 2015;
Pomeroy etal. 2015), and sirenians (Flamm etal. 2000).
These methods produce reliable body length estimates in a
variety of species (Webster etal. 2010; Wong and Auger-
Méthé 2018) and can be used to reveal trends in growth and
survival (Cheney etal. 2018), and to identify regional differ-
ences in morphometric patterns (van Aswegen etal. 2019).
The basic methods for acquiring these data on individual
animals require at least one camera positioned from a boat,
aircraft, or land, paired with a method for acquiring a reli-
able scale in images (Cheney etal. 2018). Scaling imagery to
precisely estimate animal body size can be accomplished in
multiple ways, including the use of a scaled reference object
of known size, calibrated cameras (Dawson etal. 2017), or
two parallel lasers mounted to a camera body projecting two
points of a calibrated distance onto animal bodies (Durban
and Parsons 2006; Rohner etal. 2015).
Unmanned aerial vehicles (hereafter “drones”) are ideal
platforms for low-cost and non-invasive morphometric
measurements of marine mammal body size because of their
capacity for precision flight, and the ability to carry numer-
ous sensor packages for scaling and observation (Smith etal.
2016; Schofield etal. 2019). Drone-based photogrammetry
has been applied to investigating the health, body condition,
energetics, and reproductive biology of large-bodied marine
animal species including pinnipeds (Allan etal. 2019), killer
whales (Orcinus orca) (Durban etal. 2015), blue whales
(Balaenoptera musculus) (Durban etal. 2016), and hump-
back whales (Megaptera novaeangliae) (Dawson etal. 2017;
Christiansen etal. 2019; Aoki etal. 2021). These studies can
provide compelling data needed for identifying the causes of
population impacts such as mortality events. For example,
Christiansen etal. (2021) identified poor body condition of
eastern North Pacific gray whales (Eschrichtius robustus)
associated with an unusual mortality event likely driven by
starvation of animals. Studies developing and testing basic
photogrammetry techniques using low-cost small multirotor
aircrafts can improve our ability to derive meaningful body
condition indices for monitoring cryptic and endangered
species.
The West Indian manatee (Trichechus manatus) — com-
prised of two subspecies, the Antillean manatee (T. m.
manatus) and the Florida manatee (T. m. latirostris) — are
threatened by extensive exploitation and high mortality rates
from current human impacts. The Antillean subspecies is
considered Endangered (Self-Sullivan and Mignucci-Gian-
noni 2008), and inhabits diverse coastal and riverine habitats
throughout the Caribbean Sea, Mexico, Central America,
and northern South America (Quintana-Rizzo and Reynolds
III 2010). Their populations are generally small and display
limited genetic diversity and dispersal (Garcia‐Rodriguez
etal. 1998; Hunter etal. 2012), with an estimate of less than
5700 individuals for the entire subspecies (Deutsch etal.
2008; Self-Sullivan and Mignucci-Giannoni 2008). Mana-
tees face high levels of mortality from natural phenomena
(e.g., algal blooms, epizootic diseases; Foley etal. 2019;
Rodríguez etal. 2019), intensive hunting and other anthropo-
genic causes (e.g., watercraft collision, bycatch; O'Shea etal.
1985; Bonde etal. 2004). These impacts have led to local
extirpation of manatees (e.g., in some areas of the Veracruz
State, Mexico; Serrano etal. 2017) exposing the subspecies
to major population-level threats and causing species-wide
decline (Domning 1982; Castelblanco-Martínez etal. 2012).
Investigations into the health and body condition of West
Indian manatees have been greatly facilitated by several
long-term capture and release efforts providing researchers
with the data needed to assess animal health in systematic
regional studies (Bonde etal. 2012; Stamper and Bonde
2012). Body condition indices (BCIs) based on length,
umbilical girth, and mass have been developed for assess-
ing individual health and evaluating the condition of Flor-
ida manatees (Harshaw etal. 2016) and Antillean manatees

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
(Castelblanco-Martinez etal. 2021). Flamm etal. (2000)
demonstrated that aerial video photogrammetry from a teth-
ered airship was effective for determining the size of Florida
manatees and estimating their life-stage structure. However,
aerial imaging of free-ranging manatees is complicated by
the small proportion of time manatees spend lying flat at
the surface in a position where body size can be reliably
estimated, and by the challenge of validating aerial estimates
to the actual body size of wild animals. Thus, the accuracy
of different aerial photogrammetry methods for studies of
manatee health and biology should be tested on novel plat-
forms with good control data of animals of known size.
Few studies have used aerial drones to study manatees. In
clear shallow waters, small aerial drones improved the detec-
tion of Antillean manatees and enabled observations of their
behavior (Ramos etal. 2018), and photo-identification of
individuals (Landeo-Yauri etal. 2020). Behavioral reactions
to the overhead drone during approaches and stable hovering
were most prevalent at low altitudes (< 20m) suggesting
non-invasive use of these systems should prioritize higher
altitude flight (Ramos etal. 2018). Using the same dataset,
Landeo-Yauri etal. (2020) developed a visual classification
method for photo-ID of individual manatees demonstrating
its use for reidentifying a portion of the population. The
long-term conservation and protection efforts of manatee
populations would benefit from small drone surveys and
advancements of image/video post-processing techniques
for developing non-invasive platforms for manatee health
assessments (Raoult etal. 2020).
Here, we conducted a series of experiments to test the
efficiency of photogrammetry using small aerial drones to
estimate body size and body condition of Antillean mana-
tees. Our goals were: (1) to test if photogrammetry meas-
urements from drones provide reliable estimates of manatee
body size using a scaled reference object compared to the
ground sampling distance and onboard sensor data; (2) to
examine if the addition of a LiDAR sensor for altimetry
improves accuracy in body size estimates; and (3) to use
these data to investigate the resulting differences in BCI pro-
duced by manual measurements and drone-based methods
for health assessments of Antillean manatees.
Methods
Study design
We conducted two experiments to assess the accuracy of
drone-based methods for measuring manatee body size and
using these measures to estimate their body condition. We
provide a detailed workflow outlining steps undertaken in
this study in Fig.1. In Experiment 1, we examined the differ-
ences in accuracy of manatee body size estimates acquired
from drone-based photogrammetry of manatees with and
without a reference object of known size to scale imagery.
In Experiment 2, we compared the accuracy of photogram-
metry between a drone equipped with a LiDAR package for
accuracy altimetry to a drone without LiDAR. The data from
Experiment 1 were then used to develop preliminary body
condition estimates for manatees using manual straight-
line measures of body length and curvilinear measures of
umbilical girth (Fig.2) compared to photogrammetry-based
straight-line measures of body length and maximum width.
Study site andsubjects
We conducted our experiments with 18 captive Antillean
manatees of various ages of both sexes (Table1) at four
Dolphin Discovery facilities in the state of Quintana Roo on
the Caribbean Sea coast of Mexico (Fig.2). Manatees were
classified based on total straight-line body length as adults
(> 225cm), juveniles (176–225cm), or calves (< 175cm)
(Mignucci-Giannoni etal. 2000). All manatees were housed
in the same pool with at least one other manatee (Table1).
The health and welfare of all animals were regularly assessed
during veterinary exams and all manatees were estimated to
be in the good physical condition and in good health.
Equipment
Two different consumer-grade multirotor drone models
were used: a DJI Phantom 4 Professional V1.0 (“Phantom
4” hereafter) in Experiment 1; and one DJI Mavic Pro 2 was
used as is (“Mavic” hereafter); and a second Mavic equipped
with a custom-built LiDAR/GPS sensor package (Dawson
etal. 2017) in Experiment 2 (Fig.2). The LiDAR package’s
data-logging system was powered by a 4500-mw lithium
battery and recorded altitude and a GPS location through-
out flights (Dawson etal. 2017). The package was mounted
to the bottom of the body of the aircraft, oriented straight
down aligned with the onboard camera to ensure a reliable
match from the drone imagery to the sensor data for precise
altimetry (Fig.2). Altitude measures from the altimeter were
time-synced with drone flights using a stopwatch in GMT.
Aircraft were manually flown by experienced pilots
(EAR, SLY, GR) via remote control and a mounted tablet
(i.e., Apple iPad or the DJI built-in screen on the remote of
the Mavic) to monitor flight parameters and animal behav-
ior through the live-streaming aerial video. All drones were
equipped with gimbal-mounted high-resolution cameras
filming in 3840 × 2160 pixels at 24 frames per second. Video
imagery was later examined to extract individual frames
at specific times. Drones were launched from the ground
(Experiment 1) or from atop two plastic crates (take-off
height: 0.5m; Experiment 2) from a location ~ 50m from
manatee pools to avoid animal disturbance. The aircraft was

E.A.Ramos et al.
