# Analysis of Elephant Movement in Sub-Saharan Africa: Ecological, Climatic,
and Conservation Perspectives

Matthew Hines<EMAIL_ADDRESS>0009-0001-0710-0923 ,  Gregory Glatzer
<EMAIL_ADDRESS>0009-0006-5172-5652 ,  Shreya Ghosh<EMAIL_ADDRESS>0000-0002-6970-8889 College of IST, The Pennsylvania State UniversityState
CollegePAUSA and  Prasenjit Mitra  College of IST, The Pennsylvania State
UniversityState CollegePAUSA L3S Research Center, Leibniz
UniversityHannoverGermany<EMAIL_ADDRESS>0000-0002-7530-9497

(2023)

###### Abstract.

The interaction between elephants and their environment has profound
implications for both ecology and conservation strategies. This study presents
an analytical approach to decipher the intricate patterns of elephant movement
in Sub-Saharan Africa, concentrating on key ecological drivers such as
seasonal variations and rainfall patterns. Despite the complexities
surrounding these influential factors, our analysis provides a holistic view
of elephant migratory behavior in the context of the dynamic African
landscape. Our comprehensive approach enables us to predict the potential
impact of these ecological determinants on elephant migration, a critical step
in establishing informed conservation strategies. This projection is
particularly crucial given the impacts of global climate change on seasonal
and rainfall patterns, which could substantially influence elephant movements
in the future. The findings of our work aim to not only advance the
understanding of movement ecology but also foster a sustainable coexistence of
humans and elephants in Sub-Saharan Africa. By predicting potential elephant
routes, our work can inform strategies to minimize human-elephant conflict,
effectively manage land use, and enhance anti-poaching efforts. This research
underscores the importance of integrating movement ecology and climatic
variables for effective wildlife management and conservation planning.
Codebase available at https://github.com/shreyaghosh-2016/Elephant-movement

Movement ecology, trajectory analysis, spatio-temporal data

††journalyear: 2023††copyright: acmlicensed††conference: ACM SIGCAS/SIGCHI
Conference on Computing and Sustainable Societies; August 16–19, 2023; Cape
Town, South Africa††booktitle: ACM SIGCAS/SIGCHI Conference on Computing and
Sustainable Societies (COMPASS ’23), August 16–19, 2023, Cape Town, South
Africa††price: 15.00††doi: 10.1145/3588001.3609357††isbn:
979-8-4007-0149-8/23/08††ccs: Information systems Information retrieval††ccs:
Information systems Data management systems††ccs: Computing methodologies
Machine learning

##  1\. Introduction

The intricate dynamics of elephant movement across Sub-Saharan Africa pose
significant scientific interest and substantial conservation challenges
(huang2022mapping, ). These movements, driven by various ecological features
such as water availability, temperature, and elevation, provide critical
insights into their behavior, habitat preferences, and survival strategies
(beirne2021african, ; branco2019determinants, ). As such, a comprehensive
analysis of elephant movements offers a valuable foundation for effective
conservation planning and facilitates a more sustainable coexistence with
these majestic creatures in their natural habitats.

Previous studies (evans2020natural, ; beirne2020climatic, ; de2019using, )
have outlined the role of water sources, temperature variations, and
topography in influencing elephant movements. However, a comprehensive
understanding necessitates the integration of additional factors such as
seasonal and rainfall patterns, which have not been extensively examined to
date. In the context of Sub-Saharan Africa, the two predominant seasons - the
dry and wet seasons - manifest contrasting temperature, humidity, and rainfall
conditions that are likely to significantly influence elephant migratory
behavior. To address these gaps, our study employs a synergistic approach that
combines _Feature Engineering_ , _Clustering_ , and _Movement Modeling_
techniques to analyze elephant movement data enhanced with elevation and water
data obtained via Google’s APIs. By utilizing the HDBSCAN clustering
algorithm, our goal is to understand the relationship between the elephants’
environmental variables and their behavior. Furthermore, we leverage the
MoveHMM111https://cran.r-project.org/web/packages/moveHMM/index.html,
Trajr222https://cran.r-project.org/web/packages/trajr/index.html, and
MoveVis333https://movevis.org/ packages to conduct comprehensive modeling of
elephant movement data.

One notable contribution of our work is the integration of climate change
impacts into the analysis, recognizing their potential serious implications
for future elephant travel routes. Understanding the shifts in seasonal
patterns due to climate change is crucial in predicting likely changes in
elephant movements and establishing effective conservation strategies. By
incorporating climate change projections into our modeling, we can identify
areas that may become more or less suitable for elephant populations, aiding
in proactive conservation planning.

In addition to climate change, we consider rainfall patterns as a critical
determinant of elephant movements. As elephants are known to adjust their
travel routes according to water availability, a comprehensive analysis of
rainfall patterns can provide predictive insights into future elephant
migratory routes. This information is highly valuable, as it offers potential
benefits to both human populations and elephants. Accurate predictions of
elephant movements can guide local residents, ensuring minimal disruption to
their lives, and inform conservation authorities to enhance protection
measures against poaching threats.

