A COMPARATIVE ANALYSIS OF HIPPOCAMPUS SIZE AND ECOLOGICAL FACTORS IN PRIMATES A thesis submitted to Kent State University in partial fulfillment of the requirements for the degree of Master of Arts by Melissa Edler August 2007
Thesis written by Melissa Edler B.A., Kent State University, 2000 M.A., Kent State University, 2007 Approved by Dr. Chet C. Sherwood, Advisor Dr. Richard S. Meindl, Chair, Department of Anthropology Dr. John R. D. Stalvey, Dean, College of Arts and Sciences
ii TABLE OF CONTENTS
LIST OF FIGURES…………………………………………………………………… v
LIST OF TABLES……………………………………………………………………. vii
ACKNOWLEDGEMENTS…………………………………………………………… ix
Chapter
I. INTRODUCTION………………………………………………………… 1
Overview…………………………………………………………………... 1 Role of the Hippocampus in Memory………………………………...... 2 Spatial Memory……………………………………………………………. 7 Place Cells…………………………………………………………………. 9 Spatial View Cells…………………………………………………………. 12 Use of Spatial Memory in Foraging…………………………………....….. 17 Cognitive Adaptations to the Environment………………………………... 21
II. METHODS………………………………………………………………... 26
Species Analyses…………………………………………………………... 31 Independent Contrast Analyses……………………………………………. 33
III. RESULTS………………………………………………………………….. 37
Percentage of Frugivorous Diet……………………………………………. 37 Percentage of Folivorous Diet……………………………………………... 39 Percentage of Insectivorous Diet…………………………………………... 42
iii Home Range……………………………………………………………….. 44 Diurnal Activity Pattern……………………………………………………. 47 Nocturnal Activity Pattern…………………………………………….….... 48 Arboreal Habitat……………………………………………………………. 49 Semi Terrestrial Habitat……………………………………………………. 50 Terrestrial Habitat………………………………………………………….. 51
IV. DISCUSSION……………………………………………………………… 52
Diet and Hippocampus Volume……………………………………………. 52 Home Range and Hippocampus Volume…………………………………… 56 Activity Patterns and Hippocampus Volume………………………………. 59 Habitats and Hippocampus Volume……………………………………….. 61
V. CONCLUSION…………………………………………………………….. 65
REFERENCES………………………………………………………………………..... 67
iv LIST OF FIGURES
Figure 1: Hippocampus and Surrounding Cortices…………………………………… 3 Figure 2: Information Flow through Medial Temporal Lobe………………………… 4 Figure 3: Odor Discrimination Test………………………………………………….. 6 Figure 4: Radial Arm Maze Test……………………………………………………... 8 Figure 5: Place Cells in the Hippocampus…………………………………………… 10 Figure 6: Microcircuits of the Hippocampus…………………………………………. 16 Figure 7: Phylogenetic Tree for Primates……………………………………………... 36 Figure 8: Plot of Log Hippocampus Volume versus Percentage of Frugivorous Diet…………………………………………………………….. 38 Figure 9: Plot of Hippocampus Residual from Medulla Oblongata versus Percentage of Frugivorous Diet……………………………………… 38 Figure 10: Plot of Hippocampus/Medulla Oblongata Ratio versus Percentage of Frugivorous Diet…………………………………………….. 39 Figure 11: Plot of Log Hippocampus Volume versus Percentage of Folivorous Diet……………………………………………………………… 40 Figure 12: Plot of Hippocampus Residual from Medulla Oblongata versus Percentage of Folivorous Diet……………………………………….. 41 Figure 13: Plot of Hippocampus/Medulla Oblongata Ratio versus Percentage of Folivorous Diet………………………………………………. 41
v Figure 14: Plot of Log Hippocampus Volume versus Percentage of Insectivorous Diet…………………………………………… 43 Figure 15: Plot of Hippocampus Residual from Medulla Oblongata versus Percentage of Insectivorous Diet…………………………………… 43 Figure 16: Plot of Hippocampus/Medulla Oblongata Ratio versus Percentage of Insectivorous Diet…………………………………………… 44 Figure 17: Plot of Log Hippocampus Volume versus Log Home Range…………….. 46 Figure 18: Plot of Hippocampus Residual from Medulla Oblongata versus Log Home Range……………………………………………………. 46 Figure 19: Plot of Hippocampus/Medulla Oblongata Ratio versus Log Home Range…………………………………………………………… 47 Figure 20: Plot of Log Body Mass versus Percentage of Frugivorous Diet…………. 54
vi LIST OF TABLES Table 1: Raw Data Values for Hippocampus and Medulla Volumes, Home Range Areas, Diet, Activity Pattern and Habitat .....………………… 27 Table 2: Correlation Values between Hippocampus Volume and Percentage of Frugivorous Diet………………………………………… 37 Table 3: Correlation Values between Hippocampus Volume and Percentage of Folivorous Diet………………………………………….. 40 Table 4: Correlation Values between Hippocampus Volume and Percentage of Insectivorous Diet……………………………………….. 42 Table 5: Correlation Values between Hippocampus Volume and Home Range……… 45 Table 6: Correlation Values between Hippocampus Volume and Diurnal Activity Pattern………………………………………………… 48 Table 7: Correlation Values between Hippocampus Volume and Nocturnal Activity Pattern……………………………………………… 49 Table 8: Correlation Values between Hippocampus Volume and Arboreal Habitat….. 50 Table 9: Correlation Values between Hippocampus Volume and Semi Terrestrial Habitat………………………………………………… 51 Table 10: Correlation Values between Hippocampus Volume and Terrestrial Habitat………………………………………………………. 51
vii Table 11: Correlation Values between Hippocampus Volume and Home Range after Removing Frugivorous Insectivores…………………………………… 59
viii ACKNOWLEDGEMENTS I thank Dr. Chet Sherwood for serving as my advisor all the way from
Washington, D.C. Thanks also goes to Dr. Chris Vinyard for taking the time to help me learn Mesquite and making sure that I grasped the statistical analyses. I thank Dr.
Owen Lovejoy for agreeing to serve on my thesis committee. In addition, thanks to
Dr. Emmanuel Gilissen for allowing me to use much of the primate socio ecological data he previously gathered.
I thank Marianne Blankenship for finding an old MacIntosh that would support CAIC and the techies who rigged it to work on a classic operating system.
I thank my family, especially my Mom, and friends for their support, encouragement and a kick in the butt when it was needed along the way. Finally, I thank my husband Ryan who surrendered his wife for many months to the thesis monster and who cooked dinner, cleaned house and washed laundry to allow me time to work on my thesis.
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CHAPTER I: INTRODUCTION
Overview
An animal’s ecological surroundings can provide clues to its morphology. For instance, various areas of the mammalian brain have been shown to adapt in response to environmental influences (Eisenberg and Wilson, 1978; Barton et al., 1995; Hutcheon et al., 2002). One well studied example is the hippocampus, known for its role in spatial memory, which selectively increases in response to environmental pressures in animals such as birds, rodents, bats and humans (Krebs et al., 1989; Jacobs et al., 1990; Safi and
Dechmann, 2005; Maguire et al., 2000). Whether a similar relationship exists between the primate hippocampus and environment has yet to be examined.
The goal of this study is to determine whether variation in primate hippocampal size is related to environmental factors, such as diet (frugivory, folivory or insectivory), home range size, activity pattern (diurnal or nocturnal) and habitat (arboreal, semi terrestrial or terrestrial). Based on results from prior studies, this current analysis tests the following hypotheses:
Hypothesis 1: Primates with a higher percentage of frugivory in their diet should
have larger hippocampi than those with a higher percentage of folivory or
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insectivory in their diet (Clutton Brock and Harvey, 1980; Harvey et al., 1980;
Safi and Dechmann, 2005).
Hypothesis 2: As home range size increases, hippocampus volume also should
increase in primates due, in part, to its role in spatial memory (Clutton Brock and
Harvey, 1977; Clutton Brock and Harvey, 1980; Harvey et al., 1980).
Hypothesis 3: Hippocampus volume size should not differ significantly between
diurnal and nocturnal primates (Clutton Brock and Harvey, 1980; Bicca Marques
and Garber, 2004).
Hypothesis 4: Arboreal primates view space three dimensionally and rely more
heavily on spatial abilities than terrestrial primates, thus arboreal primates should
have larger hippocampal size than terrestrial primates (Russon, 2002).
Role of the Hippocampus in Memory
Located in the medial temporal lobe, the hippocampal formation comprises the perirhinal, entorhinal, and parahippocampal cortices and the rhinal sulcus (see Figure 1).
In primates, major input connections to the hippocampus come from association areas of the cerebral cortex, including the parietal cortex, the temporal lobe’s visual and auditory areas and the frontal cortex, and the hippocampus supplies outputs to the fornix and cerebral cortex via the subiculum, entorhinal cortex and parahippocampal gyrus (see
Figure 2) (Van Hoesen, 1982; Amaral, 1987; Suzuki and Amaral, 1994; Rolls, 1999).
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Figure 1. The brain is sectioned coronally showing the hippocampus and its surrounding cortices. (Source: Bear et al., 1996)
The hippocampus plays a large role in memory and learning. Two main theories exist concerning the role of the hippocampus in memory. The episodic theory of hippocampal function suggests that the hippocampus plays a selective role in episodic memory, which involves the capacity to remember specific personal experiences and detailed series of events, with little or no contribution to semantic memory, that of facts and world knowledge (Vargha Khadem et al., 1997; Fujii et al., 2000). In contrast, the declarative theory of medial temporal lobe function argues that the hippocampus, along with surrounding cortices, contributes to both semantic and episodic memory. This view is supported by a large body of similar findings from studies in human amnesic patients
(Manns and Squire, 2002; Squire and Zola, 1998).
Scoville and Milner (1957) were the first to hypothesize that hippocampal damage might be the underlying cause of amnesia in the case of patient H.M. Due to severe
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Figure 2. Information flow through the medial temporal lobe. (Source: Bear et al., 1996)
seizures, H.M. had surgery in which an 8 cm length of the medial temporal lobe,
including two thirds of the anterior hippocampus, was removed (Bear et al., 1996). While
the operation had little effect on his perception, intelligence and personality, H.M.
developed partial retrograde amnesia (before the onset) and severe anterograde amnesia
(after the onset). Though he remembered a great deal of his childhood, he was unable to
recognize a person he met five minutes earlier. H.M. lacked the ability to form new
declarative memories (facts and events), despite retaining the ability to learn new tasks,
or create new procedural memories.
