A pattern analyser based on Bayesian network for animal movement modelling

Animal movement patterns are indicators of many factors. The grazing capacity of the landscape, and the animal grazing habits can be determined by the time spent in an area. Change in local weather, and ecological conditions cause a change in these habits, and human presence causes uncommon change in this behaviour. Since a few decades, many organizations, zoos and individuals have tagged animals to collect geo-locations data for biodiversity research as well as for management purpose. In conservation particularly, location datasets are used to map species’ home ranges, and, when used in near real-time, to enhance law enforcement, mitigate human-wildlife conflicts. While our literature review has found no previous research that combines local biotope condition, real-time or near-real-time poaching indicators with location timeseries to evaluate the change in animal behaviour, we used Garamba’s African Elephants data to implement an Animal Movement Patterns analyser using Bayesian Netwo...

 Methodology used Two types of data sets are to be considered in this work: ecological components and human indicators as well as telemetric movement timeseries. We will collect ecological components and climate variables that influence change in animals’ behavior. We will use Garamba National Park’s weather data, Digital Elevation Model and land use data to get ecological components for the training data. We will use human indicators’ timeseries. This data is regularly collected by Garamba National park. We will also use NASA Fire Information for Resource Management alerts as an additional factor that is collected to alert of any fires. Finally, we will collect telemetric historical and real-time dataset on EarthRanger platform where Garamba National Park and African Parks (the private partner) are developing their data integration. Data preparation will be conducted by overlapping geographical data and timeseries to produce the final dataset with PyGIS to link timeseries with add...

Human activities and their impact influence animals behavior, be it for adaptation to the change in the ecosystem as well as for threats escaping. These footprints are the biggest drivers for the change in natural movement. Efforts are being made to mitigate these uncontrolled changes. Data Mining and Artificial Intelligence has been used by eco-informaticians to model this change, and their work is valuable to evaluate, understand and predict change, as well as to develop mitigation strategies and adaptation catalogue. This is an ongoing process in the intersection of many domains: ecology awareness, societal adaptation, climate science, policy development and law enforcement. Since a few decades, many organizations, zoos and individuals have tagged animals to collect geo-locations data for biodiversity research as well as for management purpose. In conservation particularly, location datasets are used to map species’ home ranges, and, when used in near real-time, to enhance law enfor...