Examining the commonalities and knowledge gaps in coastal zone vegetation simulation models

Abstract

Models can be powerful tools to help scientists, policymakers, and practitioners forecast vegetation dynamics and inform management decisions in a variety of ecosystems. In coastal environments, vegetation moderates physical and hydrodynamic processes that conversely affect vegetation dynamics. Coastal management and the implementation of nature‐based solutions in the coastal environment requires models that can predict vegetation dynamics that are driven by ecological processes as well as physical processes (e.g., storms, sea level rise). To determine models that are capable of simulating biomass dynamics and assess their ability to make predictions within the context of climate change and environmental management, we reviewed coastal dynamic vegetation simulation models across the following coastal zone habitats: tidal wetlands (salt marsh, mangroves, and freshwater wetlands), seagrass beds, coastal forests (maritime and floodplain forests), and dune habitats. Fifty‐four models met the review criteria and were examined to assess their ability to simulate relevant ecological processes and the spatiotemporal scales at which they are applied. These models included a variety of exogenous and endogenous processes and integrate complex ecogeomorphological feedbacks affecting plant and soil community, hydrodynamic, and sediment transport dynamics. Most models reviewed utilized implicit approaches to predicting vegetation biomass and simulated a limited number of processes based on the principal drivers of habitat of interest. Key gaps identified were the exclusion of below‐ground biomass dynamics, limited inclusion of processes such as competition, facilitation, and succession, and the inability to simulate management actions. Future models should seek to move towards process‐based approaches where appropriate to enable their application to different systems and facilitate their use under novel environmental conditions.