As the land and sea interface, coastal foredunes are arguably the most dynamic terrestrial habitats worldwide, and will only grow more dynamic with climate change increasing the frequency, severity, and unpredictability of storm disturbances. Foredunes are biogeomorphic. Abiotic conditions influence vegetation growth and dispersal while vegetation imposes a geomorphic feedback on physical habitat state and thus storm response. Research and modeling efforts surrounding foredunes and barrier islands have predominantly, , historically been geomorphic. Given the lack of incorporation of the ecological and biological components underpinning the biogeomorphic interactions, there is a critical knowledge gap, both in our understanding of how these systems evolve over time and respond to storms. There is thus a need for a more structured and multidisciplinary, i.e. both biological and geological, approach to monitoring, protecting, and predicting foredune system dynamics to ensure their viability in the face of future storms and sea level rise. The first step in achieving this objective is to understand and accurately forecast coastal vegetation dynamics at the nexus of variable climatic conditions and associated sediment deposits. Towards this goal, we created a simple, yet complex, process-based functional community model for coastal foredune vegetation.
Results/Conclusions:
The model encompasses both relevant biological and geological components affecting foredune state in periods of relative calm and simulated storms or wind events of varied severity. Daily vegetation growth, density, and colonization on coastal dune landscapes are simulated using a grid-based, geo-referenced, stochastic individual-based modeling approach. Vegetation both responds and is affected by wind events and impacts the relative erosion or accretion of individual cells. It is thus a tool to both forecast and better understand the underpinning system level biogeomorphic interactions. Using weather data from a representative mid-Atlantic barrier island habitat, we created a dataset of different transport scenarios based on wind speed and direction. We simulate different wind events and the subsequent change in the system from these events, based on the pre-event modeled geomorphic and vegetation state. Vegetation state is photosynthesis driven with regards to density and root and shoot development, using three representative species as different dune functional plant species, Ammophila breviligulata, Spartina patens, and Morella sps. By incorporating both ecological and geological drivers, we can predict complex system dynamics towards the goal of aiding in large-scale coastal management decisions under variable climatic conditions.