00937nas a2200205 4500008004100000022001400041245014800055210006900203260001600272300001600288490000800304100002200312700002200334700002300356700001900379700002200398700002900420700002300449856025900472 2018 eng d a0378-190900aThe effect of environmental conditions on Atlantic salmon smolts’ (Salmo salar) bioenergetic requirements and migration through an inland sea0 aeffect of environmental conditions on Atlantic salmon smolts Sal cJan-10-2018 a1467 - 14820 v1011 aStrople, Leah, C.1 aFilgueira, Ramón1 aHatcher, Bruce, G.1 aDenny, Shelley1 aBordeleau, Xavier1 aWhoriskey, Frederick, G.1 aCrossin, Glenn, T. uhttp://link.springer.com/10.1007/s10641-018-0792-5http://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdfhttp://link.springer.com/article/10.1007/s10641-018-0792-5/fulltext.htmlhttp://link.springer.com/content/pdf/10.1007/s10641-018-0792-5.pdf00675nas a2200169 4500008003900000022001300039245014100052210006900193260001200262300001400274490000800288100002200296700002100318700002000339700001900359856012700378 2015 d a0025326X00aInforming Marine Spatial Planning (MSP) with numerical modelling: A case-study on shellfish aquaculture in Malpeque Bay (Eastern Canada)0 aInforming Marine Spatial Planning MSP with numerical modelling A c11/2015 a200 - 2160 v1001 aFilgueira, Ramón1 aGuyondet, Thomas1 aBacher, Cédric1 aComeau, Luc, A u//www.simulistics.com/publications/informing-marine-spatial-planning-msp-numerical-modelling-case-study-shellfish-aquacult02071nas a2200181 4500008003900000022001300039245013400052210006900186260001200255300001200267490000800279520141300287100001701700700001601717700001601733700001301749856012701762 2014 d a0924796300aA fully-spatial ecosystem-DEB model of oyster (Crassostrea virginica) carrying capacity in the Richibucto Estuary, Eastern Canada0 afullyspatial ecosystemDEB model of oyster Crassostrea virginica c08/2014 a42 - 540 v1363 aThe 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– 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–phytoplankton–zooplankton–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.1 aFilgueira, R1 aGuyondet, T1 aComeau, L A1 aGrant, J u//www.simulistics.com/publications/fully-spatial-ecosystem-deb-model-oyster-crassostrea-virginica-carrying-capacity-richib02611nas a2200277 4500008003900000022001400039245008700053210006900140520178500209100001401994700001402008700001202022700001402034700001402048700001502062700001402077700001402091700001602105700001402121700001302135700001602148700002002164700001802184700001602202856011502218 2014 d a1091-649000aMultiscale digital Arabidopsis predicts individual organ and whole-organism growth0 aMultiscale digital Arabidopsis predicts individual organ and who3 aUnderstanding how dynamic molecular networks affect whole-organism physiology, analogous to mapping genotype to phenotype, remains a key challenge in biology. Quantitative models that represent processes at multiple scales and link understanding from several research domains can help to tackle this problem. Such integrated models are more common in crop science and ecophysiology than in the research communities that elucidate molecular networks. Several laboratories have modeled particular aspects of growth in Arabidopsis thaliana, but it was unclear whether these existing models could productively be combined. We test this approach by constructing a multiscale model of Arabidopsis rosette growth. Four existing models were integrated with minimal parameter modification (leaf water content and one flowering parameter used measured data). The resulting framework model links genetic regulation and biochemical dynamics to events at the organ and whole-plant levels, helping to understand the combined effects of endogenous and environmental regulators on Arabidopsis growth. The framework model was validated and tested with metabolic, physiological, and biomass data from two laboratories, for five photoperiods, three accessions, and a transgenic line, highlighting the plasticity of plant growth strategies. The model was extended to include stochastic development. Model simulations gave insight into the developmental control of leaf production and provided a quantitative explanation for the pleiotropic developmental phenotype caused by overexpression of miR156, which was an open question. Modular, multiscale models, assembling knowledge from systems biology to ecophysiology, will help to understand and to engineer plant behavior from the genome to the field.1 aChew, Y H1 aWenden, B1 aFlis, A1 aMengin, V1 aTaylor, J1 aDavey, C L1 aTindal, C1 aThomas, H1 aOugham, H J1 aReffye, P1 aStitt, M1 aWilliams, M1 aMuetzelfeldt, R1 aHalliday, K J1 aMillar, A J uhttp://www.pnas.org/content/early/2014/08/27/1410238111.full.pdf+html?sid=66edb45d-8e99-4d84-a072-a47729a65e1402609nas a2200181 4500008003900000022001300039245009100052210006900143260001200212300001400224490000700238520204900245100001702294700001602311700001602327700001302343856007102356 2014 d a1470160X00aPhysiological indices as indicators of ecosystem status in shellfish aquaculture sites0 aPhysiological indices as indicators of ecosystem status in shell c04/2014 a134 - 1430 v393 