Морской гидрофизический журнал. 2022; 38: 105-122
Моделирование морских экосистем: опыт, современные подходы, направления развития (обзор). Часть 1. Сквозные модели
https://doi.org/10.22449/0233-7584-2022-1-105-122Аннотация
Цель. Несмотря на сравнительно недолгую историю моделирования морских систем, точкой отсчета которой является конец 1960-х – начало 1970-х гг., данное направление развивается исключительно интенсивно, и количество публикаций по моделированию морских систем исчисляется тысячами. Целью статьи является обзор накопленных в этой области достижений. Основное внимание уделено общим принципам и спектру современных подходов к моделированию морских систем. Результаты анализа и обобщения более двухсот источников – научных статей, монографий и разделов монографий, интернет-ресурсов – представлены в двух частях, публикуемых отдельно.
Методы и результаты. За последние десятилетия понимание закономерностей функционирования морских экосистем существенно возросло, как и возможности экологического мониторинга и компьютерных технологий. Одновременно в связи с увеличением количества глобальных и региональных экологических программ и проектов в области морепользования, охраны морской среды и анализа последствий изменения климата возрос спрос на количественные инструменты для поддержки инициатив по управлению рациональным использованием морских ресурсов на основе экосистемного подхода. Это привело к запросу на более сложные многокомпонентные модели и значительному росту числа такого рода моделей. Первая часть данного обзора посвящена «сквозным» (end-to-end) моделям – сложным интегративным инструментам поддержки инициатив по управлению рациональным использованием морских ресурсов.
Выводы. Способность анализировать сценарии «что, если» делает сквозные модели полезным инструментом для определения эффективных вариантов управления морскими биологическими ресурсами, в том числе управления рыболовством, и оценки влияния климатических изменений и антропогенных воздействий на все трофические уровни, включая биогеохимический цикл, микробную петлю, различные типы детрита. Эти модели не следует использовать при принятии тактических решений (в этом случае лучше работают локальные объектно-ориентированные субмодели), но они являются полезными инструментами для стратегического планирования и комплексных оценок альтернативных стратегий управления.
Список литературы
1. Using ecological models to assess ecosystem status in support of the European Marine Strategy Framework Directive / C. Piroddi [et al.] // Ecological Indicators. 2015. Vol. 58. P. 175–191. https://doi.org/10.1016/j.ecolind.2015.05.037
2. Using ecological modelling in marine spatial planning to enhance ecosystem-based management / A. Shabtay [et al.] // Marine Policy. 2018. Vol. 95. P. 14–23. https://doi.org/10.1016/j.marpol.2018.06.018
3. Интегрированная математическая модель большой морской экосистемы Баренцева и Белого морей – инструмент для оценки природных рисков и эффективного использова-ния биологических ресурсов / С. В. Бердников [и др.] // Доклады Академии наук. 2019. Т. 487, № 5. С. 566–572. https://doi.org/10.31857/S0869-56524875566-572
4. Observations and models to support the first Marine Ecosystem Assessment for the Southern Ocean (MEASO) / M. J. Brasier [et al.] // Journal of Marine Systems. 2019. Vol. 197. 103182. https://doi.org/10.1016/j.jmarsys.2019.05.008
5. Саркисян А. С. Численный анализ и прогноз морских течений. Л. : Гидрометеоиздат, 1977. 182 с.
6. Чернов И. А. Особенности сопряжения моделей циркуляции моря и морской экосистемы BMF // Труды Карельского научного центра РАН. 2018. № 7. С. 100–116. https://doi.org/10.17076/mat830
7. Крукиер Л. А. Математическое моделирование гидродинамики Азовского моря при реализации проектов реконструкции его экосистемы // Математическое моделирование. 1991. Т. 3, № 9. С. 3–20. URL: http://mi.mathnet.ru/mm2266 (дата обращения: 15.01.2022.
8. Сурков Ф. А., Крукиер Л. А., Муратова Г. В. Численное моделирование динамики Азов-ского моря при сужении гирла Таганрогского залива // Морской гидрофизический жур-нал. 1989. № 6. С. 55–62.
9. Чикин А. Л., Клещенков А. В., Чикина Л. Г. Моделирование изменения солености в Та-ганрогском заливе при штормовых нагонах // Водные ресурсы. 2019. Т. 46, № 6. С. 592–597. doi:10.31857/S0321-0596466592-597
10. Muratova G. V., Andreeva E. M. Multigrid method for solving convection-diffusion problems with dominant convection // Journal of Computational and Applied Mathematics. 2009. Vol. 226, iss. 1. P. 77–83.
11. Функционирование экосистемы Белого моря: исследование на основе математической модели трансформации органогенных веществ / А. В. Леонов [и др.] // Водные ресурсы. 2004. Т. 31, № 5. С. 556–575.
12. Селютин В. В., Задорожная Н. С. Моделирование первичных звеньев экосистемы Азовского моря // Среда, биота и моделирование экологических процессов в Азовском море. Апатиты : Изд. КНЦ РАН, 2001. C. 336–368.
13. Oxygen depletion risk assessment in shallow water bodies of the Sea of Azov region / V. Kulygin [et al.] // SGEM2016 Conference Proceedings. 2016. Book 3, Vol. 2. P. 799–806. https://doi.org/10.5593/SGEM2016/B32/S15.104
14. Численное моделирование экологического состояния Азовского моря с применением схем повышенного порядка точности на многопроцессорной вычислительной системе / А. И. Сухинов [и др.] // Компьютерные исследования и моделирование. 2016. Т. 8, вып. 1. С. 151–168. URL: http://crm.ics.org.ru/journal/article/2418/ (дата обращения: 15.01.2022.
15. Теоретические и прикладные аспекты моделирования первичной продуктивности водо-емов / Ю. А. Домбровский [и др.]. Ростов н/Д : Изд-во Рост. ун-та, 1990. 174 с.
16. Селютин В. В. Круговорот вещества и поток энергии в экологических системах: от модели системы к системе моделей // Обозрение прикладной и промышленной матема-тики. 1994. Т. 1, вып. 6. С. 957–973.
17. SPRAT: A spatially-explicit marine ecosystem model based on population balance equations / A. N. Johanson [et al.] // Ecological Modelling. 2017. Vol. 349. P. 11–25. https://doi.org/10.1016/j.ecolmodel.2017.01.020
18. Рациональное использование водных ресурсов бассейна Азовского моря: Математиче-ские модели / Под ред. И. И. Воровича. М. : Наука, 1981. 360 с.
19. Использование математической модели экосистемы Азовского моря для исследования закономерностей функционирования и структуры системы / И. И. Ворович [и др.] // До-клады АН СССР. 1981. Т. 259, № 2. С. 302–306. URL: http://mi.mathnet.ru/dan44590 (дата обращения: 15.01.2022.
