A process based model of cohesive sediment resuspension under bioturbators' influence

Abstract

Macrozoobenthos may affect sediment stability and erodibility via their bioturbating activities, thereby impacting both the short-and long-term development of coastal morphology. Process-based models accounting for the effect of bioturbation are needed for the modelling of erosion dynamics. With this work, we explore whether the fundamental allometric principles of metabolic activity scaling with individual and population size may provide a framework to derive general patterns of bioturbation effect on cohesive sediment resuspension. Experimental flumes were used to test this scaling approach across different species of marine, soft-sediment bioturbators. The collected dataset encompasses a range of bioturbators functional diversity, individual densities, body sizes and overall population metabolic rates. Measurements were collected on a range of hydrodynamic stress from 0.02 to 0.25 Pa. Overall, we observed that bioturbators are able to slightly reduce the sediment resuspension at low hydrodynamic stress, whereas they noticeably enhance it at higher levels of stress. Along the whole hydrodynamic stress gradient, the quantitative effect of bioturbators on sediment resuspension can be efficiently described by the overall metabolic rate of the bioturbating benthic communities, with significant variations across the bioturbators' taxonomic and functional diversity. One of the tested species (the gallery-builder Polychaeta Hediste diversicolor) had an effect that was partially deviating from the general trend, being able to markedly reduce sediment resuspension at low hydrodynamic stress compared to other species. By combining this trend with hydrodynamic forces, we were able to produce a process-based model of biota-mediated sediment resuspension.

Benthic species as mud patrol ‐ modelled effects of bioturbators and biofilms on large‐scale estuarine mud and morphology

“…(N m −2 ) of the sediment (Cozzoli et al, 2019;Wrede et al, 2018). As a result, the sediment flux of cohesive sediment computed by the Partheniades-Krone formulation (eq.…”

Understanding Environmental Changes in Temperate Coastal Seas: Linking Models of Benthic Fauna to Carbon and Nutrient Fluxes

“…As erosion is fundamentally linked to lateral hydrodynamic processes, it is rarely included in one-dimensional (vertical) sediment reactive-transport models. Additionally, the same animal can have both stabilizing and destabilizing effects on the sediment, making it difficult to include such effects in models (Le Hir et al, 2007;Cozzoli et al, 2019). However, there are several examples of models coupling resuspension of sediment and benthic microalgae to fauna, including effects of infaunal bivalves (Willows et al, 1998;Wood and Widdows, 2002;Orvain et al, 2012;Rakotomalala et al, 2015) and epifaunal snail tracks (Orvain et al, 2003(Orvain et al, , 2012.…”

“…van Prooijen et al ( 2011) developed a more process-based model for cohesive (muddy) sediment resuspension by deposit-feeding activities of L. balthica based on the individual rate, area and maximum depth of feeding and density of individuals. A recent study by Cozzoli et al (2019) proposed another process-based model for cohesive sediment resuspension mediated by a variety of bioturbators and demonstrated metabolic rate as a general indicator of impacts of biological activities on sediment resuspension. They concluded that the overall metabolic rate of the bioturbators was well correlated with changes in hydrodynamic energy gradients of suspended cohesive sediments, and a better predictor than size, density or biomass of the fauna.…”

“…Traitbased descriptions of pelagic groups are emerging in ecosystem models (Follows et al, 2007;Andersen et al, 2015). In general, metabolism seems to be a better predictor of biogenic mixing and resuspension than biomass, advocating the modeling approaches of Blackford (1997) and Cozzoli et al (2019). As metabolic rate is increasingly recognized as master parameter for the 'pace of life' (Gillooly et al, 2001;Brown et al, 2004b;McClain et al, 2012), this provides a promising avenue for parameterization of the 'pace of bioturbation'.…”

A Novel Instrument for Bed Dynamics Observation Supports Machine Learning Applications in Mangrove Biogeomorphic Processes

“…In addition, suspended sediment concentration is not measured in the current study due to instrument shortage, but it is a relevant input variable influencing bed dynamics (Dai et al, 2016(Dai et al, , 2018. Thus, a general machine learning predictor for intertidal morphodynamics would benefit from (1) data sets of large spatiotemporal coverage to eliminate class imbalance, (2) comprehensive data sets including all relevant variables including also biological processes (e.g., Cozzoli et al, 2019), and (3) adoption of more advanced machine learning techniques such as Recurrent Neural networks and Long Short-Term Memory (LSTM) networks (Kratzert et al, 2018;Pape et al, 2007Pape et al, , 2010. These suggestions may provide a way forward for machine learning applications in analyzing intertidal bed level dynamics data.…”