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.pdf01946nas a2200157 4500008003900000245011100039210006900150300001200219490000600231520143500237100001701672700001301689700001401702700001501716856005701731 2013 d00aEcosystem modelling for ecosystem-based management of bivalve aquaculture sites in data‑poor environment0 aEcosystem modelling for ecosystembased management of bivalve aqu a117-1330 v43 aAlthough 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.1 aFilgueira, R1 aGrant, J1 aStuart, R1 aBrown, M S uhttp://www.int-res.com/abstracts/aei/v4/n2/p117-133/00386nas a2200085 4500008003900000245009800039210007000137100001900207856007400226 2013 d00aEin Ansatz zur räumlich- dynamischen Modellierung am Beispiel der Tereskenernte im Ostpamir 0 aEin Ansatz zur räumlich dynamischen Modellierung am Beispiel der1 aHOHBERG, Georg uhttp://gispoint.de/fileadmin/user_upload/paper_gis_open/537533033.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 a

Field 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.2008.04.05902378nas a2200169 4500008003900000245009300039210006900132260001200201300001200213490000700225520179800232653000902030653001502039653003102054100001702085856010602102 2007 d00aThe EROI of U.S. offshore energy extraction: A net energy analysis of the Gulf of Mexico0 aEROI of US offshore energy extraction A net energy analysis of t c08/2007 a355-3640 v633 a

In 2004, the U.S. Department of the Interior's Minerals Management Service estimated that 49% of the oil and 57% of the natural gas yet to be discovered offshore in the United States are located in the Gulf of Mexico Outer Continental Shelf region. While the existence of these energy resources is critical to the nation's future economic well being, of equal importance is the amount of already extracted energy that will be required to deliver the new fuel to society in a useful form. The difference between the two energy quantities is the net supply. In many respects, net energy is the most relevant measure of fuel supply because it represents the energy available to produce final-demand economic goods and services. Unfortunately, there currently exists no standard procedure for determining net energy, and so the data are extremely limited and inconsistent. In this paper, we present an “energy return on investment”, or “EROI”-based approach. EROI is defined as the ratio of gross energy produced by an energy supply process to the total, direct plus indirect, energy cost of its production. If the EROI of an energy supply process is known, then it's net energy output can be derived easily given gross production data. Below, we specify an empirical computer model programmed to simulate the productivity dynamics of offshore energy extraction in the Gulf of Mexico and estimate the EROI of the "offshore process" over a twenty-year period (1985–2004). At the conclusion of the simulation, the model calculates the EROI of the process to range from 10 to 25, depending on how energy costs have been defined. In comparison, it has been estimated that the EROI of U.S. domestic petroleum extraction in the 1930s was approximately 100.

10aEROI10aNet energy10aOffshore energy extraction1 aGately, Mark u//www.simulistics.com/publications/eroi-us-offshore-energy-extraction-net-energy-analysis-gulf-mexico01633nas a2200229 4500008003900000245011300039210006900152260001300221300001000234490000700244520085700251653002001108653002501128653001501153653001301168653001801181653001301199653002301212100002101235700002001256856012701276 2007 d00aEvaluation and comparison of models and modelling tools simulating nitrogen processes in treatment wetlands 0 aEvaluation and comparison of models and modelling tools simulati bElsevier a26-490 v163 a

In this paper, two ecological models of nitrogen processes in treatment wetlands have been evaluated and compared. These models were implemented, simulated, and visualized using the Modelica modelling and simulation language [P. Fritzson, Principles of Object-Oriented Modelling and Simulation with Modelica 2.1 (Wiley-IEEE Press, USA, 2004).] and an associated tool. The differences and similarities between the MathModelica Model Editor and three other ecological modelling tools have also been evaluated. The results show that the models can well be modelled and simulated in the MathModelica Model Editor, and that nitrogen decrease in a constructed treatment wetland should be described and simulated using the Nitrification/Denitrification model as this model has the highest overall quality score and provides a more variable environment.

10aDenitrification10aEcological modelling10aEvaluation10aModelica10aNitrification10aNitrogen10aTreatment wetlands1 aEdelfeldt, Stina1 aFritzson, Peter u//www.simulistics.com/publications/evaluation-and-comparison-models-and-modelling-tools-simulating-nitrogen-processes-trea00503nam a2200157 4500008003900000020001900039245006200058210006100120260001000181300000800191653002800199653000800227100002100235700001900256856007000275 2003 d a978-047149618200aEnvironmental Modelling: Finding Simplicity in Complexity0 aEnvironmental Modelling Finding Simplicity in Complexity bWiley a43010aenvironmental modelling10aGIS1 aWainwright, John1 aMulligan, Mark uhttp://eu.wiley.com/WileyCDA/WileyTitle/productCd-0471496189.html