1 3
flown directly over target animals for ~ 15min with the cam-
era pointed straight down to film continuous video footage
of manatees at the surface, most importantly lying flat and
parallel with the water’s surface (Fig.2). All fights were
performed under wind conditions that never exceeded 5m/s.
Body size measurements
We obtained manatee body size derived from manual measure-
ments (indicated by the superscriptM) and from drone-based
photogrammetry (indicated by the superscriptD). Manual
measures include straight-line body length (SLM) and umbili-
cal girth (UGM): SLM is the straight-line measure from the
tip of the snout to the medial notch of the fluke, while UGM
is defined as the circumference at umbilical level and is typi-
cally considered the maximum girth of the manatee body (e.g.,
Harshaw 2012; Erdsack etal. 2018). Manual morphometric
measures of manatee body size were collected following stand-
ard manual methods (Lima etal. 2005), as part of regular phys-
ical monitoring in the week prior to drone flights. Manatees
were trained to stay in place and lay fully extended at the water
surface, while several people collected the measures using a
measuring tape (Fig.2). Drone-based measures obtained were
straight-line body length (SLD) and maximum width (MWD).
Straight-line body length was gathered in drone imagery (SLD)
by tracing a line from the snout to the medial notch. MWD was
defined as the maximum width of the manatee body, and was
subsequently used to obtain the UGD by applying the following
equation (adapted from Harshaw etal. 2016):
UG =MW ×𝜋.
Manual
body size
measurements
Drone flights
with DJI
Phantom 4 Pro
Images
extracted
from videos
Reference
object to
scale size
Barometer
altitude to
scale size
Measurement
of body size in
ImageJ
Body
size
values
Body
size
values
Body
size
values
Calculate body
condition index
Calculate body
condition index
Comparison
Drone flights
with DJI Mavic
Pro 2 with
LiDAR
Images
extracted
from videos
Barometer
altitude to
scale size
Body
size
values
Body
size
values
Body
size
values
Measurement
of body size in
MorphoMetrix
LiDAR
altitude to
scale size
Manual
body size
measurements
Drone flights
with DJI Mavic
Pro 2 with
LiDAR
Experiment 1 Experiment 2
Comparison
Comparison ComparisonComparison
Fig. 1 Detailed workflow outlining the various steps we undertook
in both photogrammetry experiments with captive Antillean mana-
tees at several Dolphin Discovery facilities in Quintana Roo, Mex-
ico. In Experiment 1, we flew a DJI Phantom 4 Pro recording video
over captive manatees to compare their effectiveness of the drone’s
onboard sensors compared to the use of a reference object of known
size. In Experiment 2, we flew a DJI Mavic Pro 2 and the same model
equipped with a LiDAR GPS sensor package (LiDAR drone) for pre-
cision altimetry to determine if the use of body size measures with
LiDAR improved size estimates. Images were extracted from videos
to measure manatee body size. Images in Experiment 1 were meas-
ured using ImageJ. Images in Experiment 2 were measured with Mor-
phoMetriX software (Torres and Bierlich 2020) to facilitate marine
megafauna photogrammetry. Data from Experiment 1 were then used
to calculate two body condition indices using the manual measures
and drone-based photogrammetry measures of body size

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
Experiment 1: Scaling withareference object
compared toonboard altimeter
We flew the Phantom 4 on 30 March, 31 March, and 01 April
2019 over 18 individuals (12 adults, 4 juveniles, 2 calves)
(Table1). A white polystyrene block (1m L × 0.25mW)
used as a reference object of known size was placed in the
pool floating at water level to accurately scale aerial imagery
(Fig.2). The drone was hovered for several minutes at a time
at different altitudes from 5 to 101m. Altitude data was
obtained from the onboard data storage of flight records.
Manual measurements of animal body size (standard length
and umbilical girth) were performed by the on-site veterinar-
ians within the same week of the flights.
Aerial videos filmed with the Phantom 4 were reviewed
using VideoPad v.7.4 NCH software and to extract a minimum
of 10 high-quality screenshot images (3840 × 2160 pixels)
from aerial video of manatees close to the water’s surface in a
nearly horizontal and straight position as possible for further
analysis. Straight-line body length (SL) and maximum width
(MW) of each manatee visible in drone images were meas-
ured in pixels using ImageJ software (Abràmoff etal. 2004).
Drones
Body size measurements
DJI Phantom 4 Pro
DJI Mavic Pro 2
DJI Mavic Pro 2 with
LiDAR
UG
(a) (d)
(b)
MW
Reference object
SL
SL Exp.#1
Exp.#2
Exp.#2
(c) MorphoMetriX
MDD
M
Fig. 2 Elements of the photogrammetry experiments. a Straight-
line body length (SLD) and maximum width (MWD) of each mana-
tee were measured in individual images of manatees extracted from
high-resolution aerial drone video recordings. Straight-line total
body length (SLM) and umbilical girth (UGM) were measured manu-
ally during veterinary exams. The veterinary team at Dolphin Dis-
covery used a measuring tape held over the stationary animal lying
flat at the surface to determine the straight-line total body length of a
manatee. Manatees were trained to perform this behavior (Lima etal.
2005), ensuring less variability due to animal curvature and position.
b A white polystyrene block (1m L × 0.25 m W) placed in manatee
pools during flights served as a reference object to scale animal size
in aerial video imagery. c Output of MorphoMetriX illustrating body
size measurements of Julieta at Puerto Aventuras collected with a
DJI Mavic Pro 2 at a flight altitude of 50m. In MorphometriX, the
number of width segments (yellow-colored segments) was set to 10,
representing 10% increments of the length measurement. d Images of
each of the drones used in this study: DJI Phantom 4 Pro in Experi-
ment 1 and two DJI Mavic Pro’s in Experiment 2, one equipped with
a custom LiDAR/GPS package and the other without

E.A.Ramos et al.
1 3
A reference object (RO) was used to scale manatee size in
aerial imagery (e.g., if the length of the reference object in the
image was equal to 100 pixels, the obtained rate was 0.01m/
pix). An alternate method, using flight altitude data from the
onboard altimeter, image size (in pixels), camera sensor width,
and focal distance, was also applied to calculate the ground
sampling distance (GSD), which is another form of image rate
(m/pix) used to scale manatee size:
To determine how accurate each measure was relative to
SLM, we calculated the differences between SLM and values
produced with a reference object (SLD,RO) and ground sam-
pling distance (SLD,GSD).
Experiment 2: Comparison ofLiDAR equipped drone
versusnon‑LiDAR equipped drone
Flights with the Mavics were conducted on 16 and 17 Octo-
ber 2019 (Table1). For these flights, we selected a single
adult manatee (Julieta) as representative of a healthy indi-
vidual for which the straight-line body length (SLM) was
measured by on-site veterinarians following standardized
GSD = [
sensor width(mm)× f light alt itude(m)
]
[
focal lengt h(mm)× image width(pix)
].
procedures. We flew over Julieta the day of the first series
of flights using the Mavic (no-LiDAR drone) and the Mavic
with the custom-built LiDAR/GPS sensor package (LiDAR
drone). To examine the effect of imaging manatee bodies
at different altitudes on size measurements, each drone was
flown at altitudes of 70m, 50m, and 30m twice (first during
descent flight and second during ascent flight) where it was
kept in a stationary hovering position for two consecutive
minutes.
Individual frames were extracted from video files
from both Mavics using VLC media player (VideoLAN
organization) and saved as .tiff files to preserve resolu-
tion. Twenty images were created per altitude per flight,
resulting in 40 images per altitude and 120 images per
flight totaling 600 images. Three flights were conducted
using the LiDAR mounted drone and two flights were
conducted with the standard drone without LiDAR. As a
result, 360 images were analyzed for the drone equipped
with the altimeter, and 240 images for the drone without
LiDAR. Analysis was restricted to images in which the
focal individual was in a nearly parallel position to the
surface presenting minimal body curve.
