Expensive Brains: "Brainy" Rodents have Higher Metabolic Rate.

Brains are the centers of the nervous system of animals, controlling the organ systems of the body and coordinating responses to changes in the ecological and social environment. The evolution of traits that correlate with cognitive ability, such as relative brain size is thus of broad interest. Brain mass relative to body mass (BM) varies among mammals, and diverse factors have been proposed to explain this variation. A recent study provided evidence that energetics play an important role in brain evolution (Isler and van Schaik, 2006). Using composite phylogenies and data drawn from multiple sources, these authors showed that basal metabolic rate (BMR) correlates with brain mass across mammals. However, no such relationship was found within rodents. Here we re-examined the relationship between BMR and brain mass within Rodentia using a novel species-level phylogeny. Our results are sensitive to parameter evaluation; in particular how species mass is estimated. We detect no pattern when applying an approach used by previous studies, where each species BM is represented by two different numbers, one being the individual that happened to be used for BMR estimates of that species. However, this approach may compromise the analysis. When using a single value of BM for each species, whether representing a single individual, or available species mean, our findings provide evidence that brain mass (independent of BM) and BMR are correlated. These findings are thus consistent with the hypothesis that large brains evolve when the payoff for increased brain mass is greater than the energetic cost they incur.

Introduction

Brain mass (BrM) varies across mammals. While BrM scales with body mass (BM), several other factors seem to influence BrM, such as sociability, environmental and dietary specializations, as well as energetic costs of brain tissue (Mace et al., 1981). Large brains contain more neurons and neural connections, and thus have greater potential for information processing. Large brains also tend to be more modular, which allows a great amount of connections between neurons (Krubitzer and Kaas, 2005). Hence, increased brain mass and potential for neural connections may have facilitated large brained mammals to colonize complex habitats, develop sensory systems and evolved complex societies (e.g., Budeau and Verts, 1986).

While there are many potential benefits of large brains, brain tissue is costly. For example, the mass-specific metabolic rate of the human brain is nine times higher than that of the body as a whole (Martin, 1981). The metabolic costs by having a large brain must be paid for by a direct mother metabolic constraint or by a trade-off mechanism between brain mass and energy consumption by other functions (e.g., Gibbons, 1998; Pitnick et al., 2006). Other energy allocations such as relative costs of flight and reproductive strategy in birds may also be reduced to shunt energy to an enlarged brain (Isler and van Schaik, 2006). Recent examples also show that BrM may become decoupled from BM over short time spans. For example, Gonzalez-Voyer et al. (2009) found that Tanganyikan cichlid BM exhibited recent bursts of rapid evolution, a process that is consistent with divergence linked to ecological specialization, while BrM showed no bursts of divergence but evolved in gradual manner, consistent with energetic constraints to rapid BM change.

Originally, Aiello and Wheeler (1995) proposed that a primate is able to meet the high metabolic cost of a large brain without incurring a compensatory increase in relative basal metabolic rate (BMR) by decreasing the amount of other metabolically expensive tissues (i.e., heart, lung, kidney, liver, and gastrointestinal tract). A similar hypothesis was recently proposed for fish (Kaufman et al., 2003). Recently, Isler and van Schaik (2009) using a large compilation of brain size, BM, and life history data, found evidence that an energetically costly increase in brain size has to be met by either increasing the total energy budget of a species or by compensating changes of energy allocation to other maintenance functions, such as digestion or growth and offspring production, or a combination of these. Martin, 1981, 1996), for example, found that the energetic investment of the mammalian mother during the development of the fetus and during the postnatal life up to the time of weaning resulted in a weak link between BMR and brain mass. Similarly, Jones and MacLarnon (2004) showed that for certain clades of bats, maternal investment plays an important role in the adult brain mass. While a number of studies have thus focused on these mechanisms of development, little is known about the evolutionary relationship between BMR and BrM. Very few studies have tested the generality of the “costly brain” hypotheses across multiple species with different evolutionary histories, and using phylogenetic approaches.

