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Article
Reconstructing dietary ecology of extinct strepsirrhines (Primates,
Mammalia) with new approaches for characterizing and analyzing
tooth shape
Ethan L. Fulwood* , Shan Shan, Julia M. Winchester, Tingran Gao, Henry Kirveslahti,
Ingrid Daubechies, and Doug M. Boyer
Abstract.—The morphological and ecological diversity of lemurs and lorisiformes once rivaled that of the
rest of the primate order. Here, we assemble a dataset of 3D models representing the second mandibular
molars of a wide range of extant and fossil strepsirrhines encompassing this diversity. We use these mod-
els to distill quantitative descriptors of tooth form and then analyze these data using new analytical meth-
ods. We employ a recently developed dental topography metric (ariaDNE), which is less sensitive to
details of random error in 3D model quality than previously used metrics (e.g., DNE); Bayesian multi-
nomial modeling with metrics designed to measure overfitting risk; and a tooth segmentation algorithm
that allows the shapes of disaggregated tooth surface features to be quantified using dental topography
metrics. This approach is successful at reclassifying extant strepsirrhine primates to known dietary ecology
and indicates that the averaging of morphological information across the tooth surface does not interfere
with the ability of dental topography metrics to predict dietary adaptation. When the most informative
combination of dental topography metrics is applied to extinct species, many subfossil lemurs and the
most basal fossil strepsirrhines are predicted to have been primarily frugivorous or gummivorous. This
supports an ecological contraction among the extant lemursand the importance of frugivory in the origins
of crown Strepsirrhini, potentially to avoid competition with more insectivorous and folivorous members
of Paleogene Afro-Arabian primate faunas.
Ethan L. Fulwood
†
. Department of Neuroscience, Washington University in St. Louis School of Medicine, St. Louis,
Missouri 63110, U.S.A.; and Department of Evolutionary Anthropology, Duke University, Durham, North
Carolina 27708, U.S.A. E-mail: ethanfulwood@upike.edu
†
Present address: Kentucky College of
Osteopathic Medicine, Pikeville, Kentucky 41501, U.S.A.
Shan Shan
‡
and Ingrid Daubechies. Department of Mathematics, Duke University, Durham, NorthCarolina 27708
U.S.A.
‡
Present address: Department of Mathematics and Statistics, Mt. Holyoke College, South
Hadley, Massachusetts 01075, U.S.A.
Julia M. Winchester and Doug M. Boyer. Department of Evolutionary Anthropology, Duke University, Durham,
North Carolina 27708, U.S.A.
Tingran Gao. Department of Statistics, University of Chicago, Chicago, Illinois 60637, U.S.A.
Henry Kirveslahti. Department of Statistical Science, Duke University, Durham, North Carolina 27708, U.S.A.
Accepted: 27 January 2021
*Corresponding author.
Introduction
Precisely occluding heterodont dentition
unlocked a range of efficient food-processing
strategies, selecting for a close fit between fine
aspects of tooth shape and dietary strategy in
mammals (Simpson 1933; Crompton 1970;
Ungar 2010; Bhullar et al. 2019). Tooth shape
is expected to vary with the material properties
of the plant and animal parts that mammals
exploit for food (Yamashita 1998; Lucas 2004;
Ungar 2007,2010). Most attempts to link
tooth shape to dietary adaptation have focused
on the molars and premolars, as incisors and
canines are under selection to facilitate inges-
tion of food items that may relate more to
geometry or propensity for escape than to
food material properties, and also for sociosex-
ual functions (Kay 1975,1977; Kay and Hylan-
der 1978; Kay and Simons 1980; Kay and Covert
1984; Kay and Ungar 1997; Yamashita 1998,
Paleobiology, 2021, pp. 1–20
DOI: 10.1017/pab.2021.9
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Paleontological Society. 0094-8373/21
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2003; Lucas 2004; Boyer 2008; Bunn and Ungar
2009; Ungar 2010; Bunn et al. 2011; Winchester
et al. 2014; Allen et al. 2015; López-Torres et al.
2017; Pineda-Munoz et al. 2017; Selig et al.
2019).
Molar Shape and Dietary Adaptation.—
Descriptive studies of tooth shape have long
linked the qualitative form of the mammalian
molar to aspects of diet (Gregory 1922; Simp-
son 1933; Crompton 1970). Quantitative
approaches for describing the occlusal surface
of teeth have taken two contrasting approaches:
shearing metrics and dental topography
metrics. Shearing quotients (SQ) and shearing
ratios (SR) disaggregate and measure features
on the tooth surface related to “shearing”or,
more generally, food fragmentation, achieved
through the interaction of blades on the tooth
surface (Kay 1975,1978; Kay and Covert 1984;
Yamashita 1998; Lucas 2004). SQ and SR are
measured as the sum of the lengths of the shear-
ing structures of a tooth normalized to tooth
length. This measurement appears to reflect
the proportion of structural carbohydrates in
the diets of primates and is effective in distin-
guishing folivores and insectivores from frugi-
vores (Kay and Covert 1984). Folivores and
insectivores resemble one another in shearing
capacity, however, and must be distinguished
using body size (Kay 1975; Kay and Covert
1984).
Comparisons using SQ and SR require the
identification of homologous shearing struc-
tures across a sample of different taxa, which
makes some contrasts impossible and others
misleading if comparing teeth with different
fundamental geometries (Kay and Simons
1980; Kay and Ungar 1997). SQ and SR also can-
not characterize many other potentially adap-
tive features of the occlusal surface, including
tooth height, tooth surface complexity, or the
projection of sharp cusps (Ungar and William-
son 2000).
Dental topography metrics are designed to
address these issues by abstracting functional
information from a continuous occlusal surface.
Dental topography metrics have the advan-
tages over shearing metrics of (1) incorporating
additional potentially relevant functional infor-
mation, (2) ease of automation for the analysis
of large samples, and (3) diminished reliance
on the identification of homologous structures
upon comparison among phylogenetically dis-
parate taxa (Ungar and Williamson 2000; Boyer
2008; Bunn et al. 2011; Winchester et al. 2014;
Winchester 2016). Whole occlusal surfaces
may also have “emergent”functional proper-
ties that result from the interaction of multiple
surface features and would not be captured
by discretizing measurements of tooth surface
structures (Winchester 2016).
Three dental topography metrics have been
extensively applied to reconstruct dietary ecol-
ogy in primates: relief index (RFI), Dirichlet
normal energy (DNE), and orientation patch
count (OPC). RFI measures crown relief, or
the projection of the occlusal surface into
space, a straightforward method of describing
the functional surface available for processing
food. RFI quantifies occlusal relief using a
ratio of the area of the crown surface (“3D”
area) to the cross-sectional area of the tooth
footprint (Ungar and Williamson 2000; Boyer
2008). Crown surface can be measured from
the lowest point of the talonid basin (sensu
Ungar and Williamson 2000) or from the
enamel–cementum junction (sensu Boyer
2008). RFI is expected to correlate with SQ
and SR, as both capture the elaboration of
occlusal features used to process structural car-
bohydrates (Boyer 2008; Bunn et al. 2011).
However, in capturing the projection of the
tooth into space without regard for the identity
of individual tooth features, it is less sensitive to
questions of homology. In capturing the walls
of the tooth crown, RFI sensu Boyer (2008)
also measures hypsodonty, important as an
adaptation to resist attritional agents found in
many plant tissues (Fortelius et al. 2002; Jardine
et al. 2012).
DNE measures the curvature of the occlusal
surface as deviation in normal “energy”from
a plane (Bunn et al. 2011; Winchester 2016;
Shan et al. 2019). Dirichlet’s energy is used by
mathematicians to describe the variability of a
function (Spagnolo 1976). DNE applies this
energy calculation approach to a digitized
tooth surface. DNE is measured as a sum of
the energies describing the change in orienta-
tion of mesh polygon “normal vector”(a line
perpendicular to the polygon face) across a sur-
face. DNE and RFI both capture tooth
ETHAN L. FULWOOD ET AL.2
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sharpness, which, like shearing crest length, is
associated with the processing of tough struc-
tural carbohydrates. DNE, however, may be
relatively less affected by wear than SQ, SR,
and RFI, and can be calculated on digital sur-
faces independent of the orientation of the
tooth, a major advantage in automating the
analysis of large numbers of specimens (Bunn
et al. 2011).
Mesh analysis methods, especially DNE, are
sensitive to details of mesh quality and prepar-
ation, especially the number of mesh vertices
and the iterative application of smoothing algo-
rithms (Spradley et al. 2017; Berthaume et al.
2019). A recently described implementation of
DNE, “ariaDNE,”was developed to address
these issues, allowing for comparisons among
studies and concatenation of larger datasets
(Shan et al. 2019). This metric is calculated by
integrating measurements of the vertex-by-vertex
normal energies of meshes over local “band-
widths,”which capture tooth surface features at
different resolutions. Lower bandwidths capture
smaller tooth surface features, which mayor may
not reflect functional adaptations of teeth. The
sum of ariaDNE values calculated foreach vertex
is comparable to the DNE of Bunn et al. (2011),
which was designed principally to describe
tooth sharpness. The variance of vertex-by-vertex
ariaDNE across the surface can also be calcu-
lated, which may capture some aspects ofthe dis-
tribution of sharp and flat features on a tooth
surface. The ability of ariaDNE to reclassify a lim-
ited number of platyrrhine teeth to genus has
been examined (Shan et al. 2019), but correlation
between these metrics and dietary ecology has
not been examined in a large primate sample
like that used in the validation of DNE sensu
Bunn et al. (2011).
Shearing metrics, RFI, and DNE measure the
shape of the tooth surface as a correlate of a
tooth’s projection into space. OPC is distinctive
in measuring the complexity of a tooth surface
as a count of slopes sharing a single aspect
(Evans et al. 2007; Evans and Jernvall 2009;
Evans 2013; Pineda-Munoz et al. 2017; Evans
and Pineda-Munoz 2018). The calculation of
OPC proceeds by first identifying regions of a
digital model sharing a slope of the same orien-
tation in one of a set number of cardinal direc-
tions (typically eight). The number of “patches”
of cells sharing slopes of the same orientation is
then counted. A simple tooth like the carnassial
of a hypercarnivore will have a relatively low
OPC, while a tooth with many intersecting
crests and cusps or, alternatively, a tooth with
a high degree of enamel crenulation will have
more patches, yielding a higher score. OPC
attempts to quantify the “number of tools”pre-
sent on a dental surface, which makes it unique
as a topography metric, most of which describe
the “shape of the tools”(Evans et al. 2007).
