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Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change, and marsh migration into retreating coastal forests is a primary mechanism for marsh survival in the face of global sea‐level rise. Models of coastal transgression typically assume inundation of a static topography and instantaneous conversion of forest to marsh with rising seas. In contrast, here we use four decades of satellite observations to show that many low‐elevation forests along the US mid‐Atlantic coast have survived despite undergoing relative sea‐level rise rates (RSLRR) that are among the fastest on Earth. Lateral forest retreat rates were strongly mediated by topography and seawater salinity, but not directly explained by spatial variability in RSLRR, climate, or disturbance. The elevation of coastal tree lines shifted upslope at rates correlated with, but far less than, contemporary RSLRR. Together, these findings suggest a multi‐decadal lag between RSLRR and land conversion that implies coastal ecosystem resistance. Predictions based on instantaneous conversion of uplands to wetlands may therefore overestimate future land conversion in ways that challenge the timing of greenhouse gas fluxes and marsh creation, but also imply that the full effects of historical sea‐level rise have yet to be realized.

 

Coastal landscapes are naturally shifting mosaics of distinct ecosystems that are rapidly migratingwith sealevel rise. Previous work illustrates that transitions among individual ecosystems have disproportionate impacts on the global carbon cycle, but this cannot address nonlinear interactions between multiple ecosystems that potentially cascade across the coastal landscape. Here, we synthesize carbon stocks, accumulation rates, and regional land cover data over 36 years (1984 and 2020) for a variety of ecosystems across a large portion of the rapidly transgressing mid-Atlantic coast. The coastal landscape of the Virginia Eastern Shore consists of temperate forest, salt marsh, seagrass beds, barrier islands, and coastal lagoons. We found that rapid losses and gains within individual ecosystems largely offset each other, which resulted in relatively stable areas for the different ecosystems, and a 4% (196.9 Gg C) reduction in regional carbon storage. However, new metrics of carbon replacement times indicated that it would take only 7 years of carbon accumulation in surviving ecosystems to compensate this loss. Our findings reveal unique compensatory mechanisms at the scale of entire landscapes that quickly absorb losses and facilitate increased regional carbon storage in the face of historical and contemporary sea-level rise. However, the strength of these compensatory mechanisms may diminish as climate change exacerbates the magnitude of carbon losses. 

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. 

Ecosystem connectivity tends to increase the resilience and function of ecosystems responding to stressors. Coastal ecosystems sequester disproportionately large amounts of carbon, but rapid exchange of water, nutrients, and sediment makes them vulnerable to sea level rise and coastal erosion. Individual components of the coastal landscape (i.e., marsh, forest, bay) have contrasting responses to sea level rise, making it difficult to forecast the response of the integrated coastal carbon sink. Here we couple a spatially-explicit geomorphic model with a point-based carbon accumulation model, and show that landscape connectivity, in-situ carbon accumulation rates, and the size of the landscape-scale coastal carbon stock all peak at intermediate sea level rise rates despite divergent responses of individual components. Progressive loss of forest biomass under increasing sea level rise leads to a shift from a system dominated by forest biomass carbon towards one dominated by marsh soil carbon that is maintained by substantial recycling of organic carbon between marshes and bays. These results suggest that climate change strengthens connectivity between adjacent coastal ecosystems, but with tradeoffs that include a shift towards more labile carbon, smaller marsh and forest extents, and the accumulation of carbon in portions of the landscape more vulnerable to sea level rise and erosion.

 

