Body Mass Measurements and Their Effects on Calculating Encephalization Quotients in Mammals

Home , Brain size, Encephalization quotient

BODY MASS MEASUREMENTS AND THEIR EFFECTS ON CALCULATING ENCEPHALIZATION QUOTIENTS IN MAMMALS

A THESIS

Presented to the University Honors Program

California State University, Long Beach

In Partial Fulfillment

of the Requirements for the

University Honors Program Certificate

Chika Okeke

Fall 2017 I, THE UNDERSIGNED MEMBER OF THE COMMITTEE,

HAVE APPROVED THIS THESIS

BODY MASS MEASUREMENTS AND THEIR EFFECTS ON CALCULATING ENCEPHALIZATION QUOTIENTS IN MAMMALS

Chika Okeke

______

Theodore Stankowich, Ph.D. (Thesis Advisor) Department of Biology

California State University, Long Beach

Fall 2017

ABSTRACT

BODY MASS MEASUREMENTS AND THEIR EFFECTS ON CALCULATING ENCEPHALIZATION QUOTIENTS IN MAMMALS By

Chika J. Okeke

Fall 2017

Multiple intelligence and comparative animal behavior analysis studies use encephalization quotient (EQ) for their analyses. Most of these studies use the actual brain mass of each specimen in addition to an average of the body mass to generate EQ.

However, using the average instead of individual-specific data could lead to bias if there is a significant difference between an individual’s actual body mass and the average published body masses. Using Sciuridae as my model, I measured the skull length and volume as well as the body mass of several species. These data were used to calculate the

EQ of each species and then compared to EQs generated by using average body masses from published literature. The result of this project may improve the accuracy of future studies on the relationships between encephalization quotients and performance cognitive tasks.

ACKNOWLEDGEMENTS

I would like to thank my research advisor, Theodore Stankowich, for helping me formulate and write this thesis. I enjoyed learning about general biological research despite majoring in cell biology. I would also like to thank Jim Dines for giving me access to the Los Angeles Natural History Museum Collections, and the California State

University, Long Beach, and its Honors Program for providing me with the opportunity to gain research experience as an undergraduate.

I would like also like to thank my parents, Sunny and Jacqueline Okeke, for loving and supporting me in whatever I choose to pursue. Thank you for your encouragement throughout my research experience and my undergraduate career.

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TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

CHAPTER

1. INTRODUCTION ...... 1

2. METHODS ...... 5

3. RESULTS ...... 9

4. DISCUSSION ...... 15

REFERENCES ...... 19

LIST OF TABLES

TABLE Page

1. Data Summary ...... 10

2. Results Summary ...... 10

LIST OF FIGURES

FIGURE Page

1. Pruned Sciuridae Species Tree ...... 8

2. EQ Comparisons of Squirrels of the World Data and Actual Mass ...... 11

3. EQ Comparisons of Mammals of the World Data and Actual Mass ...... 12

4. EQ Comparisons of Mass of Mammals Data and Actual Mass ...... 13

5. EQ Comparisons of Length and Actual Mass...... 14

CHAPTER 1

INTRODUCTION

Finding the best measure of intelligence is a feat that scientists have long sought after. The key to intelligence studies is to collect data that is both available and comparable across multiple species making intelligence quite difficult to quantify [1].

Additionally, scientists have not come to a consensus on the definition of intelligence because it is measured by cognitive abilities [1]. Cognition may be deemed as social, sensory, or even mechanical which further complicates setting a standard definition of intelligence [2]. Because of this, studies on cognition often have operational definitions of intelligence. These operational definitions have led to multiple studies measuring intelligence via various methods such as cerebral cortex size, neocortex ratio, and even neuronal index. One well-known, erroneous measure of intelligence is absolute brain size. Absolute brain size is a measure of intelligence that declares bigger brains correlated with higher intelligence. According to this standard, the cognitive capabilities of both elephants and blue whales would surpass that of humans today [2]. Relative brain size assumes that the relationship between brain size and body size is linear and suggests that a larger brain in comparison to body size is indicative of higher intelligence. Though brain size does increase with increasing body mass [3], this relationship is not linear which rules out relative brain size as a reasonable measure of intelligence [4].

1 When the relationship between to variables is not linear, scaling is used to account for the variability in that relationship. For intelligence studies, allometric scaling is employed to account for the non-linear relationship between brain size and body mass. The basic allometric equation is y = a·xb which stems from the equation log y = log a + b log x.

Using the log-transformed equation for a comparison between two variables would produce a straight line thereby accounting for the variation between the two variables.