Much of the promise of quantitative
descriptors of tooth shape lies in their potential
application to the fossil record. Application to
fossil organisms requires descriptors to be
validated on a sample of extant taxa of
known dietary ecology. Primates have been a
major focus of these studies since the first
description of SQ, partially because of a persist-
ent interest in the fossil record of primates and
partially because of their subtly distinct but
relatively well-studied dietary ecologies (Kay
1975). Studies reconstructing diet in fossil
organisms generally recommend combining
multiple shape descriptors (an approach
referred to as “multiproxy dental morphology
analysis”by Pineda-Munoz et al. [2017]), as
this consistently improves model reclassifica-
tion rates, particularly when using linear
discriminant function analysis (DFA), by
improving model fit (Bunn et al. 2011;
Winchester et al. 2014; Allen et al. 2015;
Pineda-Munoz et al. 2017). However, the
addition of model parameters introduces the
danger of overfitting by allowing models to
learn too much from the in-sample dataset.
Overfitting compromises out-of-sample predic-
tion by modeling noise in the multidimensional
distribution of tooth shape parameters in
addition to any biological signal. Bayesian
approaches to parameter regularization and
model comparison can combat overfitting
(McElreath 2015) but have not previously
been applied to tests of the relationship
between tooth shape and dietary ecology.
Disaggregating Adaptation in Tooth Surface
Features.—The ability of mesh analysis meth-
ods to capture the shape of an entire tooth sur-
face is both a strength and a weakness, as
disaggregated regions of the tooth surface
may play distinct functional roles that dental
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 3
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topography metrics have the potential to “aver-
age”away (Allen et al. 2015). Individual shear-
ing crests and crushing basins may play
particular roles in food fracture or in constrain-
ing the movements of the lower jaw in space as
it comes into occlusion (Simpson 1933; Kay and
Hiiemae 1974;Kay1975,1977; Sheine and Kay
1982). Past work testing these hypotheses has
used approximations of areas and shapes
derived from linear measurements of tooth sur-
face features (Kay and Hiiemae 1974;Kay1975;
Sheine and Kay 1982; Allen et al. 2015). Newly
developed shape-segmentation methods intro-
duced here allow the shapes of regions of the
tooth surface to be analyzed quantitatively
using the dental topographic approaches
employed on whole-tooth surfaces. Metrics
using the ariaDNE implementation of DNE
are particularly appropriate for this analytical
approach, as they are orientation invariant
and show less sensitivity to variation in mesh
face count than other dental topography
metrics (Shan et al. 2019).
Objectives.—The goals of this paper are two-
fold. First, combinations of dental topography
metrics, including ariaDNE and Bayesian fit-
ting approaches, are validated on a sample of
extant strepsirrhine primates. This builds on
the description of ariaDNE and instructions
for use presented in Shan et al. (2019). Second,
high-performing combinations of metrics are
applied to fossil and subfossil strepsirrhines
to better understand strepsirrhine dietary evo-
lution, particularly in the context of lemur
origins.
A series of tests are first run on the extant
strepsirrhine dataset. The dietary reclassifica-
tion utility of the recent ariaDNE implementa-
tion of DNE, both summed across vertices and
as a coefficient of variation of vertex-by-vertex
values across the surface, is assessed on a
sample of second lower molars from extant
strepsirrhine primates. Bayesian models are
constructed using regularizing priors, and
model comparison metrics are used to address
the potential for overfitting when combining
ariaDNE with the additional dental topography
metrics RFI and OPC. Finally, six bandwidths
of ariaDNE averaging are compared to deter-
mine the highest performing in dietary reclassi-
fication. Dietary signal from disaggregated
second lower molar segments is also investi-
gated. If individual tooth structures are under
relatively independent selection for function in
food processing, then the dietary signal of
aggregated tooth segment shapes should be
higher than that of tooth surfaces considered
as a single mesh (Allen et al. 2015).
High-performing combinations of dental
topography metrics are then used to recon-
struct the dietary ecology of seven fossil strep-
sirrhine taxa from the Tertiary of Africa and
Asia and specimens representing seven
recently extinct lemur genera, known only
from subfossils. These reconstructions are com-
pared to existing understandings of the dietary
ecology of these taxa, derived from descriptive
analyses of tooth shape, the calculation of
shearing quotients, and dental microwear (Jun-
gers et al. 2002; Godfrey et al. 2004,2006,2012;
Marivaux et al. 2013).
Fossil taxa include the stem strepsirrhine Dje-
belemur martinezi; the fossil lorisiforms Karanisia
clarki,Komba robustus,Nycticeboides simpsoni,
and Wadilemur elegans; and the fossil chiromyi-
form lemurs Plesiopithecus teras and Propotto lea-
kyi. Of these taxa, previous studies have
quantitatively assessed diet preference in D.
martinezi,K. clarki,P. teras, and W. elegans.Dje-
belemur martinezi has been reconstructed as pri-
marily insectivorous using shearing quotients
and dental microwear (Marivaux et al. 2013).
Shearing quotients and body-size reconstruc-
tions have reconstructed P. teras as frugivorous
and K. clarki and W. elegans as frugivorous and
insectivorous (Kirk and Simons 2000; Marivaux
et al. 2013). Karanisia clarki was also classified as
an omnivore in the dental topographic analysis
of Patel et al. (2017), a result consistent with fru-
givory/insectivory. López-Torres et al. (2020)
report evidence from the distribution of enamel
in the anterior dentition of K. clarki that it con-
sumed a significant amount of tree exudates
and should be thought of as an obligate gum-
mivore. Qualitative arguments have been
advanced for an insectivorous diet in K. robus-
tus and for a diet consisting primarily of fruit
(or at least not of leaves) in N. simpsoni and
P. leakyi (Walker 1969; MacPhee and Jacobs
1986; McCrossin 1992).
The recently extinct subfossil lemurs
represent a diverse fauna, and dental
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microwear, shearing quotients, and dental top-
ography metrics have all been applied to recon-
structing their dietary ecologies (Jungers et al.
2002; Godfrey et al. 2004,2006,2012). The sam-
ple examined here includes individuals from
the extinct families Archaeolemuridae (Archae-
olemur and Hadropithecus), Palaeopropithecidae
(Babakotia,Mesopropithecus,Palaeopropithecus)
and Megaladapidae (Megaladapis), and the
genus Pachylemur in the family Lemuridae.
Palaeopropithecids and Megaladapis appear to
have been primarily folivorous, while Pachyle-
mur shared a primarily frugivorous diet with
its extant lemurid relatives (Godfrey 2017).
The archaeolemurids show an unusual dental
morphology most like that of some cercopith-
ecoid monkeys and suggesting a diet requiring
frequent hard-object processing (Godfrey et al.
2005,2016).
Methods
Sample.—Metrics were calculated on digi-
tized scans of 218 second lower molars from
40 extant strepsirrhine species representing 22
genera. Specimens were drawn from every
lemur genus but the rare hairy-eared dwarf
lemur Allocebus and the adaptively unusual
aye-aye Daubentonia, whose molar morphology
is unlikely to reflect its diet of defended grubs
and fruits, and from every lorisiform genus
but the recently erected Paragalago (Masters
et al. 2017) (Supplementary Table 1). The sam-
ple builds on tooth scans compiled by Bunn
et al. (2011) and is processed using a similar
protocol. Mesh “.ply”surfaces were produced
from microcomputed tomography (microCT)
scans of osteological specimens or epoxy casts
made from polyvinylsiloxene molds using pro-
prietary segmenting and smoothing functions
in Avizo (v. 8) to facilitate cropping using the
natural contour of the tooth crown (Visualiza-
tion Sciences Group, Burlington, Mass., USA).
The second lower molar was cropped from
each mesh at the enamel–cementum junction
using Geomagic (3D Systems, Rock Hill, S.C.,
USA), and individual teeth were simplified to
10,000 faces and smoothed over 20 iterations
using smoothing functions in Avizo. Smooth-
ing was kept to 20 iterations at each step to
avoid the introduction of mesh irregularities
at higher numbers of iterations (Spradley et al.
2017). Teeth that showed minimal wear were
selected a priori. However, distributions of cal-
culated values were also examined post hoc,
and teeth that were significant outliers and vis-
ibly more worn than other specimens in the
sample for each taxon were then excluded
(one specimen of Arctocebus, one specimen of
Euoticus, one specimen of Lepilemur, three spe-
cimens of Microcebus, one specimen of Prole-
mur, and one specimen of Propithecus).
Three dietary categories were used, with taxa
assigned to each category based on data on the
proportional representation of foods from each
category in diets observed in studies of wild
populations (Charles-Dominique 1977,1979;
Hladik 1979; Bearder and Martin 1980; Hladik
et al. 1980; Ganzhorn et al. 1985; Harcourt
1986,1991; Harcourt and Nash 1986; Nash
1986; Masters et al. 1988; Overdorff 1992; Ster-
ling et al. 1994; Hemingway 1996; Overdorff
et al. 1997; Balko 1998; Fietz and Ganzhorn
1999; Vasey 2000,2002; Thalmann 2001; Britt
et al. 2002; Nekaris and Rasmussen 2003;Pow-
zyk and Mowry 2003; Streicher 2004,2009;
Nekaris 2005; Gould 2006; Norscia et al. 2006;
Wiens et al. 2006; Lahann 2007; Dammhahn
and Kappeler 2008; Burrows and Nash 2010;
Olson et al. 2013; Rode-Margono et al. 2014;
Sato et al. 2016; Erhart et al. 2018). If members
of a genus consumed the greatest component
of their diet from leaves or insects, the genus
was classified as folivorous or insectivorous,
respectively. Taxa that consumed the greatest
part of their diet from fruits, gums, and other
plant reproductive structures were classified
as frugivorous (Supplementary Table 1).
Dental Topography Metrics.—Functional
tooth shape was quantified using the dental
topography metrics DNE, RFI, and OPC.
DNE was calculated both sensu Bunn et al.
(2011) using the R package molaR (Pampush
et al. 2016b) and as ariaDNE using functions
in MATLAB (Shan et al. 2019). The sum of ari-
aDNE at each vertex and its coefficient of vari-
ation (CV) across the tooth surface (here called
ariaDNE and ariaDNE CV, respectively) were
each calculated. RFI was calculated using the
open-source stand-alone program Morphotes-
ter (Winchester 2016). RFI calculation on one
specimen of Varecia variegata (USNM 84383)
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failed in Morphotester and was performed
using molaR. OPC was calculated using the
“orientation patch count rotated (OPCR)”
approach implemented in molaR. OPCR
accounts for deviation in the orientation of the
tooth on the x,y plane by averaging the counts
calculated over 45° rotations (Evans and Jern-
vall 2009). OPCR was calculated using
3D-OPCR functions, which differs from the
DEM-OPCR approach taken by previous stud-
ies examining large samples of strepsirrhine
primates (Bunn et al. 2011; Winchester et al.
2014). 3D-OPCR calculates OPCR from the
orientation of the polygons directly, without
first converting the occlusal topography into a
digital elevation model (DEM), and appears
to better reflect the complexity of tooth surface
features (Winchester 2016). Variables were
transformed to z-scores before analysis through
the division of each data point by the sample
standard deviation and the subtraction of the
sample mean of each variable in order to cor-
rect for differences in the scale of each variable
and to improve model fit.
