Drivers of Elevation Change in a Retreating Coastal Forest

Drivers of Elevation Change in a Retreating Coastal Forest | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Drivers of Elevation Change in a Retreating Coastal Forest Tyler Messerschmidt, Keryn B. Gedan, Matthew L. Kirwan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7236740/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Estuaries and Coasts → Version 1 posted 5 You are reading this latest preprint version Abstract The future of coastal marshes depends in part on their ability to migrate inland with sea level rise into coastal forests, but projections of land conversion commonly assume that migration occurs over a static terrestrial topography. Here, we assess the assumption of a static topography in a rapidly retreating, coastal upland forest by measuring surface accretion, subsurface expansion or subsidence, and net elevation change across a gradient of salinity and inundation stress. We found that salt-affected forest soils were largely stable over the 5-year study period, exhibiting limited elevation change (-0.24–1.5 mm yr − 1 ) in most stages of forest health. This stability was achieved by relatively equal and offsetting rates of accretion and subsidence that were each greater than expected. Surface accretion rates were positively correlated with the total cover of Morella cerifera shrubs rather than Phragmites australis , an invasive reed that dominates retreating coastal forests in the mid-Atlantic. Subsidence was greatest in higher elevation forest areas and attributed to root zone collapse that occurred prior to ongoing tree mortality. Net elevation-change rates were highest in the marsh-forest transition zone (2.94 mm yr − 1 ), which is characterized by dead trees and fully developed marsh vegetation. Though this study measured relatively low elevation change rates in most portions of a retreating coastal forest, similarity between transition zone and adjacent salt marsh rates suggest that elevation change rates respond quickly to marsh migration and are elevated by the time of extensive tree mortality. Accretion Coastal Landscape Change Marsh Migration Morella cerifera Phragmites australis Surface Elevation Table Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Sea level rise is increasing tidal flooding, storm surges, and salinization of groundwater in ways that threaten both human infrastructure and natural ecosystems. Salt marshes are highly valued ecosystems along global coastlines that provide protection from storms (Temmerman et al. 2013 ), improve water quality (Barbier et al. 2011 ), support estuarine and marine food webs (Colombano et al. 2021 ), and sequester carbon (Mcleod et al. 2011 ). Salt marshes grow vertically through soil organic matter accumulation and sediment trapping, but must migrate inland to survive rates of sea level rise that are faster than their ability to build soil (Kirwan and Megonigal, 2013 ). The replacement of coastal forests with marshes has been widely documented in regions of the United States where sea level rise intersects a gently sloping topography (Kirwan and Gedan, 2019 ; White et al., 2022 ). Rates of coastal forest retreat are accelerating in parallel with rates of relative sea level rise (Schieder and Kirwan, 2019 ), resulting in marsh migration rates that have compensated for erosion and drowning in portions of the mid-Atlantic and Gulf of Mexico (Schieder et al., 2018 ; Chen and Kirwan, 2023; Raabe and Stump 2016). Future projections of marsh area change under sea level rise indicate that the fate of marshes is largely tied to their ability to migrate inland (Kirwan et al., 2016 ; Schuerch et al. 2018 ; Osland et al., 2022 ). However, projections of upland conversion to marsh rely on a passive inundation, or “bath-tub model,” approach in which upland topography is constant through time (Schuerch et al., 2018 ; Osland et al., 2022 ; Molino et al., 2022 , Linhoss et al. 2015 , Geselbracht et al. 2011 , Farron et al. 2020 ). This approach predicts widespread conversion of uplands to wetlands with gradual sea-level rise, but observed rates of coastal forest retreat are only about half the rate expected by sea level rise propagating across a static topography (Chen and Kirwan 2024 ). Furthermore, assumptions of a static topography contrast with the general understanding that feedbacks between flooding, plant productivity, and sediment transport control surface elevations of other coastal ecosystems such as mangroves, marshes, and tidal forests (FitzGerald et al., 2008 ; Kirwan and Megonigal, 2013 ; Saintilan et al., 2023; Krauss et al., 2024 ). Because coastal hydrology and ecology are intertwined (Kirwan et al., 2025 ; Fagherazzi et al., 2025 ), and both forests and marshes are sensitive to small changes in elevation (Krauss et al., 2010 ), gains or losses of mere centimeters may significantly impact coastal forest retreat rates. Upland forest soils are usually considered as geomorphically stable over decadal timescales, with detrital soil inputs largely balanced by respiratory losses (Schimel 1995 ), but several geomorphic processes may contribute to elevation gain in coastal forests. Deposition of autochthonous material from litterfall is likely the dominant source of surface accretion. Litterfall can make up 60% of total aboveground productivity in tidal freshwater forests (Stahl et al. 2018 ) and > 70% in boreal forests (Bhatti and Jassal 2014 ). Increased inundation with saltwater may inhibit both aerobic and anaerobic decomposition of soil organic matter (Setia et al., 2011; Qu et al., 2019), potentially allowing for greater surface accretion in salt-affected forests, but observed outcomes are subtle and highly variable (Stagg et al., 2017; Helton et al., 2019; Wen et al., 2019 ; Smith et al., 2024). Hurricanes and extreme flooding events may also deposit sediment and organic matter (i.e., wrack) at the marsh-upland transition (Tate and Battaglia 2013 ). Additionally, colonization of the understory by salt- and flood-tolerant species can rapidly build elevation through increased litter accumulation, belowground biomass production, and sediment trapping. Morella cerifera thickets can substantially increase litterfall (Brantley and Young 2007), and accretion rates within mature Phragmites australis stands can reach 10 mm yr − 1 (Rooth et al. 2003 ), potentially increasing elevation gain where these species establish or increase in cover. Alternatively, introduction of labile carbon (dying roots) and pulses of sulfate-rich seawater may enhance decomposition leading to elevation loss (Weston et al. 2011 , Chambers et al. 2019 ). Whether saltwater intrusion facilitates or hinders decomposition is directly related to duration of flooding, soil organic C, and previous hydrologic conditions. (Stagg et al. 2017, Wen et al. 2018, Smith et al. 2024). Excessive flooding may also substantially alter organic soil structure leading to destabilization (Chambers et al. 2019 ). Additionally, flooding-induced tree mortality can lead to subsidence due to loss of root turgor pressure and collapse of the rooting zone. Elevation losses of 0.5–10 cm yr − 1 following hurricanes have been documented in mangroves, tidal freshwater forests, and pocosins (Cahoon et al. 2003 , Miller et al. 2021 , Middleton and David 2022 ). Breakthroughs in understanding of the ecogeomorphic processes driving elevation gain in coastal ecosystems have come largely through repeat elevation measurements associated with the Surface Elevation Table-Marker Horizon (SET-MH) approach (Cahoon et al. 1995 , Webb et al., 2013 , Saintilan et al., 2022 ). The combination of shallow and deeply anchored SETs reveal elevation changes that occur in shallow (root zone) and deep (sub root zone) portions of the soil profile. Horizon marker layers in the soil can further differentiate surface accretion. Integrating the results of these tools can help to infer which processes are responsible for net gain or loss in elevation at the soil surface, which has clear feedbacks to inundation in the coastal zone. Surface elevation tables have been widely adopted in coastal ecosystems such as marshes (Covington 2020 , Saintilan et al. 2022 ), mangroves (Krauss et al. 2010 , Lovelock et al. 2015 ), and, more recently, tidal freshwater forests (Stagg et al. 2017, Krauss et al. 2024 ), pocosins (Moorman et al. 2024 ) and coastal scrub/shrubland (Cook et al. 2016 , Ardon et al., in review). However, elevation change rates have not been reported for upland coastal forests. Here, we investigate surface elevation change using the SET-MH approach to expand the coastal geomorphic framework into a salt-affected upland forest. Root zone subsidence in forest soils prior to forest retreat would indicate that forest retreat and marsh migration might advance more quickly than static models predict. Alternatively, substantial accretion of forest soils would potentially explain observed lags between sea level rise and forest retreat. More generally, we aim to understand how the elevation dynamics of salt-stressed forests may change over time and whether these processes enhance or mitigate future marsh migration. Study Site Our study focuses on a well-studied coastal maritime forest on Virginia’s Atlantic Coast. This area is located in the mid-Atlantic sea level rise hotspot, where relative sea level rise rates are faster than eustatic rates (Sallenger et al. 2012 ) and influenced by deep subsidence rates of 1–3 mm yr − 1 (Eggleston and Pope 2013 , Ohenen et al. 2023). The rural and low-lying mid-Atlantic coast experiences rapid marsh migration into uplands, making it an ideal location to study the elevation dynamics of marsh transgression. Approximately 220 km 2 of coastal forests have converted to marsh in the mid-Atlantic since 1984 (Chen and Kirwan, 2024 ), and an additional 1050 km 2 − 3748 km 2 of conversion is projected for the Chesapeake Bay by 2100 (Molino et al 2022 ). The study site is located within The Nature Conservancy’s Brownsville Nature Preserve, Nassawadox, VA (37.464814 ° N, -75.835185 ° W) and adjacent to Upper Phillips Creek marsh (UPC), a focal study area of the Virginia Coast Reserve (VCR) Long-Term Ecological Research (LTER) program (Fig. 1 ). Due to its exceedingly low slope (0.0006 m/m) (Fig. 1 ), this area has undergone significant coastal transgression, highlighted by the conversion of upland forest to organic-rich high marsh (Brinson et al. 1995 ). Marsh transgression is occurring at rates between 0.3 to 1 m yr − 1 (Fig. 1 ) and total marsh area increased by 0.9 ha between 2000 and 2017 (Flester and Blum 2020 ). The studied forest is an upland, maritime forest comprised mainly of loblolly pine ( Pinus taeda ) with black tupelo ( Nyssa sylvatica) , red maple (Acer rubrum) , and American holly ( Ilex opaca) interspersed at higher elevations (Langston et al., 2025). The forest understory is composed mainly of grasses Phragmites australis , Panicum virgatum and Chasmanthium laxum and the thicket-forming shrub Morella cerifera. Eastern red cedar ( Juniperus virginiana ), P, australis , and shrubs such as Baccharis halimifolia and Iva frutescens dominate the forest-marsh interface. The average forest elevation is 1.15 m, which is only 20 cm higher than the adjacent marshes (Table 1). The entire study area is within 15 cm of the Highest Astronomical Tide (1.07m NAVD88) converted from observed water levels at Wachapreague, VA (Station 8631044, NOAA Tides and Currents, Blum et al. 2021 ). Over the study period, groundwater levels exceed surface elevations upwards of 100 days yr − 1 at lower elevations (Pratt et al. 2025 ). The Brownsville forest was delineated into four forest health zones in 2019, representing a gradient of flooding and salinity stress: high, mid, low and transitional forest (Fig. 1 ) (Smith and Kirwan 2021 ; Langston et al., 2025). High forest was initially defined as largely unaffected by saltwater intrusion and containing a mixed species composition of both coniferous and deciduous tree species. The mid forest displays early signs of salt stress including deciduous tree mortality but contains a healthy, coniferous population. Low forest is defined by higher salt stress, indicated by limited tree regeneration, increasing conifer stress and standing dead trees. The transition zone contains ~ 50% adult tree mortality, no regeneration, and an understory of typical high marsh species. Since 2019, the shrub M. cerifera has expanded by 45–65% in high and mid forest plots, while in the low forest, there has been steady spread of invasive Phragmites australis (Sward et al. 2023 ). Methods Surface Elevation Tables Changes in forest soil elevations were measured using the surface elevation table - marker horizon (SET-MH) approach that quantifies both surface accretion and net elevation change with an accuracy of 1–2 mm (Cahoon 2002, Callaway 2013, Lynch 2015). SETs consist of a deep benchmark driven to refusal, on which a mechanical leveling arm is attached at fixed positions around the mark, allowing for repeated measurements of net elevation change with respect to the base of the benchmark (Cahoon 2002). It is paired with a marker horizon, which consists of a feldspar layer established on the soil surface, on top of which surface accretion can be measured. The calculated difference between surface accretion (MH) and net elevation change (SET) allows for an estimate of total subsurface processes between the marker horizon and benchmark base (Cahoon et al. 1995 ). To examine the contribution of root zone processes to total subsurface elevation change, 3-inch diameter, capped, aluminum pipes were installed to a depth of ~ 30cm. (Langley et al. 2009 ) (Fig. 2 a). These pipes have 1-inch holes drilled into them to allow for root ingrowth (Walters et al. in prep). Four SETs were installed in each zone to depths between 16 and 21 m. The high, mid, and low forest zones were installed in March 2019 and the transition zone in August 2020 (Table 1). Each SET was paired with a marker horizon and 1–2 shallow pipes driven to ~ 30 cm. Elevation measurements were taken approximately 3 times per year, beginning in October 2019 (high, mid, and low forest) and January 2021 (transition zone). To measure elevation at each SET, the arm was leveled at 4 equidistant positions approximately 0.5 m from the benchmark. At each position, leaf litter was gently removed by hand (Fig. 2 b) taking care not to disturb the underlying surface. Partially decomposed litter less than ~ 2 cm in size was left in place. Pinecones and branches were removed from the measurement area only if their removal did not disturb the forest soil. Any foreign object (stick, rock, cone, etc.) partially buried beneath the surface was left in place but excluded from the analysis. Nine fiberglass pins were then lowered to the exposed surface of the underlying forest soil. Elevation was measured as the distance between the arm and top of each pin. Leaf litter was removed (as above) from a small section of the marker horizon before measuring the depth of the feldspar layer from the revealed soil surface. Beginning in February of 2022, the SET arm was leveled over the aluminum pipes and their heights were measured. After the measurements at each SET position and marker horizon were complete, the leaf litter was gently returned to its original position. Statistical Approach Net elevation change rates were calculated using the ‘regress then average’ method (Lynch et al. 2015 , Russel et al. 2022). Elevation trends were first regressed through time at the individual pin level, then averaged by arm position and finally aggregated to the entire plot. Each plot estimate, therefore, reflects the average of 36 individual pin measurements. Marker horizon derived accretion was then subtracted from plot level net elevation change to calculate total subsurface change, where a negative subsurface change indicates subsidence, while a positive value reflects subsurface expansion. Elevation change within the top 30 cm, here referred to as root zone subsurface change is calculated as the difference between average SET cumulative change and shallow pipe derived cumulative change at each time step. Subsurface change below 30 cm, referred to as shallow hydro-geologic change, is the difference between total subsurface and root zone elevation change. One-sided t-tests were used to determine if average rates of change were greater than 0 (no change) and 4.1 mm yr − 1 (local relative sea level rise). Local relative sea level rise was calculated between 1997 and 2017 based on NOAA’s Wachapreague Station (ID 8631044) and converted to water levels at nearby Upper Phillips Creek (Blum et al. 2021 ). A paired t-test was used to compare net elevation change and surface accretion. The statistical significance of differences in net elevation change, accretion, and subsidence between forest zones was assessed using a one-way ANOVA in R Version (4.4.1) (R Core Team 2023). Tukey’s HSD post hoc test was used to evaluate the significance of pairwise comparisons. All statistical tests were conducted with an α-level of 0.05 and N = 4 for each forest zone. Environmental Variables Vegetation and soils Vegetation cover was measured in August 2024 within a 3 m x 3 m quadrat, centered around each SET benchmark. The percentage cover of each understory species was estimated via grid point-intercept at multiple height layers to account for vertical stratification, therefore total cover could exceed 100%. This approach captured the complexity of vegetation structure in the plots. To simplify statistical analyses, species were subsequently grouped by functional plant types (i.e., upland grasses, shrubs, and marsh grasses). Measurements of vegetation cover were supplemented with existing litterfall, porewater and soil datasets collected in 2022 (Gedan et al. 2022 , 2023 ). Litterfall was collected monthly using three replicate litter baskets, each measuring 25 cm x 25 cm, positioned randomly within each plot. Litter was oven-dried at 60°C to a constant mass and then weighed to determine biomass contributions. In this study, only annual totals of litterfall biomass are presented. Soil porewater samples were collected opportunistically following significant rainfall events to capture natural fluctuations in soil salinity. Samples were taken from the upper 20 cm of soil using a push-point sampler (MHE Products) and averaged to represent the salinity of the entire plot. Salinity was measured in situ using a handheld meter, following the methodology outlined by Sward et al. ( 2023 ). Surface soil samples were collected from each plot to assess soil conductivity and water content. These samples were air-dried and homogenized to ensure consistency. Conductivity was measured using a laboratory 5:1 dilution. Carbon and nitrogen content of the surface soils were determined by LECO CN828 elemental analyzer. S tatistical Analyses Stepwise regression analysis was conducted to identify the most significant predictors of net elevation change, accretion, and subsurface change. The stepwise regressions were run with and without transition zone elevation change metrics, since the soil environmental data was not collected in the transition zone. In the model including all forest zones, the explanatory variables included elevation (NAVD88), shrub cover, Phragmites cover and marsh grass cover. The model excluding the transition zone also included litterfall, soil moisture content, soil conductivity and porewater salinity as variables. Starting with an initial intercept model, variables were added or removed based on Akaike Information Criterion (AIC) to balance model complexity with predictive accuracy. All analyses were performed in R version [4.4.1] (R Core Team 2023). Results Elevation trends from the retreating upland forest indicate minimal net elevation change over the approximately 5-year record across the high, mid, and low forest with rates of individual SETs ranging from − 1.6 to 4.31 mm yr⁻¹ (Table 1). The transition zone gained elevation at the highest rate (2.94 mm yr⁻¹), followed by the mid (1.51 mm yr⁻¹), low (0.7 mm yr⁻¹) and high (-0.23 mm yr⁻¹) forest zones (Fig. 3 ). Net elevation change was significantly greater than zero in the mid, low, and transition zones (p = .007, 0.031, 0.031, Supp. Table 1) but not the high forest (p = 0.68). Of the three zones with net elevation change rates greater than 0, only the transition zone was able to keep pace with local rates of sea level rise (4.1 mm yr⁻¹ for 2005–2020) (Blum et al. 2021 ). Surface accretion rates were highly variable and ranged from 1.1 to 5.3 mm yr − 1 (Table 1) with the highest rates observed in the mid forest (4.5 mm yr⁻¹), though these were not significantly different from those in the high (2.9 mm yr⁻¹), low (2.63 mm yr⁻¹), and transition zones (2.76 mm yr⁻¹) (Table 2). Surface accretion rates were not significantly different from net elevation change in the transition zone (p = 0.88) (Fig. 4 ). Conversely, surface accretion rates were significantly greater than net elevation change in the mid, and low forest zones (p = 0.0002 and 0.007, respectively Supp. Table 2), while the difference in the high forest was marginally significant (p = 0.07). This difference in surface accretion and net elevation gain highlights the importance of subsurface processes in driving elevation change rates in these zones. Total subsurface change ranged from − 5.8 to 2.6 mm yr⁻¹ (Table 1) with the greatest subsidence rates observed in the minimally salt stressed high (-3.1 mm yr⁻¹) and mid forest (-3.0 mm yr⁻¹), although these were not significantly different from subsidence in the low forest (-1.9 mm yr⁻¹) (Table 2). Subsurface change in the transition zone was near zero (0.17 mm yr⁻¹) but highly variable (-2.4 to 2.64 mm yr⁻¹) (Fig. 3 ). Below the rooting zone (> 30 cm depth), the mid (1.56 mm yr⁻¹) and low forest (2.30 mm yr⁻¹) exhibited expansion, while the high forest (-0.56 mm yr⁻¹) and transition zone (-1.13 mm yr⁻¹) experienced subsidence. Within the rooting zone (< 30 cm depth), the high (-2.58 mm yr − 1 ), mid (-4.54 mm yr⁻¹) and low (-5.55 mm yr⁻¹) forest subsided, while only the transition zone (1.3 mm yr⁻¹) exhibited root zone expansion (Table 2). The multiple linear regression analysis identified several significant interactions influencing net elevation change, accretion, and subsurface processes (Table 4). Net elevation change was strongly correlated with both elevation and percent shrub cover. Net elevation change was positively correlated with the elevation of SET benchmarks in the high, mid, and low forest (Fig. 5 a) but negatively correlated when the transition zone was included (Fig. 5 b). In the high, mid, and low forest, shrub cover was positively correlated with net elevation change, though it was not a significant predictor across the entire forest health gradient (Fig. 5 c, d). However, when analyzing surface accretion in isolation, shrub cover was the only significant predictor across all zones (p = 0.013) (Table 3). While elevation was negatively correlated with accretion across all zones (Fig. 5 ), it was not statistically significant on its own but remained an important variable in our multiple linear model. Lastly, subsurface changes were positively correlated with elevation and soil conductivity, and negatively correlated with soil moisture and shrub cover (Fig. 5 e, 6 ). However, only shrub cover significantly influenced subsurface change at all forest zones (Fig. 5 f, 6 ). Discussion Future projections of marsh migration into retreating coastal forests rely on assumptions of a static topography, where the elevation of upland and wetland forests does not change through time (Linhoss et al. 2015 ; Schuerch et al., 2018 ; Osland et al., 2022 , Molino et al., 2022 ). In contrast, detailed elevation-change measurements in coastal marshes (Blum et al., 2021 , Saintilan et al., 2022 ), mangroves (Rogers et al., 2005 , Krauss et al., 2010 , Feher et al., 2024 ), and forested wetlands (Ardon et al., in review ,Stagg et al. 2016 ; Krauss et al., 2024 ) indicate a dynamic topography where a variety of ecogeomorphic feedbacks influence rates of accretion, subsidence, and net elevation change (Cadol et al., 2014, Rogers et al. 2022 , Lovelock et al., 2025 ). Although little work has been conducted in upland forests, observations from other coastal ecosystems indicate that vegetation type and inundation duration are primary drivers of elevation change through their impacts on surface accretion and belowground soil processes. In this study, we attempt to extend this conceptual framework to salt-affected upland forests. Influence of vegetation In marshes, species composition directly influences rates of elevation change, largely due to differences in productivity (Calvo-Cubero et al. 2013) and sediment trapping capacity (Boorman et al. 1998, Rooth et al. 2003 ). Consistent with the influence of species composition in marshes, we found that vegetation type had a strong influence on the rate of net elevation change across the retreating upland forest. Total shrub cover, dominated by the shrub Morella cerifera , was the most important variable controlling net elevation change, surface accretion, and subsurface expansion or contraction (Fig. 5 ). Over the study period, the mid forest zone experienced significant increases in M. cerifera (Sward et al. 2023 ) and the greatest rates of surface accretion (Fig. 4 ). Litter inputs from densely packed shrubs may have contributed to higher accretion rates observed in the mid forest zone. In the subsurface, increased shrub cover resulted in greater elevation loss (Fig. 5 e, f). Shrub encroachment by M. cerifera at the study site is likely occurring in response to increased light availability from tree stress and mortality (Sward et al. 2023 ). Therefore, subsurface subsidence in shrub-dominated plots may reflect root-zone collapse (e.g. Krauss et al., 2024 ), where root mortality and loss of turgor pressure occur even prior to tree mortality (Ding et al. 2023 . The cooccurrence of shrub encroachment and tree root collapse in the mid forest might explain why there were opposite responses in surface and subsurface contributions to elevation change. Alternatively, a nitrogen-fixing shrub, M. cerifera increases soil nutrients, moisture, and winter temperatures (Thomspon et al. 2017) potentially stimulating decomposition of soil organic matter in its rhizosphere (Smith et al. 2023). Surprisingly, the invasive reed Phragmites australis did not significantly influence any of the elevation change metrics across forest zones (Figs. 4 , 5 ). Previous studies indicate that Phragmites contributes to rapid marsh elevation change through increased sediment trapping and high plant productivity (Rooth and Stevenson, 1999; Rooth et al., 2002). Accretion rates in P. australis stands may be 3–4 mm yr⁻¹ greater than adjacent native marsh (Rooth et al. 2003 ), driven by above and belowground organic matter accumulation (Rooth and Stevenson 1999). In contrast, we found that subsurface losses were highest in plots with the greatest P. australis cover (MF 6, TZ 4, LF 5; Table 2) (Fig. 4 ). Given its high abundance in stressed forest plots, the positive influence of P. australis on net elevation may be offset by subsidence related to tree mortality. Conversely, P. australis may contribute to subsurface losses due to decomposition, stimulated by increased standing stocks of nitrogen (Windham and Meyerson 2003 ) and higher rhizosphere oxidation in P. australis stands (Windham and Lathrop 1999 ). Additionally, P. australis stems are more resistant to decomposition than tree leaves or native marsh grasses (Meyerson et al. 2000 ) and therefore may accumulate primarily as standing dead stems and take up to 7 years for enhanced rates of accretion to be reflected in the SET-MH record (Rooth et al. 2003 ). Given the thick thatch layer (personal observation), relatively short marker horizon record in the transition zone, and distance from tidal inputs the full contribution of Phragmites to surface accretion is likely yet to be realized. Belowground dynamics Subsidence and elevation loss is common in coastal landscapes and has been documented in marshes (Keogh et al. 2021, Cahoon et al. 1995 ), mangroves (Cahoon and Lynch 1997 , Rogers et al. 2005 ), tidal freshwater forests (Krauss et al. 2024 ), and pocosins (Miller et al. 2021 ). Subsidence is often in the form of autocompaction (Kaye and Barghoorn 1964 , Cahoon et al. 1995 ; Saintilan et al., 2022 ) an entirely physical process defined as the compression of organic rich peat beneath its own weight. Additional subsidence has been documented across these ecosystems, driven by environmental stressors such as salinization and inundation. For example, losses of 11 mm yr − 1 were documented in Honduran mangroves following a large hurricane (Cahoon et al. 2002 ) and a Mississippi River deltaic marsh lost 15 cm over several years in response to increased tidal inundation (DeLaune et al. 1994 ). A study of Louisiana marshes indicates that over 60% of subsidence occurs in the top 5 m (Jankowski et al. 2017, Keogh and Tornqvist 2019) while in Australia, marsh subsidence was found to be largely confined to the organic-rich top 80 cm (Rogers and Saintilan 2021). Consistent with widespread observations of subsidence in other coastal ecosystems, shallow subsidence was observed in 13 of 16 SET locations across the studied retreating coastal forest (Fig. 7 ). Previous work suggests that tree mortality can increase rates of subsidence and lead to net decreases in surface elevation at rates exceeding 10 mm yr − 1 (Cahoon et al. 2002 ; Miller et al. 2021 ). However, we found that total subsidence was greatest in the high and mid forest, where tree mortality is lower than in the low forest zone (Table 3). This is perhaps consistent with modeling efforts that have shown that decreases in root biomass precede canopy stress and tree mortality in coastal forests (Ding et al. 2023 ). Shallow subsidence (< 30 cm) was greatest in the mid (-4.54 mm yr⁻¹) and low (-5.55 mm yr⁻¹) forest zones. In contrast with observations of tree mortality leading to subsidence and elevation loss in other forested wetland ecosystems (Middleton et al. 2020, Cahoon et al. 2003 ), the mid and low forest exhibited expansion below the rooting zone (1.56 and 2.3 mm yr⁻¹ respectively) and rates of net elevation change were generally positive (0.7 − 1.51 mm yr − 1 ). Shallow subsidence rates in the mid and low forest ranged from − 2.98 to -1.93 mm yr − 1 but were offset by surface accretion (Fig. 4 ). Similar rates of shallow subsidence have been documented in stressed tidal freshwater forests (-3.8 to -7.6 mm yr⁻¹), largely driven by losses in the rooting zone (Krauss et al. 2024 , Stagg et al. 2016 ). Autocompaction is not a probable cause of forest subsidence, as the organic layer is thin (5–10 cm, Smith et al. 2019) and underlain by thick consolidated clay (Supp. Figure 1 ). Instead, root zone subsidence was likely due to detrimental effects of inundation and salinity on mature Loblolly pine, which distribute up to 80% of salt sensitive, fine roots within the top 20 cm of soil (Mou et al. 1995 ). Expansion below the rooting zone was only documented in the mid and low forest zones, while the high forest and transition zone subsided at depths below the rooting zone (i.e. 30cm) (Table 2). This heterogeneity was surprising given the relatively small distance between forest zones. Similar patterns of localized expansion and subsidence were observed in a South Carolina freshwater forest and were attributed to small scale differences in hydrology (Stagg et al. 2017). In the studied upland forest, expansion below the rooting zone in the mid and low forest may be due to the establishment of M. cerifera and P. australis , the roots of which frequently reach depths greater than 30 cm (Moore et al. 2012 ). Alternatively, the expansion may be due to the swelling of clay-rich soils in response to inundation with saline water. Clay matrix expansion by up to 10% has been documented in laboratory studies (Pedarla et al. 2012 ). Like the high forest, the transition zone experienced shallow subsidence below the root zone. This subsidence may be at least partially due to autocompaction, as the depth of organic matter here is ~ 20cm (Smith et al. 2019). Expansion in the root zone of 1.3 mm yr − 1 offset the subsidence below 30cm, resulting in near zero subsurface elevation change in the transition zone. This pattern of surface accretion and expansion in the shallow zone, is similar to the elevation trends of the nearby high marsh, which from 2009–2024 gained elevation at a rate of 3.5 mm yr − 1 , largely driven by surface accretion (3.1 mm yr − 1 ) and subsurface expansion (0.4 mm yr − 1 ) (Blum et al. 2021 ). Influence of Elevation and Flooding Frequency Across coastal ecosystems, rates of net elevation change are largely a function of elevation and flooding frequency (Kirwan and Megonigal, 2013 ). In coastal systems, lower elevations are more frequently flooded, resulting in increased potential for sediment deposition (Stumpf 1983, Friedrichs and Perry 2001) and enhanced exchange of nutrients leading to greater above and belowground productivity (Morris et al. 2002 , Kirwan and Guntenspergen 2012). A recent global review of nearly 400 surface elevation tables identified rates of relative sea level rise and hydroperiod as the most important factors driving marsh net elevation change (Saintilan et al. 2021). We find that this relationship between elevation and elevation change generally holds true for the forest-to-marsh transition, where the lowest elevation transition zone SETs exhibit the greatest net elevation change rates (Fig. 4 ), and elevation is a significant control of surface accretion rates across all forest zones (Fig. 5 ). Unlike other coastal systems, such as marshes and mangroves, where mineral sediment deposition can reach several mm yr − 1 (Cahoon and Lynch 1997 , Krauss et al. 2024 , Morris et al. 2002 , Kirwan and Megonigal 2013 ), deposition of mineral sediment in coastal forests is unlikely. Due to the distance from the nearest tidal channel (> 500 m) any flood waters are likely to be depleted of sediment well before reaching the transition zone. This is supported by high surface organic matter content of sediment cores in the transition zone and low forest (Supp. Figure 1 ). Though the impacts of tidal inundation on sediment deposition are minimal in coastal upland forests, lower elevations are nonetheless associated with more frequent soil saturation (Nordio et al. 2021, Callahan et al. 2017 ) due to their proximity to shallow groundwater and propensity to collect surface rainwater. Like the adjacent high marsh, the waterlogged, anerobic soils at the forest-marsh ecotone may limit decomposition (Piaszczyk et al. 2022, Janousek et al. 2017) and facilitate the establishment of productive marsh species (Shaw et al. 2022, Smith et al. 2013) that in turn increase belowground biomass relative to that of the nearby low and mid forests. Elevation change rates that increase with increasing elevation between the low forest and high forest are in contrast with elevation change rates that decrease with increasing elevation in long-measured SETs in the adjacent salt marsh (Blum et al., 2021 ) (Fig. 7 ). By synthesizing upland forest and salt marsh elevation changes, we find that the relationship between elevation and net elevation change shifts from negative to positive at an elevation very close to the highest astronomical tide (HAT, 1.07m; NOAA Station ID 8631044), suggesting a switch in the processes driving net elevation change associated with large changes in flooding regimes. For example, the low forest zone is flooded by approximately 33 tides per year, while the transition zone is flooded by approximately 140 tides per year (Supp. Table 3). This difference in flooding regime may correspond to a threshold in which additional flooding leads to enhanced soil organic matter accumulation in wetlands but enhanced subsidence in forest and shrub dominated zones. Elevation change rates that increase with increasing elevation in upland forests may lead to the development of microtopography, characterized by high elevation hummocks and low elevation hollows. Hummock-hollow microtopography is commonly found in global wetlands (Diamond et al. 2020), especially in tidal freshwater forested wetlands (Krauss et al. 2024 ) and pocosins (Brinson 1991 ) because small scale variations in elevation may cause dramatic differences in inundation, soil moisture and salinity (Wallis and Raulings, 2011). Hummocks tend to be areas of greater productivity due to reduced hydraulic stress. Once established, this microtopography tends to be preserved as shallow subsidence offsets accretion more in hollows than hummocks (Krauss et al. 2024 ). Hummocks also provide relief from wet conditions and may even be underlain with small unsaturated zones (Hsueh et al. 2016). A similar pattern of hummock-hollow formation and maintenance may be occurring in the studied coastal upland forest, where elevation gains are directly related to initial surface elevation (Fig. 7 ). This is consistent with elevation variance that is greater within individual plots and forest zone than between forest zones (pers. observation). Additionally, both rates of subsidence and accretion were more strongly controlled by elevation and vegetation type than by forest zone, suggesting the importance of localized, small-scale controls on elevation change rates. In tidal freshwater forested wetlands, maintenance of the “hummock-hollow” microtopograhy is an important factor for the resistance of transition to oligohaline marsh (Noe et al 2013 , Krauss et al. 2024 ). Similarly, poorly explained variance in the elevation of the marsh-forest boundary across the Chesapeake Bay region has been attributed to microtopograhy (Molino et al., 2023 JGR), and inundation relief provided by hummocks may slow the transition from forest to marsh. Conclusions and implications for coastal transgression We measured relatively small changes in the elevation of a retreating upland forest soil (Fig. 3 ), which at least tentatively supports assumptions of a static upland topography in models of coastal transgression (Schuerch et al., 2018 ; Osland et al., 2022 ; Molino et al., 2022 ). This finding is in contrast to observations of extensive subsidence and net decreases in the elevation of other coastal forests following mortality events (e.g. Miller et al., 2021 ). Large storms, which were largely absent during the study period, may explain this deviation. For example, elevation losses of ~ 1.1 cm yr − 1 in mangroves (Cahoon et al. 2003 ) and 10 cm yr − 1 in forested wetlands (Middleton and David, 2022 ) have been documented using SET-MH following hurricanes. In each instance, tree mortality driven subsidence resulted in rapid conversion to marsh, whereas forest to marsh conversion is gradual at our study site (Fig. 1 ). Conversely, storms may increase accretion through a pulse of leaf litter and woody debris, wrack deposits or inorganic sediment. For example, Hurricane Hugo deposited up to 3 cm of mud into a South Carolina pine forest (Gardner et al. 1991). In this study we found no evidence of either a significant subsidence or depositional event, though we were not able to directly measure storm effects during the study period. Therefore, over short time scales and in the absence of large storms, assumptions of a static upland topography may be valid. Observations of slow net-changes in soil elevation belie more dynamic responses to vegetation and inundation expressed in the individual components of elevation change (i.e. surface accretion, and subsurface expansion or subsidence). Even in the absence of a major storm event, transgression of vegetation types are occurring rapidly in the studied forest, including the encroachment of woody shrubs (Sward et al., 2023 ) and invasive Phragmites (Shaw et al., 2023), and reductions in the biomass of even the high forest zones (Chen and Kirwan, 2022 ). While we measured subsidence rates of up to 5mm yr − 1 in most forest zones that may correspond with declines in forest health, subsidence was largely compensated for by surface accretion (Fig. 4 ). Finally, we observed net surface accretion paired with belowground expansion in the transition zone, such that net elevation change rates in developing marshes are nearly equivalent to adjacent, fully developed salt marshes (Fig. 7 ). Together, these observations suggest that the elevation of forest soils are relatively stable, but that elevation change rates and their components respond rapidly to marsh migration into retreating coastal forests. Declarations The authors have no competing interests to declare that are relevant to the content of this article Funding for this project was provided by The National Science Foundation’s Critical Zone Network (2012484) and Long-Term Ecological Research (1832221 and 2425178) programs. Additional support was provided by the U.S. Geological Survey Chesapeake Bay Studies program. We appreciate assistance from Joel Carr and David Walters with the initial SET install and measurement protocols, and data collection assistance from the Virginia Coast Reserve staff. We thank The Nature Conservancy for access to the field site. Acknowledgements Funding for this project was provided by The National Science Foundation’s Critical Zone Network (2012484) and Long-Term Ecological Research (1832221 and 2425178) programs. Additional support was provided by the U.S. Geological Survey Chesapeake Bay Studies program. We appreciate assistance from Joel Carr and David Walters with the initial SET install and measurement protocols, and data collection assistance from the Virginia Coast Reserve staff. We thank The Nature Conservancy for access to the field site. References Anisfeld, S. C., Cooper, K. R., & Kemp, A. C. (2017). Upslope development of a tidal marsh as a function of upland land use. Global Change Biology , 23 (2), 755–766. https://doi.org/10.1111/gcb.13398 Anthony, E., Brunier, G., Besset, M. et al. Linking rapid erosion of the Mekong River delta to human activities. Sci Rep 5, 14745 (2015). https://doi.org/10.1038/srep14745 Ardón, M., Lazaro, P., Emanuel, R., & others. (2025, June 18). Surface elevation trends in natural and restored coastal forested wetlands reveal vulnerability to saltwater intrusion and sea level rise (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-6830356/v1 Barbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C. & Silliman, B.R. (2011), The value of estuarine and coastal ecosystem services. Ecological Monographs, 81: 169-193. https://doi.org/10.1890/10-1510.1 Bhatti, Jagtar & Jassal, Rachhpal. (2014). Long term aboveground litterfall production in boreal jack pine (Pinus banksiana) and black spruce (Picea mariana) stands along the Boreal Forest Transect Case Study in western central Canada. Écoscience. 