Basal metabolic rate is a fundamental parameter in comparative studies and lineages-specific exponents characterize its allometric scaling (White et al., 2009). Recently, Isler and van Schaik (2006) controlling both for BM and phylogentic relationships found evidence that BMR correlated with BrM in large groups of mammals. However, BrM explained a small % of the variation in metabolic rate at the species and family level (2.6 and 10.4%, respectively). At higher taxonomic levels, independent contrast (IC) revealed a significant correlation only for primates.

It is possible that the use of composite phylogenies lacking resolution and accurate estimates of branch lengths (BL) may have obscured the underlying patterns (e.g., Malia et al., 2003). Other potential confounding variables are the different possible ways of controlling for species BM. Here we generated a novel phylogeny of wild rodents using Bayesian analysis of cytochrome b sequence data. We included species where high-quality BMR, BrM, and BM data are available. We then used this analysis to test the hypothesis that BMR and BrM are correlated within rodents, after taking into consideration both BM and phylogeny.

Materials and Methods

Cytochrome b for 132 rodent species and six rabbits as outgroups (Wilson and Reeder, 2005) were downloaded from GenBank, and one sequence donated (Table A1 in Appendix). Cytochrome b was chosen as that marker has proven to be of high utility for species level phylogenetics (May-Collado and Agnarsson, 2006; Agnarsson et al., 2010, 2011)

Table A1

List of species and GenBank accesses of rodent and rabbit for the cytochrome b mitochondrial gene. Data on the .

