Investigating Movement Patterns of Prime Bull African Elephants in the

Associated Private Nature Reserves of Kruger National Park,

South Africa

Patrick Freeman and Lucia Herrero

Abstract

Partnering with Save the Elephants-South Africa, one of the premier elephant research and

conservation organizations in Southern Africa, we will be mapping and analyzing the

movements of ten adult, large-tusked bull elephants throughout the network of private reserves

that link directly into the great Kruger National Park in South Africa. Understanding the

movements of these high-profile, high-risk animals in this protected area network is integral to

the successful management of elephant populations in this transboundary ecosystem.

Background and Context According to the World Wildlife Fund, the African savannah elephant is a threatened species,

still recovering from decimating levels of hunting during the 1970s and 1980s. Elephants are a

critical factor in maintaining ecosystem health because of their large-scale interactions with their

environment. Often referred to as “ecosystem engineers,” elephants can greatly alter their

surroundings as they browse on trees, disperse seeds from those trees across, and clear forest

habitat to make way for more open grasslands. Thus, they can also be seen as a keystone species,

supporting a broad network of other wildlife species, both plant and animal. Because elephants

require such a large range, they face habitat loss and population fragmentation with increasing

land development, a problem that is gripping most of the regions that make up the African

elephant’s current range.

Additionally, elephants are also in the throes of extremely high rates of illegal hunting to fuel a

burgeoning demand for ivory products in the Far East, namely China, with estimates of total

losses to the total African elephant population summing to somewhere near 35,000 elephants per

year. Cynthia Moss, one of the world’s foremost elephant researchers, has said that if poaching

rates continue at present levels we may be living in a world without wild African elephants

within the next two decades (Stein, 2012).

Most of these losses have been sustained in East Africa, however, and elephant populations in

Southern Africa are actually on the increase in many range-states. While this is good news, there

are significant challenges associated with maintaining high elephant densities within established

protected areas, primarily dealing with the over-exploitation of bounded ecosystems by highly

destructive elephant feeding. There is also growing worry that the poaching activity that has been

largely contained to the East and Central African range states will spread into the protected

strongholds in southern Africa. Thus, research into elephant ecology in these regions is both

timely and necessary for the continuing successful management of this incredibly important

species.

In order to protect elephant habitats, a number of reserves have been established throughout

Africa. The Associated Private Nature Reserves, adjoining the famous Kruger National Park, in

South Africa prove to be an interesting study (Figure 1). The APNR includes the Balule,

Klaserie, Umbabat and Timbavati Private Nature Reserves, a network of private landholdings

that link directly into the Kruger National Park to create a massive tract of open space for animal

use ranging from Balule’s westernmost boundary all the way to the Mozambican border in the

East and up to the Zimbabwe border to the North (see map below for reference). Recently, the

fencing between this collection of connecting private reserves was removed, allowing elephants

to move freely across the landscape and thus there is a significant amount of interest in

understanding the way that elephant populations are utilizing this ecosystem.

Figure 1. Locator Map of the Associated Private Nature Reserves

Connections to GIS

A number of studies have used GIS to interpret elephant collar data in order to learn more about

elephant movement and ecosystem impact. The majority of this research is focused on two key

areas: understanding elephant home ranges, and understanding the relationships between

elephants and natural or artificial resources.

In the study “Elephant movements and home range determinations using GPS/ARGOS satellites

and GIS programme: Implication to conservation in southern Tanzania,” Mpanduji and

Ngomello (2007) used GPS to track elephant movement in the Selous‐Niassa Corridor, a

protected area which spreads through Tanzania and Mozambique, and links two important

wildlife reserves, Selous and Niassa. A GIS spatial analysis revealed that a significant subset of

the elephant population in the Selous-Niassa area had large home ranges—that is, they were

highly mobile and regularly traveled within the full extent of the Corridor. Furthermore, the GIS

analysis shows that the large home range elephants relied on the Selous-Niassa Corridor to safely

travel between the two main reserves (Mpanduji and Ngomello 2007). This suggests that it is of

vital importance to protect these lands in order for elephants to maintain their traditional

lifeways.