20. Общая характеристика и описание имитационной модели Азовского моря / И. И. Ворович [и др.] // Доклады АН СССР. 1981. Т. 256, № 5. С. 1052–1056. URL: http://mi.mathnet.ru/dan44234 (дата обращения: 15.01.2022.
21. Селютин В. В. Малые параметры и редуцированные экологические модели // Экология. Экономика. Информатика. Серия: Системный анализ и моделирование экономических и экологических систем. Ростов н/Д : Изд-во ЮНЦ РАН, 2019. Т. 1, вып. 4. С. 241–244. https://doi.org/10.23885/2500-395X-2019-1-4-241-244
22. Daewel U., Schrum C., Macdonald J. I. Towards end-to-end (E2E) modelling in a consistent NPZD-F modelling framework (ECOSMO E2E_v1.0): application to the North Sea and Baltic Sea // Geoscientific Model Development. 2019. Vol. 12, iss. 5. P. 1765–1789. https://doi.org/10.5194/gmd-12-1765-2019
23. Hannah C., Vezina A., St. John M. The case for marine ecosystem models of intermediate complexity // Progress in Oceanography. 2010. Vol. 84, iss. 1–2. P. 121–128. https://doi.org/10.1016/j.pocean.2009.09.015
24. Закономерности экосистемных процессов в Азовском море / Г. Г. Матишов [и др.]. М. : Наука, 2006. 304 c.
25. Selyutin V., Shabas I. A simplified approach to designing of compartmental models of spatial-temporal dynamics and matter cycling in aquatic ecosystems // 17th International Multidisci-plinary Scientific GeoConference SGEM 2017 : Proceedings. 2017. Book 21. P. 45–52. https://doi.org/10.5593/sgem2017/21/S07.007
26. Явная модель поискового поведения хищника / Ю. В. Тютюнов [и др.] // Журнал общей биологии. 2002. Т. 63, № 2. С. 137–148.
27. Tyutyunov Yu. V., Titova L. I. Simple models for studying complex spatiotemporal patterns of animal behavior // Deep Sea Research Part II: Topical Studies in Oceanography. 2017. Vol. 140. P. 193–202. https://doi.org/10.1016/j.dsr2.2016.08.010
28. Tyutyunov Yu. V., Zagrebneva A. D., Azovsky A. I. Spatiotemporal pattern formation in a prey-predator system: The case study of short-term interactions between diatom microalgae and microcrustaceans // Mathematics. 2020. Vol. 8, iss. 7. 1065. https://doi.org/10.3390/math8071065
29. Huse G., Giske J., Salvanes A. G. V. Individual-based models // Handbook of Fish Biology and Fisheries / Eds. P. J. Hart, J. D. Reynolds. Oxford : Blackwell Science, 2002. Vol. 2. Chapter 11. P. 228–248. https://doi.org/10.1002/9780470693919.ch11
30. DeAngelis D. L., Mooij W. M. Individual-based modeling of ecological and evolutionary pro-cesses // Annual Review of Ecology, Evolution, and Systematics. 2005. Vol. 36. P. 147–168. https://doi.org/10.1146/annurev.ecolsys.36.102003.152644
31. Grimm V., Railsback S. F. Individual-based Modeling and Ecology. Princeton : Princeton university press, 2005. P. 448.
32. Breckling B., Middelhoff U., Reuter H. Individual-based models as tools for ecological theory and application: understanding the emergence of organizational properties in ecological systems // Ecological Modelling. 2006. Vol. 194, iss. 1–3. P. 102–113. https://doi.org/10.1016/j.ecolmodel.2005.10.005
33. A model of loggerhead sea turtle (Caretta caretta) habitat and movement in the oceanic North Pacific / M. Abecassis [et al.] // PLОS ONE. 2013. Vol. 8, iss. 9. e73274. https://doi.org/10.1371/journal.pone.0073274
34. An individual-based model of skipjack tuna (Katsuwonus pelamis) movement in the tropical Pacific ocean / J. Scutt Phillips [et al.] // Progress in Oceanography. 2018. Vol. 164. P. 63–74. https://doi.org/10.1016/j.pocean.2018.04.007
35. Process study of circulation in the Pearl River Estuary and adjacent coastal waters in the wet season using a triply-nested circulation model / X. Ji [et al.] // Ocean Modelling. 2011. Vol. 38, iss. 1–2. P. 138–160. https://doi.org/10.1016/j.ocemod.2011.02.010
36. Three-dimensional hydrodynamic-eutrophication model (HEM-3D): application to Kwang-Yang Bay, Korea / K. Park [et al.] // Marine Environmental Research. 2005. Vol. 60, iss. 2. P. 171–193. https://doi.org/10.1016/j.marenvres.2004.10.003
37. Interactive visualization of marine pollution monitoring and forecasting data via a Web-based GIS / M. Kulawiak [et al.] // Computers & Geosciences. 2010. Vol. 36, iss. 8. P. 1069–1080. https://doi.org/10.1016/j.cageo.2010.02.008
38. Science plan and implementation strategy / Eds. J. Hall [et al.]. Stockholm : IGBP Secretariat, 2005. 76 p. (IGBP Report ; No. 52).
39. Ecosystem-based fishery management / E. K. Pikitch [et al.] // Science. 2004. Vol. 305, iss. 5682. P. 346–347. https://doi.org/10.1126/science.1098222
40. The Norwegian ecosystem-based management plan for the Barents Sea / E. Olsen [et al.] // ICES Journal of Marine Science. 2007. Vol. 64, iss. 4. P. 599–602. https://doi.org/10.1093/icesjms/fsm005
41. Dealing with uncertainty in ecosystem models: The paradox of use for living marine resource management / J. S. Link [et al.] // Progress in Oceanography. 2012. Vol. 102. P. 102–114. https://doi.org/10.1016/j.pocean.2012.03.008
42. Patrick W. S., Link J. S. Myths that continue to impede progress in ecosystem-based fisheries management // Fisheries. 2015. Vol. 40, iss. 4. P. 155–160. https://doi.org/10.1080/03632415.2015.1024308
43. Plagányi E. E. Models for an Ecosystem Approach to Fisheries. Rome : Food and Agriculture Organization of the United Nations, 2007. 108 p.
