Investigating Movement Patterns of Prime Bull African Elephants in the Associated Private Nature Reserves of Kruger National Park, South Africa

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1 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.

2 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

3 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:

4 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 hotspots 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.

5 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,

6 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.

7 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.

8 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 highdensity 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.

9 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 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

10 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, 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 coldspots 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,

11 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 hotspot 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,

12 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,

13 Figure 5. Clusters in Classic s Dry Season Movements, 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.

14 Figure 6. Brazen s Clustering Events

15 Figure 7. Brazen s Wet Season Clustering Events, Figure 8. Brazen s Dry Season Clustering Events, 2006

16 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

17 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 r 2 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

18 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 r 2 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 r 2 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 pointwater 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.

19 Figure 13. Clustering Events for All Bulls, Figure 14. Geographically Weighted Regression Residuals Distribution for All Bulls,

20 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.

21 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): "Frequently Asked Questions about Elephants." Elephant FAQ. International Union for the Conservation of Nature, Web. 07 Dec 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, (2007): "Kruger National Park." SANParks - Africa's Premier Wildlife Tourism Destinations. South African National Parks, Web. 05 Dec Loarie, Scott R., Rudi J. Van Aarde, and Stuart L. Pimm. "Fences and artificial water affect African savannah elephant movement patterns." Biological conservation (2009): 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 Lateline. Australian Broadcasting Corporation, 7 Nov Web. 12 Nov Transcript

22 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/ Brazen 10/20/2005 2/17/2007 2,088 Captain 4/13/2007 3/21/2011 3,449 Hook Classic 5/24/ /31/ ,689 Everest 9/27/2006 7/29/ ,425 Gower 10/24/2006 3/13/ ,217 Intwanda TBD TBD 15,964 Mac 4/19/2005 7/9/ Matambu 7/24/ /23/ ,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 % Colophospermum mopane dense woodland and shrubveld % (thicket) Combretum apiculatum - Xerophyta retinervis low thicket % Phragmites australis river beds % Combretum apiculatum - Grewia bicolor low thicket % Terminalia sericea - Combretum zeyheri - Pterocarpus % rotundifolius - open woodland Colophospermum mopane - Combretum apiculatum woodland % Acacia nigrescens - Terminalia prunioides woodland % Combretum apiculatum - Sclerocarya birrea - Strychnos % madagascariensis open woodland Acacia nigrescens - Combretum hereroense open woodland %

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24 Hot-Spot Analysis Model

25 OLS Regression Analysis Model

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