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ResearchCite this article: Verde Arregoitia LD, FisherDO, Schweizer M. 2017 Morphology capturesdiet and locomotor types in rodents. R. Soc.open sci. 4: 160957.http://dx.doi.org/10.1098/rsos.160957

Received: 24 November 2016Accepted: 15 December 2016

Subject Category:Biology (whole organism)

Subject Areas:ecology/evolution/taxonomy and systematics

Keywords:discriminant analysis, ecomorphology,non-metric multi-dimensional scaling,phylomorphospace, size correction

Author for correspondence:Luis D. Verde Arregoitiae-mail: [email protected]

†Present address: Museo de Zoología ‘AlfonsoL. Herrera’, Facultad de Ciencias, UniversidadNacional Autónoma de México, A.P. 70-399,Ciudad Universitaria, México, 04510, México.

Electronic supplementary material is availableonline at https://dx.doi.org/10.6084/m9.figshare.c.3659351.

Morphology captures dietand locomotor typesin rodentsLuis D. Verde Arregoitia1,†, Diana O. Fisher2 and

Manuel Schweizer11Naturhistorisches Museum Bern, Bernastrasse 15, Bern 3005, Switzerland2School of Biological Sciences, University of Queensland, St Lucia, Queensland 4072,Australia

LDVA, 0000-0001-9520-6543

To understand the functional meaning of morphologicalfeatures, we need to relate what we know about morphologyand ecology in a meaningful, quantitative framework. Closelyrelated species usually share more phenotypic features thandistant ones, but close relatives do not necessarily have thesame ecologies. Rodents are the most diverse group of livingmammals, with impressive ecomorphological diversification.We used museum collections and ecological literature togather data on morphology, diet and locomotion for 208species of rodents from different bioregions to investigatehow morphological similarity and phylogenetic relatednessare associated with ecology. After considering differencesin body size and shared evolutionary history, we find thatunrelated species with similar ecologies can be characterizedby a well-defined suite of morphological features. Ourresults validate the hypothesized ecological relevance ofthe chosen traits. These cranial, dental and external (e.g.ears) characters predicted diet and locomotion and showedconsistent differences among species with different feedingand substrate use strategies. We conclude that when ecologicalcharacters do not show strong phylogenetic patterns, we cannotsimply assume that close relatives are ecologically similar.Museum specimens are valuable records of species’ phenotypesand with the characters proposed here, morphology can reflectfunctional similarity, an important component of communityecology and macroevolution.

1. BackgroundUnderstanding how morphological features vary among specieswith different ecological habits is not trivial, given our limitedunderstanding of the ecology of many living species, evenin well-studied groups such as mammals. In vertebrates,variation in ecological attributes such as feeding and substrate

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rsos.royalsocietypublishing.orgR.Soc.opensci.4:160957

................................................use is commonly associated with variation in morphology [1]. The form–function relationship also haselements of phylogenetic relatedness, chance and common adaptive response that remain understudied[2,3]. With the increasing detail and availability of phylogenetic data, the relationship between form andfunction can be studied in an evolutionary context [4].

From an adaptive evolutionary standpoint, ecology and morphology are linked by commonfunctional demands [5]. In some cases, however, morphology may simply reflect retained ancestralfeatures rather than adaptation to present conditions [6]. Because evolution is a branching processand traits tend to be more conserved than random [7], phylogenetic relatedness is assumed to reflectecological similarity (i.e. phylogenetic signal). The assumption of strong phylogenetic signal in ecologicaltraits has led to studies that use ecological, morphological and phylogenetic metrics interchangeably. Forexample: when morphology is used as a surrogate for species’ functional roles in ecological assemblages[8], and phylogenetic distance between species as a measure of ecological similarity [9]. However, speciesdo not necessarily retain ancestral ecological characteristics [10,11].

Phylogeny, morphology and ecology are interconnected—a squirrel is still a squirrel in each ofthese regards, so it is not a matter of one approach being better than another, but a question ofexamining the phylogenetic patterns in morphology and ecology in order to make meaningful ecologicalor evolutionary interpretations. Instead of using only morphology or phylogenetic affinities to inferecological, and thus functional similarity between species, we need to first study the functionalrelationships between morphology and ecology in a phylogenetic comparative context. Once we identifyecologically relevant morphological variables, we can interpret similarity between species in ecologicalterms and use this measure of similarity to address other ecological questions (e.g. competition, disparityand biological invasions). We can measure large numbers of morphological traits using natural historycollections and can interpret them in functional terms, even for ecologically undescribed and poorlyknown species.

Rodents are the most diverse extant mammal group, with over 2200 described species [12].The order spans a wide array of body sizes and shows great diversity in locomotor habits andfeeding ecology, having evolved aquatic, arboreal, fossorial, jumping and gliding forms, with awide array of feeding preferences that include animal- and plant-eating specialists. For smallmammals, relatively subtle changes to the morphology of the bones and soft tissues can havedramatic functional consequences, making rodents a good example of ecological specialization withand without radical morphological changes [13]. The average rodent can climb, dig and swimwithout extensive morphological specializations [14]. Nonetheless, specialist forms have evolved (oftenindependently) and can be found in nearly every non-marine habitat. Rodents are also major ecosystemcomponents, given their position in food chains and their importance in soil tillage, seed dispersal andpollination [15].

