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EDITED BY

XIAOTONG WANG



Ludong University, China

REVIEWED BY

VESA PAAJANEN



Faculty of Science and Forestry, University of Eastern Finland, Finland

HAO ZHANG



China Agricultural University, China

The editor and reviewers' affiliations are the latest provided on their Loop
research profiles and may not reflect their situation at the time of review.

TABLE OF CONTENTS

 * * Abstract
   * Introduction
   * Materials and Methods
   * Results
   * Discussion
   * Conclusion
   * Data Availability Statement
   * Author Contributions
   * Funding
   * Conflict of Interest
   * Publisher’s Note
   * Acknowledgments
   * Supplementary Material
   * References




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ORIGINAL RESEARCH ARTICLE

Front. Physiol., 22 September 2021 | https://doi.org/10.3389/fphys.2021.690029


ELECTROCARDIOGRAPHIC SCALING REVEALS DIFFERENCES IN ELECTROCARDIOGRAM INTERVAL
DURATIONS BETWEEN MARINE AND TERRESTRIAL MAMMALS

Rhea L. Storlund1,2*, David A. S. Rosen2,3 and Andrew W. Trites1,2
 * 1Department of Zoology, University of British Columbia, Vancouver, BC, Canada
 * 2Marine Mammal Research Unit, Institute for the Oceans and Fisheries,
   University of British Columbia, Vancouver, BC, Canada
 * 3Vancouver Aquarium, Vancouver, BC, Canada

Although the ability of marine mammals to lower heart rates for extended periods
when diving is well documented, it is unclear whether marine mammals have
electrophysiological adaptations that extend beyond overall bradycardia. We
analyzed electrocardiographic data from 50 species of terrestrial mammals and 19
species of marine mammals to determine whether the electrical activity of the
heart differs between these two groups of mammals. We also tested whether
physiological state (i.e., anesthetized or conscious) affects electrocardiogram
(ECG) parameters. Analyses of ECG waveform morphology (heart rate, P-wave
duration, and PQ, PR, QRS, and QT intervals) revealed allometric relationships
between body mass and all ECG intervals (as well as heart rate) for both groups
of mammals and specific differences in ECG parameters between marine mammals and
their terrestrial counterparts. Model outputs indicated that marine mammals had
19% longer P-waves, 24% longer QRS intervals, and 21% shorter QT intervals. In
other words, marine mammals had slower atrial and ventricular depolarization,
and faster ventricular repolarization than terrestrial mammals. Heart rates and
PR intervals were not significantly different between marine and terrestrial
mammals, and physiological state did not significantly affect any ECG parameter.
On average, ECG interval durations of marine and terrestrial mammals scaled with
body mass to the power of 0.21 (range: 0.19–0.23) rather than the expected
0.25—while heart rate scaled with body mass to the power of –0.22 and was
greater than the widely accepted –0.25 derived from fractal geometry. Our
findings show clear differences between the hearts of terrestrial and marine
mammals in terms of cardiac timing that extend beyond diving bradycardia. They
also highlight the importance of considering special adaptations (such as
breath-hold diving) when analyzing allometric relationships.




INTRODUCTION

Cardiac anatomy and function are widely conserved across mammalian species. Just
as heart mass increases with increasing body mass (Prothero, 1979), the timing
of cardiac electrical signal conduction is also expected to scale with body mass
in mammals because the timing of cardiac filling and contraction must also
increase to maintain proper cardiac function (Meijler and Meijler, 2011).
Comparative analyses of mammalian electrocardiograms (ECGs) demonstrate that
there is a characteristic pattern to electrical depolarization, and that the
timing is fairly consistent for all species when body mass is accounted for
Günther and Morgado (1997). These patterns not only hold for the allometric
relationship between heart rate and body mass, but also hold for other aspects
of cardiac electrical activity. This includes the timing of individual
components of the PQRST wave that reflect cycles of depolarization and
repolarization of various portions of the heart, and produce the characteristic
ECG waveform.

