Search

Mangroves have among the highest carbon densities of any tropical forest. These 'blue carbon' ecosystems can store large amounts of carbon for long periods, and their protection reduces greenhouse gas emissions and supports climate change mitigation. Incorporating mangroves into Nationally Determined Contributions to the Paris Agreement and their valuation on carbon markets requires predicting how the management of different land‐uses can prevent future greenhouse gas emissions and increase CO2 sequestration. We integrated comprehensive global datasets for carbon stocks, mangrove distribution, deforestation rates, and land‐use change drivers into a predictive model of mangrove carbon emissions. We project emissions and foregone soil carbon sequestration potential under 'business as usual' rates of mangrove loss. Emissions from mangrove loss could reach 2391 Tg CO2 eq by the end of the century, or 3392 Tg CO2 eq when considering foregone soil carbon sequestration. The highest emissions were predicted in southeast and south Asia (West Coral Triangle, Sunda Shelf, and the Bay of Bengal) due to conversion to aquaculture or agriculture, followed by the Caribbean (Tropical Northwest Atlantic) due to clearing and erosion, and the Andaman coast (West Myanmar) and north Brazil due to erosion. Together, these six regions accounted for 90% of the total potential CO2 eq future emissions. Mangrove loss has been slowing, and global emissions could be more than halved if reduced loss rates remain in the future. Notably, the location of global emission hotspots was consistent with every dataset used to calculate deforestation rates or with alternative assumptions about carbon storage and emissions. Our results indicate the regions in need of policy actions to address emissions arising from mangrove loss and the drivers that could be managed to prevent them. [ABSTRACT FROM AUTHOR]

Multiple stressors are ubiquitous in coastal ecosystems as a result of increased human activity and development along coastlines. Accurately assessing multiple stressor effects is essential for predicting stressor impacts and informing management to efficiently and effectively mitigate potentially complex ecological responses. Extracting relevant information on multiple stressor studies conducted specifically within coastal wetlands is not possible from existing reviews, posing challenges in highlighting knowledge gaps and guiding future research. Here, we systematically review manipulative studies that assess multiple anthropogenic stressors within saltmarsh, mangrove, and seagrass ecosystems. In the past decade, there has been a rapid increase in publications, with seagrasses receiving the most attention (76 out of a total of 143 studies). Across all studies, nutrient loading and temperature were tested most often (N = 64 and N = 48, respectively), while the most common stressor combination was temperature with salinity (N = 12). Stressor application and study design varied across ecosystems. Studies are mostly conducted in highly controlled environments, without considering how natural variations in the physicochemical environment of coastal ecosystems may influence stressor intensity and timing under these conditions. This may result in vastly different ecological responses across levels of biological organisation. Shifting focus from univariate analytical approaches to multivariate, particularly path analysis, will help elucidate complex ecological relationships and highlight direct and indirect effects of multiple stressors in coastal ecosystems. There is a solid foundation of multiple stressor research in coastal wetlands. However, we recommend future research enhance ecological realism in experimental design by studying the effects of stressor combinations whilst accounting for spatiotemporal variability that reflects natural conditions of coastal ecosystems. Image 1 • Combinations of anthropogenic stressors are degrading coastal wetlands globally. • Multiple stressor studies focus on seagrass, and effects of nutrients and temperature. • Relevant stressor pairs, such as sedimentation and contaminants, are unstudied. • Experiments are conducted in controlled conditions with reduced ecological realism. • Varying stressor intensity and timing to reflect natural systems will enhance realism. [ABSTRACT FROM AUTHOR]