Feeding and substrate use (diet and locomotion) are important ecological attributes which havebeen related to morphology in rodents [16,17]. In most cases, however, the sample of species analysedwas low or constrained to a single family, using characters that are not accessible without specializedequipment or postcranial skeletal material that is often not available at natural history collections,despite the call for skin-plus-skeleton preparation as the standard mammalian museum specimen[18]. Previous studies on rodents have identified consistent differences in morphology that relate tofunctionally important traits, and found differences in postcranial skeletal and appendicular charactersamong climbing, digging, swimming and jumping species [13,14,19]. The versatility of rodent feedingbehaviours is also evident in their feeding apparatus [20]. Molar crown descriptors discriminated thediets of extant muroids [21] and incisor morphology reflected the diet of 11 caviomorph genera [22]. Amorphometric analysis related dental morphology to diet, using only the outlines of the first upper molarof extant murines [23].

In this study, we examine the morphological variation in rodents with different feeding and locomotorstrategies. We address the existing gaps in establishing functional relationships between species’morphology and ecology in a quantitative, phylogenetic framework. We aim to: (i) examine and interpretthe relationship between morphology and ecology in rodents, using feeding and locomotion strategies,while taking phylogenetic relationships into account; (ii) validate the ecological relevance of an accessibleset of morphological traits with a sample that includes all ecomorphs and multiple families; (iii) identifyhow these traits vary between groups and (iv) infer the ecology of ecologically uncharacterized speciesusing morphological data. If our chosen characters are ecologically relevant and subject to strongselective pressures, we should not find strong phylogenetic patterns. Ecologically and morphologicallysimilar species in this order should converge in morphospace when we account for the phylogeneticcomponent.



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................................................

HB T

FF

HF

E

Vib

ACPHMC

LR

LMT

BIT

ZB

CBL

UM

Figure 1. Morphological characters examined. See table 1 for descriptions.

2. Material and methods2.1. Morphological dataWe collected data on seven craniodental and seven external measurements (figure 1 and table 1) for208 rodent species from 10 different families (figure 2; electronic supplementary material, table S1),using data from previous studies provided by A. Miljutin, and from a total of 2014 specimensexamined by LDVA at natural history collections (electronic supplementary material, appendix). These14 characters were identified to be ecology-dependent in a previous study that considered 48 characters(25 craniodental and 23 external) and their correlations with well-defined feeding and locomotor andecological strategies for the rodent fauna of the Baltic region [25]. In the same study, the chosen traits alsoshowed high interspecific variability and low intraspecific variability. Our choice of species was drivenby the availability of undamaged skins and skulls at the collections, but we consider that these speciesare representative of the overall taxonomic and ecological diversity within the rodent fauna of Australiaand neighbouring islands (Sahul), the Baltic region, Madagascar and Mesoamerica.

We recorded standard external measurements (body mass, head and body length, tail length, earlength and length of the hind foot; table 1) from specimen labels. All other linear measurements weretaken by LDVA to the nearest 0.01 mm using digital callipers (Fowler UltraCal Mark IV). The angle ofthe condylar process (ACP) was measured from digital photographs of the lateral view of the mandibleusing IMAGEJ (National Institutes of Health, Bethesda, MD, USA).

2.2. Phylogenetic treesTo quantify the inferred evolutionary relationships between the species that we examined, we extracted100 trees (at random) from a set of 1000 generated under a heuristic–hierarchical Bayesian framework in arecent species-level mammal phylogeny [24]. In this approach, species with large quantities of moleculardata are placed in the phylogeny according to these data, while species with lower quantities of data areadded under steadily stricter restrictions depending on what is known about their affinities. Each tree



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................................................Table 1. Description of morphological characters. All characters were measured in millimetres except ACP, measured in degrees.

external characters

HB head and body length: distance from the tip of the nose to the base of the tail. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

T length of tail: distance from the base of the tail to its tip without terminal hairs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

E ear length: distance from the basal notch to the tip without terminal hairs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Vib length of vibrissae: length of the longest mystacial vibrissa from the base to the tip in its natural position. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HF length of hind foot: distance from the heel to the tip of the longest digit without claw. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

FF length of forefoot: distance from the notch between the radius and carpus to the tip of the longest digitwithout claw, measured parallel to the manual axis

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

UM length of forefoot claw: distance from the base of the longest claw on its inferior surface to the tip

craniodental characters

CBL condylobasal length: distance from the border between the anterior surface of the upper incisors andintermaxilla to the posterior surfaces of the occipital condyles measured parallel to the cranial axis

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LR length of rostrum: distance from the tip of the nasal bones to the anterior edge of the zygomatic arch,measured level with the nasals and parallel to the cranial axis

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ZB zygomatic breadth: greatest breadth across the zygomatic arches. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

BIT breadth across incisor tips: distance across the tips of the incisors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LMT alveolar length of maxillary tooth row: distance from the anterior edge of the alveolus of the maxillary toothrow’s first tooth to the posterior edge of the alveolus of the third molar

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HMC height of mandibular corpus: distance from the anteriodorsal part of the first molar to the ventral surface of themandibular corpus, measured perpendicular to the masticatory surface of the mandibular tooth row

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ACP angle of condylar process: angle between the tangent to the ventral surface of the mandibular corpus parallelto the masticatory surface of the mandibular tooth row and the line connecting the tangent’s contact pointwith the axis of the mandibular condyle

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

represents a slightly different phylogenetic hypothesis, so covariance among species across tree replicatesmay affect any comparative analyses. Consequently, rather than using a single tree and assuming that therelationships are known without error, we repeated all our comparative analyses across multiple trees.This approach allows us to examine the variation in parameter estimates arising from differences amongtree topologies [26].