In a more recent example of human amnesia, Manns et al. (2003) examined the performance of a group of hippocampal damaged patients versus matched controls for
memory of news events. Events were either encountered before or after the onset of
amnesia. Researchers found patients with hippocampal damage exhibited anterograde
amnesia with significant impairments in recalling and answering multiple choice
questions about news events that occurred after the onset of amnesia and partial
retrograde amnesia for events occurring one to ten years before the onset of amnesia
(Manns et al., 2003). Conversely, memory for events occurring eleven to thirty years
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before the onset of amnesia was not affected (Manns et al., 2003). Thus, results are
consistent with the declarative view that the hippocampus is involved in not only episodic
memory but also semantic memory.
A similar model of human amnesia in primates was created by Squire and Zola
Morgan (1991) in a series of experiments. Three groups of monkeys (Macaca fascicularis ) were each given a different type of lesion, one encompassing both the hippocampus and amygdala lesion (H+A+), one restricted to only the hippocampus (H+) and a hippocampus and surrounding cortices lesion (H++) (Squire and Zola Morgan,
1991). In delayed non matching to sample tasks, monkeys with the H+A+ lesion, like human amnesic patients, were severely impaired on a number of memory tasks but were entirely normal at acquiring and retaining skills (Squire and Zola Morgan, 1991). Even those with only the H+ lesion had statistically significant memory impairment after preoperative training (Squire and Zola Morgan, 1991). The most severely impaired monkeys were those with bilateral H++ lesions (Squire and Zola Morgan, 1991). These findings emphasize that the hippocampus itself is important for memory, that the hippocampus is dependent on input from surrounding cortices, and that damage limited to the hippocampus is sufficient to cause significant memory impairment.
Experiments with rodents have led to the same conclusion. In an experiment to test for sequential order memory, rats were presented with a series of five randomly selected odors (Fortin et al., 2002). Animals were rewarded for selecting the odor that had appeared earlier in the series. Control rats performed well, while those with
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Figure 3. Example of Eichenbaum’s odor discrimination experiment. (Source: Adapted from Eichenbaum, Fagan, Mathews and Cohen, 1988)
hippocampal lesions performed at near chance levels (Fortin et al., 2002). In a second
test, the same rats were examined on their ability to recognize odors in a previous series
and were rewarded when they selected the odor that was not presented in the prior
sequence (Fortin et al., 2002). Rats with hippocampal lesions performed as well as
normal rats on the recognition probes; therefore, rats with hippocampal damage could
recognize odors but lacked the ability to remember their sequential order (Fortin et al.,
2002). This supports the view that the hippocampus also plays a specific and fundamental
role in episodic memory.
Eichenbaum et al. (2005) developed a task that assessed episodic memory
involving a combination of odors presented in unique places in a specific order. During
each trial, rats sequentially sampled a unique series of four rewarded odor stimulus cups,
each in a different place along the edge of an open field (Eichenbaum et al., 2005). Then,
memory for the order of those events was tested by presenting a choice between a pair of
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odor cups in their original locations (see Figure 3) (Eichenbaum et al., 2005). Control rats performed well above chance (76.2 percent), indicating that they use both odor and
spatial cues in creating their judgments about the order of events (Eichenbaum et al.,
2005). Not surprisingly, rats with hippocampal damage failed on both aspects of this task
(Eichenbaum et al., 2005). Though the animals seemed to possess some form of memory
for both spatial and olfactory cues, they could not combine these cues effectively to
determine the correct order of events (Eichenbaum et al., 2005).
Spatial Memory
In the brain, declarative memories are organized in a way that spatial parameters
of the original sensory stimuli—their relative distance and direction—are accurately
represented (Squire and Zola, 1996; Ludvig et al., 2003). This enables the brain to
generate a cognitive map of the environment, perhaps composed of engrams encoding
objects in space (Tolman, 1948; O’Keefe and Nadel, 1978). The selection, consolidation
and association of these engrams are termed ‘spatial memory formation’ (Tolman, 1948;
O’Keefe and Nadel, 1978). Spatial memory is a type of episodic memory used in performing various spatial tasks.
Research on the rat hippocampus suggests that it performs a function in spatial
memory. In analyses of the hippocampus, Rolls and his colleagues (1989) found that rats
with hippocampal damage have an impaired ability to correctly run in an 8 arm maze,
which consists of passageways that radiate from a central platform (see Figure 4). A
normal rat explores each arm until it finds the food at the end of the arm. With practice,
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Figure 4. Following a rat through a radial arm maze. (a) An 8 arm radial maze. (b) The path of a rat through a maze in which all the arms contain food. (c) If a rat learns that 4 of the 8 arms never contain food, it will ignore these and follow a path to only the baited arms. (Sources: Part a, Bear et al., 1996; Parts b and c, Adapted from Cohen and Eichenbaum, 1993)
the rat becomes efficient at finding all the food, going down each arm of the maze once and using visual or other cues around the maze to remember where it has already been.
Rats that have hippocampal damage prior to being put in the maze still learn to go down the arms of the maze and eat the food placed at the end of each arm (Rolls et al., 1989;
Bear et al., 1996). However, they never learn to do this efficiently; rats with hippocampal lesions visit the same arms more than once, even after finding no food after the first trip, and they leave other arms containing food unexplored for an abnormally long time (Rolls et al., 1989; Bear et al., 1996). These rats learn the task but cannot seem to remember
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which arms they have already been down and lack the ability to form a cognitive spatial
map (Rolls et al., 1989).
Spatial function in the rat hippocampus also is involved with path integration. In a
series of ablation studies, Whishaw and colleagues (1997) found that the hippocampus
uses idiothetic information as an estimate of the animal's current environmental location,
derived from the animal's own movements through space in reference to a known starting point. Therefore, the rat hippocampus helps integrate signals of the animal's movement
over time (Whishaw and Jarrard, 1996; Whishaw and Tomie, 1997; Whishaw et al.,
1997; Gaffan, 1998).
Place Cells
Multiple neurons in the rat hippocampus selectively respond when the animal is in
a particular location in its environment; these neurons are known as place cells (O’Keefe
and Dostrovsky, 1971). Using a microelectrode implanted in the rat’s hippocampus,
O’Keefe and Dostrovsky (1971) found specific cells fired based on which corner of a box
the rat moved into. For example, when the rat moved to the northwest corner the cell began firing, and when the rat moved out of the corner, the firing stopped. The particular cell only responded when the rat was in that one portion of the box; this location is known as the neuron’s place field (O’Keefe and Dostrovsky, 1971).
The location of the place field is related to sensory input such as visual stimuli in the environment (O’Keefe and Dostrovsky, 1971). In the same experiment, O’Keefe and
Dostrovsky (1971) painted images above the four corners, such as a star above the
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Figure 5. Place cells in the hippocampus. A rat explores a small box (left panels); then a partition is removed so that it can explore a larger area (center and right panels). (a) Color coding indicates the area in the box where one place cell in the hippocampus responds: red = large response, yellow = moderate response, light blue = weak response, dark blue = no response. This cell has a place field in the small upper box; when the partition is removed, it stays in the same location. (b) An electrode is next to a cell in the hippocampus that does not respond when the animal is in the small upper box (left). Over the next 20 minutes, a place field develops in the new larger box (center and right). (Source: Adapted from Wilson and McNaughton, 1993) northwest corner and a happy face above the southeast corner. They removed the rat from the box, blindfolded it, and then secretly rotated the box 180 degrees so that the northwest corner had the happy face and the southeast corner had the star. Then researchers returned the rat to the box and removed the blindfold. When the rat went into the corner near the star, the neuron became active, which demonstrates that the response can be based on visual stimuli (O’Keefe and Dostrovsky, 1971). As long as the animal has had enough time to explore its environment, the place cells will be location specific
(O’Keefe and Dostrovsky, 1971; Bear et al., 1996).
Place cells are dynamic and can alter their place fields based on the current environment. O’Keefe and Dostrovsky (1971) examined place fields of several cells when a rat was put in a small box and able to explore it. Then they cut a hole in the side of the box, so the animal could explore a larger area. Initially no place fields existed
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outside the box, but after the rat explored its expanded area, some cells developed place
fields in the new, external location (see Figure 5).
Additional experiments have been conducted to characterize the information used
in constructing place cells and what role they might play in information processing and
memory mechanisms (O’Keefe, 1979; McNaughton et al., 1983; O’Keefe, 1984; Muller
et al., 1991; Markus et al., 1995; Nadel and Eichenbaum, 1999; Shapiro and Eichenbaum,
1999; Fenton et al., 2000; Redish et al., 2001; Hölscher et al., 2004). In a recent test,
Ferbinteanu and Shapiro (2003) examined the mnemonic signals of hippocampal cells in
rats with damage to the fornix, a major output pathway from the hippocampus, during the performance of a radial arm maze alternation task. Beyond typical hippocampal place cell
activity, they also found a second activity pattern termed “journey dependent” spatial
activity, where cells fired in a particular location in the maze during a specific journey
(Ferbinteanu and Shapiro, 2003).
Two different types of journey dependent activity were described. The first type
of journey dependent activity was observed on the goal arm and was dependent on the
identity of the start arm visited on that trial (Ferbinteanu and Shapiro, 2003). Therefore,
these cells signaled information about the previously visited place (retrospective signal).
The second type of journey dependent activity was detected on the start arm and was
dependent on the goal arm about to be chosen (prospective signal) (Ferbinteanu and
Shapiro, 2003). Hence, hippocampal neurons signal information not only about the
immediate past (retrospective signal) and the present (classic place cell activity) but also
the imminent future (prospective signal). Moreover, because almost half of the journey
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dependent cells in the goal arm maintained their spatial activity during detour trials, when
the path between the start and goal arm was not direct, this suggests that these cells did
not depend on the spatial trajectory or the specific combinations of body movements
made by the animal (Ferbinteanu and Shapiro, 2003). Instead, this activity appears to
signal selective information about particular goal directed journeys, in part, confirming
that the hippocampus is involved in path integration.
