Accepted Manuscript

Title: GPS technology as a proxy tool for determiningrelationships in social animals: An example with Africanelephants

Author: C.E. Hacker K.M. Horback L.J. Miller

PII: S0168-1591(14)00314-1DOI: http://dx.doi.org/doi:10.1016/j.applanim.2014.12.005Reference: APPLAN 4000

To appear in: APPLAN

Received date: 12-7-2014Revised date: 4-12-2014Accepted date: 7-12-2014

Please cite this article as: Hacker, C.E., Horback, K.M., Miller, L.J.,GPStechnology as a proxy tool for determining relationships in social animals:An example with African elephants, Applied Animal Behaviour Science (2014),http://dx.doi.org/10.1016/j.applanim.2014.12.005

This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.

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Highlights

Use of GPS technology in assessing relationships among zoo elephants was

explored.

GPS data, behavioral data and input from management were compared.

Relationships between all three were found.

Results suggest GPS data can be a proxy tool for examining social relationships.

*Research Highlights

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GPS technology as a proxy tool for determining relationships in social animals: An example with

African elephants

CE Hackera,b,1

, KM Horbackc, LJ Miller

d

a Institute for Conservation Research

San Diego Zoo Global

15600 San Pasqual Valley Road

Escondido, CA 92027, USA

b Department of Biology

Western Kentucky University

1906 College Heights Blvd. #11080

Bowling Green, KY 42101, USA

charlotte.hacker230@topper.wku.edu

c Swine Teaching and Research Center

University of Pennsylvania

School of Veterinary Medicine

382 West Street Road

Kennett Square, PA 19348, USA

khorback@vet.upenn.edu

d Chicago Zoological Society – Brookfield Zoo

3300 Golf Road

Brookfield, IL 60513, USA

Lance.Miller@czs.org

Running head: Determining social relationships with GPS

1 Current address/corresponding author:

Charlotte E. Hacker

Biology Department

Western Kentucky University

1906 College Heights Blvd. #11080

Bowling Green, KY 42101

1 410 920 1953

charlotte.hacker230@topper.wku.edu

*Revised Manuscript (Clean version)

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GPS technology as a proxy tool for determining relationships among social animals: An example 1

with African elephants 2

CE Hacker, KM Horback, and LJ Miller 3

The potential application of GPS technology in determining relationships among social 4

animals was addressed in this study of eight African elephants residing at the San Diego Zoo 5

Safari Park in Escondido, CA, USA between 2009 and 2011. GPS coordinates were collected 6

over nine 24 h periods from eight different elephants. The average distances between individuals 7

were then calculated for the morning, afternoon and evening time periods as well as for the entire 8

24 h. Behavioral data were collected to calculate rates of both positive and negative interactions 9

between elephants as well as David’s Scores to measure sociality. Lastly, input from the 10

management staff regarding the elephants’ social relations was utilized to determine pairs who 11

may display high levels of social proximity as well as the construction of a dominance structure. 12

Significant correlations were found between the social relations determined by animal 13

management staff and the GPS morning data (r = -0.431, P = 0.022), the social relations 14

determined by animal management staff and the GPS daily data (r = -0.401, P = 0.034), the 15

corrected David’s scores and the GPS daily data (r = 0.471, P = 0.012), the early time period (r = 16

0.614, P = 0.001), the morning time period (r = 0.441, P = 0.020) and the afternoon time period (r 17

= 0.474, P = 0.012) and the rate of positive social interactions and the GPS evening data (r = -18

0.386, P = 0.042). These results suggest that GPS technology can be used as a proxy tool in 19

determining social relationships. GPS devices can aid in animal behavior research by eliminating 20

the need for an observer and thereby relieving time and staff restraints. Planning the daily 21

management of animals around their known social groups can potentially increase overall animal 22

welfare and safety for caretakers. For example, keeping the animals in their known social groups 23

could decrease stress and the potential for aggressive behavior during training, transport, shifting 24

of individuals or groups between exhibits, and general husbandry. 25

Keywords: African elephants; Loxodonta Africana; zoo; social behavior; technology 26