Encephalization Quotient is a scaled relationship between actual brain mass of an animals and its expected brain mass for its body size. Therefore, high encephalization quotient is said to correlate with high intelligence [1, 2]. EQ has been found to be a reliable measurement of intelligence across multiple taxonomic groups. The EQ for humans is

7.4-7.8 indicating that humans have brains that are over seven times larger than what is expected for their body mass [1].

Additionally, EQ is also used in comparative animal behavior studies. Stankowich and Romero found that as encephalization quotient decreases, animal defense mechanisms increases [5]. This relationship stems from an energetic tradeoff between cognition and defenses [5]. They assert that because brain development requires lots of energy in order to perform cognitive tasks, mammals under ongoing predation will employ most of their energy in defense mechanisms such as spines, body armor, and toxic sprays [5]. A study by Iwaniuk et al. [6] found that high EQs are directly related to playful activity across mammals and that playful activity has become more convoluted and common with elevated EQs over time [6]. Ortega and Bekoff also found that bird species with the largest brains played the most [7]. EQ is also used in paleobiology to study brain development in extant and extinct species [8, 9, 10].

2 In this paper, I investigate whether the customary measures of EQ incite impartiality in EQ estimations across mammalian species. EQ is typically measured by quantifying brain volumes of a specimen from a museum, converting those brain volumes to brain mass, and then comparing the brain masses to published database’s body mass averages for that species [5, 8, 9]. Using the actual body masses of the individuals the skulls came from to calculate EQ would, however, yield the true EQ. The opportunity for bias lies in the notion that published body masses may vary significantly from the actual body masses of the animals being measured for study. Comparison of actual body mass EQ and published body mass EQ will show if published masses drastically change the true value of EQ. For example, trophy species (e.g., large deer and antelope) often come from a few local collecting trips and are not representative of the entire species. Subsequently, using data from only one collection may yield only local data (e.g. body size, body length, brain mass) that do not accurately reflect the complete species. In addition, collected specimens tend to be larger than the species average as well. Species in captivity also vary in dimensions from their native counterparts. Therefore, using published mass averages only from these collections would skew EQs of the species under study. I propose that using the specimen’s actual body mass instead of a published quantity will yield more accurate EQ measurements and that skull length can serve as a proxy for the actual body mass of an animal when the actual body mass is unavailable.

For most specimens, body mass is not recorded when the specimen is added to the collection, which is typically why EQ studies use published averages for their EQ calculations; but if length or another measure is shown to be a reasonable predictor of actual body mass, an acceptable EQ can still be calculated even in the absence of the

3 specimen’s actual mass. This would be especially useful for computing EQs for larger animals and validate studies that interpret and analyze the EQs of larger animals. For this study, I selected skulls from the family Sciuridae and only included skulls for which the body mass was also noted. The difference in EQ scores derived from actual masses as opposed to published masses Sciuridae is likely minor due to their abundance in collections and the fact that they are well-studied, which makes their published body masses more accurate. However, differences between actual EQ and EQs calculated using average published masses may differ substantially for larger specimens.

CHAPTER 2

METHODS

The data for this study included brain mass, body mass, and skull length measurements from the family Sciuridae. The two sources of data were the California

State University, Long Beach (CSULB) and the Natural History Museum of Los Angeles

County (LACM) collections. Sciuridae was chosen for my study because specimens from this family are abundant in both collection sites and usually include the body mass of the animal. Moreover, their skulls are standardly shaped and large enough to confidently measure brain volume using beads and dimensions using calipers. The original goal of my data collection was to obtain five males and five females for each species. However, this goal was not achieved for each species due to other restrictions such as unnoted body mass and skull damage. The first step was noting the actual body mass on the skin tag so that an EQ score could be calculated for that species. If a skin tag did not include the body mass, it could not be used in the data set. One hundred forty-six skulls representative of thirty-one species were considered. This number was reduced based on the ability to measure the volume and length of the skull and the number of specimens available for each species. Therefore, if the skull was too damaged to measure the volume or length, it also was not included in the data set. This led to modifications to my original collection criteria. Instead, only species for which five or more specimens—that is, specimens in good condition and with a noted body mass—could be obtained were

5 included in the analysis; thus, the data set was narrowed to 105 skulls across 11 species for this study. To measure the brain size, three-millimeter glass beads were funneled into the foramen magnum of the skull until it was completely full. These beads were then transferred to a 10-mL graduated cylinder to obtain the volume to the nearest 0.2 milliliters. To convert brain volume to brain mass, I multiplied the volume by 1.036 g ml-

1 [11]. Using digital calipers, the skull length was measured from the anterior tip of the premaxilas to the posterior most point of the occipital bone. The width of the skull, that is from the zygomatic arch of one side of the skull to the other, was also measured in addition to the height of the skull. However, this data was not reflected in the ultimate dataset. In order to compare my calculated EQ scores with published scores, the masses from three different sources—Squirrels of the World (SOTW) [12], Mammals of the

World (MOTW) [13], and Mass of Mammals (MOM) database [14]—were cataloged in my data set. Average body masses in these sources were given as averages of the entire species or separate averages for males and females of a specific species. When both male and female averages were given, I took an average of both values to get a single value for body mass. If only a male or only a female average was provided, then that was the only value included in the average for that species.