Shape Segmentation.—Each tooth in the sam-
ple was segmented into 15 regions using the
hecate MATLAB package. This algorithm
automatically identifies corresponding shape
regions across all tooth surfaces in a consistent
manner, using high-quality point-to-point
mappings between all pairs of surfaces. These
regions represent parts of the tooth that vary
together across a given tooth sample and are
intended to correspond to the sorts of
structures commonly identified and named
by morphologists, such as cusps and crests.
The algorithms implemented in hecate can be
summarized in three phases. First, continuous
Procrustes distances (Al-Aifari et al. 2013)are
calculated between all pairs of sample meshes,
using whole-surface meshes instead of
sequences of landmarks as described in Gao
et al. (2018). The distances computed character-
ize pairwise (dis-)similarity among all surfaces
by minimizing an energy function over all
admissible maps between two disk-type sur-
faces, thus leading to an energy-minimizing
point-to-point correspondence map associated
with the distance value. Because the computa-
tions at this stage are all carried out in a pairwise
manner, these point-to-point correspondence
maps are generally not transitive. Next, the pair-
wise distances and their associated correspond-
ence maps are assembled into a large-scale
“random walk”modeling how a hypothetical
“particle”would travel from tooth surface to
tooth surface following the guidance of the cor-
respondence maps; such a probabilistic model is
known as a horizontal random walk (Gao 2015,
2021). Intuitively, when the particle is con-
strained to hop within similar surfaces (with
small pairwise distance), the particle will slowly
drift in position but mostly remain within corre-
sponding regions of local geometric similarity;
in contrast, when jumping across highly dissimi-
lar surfaces (with large pairwise distance), the
particle will quickly deviate from its regular rou-
tine and become visible all over any single tooth
surface in the sample.
hecate leverages this phenomenon to identify
corresponding regions across tooth surfaces by
constructing a horizontal random walk matrix
encoding the transition probability from each
vertex on a triangular mesh to vertices on
other triangular meshes, computed from the
continuous Procrustes distance and the corres-
pondence maps. Finally, eigen-decomposition
of this horizontal random walk matrix provides
a way to embed all tooth surfaces into a com-
mon “template”in the spectral domain. In this
spectral representation, the tooth surfaces are
all registered onto a virtual “common domain”
to provide a basis for transitive, consistent com-
parisons across all surfaces in the sample.
Machine learning techniques such as k-means
clustering can then be applied to this spectral
representation; the kgroups of point clouds in
the spectral domain can be mapped back to cor-
responding regions on the tooth surfaces, gen-
erating consistent segments across all surfaces.
The entire algorithmic workflow is integrated
into the MATLAB software package hecate. This
package takes as an input a set of meshes of
whole teeth and outputs nmesh files, represent-
ing the number (n) of segments requested by the
user. Input mesh files do not require processing
beyond the “cleaning”routinely done in studies
of dental topography (Winchester et al. 2014).
These mesh files can then be analyzed using
orientation-invariant dental topography metrics
like DNE. Metrics that rely on meshes sharing a
consistent orientation can be misleading,
ETHAN L. FULWOOD ET AL.6
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however, as these meshes are oriented accord-
ing to their positions in the teeth from which
they have been segmented. Downloads of
scripts to implement hecate in MATLAB and
additional documentation are available from
Winchester (2020).
Dietary Signal and Dental Topography
Metrics.—The reclassification utility of ari-
aDNE implementations of DNE in combination
with other dental topography metrics was
assessed and compared with earlier implemen-
tations of DNE using the extant strepsirrhine
sample in both DFA and multinomial model-
ing paradigms. Models were constructed
using each of six bandwidths of ariaDNE and
DNE sensu Bunn et al. (2011) combined with
RFI and OPC, creating models of nested
complexity.
First, reclassification success rates from a
cross-validated (“leave-one-out”)DFAwas
performed using the package MASS in
R. Successful reclassifications of genera using
genus mean values for each dental topography
metric and the average successful reclassifica-
tions of the specimens within each genus are
both reported from the DFA.
Second, likelihood of membership in each of
the three dietary categories was modeled in a
Bayesian, multilevel framework using func-
tions in the R packages brms and the Stan
engine (Bürkner 2017). Four chains were run
over 3000 iterations, with a 1000-iteration
warm-up. Chain convergence was assessed
using the rhat parameter, the number of effect-
ive samples returned, and visual inspection of
the chain trace plots. Bayesian, multilevel meth-
ods, which have not previously been applied to
dietary classification from dental topography
metrics, were chosen to complement a more
traditional linear discriminant analysis
approach for two broad reasons. First, they
allow for clustering in the data to be modeled
as group-specific intercepts, which is useful
for analyzing multiple specimens from single
taxa or multiple segmented regions from a sin-
gle molar, and for the joint modeling of phylo-
genetic covariance among taxa. Second,
through the use of regularizing priors and
model comparison metrics, they allow the
explicit minimization and measurement of
overfitting risk.
Models were constructed using the software
default 0.8 bandwidth of ariaDNE, which, as
discussed in the “Results,”was the highest per-
forming in DFA. ariaDNE bandwidths corres-
pond to the degree of local averaging around
each vertex. For each vertex, the calculation of
ariaDNE assigns a weight to the rest of the
points in the mesh that is proportionate to the
inverse square of the bandwidth value. Each
bandwidth expresses a length of the same
units as the distance between any two vertices
in the mesh (Shan et al. 2019).
ariaDNE values were combined variously
with ariaDNE 0.8 CV, RFI, and OPC in models
of nested complexity using a sample of individ-
ual tooth specimens; in a multilevel framework
with different slopes for each genus; and in a
multilevel framework that incorporates phylo-
genetic covariance among genera using a con-
sensus phylogeny from Herrera and Dávalos
(2016). Phylogenetic covariance is modeled in
brms using a phylogenetic mixed modeling
approach that jointly estimates and incorpo-
rates phylogenetic signal into the strength of
the covariance relationships (Housworth et al.
2004). The estimated phylogenetic signal
can be reported as the proportion of the vari-
ance explained by the modeled covariance
and is analogous to Pagel’s lambda metric
(Housworth et al. 2004; Bürkner 2020). Models
of different complexity were compared using
the “Pareto-Smoothed importance sampling
leave-one-out cross validation”approximation
(LOOIS) implemented in the loo package in R
and accessed through brms (Vehtari et al.
2017). This metric efficiently approximates a
model’s leave-one-out reclassification success.
This allows the measurement of overfitting
risk, which reflects the ability of a model to pre-
dict out-of-sample outcomes, an important
consideration in models generated for applica-
tion to the fossil record.
Dietary Signal in the Segmented Molar.—ari-
aDNE values (at the 0.08 bandwidth of local
averaging) were calculated on the sample of
second lower molars segmented using the hec-
ate method. Categorical multilevel models
were constructed in a Bayesian framework,
with specimens in each genus permitted to
share independent intercepts. Two partitions
of the data were considered. The likelihood of
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 7
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membership in each dietary category was mod-
eled as a function of the shapes of each seg-
ment, with each tooth permitted to have an
independent intercept and each class of seg-
ments permitted to share an independent inter-
cept and slope. Likelihoods were also modeled
using ariaDNE values calculated for each con-
tinuous surface. ariaDNE was used as a metric
because it can be compared among surfaces,
such as those of tooth segments with different
areas, which differ in mesh face count (Shan
et al. 2019). Models were constructed to account
for the clustering of specimens within genus
but did not incorporate phylogenetic covari-
ance. Predictive models were compared using
LOOIS (Vehtari et al. 2017).
Reconstructed Dietary Ecology in Fossil
Strepsirrhines and Subfossil Lemurs.—Two
approaches were taken to reconstruct dietary
ecology in the extinct strepsirrhine sample.
DFA models were constructed using genus
mean data (“genus DFA”) and specimen-level
data, with the mean reconstructed probability
of membership in each dietary category
among all of the individual specimens reported
for each genus (“specimen DFA”). DFA model-
ing used the combination of variables that were
the highest performing in reclassifying extant
taxa using DFA leave-one-out cross validation.
A Bayesian multilevel model was also con-
structed incorporating the maximally inform-
ative combination of variables that reported
an acceptable (k< 0.7) overfitting risk as deter-
mined through LOOIS comparisons (krepre-
senting a parameter of the pareto fittothe
importance ratios of each leave-one-out
model). This model incorporates specimen-level
clustering within genera byallowing each genus
an independent intercept. Each extant taxon was
stripped of information about its dietary ecol-
ogy and classified to dietary ecology iteratively
using the same approach. This allows the classi-
fications of extinct taxa to be contextualized
using informationally analogous reclassifica-
tions of extant taxa of known diet.
Institutional Abbreviations.—CBI, Office
National des Mines, Tunis, Tunisia; DLC/
DPC, Division of Fossil Primates, Duke
Lemur Center, Durham, N.C.; MCZ, Museum
of Comparative Zoology, Harvard University,
Cambridge Mass.; USNM, National Museum
of Natural History, Smithsonian Institute,
Washington, D.C.
Results
Dental Topography Metrics and Dietary Cat-
egory.—Calculated values for the 0.08 band-
width of ariaDNE and ariaDNE CV, OPC,
and RFI for each genus are presented in Supple-
mentary Table 2. At each bandwidth, ariaDNE
was highest in insectivores and lowest in frugi-
vores, while ariaDNE CV was highest in foli-
vores and lowest in insectivores. DNE sensu
Bunn et al. (2011) was highest in folivores and
lowest in frugivores. OPC was also highest in
folivores and lowest in frugivores. RFI was
highest in insectivores and lowest in frugivores.
Values for each dietary group overlapped at the
upper and lower ends of their ranges, but
means were separated by at least 1 SE for
every metric (Supplementary Table 3).
Whole-Tooth Reclassification Success.—Imple-
mentations of ariaDNE showed the highest
DFA reclassification success of all dental
topography metrics (Table 1). The highest-
performing combination of variables (ariaDNE
sum and CV at the 0.08 bandwidth combined
with RFI and OPC) reclassified strepsirrhine
genera to the correct dietary ecology with
100% accuracy using genus means and with
86% accuracy when measured from the average
reclassification success of the specimens within
each genus. The position of each extant strepsir-
rhine genus on the two linear discriminant axes
of a model combining ariaDNE 0.08, ariaDNE
0.08 CV, RFI, and OPC are presented in
Figure 1. ariaDNE 0.08 is strongly correlated
with the first linear discriminant axis (LD1)
(r
2
= 0.95), ariaDNE 0.08 CV is strongly nega-
tively correlated with LD2 (r
2
=−0.94); RFI is
moderately correlated with LD1 (r
2
= 0.55),
and OPC is moderately negatively correlated
with LD2 (r
2
=−0.60).
Whole-Tooth Multinomial Modeling.—Chains
from all models converged with a rhat of 1
and acceptable effective sample size (Supple-
mentary Material). With each specimen treated
as an independent data point, genus clustering
accounted for, and phylogenetic covariance
modeled, LOOIS preferred the combination of
the 0.08 bandwidth of ariaDNE with its CV,
ETHAN L. FULWOOD ET AL.8
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OPC, and RFI (“all parameters model”)
(Table 2). Comparisons among data partitions
preferred the model that clustered specimens
by genus over the unclustered model and the
model that included phylogenetic information.