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1. Introduction 27

Sociality can be viewed as a life-history tactic that increases the overall fitness of group 28

mammals (Silk, 2007). According to behavioral ecology theory, conspecifics are expected to 29

associate with one another so long as the benefits of sociality exceed the costs (Silk, 2007). The 30

differences found in animal societies comes from evolutionary trajectories guided by the 31

ecological factors responsible for the costs and benefits of social interactions in the surrounding 32

environment (Gompper, 1996). Sociality can provide benefits such as protection (Macdonald, 33

1983), higher quality habitats (Mosser and Packer, 2009), valuable resources (Pinter-Wollman et 34

al., 2009), assistance with raising young (Lee, 1987), longevity (Silk et al., 2010) and decreased 35

rates of infanticide (Packer et al., 1990). The detriments to sociality can include increased feeding 36

competition (Silk, 2007), greater likelihood of disease transmission (Gompper, 1996), loss of 37

reproductive fitness (Armitage, 1999), intra-species aggression, cannibalism (Whitehead, 1997) 38

and increased detection by predators or prey (Beck et al., 2011). 39

Determining the social relationships within animal groups is important for a range of 40

fundamental and applied purposes (Whitehead, 1997). Because social organization defines an 41

important class of ecological relationships related to population biology (Whitehead, 1997), its 42

knowledge is a cornerstone for the advancement of both ecological and zoological management. 43

This particularly holds true with the increase in ecotourism (Coria and Calfucura, 2012), overall 44

concern for animal welfare (Tarou et al., 2007) and reported cases of human-wildlife conflict 45

(Dickman, 2010). Determining social relationships would be especially helpful in regards to both 46

Asian and African elephants (Elephas maximus and Loxodonta africana respectively) which pose 47

greater challenges than most other species due to their size and intricate relationships (Veasey, 48

2006). 49

Female elephant populations are composed of flexible fission-fusion societies arranged in 50

hierarchies with subordinate individuals harboring ecological costs and dominant individuals 51

reaping ecological benefits (Archie et al., 2006a; Bang et al., 2010). Females stay with their natal 52

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core group for life (Archie et al., 2006b) and therefore develop a number of non-random, stable 53

and well-developed relationships (Spinage, 1994; Wittemyer et al., 2005). Male elephants leave 54

their natal core group upon maturity and establish bachelor herds (Evans and Harris, 2008). As 55

males reach sexual maturity, they are likely to live out solitary lives but may temporarily rejoin 56

herds during breeding season (Archie et al., 2006; Druce et al., 2006; Schulte, 2000). 57

Social relations in animals are enigmatic and difficult to assess (Wittemyer et al., 2005). 58

Historically, there are two traditional methods for determining social structures. One is through 59

the documentation of behavioral observations (Altmann, 1974; Archie et al., 2006a; Gadgil and 60

Nair, 1984; Pepper et al., 1999; Wittemyer and Getz, 2007; Wittemyer et al., 2007). Because 61

dominant animals in a group are more likely to have bold and aggressive personalities (Cote et 62

al., 2010), past studies have used the occurrence of agonistic behavior among individuals as an 63

indicator of rank. Affiliative behaviors that promote group cohesion such as grooming and 64

touching may also be recorded in an effort to gauge positive relationships among individuals. 65

Unfortunately, general observations often over-look subtle social interactions, such as low-66

frequency vocalizations and chemical cues, that determine the true structure of the social dynamic 67

(Swaisgood and Schulte, 2010), and the researcher may face observer fatigue if observations are 68

undertaken over long periods of time (Altmann, 1974; Martin and Bateson, 2007). The second 69

method is the documentation of patterns of association (Archie et al., 2006b; Wittemyer et al., 70