Spatial View Cells
In addition to rodents, primates with hippocampal damage also show spatial
memory deficits in object place memory tasks, which require a whole scene or snapshot
like memory (Mishkin, 1982; Gaffan and Saunders, 1985; Gaffan, 1987; Gaffan, 1994;
Rolls, 1999). For example, macaques and humans with damage to the hippocampus or
fornix are impaired in tasks, where the location of objects and the places where responses
should be made, must be remembered (Smith and Milner, 1981; Gaffan and Saunders,
1985; Parkinson et al., 1988; Rolls, 1999).
Neurons that respond in relation to the location of the animal, like place cells in
the rat hippocampus, have not been found in the primate hippocampus (Rolls, 1999).
Instead, researchers have found spatial view cells, which respond to the place at which
the primate is looking (Watanabe and Niki, 1985; Rolls et al., 1989; Rolls and O’Mara,
1993, 1995; Rolls et al., 1997; Rolls et al., 1998; Rolls, 1999). When Watanabe and Niki
(1985) analyzed hippocampal neuronal activity in monkeys performing a delayed spatial
response task, they reported that 6.4 percent of hippocampal neurons responded
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differently while the monkey was remembering left as compared to right. Moreover in the
same type of spatial response learning task, Miyashita et al. (1989) found 14 percent of
hippocampal neurons responded to particular combinations of visual stimuli and spatial
responses.
In addition to delayed response tasks, spatial view cells in the primate
hippocampus help provide the spatial representation that enables primates to perform
object place memory tests (Rolls et al., 1989; Rolls et al., 2005). In an analysis involving both a delayed spatial response task and an object place memory task, Cahusac et al.
(1989) probed monkey hippocampal activity. In the object place memory task, the
monkey was shown a sample stimulus in one position on a video screen. After a two
second delay, the same or a different stimulus was shown in either the same or a different position. The monkey was required to remember the sample and its position, and if both
matched the delayed stimulus, it licked to obtain fruit juice. Of the 600 neurons analyzed
in this task, 3.8 percent responded differently for the different spatial positions (Cahusac
et al., 1989). Therefore, some hippocampal neurons respond differently for stimuli shown
in different positions in space, and some respond differently when the monkey is
remembering different positions in space.
A unique aspect of primate spatial view cells is their ability to maintain their
spatial properties for periods of several minutes in the dark (Robertson et al., 1998). For
instance, even when visual details of the view were completely obscured by floor to
ceiling black curtains, many of the spatial neurons responded when the monkey looked
towards where the view had been (Robertson et al., 1998). In this case, these cells used
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idiothetic cues, including eye position and head direction, to trigger memory recall
(Robertson et al., 1998).
Consequently, primates, with their highly developed visual and eye movement
control systems, can explore and remember information about what is present at locations
in the environment without having to visit those places (Rolls, 1999). Spatial view cells
are useful in that they provide a representation of space that does not depend on exactly
where the monkey is, and that could be associated with items that might be present in
those spatial locations. For instance, a primate could use this representation to remember
where it had seen ripe fruit.
As spatial view cells respond to the place at which a primate is looking, these
cells code for particular locations in allocentric (world based) space (Rolls et al., 1989;
Feigenbaum and Rolls, 1991; Rolls et al., 1998; Georges François et al., 1999; Rolls,
1999). To determine the proportion of spatial view cells that code for egocentric versus
allocentric space, Feigenbaum and Rolls (1991) examined whether spatial fields in the
hippocampus use egocentric or allocentric coordinates. They found 10 percent of spatial
neurons were egocentric, meaning the responses remained in the same position relative to
the monkey’s body axis whether the screen was moved or the monkey was rotated or
moved to a different position (Feigenbaum and Rolls, 1991). However, 46 percent of the
spatial neurons analyzed were allocentric, in which the responses remained in the same position on the screen or in the room when the monkey was rotated or moved to a
different position (Feigenbaum and Rolls, 1991).
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Another function of the primate hippocampus may be to associatively combine
spatial and reward information to provide a representation of the rewards available at
different spatial locations (Marr, 1971; Rolls, 1989, 1996; Treves and Rolls, 1994; Rolls
and Treves, 1998; Rolls et al., 2002). This association between spatial view
representations and rewards might offer a basis for remembering where different rewards
are located in space. To test this idea, Rolls and Xiang (2005) recorded neuronal activity
in the hippocampus, while rhesus macaques performed a reward–place association task.
Each spatial scene shown on a video monitor had one location that, if touched, yielded a preferred fruit juice reward and a second location that yielded a less preferred juice
reward. Additionally, each scene had different locations for the different rewards. Of 312
neurons analyzed in the hippocampus, 18 percent responded more to the location of the preferred reward in different scenes, and five percent responded to the location of the
less preferred reward (Rolls and Xiang, 2005). When locations of the preferred rewards
in the scenes were reversed, 60 percent of 44 hippocampal neurons tested switched the
location to which they responded, showing that the reward–place associations could be
altered (Rolls and Xiang, 2005). However, the majority (82 percent) of these same
neurons did not respond to reward associations in a visual discrimination, object–reward
association task (Rolls and Xiang, 2005). Therefore, the primate hippocampus contains a
representation of the reward associations of allocentric locations.
Associating together such a spatial representation with a representation of an
object could be employed through an autoassociation network implemented by the
recurrent collateral connections of the CA3 hippocampal pyramidal cells (Rolls, 1989;
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Figure 6. Microcircuits of the hippocampus. (Source: Bear et al., 1996)
Rolls, 1996; Rolls and Treves, 1998; Treves and Rolls, 1992; Treves and Rolls, 1994).
Within the hippocampus, a three stage sequence of processing consists of the dentate granule cells, the CA3 pyramidal cells and the CA1 pyramidal cells (see Figure 6). A key hypothesis is that the hippocampal CA3 recurrent collateral connections which spread throughout the CA3 region provide a single autoassociation network that enables the firing of any set of CA3 neurons representing one part of a memory to be associated together with the firing of any other set of CA3 neurons representing another part of the same memory (Marr, 1971; Rolls and Stringer, 2005).
In neuropsychological analyses, Rolls et al. (1998) quantitatively assessed information about the spatial environment represented by hippocampal spatial view cells.
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They found that the information conveyed by a hippocampal neuron is roughly
independent of that carried by other hippocampal neurons (Rolls et al., 1998). In other
words, the number of stimuli, in this case locations in space that can be encoded by a population of neurons in the primate hippocampus, increases exponentially as the number
of cells in the sample increases (Rolls et al., 1998). The conclusion is that the number of
stimuli that can be encoded increases exponentially with the number of cells in the
representation expressed by this population of hippocampal neurons. A mechanism that
has been suggested to contribute to this is the pattern separation performed by the dentate
granule cells operating as a competitive network and by the mossy fiber projection to the
CA3 cells (Rolls, 1989; Rolls and Treves, 1998; Treves and Rolls, 1992).
Use of Spatial Memory in Foraging
In the wild, animals benefit from an ability to remember the location of different feeding sites. Mustached ( Saguinus mystax ) and saddle back ( Saguinus fuscicollis )
tamarins have shown the ability to maintain detailed knowledge of the distribution and
location of multiple tree species in their home range (Garber, 1989). The tamarins
concentrated their feeding efforts on a small number of target tree species, visiting an
average of thirteen trees per day (Garber, 1989). In 70 percent of all cases, the nearest
tree of a target species was selected as the next feeding site, and tamarins traveled using a
straight line path (Garber, 1989). The goal directed foraging abilities of these tamarins
may help offset the patchiness of fruit and exudates that make up part of their diet.
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To demonstrate that primates use spatial memory in foraging for food, Ludvig et
al. (2003) examined spatial memory formation in freely moving squirrel monkeys
(Saimiri sciureus ), by allowing the monkey to move in a large test chamber, collecting
food from a set of eight baited ports interspersed among empty ones. The outside of the ports were identical and pieces of food were invisible. They found that the monkeys
could readily form spatial memory of significant locations, which was reflected in the
continuous improvement in their performance of the task (Ludvig et al., 2003).
In another study, Menzel et al. (2002) used artificial language as a tool for the
study of spatial memory organization in bonobos (Pan paniscus ). In the first experiment,
they showed the bonobo a road sign just outside its indoor sleeping area. The sign
indicated, by means of arbitrarily designated geometrical shapes (lexigrams), where food
was hidden. Only two of the fifteen locations were visible from the sign. Distances
ranged up to 170 meters from the sign. In 99 of 127 test trials, the bonobo went directly
to the designated location on its first move (Menzel et al., 2002). In a second experiment,
researchers presented the road sign at varied points in the woods rather than at the
original fixed place. In these trials, the goal was a preferred toy. The bonobo’s human
companions were never told the location of the goal and distances were up to 650 meters.
In all 12 trials, the bonobo led its companions to the designated place via an efficient path
(Menzel et al., 2002). The bonobo appeared to be able to move, based on the information provided by a lexigram, from almost any arbitrary starting location in its 20 hectare
environment to any one of the numerous goal locations (Menzel et al., 2002). This
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strongly suggests that the bonobo used information contained in a lexigram road sign to
discriminate and to choose which distant location to visit (Menzel et al., 2002).
In addition to the location of food, animals also have the ability to remember the
relative quality of different feeding sites and to flexibly adapt their foraging behavior to
changing environmental conditions. In part, the hippocampus helps animals create a
representation of these allocentric locations and the rewards associated with each
location.
To demonstrate that animals not only use spatial and episodic memory in foraging
for food, but also distinguish between food quality, Clayton and Dickinson (1998)
explored the natural caching behavior of scrub jays. Birds cached both worms and peanuts in a variety of locations. Normally, scrub jays prefer worms over peanuts, and if
recovery was allowed within a few hours after caching, the jays recovered worms first
(Clayton and Dickinson, 1998). Conversely, when recovery was not allowed for several
days after caching, jays recovered the peanuts first, because the worms had degraded and
were no longer palatable (Clayton and Dickinson, 1998). Since scrub jays were capable
of selecting the type of food depending on the time since caching, Clayton and Dickinson
(1998) concluded that jays remembered what had been cached, as well as where and
when each item was cached.
