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

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.

An Analysis of Elephants' Movement Data in Sub-Saharan Africa Using Clustering

Understanding the movement of animals is crucial to conservation efforts. Past research often focuses on factors affecting movement, rather than locations of interest that animals return to or habitat. We explore the use of clustering to identify locations of interest to African Elephants in regions of Sub-Saharan Africa. Our analysis was performed using publicly available datasets for tracking African elephants at Kruger National Park (KNP), South Africa; Etosha National Park, Namibia; as well as areas in Burkina Faso and the Congo. Using the DBSCAN and KMeans clustering algorithms, we calculate clusters and centroids to simplify elephant movement data and highlight important locations of interest. Through a comparison of feature spaces with and without temperature, we show that temperature is an important feature to explain movement clustering. Recognizing the importance of temperature, we develop a technique to add external temperature data from an API to other geospatial datasets that would otherwise not have temperature data. After addressing the hurdles of using external data with marginally different timestamps, we consider the quality of this data, and the quality of the centroids of the clusters calculated based on this external temperature data. Finally, we overlay these centroids onto satellite imagery and locations of human settlements to validate the real-life applications of the calculated centroids to identify locations of interest for elephants. As expected, we confirmed that elephants tend to cluster their movement around sources of water as well as some human settlements, especially those with water holes. Identifying key locations of interest for elephants is beneficial in predicting the movement of elephants and preventing poaching. These methods may in the future be applied to other animals beyond elephants to identify locations of interests for them.