SiMoCo: the viability of a prototype platform for a coastal monitoring system: a case study


Coastal zones are among the most productive areas in the world, offering a wide variety of valuable habitats and ecosystems services. Despite the low population density in the Brazilian coastal zone, their distribution is quite concentrated near some coastal cities and state capitals. This concentration places enormous pressure on coastal resources. Therefore, the main objective of this paper is to present an overview on the current status of SiMoCo (Sistema de Monitoramento Costeiro, or Coastal Monitoring System in English) project as a possible early warning system that can be integrated to the Brazilian Coastal Management Information System. This prototype platform provides a real-time access to the composition, organization and simulation of planktonic communities. First, our results demonstrate such a system detecting a target dinoflagellate; second, we apply structural and functional indexes to compare and characterize the ecological networks from two different coastal areas. Conclusions are made about SiMoCo’s feasibility and its possible contribution to the decision-making process within integrated coastal zone management (ICZM) strategies.

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  1. 1.

    Andrade LP, Espíndola RP, Ebecken NFF (2014) Community detection by an efficient ant colony approach. In: Proceedings of the 21st international symposium on methodologies for intelligent systems, Roskilde, DNK, 2014

  2. 2.

    Bonamano S, Piermattei V, Madonia A, Paladini de Mendoza F, Pierattini A, Martellucci R, Stefanì C, Zappalà G, Marcelli M (2015) The Civitavecchia coastal environment monitoring system (C-CEMS): an integrated approach to the study of coastal oceanographic processes. Ocean Sci Discuss 12:1595–1623

    Article  Google Scholar 

  3. 3.

    Borges AV, Gypens N (2010) Carbonate chemistry in the coastal zone responds more strongly to eutrophication than to ocean acidification. Limnol Oceanogr 55:346–353

    Article  Google Scholar 

  4. 4.

    Borja A, Bricker SB, Dauer DM, Demetriades NT, Ferreira JG, Forbes AT, Hutchings P, Jia X, Kenchington R, Marques JC, Zhu C (2008) Overview of integrative tools and methods in assessing ecological integrity in estuarine and coastal systems worldwide. Mar Pollut Bull 56:1519–1537

    Article  Google Scholar 

  5. 5.

    Carey MP, Levin PS, Townsend H, Minello TJ, Sutton GR, Francis TB, Harvey CJ, Toft JE, Arkema KK, Burke JL, Kim CK, Guerry AD, Plummer M, Spiridonov G, Ruckelshaus M (2013) Characterizing coastal foodwebs with qualitative links to bridge the gap between the theory and the practice of ecosystem-based management. ICES J Mar Sci: 1–12

  6. 6.

    Carbonel CAAH (2003) Modelling of upwelling–downwelling cycles caused by variable wind in a very sensitive coastal system. Cont Shelf Res 23:1559–1578

    Article  Google Scholar 

  7. 7.

    Carvalho WF, Granéli E (2006) Acidotropic probes and flow cytometry: a powerful combination for detecting phagotrophy in mixotrophic and heterotrophic protists. Aquat Microb Ecol 44:85–96

    Article  Google Scholar 

  8. 8.

    Coelho-Souza SA, Pereira GC, Coutinho R, Guimarães JRD (2013) Yearly variations of bacterial production in the Arraial do Cabo protection area (Cabo Frio upwelling region): an evidence of anthropogenic pressure. Braz J Microbiol 44(4):1349–1357

    Article  Google Scholar 

  9. 9.

    Copat C, Brundo MV, Arena G, Grasso A, Oliveri Conti G (2012) Seasonal variation of bioaccumulation in Engraulis encrasicolus (Linneaus, 1758) and related biomarkers of exposure. Ecotoxicol Environ Saf 86:31–37

    Article  Google Scholar 

  10. 10.

    De Leo GA, Levin S (1997) The multifaceted aspects of ecosystem integrity. Conserv Ecol.

  11. 11.

    Dubelaar GBJ, Geerders PJF (2004) Innovative technologies to monitor plankton dynamics scanning flow cytometry: a new dimension in real-time, in-situ water quality monitoring. Sea Technol: 15–21

  12. 12.

    Dunne JA, Williams SJ, Martinez ND (2002) Food-web structure and network theory: the role of connectance and size. PNAS 99(20):12917–12922

    Article  Google Scholar 

  13. 13.

    EPA - United States Environmental Protection Agency (1993) Guidance specifying management measures for sources of nonpoint pollution in coastal waters. EPA 840-B-92-002.

  14. 14.

    Ferrarini A (2011) Fuzzy cognitive maps outmatch loop analysis in dynamic modeling of ecological systems. Comput Ecol Softw 1(1):55–59

    Google Scholar 

  15. 15.

    Ferreira AP (2013) Polychlorinated biphenyl (PCB) congener concentrations in aquatic birds. Case study: Ilha Grande Bay, Rio de Janeiro, Brazil. An Acad Bras Cienc 85(4):1379–1388

    Article  Google Scholar 

  16. 16.