44. Ecosystem model skill assessment. Yes we can! / E. Olsen [et al.] // PLОS One. 2016. Vol. 11, iss. 1. e0146467. https://doi.org/10.1371/journal.pone.0146467
45. An integrated approach is needed for ecosystem based fisheries management: insights from ecosystem-level management strategy evaluation / E. A. Fulton [et al.] // PLОS One. 2014. Vol. 9, iss. 1. e84242. https://doi.org/10.1371/journal.pone.0084242
46. Reconciling complex system models and fisheries advice: Practical examples and leads / S. Lehuta [et al.] // Aquatic Living Resources. 2016. Vol. 29, no. 2. 208. https://doi.org/10.1051/alr/2016022
47. Ecosystem oceanography for global change in fisheries / P. M. Cury [et al.] // Trends in Ecol-ogy & Evolution. 2008. Vol. 23, iss. 6. P. 338–346. https://doi.org/10.1016/j.tree.2008.02.005
48. End-to-end models for the analysis of marine ecosystems: Challenges, issues, and next steps / K. A. Rose [et al.] // Marine and Coastal Fisheries. 2010. Vol. 2, iss. 1. P. 115–130. https://doi.org/10.1577/C09-059.1
49. Complexity of coupled human and natural systems / J. Liu [et al.] // Science. 2007. Vol. 317, iss. 5844. P. 1513–1516. doi:10.1126/science.1144004
50. Interactions between changes in marine ecosystems and human communities / R. I. Perry [et al.] // Marine ecosystems and global change / M. Barange [et al.] (Eds.). New York : Oxford University Press, 2010. Chapter 8. P. 221–251. doi:10.1093/acprof:oso/9780199558025.003.0008
51. Towards end-to-end models for investigating the effects of climate and fishing in marine eco-systems / M. Travers [et al.] // Progress in Oceanography. 2007. Vol. 75, iss. 4. P. 751–770. https://doi.org/10.1016/j.pocean.2007.08.001
52. Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela / M. Travers [et al.] // Ecological Modelling. 2009. Vol. 220, iss. 21. P. 3089–3099. https://doi.org/10.1016/j.ecolmodel.2009.08.016
53. Shin Y.-J., Cury P. Exploring fish community dynamics through size-dependent trophic inter-actions using a spatialized individual-based model // Aquatic Living Resources. 2001. Vol. 14, iss. 2. P. 65–80. https://doi.org/10.1016/S0990-7440(01)01106-8
54. Christensen V., Walters C. J. Ecopath with Ecosim: methods, capabilities and limitations // Ecological Modelling. 2004. Vol. 172, iss. 2–4. P. 109–139. https://doi.org/10.1016/j.ecolmodel.2003.09.003
55. Lehodey P., Senina I., Murtugudde R. A Spatial ecosystem and populations dynamics model (SEAPODYM) – Modeling of tuna and tuna-like populations // Progress in Oceanography. 2008. Vol. 78, iss. 4. P. 304–318. https://doi.org/10.1016/j.pocean.2008.06.004
56. Senina I., Sibert J., Lehodey P. Parameter estimation for basin-scale ecosystem-linked popula-tion models of large pelagic predators: application to skipjack tuna // Progress in Oceanography. 2008. Vol. 78, iss. 4. P. 319–335. https://doi.org/10.1016/j.pocean.2008.06.003
57. Shifting from marine reserves to maritime zoning for conservation of Pacific bigeye tuna (Thunnus obesus) / J. Sibert [et al.] // Proceedings of the National Academy of Sciences of the United States of America. 2012. Vol. 109, no. 44. P. 18221–18225. https://doi.org/10.1073/pnas.1209468109
58. Modeling environmental effects on the size-structured energy flow through marine ecosystems. Part 1: The model / O. Maury [et al.] // Progress in Oceanography. 2007. Vol. 74, iss. 4. P. 479–499. https://doi.org/10.1016/j.pocean.2007.05.002
59. Ecosystem model specification within an agent based framework / R. Gray [et al.]. Hobart, Tasmania : CSIRO, 2006. 127 p. (North West Shelf Joint Environmental Management Study Technical Report ; vol. 16.
60. Fulton E. A. Approaches to end-to-end ecosystem models // Journal of Marine Systems. 2010. Vol. 81, iss. 1–2. P. 171–183. https://doi.org/10.1016/j.jmarsys.2009.12.012
61. Lessons in modelling and management of marine ecosystems: the Atlantis experience / E. A. Fulton [et al.] // Fish and Fisheries. 2011. Vol. 12, iss. 2. P. 171–188. https://doi.org/10.1111/j.1467-2979.2011.00412.x
62. Ortega-Cisneros K., Cochrane K., Fulton E. A. An Atlantis model of the southern Benguela upwelling system: Validation, sensitivity analysis and insights into ecosystem functioning // Ecological Modelling. 2017. Vol. 355. P. 49–63. https://doi.org/10.1016/j.ecolmodel.2017.04.009
63. Atlantis User’s Guide Part I: General Overview, Physics & Ecology / A. Audzijonyte [et al.]. Hobart, Australia : CSIRO, 2017. 226 p. URL: https://research.csiro.au/atlantis/wp-content/uploads/sites/52/2021/10/AtlantisUserGuide_PartI.pdf (date of access: 15.01.2022.
64. Atlantis user’s guide part II: Socio-economics / A. Audzijonyte [et al.]. Hobart, Australia : CSIRO, 2017. 109 p. URL: https://research.csiro.au/atlantis/wp-content/uploads/sites/52/2021/10/AtlantisUserGuide_PartII.pdf (date of access: 15.01.2022.
65. Kaplan I. C., Holland D. S., Fulton E. A. Finding the accelerator and brake in an individual quota fishery: linking ecology, economics, and fleet dynamics of US West Coast trawl fisher-ies // ICES Journal of Marine Science. 2014. Vol. 71, iss. 2. P. 308–319. https://doi.org/10.1093/icesjms/fst114
66. Exploring Lake Victoria ecosystem functioning using the Atlantis modeling framework / C. Nyamweya [et al.] // Environmental Modelling & Software. 2016. Vol. 86. P. 158–167. https://doi.org/10.1016/j.envsoft.2016.09.019
67. Fulton E. A., Smith A. D. M., Smith D. C. Alternative management strategies for southeast Australian commonwealth fisheries: Stage 2: Quantitative management strategy evaluation. CSIRO, 2007. 378 p.
68. Set-up of the Nordic and Barents Seas (NoBa) Atlantis model / C. Hansen [et al.]. Havforskningsinstituttet, 2016. 110 p. (Fisken og Havet ; no. 2. URL: http://hdl.handle.net/11250/2408609 (date of access: 15.01.2022.
69. Do marine ecosystem models give consistent policy evaluations? A comparison of Atlantis and Ecosim / R. E. Forrest [et al.] // Fisheries Research. 2015. Vol. 167. P. 293–312. https://doi.org/10.1016/j.fishres.2015.03.010
70. Comparing the steady state results of a range of multispecies models between and across geographical areas by the use of the jacobian matrix of yield on fishing mortality rate / J. Pope [et al.] // Fisheries Research. 2019. Vol. 209. P. 259–270. https://doi.org/10.1016/j.fishres.2018.08.011
71. End-to-end model of Icelandic waters using the Atlantis framework: Exploring system dy-namics and model reliability / E. Sturludottir [et al.] // Fisheries Research. 2018. Vol. 207. P. 9–24. https://doi.org/10.1016/j.fishres.2018.05.026
72. Sensitivity of the Norwegian and Barents Sea Atlantis end-to-end ecosystem model to param-eter perturbations of key species / C. Hansen [et al.] // PLОS ONE. 2019. Vol. 14, iss. 2. e0210419. https://doi.org/10.1371/journal.pone.0210419
73. Grimm V., Railsback S. F. Individual-based modeling and ecology. Princeton : Princeton Uni-versity Press, 2005. 448 p.
