Comparing survey and multiple recruitment–mortality models to assess growth rates and population projections

• Published in: Ecology and Evolution
• Main Authors: Severud, William J., DelGiudice, Glenn D., Bump, Joseph K.
• Other Authors: National Science Foundation
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2019
• Subjects: Alces alces
• Online Access: http://dx.doi.org/10.1002/ece3.5725; https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.5725; https://onlinelibrary.wiley.com/doi/full-xml/10.1002/ece3.5725

Abstract Estimation of population trends and demographic parameters is important to our understanding of fundamental ecology and species management, yet these data are often difficult to obtain without the use of data from population surveys or marking animals. The northeastern Minnesota moose ( Alces alces Linnaeus, 1758) population declined 58% during 2006–2017, yet aerial surveys indicated stability during 2012–2017. In response to the decline, the Minnesota Department of Natural Resources (MNDNR) initiated studies of adult and calf survival to better understand cause‐specific mortality, calf recruitment, and factors influencing the population trajectory. We estimated population growth rate ( λ ) using adult survival and calf recruitment data from demographic studies and the recruitment–mortality (R‐M) Equation and compared these estimates to those calculated using data from aerial surveys. We then projected population dynamics 50 years using each resulting λ and used a stochastic model to project population dynamics 30 years using data from the MNDNR's studies. Calculations of λ derived from 2012 to 2017 survey data, and the R‐M Equation indicated growth (1.02 ± 0.16 [ SE ] and 1.01 ± 0.04, respectively). However, the stochastic model indicated a decline in the population over 30 years ( λ = 0.91 ± 0.004; 2014–2044). The R‐M Equation has utility for estimating λ , and the supporting information from demographic collaring studies also helps to better address management questions. Furthermore, estimates of λ calculated using collaring data were more certain and reflective of current conditions. Long‐term monitoring using collars would better inform population performance predictions and demographic responses to environmental variability.