ESTIMATING MOOSE ABUNDANCE BY USING STATISTICAL POPULATION RECONSTRUCTION TO FILL TEMPORAL GAPS IN AERIAL SURVEY DATA

Abstract

Changes in moose populations are often evaluated using aerial surveys, which are expensive and dependent upon weather conditions and logistical constraints that sometimes preclude their completion each year. Statistical population reconstruction (SPR) provides a versatile framework for combining existing information from aerial surveys with auxiliary data such as telemetry-derived survival estimates to fill the temporal gaps in these surveys. We examined the performance of SPR in estimating moose abundance and other demographic characteristics in Minnesota during a year when the survey was not flown (2021; due to the COVID-19 pandemic) by combining data from 5 separate telemetry studies with aerial survey results from 2005–2020 and 2022–2024. We estimated an overall abundance of 3,212 moose (95% CI = 2,130–3,883) in 2021, with a corresponding bull-to-cow ratio of 0.84 (95% CI = 0.46–1.33) and a calf-to-cow ratio of 0.38 (95% CI = 0.22–0.70), which are consistent with previous survey results and independent population models. We used Leave-One-Out Cross-Validation (LOOCV) to confirm the accuracy and precision of these estimates, and to explore how additional years of missing aerial survey data would have impacted reconstructed estimates of demographic parameters. This validation analysis demonstrated that missing a single year of aerial survey data did not substantially impact model performance, and that additional years of missing data would have resulted in small but steady decreases in both the accuracy and precision of model-derived estimates. Our research also highlights the need for telemetry studies of yearling moose survival to improve the performance of SPR and provide a clearer picture of moose demography in Minnesota.