Phylogeny, Ecology, and Heart Position in Snakes
Author(s): Gabriel E. A. Gartner, James W. Hicks, Paulo R. Manzani, Denis V. Andrade,
Augusto S. Abe, Tobias Wang, Stephen M. Secor, and Theodore Garland Jr.
Source: Physiological and Biochemical Zoology, Vol. 83, No. 1 (January/February 2010), pp. 43-
54
Published by: The University of Chicago Press
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43

Phylogeny, Ecology, and Heart Position in Snakes

* E-mail: ggart001@ucr.edu.
†E-mail: jhicks@uci.edu.
‡Present address: Universidade Estadual de Campinas-UNICAMP, Departa-

mento de Zoologia–Instituto de Biologia, Campinas-SP 13083-970, Brazil; e-

mail: pmanzani@rc.unesp.br.
§E-mail: denis@rc.unesp.br.
kE-mail: asabe@rc.unesp.br.
#E-mail: tobias.wang@biology.au.dk.

** E-mail: ssecor@biology.as.ua.edu.
††Corresponding author; e-mail: tgarland@ucr.edu.

Physiological and Biochemical Zoology 83(1):43–54. 2010. � 2010 by The
University of Chicago. All rights reserved. 1522-2152/2010/8301-8173$15.00
DOI: 10.1086/648509

Gabriel E. A. Gartner1,*
James W. Hicks2,†

Paulo R. Manzani3,‡

Denis V. Andrade3,4,§

Augusto S. Abe3,4,k

Tobias Wang5,#

Stephen M. Secor6,**
Theodore Garland Jr.1,††

1Department of Biology, University of California, Riverside,
California 92521; 2Department of Ecology and Evolutionary
Biology, University of California, Irvine, California 92697-
2525; 3Departamento de Zoologia, Universidade Estadual
Paulista, Rio Claro, São Paulo 13506-900, Brazil; 4Instituto
Nacional de Ciência e Tecnologia em Fisiologia Comparada;
5Zoophysiology, Department of Biological Sciences,
University of Aarhus, Denmark; 6Department of Biological
Sciences, Box 870344, University of Alabama, Tuscaloosa,
Alabama 35487

Accepted 3/21/2009; Electronically Published 12/7/2009

Online enhancements: data file, appendixes.

ABSTRACT

The cardiovascular system of all animals is affected by gravi-
tational pressure gradients, the intensity of which varies ac-
cording to organismic features, behavior, and habitat occupied.
A previous nonphylogenetic analysis of heart position in
snakes—which often assume vertical postures—found the heart
located 15%–25% of total body length from the head in ter-
restrial and arboreal species but 25%–45% in aquatic species.
It was hypothesized that a more anterior heart in arboreal
species served to reduce the hydrostatic blood pressure when
these animals adopt vertical postures during climbing, whereas
an anterior heart position would not be needed in aquatic
habitats, where the effects of gravity are less pronounced. We

analyzed a new data set of 155 species from five major families
of Alethinophidia (one of the two major branches of snakes,
the other being blind snakes, Scolecophidia) using both con-
ventional and phylogenetically based statistical methods. Gen-
eral linear models regressing log10 snout-heart position on log10

snout-vent length (SVL), as well as dummy variables coding
for habitat and/or clade, were compared using likelihood ratio
tests and the Akaike Information Criterion. Heart distance to
the tip of the snout scaled isometrically with SVL. In all in-
stances, phylogenetic models that incorporated transformation
of the branch lengths under an Ornstein-Uhlenbeck model of
evolution (to mimic stabilizing selection) better fit the data as
compared with their nonphylogenetic counterparts. The best-
fit model predicting snake heart position included aspects of
both habitat and clade and indicated that arboreal snakes in
our study tend to have hearts placed more posteriorly, opposite
the trend identified in previous studies. Phylogenetic signal in
relative heart position was apparent both within and among
clades. Our results suggest that overcoming gravitational pres-
sure gradients in snakes most likely involves the combined
action of several cardiovascular and behavioral adaptations in
addition to alterations in relative heart location.

Introduction

With the exception of body length, which varies by two orders
of magnitude, the snake bauplan is conserved across all taxa
(Greene and McDiarmid 2005). All snakes are limbless, elon-
gate, and lack a pectoral girdle; their anatomy is elegantly mod-
ified to fit a tubular body plan (Greene 1997; Cohn and Tickle
1999). Although they might be viewed as occupying a relatively
small region of “morphospace” with respect to basic body plan,
snakes have radiated extensively in both number of species
(13,100 named; Uetz et al. 2007) and behavioral ecology. Even
within the apparent restrictions associated with a limbless and
elongate lifestyle, snakes have evolved to occupy almost all eco-
logical niches, including fully aquatic and pelagic sea snakes,
arboreal species that rarely if ever come to the forest floor,
snakes that glide, and completely fossorial burrowers (Greene
1997; Martins et al. 2008).

Despite its evolutionary success, the snake body plan is nev-
ertheless subject to constraint at several levels. Gravity, in par-
ticular, may significantly affect the cardiovascular function of
snakes, which are in essence long fluid-filled tubes (Lillywhite
1987). In a vertical column of fluid (e.g., arteries and veins),
gravity creates a vertical pressure gradient that increases with
the height of the tube (i.e., rgh; where r is the density of blood,
g is the acceleration due to gravity, and h is the vertical height
of the fluid column above or below a reference plane; in this

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44 Gartner, Hicks, Manzani, Andrade, Abe, Wang, Secor, and Garland

Figure 1. Phylogeny used for statistical analyses, with Pagel’s (1992) arbitrary branch lengths. See Appendix B for details of tree construction
and Appendix C for an electronic version (both appendixes are available in the online edition of Physiological and Biochemical Zoology).

case the heart). Gravitational pressure gradients have several
physiological consequences. For example, increased gravita-
tional pressure will distend the distal veins below the heart and
may cause significant blood pooling and increased plasma leak-
age into the surrounding tissues. In addition, venous blood
pooling will tend to reduce cardiac filling and cardiac output
that if not compensated by an appropriate baroreceptor re-
sponse can decrease arterial blood pressure and blood flow to
the brain. Therefore, animals—particularly those that are up-
right or assume vertical postures—must find solutions to in-
creased gravitational pressure gradients on the cardiovasuclar
system. In the giraffe, for example, several mechanisms are
known to work together to prevent edema in the lower ex-
tremities, including thick and impermeable capillary basement
membranes, arterial-wall hypertrophy, and a prominent lym-
phatic system (Willamson et al. 1971; Nilsson et al. 1988; Har-
gens 1991).