In addition, two studies have attempted to quantify the relationship between elephant movement

and the distribution of water sources. In “Do artificial waterholes influence the way herbivores

use the landscape? Herbivore distribution patterns around rivers and artificial surface water

sources in a large African savanna park” Smit et al. (2007) tested if soil type, water source type

(natural or artificial), and water source availability influenced the migratory patterns of large

herbivores, including elephants. Using GIS, Smit et al. (2007) performed a density analysis to

determine that artificial waterholes significantly influenced the way large herbivores are

distributed within a given area.

These findings are supported by the study “Fences and artificial water affect African savannah

elephant movement patterns.” In this study, Loarie et al. (2009) attempt to understand how

artificial water sources and fences affect elephant movements. Using data acquired from GPS

tracking system, Loarie et al. (2009) used GIS and an ANOVA analysis to analyze migratory

patterns according to seasonality. They determined that human intervention, via artificial fencing

and water sources, reduces seasonal differences in elephant movement (Loarie et al. 2009). In

turn, they suggest that reduced migration creates the possibility for elephants to overexploit

resources (Loarie et al. 2009).

These studies raise critical questions about ethical and responsible conservation—at what point

does human interaction become helpful or potentially harmful? They serve as a guide for the

kinds of questions that will motivate this project, and provide methods that may prove fruitful in

the analysis.

Study Objectives It is our objective to better understand how the elephants residing within the APNR occupy this

space and interact with their environment. In order to achieve this, we have collaborated with

Save the Elephants-South Africa (STE-SA) to answer these questions. STE-SA is one of the

leading elephant research and conservation organizations currently operating in Southern Africa.

STE-SA has been running a strong elephant collaring operation since the early 2000s in order to

obtain vital information on the movements and spatial ecology of elephants that move

throughout the transboundary ecosystems of the APNR and Kruger National Park.

Using the GPS collaring data points collected by STE-SA, we aim to develop a methodology for

answering the following questions:

Objective 1: Understanding Elephant Movements Do individuals exhibit a preference for parts of their range, or are their movements evenly

dispersed over the entirety of this reserve network? Are there hot-spots of activity? Do these hot-

spots shift in response to changes in seasons? Additionally, for those individuals that have

several years’ worth of data collected, do general trends in spatial utilization illustrate shifts over

time or are they relatively constant?

Objective 2: Understanding Relationships between Clusters and the Environment

Are there any discernible relationships between the proximity of clustered elephant movements

to certain vegetation types and to the nearest point-water source? If so, can these relationships be

used to establish a model to predict where elephants might spend the majority of their time?

Projected Coordinate System for Project For our analysis, we will be utilizing the Universal Transverse Mercator projected coordinate

system. Our study area falls completely within Zone 36S of the Universal Transverse Mercator

grid and over a relatively small amount area, thus minimizing distortion when data is projected.

Therefore, our analysis that calculates distance and area will be rigorous and minimally distorted.

Thematic Layers to be Used in Analysis

Elephant GPS Collar Data:

Data Type: Vector, points

Source: Save the Elephants – South Africa sponsored GPS collars

Original Coordinate System: WGS1984

Projected Coordinate System: Tete UTM – Zone 36S

Time Period: Data provided was highly variable both temporally and in volume. Some

individuals had many years of consecutive data from their collars while others only had several

months and much fewer points.

As stated above, STE-SA has engaged in an intensive elephant-collaring operation since the

early 2000s in an effort to understand the spatial ecology of the elephant populations that

frequent the Kruger National Park-Greater Kruger transboundary ecosystem that includes the

APNR. While the organization has long kept a photographic database that catalogues observed

individuals within the Greater Kruger ecosystem, the tracking of individual movements has been

an incredibly powerful conservation tool that allows us to understand how these animals utilize

the space available to them as well as continue to advocate for their protection. Large bull

elephants, given both their prodigious appetites and prime breeding status can range over large

tracts of the landscape in the search for resources and mating opportunities. Knowing this, STE-

SA’s ongoing campaign to track the movements of large, senior bulls in this ecosystem has

yielded large amounts of data regarding the usage of space by several individuals. STE-SA has

provided the raw GPS collar data for a total of 10 individual elephants designated as prime bulls

within the APNR. These males are considered to be of prime breeding age and potential and thus

represent a very important demographic to the continuing health of elephant populations in this

reserve network. Additionally, senior bulls act as repositories of ecological knowledge and thus

when serving as mentors to young bulls, can transfer this knowledge accordingly, making them

of particular interest within the context of social behavior studies.

