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Impacts of invasive species are often context specific due to varying ecological interactions. Physical structure of environments hosting invaders is also potentially important but has received limited attention. An invasive macroalga,Agarophyton vermiculophyllum, has spread across the northern hemisphere with mixed positive, neutral and negative effects on resident species.Agarophytoncolonizes mudflats that vary in topography due to interactions of sediments with hydrodynamic forces. We tested the hypothesis that mudflat geomorphology moderates the effect ofAgarophytonon shorebirds and invertebrates. We surveyed 30 mudflats in the Virginia Coast Reserve quantifying elevation and topography. Invertebrate and bird abundances were also quantified. Mudflat geomorphology ranged from smooth to hummocky and was correlated with invertebrate and shorebird abundance and interactions based on piecewise structural equation models. After accounting for geomorphology,Agarophytonhad little effect on invertebrate abundance. Shorebird numbers were differentially influenced by mudflat topography, with positive correlations to invertebrates (worms) on smooth mudflats, and to macroalgae on hummocky mudflats. These differences are likely to be due to sediment properties in interaction with structural changes induced byAgarophytonmats that affect prey accessibility for birds. Even on apparently simple mudflats, geomorphic structure emerged as important, modifying invasive species impacts and differentially influencing consumers.

Determining when a disturbance has occurred, its severity, and when the system recovered, is important to numerous questions in the aquatic sciences. This problem can be conceptualized as the timing and degree of perturbation from a typical state, and when the system returns to that typical state. We present an algorithm for detecting disturbance and recovery designed for high‐frequency time series, e.g., data produced by automated sampling devices in instrumented buoys and flux towers. The algorithm quantifies differences in the empirical cumulative distribution functions of moving windows over reference and evaluation periods, and is sensitive to changes in the mean, variance, and higher statistical moments. Tests on simulated data show it accurately identifies disturbance and recovery. Three case studies illustrate the application of our algorithm in different empirical settings. A case study on dissolved oxygen in a Florida, USA estuary following a hurricane identified the disturbance and recovery 73 d later. A case study on air temperature and net ecosystem exchange in the Florida everglades identified cold snaps coinciding with periods of reduced carbon uptake. A case study on rotifer abundance following zebra mussel invasion in the Hudson River, NY showed rotifer collapse following invasion and recovery over a decade later. Methods such as ours can improve understanding response to disturbance and facilitate comparative and synthetic study of disturbance impacts across ecosystems.

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 km²lagoon 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 testedτalong 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.