Among mammals, the marine species have anatomical and physiological adaptations
to breath-hold diving that may cause the electrical activity of their hearts to
differ from terrestrial mammals. When diving, the hearts of marine mammals must
cope with extreme physiological changes, including bradycardia and
vasoconstriction (Blix et al., 1984). Marine mammals regularly achieve lower
heart rates while diving than predicted for a similarly sized terrestrial mammal
(Fedak et al., 1988; e.g., Castellini and Zenteno-Savin, 1997; Mcdonald and
Ponganis, 2014; Goldbogen et al., 2019), as well as higher heart rates than
predicted while at the surface (Fedak et al., 1988; Castellini and
Zenteno-Savin, 1997; Noren et al., 2012; e.g., Bickett et al., 2019). These
heart rate fluctuations make it challenging to predict how ECG intervals and
heart rates compare between marine and terrestrial mammals. Differences in the
scaling of electrocardiogram (ECG) parameters between marine and terrestrial
mammals may be expected due to differences in physiology (e.g., diving
bradycardia), gross anatomy (e.g., the general shape of the heart), and tissue
composition (e.g., the number and orientation of cardiac muscle fibers,
adaptations to the conduction system of the heart).

ECG measurements should be taken under standardized physiological conditions to
provide valid comparisons between species (e.g., from calm, healthy
individuals). However, in practice, ECGs are recorded under a variety of
conditions with one of the most common being under anesthesia. Anesthesia can
complicate comparisons because it is known to affect heart rate (Picker et al.,
2001; Nishiyama, 2016) and prolong QT intervals (Yildirim et al., 2004). Hence,
the effects of anesthesia must be accounted for in any comparison of cardiac
electrophysiology.

As with anesthesia, changes in activity state (e.g., rest, exercise, apnea) are
also known to affect ECG intervals in terrestrial mammals (e.g., Simoons and
Hugenholtz, 1975). However, it is less clear how ECG intervals change with
activity in marine mammals, because their apparent resting state at the water’s
surface or on land may not be equivalent to that of terrestrial mammals. As
such, recording ECGs while marine mammals are submerged may be a more
comparative measure (although these are rarely available). This potential
difference in activity states of marine and terrestrial mammals may explain the
higher-than-expected heart rates recorded from marine mammals at the surface
(Fedak et al., 1988; Castellini and Zenteno-Savin, 1997; Noren et al., 2012;
e.g., Bickett et al., 2019), and would lead to expected differences in the ECG
intervals as well.

Differences in cardiac anatomy provide further reason to investigate ECG scaling
in marine and terrestrial mammals. While the hearts of marine and terrestrial
mammals are generally anatomically similar (Drabek, 1975; Rowlatt, 1990), the
shape of marine mammal hearts may result in different cardiac electrical
activity. For example, the broadness of pinniped hearts (Drabek, 1975; Rowlatt,
1990) may increase the duration of ventricular depolarization because the
distance the signal must travel is greater, thereby resulting in a longer QRS
interval on an ECG. Other changes to the amount and distribution of cardiac
muscle will affect how electrical signals travel through the heart, and hence
the duration of their ECG intervals.

In addition to gross morphological differences, dissections have also revealed
differences in myocardial cell structure that may cause signal conduction in
marine mammal hearts to deviate from the typical mammalian pattern. For example,
cetaceans such as bowhead whales (Balaena mysticetus; Pfeiffer, 1990), sperm
whales (Physeter macrocephalus; White and Kerr, 1917), pilot whales
(Globicephala sp.), Atlantic bottlenose dolphins (Tursiops truncatus), Pacific
white-sided dolphins (Lagenorhynchus obliquidens), and Amazon river dolphins
(Inia geoffrensis; Simpson and Gardner, 1972) have unusually large Purkinje
fibers that are thought to increase signal conduction velocity from the
atrioventricular (AV) node to the ventricular myocardium (van Nie, 1986).
Similarly, Todd fibers found in the right atrial wall of white-beaked dolphins
(Lagenorhynchus albirostris) are suspected of increasing signal conduction
velocity between the sinoatrial (SA) and AV nodes (van Nie, 1987). The function
of these specialized conduction tissues is unknown, but it has been suggested
that they may be beneficial for rapid heart rate transitions such as those
observed in marine mammals as they dive and resurface (van Nie, 1986, 1987),
thereby making marine mammals better equipped to rapidly decrease their heart
rates when diving and increase their heart rates when surfacing to breathe.