Aerial manatee body size measurements were extracted
with the software MorphoMetriX (Torres and Bierlich
2020). MorphoMetriX allows the user to input flight and
Table 1 Information on the study location, drone flights, and the Antillean manatees (n = 18) imaged during our photogrammetry tests at Dol-
phin Discovery facilities in the Mexican Caribbean
A adult, J juvenile, C calf, F female, M male. No. of images = Number of images used in photogrammetry experiment. “-” indicates that the indi-
vidual was not included due to insufficient images of high-quality
Exp. no Facility Date No. of flights Name Age Age class Sex No. of images
1 Isla Mujeres 2019-Mar-30 4 África 2 J F 14
César > 11 A M 45
Fabián 9 A M 54
Sabina > 11 A F 23
Puerto Aventuras (Pool 1) 2019-Mar-31 3 Bombom 3 J F 16
Dorothy 15.6 A F 22
Julieta 17.9 A F 28
Michelin 0.9 C M 28
Nohoch 2 A M 18
Puerto Aventuras (Pool 2) 2019-Mar-31 1 Conchis 4.6 J F –
Claudia 4.3 A F 10
Dreams 2019-Mar-31 1 Lorenzo 6.5 A M –
Pablo 21.1 A M –
Quijote 11 A M –
Cozumel 2018-Apr-01 2 Angel 16 A M 25
Edgar 8 A M 25
Roberto 8 A M –
Yoltzin 10 A M –
2 Puerto Aventuras (Pool 1) 2019-Oct-16 2 Julieta 17.9 A F 240
2019-Oct-17 3 Julieta 17.9 A F 360

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
sensor parameters including Image ID, focal length (mm),
altitude (m), and pixel dimension (mm/pixel). Focal length
of the DJI Mavic Pro 2 camera was 26mm (DJI: www.
dji. com/ mavic/ inf o# sp ecs) and pixel dimension was set to
0.008666mm/pixel following Torres and Bierlich (2020).
Each image was individually uploaded and processed in
MorphoMetriX to measure manatee body length. Straight-
line body length measurements began at the tip of the
snout and ended at the medial notch of the fluke. Data
were then exported as CSV files.
Statistical analysis
For Experiment 1, comparisons between manual and drone
measures were made using only body length (SL) to evalu-
ate the accuracy of drone-based measurements, because the
drone-based umbilical girth (UGD) introduces an additional
bias since it is calculated as a perfect circumference based on
the linear maximum width (MWD). A student's t-test was used
to compare the ΔSLmanual-drone obtained with both methods
employed for scaling (RO and GSD). The mean and standard
deviation (SD) were calculated on manual and drone-based
body size measures (SL and UG) to compare their relative
variability in measurements.
For Experiment 2, a linear mixed model (LME) was used to
examine whether body length estimates differed between the
LiDAR drone vs. the non-LiDAR drone and between different
flight altitudes. Drone types (LiDAR and non-LiDAR) and
flight altitudes (30m, 50m, and 70m) as well as the interac-
tion between the two factors were included as fixed effects in
the LME. As it is possible that our selected aerial images may
not have been satisfactory units of replication, we included
Image ID nested in a flying altitude of a flight as a random
effect in our LME to control for pseudoreplication and the
likely non-independence of the series of images analyzed. The
LME was conducted using the nlme package in R version 3.6.1
(The R Foundation for Statistical Computing: www.r- proje ct.
org).
Body condition index
Morphometric measurements of manatee body size taken dur-
ing veterinary exams were used to calculate a body condition
index (BCI) for Antillean manatees according to Harshaw etal.
(2016) and Castelblanco-Martinez etal. (2021):
BCI is calculated as a ratio, SLD and UGD are expressed in
metric units. The resulting BCI values are expected to range
from approximately 0.6–1 for manatees in good health, and
individuals with BCI values close to 1 were considered poten-
tially overweight (Harshaw etal. 2016).
BCI = UG
SL
.
To determine the accuracy of the photogrammetry-based
measures of manatee body size, we compared BCI values
derived from manual body size measurements (BCIM) and
drone-based photogrammetry measurements (BCID).
Results
Experiment 1
During 11 flights performed with the Phantom 4, we recorded
173min of aerial video recordings. We obtained suitable
images (straight position close to the water surface; n = 308)
for 12 of the 18 manatees (Table1). The mean flight alti-
tude for the selected images was 28.5m (SD = 21.6 m,
mode = 30m, range = 5–101m) and the most useful images
(84%) were captured below 40m.
Accuracy ofdrone‑based size measures withareference
object andwithanonboard altimeter
For most animals, SLD estimated with both methods were
normally distributed (Fig.3a and b). The difference between
SLM (actual body size) and SLD (estimated) was not statisti-
cally significantly different when comparing the methods used
(t-test: n = 308; t = 1.553; p = 0.121). SLD were similar in size
and deviations from SLM in all manatees measured with the
reference object or with GSD (Fig.3). The difference was
marginally lower when using a reference object (RO) to scale
imagery (mean ± SD = 21.3 ± 11.59cm) t han when calculated
with GSD (mean ± SD = 22.8 ± 12.40cm). The relative er ror
(|SLM–SLD|/SLM) (Fig.3c and d) obtained with the reference
object was similarly lower (mean ± SD = 8.2 ± 3.83%) than
when using GSD (mean ± SD = 8.7 ± 4.31%). Thus, we used
the reference object-based values to present information of
manatee body size values.
Manual anddrone‑based manatee body size values
The mean values (± SE) of SLD,RO and UGD,RO were calculated
for adults (SL: 253.8 ± 1.75cm; UG: 191.3 ± 1.46cm), juve-
niles (SL: 210.7 ± 1.17cm; UG: 151.5 ± 1.00cm) and calves
(SL: 162.2 ± 1.56cm; UG: 125.2 ± 2.16cm). The values of SL
and UG from manual and drone-based measurements (RO-
based values, mean ± SD) are presented in Table2 for all man-
atees (n = 12). Across all animals combined, SLM values were
higher and more variable (mean ± SD = 252.7 ± 48.38cm) than
values for SLD,RO (mean ± SD = 231.1 ± 42.16cm). UGM val-
ues were lower (mean ± SD = 167.3 ± 33.59cm) than values
for UGD,RO (mean ± SD = 173.2 ± 33.18cm). In general, mana-
tee body size estimates from photogrammetry overestimated
umbilical girth and underestimated body length (Table2,
Fig.4).

E.A.Ramos et al.
1 3
Fig. 3 Split violin plots and overlapping boxplots displaying photo-
grammetry-based estimates of the straight-line body length (cm) of a
female and b male manatees per individual and sex using a scaled ref-
erence object compared to the ground sampling distance (GSD), and
the relative differences between these two measures from actual body
size (SLM) for c females and d males. Data were collected at altitudes
of 5–40m. Black dots represent outliers. The black bar in the boxplot
represents the median. The bottom of the box represents the 25th per-
centile and top represents the 75th percentile. Body length measure-
ments were similar when using a reference object or GSD. Body size
measurements were more variable in males than females when using
barometric values of altitude to scale imagery (onboard altimeter) as
compared to a reference object. The number of photos analyzed for
body size is listed in Table1
Table 2 Values for manual body
size measurements (cm) and
resulting body condition indices
of Antillean manatees
Manual size measurements: straight-line total body length (SLM) and umbilical girth (UGM). Photogram-
metry-based size measurements: straight-line total body length (SLD) and umbilical girth (UGD). A adult, J
juvenile, C calf, F female, M male. BCIM = Body condition index using SLM and UGM; BCID = Body con-
dition index using SLD and UGD. Standard deviations of drone-based body size estimates and body condi-
tion indices are listed in parenthesis. Manual measurements were not replicated per individual
Name Age Age class Sex SLMSLDUGMUGDBCIMBCID
Africa 2 J F 188 174.1(± 6.80) 138 142.2 (± 8.26) 0.73 0.82 (± 0.06)
Bombom 3 J F 220 210.6 (± 6.87) 154 150.1 (± 7.08) 0.70 0.71 (± 0.04)
Claudia 4.3 A F 241 201.6 (± 3.20) 142 146.8 (± 4.18) 0.59 0.73 (± 0.02)
Dorothy 15.6 A F 319 280.0 (± 4.55) 234 208.5 (± 5.89) 0.73 0.75 (± 0.02)
Julieta 17.9 A F 332 292.8 (± 7.01) 220 232.4 (± 8.43) 0.66 0.79 (± 0.03)
Sabina > 11 A F 280 264.2 (± 7.20) 189 210.2 (± 9.05) 0.68 0.80 (± 0.03)
Angel 16 A M 276 258.7 (± 6.42) 178 190.3 (± 5.26) 0.65 0.74 (± 0.03)
Cesar > 11 A M 280 258.8 (± 5.33) 160 180.2 (± 6.76) 0.57 0.70 (± 0.03)
Edgar 8 A M 259 242.9 (± 10.46) 157 173.4 (± 5.53) 0.61 0.715 (± 0.05)
Fabian 9 A M 237 217.1 (± 9.42) 166 172.9 (± 6.95) 0.70 0.798 (± 0.05)
Michelin 0.9 C M 170 156.2 (± 4.70) 117 116.7 (± 6.34) 0.69 0.748 (± 0.04)
Nohoch 2 A M 230 215.7 (± 5.56) 152 155.4 (± 5.13) 0.66 0.721 (± 0.03)

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
Experiment 2
Body length estimates significantly differed between drone
type (F1,594 = 140.67; p < 0.0001) and flight altitudes
(F2,594 = 48,36; p < 0.0001) (Fig.5), however, the interac-
tion between the two factors was not significant (Drone type
× flight altitudes: F2,594 = 2.94; p = 0.054).