Lakes represent as much as ∼25% of the total land surface area in lowland permafrost regions. Though decreasing lake area has become a widespread phenomenon in permafrost regions, our ability to forecast future patterns of lake drainage spanning gradients of space and time remain limited. Here, we modeled the drivers of gradual (steady declining lake area) and catastrophic (temporally abrupt decrease in lake area) lake drainage using 45 years of Landsat observations (i.e. 1975–2019) across 32 690 lakes spanning climate and environmental gradients across northern Alaska. We mapped lake area using supervised support vector machine classifiers and object based image analyses using five-year Landsat image composites spanning 388 968 km². Drivers of lake drainage were determined with boosted regression tree models, using both static (e.g. lake morphology, proximity to drainage gradient) and dynamic predictor variables (e.g. temperature, precipitation, wildfire). Over the past 45 years, gradual drainage decreased lake area between 10% and 16%, but rates varied over time as the 1990s recorded the highest rates of gradual lake area losses associated with warm periods. Interestingly, the number of catastrophically drained lakes progressively decreased at a rate of ∼37% decade⁻¹from 1975–1979 (102–273 lakes draining year⁻¹) to 2010–2014 (3–8 lakes draining year⁻¹). However this 40 year negative trend was reversed during the most recent time-period (2015–2019), with observations of catastrophic drainage among the highest on record (i.e. 100–250 lakes draining year⁻¹), the majority of which occurred in northwestern Alaska. Gradual drainage processes were driven by lake morphology, summer air and lake temperature, snow cover, active layer depth, and the thermokarst lake settlement index (R²_adj= 0.42, CV = 0.35,p< 0.0001), whereas, catastrophic drainage was driven by the thawing season length, total precipitation, permafrost thickness, and lake temperature (R²_adj= 0.75, CV = 0.67,p< 0.0001). Models forecast a continued decline in lake area across northern Alaska by 15%–21% by 2050. However these estimates are conservative, as the anticipated amplitude of future climate change were well-beyond historical variability and thus insufficient to forecast abrupt ‘catastrophic’ drainage processes. Results highlight the urgency to understand the potential ecological responses and feedbacks linked with ongoing Arctic landscape reorganization.

 

Forty-five years (i.e. 1975-2019) of Landsat observations were used to map the spatiotemporal patterns of lake drainage in northern Alaska. All Landsat data was pre-processed by the United States Geological Survey and downloaded by google earth engine in a radiometrically, atmospherically, and geometrically terrain-corrected state. We used Landsat surface reflectance products acquired from the Multispectral Scanner (MSS), Terrestrial Mapper (TM), Enhanced Terrestrial Mapper Plus (ETM+), and Operational Land Imager (OLI) sensors to compute eight image mosaics for the ice-free period (June 15 to September 1) at five-year time-periods. This data product represents the change in lake area between time-periods or epochs. All data used to spatially identify patterns of lake change are presented in a map (LakeDrainChg.tif), where the associated morphometric controls on drainage are summarized for each of the ~33,000 lake boundaries (Lake_Drainage.shp). All data can also be viewed within Google Earth Engine (https://code.earthengine.google.com/?accept_repo=users/mjlara71/LakeDrainage; accessed on 13 September 2021) or clone Git repository (git clone https://earthengine.googlesource.com/users/mjlara71/LakeDrainage ; accessed on 13 September 2021). 

More than four decades’ high-resolution (~1 meter (m)) remote sensing observation in upland and lowland tundra revealed divergent pathways of shrub-cover responses to fire disturbance and climate change during 1951 to 2016 in the Noatak National Preserve of northern Alaska. We set up 114 study sites (250 m by 250 m) in burned and the adjacent unburned upland and lowland tundra using stratified random sampling. Specifically, all sites were placed with a minimum distance of 500 m apart from one another, and the unburned sites were located in areas greater than 500 m and less than 2,000 m radius surrounding the fire perimeters. To achieve an unbiased representation of tundra types (upland and lowland tundra) and fire severity levels (high, moderate, low, and unburned), a minumun of 12 study sites were randomly assigned to each tundra type × fire severity group. We then analyzed decadal-scale shrub cover change in each study site using supervised support vector machine classifier (ArcGIS 10.5). The data was presented as shrub cover (m2 ha (hectare)-1) at years before fire and after fire, where negative values of Year Since Fire (YSF) correspond to the number of years before fire, and positive values are the number of years after fire. Our results revealed that shrub expansion in the well-drained uplands was largely enhanced by fire disturbance, and it showed positive correlation with fire severity. In contrast, shrub cover decreased in lowland tundra after fire, which triggered thermokarst-associated water impounding and resulted in ~ 50% loss of shrub cover over three decades.