Abstract

Topic: Moose management and monitoring

Using Statistical Population Reconstruction to Fill Temporal Gaps in Aerial Survey Estimates of Moose Abundance and Demographics

Sergey S Berg¹, Christopher Stocken², Morgan Swingen³, Ron Moen⁴, Steve Windels⁵, Michelle Carstensen⁶, Seth Moore⁷, Anna Weesies⁷, Tiffany Wolf⁸, William J. Severud⁹

Department of Computer and Data Sciences, University of St. Thomas, 2115 Summit Avenue, St. Paul, MN 55105, USA
Department of Computer and Data Sciences, University of St. Thomas, St. Paul, MN 55105, USA
1854 Treaty Authority, 4428 Haines Road, Duluth, MN 55811 USA
Natural Resources Research Institute, University of Minnesota Duluth, 5013 Miller Trunk Highway, Duluth, MN 55811 USA
National Park Service, Voyageurs National Park, 360 Highway 11 East, International Falls, MN 56649 USA
Minnesota Department of Natural Resources, Wildlife Health Program, 5463 West Broadway, Forest Lake, MN 55025 USA
Grand Portage Band of Lake Superior Chippewa, Department of Natural Resources, 27 Store Road, Grand Portage, MN 55605 USA
Department of Veterinary Population Medicine, University of Minnesota, 1988 Fitch Avenue, Saint Paul, MN 55108 USA
Department of Natural Resource Management, South Dakota State University, Box 2140B, McFadden Biostress Laboratory 138, Brookings, SD 57006, USA

Background: Estimating moose abundance over the large spatial scales at which many management decisions are made often relies on aerial surveys conducted during the late autumn and winter months when plentiful snow allows for visual detection of animals from above. These aerial surveys, however, present numerous logistical challenges that include insufficient snow cover, high costs, adverse weather conditions, personnel availability, and other external factors. Statistical population reconstruction (SPR) may provide a reliable and cost-effective means of estimating moose abundance and other demographic parameters during years when aerial surveys are not conducted by integrating survey data from other years with survival information from radiotelemetry studies. Objectives: Our objective was to quantify how the amount of available aerial survey and radio telemetry data, as measured by the number, age, sex of monitored animals each year, influences the accuracy and precision with which SPR can be used to fill temporal gaps in demographic estimates typically derived from aerial surveys. Methods: We used a Monte Carlo simulation to generate aerial survey and radiotelemetry data for moose populations experiencing a wide range of demographic conditions and survey intensities. We then used a combination of Pearson correlation and relative absolute deviation (RAD) to quantify the accuracy and precision of model estimates of overall abundance and bull- and calf-to-cow ratios. Results: Given sufficient radio-telemetry data, model estimates of overall abundance and demographic ratios were highly correlated with simulated values and had relatively low RAD when the number of years of missing aerial data were few. As the number of years without aerial surveys increased, the accuracy and precision of these model-derived estimates decreased steadily. As expected, model performance was also strongly influenced by the amount and precision of available radiotelemetry and aerial survey data. Conclusions: We encourage wildlife managers to explore using SPR models to fill any temporal gaps that may arise in aerial survey estimates of moose demographics due to insufficient snow cover, high costs, or other factors. We also recommend that agencies perform cost-benefit analyses to assess if the reduced cost associated with foregoing aerial surveys is worth the corresponding loss in accuracy and precision.