Comparisons of the cardiac electrical activity of marine and terrestrial mammals
are needed to identify how differences between these two groups influence
scaling arguments. Large scale comparisons across taxa can miss interesting
trends because of large interindividual and interspecies variation, especially
when all groups are not represented equally, or by neglecting important grouping
factors, such as physiological state (i.e., anesthetized or conscious) and
ecological group (i.e., marine or terrestrial). Previous allometric analyses of
ECG intervals among mammals generally show that scaling is similar between
marine and terrestrial mammals (Meijler and van der Tweel, 1986; Meijler, 1990;
Noujaim et al., 2004; Meijler and Meijler, 2011). However, the low number of
marine mammal species included in these studies prevented direct statistical
comparisons between marine and terrestrial mammals. We therefore amassed a
dataset of published ECG parameters including 19 species of marine mammals to
determine whether cardiac electrophysiology differs between marine and
terrestrial mammals when accounting for differences in measurement conditions.


MATERIALS AND METHODS


META-ANALYSIS

We amassed an ECG dataset from 83 species (representing over 2,000 individuals)
of marine and terrestrial mammals for analysis. The majority of the data were
obtained from the scientific literature, but we also added data we collected
from three species of marine mammals—Steller sea lions, northern fur seals, and
walrus. We extracted values for heart rate, P-wave, and PQ, PR, QRS, and QT
interval durations, and recorded information about the source group or
individual including age, sex, physiological state (anesthetized or conscious)
and body mass, when available. We lumped PQ intervals in with the PR interval
comparisons because these intervals are both measured from the onset of atrial
depolarization to the onset of ventricular depolarization, despite the slight
difference in terminology. We report a single mean or mid-range value for each
species that had sample sizes ranging from a single animal to hundreds of
individuals (e.g., dogs; Supplementary Table 1). One data point per species per
physiological state was used in the analyses to give each species equal weight,
independent of sample size.

We categorized the data based on the physiological state of the individual to
account for possible effects of anesthesia on cardiac electrophysiology. In our
dataset, we classified measurements as being taken under anesthesia when the
majority (>90%) of the individuals in the sample were known to be anesthetized.
In 22 out of 23 species recorded as anesthetized, all individuals of the species
were anesthetized and for the 23rd species, 9 out of 10 individuals were
anesthetized. Measurements from 51 species came from individuals that were not
anesthetized (i.e., “conscious”). Data from five species [cats (Felis catus),
dogs (Canis familiaris), mice (Mus musculus), guinea pigs (Cavia porcellus), and
northern elephant seals (Mirounga angustirostris)] allowed us to report
conscious and anesthetized ECG parameters separately. We excluded 24 species
from our analyses because no information regarding physiological state
(anesthetized or conscious) was available. Our final dataset consists of 69
mammalian species (19 marine and 50 terrestrial), representing 1670 individuals
(Supplementary Table 1).

To calculate representative ECG parameters for each species under a specific
physiological state, we first calculated means and midranges for each individual
source. Averages were preferable, but when these were unavailable, midranges
were used as proxies. To combine data from multiple sources, we calculated
weighted averages and midranges using sample size as the weighting factor. When
sample size was unavailable, we assigned a value of one as the weighting factor
because each report had to have come from at least one individual. Therefore,
reports from species for which no sample size was stated may be underrepresented
in the calculated average for that species.

For terrestrial mammals, we followed the procedure for harvesting ECG data and
estimating species’ masses as outlined in Günther and Morgado (1997) with some
modifications. Our source for terrestrial ECG data was Grauwiler (1965), who
reported ECG parameters for a wide variety of mammals. Often, body mass was not
indicated so we estimated body mass by matching reported information about the
individual or group, such as age and sex, to corresponding species information
from additional literature sources (Supplementary Table 1). When mass estimates
could not be informed by age and sex, we used a general average species mass
from the available literature. In many cases, estimates of mass were from only
one or a few individuals.