Once an individual has been collared, their geographic coordinates are relayed to a satellite using

a specified data capture regime (e.g. four times per day; this variable also varied across

individuals) for compilation in a database. A summary table of the total number of GPS locations

for each animal has been included in Table 1 in the Appendix for review. Total GPS location

records range from 166 to over 30,000 and records per individual and span ranges of several

months to several years. It is important to mention that not all of these bulls were collared at the

same time nor were they all from the same social group. Instead, they are collared when (a)

resources can be obtained to purchase a collar and arrange for the darting of the animal to outfit

it with a collar and (b) when the animal is accessible for darting.

Additionally, the data provided to us has been clipped to the APNR boundaries despite there

being no boundaries between the APNR and the adjoining Kruger National Park because our

analysis focuses solely on the space utilization of these bulls within the APNR. As much of this

GPS collar research is still under active development determining the ideal number of GPS

locations to relay per day is still being tested. As such, data for each individual varies in this

characteristic and steps are being taken to sort the data to determine the data collection regime

for each animal. Information about this variance can be found in the summary tables in the

Appendix. These facts will be taken into account during analysis to produce the most informative

and statistically sound results possible.

This layer will be used in a hot-spot and cold-spot analysis of the density of elephant locations

across the landscape. More details about this analysis can be found in the Methodology Outline

section. This dataset will also be used in the linear regression model to determine the relationship

between the location of these bulls and other factors like the distance to the nearest point-water

source and vegetation type.

Vegetation Community Data:

Data Type: Vector, polygons

Source: Save the Elephants – South Africa in conjunction with ecological monitoring teams in

Timbavati, Klasserie, and Umbabat Private Nature Reserves

Date of Collection: Unknown

Original Coordinate System: Assumed to be WGS1984

Projected Coordinate System: Tete UTM – Zone 36S

STE-SA has also provided us with data on the dominant vegetation communities present in this

network of reserves. Unfortunately only the vegetation communities in Umbabat, Timbavati, and

Klasserie Private Nature Reserves have been mapped to date. In total there are 24 different

classes of vegetation that have been mapped. Vegetation polygons were generated through a

field-based plant sample collection and ground-truthing with a GPS. While the overarching

ecosystem present in the APNR falls under the dry woodland savanna label, there are multiple

variations in the vegetative make-up of this landscape. A table of the top ten most prevalent

vegetation communities has been provided in the Appendix (Table 2). The vegetation

community that makes up the most area in the reserve is the Acacia nigrescens (knobthorn) –

Combretum apiculatum (red bush willow) open woodland, with a total of around 13% of the total

area of the reserve being occupied by this dominant vegetation. The second most prevalent

community in the APNR is the Colophospermum mopane dense woodland and shrubveld,

making up just over 11% of the total area of the APNR. It is of note that all of these species of

tree are heavily exploited by elephants and thus we investigated whether or not our study

individuals are choosing to occupy areas dominated by these vegetation communities through the

use of our regression model.

Water Points:

Data Type: Vector, points

Source: Save the Elephants – South Africa in communication with regional ecological

monitoring teams

Date of Collection: Up-to-date as of 2012

Original Coordinate System: WGS1984

Projected Coordinate System: Tete UTM – Zone 36S

STE-SA has provided us with GIS layers for all of the point-water sources in the APNR as well

as the layers for some of the rivers in the region. These data were collected with the help of the

ecological monitoring teams of each reserve and mapped using a GPS waypoint and sent to us

via ESRI shapefiles. A map of these point water sources is included in the Appendix. In total

there are 364 point-water sources within the boundaries of the three major reserves that make up

the APNR that we also have vegetation data for (Klasserie, Umbabat, and Timbavati Private

Nature Reserves). Elephant movements in a similar ecosystem to that found in the APNR have

been shown to be heavily influenced by water availability (Loarie, et al. 2009) and thus it is

important to understand how this particular elephant demographic is influenced by this variable

within this tightly transboundary ecosystem. We will be employing these point-water source

locations to build our regression model to determine if there are any relational trends between the

movement of these bulls and their relative distance to the nearest water source.

