Use of random forest model to identify the relationships among vegetative species, salt marsh soil properties, and interstitial water along the atlantic coast of georgia

Abstract

Saltmarshes, known to be ecologically sensitive areas, face disturbances such as vegetation dieback due to anthropogenic activities such as construction. The current construction specifications recommended by state highway agencies do not specifically require documenting or restoring any prior saltmarsh soil/interstitial water properties, nor do they require re-establishing saltmarsh vegetation; restoring the abiotic properties and appropriate vegetation would enhance the long-term functionality and ecology of a disturbed area. In order to have a successful restoration of disturbed saltmarshes with healthy vegetation, the relationship between vegetative species and the properties of saltmarsh soils and interstitial water must be fully understood. In this study, field and laboratory tests were conducted for the soil samples from eight different saltmarsh sites in the Southeastern US Atlantic coastal region, followed by the development of a random forest model; the aim is to identify correlation among saltmarsh predominant vegetation types, redox potential, and salinity. The results reveal that moisture content and sand content are two main drivers for the bulk density of saltmarsh soils, which directly affect plant growth and likely root development. Moreover, it is concluded that deploying modern machine learning algorithms, such as random forest, can help to identify desirable saltmarsh soil/water properties for re-establishing vegetative cover with the reduced time after construction activities.