@article {4263,
	title = {A fully-spatial ecosystem-DEB model of oyster (Crassostrea virginica) carrying capacity in the Richibucto Estuary, Eastern Canada},
	journal = {Journal of Marine Systems},
	volume = {136},
	year = {2014},
	month = {08/2014},
	pages = {42 - 54},
	abstract = {The success of shellfish aquaculture as well as its sustainability relies on adjusting the cultured biomass to local
ecosystem characteristics. Oyster filter-feeding activity can control phytoplankton concentration, reaching severe
depletion in extreme situations, which can threaten ecological sustainability. A better understanding of oyster{\textendash}
phytoplankton interaction can be achieved by constructing ecosystem models. In this study, a fully-spatial hydro-
dynamic biogeochemical model has been constructed for the Richibucto Estuary in order to explore oyster carry-
ing capacity. The biogeochemical model was based on a classical nutrient{\textendash}phytoplankton{\textendash}zooplankton{\textendash}detritus
(NPZD) approach with the addition of a Dynamic Energy Budget (DEB) model of Crassostrea virginica. Natural
variation of chlorophyll was used as a benchmark to define a sustainability threshold based on a resilience frame-
work. Scenario building was applied to explore carrying capacity of the system. However, the complex geomor-
phology of the Richibucto Estuary and the associated heterogeneity in water residence time, which is integral in
estuarine functioning, indicate that the carrying capacity assessment must be specific for each area of the system.
The model outcomes suggest that water residence time plays a key role in carrying capacity estimations through
its influence on ecological resistance.},
	issn = {09247963},
	doi = {10.1016/j.jmarsys.2014.03.015},
	author = {R. Filgueira and Guyondet, T. and Comeau, L.A. and J. Grant}
}
@article {4243,
	title = {Physiological indices as indicators of ecosystem status in shellfish aquaculture sites},
	journal = {Ecological Indicators},
	volume = {39},
	year = {2014},
	month = {04/2014},
	pages = {134 - 143},
	abstract = {The filtration activity of cultured mussels may exert a strong control on phytoplankton populations. Given that phytoplankton constitutes the base of marine food webs, carrying capacity in shellfish aquaculture sites has been commonly studied in terms of phytoplankton depletion. However, spatial and temporal variability of phytoplankton concentration in coastal areas present a methodological constraint for using phytoplankton depletion as an indicator in monitoring programs, and necessitates intensive field campaigns. The main goal of this study is to explore the potential of different bivalve performance indices for use as alternatives to phytoplankton depletion as cost-effective indicators of carrying capacity. For that, a fully spatial hydrodynamic{\textendash}biogeochemical coupled model of Tracadie Bay, an intensive mussel culture embayment located in Prince of Edward Island (Canada), has been constructed and scenario building has been used to explore the relationship between phytoplankton depletion and bivalve performance. Our underlying premise is that overstocking of bivalves leads to increased competition for food resources, i.e. phytoplankton, which may ultimately have a significant effect on bivalve growth rate and performance. Following this working hypothesis, the relationships among bay-scale phytoplankton depletion and three bivalve physiological indices, one static, condition index, and two dynamic, tissue mass and shell length growth rates, have been simulated. These three metrics present methodological advantages compared to phytoplankton depletion for incorporation into monitoring programs. Although significant correlations among phytoplankton depletion and the three physiological indices have been observed, shell length growth rate is shown as the most sensitive indicator of carrying capacity, followed by tissue mass growth rate and then by condition index. These results demonstrate the potentiality of using bivalve physiological measurements in monitoring programs as indicators of ecosystem status.},
	issn = {1470160X},
	doi = {10.1016/j.ecolind.2013.12.006},
	url = {http://www.sciencedirect.com/science/article/pii/S1470160X13004962},
	author = {R. Filgueira and Guyondet, T. and Comeau, L.A. and J. Grant}
}
@article {4241,
	title = {Ecosystem modelling for ecosystem-based management of bivalve aquaculture sites in data-poor environment},
	journal = {Aquacult Environ Interact},
	volume = {4},
	year = {2013},
	pages = {117-133},
	chapter = {117},
	abstract = {Although models of carrying capacity have been around for some time, their use in aquaculture management has been limited. This is partially due to the cost involved in generating and testing the models. However, the use of more generic and flexible models could facilitate the implementation of modelling in management. We have built a generic core for coupling biogeochemical and hydrodynamic models using Simile (www.simulistics.com), a visual simulation environment software that is well-suited to accommodate fully spatial models. Specifically, Simile integrates PEST (model-independent parameter estimation, Watermark Numerical Computing, www.pesthomepage.org), an optimization tool that uses the Gauss-Marquardt-Levenberg algorithm and can be used to estimate the value of a parameter, or set of parameters, in order to minimize the discrepancies between the model results and a dataset chosen by the user. The other critical aspect of modelling exercises is the large amount of data necessary to set up, tune and groundtruth the ecosystem model. However, ecoinformatics and improvements in remote sensing procedures have facilitated acquisition of these datasets, even in data-poor environments. In this paper we describe the required datasets and stages of model development necessary to build a biogeochemical model that can be used as a decision-making tool for bivalve aquaculture management in data-poor environments.},
	doi = {10.3354/aei00078},
	url = {http://www.int-res.com/abstracts/aei/v4/n2/p117-133/},
	author = {R. Filgueira and J. Grant and R. Stuart and M. S. Brown}
}
