Predicting benthic macroalgal abundance in shallow coastal lagoons from geomorphology and hydrologic flow patterns

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 km(2)). 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 km(2)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 (tau) 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 (tau= 0.01-1.07). Models tested tau along with mean elevation, fetch, and water residence time as predictors of macroalgal abundance.tau, 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.