• Wang, Guiming M.
  Dept of Wildlife, Fisheries and Aquaculture, Mississippi State Univ., Mississippi State, MS
  
• Guillaumet, Alban
  Dept of Wildlife, Fisheries and Aquaculture, Mississippi State Univ., Mississippi State, MS
  
• Hobbs, N. Thompson
  Natural Resources Ecology Laboratory, and Graduate Degree Program in Ecology, Colorado State Univ., Fort Collins, CO
  
• Slade, Norman A.
  Dept of Ecology and Evolutionary Biology and Museum of Natural History/Biodiversity Research Center, Univ. of Kansas, Lawerence, KS
  
• Merritt, Joseph F.
  Illinois Natural History Survey, Champaign, IL
  
• Getz, Lowell L.
  Dept of Animal Biology, Univ. of Illinois at Urbana-Champaign, Urbana, IL
  
• Hunter Jr. Malcolm L.
  Dept of Wildlife Ecology, Univ. of Maine, Orono, ME
  
• Witham, Jack
  Dept of Wildlife Ecology, Univ. of Maine, Orono, ME
  
• Vessey, Stephen H.
  Dept of Biological Sciences, Bowling Green State Univ., Bowling Green, OH
  

Deterministic feedbacks within populations interact with extrinsic, stochastic processes to generate complex patterns of animal abundance over time and space. Animals inherently differ in their responses to fluctuating environments due to differences in body sizes and life history traits. However, controversy remains about the relative importance of deterministic and stochastic forces in shaping population dynamics of large and small mammals. We hypothesized that effects of environmental stochasticity and density dependence are stronger in small mammal populations relative to their effects in large mammal populations and thus differentiate the patterns of population dynamics between them. We conducted an extensive, comparative analysis of population dynamics in large and small mammals to test our hypothesis, using seven population parameters to describe general dynamic patterns for 23 (14 species) time series of observations of abundance of large mammals and 38 (21 species) time series for small mammals. We used state-space models to estimate the strength of direct and delayed density dependence as well as the strength of environmental stochasticity. We further used phylogenetic comparative analysis to detect differences in population dynamic patterns and individual population parameters, respectively, between large and small mammals. General population dynamic patterns differed between large and small mammals. However, the strength of direct and delayed density dependence was comparable between large and small mammals. Moreover, the variances of population growth rates and environmental stochasticity were greater in small mammals than in large mammals. Therefore, differences in population response to stochastic forces and strength of environmental stochasticity are the primary factor that differentiates population dynamic patterns between large and small mammal species.