LOOIS preferences across alternate parameter
combinations in the clustered and phylogenetic
methods were mostly relatively small, how-
ever, and all combinations of variables were
in the “good”or “ok”range (k < 0.7), with >
98% in the “good”range (k < 0.5). Because of
this, the unclustered Bayesian model is
TABLE 1. Reclassifications to dietary ecology in extant strepsirrhines using discriminant function analysis (DFA).
Reclassification success using genus means is reported, with the average reclassification success of the specimens in each
genus reported in parentheses. Rows indicate different bandwidths and columns different combinations of variables. DNE,
Dirichlet normal energy; SD, coefficient of variation of DNE; RFI, relief index; OPC, orientation patch count.
DNE DNE + SD DNE + SD + RFI DNE + OPC DNE + SD + OPC DNE + SD + OPC + RFI
DNE sensu
Bunn et al. 2011
0.55 (0.59) NA NA 0.82 (0.59) NA NA
ariaDNE 02 0.73 (0.64) 0.64 (0.68) 0.68 (0.68) 0.73 (0.68) 0.64 (0.73) 0.64 (0.68)
ariaDNE 04 0.77 (0.73) 0.68 (0.68) 0.77 (0.73) 0.73 (0.77) 0.73 (0.82) 0.68 (0.77)
ariaDNE 06 0.77 (0.73) 0.73 (0.86) 0.77 (0.91) 0.73 (0.82) 0.77 (0.82) 0.86 (0.77)
ariaDNE 08 0.68 (0.68) 0.86 (0.77) 0.95 (0.82) 0.77 (0.82) 0.82 (0.82) 100 (0.86)
ariaDNE 10 0.59 (0.59) 0.95 (0.82) 0.95 (0.82) 0.77 (0.82) 0.95 (0.86) 0.91 (0.86)
ariaDNE 12 0.68 (0.64) 0.91 (0.77) 0.95 (0.82) 0.86 (0.73) 0.82 (0.82) 0.86 (0.82)
FIGURE 1. Plots of genus means along linear discriminant axes of discriminant function analysis (DFA). Model constructed
using ariaDNE 0.08 with ariaDNE coefficient of variation (CV), relief index (RFI), and orientation patch count (OPC),
including reconstructed subfossil lemurs. Ac, Arctocebus; Al, Archaeolemur;Av,Avahi; Ba, Babakotia; Ch, Cheirogaleus; Dj,
Djebelemur; El, Eulemur; Et, Euoticus; Ga, Galago; Gg, Galagoides; Hd, Hadropithecus; Hp, Hapalemur; In, Indri; Ka, Karanisia;
Ko, Komba; Lm, Lemur; Lo, Loris; Lp, Lepilemur; Mc, Microcebus; Mg, Megaladapis; Ms, Mesopropithecus; Mz, Mirza; Nd, Nyc-
ticebus;Ny,Nycticeboides; Ot, Otolemur;Pa,Palaeopropithecus; Pc, Pachylemur;Pe,Perodicticus; Ph, Phaner; Pl, Plesiopithecus;
Pm, Prolemur; Pp, Propithecus; Pt, Propotto; Sc, Sciurocheirus;Va,Varecia;Wa,Wadilemur.
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 9
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discussed and figured below in order to expli-
cate the influence of the dental topography
metrics studied on the probability of member-
ship in each diet group. This model is chosen
because it demonstrates the same directional
trends as more complex models, but with nar-
rower probability intervals, making the trends
clearer to visualize. Full model parameter esti-
mates are included in the Supplementary
Material. Coefficient values reported below
represent the mean of the posterior distribution
estimated by each model.
In the unclustered all parameters model,
high ariaDNE values predict insectivorous
diets (coefficient value predicting membership
in category, relative to frugivory = 6.66); inter-
mediate ariaDNE values predict folivorous
diets (coefficient value predicting membership
in category, relative to frugivory = 1.62); and
low ariaDNE values predict frugivorous diets
(Fig. 2). The molars of insectivorous strepsir-
rhines show the highest ariaDNE values, sup-
porting this observation. High CV values
predict folivorous diets (coefficient value pre-
dicting membership in category, relative to fru-
givory = 1.78); intermediate CV values predict
frugivorous diets; and low CV values predict
insectivorous diets (coefficient value predicting
membership in category, relative to frugivory
=−5.62). This reflects the concentration of foliv-
orous taxa among the strepsirrhines with the
highest ariaDNE CV values.
High OPC was positively predictive of foliv-
ory (coefficient value predicting membership
in category, relative to frugivory = 0.79) and
negatively predictive of insectivory (coefficient
value predicting membership in category, rela-
tive to frugivory = −0.85) (Fig. 2). Conditional
on the strong relationships with ariaDNE and
ariaDNE CV, high RFI was actually negatively
predictive of folivory (coefficient value predict-
ing membership in category, relative to frugiv-
ory = −0.18) and insectivory (coefficient value
predicting membership in category, relative to
frugivory = −1.45), despite being absolutely
higher in these groups than among frugivores.
Segmented Tooth Multinomial Modeling.—The
hecate shape-segmentation algorithms were
successful in isolating regions of local shape
similarity corresponding to commonly identi-
fied cusps, crests, and basins (Fig. 3). However,
models using the ariaDNE values of each seg-
ment performed no better in reclassification
than models using ariaDNE summed across
the tooth surface (LOOIS = −9.7 for whole-
tooth model and −9.7 for segmented model)
(Fig. 4). This indicates that the extra information
provided by ariaDNE calculations on each seg-
mented mesh did not improve dietary signal.
Classification of Extinct Strepsirrhines.—The
dietary ecologies of extinct strepsirrhines were
reconstructed using genus mean and specimen
values in DFA and Bayesian multilevel models
that included all parameters at the ariaDNE 0.8
bandwidth. Genus DFA and specimen DFA clas-
sifications are reported in Table 3. Means from
the posterior probabilities of Bayesian model
classifications included ariaDNE 0.08, ariaDNE
CV 0.08, RFI, and OPC, and are reported in
Table 4.IntheDFAmodels,Archaeolemur,Djebe-
lemur,Karanisia,Megaladapis,Mesopropithecus,
Hadropithecus,Pachylemur,Plesiopithecus,Pro-
potto,andWadilemur were all reconstructed as
frugivores; Babakotia,Nycticeboides,andPalaeo-
propithecus as folivores. Komba was reconstructed
as frugivorous in the genus mean model and as
insectivorous in the specimen classification
model (Fig. 5). The Bayesian model differs
from the genus mean DFA in classifying Nyctice-
boides as frugivorous and Komba as insectivorous
(Tables 3,4).
Discussion
The ariaDNE implementation of DNE has
considerable value in describing dietary
TABLE 2. Comparisons among pareto-smoothed
importance sampling leave-one-out cross validation
approximation (LOOIS) information criteria calculated on
models constructing using the 0.8 ariaDNE bandwidth.
DNE, Dirichlet normal energy; CV, coefficient of variation
of DNE; OPC, orientation patch count; RFI, relief index.
Unclustered
Genus
cluster
Phylogenetic
cluster
DNE 341.4 297.7 308.1
DNE + CV 249.3 179.7 265.3
DNE + OPC 314.8 259.4 298.4
DNE + RFI 330.1 278.7 306.9
DNE+CV+
OPC
237.1 167.5 257.0
DNE+CV+
RFI
238.4 163.1 265.2
All metrics 226.6 151.8 257.0
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ecology in strepsirrhines, particularly when
using larger bandwidths of local averaging.
At all bandwidths, ariaDNE outperforms trad-
itional DNE in dietary reclassification. ariaDNE
can be implemented in both DFA and Bayesian
multinomial frameworks, and neither overfit-
ting, when combined with additional metrics,
nor averaging of functional information across
the occlusal surface appears to represent
major confounding factors. ariaDNE and
ariaDNE CV used in combination add a valu-
able new dimension to dietary discrimination,
particularly in distinguishing primate folivores
from insectivores, a task that has traditionally
proved difficult without the inclusion of add-
itional body-size information.
Insectivorous teeth show surfaces with high
average curvature across the surface, while foli-
vores show moderate average curvature but
high variability. Both insects and leaves require
consumers to fragment relatively tough struc-
tural carbohydrates (Kay 1975; Lucas 2004;
Ungar 2010). In leaves, structural carbohy-
drates are packaged within cellulose fibers
and ligneous cell walls, while in insects they
form the structural component of chitinous
exoskeletons (Vincent 1990; Strait 1993; Strait
and Vincent 1998; Lucas 2004). The common
demands of these food materials explain the
common elaboration of shearing crests and
FIGURE 2. Relative probability of membership in each dietary category over the scaled range of values of ariaDNE band-
width 0.08 estimated by multinomial Bayesian modeling (without including genus clustering or phylogenetic covariance)
over the scaled range of values of A, ariaDNE; B, ariaDNE coefficient of variation (CV); C, relief index (RFI); D, orientation
patch count (OPC). Shaded area represents consistencyinterval of middle 95% of the mass of the posterior distribution. FL,
folivory; FG, frugivory; IN, insectivory.
FIGURE 3. Regional segmentation of lower second molars
created by hecate algorithms. A, Arctocebus;B,Avahi;C,
Cheirogaleus.
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other tooth cutting surfaces in both insectivores
and folivores (Kay 1975; Yamashita 1998).
However, leaves and insects differ in many
important respects as potential food items.
Insect exoskeletons are both tough (requiring
continuous application of force to propagate
cracks) and stiff (requiring high concentrations
of force to initiate cracks) (Strait 1993; Strait and
Vincent 1998; Evans and Sanson 2003). The
toughness of insect exoskeletons selects for
the elaboration of blades, which can propagate
cracks linearly and prevent the puncturing of a
material without crack propagation, but the
stiffness of exoskeletons selects for the develop-
ment of blade edges and pointed cusps with
minimal radius of curvature in three dimen-
sions (Strait 1993; Evans and Sanson 2003). Ani-
mal matter is also highly elastic, which makes
securing food items between interacting
molar structures difficult, a problem best
solved by high crests around deep basins (Strait
1997). Strepsirrhine insectivores appear to
arrive at a morphological compromise by
developing sharp cusps and narrow molar
basins connected by sharp shearing crests.
This occlusal topography is characterized by
uniformly high curvature, yielding a high ari-
aDNE with low variance.
Leaves are also tough, but generally less stiff,
with a planar geometry that minimizes the
FIGURE 4. Relative probability of membership in each dietary category over the scaled range of values of ariaDNE band-
width 0.08 evaluated alone on A, each tooth surface; and B, segmented tooth surfaces. Shaded area represents consistency
interval of middle 95% of the mass of the posterior distribution.