74. Uchmański J., Grimm V. Individual-based modelling in ecology: what makes the difference? // Trends in Ecology & Evolution. 1996. Vol. 11, iss. 10. P. 437–441. http://dx.doi.org/10.1016/0169-5347(96)20091-6
75. Individual-based model of yellow perch and walleye populations in Oneida Lake / K. A. Rose [et al.] // Ecological Monographs. 1999. Vol. 69, iss. 2. P. 127–154. https://doi.org/10.2307/2657234
76. Moss S., Pahl-Wostl C., Downing T. Agent-based integrated assessment modelling: the exam-ple of climate change // Integrated Assessment. 2001. Vol. 2, no. 1. P. 17–30.
77. Shin Y.-J., Cury P. Simulation of the effects of marine protected areas on yield and diversity using a multispecies, spatially explicit, individual-based model // Spatial processes and management of marine populations : proceedings of the Symposium on spatial processes and management of marine populations, October 27–30, 1999, Anchorage, Alaska / Eds. Gordon H. Kruse [et al.]. Fairbanks, Alaska : University of Alaska, 2001. Vol. 17. P. 627–642. (Lowell Wakefield Fisheries Symposia Series.
78. Shin Y.-J., Shannon L. J., Cury P. M. Simulations of fishing effects on the southern Benguela fish community using an individual-based model: learning from a comparison with ECOSIM // African Journal of Marine Science. 2004. Vol. 26, iss. 1. P. 95–114. https://doi.org/10.2989/18142320409504052
79. Simulating and testing the sensitivity of ecosystem-based indicators to fishing in the southern Benguela ecosystem / M. Travers [et al.] // Canadian Journal of Fisheries and Aquatic Sciences. 2006. Vol. 63, no. 4. P. 943–956. https://doi.org/10.1139/f06-003
80. Multiple Use Management Strategy Evaluation for the North West Shelf: Results and Discus-sion / L. R. Little [et al.]. Hobart, Tasmania : CSIRO, 2006. 50 p. (North West Shelf Joint En-vironmental Management Study Technical Report ; Vol. 18.
81. Multiple-use management strategy evaluation for coastal marine ecosystems using InVitro / A. D. McDonald [et al.] // Complex Science for a Complex World: Exploring Human Ecosys-tems with Agents / P. Perez, D. Batten (Eds.). Canberra : ANU Press, 2006. Chapter 13. P. 283–300. URL: https://www.jstor.org/stable/j.ctt2jbhz2.19 (дата обращения: 15.01.2022.
82. An agent-based modelling approach to evaluation of multiple-use management strategies for coastal marine ecosystems / A. D. McDonald [et al.] // Mathematics and Computers in Simu-lation. 2008. Vol. 78, iss. 2–3. P. 401–411. https://doi.org/10.1016/j.matcom.2008.01.039
83. Modelling for management: news from Ningaloo / E. A. Fulton [et al.] // Ningaloo Research Progress Report: Discovering Ningaloo – latest findings and their implications for manage-ment / K. Waples, E. Hollander (Eds.). Department of Environment and Conservation, WA, 2008. P. 91–94.
84. A novel modeling approach for the “end-to-end” analysis of marine ecosystems / C. E. Sansores [et al.] // Ecological Informatics. 2016. Vol. 32. P. 39–52. https://doi.org/10.1016/j.ecoinf.2016.01.001
85. Balancing end-to-end budgets of the Georges Bank ecosystem / J. H. Steele [et al.] // Progress in Oceanography. 2007. Vol. 74, iss. 4. P. 423–448. https://doi.org/10.1016/j.pocean.2007.05.003
86. Coupling planktonic ecosystem and fisheries food web models for a pelagic ecosystem: de-scription and validation for the subarctic Pacific / K. A. Kearney [et al.] // Ecological Modelling. 2012. Vol. 237–238. P. 43–62. https://doi.org/10.1016/j.ecolmodel.2012.04.006
87. Steele J. H., Ruzicka J. J. Constructing end-to-end models using ECOPATH data // Journal of Marine Systems. 2011. Vol. 87, iss. 3–4. P. 227–238. https://doi.org/10.1016/j.jmarsys.2011.04.005
88. Challenges in integrative approaches to modelling the marine ecosystems of the North Atlantic: Physics to fish and coasts to ocean / J. Holt [et al.] // Progress in Oceanography. 2014. Vol. 129, part B. P. 285–313. https://doi.org/10.1016/j.pocean.2014.04.024
89. Madec G. NEMO ocean engine. France, Institut Pierre-Simon Laplace (IPSL), 2008. 300 p. (Note du Pole de Modélisation ; no. 27). doi:10.5281/ZENODO.3248739
90. Modelling environmental effects on the size-structured energy flow through marine ecosystems. Part 2: Simulations / O. Maury [et al.] // Progress in Oceanography. 2007. Vol. 74, iss. 4. P. 500–514. https://doi.org/10.1016/j.pocean.2007.05.001
91. Lehodey P., Murtugudde R., Senina I. Bridging the gap from ocean models to population dy-namics of large marine predators: A model of mid-trophic functional groups // Progress in Oceanography. 2010. Vol. 84, iss. 1–2. P. 69–84. https://doi.org/10.1016/j.pocean.2009.09.008
92. Link J. S., Fulton E. A., Gamble R. J. The northeast US application of ATLANTIS: A full system model exploring marine ecosystem dynamics in a living marine resource management context // Progress in Oceanography. 2010. Vol. 87, iss. 1–4. P. 214–234. https://doi.org/10.1016/j.pocean.2010.09.020
93. Fisheries management under climate and environmental uncertainty: control rules and per-formance simulation / A. E. Punt [et al.] // ICES Journal of Marine Science. 2013. Vol. 71, iss. 8. P. 2208–2220. https://doi.org/10.1093/icesjms/fst057
94. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0 / D. P. Tittensor [et al.] // Geoscientific Model Development. 2018. Vol. 11, iss. 4. P. 1421–1442. https://doi.org/10.5194/gmd-11-1421-2018
95. Projecting changes in the distribution and productivity of living marine resources: A critical review of the suite of modelling approaches used in the large European project VECTORS / M. A. Peck [et al.] // Estuarine, Coastal and Shelf Science. 2018. Vol. 201. P. 40–55. https://doi.org/10.1016/j.ecss.2016.05.019
Morskoy Gidrofizicheskiy Zhurnal. 2022; 38: 105-122
Modeling of Marine Ecosystems: Experience, Modern Approaches, Directions of Development (Review). Part 1. End-to-End Models
https://doi.org/10.22449/0233-7584-2022-1-105-122Abstract
Purpose. Despite a relatively short history of marine systems modeling, which started in late 1960s – early 1970s, this discipline is developing quite intensively. Publications on marine system modeling number in the thousands. The purpose of the article is to review the achievements accumulated in this field. The main attention is paid to the general principles in marine systems modeling, and to the spectrum of the applied modern approaches. The results of analysis of more than 200 sources, i. e. research papers, monographs, sections in books, internet-resources, are summarized in the paper of two parts published separately.