2.3. Ecology dataUsing published data, we classified each species into categories that reflect their substrate use andfeeding strategies. We used information on feeding habits, primary dietary items, and use of differentlocomotor modes and substrate types to assign species into four dietary categories and seven locomotorgroups (tables 2 and 3), using a modification of existing categories [14,20,27]. We did not consideromnivory as a diet type, and instead assigned most species traditionally described as omnivorousinto the generalized herbivore (GH) category, recognizing that generalized herbivores opportunisticallyconsume animal matter and fungi. We were able to assign 185 species into a locomotor mode using atleast one source. For feeding strategies, detailed natural history information is sparser and we couldonly assign 166 species into a diet class (electronic supplementary material, appendix). Species for whichwe could not confidently assign ecological categories were classified as unknown (U), and their dietand locomotion were predicted using morphology with the predictive framework that is built intodiscriminant analyses.

2.4. Data processing and analysisWe carried out all statistical tests in R v. 3.2.5 [28]. All data and code are provided as the electronicsupplementary material (see Data accessibility section). We used the package dplyr [29] for all data



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Muridae

Sciuridae

Gliridae

Echymyidae

Capromyidae

Ctenomyidae

Octodontidae

Abrocomidae

Caviidae

Dasyproctidae

Erethizontidae

Bathygeridae

Hystricidae

Nesomyidae

Cricetidae

GeomydaeHeteromyidae

DipodidaeSpalacidae

Figure 2. Sampled species in this study (black lines) within the phylogeny of Rodentia (one randomly selected tree from a set of 100),derived from the species-level mammalian phylogeny of Faurby & Svenning [24]. Families with 10 or more species are marked withcoloured bands and labelled.

Table 2. Locomotor categories.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

terrestrial (T) Rarely swims or climbs, may dig to make a burrow (but not extensively), may show saltatory behaviour(quadrupedal only), never glides (e.g. rats and mice)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

semiaquatic (Sa) Regularly swims for dispersal, escape or foraging (e.g. beavers and muskrats). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

arboreal (A) Capable of and regularly seen climbing for escape, shelter or foraging (includes scansorial species; e.g. treesquirrels and erethizontid porcupines)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

semifossorial (Sf) Regularly digs to build burrows for shelter, but does not forage underground (e.g. ground squirrels). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

fossorial (F) Regularly digs to build extensive burrows as shelter or for foraging underground (e.g. gophers and mole rats).Displays a predominantly subterranean existence

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ricochetal (R) Capable of jumping behaviour characterized by simultaneous use of the hind limbs, commonly bipedal (e.g.kangaroo rats)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

gliding (G) Capable of gliding through the use of a patagium, commonly forages in and rarely leaves trees (e.g. flyingsquirrels)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

processing and manipulation workflows, and we mention the relevant functions and packages for allour statistical analyses below.

2.5. Size correctionThe species in our sample ranged in size (body mass) from 10 (pygmy mice and jerboas) to over 10 000 g(beaver), a 2000-fold range covering the range of sizes of most extant rodents [30]. To remove thevariation in trait values that occurs simply because species vary in size, we chose to analyse the residualvariation in each character after removing the correlation with a measure of overall body size. Out ofthree highly correlated (non-central correlations greater than 0.95) candidate proxies of body size (bodymass, condylobasal length and head–body length), we selected body mass as a measure of overall body



6


................................................Table 3. Diet categories.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

carnivore (C) Diet composed primarily of animal matter, including some vertebrate or larger invertebratematerial (e.g. grasshopper mice)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

insectivore (I) Diet composed of animal matter, but primarily small arthropods (insects and chelicerates), grubsor earthworms (e.g. shrewmice)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

generalized herbivore (GH) Diet composed primarily of plant matter, mostly soft leafy vegetation, fruits or seeds. Diet alsoincludes fungi and animal matter in varying amounts (e.g. spiny pocket mice)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

specialized herbivore (SH) Diet composed exclusively of plant matter, including large amounts of particularly fibrous ordifficult to process plants (e.g. grass, bark or roots) or dust and grit (e.g. Australianbroad-toothed rat)

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

dimension because we considered it a suitable whole-body descriptor that has known relationships withspecies’ ecology. With each of the 100 trees in our set, we performed phylogenetic generalized least-squares (PGLS) regressions [31,32] of the log-transformed morphological traits (except the angle of thecondylar process, which we did not expect to vary with size) and body mass, using the ‘lambda’ method[33] to obtain the error structure for the models with the phyl.resid function in the phytools [34] package.We averaged the set of residuals from each of the 100 trees to incorporate phylogenetic uncertainty intothe size-correction process [35].