21. 301-314.. Blum, L. K., Christian, R. R., Cahoon, D. R., & Wiberg, P. L. (2021). Processes Influencing Marsh Elevation Change in Low- and High-Elevation Zones of a Temperate Salt Marsh. Estuaries and Coasts, 44(3), 818–833. https://doi.org/10.1007/s12237-020-00796-z Brantley, S.T., Young, D.R. Shifts in litterfall and dominant nitrogen sources after expansion of shrub thickets. Oecologia 155 , 337–345 (2008). https://doi.org/10.1007/s00442-007-0916-7 Brinson, M.M. Landscape properties of pocosins and associated wetlands. Wetlands 11 (Suppl 1), 441–465 (1991). https://doi.org/10.1007/BF03160761 Brinson, M. M., Christian, R. R., & Blum, L. K. (1995). Multiple States in the Sea-Level Induced Transition from Terrestrial Forest to Estuary (Vol. 18, Issue 4, pp. 648–659). https://about.jstor.org/terms Brinson, M.M., & R.R. Christian. (1999). Stability of Juncus roemerianus patches in a salt marsh. Wetlands 19 (1): 65–70. Cahoon, D. R., & Lynch, J. C. (1997). Vertical accretion and shallow subsidence in a mangrove forest of southwestern Florida, U.S.A. In Mangroves and Salt Marshes (Vol. 1, pp. 173–186). Kluwer Academic Publishers. Cahoon, D. R., Lynch, J. C., Perez, B. C., Segura, B., Holland, R. D., Stelly, C., Stephenson, G., & Hensel, P. (2002). High precision measurements of wetland sediment elevation: II. The rod surface elevation table. Journal of Sedimentary Research, 72(5), 734–739. https://doi.org/10.1306/020702720734 Cahoon, D.R., Hensel, P., Rybczyk, J., McKee, K.L., Proffitt, C.E. & Perez, B.C. (2003), Mass tree mortality leads to mangrove peat collapse at Bay Islands, Honduras after Hurricane Mitch. Journal of Ecology, 91: 1093-1105. https://doi.org/10.1046/j.1365-2745.2003.00841.x Cahoon, D. R., Reed, D. J., & Day, J. W.. (1995). Estimating shallow subsidence in microtidal salt marshes of the southeastern United States: Kaye and Barghoorn revisited. In MARINE GEOLOGY ELSEVIER Marine Geology (Vol. 128, p. 9). Callahan, T.J.; Amatya, D.M.; Stone, P.A. (2017). Coastal Forests and Groundwater: Using Case Studies to Understand the Effects of Drivers and Stressors for Resource Management. Sustainability 9, 447. https://doi.org/10.3390/su9030447 Callaway, J.C., Cahoon, D.R. & Lynch, J.C. (2013). The Surface Elevation Table–Marker Horizon Method for Measuring Wetland Accretion and Elevation Dynamics. In Methods in Biogeochemistry of Wetlands (eds R.D. DeLaune, K.R. Reddy, C.J. Richardson and J.P. Megonigal). Carr, J., Guntenspergen, G., & Kirwan, M. (2020). Modeling Marsh-Forest Boundary Transgression in Response to Storms and Sea-Level Rise. Geophysical Research Letters, 47(17).https://doi.org/10.1029/2020GL088998 Chambers, L. G., Steinmuller, H. E., & Breithaupt, J. L. (2019). Toward a mechanistic understanding of “peat collapse” and its potential contribution to coastal wetland loss. Ecology, 100(7). https://doi.org/10.1002/ecy.2720 Chen, Y., & Kirwan, M. L. (2022). Climate-driven decoupling of wetland and upland biomass trends on the mid-Atlantic coast. Nature Geoscience, 15 , 913–918. https://doi.org/10.1038/s41561-022-01041-x Chen, Y., & Kirwan, M. L. (2024). Upland forest retreat lags behind sea-level rise in the mid-Atlantic coast. Global Change Biology, 30(1). https://doi.org/10.1111/gcb.17081 Colombano, D.D., Litvin, S.Y., Ziegler, S.L. et al. (2021). Climate Change Implications for Tidal Marshes and Food Web Linkages to Estuarine and Coastal Nekton. Estuaries and Coasts 44, 1637–1648 https://doi.org/10.1007/s12237-020-00891-1 Conner, W.H. & Askew, G.R. (1992). Response of baldcypress and loblolly pine seedlings to short-term saltwater flooding. Wetlands 12, 230–233 https://doi.org/10.1007/BF03160614 Conner, W., Whitmire, S., Duberstein, J., Stalter, R., & Baden, J. (2022). Changes within a South Carolina Coastal Wetland Forest in the Face of Rising Sea Level. Forests, 13(3). https://doi.org/10.3390/f13030414 Cook, C. E., Boyle, M. F., & Rankin, N. M. (2016). Vegetation of coastal wetland elevation monitoring sites on National Wildlife Refuges in the South Atlantic geography: 1st status assessment repor t . U.S. Fish and Wildlife Service. Covington, J.S. (2020). An inventory of surface elevation tables installed on National Wildlife Refuge System lands. U.S. Fish and Wildlife Service, Falls Church, VA. 16 pp. Craft, C.B., (2012). Tidal freshwater forest accretion does not keep pace with sea level rise. Global Change Biology, 18(12), pp.3615-3623. Denslow, J. S., & Battaglia, L. L. (2002). Stand Composition and Structure Across a Changing Hydrologic Gradient: Jean Lafitte National Park, Louisiana, USA. In Wetlands (Vol. 22, Issue 4, pp. 738–752). Delaune, Ronald & Nyman, John & Jr, W.H. (1994). Peat collapse, ponding, and wetland loss in a rapidly submerging coastal marsh. Journal of Coastal Research. 10. 1021-1030. Ding, J., McDowell, N., Fang, Y., Ward, N., Kirwan, M.L., Regier, P., Megonigal, P., Zhang, P., Zhang, H., Wang, W. & Li, W., (2023). Modeling the mechanisms of conifer mortality under seawater exposure. New Phytologist, 239(5), pp.1679-1691. Duberstein JA, Krauss KW, Baldwin MJ, et al. (2020). Small gradients in salinity have large effects on stand water use in freshwater wetland forests. Forest Ecology and Management 473:118308. Eggleston, J. & Pope, J. (2013). Land Subsidence and Relative Sea-Level Rise in the Southern Chesapeake Bay Region. U.S. Geological Survey Circular 1392. Fagherazzi, S., Anisfeld, S. C., Blum, L. K., Long, E. V., Feagin, R. A., Fernandes, A., Kearney, W. S., & Williams, K. (2019). Sea level rise and the dynamics of the marsh-upland boundary. Frontiers in Environmental Science, 7(FEB). https://doi.org/10.3389/fenvs.2019.00025 Fagherazzi, S., Nordio, G., Boaga, J., Cassiani, G., Michael, H., Pratt, D., Messerschmidt, T., Kirwan, M. and Stotts, S. (2025), The Ecohydrology of Coastal Ghost Forests. Ecohydrology, 18: e70020. https://doi.org/10.1002/eco.70020 Farron, S. J., Hughes, Z. J., & FitzGerald, D. M. (2020). Assessing the response of the Great Marsh to sea-level rise: Migration, submersion or survival. Marine Geology, 425. https://doi.org/10.1016/j.margeo.2020.106195 Feagin, R. A., Luisa Martinez, M., & Mendoza-Gonzalez, G. (2010). Salt Marsh Zonal Migration and Ecosystem Service Change in Response to Global Sea Level Rise: A Case Study from an Urban Region. In Costanza Source: Ecology and Society (Vol. 15, Issue 4). Feher, L. C., Osland, M. J., Anderson, G. H., Vervaeke, W. C., Krauss, K. W., Whelan, K. R. T., Balentine, K. M., Tiling-Range, G., Smith, T. J., & Cahoon, D. R. (2020). The Long-Term Effects of Hurricanes Wilma and Irma on Soil Elevation Change in Everglades Mangrove Forests. Ecosystems, 23(5), 917–931. https://doi.org/10.1007/s10021-019-00446-x Feher, L. C., Osland, M. J., McKee, K. L., & others. (2024). Soil elevation change in mangrove forests and marshes of the Greater Everglades: A regional synthesis of surface elevation table–marker horizon (SET-MH) data. Estuaries and Coasts, 47, 2027–2056. https://doi.org/10.1007/s12237-022-01141-2 FitzGerald, Duncan & Fenster, Michael & Argow, Brittina & Buynevich, Ilya. (2008). Coastal Impacts Due to Sea-Level Rise. Annu. Rev. Earth Planet. Sci.. 36. 10.1146/annurev.earth.35.031306.140139. Flester, J. A., & Blum, L. K. (2020). Rates of Mainland Marsh Migration into Uplands and Seaward Edge Erosion are Explained by Geomorphic Type of Salt Marsh in Virginia Coastal Lagoons. https://doi.org/10.1007/s13157-020-01390-6/Published Galloway, D.L., Jones, D.R., & Ingebritsen, S.E. (1999) Land Subsidence in the United States. Vol. 1182. Geological Survey (USGS) Gedan, K., J. MacGregor, R. Mohammadi, & Khan, A. (2022). Forest Transition Experiment - Leaf Litter in a Coastal Virginia Forest ver. 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/fcdcb56064634ac5df489c572ed4a2ed (Accessed 2024-09-16). Gedan, K., J. MacGregor, R. Mohammadi, & A. Khan. 2023. Forest Transition Experiment - Vegetation Monitoring on a Coastal Virginia Forest, 2019-2022 ver. 6. Environmental Data Initiative. https://doi.org/10.6073/pasta/a7de61660e2fa73ce1899200cc64744b (Accessed 2024-09-16). Geselbracht, L., Freeman, K., Kelly, E., Gordon, D. R., & Putz, F. E. (2011). Retrospective and prospective model simulations of sea level rise impacts on Gulf of Mexico coastal marshes and forests in Waccasassa Bay, Florida. Climatic Change, 107(1), 35–57. https://doi.org/10.1007/s10584-011-0084-y Gleason, M.L., Elmer, D.A., Pien, N.C. et al. (1979). Effects of stem density upon sediment retention by salt marsh cord grass, Spartina alterniflora loisel. Estuaries 2, 271–273 https://doi.org/10.2307/1351574 Hacke, G.; Sperry, J.S.; Ewers, B.E.; Ellsworth, D.S.; Schäfer, K.V.R.; Oren, R. 2000. Influence of soil porosity on water use in Pinus taeda . Oecologa (2000) 124:495-505 He, Yangbo & DeSutter, Thomas & Prunty, Lyle & Hopkins, David & Jia, Xinhua & Wysocki, Douglas. (2012). Evaluation of 1:5 soil to water extract electrical conductivity methods. Geoderma. s 185–186. 12–17. 10.1016/j.geoderma.2012.03.022. Hladik, Christine & Alber, Merryl. (2014). Classification of salt marsh vegetation using edaphic and remote sensing-derived variables. Estuarine, Coastal and Shelf Science. 141. 10.1016/j.ecss.2014.01.011. Kaye, C. A., & Barghoorn, E. S. (1964). Late Quaternary sea-level change and crustal rise at Boston, Massachusetts, with notes on the autocompaction of peat. GSA Bulletin, 75(2), 63–80. https://doi.org/10.1130/0016-7606(1964)75[63:LQSCAC]2.0.CO;2 Kennish, M.J., (2001). Coastal Salt Marsh Systems in the U.S.: A Review of Anthropogenic Impacts. Journal of Coastal Research, 17(3), 731-748. Kirwan M.L., Megonigal J.P. (2013). Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504:53–60 Kirwan, M. L., D. C. Walters, W. G. Reay, and J. A. Carr (2016), Sea level driven marsh expansion in a coupled model of marsh erosion and migration, Geophys. Res. Lett. , 43, 4366–4373, doi:10.1002/2016GL068507. Kirwan, M. L. & Gedan, K. B. (2019). Sea-level driven land conversion and the formation of ghost forests. Nature Climate Change, 9(6), 450–457. https://doi.org/10.1038/s41558-019-0488-7 Kirwan, M. L., Michael, H. A., Gedan, K. B., Tully, K. L., Fagherazzi, S., McDowell, N. G., Molino, G. D., Pratt, D., Reay, W. G., & Stotts, S. (2025). Feedbacks regulating the salinization of coastal landscapes. Annual Review of Marine Science, 17 (1), 461–484. https://doi.org/10.1146/annurev-marine-070924-031447 Krauss, K.W., Cahoon, D.R., Allen, J.A. et al. Surface Elevation Change and Susceptibility of Different Mangrove Zones to Sea-Level Rise on Pacific High Islands of Micronesia. Ecosystems 13 , 129–143 (2010). https://doi.org/10.1007/s10021-009-9307-8 Krauss, K. W., Noe, G. B., Duberstein, J. A., Cormier, N., From, A. S., Doody, T. R., Conner, W. H., Cahoon, D. R., & Johnson, D. J. (2024). Presence of Hummock and Hollow Microtopography Reflects Shifting Balances of Shallow Subsidence and Root Zone Expansion Along Forested Wetland River Gradients. Estuaries and Coasts. https://doi.org/10.1007/s12237-023-01227-5 J.A. Langley, K.L. McKee, D.R. Cahoon, J.A. Cherry, & J.P. Megonigal, Elevated CO2 stimulates marsh elevation gain, counterbalancing sea-level rise, Proc. Natl. Acad. Sci. U.S.A. 106 (15) 6182-6186, https://doi.org/10.1073/pnas.0807695106 (2009). Langston A.K., Kaplan D.A., & Putz F.E. (2017). A casualty of climate change? Loss of freshwater forest islands on Florida's Gulf Coast. Glob Change Biol. 23: 5383 5397. https://doi.org/10.1111/gcb.13805 Linhoss, A. C., Kiker, G., Shirley, M., & Frank, K. (2015). Sea-level rise, inundation, and marsh migration: Simulating impacts on developed lands and environmental systems. Journal of Coastal Research, 31(1), 36–46. https://doi.org/10.2112/JCOASTRES-D-13-00215.1 Lynch, J., Hensel, P., & Cahoon, D. (2015). The Surface Elevation Table and Marker Horizon Technique: A protocol for monitoring wetland elevation dynamics. https://doi.org/10.13140/RG.2.1.5171.9761 Lovelock, C., Cahoon, D., Friess, D. et al. (2015). The vulnerability of Indo-Pacific mangrove forests to sea-level rise. Nature 526 , 559–563. https://doi.org/10.1038/nature15538 Lovelock, C. E., Ball, M. C., Brothers, N., Pearse, A., & Reef, R. (2025). Dynamics of surface accretion and surface elevation differ between river- and tide-dominated settings in tropical mangroves. Limnology and Oceanography, 70 , 1424–1437. https://doi.org/10.1002/lno.70024 McLeod, E., Chmura, G., Bouillon, S., Salm, R., Björk, M., Duarte, C., Lovelock, C., Schlesinger, W., & Silliman, B. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552-560. https://doi.org/10.1890/110004 Messerschmidt, T.C., S. Wittyngham & ML. Kirwan. (2022). Vegetation and physical characteristics of Chesapeake Bay retreating Coastal Forests 2022 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/92071e5fc49431114a6453d7ed205298 Meyerson, L. A., K. Saltonstall, L. Windham, E. Kiviat, & S. Findlay. (2000). A comparison of Phragmites australis in freshwater and brackish marsh environments in North America. Wetlands Ecology and Management 8: 89–103 Middleton, B.A. & David, J.L., 2022. Trends in vegetation and height of the topographic surface in a tidal freshwater swamp experiencing rooting zone saltwater intrusion. Ecological Indicators, 145, p.109637 Miller, C., Rodriguez, A., & Bost, M. (2021). Sea-level rise, localized subsidence, and increased storminess promote saltmarsh transgression across low-gradient upland areas. Quaternary Science Reviews, 265, 107000. https://doi.org/10.1016/j.quascirev.2021.107000 Molino, G. D., Carr, J. A., Ganju, N. K., & Kirwan, M. L. (2022). Variability in marsh migration potential determined by topographic rather than anthropogenic constraints in the Chesapeake Bay region. Limnology And Oceanography Letters, 7(4), 321–331. https://doi.org/10.1002/lol2.10262 Moore, G. E., Burdick, D. M., Peter, C. R., & Keirstead, D. R. (2012). Belowground biomass of Phragmites australis in coastal marshes. Northeastern Naturalist, 19(4), 611–626. https://doi.org/10.1656/045.019.0406 Moorman, M.C., Ladin, Z.S., Tsai, E. et al. Will They Stay or Will They Go — Understanding South Atlantic Coastal Wetland Transformation in Response to Sea-Level Rise. (2024). Estuaries and Coasts 47 , 2011–2026. https://doi.org/10.1007/s12237-023-01225-7 Morris, J.T., Sundareshwar, P.V., Nietch, C.T., Kjerfve, B. and Cahoon, D.R. (2002), RESPONSES OF COASTAL WETLANDS TO RISING SEA LEVEL. Ecology, 83: 2869-2877. https://doi.org/10.1890/0012-9658 Mou, P., et al. (1995). Spatial distribution of roots in sweetgum and loblolly pine monocultures and relations with above-ground biomass and soil nutrients. Functional Ecology, 9(4), 689–699. https://doi.org/10.2307/2390162 Noe, G.B., Krauss, K.W., Lockaby, B.G. et al. (2013). The effect of increasing salinity and forest mortality on soil nitrogen and phosphorus mineralization in tidal freshwater forested wetlands. Biogeochemistry 114, 225–244. https://doi.org/10.1007/s10533-012-9805-1 Nordio, G., Gedan, K., & Fagherazzi, S. (2024). Storm Surges and Sea Level Rise Cluster Hydrological Variables Across a Coastal Forest Bordering a Salt Marsh. Water Resources Research, 60(2), e2022WR033931. https://doi.org/10.1029/2022WR033931 Osland, M. J., Chivoiu, B., Enwright, N. M., Thorne, K. M., Guntenspergen, G. R., Grace, J. B., Dale, L. L., Brooks, W., Herold, N., Day, J. W., Sklar, F. H., & Schwarzenzki, C. M. (2022). Migration and transformation of coastal wetlands in response to rising seas. Science Advances, 8 (26), eabo5174. https://doi.org/10.1126/sciadv.abo5174 Pedarla, A., Puppala, A., Hoyos, L., Vanapalli, S., & Zapata, C. (2012). SWRC modelling framework for evaluating volume change behavior of expansive soils. In Proceedings of the 11th International Conference on Expansive Soils (pp. 305-309). https://doi.org/10.1007/978-3-642-31116-1_30 Poulter, B., Qian, S. S., & Christensen, N. L. (2009). Determinants of coastal treeline and the role of abiotic and biotic interactions. Plant Ecology, 202(1), 55–66. https://doi.org/10.1007/s11258-008-9465-3 Pratt, D., R. McQuiggan, E. S. Bacmeister, A. Sprague-Getsy, H. Michael (2025). Coastal Critical Zone Dataset: Groundwater and soil moisture data collected in the Coastal Critical Zone of the Delmarva Peninsula, United States, HydroShare, http://www.hydroshare.org/resource/845670d788eb4385a17b1699bdc0383d Raabe, E. A., & Stumpf, R. P. (2015). Expansion of tidal marsh in response to sea-level rise: Gulf Coast of Florida, USA. Estuaries and Coasts, 39(1), 145–157. https://doi.org/10.1007/s12237-015-9974-y Rogers, K., Saintilan, N., & Cahoon, D. (2005). Surface elevation dynamics in a regenerating mangrove forest at Homebush Bay, Australia. Wetlands Ecology and Management, 13 , 587–598. https://doi.org/10.1007/s11273-004-0003-3 Rogers, K., Saintilan, N., & Copeland, C. (2012). Modelling wetland surface elevation dynamics and its application to forecasting the effects of sea-level rise on estuarine wetlands. Ecological Modelling, 244, 148–157. https://doi.org/10.1016/j.ecolmodel.2012.06.014 Rogers, Kerrylee, et al. (2019). “Wetland Carbon Storage Controlled by Millennial-Scale Variation in Relative Sea-Level Rise.” Nature., vol. 567, no. 7746, pp. 91–95, https://doi.org/10.1038/s41586-019-0951-7. Rogers, K., Zawadzki, A., Mogensen, L., & Saintilan, N. (2022). Coastal wetland surface elevation change is dynamically related to accommodation space and influenced by sedimentation and sea-level rise over decadal timescales. Frontiers in Marine Science, 9 , Article 807588. https://doi.org/10.3389/fmars.2022.807588 Rooth, J. E., Stevenson, J. C., & Cornwell, J. C. (2003). Increased Sediment Accretion Rates Following Invasion by Phragmites australis: The Role of Litter. Estuaries, 26(2), 475–483. http://www.jstor.org/stable/1353362 Russell, B. T., Cressman, K. A., Schmit, J. P., Shull, S., Rybczyk, J. M., & Frost, D. L. (2022). How should surface elevation table data be analyzed? A comparison of several commonly used analysis methods and one newly proposed approach. Environmental and Ecological Statistics, 29(2), 359–391. https://doi.org/10.1007/s10651-021-00524-1 Saintilan N., Kovalenko KE, Guntenspergen G, Rogers K, Lynch JC, Cahoon DR, Lovelock et al. (2022). Constraints on the adjustment of tidal marshes to accelerating sea level rise. Science Jul 29;377(6605):523-527. doi: 10.1126/science.abo7872. Saintilan, N., Horton, B., Törnqvist, T.E. et al. (2023a). Widespread retreat of coastal habitat is likely at warming levels above 1.5 °C. Nature 621, 112–119. https://doi.org/10.1038/s41586-023-06448-z Saintilan, N., Sun, Y., Lovelock, C. E., Rogers, K., Goddard, M., Hutley, L. B., Kelleway, J., Mosley, L., Dittmann, S., Cormier, N., Lal, K. K., & Jones, A. (2023b). Vertical Accretion Trends in Australian Tidal Wetlands. Estuaries and Coasts. https://doi.org/10.1007/s12237-023-01267-x Sallenger, A., Doran, K. & Howd, P. (2012). Hotspot of accelerated sea-level rise on the Atlantic coast of North America. Nature Clim Change 2, 884–888. https://doi.org/10.1038/nclimate1597 Schieder, N. W., Walters, D. C., & Kirwan, M. L. (2018). Massive Upland to Wetland Conversion Compensated for Historical Marsh Loss in. 41(4), 940–951. https://doi.org/10.1007/sl2237-017-0336-9 Schimel, D.S., (1995). Terrestrial ecosystems and the carbon-cycle. Global Change Biology 1, 77–91 Schuerch, M., Spencer, T., Temmerman, S., Kirwan, M. L., Wolff, C., Lincke, D., McOwen, C. J., Pickering, M. D., Reef, R., Vafeidis, A. T., Hinkel, J., Nicholls, R. J., & Brown, S. (2018). Future response of global coastal wetlands to sea-level rise. Nature, 561(7722), 231–234. https://doi.org/10.1038/s41586-018-0476-5 Smith, A. J., & Kirwan, M. L. (2021). Sea Level-Driven Marsh Migration Results in Rapid Net Loss of Carbon. Geophysical Research Letters, 48(13). https://doi.org/10.1029/2021GL092420 Smith, J. A. M. (2013). The Role of Phragmites australis in Mediating Inland Salt Marsh Migration in a Mid-Atlantic Estuary. PLoS ONE, 8(5). https://doi.org/10.1371/journal.pone.0065091 Stagg, C. L., Krauss, K. W., Cahoon, D. R., Cormier, N., Conner, W. H., & Swarzenski, C. M. (2016). Processes contributing to resilience of coastal wetlands to sea-level rise. Ecosystems, 19(8), 1445–1459. https://doi.org/10.1007/s10021-016-0015-x Stagg, C. L., Baustian, M. M., Perry, C. L., Carruthers, T. J. B., & Hall, C. T. (2018). Direct and indirect controls on organic matter decomposition in four coastal wetland communities along a landscape salinity gradient. Journal of Ecology, 106(5), 655–670. https://doi.org/10.1111/1365-2745.12901 Stahl, M., Widney, S., & Craft, C. (2018). Tidal freshwater forests: Sentinels for climate change. Ecological Engineering, 116, 104–109. https://doi.org/10.1016/j.ecoleng.2018.03.002 Sward, R., Philbrick, A., Morreale, J., Baird, C. J., & Gedan, K. (2023). Shrub expansion in maritime forest responding to sea level rise. Frontiers in Forests and Global Change, 6. https://doi.org/10.3389/ffgc.2023.1167880 Tate, A.S. & Battaglia, L.L., (2013). Community disassembly and reassembly following experimental storm surge and wrack application. Journal of Vegetation Science, 24(1), pp.46-57. Temmerman, S., Meire, P., Bouma, T. et al. (2013). Ecosystem-based coastal defence in the face of global change. Nature 504, 79–83. https://doi.org/10.1038/nature12859 Thompson, J. A., J. C. Zinnert, and D. R. Young. (2017). Immediate effects of microclimate modification enhance native shrub encroachment. Ecosphere 8(2): e01687. 