With the growing recognition that effective action on climate change will require a combination of emissions reductions and carbon sequestration, protecting, enhancing and restoring natural carbon sinks have become political priorities. Mangrove forests are considered some of the most carbon-dense ecosystems in the world with most of the carbon stored in the soil. In order for mangrove forests to be included in climate mitigation efforts, knowledge of the spatial distribution of mangrove soil carbon stocks are critical. Current global estimates do not capture enough of the finer scale variability that would be required to inform local decisions on siting protection and restoration projects. To close this knowledge gap, we have compiled a large georeferenced database of mangrove soil carbon measurements and developed a novel machine-learning based statistical model of the distribution of carbon density using spatially comprehensive data at a 30 m resolution. This model, which included a prior estimate of soil carbon from the global SoilGrids 250 m model, was able to capture 63% of the vertical and horizontal variability in soil organic carbon density (RMSE of 10.9 kg m −3 ). Of the local variables, total suspended sediment load and Landsat imagery were the most important variable explaining soil carbon density. Projecting this model across the global mangrove forest distribution for the year 2000 yielded an estimate of 6.4 Pg C for the top meter of soil with an 86-729 Mg C ha −1 range across all pixels. By utilizing remotely-sensed mangrove forest cover change data, loss of soil carbon due to mangrove habitat loss between 2000 and 2015 was 30-122 Tg C with >75% of this loss attributable to Indonesia, Malaysia and Myanmar. The resulting map products

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Aim We developed a set of statistical models to improve spatial estimates of mangrove aboveground biomass (AGB) based on the environmental signature hypothesis (ESH). We hypothesized that higher tidal amplitudes, river discharge, temperature, direct rainfall and decreased potential evapotranspiration explain observed high mangrove AGB. Location Neotropics and a small portion of the Nearctic region. Methods A universal forest model based on site‐level forest structure statistics was validated to spatially interpolate estimates of mangrove biomass at different locations. Linear models were then used to predict mangrove AGB across the Neotropics. Results The universal forest site‐level model was effective in estimating mangrove AGB using pre‐existing mangrove forest structure inventories to validate the model. We confirmed our hypothesis that at continental scales higher tidal amplitudes contributed to high forest biomass associated with high temperature and rainfall, and low potential evapotranspiration. Our model explained 20% of the spatial variability in mangrove AGB, with values ranging from 16.6 to 627.0 t ha−1 (mean, 88.7 t ha−1). Our findings show that mangrove AGB has been overestimated by 25–50% in the Neotropics, underscoring a commensurate bias in current published global estimates using site‐level information. Main conclusions Our analysis show how the ESH significantly explains spatial variability in mangrove AGB at hemispheric scales. This finding is critical to improve and explain site‐level estimates of mangrove AGB that are currently used to determine the relative contribution of mangrove wetlands to global carbon budgets. Due to the lack of a conceptual framework explicitly linking environmental drivers and mangrove AGB values during model validation, previous works have significantly overestimated mangrove AGB; our novel approach improved these assessments. In addition, our framework can potentially be applied to other forest‐dominated ecosystems by allowing the retrieval of extensive databases at local levels to generate more robust statistical predictive models to estimate continental‐scale biomass values.

Comprised of 17 named tropical storms, 6 of which were major hurricanes, the 2017 Atlantic hurricane season ranked as one of the most damaging and costly hurricane seasons on record. In addition to socio-economic impacts, many previous studies have shown that important coastal ecosystems like mangroves are shaped by severe storms. However, little is known about how the cumulative effects of storms over entire hurricane seasons affect mangroves across large regions. We used satellite imagery from the entire Caribbean and Gulf of Mexico region to show that 2017 resulted in disproportionate mangrove damage compared to baseline responses over the previous 8 years. Specifically, we observed 30 times more mangrove damage, via a reduction in the normalized difference vegetation index (NDVI), during 2017 compared to any of the eight previous hurricane seasons, and most (72%) of this damage persisted throughout the 7 month post-hurricane season period as indicated by no NDVI recovery. Furthermore, wind speed, rainfall, and canopy height data showed that mangrove damage primarily resulted from high maximum wind speeds, but flooding (cumulative rainfall), previous storm history, and mangrove structure (canopy height) were also important predictors of damage. While mangroves are known to be resilient to hurricane impacts, our results suggest that increasingly frequent mega-hurricane seasons in the Caribbean region will dramatically alter mangrove disturbance dynamics.

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