Using residuals from a regression to deal with size variation removes size and also allometric variationfrom the dataset, leaving ‘non-allometric shape’ information. This focus on non-allometric morphologicalvariation in relation to ecology may limit our interpretation, because an animal’s size is likely to influencethe niches it can occupy. We used the phylANOVA function in phytools [34] to run simulation-basedphylogenetic analysis of variance [36] to test for differences in body size (using two proxies: bodymass and condylobasal length) among species with different diets and locomotion modes. If ecologicalhabits relate strongly with body size, we would need to use an alternative size-only correction thatkeeps allometric variation such as the log-shape ratio approach of Mosimann [37] to examine howmuch diversity and homoplasy there is that has evolved other than by simple allometric scaling duringspeciation.

2.6. Ecology and morphologyBefore using discriminant analyses to examine the morphological changes associated with differencesin diet and locomotion, we used phylogenetic multivariate analyses of variance across the set of trees(phylogenetic MANOVAs) to first test whether or not we had significantly different ecological groups.To fit the models, we used a Procrustes distance PGLS (procD.pgls function in the geomorph [38] package)on the size-corrected data. This test is designed to take a matrix of morphological variables as a responsewhile including the phylogeny in the error term.

Afterwards, we were interested in examining the between-class variance in the data, so weused discriminant analyses. Discriminant analyses are supervised methods, using known classlabels to maximize the separation among species belonging to different ecological classes. Weconducted a phylogenetically informed, flexible discriminant function analysis (pFDA) to examinethe relationship between morphological variables and ecological categories. Flexible discriminantanalysis is a generalization of linear discriminant analysis; it reduces a discrimination problem to aregression problem [39], making it compatible with a PGLS framework [40,41]. pFDA has been usedto relate ecology with morphology for different taxonomic groups [42–46]. Standard and phylogeneticdiscriminant analyses rely on functions from the R package mda [47].

The degree of phylogenetic bias removed (assuming Brownian motion evolution) in pFDA isdetermined by Pagel’s lambda. The optimal value of lambda for a given tree topology can be foundby identifying the value that minimizes the residual sum of squares of the linear fit between thephylogenetically corrected matrices containing the continuous and categorical data for each species [42].For each of the 100 trees, we estimated the optimal value of Pagel’s lambda. A lambda value of zeroindicates that phylogeny has no importance in the model, equivalent to a non-phylogenetic analysis. Iflambda equals one, phylogeny is an important component of the model, with the residuals followinga Brownian motion model of evolution [43,48]. In this case, phylogenetic correction is necessary to



7


................................................ensure that the resulting projections into discriminant space are evolutionarily orthogonal. Previousstudies that used pFDA showed that even small differences in the lambda values used to account forphylogenetic covariance among species led to different sets of classifications and error rates [49], sodespite the low lambda values (see Results), we repeated all analyses using both phylogenetic andstandard (non-phylogenetic) flexible discriminant analysis.

After running the discriminant analyses, we used the R package DiscriMiner [50] to calculate Wilks’lambda and evaluate the discriminant power of the independent variables. Wilks’ lambda represents theproportion of the total variance in the discriminant scores not explained by differences among groupsfor a given variable. Wilks’ lambda statistic can be mathematically adjusted to a statistic which hasapproximately an F distribution for significance testing. Variables with low values for Wilks’ lambdahave higher discriminatory power.

2.7. MorphospaceWe measured an angle in addition to the linear measurements, and because there is no naturalrelation among the scales and units of measurement for these variables, the morphospace resultingfrom our flexible discriminant analysis approach would not have a Euclidean structure [51]. Thismeans that a meaningful interpretation of the spread and spacing of taxa within this morphospaceis not guaranteed (for example: interpreting the distances between points on a two-dimensional plotas morphological dissimilarity). Discriminant function analyses and analyses of variance with mixedangular and linear data are statistically valid, and they provide important and useful informationabout how morphology varies among different ecologies, the likely ecology of taxa according to theirmorphology, and the predictive power of different measurements. However, reducing the dimensionalityof morphological data for visualization is a common and helpful tool in ecomorphology. We complementour phylogenetic discriminant function approach with a non-metric multi-dimensional scaling (nMDS)analysis to visualize the variation in morphological variables as described by two nMDS axes, in which‘closer’ morphologies are more similar than more ‘distant’ morphologies [51]. We used Sammon’snonlinear mapping [52,53] with the sammon function in the MASS package [52] to collapse multi-dimensional morphological data onto a two-dimensional space while minimizing a stress function thatreflects goodness of fit. Because nMDS is an unsupervised analysis, we labelled the species in theresulting morphospace with the ecological categories predicted by the discriminant analyses.

2.8. Phylogenetic signalQuantifying the phylogenetic patterns in all of our data is an important first step that can help us interpretall the results in this study. In addition to estimating the optimal value of lambda for pFDA, we measuredphylogenetic signal separately for body size, morphological measurements and ecological habits. Forbody size, we calculated the K statistic of Blomberg et al. [11] with our body mass data. For morphologicaldata, we calculated Kmult [54], a generalization of the K statistic for multivariate data. K and Kmult arestandardized metrics that quantify the degree to which variation in a trait or set of traits is explained bythe structure of a given phylogeny. K < 1 indicates that closely related species resemble each other lessthan expected under a Brownian motion model of trait evolution. K > 1 means that closely related speciesare more similar than predicted by the model. To quantify the phylogenetic signal for the discrete dietaryand locomotor categories, we followed a recent proposal [55] to incorporate evolutionary models intoMantel tests in order to resolve previous issues of lower statistical power and higher type I error rates.