TABLE 3. Reconstructions of dietary ecology in extinct strepsirrhines using discriminant function analysis (DFA).
Reconstructed probabilities using genus means are reported, with the average reconstructions of all of the specimens in
each genus reported in parentheses.
Group Genus Frugivory Folivory Insectivory
Godfrey et al. 2004;
Marivaux et al. 2013;
López-Torres et al. 2020
Archaeolemuridae Archaeolemur 100% (85.1%) 0% (14.9%) 0% (0%) Fruit, hard objects
Hadropithecus 70.8% (62.6%) 29.1% (37.3%) 0% (0.17%) Fruit, hard objects
Palaeopropithecidae Babakotia 0% (5.9%) 100% (94.1%) 0% (0%) Seed, fruit, foliage
Mesopropithecus 98.7% (58.1%) 1% (28.2%) 003% (13.7%) Seed, fruit, foliage
Palaeopropithecus 0% (16.4%) 99.9% (83.4%) 0% (0.15%) Seed, fruit, foliage
Megaladapidae Megaladapis 80.2% (65.5%) 19.8% (33.3%) 0% (0.12%) Leaves
Lemuridae Pachylemur 100% (93.4%) 0.00% (3.41%) 0% (3.18%) Fruit
Stem Strepsirrhini Djebelemur 98% (60.5%) 0% (5.7%) 2% (33.8%) Insects, fruit
Lorisiformes Karanisia 97.7% (61.2%) 0.2% (38.4%) 0% (0.04%) Fruit/gums
Lorisiformes Komba 82.4% (23.5%) 0% (1.5%) 17.6% (75%) NA
Lorisiformes Nycticeboides 3.9% (30.1%) 93.4% (59.2%) 2.7% (10.7%) NA
Chiromyiformes Plesiopithecus 91.5% (70.5%) 8.5% (29.2%) 0% (0.03%) Fruit
Chiromyiformes Propotto 99.9% (86.2%) 0.01% (13.8%) 0% (0%) NA
Lorisiformes Wadilemur 99.5% (61%) 0% (8.2%) 0.04% (30.8%) Fruit
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ability for cracks to spread elastically through
their tissue (Yamashita 1998; Lucas 2004;
Ungar 2010). This combination of properties
selects for the elaboration of elongated blades
that interact to slice leaves and shallow basins
against which leaves can be triturated (Yama-
shita 1998; Cuozzo and Yamashita 2006). The
differences in curvature between open basins
and high crests is captured by the relatively
high CV of ariaDNE values across the tooth
surfaces of folivores. Strepsirrhine folivores
also develop multiple, intersecting blades that
yield higher tooth surface complexity and a
high OPC, as also observed in herbivorous
rodents and carnivorans (Evans et al. 2007).
Plants have evolved a range of adaptations to
resist mammalian predation by increasing the
rate of dental wear in leaf consumers (Vincent
1990; Lucas 2004; Ungar 2010). Strepsirrhine
folivores appear to have adapted to resist this
wear by increasing crown height, as captured
by RFI (Boyer 2008; Pampush et al. 2016a).
TABLE 4. Reconstructions of dietary ecology (means of the posterior probabilities distributions of likelihood of
membership in each dietary category) in extinct strepsirrhines from Bayesian multilevel model.
Group Genus Frugivory Folivory Insectivory
Godfrey et al. 2004;
Marivaux et al. 2013;
López-Torres et al. 2020
Archaeolemuridae Archaeolemur 72% 27.9% 0.1% Fruit, hard objects
Hadropithecus 64.6% 35% 3.7% Fruit, hard objects
Palaeopropithecus Babakotia 15.7% 84.3% 0% Seed, fruit, foliage
Mesopropithecus 64.9% 20.6% 14.5% Seed, fruit, foliage
Palaeopropithecus 45.5% 54.4% 0.01% Seed, fruit, foliage
Megaladapidae Megaladapis 70% 28.4% 1.4% Leaves
Lemuridae Pachylemur 76.9% 7.2% 15.9% Fruit
Stem Strepsirrhini Djebelemur 60.3% 1.9% 37.9% Insects, fruit
Lorisiformes Karanisia 61.1% 34.3% 4.6% Fruit/gums
Lorisiformes Komba 42.6% 0.4% 56.9% NA
Lorisiformes Nycticeboides 62.5% 32.6% 5% NA
Chiromyiformes Plesiopithecus 73.9% 25.8% 0.2% Fruit
Chiromyiformes Propotto 76.5% 22.8% 0.7% NA
Lorisiformes Wadilemur 59.6% 4.6% 35.8% Fruit
FIGURE 5. Ternary diagrams of the probability of reconstructed dietary ecologies from fossil and extant strepsirrhines. A,
Dietary classification of each specimen averaged by genus, using discriminant function analysis (DFA) model incorporat-
ing ariaDNE 0.08, ariaDNE 0.08 coefficient of variation (CV), relief index (RFI), and orientation patch count (OPC); B, Diet-
ary classification of each specimen averaged by genus, using Bayesian multilevel model incorporating ariaDNE 0.08,
ariaDNE 0.08 CV, RFI, and OPC. FL, folivory; FG, frugivory; IN, insectivory. Ac, Arctocebus; Al, Archaeolemur;Av,
Avahi; Ba, Babakotia; Ch, Cheirogaleus; Dj, Djebelemur; El, Eulemur; Et, Euoticus; Ga, Galago; Gg, Galagoides; Hd, Hadropithecus;
Hp, Hapalemur; In, Indri; Ka, Karanisia; Ko, Komba; Lm, Lemur; Lo, Loris; Lp, Lepilemur; Mc, Microcebus; Mg, Megaladapis; Ms,
Mesopropithecus; Mz, Mirza; Nd, Nycticebus;Ny,Nycticeboides; Ot, Otolemur;Pa,Palaeopropithecus; Pc, Pachylemur;Pe,Per-
odicticus; Ph, Phaner; Pl, Plesiopithecus; Pm, Prolemur; Pp, Propithecus; Pt, Propotto; Sc, Sciurocheirus;Va,Varecia;Wa,
Wadilemur.
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 13
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Frugivores (which in this sample include
gummivores) are thought to habitually con-
sume foods with low toughness, although
hard seed predation is important to some spe-
cies (Godfrey et al. 2004; Lucas 2004; Ungar
2010). This lack of dietary structural carbohy-
drates is reflected in the low ariaDNE, ariaDNE
CV, RFI, and OPC values characterizing frugiv-
orous strepsirrhines in this sample. This sup-
ports the suggestion that the elaboration of
shearing tooth structures is less important in
processing diets characterized by lower levels
of structural carbohydrates (Kay 1975; Bunn
et al. 2011; Boyer 2008; Ungar 2010; Winchester
et al. 2014).
The combination of Bayesian modeling and
the use of explicit overfitting metrics largely
supports the suitability of combinations of ari-
aDNE, RFI, and OPC for out-of-sample recon-
struction. Metric comparisons supported the
use of more highly parameterized models, in
line with the multiproxy approach recom-
mended by Pineda-Munoz et al. (2017). Models
using disaggregated structures of the lower
molar failed to outperform models constructed
using ariaDNE values calculated for the whole
surface. This suggests that, contrary to the con-
cerns of Allen et al. (2015), tooth occlusal sur-
faces are under selection as integrated units
for maximizing food fragmentation. Dental
topography metrics calculated on occlusal sur-
faces do not appear to aggregate away dietary
information reflected in disaggregated shear-
ing crests, and instead may capture emergent
properties of interacting tooth crown structures
(Winchester 2016).
Genus mean and specimen-level DFA mod-
els suggest that a majority of the recently extinct
subfossil lemur genera subsisted on fruits. It
has been observed that lemur faunas are
depauperate of frugivores when compared
with similar primate communities on other
landmasses (Ganzhorn 1992; Goodman and
Ganzhorn 1997; Wright et al. 2005). This frugi-
vore depauperate fauna may have resulted
from the recent extinction of some large-
bodied, specialized frugivores and hard-object
feeders (especially the lemurid Pachylemur
and the archaeolemurids Archaeolemur and
Hadropithecus). The extinction of large-bodied
frugivores would be consistent with a broader
pattern of ecological contraction in lemur com-
munities hypothesized to have occurred over
the Quaternary (Godfrey et al. 2006,2012).
Dietary reconstructions of subfossil lemurs
using the all-parameters DFA models are
largely consonant with reconstructions based
on dental microwear and the elaboration of
shearing quotients, both of which predict
many subfossil lemur genera to have been fru-
givorous or hard-object feeding, with the
exception of the sloth lemurs (Palaeopropithe-
cidae) and Megaladapis (Jungers et al. 2002;
Godfrey et al. 2004,2006,2012; Scott et al.
2009)(Fig. 6). Models reconstruct two of the
sloth lemurs, Palaeopropithecus and Babakotia,
as folivorous, as expected. The palaeopropithe-
cid Mesopropithecus, however, seems aberrant
in this regard, as its dietary ecology was recon-
structed as frugivorous with relatively high
confidence by DFA and Bayesian methods. Its
molar structure resembles that of Indri and Pro-
pithecus, with ariaDNE and ariaDNE CV values
most like Propithecus, both of which were
reclassified as frugivorous by the Bayesian
model (Table 5). Propithecus is known to exhibit
a significant degree of seasonal diet switching
toward fruits and seeds, and this ecology may
have characterized Mesopropithecus or its ances-
tors (Godfrey et al. 2004; Norscia et al. 2006).
The position of some paleopropithecids,
although reconstructed as folivorous, far out-
side the ecological distribution of the extant
species also underlines the extent to which
some subfossil lemurs may lack clear ecological
analogues among the extant fauna.
Both DFA and Bayesian models classified
Megaladapis as frugivorous. This was surpris-
ing, as this taxon exhibits long shearing crests
and a strongly folivorous microwear signal
(Jungers et al. 2002; Godfrey et al. 2004). The
signal for frugivory in Megaladapis seems to
arise from its relatively low ariaDNE CV. The
long, continuous crests displayed by Megalada-
pis molars may have lower variability in vertex
bending than the shorter, intersecting crests of
other lemur folivores. Sixty-six percent of
specimens from the morphologically similar
Lepilemur were also misclassified by the
Bayesian model as frugivorous, suggesting
that ariaDNE CV may struggle to characterize
this dental configuration.
ETHAN L. FULWOOD ET AL.14
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FIGURE 6. Strepsirrhine second molars plotted by scaled ariaDNE 0.08 and ariaDNE 0.08 coefficient of variation (CV)
values. Ac, Arctocebus; Al, Archaeolemur;Av,Avahi; Ch, Cheirogaleus; Dj, Djebelemur; El, Eulemur; Et, Euoticus; Ga, Galago;
Gg, Galagoides; Hd, Hadropithecus; Hp, Hapalemur; In, Indri; Ka, Karanisia; Ko, Komba; Lm, Lemur; Lo, Loris; Lp, Lepilemur;
Mc, Microcebus; Mg, Megaladapis; Ms, Mesopropithecus; Mz, Mirza; Nd, Nycticebus;Ny,Nycticeboides; Ot, Otolemur;Pa,
Palaeopropithecus; Pc, Pachylemur;Pe,Perodicticus; Ph, Phaner; Pl, Plesiopithecus; Pm, Prolemur; Pp, Propithecus; Pt, Propotto;
Sc, Sciurocheirus;Va,Varecia;Wa,Wadilemur.
