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Barrier coastlines and their associated ecosystems are rapidly changing. Barrier islands/spits, marshes, bays, and coastal forests are all thought to be intricately coupled, yet an understanding of how morphologic change in one part of the system affects the system altogether remains limited. Here we explore how sediment exchange controls the migration of different ecosystem boundaries and ecosystem extent over time using a new coupled model framework that connects components of the entire barrier landscape, from the ocean shoreface to mainland forest. In our experiments, landward barrier migration is the primary cause of back‐barrier marsh loss, while periods of barrier stability can allow for recovery of back‐barrier marsh extent. Although sea‐level rise exerts a dominant control on the extent of most ecosystems, we unexpectedly find that, for undeveloped barriers, bay extent is largely insensitive to sea‐level rise because increased landward barrier migration (bay narrowing) offsets increased marsh edge erosion (bay widening).

To understand surface biogeophysical processes, accurately evaluating the geographical and temporal fluctuations of soil moisture is crucial. It is well known that the surface soil moisture content (SMC) affects soil reflectance at all solar spectrum wavelengths. Therefore, future satellite missions, such as the NASA Surface Biology and Geology mission, will be essential for mapping and monitoring global soil moisture changes. Our study compares two widely used moisture retrieval models: the multilayer radiative transfer model of soil reflectance (MARMIT) and the soil water parametric (SWAP)‐Hapke model. We evaluated the SMC retrieval accuracy of these models using unmanned aerial systems (UAS) hyperspectral imagery and goniometer hyperspectral data. Laboratory analysis employed hyperspectral goniometer data of sediment samples from four locations reflecting diverse environments, while field validation used hyperspectral UAS imaging and coordinated ground truth collected in 2018 and 2019 from a barrier island beach at the Virginia Coast Reserve Long‐Term Ecological Research site. The (SWAP)‐Hapke model achieves comparable accuracy to MARMIT using laboratory hyperspectral data but is less accurate when applied to UAS hyperspectral imagery than the MARMIT model. We proposed a modified version of the (SWAP)‐Hapke model, which achieves better results than MARMIT when applied to laboratory spectral measurements; however, MARMIT's performance is still more accurate when applied to UAS imagery. These results are likely due to differences in the models' descriptions of multiply‐scattered light and MARMIT's more detailed description of air‐water interactions.

Understanding and attributing changes to water quality is essential to the study and management of coastal ecosystems and the ecological functions they sustain (e.g., primary productivity, predation, and submerged aquatic vegetation growth). However, describing patterns of water clarity—a key aspect of water quality—over meaningful scales in space and time is challenged by high spatial and temporal variability due to natural and anthropogenic processes. Regionally tuned satellite algorithms can provide a more complete understanding of coastal water clarity changes and drivers. In this study, we used open‐access satellite data and low‐cost in situ methods to improve estimates of water clarity in an optically complex coastal water body. Specifically, we created a remote sensing water clarity product by compiling Landsat‐8 and Sentinel‐2 reflectance data with long‐term Secchi depth measurements at 12 sites over 8 years in a shallow turbid coastal lagoon system in Virginia, USA. Our satellite‐based model explained ∼33% of the variation in in situ water clarity. Our approach increases the spatiotemporal coverage of in situ water clarity data and improves estimates from bio‐optical algorithms that overpredicted water clarity. This could lead to a better understanding of water clarity changes and drivers to better predict how water quality will change in the future.

Restoration aims to reverse the global declines of foundation species, but it is unclear how project attributes, the physical setting, and antecedent conditions affect restoration success. In coastal seas worldwide, oyster reef restoration is increasing to counter historical habitat destruction and associated declines in fisheries production and biodiversity. Yet, restoration outcomes are highly variable and the factors that enhance oyster production and nekton abundance and diversity on restored reefs are unresolved. To quantify the drivers of oyster restoration success, we used meta‐analysis to synthesize data from 158 restored reefs paired with unstructured habitats along the United States Gulf and Atlantic coasts. The average recovery of oyster production was 65% greater in subtidal (vs. intertidal) zones, 173% greater in polyhaline (vs. mesohaline) environments and increased with tidal range, demonstrating that physical conditions can strongly influence the restoration success of foundation species. Additionally, restoration increased the relative abundance and richness of nektonic fishes and invertebrates over time as reefs aged (at least 8 years post‐construction). Thus, the restoration benefits for provisioning habitat and enhancing biodiversity accrue over time, highlighting that restoration projects need multiple years to maximize ecosystem functions. Furthermore, long‐term monitoring of restored and control sites is needed to assess restoration outcomes and associated drivers. Last, our work reveals data constraints for several potential drivers of restoration outcomes, including reef construction material, reef dimensions, harvest pressure and disease prevalence. More experimental and observational studies are needed to target these factors and measure them with consistent methods across studies. Our findings indicate that the assisted recovery of foundation species yields several enhancements to ecosystem services, but such benefits are mediated by time and environmental conditions.