For at least two reasons, snakes are an interesting group in
which to study potential cardiovascular adaptations that
counter gravitational pressure gradients. First, some species are
completely terrestrial and therefore rarely encounter changes
in gravity, whereas arboreal species frequently assume vertical
postures while climbing, and aquatic species are less vulnerable
to the effects of gravity. Second, the conservative body plan
eliminates the confounding effects of appendages.

The utility of snakes as models to study the effects of gravity
and adaptations of the cardiovascular system to gravitational
pressure gradients has been previously reported (Lillywhite
1987, 1988; Seymour 1987). The most intriguing adaptive hy-
pothesis to emerge from these studies is that heart position
should correlate with behavioral ecology or habitat. Specifically,
in nonaquatic snakes that frequently assume vertical postures,
the height of the heart-head blood column should be reduced,
thus reducing cardiac work. In contrast, aquatic species would
have more centrally placed hearts because of the more dense
and less gravity-stressing nature of the medium they occupy
(water). Finally, terrestrial species would have a heart position

intermediate between aquatic and arboreal forms (Seymour and
Lillywhite 1976; Lillywhite 1987, 1988; Seymour 1987; Lilly-
white and Henderson 1993; Lillywhite et al. 1996a). Although
this general adaptive hypothesis seems reasonable, it must be
compared with alternative explanations to describe the evo-
lutionary origin and maintenance of traits (e.g., see Gould and
Lewontin 1979; Greene 1986; Garland et al. 1993; Garland and
Adolph 1994; Garland and Carter 1994; Rose and Lauder 1996;
Clobert et al. 1998; Orzack and Sober 2001; Blomberg and
Garland 2002; Garland et al. 2005).

In this article, we analyze heart position in 155, primarily
South American, snake species or subspecies to investigate the
generality of the heart position/habitat hypothesis using both
phylogenetic and nonphylogenetic statistical models. We ac-
count for phylogenetic “effects” (in the statistical sense) in two
ways. The first is by use of a phylogenetic tree that is hierarchical
(as shown in Fig. 1) versus one that has no hierarchical structure
and appears as a “star” (one large polytomy). The hierarchical
phylogeny, and alterations of it under an Ornstein-Uhlenbeck
(OU) model of character evolution (see “Methods”), implies
that related species will tend to be similar for the dependent
variable of interest, in our case heart position. The second way
that we allow for phylogenetic effects in statistical models is by
coding major branches of the tree for our included species as
different levels in a categorical factor (as shown in Fig. 1, we
recognized seven different lineages or “clades”). By use of ln
maximum likelihood ratio tests (LRTs), we can compare the
fit of models that assume (1) a star phylogeny (implying no
expected tendency for relatives to resemble each other), (2) the
hierarchical phylogeny shown in Figure 1, or (3) the hierarchical
phylogeny transformed under an OU model of evolution (see
“Methods”). We also use LRTs to compare those three models
with their counterparts that include the clade variable (which
we can refer to as models 4, 5, and 6, respectively). Importantly,
this approach is not the same as nested ANOVAs based on
taxonomy rather than phylogeny per se (see review in Harvey
and Pagel 1991). Those approaches could implement a star

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Predictors of Heart Position in Snakes 45

Table 1: Families or subfamilies included in the data set and their habitat distributions

Xenodontinae Dipsadinae Natricinae Colubrinae Elapidae Viperidae Boidae Total

Terrestrial 35 6 1 11 2 22 6 83
Fossorial 4 2 … 2 5 … 1 14
Arboreal 5 6 … 15 … 5 3 34
Semiaquatic 7 … 11 … 0 2 3 23
Aquatic … … … … 1a … … 1a

Total 51 14 12 28 8 29 13 155
a The aquatic Micrurus surinamensis was recoded as semiaquatic for purposes of statistical analyses.

phylogeny with categorical variables to represent membership
in different named groups at one or more taxonomic levels
(e.g., order, family, genus) but could not incorporate the full
branching structure either among groups at a given level or
within a group.

We also apply the above-listed six models with a separate
categorical variable that codes for variation in an “ecological”
factor—namely habitat (Table 1: terrestrial, fossorial, arboreal,
semiaquatic)—and compare their fit with LRTs. Finally, we use
the Akaike Information Criterion (AIC) to compare the fit of
nonnested models that include the habitat variable, the clade
variable, or both. These AIC comparisons allow for simulta-
neous consideration of the statistical effects of both phylogeny
and ecology on relative heart position. Our overall statistical
approach is widely applicable to many questions in comparative
biology (see also Huey et al. 2009; Swanson and Garland 2009).

Methods

Heart Position, Habitat, and Body Size

We gathered data on snake heart position in 155 taxa repre-
senting seven major families and subfamilies from both new
material and museum specimens (Table 1; see App. A in the
online edition of Physiological and Biochemical Zoology for
data). Snakes came primarily from a data set collected in Brazil
by P. R. Manzani and D. V. Andrade (Manzani 1995; N p

species). The remaining species were measured by G. E. A120
Gartner ( species) and S. M. Secor ( species). WeN p 8 N p 27
used the mean snout-heart length (SHL) when multiple indi-
viduals of a species were measured (App. A). In general, only
adult snakes were used. Heart distance was measured by making
a ventral incision from the neck until reaching the heart, then
measuring the distance from the tip of the snout to the top of
the atria. Snout-vent length (SVL) was measured from the tip
of the rostral scale to the cloaca on the ventral side of the
animal. Note that in Seymour (1987, p. 90) “the distances
[were] measured between the head (eye), heart and tip of tail.”
This difference in measurements is, more likely than not, neg-
ligible from a hydrostatic standpoint, as it is unlikely that the
evolution of the circulatory system is driven by such minimal
differences in pressure.

