Ground beetles (Coleoptera:Carabidae) are generalist predators frequently encountered in agricultural landscapes. High ground beetle populations may contribute to arthropod pest management on farms. Understanding the life history of these species, particularly the habitat requirements of species that over-winter as adults is important to managing agroecosystems to encourage these beneficial insects. Soil samples were taken in winter from a variety of habitats on two Western Oregon farms to compare the abundance of ground beetles and other generalist predators in different on farm habitats. Nonmetric Multidimensional Scaling ordination based on Sorensen distances was used to compare the invertebrate communities of the samples. Four methods were compared to test whether the 12 species of ground beetles that were collected from the samples were responding to the dominant environmental gradients in the same way. The first method was simply by visually comparing the average location of each species in the ordination and identifying groups. The second method involved calculating standardized chi squared distances for the abundances of each species pair. The third method used ordination scores as predictors in a nonparametric multiple regression of species abundances. Correlations of the fitted values of each species were used to compare the species responses to environmental gradients. Finally the Spatial Analysis by Distance Indices method (SADIE) was used to test whether species were associated in ordination space.
Results/Conclusions
The different methods of testing for group membership resulted in the elimination of different subsets of carabids from the a priori group of all carabids. The elimination of these species reduced the variability in the number of individuals of the group found in each type of habitat. That reduction in variability increased the effect of habitat as measured by one way anova tests comparing ground beetle abundance in the different habitat types. By identifying which species belong to life history based functional groups a better model for predicting growing season densities of these important predators can be created.