Species	BrM	BMR	BMR*	BM	GenBank Accession#
ORDER RODENTIA
Family Aplodontiidae
Aplodontia rufa	1.95	6.03	McNab (1979b)	6.69	AJ389528
FAMILY AGOUTIDAE
Agouti (Cuniculus) paca	3.40	7.92	Arends and McNab (2001)	9.00	AY206551
FAMILY SCIURIDAE
Ammospermophilus leucurus	0.85	4.54	Lovegrove (2000), Chappell and Bartholomew 1981a,b	4.66	AY685488
Cynomys ludovicianus	1.79	6.05	Reinking et al. (1977)	6.68	AF157890
Glaucomys volans	0.59	4.51	Lovegrove (2003)	4.29	AJ389531
Marmota monax	2.37	6.50	Benedict (1938)	8.34	AF157953
Marmota flaviventris	2.33	7.34	Reinking et al. (1977)	8.52	AF143927
Paraxerus cepapi	0.51	4.98	Viljoen (1985)	5.26	U59179
Sciurus aberti	1.92	6.07	Golightly and Ohmart (1978)	6.44	U10163
Sciurus carolinensis	1.97	5.91	Bolls and Perfect (1972)	6.35	U46167
Spermophilus beldingi	1.19	5.07	Lovegrove (2003)	5.57	AF157951
Spermophilus richardsonii	1.21	5.08	Lovegrove (2003)	5.87	S73150
Spermophilus tridecemlineatus	0.86	4.64	Lovegrove (2003)	4.93	AF157877
Spermophilus beecheyi	1.63	5.76	Baudinette (1972)	6.38	AF157918
Spermophilus tereticaudus	0.60	4.54	Hudson et al. (1972)	5.05	AF157940
Spermophilus lateralis	1.18	5.46	Lovegrove (2003)	5.51	AF157950
Spermophilus parryii	1.46	6.25	Geiser (1988)	6.18	AY428024
Spermophilus undulatus	1.58	6.50	Casey et al. (1979)	6.59	AF157912
Spermophilus townsedi	0.72	4.86	Lovegrove (2003)	5.29	AF157949
Tamiasciurus hudsonicus	1.34	5.54	Pauls (1981)	5.24	AF147643
Tamias striatus	0.77	4.50	Wang and Hudson (1971)	4.55	AF147670
Tamias amoenus	0.33	4.57	Kenagy and Vleck (1982), Jones and Wang (1976)	3.93	AF147630
Tamias minimus	0.47	4.29	Jones and Wang (1976), Willems and Armitage (1975)	3.81	AF147649
Tamias palmeri	0.68	4.73	Yousef et al. (1974)	4.11	AF147655
Xerus inauris	1.34	5.74	Lovegrove (2003)	6.44	AY452689
FAMILY GEOMYIDAE
Geomys bursarius	0.71	4.93	Bradley and Yousef (1975)	5.31	AY393941
Thomomys talpoides	0.33	4.86	Lovegrove (2003)	4.79	AF215809
FAMILY HETEROMYIDAE
Chaetodipus hispidus	−0.39	3.87	Lovegrove (2003)	3.66	AF172832
Dipodomys microps	0.19	4.10	Lovegrove (2003)	4.09	AY926385
Dipodomys agilis	0.29	4.16	Lovegrove (2003)	4.12	U65303
Dipodomys deserti	0.55	4.54	Lovegrove (2003)	4.87	AY926381
Dipodomys heermanni	0.31	4.29	Hinds and Rice-Warner (1992)	4.09	AY926369
Dipodomys merriami	0.05	3.79	Lovegrove (2003)	3.63	AF 173502
Dipodomys ordii	0.32	4.16	Lovegrove (2003)	3.99	AY926365
Heteromys anomalus	0.02	4.61	Arends and McNab (2001)	4.25	DQ168468
Heteromys desmarestianus	0.08	4.60	Hinds and MacMillen (1985)	4.28	DQ168467
Liomys salvini	−0.26	3.88	Lovegrove (2003)	3.80	DQ168546
Microdipodops megacephalus	0.51	3.43	Lovegrove (2003)	2.65	AY926362
Perognathus flavus	−1.20	2.85	Hinds and MacMillen (1985)	2.17	DQ168551
Perognathus longimembris	−1.80	2.53	Lovegrove (2003)	2.12	U65302
FAMILY DIPODIDAE
Jaculus jaculus	0.19	4.52	Hooper and Hilali (1972)	4.01	AJ416890
Napaeozapus insignis	−0.73	3.68	Brower and Cade (1966)	3.14	AJ389535
Zapus hudsonicus	−0.84	3.81	Lovegrove (2003)	2.88	DQ664918
FAMILY CRICETIDAE
Arvicola terrestris	0.49	4.73	Lovegrove (2003)	5.13	AF119269
Cricetus cricetus	0.90	5.45	Lovegrove (2000), Hart (1971)	5.85	AY275109