    Frankel DS, Olson RJ, Frankel SL, Chisholm SW (1989) Use of a neural net computer system for analysis of flow cytometric data of phytoplankton populations. Cytometry 10:540–550

    Article  Google Scholar 

  17. 17.

    Glasgow HB, Burkholder JM, Reed RE, Lewitus AJ, Kleinman JE (2004) Real-time monitoring of water quality: a review of current applications, and advancements in sensor, telemetry, and computing technologies. J Exp Mar Biol Ecol 300:409–448

    Article  Google Scholar 

  18. 18.

    Guimerà R, Stouffer DB, Sales-Pardo M, Leicht EA, Newman MEJ, Amaral LAN (2010) Origin of compartmentalization in food webs. Ecology 91:2941–2951

    Article  Google Scholar 

  19. 19.

    Hamza-Chaffai A (2014) Usefulness of bioindicators and biomarkers in pollution biomonitoring. Inter J Biotechnol Wellness Ind 3:19–26

    Article  Google Scholar 

  20. 20.

    He D, Liu J, Liu D, Jin D, Jia Z (2011) Ant colony optimization for community detection in large-scale complex networks. In: 7th international conference on natural computation, 1151–1155, Shangai, CHN, 2011

  21. 21.

    Keister JE, Bonnet D, Chiba S, Johnson CL, Mackas DL, Escribano R (2012) Zooplankton population connections, community dynamics, and climate variability. ICES J Mar Sci 69:347–350

    Article  Google Scholar 

  22. 22.

    Krause AE, Frank KA, Mason DM, Ulanowicz RE, Taylor WW (2003) Compartments revealed in food-web structure. Nature 426:282–285

    Article  Google Scholar 

  23. 23.

    Lo Kenneth, Brinkman RR, Gottardo R (2008) Automated gating of flow cytometry data via robust model based clustering. Cytometry 73A(4):321–332

    Article  Google Scholar 

  24. 24.

    Lacerda LD, Pfeiffer WC, Fiszman M (1987) Heavy metal distribution, availability and fate in Sepetiba Bay, SE Brazil. Sci Total Environ 65:163–173

    Article  Google Scholar 

  25. 25.

    Landsberg J (2002) The effects of harmful algae blooms on aquatic organisms. Rev Fish Sci 10(2):1–412

    Article  Google Scholar 

  26. 26.

    Latham II, Luke G (2006) Network flow analysis algorithms. Ecol Model 192(3–4):586–600

    Article  Google Scholar 

  27. 27.

    Leibold MA (1996) A graphical model of keystone predators in food webs: trophic regulation of abundance, incidence, and diversity patterns in communities. Am Nat 147(5):784–812

    Article  Google Scholar 

  28. 28.

    Longo G, Trovato M, Mazzei V, Ferrante M, Conti GO (2013) Ligia italic (Isopoda, Oniscidea) as bioindicator of mercury pollution of marine rocky coasts. PLoS One 8(3):e58548

    Article  Google Scholar 

  29. 29.

    McFarland MN, Rines J, Sullivan J, Donaghay P (2015) Impact of phytoplankton size and physiology on particle optical properties determined with scanning flow cytometry. Mar Eco Prog Ser 531:43–61

    Article  Google Scholar 

  30. 30.

    McPhaden MJ, Busalacchi AJ, Cheney R, Donguy JR, Gage KS, Halpern D, Ji M, Julian P, Meyers G, Mitchum Gary T, Niiler PP, Picaut J, Reynolds RW, Smith N, Takeuchi K (1998) The tropical ocean-global atmosphere observing system: a decade of progress. Mar Sci Fac Publ, Paper 45

    Google Scholar 

  31. 31.

    MMA (2014) Ministerio do Meio Ambiente. (in Portuguese). Accessed on 14 April 2015

  32. 32.

    Molisani MM, Marins RV, Machado W et al (2004) Environmental changes in Sepetiba Bay, SE Brazil. Reg Environ Change 4:17–27

    Article  Google Scholar 

  33. 33.

    Montoya JM, Sole RV (2000) Small world patterns in food webs. arXiv:cond-mat/0011195v1 [cond-mat.dis-nn]

  34. 34.

    Nicholls RJ, Wong PP, Burkett VR, Codignotto JO, Hay JE, McLean RF, Ragoonaden S, Woodroffe CD (2007) Coastal systems and low-lying areas. Climate change: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. In: Parry ML, Canziani OF, Palutikof JP, van der Linden PJ, Hanson CE (eds). Cambridge University Press, Cambridge, UK, 315–356

  35. 35.

    O`Gorman EJ, Fitch JE, Crowe TP (2012) Multiple anthropogenic stressors and the structural properties of food webs. Ecology 93(3):441–448

    Article  Google Scholar 

  36. 36.

    Payet JP, Suttle CA (2013) To kill or not to kill: the balance between lytic and lysogenic viral infection is driven by trophic status. Limnol Oceanogr 58:465–474

    Article  Google Scholar 

  37. 37.