Snakes were categorized with respect to general habitat usage
using literature accounts—primarily field guides and various
works on localized snake faunas (e.g., Wright and Wright

1957)—and the authors’ own observations and experiences
with many of the included species. Fossorial species actively
burrow and are found underground or in litter, and most pos-
sess obvious morphological adaptations for burrowing (e.g.,
Typhlops). Semiaquatic species are commonly found in water,
where they often feed or flee from predators but frequently
take to the shore to bask, reproduce, etc. (e.g., Nerodia). Ar-
boreal species are often long and gracile in appearance and are
most frequently encountered in trees or low-lying shrubs (e.g.,
Boiga, Corallus). Terrestrial species lack any obvious morpho-
logical adaptations to the terrestrial realm and thus cannot be
easily classified into any of the other groups (e.g., most elapids,
Pituophis, Drymarchon). When possible, habitat categories were
chosen to reflect those of a previous study (Seymour 1987).
Thus, the terrestrial group not only includes obviously terres-
trial animals found away from water or trees but also those
snakes occasionally found swimming or climbing (as most
snakes appear to be able to swim and climb to some extent).
Unlike in Seymour (1987), our data set included only one
species that might be considered aquatic (Micrurus surinamen-
sis), so it was coded as semiaquatic for purposes of statistical
analyses.

Phylogeny Construction

We constructed a composite tree using phylogenetic hypotheses
from previously published studies. We began with higher-level
relationships uniting the major lineages of snakes and nested
less inclusive groups (lower-level relationships) within this
framework. Our initial intent was to use the best available
phylogenetic estimate at each hierarchical level rather than
combining a number of phylogenies for a particular group.
Most published trees, however, contained only a few taxa of
interest for any particular group, and so we were often forced
to use multiple trees—each of which may have employed dif-
ferent characters and methods in their analyses—to place par-
ticular taxa into our tree.

When a number of trees were available for a given group,
we followed the methods of de Queiroz and Rodrı́guez-Robles
(2006): maximum likelihood trees were preferred over those
obtained by other methods (e.g., parsimony), and strict con-
sensus trees were used when available. If a lower-level group
had multiple trees available to choose from, then we used the
number of characters and the number of taxa to differentiate

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46 Gartner, Hicks, Manzani, Andrade, Abe, Wang, Secor, and Garland

Table 2: Allometry of snout-heart length in relation to snout-vent length

Model Slope SE y-intercept SE r2

ln Maximum
Likelihood AIC

Conventional:
Least squares 1.027 .0489 �.729 .1375 .742 118.791 �231.6
Reduced major axis 1.193 �1.191

Phylogenetic:
Least squares .926 .0351 �.405 .1204 .820 168.798 �331.6
Reduced major axis 1.023 �.679
Regression with OU transforma .937 .0362 �.447 .1061 .814 174.916 �341.8

a REML estimate of .d p 0.7306

Figure 2. Isometry (slope not statistically different from 1.00) of snout-
heart length in relation to snout-vent length for 155 species or sub-
species of snakes. Line is conventional (nonphylogenetic) least squares
linear regression (slope p 1.027, ; see Table 2 for this andSE p 0.0489
alternatives).

among them. For instance, if two trees had similar numbers
of taxa, we chose the tree with the greater number of characters
(and vice versa).

In a few instances, the amount of clade support (i.e., boot-
strap values) influenced our decision on tree selection. In those
cases where no phylogenetic hypotheses could be found (par-
ticularly a problem for species-level relationships among the
Xenodontinae) or where there was particularly weak nodal sup-
port, we collapsed clades to maintain a more conservative ap-
proach to our analysis.

Figure 1 shows the topology of the final tree and indicates
the seven major clades identified in statistical analyses. Details
of tree construction can be found in Appendix B in the online
edition of Physiological and Biochemical Zoology. For statistical
analyses, branch lengths were set by the arbitrary method of
Pagel (1992), as shown in Figure 1, using the DOS PDTREE
program (Garland et al. 1999; Garland and Ives 2000). Ap-
pendix C in the online edition of Physiological and Biochemical
Zoology presents the tree in a standard electronic format.

Statistical Analyses

SVL and SHL were log10 transformed before analyses. To test
and quantify phylogenetic signal, we used the methods of Blom-
berg et al. (2003).

We then described the simple allometry of snout-heart po-
sition in relation to SVL in the entire data set ( ) byN p 155
use of ordinary least squares (OLS) linear regression and re-
duced major axis (RMA), with both conventional (i.e., non-
phylogenetic or assuming a star phylogeny) and phylogenetic
versions, using the DOS PDTREE program. PDTREE employs
phylogenetically independent contrasts, which yields estimates
that are the same as from a phylogenetic generalized least
squares analysis (PGLS; Garland et al. 2005; Lavin et al. 2008).
However, PDTREE provides certain statistics that are not cur-
rently available in most programs for PGLS. It is well known
that OLS slopes will underestimate the true scaling relation
when the independent variable (in this case, log10 SVL) contains
“measurement error,” and if the amount of such error is not
known, then the RMA slope often gives a reasonable estimate
(Rayner 1985; Warton et al. 2006; Ives et al. 2007). To obtain
the likelihood of the alternative models, we used the Matlab
Regressionv2.m program developed by A. R. Ives and T. Gar-

land Jr. (Lavin et al. 2008; for examples of applications, see
Buchwalter et al. 2008; Jeffery et al. 2008; Warne and Charnov
2008; Huey et al. 2009; Swanson and Garland 2009).

