Our study aims to enhance process understanding of the long-term (decadal and longer) cyclic marsh dynamics by identifying the mechanisms that translate large-scale physical forcing in the system into vegetation change, in particular (i) the initiation of lateral erosion on an expanding marsh, and (ii) the control of seedling establishment in front of an eroding marsh-cliff. Short-term sediment dynamics (i.e., seasonal and shorter changes in sediment elevation) at the mudflat causes variation in mudflat elevation over time (dz TF ). The resulting difference in elevation between the tidal flat and adjacent marsh (DZ) initiates lateral marsh erosion. Marsh erosion rate was found to depend on sediment type and to increase with increasing DZ and hydrodynamic exposure. Laboratory and field experiments revealed that seedling establishment was negatively impacted by an increasing dz TF . As the amplitude of dz TF increases towards the channel, expanding marshes become more prone to lateral erosion the further they extend on a tidal flat, and the chance for seedlings to establish increases with the distance that marsh has eroded back towards the land. This processbased understanding, showing the role of sediment dynamics as explanatory factor for marsh cyclicity, is important for protecting and restoring valuable marsh ecosystems. Overall, our experiments emphasize the need for understanding the connections between neighbouring ecosystems such as mudflat and salt marsh.

Accelerated sea level rise (SLR) in the twenty-first century will result in unprecedented coastal recession, threatening billions of dollars worth of coastal developments and infrastructure. Therefore, we cannot continue to depend on the highly uncertain coastal recession estimates obtained via the simple, deterministic method (Bruun rule) that has been widely used over the last 50 years. Furthermore, the emergence of risk management style coastal planning frameworks is now requiring probabilistic (rather than deterministic, single value) estimates of coastal recession. This paper describes the development and application of a process based model (PCR model) which provides probabilistic estimates of SLR driven coastal recession. The PCR model is proposed as a more appropriate and defensible method for determining coastal recession due to SLR for planning purposes in the twenty-first century and beyond.

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