2005). By recording who associates with whom, social relationships can be inferred. But once 71

again, these patterns can only be assessed for a few h at a time due to observer fatigue (Altmann, 72

1974; Martin and Bateson, 2007) and distances from one conspecific to another are visual 73

estimates. Furthermore, the obstruction of the observer’s vision by an exhibit feature and the 74

misidentification of individuals may skew collected data in both approaches. Finding alternative 75

methods that are more time efficient and quantitative and lack observer fatigue, such as the 76

analysis of GPS coordinates as a measure of distance from one individual to another through 77

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time, may provide a superior proxy tool to assess the complex relationships that social animals 78

share. 79

Global Positioning System technology (Theiss et al., 2005) has been used in the wild to 80

study topics such as movement, home range, inter-group hierarchies and walking rates (Birkett et 81

al., 2012; Boettiger et al., 2011; Druce et al., 2008; Foguekem, 2009; Foley, 2002; Harris et al., 82

2008; Jackson et al., 2008; Loarie et al., 2009). In farming, GPS technology is increasingly used 83

to assess and improve livestock welfare by recording coordinates of transport trucks in real time 84

and measuring activity levels and movements within groups of animals (Gebresenbet et al., 2003; 85

Rushen et al., 2012). In zoos, GPS devices have been used minimally but have aided in the study 86

of walking rates in elephants (Leighty et al., 2009; Miller et al., 2012). To our knowledge, GPS 87

devices have not been used as a proxy tool within an elephant herd to examine intraspecific 88

relations. As the technology being investigated in this study is most applicable for exploring 89

relationships among social species, testing its methodologies is best suited for a socially 90

intelligent species within a zoological institution. In zoos, the security of vital resources such as 91

food and water may lead to a social structure that is different than that seen in the wild. However, 92

dominance hierarchies still exist (Schulte, 2000). Exploring these relationships may help 93

management in determining appropriate group settings to ensure species-appropriate levels of 94

social behavior. The objective of this study is to test the usability of GPS technology as a proxy 95

tool for determining the social structure of animals by correlating social relations and dominance 96

information already known about the African elephant herd through anecdotal observations by 97

elephant care staff and previously collected behavioral data. 98

2. Methods 99

2.1 Subjects and Exhibits 100

At the time of the study from 2009 to 2011, the San Diego Zoo Safari Park’s African 101

elephant herd in Escondido, CA, USA consisted of one adult male, six adult females, and their 102

offspring, all managed via protected contact (Table 1). Based on animal care safety 103

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recommendations, eight elephants (seven adults and one sub-adult) were used for the study. 104

These eight elephants represented those old enough to participate in training which would allow 105

for the safe and successful application of GPS collars. Additionally, the remaining elephants were 106

3 years of age or younger and thus would have been heavily dependent on their mothers, likely 107

not straying too far for any extended period of time. All seven of the adults were part of a wild 108

herd transferred from Kruger National Park in South Africa to Swaziland in 1994 and then from 109

Swaziland to the San Diego Zoo Safari Park in 2003 after a scheduled cull threatened their lives. 110

Estimates of birth year are circa 1990. The sub-adult used in the study was the first birth of the 111

park’s herd in early 2004. The elephants were kept in a 13,000 m2 exhibit with one indoor barn 112

measuring 93 m2 and another indoor barn measuring 21 m

2. A complete description of husbandry 113

procedures and exhibit details can be found in Andrews et al. (2004). 114

2.2 Design and Procedure 115

2.2.1 GPS Data Collection 116

Positive reinforcement over a period of several months was used for desensitizing the 117

elephants to the leather collar housing the GPS device (See Miller et al., 2012 for full 118

description). The elephants were trained to stand parallel to a safety barrier where handlers could 119

reach through and place the collar on top of the elephant’s neck, then secure it underneath. The 120

GPS tracking units (Qstarz BT-Q1000X [Taipei, Taiwan]) were incased in waterproof containers 121