Next, we examined the effects of body size (SVL), clade, and
ecology using conventional multiple regressions with dummy
variables that code for clade membership and habitat category
(i.e., ANCOVA with parallel slopes). Formally, a “clade” is de-
fined as a monophyletic group of organisms, including the
ancestor and all descendant species. Practically, few if any com-
parative studies can include all members of a given clade be-
cause of extinctions and/or inaccessibility of living represen-
tatives. In this article, we use “clade” to refer to all of the species
included in the available data set that are members of a formal
clade (e.g., Colubrinae, Viperidae, Elapidae). For analyses, Re-

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Predictors of Heart Position in Snakes 47

Table 3: Alternate regression models for predicting heart position of snakes

Model

Conventional
(OLS)

Phylogeny
(PGLS)

Phylogeny with
OU Transform
(RegOU)

Mean SE

SE of
the
Estimate

r2 for
Model

REML
Estimate
of d (OU
Transform)ln ML AIC ln ML AIC ln ML AIC

SVL (simple allometry) 118.791 �231.6 168.798 �331.6 174.916 �341.8 6.210E�03 .07880 .8140 .7306
SVL � habitat 126.673 �241.3 178.459 �344.9 184.909 �355.8 5.567E�03 .07462 .8367 .7356
SVL � clade 187.324 �356.6 170.298 �322.6 193.673 �367.3 5.114E�03 .07151 .8602 .3024
SVL � clade � habitat 205.625 �387.3 180.134 �336.2 209.749 �393.5 4.234E�03 .06507 .8936 .1975
SVL � clade �

“arboreal” 204.655 �389.3 179.974 �339.9 209.075 �396.2 4.210E�03 .06489 .8923 .2034

Note. ML p maximum likelihood. “Conventional (OLS)” indicates ordinary least squares (multiple) regression, which is mathematically equivalent to assuming

a “star” phylogeny with no hierarchical structure. “Phylogeny” indicates generalized least squares analysis (PGLS), which is mathematically equivalent to phylo-

genetically independent contrasts. “Phylogeny with OU Transform (RegOU)” is a regression model in which the residuals are modeled as an Ornstein-Uhlenbeck

process. RegOU models contain one more parameter than their OLS or PGLS counterparts, so whether a RegOU model fits the data significantly ( )P ! 0.05

better can be tested by comparing twice the difference in ln likelihoods with the value 3.841 (the ninety-fifth percentile of the distribution of x2 with 1 df). Similar

ln likelihood ratio tests can be used to compare models within the OLS, PGLS, or RegOU columns when one is a nested subset of the other (e.g., SVL � habitat

vs. SVL but not SVL � habitat vs. SVL � clade). The AIC (see “Methods”) can be used to compare any models, with smaller (more negative) values indicating

a better fit. As a rule of thumb, models whose AIC is !2 units larger than the best model can also be said to have substantial support. See “Methods” and Lavin

et al. (2008) for further explanation. For the models listed in the bottom row (SVL � clade � “arboreal”), the arboreal variable was always highly significant

(all ), and the partial regression coefficient was always positive, indicating that heart position is more posterior than for nonarboreal species.P ! 0.0001

gressionv2.m automatically recoded clade as a series of six 0–
1 dummy variables.

Our simplest model used only SVL as an independent var-
iable and hence was just a linear regression. Subsequent models
added additional variables along with SVL (e.g., SVL � clade
or SVL � habitat). Our most complex or “full” model included
SVL, clade, and habitat. Based on inspection of the partial
regression coefficients for that most complex model (see “Re-
sults”), it was apparent that the major effect of habitat was that
arboreal animals were different from all three other habitat
types. Therefore, we ran one additional model that considered
SVL, clade, and only arboreal animals as a distinct category.

All analyses were then repeated using PGLS ANCOVA models
with the Regressionv2.m program. Finally, we implemented
phylogenetic ANCOVAs with a branch-length transformation
parameter based on the OU model of evolution, termed
“RegOU” (Lavin et al. 2008). The OU process has been sug-
gested as a way to mimic the effects of stabilizing selection (e.g.,
see Felsenstein 1988; Garland et al. 1993; Blomberg et al. 2003;
Halsey et al. 2006; Lavin et al. 2008; see also Martins and
Hansen 1997). The statistical procedure begins with a user-
specified phylogenetic tree (as shown in Fig. 1), then moves
the internal nodes of the tree up and down, thus simultaneously
stretching and contracting the branch lengths above and below
the nodes. Small values of the OU transformation parameter
(d) yield trees that are more starlike (i.e., long terminal branches
and short internode branches near the root), whereas values
of d greater than unity yield trees that are even more hierarchical
than the original tree. A d value of exactly unity yields the
original tree. The regression model in question is fitted using
the entire range of stretched and compressed trees. The tree
that yields the lowest mean squared error (residual variance)
has the highest likelihood, and it is used to compute regression
statistics. All of the multiple regression analyses were performed

in Matlab version 7.0 using Regressionv2.m (Lavin et al. 2008).
For the conventional (nonphylogenetic) multiple regressions,
analyses were also run in SPSS for Windows version 11.5 as a
verification, and results were identical to those of Regres-
sionv2.m. The fit of all alternate models considered was com-
pared using the AIC, computed in the smaller-is-better form:

.AIC p (�2 # ln ML likelihood) � (2 # no. parameters)
AIC is particularly well suited to situations such as this, where

numerous nonnested models are being compared (e.g., see
Lavin et al. 2008). As a rule of thumb, models whose AIC is
!2 units larger can also be said to have substantial support,
whereas a difference of 4–7 indicates considerably less support,
and a difference 110 indicates essentially no support (Burnham
and Anderson 2002, p. 70). Where one model was a nested
subset of the other, we compared them by ln maximum like-
lihood ratio tests (LRTs), where twice the difference in ln like-
lihoods is assumed to be distributed asymptotically as a x2

distribution with degrees of freedom equal to the difference in
the number of parameters in the two models. If an LRT and/
or comparison of the AIC indicates that the phylogenetic ver-
sion of the model is better than the nonphylogenetic version,
then one can conclude that “phylogenetic signal”—the ten-
dency for similar species to resemble one another (Blomberg
and Garland 2002; Blomberg et al. 2003)—is present in the
residuals of the nonphylogenetic model. From a more general
perspective, if the clade variable is statistically significant in a
model, then phylogenetic position is also important in pre-
dicting the dependent variable (i.e., heart position in this ar-
ticle) after controlling statistically for other independent vari-
ables in the model.

