Indicators can monitor ecological environment changes and help maintain ecological balance. Bioindicators are divided into animal, plant, and microbial indicators, of which animal and plant indicators have previously been the most researched, but microbial indicators have drawn attention recently owing to their high sensitivity to the environment and their potential for use in monitoring environmental changes. To date, reviews of studies of animals and plants as indicator species have frequently been conducted, but reviews of research on microorganisms as indicator species have been rare. In this review, we summarize and analyze studies using microorganisms as indicator species in a variety of ecosystems, such as forests, deserts, aquatic and plateau ecosystems, and artificial ecosystems, which are contained in wetlands, farmlands, and mining ecosystems. This review provides useful information for the further use of microorganisms as indicators to reflect the changes in different environmental ecosystems.

In recent years, invasive alien plants (IAPs) have caused serious ecological disasters and economic losses in China. This study combined three IAP species richness-related indices (species richness of IAPs, first records of IAPs, and the relative species richness of IAPs), as well as indices reflecting distribution and dispersal patterns (average similarity coefficient of IAPs) and invasiveness (average risk score of IAPs), to conduct an integrated regional-invasion risk assessment based on the principal component analysis (PCA) method. Partial least-squares (PLS) regression was conducted to explore the explanatory power of 12 environmental and anthropogenic factors on different invasion indices. The results indicated that coastal provinces and Yunnan had high IAP introduction risk, as well as high synthetic-risk scores. The dispersal of IAPs in mid-latitude provinces should be particularly prevented. For species richness of IAPs, more environmental factors with variable importance for the project (VIP) values higher than 1 were retained in the optimal model, reflecting the importance of environmental filtering on IAPs. Visitors were the most important predictor for first records of IAPs. Compared to species richness (R² = 79.5%), first records were difficult to predict (R² = 60.4%) and were influenced by anthropogenic factors. There was spatial distribution congruence of various families of IAPs. Generally, the correlations of the residuals of species richness were still significant, with 0.421 (p < 0.05) as the lowest Pearson correlation coefficient, which indicated that external factors could not fully explain the spatial distribution congruence. These findings could enrich the relevant research on IAP invasion mechanisms and provide suggestions for regional IAP detection and response.