The same paradigm was adapted by Hampton et al. (2005) in an experiment with
rhesus monkeys. In the study phase of each trial, monkeys found a preferred and a less preferred food reward in a trial unique array of three locations in a large room. After an
hour, monkeys returned to the test room, where they found foods placed as during the
20
study period. Twenty five hours later, the monkeys again searched the room, but now the preferred food was replaced with a distasteful food remnant, while the less preferred food
was still present. Although monkeys remembered the locations of the foods, they did not
learn that the preferred food was available after the short, but not after the long delay
(Hampton et al., 2005). Thus, monkeys demonstrated long term memory for the type
(recognition memory) and location of food (spatial memory) but failed to demonstrate
sensitivity to when they acquired that knowledge (episodic memory) (Hampton et al.,
2005).
This memory pattern for the location and type of food reward, without concurrent
memory for the time at which this information was acquired, is not restricted to rhesus
monkeys. In experiments that closely paralleled the work with scrub jays, rats carried preferred cheese chunks and less preferred pretzel pieces to boxes located at the ends of
the arms of an 8 arm radial maze where the rats left the foods (Bird et al., 2003). Cheese
was made unpalatable by treatment with a quinine solution; for half the rats, cheese was
degraded after short delays and after long delays for the other half. The rats showed
reliable memory for the type and location of food, but they did not learn to search pretzel
locations selectively after delays associated with quinine treatment of the cheese (Bird et
al., 2003). In a related set of experiments, rats also failed to learn to reenter the first arm
of the maze visited during a session to receive a preferred reward (Roberts and Roberts,
2002).
In addition to birds, primates and rodents, pigs have the ability to discriminate between food sites of different relative value and to remember their respective locations.
21
Held et al. (2005) investigated whether domestic pigs remembered the locations of food
sites of different relative value, and how a restricted retrieval choice affects their foraging behavior. Nine juvenile female pigs were trained to relocate two food sites out of a possible eight in a spatial memory task. The two baited sites contained different amounts
of food and an obstacle was added to the smaller amount to increase handling time. When
allowed to retrieve both baits, pigs showed no preference for retrieving a particular food
type first (Held et al., 2005). When allowed to retrieve one bait, the animals significantly preferred retrieving the larger amount (Held et al., 2005). In control trials, obstacles were present with both baits. Pigs continued to retrieve the larger bait, indicating they could
discriminate between the two food sites on the basis of quantity or profitability and
adjusted their behavior accordingly when the relocation choice was restricted (Held et al.,
2005).
Cognitive Adaptations to the Environment
How mammalian brain regions develop remains a controversial topic. The two
divergent concepts propose that either the whole brain of an organism changes in size or
selection operates on individual neurocognitive systems. Known as the phylogenetic
constraint hypothesis, researchers argue that the set up of a common ancestor’s brain
constrains development so that the size of its constituent structures changes in a
coordinated fashion (Finlay and Darlington, 1995). On the other hand, the mosaic theory
suggests that selection can act on brain regions independently of changes in whole brain
size (Harvey and Krebs, 1990; Barton et al., 1995; Barton and Harvey, 2000).
22
The mammalian brain consists of a number of functionally distinct systems, so it
is possible that natural selection on particular behavioral capacities could cause size
changes in the systems responsible for those capacities (Eisenberg, 1981; Harvey and
Krebs, 1990; Finlay and Darlington, 1995). In an analysis among brain structures in primates and insectivores using independent contrast testing, Barton and Harvey (2000)
found significant partial correlations even after accounting for the effects of change in
other structures. Results showed insectivore cerebellum size significantly correlates with
neocortex size when diencephalon size is excluded (p = 0.0001), and cerebellum size in primates significantly correlates with diencephalon size when neocortex size is excluded
(p = 0.003) (Barton and Harvey, 2000).
In addition, Barton and Harvey (2000) tested for correlated evolutionary change
in the individual components of six specific functional systems, including olfactory
system, hippocampal formation, amygdala, sensory motor system, visual system and
auditory system. In all instances, components of functional systems evolved together
independently of evolutionary size change in other structures and in the rest of the brain
(Barton and Harvey, 2000). Therefore, mammalian brain evolution involved size changes
concentrated in specific structures and functional systems (Barton and Harvey, 2000).
One potential explanation is the spatiotemporal mapping hypothesis, which states
that the demands of exploiting ecological resources across time and space accounts for
cognitive variation in primates (Allman, 1977; Clutton Brock and Harvey, 1980; Milton,
1981). Early researchers found interspecific associations between relative brain size and
home range size and frugivory, which are thought to correspond with the ecological
23
demands of spatiotemporal mapping (Clutton Brock and Harvey, 1980; Harvey et al.,
1980). Similarly, investigators have shown positive interspecific correlations between
frugivory and relative neocortex size (Sawaguchi, 1992; Barton, 1996).
In a recent comparative study of orangutans from Borneo and Sumatra, Taylor
and van Schaik (2007) demonstrated the first evolutionary connection in primates between diet and brain size. They found that orangutans confined to the northeastern part
of Borneo, an area where food supplies are frequently scarce, may have evolved through
the process of natural selection comparatively smaller brains, accompanied by slightly
smaller body size, than the orangutans who inhabit the more favorable environment of
Sumatra (Taylor and van Schaik, 2007).
A particular brain structure that has been shown in birds, bats and humans to vary
in size due to selective environmental pressures is the hippocampus (Krebs et al., 1989;
Safi and Dechmann, 2005; Maguire et al., 2000). Like mammals, the hippocampal
formation has been implicated in spatial learning in birds (Colombo and Broadbent,
2000). Numerous studies reported that food hoarding species possess larger hippocampi
than non hoarding relatives, and that species classified as large scale hoarders have larger
hippocampi than more generalized hoarders (Krebs et al. 1989, 1990; Sherry et al., 1989;
Healy and Krebs, 1992, 1996; Hampton et al., 1995; Basil et al., 1996).
In a phylogenetic analysis of 55 bird species, Garamszegi and Eens (2004) studied
the interspecific association between food hoarding and hippocampus size. After
adjusting for allometric effects, the relative volume of the hippocampus and the relative
size of the entire brain were each positively related to the degree of food hoarding
24
specialization; thus, adaptive specialization to food hoarding leads to the enlargement of
neural regions (Garamszegi and Eens, 2004).
Prior studies on encephalization in bats found an influence of diet on brain size,
with frugivorous bats having larger hippocampi than animal eating bats (Eisenberg and
Wilson, 1978; Pirlot and Jolicoeur, 1982; Jolicoeur et al., 1984; Harvey and Krebs, 1990;
Barton et al., 1995; Hutcheon et al., 2002). In a more recent study, Safi and Dechmann
(2005) show that neuronal capacities in bats likely coevolved with flight morphology,
under selection imposed by habitat complexity. Using wing morphology as an indirect
measure of the bat’s complex foraging habitat, they examined the correlation between
wing size and the brain structures connected with hearing (auditory nuclei), smell
(olfactory bulb) and spatial memory (hippocampus), while controlling for phylogeny and body mass (Safi and Dechmann, 2005). Safi and Dechmann (2005) found a significant
increase in size of the hippocampus with increasing wing area, even after accounting for phylogeny. Thus, bats with small wing areas that hunt insects in open spaces, have low
agility and maneuverability and rely on speed, have smaller hippocampi than species with
larger wing areas that are slower, but posses better maneuverability to forage in complex
forested habitats for fruit and animals (Norberg and Rayner, 1987; Norberg, 1994; Safi
and Dechmann, 2005).
Finally, in a study examining the hippocampi of humans, Maguire et al. (2000)
compared the brains of licensed London taxis drivers with extensive navigational
experience to those of control subjects who did not drive taxis. They found the posterior
hippocampi of taxi drivers were significantly larger relative to those of the control
25
subjects and that posterior hippocampal volume correlated positively with the amount of
time spent as a taxi driver (Maguire et al., 2000). Therefore, the human brain may have
the capacity for local plastic change in response to environmental demands, and the
hippocampus may be selectively pressured by environmental demands to increase or
decrease.
CHAPTER TWO:
METHODS
Hippocampus and medulla oblongata volumes (in cubic millimeters) for 42 species of primates were taken from Stephan et al. (1981), with the exception of Pongo pygmaeus hippocampus and medulla oblongata volumes (Sherwood et al., 2004;
Sherwood, 2005). Percentages of diet types, home range size (in hectares), activity patterns and habitat data were found throughout the published literature (see Table 1 for raw data and complete list of sources).
Diet percentages were broken down into three categories—frugivory, folivory and insectivory. Percent of frugivorous diet included the feeding time, food intake or stomach content devoted to fruit, seeds or gums; percent of folivorous diet included the feeding time, food intake or stomach content devoted to leaves, stems and flowers; and percent of insectivorous diet included feeding time, food intake or stomach content devoted to animal matter (Chivers, 1994).
Primates were separated into two types of activity patterns, diurnal or nocturnal.
All prosimians except the larger lemurs of Madagascar are nocturnal and all anthropoids except Aotus trivirgatus are diurnal.
