摘要 : The simultaneous effect of the environmental factors as determinants of soil invertebrate community structures is little understood. This study was carried out at two depths (0-10, 10-20 cm) in the forest ecosystem located in Gole... 展开 The simultaneous effect of the environmental factors as determinants of soil invertebrate community structures is little understood. This study was carried out at two depths (0-10, 10-20 cm) in the forest ecosystem located in Golestan province, north of Iran. The abundance, diversity, and richness of twelve taxonomic groups of soil invertebrates were modeled using three groups of environmental attributes consisted of topographic attributes and vegetation indices, soil properties and tree diversity indices as predictors. Genetic algorithm was applied to identify and select the effective environmental attributes; the non-linear machine learning, random forest (RF), was employed to predict abundance, diversity, and richness of invertebrates and to determine the most important variables controlling the horizontal and vertical distribution of invertebrates in the forest ecosystem. Results showed that RF was an accurate model for predicting abundance (RMSE = 0.15), diversity (RMSE = 0.18) and richness (overall accuracy = 0.51, kappa = 0.34) of soil invertebrates at the first depth. Similarly, at the second depth, RF showed a good prediction for abundance (RMSE = 0.20), diversity (RMSE = 0.18) and richness (overall accuracy = 0.23, kappa = 0.12) of soil invertebrates. Horizontal distribution of invertebrates' abundance was affected by soil microbial respiration (Resp), land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Shannon index of trees and soil moisture, while vertical distribution of invertebrates' abundance was more related to soil organic carbon (SOC) and soil moisture. Variable importance analysis confirmed that horizontal and vertical types of distribution of Shannon index of soil invertebrates were affected by the same factors, including land temperature, NDVI, soil respiration and moisture. Also, soil moisture, soil respiration, NDVI and richness of trees had significant effects on horizontal distribution of soil invertebrates' richness, but silt, Normalized Ratio Vegetation Index (NRVI), soil respiration and moisture had positive effects on vertical distribution of richness. In conclusion, that digital soil mapping (DSM) model could be apparently considered as a powerful and economical method in biodiversity modeling. Other supplementary data such as invertebrates feeding web are also suggested as the input dataset for further investigations. 收起