Clethrionomys (Myodes) rutilus	−0.58	4.34	Rosenmann et al. (1975)	2.87	AF119274
Clethrionomys (Myodes) gapperi	−0.58	4.09	Lovegrove (2003)	2.83	AF272633
Clethrionomys (Myodes) glareolus	−0.65	4.15	Hart (1971)	2.88	AY309419
Clethrionomys (Myodes) rufocanus	−0.49	4.08	McNab (1992)	3.65	AY309418
Dicrostonyx groenlandicus	−0.33	4.59	McNab (1992)	4.23	AJ131444
Isthmomys pirrensis	0.54	4.80	Hill (1975)	4.58	EF989945
Lemmus sibiricus	0.20	5.06	Lovegrove (2003)	4.17	AJO12671
Lemmus lemmus	−0.15	5.26	Hissa (1970)	4.23	AY219145
Megadontomys thomasi	0.23	4.82	Lovegrove (2000), Hart (1971)	4.49	EF989949
Mesocricetus auratus	0.07	4.99	Hart (1971)	4.72	AF119265
Microtus agrestis	−0.56	4.15	McDevitt and Speakman (1996)	3.10	DQ662102
Microtus arvalis	−0.60	4.13	Lovegrove (2000), Ishii et al. (1996)	3.41	AM991098
Microtus guentheri	−0.37	4.38	Haim and Izhaki (1993)	3.94	AY513807
Microtus mexicanus	−0.62	3.85	McNab (1992)	3.56	AF163897
Microtus townsendii	−0.13	4.50	Kenagy and Vleck (1982)	3.82	AF163906
Microtus pinetorum	−0.58	4.07	McNab (1992)	3.21	AF163904
Microtus ochrogaster	−0.34	4.50	Lovegrove (2003)	3.82	AF163901
Microtus longicaudus	−0.36	4.30	Lovegrove (2003)	3.86	AF187160
Microtus pennsylvanicus	−0.26	4.40	Lovegrove (2003)	3.62	AF119279
Microtus montanus	−0.37	4.40	Lovegrove (2003)	3.68	AF119280
Microtus californicus	−0.25	4.22	McNab (1992)	3.77	AF163891
Myopus schisticolor	−0.58	4.54	Saarela and Hissa (1993)	3.51	EU165268
Neotoma albigula	0.77	4.90	McNab (1986)	5.41	AF108704
Neotoma fuscipes	0.98	4.99	McNab (1970)	5.26	AF376475
Neotoma cinerea	0.97	5.13	McNab (1986)	5.80	AF186799
Ochrotomys nuttalli	−0.37	3.30	Layne and Dolan (1975)	3.07	AY195798
Ondatra zibethicus	1.55	6.47	McNab (1992)	7.22	AF119277
Oligoryzomys longicaudatus	−0.40	3.93	Bozinovic and Rosenmann (1988)	3.28	AY452198
Onychomys torridus	−0.53	3.39	Whitford and Conley (1971)	3.04	EF989967
Peromyscus boylii	−0.34	3.99	Mazen and Rudd (1980)	3.31	AY322506
Peromyscus gossypinus	−0.39	3.61	Glenn (1970), Tannenbaum and Pivorun (1988)	3.18	DQ973102
Peromyscus leucopus	−0.46	3.52	Lovegrove (2003)	2.97	EF989980
Peromyscus polionotus	−0.87	3.07	Glenn (1970)	2.76	EF989995
Peromyscus maniculatus	−0.50	3.60	Lovegrove (2003)	2.93	AF119261
Peromyscus californicus	−0.09	4.06	Lovegrove (2003)	3.70	AF155393
Peromyscus eremicus	−0.62	3.48	Lovegrove (2003)	2.99	AY322503
Peromyscus crinitus	−0.63	3.44	Lovegrove (2003)	2.61	AY376413
Peromyscus truei	−0.29	3.83	Lovegrove (2003)	3.36	AF108703
Peromyscus megalops	0.11	4.51	McNab (1988)	4.09	DQ861377
Phyllotis darwini	−0.66	4.27	Bozinovic and Rosenmann (1988)	3.91	AY956728
Podomys floridanus	−0.06	3.95	Glenn (1970)	3.70	EF989977
Reithrodontomys megalotis	−0.92	3.11	Tomasi (1985), Pearson (1960)	2.37	EF990008
Sigmodon hispidus	0.13	5.44	Bowers (1971), Scheck (1982)	4.89	AF108702
Scotinomys teguina	−0.87	3.44	Hill and Hooper (1971)	2.42	EF990029
Scotinomys xerampelinus	−0.65	3.46	Hill and Hooper (1971)	2.72	AF108706
FAMILY SPALACIDAE
Nannospalax (Spalax) ehrenbergi	0.63	4.70	Lovegrove (2003)	5.28	AJ416891
Tachyoryctes splendens	0.69	5.02	McNab (1979b)	5.46	AF160602
FAMILY MURIDAE
Acomys cahirinus	−0.43	3.83	Shkolnik and Borut (1969)	3.74	Z96053