When compared to the known body length of the mana-
tee Julieta (314cm), estimates obtained through analysis
of images collected with the standard drone (non-LiDAR
drone) always overestimated body length with greater dif-
ferences reported for 50m and 70m flight altitudes (mean
Δ SLmanual-drone: at 30m = 0.14m; at 50m = 0.37m; at
70m = 0.29 m) (Fig. 6). Greater accuracy was obtained
for lower altitude flights (30m) with the standard drone.
Body length estimates calculated using the LiDAR-equipped
drone equipped were more accurate (mean Δ SLmanual-drone:
at 30m = − 0.19m; at 50m = 0.16m; at 70m = − 0.03m).
Smaller differences in body length estimates were obtained
for images collected during 70m altitude drone flights
(Fig.6).
Assessment ofmanatee body condition index
The BCI values obtained from manual body size measure-
ments (BCIM) were lower for all individuals compared to
BCID (Table2, Fig.7). The average BCID for all individu-
als was 0.75 ± 0.05 (mean ± SD) and ranged from 0.70 to
0.82. The average BCIM for all individuals was 0.66 ± 0.05
(mean ± SD), ranging from 0.57 to 0.73.
Fig. 4 Scatterplot of the manatee umbilical girth (cm) and body
length (cm) measured manually versus from drone-based imagery
(mean ± SD). Trend lines were positive and similar for body size esti-
mates from both methods
Fig. 5 Comparison of differences in measurements of the body length
of a single adult Antillean manatee imaged from a DJI Mavic Pro 2
(no-LiDAR drone:n30 meters = 80; n50 meters = 80; n70 meters = 80) and the
same model drone equipped with a custom-built LiDAR package for
precision altimetry (LiDAR drone: n30 meters = 120; n50 meters = 120;
n70 meters = 120). Body length was defined as the straight-line body
length (SLD) measured from the tip of the snout to the medial tip of
the tail using a measuring tape extended lengthwise next to the ani-
mal lying flat at the surface. The red dotted line represents straight-
line total body length (SLM) acquired during regular veterinary
exams. Flights with the LiDAR drone flown at an altitude of 70m
produced the most precise measurements of body size
Fig. 6 Comparison of the accuracy of body length measurements of
a single adult Antillean manatee (Julieta) imaged with a DJI Mavic
Pro 2 (no-LiDAR drone) and the same model equipped with a cus-
tom-built LiDAR system for precision altimetry (LiDAR drone). Δ
SLmanual-drone represents the difference in meters between straight-line
body length measured manual with a tape ruler (SLM) or from aerial
drone imagery (SLD). The manatee graphic is scaled to SLM. The
LiDAR drone provided more precise body length measurements of
manatees than the no-LiDAR drone

E.A.Ramos et al.
1 3
Discussion
Advancing new methods for assessing marine mammal
health is critical to supporting wildlife management and
conservation efforts. In this study, we flew small consumer-
grade drones with and without a custom LiDAR-based altim-
eter and compared methods for estimating the body size and
body condition of Antillean manatees. Using a scaled refer-
ence object resulted in a similar error when compared to the
use of the ground sampling distance derived from imagery
and the drone’s onboard sensors, hence, using a reference
object is equally efficient when flight data is unavailable
or when the drone altimeter data is unreliable. Imaging
from the drone equipped with the LiDAR altimeter showed
improved accuracy in body size measurements compared
to that from an out-of-the-box drone. Drone-based size
measures of manatees used to calculate a photogrammetry-
based body condition index for Antillean manatees enabled
a preliminary assessment for drone-based BCI studies of the
subspecies. Our study provides strong support for the use
of small drones for robust measurement of the body size of
Antillean manatees.
The LiDAR-equipped aircraft provided more reliable esti-
mates of manatee body size than the built-in altimeter of the
Mavic Pro 2 with a resulting suitable accuracy to measure
individual manatees. Consumer-grade DJI drones use baro-
metric pressure and built-in GPS to determine altitude, and
these methods introduced major inaccuracies into altitude
estimations in drones without LiDAR. Drift in DJI barom-
eter can skew altitude readings in a single flight (Colefax
etal. 2019). Thus, in flights conducted without the LiDAR,
erroneous altitude readings from DJI sensors are likely con-
tributors to less precise body size measurements and BCI
values. Additionally, pixel issues in imagery acquired at
high altitudes may have impacted the accuracy of measure-
ments in all flights in the non-LiDAR drone compared to the
LiDAR-equipped drone. These findings support the use of
LiDAR as a low-cost enhancement to research using small
drones for photogrammetry that enables improvements in the
accuracy of altitude data, increasing the reliability of body
size measurements of animals (Dawson etal. 2017).
Collecting reliable drone-based morphometric measure-
ments of manatees was hindered by several factors inherent
in the use of overhead imagery in place of manual meas-
urements of animal body size. Differences in body length
(SL) in drone-based measurements likely arose from the
differences in measuring the straight-line length of mana-
tees from above during drone flights, where animal bodies
are naturally curved downward at the posterior and anterior
ends and result in shorter body lengths as compared to the
length measured manually when the animals were station-
ary at the surface and fully extended (a trained behavior in
these animals). Considering the differences between meas-
ures from drone-imagery and manual measures, developing
a correction factor to account for the disparity is essential.
We calculated the difference between body size values
obtained through photogrammetry and manual methods
(ΔSLmanual-drone), resulting in a correction factor that should
be considered preliminary due to our small sample size. The
umbilical girth (UG) was not used for comparisons between
manual and drone-based methods, as it could not be meas-
ured directly in the drone imagery, but obtained using the
Africa Bombom Claudia Dorothy Julieta Sabina AngelCesar EdgarFabian Michelin Nohoch
0.65
0.75
0.85
0.95
Body condition index
Female Male
Manatee
Fig. 7 Violin plots and overlapping boxplots depicting body condi-
tion index (BCI) values for female and male manatees. The plots rep-
resent straight-line photogrammetry-based measurements of mana-
tee body size (BCID). Black dots mark outliers. The black bar in the
boxplot represents the median. The bottom of the box marks the 25th
percentile and top marks the 75th percentile. The single red bar repre-
sents the BCI value derived from the single set of manual size meas-
ures of manatees (BCIM) using straight-line body length (SLM) and
umbilical girth (UGM). BCID measurements resulted in overestima-
tion in manatee body size by approximately 10% compared to BCIM

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
straight-line maximum width (MWD) assuming width as the
diameter of the circle UG. The use of MWD to estimate UGD
resulted in slight overestimation compared to UGM and sub-
sequent differences regarding drone-based BCI values com-
pared to BCI values derived from manual measurements.
The development of 3D models of manatees similar to those
created for harbor porpoises (Phocoena phocoena) (Irschick
etal. 2021) and right whales (Eubalaena sp.) (Christiansen
etal. 2019) will enable volumetric estimation of animals in
the wild, potentially providing the ability to test other BCIs
in use with manatees (Harshaw etal. 2016; Castelblanco-
Martínez etal. 2021).
In addition, future efforts should also be directed at deter-
mining the variance introduced by environmental factors and
strategies for overcoming their effects. In our data, the time
of day of overhead imaging was a critical choice for success-
ful photogrammetry. High levels of glare from late morning
to mid-afternoon and shadows cast by manatee bodies made
it challenging to capture high-quality imagery. Similarly, the
presence of water surface ripples may limit the efficiency of
the imagery method as the shape of the manatee bodies can
be distorted by the ripples. Additionally, the behavior, habi-
tat use, and environmental factors must be considered when
using photogrammetry to study wild aquatic animals; for
example, manatees do not usually spend much time in a flat,
straight position while near the surface, which hindered our
efforts to obtain suitable images from all individuals evalu-
ated. Manatees also occupy a variety of turbid habitats with
poor visibility that may be unsuitable for drone imaging.
This illustrates the difficulties associated with aerial pho-
togrammetry applied to our target species, and the need for
further tests and improved protocols to successfully imple-
ment drones for photogrammetry of manatees in their natu-
ral habitats. Despite these difficulties, approaching animals
in the wild is logistically challenging and pinpointing their
location is often infeasible, thus, drone-based assessments
offer a non-invasive alternative with the capacity to collect
higher volumes of data at lower costs.
Body condition for captive manatees (mean = 0.66) was
similar to the average BCI (UG/SL) in healthy wild Antil-
lean manatees from the Mexican Caribbean (Castelblanco-
Martínez etal. 2021). Florida manatees (0.75; Harshaw
etal. 2016) had higher BCIs than Antillean manatees. The
Florida subspecies is larger than the Antillean subspecies
with stockier body size (Wong etal., 2012) with differing
shape (Johnson 2019) and growth rates (Castelblanco-Mar-
tínez etal. 2014) resulting in a higher average BCI. Body
size and shape differences between the subspecies such as
the larger size of Florida manatees (Domning and Hayek
1986) may be driven by thermoregulatory adaptations to
colder habitats (e.g., Allen’s rule; Allen 1877, Bergmann’s
rule; Bergmann 1847), which should be taken into account
in the comparison of morphometric BCIs between the two
subspecies. Differences in the BCIs between two manatee
subpopulations may have been attributable to the need to
increase body fat in the colder northern waters relative to
more southern areas (Harshaw etal. 2016).