3. Results3.1. Size correctionThe PGLS models had low variation in the slope and intercept estimates across the 100 tree replicates,suggesting that our size corrections were robust to phylogenetic uncertainty (electronic supplementarymaterial, table S2). We used the means of the residuals across all trees so that our conclusions did notdepend on the assumption of any single tree being ‘correct’. We found no differences in body size amongspecies with different diet types (phylogenetic ANOVA; p > 0.05 for 81/100 trees for body mass andp > 0.05 for 99/100 trees for condylobasal length), and body size did not differ among locomotor modeseither (phylogenetic ANOVA; p > 0.05 for 100/100 trees for body mass and p > 0.05 for 100/100 trees for



8


................................................Table 4. Phylogenetic signal. Summary statistics for various measurements of phylogenetic signal run across a set of 100 phylogenetictrees.

character(s) median valueminimumvalue

maximumvalue metric

significancetestinga

optimal lambda (diet) 0.04 0.04 0.07 Pagel’s lambda n.a.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

optimal lambda (locomotion) 0.04 0.03 0.05 Pagel’s lambda n.a.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

body size (mass in grams) 0.18 0.01 0.62 Blomberg’s K 98/100. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

craniodental and mandibularmeasurements

0.18 0.01 0.65 Kmult 98/100

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

external measurements 0.20 0.02 0.65 Kmult 99/100. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

diet type −0.10 −0.11 −0.08 EMMantel test (Mantelcorrelation)

0/100

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

locomotor mode 0.36 0.35 0.38 EMMantel test (Mantelcorrelation)

100/100

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

aThe number of trees for which tests against a null hypothesis of no phylogenetic signal (e.g. by permutation tests) attained statistical significance(α = 0.05).

condylobasal length). In the light of these results, we consider the use of residuals for further analysesto be appropriate.

3.2. Phylogenetic signal and phylogenetic discriminant analysesWe found weak (less than expected under a Brownian motion model) but non-random phylogeneticsignal for body size (mass in grams), craniodental measurements and external measurements (table 4).Using the evolutionary-model Mantel test [55], we found no evidence of phylogenetic signal in diet type(very weak, non-significant Mantel correlations between phylogenetic and trait distances). Locomotormodes appear to be more conserved, and we found evidence of low phylogenetic signal for these habits(weak but significant Mantel correlation; table 4).

Optimal lambda values for diet type and locomotor mode were close to 0 across the tree set (table 4).Considering the predicted classes and the class means in discriminant space, the classification abilityand results of FDA were quantitatively indistinguishable from pFDA even across 100 slightly differentphylogenetic hypotheses. The discriminant scores for standard and phylogenetic FDA showed highcorrelation (always above 0.95; Pearson’s correlation), meaning no discernible shift between the positionof species in (affine) morphospace and phylomorphospace.

3.3. DietRunning phylogenetic MANOVAs, we found that morphology differs significantly among diet types(d.f. = 3, p < 0.05 for 98/100 trees). Using standard flexible discriminant analysis for dietary categories,morphological data allowed for the correct classification of 89% of species into their diet categories(cross-validated error rate from ten random partitions of the training data). Discrimination was bestfor generalized herbivores and specialized herbivores (GH and SH; 99 and 81% correct classification,respectively), and worst for carnivorous and insectivorous species (C and I; 50% correct classification).The first discriminant function (DF1) accounted for 76.38% of the total between-group variance. DF2accounted for 16.98% of the between-group variance. DF3 explained the remaining 6.64% of thewithin-group variance.

Examining the correlations between the morphological characters and the linear discriminants,measurements related to the masticatory apparatus (ACP, LMT, BIT and HMC) had the highest influenceon the discriminant axes (table 5). Several variables load onto the different DFs simultaneously, but DF1was driven mainly by ACP. Along this axis, specialized herbivores (e.g. voles) with very pronouncedcondylar processes, longer maxillary tooth rows, wider incisors and a thicker mandible had lowscores, while animal-eating (insectivorous and carnivorous) species with the least pronounced condylarprocesses and more gracile mandibles scored highest on DF1. DF2 mostly reflected the length of themaxillary tooth row. This axis captured the distinctive combination of features of the New Guinea moss



9


................................................Table 5. Discriminant structure. Correlations between morphological variables and discriminant functions for the standard (non-phylogenetic) FDAondiet typeand locomotionmode. Figures in italics represent significant correlations (Holm-correctedp-values smallerthan 0.05).

diet locomotion

character DF1 DF2 DF3 DF1 DF2 DF3 DF4 DF5 DF6

LR 0.28 0.2 0.29 −0.48 0.14 0.33 −0.06 0.5 −0.01. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ZB −0.14 0.32 −0.11 0.41 −0.03 −0.58 −0.35 0.05 −0.39. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

BIT −0.52 −0.36 −0.23 −0.36 −0.25 −0.09 −0.28 0.25 0.03. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LMT −0.64 0.4 0.34 −0.08 0.09 0.06 0.11 0.13 −0.54. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HMC −0.48 0.43 −0.46 0.15 −0.08 −0.32 −0.3 −0.19 0.17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ACP −0.88 0 −0.19 0.03 −0.47 −0.24 0.27 −0.05 0.2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