Results

SVL (log10 transformed) showed relatively low ( ; cf.K p 0.265
Blomberg et al. 2003) but statistically significant ( )P ! 0.001

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48 Gartner, Hicks, Manzani, Andrade, Abe, Wang, Secor, and Garland

Table 4: Full model including habitat and clade variables to predict log10 snout-heart length (mm), analyzed by
conventional multiple regression and phylogenetically with an OU transform

Variable

Conventional (OLS) Phylogenetic with OU Transform (RegOU)

Coefficient SE F df P Coefficient SE F df P

y-intercept �.6465 .0964 44.94 �.5746 .0968 35.26
log10 SVL (mm) .9694 .0331 858.82 1, 144 .0039 .9454 .0333 806.77 1, 144 !.0001
Arboreal .0914 .0148 38.14 1, 144 !.0001 .0879 .0155 32.06 1, 144 !.0001
Fossorial .0191 .0217 .77 1, 144 .3813 .0199 .0230 .75 1, 144 .3879
Semiaquatic .0225 .0196 1.31 1, 144 .2537 .0156 .0227 .48 1, 144 .4895
Xenodontinae �.0049 .0174 .08 1, 144 .7789 �.0094 .0224 .18 1, 144 .672
Dipsadinae .0691 .0232 8.87 1, 144 .0034 .0564 .0307 3.37 1, 144 .0685
Natricinae �.0448 .0297 2.28 1, 144 .1335 �.0483 .0366 1.74 1, 144 .1892
Boidae .1226 .0235 27.14 1, 144 !.0001 .1314 .0303 18.81 1, 144 !.0001
Elapinae .0718 .0301 5.70 1, 144 .0183 .0666 .0355 3.52 1, 144 .0627
Viperidae .2153 .0187 132.27 1, 144 !.0001 .2079 .0229 82.49 1, 144 !.0001
Habitat 12.79 3, 144 !.0001 10.71 3, 144 !.0001
Clade 42.47 6, 144 !.0001 25.09 6, 144 !.0001

Note. For the habitat variable, terrestrial is arbitrarily chosen as the base group for comparison. For the clade variable, Colubridae is arbitrarily

chosen as the base group for comparison. Thus, all coefficients and significance levels for the individual dummy variables within these categorical

variables are relative to those base groups. Overall tests for habitat and clade are at the bottom. For the OLS model, rate of evolution (MSE) p
0.004438, SE of estimate (SEE) p 0.06620, model r2 p 0.9160, ln maximum likelihood of model p 205.625, AIC p �387.250, AICc p �385.053.

For the RegOU model, MSE p 0.004234, SEE p 0.06507, model r2 p 0.8936, REML estimate of OU transformation parameter (d) p 0.1975, ln

maximum likelihood of model p 209.749, AIC p �393.499, AICc p �390.917.

phylogenetic signal. Once corrected for its association with log10

SVL, log10 heart position showed higher signal ( ,K p 0.580
).P ! 0.001

Among all taxa, heart position scales isometrically with body
size (Table 2; Fig. 2). The 95% confidence interval (CI; 0.931–
1.124) about the OLS regression slope (1.027) includes unity,
although, as must be the case, the RMA slope is higher (RMA
slope p 1.193; RMA p OLS/r). The 95% CI (0.856–0.995)
about the PGLS slope (0.926) excludes unity, but again, the
RMA slope is higher (1.023). The phylogenetic regression with
an OU transform (RegOU) is the best-fitting model (based on
likelihood and AIC; Table 2) and has a slope of 0.937 with a
95% CI of 0.865–1.008.

Alternate models that include clade and/or habitat are pre-
sented in Table 3. Based on the AIC values (smaller is better),
the relative fit of the models with various independent variables
is the same for conventional OLS and RegOU models, im-
proving in the following order: SVL (simple allometry), SVL �
habitat, SVL � clade, SVL � clade � habitat, SVL � clade �
“arboreal” (i.e., a single category of habitat vs. all others). How-
ever, for all models, the RegOU versions (which contain one
more parameter) are significantly better than their OLS
counterparts based on ln likelihood ratio (LR) tests, with the
largest P value being 0.0041 for the model that includes
SVL � clade � habitat. For both OLS and RegOU models, the
best-fitting model, based on lowest AIC, includes SVL, clade,
and a single dummy variable for arboreal snakes (rather than
the set of three dummy variables to recognize all four habitat
categories). This emphasizes that, for our data set, the major
habitat effect on snake heart position is that arboreal snakes

have more posteriorly positioned hearts (see partial regression
coefficients for the full models in Table 4).

Table 3 also presents PGLS models, which incorporate the
phylogeny with untransformed branch lengths, as shown in
Figure 1. In all cases, these models are significantly worse than
their RegOU counterparts (which contain one additional pa-
rameter), based on LR tests (largest ). Therefore,P p 0.0005
we defer further consideration of these models to the
“Discussion.”

Discussion

We used a statistical approach that incorporates phylogenetic
information to develop models that address whether ecological
or historical factors (or a combination of the two) most affect
relative heart position in snakes and to test for the presence of
phylogenetic signal in this trait. We determined which version
of a given model—OLS, PGLS, or RegOU—better fit the data
by use of ln LRTs and by comparing AIC values.