26 27
Table 1. Raw data values for hippocampus and medulla volumes, home range areas, diet, activity patterns and habitat Species HV MV HR FR FO IN AP HB Alouatta seniculus 1,2,3,6 1325.00 1840.00 221.36 42.00 53.00 0.00 D A Aotus trivirgatus 1,2,4 539.00 686.00 316.23 65.00 30.00 5.00 N A Ateles geoffroyi 1,2 1366.00 1834.00 774.60 80.00 20.00 1.00 D A Avahi laniger 1,5,6 476.00 549.00 144.91 25.00 75.00 0.00 N A Callicebus moloch 1,2,4,7,8 588.00 787.00 204.94 65.00 28.00 4.00 D A Callimico goeldii 1,9,10,11 281.00 460.00 681.91 29.00 0.00 41.00 D S Callithrix jacchus 1,12 221.00 318.00 114.02 100.00 0.00 0.00 D A Cebus albifrons 1,8 1029.00 1896.00 1224.74 36.00 0.00 64.00 D A Cercocebus albigena 1,2 1485.00 2708.00 2024.85 61.00 5.00 24.00 D A Cercopithecus ascanius 1,13,14 1154.00 1583.00 1095.45 47.70 22.50 16.00 D T Cercopithecus mitis 1,2,6 1366.00 1999.00 374.17 45.00 39.00 10.00 D A Cheirogaleus medius 1,6,15,16 175.00 206.00 200.00 85.00 5.00 10.00 N A
1 Stephan et al., 1981 2 Clutton Brock and Harvey, 1977b 3 Crockett and Eisenberg, 1986 4 Sussman, 1999 5 Ganzorn et al., 1985 6 Fleagle et al., 1999 7 Kinzey, 1981 8 Terborgh, 1983 9 Heltne et al., 1981 10 Lee, 2001 11 Porter, 2001 12 Hubrecht, 1984, 1985 13 Chapman and Chapman, 2000 14 Davis, 2002 15 Harvey et al., 1986
28
Table 1 continued Cheirogaleus major 1,6,17 332.00 370.00 200.00 80.00 0.00 20.00 N A Colobus badius 1,2 1671.00 2007.00 820.37 8.00 78.30 0.00 D A Daubentonia madagascariensis 1,6,10,18,19 1776.00 1517.00 1122.50 50.00 0.00 50.00 N A Erythrocebus patas 1,2,20 1591.00 2616.00 7211.10 44.10 48.30 8.10 D T Galago senegalensis moholi 1,3,6,21,22,23 261.00 254.00 252.98 0.00 48.00 52.00 N A Galagoides demidovii 1,2 152.00 169.00 100.00 29.00 0.00 70.00 N A Gorilla gorilla 1,2 4781.00 7509.00 2489.98 2.00 86.00 0.00 D T Hapalemur simus 1,6,10,24 525.00 485.00 787.40 0.50 98.00 0.00 N A Hylobates lar 1,2,25 2673.00 2251.00 650.38 30.00 29.00 11.00 D A Indri indri 1,2 1520.00 1342.00 479.58 41.00 57.00 0.00 D A Lagothrix lagotricha 1,10,12,26 1381.00 1742.00 2738.61 79.00 20.00 0.00 D A Lemur fulvus 1,2 752.00 909.00 94.87 25.00 71.00 0.00 D A Lepilemur ruficaudatus 1,6,18 380.00 433.00 100.00 25.00 75.00 0.00 N A Loris tardigradus 1,2 191.00 233.00 100.00 15.00 0.00 85.00 N A Macaca mulatta 1,2,6 1353.00 1992.00 3872.98 58.00 19.00 23.00 D S
16 Older, 1999 17 Cooper, 2000 18 Nowak, 1999 19 Jansa, 1999 20 Isbell, 1998 21 Bearder and Doyle, 1974 22 Bearder and Martin, 1980 23 Jolly, 1972 24 Tan, 1999 25 Chivers, 1984 26 Stone, 2001
29
Table 1 continued Microcebus murinus 1,2,6,10,27 100.00 99.10 44.72 48.00 12.00 40.00 N A Miopithecus talapoin 1,2,28 684.00 1139.00 1095.45 52.00 2.00 43.00 D S Nasalis larvatus 1,29,30 1966.00 2945.00 1118.03 50.00 50.00 0.00 D S Nycticebus coucang 1,3,10,31,32 566.00 528.00 236.64 60.00 0.00 30.00 N A Otolemur crassicaudatus 1,3,18,21 461.00 540.00 264.58 33.00 63.00 4.00 N A Pan troglodytes 1,2 3779.00 5817.00 3464.10 68.00 28.00 4.00 D S Papio anubis 1,2 3398.00 5297.00 4929.50 63.00 7.80 10.00 D S Perodicticus potto 1,2 607.00 680.00 316.23 76.00 0.00 10.00 N A Pithecia monachus 1,12,18,33 834.00 1009.00 331.66 71.00 16.00 0.00 D A Pongo pygmaeus 2,34,35,36 2567.00 5172.00 2645.75 59.50 25.00 14.00 D A Propithecus verreauxi 1,2 1043.00 1223.00 275.68 40.00 41.00 0.00 D A Saguinus oedipus 1,10,37,38,39 259.00 345.00 291.55 38.40 18.50 40.00 D A Saimiri sciureus 1,8,40 352.00 722.00 1581.14 18.00 0.00 82.00 D A
27 Hladik et al., 1980 28 Gautier Hion, 1973 29 Kawabe and Mano, 1972 30 Bennett, 1986 31 Wiens, 2002 32 Ballenger, 2001 33 Buchanan et al., 1981 34 Rijksen, 1978 35 Sherwood et al., 2004 36 Sherwood et al., 2005 37 Bridgeman, 2002 38 Garber, 1984 39 Clutton Brock and Harvey, 1980
30
Table 1 continued Tarsius spectrum 1,41 153.00 207.00 100.00 0.00 0.00 100.00 N A Tarsius syrichta 1,42 138.00 185.00 122.47 0.00 0.00 100.00 N A HV = hippocampus volume (mm³), MV = medulla oblongata volume (mm³), HR = home range area (m), FR = % frugivorous diet, FO = % folivorous diet, IN = % insectivorous diet, AP = activity pattern (D = diurnal, N = nocturnal) HB = habitat (A = arboreal, S = semi terrestrial, T = terrestrial)
40 Baldwin and Baldwin, 1972 41 Mackinnon and Mackinnon, 1980 42 Kubicek, 1999
31
Primates were classified into one of three habitats, arboreal, semi terrestrial and
terrestrial, based on reports of their behavior in the literature. Arboreal primates are
defined as those that never or rarely come to the ground; semi terrestrial primates were
defined as those that regularly come to the ground from higher forest levels to feed or
drink; and terrestrial primates were defined as those that never or rarely climb trees.
Species analyses
Using SPSS version 13.0 (Apache Software Foundation, 2004), hippocampus
volume, medulla oblongata volume and home range size were log transformed to obtain
normal distribution. Categorical variables, such as activity patterns and habitats, were
recoded for regression analyses. A separate column for each categorical variable,
including diurnal activity, nocturnal activity, arboreal habitat, semi terrestrial habitat and
terrestrial habitat, was created; then each variable was recoded for analysis. A value of
1.0 was assigned if the species belonged to the particular activity pattern or habitat and a
value of zero was assigned if the animal did not (e.g. Daubentonia madagascariensis was
assigned a value of one in the nocturnal and arboreal columns and a value of zero for the
diurnal, semi terrestrial and terrestrial columns).
The comparative neuroanatomical approach assumes cognitive ability
corresponds with brain size or the size of brain structures implicated in cognitive tasks
(Deaner et al., 2000). However, rather than considering the absolute size of the brain or brain structures, comparative neuroanatomical studies scale these structures relative to
other biological variables, such as body weight. While many parts of the body, including
32
the brain, increase with overall body weight, a cause and effect relation is not implied;
rather, body weight is likely a substitute measure for an undefined, underlying variable
(Harvey and Krebs, 1990). In fact, body weight may not be a particularly suitable
reference variable for scaling with the brain or various brain structures (Harvey and
Krebs, 1990; Deaner et al., 2000). Body weight in primates can show cyclic fluctuations
due to seasonal and life history patterns of fat deposition (Cowlishaw and Dunbar, 2000).
For example, male squirrel monkeys become visually heavier during the annual mating
season, through selection to maximize competitive ability compared to other males
(Boinski, 1987). Conversely, during lean seasons tamarins can lose a considerable
amount of body mass (Goldizen et al., 1988). In contrast, brain weight varies very little
during an adult’s lifetime, and therefore, may correlate more highly with another
morphological, life history or environmental variable (Harvey and Krebs, 1990). Thus,
two additional values using the hindbrain structure medulla oblongata were generated for
scaling purposes: hippocampus volume residuals from a log log least squares regression
on medulla oblongata volume and a ratio value of hippocampus volume divided by
medulla oblongata volume (Sherwood et al., 2005). The medulla is located in the
hindbrain, which serves as an important avenue for information traveling from the
forebrain to the spinal cord (Bear et al., 1996). The hindbrain contains neurons that
contribute to the processing of sensory information and control of voluntary movement,
and the medulla contains neurons that perform several sensory and motor functions
neurons that relay sensory information from the spinal cord, where inputs provide
information about where the body is in space, to the thalamus (Bear et al., 1996).
33
To determine whether hippocampus volume size correlates with diet
(frugivorous, folivorous, insectivorous), home range size, activity patterns (diurnal,
nocturnal) or habitat (arboreal, semi terrestrial, terrestrial), least squares regression
analyses were conducted for each of the above variables and log hippocampus volume,
hippocampus volume residuals from a log log least squares regression medulla oblongata
and the hippocampus/medulla oblongata volume ratio.
Independent contrasts analyses
Statistical analyses for continuous variables were based on independent contrasts
(Felsenstein, 1985; Harvey and Pagel, 1991; Pagel, 1999), generated with the software
Mesquite version 1.11 (Maddison and Maddison, 2006) using the PDAP package
(Midford et al., 2003). Phylogenetically independent contrasts for categorical variables
were generated using the CAIC computer program (Purvis and Rambaut, 1995).
Independent contrasts, or the standardized differences calculated between sister
taxa at each phylogenetic node, are estimates of the evolutionary change in a variable
since the divergence of the sister taxa from their common ancestor (Nunn and Barton,
2000). Thus, independent contrasts provide a way to test for the presence of an
evolutionary relationship. This approach acknowledges that species have a common
history represented by a hierarchical and branched phylogeny.
Independent contrasts provide some advantages over species analysis. ICs are less
affected by grade shifts (Felsenstein, 1985; Ridley, 1989; Garland et al., 1993; Price,
1997; Purvis and Webster, 1999). Grade shifts occur when a group of species shares a
34
character state through common ancestry that produces a shift in the relationship between
the main variables without a change in their slope (Garland et al., 1992). However, with
independent contrasts a single data point representing the node where the grade shift is
located will produce less bias than multiple data points in a species analysis (Garland et
al., 1992). Therefore, contrasts analyses may be more appropriate than species analyses.
