Big Data Analytics and Statistical Modeling in Wildlife Population Studies
Keywords:
Wildlife population, big data analytics, statistical modeling, occupancy models, capture-recapture models, spatial capture-recapture, integrated population models, data management, data visualization, machine learning, GPS tracking, camera traps, acoustic monitoring, remote sensing, citizen scienceAbstract
Wildlife population studies are increasingly reliant on "big data," encompassing vast and complex datasets generated by tracking technologies, remote sensing, and citizen science initiatives. This data presents both opportunities and challenges for researchers seeking to understand population dynamics, habitat use, and conservation challenges. This paper explores the synergistic roles of big data analytics and statistical modeling in extracting meaningful insights from this data. We discuss various statistical modeling approaches, including occupancy models, capture-recapture models, and spatial capture-recapture models, highlighting their applications in estimating population size, distribution, and demographic parameters. We also examine the role of big data analytics in handling, processing, and visualizing large datasets, emphasizing the importance of data management, quality control, and integration of multiple data sources. This paper underscores the transformative potential of big data analytics and statistical modeling in advancing wildlife research and conservation.