For marine mammals, we used ECG data that were previously published for 16
species, to which we added data for three additional species (Steller sea lions,
northern fur seals, and a walrus; see Storlund et al., 2021 for detailed
methods). Masses for all marine mammal individuals were documented at the time
of the ECG recordings either as an estimate (for the large whales) or by direct
measurement.

For all species, heart rate data were either explicitly stated or were
calculated as 60 divided by the RR interval. Midrange heart rates were
calculated and used when averages were not reported. For species that had
multiple types of heart rate data available, the reported value used for
analysis was selected based on the estimate requiring the fewest number of
calculations. Our ranking system from highest to lowest preference was heart
rate, heart rate calculated from the RR interval, heart rate midrange, and
finally heart rate midrange calculated from RR midrange.

The final data set included ECG parameters published from 1933 to 2021. No data
was excluded based on publication date because standards for measuring ECGs have
not changed significantly over this time frame (e.g., Lewis, 1913; Wilson et
al., 1954; Bickett et al., 2019).


STATISTICAL ANALYSIS

All statistical analyses were performed using R (v.3.6.3; R Core Team, 2020) and
RStudio (v.1.2.5042; RStudio Team, 2020). We fit linear models to test the
effects of body mass, ecological group (marine or terrestrial) and physiological
state (conscious or anesthetized) on ECG parameters. Body mass and ECG
parameters were log10-transformed to linearize the data prior to model fit.
Initially, we fit saturated three-way interaction models between body mass,
ecological group, and physiological state for each ECG parameter. The most
parsimonious model for each relationship was determined using visual inspections
of interaction plots using the function plot_model (package sjPlot v.2.8.4;
Lüdecke, 2020), followed by likelihood ratio tests using the function lrtest
(package lmtest; Zeileis and Hothorn, 2002). We performed multiple linear
regression on the most parsimonious models to evaluate the effect of each
independent variable. To check that each model met the assumptions of multiple
linear regression analysis, we visually inspected scatterplots of body mass and
each ECG interval, Q-Q plots, scatterplots of the predicted values and
residuals, and evaluated Variance Inflation Factor values using the function
vif() (package car; Fox and Weisberg, 2019). All data met the assumptions of
linearity, multivariate normality, no multicollinearity, and homoscedasticity.
Results were assumed to be significant for p < 0.05.


RESULTS

The effects of ecological group, physiological state, body mass, and
interactions between these factors were specific to each ECG parameter (Tables
1, 2). All ECG parameters significantly correlated to body mass (Table 1, p <
0.001). Heart rate decreased with body mass, while P-wave, PR, QRS, and QT
durations increased with body mass (detailed below). Differences between marine
and terrestrial mammals were detected in P, QRS, and QT durations, but not in
heart rate, or PR durations when mass was accounted for. There were no
significant differences between anesthetized and conscious mammals and no
interaction effects between body mass, ecological group and physiological state
for any ECG parameter.


TABLE 1

Table 1. Multiple linear regression model parameters for relationships between
body mass (BM), ecological group (EG), physiological state (PS), and ECG
parameters. In these models, the factor EG is 1 for a marine mammal and 0 for a
terrestrial mammal and the factor PS is 1 for anesthetized individuals and 0 for
conscious individuals.



TABLE 2

Table 2. Linear regressions and allometric equations describing the relationship
between body mass (BM) and ECG parameters for mammals separated by ecological
group as appropriate.




Heart rate scaled with body mass to the power of −0.221 and correlated with body
mass over a range of 0.017 kg (mouse) to 70,000 kg (blue whale; Figure 1A and
Table 2) while PR interval duration scaled with body mass to the power of 0.208
and correlated with body mass over a range of 0.017 kg (mouse) to 32,000 kg (fin
whale; Figure 1B and Table 2). Both heart rate and PR interval duration did not
vary significantly with ecological group and physiological state (Table 1).