In addition, prior research has shown that contrasts analyses results are less prone to
greater than expected Type I and II error rates than results from species analysis (Martins
and Garland, 1991; Purvis et al., 1994; Diaz Uriarte and Garland, 1996, 1998; Harvey
and Rambaut, 1998). Finally, independent contrasts reduce the chance of obtaining a
false correlation, as a result of related species sharing similar traits due to their phylogeny.
Analyses of 42 primate species were completed at a species level. In all but four
cases, only one species was used for each genus. The four genera that contained two
species were Cercopithecus , Cheirogaleus , Galago (and Galagoides) and Tarsius . A
species level phylogenetic tree was created in Mesquite using the genus level phylogeny
of primates in Goodman et al. (2005) (see Figure 7). Because the tree contained four soft polytomies, an additional four degrees of freedom were subtracted (Purvis and Garland,
1993; Garland and Diaz Uriarte, 1999). Since exact branch lengths were unknown, each branch was set equally to the arbitrary value of 1.0 (Garland et al., 1992).
The same log hippocampus volume, hippocampus volume and
hippocampus/medulla oblongata volume ratio values used in the species analyses also
were utilized in the contrast analyses. Plots of the absolute values of the standardized
35
independent contrasts versus the standard deviation showed no correlation for all
variables analyzed in this study, suggesting that the arbitrarily equalized branch lengths
standardized the contrasts (Diaz Uriarte and Garland, 1996, 1998). Using positivized Y
contrast versus X contrast plots for all continuous variables, the individual effect of
frugivorous, folivorous and insectivorous diet and home range size on log hippocampus
volume, hippocampus volume residuals and the hippocampus/medulla oblongata volume
ratio was investigated using the Mesquite PDAP package (Garland et al., 1992; Midford
et al., 2006; Maddison and Maddison, 2006). Regressions were forced through the origin
(Purvis and Rambaut, 1995). All statistical tests were two tailed.
Using the BRUNCH algorithm of CAIC, categorical variables, including diurnal
and nocturnal activity patterns and arboreal, semi terrestrial, and terrestrial habitats, were
tested against log hippocampus volume, hippocampus volume residuals from a log log
least squares regression medulla oblongata and the hippocampus/medulla oblongata
volume ratio. Unique letter codes were assigned to each branch of the primate phylogenetic tree, and branch lengths were set to be equal.
36
Figure 7. Primate phylogenetic tree (Source: Goodman et al., 2005)
CHAPTER THREE:
RESULTS
Percentage of Frugivorous Diet
For both species analysis and independent contrasts analysis, there is not a significant correlation between percentage of frugivorous diet and hippocampus volume regardless of the method used for size adjustment (see Table 2; see Figures 8 through10).
Table 2. Correlation values for hippocampus volume (mm 3) and percentage of frugivorous diet in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 0.007 0.171 0.029 1.210 1.100 0.278 Hippocampus residual from medulla 41 0.000 0.036 0.001 0.053 0.230 0.819 Hippocampus/medulla ratio 41 0.000 0.073 0.005 0.214 0.463 0.646 Independent Contrasts a df b r r² F t p b Log hippocampus volume 36 0.005 0.222 0.049 2.080 1.442 0.755 Hippocampus residual from medulla 36 0.000 0.071 0.005 0.204 0.451 1.000 Hippocampus/medulla ratio 36 0.000 0.082 0.002 0.111 0.333 1.000 a Number of contrasts = 41 (Using Mesquite) b Two tailed
37 38
Figure 8. Plot of log hippocampus volume contrasts versus percentage of frugivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Figure 9. Plot of hippocampus volume residual contrasts (from a log log least squares regression on medulla oblongata volume) versus percentage of frugivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
39
Figure 10. Plot of hippocampus/medulla oblongata volume ratio contrasts versus percentage of frugivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Percentage of Folivorous Diet
In the species analysis, percentage of folivorous diet is significantly, positively
correlated with hippocampus volume, using both the log value and the size corrected
hippocampus volume residual from medulla oblongata volume (see Table 3). When a
ratio of hippocampus and medulla oblongata volumes is utilized, the result is borderline
non significant (p = 0.058). Conversely, in the independent contrasts analysis, there is not
a significant relationship between hippocampus volume and percentage of folivorous diet
regardless of the method used for size adjustment (see Figures 11 through 13).
40
Table 3. Correlation values for hippocampus volume (mm 3) and percentage of folivorous diet in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 0.011 0.329 0.108 4.866 2.206 0.033 Hippocampus residual from medulla 41 0.002 0.353 0.124 5.685 2.384 0.022 Hippocampus/medulla ratio 41 0.000 0.294 0.087 3.795 1.948 0.058 Independent Contrasts a df b r r² F t p b Log hippocampus volume 36 0.005 0.202 0.040 1.709 1.307 0.211 Hippocampus residual from medulla 36 0.001 0.148 0.022 0.906 0.951 0.348 Hippocampus/medulla ratio 36 0.000 0.143 0.020 0.835 0.913 0.871 a Number of contrasts = 41 (Using Mesquite) b Two tailed
Figure 11. Plot of log hippocampus volume contrasts versus percentage of folivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
41
Figure 12. Plot of hippocampus volume residual contrasts (from a log log least squares regression on medulla oblongata volume) versus percentage of folivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Figure 13. Plot of hippocampus/medulla oblongata volume ratio contrasts versus percentage of folivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
42
Percentage of Insectivorous Diet
Percentage of insectivorous diet significantly and negatively correlates with
hippocampus volume, except when using a ratio of hippocampus and medulla oblongata
volume (p>0.10) in species analysis (see Table 4). Even after controlling for phylogeny,
the effect of insectivorous diet remains significant, except when using the
hippocampus/medulla oblongata volume ratio (p = 0.07) as a size adjustment measure
(see Figures 14 through 16).
Table 4. Correlation values for hippocampus volume (mm 3) and percentage of insectivorous diet in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 0.019 0.552 0.305 17.514 4.185 0.000 Hippocampus residual from medulla 41 0.002 0.359 0.129 5.910 2.431 0.020 Hippocampus/medulla ratio 41 0.000 0.240 0.058 2.441 1.562 0.126 Independent Contrasts a df b r r² F t p b Log hippocampus volume 36 0.013 0.509 0.259 14.040 3.747 0.004 Hippocampus residual from medulla 36 0.002 0.329 0.108 4.860 2.204 0.059 Hippocampus/medulla ratio 36 0.000 0.282 0.079 3.459 1.859 0.255 a Number of contrasts = 41 (Using Mesquite) b Two tailed
43
Figure 14. Plot of log hippocampus volume contrasts versus percentage of insectivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Figure 15. Plot of hippocampus volume residual contrasts (from a log log least squares regression on medulla oblongata volume) versus percentage of insectivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
44
Figure 16. Plot of hippocampus/medulla oblongata volume ratio contrasts versus percentage of insectivorous diet positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Home Range
The species value of log hippocampus volume shows a significant, positive correlation with home range (see Table 5), explaining almost 60 percent of variance.
When controlling for medulla oblongata volume using the hippocampus residual, home range size was not significantly correlated with hippocampus volume. Surprisingly, when a ratio of hippocampus volume and medulla oblongata volume was used for scaling purposes, a significant, negative correlation was found (p<0.05).
When accounting for phylogeny, the log value of hippocampus volume was significantly correlated with home range size, while the size adjusted residual was borderline significant (p = 0.059) and the ratio value showed a non significant relationship (see Figures 17 through 19).
45
Table 5. Correlation values for hippocampus volume (mm 3) and home range area (ha) in 42 primate species. b Species Data df b r r² F t p Log hippocampus volume 41 0.601 0.769 0.591 57.850 7.606 0.000 Hippocampus residual from medulla 41 0.027 0.167 0.028 1.148 1.072 0.290 Hippocampus /medulla ratio 41 0.009 0.358 0.128 5.881 2.425 0.020 Independent a b Contrasts df b r r² F t p Log hippocampus volume 36 0.360 0.528 0.278 15.475 3.933 0.000 Hippocampus residual from medulla 36 0.028 0.149 0.022 0.912 0.955 0.059 Hippocampus /medulla ratio 36 0.001 0.063 0.003 0.160 0.400 0.255 a b Number of contrasts = 41 (Using Mesquite) Two tailed
46
Figure 17. Plot of log hippocampus volume contrasts versus log home range positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Figure 18. Plot of hippocampus volume residual contrasts (from a log log least squares regression on medulla oblongata volume) versus log home range positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
47
Figure 19. Plot of hippocampus/medulla oblongata volume ratio contrasts versus log home range positivized contrasts. Regression lines through origin: Black is ordinary least squares. Green is major axis. Red is reduced major axis.
Diurnal Activity Pattern
Using species analysis, a diurnal activity pattern was significantly correlated
(p<.05) with hippocampus volume (see Table 6). Species data for the hippocampus/medulla oblongata volume ratio also showed a significant correlation, though, the size adjusted residual of hippocampus volume showed only borderline significance (p = 0.063) with diurnality. When controlling for phylogeny, the relationship was not significant.
48
Table 6. Correlation values for hippocampus volume (mm 3) and diurnal activity in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 1.276 0.630 0.397 26.324 5.131 0.000 Hippocampu s residual from medulla 41 0.119 0.289 0.084 3.647 1.910 0.063 Hippocampu s/medulla ratio 41 0.030 0.482 0.233 12.120 3.481 0.001 Independent Contrasts a df F p b Hippocampu s residual from medulla 4 0.200 0.687 a Using CAIC brunch algorithm b Two tailed
Nocturnal Activity Pattern
Similar to diurnal activity analyses, nocturnal activity is significantly correlated using species values for log hippocampus volume and for the hippocampus/medulla oblongata ratio (see Table 7). However, in the independent contrasts analysis, hippocampus volume residual was not significantly correlated with nocturnal activity.
49
Table 7. Correlation values for hippocampus volume (mm 3) and nocturnal activity in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 1.279 0.623 0.388 25.412 5.041 0.000 Hippocampu s residual from medulla 41 0.093 0.223 0.050 2.096 1.448 0.155 Hippocampu s/ medulla ratio 41 0.025 0.407 0.165 7.928 2.816 0.008 Independent Contrasts a df F p b Hippocampu s residual from medulla 4 0.080 0.801 a Using CAIC brunch algorithm b Two tailed
Arboreal Habitat
Species analysis for log hippocampus volume and hippocampus/medulla oblongata volume ratio showed a significant correlation with arborealism. However, when corrected for phylogeny, a significant correlation was not found with residual hippocampus volume (see Table 8).