Our ability to capture drone-based body measurements
of captive manatees and compare them with physical size
measurements of the same animals provided a rare opportu-
nity to ground-truth our photogrammetry methods. Despite
our limited sample size, captive manatees in this study
were native to Mexico and in good health. BCI values var-
ied across our sample of manatees highlighting the need
to account for individual variability in manatee body size
and health condition based on age and other possible factors
(e.g., percentage of body fat, volume of gas). The BCI we
calculated was used to compare to a large database of animal
morphometric data of Antillean manatees from populations
throughout their range revealed potential ecotypes with dif-
ferent BCIs across riverine and marine coastal ecosystems
(Castelblanco-Martínez etal. 2021). To effectively ground-
truth drone-based photogrammetry for wild Antillean mana-
tees, comparisons of morphometric measurements and other
BCI values of individually tracked manatees sampled over
time and in different regions to reveal temporal trends (e.g.,
seasonal, yearly) regarding their health would be useful. The
relative ease of flying drones over manatees and waiting for
surfacings where most of their body is visible can facilitate
assessments of large numbers of animals when manatees are
clustered together, for example, with hundreds of Florida
manatees at their warm-water aggregation sites.
Assessments of BCI for manatees will be improved
with the use of visual indices now associated with quanti-
fied BCI values of Antillean manatees captured in health
assessments (Castelblanco-Martínez etal. 2021). For
example, emaciated manatees exhibit extensive weight loss
and depletions in muscle and fats, detectable in the low
nuchal fat deposits around the neck (i.e., “peanut head”)
and the visible protrusion of skeletal structure of the ribs,
scapulae, pelvis, spine, and head (Harvey etal. 2019).
Precise measurement of scars and wounds of injured
manatees facilitates an understanding of how many indi-
viduals in populations survive vessel collisions (Langtimm
etal. 1998) and provide insight into the level of threats
manatees face from boating activity (Beck etal. 1982).
All three species of manatee regularly live-strand from
health-related issues due to natural causes and anthro-
pogenic impacts like boat strikes (Adimey etal. 2012).
Stranded manatees are typically underweight and in poor
health, requiring extensive short- to long-term rehabilita-
tion and eventual release, a task recognized as essential to
their conservation (e.g., Adimey etal. 2012; Normande
etal. 2015; Ball etal. 2020). Drone observations provide
a useful way to remotely track animal health following
release according to its measured BCI, reducing the need

E.A.Ramos et al.
1 3
for close boat approaches to document animal condition
in post-release monitoring. For example, non-standardized
methods of viewing animal condition through aerial drone
videos are regularly employed by Wildtracks in Belize—a
manatee rescue and rehabilitation center—to monitor man-
atees during releases and remotely observe the animal in
the following months for visually detectable signs of poor
health (e.g., abnormal movement, weight loss; Z. Walker,
personal communication). Drones can also be applied to
determine the size and age of calves relative to their stages
of dependency and to track the pregnancy status of adult
females, data essential for local population management in
areas with small populations (Self-Sullivan and Mignucci-
Giannoni 2012).
The development of novel, reliable, non-invasive, low-
cost methods for photogrammetry opens new avenues for
measuring the body size and assessing the health condi-
tion of rare and endangered marine megafauna like Antil-
lean manatees. We foresee the combination of photo-
grammetry-based health and body condition assessments,
physiological sampling (e.g., blood samples, urine, blow
sampling), and information on local population character-
istics and behavior (e.g., size, food availability, preferred
species) to help inform on regional threats and produce
a more robust picture of the effectiveness of visual body
condition examinations at identifying underlying health
issues. Comparisons within and across populations will
help to elucidate differential impacts faced by manatees
and to provide critical insights into their welfare and con-
servation needs regionally and species-wide.
Supplementary Information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s42991- 022- 00228-4.
Acknowledgements We are very grateful to Dolphin Discovery Aquar-
iums for allowing us to perform our tests in their facilities. Thanks to
Chris and Iain Kerr at Ocean Alliance for building the LiDAR sensor
for us. Big thanks to all the staff and manatee caretakers for their help
during our tests, especially to Adriana Mingramm, Rocío Fraustro and
Luz Garduño, who provided the biometrics data for the manatees they
have under their care. Thanks to Carlos Niño-Torres for comments on
the first phase of the experiments. We also thank RIU hotels and the
Lowry Park Zoo for supporting our field stays and trips.
Author contributions All authors contributed to the study conception
and design. Drone flights, data collection and analysis were performed
by SLY, EAR, AHQ, MRA, and GR. The first draft of the manuscript
was written by EAR, SL-Y, NC-M, and GR and all authors commented
and edited previous versions of this manuscript. All authors read and
approved the final manuscript.
Funding No funding was received for conducting this study.
Availability of data and material The datasets generated during and/or
analyzed during this study are not publicly available due to their use in
multiple studies in preparation but are available from the corresponding
author on reasonable request.
Declarations
Conflict of interest The authors have no relevant financial or non-fi-
nancial interests to declare.
Ethics approval This was an observational study and received ethics
approval from the internal veterinary leadership at the Dolphin Dis-
covery aquarium facilities in Mexico.
References
Abràmoff MD, Magalhães PJ, Ram SJ (2004) Image processing with
ImageJ. Biophotonics Int 11:36–42
Adimey NM, Mignucci-Giannoni A, Auil Gomez N, Da Silva VM,
Alvite C, Morales-Vela B, Rosas F (2012) Manatee rescue, reha-
bilitation, and release efforts as a tool for species conservation.
In: Hines EM, Reynolds JE III, Aragones L, Mignucci-Giannoni
A, Marmontel M (eds) Sirenian conservation: issues and strate-
gies in developing countries, Florida. University Press of Florida,
Florida, pp 205–217
Allan BM, Ierodiaconou D, Hoskins AJ, Arnould JP (2019) A rapid
UAV method for assessing body condition in fur seals. Drones
3:2. https:// doi. org/ 10. 3390/ drone s3010 024
Allen JA (1877) The influence of physical conditions in the genesis of
species. Rad Rev 1:108–140
Allen AC, Beck CA, Bonde RK, Powell JA, Gomez NA (2018) Diet of
the Antillean manatee (Trichechus manatus manatus) in Belize,
Central America. J Mar Biol Assoc UK 98:1831–1840. https://
doi. org/ 10. 1017/ S0025 31541 70001 82
Alves-Stanley CD, Worthy GA, Bonde RK (2010) Feeding preferences
of West Indian manatees in Florida, Belize, and Puerto Rico as
indicated by stable isotope analysis. Mar Ecol Prog Ser 402:255–
267. https:// doi. org/ 10. 3354/ meps0 8450
Aoki K, Isojunno S, Bellot C, Iwata T, Kershaw J, Akiyama Y, Mar-
tín López LM, Ramp C, Biuw M, Swift R, Wensveen PJ (2021)
Aerial photogrammetry and tag-derived tissue density reveal pat-
terns of lipid-store body condition of humpback whales on their
feeding grounds. Proc Royal Soc B 1943:20202307. https:// doi.
org/ 10. 1098/ rspb. 2020. 2307
Apprill A, Miller CA, Moore MJ, Durban JW, Fearnbach H, Barrett-
Lennard LG (2017) Extensive core microbiome in drone-captured
whale blow supports a framework for health monitoring. mSys-
tems 2:e00119-17. https:// doi. org/ 10. 1128/ mSyst ems. 00119- 17
Ball RL, Malmi M, Zgibor J (2020) Trends of the Florida manatee
(Trichechus manatus latirostris) rehabilitation admissions 1991–
2017. PLoSOne 15:e0223207. https:// doi. org/ 10. 1371/ journ al.
pone. 02232 07
Beck CA, Bonde RK, Rathbun GB (1982) Analyses of propeller
wounds on manatees in Florida. J Wild Manage 46:531–535.
https:// doi. org/ 10. 2307/ 38086 75
Bergmann C (1847) About the relationships between heat conservation
and body size of animals. Goett Stud 1:595–708
Best P, Rüther H (1992) Aerial photogrammetry of southern right
whales, Eubalaena australis. J Zool 228:595–614. https:// doi.
org/ 10. 1111/j. 1469- 7998. 1992. tb044 58.x
Bonde RK, Aguirre AA, Powell J (2004) Manatees as sentinels of
marine ecosystem health: Are they the 2000-pound Canaries?