Aethomys (Micaelamys) namaquensis	−0.66	3.87	Lovegrove (2003)	3.83	EU3 49731
Apod em us flavicollis	−0.36	5.03	Lovegrove (2003)	3.48	AF159392
Apodemus sylvaticus	−0.53	4.04	Lovegrove (2003)	3.17	AF159395
Conilurus penicillatus	0.65	5.09	Hinds and Rice-Warner (1992)	5.01	AM910935
Desmodillus auricularis	−0.05	4.47	Downs and Perrin (1994)	3.83	AJ851272
Gerbillurus paeba	−0.17	3.55	Downs and Perrin (1990)	3.18	AJ430557
Gerbillus nanus	−0.51	3.19	Lovegrove (2003)	2.64	AJ851270
Hydromys chrysogaster	1.47	6.27	Dawson and Fanning (1981)	6.55	AM408339
Mastomys natalensis	−0.26	3.49	Haim and Fourie (1980)	4.06	AY751296
Meriones unguiculatus	0.12	4.34	Weiner and Gorecki (1981)	4.05	AF119264
Micromys minutus	−1.24	3.05	Lovegrove (2000), Hart (1971)	1.86	AB201996
Mus minutoides	−1.31	3.10	Lovegrove (2003)	1.61	AY057816
Notomys alexis	−0.04	3.81	MacMillen and Lee (1970)	3.61	AY176318
Otomys irroratus	0.32	4.44	Haim (1987)	4.95	AH012645
Par atomys brantsii	−0.48	4.43	Du Plessis et al. (1989)	4.57	AF141224
Pseudomys hermannsburgensis	−0.71	3.15	MacMillen et al. (1972)	2.42	AY176321
Rattus sordidus	0.53	4.67	Collins and Bradshaw (1973)	5.03	EF186477
Rattus fuscipes	0.49	4.44	Collins (1973)	4.91	EF186439
Rattus rattus	0.33	5.12	McNab (1988)	5.01	AB033702
Rhabdomys pumilio	−0.37	3.47	Haim (1987)	3.72	AF533116
Stochomys longicaudatus	0.24	4.58	Lovegrove (2000)	4.17	EU292149
Tatera indica	0.61	4.33	Goyal et al. (1981)	4.94	AJ430563
Tatera (Gerbilliscus) afra	0.44	5.20	Duxbury and Perrin (1992)	4.17	AJ430560
Thallomys paedulcus	−0.20	4.47	Lovegrove et al. (1991)	4.39	DQ381926
FAMILY NESOMYIDAE
Cricetomys gambianus	1.88	6.93	Lovegrove (2003)	7.05	AF160614
Saccostomus campestris	−0.20	3.94	Haim et al. (1991)	3.92	EF529796
FAMILY BATHYERGIDAE
Cryptomys hottentotus	−0.66	4.17	Lovegrove (2003)	4.84	AY425891
Heliophobius argenteocinereus	0.36	4.31	McNab (1979a)	5.08	U87527
Heterocephalus glaber	−0.65	3.25	Lovegrove (2003)	4.11	AF155870
FAMILY CHICHILLIDAE
Chinchilla lanigera	1.66	5.64	Lovegrove (2003)	6.26	AF283981
Lagostomus maximus	2.80	7.55	Arends and McNab (2001)	8.45	AF245485
FAMILY CAVIIDAE
Cavia porcellus	1.55	5.85	Arends and McNab (2001)	6.01	NC_000884.1
FAMILY HYDROCHAERIDAE
Hydrochaeris (Hydrochoerus) hydrochaeris	4.32	8.79	Arends and McNab (2001)	10.81	Unpublished**
FAMILY DASYPROCTIDAE
Dasyprocta leporina	3.13	7.35	Arends and McNab (2001)	8.01	AF437783
FAMILY OCTODONTIDAE
Spalacopus cyanus	0.46	4.53	Lovegrove (2003)	4.53	AF007061
Octodon degus	0.73	5.15	Lovegrove (2003)	5.35	AF422914
FAMILY HYSTRICIDAE
Hystrix africaeaustralis	3.11	7.77	Haim et al. (1990)	9.78	X70674
FAMILY GLIRIDAE
Myoxus (Glis) glis	0.57	5.06	Geiser (1988)	4.86	NC_001892.1
FAMILY PEDETIDAE
Pedetes capensis	2.41	6.68	Lovegrove (2000)	8.06	U59176
Out groups
Order Lagomorpha
FAMILY OCHOTONIDAE
Ochotona alpina					DQ335487
Ochotona turuchanensis					DQ335507
Ochotona princeps					NC_005358.1
FAMILY LEPORIDAE
Lepus europaeus					NC_004028.1
Oryctolagus cuniculus					EU285255
Pentalagus furnessi					AY292720
Pronolagus crassicaudatus					AY292738
Pronolagus randensis					AY292737
Romerolagus diazi					AY292734