T 0.45 0.12 0.04 0.24 0.5 −0.35 0.45 0.44 −0.15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

E 0.42 0.4 0.06 0.38 0.6 0.21 0.03 0.2 −0.06. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Vib 0.49 0.3 0.07 0.52 0.57 −0.03 −0.03 −0.06 −0.03. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HF 0.32 0.11 0.15 0.78 0.19 −0.23 0.24 0.21 −0.18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

FF −0.06 −0.19 0.19 −0.63 0.12 −0.36 −0.02 −0.06 −0.04. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

UM −0.25 −0.09 −0.13 0.46 −0.3 −0.43 −0.37 0.05 0.1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Table 6. Discriminant power of independent variables.

diet locomotion

character Wilks’ lambda F-statistic p-value Wilks’ lambda F-statistic p-value

ACP 0.426 72.721 0 0.819 6.579 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LMT 0.611 34.413 0 0.966 1.044 0.398. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HMC 0.715 21.472 0 0.899 3.368 0.004. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

BIT 0.738 19.144 0 0.763 9.288 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Vib 0.788 14.498 0 0.56 23.397 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

E 0.807 12.879 0 0.618 18.455 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

T 0.847 9.728 0 0.67 14.713 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LR 0.911 5.261 0.002 0.695 13.082 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

HF 0.914 5.05 0.002 0.411 42.779 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ZB 0.945 3.167 0.026 0.657 15.551 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

UM 0.947 3.026 0.031 0.624 17.972 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

FF 0.976 1.325 0.268 0.604 19.572 0. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

mice Pseudohydromys ellermani, which have a short, stout rostrum with retracted nasals and extremereductions in molar size and number [56].

When we quantified the discriminant power of the independent variables, most variables contributedto the discrimination of species into dietary classes to some extent. All the variables except forefootlength were significantly correlated with at least one of the discriminant axes. All the measurementsexcept forefoot length (FF) showed significant contribution to the discriminant functions, and the twovariables with the highest power were ACP and LMT (table 6).

3.4. LocomotionWith the phylogenetic MANOVAs, we found that morphology tends to differ among locomotionmodes (d.f. = 6, p < 0.05 for 77/100 trees), but this result was somewhat sensitive to uncertainty in



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................................................

arboreal (A)fossorial (F)

gliding (G)ricochetal (R)semiaquatic (Sa)

semifossorial (Sf)

terrestrial (T)

nMDS axis 1

nMD

S ax

is 2

dietcarnivore (C)

generalizedherbivore (GH)

insectivore (I)

specializedherbivore (SH)

locomotion

(a) (b)

−0.2

−0.1

0

0.1

0.2

0.3

−0.2

−0.1

0

0.1

0.2

0.3

−0.2 0.20−0.2 0.20

Figure 3. Morphospace plot of nMDS ordination (Sammon’s nonlinear mapping) of morphological data for 208 species of rodents basedon 12 variables. Each species is labelled (colour and shape) with its predicted diet (a) and locomotion (b) from a flexible discriminantanalysis (FDA). Species with no information on ecological habits in the training dataset (42 for diet and 35 for locomotion) are indicatedby black outlines. Differences in classification between predicted and known diets (nine for diet and 35 for locomotion) are labelled withthe originally assigned category.

the phylogenetic hypotheses. Morphological data allowed for the correct classification of 69% ofspecies into their ‘correct’ locomotor modes by the FDA (cross-validated error rate). All fossorial,gliding and ricochetal species were correctly classified, followed by terrestrial species (T; 85% correctclassification), semiaquatic species (Sa; 83%), semifossorial species (Sf; 70%) and finally arboreal species(A; 66%). The first discriminant function (DF1) accounted for 71.95% of the total between-group variance.DF2 accounted for 14.27% of the between-group variance. External characters, and appendicularmeasurements in particular, had the strongest correlations with the linear discriminants. DF1 separatedjumping species with elongated hind feet (HF), longer claws on the forefeet (UM) and shorter forefeet(FF) than those with other locomotion modes. DF2 had strong correlations with ear (E), tail (T) andvibrissae length (Vib). Arboreal species with long tails, ears and vibrissae score higher on this axis, whilesemifossorial, semiaquatic and fossorial species score lower (table 5). All the variables except LMT hada significant contribution to the discriminant functions, and the variables with the highest discriminantpower based on Wilks’ lambda were HF, FF and Vib (table 6).

3.5. MorphospaceIn our brief exploration of how the species in our sample occupy a morphospace with Euclideanproperties, the nMDS with Sammon’s nonlinear mapping allowed us to construct a new lower-dimensional dataset with a structure as similar to the original morphometric dataset as possible. Wecombined this two-dimensional space with the predictions of the discriminant analyses to visualize theoverlap or separation of species with different ecological habits in morphological space. For diet types:generalized herbivores occupied a vast portion of the morphospace (figure 3a) without much overlapwith the other three diet types. In terms of locomotor modes, terrestrial species overlapped considerablywith arboreal, gliding and semifossorial species (figure 3b) but not with the other locomotor modes(ricochetal, semiaquatic and fossorial) that have marked structural requirements (e.g. elongated hindfeet that increase stride length when jumping or robust appendages for digging).