TABLE 5. Reconstructions of dietary ecology (means of the posterior probabilities distributions of likelihood of
membership in each dietary category) in extant strepsirrhines from Bayesian multilevel model.
Group Genus Frugivory Folivory Insectivory Diet
Lorisidae Arctocebus 17.7% 3.8% 78.5% Insectivory
Loris 27.7% 8.2% 64.2% Insectivory
Nycticebus 68.3% 26.1% 5.7% Frugivory
Perodicticus 65.7% 33.6% 0.7% Frugivory
Galagidae Euoticus 54.3% 24.5% 21.2% Frugivory
Galago 33.7% 5% 61.2% Insectivory
Galagoides 41.1% 9.8% 49.1% Insectivory
Otolemur 69.4% 8% 22.7% Frugivory
Sciurocheirus 65.5% 13.1% 21.3% Frugivory
Cheirogaleidae Cheirogaleus 76.4% 21.5% 2.1% Frugivory
Microcebus 56.9% 8.6% 34.5% Insectivory
Mirza 72.3% 11.4% 16.3% Frugivory
Phaner 74.5% 15.1% 10.3% Frugivory
Lepilemuridae Lepilemur 66.6% 24.3% 9% Folivory
Lemuridae Eulemur 56.4% 38.8% 4.8% Frugivory
Hapalemur 65% 20.4% 14.5% Folivory
Lemur 60.6% 28.9% 10.5% Frugivory
Prolemur 49% 44.3% 6.7% Folivory
Varecia 69.9% 26.1% 3.9% Frugivory
Indriidae Avahi 43.8% 36.6% 19.6% Folivory
Indri 60.2% 37.1% 2.7% Folivory
Propithecus 57.8% 38.6% 3.7% Folivory
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 15
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Dental topography metrics were also applied
to a sample of fossil strepsirrhines from the
Paleogene and Neogene of Africa and Asia
that includes a stem strepsirrhine, early fossil
lorisiforms, and stem members of the
chiromyiform lineage today represented by
the aye-aye Daubentonia. Among these taxa,
genus means strongly support frugivory
(including potentially gummivory) in all taxa
but Nycticeboides and Komba, with averaged
specimen reclassifications indicating greater
uncertainty but modally consistent results, des-
pite the small size and apparently sharp teeth
of Djebelemur and the Paleogene lorisiforms
Wadilemur and Karanisia (Fig. 7). This is largely
consistent with dietary reconstruction of the
Paleogene species using shearing quotients
(Kirk and Simons 2000; Marivaux et al. 2013).
The living mouse lemur Microcebus, which
was reclassified by the DFA as an insectivore
but by the Bayesian model as frugivorous,
also consumes small fruits and gums, and
would seem to represent the most likely eco-
logical analogue for these early Paleogene
taxa. However, Microcebus shows higher ari-
aDNE values than any of the three Paleogene
genera (Fig. 8). This may capture the greater
elaboration of flat trigonid and talonid basins
in Djebelemur and Wadilemur, which could
serve as crushing surfaces for processing
small fruits, and the relatively high trigonid
with low cusps of Karanisia. In these characters,
these taxa seem to resemble the extant Euoticus,
which is classified here as a frugivore/gummi-
vore but is among the members of this category
with the highest ariaDNE values. It seems
likely that, like Euoticus,Djebelemur,Karanisia,
and Wadilemur supplemented a frugivorous or
gummivorous diet with insect protein. ariaDNE
values from Paleogene fossil strepsirrhines over-
lap the distribution of both Microcebus and Euoti-
cus, with a mean that is lower than both. The
ecological distinction between small-bodied
frugivore-insectivores and insectivore-frugivores
may be ultimately difficult to detect using dental
topographic analysis.
DFA, but not Bayesian, models reconstructed
Nycticeboides as folivorous, a surprising result,
as no extant lorisiforms consume leaves as a
significant dietary component. Nycticeboides
FIGURE 7. Occlusal view of the m2 of Paleogene strepsirrhines, Microcebus, and Euoticus.A,Microcebus (DLC 893m); B, Euo-
ticus (MCZ 17591); C, Djebelemur (CBI 366); D, Wadilemur (DPC 16872); E, Karanisia (DPC 21456K).
FIGURE 8. Box plot comparing specimen ariaDNE values of
Euoticus,Microcebus, and Paleogene fossil taxa Djebelemur,
Karanisia, and Wadilemur. IN, insectivory; FG, frugivory;
X, unknown.
ETHAN L. FULWOOD ET AL.16
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shares with strepsirrhine folivores high ari-
aDNE CV, reflecting the development of
sharp shearing crests and relatively flat basins.
A qualitative description of the dentition of
Nycticeboides simpsoni noted the development
of shearing crests but argued against the likeli-
hood that this taxon regularly consumed leaves
due to its small size and phylogenetic bracket-
ing (MacPhee and Jacobs 1986). Its body-size
reconstruction is ambiguous, however, and it
may have exceeded 500 g, placing it above
“Kay’s threshold,”the approximate mass at
which leaves become a more efficient source
of dietary protein for primates than insects
(Kay 1975; MacPhee and Jacobs 1986). This
may indicate greater ecological diversity
among Asian lorisiforms during the Miocene.
The reconstructed diets of Paleogene strep-
sirrhines suggest that adaptations for frugivory
or gummivory among Eocene crown and near-
crown (Djebelemurinae) strepsirrhines distin-
guished them ecologically from the more
insectivorous Afrotarsius and folivorous adapi-
forms and anthropoids (Kirk and Simons
2000). The exploitation of angiosperm repro-
ductive structures may have played a signifi-
cant role in the early adaptive history of
strepsirrhines in Afro-Arabia. As strepsirrhines
expanded into habitats without incumbent pri-
mate insectivores (including Madagascar and
South Asia), and after the extinction of main-
land African insectivores like Afrotarsius,
some strepsirrhine taxa apparently shifted
into more specialist insectivore roles.
Acknowledgments
This paper emerged from a dissertation chap-
ter to which G. Gunnell, R. Kay, D. McShea,
E. St. Clair, C. Wall, and B. Williams gave help-
ful guidance. Helpful comments from N. Vitek,
M. Silcox, and anonymous reviewer improved
an earlier version of the article. Many under-
graduates in the Boyer Lab also assisted in pro-
cessing scans, especially K. Montane and
M. Schaeffer. L. Godfrey, E. Seiffert, and the col-
lections staff at the Smithsonian National
Museum of Natural History, the American
Museum of Natural History, the Field Museum
of Natural History, the Natural History
Museum (UK), and the Division of Fossil
Primates at the Duke Lemur Center all provided
valuable access to specimens. Funding was pro-
vided by the Duke Graduate School dissertation
research domestic travel grant and summer
research support and by the grants NSF BCS
1552848 to D.M.B., NSF BCS 130405 to D.M.B.
and Elizabeth St. Clair, and NSF BCS 1825129
to D.M.B. and A. Harrington.
Data Availability Statement
Data available from the Dryad Digital
Repository: https://doi.org/10.5061/dryad.
4mw6m908m.
Literature Cited
Al-Aifari, R., I. Daubechies, and Y. Lipman. 2013. Continuous Pro-
crustes distance between two surfaces. Communications on Pure
and Applied Mathematics 66:934–964.
Allen, K.L., S. B. Cooke, L. A. Gonzales, and R. F. Kay. 2015. Dietary
inference from upper and lower molar morphology in platyrrhine
primates. PLoS ONE 10:e0118732.
Balko, E.A. 1998. A behaviorally plastic response to forest compos-
ition and logging disturbance by Varecia variegata in Ranomafana
National Park, Madagascar. Ph.D. dissertation. Syracuse Univer-
sity, Syracuse, N.Y.
Bearder, S. K., and R. D. Martin. 1980. Acacia gum and its use by
bushbabies, Galago senegalensis (Primates: Lorisidae). Inter-
national Journal of Primatology 1:103–128.
Berthaume, M. A., J. Winchester, and K. Kupczik. 2019. Effects of
cropping, smoothing, triangle count, and mesh resolution on 6
dental topographic metrics. PLoS ONE 14:e0216229.
Bhullar, B. A. S., A. R. Manafzadeh, J. A. Miyamae, E. A. Hoffman,
E. L. Brainerd, C. Musinsky, and A. W. Crompton. 2019. Rolling
of the jaw is essential for mammalian chewing and tribosphenic
molar function. Nature 566:528–532.
Boyer, D. M. 2008. Relief index of second mandibular molars is a
correlate of diet among prosimian primates and other euarchon-
tan mammals. Journal of Human Evolution 55:1118–1137.
Britt, A., N. J. Randriamandratonirina, K. D. Glasscock, and B.
R. Iambana. 2002. Diet and feeding behaviour of Indri indri in a
low-altitude rain forest. Folia Primatologica 73:225–239.
Bunn, J. M., and P. S. Ungar. 2009. Dental topography and diets of
four Old World monkey species. American Journal of Primat-
ology 71:466–477.
Bunn, J. M., D. M. Boyer, Y. Lipman, E. M. St. Clair, J. Jernvall, and
I. Daubechies. 2011. Comparing Dirichlet normal surface energy
of tooth crowns, a new technique of molar shape quantification
for dietary inference,with previousmethodsin isolationand in com-
bination. American Journal of Physical Anthropology 145:247–261.
Bürkner, P. C. 2017. brms: an R package for Bayesian multilevel
models using Stan. Journal of Statistical Software 80:1–28.
Bürkner, P. C. 2020. Estimating phylogenetic multilevel models
with brms. https://cran.r-project.org/web/packages/brms/
vignettes/brms_phylogenetics.html, accessed 7 January 2021.
Burrows, A. M., and L. T. Nash. 2010. Searching for dental signals of
exudativory in Galagos. Pp. 211–233 in A. M. Burrows and L.
T. Nash, eds. The evolution of exudativory in primates. Springer,
New York.
Charles-Dominique, P. 1977. Ecology and behaviour of nocturnal
primates: prosimians of equatorial West Africa. Columbia Uni-
versity Press, New York.
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 17
https://www.cambridge.org/core/terms. https://doi.org/10.1017/pab.2021.9
Downloaded from https://www.cambridge.org/core. IP address: 173.81.249.1, on 26 Mar 2021 at 11:38:44, subject to the Cambridge Core terms of use, available at
Charles-Dominique, P. 1979. Field studies of lorisid behavior: meth-
odological aspects. Study of Prosimian Behavior. Academic Press,
Cambridge, Mass.
Crompton, A. W. 1970. Functional significance of the therian molar
pattern. Nature 227:197–199.