Overall, this study proposes a multi-faceted approach to understanding
elephant movement patterns, leveraging advanced computational techniques to
decode the complex interplay of various ecological variables. By offering
predictive insights into elephant movements, this research seeks to contribute
significantly to the realms of movement ecology and sustainable conservation
planning. By integrating climate change impacts and rainfall patterns into our
analysis, we aim to provide a more comprehensive understanding of elephant
movements and support informed decision-making for effective conservation
strategies in Sub-Saharan Africa and beyond.

##  2\. Background

###  2.1. Related Works

Understanding the complexities of elephant movement and migration has long
been a subject of interest for ecologists and conservationists. Clements et
al. (clements2014objective, ) developed a spatially explicit model of elephant
movement in response to landscape heterogeneity and poaching risk, thereby
providing a valuable foundation for the methodological approach used in our
study. The authors demonstrated the importance of understanding landscape
characteristics and threats to effective conservation strategy planning.
Further insights into the effects of seasonal variations on elephant movement
were provided by Loarie et al. (loarie2009elephant, ). They emphasized the
important influence of water availability on elephant migration, a factor
closely linked to seasonal and rainfall patterns. They highlighted the urgency
of understanding these migratory patterns in the context of climate change.
Studies on human-elephant conflict, such as that by Fernando et al.
(fernando2008review, ), have underscored the need for informed conservation
strategies that take into account potential elephant routes. This line of
research reinforces the importance of our analytical approach, which predicts
elephant routes and thus has direct applications for minimizing human-elephant
conflict and enhancing anti-poaching efforts. The relationship between
elephant movement and climate change, as investigated by Wall et al.
(wall2014novel, ), highlights the potential impact of changing seasonal and
rainfall patterns on elephant migratory behavior. The role of climate change
in altering the migration patterns of elephants was further studied by another
work (kittle2016landscape, ). They used GPS telemetry to track the movements
of elephants in relation to changes in weather and climate conditions. Their
research aligns with our study, furthering the understanding of how climate
change impacts elephant migration. This understanding underscores the
importance of our work in predicting these changes and establishing effective
conservation strategies.

Naidoo et al. (naidoo2016estimating, ) proposed a model to analyze the impact
of environmental features such as vegetation and water sources, as well as
human threats, on the spatial distribution of elephants in Kruger National
Park, South Africa. This work aligns with our study’s aim to understand
elephant movement in Sub-Saharan Africa in relation to ecological variables
and threats. Boettiger et al. (boettiger2011inferring, ) developed an early
warning system based on monitoring elephant movement and behavior in real-time
to predict poaching threats. This work contributes to our understanding of
using movement data to inform conservation strategies, a concept central to
our study. The impact of climate change on elephant movement was investigated
by Shannon et al. (shannon2009affects, ). Their study emphasized the
importance of understanding elephant response to rainfall variability,
particularly in the context of climate change, a crucial component of our
study.

The impact of spatial factors on elephants’ movement has been extensively
explored in previous studies. Sitompul et al. (sitompul2013spatial, ) employed
spatial data to determine that elephants tend to stay in proximity to the edge
of canopy coverage. Mills et al. (mills2018forest, ) analyzed the influence of
vegetation and seasonal variables on elephant movement. Human-Elephant
Conflict (HEC) has also been a subject of investigation using spatial data.
Tripathy et al. (tripathy2021descriptive, ) utilized KMeans clustering to
identify hotspots of HEC. Sitati et al. (sitati2003predicting, ) examined the
relationship between road proximity and crop raiding by elephants. Granados et
al. (granados2012movement, ) focused on the influence of human infrastructure
and settlements on elephant movement, particularly emphasizing seasonal
patterns and crop raiding. In a broader context, Lamb et al. (lamb2020space, )
explored the application of clustering algorithms to elucidate groupings
within animal movement data.

While our work does not directly address HEC, it contributes to the existing
body of knowledge by employing multiple clustering algorithms to identify
human settlements that elephants seem to inhabit. Through spatial analysis, we
aim to provide insights into the spatial distribution of elephant presence,
thus adding to the understanding of their movement patterns and potential
interactions with human settlements. Animal movement studies, especially of
large mammals like elephants, are of considerable interest to ecologists,
conservationists, and data scientists. Current research on elephant movement
primarily considers key ecological factors like water availability,
temperature, and elevation to understand elephant behavior and predict their
migratory routes. The interest for deeper understanding has led to the
utilization of advanced analytical techniques, including feature engineering
and clustering algorithms in data science, to enrich elephant data and discern
patterns. Simultaneously, movement modeling tools like MoveHMM, Trajr, and
MoveVis have been used to build robust models of elephant movements, providing
insights into their behavior based on environmental factors. However, these
studies largely focus on a narrow set of ecological variables, with the
influence of broader climatic phenomena such as seasonal and rainfall
patterns, and global warming impacts on elephant movements relatively
unexplored. Our study expands upon this existing knowledge, incorporating
seasonal variations, rainfall patterns, and global warming effects to enhance
predictability of elephant movements, contributing to a more sustainable
coexistence of humans and elephants in Sub-Saharan Africa.

