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Abstract. The fortedata R package is an open data notebook from the Forest Resilience ThresholdExperiment (FoRTE) – a modeling and manipulative field experiment that teststhe effects of disturbance severity and disturbance type on carbon cyclingdynamics in a temperate forest. Package data consist of measurements ofcarbon pools and fluxes and ancillary measurements to help analyze andinterpret carbon cycling over time. Currently the package includes data andmetadata from the first three FoRTE field seasons, serves as a central,updatable resource for the FoRTE project team, and is intended as a resourcefor external users over the course of the experiment and in perpetuity.Further, it supports all associated FoRTE publications, analyses, andmodeling efforts. This increases efficiency, consistency, compatibility, and productivity while minimizing duplicated effort and error propagation thatcan arise as a function of a large, distributed and collaborative effort.More broadly, fortedata represents an innovative, collaborative way of approachingscience that unites and expedites the delivery of complementary datasets tothe broader scientific community, increasing transparency andreproducibility of taxpayer-funded science. The fortedata package is available via GitHub:https://github.com/FoRTExperiment/fortedata (last access: 19 February 2021), and detaileddocumentation on the access, used, and applications of fortedata are available athttps://fortexperiment.github.io/fortedata/ (last access: 19 February 2021). The first publicrelease, version 1.0.1 is also archived athttps://doi.org/10.5281/zenodo.4399601 (Atkins et al., 2020b). All data products are also available outside of thepackage as .csv files: https://doi.org/10.6084/m9.figshare.13499148.v1 (Atkins et al., 2020c). 

Relative sea level rise (SLR) increasingly impacts coastal ecosystems through the formation of ghost forests. To predict the future of coastal ecosystems under SLR and changing climate, it is important to understand the physiological mechanisms underlying coastal tree mortality and to integrate this knowledge into dynamic vegetation models.

We incorporate the physiological effect of salinity and hypoxia in a dynamic vegetation model in the Earth system land model, and used the model to investigate the mechanisms of mortality of conifer forests on the west and east coast sites of USA, where trees experience different form of sea water exposure.

Simulations suggest similar physiological mechanisms can result in different mortality patterns. At the east coast site that experienced severe increases in seawater exposure, trees loose photosynthetic capacity and roots rapidly, and both storage carbon and hydraulic conductance decrease significantly within a year. Over time, further consumption of storage carbon that leads to carbon starvation dominates mortality. At the west coast site that gradually exposed to seawater through SLR, hydraulic failure dominates mortality because root loss impacts on conductance are greater than the degree of storage carbon depletion.

Measurements and modeling focused on understanding the physiological mechanisms of mortality is critical to reducing predictive uncertainty.