EcoHealth 1:255–262. https:// doi. org/ 10. 1007/ s10393- 004- 0095-5
Bonde RK, Garrett A, Belanger M, Askin N, Tan L, Wittnich C (2012)
Biomedical health assessments of the Florida manatee in Crystal
River—providing opportunities for training during the capture,
handling, and processing of this endangered aquatic mammal. J

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
Mar Anim Ecol 5:17–28. https:// jmate. ca/ wp- conte nt/ uploa ds/
2020/ 12/ techn iques- vol5- iss2. pdf
Booth CG, Sinclair RR, Harwood J (2020) Methods for monitoring
for the population consequences of disturbance in marine mam-
mals: a review. Front Mar Sci 7:115. https:// doi. org/ 10. 3389/
fmars. 2020. 00115
Bräger S, Chong A (1999) An application of close range photogram-
metry in dolphin studies. Photogramm Rec 16:503–517. https://
doi. org/ 10. 1111/ 0031- 868X. 00139
Burnett JD, Lemos L, Barlow D, Wing MG, Chandler T, Torres LG
(2019) Estimating morphometric attributes of baleen whales
with photogrammetry from small UASs: a case study with blue
and gray whales. Mar Mammal Sci 35:108–139. https:// doi. org/
10. 1111/ mms. 12527
Castelblanco-Martínez DN, Nourisson C, Quintana-Rizzo E, Padilla-
Saldivar JA, Schmitter-Soto JJ (2012) Potential effects of human
pressure and habitat fragmentation on population viability of
the Antillean manatee Trichechus manatus manatus: a predic-
tive model. Endanger Species Res 18:129–145. https:// doi. org/
10. 3354/ esr00 439
Castelblanco-Martínez DN, Morales-Vela B, Padilla-Saldívar JA
(2014) Using craniometrical predictors to infer body size of
Antillean manatees. Mammalia 78:109–115. https:// doi. org/ 10.
1515/ mamma lia- 2012- 0136
Castelblanco-Martínez DN, Landeo-Yauri SS, Ramos EA, Rieucau
G, Bonde RK, Álvarez-Alemán A, Beck CA, Powell J, Galves
J, Caicedo-Herrera D, Morales-Vela B, Padilla-Saldívar J, Oli-
vera LD, Jiménez-Domínguez D, Reid J, Butler S, Attademo F,
Luna F, Mignucci-Gianonni A (2021) Analysis of body condi-
tion indices reveals different ecotypes of the Antillean manatee.
Sci Rep 11:19451. https:// doi. org/ 10. 1038/ s41598- 021- 98890-0
Cheney B, Wells R, Barton T, Thompson P (2018) Laser photo-
grammetry reveals variation in growth and early survival in
free-ranging bottlenose dolphins. Anim Conserv 21:252–261.
https:// doi. org/ 10. 1111/ acv. 12384
Christiansen F, Dujon AM, Sprogis KR, Arnould JP, Bejder L (2016)
Noninvasive unmanned aerial vehicle provides estimates of the
energetic cost of reproduction in humpback whales. Ecosphere
7:e01468. https:// doi. org/ 10. 1002/ ecs2. 1468
Christiansen F, Sironi M, Moore MJ, Di Martino M, Ricciardi M,
Warick HA, Irschick DJ, Gutierrez R, Uhart MM (2019) Esti-
mating body mass of free-living whales using aerial photogram-
metry and 3D volumetrics. Methods Ecol Evol 10:2034–2044.
https:// doi. org/ 10. 1111/ 2041- 210X. 13298
Christiansen F, Dawson SM, Durban JW, Fearnbach H, Miller CA,
Bejder L, Uhart M, Sironi M, Corkeron P, Rayment W, Leunis-
sen E (2020a) Population comparison of right whale body con-
dition reveals poor state of the North Atlantic right whale. Mar
Ecol Prog Ser 640:1–16. https:// doi. org/ 10. 3354/ meps1 3299
Christiansen F, Sprogis KR, Gross J, Castrillon J, Warick HA,
Leunissen E, Bengtson-Nash S (2020b) Variation in outer blub-
ber lipid concentration does not reflect morphological body con-
dition in humpback whales. J Exp Biol 223:jeb213769. https://
doi. org/ 10. 1242/ jeb. 213769
Christiansen F, Rodríguez-González F, Martínez-Aguilar S, Urbán
J, Swartz S, Warick H, Vivier F, Bejder L (2021) Poor body
condition associated with an unusual mortality event in gray
whales. Mar Ecol Prog Ser 658:237–252. https:// doi. org/ 10.
3354/ meps1 3585
Clutton-Brock T, Sheldon BC (2010) Individuals and populations:
the role of long-term, individual-based studies of animals in
ecology and evolutionary biology. Trends Ecol Evol 25:562–
573. https:// doi. org/ 10. 1016/j. tree. 2010. 08. 002
Colefax AP, Butcher PA, Pagendam DE, Kelaher BP (2019) Reli-
ability of marine faunal detections in drone-based monitoring.
Ocean Coast Manag 174:108–115. https:// doi. org/ 10. 1016/j.
oceco aman. 2019. 03. 008
Dawson SM, Bowman MH, Leunissen E, Sirguey P (2017) Inexpensive
aerial photogrammetry for studies of whales and large marine
animals. Front Mar Sci 4:366. https:// doi. org/ 10. 3389/ fmars. 2017.
00366
Deutsch CJ, Self-Sullivan C, Mignucci-Giannoni A (2008) Trichechus
manatus. The IUCN Red List of Threatened Species 2008:
e.T22103A9356917. https:// doi. org/ 10. 2305/ IUCN. UK. 2008.
RLTS. T2210 3A935 6917. en
Domning DP (1982) Commercial exploitation of manatees Trichechus
in Brazil c. 1785–1973. Biol Conserv 22:101–126. https:// doi. org/
10. 1016/ 0006- 3207(82) 90009-X
Domning DP, Hayek LA (1986) Interspecific and intraspecific morpho-
logical variation in manatees (Sirenia: Trichechus). Mar Mamm
Sci 2:87–144. https:// doi. org/ 10. 1111/j. 1748- 7692. 1986. tb000
34.x
Dulisz B, Nowakowski JJ, Górnik J (2016) Differences in biometry
and body condition of the House Sparrow (Passer domesticus) in
urban and rural population during breeding season. Urban Ecosyst
19:1307–1324. https:// doi. org/ 10. 1007/ s11252- 016- 0546-0
Durban J, Parsons K (2006) Laser-metrics of free-ranging killer
whales. Mar Mammal Sci 22:735–743. https:// doi. org/ 10. 1111/j.
1748- 7692. 2006. 00068.x
Durban J, Fearnbach H, Barrett-Lennard L, Perryman W, Leroi D
(2015) Photogrammetry of killer whales using a small hexacopter
launched at sea. J Unmanned Veh Sys 3:131–135. https:// doi. org/
10. 1139/ juvs- 2015- 0020
Durban JW, Moore MJ, Chiang G, Hickmott LS, Bocconcelli A, Howes
G, Bahamonde PA, Perryman WL, LeRoi DJ (2016) Photogram-
metry of blue whales with an unmanned hexacopter. Mar Mammal
Sci 32:1510–1515. https:// doi. org/ 10. 1111/ mms. 12328
Erdsack N, Phillips SRM, Rommel SA, Pabst DA, McLellan WA,
Reynolds JE (2018) Heat flux in manatees: an individual matter
and a novel approach to assess and monitor the thermal state of
Florida manatees (Trichechus manatus latirostris). J Comp Phys-
iol B 188:717–727. https:// doi. org/ 10. 1007/ s00360- 018- 1152-7
Fair PA, Schaefer AM, Romano TA, Bossart GD, Lamb SV, Reif JS
(2014) Stress response of wild bottlenose dolphins (Tursiops
truncatus) during capture–release health assessment studies. Gen
Comp Endocrinol 206:203–212. https:// doi. org/ 10. 1016/j. ygcen.
2014. 07. 002
Fearnbach H, Durban JW, Ellifrit DK, Balcomb KC (2018) Using
aerial photogrammetry to detect changes in body condition of
endangered southern resident killer whales. Endanger Species Res
35:175–180. https:// doi. org/ 10. 3354/ esr00 883
Flamm RO, Owen ECG, Owen CFW, Wells RS, Nowacek D (2000)
Aerial videogrammetry from a tethered airship to assess manatee
life-stage structure. Mar Mammal Sci 16:617–630. https:// doi. org/
10. 1111/j. 1748- 7692. 2000. tb009 55.x
Foley AM, Stacy BA, Schueller P, Flewelling LJ, Schroeder B, Minch
K, Fauquier DA, Foote JJ, Manire CA, Atwood KE, Granholm AA
(2019) Assessing Karenia brevis red tide as a mortality factor of
sea turtles in Florida, USA. Dis Aquat Org 132:109–124. https://
doi. org/ 10. 3354/ dao03 308
Garcia-Rodriguez AI, Bowen BW, Domning D, Mignucci-Giannoni
AA, Marmontel M, Montoya-Ospina RA, Morales-Vela B, Rudin
M, Bonde RK, McGuire PM (1998) Phylogeography of the West
Indian manatee (Trichechus manatus): how many populations and
how many taxa? Mol Ecol 7:1137–1149. https:// doi. org/ 10. 1046/j.