Methods and Results. Over the past decades, our understanding of the patterns of marine ecosystems functioning has increased significantly, as well as the possibilities of ecological monitoring and information technologies. At the same time, the increasing number of global and regional environmental programs and projects in the field of rational use of marine resources, protection of marine ecosystems, and assessment of the climate change impacts has resulted in growth of demands for quantitative tools providing the ecosystem-based support of the initiatives in rational management of sea resources. This, in its turn, has required more complex multi-component models and led to significant increase in the number of such models. The first part of this review is focused on the end-to-end models which represent the complex integrative tools assisting in taking correct decisions for rational management of marine resource.
Conclusions. Providing testing of the scenarios “what if”, the end-to-end models are the effective modeling instruments for assessing the consequences of climatic and anthropogenic impacts on all the trophic levels of marine ecosystems including bio-geo-chemical cycle, microbial loop, and various kinds of detritus. These models are not intended for taking tactical decisions (in such cases, local object-oriented sub-models should be used), but they are indispensable instruments in strategic planning and complex assessing of the management strategies.
References
1. Using ecological models to assess ecosystem status in support of the European Marine Strategy Framework Directive / C. Piroddi [et al.] // Ecological Indicators. 2015. Vol. 58. P. 175–191. https://doi.org/10.1016/j.ecolind.2015.05.037
2. Using ecological modelling in marine spatial planning to enhance ecosystem-based management / A. Shabtay [et al.] // Marine Policy. 2018. Vol. 95. P. 14–23. https://doi.org/10.1016/j.marpol.2018.06.018
3. Integrirovannaya matematicheskaya model' bol'shoi morskoi ekosistemy Barentseva i Belogo morei – instrument dlya otsenki prirodnykh riskov i effektivnogo ispol'zova-niya biologicheskikh resursov / S. V. Berdnikov [i dr.] // Doklady Akademii nauk. 2019. T. 487, № 5. S. 566–572. https://doi.org/10.31857/S0869-56524875566-572
4. Observations and models to support the first Marine Ecosystem Assessment for the Southern Ocean (MEASO) / M. J. Brasier [et al.] // Journal of Marine Systems. 2019. Vol. 197. 103182. https://doi.org/10.1016/j.jmarsys.2019.05.008
5. Sarkisyan A. S. Chislennyi analiz i prognoz morskikh techenii. L. : Gidrometeoizdat, 1977. 182 s.
6. Chernov I. A. Osobennosti sopryazheniya modelei tsirkulyatsii morya i morskoi ekosistemy BMF // Trudy Karel'skogo nauchnogo tsentra RAN. 2018. № 7. S. 100–116. https://doi.org/10.17076/mat830
7. Krukier L. A. Matematicheskoe modelirovanie gidrodinamiki Azovskogo morya pri realizatsii proektov rekonstruktsii ego ekosistemy // Matematicheskoe modelirovanie. 1991. T. 3, № 9. S. 3–20. URL: http://mi.mathnet.ru/mm2266 (data obrashcheniya: 15.01.2022.
8. Surkov F. A., Krukier L. A., Muratova G. V. Chislennoe modelirovanie dinamiki Azov-skogo morya pri suzhenii girla Taganrogskogo zaliva // Morskoi gidrofizicheskii zhur-nal. 1989. № 6. S. 55–62.
9. Chikin A. L., Kleshchenkov A. V., Chikina L. G. Modelirovanie izmeneniya solenosti v Ta-ganrogskom zalive pri shtormovykh nagonakh // Vodnye resursy. 2019. T. 46, № 6. S. 592–597. doi:10.31857/S0321-0596466592-597
10. Muratova G. V., Andreeva E. M. Multigrid method for solving convection-diffusion problems with dominant convection // Journal of Computational and Applied Mathematics. 2009. Vol. 226, iss. 1. P. 77–83.
11. Funktsionirovanie ekosistemy Belogo morya: issledovanie na osnove matematicheskoi modeli transformatsii organogennykh veshchestv / A. V. Leonov [i dr.] // Vodnye resursy. 2004. T. 31, № 5. S. 556–575.
12. Selyutin V. V., Zadorozhnaya N. S. Modelirovanie pervichnykh zven'ev ekosistemy Azovskogo morya // Sreda, biota i modelirovanie ekologicheskikh protsessov v Azovskom more. Apatity : Izd. KNTs RAN, 2001. C. 336–368.
13. Oxygen depletion risk assessment in shallow water bodies of the Sea of Azov region / V. Kulygin [et al.] // SGEM2016 Conference Proceedings. 2016. Book 3, Vol. 2. P. 799–806. https://doi.org/10.5593/SGEM2016/B32/S15.104
14. Chislennoe modelirovanie ekologicheskogo sostoyaniya Azovskogo morya s primeneniem skhem povyshennogo poryadka tochnosti na mnogoprotsessornoi vychislitel'noi sisteme / A. I. Sukhinov [i dr.] // Komp'yuternye issledovaniya i modelirovanie. 2016. T. 8, vyp. 1. S. 151–168. URL: http://crm.ics.org.ru/journal/article/2418/ (data obrashcheniya: 15.01.2022.
15. Teoreticheskie i prikladnye aspekty modelirovaniya pervichnoi produktivnosti vodo-emov / Yu. A. Dombrovskii [i dr.]. Rostov n/D : Izd-vo Rost. un-ta, 1990. 174 s.
16. Selyutin V. V. Krugovorot veshchestva i potok energii v ekologicheskikh sistemakh: ot modeli sistemy k sisteme modelei // Obozrenie prikladnoi i promyshlennoi matema-tiki. 1994. T. 1, vyp. 6. S. 957–973.
17. SPRAT: A spatially-explicit marine ecosystem model based on population balance equations / A. N. Johanson [et al.] // Ecological Modelling. 2017. Vol. 349. P. 11–25. https://doi.org/10.1016/j.ecolmodel.2017.01.020
18. Ratsional'noe ispol'zovanie vodnykh resursov basseina Azovskogo morya: Matematiche-skie modeli / Pod red. I. I. Vorovicha. M. : Nauka, 1981. 360 s.
19. Ispol'zovanie matematicheskoi modeli ekosistemy Azovskogo morya dlya issledovaniya zakonomernostei funktsionirovaniya i struktury sistemy / I. I. Vorovich [i dr.] // Do-klady AN SSSR. 1981. T. 259, № 2. S. 302–306. URL: http://mi.mathnet.ru/dan44590 (data obrashcheniya: 15.01.2022.