aThe 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–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.1 aFilgueira, R1 aGuyondet, T1 aComeau, L A1 aGrant, J uhttp://www.sciencedirect.com/science/article/pii/S1470160X1300496201899nas a2200157 4500008003900000245009200039210006900131260001200200300001400212520131800226100002201544700002101566700001901587700001501606856012001621 2014 d00aStorm-induced changes in coastal geomorphology control estuarine secondary productivity0 aStorminduced changes in coastal geomorphology control estuarine c01/2014 an/a - n/a3 aEstuarine ecosystems are highly sensitive not only to projected effects of climate change such as ocean warming, acidification, and sea-level rise but also to the incidence of nor'easter storms and hurricanes. The effects of storms and hurricanes can be extreme, with immediate impact on coastal geomorphology and water circulation, which is integral to estuarine function and consequently to provision of ecosystem services. In this article, we present the results of a natural estuarine-scale experiment on the effects of changes in coastal geomorphology on hydrodynamics and aquaculture production. A bay in Prince Edward Island, Canada, was altered when a nor'easter storm eroded a second tidal inlet through a barrier island. Previous field and modeling studies allowed a comparison of prestorm and post-storm circulation, food limitation by cultured mussels, and aquaculture harvest. Dramatic increases in mussel production occurred in the year following the opening of the new inlet. Model studies showed that post-storm circulation reduced food limitation for cultured mussels, allowing greater growth. Climate change is expected to have severe effects on the delivery of marine ecosystem services to human populations by changing the underlying physical-biological coupling inherent to their functioning.1 aFilgueira, Ramón1 aGuyondet, Thomas1 aComeau, Luc, A1 aGrant, Jon uhttp://onlinelibrary.wiley.com/doi/10.1002/2013EF000145/abstract;jsessionid=DBD0FB7B2443BD9C9D658F85A42F41FD.f04t0400507nas a2200145 4500008003900000245008700039210007100126300001000197490000600207100002200213700001500235700002000250700002000270856007100290 2012 d00aA physical–biogeochemical coupling scheme for modeling marine coastal ecosystems0 aphysical–biogeochemical coupling scheme for modeling marine coas a71-800 v71 aFilgueira, Ramón1 aGrant, Jon1 aBacher, Cédric1 aCarreau, Michel uhttp://www.sciencedirect.com/science/article/pii/S157495411100097503363nas a2200445 4500008003900000020001800039245010000057210006900157260001400226300001200240490000600252520204200258653005002300653002902350653002202379653002102401653002502422100001702447700001702464700002102481700001602502700002202518700001702540700001802557700001302575700001702588700001602605700001402621700001602635700001402651700001702665700001602682700001502698700001302713700001702726700001602743700001702759700001402776856012702790 2008 d a978008056886700aChapter Seven Integrated Modelling Frameworks for Environmental Assessment and Decision Support0 aChapter Seven Integrated Modelling Frameworks for Environmental b Elsevier a101-1180 v33 a
In this chapter we investigate the motivation behind the development of modelling frameworks that explicitly target the environmental domain. Despite many commercial and industrial-strength frameworks being available, we claim that there is a definite niche for environmental-specific frameworks. We first introduce a general definition of what is an environmental integrated modelling framework, leading to an outline of the requirements for a generic software architecture for such frameworks. This identifies the need for a knowledge layer to support the modelling layer and an experimentation layer to support the execution of models.
The chapter then focuses on the themes of knowledge representation, model management and model execution. We advocate that appropriate knowledge representation and management tools can facilitate model integration and linking. We stress that a model development process adhering to industry standards and good practices, called “model engineering,” is to be pursued. We focus on the requirements of the experimental frame, which can ensure transparency and traceability in the execution of simulation scenarios and optimisation problems associated with complex integrated assessment studies.
A promising trend for knowledge representation is the use of ontologies that have the capacity to elicit the meaning of knowledge in a manner that is logical, consistent and understandable by computers and the knowledge worker community. This new path in knowledge-based computing will support retention of institutional knowledge, while putting modelling back in the hands of modellers. Environmental modelling will then become a conceptual activity, focusing on model design rather than model implementation, with code generation being delegated to some degree to ontology-aware tools. In this respect, we envision the whole model lifecycle to change drastically, becoming more of a theoretical activity and less of a coding-intensive, highly engineering-oriented task.