Restoration of foundation species promises to reverse environmental degradation and return lost ecosystem services, but a lack of standardized evaluation across projects limits understanding of recovery, especially in marine systems. Oyster reefs are restored to reverse massive global declines and reclaim valuable ecosystem services, but the success of these projects has not been systematically and comprehensively quantified. We synthesized data on ecosystem services associated with oyster restoration from 245 pairs of restored and degraded reefs and 136 pairs of restored and reference reefs across 3500 km of U.S. Gulf of Mexico and Atlantic coastlines. On average, restoration was associated with a 21‐fold increase in oyster production (mean log response ratio = 3.08 [95% confidence interval: 2.58–3.58]), 34–97% enhancement of habitat provisioning (mean community abundance = 0.51 [0.41–0.61], mean richness = 0.29 [0.19–0.39], and mean biomass = 0.69 [0.39–0.99]), 54% more nitrogen removal (mean = 0.43 [0.13–0.73]), and 89–95% greater sediment nutrients (mean = 0.67 [0.27–1.07]) and organic matter (mean = 0.64 [0.44–0.84]) relative to degraded habitats. Moreover, restored reefs matched reference reefs for these ecosystem services. Our results support the continued and expanded use of oyster restoration to enhance ecosystem services of degraded coastal systems and match many functions provided by reference reefs.

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

Global declines of foundation species have reduced ecological function at population, community, and ecosystem levels. Restoration of foundation species promises to counter such losses, despite unknown recovery timelines, undefined benchmarks, and uncertainty about whether restored ecosystems approximate natural ones. Here, we demonstrate through a 15‐year large‐scale experiment in coastal Virginia, USA, that restored oyster reefs can quickly recover multiple ecological functions and match natural reefs. Specifically, abundances of oysters and a key crab mesopredator on restored reefs equaled reference reefs in approximately 6 years, indicating that restoration can initiate rapid, sustained recovery of foundation species and associated consumers. As reefs matured and accrued biomass, they became more temporally stable, suggesting that restoration can increase resilience and may stabilize those ecosystem processes that scale with foundation species biomass. Together, these results demonstrate that restoration can catalyze rapid recovery of imperiled coastal foundation species, reclaim lost community interactions, and help reverse decades of degradation.

This paper presents a new empirical model, called the cumulative storm impact index (CSII), that quantifies the impact of coastal storms on sandy beaches. The new model utilizes user‐defined storm data to incorporate both individual storm magnitude and the cumulative effect of successive storms into an index, which is a proxy for beach erosion at a given time. Applying this model to long‐term water‐level data from a Virginia tide gauge showed that the greatest storm impact resulted not from the larger individual storms, such as the Ash Wednesday nor'easter of 1962, the “Perfect Storm” of 1991, or Hurricane Sandy of 2012, but rather from especially stormy winter seasons that occurred during the twenty‐first century. Additionally, the CSII model uncovered a trend—not detectable by single storm impact analyses—toward greater storm impacts, which began c. 1980 and continued to the present day. Finally, comparative analyses using wave power as a storm index shows CSII can capture decadal or seasonal scale storminess. We expect this model to have utility in many areas of the coastal sciences and engineering, including developing holistic response models, quantifying erosion potential at other locations, and managing coastal ecosystems.

The relationship between biodiversity and stability, or its inverse, temporal variability, is multidimensional and complex. Temporal variability in aggregate properties, like total biomass or abundance, is typically lower in communities with higher species diversity (i.e., the diversity–stability relationship [DSR]). At broader spatial extents, regional‐scale aggregate variability is also lower with higher regional diversity (in plant systems) and with lower spatial synchrony. However, focusing exclusively on aggregate properties of communities may overlook potentially destabilizing compositional shifts. It is not yet clear how diversity is related to different components of variability across spatial scales, nor whether regional DSRs emerge across a broad range of organisms and ecosystem types. To test these questions, we compiled a large collection of long‐term metacommunity data spanning a wide range of taxonomic groups (e.g., birds, fish, plants, invertebrates) and ecosystem types (e.g., deserts, forests, oceans). We applied a newly developed quantitative framework for jointly analyzing aggregate and compositional variability across scales. We quantified DSRs for composition and aggregate variability in local communities and metacommunities. At the local scale, more diverse communities were less variable, but this effect was stronger for aggregate than compositional properties. We found no stabilizing effect of γ‐diversity on metacommunity variability, but β‐diversity played a strong role in reducing compositional spatial synchrony, which reduced regional variability. Spatial synchrony differed among taxa, suggesting differences in stabilization by spatial processes. However, metacommunity variability was more strongly driven by local variability than by spatial synchrony. Across a broader range of taxa, our results suggest that high γ‐diversity does not consistently stabilize aggregate properties at regional scales without sufficient spatial β‐diversity to reduce spatial synchrony.

Storm surge has the potential to significantly increase suspended sediment flux to microtidal marshes. However, the overall effects of storm surge on microtidal marsh deposition have not been well quantified, with most modeling studies focusing on regular (astronomical) tidal flooding. Here we applied the Delft3D model to a microtidal bay‐marsh complex in Hog Bay, Virginia to quantify the contributions of storm surge to marsh deposition. We validated the model using spatially distributed hydrodynamic and suspended sediment data collected from the site and ran model simulations under different storm surge conditions with/without storm‐driven water level changes. Our results show that episodic storm surge events occurred 5% of the time at our study site, but contributed 40% of marsh deposition during 2009–2020. Our simulations illustrate that while wind‐driven waves control sediment resuspension on tidal flats, marsh deposition during storms was largely determined by tidal inundation associated with storm‐driven water levels. A moderate storm surge event can double sediment flux to most marshes around the bay and deliver more sediment to the marsh interior compared to simulations that include wind waves but not storm surge variations in water levels. Simulations of bay and marsh response to different storm surge events with varying magnitude of storm surge intensity reveal that total marsh deposition around the bay increased linearly with storm surge intensity, suggesting that future changes to storm magnitude and/or frequency would have significant implications for sediment supply to marshes at our study site.