###  2.2. Problem Formulation

The overarching problem addressed by our research pertains to the
comprehensive understanding and prediction of elephant movement patterns
across Sub-Saharan Africa. This problem can be broken down into the following
specific challenges:

Seasonal Influence on Elephant Movements: While previous studies have analyzed
various determinants of elephant movement, including water availability,
temperature, and elevation, the influence of seasonal changes remains largely
unexplored. We hypothesize that the contrasting weather, humidity, and
rainfall conditions in the dry and wet seasons significantly impact elephant
migration behavior. However, the relationship between seasonal patterns and
elephant movements needs rigorous empirical investigation.

Impacts of Rainfall Patterns: Elephants are known to migrate in response to
water availability, which is significantly influenced by rainfall. Yet, the
role of rainfall patterns, including their temporal and spatial distribution,
in determining elephant movement routes remains unclear. A comprehensive
analysis of these patterns can potentially enhance the predictability of
elephant movements.

Effects of Climate Change: Global warming is likely to cause shifts in
seasonal patterns and rainfall distributions, which could significantly alter
elephant migration routes. Understanding these impacts requires the
integration of climate change projections with the analysis of elephant
movements. The challenge lies in the complexity and uncertainty associated
with climate change impacts and their translation into elephant movement
behaviors.

Predicting Elephant Movements and Analysing Movement Charecteristics: Given
the various factors influencing elephant movements, predicting their future
routes is a highly complex problem. Accurate predictions are essential for
informing conservation strategies and facilitating human-elephant coexistence.
We propose a data-driven approach, combining feature engineering, clustering,
and movement modeling techniques that can help to predict elephant movements
based on an expanded set of ecological variables. The resolution of these
challenges would significantly enhance our understanding of elephant behaviors
and contribute to more effective conservation planning.

##  3\. Proposed Framework

This work follows a three-step approach to understanding elephant movements in
Africa, employing Feature Engineering, Clustering, and Movement Modeling.

###  3.1. Feature Engineering

This initial step involves the integration of elevation and water body data
into the existing elephant data. The elevation data is obtained via the Open
Elevation API and is combined with each instance of the elephant data. On the
other hand, the water body information is sourced in the form of polygon
shapefiles. These shapefiles are processed using the nearbyWaterBodies()
function, which generates edge points of each polygon shapefile. This
information is subsequently utilized by the KMeans algorithm to identify
prominent water bodies. Here, the elevation data is acquired and combined with
each data point of the existing elephant data. Let’s denote the data point as
P and the elevation data as E. Each data point P(i) is combined with its
corresponding elevation data E(i).

(1) $P(i)^{\prime}=P(i)\cup E(i)$

Where P(i)’ is the new data point.

###  3.2. Clustering

Spatial movement data derived from animals in real-world scenarios often
exhibit complex patterns. For instance, elephants have been observed to engage
in shuttling motion, fluctuating between shaded areas and water bodies.
Additionally, they can traverse significant distances over time without
intermittent pauses or resting spots. Such nature of movement necessitates a
strategy to distinguish between the shuttling motion and ”locations of
interest” within the spatial data. These locations of interest can be
determined as regions where multiple data points congregate into a dense
cluster, indicating frequent returns or prolonged stay by the elephants.
Conversely, sporadic, thinly scattered points, symbolizing elephants’ motion
towards their next rest area, should be recognized as noise.

To identify these locations of interest, we resort to clustering. Considering
the shuttling behaviour of elephants, a critical requirement is the ability to
differentiate clusters from noise. The DBSCAN algorithm lends itself as a
suitable candidate for this task owing to its inherent concept of noise, a
feature absent in many popular clustering algorithms such as K-Means. We
utilized the scikit-learn implementation of DBSCAN, incorporating a
preprocessing step using scikit-learn’s StandardScaler. The refined data set
(after pre-processing) is subjected to clustering analysis, utilizing
Hierarchical Density-Based Spatial Clustering of Applications with Noise
(HDBSCAN) and Agglomerative Clustering (AGGLO) algorithms.

_(1) HDBSCAN:_ An enhancement of the DBSCAN algorithm, HDBSCAN allows for the
accommodation of clusters with varying densities. This algorithm replaces the
conventional epsilon parameter with min_cluster_size, enabling easier
optimization by defining the smallest cluster size of interest in the study.

_(2) AGGLO:_ AGGLO is particularly effective in handling elongated and thin
clusters. However, unlike HDBSCAN, AGGLO incorporates all noise present in the
data set.

In the HDBSCAN algorithm, it creates clusters by connecting areas of the
dataset where the distance between points is lower than a threshold. In the
case of AGGLO, it’s a type of hierarchical clustering where it starts with
each point as a separate cluster and merges clusters based on a distance
metric.

