Landscape factors can significantly influence arthropod populations. The economically important brown stink bug, Euschistus servus (Say) (Hemiptera: Pentatomidae), is a native mobile, polyphagous and multivoltine pest of many crops in southeastern United States and understanding the relative influence of local and landscape factors on their reproduction may facilitate population management. Finite rate of population increase (λ) was estimated in four major crop hosts - maize, peanut, cotton, and soybean - over 3 yr in 16 landscapes of southern Georgia. A geographic information system (GIS) was used to characterize the surrounding landscape structure. LASSO regression was used to identify the subset of local and landscape characteristics and predator densities that account for variation in λ. The percentage area of maize, peanut and woodland and pasture in the landscape and the connectivity of cropland had no influence on E. servus λ. The best model for explaining variation in λ included only four predictor variables: whether or not the sampled field was a soybean field, mean natural enemy density in the field, percentage area of cotton in the landscape and the percentage area of soybean in the landscape. Soybean was the single most important variable for determining E. servus λ, with much greater reproduction in soybean fields than in other crop species. Penalized regression and post-selection inference provide conservative estimates of the landscape-scale determinants of E. servus reproduction and indicate that a relatively simple set of in-field and landscape variables influences reproduction in this species.
Bibliographical noteFunding Information:
We thank Andy Hornbuckle, Melissa Thompson, and numerous student workers for their help in the field. We also thank two anonymous reviewers for their comments which have greatly improved the manuscript. The project was supported by the National Institute of Food and Agriculture (grant number 2008-35302-04709 to D.A.A., D.M.O., and J.R.R.).
- brown stink bug
- least angle regression