Partition Method On Inhabitable Area Of Oedaleous Asiaticus B. Bikeo

Inhabitable area of grasshopper are the regions where can provide proper habitat for grasshopper growth. There are many ecological factors that can affect the distribution of inhabitable area. As an important ecological factor, plants play an important role in grasshopper growth and population dynamics. This paper analyzed the composition characteristics of grassland and the relationship with grasshopper to build partition method of inhabitable area based on digitization and visualization of grassland subtype, which had important practical significance in grasshopper monitoring and control.This paper analyzed the composition charateristics about plants in grassland. Using plant species dominance to evaluate grassland subtype, we built digital naming method. Ranking the plant according dominance in descending order,(1) If the dominance of the first plant was greater than 50%, using this plant to name grassland subtype;(2) When out of condition(1), If the sum of dominance of the first and second plant was greater than 75%, using these two plants to name grassland subtype;(3)When out of condition(1) and(2), using the first, second and third plant to name grassland subtype. According this naming method, studying area could be divided into five subtype. Proportion of subtype named by one plant, two palnts, and three plants were 61.53%, 19.76% and 18.71%, respectively. Using geographic information system(ArcGIS) to achieve the visualization of subtype through layer handling and overlaying, the visualized and quantitative observing of subtype and it plant composition could come true. So the digitalization and visualization could provide numerous data of plants and operation platform for partition method of grasshopper inhabitable area.Using Oedaleous asiaticus B. Bikeo as a model species, biological studies showed that the plant order of being good for O. asiaticus was Stipa krylovii Roshev>Cleistogenes squarrosa(Trin.) Keng>Leymus chinensis(Trin.) Tzvel> Artemisia frigida Willd. Sp. Pl.> Caragana microphylla Lam.. Plant communities of Stipa krylovii-dominated were beneficial for O. asiaticus growth and development. O. asiaticus preferred feeding Stipa krylovii and Cleistogenes squarrosa(0.25≤RFN<0.5), but fed Leymus chinensis little(0.025≤RFN<0.25). O. asiaticus feeding preference of three plants could change for variable plant communities. Linear-regression analysis of feeding amount of C. squarrosa and S. krylovii was carried out, which showed that feeding amount of these two plants was siginificant linearly dependent in 4th instar(F=661.54, R2=0.9525, p<0.0001), 5th instar(F=41.33, R2=0.5560, p<0.0001) and adults(F=181.36, R2=0.8461, p<0.0001). Due to this remarkable negative correlation, we could conclude that S. krylovii and C. squarrosa can substitute each other in the food structure of O. asiaticus. Feeding preference(diets selectivity index SI) for L. chinensis could affect feeding preference for S. krylovii, this relation was significant negative correlation(correlation coefficient r=-0.5437, p<0.0001). S. krylovii and C. squarrosa were the key factors which affecting inhabitable area of O. asiaticus mostly. Those studies could provide biological basis for the partition of O. asiaticus inhabitable area.The analysis based on ecology showed that the diversity of plant biomass was the key factor, which affected grasshopper emergence mostly(the best projection direction a=0.6725). And, for O. asiaticus, Gramineae dominance was the key factor(a=0.6725), the second was S. krylovii dominance(a=0.6725). Besides, projection pursuit model for comprehensive evaluation in risk assess in grasshoppers infection and habitat vegetation was used and verified here. There was a significant linear relation(p<0.01) between the projection eigenvalue(Zi) with grasshoppers density. The occurrence of grasshoppers could be predicted according to the projection eigenvalue(Zi). The bigger the value of Zi is, the higher risk the grasshopper occurrence with. Zi could be used as inhabitable index of grasshopper. Comprehensive evaluation based on projection pursuit model provided a reliable method for partition of grasshopper inhabitable area.