1365- 294x. 1998. 00430.x
Goebel ME, Perryman WL, Hinke JT, Krause DJ, Hann NA, Gardner S,
LeRoi DJ (2015) A small unmanned aerial system for estimating
abundance and size of Antarctic predators. Polar Biol 38:619–630.
https:// doi. org/ 10. 1007/ s00300- 014- 1625-4

E.A.Ramos et al.
1 3
Gray PC, Bierlich KC, Mantell SA, Friedlaender AS, Goldbogen JA,
Johnston DW (2019) Drones and convolutional neural networks
facilitate automated and accurate cetacean species identification
and photogrammetry. Methods Ecol Evol 10:1490–1500. https://
doi. org/ 10. 1111/ 2041- 210X. 13246
Harshaw LT (2012) Evaluation of the nutrition of Florida manatees
(Trichechus manatus latirostris) (Doctoral dissertation, University
of Florida)
Harshaw LT, Larkin IV, Bonde RK, Deutsch CJ, Hill RC (2016) Mor-
phometric body condition indices of wild Florida manatees (Tri-
chechus manatus latirostris). Aquat Mamm 42:428. https:// doi.
org/ 10. 1578/ AM. 42.4. 2016. 428
Harvey JW, Harr KE, Murphy D, Walsh MT, de Wit M, Deutsch CJ,
Bonde R (2019) Serum iron analytes in healthy and diseased
Florida manatees (Trichechus manatus latirostris). J Comp Path
173:58–70. https:// doi. org/ 10. 1016/j. jcpa. 2019. 10. 006
Harwood LA, Smith TG, Melling, H (2000) Variation in reproduction
and body condition of the ringed seal (Phoca hispida) in western
Prince Albert Sound, NT, Canada, as assessed through a harvest-
based sampling program. Arctic 53:422–431. https:// www. jstor.
org/ stable/ 40512 255
Hunter M, Auil-Gomez N, Tucker K, Bonde R, Powell J, McGuire
P (2010) Low genetic variation and evidence of limited disper-
sal in the regionally important Belize manatee. Anim Conserv
13:592–602. https:// doi. org/ 10. 1111/j. 1469- 1795. 2010. 00383.x
Hunter ME, Mignucci-Giannoni AA, Tucker KP, King TL, Bonde RK,
Gray BA, McGuire PM (2012) Puerto Rico and Florida manatees
represent genetically distinct groups. Conserv Gen 13:1623–1635.
https:// doi. org/ 10. 1007/ s10592- 012- 0414-2
Irschick DJ, Martin J, Siebert U, Kristensen JH, Madsen PT, Christian-
sen F (2021) Creation of accurate 3D models of harbor porpoises
(Phocoena phocoena) using 3D photogrammetry. Mar Mammal
Sci 37:482–491. https:// doi. org/ 10. 1111/ mms. 12759
Johnson J (2019) Is natural selection shaping Florida manatees? An
investigation into the body shapes between the subspecies of the
West Indian manatee. Undergraduate thesis. Andrews University,
Berrien Springs, Michigan
Johnston D, Rayment W, Dawson SM (2022) Morphometrics and body
condition of southern right whales on the calving grounds at Port
Ross, Auckland Islands. Mamm Biol. https:// doi. org/ 10. 1007/
s42991- 021- 00175-6
Karczmarski L, Chan SCY, Rubenstein DI, Chui SYS, Cameron EZ
(2022a) Individual identification and photographic techniques in
mammalian ecological and behavioural research – Part 1: Meth-
ods and concepts. Mamm Biol (Special Issue) 102(2). https:// link.
sprin ger. com/ journ al/ 42991/ volum es- and- issues/ 102-2
Karczmarski L, Chan SCY, Chui SYS, Cameron EZ (2022b) Individual
identification and photographic techniques in mammalian ecologi-
cal and behavioural research – Part 2: Field studies and applica-
tions. Mamm Biol (Special Issue) 102(3). https:// link. sprin ger.
com/ journ al/ 42991/ volum es- and- issues/ 102-3
Kendrick GA, Nowicki R, Olsen YS, Strydom S, Fraser MW, Sinclair
EA, Statton J, Hovey RK, Thomson JA, Burkholder D, McMahon
KM (2019) A systematic review of how multiple stressors from
an extreme event drove ecosystem-wide loss of resilience in an
iconic seagrass community. Front Mar Sci 6:455. https:// doi. org/
10. 3389/ fmars. 2019. 00455
Krause DJ, Hinke JT, Perryman WL, Goebel ME, LeRoi DJ (2017) An
accurate and adaptable photogrammetric approach for estimating
the mass and body condition of pinnipeds using an unmanned
aerial system. PLoSOne 12:e0187465. https:// doi. org/ 10. 1371/
journ al. pone. 01874 65
Landeo-Yauri SS, Ramos EA, Castelblanco-Martínez DN, Niño-Torres
CA, Searle L (2020) Using small drones to photo-identify Antil-
lean manatees: a novel method for monitoring an endangered
marine mammal in the Caribbean Sea. Endanger Species Res
41:79–90. https:// doi. org/ 10. 3354/ esr01 007
Langtimm CA, O’Shea TJ, Pradel R, Beck CA (1998) Estimates of
annual survival probabilities for adult Florida manatees (Tri-
chechus manatus latirostris). Ecology 79:981–997. https:// doi.
org/ 10. 1890/ 0012- 9658(1998) 079[0981: EOASPF] 2.0. CO;2
Lanyon JM, Sneath HL, Long T, Bonde RK (2010) Physiological
response of wild dugongs (Dugong dugon) to out-of-water sam-
pling for health assessment. Aquat Mamm 36:46–58. https:// doi.
org/ 10. 1578/ AM. 36.1. 2010. 46
Larrat S, Lair S (2021) Body condition index in beluga whale (Delphi-
napterus leucas) carcasses derived from morphometric measure-
ments. Mar Mamm Sci. https:// doi. org/ 10. 1111/ mms. 12855
Lima DS, Vergara-Parente JE, Young RJ, Paszkiewicz E (2005) Train-
ing of Antillean manatee Trichechus manatus manatus Linnaeus,
1758 as a management technique for individual welfare. LAJAM.
https:// doi. org/ 10. 5597/ lajam 00071
McKinney MA, Atwood T, Dietz R, Sonne C, Iverson SJ, Peacock E
(2014) Validation of adipose lipid content as a body condition
index for polar bears. Ecol Evol 4:516–527. https:// doi. org/ 10.
1002/ ece3. 956
Meise K, Mueller B, Zein B, Trillmich F (2014) Applicability of sin-
gle-camera photogrammetry to determine body dimensions of pin-
nipeds: Galapagos sea lions as an example. PLoSOne 9:e101197.
https:// doi. org/ 10. 1371/ journ al. pone. 01011 97
Mignucci-Giannoni AA, Montoya-Ospina RA, Jiménez-Marrero NM,
Rodríguez-López MA, Williams EH Jr, Bonde RK (2000) Mana-
tee mortality in Puerto Rico. Environ Manage 25:189–198. https://
doi. org/ 10. 1007/ s0026 79910 015
Morfeld KA, Meehan CL, Hogan JN, Brown JL (2016) Assessment
of body condition in African (Loxodonta africana) and Asian
(Elephas maximus) elephants in North American zoos and man-
agement practices associated with high body condition scores.
PLoSOne 11:e0155146. https:// doi. org/ 10. 1371/ journ al. pone.
01551 46
Norkaew T, Brown JL, Bansiddhi P, Somgird C, Thitaram C, Pun-
yapornwithaya V, Punturee K, Vongchan P, Somboon N, Khon-
mee J (2018) Body condition and adrenal glucocorticoid activ-
ity affects metabolic marker and lipid profiles in captive female
elephants in Thailand. PLoSOne 13:e0204965. https:// doi. org/ 10.