10.1002/ecs2.1687 Turaski, S.J. (2002). Spatial and temporal controls on saturated overland flow in a regularly flooded salt marsh. MS Thesis, University of Virginia Charlottesville, VA. Walters, D. C., Carr, J. A., Hockaday, A., Jones, J. A., McFarland, E., Kovalenko, K. E., Kirwan, M. L., Cahoon, D. R., & Guntenspergen, G. R. (2021). Experimental Tree Mortality Does Not Induce Marsh Transgression in a Chesapeake Bay Low-Lying Coastal Forest. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.782643 Wang, C., & S. Temmerman (2013), Does biogeomorphic feedback lead to abrupt shifts between alternative landscape states?: An empirical study on intertidal flats and marshes, J. Geophys. Res. Earth Surf., 118, 229–240, doi:10.1029/2012JF002474. Webb, E. L., Friess, D. A., Krauss, K. W., Cahoon, D. R., Guntenspergen, G. R., & Phelps, J. (2013). A global standard for monitoring coastal wetland vulnerability to accelerated sea-level rise. Nature Climate Change, 3(5), 458–465. https://doi.org/10.1038/nclimate1756 Wen, Yongli & Bernhardt, Emily & Deng, Wenbo & Liu, Wenjuan & Yan, Junxia & Baruch, Ethan & Bergemann, Christina. (2019). Salt effects on carbon mineralization in southeastern coastal wetland soils of the United States. Geoderma. 339. 31-39. 10.1016/j.geoderma.2018.12.035. Weston, N.B., Vile, M.A., Neubauer, S.C., Velinsky, D.J., (2011). Accelerated microbial organic matter mineralization following salt-water intrusion into tidal freshwater marsh soils. Biogeochemistry 102, 131–151 Williams, K., Meads, M.V. & Sauerbrey, D.A. (1998), The roles of seedling salt tolerance and resprouting in forest zonation on the west coast of Florida, USA . Am. J. Bot., 85: 1745-1752. https://doi.org/10.2307/2446509 Williams, K., Ewel, K. C., Stumpf, R. P., Putz, F. E., & Workman, T. W. (1999). Sea-level rise and coastal forest retreat on the west coast of Florida, USA. Ecology, 80(6), 2045–2063. https://doi.org/10.2307/176677 Williams, K., MacDonald, M., & Sternberg, S. L. (2003). Interactions of storm, drought, and sea-level rise on coastal forest: a case study. J. Coast. Res. 19, 1116–1121. Windham, L., & R. G. Lathrop. (1999). Effects of Phragmites australis (common reed) invasion on aboveground biomass and soil properties in a brackish tidal marsh of the Mullica River. Estuaries 22: 927–93 Windham, L., & L. A. Meyerson. 2003. Effects of common reed (Phragmites australis) expansions on nitrogen dynamics of tidal marshes of the northeastern US. Estuaries 26, no. 2B:452–464. White, E. E., Ury, E. A., Bernhardt, E. S., & Yang, X. (2022). Climate change driving widespread loss of coastal forested wetlands throughout the North American Coastal Plain. Ecosystems, 25 (4), 812–827. https://doi.org/10.1007/s10021-021-00686-w Tables Table 1 Elevation, vegetation characteristics, and elevation change rates for each individual SET. Vegetation cover is expressed as a percent at each individual canopy height. Rates of surface accretion, net elevation change, and subsurface change expressed as mm yr -1 . A negative subsurface change indicates subsidence and a positive value indicates subsurface expansion Table 2 Differences among forest and transition zones for surface accretion, net elevation change, and subsurface change expressed as mm yr -1 . Contribution of shallow root zone processes are calculated as the difference between total subsurface change and deep subsurface change (derived from shallow SET benchmarks). A negative subsurface value indicates subsidence, while a positive value indicates belowground expansion. Statistical differences between zones (determined via Tukey HSD) are shown in superscript Table 3 P-values from linear models comparing net elevation change, accretion, and subsurface change with environmental drivers (e.g., litter depth, soil moisture). P-values represent the significance of each environmental variable in predicting elevation change responses. Significant values (p < 0.1) are highlighted in bold. Here we use α = 0.1 to reduce the risk of missing potential correlations, as this was an exploratory analysis to identify patterns for further investigation Supplementary Files SFigSTable.docx Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Estuaries and Coasts → Version 1 posted Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 05 Aug, 2025 Editor invited by journal 03 Aug, 2025 Editor assigned by journal 28 Jul, 2025 First submitted to journal 28 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7236740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":496130518,"identity":"22cb16d8-a113-4cea-ac63-d8b1309c3a35","order_by":0,"name":"Tyler Messerschmidt","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHADHgaGBDYGOQZmMI8ZnzoULQbGJGphYDNIbGAgoMWe/XTiwx9/bPLl288e3fCg7E/6huPcyR8YKqxhejFt4cndbMzblma54Uxe2o2Ecwa5Gw7zbpNgOJOOW4sE7zZpxobDBgYSPGY3EtsMcmc2825jYGw7jE/L9p8//vw3kJ8B0ZIu2cy7+QPjP7xatjHwsB0wYLgB0ZLAz8y7QQJoL24tZ3I3S/O2JRsYnMkxA/rF2LCfGeiXhGPpxri0sLef3fjxxx87A/n2M2Y3f5TJybPxn9384UONtSwuLThAAmnKR8EoGAWjYBSgAQDULVZGqLBUVgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5494-4352","institution":"William \u0026 Mary Virginia Institute of Marine Science","correspondingAuthor":true,"prefix":"","firstName":"Tyler","middleName":"","lastName":"Messerschmidt","suffix":""},{"id":496130519,"identity":"afa84eaf-6fd8-4229-8965-127ca39b2c4c","order_by":1,"name":"Keryn B. Gedan","email":"","orcid":"","institution":"George Washington University","correspondingAuthor":false,"prefix":"","firstName":"Keryn","middleName":"B.","lastName":"Gedan","suffix":""},{"id":496130520,"identity":"6230a526-bd5e-49ee-b9e1-2ac4199ad373","order_by":2,"name":"Matthew L. Kirwan","email":"","orcid":"","institution":"William \u0026 Mary Virginia Institute of Marine Science","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"L.","lastName":"Kirwan","suffix":""}],"badges":[],"createdAt":"2025-07-28 19:07:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7236740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7236740/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12237-025-01624-y","type":"published","date":"2025-12-09T15:58:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88643441,"identity":"d72044ad-f71a-4142-8627-837e0a861db8","added_by":"auto","created_at":"2025-08-08 16:15:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":147077,"visible":true,"origin":"","legend":"\u003cp\u003eRetreating coastal forest study site. A. The Brownsville Preserve Forest is located near Phillips Creek (SW portion of image) on Virginia’s Atlantic Coast (inset). Locations of the surface elevation tables (SETs) are shown in white. SETs are labeled by first letter denoting forest zone (High, Mid, Low, Transition) and plot number. Yellow (1949) and blue (2017) lines represent historic marsh-forest boundaries delineated from aerial imagery from Virginia Department of Transportation and Virginia Base Mapping Program respectively. Forest retreat rates (0.5 m yr⁻¹ from 1949–2017) were determined using linear regression in Digital Shoreline Analysis System (DSAS). The solid white line represents an elevation transect from healthy forest to low marsh with core locations (red dots) for the stratigraphic profile shown in 1B. B. Surface elevation and stratigraphic profile that was determined via RTK GPS survey, where all elevations are relative to NAVD88, which approximates mean sea level in the region. The green-shaded area represents organic-rich marsh soils and the pink-shaded area represents the underlying mineral-rich, terrestrial sediment, both interpolated from eight sediment cores (Supp Figure 2)\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/2e8a07c06a74090aa8366511.jpg"},{"id":88641017,"identity":"005b1c74-e27a-4387-bc67-5483ccdc927a","added_by":"auto","created_at":"2025-08-08 16:07:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":286634,"visible":true,"origin":"","legend":"\u003cp\u003ePhotographs of SET measurement approach. A) Photograph of the high forest floor, with a shallow SET pipe in the background. B) Photograph of forest floor, with leaf litter gently removed revealing soil surface prior to measurement. Leaf litter is replaced upon completion of each measurement\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/96f48d1c1cc7ff05cdd99022.jpg"},{"id":88640998,"identity":"6cd6747b-2114-4ff5-aaa6-e20664a4c680","added_by":"auto","created_at":"2025-08-08 16:07:56","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":85800,"visible":true,"origin":"","legend":"\u003cp\u003eAverage cumulative elevation change (mm) of each forest zone at Brownsville Forest shown in the High (green), Mid (blue), and Low (orange) forest. Error bars reflect standard error at each timestep. Average elevation change rates (mm yr\u003csup\u003e-1\u003c/sup\u003e) calculated via linear regression are shown in the legend\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/8265450213242807f64b2a21.jpg"},{"id":88645317,"identity":"71a54b6a-a661-4b3a-a3e0-2992998a053c","added_by":"auto","created_at":"2025-08-08 16:23:56","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61734,"visible":true,"origin":"","legend":"\u003cp\u003eRates of accretion (blue), subsidence (green), and net elevation change (black) for each individual SET. SET numbering scheme follows previously installed vegetation plots (Smith and Kirwan 2021). Accretion rates are calculated as the thickness of material above the marker horizon divided by time since deployment. Net elevation change is calculated as the average change rate of each pin (n=36) at a given SET. Subsidence is calculated as the difference between accretion and net elevation change. Negative values indicate subsidence (elevation loss) and positive values indicate subsurface expansion\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/f593581007e0cb510a445501.jpg"},{"id":88640997,"identity":"f43481b8-9801-428e-ac62-de737d1ad992","added_by":"auto","created_at":"2025-08-08 16:07:56","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":67377,"visible":true,"origin":"","legend":"\u003cp\u003eImportance of each environmental variable shown. Importance calculated as the absolute value of the t-statistic for each model parameter in the full linear model. On the left shows only High, Mid, and Low forest. While the plots on the right include the transition zone. The models including the transition zone are limited in their parameters due to data not being collected at these sites. The significant predictive variables in each multiple linear regression are denoted by a +/- indicating the direction of the relationship. P-values of the multiple linear models containing only significant variables are shown in parentheses\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/241e879ed30a9eb407b311dd.jpg"},{"id":88645319,"identity":"b6969ff9-499e-4785-89da-77972912431c","added_by":"auto","created_at":"2025-08-08 16:23:56","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":44097,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between shrub cover (%) and net elevation change (A), Surface Accretion (B), and Total Subsurface Change (C) for each SET. Each panel shows a linear regression with 95% confidence intervals (gray shading)\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/5d7205b4017b5b5e4b6deb8f.jpg"},{"id":88646020,"identity":"db474d9e-b2c5-4805-b560-cdb6e77ee4ee","added_by":"auto","created_at":"2025-08-08 16:31:56","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":30155,"visible":true,"origin":"","legend":"\u003cp\u003eNet elevation change for each forest SET (this study) compared to long-established SETs in the adjacent marsh (green dots, Blum et al. 2021). Transition Zone SETs are highlighted in green circle. Best fit lines are shown in black and determined using the segmented package in R (Muggeo, 2008). 95% confidence intervals for each segmented regression are shown in gray\u003c/p\u003e","description":"","filename":"7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/586d4bb56f9ff1bd17b8a2af.jpg"},{"id":98243909,"identity":"630f580c-6d9e-4935-9f12-ca65b0f33c87","added_by":"auto","created_at":"2025-12-15 16:11:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1627566,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/e74f355a-32b4-45c1-8308-db747cf15746.pdf"},{"id":88640995,"identity":"e9e7ea5c-bb74-4fa7-8bc1-e25ece57bb9a","added_by":"auto","created_at":"2025-08-08 16:07:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":114020,"visible":true,"origin":"","legend":"","description":"","filename":"SFigSTable.docx","url":"https://assets-eu.researchsquare.com/files/rs-7236740/v1/010439aa5ccbf20549b19cda.docx"}],"financialInterests":"","formattedTitle":"Drivers of Elevation Change in a Retreating Coastal Forest","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSea level rise is increasing tidal flooding, storm surges, and salinization of groundwater in ways that threaten both human infrastructure and natural ecosystems. Salt marshes are highly valued ecosystems along global coastlines that provide protection from storms (Temmerman et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), improve water quality (Barbier et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), support estuarine and marine food webs (Colombano et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and sequester carbon (Mcleod et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Salt marshes grow vertically through soil organic matter accumulation and sediment trapping, but must migrate inland to survive rates of sea level rise that are faster than their ability to build soil (Kirwan and Megonigal, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The replacement of coastal forests with marshes has been widely documented in regions of the United States where sea level rise intersects a gently sloping topography (Kirwan and Gedan, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; White et al., \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Rates of coastal forest retreat are accelerating in parallel with rates of relative sea level rise (Schieder and Kirwan, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), resulting in marsh migration rates that have compensated for erosion and drowning in portions of the mid-Atlantic and Gulf of Mexico (Schieder et al., \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen and Kirwan, 2023; Raabe and Stump 2016).\u003c/p\u003e\u003cp\u003eFuture projections of marsh area change under sea level rise indicate that the fate of marshes is largely tied to their ability to migrate inland (Kirwan et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Schuerch et al. \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osland et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, projections of upland conversion to marsh rely on a passive inundation, or “bath-tub model,” approach in which upland topography is constant through time (Schuerch et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osland et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Molino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Linhoss et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Geselbracht et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Farron et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This approach predicts widespread conversion of uplands to wetlands with gradual sea-level rise, but observed rates of coastal forest retreat are only about half the rate expected by sea level rise propagating across a static topography (Chen and Kirwan \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, assumptions of a static topography contrast with the general understanding that feedbacks between flooding, plant productivity, and sediment transport control surface elevations of other coastal ecosystems such as mangroves, marshes, and tidal forests (FitzGerald et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Kirwan and Megonigal, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Saintilan et al., 2023; Krauss et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Because coastal hydrology and ecology are intertwined (Kirwan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Fagherazzi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), and both forests and marshes are sensitive to small changes in elevation (Krauss et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), gains or losses of mere centimeters may significantly impact coastal forest retreat rates.\u003c/p\u003e\u003cp\u003eUpland forest soils are usually considered as geomorphically stable over decadal timescales, with detrital soil inputs largely balanced by respiratory losses (Schimel \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), but several geomorphic processes may contribute to elevation gain in coastal forests. Deposition of autochthonous material from litterfall is likely the dominant source of surface accretion. Litterfall can make up 60% of total aboveground productivity in tidal freshwater forests (Stahl et al. \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and \u0026gt; 70% in boreal forests (Bhatti and Jassal \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Increased inundation with saltwater may inhibit both aerobic and anaerobic decomposition of soil organic matter (Setia et al., 2011; Qu et al., 2019), potentially allowing for greater surface accretion in salt-affected forests, but observed outcomes are subtle and highly variable (Stagg et al., 2017; Helton et al., 2019; Wen et al., \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Smith et al., 2024). Hurricanes and extreme flooding events may also deposit sediment and organic matter (i.e., wrack) at the marsh-upland transition (Tate and Battaglia \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additionally, colonization of the understory by salt- and flood-tolerant species can rapidly build elevation through increased litter accumulation, belowground biomass production, and sediment trapping. \u003cem\u003eMorella cerifera\u003c/em\u003e thickets can substantially increase litterfall (Brantley and Young 2007), and accretion rates within mature \u003cem\u003ePhragmites australis\u003c/em\u003e stands can reach 10 mm yr\u003csup\u003e− 1\u003c/sup\u003e (Rooth et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), potentially increasing elevation gain where these species establish or increase in cover.