*Original sources for BMR data,

**Sequence (714 .

Sequences were aligned using ClustalX 1.83 (Thompson et al., 1997) via Mesquite (Maddison and Maddison, 2008). The preferred model for the Bayesian analyses was selected with Modeltest (Posada and Crandall, 2001) using the AIC criterion (Posada and Buckley, 2004). The best-fitting model was GTR + γ + I (Yang, 1994). Bayesian analyses were carried out using MrBayes V3.12 (Huelsenbeck and Ronchist, 2001) with the settings as specified in Agnarsson and May-Collado (2008). The Markov chain Monte Carlo search was ran with 10,000,000 generations sampling the Markov chain every 1,000 generations, and the sample points of the first 7,000,000 generations were removed (“burnin”), after which the chain had reached stationarity.

Data on the log of BrM and BM (g), and BMR (cm3O2/h) were used in this study (Table A1 in Appendix). For studies comparing traits among species, such as regression analyses, it is necessary to account for phylogenetic relationships among the compared species (Felsenstein, 1985). Ignoring phylogenetic relationships can lead to pseudoreplication as species are not independent data points, rather independent evolutionary changes in the traits being compared are the data points, or the IC (see Felsenstein, 1985; May-Collado et al., 2007) among species and lineages. To describe the evolutionary relationship between BMR and BrM we performed various phylogenetic analyses. (i) The PDAP module in Mesquite (Midford et al., 2008) was used to estimate IC (Felsenstein, 1985). We used BL as estimated by MrBayes testing them for statistic appropriateness using PDAP. To correct for BM we regressed BMR and BrM against BM and subsequently regressed the residuals from these regressions (Garland et al., 1993). If the residuals are correlated then that is consistent with a relationship among these variables (BMR and BrM), that is independent of the BM of, and phylogenetic relationship among, species (e.g., May-Collado et al., 2007). Regression of residuals was performed using SPSS 2007 (SPSS Inc.). We also regressed BMR and BrM directly. (ii) To evaluate the correlated evolution among BMR, BrM, and BM, we assess the phylogenetic effect on the trends in character relationships between taxa (i.e., the observed pattern) using the best model of evolution that was found for each character. To do this we evaluated the significance of the relationships between the pair of characters using a measure of correlated evolution (CORR) in a Bayesian framework implemented in BayesTrait 1.0 (Pagel and Meade, 2007), assessing the probability of positively correlated (CORR > 0) and negatively correlated evolution (CORR < 0). As the null hypothesis we used a model in which the covariance between characters was set to zero (i.e., complete character independence, CORR = 0), and the alternative hypothesis was, then, the observed covariance between characters (Pagel, 1999a, 1999b). If the null hypothesis was rejected (i.e., a significant historical relationship between characters exists), then we concluded that the phylogenetic relationship and the models of evolution of the characters influence the observed patterns, and we corroborate the hypothesis of correlated evolution between BMR, BrM, and BM.

These methods account for phylogenetic uncertainty by running analyses across multiple trees. We used a Bayesian approach based on maximum likelihood with 10 test per tree and estimating Pagel (1999a,b) escalated phylogenetic parameters (Table A2 in Appendix). The sign test was used for statistical comparisons (Zar, 1996) with STATISTICA 6.0 (StatSoft, 2001).

Table A2

Results of Bayesian estimations of lambda (λ) and kappa (κ) phylogenetic parameters of Pagel (. In this approach: (1) λ, reveals whether the phylogeny fits to the patterns of covariance among species for a given trait. If a trait is not evolving among species this parameter will take the value 0, indicating that a phylogenetic correction is not necessary. If traits are evolving as expected given the tree topology, λ, takes the value of 1.0. Values of λ = 1.0 are consistent with the constant-variance model (sometimes called Brownian motion) and is therefore a correct representation of the data; (2) κ scales the relationship between individual branch lengths and trait evolution (Pagel, 1994, 2002). The value of this parameter is the power to which individual branch lengths should be raised in order to maximize the fit of the model of evolution to the data. If κ is 1, trait evolution is directly proportional to branch length and, then, the gradual mode of trait evolution is better supported. Values of κ greater than 1 signify proportionally more evolution in longer branches. Values of κ less than 1 signify proportionally more evolution in shorter branches. In the extreme case of κ = 0, trait evolution is independent of branch length, which is consistent with a punctuational mode of evolution.