FIGURE 1

Figure 1. Relationships between body mass and heart rate (A), and PR interval
duration (B) in mammals. Mass was the best predictor for these three ECG
parameters. Ecological group (terrestrial mammals shown in green squares, marine
mammals shown in blue circles) and physiological state (conscious mammals
indicated by closed squares and circles, anesthetized mammals indicated by open
squares and circles) had no effect on heart rate or PR interval. Regression
equations for each parameter are provided in Table 2.




In all mammals, P-wave duration scaled with body mass to the power of 0.209
(Table 2 and Figure 2A). P-wave duration increased with body mass over a range
of 0.017 kg (mouse) to 3,320 kg (Asian elephant) and depended on ecological
group (Table 1, p < 0.001). Marine mammals had 19% longer P-waves than
terrestrial mammals when mass was accounted for Table 1, p < 0.05. For example,
a marine mammal weighing 100 kg would have a P-wave duration of 0.095 s, whereas
a terrestrial mammal of the same mass would have a P-wave duration of 0.077 s.
P-wave duration did not vary significantly with physiological state (Table 1).


FIGURE 2

Figure 2. Relationships between body mass, ecological group and P wave duration
(A), QRS complex duration (B), and QT interval duration (C) in mammals. Mass was
the best predictor for these three ECG parameters. However, durations also
depended on ecological group (terrestrial mammals shown in green squares, marine
mammals shown in blue circles), but not on physiological state (conscious
mammals indicated by closed squares and circles, anesthetized mammals indicated
by open squares and circles). Regression equations for each parameter are
provided in Table 2.




In all mammals, QRS duration scaled with body mass to the power of 0.189 (Table
2 and Figure 2B). QRS duration increased with body mass over a range of 0.017 kg
(mouse) to 70,000 kg (blue whale) and depended on ecological group (Table 1, p <
0.001). Marine mammals had 24% longer QRS intervals than terrestrial mammals
when mass is accounted for. For example, a 100 kg marine mammal would have a QRS
complex of 0.092 s, while a terrestrial mammal of the same mass would have a QRS
complex of 0.070 s. QRS duration did not vary significantly with physiological
state (Table 1).

In all mammals, QT interval duration scaled with body mass to the power of 0.225
(Table 2 and Figure 2C). QT interval duration increased with body mass over a
range of 0.17 kg (mouse) to 70,000 kg (blue whale; Table 2, p < 0.001) and
depended on ecological group (Figure 2C). Marine mammals had 21% shorter QT
interval durations than terrestrial mammals when accounting for body mass. For
example, a 100 kg marine mammal would have a QT interval of 0.281 s, while a
terrestrial mammal of the same mass would have a QT interval of 0.340 s. QT
duration did not vary significantly with physiological state (Table 1).


DISCUSSION


MARINE AND TERRESTRIAL MAMMAL ELECTROCARDIOGRAM COMPARISON

Our results show that the durations of some components of the ECG waveform
differ between marine and terrestrial mammals. Marine mammals have longer
P-waves, wider QRS complexes, and shorter QT intervals than terrestrial mammals.
These differences indicate that marine mammals have prolonged atrial and
ventricular depolarization, and shortened ventricular repolarization compared to
terrestrial mammals. In other words, conduction through the atria and ventricles
in marine mammals is slower, and repolarization through the ventricles is faster
than in terrestrial mammals. These findings likely reflect differences in
cardiac anatomy and physiology between these two ecological groups.

We detected several differences between the ECGs of marine and terrestrial
mammals despite the large between-species variation observed for all parameters.
Only about one quarter of the species we studied fell on or were close to the
respective (marine or terrestrial) allometric regression lines, indicating that
body mass and ecological group are not the only factors influencing electrical
signal conduction through the myocardium. Data points that fall far from the
regression lines highlight species-specific differences in cardiac
electrophysiology that may relate to unique cardiac adaptations. It is also
important to note that the significant differences between marine and
terrestrial mammals may not apply to all of the species we grouped within these
categories because the hearts of some marine mammals perform more similarly to
the hearts of terrestrial mammals and vice versa. Despite this, our comparison
showed clear differences in cardiac electrophysiology between marine and
terrestrial mammals.