\u003c/p\u003e\u003cp\u003eAlternatively, introduction of labile carbon (dying roots) and pulses of sulfate-rich seawater may enhance decomposition leading to elevation loss (Weston et al. \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e2011\u003c/span\u003e, Chambers et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Whether saltwater intrusion facilitates or hinders decomposition is directly related to duration of flooding, soil organic C, and previous hydrologic conditions. (Stagg et al. 2017, Wen et al. 2018, Smith et al. 2024). Excessive flooding may also substantially alter organic soil structure leading to destabilization (Chambers et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, flooding-induced tree mortality can lead to subsidence due to loss of root turgor pressure and collapse of the rooting zone. Elevation losses of 0.5–10 cm yr\u003csup\u003e− 1\u003c/sup\u003e following hurricanes have been documented in mangroves, tidal freshwater forests, and pocosins (Cahoon et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Miller et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Middleton and David \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBreakthroughs in understanding of the ecogeomorphic processes driving elevation gain in coastal ecosystems have come largely through repeat elevation measurements associated with the Surface Elevation Table-Marker Horizon (SET-MH) approach (Cahoon et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1995\u003c/span\u003e, Webb et al., \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Saintilan et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The combination of shallow and deeply anchored SETs reveal elevation changes that occur in shallow (root zone) and deep (sub root zone) portions of the soil profile. Horizon marker layers in the soil can further differentiate surface accretion. Integrating the results of these tools can help to infer which processes are responsible for net gain or loss in elevation at the soil surface, which has clear feedbacks to inundation in the coastal zone. Surface elevation tables have been widely adopted in coastal ecosystems such as marshes (Covington \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Saintilan et al. \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mangroves (Krauss et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Lovelock et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and, more recently, tidal freshwater forests (Stagg et al. 2017, Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), pocosins (Moorman et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and coastal scrub/shrubland (Cook et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Ardon et al., in review). However, elevation change rates have not been reported for upland coastal forests.\u003c/p\u003e\u003cp\u003eHere, we investigate surface elevation change using the SET-MH approach to expand the coastal geomorphic framework into a salt-affected upland forest. Root zone subsidence in forest soils prior to forest retreat would indicate that forest retreat and marsh migration might advance more quickly than static models predict. Alternatively, substantial accretion of forest soils would potentially explain observed lags between sea level rise and forest retreat. More generally, we aim to understand how the elevation dynamics of salt-stressed forests may change over time and whether these processes enhance or mitigate future marsh migration.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStudy Site\u003c/b\u003e\u003c/p\u003e\u003cp\u003eOur study focuses on a well-studied coastal maritime forest on Virginia’s Atlantic Coast. This area is located in the mid-Atlantic sea level rise hotspot, where relative sea level rise rates are faster than eustatic rates (Sallenger et al. \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and influenced by deep subsidence rates of 1–3 mm yr\u003csup\u003e− 1\u003c/sup\u003e (Eggleston and Pope \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Ohenen et al. 2023). The rural and low-lying mid-Atlantic coast experiences rapid marsh migration into uplands, making it an ideal location to study the elevation dynamics of marsh transgression. Approximately 220 km\u003csup\u003e2\u003c/sup\u003e of coastal forests have converted to marsh in the mid-Atlantic since 1984 (Chen and Kirwan, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and an additional 1050 km\u003csup\u003e2\u003c/sup\u003e − 3748 km\u003csup\u003e2\u003c/sup\u003e of conversion is projected for the Chesapeake Bay by 2100 (Molino et al \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe study site is located within The Nature Conservancy’s Brownsville Nature Preserve, Nassawadox, VA (37.464814\u003csup\u003e°\u003c/sup\u003e N, -75.835185\u003csup\u003e°\u003c/sup\u003e W) and adjacent to Upper Phillips Creek marsh (UPC), a focal study area of the Virginia Coast Reserve (VCR) Long-Term Ecological Research (LTER) program (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Due to its exceedingly low slope (0.0006 m/m) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), this area has undergone significant coastal transgression, highlighted by the conversion of upland forest to organic-rich high marsh (Brinson et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Marsh transgression is occurring at rates between 0.3 to 1 m yr\u003csup\u003e− 1\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) and total marsh area increased by 0.9 ha between 2000 and 2017 (Flester and Blum \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe studied forest is an upland, maritime forest comprised mainly of loblolly pine (\u003cem\u003ePinus taeda\u003c/em\u003e) with black tupelo (\u003cem\u003eNyssa sylvatica)\u003c/em\u003e, red maple \u003cem\u003e(Acer rubrum)\u003c/em\u003e, and American holly (\u003cem\u003eIlex opaca)\u003c/em\u003e interspersed at higher elevations (Langston et al., 2025). The forest understory is composed mainly of grasses \u003cem\u003ePhragmites australis\u003c/em\u003e, \u003cem\u003ePanicum virgatum\u003c/em\u003e and \u003cem\u003eChasmanthium laxum\u003c/em\u003e and the thicket-forming shrub \u003cem\u003eMorella cerifera.\u003c/em\u003e Eastern red cedar (\u003cem\u003eJuniperus virginiana\u003c/em\u003e), \u003cem\u003eP, australis\u003c/em\u003e, and shrubs such as \u003cem\u003eBaccharis halimifolia\u003c/em\u003e and \u003cem\u003eIva frutescens\u003c/em\u003e dominate the forest-marsh interface. The average forest elevation is 1.15 m, which is only 20 cm higher than the adjacent marshes (Table\u0026nbsp;1). The entire study area is within 15 cm of the Highest Astronomical Tide (1.07m NAVD88) converted from observed water levels at Wachapreague, VA (Station 8631044, NOAA Tides and Currents, Blum et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Over the study period, groundwater levels exceed surface elevations upwards of 100 days yr\u003csup\u003e− 1\u003c/sup\u003e at lower elevations (Pratt et al. \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe Brownsville forest was delineated into four forest health zones in 2019, representing a gradient of flooding and salinity stress: high, mid, low and transitional forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Smith and Kirwan \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Langston et al., 2025). High forest was initially defined as largely unaffected by saltwater intrusion and containing a mixed species composition of both coniferous and deciduous tree species. The mid forest displays early signs of salt stress including deciduous tree mortality but contains a healthy, coniferous population. Low forest is defined by higher salt stress, indicated by limited tree regeneration, increasing conifer stress and standing dead trees. The transition zone contains ~ 50% adult tree mortality, no regeneration, and an understory of typical high marsh species. Since 2019, the shrub \u003cem\u003eM. cerifera\u003c/em\u003e has expanded by 45–65% in high and mid forest plots, while in the low forest, there has been steady spread of invasive \u003cem\u003ePhragmites australis\u003c/em\u003e (Sward et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eSurface Elevation Tables\u003c/b\u003e\u003c/p\u003e\u003cp\u003eChanges in forest soil elevations were measured using the surface elevation table - marker horizon (SET-MH) approach that quantifies both surface accretion and net elevation change with an accuracy of 1–2 mm (Cahoon 2002, Callaway 2013, Lynch 2015). SETs consist of a deep benchmark driven to refusal, on which a mechanical leveling arm is attached at fixed positions around the mark, allowing for repeated measurements of net elevation change with respect to the base of the benchmark (Cahoon 2002). It is paired with a marker horizon, which consists of a feldspar layer established on the soil surface, on top of which surface accretion can be measured. The calculated difference between surface accretion (MH) and net elevation change (SET) allows for an estimate of total subsurface processes between the marker horizon and benchmark base (Cahoon et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). To examine the contribution of root zone processes to total subsurface elevation change, 3-inch diameter, capped, aluminum pipes were installed to a depth of ~ 30cm. (Langley et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). These pipes have 1-inch holes drilled into them to allow for root ingrowth (Walters et al. in prep).\u003c/p\u003e\u003cp\u003eFour SETs were installed in each zone to depths between 16 and 21 m. The high, mid, and low forest zones were installed in March 2019 and the transition zone in August 2020 (Table\u0026nbsp;1). Each SET was paired with a marker horizon and 1–2 shallow pipes driven to ~ 30 cm. Elevation measurements were taken approximately 3 times per year, beginning in October 2019 (high, mid, and low forest) and January 2021 (transition zone). To measure elevation at each SET, the arm was leveled at 4 equidistant positions approximately 0.5 m from the benchmark. At each position, leaf litter was gently removed by hand (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) taking care not to disturb the underlying surface. Partially decomposed litter less than ~ 2 cm in size was left in place. Pinecones and branches were removed from the measurement area only if their removal did not disturb the forest soil. Any foreign object (stick, rock, cone, etc.) partially buried beneath the surface was left in place but excluded from the analysis. Nine fiberglass pins were then lowered to the exposed surface of the underlying forest soil. Elevation was measured as the distance between the arm and top of each pin. Leaf litter was removed (as above) from a small section of the marker horizon before measuring the depth of the feldspar layer from the revealed soil surface. Beginning in February of 2022, the SET arm was leveled over the aluminum pipes and their heights were measured. After the measurements at each SET position and marker horizon were complete, the leaf litter was gently returned to its original position.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Approach\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNet elevation change rates were calculated using the ‘regress then average’ method (Lynch et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Russel et al. 2022). Elevation trends were first regressed through time at the individual pin level, then averaged by arm position and finally aggregated to the entire plot. Each plot estimate, therefore, reflects the average of 36 individual pin measurements. Marker horizon derived accretion was then subtracted from plot level net elevation change to calculate total subsurface change, where a negative subsurface change indicates subsidence, while a positive value reflects subsurface expansion. Elevation change within the top 30 cm, here referred to as root zone subsurface change is calculated as the difference between average SET cumulative change and shallow pipe derived cumulative change at each time step. Subsurface change below 30 cm, referred to as shallow hydro-geologic change, is the difference between total subsurface and root zone elevation change. One-sided t-tests were used to determine if average rates of change were greater than 0 (no change) and 4.1 mm yr\u003csup\u003e− 1\u003c/sup\u003e (local relative sea level rise). Local relative sea level rise was calculated between 1997 and 2017 based on NOAA’s Wachapreague Station (ID 8631044) and converted to water levels at nearby Upper Phillips Creek (Blum et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A paired t-test was used to compare net elevation change and surface accretion. The statistical significance of differences in net elevation change, accretion, and subsidence between forest zones was assessed using a one-way ANOVA in R Version (4.4.1) (R Core Team 2023). Tukey’s HSD post hoc test was used to evaluate the significance of pairwise comparisons. All statistical tests were conducted with an α-level of 0.05 and N = 4 for each forest zone.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEnvironmental Variables\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eVegetation and soils\u003c/b\u003e\u003c/p\u003e\u003cp\u003eVegetation cover was measured in August 2024 within a 3 m x 3 m quadrat, centered around each SET benchmark. The percentage cover of each understory species was estimated via grid point-intercept at multiple height layers to account for vertical stratification, therefore total cover could exceed 100%. This approach captured the complexity of vegetation structure in the plots. To simplify statistical analyses, species were subsequently grouped by functional plant types (i.e., upland grasses, shrubs, and marsh grasses).\u003c/p\u003e\u003cp\u003eMeasurements of vegetation cover were supplemented with existing litterfall, porewater and soil datasets collected in 2022 (Gedan et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Litterfall was collected monthly using three replicate litter baskets, each measuring 25 cm x 25 cm, positioned randomly within each plot. Litter was oven-dried at 60°C to a constant mass and then weighed to determine biomass contributions. In this study, only annual totals of litterfall biomass are presented. Soil porewater samples were collected opportunistically following significant rainfall events to capture natural fluctuations in soil salinity. Samples were taken from the upper 20 cm of soil using a push-point sampler (MHE Products) and averaged to represent the salinity of the entire plot. Salinity was measured in situ using a handheld meter, following the methodology outlined by Sward et al. (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Surface soil samples were collected from each plot to assess soil conductivity and water content. These samples were air-dried and homogenized to ensure consistency. Conductivity was measured using a laboratory 5:1 dilution. Carbon and nitrogen content of the surface soils were determined by LECO CN828 elemental analyzer.\u003c/p\u003e\u003cp\u003e\u003cem\u003eS\u003c/em\u003e\u003cb\u003etatistical Analyses\u003c/b\u003e\u003c/p\u003e\u003cp\u003eStepwise regression analysis was conducted to identify the most significant predictors of net elevation change, accretion, and subsurface change. The stepwise regressions were run with and without transition zone elevation change metrics, since the soil environmental data was not collected in the transition zone. In the model including all forest zones, the explanatory variables included elevation (NAVD88), shrub cover, \u003cem\u003ePhragmites\u003c/em\u003e cover and marsh grass cover. The model excluding the transition zone also included litterfall, soil moisture content, soil conductivity and porewater salinity as variables. Starting with an initial intercept model, variables were added or removed based on Akaike Information Criterion (AIC) to balance model complexity with predictive accuracy. All analyses were performed in R version [4.4.1] (R Core Team 2023).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eElevation trends from the retreating upland forest indicate minimal net elevation change over the approximately 5-year record across the high, mid, and low forest with rates of individual SETs ranging from \u0026minus;\u0026thinsp;1.6 to 4.31 mm yr⁻\u0026sup1; (Table\u0026nbsp;1). The transition zone gained elevation at the highest rate (2.94 mm yr⁻\u0026sup1;), followed by the mid (1.51 mm yr⁻\u0026sup1;), low (0.7 mm yr⁻\u0026sup1;) and high (-0.23 mm yr⁻\u0026sup1;) forest zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Net elevation change was significantly greater than zero in the mid, low, and transition zones (p\u0026thinsp;=\u0026thinsp;.007, 0.031, 0.031, Supp. Table\u0026nbsp;1) but not the high forest (p\u0026thinsp;=\u0026thinsp;0.68). Of the three zones with net elevation change rates greater than 0, only the transition zone was able to keep pace with local rates of sea level rise (4.1 mm yr⁻\u0026sup1; for 2005\u0026ndash;2020) (Blum et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSurface accretion rates were highly variable and ranged from 1.1 to 5.3 mm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e (Table\u0026nbsp;1) with the highest rates observed in the mid forest (4.5 mm yr⁻\u0026sup1;), though these were not significantly different from those in the high (2.9 mm yr⁻\u0026sup1;), low (2.63 mm yr⁻\u0026sup1;), and transition zones (2.76 mm yr⁻\u0026sup1;) (Table\u0026nbsp;2). Surface accretion rates were not significantly different from net elevation change in the transition zone (p\u0026thinsp;=\u0026thinsp;0.88) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Conversely, surface accretion rates were significantly greater than net elevation change in the mid, and low forest zones (p\u0026thinsp;=\u0026thinsp;0.0002 and 0.007, respectively Supp. Table\u0026nbsp;2), while the difference in the high forest was marginally significant (p\u0026thinsp;=\u0026thinsp;0.07). This difference in surface accretion and net elevation gain highlights the importance of subsurface processes in driving elevation change rates in these zones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTotal subsurface change ranged from \u0026minus;\u0026thinsp;5.8 to 2.6 mm yr⁻\u0026sup1; (Table\u0026nbsp;1) with the greatest subsidence rates observed in the minimally salt stressed high (-3.1 mm yr⁻\u0026sup1;) and mid forest (-3.0 mm yr⁻\u0026sup1;), although these were not significantly different from subsidence in the low forest (-1.9 mm yr⁻\u0026sup1;) (Table\u0026nbsp;2). Subsurface change in the transition zone was near zero (0.17 mm yr⁻\u0026sup1;) but highly variable (-2.4 to 2.64 mm yr⁻\u0026sup1;) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Below the rooting zone (\u0026gt;\u0026thinsp;30 cm depth), the mid (1.56 mm yr⁻\u0026sup1;) and low forest (2.30 mm yr⁻\u0026sup1;) exhibited expansion, while the high forest (-0.56 mm yr⁻\u0026sup1;) and transition zone (-1.13 mm yr⁻\u0026sup1;) experienced subsidence. Within the rooting zone (\u0026lt;\u0026thinsp;30 cm depth), the high (-2.58 mm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), mid (-4.54 mm yr⁻\u0026sup1;) and low (-5.55 mm yr⁻\u0026sup1;) forest subsided, while only the transition zone (1.3 mm yr⁻\u0026sup1;) exhibited root zone expansion (Table\u0026nbsp;2).