(PelicanTM Micro Case #1020 [San Antonio, TX, USA]) and were affixed to the leather collars 122

(TechNicol LTD [Cambridge, UK]). Each collar weighed approximately 1.87 kg (see Rothwell et 123

al., 2011 for details). The GPS units were reported by the manufacturing company to be accurate 124

within 2.5m and previous testing surrounding its precision was preformed in an earlier study 125

(Miller et al., 2012). GPS location points were collected every 5 s, and included time, 126

coordinates, and related accuracy measures in its memory. The data were then downloaded from 127

the GPS unit to a PC computer and opened using the GPS device’s accompanying utility software 128

(QTravel V1 [Taipei, Taiwan]). Data were collected from the GPS collars between November 129

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2009 and February 2010. One 24 h (12:00 – 11:59 h the following day) trial was conducted once 130

a week for a total of 9 weeks. 131

2.2.2 Behavioral Data Collection 132

Behavioral data were collected from May to August of 2010 and 2011 as part of a 133

separate study examining elephant personality. Observations occurred during the day: 5:00 h to 134

9:00 h and 11:00 h to 15:00 h, and during the night: 17:00 h to 21:00 h and 21:00 h to 01:00 h. 135

Focal follows lasting 15 min in duration were conducted on each participating elephant during 136

each morning and night period (Horback et al., 2013). Behavioral states were collected using 1 137

min scan sampling methods (Altmann, 1974) and behavioral events were recorded continuously 138

using all-occurrence sampling (Martin and Bateson, 1993). Events recorded included solitary 139

behaviors, positive social behaviors (e.g., body touch, social play) and negative social behaviors 140

(e.g., charge, head butt). For a complete list of operational definitions see Horback et al. (2013). 141

2.2.3 Structure and Relationships Data Collection 142

The safari park’s elephant care staff were asked on one occasion to provide their 143

knowledge of the elephants’ social relations and dominance structure of individuals. This 144

included asking about elephant pairs they regularly witnessed in close proximity to one another 145

through their own anecdotal observations during daily animal management. A dominance 146

structure was constructed using the information (Fig. 1). Three different analyses were used to 147

interpret the information provided (further explanation to follow below). 148

- Insert figure 1 about here - 149

2.3 Data Analysis 150

2.3.1 GPS Data Analysis 151

The GPS data set for each elephant was condensed from 5 s intervals to 15 min intervals 152

for the 24 h period (12:00 – 11:45 h). Interval points for each pair of elephants within 5 s of one 153

another were chosen. If the GPS device communicated with less than 6 satellites, the fix was 154

determined to be erroneous. Horizontal dilution of precision, or HDOP, is a measure fix of data 155

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quality and refers to the horizontal geometry of the satellites. The lower the HDOP value, the 156

more accurate the fix measurement. Locations that were products of fixes with HDOP values 157

greater than 2 were deleted to ensure accuracy. The distance between elephants at that 15 min 158

interval was then calculated in the Excel program. Next, the average distance from one elephant 159

to another was found for the entire 24 h daily period, from the afternoon 12:00 to 17:45 h period, 160

from the evening 18:00 to 23:45 h period, from the early morning 0:00 to 05:45 h period and 161

from the morning 6:00 to 11:45 h period for each of the nine trials. The various time periods were 162

chosen so as to split the whole 24 h period into quarterly time frames for closer analysis of social 163

activity based on time of day. Cumulative averages across the 9 days were then calculated for 164

each of the different time periods to determine the average distance between individuals over the 165

trials. 166

2.3.2 Behavior Analysis 167

Cumulatively, 640 h of observational data were collected. For this study, only behavioral 168

events related to social behaviors for the eight collared elephants were examined for analysis 169

