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Climate-driven decoupling of wetland and upland biomass trends on the mid-Atlantic coast

Nature Geoscience volume 15, pages 913–918 (2022)Cite this article

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Abstract

Coastal ecosystems represent a disproportionately large but vulnerable global carbon sink. Sea-level-driven tidal wetland degradation and upland forest mortality threaten coastal carbon pools, but responses of the broader coastal landscape to interacting facets of climate change remain poorly understood. Here, we use 36 years of satellite observations across the mid-Atlantic sea-level rise hotspot to show that climate change has actually increased the amount of carbon stored in the biomass of coastal ecosystems despite substantial areal loss. We find that sea-level-driven reductions in wetland and low-lying forest biomass were largely confined to areas less than 2 m above sea level, whereas the otherwise warmer and wetter climate led to an increase in the biomass of adjacent upland forests. Integrated across the entire coastal landscape, climate-driven upland greening offset sea-level-driven biomass losses, such that the net impact of climate change was to increase the amount of carbon stored in coastal vegetation. These results point to a fundamental decoupling between upland and wetland carbon trends that can only be understood by integrating observations across traditional ecosystem boundaries. This holistic approach may provide a template for quantifying carbon–climate feedbacks and other aspects of coastal change that extend beyond sea-level rise alone.

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Fig. 1: Correspondence between NDVI trend and vegetation shift in the mid-Atlantic coast of North America, a hotspot for accelerated SLR.
Fig. 2: Spatial extent of SLR impacts in coastal ecosystems.
Fig. 3: NDVI trend and the associated AGB change by vegetation type.
Fig. 4: Environmental drivers for regional patterns of NDVI trend.

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Data availability

All Landsat Level-1 surface reflectance images are publicly available from the United States Geological Survey EarthExplorer (https://earthexplorer.usgs.gov/) or via Google Cloud Landsat dataset (https://cloud.google.com/storage/docs/public-datasets/landsat). All field-based biomass data are detailed in refs. 25,68,69,70,71,72 and available in the Virginia Coast Reserve Long-Term Ecological Research repository (http://www.vcrlter.virginia.edu/cgi-bin/browseData.cgi). The landcover maps and the NDVI trend map are publicly available at the Environmental Data Initiative data repository (https://doi.org/10.6073/pasta/4ae5ac3fbdb6a20dcdcb2ff36487d292).

Code availability

The study does not report original code. All code used this study is available from the corresponding author upon reasonable request.

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Acknowledgements

Primary funding for this work comes from the National Science Foundation (no. 1654374, M.L.K.; no. 1832221, M.L.K; and no. 2012670, M.L.K.) with additional support from the US Department of Energy, Office of Biological and Environmental Research Program (DE-SC0021112, M.L.K.). T. Messerschmidt and A. Smith helped with field work. This is contribution 4115 of the Virginia Institute of Marine Science, William & Mary.

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  1. Virginia Institute of Marine Science, William & Mary, Gloucester Point, VA, USA

    Yaping Chen & Matthew L. Kirwan

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  1. Yaping Chen
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Y.C. designed the study, performed the analysis and wrote the initial draft. M.L.K conceived the idea, contributed to the study design and revised the manuscript. Both authors interpreted the data.

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Correspondence to Yaping Chen.

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Nature Geoscience thanks Marcelo Ardon, Melinda Martinez and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: James Super, in collaboration with the Nature Geoscience team.

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Extended data

Extended Data Fig. 1 Map of the U.S. mid-Atlantic study region.

Green circles denote field sites of aboveground biomass observations. The elevation map refers to the CoNED DEM54. Map created using the Ocean Basemap in ArcGIS (v10.7).

Extended Data Fig. 2 Climate change observed in the study region.

The vertical bars represent annual mean temperature (left) and annual total precipitation (right), recorded in the nearest NOAA station in Dover, Delaware. The black lines refer to the 5-yr moving average. The dotted lines represent linear regression that show significant upward trend of long-term temperature and precipitation from 1980 onward. The observed climate data was used for illustrative purposes only. The climate inputs for our boosted regression tree analysis refers to the spatially explicit PRISM datasets.

Extended Data Fig. 3 Identifying timing for NDVI trend analysis.

a. Sites selected randomly for evaluating monthly NDVI patterns. b. Monthly NDVI pattern of each vegetation type for the most recent five years. The number of sites (n) selected for marsh, transition forest, deciduous upland forest and evergreen upland forest is respectively 4274, 3117, 3808 and 4098. The shaded areas indicate peak growing season when NDVI is maximized and stays relatively consistent. All data are shown as mean ± 1 standard deviation. The elevation data refers to the CoNED DEM54. Map created using the Ocean Basemap in ArcGIS (v10.7).

Extended Data Fig. 4 Relationship between peak growing-season NDVI and aboveground biomass.

The mean linear regression trendline is bounded by 95% confidence interval (shaded area). The solid and open symbols correspond respectively to marsh and forest. All biomass data (mean ± 1 standard deviation, n = 125) were accessed from the Long Term Ecological Research Network database indicated in Extended Data Fig. 1. The y-axis is plotted on a logarithmic scale. The regression function is (aboveground biomass) = 0.05 × e6.02 × (NDVI), (P < 0.0001, F-statistic = 378.5, and RMSE = 0.5766).

Extended Data Fig. 5 Cross-comparison of NDVI between Landsat sensors.

Scatterplots of NDVI by Landsat-5 TM (left) and by Landsat-8 OLI (right) were plotted against NDVI by Landsat-7 ETM + . The solid lines refer to linear regression and dotted lines represent the 1:1 Line superimposed for reference.

Extended Data Fig. 6 Mann-Kendall test for significant NDVI trends (P < 0.1) in the study region.

The inserted map shows all areas within 0-5 m above sea level that demonstrate statistically significant increases or decreases in NDVI during 1984 to 2020. The density plot summarizes distribution pattern of statistical results among all pixels, and the significance level at P = 0.1 is indicated by the dotted red line. The elevation map refers to the CoNED DEM54. Map created using the Ocean Basemap in ArcGIS (v10.7).

Extended Data Fig. 7 Flowchart for landcover mapping and validation.

All training and validation sites were randomly generated in ArcGIS (v10.7) within 0-5 m above sea level. The elevation data refers to the CoNED DEM54.

Extended Data Table 1 Landcover classes and their definitions used in this study94
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Extended Data Table 2 Input datasets for random forest classifier95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110
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Extended Data Table 3 Classification accuracy of landcover maps
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Chen, Y., Kirwan, M.L. Climate-driven decoupling of wetland and upland biomass trends on the mid-Atlantic coast. Nat. Geosci. 15, 913–918 (2022). https://doi.org/10.1038/s41561-022-01041-x

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