\u003c/p\u003e\u003cp\u003eThe multiple linear regression analysis identified several significant interactions influencing net elevation change, accretion, and subsurface processes (Table\u0026nbsp;4). Net elevation change was strongly correlated with both elevation and percent shrub cover. Net elevation change was positively correlated with the elevation of SET benchmarks in the high, mid, and low forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) but negatively correlated when the transition zone was included (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). In the high, mid, and low forest, shrub cover was positively correlated with net elevation change, though it was not a significant predictor across the entire forest health gradient (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec, d). However, when analyzing surface accretion in isolation, shrub cover was the only significant predictor across all zones (p\u0026thinsp;=\u0026thinsp;0.013) (Table\u0026nbsp;3). While elevation was negatively correlated with accretion across all zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), it was not statistically significant on its own but remained an important variable in our multiple linear model. Lastly, subsurface changes were positively correlated with elevation and soil conductivity, and negatively correlated with soil moisture and shrub cover (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). However, only shrub cover significantly influenced subsurface change at all forest zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFuture projections of marsh migration into retreating coastal forests rely on assumptions of a static topography, where the elevation of upland and wetland forests does not change through time (Linhoss et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Schuerch et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osland et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Molino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In contrast, detailed elevation-change measurements in coastal marshes (Blum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Saintilan et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), mangroves (Rogers et al., \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Krauss et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Feher et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and forested wetlands (Ardon et al., in review ,Stagg et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Krauss et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) indicate a dynamic topography where a variety of ecogeomorphic feedbacks influence rates of accretion, subsidence, and net elevation change (Cadol et al., 2014, Rogers et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Lovelock et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although little work has been conducted in upland forests, observations from other coastal ecosystems indicate that vegetation type and inundation duration are primary drivers of elevation change through their impacts on surface accretion and belowground soil processes. In this study, we attempt to extend this conceptual framework to salt-affected upland forests.\u003c/p\u003e\u003cp\u003e\u003cb\u003eInfluence of vegetation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn marshes, species composition directly influences rates of elevation change, largely due to differences in productivity (Calvo-Cubero et al. 2013) and sediment trapping capacity (Boorman et al. 1998, Rooth et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Consistent with the influence of species composition in marshes, we found that vegetation type had a strong influence on the rate of net elevation change across the retreating upland forest. Total shrub cover, dominated by the shrub \u003cem\u003eMorella cerifera\u003c/em\u003e, was the most important variable controlling net elevation change, surface accretion, and subsurface expansion or contraction (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Over the study period, the mid forest zone experienced significant increases in \u003cem\u003eM. cerifera\u003c/em\u003e (Sward et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and the greatest rates of surface accretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Litter inputs from densely packed shrubs may have contributed to higher accretion rates observed in the mid forest zone. In the subsurface, increased shrub cover resulted in greater elevation loss (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, f). Shrub encroachment by \u003cem\u003eM. cerifera\u003c/em\u003e at the study site is likely occurring in response to increased light availability from tree stress and mortality (Sward et al. \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, subsurface subsidence in shrub-dominated plots may reflect root-zone collapse (e.g. Krauss et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e ), where root mortality and loss of turgor pressure occur even prior to tree mortality (Ding et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e. The cooccurrence of shrub encroachment and tree root collapse in the mid forest might explain why there were opposite responses in surface and subsurface contributions to elevation change. Alternatively, a nitrogen-fixing shrub, \u003cem\u003eM. cerifera\u003c/em\u003e increases soil nutrients, moisture, and winter temperatures (Thomspon et al. 2017) potentially stimulating decomposition of soil organic matter in its rhizosphere (Smith et al. 2023).\u003c/p\u003e\u003cp\u003eSurprisingly, the invasive reed \u003cem\u003ePhragmites australis\u003c/em\u003e did not significantly influence any of the elevation change metrics across forest zones (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e,\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Previous studies indicate that \u003cem\u003ePhragmites\u003c/em\u003e contributes to rapid marsh elevation change through increased sediment trapping and high plant productivity (Rooth and Stevenson, 1999; Rooth et al., 2002). Accretion rates in \u003cem\u003eP. australis\u003c/em\u003e stands may be 3–4 mm yr⁻¹ greater than adjacent native marsh (Rooth et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), driven by above and belowground organic matter accumulation (Rooth and Stevenson 1999). In contrast, we found that subsurface losses were highest in plots with the greatest \u003cem\u003eP. australis\u003c/em\u003e cover (MF 6, TZ 4, LF 5; Table\u0026nbsp;2) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Given its high abundance in stressed forest plots, the positive influence of \u003cem\u003eP. australis\u003c/em\u003e on net elevation may be offset by subsidence related to tree mortality. Conversely, \u003cem\u003eP. australis\u003c/em\u003e may contribute to subsurface losses due to decomposition, stimulated by increased standing stocks of nitrogen (Windham and Meyerson \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and higher rhizosphere oxidation in \u003cem\u003eP. australis\u003c/em\u003e stands (Windham and Lathrop \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Additionally, \u003cem\u003eP. australis\u003c/em\u003e stems are more resistant to decomposition than tree leaves or native marsh grasses (Meyerson et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and therefore may accumulate primarily as standing dead stems and take up to 7 years for enhanced rates of accretion to be reflected in the SET-MH record (Rooth et al. \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Given the thick thatch layer (personal observation), relatively short marker horizon record in the transition zone, and distance from tidal inputs the full contribution of \u003cem\u003ePhragmites\u003c/em\u003e to surface accretion is likely yet to be realized.\u003c/p\u003e\u003cp\u003e\u003cb\u003eBelowground dynamics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSubsidence and elevation loss is common in coastal landscapes and has been documented in marshes (Keogh et al. 2021, Cahoon et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1995\u003c/span\u003e), mangroves (Cahoon and Lynch \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Rogers et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), tidal freshwater forests (Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and pocosins (Miller et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Subsidence is often in the form of autocompaction (Kaye and Barghoorn \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1964\u003c/span\u003e, Cahoon et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Saintilan et al., \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) an entirely physical process defined as the compression of organic rich peat beneath its own weight. Additional subsidence has been documented across these ecosystems, driven by environmental stressors such as salinization and inundation. For example, losses of 11 mm yr\u003csup\u003e− 1\u003c/sup\u003e were documented in Honduran mangroves following a large hurricane (Cahoon et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) and a Mississippi River deltaic marsh lost 15 cm over several years in response to increased tidal inundation (DeLaune et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). A study of Louisiana marshes indicates that over 60% of subsidence occurs in the top 5 m (Jankowski et al. 2017, Keogh and Tornqvist 2019) while in Australia, marsh subsidence was found to be largely confined to the organic-rich top 80 cm (Rogers and Saintilan 2021).\u003c/p\u003e\u003cp\u003eConsistent with widespread observations of subsidence in other coastal ecosystems, shallow subsidence was observed in 13 of 16 SET locations across the studied retreating coastal forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Previous work suggests that tree mortality can increase rates of subsidence and lead to net decreases in surface elevation at rates exceeding 10 mm yr\u003csup\u003e− 1\u003c/sup\u003e (Cahoon et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Miller et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, we found that total subsidence was greatest in the high and mid forest, where tree mortality is lower than in the low forest zone (Table\u0026nbsp;3). This is perhaps consistent with modeling efforts that have shown that decreases in root biomass precede canopy stress and tree mortality in coastal forests (Ding et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Shallow subsidence (\u0026lt; 30 cm) was greatest in the mid (-4.54 mm yr⁻¹) and low (-5.55 mm yr⁻¹) forest zones. In contrast with observations of tree mortality leading to subsidence and elevation loss in other forested wetland ecosystems (Middleton et al. 2020, Cahoon et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), the mid and low forest exhibited expansion below the rooting zone (1.56 and 2.3 mm yr⁻¹ respectively) and rates of net elevation change were generally positive (0.7 − 1.51 mm yr\u003csup\u003e− 1\u003c/sup\u003e). Shallow subsidence rates in the mid and low forest ranged from − 2.98 to -1.93 mm yr\u003csup\u003e− 1\u003c/sup\u003e but were offset by surface accretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Similar rates of shallow subsidence have been documented in stressed tidal freshwater forests (-3.8 to -7.6 mm yr⁻¹), largely driven by losses in the rooting zone (Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Stagg et al. \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Autocompaction is not a probable cause of forest subsidence, as the organic layer is thin (5–10 cm, Smith et al. 2019) and underlain by thick consolidated clay (Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Instead, root zone subsidence was likely due to detrimental effects of inundation and salinity on mature Loblolly pine, which distribute up to 80% of salt sensitive, fine roots within the top 20 cm of soil (Mou et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eExpansion below the rooting zone was only documented in the mid and low forest zones, while the high forest and transition zone subsided at depths below the rooting zone (i.e. 30cm) (Table\u0026nbsp;2). This heterogeneity was surprising given the relatively small distance between forest zones. Similar patterns of localized expansion and subsidence were observed in a South Carolina freshwater forest and were attributed to small scale differences in hydrology (Stagg et al. 2017). In the studied upland forest, expansion below the rooting zone in the mid and low forest may be due to the establishment of \u003cem\u003eM. cerifera\u003c/em\u003e and \u003cem\u003eP. australis\u003c/em\u003e, the roots of which frequently reach depths greater than 30 cm (Moore et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Alternatively, the expansion may be due to the swelling of clay-rich soils in response to inundation with saline water. Clay matrix expansion by up to 10% has been documented in laboratory studies (Pedarla et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Like the high forest, the transition zone experienced shallow subsidence below the root zone. This subsidence may be at least partially due to autocompaction, as the depth of organic matter here is ~ 20cm (Smith et al. 2019). Expansion in the root zone of 1.3 mm yr\u003csup\u003e− 1\u003c/sup\u003e offset the subsidence below 30cm, resulting in near zero subsurface elevation change in the transition zone. This pattern of surface accretion and expansion in the shallow zone, is similar to the elevation trends of the nearby high marsh, which from 2009–2024 gained elevation at a rate of 3.5 mm yr\u003csup\u003e− 1\u003c/sup\u003e, largely driven by surface accretion (3.1 mm yr\u003csup\u003e− 1\u003c/sup\u003e) and subsurface expansion (0.4 mm yr\u003csup\u003e− 1\u003c/sup\u003e) (Blum et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eInfluence of Elevation and Flooding Frequency\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAcross coastal ecosystems, rates of net elevation change are largely a function of elevation and flooding frequency (Kirwan and Megonigal, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In coastal systems, lower elevations are more frequently flooded, resulting in increased potential for sediment deposition (Stumpf 1983, Friedrichs and Perry 2001) and enhanced exchange of nutrients leading to greater above and belowground productivity (Morris et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Kirwan and Guntenspergen 2012). A recent global review of nearly 400 surface elevation tables identified rates of relative sea level rise and hydroperiod as the most important factors driving marsh net elevation change (Saintilan et al. 2021). We find that this relationship between elevation and elevation change generally holds true for the forest-to-marsh transition, where the lowest elevation transition zone SETs exhibit the greatest net elevation change rates (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and elevation is a significant control of surface accretion rates across all forest zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eUnlike other coastal systems, such as marshes and mangroves, where mineral sediment deposition can reach several mm yr \u003csup\u003e− 1\u003c/sup\u003e (Cahoon and Lynch \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e, Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Morris et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, Kirwan and Megonigal \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), deposition of mineral sediment in coastal forests is unlikely. Due to the distance from the nearest tidal channel (\u0026gt; 500 m) any flood waters are likely to be depleted of sediment well before reaching the transition zone. This is supported by high surface organic matter content of sediment cores in the transition zone and low forest (Supp. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Though the impacts of tidal inundation on sediment deposition are minimal in coastal upland forests, lower elevations are nonetheless associated with more frequent soil saturation (Nordio et al. 2021, Callahan et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) due to their proximity to shallow groundwater and propensity to collect surface rainwater. Like the adjacent high marsh, the waterlogged, anerobic soils at the forest-marsh ecotone may limit decomposition (Piaszczyk et al. 2022, Janousek et al. 2017) and facilitate the establishment of productive marsh species (Shaw et al. 2022, Smith et al. 2013) that in turn increase belowground biomass relative to that of the nearby low and mid forests.\u003c/p\u003e\u003cp\u003eElevation change rates that increase with increasing elevation between the low forest and high forest are in contrast with elevation change rates that decrease with increasing elevation in long-measured SETs in the adjacent salt marsh (Blum et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). By synthesizing upland forest and salt marsh elevation changes, we find that the relationship between elevation and net elevation change shifts from negative to positive at an elevation very close to the highest astronomical tide (HAT, 1.07m; NOAA Station ID 8631044), suggesting a switch in the processes driving net elevation change associated with large changes in flooding regimes. For example, the low forest zone is flooded by approximately 33 tides per year, while the transition zone is flooded by approximately 140 tides per year (Supp. Table\u0026nbsp;3). This difference in flooding regime may correspond to a threshold in which additional flooding leads to enhanced soil organic matter accumulation in wetlands but enhanced subsidence in forest and shrub dominated zones.\u003c/p\u003e\u003cp\u003eElevation change rates that increase with increasing elevation in upland forests may lead to the development of microtopography, characterized by high elevation hummocks and low elevation hollows. Hummock-hollow microtopography is commonly found in global wetlands (Diamond et al. 2020), especially in tidal freshwater forested wetlands (Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and pocosins (Brinson \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) because small scale variations in elevation may cause dramatic differences in inundation, soil moisture and salinity (Wallis and Raulings, 2011). Hummocks tend to be areas of greater productivity due to reduced hydraulic stress. Once established, this microtopography tends to be preserved as shallow subsidence offsets accretion more in hollows than hummocks (Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Hummocks also provide relief from wet conditions and may even be underlain with small unsaturated zones (Hsueh et al. 2016). A similar pattern of hummock-hollow formation and maintenance may be occurring in the studied coastal upland forest, where elevation gains are directly related to initial surface elevation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This is consistent with elevation variance that is greater within individual plots and forest zone than between forest zones (pers. observation). Additionally, both rates of subsidence and accretion were more strongly controlled by elevation and vegetation type than by forest zone, suggesting the importance of localized, small-scale controls on elevation change rates. In tidal freshwater forested wetlands, maintenance of the “hummock-hollow” microtopograhy is an important factor for the resistance of transition to oligohaline marsh (Noe et al \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2013\u003c/span\u003e, Krauss et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Similarly, poorly explained variance in the elevation of the marsh-forest boundary across the Chesapeake Bay region has been attributed to microtopograhy (Molino et al., 2023 JGR), and inundation relief provided by hummocks may slow the transition from forest to marsh.