(Table 2). Positive and negative social interactions were determined from the ethogram 170

constructed by Horback et al. (2013). Interactions from each elephant serving as the initiator of 171

that action were then organized into their respective positive or negative categories. Positive and 172

negative interactions across the behavioral collection period were summed to determine the total 173

for each combination of initiation and receiving pairs. Rates for positive and negative interactions 174

were calculated by dividing the number of occurrences of a particular action by the number of 175

min visible. A mathematical approach to determining dominance was calculated by breaking 176

down negative social interactions into ‘wins’ and ‘losses’ between pairs of elephants to calculate 177

dominance based upon normalized David’s scores (DeVries et al., 2006). This particular 178

calculation was chosen because of it corrects for the chance of occurrence of the observed 179

outcome. 180

2.3.3 Structure and Relationships Data Collection Analysis 181

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Three different methods of analysis were used to assess the input from animal 182

management staff. The first involved using a ratio reflective of any given elephant’s dominance 183

rank. For example, ‘E1’ would be one as the highest ranking elephant, ‘E2’ would be two, ‘E3’ 184

would be three, ‘E4’, ‘E6,’ and ‘E5’ would be four due to their cyclical dominance structure 185

among themselves within the main structure, ‘E7’ would be five, and ‘E8’ would be six as the 186

lowest ranking elephant. To calculate a ratio reflective of dominance, the following formula was 187

used: (number of elephants above position in social structure) / (total number of elephants). For 188

example, elephant ‘E2’ is above six elephants in the suggested dominance structure (E8, E7, E6, 189

E5, E4, and E3) and there are eight total elephants. The equation to determine E2’s rank would 190

therefore be 6/8, equating to 0.75. 191

The second method involved taking the initiating and receiving pair from the behavioral 192

data and recording the number of elephants in the dominance structure that stood between them, 193

labeled as ‘steps’. ‘Steps’ from one individual to another were determined by calculating the 194

number of elephants between the pair in the social structure. For example, the step number 195

assigned to pair E1 and E4 would be two, because E1 and E4 are two elephants away from one 196

another in the structure. Because of the cyclic nature of the suggested relationship among 197

elephants E4, E6 and E5, they were cumulatively counted as step as all three members were 198

considered to be their own sub-group in which E4 is dominant over E6, E6 is dominant over E5, 199

and E5 is dominant over E4. 200

Lastly, pairs of elephants deemed likely to be in close social proximity based on the 201

keepers anecdotal observations, were assigned the number ‘1’. If the pair was not mentioned, it 202

was assigned the number ‘0’. 203

2.4 Statistical Analysis 204

Given that the dataset violated normality assumptions due to small sample size and 205

irregular distribution, a non-parametric Spearman’s rank correlation coefficient was used to 206

examine the relationships among the variables (SPSS 16.0 [Chicago, IL, USA]). For all statistical 207

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tests, the alpha level was set at P = 0.05. Sociograms were also created using both GPS distance 208

and positive social behaviors to visually represent elephant relationships using the software 209

program Netdraw (version 2.136 [Lexington, KY, USA]). 210

3. Results 211

A total of 6,780 GPS locations (one accurate fix for each of the eight elephants every 15 212

min period for 9 days) were successfully recorded over 240 h. Recorded behaviors consisted of 213

945 social interactions with 913 (97%) of them being positive social interactions and 32 (3%) 214

being negative. Significant relationships between the GPS data, corrected David’s scores, 215

management input and behavioral data were found (Table 3). The GPS data averaged over the full 216

24 h period negatively correlated with the social proximity data taken from the keepers’ anecdotal 217

observations (P = 0.034). The GPS data averaged from the morning time period (6:00 – 11:45 h) 218

also negatively correlated with the social proximity data taken from the keepers’ anecdotal 219

observations (P = 0.022). The GPS data positively correlated with the calculated corrected 220

David’s scores for the full 24 h period (12:00 – 11:45 h) (P = 0.012), the early time period (0:00 221

to 05:45 h) (P = 0.001), the morning time period (6:00 – 11:45 h) (P = 0.020) and the afternoon 222

time period (12:00 to 17:45 h) (P = 0.012). Lastly, the GPS data averaged over the evening time 223

period (18:00 – 23:45 h) negatively correlated with both the rate of positive social behaviors (P = 224