20. Obshchaya kharakteristika i opisanie imitatsionnoi modeli Azovskogo morya / I. I. Vorovich [i dr.] // Doklady AN SSSR. 1981. T. 256, № 5. S. 1052–1056. URL: http://mi.mathnet.ru/dan44234 (data obrashcheniya: 15.01.2022.
21. Selyutin V. V. Malye parametry i redutsirovannye ekologicheskie modeli // Ekologiya. Ekonomika. Informatika. Seriya: Sistemnyi analiz i modelirovanie ekonomicheskikh i ekologicheskikh sistem. Rostov n/D : Izd-vo YuNTs RAN, 2019. T. 1, vyp. 4. S. 241–244. https://doi.org/10.23885/2500-395X-2019-1-4-241-244
22. Daewel U., Schrum C., Macdonald J. I. Towards end-to-end (E2E) modelling in a consistent NPZD-F modelling framework (ECOSMO E2E_v1.0): application to the North Sea and Baltic Sea // Geoscientific Model Development. 2019. Vol. 12, iss. 5. P. 1765–1789. https://doi.org/10.5194/gmd-12-1765-2019
23. Hannah C., Vezina A., St. John M. The case for marine ecosystem models of intermediate complexity // Progress in Oceanography. 2010. Vol. 84, iss. 1–2. P. 121–128. https://doi.org/10.1016/j.pocean.2009.09.015
24. Zakonomernosti ekosistemnykh protsessov v Azovskom more / G. G. Matishov [i dr.]. M. : Nauka, 2006. 304 c.
25. Selyutin V., Shabas I. A simplified approach to designing of compartmental models of spatial-temporal dynamics and matter cycling in aquatic ecosystems // 17th International Multidisci-plinary Scientific GeoConference SGEM 2017 : Proceedings. 2017. Book 21. P. 45–52. https://doi.org/10.5593/sgem2017/21/S07.007
26. Yavnaya model' poiskovogo povedeniya khishchnika / Yu. V. Tyutyunov [i dr.] // Zhurnal obshchei biologii. 2002. T. 63, № 2. S. 137–148.
27. Tyutyunov Yu. V., Titova L. I. Simple models for studying complex spatiotemporal patterns of animal behavior // Deep Sea Research Part II: Topical Studies in Oceanography. 2017. Vol. 140. P. 193–202. https://doi.org/10.1016/j.dsr2.2016.08.010
28. Tyutyunov Yu. V., Zagrebneva A. D., Azovsky A. I. Spatiotemporal pattern formation in a prey-predator system: The case study of short-term interactions between diatom microalgae and microcrustaceans // Mathematics. 2020. Vol. 8, iss. 7. 1065. https://doi.org/10.3390/math8071065
29. Huse G., Giske J., Salvanes A. G. V. Individual-based models // Handbook of Fish Biology and Fisheries / Eds. P. J. Hart, J. D. Reynolds. Oxford : Blackwell Science, 2002. Vol. 2. Chapter 11. P. 228–248. https://doi.org/10.1002/9780470693919.ch11
30. DeAngelis D. L., Mooij W. M. Individual-based modeling of ecological and evolutionary pro-cesses // Annual Review of Ecology, Evolution, and Systematics. 2005. Vol. 36. P. 147–168. https://doi.org/10.1146/annurev.ecolsys.36.102003.152644
31. Grimm V., Railsback S. F. Individual-based Modeling and Ecology. Princeton : Princeton university press, 2005. P. 448.
32. Breckling B., Middelhoff U., Reuter H. Individual-based models as tools for ecological theory and application: understanding the emergence of organizational properties in ecological systems // Ecological Modelling. 2006. Vol. 194, iss. 1–3. P. 102–113. https://doi.org/10.1016/j.ecolmodel.2005.10.005
33. A model of loggerhead sea turtle (Caretta caretta) habitat and movement in the oceanic North Pacific / M. Abecassis [et al.] // PLOS ONE. 2013. Vol. 8, iss. 9. e73274. https://doi.org/10.1371/journal.pone.0073274
34. An individual-based model of skipjack tuna (Katsuwonus pelamis) movement in the tropical Pacific ocean / J. Scutt Phillips [et al.] // Progress in Oceanography. 2018. Vol. 164. P. 63–74. https://doi.org/10.1016/j.pocean.2018.04.007
35. Process study of circulation in the Pearl River Estuary and adjacent coastal waters in the wet season using a triply-nested circulation model / X. Ji [et al.] // Ocean Modelling. 2011. Vol. 38, iss. 1–2. P. 138–160. https://doi.org/10.1016/j.ocemod.2011.02.010
36. Three-dimensional hydrodynamic-eutrophication model (HEM-3D): application to Kwang-Yang Bay, Korea / K. Park [et al.] // Marine Environmental Research. 2005. Vol. 60, iss. 2. P. 171–193. https://doi.org/10.1016/j.marenvres.2004.10.003
37. Interactive visualization of marine pollution monitoring and forecasting data via a Web-based GIS / M. Kulawiak [et al.] // Computers & Geosciences. 2010. Vol. 36, iss. 8. P. 1069–1080. https://doi.org/10.1016/j.cageo.2010.02.008
38. Science plan and implementation strategy / Eds. J. Hall [et al.]. Stockholm : IGBP Secretariat, 2005. 76 p. (IGBP Report ; No. 52).
39. Ecosystem-based fishery management / E. K. Pikitch [et al.] // Science. 2004. Vol. 305, iss. 5682. P. 346–347. https://doi.org/10.1126/science.1098222
40. The Norwegian ecosystem-based management plan for the Barents Sea / E. Olsen [et al.] // ICES Journal of Marine Science. 2007. Vol. 64, iss. 4. P. 599–602. https://doi.org/10.1093/icesjms/fsm005
41. Dealing with uncertainty in ecosystem models: The paradox of use for living marine resource management / J. S. Link [et al.] // Progress in Oceanography. 2012. Vol. 102. P. 102–114. https://doi.org/10.1016/j.pocean.2012.03.008
42. Patrick W. S., Link J. S. Myths that continue to impede progress in ecosystem-based fisheries management // Fisheries. 2015. Vol. 40, iss. 4. P. 155–160. https://doi.org/10.1080/03632415.2015.1024308
43. Plagányi E. E. Models for an Ecosystem Approach to Fisheries. Rome : Food and Agriculture Organization of the United Nations, 2007. 108 p.