Methodologies

Pre-Analysis Data Preparation of GPS-Collar Data:

Original elephant GPS collar data was delivered as a database for each individual not separated

by year or season. Thus, we created a new feature class for each individual for each year (i.e.

Classic_2007) where entire datasets existed (qualified as those datasets that have points collected

from January 1 to December 31 of each year).

We then went on to break those individuals for which we have complete annual data sets into

seasonal datasets. The seasons in this region can be broadly classified into wet and dry and are

characterized by their relative levels of precipitation. According to the Kruger National Park’s

Web site, the wet season runs from October to March and the dry season runs from April to

September (“Kruger National Park”). Thus we separated our GPS-collar data into these

categories to investigate any rudimentary changes in distribution of bull movements. As entire

wet seasons are not contained within a single year and instead traverse two calendar years, we

generated data using points from October through December of the previous year and points

from January to March for the newyear to create an entire seasonal set where possible. We

acknowledge that in reality seasonality is not discrete in nature, however to test the functionality

of this analysis we utilize this assumption. This parameter can of course be changed in the future

as necessary.

Please see Appendix for full Model Builder Flowcharts.

Understanding General Distribution Patterns

In order to understand the general spatial distribution of an

individual’s total range within any given year or season,

the Mean Center and Standard Deviation Ellipse tools were

employed. For the Standard Deviation Ellipse Tool, two

standard deviations were selected to incorporate 95% of the

locations, excluding 5% with the aim of excluding outliers

from the dataset. This basic tool can track subtle changes

in the mean center of each individual’s range as well as the

dispersal of points across their range both annually and

seasonally.

Seeking Clusters of Elephant Movements

To answer some of our core questions about distribution and the clustering of movements in this

network of reserves we employed the Hot-Spot Analysis Tool. For reference, an attached image

of the entire model flowchart can be viewed for simplicity. The methodology is outlined below

in text format:

1. We started with an input dataset of

GPS-collar locations for one individual

and applied this analysis first to an

entire calendar year where applicable.

In order to minimize noise in the

dataset we first utilized the Integrate

tool to merge points within a 100m

radius into the same point. For this

step, we acknowledge the arbitrary

assumption of a distance at which we

integrate incident events. However,

upon trial-and-error we found that

integration of points using this distance

parameter resulted in higher variation in the degree of clustering of events when applying

the Collect tool, which is described in the next step. However, we acknowledge that

developing more reasoned distance at which to integrate incident points could be of

further interest to this area of research.

2. After integrating the points, we then applied the Collect tool to convert our event data

into weighted point data, essentially ascribing a numerical value to event points in a first

step to investigate potential clustering of events and providing a data source to utilize in

the later Incremental Spatial Autocorrelation analysis and Hot-Spot Analysis.

3. We then applied the Calculate Distance

Band from Neighbor Count tool to our

collected point layer in order to delineate

the maximum distance between points

such that all points in the dataset have at

least one neighbor. This forms a crucial

component of the Incremental Spatial

Autocorrelation tool.

4. We then employed the Incremental

Spatial Autocorrelation tool on our

collected data layer to determine the

distance band at which we should

operate our Hot Spot Analysis.

5. Finally, we utilized the Hot Spot Analysis tool to

understand the clustering of high-density and

low-density points (i.e. hot spots and cold spots)

across the reserve network. Utilizing the Fixed

Distance Band option, we input the First Peak

distance from the Incremental Spatial

Autocorrelation tool as this measurement

indicates the neighborhood distance at which the

clustering of our collected events becomes

meaningful. This then produces a thematic layer

based on the z-scores of the clustered data,

essentially delineating areas of statistically

significant clustering of high-density points that

meet our analysis criteria (i.e. hot-spot zones will

illustrate high degrees of clustering of high-

density collected events).

Aggregating All Bull Events to Investigate Potential Reasons for Clustering For this portion of our analysis we decided to aggregate the movements of our ten sample bulls

across all years to maximize our sample size and determine if any trends in the clustering of our

total points could be determined by our aforementioned environmental factors of interest. We

realize the limitations in aggregating this dataset given that not all bulls had the same sample

size, were not collected during the same time period, nor were fitted with collars with the same

GPS-location sampling regime. However, in performing this regression we aimed to develop a

first step in this methodology for determining whether a linear predictive model could be applied

to sample datasets similar to this in the future.