Our general findings were that habitat, clade membership,
and phylogenetic position within clades (and/or interclade re-
lations) all accounted for some of the variation in relative heart
position. The fact that the RegOU models fit better than the
nonphylogenetic models even when clade is included as a factor
(Table 3) means that the hierarchical structure within and/or
among clades reflects some of the resemblance among related
species (i.e., phylogenetic signal) in relative heart position. With
respect to habitat, arboreal snakes had the most posteriorly
placed hearts relative to SVL; with respect to clade, the Viper-
idae had the most posterior hearts (Fig. 3; Table 4).

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Predictors of Heart Position in Snakes 49

Figure 3. Snout-heart length in relation to snout-vent length for 155
taxa of snakes, indicating the clade that is most different (A, Viperidae)
and the habitat classification (B, arboreal) that is most different from
other snakes.

In all cases, the RegOU versions of models performed sig-
nificantly better (based on LR tests) than their OLS or PGLS
counterparts. The relative fit of the PGLS models, as shown in
Table 3, reveals an interesting situation. The PGLS models in-
corporate the phylogeny with branch lengths as shown in Figure
1 and do not allow any branch-length transformations to im-
prove the fit of the statistical model to the data. As compared
with OLS models, their PGLS counterparts had much higher
likelihoods when the clade variable was not in the model but
lower likelihoods when it was included. This reflects a trade-
off in the sense that the phylogenetic signal (the tendency for
related species to resemble each other; Blomberg and Garland
2002) present in the residuals can be apportioned either gen-
erally throughout the tree or among the specified clades but
not both, given the branch lengths shown in Figure 1. However,
when the branch lengths are allowed to vary to optimize fit in
the RegOU models, the nodes are pulled toward the root (es-
timated d value ∼ 0.2, where 0 p a star and 1.0 p the original
tree), thus making the tree more starlike than shown in Figure
1, and models that include clade are much better (difference
in AIC p 11.5–40.4) than those that do not. In these RegOU
models that include clade, phylogenetic signal thus exists both
among clades and among species within clades (or in the form
of related clades resembling each other).

The importance of estimating a branch-length transforma-
tion parameter simultaneously with estimating parameters in
a phylogenetic regression model was first emphasized by Grafen
(1989; see review in Lavin et al. 2008). Similarly, Garland et al.
(1992) emphasized the importance of various diagnostics and
possible transformations of branch lengths when implementing
phylogenetically independent contrasts (see also Dı́az-Uriarte
and Garland 1998). These points are now well accepted in the
comparative-method literature (e.g., Martins and Hansen 1997;
Freckleton et al. 2002; Halsey et al. 2006; Duncan et al. 2007),
and our study provides another clear example of how analyses
can be improved by adding this flexibility. Moreover, debates
about the importance of ecology versus phylogeny can be ad-
dressed statistically by comparison of a range of models in
between a star and the original starter tree (e.g., Fig. 1) while
simultaneously testing the effects of including ecological (e.g.,
habitat) and/or phylogenetic (clade membership) variables in
alternate models (see also Huey et al. 2009; Swanson and Gar-
land 2009).

In our study, the single best predictor of heart position was
body size (log10 SVL), which is unsurprising given the large size
range in the data set (Tables 2, 4; Figs. 2, 3). With body size
in the model, clade membership (see Fig. 1 for the seven clades
identified for analyses) appeared to be a significantly better
predictor of heart position than habitat (four categories iden-
tified) for both nonphylogenetic (AIC values of �356.6 for
clade vs. �241.3 for habitat) and RegOU models (�367.3 for
clade vs. �355.8 for habitat). Moreover, those same AIC values
indicate the clear superiority of the phylogenetic RegOU models
as compared with the nonphylogenetic OLS models. Thus, rel-
ative heart position of snakes varies in relation to both differ-
ences among the major branches of the phylogenetic tree and

the detailed hierarchical structure of the tree. Stated another
way, phylogenetic signal (sensu Blomberg and Garland 2002;
Blomberg et al. 2003) is apparent both within and among clades
(see also above). The best models, however, incorporated both
clade and habitat on a hierarchical tree, and the single best

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50 Gartner, Hicks, Manzani, Andrade, Abe, Wang, Secor, and Garland

Figure 4. Tail length versus snout-vent length from a published data
set for 65 South American taxa (Martins and Oliveira 1998). Note that
arboreal species generally have longer tails (see text).

model included clade and only one aspect of habitat—inclusion
in the arboreal category.

It is clear that both phylogeny and habitat are important in
predicting heart position. For instance, in viperids, Seymour
(1987) noted that the heart is generally shifted posteriorly (per-
haps inherited from their common ancestor), but in arboreal
members of the clade, the heart is positioned more anteriorly
than in terrestrial members, which Seymour interpreted as an
adaptation. Given their unusual heart positions, we also ana-
lyzed the viperids alone ( species), for which three hab-N p 29
itat categories are represented (fossorial snakes are lacking).
The ln likelihoods of the OLS, PGLS, and RegOU ANCOVA
models were 45.0929, 44.8680, and 47.3945, respectively. Based
on likelihood ratio tests, the RegOU model (estimated d p

) is significantly better than either the OLS (0.4630 P p
) or PGLS model ( ). The effect of habitat was0.0319 P p 0.0246

not statistically significant in any of the models ( for theP 1 0.5
RegOU model).

For completeness, we also analyzed the Xenodontinae
( ) and Colubrinae ( ) alone. Results for Xeno-N p 51 N p 28
dontinae were similar to those for viperids; that is, RegOU was
the best-fitting model, and habitat was not significant (P 1

for the RegOU model). For Colubrinae, the conventional0.5
OLS model was best, and habitat was highly significant (P !

), with the effect attributable to arboreal species having0.0001
hearts placed more posteriorly (partial regression coefficient p
0.1074, ). Thus, the habitat effect within ColubrinaeP ! 0.0001
alone is consistent with that shown in the overall analysis of
all species (Table 4; Fig. 3).

