Prediction of the spatial distribution of soil arthropods using a random forest model: A case study in Changtu County, Northeast China

Abstract

Soil arthropods with a body size of more than 2 mm are a significant component of terrestrial ecosystems and play an important role in biogeochemical cycles. Understanding the spatial distribution of soil arthropods is crucial for biodiversity conservation and land remediation. Studies on the spatial distribution of soil arthropods have mostly been performed at a small scale. However, due to the difficulties in obtaining soil arthropods, research on their spatial distribution patterns at a large scale is scarce and the details remain unclear. Therefore, it is important to clarify the spatial distribution pattern of soil arthropods at a large scale and the influence of environmental variables on them to develop targeted protection measures. In this study, we used a random forest model combined with environmental variables to map the spatial distribution of soil arthropods in cultivated land in Changtu County, China, and evaluated the prediction accuracy of the model. In addition, we ranked the relative importance of environmental variables to understand their influence on the spatial distribution of soil arthropods. The results showed the following: 1) The mean absolute prediction error was 13.9, the root mean square error was 21.5, R2 was 0.53, and Lin’s concordance correlation coefficient of the model was 0.48, which indicated that random forest can be used to predict the spatial distribution of soil arthropods at a large scale. 2) The nature index of agricultural land, which represents the comprehensive quality of cultivated lands, and landscape division index were the two most important environmental variables affecting the spatial distribution of soil arthropods, with relative importance values of 19.3 % and 12.9 %, respectively. 3) The number of soil arthropods was low in the study area and the soil arthropods were aggregated in their distribution. We believe that accurately predicting the distribution of soil arthropods can aid in the rational distribution and management of farmland landscape and the protection of soil arthropods.