Methodology for Calculating Distance of Clustering Events to Nearest Water Source:

Elephants depend heavily on water resources, and the existence of year-round point-water

sources has been shown to influence elephant movements in the Kruger National Park

ecosystem. In order to prepare our data for use in our regression model, we calculated the

distance of each collected point to the nearest point-water source utilizing the Near tool. This

provided an important explanatory variable in our Ordinary Least Squares Regression model,

which will be explained further below.

Methodology for Spatially Joining Points with Vegetation Type:

In order to attempt to answer our questions regarding the usage of certain vegetation types by our

study individuals we utilized the Spatial Join tool to append the information of the vegetation

type occupied by each collected point reading. This provided the second explanatory variable in

our OLS regression.

Ordinary Least Squares Regression Analysis

This analysis will show whether there is any statistically significant correlation between

collected clustering events, the distance to nearest water source from those points of collection,

and the dominant vegetation community that the clustering event resides in. For this analysis our

dependent variable was the numeric value attributed to each collected point. Our explanatory

variables were the dominant vegetation type and the distance to the nearest water source (in

meters). Unfortunately the development of our model ran into a critical misstep in that we did

not develop a relative ranking of our vegetation type and thus the results from this model that

included both of these variables

Geographically Weighted Regression Analysis

We chose to run a Geographically Weighted Regression to see how any relationships between

elephant movements and distance to nearest water source may change within the APNR. This

analysis differs from the Ordinary Least Squares Regression in that it attempts to determine how

a variable might change across space. The OLS Regression creates a global model based on the

explanatory variables. In contrast, the GWR creates a local model of the variables we are trying

to predict by developing a regression model for each factor in the analysis. This is significant

because a specific explanatory variable may be more important in one area than another. For

example, it may be that distance to nearest water source better explains elephant clustering in

the northern part of the APNR than in the south, where clustering might be attributed to some

other combination of factors.

Results and Discussion:

Operating with the constraints of time and the sheer volume of data provided to us in the form of

GPS locations, we chose to test our methodologies for clustering analysis on one individual with

a large dataset spanning several years and one individual with a smaller dataset to demonstrate

the application of this methodology to datasets of various scales. We chose two study

individuals, named Brazen and Classic, that met these criteria. Classic’s data set was compiled of

thousands of points spanning full calendar years from 2005 to 2012. Brazen’s GPS collar data

spanned the years of 2005 to 2007 with a full calendar year’s worth of data for 2006 but still

encompassing one complete set of wet season months and one complete set of dry seasons

events.

Mean-Center Analysis Results

By performing this analysis we could glimpse at a very rudimentary scale the mean center point

of our selected bulls’ movements. Figure 2 ilustrates which plots the mean center for each

calendar year, it is illustrated that Classic’s movements are highly centered in the north of the

APNR, occupying spaces that fall between two major river systems that span Umbabat and

Timbavati Private Nature Reserves. The relative lack of drastic movements in the location of this

bull’s mean center suggests that this animal has a core range and that further analysis of his

home range can and should be applied to understand how he occupies both space in the APNR

and the adjacent Kruger National Park.

Figure 2. Mean Centers of Classic’s Movements, 2005-2012

Hot-Spot Clustering Analysis Results

The results of our hot-spot clustering analysis did indicate that there was statistically significant

clustering of both high and low-density events for individuals, resulting in hot-spots and cold-

spots of clustering events in various regions within the APNR. This analysis essentially

answered our first objective of determining if bull movements illustrated any clustering

tendencies with a resounding yes, utilizing the parameters of our model. If you reference the

maps below you can see this clustering illustrated visually for both of our sample individuals,

Classic and Brazen.

From this analysis we can see that over the entirety of his collared history, Classic has

demonstrated a strong preference for the far northern part of the APNR, relatively close to the

border into Kruger National Park, illustrated by the bright red clusters (Figure 3). We also see

that while his activity illustrates clustering of high-density events, clustering of low-density

events also occurs on the periphery of his range much farther south and to the west of his hot-

spot clusters. This indicates that while his ranging activity does extend into these areas, the

concentration of his activity over this seven-year time span is much lower than his core activity

in the north. This compares well with the previous analysis we performed on the mean center of

his incident movements and demonstrates that there appears to be a core usage of this small area

of his total range.