Alternate Functional Hypotheses for Variation in
Heart Position

The anterior heart position of arboreal snakes is hypothesized
to reduce the cardiac work required to lift the blood to the
head when in the vertical orientation (Seymour 1987; Lillywhite

1988). Alternatively, Badeer (1998) hypothesized that heart po-
sition is optimized for cardiac-filling pressures and is related
to the compliance of the vessels above and below the heart.
Briefly, this alternative view is based on the physical principles
that determine the hydrostatic indifference point (HIP) in a
vertically oriented vascular system (Wagner 1886; Clark et al.
1934; Gauer and Thron 1965). The HIP is a unique reference
within the venous circulation where blood pressure is unaf-
fected by vertical orientation (Gauer and Thron 1965; Buckner
et al. 1999). HIP is determined by in vivo compliance of the
veins above and below the heart. In the upright position, if the
dependent veins (vessels below the heart) are highly compliant
relative to the vessels above the heart, then the venous HIP
will shift below heart level (Gauer and Thron 1965). The in-
ferior location of the HIP results in a reduction in venous return
and decreased cardiac-filling pressure (Buckner et al. 1999; Jar-
vis et al. 2007). Conversely, reducing the compliance (stiffening)
of the dependent vessels can raise the HIP above heart level
(Gauer and Thron 1965) and increase cardiac-filling pressures.
The colocalization of the HIP and heart ensures that cardiac
filling remains relatively stable despite changes in vertical ori-
entation (Buckner et al. 1999). Based on these fundamental
hemodynamic principles, Badeer (1998) hypothesized that the
position of the heart in snakes should be correlated with the
HIP.

Several studies have shown that arboreal snakes have less
compliant caudal vessels compared with other terrestrial and
aquatic species. In these species, the integument is tightly cou-
pled to the underlying tissues, which will help prevent venous
pooling in the upright position (Lillywhite 1993, 1996; Lilly-
white et al. 1996b). Interestingly, this antigravity feature of the
integument is similar to the “skin and fascial antigravity suit”
seen in the legs of giraffes (Hargens et al. 1987), thus providing
an apparent example of convergent evolution in function. In
snakes, a more posterior location of the heart will also act in
preventing blood from pooling below the heart when adopting
an upright position (see the preceding discussion about HIP).
Given that both decreased compliance (Lillywhite 1993, 1996;
Lillywhite et al. 1996b) and posterior heart location (this study)
are prevalent among arboreal species, this seems to indicate
venous pooling and the associated risk of edema formation as
an important gravitational stressor. Thus, previous adaptive
scenarios placing the heart of arboreal snakes closer to the head
may have overweighted the importance of ensuring an adequate
blood supply to the head in detriment of the importance of
venous return from the regions below the heart. Obviously,
ensuring an adequate blood flow—above and below the heart—
is important both on the arterial and venous side, and this is
only made possible by the action of an orchestrated suite of
cardiovascular and behavioral adaptations rather than to al-
terations in heart location alone. This, combined with differ-
ences in the intensity of the gravitational stress imposed by
different habitats, may help to explain the absence of any ex-
pected correlation between heart location and habitat for
aquatic and terrestrial species. Future studies should address
this question in addition to determining the compliance and

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Predictors of Heart Position in Snakes 51

Table 5: ANCOVA of snake heart position with log10 estimated total length as the covariate

Model

Conventional
(OLS)

Phylogeny
(PGLS)

Phylogeny with
OU Transform
(RegOU)

Mean
Squared
Error

SE of
the
Estimate

r2 for
Model

REML
Estimate
of d (OU
Transform)ln ML AIC ln ML AIC ln ML AIC

Total length � clade �
“arboreal” 204.655 �389.3 179.974 �339.9 209.075 �396.2 4.210E�03 .06489 .8923 .2034

P for clade !.0001 .7890 !.0001
P for arboreal .0575 .3469 .0846

Note. ML p maximum likelihood. See note to Table 3. The partial regression coefficient for the arboreal dummy variable was always positive, indicating that

heart position is more posterior than for nonarboreal species.

Table 6: Heart position as a percentage of body length

Percentage of

Total Length
(Seymour
1987)

Estimated Total
Length (This
Study)

Snout-Vent
Length (This
Study)

Arboreal 17.4 (2.2) 17.9 (4.1) 25.6 (5.8)
Terrestrial 18.8 (3.2) 15.6 (2.9) 19.4 (3.7)
Semiaquatic 22.7 (4.8) 15.7 (2.3) 19.4 (2.8)
Fossorial 23.6 (7.9) 17.6 (2.9) 21.8 (3.5)
Viperidae 33.3 (4.5) 25.2 (3.8) 31.9 (4.2)
Aquatic 33.4 (5.7)

Note. Data are means, with SDs in parentheses.

the HIP of snakes from a variety of habitats, thus allowing
direct tests of the hypothesis that heart position is correlated
with HIP.

Variation in Tail Length as a Possible Confounding Factor

Seymour (1987) grouped snakes into six categories—arboreal,
terrestrial, aquatic, semiaquatic, fossorial, and Viperidae—then
analyzed heart position as a percent of total length (measured
as eye to tail tip). ANOVA indicated highly significant group
differences, with arboreal having the most anteriorly placed
hearts (Table 6). In contrast, we analyzed SHL by ANCOVA
with SVL as the covariate and found that arboreal snakes have
more posteriorly placed hearts (Fig. 3; Table 4). One possible
explanation for this discrepancy is that arboreal snakes in our
data set tend to have relatively long tails (H. B. Lillywhite,
personal communication). Indeed, some previous studies have
found that arboreal snakes often do have relatively long tails
(Goldsmith 1984; Guyer and Donnelly 1990; Lillywhite and
Henderson 1993).