10aenvironmental integrated modelling frameworks10aknowledge representation10amodel engineering10amodel management10amodelling frameworks1 aRizzoli, A E1 aLeavesley, G1 aII, Ascough, J C1 aArgent, R M1 aAthanasiadis, I N1 aBrilhante, V1 aClaeys, F H A1 aDavid, O1 aDonatelli, M1 aGijsbers, P1 aHavlik, D1 aKassahun, A1 aKrause, P1 aQuinn, N W T1 aScholten, H1 aSojda, R S1 aVilla, F1 aJakeman, A J1 aVoinov, A A1 aRizzoli, A E1 aChen, S H u//www.simulistics.com/publications/chapter-seven-integrated-modelling-frameworks-environmental-assessment-and-decision-sup00709nas a2200205 4500008003900000245009900039210006900138100002000207700002500227700002500252700002400277700002600301700001800327700002100345700001800366700002200384700002300406700002000429856005400449 2008 d00aCONCEPT MAPS FOR COMBINING HARD AND SOFT SYSTEM THINKING IN THE MANAGEMENT OF SOCIO-ECOSYSTEMS0 aCONCEPT MAPS FOR COMBINING HARD AND SOFT SYSTEM THINKING IN THE 1 aSalerno, Franco1 aCuccillato, Emanuele1 aMuetzelfeldt, Robert1 aGiannino, Francesco1 aBajracharya, Birendra1 aCaroli, Paolo1 aViviano, Gaetano1 aStaiano, Anna1 aFabrizio Cartenì1 aMazzoleni, Stefano1 aTartari, Gianni uhttp://cmc.ihmc.us/cmc2008papers/cmc2008-p190.pdf01794nas a2200229 4500008003900000245012200039210006900161260001200230300001300242490000700255520108800262653001101350653001401361653001601375653001501391653002101406653001701427100002101444700002201465700002101487856005601508 2008 d00aThe effect of vegetation on pesticide dissipation from ponded treatment wetlands: Quantification using a simple model0 aeffect of vegetation on pesticide dissipation from ponded treatm c07/2008 a999-10050 v723 aField data shows that plants accelerate pesticide dissipation from aquatic systems by increasing sedimentation, biofilm contact and photolysis. In this study, a graphical model was constructed and calibrated with site-specific and supplementary data to describe the loss of two pesticides, endosulfan and fluometuron, from a vegetated and a non-vegetated pond. In the model, the major processes responsible for endosulfan dissipation were alkaline hydrolysis and sedimentation, with the former process being reduced by vegetation and the latter enhanced. Fluometuron dissipation resulted primarily from biofilm reaction and photolysis, both of which were increased by vegetation. Here, greater photolysis under vegetation arose from faster sedimentation and increased light penetration, despite shading. Management options for employing constructed wetlands to polish pesticide-contaminated agricultural runoff are discussed. The lack of easily fulfilled sub-models and data describing the effect of aquatic vegetation on water chemistry and sedimentation is also highlighted.
10aCotton10aHerbicide10aInsecticide10aMacrophyte10aPhytoremediation10aRunoff water1 aRose, Michael, T1 aCrossan, Angus, N1 aKennedy, Ivan, R uhttp://dx.doi.org/10.1016/j.chemosphere.2008.04.05902788nas a2200265 4500008003900000245013300039210006900172260001100241300001200252490000800264520188500272653001602157653002202173653002002195653002102215653001202236100001502248700002402263700002402287700002002311700002002331700002602351700001802377856012702395 2007 d00aA box model of carrying capacity for suspended mussel aquaculture in Lagune de la Grande-Entrée, Iles-de-la-Madeleine, Québec 0 abox model of carrying capacity for suspended mussel aquaculture c1/2007 a193-2060 v2003 aAn object-oriented model of environment–mussel aquaculture interactions and mussel carrying-capacity within Lagune de la Grande-Entrée (GEL), Iles-de-la-Madeleine, Québec, was constructed to assist in development of sustainable mussel culture in this region. A multiple box ecosystem model for GEL tied to the output of a hydrodynamic model was constructed using Simile software, which has inherent ability to represent spatial elements and specify water exchange between modelled regions. Mussel growth and other field data were used for model validation. Plackett–Burman sensitivity analysis demonstrated that a variety of bioenergetic parameters of zooplankton and phytoplankton submodels were important in model outcomes. Model results demonstrated that mussel aquaculture can be further developed throughout the lagoon. At present culture densities, phytoplankton depletion is minimal, and there is little food limitation of mussel growth. Results indicated that increased stocking density of mussels in the existing farm will lead to decreased mass per individual mussel. Depending on the location of new farm emplacement within the lagoon, implementation of new aquaculture sites either reduced mussel growth in the existing farm due to depletion of phytoplankton, or exhibited minimum negative impact on the existing farm. With development throughout GEL, an excess of phytoplankton was observed during the year in all modelled regions, even at stocking densities as high as 20 mussels m−3. Although mussels cultured at this density do not substantially impact the ecosystem, their growth is controlled by the flux of phytoplankton food and abundance of zooplankton competitors. This model provides an effective tool to examine expansion of shellfish farming to new areas, balancing culture location and density.
10aAquaculture10aCarrying-capacity10aEcosystem model10aMagdalen Islands10aMussels1 aGrant, Jon1 aCurran, Kristian, J1 aGuyondet, Thomas, L1 aTita, Guglielmo1 aBacher, Cédric1 aKoutitonsky, Vladimir1 aDowd, Michael u//www.simulistics.com/publications/box-model-carrying-capacity-suspended-mussel-aquaculture-lagune-de-la-grande-entr-e-ile