Figure 3. Classic’s Clustering Events, 2005-2012

As a first step to investigate any differences in the instances of clustering across seasons (wet v.

dry) we aggregated the wet season and dry season GPS location data from all the complete

calendar years for which we had data for Classic. As his data set spanned such a large number of

years relative to the other individuals that we have at our disposal, he made the ideal test subject

for running this analysis. It apparent from Figures 4 and 5 that the distribution of incidents of

extremely high clustering of high-density events is greater during the wet season months of

October through March than during the dry season months of April through September.

Hypotheses for this differentiation in clustering are varied. The proliferation of abundant natural

water sources and the increase in primary production during the growing seasons seen in the wet

season in this reserve network may be related to the relative concentrations and distribution of

clustered events but more study is needed on this matter. Again, this is a very coarse scale of

analysis and with more time finer-scale analyses on the clustering of Classic’s movements per

each annual seasonal cycle could be performed and could potentially reveal more patterns in his

spatial utilization.

Figure 4. Clusters in Classic’s Wet Season Movements, 2004-2012

Figure 5. Clusters in Classic’s Dry Season Movements, 2005-2012

Brazen’s clustering tells a different story than Classic’s. Hot-spot analysis on his total

movements for the duration of his collared history has indicated that clustering of high density

incidents is much more concentrated along the southwestern portion of Timbavati Private Nature

Reserve (Figure 6). This analysis also indicated a large cold-spot, or clustering of low-density

points farther to the north, closer to the areas where we see the highest clustering of high-density

points for Classic (Figure 3). We also performed a seasonal analysis on Brazen’s smaller dataset

and yielded primarily inconclusive changes in his total distribution of hot and cold clusters

(Figures 7 and 8). In the wet season spanning 2005 and 2006, the distribution of his high-density

clustering events shifted slightly more southward, however, it appears that the distribution of his

low-density points becomes larger during the wet season, suggesting that during this time period

he was moving over these distances but not spending much time there, which would theoretically

lead to incidents of clustering utilizing our analysis methodologies.

Figure 6. Brazen’s Clustering Events 2005-2007

Figure 7. Brazen’s Wet Season Clustering Events, 2005-2006

Figure 8. Brazen’s Dry Season Clustering Events, 2006

We also compared hot-spot clustering events of both Classic and Brazen within the same year

(2005) (Figures 9 and 10). Of potential interest is the fact that the hot-spots for both bulls, which

can be used as a proxy for understanding where the density of their habitation is the greatest, do

not overlap within this calendar year as you can see in the maps below. And while not

necessarily indicative of the total potential interaction between these two animals of similar age

classes, it is nonetheless suggestive that further analyses on the potential social factors that

dictate ranging activity and bull-bull interaction could be performed to illuminate more nuanced

patterns of space usage. Ultimately, the determination that the movement patterns of these

sample individuals did exhibit clustering provides the basis from which to perform additional

research on the specific factors, if any, that result in this clustering behavior.

Figure 9. Brazen’s Clustering Events, 2005

Figure 10. Classic’s Clustering Events 2005

Linear Regression Results In addition to understanding how elephant movements are distributed throughout the APNR, we

were also interested in trying to describe the motivating factors behind these movements. We

hypothesize that the elephant’s environment plays a large role in determining where an elephant

tends to cluster. With this in mind, we hoped to establish a predictive model using the total

collected points for all prime bulls, the corresponding vegetation type, and the distance to nearest

water source to better understand why elephant clustering occurs. Furthermore, we hope to

establish a methodology for using Ordinary Least Squares Regression and Geographically

Weighted Regression for future studies of elephant movements.

Ordinary Least Squares Regression Results

In the OLS, we set the weighted average location (ICOUNT, from collected points) as the

dependent variable and the vegetation types and distance to nearest water source as the

explanatory variables. The OLS resulted in an adjusted r2 value of .03. This suggests that the

location of approximately three percent of the clustered points can be explained by the kind of

vegetation the point is associated with and how far that point is from water. Based on what we

know about elephant resource dependency, we find this value to be very low.

However, we are not entirely surprised by this result. Ordinary Least Squares Regression relies

on a number of assumptions to create a linear model, and unfortunately, given our dataset, we

were not able to meet these requirements. Most importantly, the OLS requires that the

relationships between all the variables are linear. This is not the case, and furthermore, the data

did not become more linear using logarithmic or exponential transformations. In addition, our

results did not have a constant standard deviation, which is also a requirement for linear models.

