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Abstract Predator–prey interactions are important for regulating populations and structuring communities but are affected by many dynamic, complex factors across large scales, making them difficult to study. Integrated population models (IPMs) offer a potential solution to understanding predator–prey relationships by providing a framework for leveraging many different datasets and testing hypotheses about interactive factors. Here, we evaluate the coyote–deer (Canis latrans–Odocoileus virginianus) predator–prey relationship across the state of North Carolina (NC). Because both species have similar habitat requirements and may respond to human disturbance, we considered net primary productivity (NPP) and urbanization as key mediating factors. We estimated deer survival and fecundity by integrating camera trap, harvest, and biological and hunter observation datasets into a two‐stage, two‐sex Lefkovich population projection matrix. We allowed survival and fecundity to vary as functions of urbanization, NPP, and coyote density, and projected abundance forward to test eight hypothetical scenarios. We estimated initial average deer and coyote densities to be 11.83 (95% CI: 5.64, 20.80) and 0.46 (95% CI: 0.02, 1.45) individuals/km2, respectively. We found a negative, though highly uncertain, relationship between the current levels of coyote density and deer fecundity in most areas that became more negative under hypothetical conditions of lower NPP or higher urbanization, leading to lower projected deer abundances. Though not conclusive, our results indicate the possibility that coyotes could have stronger effects on deer populations in NC if their densities rise, but primarily in less productive and/or more suburban habitats. Our case study provides an example of how IPMs can be used to better understand the complex relationships between predator and prey under changing environmental conditions. |
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