Let’s denote $d(x,y)$ as the distance between points $x$ and $y$. For HDBSCAN,
it looks for $x,y$ such that $d(x,y)<\epsilon$, where $\epsilon$ is a
threshold distance. For AGGLO, it starts with each point as its own cluster
and repeatedly merges the closest pair of clusters.

###  3.3. Temperature Influence in Clustering

In light of the findings from existing research works, underscoring the
significance of temperature in explaining the shuttling motion of elephants,
we incorporated temperature into our analysis. Accordingly, we executed DBSCAN
under two feature spaces: (1) Temperature-influenced - comprising temperature,
latitude, and longitude, and (2) Without Temperature influence – encompassing
only latitude and longitude. The latter feature space excludes the temperature
feature to evaluate its impact on the clustering algorithm’s efficiency in
identifying locations of interest.

####  3.3.1. Obtaining Historical Temperature Data

However, such temperature analysis is only feasible when temperature data is
available. Certain studies concerning elephant movement (Tsalyuk et al.
(tsalyuk2019temporal, ); Wall et al. (wall2014data, )) lack a temperature
feature. Therefore, we explored methods to approximate temperature data from
other data sources. Using the meteostat python package and API, we identified
weather stations proximate to the study site. The historical data was queried
and appended to the study data, enabling calculation of Temperature-influenced
centroids that would have been impossible to calculate otherwise.

The procedure entailed three key steps: (1) Identifying a nearby weather
station, (2) Matching timestamps with the queried data, and (3) Evaluating the
capability of the appended historical temperature data in calculating
temperature-influenced centroids.

For the first step, the median latitude and longitude of the given elephant’s
movement data was computed, which was then used to query a nearby station. The
second step involved normalizing and interpolating the time series data from
the station, provided by meteostat, to ensure a higher temporal granularity
that matches the given data. In the third step, the correlation between the
historical station data and Kruger temperature data was evaluated using the
coefficient of determination, R-squared.

Our results indicate a moderate correlation between the study data and the
station data. This correlation, combined with the performance of the
Temperature-influenced centroids with the weather data, gives us confidence to
extend this technique to datasets that lack temperature data. Based on our
experiment with elephant AM306 (See Figure 1) from the Kruger dataset, we
found that the Temperature-influenced feature space aided in revealing more
nuanced locations of interest within the larger clusters identified by the
Without Temperature influence feature space.

####  3.3.2. Fuzzy Timestamp Matching

Fuzzy timestamp matching is an advanced data processing technique that matches
timestamps not based on exact equality but within a certain tolerance level.
This tolerance level, or fuzzy threshold, is usually calculated by taking half
of the median of the difference of timestamps in the dataset. The mathematical
representation of the fuzzy timestamp matching process could be described as
follows:
Given two timestamps, $t1$ and $t2$, and a tolerance level $\delta$, the
timestamps $t1$ and $t2$ are said to match if:

(2) $|t1-t2|\leq\delta$

where $|t1-t2|$ denotes the absolute difference between the timestamps $t1$
and $t2$. In this case, $\delta$ is calculated as:

(3) $\delta=0.5*median(|t[i+1]-t[i]|),\forall i\;1\;to\;N-1$

where N is the total number of timestamps, and t[i] represents the ith
timestamp in the ordered sequence. This fuzzy matching approach increases the
likelihood of matches and can help to mitigate data loss when aligning data
from different sources or with different temporal resolutions. However, it is
important to note that this technique may also introduce some uncertainty into
the analysis due to the mismatched timestamps. Hence, an appropriate balance
between data retention and accuracy should be maintained while deciding the
value of $\delta$.

The integration of weather station temperature data with animal movement
datasets presented a significant challenge due to the relatively low
percentage of matching timestamps. For instance, in the case of AM189 from
Etosha, a mere 19.662% of timestamps corresponded. This limited overlap
signifies a considerable loss of data, which undermines the analysis. To
address this issue, we utilized ”fuzzy” timestamp matching. This method
extends the criteria of a match beyond exact timestamp equality, incorporating
a pre-defined threshold for the discrepancy between two timestamps that still
qualifies them as a match. The mathematical formulation of this concept is as
follows:

Figure 1. Comparing feature spaces with and without temperature on elephant AM306 from the Kruger dataset. Black Xs represent centroids calculated with temperature included in the feature space. Large colored dots represent centroids of clusters calculated solely on location data. Parameters: Without temp-influenced epsilon=0.1, minPts=35. Temp-influenced epsilon=0.2, minPts=50 Figure 2. The difference between temperature data from Elephant AM105 in the Kruger dataset, and corresponding temperature data from the meteostat weather station API. Metric | Value
---|---
R-squared (zero-centered) | 0.6871044690549571
Offset (study – station) | 9.840106696689293
% of timestamps found | 61.6%
Table 1. Statistics for Figure 2

Given two timestamps t1 and t2, and a tolerance level (or fuzzy threshold)
$\delta$, the timestamps t1 and t2 are said to match if the absolute
difference between them, denoted as —t1 - t2—, does not exceed $\delta$. The
fuzzy threshold $\delta$ is calculated as half the median of the differences
between all sequential pairs of timestamps in the dataset.