This was evident from the standardized residuals plot, which is strongly heteroskedastic (Figure

11). We can see that there is a significant amount of clumping in the overestimated residuals.

Finally, a linear model necessitates that the residuals are normally distributed. Looking at Figure

12, we see that this is not the case; the residuals are strongly skewed towards the right. Because

of these issues, we find that the linear model created by the OLS is not reliable. However, it

forms a necessary first step in understanding the relationships between elephant movements and

the environment.

Figure 11. Standardized Residual Plot Figure 12. Histogram of Standardized Residuals

Geographically Weighted Regression Results

In addition to the OLS, we also performed a Geographically Weighted Regression. The GWR is

significant because it takes into account spatial variation by creating a linear model for each

variable at the local level. Thus, the GWR will allow us to see how the relationship between

elephant clustering and distance to nearest water source varies across the APNR.

For this GWR, we set the weighted average location (ICOUNT, from collected points) of the

total bulls as the dependent variable and the calculated distance to nearest point-water source as

the explanatory variable. The GWR resulted in an adjusted r2 value of .54. This means that the

location of approximately fifty-four percent of the clustered points can be explained how far they

are from the nearest point-water source. This adjusted r2 value is quite significant, and it is

exciting that such a strong correlation can be drawn between elephant clustering and the presence

of water.

However, the Geographically Weighted Regression is still a linear model and is therefore subject

to all the assumptions required for the Ordinary Least Squares Regression. Because of this, we

find that our model is unreliable. In addition to this, the Akaike Information Criterion (AIC) for

the GWR was 72,000. This lends considerable doubt to the accuracy of our findings. Finally, in

order for the GWR to be as accurate as possible, it requires that all of the explanatory variables

are included. Since we only had an accurate data set for artificial point-water source locations,

we could not include a number of interesting other variables that may help explain elephant

clustering, like the vegetation type, distance to natural water sources, like rivers and seasonal

drainages, and distance to human infrastructures, like roads, settlements, and park boundaries.

Therefore, it is likely that the GWR has over-attributed significance to distance to artificial point-

water sources, because it was unable to account for any other explanatory variables.

Interestingly, when we compare the results of the hot- and cold-spot analysis for the total bulls

and the results of the GWR, we see that the areas with the highest amount of clustering

correspond with the areas in the GWR that are the most over- and under-predicted (indicated by

red and blue dots respectively) (Figures 13 and14). In contrast, the areas that do not show

evidence of statistically significant high or low clustering are the areas in the GWR model that

are the most well-predicted. This further illustrates that our model needs work in order to be able

to accurately predict the relationship between elephant clustering and distance to point-water

source.

Figure 13. Clustering Events for All Bulls, 2004-2012

Figure 14. Geographically Weighted Regression Residuals Distribution for All Bulls, 2004-2012

Conclusions The completion of this project has allowed for the development of a methodology for assessing

the potential for clustering in the movements of highly mobile, large mammals in an open

savanna ecosystem. The determination that individuals do exhibit this behavior and that their

clustering activity relative to one another is idiosyncratic suggests that there are a host of factors

that determine the relative space utilization of elephants in the Greater Kruger National Park

ecosystem. In the future, analyses of this type could be more illuminating if the ranging activity

of each of these bull elephants could be completed with the knowledge of each bull’s hormonal

state. Bull elephants of this age class cycle hormonally, coming into a rut known as musth,

known to be accompanied by increased rates of travel as bulls seek out receptive females. This

rut is accompanied by surges in testosterone levels that in turn can lead to increased aggression

and could also perhaps explain how bulls arrange themselves on the landscape as they either

avoid conflict or compete with other males for access to females. Additionally, with more time,

being able to rank the relative suitability of each of the represented vegetation types within this

reserve network would perhaps allow us to better understand how to develop a proper predictive

model for understanding where we might be most likely to see clustering events, if in fact the

dominant vegetation community of a certain area has bearing on this variable. Given the nature

of the GPS-location datasets and its inability to meet much of the criteria required for a linear

regression model, more time is needed to develop a predictive model relating environmental

factors to elephant movements in the future.