Cuozzo, F. P., and N. Yamashita. 2006. Impact of ecology on the
teeth of extant lemurs: a review of dental adaptations, function,
and life history. Pp. 67–96 in L. Gould and M. L. Sauther, eds.
Lemurs: ecology and adaptation. Springer, New York.
Dammhahn, M., and P. M. Kappeler 2008. Small-scale coexistence
of two mouse lemur species (Microcebus berthae and M. murinus)
within a homogeneous competitive environment. Oecologia
157:473–483.
Erhart, E. M., S. R. Tecot, and C. Grassi. 2018. Interannual variation
in diet, dietary diversity, and dietary overlap in three sympatric
strepsirrhine species in southeastern Madagascar. International
Journal of Primatology 39:289–311.
Evans, A. R. 2013. Shape descriptors as ecometrics in dental ecol-
ogy. Hystrix: Italian Journal of Mammalogy 24:133–140.
Evans, A. R., and J. Jernvall. 2009. Patterns and constraints in carni-
voran and rodent dental complexity and tooth size. Journal of
Vertebrate Paleontology 29:24A.
Evans, A. R., and S. Pineda-Munoz. 2018. Inferring mamm al dietary
ecology from dental morphology. Pp. 37–51 in D. A. Croft, D. Su,
and S. W. Simpson, eds. Methods in paleoecology. Springer,
New York.
Evans, A. R., and Sanson, G. D. 2003. The tooth of perfection: func-
tional and spatial constraints on mammalian tooth shape. Bio-
logical Journal of the Linnean Society 78:173–191.
Evans, A. R., G. P. Wilson, M. Fortelius, and J. Jernvall. 2007. High-
level similarity of dentitions in carnivorans and rodents. Nature
445:78–81.
Fietz, J., and J. U. Ganzhorn. 1999. Feeding ecology of the hiberna-
ting primate Cheirogaleus medius: how does it get so fat? Oecologia
121:157–164.
Fortelius, M., J. Eronen, J. Jernvall, L. Liu, D. Pushkina, J. Rinne,
A. Tesakov, I. Vislobokova, Z. Zhang, and L. Zhou. 2002. Fossil
mammals resolve regional patterns of Eurasian climate change
over 20 million years.Evolutionary Ecology Research4:1005–1016.
Ganzhorn, J. U. 1992. Leaf chemistry and the biomass of folivorous
primates in tropical forests. Oecologia 91:540–547.
Ganzhorn, J. U., J. P. Abraham, and M. Razanahoera-Rakotomalala.
1985. Some aspects of the natural history and food selection of
Avahi laniger. Primates 26:452–463.
Gao, T. 2015. Hypoelliptic diffusion maps and their applications in
automated geometric morphometrics. Ph.D. thesis. Duke Univer-
sity, Durham, N.C.
Gao, T. 2021. The diffusion geometry of fibre bundles: horizontal
diffusion maps. Applied and Computational Harmonic Analysis
50:147–215.
Gao T., G. S. Yapuncich, I. Daubechies, S. Mukherjee, and D.
M. Boyer. 2018. Development and assessment of fully automated
and globally transitive geometric morphometric methods, with
application to a biological comparative dataset with high inter-
specific variation. Anatomical Record 301:636–658.
Godfrey, L. R. 2017. Subfossil lemurs. In A. Fuentes et al., eds. The
international encyclopedia or primatology. Wiley, New York.
Godfrey, L. R., G. M. Semprebon, W. L. Jungers, M. R. Sutherland, E.
L. Simons, and N. Solounias. 2004. Dental use wear in extinct
lemurs: evidence of diet and niche differentiation. Journal of
Human Evolution 47:145–169.
Godfrey, L. R., G. M. Semprebon, G. T. Schwartz, W. L. Jungers, E.
K. Flanagan, F. P. Cuozzo, and S. J. King. 2005. New insights into
old lemurs: the tropic adaptations of the Archaeolemuridae.
International Journal of Primatology 26:825–854.
Godfrey, L. R., W. L. Jungers, and G. T. Schwartz. 2006. Ecology and
extinction of Madagascar’s subfossil lemurs. Pp. 41–64 in
L. Gould and M. L. Sauther, eds. Lemurs: ecology and adaptation.
Springer, New York.
Godfrey, L. R., J. M. Winchester, S. J. King, D. M. Boyer, and
J. Jernvall. 2012. Dental topography indicates ecological contrac-
tion of lemur communities. American Journal of Physical Anthro-
pology 148:215–227.
Godfrey, L. R., R. E. Crowley, K. M. Muldoon, E. A. Kelley, S.
J. King, A. W. Best, and A. Berthaume. 2016. What did Hadropithe-
cus eat, and why should palaeoanthropologists care? American
Journal of Primatology 78:1098–1112.
Goodman, S. M., and J. U. Ganzhorn. 1997. Rarity of figs (Ficus)on
Madagascar and its relationship to a depauperate frugivore com-
munity. Revue d’Ecologie 52:321–329.
Gould, L. 2006. Lemur catta ecology: what we know and what we
need to know. Pp. 255–274 in L. Gould and M. L. Sauther, eds.
Lemurs: ecology and adaptation. Springer, New York.
Gregory, W. K. 1922. Origin and evolution of the human dentition.
Williams and Wilkins, Baltimore.
Harcourt, C. 1986. Seasonal variation in the diet of South African
galagos. International Journal of Primatology 7:491–506.
Harcourt, C. 1991. Diet and behaviour of a nocturnal lemur, Avahi
laniger, in the wild. Journal of Zoology 223:667–674.
Harcourt, C. S., and L. T. Nash. 1986. Species differences in substrate
use and diet between sympatric galagos in two Kenyan coastal
forests. Primates 27:41–52.
Hemingway, C. A. 1996. Morphology and phenology of seeds and
whole fruit eaten by Milne-Edwards’sifaka, Propithecus diadema
edwardsi, in Ranomafana National Park, Madagascar. Inter-
national Journal of Primatology 17:637–659.
Herrera, J. P., and L. M. Dávalos. 2016. Phylogeny and divergence
times of lemurs inferred with recent and ancient fossils in the
tree. Systematic Biology 65:772–791.
Hladik, C. M. 1979. Diet and ecology of prosimians. Pp. 307–357 in
G. A. Doyle, ed. Study of prosimian behavior. Academic Press,
Cambridge, Mass.
Hladik, C. M., P. Charles-Dominique, and J. J. Petter. 1980. Feeding
strategies of five nocturnal prosimians in the dry forest of the west
coast of Madagascar. Pp. 41–73 in P. Charles-Dominique, ed. Noc-
turnal Malagasy primates: ecology, physiology, and behaviour.
Academic Press, Cambridge, Mass.
Housworth, E. A., E. P. Martins, and M. Lynch. 2004. The phylogen-
etic mixed model. American Naturalist 163:84–96.
Jardine, P. E., C. M. Janis, S. Sahney, and M. J. Benton. 2012. Grit not
grass: concordant patterns of early origin of hypsodonty in Great
Plains ungulates and Glires. Palaeogeography, Palaeoclimat-
ology, and Palaeoecology 365:1–10.
Jungers, W. L., L. R. Godfrey, E. L. Simons, R. E. Wunderlich, B.
G. Richmond,and P. S. Chatrath. 2002. Ecomorphology and behav-
ior of giant extinct lemurs from Madagascar. Pp. 371–411 in
J. Plavcan, R. F. Kay, W. Jungers, and C. P. van Schaik, eds. Recon-
structing behaviorin the primate fossil record. Springer,New York.
Kay, R. F. 1975. The functional adaptations of primate molar teeth.
American Journal of Physical Anthropology 43:195–215.
Kay, R. F. 1977. The evolution of molar occlusion in the Cercopithe-
cidae and early catarrhines. American Journal of Physical Anthro-
pology 46:327–352.
Kay, R. F. 1978. Molar structure and diet in extant Cercopithecidae.
Pp. 309–339 in M. F. Teaford, M. M. Smith, and M. W. J. Ferguson,
eds. Development, function, and evolution of teeth. Academic
Press, New York.
Kay, R. F., and H. H. Covert. 1984. Anatomy and behaviour of
extinct primates. Pp. 467–508 in D. J. Chivers, B. A. Wood, and
A. Bilsborough, eds. Food acquisition and processing in primates.
Springer, New York.
Kay, R. F., and K. M. Hiiemae. 1974. Jaw movement and tooth use in
recent and fossil primates. American Journal of Physical Anthro-
pology 40:227–256.
ETHAN L. FULWOOD ET AL.18
https://www.cambridge.org/core/terms. https://doi.org/10.1017/pab.2021.9
Downloaded from https://www.cambridge.org/core. IP address: 173.81.249.1, on 26 Mar 2021 at 11:38:44, subject to the Cambridge Core terms of use, available at
Kay, R. F., and W. L. Hylander. 1978. The dental structure of mam-
malian folivores with special reference to Primates and Phalan-
geroidea (Marsupialia). Pp. 173–191 in G. G. Montgomery, ed.
The ecology of arboreal folivores. Smithsonian Institution Press,
Washington, D.C.
Kay, R. F., and E. L. Simons. 1980. The ecology of Oligocene African
Anthropoidea. International Journal of Primatology 1:21–37.
Kay, R. F., and P. S. Ungar. 1997. Dental evidence for diet in some
Miocene catarrhines with comments on the effects of phylogeny
on the interpretation of adaptation. Pp. 131–151 in D. R. Begun,
C. V. Ward, and M. D. Rose, eds. Function, phylogeny, and fos-
sils. Springer, New York.
Kirk, E. C., and E. L. Simons. 2000. Diets of fossil primates from the
Fayum Depression of Egypt: a quantitative analysis of molar
shearing. Journal of Human Evolution 40:203–229.
Lahann, P. 2007. Feeding ecology and seed dispersal of sympatric
cheirogaleid lemurs (Microcebus murinus,Cheirogaleus medius,
Cheirogaleus major) in the littoral rainforest of south-east Mada-
gascar. Journal of Zoology 271:88–98.
López-Torres,S.,Keegan,S.R.,PrufrockK.A.,Lin,D.,andSilcox,M.T.
2017. Dental topographic analysis of paromomyid (Plesiadapi-
formes, Primates) cheek teeth: morethan 15 million years of changing
surfaces and shifting ecologies. Historical Biology 30(1–2):76–88.
López-Torres, S., K. R. Selig, A. M. Burrows, and M. T. Silcox. 2020.
The toothcomb of Karanisia clarki: was this species an exudate-
feeder? Pp. 67–75 in K. A.-I. Nekaris and A. M. Burrows, eds.
Ecology and conservation of lorises and pottos. Cambridge Uni-
versity Press, Cambridge.
Lucas, P. W. 2004. Dental functional morphology: how teeth work.
Cambridge University Press, Cambridge.
MacPhee, R. D. E., and L. L. Jacobs. 1986. Nycticeboides simpsoni and
the morphology, adaptations, and relationships of Miocene
Siwalik Lorisidae. Contributions to Geology, University of Wyo-
ming Special Paper 3:131–161.