To compare EQs using published masses versus EQs with actual masses and to see if length is an acceptable predictor of EQ, I ran linear regression and phylogenetic least squares analysis in R v.3.4.1 [15] using the ‘ape [16],’ ‘caper [17],’ ‘geiger [18],’ ‘nlme

[19],’ and ‘phytools’ [20] packages. First, I imported a phylogenetic tree from Faurby and

Svenning’s paper published Molecular Phylogenetics and Evolution [21]. This tree was then pruned down to only the species of interest. All body mass and length measurements

6 were log-transformed before analysis. I then ran several linear regressions (Reg) comparing brain mass to each measure of body mass and skull length. To correct for species relatedness, I repeated all comparisons using phylogenetic generalized least squares (PGLS) analyses. The slopes and intercepts generated from these statistical tests were incorporated into my EQ calculation. I used Boddy et al.’s allometric formula, E = kP훼, where E = brain mass, P = body mass, k = 10y-intercept, and the allometric exponent 훼

= slope to determine my EQ formula (i.e. EQ = brain mass/(kP훼) [8]. To compare the EQ scores from the actual body mass analysis to the EQs from other body mass measures

(i.e. SOTW, MOTW, MOM, and Length) I calculated correlation coefficients between each pair of EQs and calculated 95% confidence interval (CI) of the slopes of every regression and PGLS analysis. Non-overlapping confidence intervals would suggest that the measures produced significantly different EQ values.

FIGURE 1: Pruned phylogenetic tree containing only the species of interest

CHAPTER 3

RESULTS

The slopes generated from the linear regression and PGLS tests showed that actual mass and the masses from the published data sources were not significantly different

(Table 2). Correlations between EQ measures of actual mass and the SOTW, MOTW, or

MOM were all fairly strong (0.82-0.85). While the slopes of the actual mass EQ and skull length EQ are not comparable due to scaling differences (cite the scaling book here), the correlation between EQ measures produced by actual mass and skull length was much lower (0.4) than the correlations with published mass measures. Results from the PGLS analyses were identical to those for the uncorrected regression analyses.

9 Table 1: Species names, total number of samples, actual mass averages, SOTW mass averages, MOTW mass averages, MOM mass averages, skull volume averages, and skull length averages. Binomial Count Actual Mass SOTW MOM MOTW Volume Length Ammospermophilus 13 123.408 154.500 160.398 160.500 2.619 40.387 nelsoni Heliosciurus 11 318.730 298.000 371.501 370.200 5.468 52.290 rufobrachium Heliosciurus 11 276.125 298.000 290.998 300.000 5.450 50.993 ruwenzorii Paraxerus cepapi 10 154.900 243.000 180.000 183.100 3.187 40.588 Sciurus niger 9 771.414 934.000 761.904 950.000 8.537 64.666 Spermophilus 11 170.355 194.467 190.999 168.850 2.954 41.411 lateralis Spermophilus 7 435.857 647.750 691.640 672.500 6.083 57.674 variegatus Tamias amoenus 5 49.120 50.633 50.500 50.650 1.884 33.918 Tamias townsendii 5 76.460 83.467 74.776 73.050 2.392 39.022 Tamiasciurus 10 246.670 203.100 224.999 203.100 4.632 48.720 douglasii Tamiasciurus 13 224.338 204.000 201.169 203.500 4.551 47.376 hudsonicus

Table 2: summary of results including the uncorrected and corrected slopes, kappa values, and confidence intervals. Measurement U-Slope U- Correl U-95% CI PGLS-Slope PGLS-Correl PGLS-95% Variable CI Actual Mass 0.581 --- (0.666, 0.496) 0.569 --- (0.662, 0.476)

SOTW 0.5206 0.831 (0.666, 0.375) 0.489 0.834 (0.596, 0.381)

MOTW 0.509 0.823 (0.627, 0.392) 0.478 0.826 (0.580, 0.376)

MOM 0.530 0.850 (0.660, 0.399) 0.496 0.845 (0.607, 0.385)

Length 2.398 0.399 (2.656, 2.141) 2.238 0.475 (2.481, 1.994)

