Modelling the spatial heterogeneity of ecological processes in an intertidal estuarine bay: dynamic interactions between bivalves and phytoplankton

Abstract

Spatial patterns in ecological communities result from a combination of physical and biological factors. In an estuarine intertidal bay, spatial differences have been found in the structure of phytoplanktonic communities and in the biological performance of cultivated oysters. It has been hypothesised that trophic heterogeneity exists, although the mechanisms controlling it remain undefined. Spatial and temporal interactions in the structure of phytoplanktonic biomass and in the biological performance of cultivated oysters were highlighted in this estuarine intertidal bay using a 2-dimensional hydrodynamic model coupled to a nutrient -phytoplankton-zooplankton bivalve food web model. The coupled models allowed a reproduction in space and time of variations in the main variables (i.e. nutrients, chlorophyll a (chl a) and bivalve growth and reproduction). Spatial patterns of chl a at the bay scale showed a dichotomy between the eastern and western parts of the bay, with a sharp drop in concentrations above the oyster area. At the smallest scale, significant spatial heterogeneity was obtained in terms of oyster dry weight (DW), with a difference of around 3.0 g between the lowest and the highest oyster DW. Influences of physical and biological factors were discriminated for spatial patterns of phytoplankton at a large scale and for spatial patterns of bivalves at a small scale. Bivalve density, immersion time (i.e. feeding time) and current velocity were identified as the main factors controlling the spatial patterns of phytoplankton and bivalve growth. The results of the model indicate that the effects of spatial scales are much larger than those of temporal scales; this conclusion differs from that expected through observations only. Top-down effects of oysters on phytoplankton biomass at local scales were revealed, whereas bottom-up effects drove primary productivity at the whole bay scale. In general, we conclude that spatial modelling is particularly appropriate to reveal spatial properties which would be difficult to observe directly. Knowledge of ecosystem functioning would be enhanced accordingly.

Numerical modelling of spatio-temporal variability of growth of Mytilus edulis (L.) and influence of its cultivation on ecosystem functioning

“…Brigolin et al, 2009;Cugier et al, 2010;Grangeré et al, 2009;Grangeré et al, 2010;Grant et al, 2008;Guyondet et al, 2010 andRen et al, 2010); is accounting for the allocation of N and C in mussels and the way the ammonium excretion is formulated, which is important in modelling the carrying capacity of the systems, as NH 4 is the preferential nutrient for phytoplankton and will have an impact on the primary productivity also affecting the overall biogeochemical cycle. The capacity of mussel farming to alter the structure of the pelagic ecosystem has been demonstrated in Figure 11.…”

“…Several authors have dynamically coupled DEB models with biogeochemical models to provide feedbacks from aquaculture farms to phytoplankton and nutrient dynamics. Grangeré et al (2010) and Grangeré et al (2009) accounted for food uptake, biodeposition and excretion of the Pacific oyster, with ammonia excretion rates arbitrarily specified as a fraction of ingested nitrogen. In both studies, food uptake was calculated from the predicted filtration rates and converted from energy to mass units using appropriate conversion factors.…”

Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environments

“…Héral 1993, Ferreira et al 2009 and manage ecosystem-level impacts (e.g. Grangeré et al 2010, Byron et al 2011). These models vary in complexity from simple ratio-based budgets (e.g.…”

Phytoplankton community distribution in relation to environmental parameters in three aquaculture systems in a Chinese subtropical eutrophic bay

“…Similarly, shellfish cultures (e.g. oyster, mussel and clam) exhibit strong top-down control of primary production by filtering large volumes of phytoplankton from the water column (Dupuy et al 2000, Souchu et al 2001, Lefebvre et al 2009, Grangeré et al 2010.…”