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Seagrass meadows are important carbon sinks in the global coastal carbon cycle yet are also among the most rapidly declining marine habitats. Their ability to sequester carbon depends on flow–sediment–vegetation interactions that facilitate net deposition, as well as high rates of primary production. However, the effects of seasonal and episodic variations in seagrass density on net sediment and carbon accumulation have not been well quantified. Understanding these dynamics provides insight into how carbon accumulation in seagrass meadows responds to disturbance events and climate change. Here, we apply a spatially resolved sediment transport model that includes coupling of seagrass effects on flow, waves, and sediment resuspension in a seagrass meadow to quantify seasonal rates of sediment and carbon accumulation in the meadow. Our results show that organic carbon accumulation rates were largely determined by sediment accumulation and that they both changed non‐linearly as a function of seagrass shoot density. While seagrass meadows effectively trapped sediment at meadow edges during spring–summer growth seasons, during winter senescence low‐density meadows (< 160 shoots m−2) were erosional with rates sensitive to density. Small variations in winter densities resulted in large changes in annual sediment and carbon accumulation in the meadow; meadow‐scale (hundreds of square meters) summer seagrass dieback due to marine heatwaves can result in annual erosion and carbon loss. Our findings highlight the strong temporal and spatial variability in sediment accumulation within seagrass meadows and the implications for annual sediment carbon burial rates and the resilience of seagrass carbon stocks under future climate change. [ABSTRACT FROM AUTHOR]

Macroalgae structure coastal ecosystems affecting metabolism, nutrient dynamics, and food webs. Spatially explicit prediction of macroalgal abundance is critical for understanding coastal ecosystems and trajectories. However, models of macroalgal distribution tend to be mechanistic and generalize poorly, or biogeographic and too coarse to use over spatial scales most appropriate to ecosystem research and management (1–100 km2). Our objective was to develop spatial distribution models for benthic macroalgae in soft‐sediment environments. We compared macroalgal abundance quantified as percent cover, with environmental drivers on 1 ha intertidal flats in a > 900 km2 lagoon system along the Atlantic Coast of Virginia, U.S.A. Physical drivers of macroalgae (e.g., depth‐mediated light availability, exposure to waves) are related to bed morphology. We developed a novel topographic index (τ) to determine whether bed morphology predicts macroalgal abundance. This topographic index described variation in elevation occurring over spatial scales relevant to macroalgae, ranging from smooth to hummocky (τ = 0.01–1.07). Models tested τ along with mean elevation, fetch, and water residence time as predictors of macroalgal abundance. τ, and the interaction with water residence time, were most strongly related to macroalgal abundance. Hummocky flats accumulated less macroalgae than smoother flats, but exceptions occurred with short residence times. Model error (root mean square error) was low, varying between 8% and 18% across models. These models, based on readily measured physical features, are a useful approach for assessing macroalgal abundance in relation to shoreline hardening, species invasions, sea‐level rise, and changing sedimentation affecting coastal ecosystems. [ABSTRACT FROM AUTHOR]

We investigated the size and shape of the area on the sediment surface, the so-called footprint, that contributes to the flux in subaqueous eddy correlation measurements. Tracer tracking simulations were performed for a dissolved conservative tracer released from the sediment surface into a current-driven flow not affected by density stratifications and surface waves. Simulations revealed that the footprint length (l) can be calculated as l = -2.783 - 158.7h + 159.2h² - 120.8h log(z0) (all units in m) for eddy correlation measurements heights (h) between 0.05 and 0.3 m above the sediment surface and for sediment surface roughness parameter (z0) values between 7.04 x 10-6 and 0.01 m. The upstream distance (xmax) to the location that contributes the strongest flux signal can likewise be estimated as xmax = -0.09888 - 11.53h + 10.25h² - 6.650h log (z0). Because vertical turbulent mixing scales with mean current velocity, l and xmax are independent of current velocity. The footprint width (w) can be calculated as w = 6.531h. These expressions were developed for water depths (H) of H > 27h. In the depth interval 6.7h > H > 27h, l can be calculated by multiplying the length, as given above, by the factor 1 + 8.347exp(-0.2453 H/h), whereas xmax is independent of H. For H > 6.7h, the tracer transfer rate over the air-water interface controls the size and shape of the footprint. All expressions are valid for isotropic turbulence, but as a first-order estimate, the expressions for l and xmax also hold for anisotropic conditions. In contrast, w scales with sqrt(Ey/Ex), where Ey and Ez are the transverse and the vertical eddy diffusivity, respectively. Finally, we describe how site-specific values of z0 and levels of anisotropy in a turbulent near-bottom flow can be extracted directly from eddy correlation measurements [ABSTRACT FROM AUTHOR]