50
Table 8. Correlation values for hippocampus volume (mm 3) and arboreal habitat in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 1.010 0.422 0.178 8.651 2.941 0.005 Hippocampus residual from medulla 41 0.122 0.250 0.062 2.666 1.633 0.110 Hippocampus/ medulla ratio 41 0.026 0.357 0.128 5.858 2.420 0.020 Independent Contrasts a df F p b Hippocampus residual from medulla 6 0.560 0.486 a Using CAIC brunch algorithm b Two tailed
Semi-Terrestrial Habitat
Semi terrestrial habitat is not significantly correlated in the species analysis with log hippocampus volume or the size adjusted residual; however, the hippocampus/medulla oblongata volume ratio does demonstrate a significant, negative correlation with semi terrestrial habitat (see Table 9). The hippocampus volume residual does not significantly correlate with semi terrestrialism when using independent contrasts.
51
Table 9. Correlation values for hippocampus volume (mm 3) and semi terrestrial habitat in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 0.767 0.273 0.074 3.217 1.794 0.080 Hippocampus residual from medulla 41 0.134 0.234 0.055 2.321 1.524 0.135 Hippocampus/ medulla ratio 41 0.027 0.312 0.097 4.304 2.075 0.045 Independent Contrasts a df F p b Hippocampus residual from medulla 5 0.200 0.678 a Using CAIC brunch algorithm b Two tailed
Terrestrial Habitat
Terrestrialism is significantly correlated with log hippocampus volume at the species level (p = 0.053). However, when size adjusted residuals and ratios are used in both species data and independent contrasts analyses, hippocampus volume is not significantly correlated with terrestrial habitat (see Table 10).
Table 10: Correlation values for hippocampus volume (mm 3) and terrestrial habitat in 42 primate species. Species Data df b r r² F t p b Log hippocampus volume 41 1.150 0.301 0.091 3.989 1.997 0.053 Hippocampus residual from medulla 41 0.062 0.080 0.006 0.258 0.508 0.614 Hippocampus/medulla ratio 41 0.017 0.146 0.021 0.871 0.933 0.356 Independent Contrasts a df F p b Hippocampus residual from medulla 3 0.340 0.621 a Using CAIC brunch algorithm b Two tailed
CHAPTER FOUR:
DISCUSSION Diet and Hippocampus Volume Relative brain size has been shown to be greater in frugivorous mammal species
than in folivorous species (Stephan and Pirlot, 1970; Clutton Brock and Harvey, 1980;
Harvey et al., 1980; Mace et al., 1981). One specific brain structure, the hippocampus,
can vary in size due to selective environmental pressures, such as diet or a need for
spatial memory, in birds, bats and humans (Krebs et al., 1989; Safi and Dechmann, 2005;
Maguire et al., 2000). Thus, the expectation in this study was that primates with a higher percentage of frugivory in their diet should have larger hippocampi than those with a
higher percentage of folivory or insectivory in their diet (Clutton Brock and Harvey,
1980; Harvey et al., 1980; Safi and Dechmann, 2005). This is the first such study that
examines the relationship between hippocampus volume size and diet in primates.
Surprisingly, original results showed that hippocampus volume size is not
correlated with a frugivorous diet in primates. This may be due to potential exceptions to
the rule that primates with mainly insectivorous diets tend to be smaller bodied, while
those with larger body mass typically have a more folivorous diet (Kay and Simons,
1980). Uniquely, frugivores can be split into two groups, the smaller bodied frugivorous
52 53
insectivore and the larger bodied frugivorous folivore. Indeed, when plotting log body
mass against percentage of frugivorous diet (see Figure 20), outliers included
Cheirogaleus, Microcebus, Galagoides and Callithrix , which fall into the frugivorous
insectivore diet category, small in body mass yet have a high percentage of frugivory in
their diet. To test this idea further, the outlying species were removed and a least squares
regression analysis was run. A significant, positive correlation was found between log
hippocampus volume and percentage of frugivorous diet (p = 0.007, b = 0.016, t = 2.857,
r = 0.435), and between the size adjusted hippocampus volume residual from medulla
oblongata volume and percentage of frugivorous diet (p = 0.016, b = 0.014, t = 2.524, r =
0.392). Even after accounting for phylogeny through independent contrasts, a significant, positive correlation was found using both the log value (p = 0.018, b = 0.011, t = 2.779, r
= 0.435) and size corrected residual of hippocampus volume (p = 0.009, b = 0.012, t =
3.050, r = 0.468) and frugivorous diet. Thus, primates with frugivorous folivorous diets
have significantly larger hippocampal volume size than primates with primarily
frugivorous insectivorous, folivorous or insectivorous diets. Presumably, this is due to
the idea that frugivorous primates need increased spatial memory skills to locate tree
species that produce ripe fruit versus the more readily available food supply used by
folivorous or insectivorous primates.
54
Figure 20. Plot of log body mass (g) versus percentage of frugivorous diet.
55
At first glance, primates with a highly folivorous diet show a significant increase
in hippocampus volume; however, when phylogeny was controlled for, percentage of
folivorous diet did not significantly contribute to an increase in hippocampus volume
size. As brain volume increases with body mass, and hippocampus volume increases with brain volume, such a difference between the species analysis and independent contrasts
analysis is likely due to folivorous species being larger bodied than insectivorous or
frugivorous animals (Kay and Simons, 1980). Thus in this study, folivorous diet had no
effect on primate hippocampal size.
Perhaps the most interesting and unexpected result was that hippocampus volume
size decreases significantly as the percentage of a primate’s insectivorous diet increases,
which was found in both species and independent contrast analyses for log hippocampus
volume and the hippocampus residual from medulla oblongata volume. Thus if a primate’s diet is highly insectivorous, it will have a smaller hippocampus than
frugivorous or folivorous species.
Many insect species, such as Diaspididae and Apionidae, occur at relatively high
densities unlike widely distributed fruit and seed resources (Fernandes and Price, 1988;
Marquis, 1991; Price et al., 1995). If a food source is dense yet limited to a small area,
there is less environmental pressure to increase spatial memory abilities. Consequently, primates with highly insectivorous diets tend to have significantly smaller hippocampus
size. In addition, small bodied, insectivorous species have smaller home range sizes,
which may require less spatial memory skills and thus smaller hippocampus volume sizes
(Clutton Brock and Harvey, 1977a; Clutton Brock and Harvey, 1977b). Another
56
explanation for the smaller hippocampal size in insectivorous primates may be based on
the idea that most insectivorous species are nocturnal and rely heavily on auditory cues to
locate fast moving prey (Dominy et al., 2001). Due to a selective increase in auditory
areas, insectivorous primates might have significantly smaller hippocampal size as a
result. In addition, the negative correlation between insectivorous diet and hippocampus
volume may explain why frugivorous insectivores affect the correlation between
frugivorous diet and hippocampus volume above. Essentially, the increase in frugivorous primates’ hippocampus volume may be equalized by the decrease in insectivorous primates’ hippocampus volume. One way to address the above issue in future research is
to break the diet categories down further into folivores, frugivore insectivores, frugivore
folivores, insectivores and gumivores.
Hence, my analyses support the idea that diet can influence hippocampus volume
size in primates. Frugivorous primates, with the exception of those that supplement their
diet with insects, have relatively larger hippocampal size than folivorous and
insectivorous primates. Unexpected, though, was the result that insectivorous primates
have significantly smaller hippocampal size than frugivorous and folivorous primates.
Home Range and Hippocampus Volume
Diet type influences home range size (Haskell et al., 2002; Ottaviani et al., 2006)
and is linked greatly to the spatial distribution of resources. The influence of diet on
home range size has been confirmed in mammals (Harestad and Bunnell, 1979; Harvey
and Clutton Brock, 1981; Gittleman and Harvey, 1982; Mace and Harvey, 1983;
57
Lindstedt et al., 1986; Ottaviani et al., 2006) and birds (Schoener, 1968; Baker and
Mewaldt, 1979; Mace and Harvey, 1983).
In primates, primarily frugivorous species have larger home ranges for their body
size than those of folivorous and insectivorous species (Clutton Brock and Harvey,
1977b; Mace and Harvey, 1983; Nunn and Barton, 2000). Studies of gorilla subspecies
have shown that western and Grauer’s, which have a more frugivorous diet than Virunga
gorillas, have larger home ranges (Yamagiwa et al., 1994, 1996; Doran and McNeilage,
2001; Goldsmith, 1999; Remis, 1997; Tutin, 1996). This is due to fruit typically being
more sparsely distributed than the more densely based food resources of leaves and
insects; yet the greater energetic gains of fruit make it possible for animals to invest in
travel and search time for fruit.
As home range size increases, brain size increases in primates, thus hippocampus
size should increase as well due, in part, to its role in spatial memory (Clutton Brock and
Harvey, 1977b; Clutton Brock and Harvey, 1980; Harvey et al., 1980). As resources become more widely distributed, a need for greater spatial memory abilities exists.
Therefore, hippocampus volume size may be selectively increased due to an
environmental pressure for increased spatial memory skills. Indeed, it appears
hippocampus volume significantly increases as home range size does in primates, though
results were borderline significant when accounting for phylogeny and controlling for
medulla oblongata volume.
One reason for such results may be the variability in methodologies used to
determine species home range size. In particular, the spatial and temporal scales utilized
58
and the method used to define home range are important (Robbins and McNeilage, 2003).
Studies of primates tend to use the grid square method to estimate home range size.
However, unless researchers observe group movements extensively, the grid cell method
can produce an underestimate of home range size, because often groups are not observed
in many grids within their home range (Chapman and Wrangham, 1993; Harris et al .,
1990; Singleton and van Schaik, 2001; Sterling et al., 2000). On the temporal scale, most
researchers use only 1 to 2 years of data to estimate home range size, but, in a seven year
study of habitat use patterns by Karisoke gorillas, Watts (1998) found home range size to be considerably larger than annual yearly ranges. In addition, variation in home range
size at a species level can be caused by geographic range (Diniz Filho and Torres, 2002;
Olifiers et al., 2004), climatic variation (Fisher and Owens, 2000), latitude (Harestad and
Bunnell, 1979; Lindstedt et al., 1986; Olifiers et al., 2004) and social organization
(Damuth, 1981; Gittleman and Harvey, 1982).
