Modeling Individual-level and Population-level Nest Success of California Condors from Movement Data

Abstract

The California Condor (Gymnogyps californianus) is a critically endangered species with populations that are not currently self-sustaining. Although understanding nest success is key to understanding trends in their populations, field monitoring of condor nests has become increasingly challenging as the number of nesting condors has increased and their range has expanded. We investigated whether California Condor nest fate could be accurately estimated from telemetry data with limited field observations. Our study focused on the southern California population of California Condors (2015-2022), and we used a recently published Bayesian hierarchical modeling framework that combines movement data and occasional field observations to estimate individual-level and population-level nest success. The model detected shifts in space use to categorize if each nest failed or if a young fledged. Estimated model parameters suggested that after nest failure, condors shifted toward more expansive space use. Additional field observations, not included as data in the model, provided evidence that we accurately categorized nest fate for 63 out of 65 California Condor nesting attempts. Finally, we scaled individual-level reproductive success to estimate annual population-level nesting success. These methods offer managers a way to reduce field monitoring efforts while still allowing for estimation of nest success, which will be key as the breeding populations of California Condors continue to grow and become more widely spread across the landscape.