0.042) and the rate of positive and negative social behaviors combined (P = 0.032). Sociograms 225

reflective of the relative level of association between elephants were created based upon average 226

maintained distance as determined by the GPS data and rate of positive social behavior between 227

pairs (Fig. 2 and 3). 228

- Insert Figures 2 and 3 about here - 229

4. Discussion 230

The data acquired from GPS technology can help in making both ecological and 231

zoological management decisions where sociality is a key component. Negative correlations were 232

found for both the morning GPS data (06:00 – 11:45 h) and the social proximity data taken from 233

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the keepers’ anecdotal observations as well as the daily GPS data (12:00 – 11:45 h) and the social 234

proximity data taken from the keepers’ anecdotal observations. As the distance between 235

individual elephants decreased, the likelihood of those individuals being in close social proximity 236

according to staff increased. These relationships suggest that animal caretaker knowledge and 237

GPS data are linked, further promoting the successful application of GPS technology in 238

determining relationships as well as the reliability of keeper knowledge and observations in 239

zoological settings and research. The stronger correlation coefficient for the morning GPS data in 240

relation to the daily averaged GPS data may be attributed to the elephant management work 241

schedule. Keepers arrive around 7:00 h and depart at approximately 15:30 h. General husbandry 242

and yard clean up occurs in the morning, where the elephants are kept in the opposing yard area 243

within keeper view. This scheduling of yard clean-up and elephants being within eyesight 244

indicate that management’s anecdotal observations that formed their input are more than likely 245

from the morning time period as opposed to the afternoon time period when they are doing keeper 246

reports, barn clean up and interacting with guests. This helps to explain the significant correlation 247

between the social relations determined by animal management staff and the morning GPS data, 248

the lack of significant relationships between the social relations determined by animal 249

management staff and any other specific time period as management carries out other duties 250

during these times of the day, and the lower but still significant correlation between the social 251

relations determined by animal management staff and the daily GPS data. This significant 252

correlation between the social relations determined by animal management staff and the daily 253

GPS data indicates that the social trends observed by the staff hold for the entire 24 h period but 254

are not as strong as those observed during the morning period, which is consistent with the 255

suggestion that keeper knowledge is vital in zoological settings as it has a number of promising 256

applications (Hosey, 2008). 257

The evening GPS data (18:00 – 23:45 h) and the rate of positive social interactions 258

among the elephants had a strong negative correlation. As the distance between individuals 259

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decreased, the number of positive social interactions increased. This relationship follows the 260

predicted pattern that elephants with a shorter distance between each other for longer periods of 261

time are more likely to display positive social behaviors towards each other. Throughout the day, 262

the elephants are called over in their family groups and shifted or moved to ensure the highest 263

level of care. Groups include one female adult and her offspring. In total, the herd consisted of 264

five groups plus one female with no offspring and the bull elephant. This may have created a bias 265

in the GPS data from 7:00 – 15:30 h when management is present and temporarily separate 266

elephants and family groups for cleaning, training, and husbandry purposes. Once the care staff 267

leaves for the day, the elephants are able to associate with whomever they choose. Therefore, 268

more accurate data based on the true relationships among individuals would have been collected 269

during the evening GPS data (18:00 – 23:45 h) time frame as the bias created by management 270

presence and interaction was absent. A number of papers have explored how management staff 271

may affect the animals in their care (Mellen, 1991; Thompson, 1989). For example, Mellen 272