In an attempt to test this proposition, we did the following.
First, we analyzed a separate, published data set for 65 South
American taxa (Martins and Oliveira 1998) that also measured
SVL (as in our study) rather than total length and also reported
tail length as a percent of total length. Tail length as a percentage
of total length was given as a range of values, so we used the
upper end of the range to compute absolute tail length for their
data set. We then computed SVL by subtraction. Figure 4 shows

that, for their data set, arboreal snakes do indeed tend to have
relatively long tails. Conventional ANCOVA of their data yields
the following equation:

log total length p 0.04634 � (1.0165410

# log SVL) � (0.06242 # arboreal),10

where “arboreal” is a dummy variable that is 1 for arboreal
species and 0 for all others. Second, we used that equation to
compute a log10 total length for all 155 species in our data set.
Third, we analyzed the data with Regressionv2.m, mimicking
the analyses presented in the bottom row of Table 3, but with
log10 total length as the covariate rather than log10 SVL. In these
analyses, the partial regression coefficient for the arboreal
dummy variable was always positive, thus again indicating that
arboreal snakes have hearts placed more posteriorly (unlike in
the analyses of Seymour 1987), although the effect was not
statistically significant (two-tailed for the best-P p 0.0846
fitting RegOU model; see Table 5).

Table 6 shows the mean values from Seymour (1987, Table
1) and from our study when snakes are grouped according to
his categories. As a percent of estimated (see above) total length,
our data set does not indicate arboreal species to have the most
anteriorly placed hearts. Therefore, we conclude that the dis-
crepancy between our analyses and those of Seymour (1987)
cannot be explained entirely by the difference in the measure
of body size used.

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52 Gartner, Hicks, Manzani, Andrade, Abe, Wang, Secor, and Garland

Figure 5. Illustration of the range of individual variation in snout-
heart length in relation to snout-vent length for eight species in which
we had at least 10 individuals per species. See “Discussion.”

Table 7: Unique and shared taxa between Seymour (1987) and this study

Shared Taxa
Unique Taxa in
This Study

Unique Taxa in
Seymour (1987) Shared Species

Acrochordidae
Boinae Agkistrodon piscivorus
Colubrinae Arizona elegans
Crotalinae Bothrops atrox

Dipsadinae Diadophis punctatus
Elapinae Elaphe obsoleta
Erycinae Lachesis muta

Homalopsinae Lampropeltis getulus
Hydrophiinae Masticophis flagellum

Natricinae Nerodia sipedon
Pythoninae Nerodia taxispilota
Typhlopidae Pituophis melanoleucus
Viperinae Thamnophis sirtalis

Xenodontinae

Note. See “Discussion.”

Caveats

A limitation of any comparative study based on data sets com-
prised of faunal surveys or literature reviews is that the various
species are not measured under “common garden” conditions
(Garland and Adolph 1991, 1994; Garland et al. 2005). Hence,
it is possible that some of the differences we have observed
among habitats or among clades might be reduced in magni-
tude if all animals had been raised under identical conditions
or even housed under identical conditions for some weeks or
months before measurement (see Garland and Adolph 1991
and references therein). On the other hand, it would not be
possible to raise all species under identical conditions because,
for example, they will not (voluntarily) eat exactly the same
types of food. In addition, heart position can shift ontogenet-
ically within a species (S. M. Secor, unpublished results), so

variation in age among species in our sample would affect
species differences to some extent.

Most of the species included here are represented by a single
individual, which obviously introduces error when comparing
species. To get some indication of how this “noise” may have
influenced our comparison, in Figure 5 we show the relation
between log10 SHL and log10 SVL for the eight species from the
Secor sample that were represented by 10 or more nonjuvenile
individuals (total ). As can be seen, the magnitude ofN p 150
the differences among some species in size-relative heart po-
sition is much larger than the range of variation observed within
species. In general, noise introduced by small sample sizes
should tend to reduce statistical power to detect effects of hab-
itat, clade, etc. Given the several statistically significant effects
we have detected (Table 3), this sampling variation was not
large enough to obscure major results. An improvement for
future analyses would be to fit regression models that explicitly
account for the magnitude of within-species variation (Ives et
al. 2007).

An additional caveat is that the taxa used in this study were
different than those used in previous studies. In particular, only
12 of the 155 taxa used in this present study were also used in
Seymour (1987; see Table 7). Moreover, 51 of our species (33%)
were South American xenodontines, a taxon lacking from Sey-
mour’s (1987) sample, whereas we lacked any fully aquatic
snakes, such as hydrophiid sea snakes, which accounted for
17% of Seymour’s sample. On the other hand, of the 14 families
and subfamilies included in one or the other of the two studies,
nine were included in both (Table 7).

A final limitation is that it is very difficult to quantify and
categorize behavior—especially in broadly defined regimes such
as “habitat” (e.g., see Jayne 1982). Very few snakes live strictly
in an arboreal regime or a terrestrial regime (sea snakes and
blind snakes being notable exceptions) but instead tend to exist
at the borders of any given behavioral spectrum (e.g., primarily

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Predictors of Heart Position in Snakes 53

terrestrial but occasionally arboreal or primarily fossorial but
occasionally terrestrial). Thus, categorization of habitat be-
comes particularly difficult for animals that can be classified as
semiarboreal or semifossorial. For our purposes, any snake that
frequently assumes vertical postures, even if commonly found
in a terrestrial environment (e.g., Pantherophis), was considered
arboreal, but one can see how tinkering with such a distinction
could have important consequences for the results and con-
clusions of a study such as this. Note, however, that our data
set does include some strictly arboreal species (e.g., Corallus
sp., Atheris squamiger, Bothriopsis bilineata, Oxybelis sp., Im-
antodes cenchoa, etc.). Because we present all of the data an-
alyzed here (App. A), it will be possible for future workers to
try various recategorizations as field data become more avail-
able. In addition, future workers can incorporate additional
ecological or behavioral predictors, such as diet (Hampton
2009).

Acknowledgments

We thank H. B. Lillywhite for helpful discussions. This study
was supported by National Science Foundation grants DEB-
0416085 (D. N. Reznick, M. S. Springer, and T.G.) and IOS-
0466139 (S.M.S.). A.S.A. and D.V.A. were supported by Fun-
dação de Amparo à Pesquisa do Estado de São Paulo and
Conselho Nacional de Desenvolvimento Cientı́fico e Tecnoló-
gico grants, and T.W. was supported by the Danish Research
Council.

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