44. Ecosystem model skill assessment. Yes we can! / E. Olsen [et al.] // PLOS One. 2016. Vol. 11, iss. 1. e0146467. https://doi.org/10.1371/journal.pone.0146467
45. An integrated approach is needed for ecosystem based fisheries management: insights from ecosystem-level management strategy evaluation / E. A. Fulton [et al.] // PLOS One. 2014. Vol. 9, iss. 1. e84242. https://doi.org/10.1371/journal.pone.0084242
46. Reconciling complex system models and fisheries advice: Practical examples and leads / S. Lehuta [et al.] // Aquatic Living Resources. 2016. Vol. 29, no. 2. 208. https://doi.org/10.1051/alr/2016022
47. Ecosystem oceanography for global change in fisheries / P. M. Cury [et al.] // Trends in Ecol-ogy & Evolution. 2008. Vol. 23, iss. 6. P. 338–346. https://doi.org/10.1016/j.tree.2008.02.005
48. End-to-end models for the analysis of marine ecosystems: Challenges, issues, and next steps / K. A. Rose [et al.] // Marine and Coastal Fisheries. 2010. Vol. 2, iss. 1. P. 115–130. https://doi.org/10.1577/C09-059.1
49. Complexity of coupled human and natural systems / J. Liu [et al.] // Science. 2007. Vol. 317, iss. 5844. P. 1513–1516. doi:10.1126/science.1144004
50. Interactions between changes in marine ecosystems and human communities / R. I. Perry [et al.] // Marine ecosystems and global change / M. Barange [et al.] (Eds.). New York : Oxford University Press, 2010. Chapter 8. P. 221–251. doi:10.1093/acprof:oso/9780199558025.003.0008
51. Towards end-to-end models for investigating the effects of climate and fishing in marine eco-systems / M. Travers [et al.] // Progress in Oceanography. 2007. Vol. 75, iss. 4. P. 751–770. https://doi.org/10.1016/j.pocean.2007.08.001
52. Two-way coupling versus one-way forcing of plankton and fish models to predict ecosystem changes in the Benguela / M. Travers [et al.] // Ecological Modelling. 2009. Vol. 220, iss. 21. P. 3089–3099. https://doi.org/10.1016/j.ecolmodel.2009.08.016
53. Shin Y.-J., Cury P. Exploring fish community dynamics through size-dependent trophic inter-actions using a spatialized individual-based model // Aquatic Living Resources. 2001. Vol. 14, iss. 2. P. 65–80. https://doi.org/10.1016/S0990-7440(01)01106-8
54. Christensen V., Walters C. J. Ecopath with Ecosim: methods, capabilities and limitations // Ecological Modelling. 2004. Vol. 172, iss. 2–4. P. 109–139. https://doi.org/10.1016/j.ecolmodel.2003.09.003
55. Lehodey P., Senina I., Murtugudde R. A Spatial ecosystem and populations dynamics model (SEAPODYM) – Modeling of tuna and tuna-like populations // Progress in Oceanography. 2008. Vol. 78, iss. 4. P. 304–318. https://doi.org/10.1016/j.pocean.2008.06.004
56. Senina I., Sibert J., Lehodey P. Parameter estimation for basin-scale ecosystem-linked popula-tion models of large pelagic predators: application to skipjack tuna // Progress in Oceanography. 2008. Vol. 78, iss. 4. P. 319–335. https://doi.org/10.1016/j.pocean.2008.06.003
57. Shifting from marine reserves to maritime zoning for conservation of Pacific bigeye tuna (Thunnus obesus) / J. Sibert [et al.] // Proceedings of the National Academy of Sciences of the United States of America. 2012. Vol. 109, no. 44. P. 18221–18225. https://doi.org/10.1073/pnas.1209468109
58. Modeling environmental effects on the size-structured energy flow through marine ecosystems. Part 1: The model / O. Maury [et al.] // Progress in Oceanography. 2007. Vol. 74, iss. 4. P. 479–499. https://doi.org/10.1016/j.pocean.2007.05.002
59. Ecosystem model specification within an agent based framework / R. Gray [et al.]. Hobart, Tasmania : CSIRO, 2006. 127 p. (North West Shelf Joint Environmental Management Study Technical Report ; vol. 16.
60. Fulton E. A. Approaches to end-to-end ecosystem models // Journal of Marine Systems. 2010. Vol. 81, iss. 1–2. P. 171–183. https://doi.org/10.1016/j.jmarsys.2009.12.012
61. Lessons in modelling and management of marine ecosystems: the Atlantis experience / E. A. Fulton [et al.] // Fish and Fisheries. 2011. Vol. 12, iss. 2. P. 171–188. https://doi.org/10.1111/j.1467-2979.2011.00412.x
62. Ortega-Cisneros K., Cochrane K., Fulton E. A. An Atlantis model of the southern Benguela upwelling system: Validation, sensitivity analysis and insights into ecosystem functioning // Ecological Modelling. 2017. Vol. 355. P. 49–63. https://doi.org/10.1016/j.ecolmodel.2017.04.009
63. Atlantis User’s Guide Part I: General Overview, Physics & Ecology / A. Audzijonyte [et al.]. Hobart, Australia : CSIRO, 2017. 226 p. URL: https://research.csiro.au/atlantis/wp-content/uploads/sites/52/2021/10/AtlantisUserGuide_PartI.pdf (date of access: 15.01.2022.
64. Atlantis user’s guide part II: Socio-economics / A. Audzijonyte [et al.]. Hobart, Australia : CSIRO, 2017. 109 p. URL: https://research.csiro.au/atlantis/wp-content/uploads/sites/52/2021/10/AtlantisUserGuide_PartII.pdf (date of access: 15.01.2022.
65. Kaplan I. C., Holland D. S., Fulton E. A. Finding the accelerator and brake in an individual quota fishery: linking ecology, economics, and fleet dynamics of US West Coast trawl fisher-ies // ICES Journal of Marine Science. 2014. Vol. 71, iss. 2. P. 308–319. https://doi.org/10.1093/icesjms/fst114
66. Exploring Lake Victoria ecosystem functioning using the Atlantis modeling framework / C. Nyamweya [et al.] // Environmental Modelling & Software. 2016. Vol. 86. P. 158–167. https://doi.org/10.1016/j.envsoft.2016.09.019
67. Fulton E. A., Smith A. D. M., Smith D. C. Alternative management strategies for southeast Australian commonwealth fisheries: Stage 2: Quantitative management strategy evaluation. CSIRO, 2007. 378 p.
68. Set-up of the Nordic and Barents Seas (NoBa) Atlantis model / C. Hansen [et al.]. Havforskningsinstituttet, 2016. 110 p. (Fisken og Havet ; no. 2. URL: http://hdl.handle.net/11250/2408609 (date of access: 15.01.2022.
69. Do marine ecosystem models give consistent policy evaluations? A comparison of Atlantis and Ecosim / R. E. Forrest [et al.] // Fisheries Research. 2015. Vol. 167. P. 293–312. https://doi.org/10.1016/j.fishres.2015.03.010
70. Comparing the steady state results of a range of multispecies models between and across geographical areas by the use of the jacobian matrix of yield on fishing mortality rate / J. Pope [et al.] // Fisheries Research. 2019. Vol. 209. P. 259–270. https://doi.org/10.1016/j.fishres.2018.08.011
71. End-to-end model of Icelandic waters using the Atlantis framework: Exploring system dy-namics and model reliability / E. Sturludottir [et al.] // Fisheries Research. 2018. Vol. 207. P. 9–24. https://doi.org/10.1016/j.fishres.2018.05.026
72. Sensitivity of the Norwegian and Barents Sea Atlantis end-to-end ecosystem model to param-eter perturbations of key species / C. Hansen [et al.] // PLOS ONE. 2019. Vol. 14, iss. 2. e0210419. https://doi.org/10.1371/journal.pone.0210419
73. Grimm V., Railsback S. F. Individual-based modeling and ecology. Princeton : Princeton Uni-versity Press, 2005. 448 p.