Heart rate comparisons are difficult because they depend on the activity states
of the animals being compared. In our study, we found no difference between the
heart rates of similar sized marine and terrestrial mammals. This may reflect
the challenge of determining what constitutes a “resting heart rate” for marine
mammals. For marine mammals, the “resting” cardiac state may be determined by
the proportion of time spent diving, surfacing, and (in the case of pinnipeds)
on land. Therefore, it may also be important to distinguish between whales,
phocids (true seals), and otariids (fur seals and sea lions). For example,
whales and seals spend more time diving, so a bradycardic heart rate might be
considered their resting state. In contrast, sea lions spend more time
hauled-out, so it might be more appropriate to measure their resting heart rate
at the surface. It is important to consider this point when interpreting the
results of this study as it is possible that some marine mammals were not “at
rest” when ECGs were recorded and therefore the data are not directly
comparable. Moving forward, defining “rest” in marine mammals will be necessary
to improve comparisons of cardiac electrophysiology between ecological groups.

Two of the differences we found in cardiac parameters in our comparison between
marine and terrestrial mammals—a longer P-wave and a wider QRS complex—suggest
that cardiac anatomy may differ between these two ecological groups. The
duration of ECG parameters is influenced by the mass of the myocardium that the
electrical signal must pass through because conduction time increases with
distance (Lewis, 1920). For example, long QRS intervals are commonly observed in
association with left ventricular hypertrophy, especially in elite athletes
(Dorn, 2007; Zelenkova and Chomahidze, 2016). Hence, the longer P-waves and QRS
complexes of marine mammals may indicate that marine mammals have greater atrial
and ventricular myocardial mass or differently shaped cardiac chambers than
terrestrial mammals. This is supported by anatomical reports of the broad,
dorsoventrally compressed hearts of phocids (Drabek, 1975; Rowlatt, 1990), a
shape that would potentially increase the signal conduction distance slowing
atrial and ventricular depolarization.

Atrial and ventricular depolarization were slower in marine mammals, but this
had no effect on overall cardiac timing (heart rates scaled the same for all
mammals) or PR interval duration. A probable explanation is that the enlarged
Purkinje fibers and Todd fibers found in some species of cetaceans increase
signal conduction velocity through the myocardium (van Nie, 1987, 1988;
Pfeiffer, 1990), thereby making up for any possible delay in overall timing.
Theoretically, enlarged Todd fibers, such as those found in white-beaked
dolphins (van Nie, 1987), could also decrease the timing of the electrical
activity to support the longer P-wave and QRS complex despite heart rate
remaining the same. However, it is more difficult to predict the effect of Todd
fibers on the ECG because conduction from the SA node to the AV node happens
concurrently with atrial depolarization.

The third difference we observed—a shorter QT interval—could be indicative of
differing activity states of marine and terrestrial mammals when ECGs were
recorded. QT intervals are largely determined by heart rate and shorten when
heart rate increases (Lecocq et al., 1989). While there were no differences in
heart rate between marine and terrestrial mammals, the shortened QT intervals
might be further evidence that the heart rates at the surface (where most ECGs
were recorded) of some marine mammals are, in fact, elevated above true resting
values. In such cases, “resting” heart rates likely occur subsurface in many
marine mammal species.

Inherent differences in the timing of cardiac action potentials between marine
and terrestrial mammals can also explain the ECG differences we found. The
duration of an action potential is determined by time-dependent and
voltage-gated membrane currents. Prolonged atrial and ventricular depolarization
can result from decreased sodium current, while short QT intervals can result
from increased potassium current or decreased calcium current (e.g., Gima and
Rudy, 2002; Antzelevitch et al., 2007; Amin et al., 2010; Gintant et al., 2011).
Comparisons of marine and terrestrial cardiac myocytes are needed to test this
hypothesis.