Finally, since such a strong correlation exists between diet, body size and home
range size, the five frugivorous insectivore species outliers (n = 5) found when plotting
log body mass versus percentage of frugivorous diet were removed (see Figure 25) and a
least squares regression was run using species and independent contrasts analyses.
Results showed a highly significant, positive correlation between log home range size
and log and size adjusted hippocampus volume size even after accounting for phylogeny
(see Table 11). Thus, my analysis supports the hypothesis that primates with larger home
ranges require better spatial memory skills, and consequently, have increased hippocampi
than primates with smaller home ranges.
59
Table 11. Correlation values for hippocampus volume (mm 3) and home range area (ha) in 37 primate species excluding frugivorous insectivores. Species Data df r t p b Log hippocampus volume 36 0.706 5.860 0.000 Hippocampus residual from medulla 36 0.714 6.027 0.000 Hippocampus/medulla ratio 36 0.336 2.109 0.042 Independent Contrasts a df r t p b Log hippocampus volume 30 0.507 3.380 0.002 Hippocampus residual from medulla 30 0.508 3.389 0.024 Hippocampus/medulla ratio 30 0.108 0.626 0.162 a Number of contrasts = 34 (Using Mesquite) b Two tailed
Activity Patterns and Hippocampus Volume
Comparative brain size in primates does not significantly differ between nocturnal and diurnal species (Clutton Brock and Harvey, 1980). In addition, using only spatial memory cues, both diurnal and nocturnal primate species have been shown to successfully relocate food rewards (Bicca Marques and Garber, 2004). Though nocturnal primates required longer exposure to the test setting than diurnal primates, this likely is due to anatomical differences between visual systems in nocturnal and diurnal primates
(Bicca Marques and Garber, 2004). For example, cortical visual areas and pathways, including both V1 and non V1 neocortex which contain many higher visual and sensory processing areas and make up approximately 50 percent of neocortex size, are larger in diurnal primates than in nocturnal primates (Van Essen et al., 1992; Barton, 1996, 1998;
Drury et al., 1996). Thus, it was expected that since diurnal and nocturnal primates show
60
no significant differences in comparative brain size or the ability to use spatial memory in
foraging, there should be no difference in hippocampal size as well (Clutton Brock and
Harvey, 1980; Bicca Marques and Garber, 2004).
In the species analysis of this study, diurnal activity was shown to significantly
increase log hippocampus volume size; however, when controlling for medulla oblongata
volume, a diurnal activity pattern resulted in a significant decrease in hippocampus
volume size. The effect was exactly the opposite when examining the correlation between
nocturnal activity and log hippocampus volume size. A nocturnal activity pattern resulted
in a significant decrease in hippocampus volume size with species data, but when
utilizing the size adjusted ratio, nocturnal activity produced a significant increase in
hippocampus volume size.
Perhaps, these results are due to the unique ability of primate spatial view cells,
which maintain their spatial properties for periods of several minutes in the dark
(Robertson et al., 1998). Located in the hippocampus, these spatial view cells may provide nocturnal primates with a somewhat equalizing ability to locate food resources using their spatial memory abilities in combination with olfaction. On the other hand, diurnal primates depend heavily on their highly developed visual and eye movement control systems, which can explore and remember information about what is present at locations in the environment without having to visit those places, to provide spatial cues to the hippocampus (Rolls, 1999). Thus, diurnal primates have smaller hippocampus volume size and may compensate with their significantly larger primary visual area in the brain than nocturnal animals.
61
In addition, Barton (1998) has demonstrated that the larger size of olfactory brain
structures and possibly auditory structures in nocturnal species has offset the smaller size
of their visual structures (Barton et al. 1995); therefore, activity timing, unlike diet, is not
consistently associated with overall brain size differences. Moreover, Barton and Harvey
(1995) found negative correlations in visual and olfactory systems within nocturnal
strepsirhines and within diurnal haplorhines, suggesting activity patterns are not the only
ecological variable associated with the evolution of these systems. Variation may be due
to the idea that some strepsirhines are not strictly nocturnal or diurnal; rather, they fall
into the category of cathemeral, where animals are active during daytime and nighttime
(Engqvist and Richard, 1991; Wright, 1992).
In both instances when using independent contrasts, a significant correlation was
not found between the hippocampus residual from medulla oblongata volume and diurnal
or nocturnal activity patterns. When ancestry is included, neither diurnal or nocturnal
activity patterns have a significant effect on hippocampus volume size. Thus when
appropriately scaling hippocampus volume with medulla volume and accounting for
ancestry, my data supports the hypothesis that nocturnal and diurnal primates do not have
significantly different hippocampus volume sizes.
Habitats and Hippocampus Volume
Relative brain size has been found to positively correlate with arboreality in
Peromyscus and Didelphidae (Lemen, 1980; Eisenberg and Wilson, 1978). Also, arboreality was found to be a better predictor than terrestriality of brain size in a study
62
examining 33 species of North American Sciurids; arboreal squirrels had significantly
larger brains for their body size than terrestrial squirrels (Meier, 1983). Additionally,
arboreality involves more complicated foraging tasks than terrestrialism, such as positioning for access and manipulation of food items (Russon, 2002). For example,
chimpanzees have more difficulty cracking nuts with stones and hunting arboreally than
terrestrially (Boesch and Boesch, 1981, 1984; Boesch and Boesch Achermann, 2000). As
arboreal positioning involves determining positions of self, it relies mainly on spatial
abilities (Russon, 2002). Since arboreal primates view space in a more three dimensional
manner than terrestrial primates, they may rely more heavily on spatial abilities than
terrestrial primates (Russon, 2002). Therefore, arboreal primates should have larger
hippocampal size than terrestrial primates.
In the current study, arboreal primates showed a significant decrease in log
hippocampus volume, but after controlling for medulla oblongata volume size, a
significant increase was shown. This may due to the overall increase in arboreal primates’ brain size, which is likely due to an increase in visual cortex areas. Semi terrestrial primates only displayed a significant decrease in hippocampus volume size when using
the size adjusted hippocampus/medulla oblongata volume ratio. However, when using
log hippocampus volume and hippocampus residuals from medulla oblongata volume, a
significant correlation was not found with semi terrestrial habitat. In terrestrial primates,
log hippocampus volume increased significantly, but when size adjusted residuals and
ratios were utilized, hippocampus volume decreased with terrestrial habitat, though not
significantly.
63
When controlling for phylogeny, a non significant correlation was found between
all three habitat types and hippocampus volume residuals. Thus, phylogeny seems to be a
major factor in the correlation between habitat and hippocampus volume and, my
hypothesis that arboreal primates should have larger hippocampus volume size than semi
terrestrial or terrestrial primates was not supported in a significant manner. Habitat has no
effect on hippocampal size.
A few factors that may have influenced these results are the correlation between body size, home range size and habitat, the ability to learn in a three dimensional
environment, and the difference in comparative brain size between arboreal mammals
versus arboreal primates. Terrestrial primate species tend to be larger bodied than
arboreal primates (Clutton Brock and Harvey, 1977a; Clutton Brock and Harvey, 1977b).
Gorillas are terrestrial and have much larger daily home ranges than arboreal orangutans
(Goodall, 1977; Rodman, 1984). Thus, terrestrial animals likely require stronger spatial
memory skills to navigate their larger home ranges than arboreal primates, which have
smaller home ranges. This may equalize any hippocampal differences between terrestrial
and arboreal primates. Additionally, humans, who evolved as terrestrial creatures, were placed in a gravity free environment and able to learn and perform tasks in a three
dimensional environment (Oman et al., 2000). Therefore, it might be possible for
terrestrial primates to adapt in certain instances and view space three dimensionally, even
when they are accustomed to viewing space two dimensionally. Finally, while small
mammals demonstrated a significant, positive correlation between comparative brain size
64
and arborealism, primates did not demonstrate the same significant result (Harvey et al.,
1980).
CHAPTER FIVE: CONCLUSION This study adds to a growing body of research in support of the mosaic theory of cognitive evolution, which suggests that brains evolved through selection on specific neural structures (Krebs, 1990; Harvey and Krebs, 1990; Healy and Guilford, 1990;
Barton and Dean, 1993; DeVoogd et al., 1993; Barton et al., 1995). While previous research has shown selection on specific brain structures such as neocortex size and cerebellum size in primates, this is the first such study to demonstrate a selective environmental pressure on primate hippocampus volume size (Barton et al., 1995; Barton and Harvey, 2000).
Original results showed that hippocampus volume size was not correlated with a frugivorous diet in primates; however, once frugivorous insectivore outliers were removed, hippocampus volume was found to increase in primates with a highly frugivorous diet. Conversely, primarily insectivorous primates have smaller hippocampi than folivorous or frugivorous primates. In addition, as home range size increases, so does hippocampus volume size in primates. Activity patterns and habitat had no effect on primate hippocampus volume size.
65 66
Future directions for this research may include more specific ecological categories, a comparison between congeneric groups, examining correlations between ecological variables and particular hippocampal neurons, different scaling measures, determining whether differences exist due to sexual dimorphism, and conducting a multiple regression analysis. Feeding categories could be further broken down into frugivorous insectivores, frugivorous folivores, folivores, insectivores and gumivores; cathemerality may be added as an activity pattern category. Also, correlation and independent contrast analyses could be run to determine similarities and differences between congeneric groups. In addition, correlations between the number of CA3 and
CA1 neurons in the hippocampus, which are responsible for input from the dentate gyrus, and ecological variables could be analyzed. Instead of using medulla oblongata volume for size adjustments, the hippocampus could be scaled with telencephalon volume or medial temporal lobe volumes. Another avenue to be explored would be whether differences in hippocampal size exist between females and males of the same species.
Finally, various potential predictors of relative hippocampus size, such as frugivorous diet, insectivorous diet and home range size, should be analyzed simultaneously using a multiple regression model to determine which variable is the strongest predictor.
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