Body Mass Measurements and Their Effects on Calculating Encephalization Quotients in Mammals
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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 BY 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. iii 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 iv LIST OF TABLES TABLE Page 1. Data Summary .................................................................................................. 10 2. Results Summary .............................................................................................. 10 v 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 vi 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