1371/ journ al. pone. 02049 65
Normande IC, Luna FDO, Malhado ACM, Borges JCG, Junior PCV,
Attademo FLN, Ladle RJ (2015) Eighteen years of Antillean
manatee Trichechus manatus manatus releases in Brazil: lessons
learnt. Oryx 49:338–344. https:// doi. org/ 10. 1017/ S0030 60531
30008 96
O’Shea TJ, Beck CA, Bonde RK, Kochman HI, Odell DK (1985) An
analysis of manatee mortality patterns in Florida, 1976–81. J Wild
Manage. https:// doi. org/ 10. 2307/ 38018 30
Perryman WL, Lynn MS (1993) Identification of geographic forms
of common dolphin (Delphinus delphis) from aerial photogram-
metry. Mar Mammal Sci 9:119–137. https:// doi. org/ 10. 1111/j.
1748- 7692. 1993. tb004 38.x
Pettis HM, Rolland RM, Hamilton PK, Brault S, Knowlton AR, Kraus
SD (2004) Visual health assessment of North Atlantic right
whales (Eubalaena glacialis) using photographs. Can J Zool
82:8–19. https:// doi. org/ 10. 1139/ z03- 207
Pirotta V, Smith A, Ostrowski M, Russell D, Jonsen ID, Grech A,
Harcourt R (2017) An economical custom-built drone for assess-
ing whale health. Front Mar Sci 21:425. https:// doi. org/ 10. 3389/
fmars. 2017. 00425
Pomeroy P, O’Connor L, Davies P (2015) Assessing use of and reac-
tion to unmanned aerial systems in gray and harbor seals during
breeding and molt in the UK. J Unmanned Veh Syst 3:102–113.
https:// doi. org/ 10. 1139/ juvs- 2015- 0013

Drone-based photogrammetry assessments ofbody size andbody condition ofAntillean manatees
1 3
Preen A, Marsh H (1995) Response of dugongs to large-scale loss
of seagrass from Hervey Bay, Queensland, Australia. Wild Res
22:507–519. https:// doi. org/ 10. 1071/ WR995 0507
Quintana-Rizzo E, Reynolds III J (2010) Regional management plan
for the West Indian manatee (Trichechus manatus). Caribbean
Environment Programme, United Nations Environment Pro-
gramme. CEP Technical Report, Kingston, Jamaica. http:// cep.
unep. org/ events- and- meeti ngs/ 4th- spaws tac-1/ IV% 20SPAW%
20STAC
Ramos EA, Maloney BM, Magnasco MO, Reiss D (2018) Bottlenose
dolphins and Antillean manatees respond to small multi-rotor
unmanned aerial systems. Front Mar Sci 5:316. https:// doi. org/
10. 3389/ fmars. 2018. 00316
Raoult V, Colefax AP, Allan BM, Cagnazzi D, Castelblanco-Martínez
N, Lerodiaconou D, Johnston DW, Landeo-Yauri S, Lyons M,
Pirotta V, Schofield (2020) Operational protocols for the use of
drones in marine animal research. Drones 4:64. https:// doi. org/
10. 3390/ drone s4040 064
Raudino HC, Tyne JA, Smith A, Ottewell K, McArthur S, Kopps AM,
Chabanne D, Harcourt RG, Pirotta V, Waples K (2019) Challenges
of collecting blow from small cetaceans. Ecosphere 10:e02901.
https:// doi. org/ 10. 1002/ ecs2. 2901
Rodríguez RAH, González AOG, Santander MMA (2019) Manatees
mortality analysis at Los Bitzales, Tabasco, by remote sensing.
Int J Latest Res Eng Technol 5:1–15
Rohner CA, Richardson AJ, Prebble CE, Marshall AD, Bennett MB,
Weeks SJ, Cliff G, Wintner SP, Pierce SJ (2015) Laser photogram-
metry improves size and demographic estimates for whale sharks.
PeerJ 3:e886. https:// doi. org/ 10. 7717/ peerj. 886
Schofield G, Esteban N, Katselidis KA, Hays GC (2019) Drones for
research on sea turtles and other marine vertebrates—a review.
Biol Conserv 238:108214. https:// doi. org/ 10. 1016/j. biocon. 2019.
108214
Self-Sullivan C, Mignucci-Giannoni AA (2008) Trichechus manatus
ssp. manatus. The IUCN Red List of Threatened Species 2008:
e.T22105A9359161. https:// doi. org/ 10. 2305/ IUCN. UK. 2008.
RLTS. T2210 5A935 9161. en
Self-Sullivan C, Mignucci-Giannoni AA (2012) West Indian mana-
tees (Trichechus manatus) in the wider Caribbean region. Sire-
nian conservation: issues and strategies in developing countries.
University Press of Florida, Florida, pp 36–46
Serrano A, del Carmen D-R, Hernández-Cabrera T, Sánchez-Rojas G,
Cuervo-López L, Basáñez-Muñoz A (2017) Is the West Indian
Manatee (Trichechus manatus) at the Brink of Extinction in the
State of Veracruz, Mexico? Aquat Mamm 43:201. https:// doi. org/
10. 1578/ AM. 43.2. 2017. 201
Smith CE, Sykora-Bodie ST, Bloodworth B, Pack SM, Spradlin TR,
LeBoeuf NR (2016) Assessment of known impacts of unmanned
aerial systems (UAS) on marine mammals: data gaps and recom-
mendations for researchers in the United States. J Unmanned Veh
Syst 4:31–44. https:// doi. org/ 10. 1139/ juvs- 2015- 0017@ juvs- vi.
2016. 01. issue-1
Speakman JR (2005) Body size, energy metabolism and lifespan. J Exp
Biol 208:1717–1730
Stamper MA, Bonde RK (2012) Health assessment of captive and
wild–caught West Indian manatees (Trichechus manatus). In:
Hines EM, Reynolds JE III, Aragones L, Mignucci-Giannoni A,
Marmontel M (eds) Sirenian conservation: issues and strategies
in developing countries, Florida. University Press of Florida,
Florida, pp 139–147
Stevenson R, Woods WA Jr (2006) Condition indices for conserva-
tion: new uses for evolving tools. Integ Comp Biol 46:1169–1190.
https:// doi. org/ 10. 1242/ jeb. 01556
Sulzner K, Johnson CK, Bonde RK, Gomez NA, Powell J, Nielsen K,
Luttrell MP, Osterhaus AD, Aguirre AA (2012) Health assess-
ment and seroepidemiologic survey of potential pathogens in wild
Antillean manatees (Trichechus manatus manatus). PLoSOne
7:e44517. https:// doi. org/ 10. 1371/ journ al. pone. 00445 17
Torres WI, Bierlich K (2020) MorphoMetriX: a photogrammetric
measurement GUI for morphometric analysis of megafauna. J
Open Source Softw 5:1825. https:// doi. org/ 10. 21105/ joss. 01825
van Aswegen M, Christiansen F, Symons J, Mann J, Nicholson K,
Sprogis K, Bejder L (2019) Morphological differences between
coastal bottlenose dolphin (Tursiops aduncus) populations iden-
tified using non-invasive stereo-laser photogrammetry. Sci Rep
9:1–14. https:// doi. org/ 10. 1038/ s41598- 019- 48419-3
Vianna JA, Bonde RK, Caballero S, Giraldo JP, Lima RP, Clark A,
Marmontel M, Morales-Vela B, De Souza MJ, Parr L, Rodríguez-
Lopez MA (2006) Phylogeography, phylogeny and hybridization
in trichechid sirenians: implications for manatee conservation.
Mol Ecol 15:433–447. https:// doi. org/ 10. 1111/j. 1365- 294X. 2005.
02771.x
Webster T, Dawson S, Slooten E (2010) A simple laser photogramme-
try technique for measuring Hector’s dolphins (Cephalorhynchus
hectori) in the field. Mar Mamm Sci 26:296–308. https:// doi. org/
10. 1111/j. 1748- 7692. 2009. 00326.x
Wells RS, Scott MD (1990) Estimating bottlenose dolphin popula-
tion parameters from individual identification and capture-release
techniques. Rep Int Whal Commn 12:407–415
Wild S, Krützen M, Rankin RW, Hoppitt WJ, Gerber L, Allen SJ (2019)
Long-term decline in survival and reproduction of dolphins fol-
lowing a marine heatwave. Curr Biol 29:R239–R240. https:// doi.
org/ 10. 1016/j. cub. 2019. 02. 047
Wong JB, Auger-Méthé M (2018) Using laser photogrammetry to
measure long-finned pilot whales (Globicephala melas). Proc N
S Inst Sci 49:269. https:// doi. org/ 10. 15273/ pnsis. v49i2. 8164
Wong AW, Bonde RK, Siegal-Willott J, Stamper MA, Colee J, Powell
JA, Reid JP, Deutsch CJ, Harr KE (2012) Monitoring oral tem-
perature, heart rate, and respiration rate of West Indian manatees
(Trichechus manatus) during capture and handling in the field.
Aquat Mamm 38:1–16. https:// doi. org/ 10. 1578/ AM. 38.1. 2012.1