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Challenges and opportunities for integrating lake ecosystem modelling approaches / W. M. Mooij [et al.] // Aquatic Ecology. - 2010. - Vol. 44, Is. 3. - P633-667, DOI 10.1007/s10452-010-9339-3 . - ISSN 1386-2588
Кл.слова (ненормированные):
Adaptive processes -- Analysis -- Aquatic -- Bifurcation -- Biodiversity -- Climate warming -- Community -- Eutrophication -- Fisheries -- Food web dynamics -- Freshwater -- Global change -- Hydrology -- Lake -- Management -- Marine -- Mitigation -- Model integration -- Model limitations -- Non-linear dynamics -- Nutrients -- Plankton -- Population -- Prediction -- Spatial -- Understanding -- adaptive management -- algorithm -- aquatic community -- biodiversity -- ecosystem modeling -- eutrophication -- fishery production -- food web -- fuzzy mathematics -- global warming -- hydrology -- lake ecosystem -- mitigation -- model test -- numerical model -- nutrient availability -- plankton -- prediction -- saline lake -- spatial analysis
Аннотация: A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models. В© 2010 The Author(s).

Scopus
Держатели документа:
Netherlands Institute of Ecology (NIOO-KNAW), Department of Aquatic Ecology, Rijksstraatweg 6, 3631 AC Nieuwersluis, Netherlands
Aarhus University, National Environmental Research Institute, Department of Freshwater Ecology, 8600 Silkeborg, Denmark
Greenland Climate Research Centre (GCRC), Greenland Institute of Natural Resources, Kivioq 2, P.O. Box 570, 3900 Nuuk, Greenland
University of Toronto, Department of Physical and Environmental Sciences, Toronto, ON M1C 1A4, Canada
Institute of Computational Modelling (SB-RAS), Siberian Federal University, 660036 Krasnoyarsk, Russian Federation
Tanzania Fisheries Research Institute (TAFIRI), Mwanza Centre, P.O. Box 475, Mwanza, Tanzania
Institute of Biophysics (SB-RAS), Akademgorodok, 660036 Krasnoyarsk, Russian Federation
University of Miami, Florida Integrated Science Centre, USGS, Coral Gables, FL 33124, United States
Wageningen University, Department of Aquatic Ecology and Water Quality, P.O. Box 47, 6700 AA Wageningen, Netherlands
Centre for Ecology and Hydrology, Lancaster Environment Centre, Lake Ecosystem Group, Algal Modelling Unit, Bailrigg, Lancaster LA1 4AP England, United Kingdom
Federal University of Alagoas, Centre for Technology, Campus A.C. Simoes, 57072-970 Maceio-AL, Brazil
Institute of Biochemistry and Biology, Department of Ecology and Ecosystem Modelling, University of Potsdam, Am Neuen Palais 10, 14469 Potsdam, Germany
Swedish University of Agricultural Sciences, Department of Aquatic Sciences and Assessment, P.O. Box 7050, 75007 Uppsala, Sweden
University of Waikato, Centre for Biodiversity and Ecology Research, Private Bag 3105, Hamilton, New Zealand
University of Western Australia, School of Earth and Environment, Crawley, WA 6009, Australia
Technische Universitat Dresden, Institute of Hydrobiology, 01062 Dresden, Germany
Technische Universitat Dresden, Neunzehnhain Ecological Station, Neunzehnhainer Str. 14, 09514 Lengefeld, Germany
Deltares, P.O. Box 177, 2600 MH Delft, Netherlands
Technion-Israel Institute of Technology, Faculty of Civil and Environmental Engineering, Technicon City, Haifa 32000, Israel
Helmholtz Centre for Environmental Research, Department of Lake Research, Brueckstrasse 3a, 39114 Magdeburg, Germany
Witteveen and Bos, P.O. Box 233, 7400 AV Deventer, Netherlands
University of Oslo, Department of Biology, P.O. Box 1066, Blindern, 0316 Oslo, Norway
UNESCO-IHE Institute of Water Education, 2601 DA Delft, Netherlands
Portland State University, Department of Civil and Environmental Engineering, Portland, OR 97207, United States
Netherlands Environmental Assessment Agency (PBL), P.O. Box 303, 3720 AH Bilthoven, Netherlands : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Mooij, W.M.; Trolle, D.; Jeppesen, E.; Arhonditsis, G.; Belolipetsky, P.V.; Chitamwebwa, D.B.R.; Degermendzhy, A.G.; DeAngelis, D.L.; De Senerpont Domis, L.N.; Downing, A.S.; Elliott, J.A.; Fragoso Jr., C.R.; Gaedke, U.; Genova, S.N.; Gulati, R.D.; Hakanson, L.; Hamilton, D.P.; Hipsey, M.R.; 't Hoen, J.; Hulsmann, S.; Los, F.H.; Makler-Pick, V.; Petzoldt, T.; Prokopkin, I.G.; Rinke, K.; Schep, S.A.; Tominaga, K.; van Dam, A.A.; van Nes, E.H.; Wells, S.A.; Janse, J.H.

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Hematological Parameters and the State of Liver Cells of Rats After Oral Administration of Aflatoxin B1 Alone and Together with Nanodiamonds [Text] / O. A. Mogilnaya [et al.] // Nanoscale Res. Lett. - 2010. - Vol. 5, Is. 5. - P908-912, DOI 10.1007/s11671-010-9571-8. - Cited References: 23. - The study was financially supported by the Russian Foundation for Basic Research (RFBR) (Grant No. 06-0490234) and the RAS Presidium (Program No. 27, Project No. 64). . - ISSN 1931-7573

РУБ Nanoscience & Nanotechnology + Materials Science, Multidisciplinary + Physics, Applied
Рубрики:
B-1
MYCOTOXINS
ADSORPTION
Кл.слова (ненормированные):
Nanodiamonds -- Aflatoxin B(1) -- Detoxification -- Adsorbent
Аннотация: Hematological parameters and the state of liver cells of rats were examined in vivo after the animals received aflatoxin B(1) (AfB(1)) alone and together with modified nanodiamonds (MND) synthesized by detonation. The rats that had received the MND hydrosol had elevated leukocyte levels, mainly due to higher granulocyte counts and somewhat increased monocyte counts compared to control rats. Hematological parameters of the rats that had received AfB(1) alone differed from those of the control rats in another way: total white blood cell counts were significantly lower due to the decreased lymphocyte counts. In rats that had consumed AfB(1) with the MND hydrosol, changes in hematological parameters were less pronounced than in rats that had consumed either AfB(1) or MND. Electron microscopy showed that hepatocytes of the rats that had received the MND hydrosol or AfB(1) with the MND hydrosol contained elevated levels of lipid inclusions and lysosomes. Hyperplasia of the smooth endoplasmic reticulum (EPR) was revealed in liver specimens of the rats that had received AfB(1). Results of the study suggest the conclusion about mutual mitigation of the effects of nanoparticles and the mycotoxin on rats blood and liver cells after AfB(1) has adsorbed on MND.

Держатели документа:
[Mogilnaya, O. A.
Puzyr, A. P.
Bondar, V. S.] Inst Biophys SB RAS, Krasnoyarsk 660036, Russia
[Baron, A. V.] Siberian Fed Univ, Inst Fundamental Biol & Biotechnol, Krasnoyarsk 660041, Russia
ИБФ СО РАН : 660036, Красноярск, Академгородок, д. 50, стр. 50

Доп.точки доступа:
Mogilnaya, O.A.; Puzyr, A.P.; Baron, A.V.; Bondar, V.S.

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