Weather Affects Grasshopper Population Dynamics in Continental Grassland Over Annual and Decadal Periods

Understanding the complex dynamics of insect herbivores requires consideration of both exogenous and endogenous factors at multiple temporal scales. This problem is difficult due to differences in population responses among closely related taxa. Increased understanding of dynamic relationships between exogenous and endogenous factors will facilitate forecasting and suggest nodes in the life cycle of economically important species susceptible to intervention by managers. This study uses an information-theoretic approach to examine the contributions of weather and density to model population densities and growth rates of nine common grasshopper species from continental U.S. grassland over 25years. In general, grass-feeding species and total grass-feeders as a functional group were most closely associated with weather during the year before hatching. Increased variability in prior growing season precipitation was associated with increased densities of Mermiria bivittata, Opeia obscura, Phoetaliotes nebrascensis, and the grass-feeding guild. Melanoplus sanguinipes densities tended to be smaller following warm fall seasons, while Amphitoruns coloradus declined during the positive phase of the North Atlantic Oscillation or after warmer than average winters. Population growth rate dynamics of all grouped species combinations were best explained by models including variability in precipitation during the prior year growing season. Large-scale Pacific Decadal Oscillation (PDO) patterns were also associated with growth rate dynamics of the mixed-feeding species group. Density showed a negative relationship with population growth rates of five species. This study indicates the importance of parental and diapause environmental conditions and the utility of incorporating long-term, readily obtained decadal weather indices for forecasting grasshopper densities and identifying critical years with regard to grasshopper management—at least to the degree that the past will continue to predict the future as global climates change.