10 FIGURE 2: Comparison of EQ values calculated from published Squirrels of the World (SOTW) masses and actual masses

FIGURE 3: Comparison of EQ values calculated from published Mammals of the World (MOTW) masses and actual masses

FIGURE 4: Comparison of EQ values calculated from published Mass of Mammals (MOM) masses and actual masses

FIGURE 5: Comparison of EQ values calculated from skull length measurements and actual masses

CHAPTER 4

DISCUSSION

My results show that the actual body mass slope does not significantly differ from that from the published masses. Since correlations between the EQs of the actual mass and the EQs of each published masses (i.e. SOTW, MOTW, and MOM) are high, this suggests that using published masses instead of actual masses is reasonable for EQ calculations for Sciuridae. This notion is further cemented by the overlapping confidence intervals for actual mass, SOTW, MOTW, MOM for both the corrected and uncorrected slopes. Conversely, the correlations for actual mass and skull length alone are notably low indicating that skull length alone is not a good predictor of EQ scores.

My data collection methods and analysis align closely with Boddy et al. Boddy et al. used both body masses and brain masses from the published literature sources. However, if the species was present in their collection, they measured its brain mass. The criteria for their data collection were that every species measured was an adult and that published data was from an original source. Like my study, they also used the averages of both males and females when available. In contrast, they did this for both brain masses and body masses. I only did this for published averages masses. Sometimes, the brain volume was recorded in the published literature so they converted the volume to brain mass by multiplying by 1.036 g mL-1 [11]. I measured the brain volumes for each sample and also converted them to brain mass by multiplying by 1.036 g mL-1 [11] Another difference

15 between my methods and Boddy et al.’s was that they utilized body weight averages from

Silva and Downing’s CRC Handbook of Mammalian Body Masses. When body weight was unavailable, I excluded that specimen from my study. In total, their analysis included

630 adult mammalian species representing 21 orders while my analysis had a much smaller sample size consisting of 105 species from 11 species Sciuridae. For their analysis, they used Midford et al.’s PDAP:PDTREE, but the techniques used to analyze data were the same for my study and Boddy et al. Both Boddy et al. and I calculated uncorrected and PGLS-slopes for each of the species in our data set and calculated EQ using this equation E = kP훼. These scores yielded similar resulting suggesting that the methods of past EQ studies are appropriate.

Additionally, I also acknowledge that intrinsic bias may have been introduced into the published EQ SOTW. SOTW only reported a male or female average body mass for some species. Those values may not may not be a true representation of those species’ average mass; thus, incorporating those averages into the SOTW EQ calculations would show an EQ that represents only one sex in of that species. Therefore, sexual dimorphisms’ influence on species size is not accounted for in some of the published EQ values. In spite of this possible bias, the EQ for SOTW was not significantly different from the other published averages or the actual EQ.

I performed my study using Sciuridae as my species of interest due to its abundance in collections and moderately-sized skulls. As a result of this specimen abundance, however, published body masses for squirrels are a fairly accurate representation of the species and yield acceptable EQs according to my results. Presumably, any species with an abundance of specimens and recorded body masses should have more accurate EQ

16 values. An important consideration is that published body mass averages may differ across geographical regions due to factors such as temperature, resource abundance, and competition, and other environmental factors. According to Bergmann’s rule, body size decreases with increasing temperatures [21]. Larger bodied animals are better at conserving heat than smaller species while smaller species are better at releasing heat than larger animals [21]. For this reason, a large body size is not necessary for high temperatures conditions, and in these high-temperature region, species are typically smaller [21]. Local collection trips contribute to collection sites. If those specimens were collected in warmer areas, they will be smaller and not a true representation of the average species size because they are not including species from colder regions. Using only species from warmer regions would skew the average actual mass and brain size.

When comparing these values to published averages, the actual mass and brain volume would be lower than expected yielding a reduced EQ. Conversely, species in collections from only colder regions would inflate the true EQ. To see if my findings can be replicated in other species, this study should be expanded to other rodents and subsequently other mammals.

In the future, I plan to perform this study again expanding my sample size to other

Orders across multiple geographic regions. Increasing the sample size and the source of the samples would yield more accurate measures for each species. I would then calculate skull volumes and use a principle components analysis (PCA) to combine the length, width, and height skull measurements. This would generate one principle component value that would be associated with overall skull size. Since this analysis would apply to multiple Orders, I would also need to calculate the volume and principle component

17 scores for every Order represented in the study. Together, volume and the principle component score may be a better predictor of actual mass than length alone. If the principle component score and volume are a reliable predictor of actual mass, it can be used to obtain a true EQ when actual body mass is not recorded for the specimen.

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