4. DiscussionFor the most part, rodents have a conserved and recognizable body plan. Overall, they are stout-bodied,short-limbed and long-tailed small mammals with a conserved suite of craniodental features for gnawing[57]. Nonetheless, rodents are the most successful group in the evolutionary history of mammals interms of taxonomic diversity, and there is at once remarkable diversification and extreme conservatismof morphological characters. After controlling for differences in body size and quantifying the effects



11


................................................of phylogenetic covariance, we found that unrelated species sharing diet and/or locomotion typesare characterized by shared morphological features, a likely indication of widespread homoplasy andconvergent evolution.

4.1. Phylogenetic patternsWe found very low phylogenetic signals for our chosen morphological and ecological traits, and nodifference in the results from phylogenetically informed and non-phylogenetic discriminant analyses.Despite this finding, we still consider that testing for phylogenetic signal and using a canonical variatesanalysis that can account for phylogenetic non-independence is a crucial first step in ecomorphologicalstudies. Convergent evolution and parallelism in rodent feeding and locomotion has been documented[58], so a species might share the same ecological attributes (and the corresponding morphology) with itsclose relatives but also with distantly related species that radiated into a similar niche. Low phylogeneticsignal is probably diminished further by the fact that, in rodents, species with different ecologies can haverelatively similar morphologies [30]. Also, there can be multiple adaptive solutions to a selective regime[2]. For example, fossorial species can be either scratch-diggers or tooth-diggers (e.g. gophers and nakedmole rats, respectively), and some have substantial morphological modifications suited to each diggingmode [59]. Conversely, similar features can evolve in response to different adaptive regimes (e.g. bothricochetal and arboreal species have very long tails).

4.2. Ecological relevance of morphological traitsIn ecomorphological studies, the question of how many and which characters to measure is crucialfor the meaningful interpretation of morphological space. Our results support the a priori expectationfor ecological relevance of the chosen variables. This correlative approach is no replacement for fine-scale studies that explicitly consider biomechanics and performance when examining the form–functionrelationship [60], but we consider that our whole-body, collections-based approach provides a useful basefor interpreting species’ morphologies at the order level. We used all craniodental and external characterstogether because they do not represent separate systems with unique biological properties, and theircontribution to the discriminant analyses reflects common functional demands. Food resources dependon the way in which they are obtained, and most behaviour involves locomotion [61]. For example:two external characters showed high discriminant power in relation to diet type: ear length (E) andvibrissae length (Vib). These sensory traits play a role in finding food, foraging and prey capture [62].We found that vibrissae were longer (in relation to body size) in arboreal species than they were interrestrial, fossorial or semiaquatic species. This result supports the notion that vibrissae are importantfor orientation in low-visibility conditions and discontinuous substrates, such as tree branches at nighttime [63]. Craniodental characters proved useful in predicting locomotion type, perhaps because theyare involved in posture and digging, and because the skull carries important sensory organs [64].

The angle of the condylar process (ACP) stood out as a powerful indicator of diet and locomotionstrategy. This measurement is not commonly used in studies of rodent morphology, but it capturedthe biomechanical consequences of gape angle and bite force on the occlusal apparatus. Specializedherbivores with robust mandibles, wide gapes and strong bite forces have higher ACP values, whileanimal-eating species have narrow ACP angles with gracile mandibles and reduced dentition. Differentvalues of ACP relate with other cranial and mandibular features (molars, jaw articulation, zygomaticstructure), including subtle changes that influence the insertion of the masticatory part of the masseter,temporal and pterygoid muscles. Given the abundance of rodent mandibles in fossil record, ACPrepresents a new opportunity for morphological inference of the ecology of fossil rodents and subsequentanalyses of the ecological dynamics of palaeocommunities.

4.3. Discrimination of ecological categoriesWe were interested in how morphology predicts ecology, and the ecological relevance of our chosenmeasurements. Assigning all the species in the training data into their ‘correct’ ecology was not ourobjective, and we consider most misclassification in our models as a result of the ecological versatility ofrodents, multiple adaptive solutions to similar selective regimes, uncertainty in the ecological literature,and in the way we interpreted vague or conflicting verbal descriptions of feeding and substrate use intodiscrete classes. While our results suggest widespread convergence and parallelism in rodent ecology,we did not explicitly test the hypothesis of convergent evolution. Recent comparative methods [65,66]



12


................................................that can detect and measure convergent evolution can be readily applied to our data. This investigationof convergence represents a rewarding topic that would enrich our present findings, as long as theevolutionary models involved in these tests are applied and interpreted correctly [67].

Certain ecological strategies are worth discussing in light of our results, such as the differencesbetween and within plant eaters and animal eaters. It is not biologically meaningful to separategeneralized herbivores and omnivores. Despite widespread use in the recent literature, omnivory isnot a cohesive category in the case of rodents. Generalized herbivores consume plant parts with littleor no cellulose (fruit, seeds, gum and nectar) and opportunistically consume animal matter and fungi.Specialized herbivores separated from other diet classes in the nMDS morphospace (figure 3). Theyconsume the vegetative parts of plants (leaves, stems and roots), which often contain grit and silica. Thesefoods are procured and processed with a suite of cranial features suited for maximum bite force whennibbling and cutting tough, fibrous foods, while maximizing grinding pressure to break down plantmatter before ingestion. The morphological ‘cluster’ of specialist herbivores included Arvicolines (volesand relatives) with varied lifestyles (semiaquatic, terrestrial and semifossorial), subterranean gophersand the European beaver, known for gnawing and grinding wood.

