Construction of a GeogDetector-based model system to indicate the potential occurrence of grasshoppers in Inner Mongolia steppe habitats

Abstract

Grasshopper plagues have seriously disturbed grassland ecosystems in Inner Mongolia, China. The accurate prediction of grasshopper infestations and control of grasshopper plagues have become urgent needs. We sampled 234, 342, 335, and 369 plots in Xianghuangqi County of Xilingol League in 2010, 2011, 2012, and 2013, respectively, and measured the density of the most dominant grasshopper species, Oedaleus decorus asiaticus, and the latitude, longitude, and associated relatively stable habitat factors at each plot. We used Excel-GeogDetector software to explore the effects of individual habitat factors and the two-factor interactions on grasshopper density. We estimated the membership of each grasshopper density rank and determined the weights of each habitat category. These results were used to construct a model system evaluating grasshopper habitat suitability. The results showed that our evaluation system was reliable and the fuzzy evaluation scores of grasshopper habitat suitability were good indicators of potential occurrence of grasshoppers. The effects of the two-factor interactions on grasshopper density were greater than the effects of any individual factors. O. d. asiaticus was most likely to be found at elevations of 1300-1400 m, flat terrain or slopes of 4-6°, typical chestnut soil with 70-80% sand content in the top 5 cm of soil, and medium-coverage grassland. The species preferred temperate bunchgrass steppe dominated by Stipa krylovii and Cleistogenes squarrosa. These findings may be used to improve models to predict grasshopper occurrence and to develop management guidelines to control grasshopper plagues by changing habitats.

PM2.5 Spatiotemporal Evolution and Drivers in the Yangtze River Delta between 2005 and 2015

“…The advantages of the geographical detector model are that it relies on few assumptions and can effectively overcome the limitations of traditional statistical methods for dealing with categorical variables. At present, it is widely used in the ecological and environmental fields [39][40][41].…”

Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

“…In addition, the geographic detector can also identify the direct interaction of the different factors of X s , that is, whether the interaction between factor X 1 and factor X 2 will increase or decrease the explanatory power of the variable Y. Because it can describe the cause and mechanism of spatial patterns of geographical elements by detecting both numerical data and qualitative data, it has been gradually applied to public health [61,62,63,64], social economy [65,66,67,68], and ecological environment [69,70,71,72]. The principle of geographic detector is shown in Figure S2, and the geographic detector model is defined as follows:q=1−true∑h=1LNhσh2Nσ2 where q indicates the explanatory power of the influencing factor of variable Y; h = 1… L is the second region of variable Y or factor X; Nh and N are the number of sample points in the region h and the number of sample points in the whole region, respectively.…”

The impact of new transportation modes on population distribution in Jing-Jin-Ji region of China

“…To discover the contribution of the potential population determinants and the interaction between them, we apply geographic detectors 29 , 30 . This approach is novel as it extracts the implicit interrelationships between risk factors and events without any assumptions or restrictions with respect to explanatory and response variables, which are difficult to model using classic epidemiological methods 31 , 32 . In this study, it is assumed that population will exhibit a spatial relationship to patterns of factors that contribute to the population dynamic.…”