(1991) found that felids were more likely to produce offspring if their keepers spent time 273

interacting with them. The significant relationship between the GPS evening data and rate of 274

positive social interactions would be expected as keeper absence would allow for more natural 275

motivations of the elephants’ physical movement and social behavior. 276

Positive correlations were found between the corrected David’s scores, the full 24 h 277

period, the early time period (0:00 to 05:45 h), the morning time period (6:00 – 11:45 h) and the 278

afternoon time period (12:00 to 17:45 h). As the distance between individuals increased so did the 279

numerical gap between them for the corrected David’s scores. The lack of correlation between the 280

evening time period and the David’s scores may again be due to keeper presence bias. As 281

mentioned above, keepers shift and work with the elephants throughout the day, specifically 282

during most of the morning and afternoon time periods. In order to minimize potential aggression 283

among the elephants during keeper led movements, elephants are ordered through exhibit gates 284

ways from least to most dominant, thus artificially creating distance between the elephants. 285

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However, keepers are absent in the early morning time period and the elephants can make social 286

decisions on their own accord. This time period had the highest reported p-value, indicating that 287

while keepers can bias data, true relationships may still be evident in statistical analysis. 288

Additional contributing factors include that the elephants could be resting during the evening time 289

period, and may be less likely to maintain distances between one another as alertness decreases 290

and that colder evening temperatures are initiating closer contact in an effort to stay warm. 291

The results from the study indicate that GPS technology can be used as a proxy tool in 292

determining social relationships among animals. The utilization of such technology in 293

determining social relationships may help animal behavior research, zoological planning and 294

ecological management. Past methods of assessing social relationships have included 295

observations looking at the presence of aggressive or affiliative behavior and association patterns 296

between individuals. Unfortunately, these methods can be less accurate due to observer fatigue, 297

the lack of identification of subtle social behaviors, misidentification of individuals, visual 298

estimates of the distance between two con-specifics and obstruction of observer’s vision. Horback 299

et al. (2012) found that GPS collars have little to no impact on the natural behavior of the 300

elephant wearing the device. It follows that as long as adequate collar training is achieved, no 301

significant differences in behavior should occur and measurements of maintained distance 302

between individuals will be accurate. Both the small sample size and the inherited bias from 303

keeper presence and management protocols which involves the physical movement of the 304

elephants wearing the GPS device highlights the need for further studies to confirm GPS 305

applicability. In the future, research should consider the use of larger, undisrupted populations to 306

better assess social relationships. 307

5. Conclusions 308

This study examined the use of GPS technology in determining social relationships by 309

comparing GPS data to behavioral data and input from animal management staff. Significant 310

correlations found between each suggest that GPS technology can be used as a proxy tool in the 311

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determination of social structures. This knowledge could be used in daily management and 312

potentially stressful situations (e.g., transport, shifting animals between exhibits, etc.) to mitigate 313

any negative changes in welfare. Broader applications include zoological planning in regards to 314

moving or breeding animals and ecological planning for wildlife reserves and management. Such 315

technology also has applications in future animal behavior research in both wild and zoological 316

settings to further explore sociality and dominance structures in both elephants and other animals. 317

6. Acknowledgements 318

The authors would like to thank the Heller Foundation for their continued financial 319

support. We would also like to thank Allison Alberts, Randy Rieches and Matt Anderson for 320

their continued support as well as Jeff Andrews for his initial work on this project. Finally, we 321

would like to thank the entire elephant team; Curtis Lehman, Mindy Albright, Jason Chadwell, 322

Keith Crew, Brian Harmon, Erin Ivory, Weston Popichak, Karissa Reinbold, Heather Rogers, 323

Rick Sanchez, Brittany Trawick and John Walko for all of their hard work which made this 324

project possible. 325

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Table 1

Sex, date of birth and lineage of the African elephants along with their collared (Y) or not

collared (N) status for this study.

