Shortgrass Steppe LTER

Changes in the Microbial Community May Determine the Biogeochemical Patterns in the Great Plains

Shortgrass Steppe LTER investigators have found that rates of nutrient cycling vary across landscape and regional scales. This biogeochemical variability can partially be attributed to patterns in plant community characteristics and abiotic and soil conditions across topographic gradients at the landscape-scale or across regional climatic gradients. However, it is also possible that concomitant changes in the microbial communities performing these biogeochemical processes occur across the same spatial scales and may therefore contribute to the observed biogeochemical trends. To assess patterns of microbial community composition across regional and landscape scales, SGS investigators sampled three grassland communities spanning a 500-mm regional precipitation gradient across the central Great Plains. Soil microbial community composition and biomass were determined using phospholipid fatty acid (PLFA) analysis. Microbial biomass increased across the regional gradient, and different microbial communities were associated with the different grassland community types. The relative abundance of fungi decreased while gram-negative anaerobic bacteria increased from shortgrass steppe to tallgrass prairie. There were no differences in microbial biomass at the landscape-scale, and the only alteration in microbial community composition between upland and lowland landscape positions was a shift toward more nonspecific bacteria in lowlands. The fact that the trends in microbial biomass and community composition at the landscape-scale were less pronounced suggests that variability in microbial community composition is larger regionally across the Great Plains than landscape variability associated with topographical features at any particular site. Alterations in the microbial community may play a role in determining the biogeochemical patterns of grasslands in the Great Plains region.

Prairie Dog Towns Have Little Affect on Cattle Grazing in Shortgrass Steppe

SGS Investigators examined how cattle graze on prairie dog towns in the shortgrass steppe of northeastern Colorado. In a study of cattle presence and behavior on 15 black-tailed prairie dog towns, cattle neither significantly preferred nor avoided the critters, much-maligned by ranchers. Vegetation cover on prairie dog towns did not significantly differ from most other habitats, but vegetation on prairie dog towns was significantly shorter on prairie dog towns. Nevertheless, foraging observations indicated that there was no significant difference between cattle foraging rates on swales (70.9 bites/min) and prairie dog towns (69.5 bites/min). Thus, cattle of the shortgrass steppe appear to use prairie dog towns in proportion to their availability and, while there, they graze as intensively as they do on habitats not inhabited by prairie dogs.

Biodiversity Measurement Tool Questioned

The species-area relationship (SAR) provides the foundation for much of theoretical ecology and conservation practice. However, by ignoring the time component, the SAR offers an incomplete model for biodiversity dynamics. Investigators at the SGS LTER used long-term data from permanent plots in Kansas grasslands to show that the increase in the number of species found with increasing periods of observation takes the same power-law form as the SAR. A statistical model including time, area, and their interaction explains 98% of variation in mean species number and demonstrates that while the effect of time depends on area, and vice versa, time has strong effects on species number even at relatively broad spatial scales. Results suggest equivalence of underlying processes in space and time and raise questions about the diversity estimates currently used by basic researchers and conservation practitioners.

Determining the Impact of Vegetation on Microclimate

Investigators at the SGS LTER site in northern Colorado conducted a 10-year study to determine the impact of vegetation on microclimate, which previously has not been adequately considered in the analysis of temperature forecasting and modeling.

A daily 850-700 mb layer mean temperature, computed from the National Center for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis, and satellite-derived greenness values, as defined by NDVI (Normalized Difference Vegetation Index), were correlated with surface maximum and minimum temperatures at six sites in northeast Colorado for the years 1989-98. The NDVI values, representing landscape greenness, act as a proxy for latent heat partitioning via transpiration. These sites encompass a wide array of environments, from irrigated-urban to short-grass prairie. The explained variance (γ² value) of surface maximum and minimum temperature by only the 850-700 mb layer mean temperature was subtracted from the corresponding explained variance by the 850-700 mb layer mean temperature and NDVI values. The subtraction shows that by including NDVI values in the analysis, the values, and thus the degree of explanation of the surface temperatures, increase by a mean of 6% for the maxima and 8% for the minima over the period March-October. At most sites, there is a seasonal dependence in the explained variance of the maximum temperatures because of the seasonal cycle of plant growth and senescence. Between individual sites, the highest increase in explained variance occurred at the site with the least amount of anthropogenic influence. This work suggests the vegetation state needs to be included as a factor in surface temperature forecasting, numerical modeling, and climate change assessments.