Impact of climatic factors on genetic diversity of Stipa breviflora populations in Inner Mongolia.

Genetic diversity of Stipa breviflora populations in the Inner Mongolian grasslands of China and its possible correlation with climatic factors was examined using geographic information systems and random amplified polymorphism DNA analysis. A total of 308 bands were produced with 28 arbitrary decamer oligonucleotide. Three major findings were demonstrated. First, the genetic diversity of S. breviflora was high but lower than that of Stipa grandis and Stipa krylovii. Second, genetic distances between the populations analyzed using the unweighted pair group method and the Mantel test had a highly positive correlation with geographical distances, indicating that spatial separation of this species in the studied area produced genetic shift in the population. Finally, both canonical correspondence and Pearson's analyses revealed strong correlations between genetic differentiation and temperature in the area. We therefore conclude that temperature variations play an important role in genetic differentiations among the investigated S. breviflora populations.

Hierarchical Bayesian scaling of soil properties across urban, agricultural, and desert ecosystems.

Ecologists increasingly use plot-scale data to inform research and policy related to regional and global environmental change. For soil chemistry research, scaling from the plot to the region is especially difficult due to high spatial variability at all scales. We used a hierarchical Bayesian model of plot-scale soil nutrient pools to predict storage of soil organic carbon (oC), inorganic carbon (iC), total nitrogen (N), and available phosphorus (avP) in a 7962-km2 area including the Phoenix, Arizona, USA, metropolitan area and its desert and agricultural surroundings. The Bayesian approach was compared to a traditional approach that multiplied mean values for urban mesic residential, urban xeric residential, nonresidential urban, agricultural, and desert areas by the aerial coverage of each land-use type. Both approaches suggest that oC, N, and avP are correlated with each other and are higher (in g/m2) in mesic residential and agricultural areas than in deserts or xeric residential areas. In addition to traditional biophysical variables, cultural variables related to impervious surface cover, tree cover, and turfgrass cover were significant in regression models predicting the regional distribution of soil properties. We estimate that 1140 Gg of oC have accumulated in human-dominated soils of this region, but a significant portion of this new C has a very short mean residence time in mesic yards and agricultural soils. For N, we estimate that 130 Gg have accumulated in soils, which explains a significant portion of "missing N" observed in the regional N budget. Predictions for iC differed between the approaches because the Bayesian approach predicted iC as a function of elevation while the traditional approach employed only land use. We suggest that Bayesian scaling enables models that are flexible enough to accommodate the diverse factors controlling soil chemistry in desert, urban, and agricultural ecosystems and, thus, may represent an important tool for ecological scaling that spans land-use types. Urban planners and city managers attempting to reduce C emissions and N pollution should consider ways that landscape choices and impervious surface cover affect city-wide soil C, N, and P storage.