By employing fuzzy timestamp matching, the percentage of matched data can be
substantially increased. For example, in the case of AG191 from Etosha,
conventional timestamp matching resulted in a match percentage of 41.85%. With
the application of fuzzy matching, this percentage rose to 74.50%.

Figure 3. Fuzzy timestamp matching results on AG191 from the Etosha dataset

Notably, these additional matches, achieved through fuzzy matching, are
proximal rather than exact timestamp correlations, falling within the defined
tolerance. The increased density of matched timestamps facilitated the
identification of the same temperature-influenced centroids as found using
precise matching, along with additional centroids, particularly in the lower-
right region of the map (See Figure 3).

Subsequent trials of fuzzy timestamp matching yielded consistent results,
enhancing the number of temperature-influenced centroids identified,
particularly in regions that initially lacked such centroids. Considering
these improvements, it is recommended that fuzzy timestamp matching be
implemented in future endeavors to identify centroids in animal movement data.

Figure 2 and Table 1 present the outcomes of conducting steps (1)-(3) on
Elephant AM105 from the Kruger dataset. When comparing the weather station
temperature data with the study temperature data, we find a moderate
correlation, as indicated by an R-squared value of 0.6978. As stated by Moore
et al. (moore2009introduction, ), an R-squared value between 0.5 and 0.7
typically signifies a moderate effect size. Prior to calculating the R-squared
value, the data was zero-centered, which normalizes the data around zero.
Nevertheless, this process is not critical for our analysis because we’re
primarily interested in the correlation between temperature values for
clustering, rather than the individual temperature values themselves. It’s
vital to pay attention to the ”% of timestamps found” data. In this instance,
temperature measurements from the weather station coincide with only 61% of
the original dataset for Elephant AM105. This level of data retention,
achieved after applying both normalization and interpolation techniques,
suggests significant data loss.

Figure 4. Applying DBSCAN to AM105 with study data and weather station data

One potential solution to this issue is fuzzy timestamp matching, which aims
to match timestamps within a certain threshold. While this method can increase
data retention, it may also compromise data quality in favor of quantity. The
application of fuzzy timestamp matching will be discussed further in the
subsequent sections. Finally, we examine the performance of DBSCAN clustering
when using weather station data. After examining the performance of DBSCAN on
the elephant AM105 weather data in the Kruger dataset (See Figure 4), it is
evident that the Temp-influenced centroids effectively detect areas of high
density, indicating the previously mentioned points of interest. Remarkably,
the station data not only identifies some of the same points of interest as
the original study data but also reveals new locations. It is important to
note that identical parameters were used for both analyses. Given the
successful performance of the Temp-Influenced centroids technique on the
weather data and the moderate correlation observed between the study data and
the station data, we can now proceed with greater confidence to apply this
methodology to datasets lacking temperature information. This opens up new
possibilities for exploring and analyzing diverse datasets using this
technique.

###  3.4. Movement Modeling

Finally, movement modeling is conducted on the processed elephant data using
the MoveHMM, Trajr, and MoveVis packages in R.

_(1) MoveHMM Model:_ MoveHMM package employs a Hidden Markov Model with a
bivariate time series to model an animal’s step length and turning angle at
each location. For our study, two states are defined for the elephants. The
first state, foraging, is characterized by small step sizes and larger turning
angles. The second state, transiting, exhibits large step sizes and small
turning angles.

Figure 5. Etosha Mapping For All Elephant Groups

_(2) Trajr Model:_ The Trajr package uses first and second order finite
derivatives to calculate velocity and acceleration from a Trajectory Object.
Additional features such as straightness and directional change can also be
computed, offering insight into sudden speed changes and areas of limited
directional change.

Figure 6. Etosha Transition Probabilities Figure 7. Kruger Transition
Probabilities

_(3) MoveVis Model:_ The MoveVis package facilitates the animation of movement
data. Spatial frames are created from a move object, which is generated using
a built-in function that reads the elephant data. The resultant animated
visuals provide an intuitive understanding of elephant movement patterns.

Figure 8. Clustering with Temperature on HDBSCAN and AGGLO AG006

In the MoveHMM model, it uses a Hidden Markov Model. The model assumes that
there is an underlying, unobservable state process that governs the dynamics
of the observable process. The state at time $t$, $S(t)$, is only dependent on
the state at time $t-1,S(t-1)$, which is represented as $P(S(t)|S(t-1))$.

In the Trajr Model, it calculates velocity and acceleration of an animal by
the Trajectory Object. Let $P(i)$ be the position at time $i$, $V(i)$ be the
velocity, and $A(i)$ be the acceleration. These can be calculated as:

(4) $\begin{split}V(i)=P(i)-P(i-1)\\\ A(i)=V(i)-V(i-1)\end{split}$

Where the subtraction of position values gives the velocity, and the
subtraction of velocity values gives the acceleration.

