Machine Learning-Embedded Spatial Modeling for Ecological Data

Music City Center 

In dynamic riverine systems, such as dammed rivers, habitat conditions can fluctuate rapidly due to environmental and anthropogenic factors. Piping plovers (Charadrius melodus), a threatened bird species, rely on sandbar habitats in these environments for nesting, brood-rearing, and foraging. Effective habitat management requires understanding their habitat usage, with vegetation reduction practices playing a key role in improving reproductive success by creating suitable habitats and reducing predation risks. This study combines Bayesian hierarchical modeling with machine learning to develop a spatial modeling framework that captures plover nesting dynamics along the Missouri River, addressing complex spatial dependencies such as discontinuities and abrupt transitions. Our findings provide valuable insights into plover nesting and habitat usage informing adaptive management strategies to support piping plover conservation efforts in dynamic riverine environments.