Impact of microphytobenthos on the sediment biogeochemical cycles: A modeling approach

Hochard, Sébastien; Pinazo, Christel; Grenz, Christian; Evans, Jessica L. Burton; Pringault, Olivier

doi:10.1016/j.ecolmodel.2010.04.002

Cited by 52 publications

(45 citation statements)

References 51 publications

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“…In estuarine environments, high spatial resolution is often required to capture hydrodynamic or bathymetric variability; if one is simply interested in algal physiology or plankton community interactions, a one-dimensional model may be sufficient, as with many applications of NPZD models (Fasham et al 1990; Oguz et al 2000; Soetaert and Middelburg 2009). Other examples of models with low spatial, but high temporal resolution include box models (Webster and Harris 2004) and sediment biogeochemical models (Hochard et al 2010; Brady et al 2013). …”

Section: Balancing Spatial and Temporal Resolutionmentioning

confidence: 99%

Progress and Challenges in Coupled Hydrodynamic-Ecological Estuarine Modeling

Ganju

Brush²,

Rashleigh

et al. 2015

Estuaries and Coasts

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Numerical modeling has emerged over the last several decades as a widely accepted tool for investigations in environmental sciences. In estuarine research, hydrodynamic and ecological models have moved along parallel tracks with regard to complexity, refinement, computational power, and incorporation of uncertainty. Coupled hydrodynamic-ecological models have been used to assess ecosystem processes and interactions, simulate future scenarios, and evaluate remedial actions in response to eutrophication, habitat loss, and freshwater diversion. The need to couple hydrodynamic and ecological models to address research and management questions is clear, because dynamic feedbacks between biotic and physical processes are critical interactions within ecosystems. In this review we present historical and modern perspectives on estuarine hydrodynamic and ecological modeling, consider model limitations, and address aspects of model linkage, skill assessment, and complexity. We discuss the balance between spatial and temporal resolution and present examples using different spatiotemporal scales. Finally, we recommend future lines of inquiry, approaches to balance complexity and uncertainty, and model transparency and utility. It is idealistic to think we can pursue a “theory of everything” for estuarine models, but recent advances suggest that models for both scientific investigations and management applications will continue to improve in terms of realism, precision, and accuracy.

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Section: Balancing Spatial and Temporal Resolutionmentioning

confidence: 99%

Progress and Challenges in Coupled Hydrodynamic-Ecological Estuarine Modeling

Ganju

Brush²,

Rashleigh

et al. 2015

Estuaries and Coasts

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“…Light incubations would have been valuable to examine the interactions of benthic microalgae and N cycling. However, our DNF data were extrapolated based on 12 hour days to reflect our assumption of very low rates during the day due to both competition with benthic microalgae for N and increased oxygen concentrations (Tobias 2007, Hochard et al 2010). …”

Section: Methodological Constraintsmentioning

confidence: 99%

Habitat-specific distinctions in estuarine denitrification affect both ecosystem function and services

2011

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Abstract. Resource limitation controls the base of food webs in many aquatic ecosystems. In coastal ecosystems, nitrogen (N) has been found to be the predominant limiting factor for primary producers. Due to the important role nitrogen plays in determining ecosystem function, understanding the processes that modulate its availability is critical. Shallow-water estuarine systems are highly heterogeneous. In temperate estuaries, multiple habitat types can exist in close proximity to one another, their distribution controlled primarily by physical energy, tidal elevation and geomorphology. Distinctions between these habitats such as rates of primary productivity and sediment characteristics likely affect material processing. We used membrane inlet mass spectrometry to measure changes in N 2 flux (referred to here as denitrification) in multiple shallow-water estuarine habitats through an annual cycle. We found significantly higher rates of denitrification (DNF) in structured habitats such as submerged aquatic vegetation, salt marshes and oyster reefs than in intertidal and subtidal flats. Seasonal patterns were also observed, with higher DNF rates occurring in the warmer seasons. Additionally, there was an interaction between habitat type and season that we attributed to the seasonal patterns of enhanced productivity in individual habitat types. There was a strong correlation between denitrification and sediment oxygen demand (SOD) in all habitats and all seasons, suggesting the potential to utilize SOD to predict DNF. Denitrification efficiency was also higher in the structured habitats than in the flats. Nitrogen removal by these habitats was found to be an important contributor to estuarine ecosystem function. The ecosystem service of DNF in each habitat was evaluated in US dollars using rates from a regional nutrient-offset market to determine the cost to replace N through management efforts. Habitat-specific values of N removal ranged from approximately three thousand U.S. dollars per acre per year in the submerged aquatic vegetation to approximately four hundred U.S. dollars per acre per year in the subtidal flat. Because of the link between habitat type and processes such as DNF, changes in habitat area and distribution will have consequences for both ecosystem function and the delivery of ecosystem services.

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“…This implies that the rates of dark O 2 flux represent a true measure of respiration and that dark and light rates of heterotrophic respiration are the same. Temporal changes in the depth distribution of redox processes resulting from diel changes in O 2 penetration may affect net effluxes during changes from dark to light conditions (Hochard et al 2010), though comparison of O 2 -based benthic microalgal photosynthesis to CO 2 -based estimates have shown good agreement (Newell et al 2002). Daily rates of benthic microalgal production were calculated as the product of the photosynthetic rate and day length.…”

Section: Benthic Microalgal Productivity Calculationsmentioning

confidence: 99%