EFFECTS OF ANESTHESIA

Anesthesia is known to affect heart rate and QT interval duration, but did not
have a noticeable effect on ECG durations in this study. This is not to say that
anesthesia does not affect ECG parameters, only that we did not detect any
differences in our study. Anesthesia was only retained in the final model
describing heart rate, and it did not have a statistically significant effect.
Due to the large variation in heart rates observed for a given mass, the effect
of anesthesia may not have been noticeable. In addition, anesthetic protocols
can have opposing effects on heart rate with some increasing heart rate (Picker
et al., 2001) while others decrease heart rate (Ko and Krimins, 2014; Ozeki and
Caulkett, 2014). Since the anesthetic agent in many of these studies was not
specified, this could also contribute to the lack of observable effect.
Anesthetic agents such as isoflurane, desflurane, and sevoflurane are also known
to cause QT intervals to increase (Yildirim et al., 2004), but that was not
apparent in the current study. In many of the published studies that we took
data from, only limited information about the anesthetic protocol used (e.g.,
type, plane, and duration of anesthesia) was available, which prevented us from
undertaking further analyses.


MAMMALIAN ELECTROCARDIOGRAM SCALING

The ECG scaling exponents we found closely agree with other previously derived
cardiac scaling exponents. Günther and Morgado (1997) found that the RR, PQ,
QRS, and QT intervals all scaled with body mass to the power of 0.20, while our
scalers ranged from 0.19 to 0.23. Noujaim et al. (2004) found that mammalian PR
intervals scale to a power of 0.24, comparatively higher than the 0.21
determined in our analysis. The similarities between our results and those of
Günther and Morgado (1997) are likely due to the similarity in methods and data
sources (e.g., Grauwiler, 1965). Additionally, our inclusion of more data from
marine mammal species may explain the lower value of our PR interval scaling
exponent compared to that of Noujaim et al. (2004), because many of the species
that we added are large bodied, putting them on the far end of the body mass
spectrum where their PR intervals could greatly impact the overall scaling
relationship.

There is debate regarding the theoretical foundation for how ECG characteristics
should scale with body mass. Recent studies examining the scaling of ECG
parameters with body size report exponents more consistent with the theoretical
one-quarter scaling law (Meijler and Meijler, 2011) than with the simple
one-third law predicted by Euclid—suggesting that fractal geometry is a more
likely explanation for how ECG parameters scale with body mass than volumetric
scaling. Still, our empirically derived scaling exponents are lower than the
theoretically predicted exponent for all of the ECG parameters we explored.
Although our observed values of ∼±0.21 are close to the widely accepted 0.25
derived from fractal geometry, small deviations in exponents will have large
impacts on the estimated range of cardiac measurements for a mammal of a given
size.

It is possible that the discrepancy between our data and the theoretical
exponents reflect variability in nature, measurement error, and technique and
operator variability. It is also possible that the theoretically derived scaling
arguments are based on a supposed “average idealized animal” that are simply
approximations meant to aid understanding of fundamental biological principles
(West and Brown, 2005). Currently, no theoretical mechanism exists to explain
our consistent allometric scaling to the power of 0.20.


LIMITATIONS

Comparing marine and terrestrial mammal ECGs is difficult because there are
relatively few published ECGs from marine mammal species. To address this
challenge, we included data from anesthetized subjects and accounted for the
potential bias associated with anesthesia by including physiological state as an
independent variable in our models. However, we could only categorize each
subject as either “anesthetized” or “conscious” because detailed anesthetic
protocols were rarely included in the published reports. This adds uncertainty
to our analysis because different types of anesthetic agents (and protocols)
affect the heart differently. Despite the potential differences in anesthetic
protocols, we did not find any effect of physiological state on any ECG
parameter, which suggests that the variation due to anesthetic protocol had less
of an effect than variation due to other sources. The potential effects of
anesthesia are a limitation that can be overcome in future studies as technology
advances and more ECGs are recorded from calm, conscious marine mammals.