Variable	BrM		BMR		BM
	Mean	SD	Mean	SD	Mean	SD
	0.962	0.027	0.865	0.067	0.991	0.007
	1.652	0.328	1.644	0.368	1.218	0.039

Results

The novel phylogeny finds support for the monophyly of each currently recognized taxonomical bat family with the exception that Heteromyidae contains Geomyidae, and one species of Muridae, Sigmodon hispidus, groups with Cricetidae (Figure A1 in Appendix). The phylogeny overall agrees well with recent rodent phylogenies at higher levels (e.g., Jansa and Weksler, 2004; Montgelard et al., 2008) and thus represents an reasonable hypothesis for to study the evolution of characters (e.g., Pagel and Harvey, 1988)

Figure A1

Majority rule consensus tree of the Bayesian analyses for Rodentia. Number at nodes represent the posterior probabilities values.

Independent contrast revealed associations between BM and BMR (p < 0.0001; r2 = 0.77), and with BrM (p < 0.0001; r2 = 0.85), and between BMR and BrM (p < 0.0001; r2 = 0.71). When using a single value to represent species mass we also found significant correlation between BMR and BrM, after accounting for BM. When using a single individual weight to represent the species, BMR explained 9.7% of the variation in BrM (p = 0.0003; r2 = 0.097; Figure 1A). Two extreme outliers affected the regression and removing these outliers resulted in much stronger regression (p < 0.0001; r2 = 0.20; Figure 1B). The outliers represent 3 species of small rodents (Cricetidae, Arvicolinae), that inhabit circumpolar Northern Hemisphere biome (Nowak, 1999). These Lemmings seem to have higher metabolic rate than typical rodents with similar BrM, which may be related to living in extreme climates requiring higher metabolism. This demonstrates that the climate and habitat as well as other potentially confounding factors (BM, food habits, substrate, a restriction to islands or highlands, use of torpor, and type of reproduction) make it difficult to demonstrate a significant correlation between BMR and BrM, even when it exists (e.g., McNab, 2008).

Figure 1

(A) Independent contrast regression analysis between BMR and BrM residuals, corrected by BM. (B) IC regression analysis between BMR and BrM residuals, corrected by BM and removing outliners (Myopus schisticolor, Lemmus sibiricus, and L. lemmus). Where BM = body mass, BMR = basal metabolic rate, and BrM = rodent brain mass.

When using species mean BM, BMR explained approximately 3% of variation in BrM (p < 0.05, r2 = 0.029). This correlation, however, disappears when using two BM values for each species, one, the estimated species mean, to calculate residuals of BrM, and the second, of the individuals used for the BMR experiments, to calculate residuals of BMR (p > 0.05).

CORR indicated significant correlations between all variables (p < 0.0001; CORR ≥ 1), with the highest correlation recorded between BM and BrM (p < 0.0001; CORR = 2.86; r2 = 0.95; Figure 2), followed by BM and BMR (p < 0.0001; CORR = 2.24; r2 = 0.92; Figure 2), and finally BMR and BrM (p < 0.0001; CORR = 1.86; r2 = 0.91; Figure 2).

Figure 2

The series of arrows in the middle of the figure indicates the model of correlated evolution between the studied characters in a Bayesian framework (for more details see . Average CORR values for each set of variables are presented above each arrow. Each inset labeled with a lower-case letter contains: on the left-hand side a line graph of a sample of 1,000 Markov Chain estimations (x-axis) of CORR (y-axis) between pairs of characters, where the continuous black line represents CORR = 0, and the continuous gray line indicates CORR = 1; and on the right-hand side a histogram of the probability distribution of covariance between pairs of characters, where the black bar indicates the average value and the gray bars indicate the lower 5% percentile (LP) and upper 95% percentile (UP). In particular, the analyzed characters were (A) BMR and BrM (LP = 1.33, UP = 2.46); (B) BM and BMR (LP = 1.50, UP = 3.12); and (C) BM and BrM (LP = 2.08, UP = 3.77).