Subject Sex Date of birth Sire Dam Collared

E1 M 1/1/1990* Unknown Unknown Y

E2 F 1/1/1990* Unknown Unknown Y

E3 F 1/1/1990* Unknown Unknown Y

E4 F 1/1/1990* Unknown Unknown Y

E5 F 1/1/1990* Unknown Unknown Y

E6 F 1/1/1990* Unknown Unknown Y

E7 F 1/1/1990* Unknown Unknown Y

E8 M 2/23/2004 Unknown E3 Y

E9 F 9/11/2006 E1 E5 N

E10 M 3/11/2007 E1 E4 N

E11 F 9/19/2007 E1 E6 N

E12 M 3/13/2009 E1 E5 N

N/A: Not available, *Date of birth is estimate for wild-caught animals

rescued from a schedule cull in Swaziland in 2003.

Table 1

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Table 2

The behavioral ethogram used to define the positive and negative social interactions between the elephants

at the San Diego Zoo Safari Park in the summers of 2010 and 2011.a

Behavior Operational Definition

Positive

Social

Behavior

Approach One elephant walks toward another elephant.

Body touch Initiation of body contact with another elephant.

Social play Trunk wrestling, shoving, butting, bullying each other.

Sharing food Eating from the same food pile, simultaneously.

Sharing objects More than one elephant simultaneously handling the same object

(e.g., rope or tree branch).

Negative

Social

Behavior

Charge Rapidly approach another animal with trunk tucked under head, head

up, and chin tuck. Attempts to contact target. Often a “silent” charge,

without trumpeting. Ears usually close to head. Often has an ear fold.

Head shake An abrupt shaking of the head that causes ears to flap; can also be

used in play.

Alert posture Standing with head raised, ears spread with bottom part of ear folded

back so that a prominent horizontal ridge appears, tail raised, trunk

raised or turned in a “Sniff” position.

Pursuit One elephant runs after another. The pursuer is attempting to reduce

the separation between animals. The elephants may be mobbing at a

fast walking pace.

Throwing Lifting or uprooting objects and throwing them in the general

direction of an opponent.

Bite The aggressor puts the tail or other body part of another elephant in its

mouth.

Head butt The aggressor charges/rams another elephant with its head. The

aggressor may hit the recipient on its side, hind legs, and front legs.

This is a side-on hit, not a hit from above.

Sparring Head to head contact between two elephants. Pushing trunks, tusking,

shove, wrestle or trunk entwine with another elephant.

a This table is representative of only the social behaviors of the African elephant herd at the San Diego Zoo

Safari Park and was taken from a comprehensive ethogram found in Horback et al. (2013).

Table 2

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Table 3

Spearman’s Correlation Coefficient among the GPS data time periods, dominance ratios,

management’s suggested hierarchy, the rate of positive social interactions, and the rate of all

social interactions combined. a

*P < 0.05 , ** P < 0.01 a There were not a sufficient number of occurrences of negative social interactions in the behavioral data set

to calculate rates or run statistical tests. b Refers to the elephant pairs that management staff regularly witnessed in close proximity to one

another through their own anecdotal observations.

Daily Early Morning Afternoon Evening

Steps Correlation

Coefficient

-0.076 0.139 0.147 0.110 -0.212

Ratio Correlation

Coefficient

-0.059 0.067 0.186 0.098 -0.269

Corrected David’s

Scores

Correlation

Coefficient

0.471* 0.614** 0.441* 0.474* 0.207

Social Proximity b Correlation

Coefficient

-0.401* -0.264 -0.431* -0.362 -0.313

Positive

Rate Correlation

Coefficient

-0.081 -0.024 -0.025 -0.141 -0.386*

Overall Rate Correlation

Coefficient

-0.133 -0.045 -0.050 -0.168 -0.406*

Table 3

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Figure 1

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Figure 2

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Figure 3

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Fig. 1

Suggested dominance structure of African elephants, from top (E1) to bottom (E8) at the San

Diego Zoo Safari Park in 2009.

Fig. 2

The average level of association between any two elephants based upon numerical values for

average daily distance (m) maintained between the pair as indicated by arrow boldness.

Fig. 3

The average level of association between any two elephants based upon numerical values of rates

of positive social interactions as indicated by arrow boldness.

Figure captions