In the MoveVis model, it creates spatial frames from a move object, where each
spatial frame F(t) at time t is a snapshot of all the position data at that
time, which can be denoted as:

(5) $F(t)=\\{P(i):i\in T(t)\\}$

Where $T(t)$ are all the indices at time $t$.

Each of these methods in the pipeline serves a distinct yet complementary role
in elucidating the factors influencing elephant movement patterns across
Africa, thereby contributing to more effective conservation strategies.

##  4\. Experimental evaluation

###  4.1. Performance Metrics for Clustering

The effectiveness of the two clustering algorithms, HDBSCAN and AGGLO, was
assessed using the elephant data that included temperature and elevation.
Figure 8 illustrates the clustering results obtained from these methods for
the elephant ID “AG006”. In comparison to the DBSCAN algorithm, HDBSCAN
demonstrated the ability to form varying density clusters.

Figure 9. Performance Metrics For Clustering

It was observed that AGGLO, despite its capability to handle thin and
elongated data shapes for clustering, did not present any advantages over
other tested clustering methods. AGGLO was found to incorporate all noise
present in the data, rendering DBSCAN and HDBSCAN more informative due to
their resilience to noise (See Figure 8).

Figure 10. State Mapping on AG004

For the evaluation of clustering algorithms, two metrics were chosen: the
Silhouette Score and the Calinski-Harabasz Index. These metrics were selected
as they effectively gauge the density, separation, and variation of the
clusters (See Figure 9).

Figure 11. Kruger Elephant Groups Pathing Mapped

The Silhouette Score demonstrated significant differences in the performance
of each clustering algorithm on each data set.

The HDBSCAN algorithm did not return any negative coefficients across the
clustering sets, and exhibited excellent scores for AG004 and AG192. It seems
that the ability to detect varying density clusters in HDBSCAN offers a
performance advantage. In terms of the Calinski-Harabasz index, HDBSCAN scored
substantially higher across all elephant groupings, suggesting that HDBSCAN
clusters were denser and more well-separated.

Figure 12. Elephant Movement Charecteristics Figure 13. Jaguar Movement
Charecteristics

###  4.2. HMM Modeling

Utilizing the MoveHMM R package, we performed Hidden Markov Model (HMM)
analysis of elephant group movement. This model uses a bivariate time series
of distance traveled between time (step size) and the change of direction
between time (turning angle).

Figure 14. Brazil Jaguars Pathing

For the analysis, initial step and angle parameters were established for each
state (See Figure 10). The plots generated for “AG004” depict two states: a
transit state characterized by larger steps with smaller turns, and a foraging
state where the elephant takes shorter steps with larger turns.

Figure 15. (a) Etosha National Park’s Wet Season as an average of all the
years; (b) Etosha National Park’s Dry Season as an average of all the years

Mapping was conducted to produce state-wise mapping for all elephant groups at
Etosha National Park (See Figure 5). These maps offer insights into key points
of interest for all groupings of elephants and help identify locations of
water and food resources. The transition probabilities of Etosha elephants
(See Figure 6) and Kruger elephants (See Figure 7) were also examined. Figure
11 depicts the movement path for elephant groups at Kruger National Park. The
results indicated that changes in temperature play a smaller role in
influencing the transition between states for Etosha elephants compared to
Kruger elephants.

Analyzing two diagrams (See Figure 15(a) and Figure 15(b)) reveals a distinct
contrast in elephant behavior between the wet and dry seasons. During the dry
season, elephants exhibit a tendency to congregate and cluster together in
specific locations, whereas in the wet season, they disperse and spread out
over a wider area. This observation highlights a clear pattern of behavior
based on the prevailing weather conditions. The dataset used for studying
elephant movements in Etosha National Park encompasses a timeframe ranging
from 2008 to 2014. Despite the span of years, a consistent pattern emerges,
demonstrating that elephants have consistently exhibited the same behavior
during every wet season and every dry season throughout the observed period.
This consistent recurrence of behavior across multiple years underscores the
reliability and repeatability of the observed pattern. By recognizing and
understanding these seasonal behavioral patterns, we can gain valuable
insights into elephant dynamics and potentially leverage this knowledge for
various applications, such as conservation efforts, resource management, and
human-elephant conflict mitigation strategies.

###  4.3. Comparison with Jaguar Movement

When comparing the movement patterns of different animal species, such as
elephants and jaguars, it becomes evident that their behaviors are influenced
by the characteristics of their respective environments. Here, we focus on
scalability of animal movement and how it can shed light on habitats in which
these animals reside.

In Kruger National Park, elephants exhibit a more linear form of movement,
characterized by traveling between specific points. Figure 12 illustrates this
behavior, showing elephants with minimal turning angles and short step
lengths. Such movement patterns can be attributed to the open safari landscape
found in the park. As elephants are large herbivores, they spend a significant
amount of their time traversing the terrain in search of resources. Their
movement from one resource to another explains the observed linear paths they
follow.

