No abstract

Historic baselines are important in developing our understanding of ecosystems in the face of rapid global change. While a number of studies have sought to determine changes in extent of exploited habitats over historic timescales, few have quantified such changes prior to late twentieth century baselines. Here, we present, to our knowledge, the first ever large-scale quantitative assessment of the extent and biomass of marine habitat-forming species over a 100-year time frame. We examined records of wild native oyster abundance in the United States from a historic, yet already exploited, baseline between 1878 and 1935 (predominantly 1885–1915), and a current baseline between 1968 and 2010 (predominantly 2000–2010). We quantified the extent of oyster grounds in 39 estuaries historically and 51 estuaries from recent times. Data from 24 estuaries allowed comparison of historic to present extent and biomass. We found evidence for a 64 per cent decline in the spatial extent of oyster habitat and an 88 per cent decline in oyster biomass over time. The difference between these two numbers illustrates that current areal extent measures may be masking significant loss of habitat through degradation.

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