Another limitation of our study is the lack of information regarding ECG
recording protocols for each subject. The sources we used rarely included
information about perceived stress, activity level, limb leads, electrode
placement, and measurement protocols. As a result, we assumed that ECG
measurements were comparable despite potential differences in protocols.
However, our results do not appear to have been undermined by this assumption
given that the trends we observed agree with previously described patterns in
mammalian ECGs.

Analyzing group differences in allometric relationships can be challenging when
the groups being compared have large differences in body mass. In our case, the
average mass of terrestrial mammals was small compared to the average mass of
marine mammals, and the masses of the two groups only overlapped over a portion
of their range. However, we felt it appropriate to retain all of the species in
the final model because we could find no indication that ECG parameters from the
smallest and largest species were outliers.


CONCLUSION

Overall, our study supports previous findings about mammalian ECG interval and
heart rate scaling, while also demonstrating the need to consider ecological
groups when making comparisons based on allometric relationships. The timing of
electrical conduction through the myocardium is altered slightly in marine
mammals, probably to maintain the timing of chamber filling and contraction.
Without this unique timing, the heart beats of marine mammals would be slowed,
which could negatively affect circulation. Clear differences in the cardiac
timing of marine mammals are likely the result of anatomical adaptations to
diving, rather than these differences being functional adaptations themselves.


DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the
article/Supplementary Material, further inquiries can be directed to the
corresponding author/s.


AUTHOR CONTRIBUTIONS

RS collected, analyzed the ECG data, and wrote the first draft of the
manuscript. All authors contributed to the conception and design of the study
and contributed to the revisions.


FUNDING

Financial support was provided by an NSERC grant to DR and a ReNewZoo
scholarship to RS.


CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any
commercial or financial relationships that could be construed as a potential
conflict of interest.


PUBLISHER’S NOTE

All claims expressed in this article are solely those of the authors and do not
necessarily represent those of their affiliated organizations, or those of the
publisher, the editors and the reviewers. Any product that may be evaluated in
this article, or claim that may be made by its manufacturer, is not guaranteed
or endorsed by the publisher.


ACKNOWLEDGMENTS

The inclusion of Steller sea lion, northern fur seal, and walrus ECGs was made
possible by the Vancouver Aquarium and IDEXX. We are especially grateful to
Marco Margiocco for sharing his ECG expertise with us, and to the Vancouver
Aquarium’s animal health team and training staff for their support with ECG
collection. We also thank Drs. Robert Shadwick, Martin Haulena, and William
Milsom for their insightful comments on earlier versions of this manuscript, and
the two reviewers whose helpful comments improved this manuscript. An older
version of this manuscript appears online as Ch. 3 of Storlund’s (2019) Master’s
thesis.


SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at:
https://www.frontiersin.org/articles/10.3389/fphys.2021.690029/full#supplementary-material


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Keywords: ECG, marine mammal, heart rate, anesthesia, allometry, cardiac timing,
comparative electrophysiology

Citation: Storlund RL, Rosen DAS and Trites AW (2021) Electrocardiographic
Scaling Reveals Differences in Electrocardiogram Interval Durations Between
Marine and Terrestrial Mammals. Front. Physiol. 12:690029. doi:
10.3389/fphys.2021.690029

Received: 01 April 2021; Accepted: 30 August 2021;
Published: 22 September 2021.

Edited by:

Xiaotong Wang, Ludong University, China

Reviewed by:

Vesa Paajanen, University of Eastern Finland, Finland
Hao Zhang, China Agricultural University, China

Copyright © 2021 Storlund, Rosen and Trites. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (CC BY).
The use, distribution or reproduction in other forums is permitted, provided the
original author(s) and the copyright owner(s) are credited and that the original
publication in this journal is cited, in accordance with accepted academic
practice. No use, distribution or reproduction is permitted which does not
comply with these terms.

*Correspondence: Rhea L. Storlund, r.storlund@oceans.ubc.ca



Disclaimer: All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated organizations, or
those of the publisher, the editors and the reviewers. Any product that may be
evaluated in this article or claim that may be made by its manufacturer is not
guaranteed or endorsed by the publisher.



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