OOS 31-1
Can the floristic quality assessment index be used to predict grassland habitat quality for beneficial arthropods?
The Conservation Reserve Program (CRP) of the USDA Farm Service sows warm season grasslands primarily to protect soils, and these grasslands are a widely used conservation strategy in agricultural landscapes for restoring biodiversity and ecological processes. Managing these grasslands for beneficial insects that provide important ecosystem services is especially important to farmers, and testing the role of different habitat quality measures for predicting beneficial insect richness and abundance is needed so that CRP grasslands can be evaluated for their conservation value quickly and management actions can be implemented. In Southwestern Ohio, 24 CRP grasslands were selected to investigate how well various vegetation-based habitat quality measures relate to beneficial insects. In particular, different measures of the Floristic Quality Assessment Index (FQAI), which has a benefit of reducing sampling effort, were compared to several plant richness measures to evaluate their role in quantifying habitat quality for beetles, bees and butterflies. In each grassland all herbaceous and woody plant species were identified and percent cover estimated within five to ten, 10m2 circular plots. A subset of the grasslands were selected to sample butterflies, bees and beetles. Data was analyzed using generalized linear and linear mixed models with site as a random variable.
Results/Conclusions
Grasslands ranged in quality as measured by all vegetation-based variables, for example FQAI calculated with plant presence-absence data ranged from 7.2 to 20.1, FQAI calculated with plant cover data ranged from 7.2 to 49.5 and native plant richness ranged from 8 to 49 species. Butterflies were sampled in 14 grasslands and a total of 533 butterflies representing 32 species were observed. Bees and beetles were sampled from 10 grasslands and a total of 2672 bees of 48 species and 3330 beetles from 146 species were captured. The best vegetation-based habitat quality predictor for butterfly richness was FQAI calculated using presence-absence of only native plant species data, which outperformed the next best predictor, the number of plant species (LRT=0.75; p<0.0001). No vegetation-based habitat quality predictors adequately explained bee richness, however all of them worked equally well for bee abundance. For beetle richness, the simpson diversity index was the best predictor and outperformed the next best, FQAI calculated using presence-absence of only native plant species data (LRT=3.09; p<0.0001). Our results indicate that measures such as FQAI that assign habitat quality via weighting plants based on their ecological breadth may be provide a more rapid assessment of CRP grassland quality for beneficial insects.