Marivaux, L., A. Ramdarshan, E. M. Essid, and W. Marzougui, H.
K. Ammar, R. Lebrun, B. Marandat, G. Merzeraud, R. Tabuce,
and M. Vianey-Liaud. 2013. Djebelemur, a tiny pre-tooth-combed
primate from the Eocene of Tunisia: a glimpse into the origin of
crown strepsirrhines. PLoS ONE 9:e80778.
Masters, J. C., W. H. R. Lumsden, and D. A. Young. 1988. Repro-
ductive and dietary parameters in wild greater galago popula-
tions. International Journal of Primatology 9:573–592.
Masters, J. C., F. Génin, S. Couette, C. P. Groves, S. D. Nash,
M. Delpero, and L. Pozzi. 2017. A new genus for the eastern
dwarf galagos (Primates: Galagidae). Zoological Journal of the
Linnaean Society 181:229–241.
McCrossin, M. L. 1992. New species of bushbaby from the Middle
Miocene of Maboko Island, Kenya. American Journal of Physical
Anthropology 89:215–233.
McElreath, R. 2015. Statistical rethinking: a Bayesian course with
examples in R and Stan. Chapman and Hall/CRC, New York.
Nash, L. T. 1986. Dietary, behavioral, and morphological aspects of
gummivory in primates. American Journal of Physical Anthro-
pology 29:113–137.
Nekaris, K. A. I. 2005. Foraging behaviour of the slender loris (Loris
lydekkerianus lydekkerianus): implications for theories of primate
origins. Journal of Human Evolution 49:289–300.
Nekaris, K. A. I., and D. T. Rasmussen. 2003. Diet and feeding
behavior of Mysore slender lorises. International Journal of Pri-
matology 24:33–46.
Norscia, I., V. Carrai, and S. M. Borgognini-Tarli. 2006. Influence of
dry season and food quality and quantity on behavior and feed-
ing strategy of Propithecus verreauxi in Kirindy,Mada gascar. Inter-
national Journal of Primatology 27:1001–1022.
Olson, E. R., R. A. Marsh, B. N. Bovard, H. L. Randrianarimanana,
M. Ravaloharimanitra, J. H. Ratsimbazafy, and T. King. 2013.
Habitat preferences of the critically endangered greater bamboo
lemur (Prolemur simus) and densities of one of its primary food
sources, Madagascar giant bamboo (Cathariostachys madagascarien-
sis), in sites with different degrees of anthropogenic and natural
disturbance. International Journal of Primatology 34:486–499.
Overdorff, D. J. 1992. Differential patterns in flower feeding by
Eulemur fulvus rufus and Eulemur rubriventer in Madagascar.
American Journal of Primatology 28:191–203.
Overdorff, D. J., S. G. Strait, and A. Telo. 1997. Seasonal variation in
activity and diet in a small-bodied folivorous primate, Hapalemur
griseus, in southeastern Madagascar. American Journal of Primat-
ology 43:211–223.
Pampush, J. D., J. P. Spradley, P. E. Morse, A. R. Harrington, K.
L. Allen, D. M. Boyer, and R. F. Kay. 2016a. Wear and its effects
of dental topography measures in howling monkeys (Alouatta pal-
liata). American Journal of Physical Anthropology 161:705–721.
Pampush, J. D., J. M. Winchester, P. E. Morse, A. Q. Vining, D.
M. Boyer, and R. F. Kay. 2016b. Introducing molaR: a new R pack-
age for quantitative topographic analysis of teeth (and other topo-
graphic surfaces). Journal of Mammalian Evolution 23:397–412.
Patel, B. A., D. M. Boyer, B. A. Perchalski, T.M. Ryan, E. M. St. Clair,
J. M. Winchester, and E. R. Seiffert. 2017. New fossils and the
paleobiology of Karanisia clarki from the late Eocene of Egypt.
American Journal of Physical Anthropology 162:310–311.
Pineda-Munoz, S., I. A. Lazagabaster, J. Alroy, and A. R. Evans.
2017. Inferring diet from dental morphology in terrestrial mam-
mals. Methods in Ecology and Evolution 8:481–491.
Powzyk, J. A., and C. B. Mowry. 2003. Dietary and feeding differ-
ences between sympatric Propithecus diadema diadema and Indri
indri. International Journal of Primatology 24:1143–1162.
Rode-Margono, E. J., V. Nijman, N. K. Wirdateti, and
K. A. I. Nekaris. 2014. Ethology of the critically endangered
Javan slow loris Nycticebus javanicus E. Geoffroy Saint-Hilaire in
West Java. Asian Primates 4:27–41.
Sato, H., L. Santini, E. R. Patel, M. Campera, N. Yamashita, I.
C. Colquhoun, and G. Donati. 2016. Dietary flexibility and feed-
ing strategies of Eulemur: a comparison with Propithecus. Inter-
national Journal of Primatology 37:109–129.
Scott, J. R., L. R. Godfrey, W. L. Jungers, R. S. Scott, E. L. Simons, M.
F. Teaford, P. S. Ungar, and A. Walker. 2009. Dental microwear
texture analysis of two families of subfossil lemurs from Mada-
gascar. Journal of Human Evolution 56:405–416.
Selig, R. S., E. J. Sargis, and M. T. Silcox. 2019. The frugivorous insec-
tivores? Functional morphological analysis of molar topography
for inferring diet in extant treeshrews (Scandentia). Journal of
Mammalogy 100:1901–1917.
Shan, S., S. Z. Kovalsky, J. M. Winchester, D. M. Boyer, and
I. Daubechies. 2019. aria DNE: a robustly implemented algorithm
for Dirichlet energy of the normal. Methods in Ecology and Evo-
lution 10:541–552.
Sheine,W. S., and R. F. Kay. 1982.A model for comparison of mastica-
tory effectiveness in primates. Journal of Morphology 172:139–149.
Simpson, G. G. 1933. Paleobiology of Jurassic mammals. Palaeobio-
logica Band V.
Spagnolo, S. 1976. Convergence in energy for elliptic operators. In
B. Hubbard, ed. Numerical solution of partial differential equa-
tions III:469–499. Elsevier, New York.
Spradley, J. P., J. D Pampush, P. E. Morse, and R. F. Kay. 2017.
Smooth operator: the effects of different 3D mesh retriangulation
protocols on the computation of Dirichlet normal energy. Ameri-
can Journal of Physical Anthropology 163:94–109.
Sterling, E. J., E. S. Dierenfeld, C. J. Ashbourne, and A. T. Feistner.
1994. Dietary intake, food composition and nutrient intake in
wild and captive populations of Daubentonia madagascariensis.
Folia Primatologica (Basel) 62:115–124.
Strait, S. G. 1993. Differences in occlusal morphology and molar size
in frugivores and faunivores. Journal of Human Evolution
25:471–484.
DIETARY ECOLOGY OF EXTINCT STREPSIRRHINES 19
https://www.cambridge.org/core/terms. https://doi.org/10.1017/pab.2021.9
Downloaded from https://www.cambridge.org/core. IP address: 173.81.249.1, on 26 Mar 2021 at 11:38:44, subject to the Cambridge Core terms of use, available at
Strait, S. G. 1997. Tooth use and the physical properties of food. Evo-
lutionary Anthropology 5:199–211.
Strait, S. G., and J. F. V. Vincent. 1998. Primate faunivores: physical
properties of prey items. International Journal of Primatology
19:867–878.
Streicher, U. 2004. Aspects of the ecology and conservation of the
pygmy loris Nycticebus pygmaeus in Vietnam. Ph.D. thesis. Lud-
wig Maximilians Universität, Munich.
Streicher, U. 2009. Diet and feeding behaviour of pygmy lorises
(Nycticebus pygmaeus) in Vietnam. American Journal of Primat-
ology 3:37–44.
Thalmann, U. 2001. Food resource characteristics in two nocturnal
lemurs with different social behavior: Avahi occidentalis and Lepi-
lemur edwardsi. International Journal of Primatology 22:287–324.
Ungar, P. S. 2007. Dental functional morphology. Pp. 39–55 in P.
S. Ungar, ed. Evolution of the human diet: the known, the
unknown, and the unknowable. Oxford University Press, Oxford.
Ungar, P. S. 2010. Mammal teeth: origin, evolution, and diversity.
Johns Hopkins University Press, Baltimore.
Ungar, P. S., and M. Williamson. 2000. Exploring the effects of tooth
wear on functional morphology: a preliminary study using dental
topographic analysis. Palaeontologia Electronica 3:1–18.
Vasey, N. 2000. Niche separation in Varecia variegata rubra and Eule-
mur fulvus albifrons: I. Interspecific patterns. American Journal of
Physical Anthropology 112:411–431.
Vasey, N. 2002. Niche separation in Varecia variegata rubra and Eule-
mur fulvus albifrons: II. Intraspecific patterns. American Journal of
Physical Anthropology 118:169–183.
Vehtari, A., A. Gelman, and J. Gabry. (2017) Practical Bayesian
model evaluation using leave-one-out cross-validation and
WAIC. Statistics and Computing 27:1413–1432.
Vincent, J. 1990. Structural biomaterials. Princeton University Press,
Princeton, N.J.
Walker, A. 1969. True affinities of Propotto leakeyi Simpson 1967.
Nature 223:647–648.
Wiens, F., A. Zitzmann, and N. A. Hussein. 2006. Fast food for slow
lorises: is low metabolism related to secondary compounds in
high-energy plant diet? Journal of Mammalogy 87:790–798.
Winchester, J. M. 2016. MorphoTester: an open source application
for morphological topographic analysis. PLoS ONE 11:e0147649.
Winchester, J. M. 2020. hecate. https://github.com/JuliaWinche-
ster/hecate, accessed 10 March 2020.
Winchester, J. M., D. M. Boyer, E. M. St. Clair, and A.
D. Gosselin-Ildari, S. B. Cooke, and J. A. Ledogar. 2014. Dental
topography of platyrrhines and prosimians: convergence and
contrasts. American Journal of Physical Anthropology 153:29–44.
Wright, P. C., V. R. Razafindratsita, S. T. Pochron, and J. Jernvall.
2005. The key to Madagascar frugivores. Pp. 121–138 in J.
L. Dew, and J. P Boubli, eds. Tropical fruits and frugivores.
Springer, New York.
Yamashita, N. 1998. Functional dental correlates of food properties
in five Malagasy lemur species. American Journal of Physical
Anthropology 106:169–188.
Yamashita, N. 2003. Food procurement and tooth use in two sym-
patric lemur species. American Journal of Physical Anthropology
121:125–133.
ETHAN L. FULWOOD ET AL.20
https://www.cambridge.org/core/terms. https://doi.org/10.1017/pab.2021.9
Downloaded from https://www.cambridge.org/core. IP address: 173.81.249.1, on 26 Mar 2021 at 11:38:44, subject to the Cambridge Core terms of use, available at