Specialized herbivores have exclusively vegetarian diets, because the symbiotic microorganisms intheir digestive tracts that help them break down cellulose require a more or less constant environment.Some species which we classified as specialized herbivores were not predicted as such on thebasis of morphology despite having well-documented herbivorous diets. These species occupied themorphospace between generalized and specialized herbivores, since they do not share the well-developed zygomasseteric complex of voles, gophers or beavers. The Australian broad-toothed mouse(Mastacomys fuscus) mostly eats grasses and is an example of such a specialized herbivore. Despitehaving broad teeth to clip pliable but tough grasses and a long maxillary tooth row to grind them, thisspecies is morphologically closer to other generalized herbivores and is most probably meeting demandsimposed by a herbivorous diet through a specialized gut morphology and with broader, more complexmolars [68]. The Mexican hairy dwarf porcupine (Sphiggurus mexicanus) is another strict vegetarian, butits arboreal habits probably give it access to young leaves and leaf buds that do not require extensivecraniodental modifications to chew.

The separation of species that specialize on soft-bodied invertebrates (insectivorous) from those thatconsume more varied animal matter (carnivorous, consuming chitinous invertebrates and vertebrates) isecologically sound in light of our results. From limited ecological data, we originally classified bothspecies of Pseudohydromys as insectivorous, but the discriminant analysis classified the eastern mossmouse (P. murinus) as carnivorous. These two species are on opposite ends of the spectrum of molarreduction within the New Guinea moss mice radiation, and it is likely that of the two, only P. ellermanispecializes on soft-bodied invertebrates that need no chewing. We had originally assigned the white-tailed antelope squirrel (Ammospermophilus leucurus) as a generalized herbivore, but under our model itwas classified as carnivorous on the basis of morphology. The predatory propensity of ground squirrels iswell documented [69], and in this case these habits seem to be reflected in their craniodental morphology.

Gliding, fossorial and jumping forms present clear differences in appendicular measurements andwere all correctly classified. Fossorial forms present the longest claws, jumping species have long tailsand hind feet and shorter forefeet, and gliding squirrels have proportionally longer limbs than non-gliding forms. Our choice of morphological characters yielded good classification of semiaquatic specieseven if we did not consider known swimming features such as waterproof fur, webbed feet and flattenedtails. Despite having different diets and using different parts of the aquatic medium, all the semiaquaticspecies except the Rakali (Australian water rat, Hydromys chrysogaster) were correctly classified by ourmodel. Like other swimming forms, this large predatory rat has small ears and stout whiskers, but it doesnot share the limb proportions of the other swimming rodents in the dataset. Terrestrial forms overlapwith both semifosorrial and arboreal forms in morphological space, leading to increased misclassificationin these three categories. For example, many deer mice, melomys and harvest mice are excellent climberswith ‘all-purpose’ morphologies, of which only a few species have been documented to lead trulyarboreal lifestyles. Similarly, the relationship between burrow use, digging ability and morphologicalmodifications are still unclear.

5. ConclusionThis study provides a useful and accessible set of characters for ecomorphological analysis and addsto our understanding of mammalian form and function. Nonetheless, we must continue collecting



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................................................morphological data and make efforts to synthesize the wealth of existing ecological data for suchan important order of mammals. The species we analysed were restricted to four biogeographicrealms, and several families with potentially unique morphological specializations are not represented.Species from a wider geographical scope should be analysed in further studies. Because of the weakrelationship between phylogeny and ecomorphology, we cannot predict the ecological similarity betweenspecies adequately using only phylogenies. However, ecological metrics derived from morphology canovercome the limitations of limited ecological data and convergent evolution in comparative studies.Voucher specimens are valuable records of species’ morphological phenotypes, and with the suitableset of characters used here, morphological similarity can be used to quantify functional similarity,an important component of research into community ecology, palaeoecology and macroecology. Ourapproach highlights the important role that museum collections can play in not just taxonomic orphylogenetic studies, but also in advancing the study of ecology [70,71].

Data accessibility. Data and R code are available as an archived GitHub repository [72], available at http://doi.org/10.5281/zenodo.201147.Authors’ contributions. L.D.V.A. and D.O.F. conceived the project. L.D.V.A. collected the measurements. L.D.V.A. and M.S.analysed the data. All authors contributed to interpreting the results and writing and revising the manuscript.Competing interests. We declare no competing interests.Funding. L.D.V.A. was supported by funding from Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship308685 and fellowship 253088. D.O.F. was supported by Australian Research Council fellowship FTll0100191.Acknowledgements. We are very grateful to Andrei Miljutin (Curator, Natural History Museum of Tartu University,Estonia) for providing data, reprints and comments on earlier versions of this manuscript. Sandy Ingleby,Heather Janetzki, Anja Divljan, Livia León Paniagua, Yiré Gómez Jiménez and Zamira Ávila-Valle provided accessto collections and assistance with specimens. L.D.V.A. wishes to thank Emma Sherratt for advice on variousmorphometric procedures and Wolfgang Nentwig for making his research visit to Switzerland possible.

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