Discussion

Brains are the centers of the nervous system of vertebrates, controlling the organ systems of the body and coordinating responses to changes in the ecological and social environment (Shultz, 2010). Although brain mass per se does not capture the complexity of brain function, there is general evidence that relative brain size roughly correlates with cognitive ability (e.g., Barton and Harvey, 2000). Hence the evolution of brain size is of broad interest, including what factors may favor and constrain the evolution of relatively large, modular and complex brains (Sol, 2009).

Basal metabolic rate is influenced by a variety of factors (BM White and Seymour, 2003; climate Lovegrove, 2000; demography Kurta and Ferkin, 1991). Furthermore, an increase in BrM results in increased costs for maintenance and information processing (Niven and Laughlin, 2008). For example, Karbowski (2007) found that in volume-specific cerebral glucose metabolic rate of different brain structures closely scales with brain volume. These results confirm that information processing in the brain requires large amounts of metabolic energy.

Here, we demonstrate a possible correlation between BMR and BrM within Rodentia. Independent of BM large brained rodents exhibit correspondingly higher BMR. These results contrast previous studies (McNab and Eisenberg, 1989). For instance, Isler and van Schaik (2006) found support for this relationship across all mammals combined, and within primates (IC p = 0.025, r2 = 0.20), but not within other orders, such as rodents. Adjustments appear to be clade-specific, the slopes of best-fit lines for BrM against BM tend to be higher in analyses of more inclusive taxa (e.g., orders and suborders) and lower in analyses of less inclusive taxa (families, subfamilies, and genera; e.g., Finarelli and Flynn, 2009). We hypothesize that the discrepancy between our findings and previous studies is potentially caused by two factors. First, the use of a composite versus primary-data based phylogenies, and second, differences in accounting for BM. Composite phylogenies often reflect taxonomy, not necessarily phylogeny, and typically lack accurate BL estimates, two aspects that reduce the efficiency of comparative tests. Accounting for BM is a complicated problem, but we argue that using more than a single value for a species, as have prior studies, may introduce confounding variables. Thus using an estimate of species BM, such as average species BM to generate BrM residuals, and the BM of the individual that happened to be used to evaluate BMR to generate BMR residuals, can likely adds noise that may obscure real patterns. If, for example, the evaluation of BMR happened to have been done on an atypically small or large animal, this would strongly affect the BM-BMR residual, and could readily obscure subtle patterns across two or more variables that are both highly correlated with BM. Instead using a single value, whether mean BM of a species, or the weight of a single individual seems at least to be a reasonable alternative. Furthermore, species mean BM is a measure independent of the available measures for BMR and BrM (typically single individuals), and as such provides a relatively neutral control unlikely to result in systematic error.

Here, we used a novel primary-data-based phylogeny with BL estimates and single estimates of species BM (species average or single individuals). Considering the compendium of factors that may contribute to BMR, the up to 20% of variation explained by BrM in rodents is high. Clearly, though, further research is necessary to understand the interplay between these variables, and ideally accurate estimates of species means based on multiple individuals would be available for each of these variables (e.g., Smith and Jungers, 1997).

Our study is also consistent with two evolutionary paths “favoring” an increase in BrM across rodent species. First, a “direct” path in which an increase in BMR correlates with an increase in BrM (Figure 2A). Alternatively, an “indirect” and additive path in which the effect of BM on BMR allows BrM to increase (Figures 2A,B). Comparatively, the direct scaling effect of BMR on BrM is the least important relationship (Figure 2A), but the importance of direct scaling increases when considering the indirect path. Dunbar and Shultz (2007) supported the indirect path scenario in primates, where BMR had a limiting effect on BrM, while BM had an effect on BrM through BMR.

Concluding Remarks

Here, we corroborate the hypothesis of Isler and van Schaik (2006) that an increase in brain mass is accompanied by an increase in basal metabolic rate, and suggests that this pattern may be general across mammals. Our findings corroborate the hypothesis that large brains evolve when the payoff for increased brain mass is greater than the energetic cost they incur (Niven and Laughlin, 2008).

Conflict of Interest Statement

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