Drivers and modelling of blue carbon stock variability in sediments of southeastern Australia

Tidal marshes, mangrove forests, and seagrass meadows are important global carbon (C) sinks, commonly referred to as coastal "blue carbon". However, these ecosystems are rapidly declining with little understanding of what drives the magnitude and variability of C associated with them, making strategic and effective management of blue C stocks challenging. In this study, our aims were threefold: (1) identify ecological, geomorphological, and anthropogenic variables associated with 30 cm deep sediment C stock variability in blue C ecosystems in southeastern Australia, (2) create a predictive model of 30 cm deep sediment blue C stocks in southeastern Australia, and (3) map regional 30 cm deep sediment blue C stock magnitude and variability. We had the unique opportunity to use a high-spatial-density C stock dataset of sediments to 30 cm deep from 96 blue C ecosystems across the state of Victoria, Australia, integrated with spatially explicit environmental data to reach these aims. We used an information theoretic approach to create, average, validate, and select the best averaged general linear mixed effects model for predicting C stocks across the state. Ecological drivers (i.e. ecosystem type or ecological vegetation class) best explained variability in C stocks, relative to geomorphological and anthropogenic drivers. Of the geomorphological variables, distance to coast, distance to freshwater, and slope best explained C stock variability. Anthropogenic variables were of least importance. Our model explained 46 % of the variability in 30 cm deep sediment C stocks, and we estimated over 2.31 million Mg C stored in the top 30 cm of sediments in coastal blue C ecosystems in Victoria, 88 % of which was contained within four major coastal areas due to the extent of blue C ecosystems (span classCombining double low line"inline-formula"ĝˆ1/487/span % of total blue C ecosystem area). Regionally, these data can inform conservation management, paired with assessment of other ecosystem services, by enabling identification of hotspots for protection and key locations for restoration efforts. We recommend these methods be tested for applicability to other regions of the globe for identifying drivers of sediment C stock variability and producing predictive C stock models at scales relevant for resource management.