\u003c/p\u003e"},{"header":"Conclusions and implications for coastal transgression","content":"\u003cp\u003eWe measured relatively small changes in the elevation of a retreating upland forest soil (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), which at least tentatively supports assumptions of a static upland topography in models of coastal transgression (Schuerch et al., \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Osland et al., \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Molino et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding is in contrast to observations of extensive subsidence and net decreases in the elevation of other coastal forests following mortality events (e.g. Miller et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Large storms, which were largely absent during the study period, may explain this deviation. For example, elevation losses of ~ 1.1 cm yr\u003csup\u003e− 1\u003c/sup\u003e in mangroves (Cahoon et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) and 10 cm yr\u003csup\u003e− 1\u003c/sup\u003e in forested wetlands (Middleton and David, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) have been documented using SET-MH following hurricanes. In each instance, tree mortality driven subsidence resulted in rapid conversion to marsh, whereas forest to marsh conversion is gradual at our study site (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Conversely, storms may increase accretion through a pulse of leaf litter and woody debris, wrack deposits or inorganic sediment. For example, Hurricane Hugo deposited up to 3 cm of mud into a South Carolina pine forest (Gardner et al. 1991). In this study we found no evidence of either a significant subsidence or depositional event, though we were not able to directly measure storm effects during the study period. Therefore, over short time scales and in the absence of large storms, assumptions of a static upland topography may be valid.\u003c/p\u003e\u003cp\u003eObservations of slow net-changes in soil elevation belie more dynamic responses to vegetation and inundation expressed in the individual components of elevation change (i.e. surface accretion, and subsurface expansion or subsidence). Even in the absence of a major storm event, transgression of vegetation types are occurring rapidly in the studied forest, including the encroachment of woody shrubs (Sward et al., \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and invasive \u003cem\u003ePhragmites\u003c/em\u003e (Shaw et al., 2023), and reductions in the biomass of even the high forest zones (Chen and Kirwan, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). While we measured subsidence rates of up to 5mm yr\u003csup\u003e− 1\u003c/sup\u003e in most forest zones that may correspond with declines in forest health, subsidence was largely compensated for by surface accretion (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Finally, we observed net surface accretion paired with belowground expansion in the transition zone, such that net elevation change rates in developing marshes are nearly equivalent to adjacent, fully developed salt marshes (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Together, these observations suggest that the elevation of forest soils are relatively stable, but that elevation change rates and their components respond rapidly to marsh migration into retreating coastal forests.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003efor this project was provided by The National Science Foundation\u0026rsquo;s Critical Zone Network (2012484) and Long-Term Ecological Research (1832221 and 2425178) programs. Additional support was provided by the U.S. Geological Survey Chesapeake Bay Studies program. We appreciate assistance from Joel Carr and David Walters with the initial SET install and measurement protocols, and data collection assistance from the Virginia Coast Reserve staff. We thank The Nature Conservancy for access to the field site.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunding for this project was provided by The National Science Foundation\u0026rsquo;s Critical Zone Network (2012484) and Long-Term Ecological Research (1832221 and 2425178) programs. Additional support was provided by the U.S. Geological Survey Chesapeake Bay Studies program. We appreciate assistance from Joel Carr and David Walters with the initial SET install and measurement protocols, and data collection assistance from the Virginia Coast Reserve staff. We thank The Nature Conservancy for access to the field site. \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnisfeld, S. C., Cooper, K. R., \u0026amp; Kemp, A. C. (2017). Upslope development of a tidal marsh as a function of upland land use. \u003cem\u003eGlobal Change Biology\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(2), 755\u0026ndash;766. https://doi.org/10.1111/gcb.13398\u003c/li\u003e\n\u003cli\u003eAnthony, E., Brunier, G., Besset, M. et al. Linking rapid erosion of the Mekong River delta to human activities. Sci Rep 5, 14745 (2015). https://doi.org/10.1038/srep14745\u003c/li\u003e\n\u003cli\u003eArd\u0026oacute;n, M., Lazaro, P., Emanuel, R., \u0026amp; others. (2025, June 18). Surface elevation trends in natural and restored coastal forested wetlands reveal vulnerability to saltwater intrusion and sea level rise (Version 1) [Preprint]. Research Square. https://doi.org/10.21203/rs.3.rs-6830356/v1\u003c/li\u003e\n\u003cli\u003eBarbier, E.B., Hacker, S.D., Kennedy, C., Koch, E.W., Stier, A.C. \u0026amp; Silliman, B.R. (2011), The value of estuarine and coastal ecosystem services. Ecological Monographs, 81: 169-193. https://doi.org/10.1890/10-1510.1\u003c/li\u003e\n\u003cli\u003eBhatti, Jagtar \u0026amp; Jassal, Rachhpal. (2014). Long term aboveground litterfall production in boreal jack pine (Pinus banksiana) and black spruce (Picea mariana) stands along the Boreal Forest Transect Case Study in western central Canada. \u0026Eacute;coscience. 21. 301-314..\u003c/li\u003e\n\u003cli\u003eBlum, L. K., Christian, R. R., Cahoon, D. R., \u0026amp; Wiberg, P. L. (2021). Processes Influencing Marsh Elevation Change in Low- and High-Elevation Zones of a Temperate Salt Marsh. Estuaries and Coasts, 44(3), 818\u0026ndash;833. https://doi.org/10.1007/s12237-020-00796-z\u003c/li\u003e\n\u003cli\u003eBrantley, S.T., Young, D.R. Shifts in litterfall and dominant nitrogen sources after expansion of shrub thickets. \u003cem\u003eOecologia\u003c/em\u003e \u003cstrong\u003e155\u003c/strong\u003e, 337\u0026ndash;345 (2008). https://doi.org/10.1007/s00442-007-0916-7\u003c/li\u003e\n\u003cli\u003eBrinson, M.M. Landscape properties of pocosins and associated wetlands. \u003cem\u003eWetlands\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e (Suppl 1), 441\u0026ndash;465 (1991). https://doi.org/10.1007/BF03160761\u003c/li\u003e\n\u003cli\u003eBrinson, M. M., Christian, R. R., \u0026amp; Blum, L. K. (1995). Multiple States in the Sea-Level Induced Transition from Terrestrial Forest to Estuary (Vol. 18, Issue 4, pp. 648\u0026ndash;659). https://about.jstor.org/terms\u003c/li\u003e\n\u003cli\u003eBrinson, M.M., \u0026amp; R.R. Christian. (1999). Stability of Juncus roemerianus patches in a salt marsh. Wetlands 19 (1): 65\u0026ndash;70.\u003c/li\u003e\n\u003cli\u003eCahoon, D. R., \u0026amp; Lynch, J. C. (1997). Vertical accretion and shallow subsidence in a mangrove forest of southwestern Florida, U.S.A. In Mangroves and Salt Marshes (Vol. 1, pp. 173\u0026ndash;186). Kluwer Academic Publishers.\u003c/li\u003e\n\u003cli\u003eCahoon, D. R., Lynch, J. C., Perez, B. C., Segura, B., Holland, R. D., Stelly, C., Stephenson, G., \u0026amp; Hensel, P. (2002). High precision measurements of wetland sediment elevation: II. The rod surface elevation table. Journal of Sedimentary Research, 72(5), 734\u0026ndash;739. https://doi.org/10.1306/020702720734\u003c/li\u003e\n\u003cli\u003eCahoon, D.R., Hensel, P., Rybczyk, J., McKee, K.L., Proffitt, C.E. \u0026amp; Perez, B.C. (2003), Mass tree mortality leads to mangrove peat collapse at Bay Islands, Honduras after Hurricane Mitch. Journal of Ecology, 91: 1093-1105. \u003cbr\u003e https://doi.org/10.1046/j.1365-2745.2003.00841.x\u003c/li\u003e\n\u003cli\u003eCahoon, D. R., Reed, D. J., \u0026amp; Day, J. W.. (1995). Estimating shallow subsidence in microtidal salt marshes of the southeastern United States: Kaye and Barghoorn revisited. In MARINE GEOLOGY ELSEVIER Marine Geology (Vol. 128, p. 9).\u003c/li\u003e\n\u003cli\u003eCallahan, T.J.; Amatya, D.M.; Stone, P.A. (2017). Coastal Forests and Groundwater: Using Case Studies to Understand the Effects of Drivers and Stressors for Resource Management. Sustainability 9, 447. https://doi.org/10.3390/su9030447\u003c/li\u003e\n\u003cli\u003eCallaway, J.C., Cahoon, D.R. \u0026amp; Lynch, J.C. (2013). The Surface Elevation Table\u0026ndash;Marker Horizon Method for Measuring Wetland Accretion and Elevation Dynamics. In Methods in Biogeochemistry of Wetlands (eds R.D. DeLaune, K.R. Reddy, C.J. Richardson and J.P. Megonigal). \u003c/li\u003e\n\u003cli\u003eCarr, J., Guntenspergen, G., \u0026amp; Kirwan, M. (2020). Modeling Marsh-Forest Boundary Transgression in Response to Storms and Sea-Level Rise. Geophysical Research Letters, 47(17).https://doi.org/10.1029/2020GL088998\u003c/li\u003e\n\u003cli\u003eChambers, L. G., Steinmuller, H. E., \u0026amp; Breithaupt, J. L. (2019). Toward a mechanistic understanding of \u0026ldquo;peat collapse\u0026rdquo; and its potential contribution to coastal wetland loss. Ecology, 100(7). https://doi.org/10.1002/ecy.2720\u003c/li\u003e\n\u003cli\u003eChen, Y., \u0026amp; Kirwan, M. L. (2022). Climate-driven decoupling of wetland and upland biomass trends on the mid-Atlantic coast. \u003cem\u003eNature Geoscience, 15\u003c/em\u003e, 913\u0026ndash;918. https://doi.org/10.1038/s41561-022-01041-x\u003c/li\u003e\n\u003cli\u003eChen, Y., \u0026amp; Kirwan, M. L. (2024). Upland forest retreat lags behind sea-level rise in the mid-Atlantic coast. Global Change Biology, 30(1). https://doi.org/10.1111/gcb.17081\u003c/li\u003e\n\u003cli\u003eColombano, D.D., Litvin, S.Y., Ziegler, S.L. et al. (2021). Climate Change Implications for Tidal Marshes and Food Web Linkages to Estuarine and Coastal Nekton. Estuaries and Coasts 44, 1637\u0026ndash;1648 https://doi.org/10.1007/s12237-020-00891-1\u003c/li\u003e\n\u003cli\u003eConner, W.H. \u0026amp; Askew, G.R. (1992). Response of baldcypress and loblolly pine seedlings to short-term saltwater flooding. Wetlands 12, 230\u0026ndash;233 https://doi.org/10.1007/BF03160614\u003c/li\u003e\n\u003cli\u003eConner, W., Whitmire, S., Duberstein, J., Stalter, R., \u0026amp; Baden, J. (2022). Changes within a South Carolina Coastal Wetland Forest in the Face of Rising Sea Level. Forests, 13(3). https://doi.org/10.3390/f13030414\u003c/li\u003e\n\u003cli\u003eCook, C. E., Boyle, M. F., \u0026amp; Rankin, N. M. (2016). Vegetation of coastal wetland elevation monitoring sites on National Wildlife Refuges in the South Atlantic geography: 1st status assessment repor\u003cem\u003et\u003c/em\u003e. U.S. Fish and Wildlife Service.\u003c/li\u003e\n\u003cli\u003eCovington, J.S. (2020). An inventory of surface elevation tables installed on National Wildlife Refuge System lands. U.S. Fish and Wildlife Service, Falls Church, VA. 16 pp.\u003c/li\u003e\n\u003cli\u003eCraft, C.B., (2012). Tidal freshwater forest accretion does not keep pace with sea level rise. Global Change Biology, 18(12), pp.3615-3623.\u003c/li\u003e\n\u003cli\u003eDenslow, J. S., \u0026amp; Battaglia, L. L. (2002). Stand Composition and Structure Across a Changing Hydrologic Gradient: Jean Lafitte National Park, Louisiana, USA. In Wetlands (Vol. 22, Issue 4, pp. 738\u0026ndash;752).\u003c/li\u003e\n\u003cli\u003eDelaune, Ronald \u0026amp; Nyman, John \u0026amp; Jr, W.H. (1994). Peat collapse, ponding, and wetland loss in a rapidly submerging coastal marsh. Journal of Coastal Research. 10. 1021-1030.\u003c/li\u003e\n\u003cli\u003eDing, J., McDowell, N., Fang, Y., Ward, N., Kirwan, M.L., Regier, P., Megonigal, P., Zhang, P., Zhang, H., Wang, W. \u0026amp; Li, W., (2023). Modeling the mechanisms of conifer mortality under seawater exposure. New Phytologist, 239(5), pp.1679-1691.\u003c/li\u003e\n\u003cli\u003eDuberstein JA, Krauss KW, Baldwin MJ, et al. (2020). Small gradients in salinity have large effects on stand water use in freshwater wetland forests. Forest Ecology and Management 473:118308.\u003c/li\u003e\n\u003cli\u003eEggleston, J. \u0026amp; Pope, J. (2013). Land Subsidence and Relative Sea-Level Rise in the Southern Chesapeake Bay Region. U.S. Geological Survey Circular 1392.\u003c/li\u003e\n\u003cli\u003eFagherazzi, S., Anisfeld, S. C., Blum, L. K., Long, E. V., Feagin, R. A., Fernandes, A., Kearney, W. S., \u0026amp; Williams, K. (2019). Sea level rise and the dynamics of the marsh-upland boundary. Frontiers in Environmental Science, 7(FEB). https://doi.org/10.3389/fenvs.2019.00025\u003c/li\u003e\n\u003cli\u003eFagherazzi, S., Nordio, G., Boaga, J., Cassiani, G., Michael, H., Pratt, D., Messerschmidt, T., Kirwan, M. and Stotts, S. (2025), The Ecohydrology of Coastal Ghost Forests. Ecohydrology, 18: e70020. https://doi.org/10.1002/eco.70020\u003c/li\u003e\n\u003cli\u003eFarron, S. J., Hughes, Z. J., \u0026amp; FitzGerald, D. M. (2020). Assessing the response of the Great Marsh to sea-level rise: Migration, submersion or survival. Marine Geology, 425. https://doi.org/10.1016/j.margeo.2020.106195\u003c/li\u003e\n\u003cli\u003eFeagin, R. A., Luisa Martinez, M., \u0026amp; Mendoza-Gonzalez, G. (2010). Salt Marsh Zonal Migration and Ecosystem Service Change in Response to Global Sea Level Rise: A Case Study from an Urban Region. In Costanza Source: Ecology and Society (Vol. 15, Issue 4).\u003c/li\u003e\n\u003cli\u003eFeher, L. C., Osland, M. J., Anderson, G. H., Vervaeke, W. C., Krauss, K. W., Whelan, K. R. T., Balentine, K. M., Tiling-Range, G., Smith, T. J., \u0026amp; Cahoon, D. R. (2020). The Long-Term Effects of Hurricanes Wilma and Irma on Soil Elevation Change in Everglades Mangrove Forests. Ecosystems, 23(5), 917\u0026ndash;931. https://doi.org/10.1007/s10021-019-00446-x\u003c/li\u003e\n\u003cli\u003eFeher, L. C., Osland, M. J., McKee, K. L., \u0026amp; others. (2024). Soil elevation change in mangrove forests and marshes of the Greater Everglades: A regional synthesis of surface elevation table\u0026ndash;marker horizon (SET-MH) data. Estuaries and Coasts, 47, 2027\u0026ndash;2056. https://doi.org/10.1007/s12237-022-01141-2\u003c/li\u003e\n\u003cli\u003eFitzGerald, Duncan \u0026amp; Fenster, Michael \u0026amp; Argow, Brittina \u0026amp; Buynevich, Ilya. (2008). Coastal Impacts Due to Sea-Level Rise. Annu. Rev. Earth Planet. Sci.. 36. 10.1146/annurev.earth.35.031306.140139.\u003c/li\u003e\n\u003cli\u003eFlester, J. A., \u0026amp; Blum, L. K. (2020). Rates of Mainland Marsh Migration into Uplands and Seaward Edge Erosion are Explained by Geomorphic Type of Salt Marsh in Virginia Coastal Lagoons. https://doi.org/10.1007/s13157-020-01390-6/Published\u003c/li\u003e\n\u003cli\u003eGalloway, D.L., Jones, D.R., \u0026amp; Ingebritsen, S.E. (1999) Land Subsidence in the United States. Vol. 1182. Geological Survey (USGS)\u003c/li\u003e\n\u003cli\u003eGedan, K., J. MacGregor, R. Mohammadi, \u0026amp; Khan, A. (2022). Forest Transition Experiment - Leaf Litter in a Coastal Virginia Forest ver. 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/fcdcb56064634ac5df489c572ed4a2ed (Accessed 2024-09-16).\u003c/li\u003e\n\u003cli\u003eGedan, K., J. MacGregor, R. Mohammadi, \u0026amp; A. Khan. 2023. Forest Transition Experiment - Vegetation Monitoring on a Coastal Virginia Forest, 2019-2022 ver. 6. Environmental Data Initiative. https://doi.org/10.6073/pasta/a7de61660e2fa73ce1899200cc64744b (Accessed 2024-09-16).\u003c/li\u003e\n\u003cli\u003eGeselbracht, L., Freeman, K., Kelly, E., Gordon, D. R., \u0026amp; Putz, F. E. (2011). Retrospective and prospective model simulations of sea level rise impacts on Gulf of Mexico coastal marshes and forests in Waccasassa Bay, Florida. Climatic Change, 107(1), 35\u0026ndash;57. https://doi.org/10.1007/s10584-011-0084-y\u003c/li\u003e\n\u003cli\u003eGleason, M.L., Elmer, D.A., Pien, N.C. et al. (1979). Effects of stem density upon sediment retention by salt marsh cord grass, Spartina alterniflora loisel. Estuaries 2, 271\u0026ndash;273 https://doi.org/10.2307/1351574\u003c/li\u003e\n\u003cli\u003eHacke, G.; Sperry, J.S.; Ewers, B.E.; Ellsworth, D.S.; Sch\u0026auml;fer, K.V.R.; Oren, R. 2000. Influence of soil porosity on water use in \u003cem\u003ePinus taeda\u003c/em\u003e. Oecologa (2000) 124:495-505\u003c/li\u003e\n\u003cli\u003eHe, Yangbo \u0026amp; DeSutter, Thomas \u0026amp; Prunty, Lyle \u0026amp; Hopkins, David \u0026amp; Jia, Xinhua \u0026amp; Wysocki, Douglas. (2012). Evaluation of 1:5 soil to water extract electrical conductivity methods. Geoderma. s 185\u0026ndash;186. 12\u0026ndash;17. 10.1016/j.geoderma.2012.03.022.\u003c/li\u003e\n\u003cli\u003eHladik, Christine \u0026amp; Alber, Merryl. (2014). Classification of salt marsh vegetation using edaphic and remote sensing-derived variables. Estuarine, Coastal and Shelf Science. 141. 10.1016/j.ecss.2014.01.011.\u003c/li\u003e\n\u003cli\u003eKaye, C. A., \u0026amp; Barghoorn, E. S. (1964). Late Quaternary sea-level change and crustal rise at Boston, Massachusetts, with notes on the autocompaction of peat. GSA Bulletin, 75(2), 63\u0026ndash;80. https://doi.org/10.1130/0016-7606(1964)75[63:LQSCAC]2.0.CO;2\u003c/li\u003e\n\u003cli\u003eKennish, M.J., (2001). Coastal Salt Marsh Systems in the U.S.: A Review of Anthropogenic Impacts. Journal of Coastal Research, 17(3), 731-748.\u003c/li\u003e\n\u003cli\u003eKirwan M.L., Megonigal J.P. (2013). Tidal wetland stability in the face of human impacts and sea-level rise. Nature 504:53\u0026ndash;60\u003c/li\u003e\n\u003cli\u003eKirwan, M. L., D. C. Walters, W. G. Reay, and J. A. Carr (2016), Sea level driven marsh expansion in a coupled model of marsh erosion and migration, \u003cem\u003eGeophys. Res. Lett.\u003c/em\u003e, 43, 4366\u0026ndash;4373, doi:10.1002/2016GL068507.\u003c/li\u003e\n\u003cli\u003eKirwan, M. L. \u0026amp; Gedan, K. B. (2019). Sea-level driven land conversion and the formation of ghost forests. Nature Climate Change, 9(6), 450\u0026ndash;457. https://doi.org/10.1038/s41558-019-0488-7\u003c/li\u003e\n\u003cli\u003eKirwan, M. L., Michael, H. A., Gedan, K. B., Tully, K. L., Fagherazzi, S., McDowell, N. G., Molino, G. D., Pratt, D., Reay, W. G., \u0026amp; Stotts, S. (2025). Feedbacks regulating the salinization of coastal landscapes. \u003cem\u003eAnnual Review of Marine Science, 17\u003c/em\u003e(1), 461\u0026ndash;484. https://doi.org/10.1146/annurev-marine-070924-031447\u003c/li\u003e\n\u003cli\u003eKrauss, K.W., Cahoon, D.R., Allen, J.A. \u003cem\u003eet al.\u003c/em\u003e Surface Elevation Change and Susceptibility of Different Mangrove Zones to Sea-Level Rise on Pacific High Islands of Micronesia. \u003cem\u003eEcosystems\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e, 129\u0026ndash;143 (2010). https://doi.org/10.1007/s10021-009-9307-8\u003c/li\u003e\n\u003cli\u003eKrauss, K. W., Noe, G. B., Duberstein, J. A., Cormier, N., From, A. S., Doody, T. R., Conner, W. H., Cahoon, D. R., \u0026amp; Johnson, D. J. (2024). Presence of Hummock and Hollow Microtopography Reflects Shifting Balances of Shallow Subsidence and Root Zone Expansion Along Forested Wetland River Gradients. Estuaries and Coasts. https://doi.org/10.1007/s12237-023-01227-5\u003c/li\u003e\n\u003cli\u003eJ.A. Langley, K.L. McKee, D.R. Cahoon, J.A. Cherry, \u0026amp; J.P. Megonigal, Elevated CO2 stimulates marsh elevation gain, counterbalancing sea-level rise, Proc. Natl. Acad. Sci. U.S.A. 106 (15) 6182-6186, https://doi.org/10.1073/pnas.0807695106 (2009).\u003c/li\u003e\n\u003cli\u003eLangston A.K., Kaplan D.A., \u0026amp; Putz F.E. (2017). A casualty of climate change? Loss of freshwater forest islands on Florida\u0026apos;s Gulf Coast. Glob Change Biol. 23: 5383 5397. \u003cbr\u003e https://doi.org/10.1111/gcb.13805 \u003c/li\u003e\n\u003cli\u003eLinhoss, A. C., Kiker, G., Shirley, M., \u0026amp; Frank, K. (2015). Sea-level rise, inundation, and marsh migration: Simulating impacts on developed lands and environmental systems. Journal of Coastal Research, 31(1), 36\u0026ndash;46. https://doi.org/10.2112/JCOASTRES-D-13-00215.1\u003c/li\u003e\n\u003cli\u003eLynch, J., Hensel, P., \u0026amp; Cahoon, D. (2015). The Surface Elevation Table and Marker Horizon Technique: A protocol for monitoring wetland elevation dynamics. https://doi.org/10.13140/RG.2.1.5171.9761\u003c/li\u003e\n\u003cli\u003eLovelock, C., Cahoon, D., Friess, D. \u003cem\u003eet al.\u003c/em\u003e(2015). The vulnerability of Indo-Pacific mangrove forests to sea-level rise. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e526\u003c/strong\u003e, 559\u0026ndash;563. https://doi.org/10.1038/nature15538\u003c/li\u003e\n\u003cli\u003eLovelock, C. E., Ball, M. C., Brothers, N., Pearse, A., \u0026amp; Reef, R. (2025). Dynamics of surface accretion and surface elevation differ between river- and tide-dominated settings in tropical mangroves. \u003cem\u003eLimnology and Oceanography, 70\u003c/em\u003e, 1424\u0026ndash;1437. https://doi.org/10.1002/lno.70024\u003c/li\u003e\n\u003cli\u003eMcLeod, E., Chmura, G., Bouillon, S., Salm, R., Bj\u0026ouml;rk, M., Duarte, C., Lovelock, C., Schlesinger, W., \u0026amp; Silliman, B. (2011). A blueprint for blue carbon: Toward an improved understanding of the role of vegetated coastal habitats in sequestering CO2. Frontiers in Ecology and the Environment, 9(10), 552-560. https://doi.org/10.1890/110004\u003c/li\u003e\n\u003cli\u003eMesserschmidt, T.C., S. Wittyngham \u0026amp; ML. Kirwan. (2022). Vegetation and physical characteristics of Chesapeake Bay retreating Coastal Forests 2022 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/92071e5fc49431114a6453d7ed205298\u003c/li\u003e\n\u003cli\u003eMeyerson, L. A., K. Saltonstall, L. Windham, E. Kiviat, \u0026amp; S. Findlay. (2000). A comparison of Phragmites australis in freshwater and brackish marsh environments in North America. Wetlands Ecology and Management 8: 89\u0026ndash;103\u003c/li\u003e\n\u003cli\u003eMiddleton, B.A. \u0026amp; David, J.L., 2022. Trends in vegetation and height of the topographic surface in a tidal freshwater swamp experiencing rooting zone saltwater intrusion. Ecological Indicators, 145, p.109637\u003c/li\u003e\n\u003cli\u003eMiller, C., Rodriguez, A., \u0026amp; Bost, M. (2021). Sea-level rise, localized subsidence, and increased storminess promote saltmarsh transgression across low-gradient upland areas. Quaternary Science Reviews, 265, 107000. https://doi.org/10.1016/j.quascirev.2021.107000\u003c/li\u003e\n\u003cli\u003eMolino, G. D., Carr, J. A., Ganju, N. K., \u0026amp; Kirwan, M. L. (2022). Variability in marsh migration potential determined by topographic rather than anthropogenic constraints in the Chesapeake Bay region. Limnology And Oceanography Letters, 7(4), 321\u0026ndash;331. https://doi.org/10.1002/lol2.10262\u003c/li\u003e\n\u003cli\u003eMoore, G. E., Burdick, D. M., Peter, C. R., \u0026amp; Keirstead, D. R. (2012). Belowground biomass of Phragmites australis in coastal marshes. Northeastern Naturalist, 19(4), 611\u0026ndash;626. https://doi.org/10.1656/045.019.0406\u003c/li\u003e\n\u003cli\u003eMoorman, M.C., Ladin, Z.S., Tsai, E. \u003cem\u003eet al.\u003c/em\u003e Will They Stay or Will They Go \u0026mdash; Understanding South Atlantic Coastal Wetland Transformation in Response to Sea-Level Rise. (2024). \u003cem\u003eEstuaries and Coasts\u003c/em\u003e \u003cstrong\u003e47\u003c/strong\u003e, 2011\u0026ndash;2026. https://doi.org/10.1007/s12237-023-01225-7\u003c/li\u003e\n\u003cli\u003eMorris, J.T., Sundareshwar, P.V., Nietch, C.T., Kjerfve, B. and Cahoon, D.R. (2002), RESPONSES OF COASTAL WETLANDS TO RISING SEA LEVEL. Ecology, 83: 2869-2877. https://doi.org/10.1890/0012-9658\u003c/li\u003e\n\u003cli\u003eMou, P., et al. (1995). Spatial distribution of roots in sweetgum and loblolly pine monocultures and relations with above-ground biomass and soil nutrients. Functional Ecology, 9(4), 689\u0026ndash;699. https://doi.org/10.2307/2390162\u003c/li\u003e\n\u003cli\u003eNoe, G.B., Krauss, K.W., Lockaby, B.G. et al. (2013). The effect of increasing salinity and forest mortality on soil nitrogen and phosphorus mineralization in tidal freshwater forested wetlands. Biogeochemistry 114, 225\u0026ndash;244. https://doi.org/10.1007/s10533-012-9805-1\u003c/li\u003e\n\u003cli\u003eNordio, G., Gedan, K., \u0026amp; Fagherazzi, S. (2024). Storm Surges and Sea Level Rise Cluster Hydrological Variables Across a Coastal Forest Bordering a Salt Marsh. Water Resources Research, 60(2), e2022WR033931. https://doi.org/10.1029/2022WR033931\u003c/li\u003e\n\u003cli\u003eOsland, M. J., Chivoiu, B., Enwright, N. M., Thorne, K. M., Guntenspergen, G. R., Grace, J. B., Dale, L. L., Brooks, W., Herold, N., Day, J. W., Sklar, F. H., \u0026amp; Schwarzenzki, C. M. (2022). Migration and transformation of coastal wetlands in response to rising seas. \u003cem\u003eScience Advances, 8\u003c/em\u003e(26), eabo5174. https://doi.org/10.1126/sciadv.abo5174\u003c/li\u003e\n\u003cli\u003ePedarla, A., Puppala, A., Hoyos, L., Vanapalli, S., \u0026amp; Zapata, C. (2012). SWRC modelling framework for evaluating volume change behavior of expansive soils. In Proceedings of the 11th International Conference on Expansive Soils (pp. 305-309). https://doi.org/10.1007/978-3-642-31116-1_30\u003c/li\u003e\n\u003cli\u003ePoulter, B., Qian, S. S., \u0026amp; Christensen, N. L. (2009). Determinants of coastal treeline and the role of abiotic and biotic interactions. Plant Ecology, 202(1), 55\u0026ndash;66. https://doi.org/10.1007/s11258-008-9465-3\u003c/li\u003e\n\u003cli\u003ePratt, D., R. McQuiggan, E. S. Bacmeister, A. Sprague-Getsy, H. Michael (2025). Coastal Critical Zone Dataset: Groundwater and soil moisture data collected in the Coastal Critical Zone of the Delmarva Peninsula, United States, HydroShare, http://www.hydroshare.org/resource/845670d788eb4385a17b1699bdc0383d\u003c/li\u003e\n\u003cli\u003eRaabe, E. A., \u0026amp; Stumpf, R. P. (2015). Expansion of tidal marsh in response to sea-level rise: Gulf Coast of Florida, USA. Estuaries and Coasts, 39(1), 145\u0026ndash;157. https://doi.org/10.1007/s12237-015-9974-y\u003c/li\u003e\n\u003cli\u003eRogers, K., Saintilan, N., \u0026amp; Cahoon, D. (2005). Surface elevation dynamics in a regenerating mangrove forest at Homebush Bay, Australia. \u003cem\u003eWetlands Ecology and Management, 13\u003c/em\u003e, 587\u0026ndash;598. https://doi.org/10.1007/s11273-004-0003-3\u003c/li\u003e\n\u003cli\u003eRogers, K., Saintilan, N., \u0026amp; Copeland, C. (2012). Modelling wetland surface elevation dynamics and its application to forecasting the effects of sea-level rise on estuarine wetlands. Ecological Modelling, 244, 148\u0026ndash;157. https://doi.org/10.1016/j.ecolmodel.2012.06.014\u003c/li\u003e\n\u003cli\u003eRogers, Kerrylee, et al. (2019). \u0026ldquo;Wetland Carbon Storage Controlled by Millennial-Scale Variation in Relative Sea-Level Rise.\u0026rdquo; Nature., vol. 567, no. 7746, pp. 91\u0026ndash;95, https://doi.org/10.1038/s41586-019-0951-7.\u003c/li\u003e\n\u003cli\u003eRogers, K., Zawadzki, A., Mogensen, L., \u0026amp; Saintilan, N. (2022). Coastal wetland surface elevation change is dynamically related to accommodation space and influenced by sedimentation and sea-level rise over decadal timescales. \u003cem\u003eFrontiers in Marine Science, 9\u003c/em\u003e, Article 807588. https://doi.org/10.3389/fmars.2022.807588\u003c/li\u003e\n\u003cli\u003eRooth, J. E., Stevenson, J. C., \u0026amp; Cornwell, J. C. (2003). Increased Sediment Accretion Rates Following Invasion by Phragmites australis: The Role of Litter. Estuaries, 26(2), 475\u0026ndash;483. http://www.jstor.org/stable/1353362\u003c/li\u003e\n\u003cli\u003eRussell, B. T., Cressman, K. A., Schmit, J. P., Shull, S., Rybczyk, J. M., \u0026amp; Frost, D. L. (2022). How should surface elevation table data be analyzed? A comparison of several commonly used analysis methods and one newly proposed approach. Environmental and Ecological Statistics, 29(2), 359\u0026ndash;391. https://doi.org/10.1007/s10651-021-00524-1\u003c/li\u003e\n\u003cli\u003eSaintilan N., Kovalenko KE, Guntenspergen G, Rogers K, Lynch JC, Cahoon DR, Lovelock et al. (2022). Constraints on the adjustment of tidal marshes to accelerating sea level rise. Science Jul 29;377(6605):523-527. doi: 10.1126/science.abo7872. \u003c/li\u003e\n\u003cli\u003eSaintilan, N., Horton, B., T\u0026ouml;rnqvist, T.E. et al. (2023a). Widespread retreat of coastal habitat is likely at warming levels above 1.5 \u0026deg;C. Nature 621, 112\u0026ndash;119. https://doi.org/10.1038/s41586-023-06448-z\u003c/li\u003e\n\u003cli\u003eSaintilan, N., Sun, Y., Lovelock, C. E., Rogers, K., Goddard, M., Hutley, L. B., Kelleway, J., Mosley, L., Dittmann, S., Cormier, N., Lal, K. K., \u0026amp; Jones, A. (2023b). Vertical Accretion Trends in Australian Tidal Wetlands. Estuaries and Coasts. https://doi.org/10.1007/s12237-023-01267-x\u003c/li\u003e\n\u003cli\u003eSallenger, A., Doran, K. \u0026amp; Howd, P. (2012). Hotspot of accelerated sea-level rise on the Atlantic coast of North America. Nature Clim Change 2, 884\u0026ndash;888. https://doi.org/10.1038/nclimate1597\u003c/li\u003e\n\u003cli\u003eSchieder, N. W., Walters, D. C., \u0026amp; Kirwan, M. L. (2018). Massive Upland to Wetland Conversion Compensated for Historical Marsh Loss in. 41(4), 940\u0026ndash;951. https://doi.org/10.1007/sl2237-017-0336-9\u003c/li\u003e\n\u003cli\u003eSchimel, D.S., (1995). Terrestrial ecosystems and the carbon-cycle. Global Change Biology 1, 77\u0026ndash;91\u003c/li\u003e\n\u003cli\u003eSchuerch, M., Spencer, T., Temmerman, S., Kirwan, M. L., Wolff, C., Lincke, D., McOwen, C. J., Pickering, M. D., Reef, R., Vafeidis, A. T., Hinkel, J., Nicholls, R. J., \u0026amp; Brown, S. (2018). Future response of global coastal wetlands to sea-level rise. Nature, 561(7722), 231\u0026ndash;234. https://doi.org/10.1038/s41586-018-0476-5\u003c/li\u003e\n\u003cli\u003eSmith, A. J., \u0026amp; Kirwan, M. L. (2021). Sea Level-Driven Marsh Migration Results in Rapid Net Loss of Carbon. Geophysical Research Letters, 48(13). https://doi.org/10.1029/2021GL092420\u003c/li\u003e\n\u003cli\u003eSmith, J. A. M. (2013). The Role of Phragmites australis in Mediating Inland Salt Marsh Migration in a Mid-Atlantic Estuary. PLoS ONE, 8(5). https://doi.org/10.1371/journal.pone.0065091\u003c/li\u003e\n\u003cli\u003eStagg, C. L., Krauss, K. W., Cahoon, D. R., Cormier, N., Conner, W. H., \u0026amp; Swarzenski, C. M. (2016). Processes contributing to resilience of coastal wetlands to sea-level rise. Ecosystems, 19(8), 1445\u0026ndash;1459. https://doi.org/10.1007/s10021-016-0015-x\u003c/li\u003e\n\u003cli\u003eStagg, C. L., Baustian, M. M., Perry, C. L., Carruthers, T. J. B., \u0026amp; Hall, C. T. (2018). Direct and indirect controls on organic matter decomposition in four coastal wetland communities along a landscape salinity gradient. Journal of Ecology, 106(5), 655\u0026ndash;670. https://doi.org/10.1111/1365-2745.12901\u003c/li\u003e\n\u003cli\u003eStahl, M., Widney, S., \u0026amp; Craft, C. (2018). Tidal freshwater forests: Sentinels for climate change. Ecological Engineering, 116, 104\u0026ndash;109. https://doi.org/10.1016/j.ecoleng.2018.03.002\u003c/li\u003e\n\u003cli\u003eSward, R., Philbrick, A., Morreale, J., Baird, C. J., \u0026amp; Gedan, K. (2023). Shrub expansion in maritime forest responding to sea level rise. Frontiers in Forests and Global Change, 6. https://doi.org/10.3389/ffgc.2023.1167880\u003c/li\u003e\n\u003cli\u003eTate, A.S. \u0026amp; Battaglia, L.L., (2013). Community disassembly and reassembly following experimental storm surge and wrack application. Journal of Vegetation Science, 24(1), pp.46-57.\u003c/li\u003e\n\u003cli\u003eTemmerman, S., Meire, P., Bouma, T. et al. (2013). Ecosystem-based coastal defence in the face of global change. Nature 504, 79\u0026ndash;83. https://doi.org/10.1038/nature12859\u003c/li\u003e\n\u003cli\u003eThompson, J. A., J. C. Zinnert, and D. R. Young. (2017). Immediate effects of microclimate modification enhance native shrub encroachment. Ecosphere 8(2): e01687. 10.1002/ecs2.1687\u003c/li\u003e\n\u003cli\u003eTuraski, S.J. (2002). Spatial and temporal controls on saturated overland flow in a regularly flooded salt marsh. MS Thesis, University of Virginia Charlottesville, VA.\u003c/li\u003e\n\u003cli\u003eWalters, D. C., Carr, J. A., Hockaday, A., Jones, J. A., McFarland, E., Kovalenko, K. E., Kirwan, M. L., Cahoon, D. R., \u0026amp; Guntenspergen, G. R. (2021). Experimental Tree Mortality Does Not Induce Marsh Transgression in a Chesapeake Bay Low-Lying Coastal Forest. Frontiers in Marine Science, 8. https://doi.org/10.3389/fmars.2021.782643\u003c/li\u003e\n\u003cli\u003eWang, C., \u0026amp; S. Temmerman (2013), Does biogeomorphic feedback lead to abrupt shifts between alternative landscape states?: An empirical study on intertidal flats and marshes, J. Geophys. Res. Earth Surf., 118, 229\u0026ndash;240, doi:10.1029/2012JF002474.\u003c/li\u003e\n\u003cli\u003eWebb, E. L., Friess, D. A., Krauss, K. W., Cahoon, D. R., Guntenspergen, G. R., \u0026amp; Phelps, J. (2013). A global standard for monitoring coastal wetland vulnerability to accelerated sea-level rise. Nature Climate Change, 3(5), 458\u0026ndash;465. https://doi.org/10.1038/nclimate1756\u003c/li\u003e\n\u003cli\u003eWen, Yongli \u0026amp; Bernhardt, Emily \u0026amp; Deng, Wenbo \u0026amp; Liu, Wenjuan \u0026amp; Yan, Junxia \u0026amp; Baruch, Ethan \u0026amp; Bergemann, Christina. (2019). Salt effects on carbon mineralization in southeastern coastal wetland soils of the United States. Geoderma. 339. 31-39. 10.1016/j.geoderma.2018.12.035.\u003c/li\u003e\n\u003cli\u003eWeston, N.B., Vile, M.A., Neubauer, S.C., Velinsky, D.J., (2011). Accelerated microbial organic matter mineralization following salt-water intrusion into tidal freshwater marsh soils. Biogeochemistry 102, 131\u0026ndash;151\u003c/li\u003e\n\u003cli\u003eWilliams, K., Meads, M.V. \u0026amp; Sauerbrey, D.A. (1998), The roles of seedling salt tolerance and resprouting in forest zonation on the west coast of Florida, USA . Am. J. Bot., 85: 1745-1752. https://doi.org/10.2307/2446509\u003c/li\u003e\n\u003cli\u003eWilliams, K., Ewel, K. C., Stumpf, R. P., Putz, F. E., \u0026amp; Workman, T. W. (1999). Sea-level rise and coastal forest retreat on the west coast of Florida, USA. Ecology, 80(6), 2045\u0026ndash;2063. https://doi.org/10.2307/176677\u003c/li\u003e\n\u003cli\u003eWilliams, K., MacDonald, M., \u0026amp; Sternberg, S. L. (2003). Interactions of storm, drought, and sea-level rise on coastal forest: a case study. J. Coast. Res. 19, 1116\u0026ndash;1121.\u003c/li\u003e\n\u003cli\u003eWindham, L., \u0026amp; R. G. Lathrop. (1999). Effects of Phragmites australis (common reed) invasion on aboveground biomass and soil properties in a brackish tidal marsh of the Mullica River. Estuaries 22: 927\u0026ndash;93\u003c/li\u003e\n\u003cli\u003eWindham, L., \u0026amp; L. A. Meyerson. 2003. Effects of common reed (Phragmites australis) expansions on nitrogen dynamics of tidal marshes of the northeastern US. Estuaries 26, no. 2B:452\u0026ndash;464.\u003c/li\u003e\n\u003cli\u003eWhite, E. E., Ury, E. A., Bernhardt, E. S., \u0026amp; Yang, X. (2022). Climate change driving widespread loss of coastal forested wetlands throughout the North American Coastal Plain. \u003cem\u003eEcosystems, 25\u003c/em\u003e(4), 812\u0026ndash;827. https://doi.org/10.1007/s10021-021-00686-w\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Elevation, vegetation characteristics, and elevation change rates for each individual SET. Vegetation cover is expressed as a percent at each individual canopy height. Rates of surface accretion, net elevation change, and subsurface change expressed as mm yr \u003csup\u003e-1\u003c/sup\u003e. A negative subsurface change indicates subsidence and a positive value indicates subsurface expansion\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"658\" height=\"372\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Differences among forest and transition zones for surface accretion, net elevation change, and subsurface change expressed as mm yr\u0026nbsp;\u003csup\u003e-1\u003c/sup\u003e. Contribution of shallow root zone processes are calculated as the difference between total subsurface change and deep subsurface change (derived from shallow SET benchmarks). A negative subsurface value indicates subsidence, while a positive value indicates belowground expansion. Statistical differences between zones (determined via Tukey HSD) are shown in superscript\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" style=\"width: 603px; height: 312.408px;\" width=\"603\" height=\"312.408\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e P-values from linear models comparing net elevation change, accretion, and subsurface change with environmental drivers (e.g., litter depth, soil moisture). P-values represent the significance of each environmental variable in predicting elevation change responses. Significant values (p \u0026lt; 0.1) are highlighted in bold. Here we use \u0026alpha; = 0.1 to reduce the risk of missing potential correlations, as this was an exploratory analysis to identify patterns for further investigation\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cimg src=\"data:image/png;base64,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\" style=\"width: 705px; height: 134.196px;\" width=\"705\" height=\"134.196\"\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Accretion, Coastal Landscape Change, Marsh Migration, Morella cerifera, Phragmites australis, Surface Elevation Table","lastPublishedDoi":"10.21203/rs.3.rs-7236740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7236740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe future of coastal marshes depends in part on their ability to migrate inland with sea level rise into coastal forests, but projections of land conversion commonly assume that migration occurs over a static terrestrial topography. Here, we assess the assumption of a static topography in a rapidly retreating, coastal upland forest by measuring surface accretion, subsurface expansion or subsidence, and net elevation change across a gradient of salinity and inundation stress. We found that salt-affected forest soils were largely stable over the 5-year study period, exhibiting limited elevation change (-0.24\u0026ndash;1.5 mm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in most stages of forest health. This stability was achieved by relatively equal and offsetting rates of accretion and subsidence that were each greater than expected. Surface accretion rates were positively correlated with the total cover of \u003cem\u003eMorella cerifera\u003c/em\u003e shrubs rather than \u003cem\u003ePhragmites australis\u003c/em\u003e, an invasive reed that dominates retreating coastal forests in the mid-Atlantic. Subsidence was greatest in higher elevation forest areas and attributed to root zone collapse that occurred prior to ongoing tree mortality. Net elevation-change rates were highest in the marsh-forest transition zone (2.94 mm yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), which is characterized by dead trees and fully developed marsh vegetation. Though this study measured relatively low elevation change rates in most portions of a retreating coastal forest, similarity between transition zone and adjacent salt marsh rates suggest that elevation change rates respond quickly to marsh migration and are elevated by the time of extensive tree mortality.\u003c/p\u003e","manuscriptTitle":"Drivers of Elevation Change in a Retreating Coastal Forest","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-08 16:07:51","doi":"10.21203/rs.3.rs-7236740/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-08-05T21:13:24+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-05T16:03:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Estuaries and Coasts","date":"2025-08-03T15:05:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T03:31:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Estuaries and Coasts","date":"2025-07-28T15:07:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"estuaries-and-coasts","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esco","sideBox":"Learn more about [Estuaries and Coasts](https://www.springer.com/journal/12237)","snPcode":"12237","submissionUrl":"https://www.editorialmanager.com/esco/","title":"Estuaries and Coasts","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9893fa70-3b28-43f0-999e-4f7cfba5ef94","owner":[],"postedDate":"August 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:03:58+00:00","versionOfRecord":{"articleIdentity":"rs-7236740","link":"https://doi.org/10.1007/s12237-025-01624-y","journal":{"identity":"estuaries-and-coasts","isVorOnly":false,"title":"Estuaries and Coasts"},"publishedOn":"2025-12-09 15:58:36","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-08-08 16:07:51","video":"","vorDoi":"10.1007/s12237-025-01624-y","vorDoiUrl":"https://doi.org/10.1007/s12237-025-01624-y","workflowStages":[]},"version":"v1","identity":"rs-7236740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7236740","identity":"rs-7236740","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}