Contrasting the elephants’ behavior, jaguars tracked on the largest floodplain
in Brazil demonstrate distinct movement patterns. Figure 13 displays the
movement plots of jaguars, which exhibit random and erratic trajectories.
Jaguars are known to exhibit high-degree turns and cover large distances in
their movement, often adopting circular paths. The environment of the
Brazilian floodplain plays a crucial role in shaping their movement behavior.
Unlike elephants, the floodplain provides a consistent distribution of
resources throughout, eliminating the need for jaguars to travel long
distances or target specific areas for foraging. Figure 14 further illustrates
this point by showcasing the pathing of jaguars in Brazil.

Analyzing individual jaguars’ movements reveals that they primarily occupy
certain territorial ranges, with some exceptions. These ranges are evident
from the mapping of each jaguar’s path. However, there may be outliers, such
as a particular jaguar that follows a distinct and precise path to and from a
specific location. Additionally, jaguars tend to stay relatively close to
bodies of water, indicating their dependence on these water sources. The
presence of water serves as an anchor, restricting jaguars’ movements to
specific areas within their habitat.

It is worth noting that both elephant and jaguar movement patterns are
influenced by the features of the land they inhabit. Elephants’ extensive
travel and pathing to specific areas are driven by the need to access
resources, while jaguars’ erratic movement and territorial ranges reflect the
abundance of resources in their environment, supplemented by the presence of
water sources. By studying and comparing the scalability of animal movement,
we can gain insights into the unique characteristics of different ecosystems
and the ways in which they shape animal behaviors.

#### Consideration of a Control Species in Elephant Habitats:

The comparison between elephant and jaguar movements provides valuable
insights about how species-specific behaviors and environmental
characteristics influence animal mobility patterns. However, to isolate unique
elephant behaviors, it may be beneficial to consider a ‘control’ species
cohabiting with elephants and having different propensities for human
conflict. For example, less conflict-prone species such as giraffes or zebras
could serve as controls. If these species demonstrate similar linear paths
between resources as observed in elephants, it could suggest that these
movement patterns are largely dictated by the shared environment and resource
availability, rather than being exclusive to elephants. Contrasting movement
patterns might indicate species-specific factors such as size or social
structure influencing unique elephant behaviors. Alternatively, considering a
species more prone to human conflict like lions or hyenas could provide
interesting insights. Shared movement patterns highlight commonalities among
species frequently encountering human conflict, while unique patterns in
elephants could expose specific behaviors making them more susceptible to such
conflicts. Such comparisons within the same habitat could help isolate
species-specific movement patterns, providing better understanding of the
unique factors influencing elephant movements.

###  4.4. Use-case with POI data

Multiple methods were employed to study elephant movement patterns. DBSCAN was
utilized to calculate clusters and centroids based on location alone (”Without
Temp-influenced”) and in combination with temperature data (”Temp-
influenced”). Both feature spaces demonstrated promising capabilities in
identifying centroids in elephant movement, with each approach uncovering
centroids that the other method missed. To augment datasets lacking
temperature information, historical weather API was employed to incorporate
temperature data, successfully enriching the data for centroid identification.
Additionally, a ”fuzzy” timestamp matching technique was applied to expand the
availability of temperature data for analysis.

To ascertain the practical significance of these techniques in real-world
applications, it is essential to overlay additional contextual information
onto the calculated centroids. This includes factors such as terrain
characteristics, satellite imagery, human settlements, water sources, and
population density. By incorporating this supplementary data, we can assess
whether the calculated centroids, both Temp-Influenced and Without Temp-
influenced, hold any meaningful insights. To facilitate exploration of the
calculated centroids and their relationship with these additional features, an
application has been developed. The application allows users to examine the
centroids interactively and visualize them in conjunction with the overlaid
contextual information444The application can be accessed at
https://g1776-elephantcentroids-app-95v7am.streamlit.app/.

Figure 16. Elephant centroids around human settlement in Etosha National Park

In Figure 16, elephant centroids in the vicinity of human settlements within
Etosha National Park, specifically at the Halali and Okaukuejo campsites, are
depicted. The centroids are represented by elephant icons, with green markers
indicating groups of centroids. These centroids encompass both Temp-Influenced
and Without Temp-influenced variants, utilizing fuzzy matching. The circular
boundary surrounding the settlement represents the extent of human population
in the area. The presence of centroids within and around this boundary
suggests the interest of elephants in the settlement and its surrounding
region. According to information from Etosha National Park’s website, both the
Halali and Okaukuejo campsites feature watering holes, which could explain the
elephants’ affinity for these areas. Additionally, centroids are observed near
bodies of water, where elephant centroids from the Kruger dataset align along
a river, with a nearby campsite visible. This implies that elephants may
prioritize the presence of rivers over human settlements, as the concentration
of centroids is primarily along the river rather than around the settlement.

