Monitoring Strategy for Mudflat Wetlands: Selecting Indicator Species Based on Principal Component Analysis

Effective and cost-efficient monitoring is crucial in wetland management strategies. Large-scale surveys are time-consuming and uneconomical. Therefore, choosing between smaller-scale or alternative surveys is an important consideration in monitoring strategies. Indicator species (IS) are single species or a small number of target species that use specific characteristics as proxies or paradigms to represent community status or environmental indicators. To interpret and monitor changes caused by mangrove removal, we applied principal component analysis (PCA) and proposed a new concept to reveal the contribution of species to each principal component, thereby quantitatively identifying selectable ISs in environmental change. ISs were selected based on the total cumulative load of each species and the load of each species in each component. According to the load score algorithm in PCA, we identified five indicator species, namely, M. brevidactylus, M. banzai, U. arcuata, U. lacteal, and U. borealis. These ISs can clearly highlight changes during mangrove removal. PCA effectively reveals the relative changes of organisms across principal components by highlighting patterns and trends. It helps to detect environmental anomalies and assess their trends.