Spatial heterogeneity of vegetation under different grazing intensities in the Northwest Heilongjiang Steppe of China

In northern China, recent degradation of semi-arid grasslands caused by overgrazing is serious. At Green Grassland Village, west of Daqing City in Heilongjiang Province, three grasslands with different degrees of degradation were surveyed. In the survey, a new method of vegetation analysis based on the beta-binomial distribution, was adopted to describe the frequency of occurrence and spatial heterogeneity for each plant species.Chenopodium centrorubrum, Cleistogenes squarrosa and a Carex species were commonly found in the three grasslands. Non-degraded grassland with light grazing was dominated by Arundinella hirta, Filifolium sibiricium, Lespedeza hedysaroides, Potentilla verticillaris, Stipa baicalensis and Thymus serpyllum. These species belong to the erect-type or do not develop large clones under light grazing. These species can be used as indicators of a well-managed grassland in this area. On the other hand, the degraded grassland with heavy grazing was dominated by Artemisia spp. and Euphorbia humifusa var. pilosa. Artemisia spp., E. humifusa var. pilosa, Eragrostis pilosa and Setaria viridis belong to the creeping-type or develop rather large stocks or stolons under heavy utilization. These species increased the spatial heterogeneity in the degraded grassland with heavy grazing.Under degraded conditions, species diversity decreased and spatial heterogeneity of the communities tended to increase.     

Predation-affected spatial pattern changes in a prey population

Predation-affected spatial pattern changes in a prey population were studied. The spatial pattern of a population of Galleria mellonella is changed by its predator, Podisus maculiventris. The pattern is affected by the frequency of attack by the predator (attack ability), the homogeneity of the attack ability within a predator population and the mobility of the predator. A mathematical model incorporating these three factors was constructed, and several computer simulations were conducted by changing the parameter values. A natural enemy population having high and homogeneous attack ability and high mobility effectively kills prey individuals.     

A grassland ecosystem model of the Xilingol steppe, Inner Mongolia, China

The natural grassland ecosystem of the Xilingol steppe has traditionally been the source of the most productive and highest quality agriculture in northern China. Unfortunately, the area is now experiencing degradation due to resource overuse. In an attempt to forecast grassland production and to sustain the ecosystem, we built a time-dependent simulation model of the ecosystem based on long-range weather forecasts (several weeks to several months). The model incorporated five state variables including above- and belowground biomass, the amount of standing dead plant material, livestock (sheep) weight, and the amount of excrement per unit ground area. Within the model, solar light energy is fixed by grassland vegetation and flows through the other variables via a variety of organism-environment interactions. The model was written using a set of simultaneous differential equations and was numerically analyzed. The values of the time-dependent parameters controlling energy flow were determined based on data accumulated in experiments and field surveys executed at a grassland experimental station located in Xilingol, as well as by reference to related literature. We used daily meteorological data including air temperature and rainfall recorded at the Xilinhot Meteorological Observatory. Simulated results for several stocking densities coincided well with the data of aboveground plant biomass observed at the experimental station in 1990, 1993, and 1997. We obtained reasonable simulation results for five stocking densities, three air temperature patterns, and five rainfall patterns. When a month-long drought, which sometimes occurs in this area, was forecast by a local weather station, a decrease in grassland production was forecast by the model. Such forecasts will assist in the management of livestock, forage preservation, and grassland conservation.