Analyzing the centroids within their surrounding context provided valuable
insights into their potential applications. It was observed that some
centroids were situated near settlements with watering holes, while others
tended to be in proximity to rivers. Interestingly, no discernible pattern
emerged to suggest that Temp-influenced centroids differed significantly from
Without Temp-influenced centroids. We initially anticipated that Temp-
influenced centroids would exhibit a greater affinity for water sources and
clusters of trees (providing shade) compared to the Without Temp-influenced
centroids. However, this expectation was not supported by the analysis.
Overall, the contextual examination of the centroids allowed for a more
comprehensive understanding of their practical utility. It highlighted the
relationship between centroids and their surroundings, indicating that some
centroids are found near settlements with watering holes, while others are
concentrated along rivers. However, no distinctive pattern emerged to
differentiate the Temp-influenced centroids from the Without Temp-influenced
centroids in terms of their preferences for water sources or tree clusters.

Figure 17. Velocity/ Acceleration of AG004

Geometry | Name | Type | # Centroids in
---|---|---|---
|  |  | settlement cluster
POINT (16.4710969 -19.0356338) | Halali | village | 23
POINT (15.295068 -17.6750468) | Ogongo | village | 10
POINT (14.5680119 -17.3940925) | Omahenene | village | 9
POINT (16.0210914 -17.9842151) | Olukonda | hamlet | 9
POINT (14.3015546 -19.8967052) | - | hamlet | 1
POINT (15.9146617 -17.2211624) | Okawe | village | 1
POINT (14.4043373 -18.7978984) | - | village | 1
POINT (14.1598649 -18.6311834) | Otjitundua | village | 1
POINT (14.960421 -20.37384) | Khorixas | town | 1
POINT (17.4722164 -17.6549121) | Omupini | hamlet | 1

Table 2. Settlement Clusters and Centroid Counts

The presence of black circles on the plot (See Figure 17) signifies that the
elephants spend a significant amount of time in those specific areas. Among
these circles, the one labeled as C is located near a man-made waterhole and a
local tourist station. This finding suggests that the elephants are likely
drawn to this location due to the availability of water and potentially other
resources. The circled area labeled as B corresponds to the vicinity of
Dolomite Camp, which serves as an explanation for its designation as an area
of interest. It is plausible that the camp provides attractants or resources
that make it appealing to the elephants. Regarding the circle labeled as A,
its placement suggests that it may represent an area abundant in food, water,
and/or shade. This inference is supported by the fact that the group of
elephants traveled westward towards this particular location. It is likely
that this area offers the necessary resources to support the elephants’ needs
during their journey. Overall, the generated plot effectively illustrates the
areas of interest for the elephant group AG004. By analyzing the distribution
of the black circles, we can identify key locations where the elephants spend
a substantial amount of time. This information can be crucial for
understanding their habitat preferences, resource utilization, and potentially
informing conservation efforts and management strategies to ensure the long-
term well-being of the elephant population.

By considering the combined centroids along with human settlements, we gain
the capability to identify settlements that elephants may potentially inhabit
at certain times. When visually inspecting the map, it becomes apparent which
settlements have a significant concentration of elephant centroids, signaling
the need for further investigation. To automate this selection process, we
applied KMeans and DBSCAN algorithms to the problem. Subsequently, we ranked
the settlements based on the number of elephant centroids present in each
respective cluster. The outcome of this process, executed on the Etosha
dataset, is presented in Table 2. This ranking provides a valuable starting
point for directing efforts towards locating elephants.

Our research into elephant movement ecology suggests several prospective
avenues for exploration, which may enhance conservation strategies.
Understanding the impacts of climate change and land use changes on elephant
migration, alongside the effects of extreme weather events, interactions with
other species, landscape connectivity, behavioral alterations due to poaching,
and responses to human infrastructure and changing rainfall patterns could
present significant contributions. A vital focus could be the role of
Artificial Intelligence (AI) and Machine Learning (ML) in analyzing extensive
data from sources like GPS collaring, and predicting elephant movements based
on varied factors like climate, land use, and poaching activities. AI and ML
could also be instrumental in real-time monitoring and providing alerts about
potential human-elephant conflicts or imminent threats like poaching, aiding
quicker responses. These directions aim to encourage further research,
underlining the significance of AI and ML tools in wildlife ecology and
conservation, and fostering sustainable human-elephant coexistence in Sub-
Saharan Africa.

##  5\. Conclusion and Future Works

This paper provides insightful findings on animal movement patterns,
specifically elephants, demonstrating the influence of temperature and land
features on their behavior. The methodologies used, when extended to analyze
jaguar data, revealed distinct behaviors and movement strategies. In future,
we plan to integrate uncertainty analysis into our predictive model as part of
our future work. This includes developing methods to quantify and visualize
error propagation within our predictions, especially considering geographical
features and human settlement influences. Furthermore, we plan to incorporate
measures to adjust the model’s confidence bounds based on these error
analyses, enabling the tool to self-calibrate as it encounters varying levels
of uncertainty across different terrain types.

## Acknowledgements

This research was partially funded by the Federal Ministry of Education and
Research (BMBF), Germany under the project LeibnizKILabor with grant No.
01DD20003.

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