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Abstract Foundation species, such as mangroves, saltmarshes, kelps, seagrasses, and oysters, thrive within suitable environmental envelopes as narrow ribbons along the land–sea margin. Therefore, these habitat‐forming species and resident fauna are sensitive to modified environmental gradients. For oysters, many estuaries impacted by sea‐level rise, channelization, and municipal infrastructure are experiencing saltwater intrusion and water‐quality degradation that may alter reef distributions, functions, and services. To explore decadal‐scale oyster–reef community patterns across a temperate estuary in response to environmental change, we resampled reefs in the Newport River Estuary (NRE) during 2013–2015 that had previously been studied during 1955–1956. We also coalesced historical NRE reef distribution (1880s–2015), salinity (1913–2015), and water‐quality‐driven shellfish closure boundary (1970s–2015) data to document environmental trends that could influence reef ecology and service delivery. Over the last 60–120 years, the entire NRE has shifted toward higher salinities. Consequently, oyster–reef communities have become less distinct across the estuary, manifest by 20%–27% lower species turnover and decreased faunal richness among NRE reefs in the 2010s relative to the 1950s. During the 2010s, NRE oyster–reef communities tended to cluster around a euhaline, intertidal‐reef type more so than during the 1950s. This followed faunal expansions farther up estuary and biological degradation of subtidal reefs as NRE conditions became more marine and favorable for aggressive, reef‐destroying taxa. In addition to these biological shifts, the area of suitable bottom on which subtidal reefs persist (contracting due to up‐estuary intrusion of marine waters) and support human harvest (driven by water quality, eroding from up‐estuary) has decreased by >75% since the natural history of NRE reefs was first explored. This “coastal squeeze” on harvestable subtidal oysters (reduced from a 4.5‐km to a 0.75‐km envelope along the NRE's main axis) will likely have consequences regarding the economic incentives for future oyster conservation, as well as the suite of services delivered by remaining shellfish reefs (e.g., biodiversity maintenance, seafood supply). More broadly, these findings exemplify how “squeeze” may be a pervasive concern for biogenic habitats along terrestrial or marine ecotones during an era of intense global change.
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Abstract Supra‐permafrost submarine groundwater discharge (SGD) in the Arctic is potentially important for coastal biogeochemistry and will likely increase over the coming decades owing to climate change. Despite this, land‐to‐ocean material fluxes via SGD in Arctic environments have seldom been quantified. This study used radium (Ra) isotopes to quantify SGD fluxes to an Arctic coastal lagoon (Simpson Lagoon, Alaska) during five sampling periods between 2021 and 2023. Using a Ra mass balance model, we found that the SGD water flux was substantial and dependent on environmental conditions. No measurable SGD was detected during the spring sampling period (June 2022), when the lagoon was partially ice‐covered. During ice‐free periods, the main driver of SGD in this location is wind‐driven lagoon water level changes, not tides, which control surface water recirculation through sediments along the lagoon boundary. A combination of wind strength and direction led to low SGD fluxes in July 2022, with an SGD flux of (6 ± 3) × 106 m3 d−1, moderate fluxes in August 2021 and July 2023, which had an average flux of (17 ± 9) × 106 m3 d−1, and high fluxes in October 2022, at (79 ± 16) × 106 m3 d−1. This work demonstrates how soil and environmental conditions in the Arctic impact Ra mobilization, laying a foundation for future SGD studies in the Arctic and shedding light on the major processes driving Ra fluxes in this important environment.
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Coastal agricultural zones are experiencing salinization due to accelerating rates of sea-level rise, causing reduction in crop yields and abandonment of farmland. Understanding mechanisms and drivers of this seawater intrusion (SWI) is key to mitigating its effects and predicting future vulnerability of groundwater resources to salinization. We implemented a monitoring network of pressure and specific conductivity (SC) sensors in wells and surface waters to target marsh-adjacent agricultural areas in greater Dover, Delaware. Recorded water levels and SC over a period of three years show that the mechanisms and timescales of SWI are controlled by local hydrology, geomorphology, and geology. Monitored wells did not indicate widespread salinization of deep groundwater in the surficial aquifer. However, monitored surface water bodies and shallow (<4m deep) wells did show SC fluctuations due to tides and storm events, in one case leading to salinization of deeper (18m deep) groundwater. Seasonal peaks in SC occurred during late summer months. Seasonal and interannual variation of SC was also influenced by relative sea level. The data collected in this study data highlight the mechanisms by which surface water-groundwater connections lead to salinization of aquifers inland, before SWI is detected in deeper groundwater nearer the coastline. Sharing of our data with stakeholders has led to the implementation of SWI mitigation efforts, illustrating the importance of strategic monitoring and stakeholder engagement to support coastal resilience.more » « less
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Abstract Submarine groundwater discharge (SGD) is an important driver of coastal biogeochemical budgets worldwide. Radon (222Rn) has been widely used as a natural geochemical tracer to quantify SGD, but field measurements are time consuming and costly. Here, we use deep learning to predict coastal seawater radon in SGD‐impacted regions. We hypothesize that deep learning could resolve radon trends and enable preliminary insights with limited field observations of groundwater tracers. Two deep learning models were trained on global coastal seawater radon observations (
n = 39,238) with widely available inputs (e.g., salinity, temperature, water depth). The first model used a one‐dimensional convolutional neural network (1D‐CNN‐RNN) framework for site‐specific gap filling and producing short‐term future predictions. A second model applied a fully connected neural network (FCNN) framework to predict radon across geographically and hydrologically diverse settings. Both models can predict observed radon concentrations withr 2 > 0.76. Specifically, the FCNN model offers a compelling development because synthetic radon tracer data sets can be obtained using only basic water quality and meteorological parameters. This opens opportunities to attain radon data from regions with large data gaps, such as the Global South and other remote locations, allowing for insights that can be used to predict SGD and plan field experiments. Overall, we demonstrate how field‐based measurements combined with big‐data approaches such as deep learning can be utilized to assess radon and potentially SGD beyond local scales.