74. Uchmański J., Grimm V. Individual-based modelling in ecology: what makes the difference? // Trends in Ecology & Evolution. 1996. Vol. 11, iss. 10. P. 437–441. http://dx.doi.org/10.1016/0169-5347(96)20091-6
75. Individual-based model of yellow perch and walleye populations in Oneida Lake / K. A. Rose [et al.] // Ecological Monographs. 1999. Vol. 69, iss. 2. P. 127–154. https://doi.org/10.2307/2657234
76. Moss S., Pahl-Wostl C., Downing T. Agent-based integrated assessment modelling: the exam-ple of climate change // Integrated Assessment. 2001. Vol. 2, no. 1. P. 17–30.
77. Shin Y.-J., Cury P. Simulation of the effects of marine protected areas on yield and diversity using a multispecies, spatially explicit, individual-based model // Spatial processes and management of marine populations : proceedings of the Symposium on spatial processes and management of marine populations, October 27–30, 1999, Anchorage, Alaska / Eds. Gordon H. Kruse [et al.]. Fairbanks, Alaska : University of Alaska, 2001. Vol. 17. P. 627–642. (Lowell Wakefield Fisheries Symposia Series.
78. Shin Y.-J., Shannon L. J., Cury P. M. Simulations of fishing effects on the southern Benguela fish community using an individual-based model: learning from a comparison with ECOSIM // African Journal of Marine Science. 2004. Vol. 26, iss. 1. P. 95–114. https://doi.org/10.2989/18142320409504052
79. Simulating and testing the sensitivity of ecosystem-based indicators to fishing in the southern Benguela ecosystem / M. Travers [et al.] // Canadian Journal of Fisheries and Aquatic Sciences. 2006. Vol. 63, no. 4. P. 943–956. https://doi.org/10.1139/f06-003
80. Multiple Use Management Strategy Evaluation for the North West Shelf: Results and Discus-sion / L. R. Little [et al.]. Hobart, Tasmania : CSIRO, 2006. 50 p. (North West Shelf Joint En-vironmental Management Study Technical Report ; Vol. 18.
81. Multiple-use management strategy evaluation for coastal marine ecosystems using InVitro / A. D. McDonald [et al.] // Complex Science for a Complex World: Exploring Human Ecosys-tems with Agents / P. Perez, D. Batten (Eds.). Canberra : ANU Press, 2006. Chapter 13. P. 283–300. URL: https://www.jstor.org/stable/j.ctt2jbhz2.19 (data obrashcheniya: 15.01.2022.
82. An agent-based modelling approach to evaluation of multiple-use management strategies for coastal marine ecosystems / A. D. McDonald [et al.] // Mathematics and Computers in Simu-lation. 2008. Vol. 78, iss. 2–3. P. 401–411. https://doi.org/10.1016/j.matcom.2008.01.039
83. Modelling for management: news from Ningaloo / E. A. Fulton [et al.] // Ningaloo Research Progress Report: Discovering Ningaloo – latest findings and their implications for manage-ment / K. Waples, E. Hollander (Eds.). Department of Environment and Conservation, WA, 2008. P. 91–94.
84. A novel modeling approach for the “end-to-end” analysis of marine ecosystems / C. E. Sansores [et al.] // Ecological Informatics. 2016. Vol. 32. P. 39–52. https://doi.org/10.1016/j.ecoinf.2016.01.001
85. Balancing end-to-end budgets of the Georges Bank ecosystem / J. H. Steele [et al.] // Progress in Oceanography. 2007. Vol. 74, iss. 4. P. 423–448. https://doi.org/10.1016/j.pocean.2007.05.003
86. Coupling planktonic ecosystem and fisheries food web models for a pelagic ecosystem: de-scription and validation for the subarctic Pacific / K. A. Kearney [et al.] // Ecological Modelling. 2012. Vol. 237–238. P. 43–62. https://doi.org/10.1016/j.ecolmodel.2012.04.006
87. Steele J. H., Ruzicka J. J. Constructing end-to-end models using ECOPATH data // Journal of Marine Systems. 2011. Vol. 87, iss. 3–4. P. 227–238. https://doi.org/10.1016/j.jmarsys.2011.04.005
88. Challenges in integrative approaches to modelling the marine ecosystems of the North Atlantic: Physics to fish and coasts to ocean / J. Holt [et al.] // Progress in Oceanography. 2014. Vol. 129, part B. P. 285–313. https://doi.org/10.1016/j.pocean.2014.04.024
89. Madec G. NEMO ocean engine. France, Institut Pierre-Simon Laplace (IPSL), 2008. 300 p. (Note du Pole de Modélisation ; no. 27). doi:10.5281/ZENODO.3248739
90. Modelling environmental effects on the size-structured energy flow through marine ecosystems. Part 2: Simulations / O. Maury [et al.] // Progress in Oceanography. 2007. Vol. 74, iss. 4. P. 500–514. https://doi.org/10.1016/j.pocean.2007.05.001
91. Lehodey P., Murtugudde R., Senina I. Bridging the gap from ocean models to population dy-namics of large marine predators: A model of mid-trophic functional groups // Progress in Oceanography. 2010. Vol. 84, iss. 1–2. P. 69–84. https://doi.org/10.1016/j.pocean.2009.09.008
92. Link J. S., Fulton E. A., Gamble R. J. The northeast US application of ATLANTIS: A full system model exploring marine ecosystem dynamics in a living marine resource management context // Progress in Oceanography. 2010. Vol. 87, iss. 1–4. P. 214–234. https://doi.org/10.1016/j.pocean.2010.09.020
93. Fisheries management under climate and environmental uncertainty: control rules and per-formance simulation / A. E. Punt [et al.] // ICES Journal of Marine Science. 2013. Vol. 71, iss. 8. P. 2208–2220. https://doi.org/10.1093/icesjms/fst057
94. A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0 / D. P. Tittensor [et al.] // Geoscientific Model Development. 2018. Vol. 11, iss. 4. P. 1421–1442. https://doi.org/10.5194/gmd-11-1421-2018
95. Projecting changes in the distribution and productivity of living marine resources: A critical review of the suite of modelling approaches used in the large European project VECTORS / M. A. Peck [et al.] // Estuarine, Coastal and Shelf Science. 2018. Vol. 201. P. 40–55. https://doi.org/10.1016/j.ecss.2016.05.019