The potential for spatial analysis to assist in elephant conservation and management in this

ecosystem as well as across the elephant’s range should not be underestimated, as it can provide

managers, scientists, and the general public an understanding of the ways these mega-herbivores

utilize the space that we have left for them. As human populations continue to climb and

elephants are faced with more challenges to sustaining themselves in these protected area

networks, the utilization of GIS will be vital in monitoring the best course that we should take to

ensure that elephants have a place to live, and live safely, for many years to come.

Acknowledgements

We would like to offer profuse thanks to Michelle Henley of Save the Elephants-South Africa

for providing us with the data to complete this class project. Utilizing real-world data has made

learning how to employ GIS a challenging, but rewarding experience.

Additional thanks to our patient instructor and teaching assistants for their help in this learning

process.

Citations

Dean, W. R. J., S. J. Milton, and F. Jeltsch. "Large trees, fertile islands, and birds in arid savanna."

Journal of Arid Environments 41.1 (1999): 61-78.

"Frequently Asked Questions about Elephants." Elephant FAQ. International Union for the

Conservation of Nature, 2011. Web. 07 Dec. 2013.

Izak P.J. Smit, Cornelia C. Grant, Bernard J. Devereux. “Do artificial waterholes influence the way

herbivores use the landscape? Herbivore distribution patterns around rivers and artificial surface

water sources in a large African savanna park.” Biological Conservation, 136.1 (2007): 85-99.

"Kruger National Park." SANParks - Africa's Premier Wildlife Tourism Destinations. South African

National Parks, 2013. Web. 05 Dec. 2013.

Loarie, Scott R., Rudi J. Van Aarde, and Stuart L. Pimm. "Fences and artificial water affect African

savannah elephant movement patterns." Biological conservation 142.12 (2009): 3086-3098.

Mpanduji, Donald G., and Kumrwa A.S Ngomello. “Elephant movements and home range

determinations using GPS/ARGOS satellites and GIS programme: Implication to conservation in

southern Tanzania.” Proceedings from the TAWIRI Annual Scientific Conference (2007).

Stein, Ginny. "Elephant Slaughter Risks Future of Species." Lateline. ABC. Australia, 7 Nov. 2012.

Lateline. Australian Broadcasting Corporation, 7 Nov. 2012. Web. 12 Nov. 2012. Transcript

Appendix

Table 1: Summary of GPS-Collar Locations per Bull in the APNR

Bull Name Date of First

Transmission

Date of Last GPS

Point

Total Number of GPS

Locations

Benjamin 3/14/2005 6/22/2005 166

Brazen 10/20/2005 2/17/2007 2,088

Captain

Hook

4/13/2007 3/21/2011 3,449

Classic 5/24/2004 12/31/2012 28,689

Everest 9/27/2006 7/29/2008 14,425

Gower 10/24/2006 3/13/2013 22,217

Intwanda TBD TBD 15,964

Mac 4/19/2005 7/9/2012 409

Matambu 7/24/2008 12/23/2012 13,090

Mellow 4/13/2007 6/30/2011 4,970

Mean # Of Locations

Standard Deviation

~10,547

~9,887

Table 2: Summary of Top Ten Vegetation Communities in the APNR

Vegetation Community Type

Total

Area (sq.

km)

Proportion

of Total

Area

Acacia nigrescens - Combretum apiculatum mixed woodland 190.2436 13.1505%

Colophospermum mopane dense woodland and shrubveld

(thicket)

171.6129 11.8627%

Combretum apiculatum - Xerophyta retinervis low thicket 153.8712 10.6363%

Phragmites australis river beds 140.4546 9.70886%

Combretum apiculatum - Grewia bicolor low thicket 118.2182 8.17178%

Terminalia sericea - Combretum zeyheri - Pterocarpus

rotundifolius - open woodland

102.2808 7.07011%

Colophospermum mopane - Combretum apiculatum woodland 99.2257 6.85893%

Acacia nigrescens - Terminalia prunioides woodland 92.6144 6.40193%

Combretum apiculatum - Sclerocarya birrea - Strychnos

madagascariensis open woodland

81.6544 5.64432%

Acacia nigrescens - Combretum hereroense open woodland 54.4672 3.76502%

Hot-Spot Analysis Model

OLS Regression Analysis Model