    Pearl MR, Swanstrom JA, Bruckman LS, Richardson TL, Shaw TJ, Sosik HM, Myrick ML (2013) Taxonomic classification of phytoplankton with multivariate optical computing. Part III: demonstration. Appl Spectrosc 67(3):640–647

    Article  Google Scholar 

  38. 38.

    Pereira GC, Andrade LP, Espínola RP, Ebecken NFF (2014) Structural analysis and static simulation of coastal planktonic networks. J Intell Learn Syst Appl 6:113–124

    Google Scholar 

  39. 39.

    Pereira GC, Ebecken NFF (2011) Combining in situ flow cytometry and artificial neural network for aquatic system monitoring. Expert Syst Appl 38:9626–9632

    Article  Google Scholar 

  40. 40.

    Pereira GC, Evsukoff A, Ebecken NFF (2009) Fuzzy modelling of chlorophyll production in a Brazilian upwelling system. Ecol Model 220:1506–1512

    Article  Google Scholar 

  41. 41.

    Pinheiro FM, Fernandez MA, Fragoso MR, Quadros JP, Camillo E Jr, dos Santos FA (2006) Assessing the impacts of organotin compounds in Ilha Grande Bay, (Rio de Janeiro, Brazil): imposex and a multiple-source dispersion model. J Coast Res SI 39:1383–1388

    Google Scholar 

  42. 42.

    Pomati S, Jokela J, Simona S, Veronesi M, Ibelings Bas W (2011) An automated platform for phytoplankton and aquatic ecosystem monitoring. Environ Sci Technol 45:9658–9665

    Article  Google Scholar 

  43. 43.

    Preece J, Rogers Y, Sharp E, Benyon D, Holland S, Carey T (1994) Human–computer interaction. Addison-Wesley

  44. 44.

    Rajwa B, Murugesan V, Ragheb K, Banada PP, Hirleman D, Lary T, Robinson JP (2008) Automated classification of bacterial particles in flow by multi-angle scatter measurement and support vector machine classifier. Cytometry 73A(4):369–379

    Article  Google Scholar 

  45. 45.

    Raven JA (1998) The twelfth Tansley Lecture. Small is beautiful: the picophytoplankton. Funct Ecol 12:503–513

    Article  Google Scholar 

  46. 46.

    Robinson JP, Durante C, cocchi M, Cossarizza A (2007) Subject classification obtained by cluster analysis and principal component analysis to flow cytometric data. 71(5): 334–344

  47. 47.

    Rossberg AG (2012) Food webs. Encyclopedia of theoretical ecology.In: Hastings A, Gross L (eds). University of California Press, Berkeley, CA (2012), 1–13

  48. 48.

    Rutten TPA, Sandee B, Hofman ART (2005) Phytoplankton monitoring by high performance flow cytometry: a successful approach? Cytom Part A 64A:16–26

    Article  Google Scholar 

  49. 49.

    Shapiro HM (2003) Practical flow cytometry, 4o edn. Wiley, Hoboken

    Google Scholar 

  50. 50.

    Sigman DM, Hain MP (2012) The biological productivity of the ocean. Nat Educ Knowl 3(10):21

    Google Scholar 

  51. 51.

    Sorensen J (1993) The international proliferation of integrated coastal zone management efforts. Ocean Coast Manag 21:129–148

    Article  Google Scholar 

  52. 52.

    Sosik HM, Olson RJ (2007) Automated taxonomic classification of phytoplankton sampled with imaging-in-flow cytometry. Limnol Oceanogr Methods 5:204–216

    Article  Google Scholar 

  53. 53.

    Stylios E, Groumpos PP (1999) Mathematical formulation of fuzzy cognitive maps. Proceedings of the 7th mediterranean conference on control and automation (MED99) Haifa, Israel, June 28–30, 1999

  54. 54.

    Stouffer DB, Bascompte J (2011) Compartmentalization increases food web persistence. PNAS 108(9):3648–3652

    Article  Google Scholar 

  55. 55.

    Taylor F, Fukuyo Y, Larson J (1995) Taxonomy of harmful dinoflagellates. In: Hallengreff GM, Andersen DM, Cembella AD (eds). Manual of harmfull marine microalgae, Unesco, France, pp 283–309

  56. 56.

    Torán F, Ramírez D, Navarro AE, Casans S, Pelegrí J, Espí JM (2001) Design of a virtual instrument for water quality monitoring across the internet. Sens Actuators B 76:281–285

    Article  Google Scholar 

  57. 57.

    UNEP, UN-HABITAT (2005) Coastal area pollution the role of cities. Local capacities for global agendas. September 2005

  58. 58.

    van Haegen SM (2013) Water innovations in the Netherlands: a brief overview. Ministry of Infrastructure and the Environment. Accessed 04 Sept 2014

  59. 59.

    Velho AMA, Aiub CAF, Corrêa SM, Soares MLG, Felzenszwalb I (2012) Preliminary study by environmental indicator measurements of sediments in a mangrove forest in Ilha Grande Bay, Rio de Janeiro, Southeastern Brazil. J Environ Prot 3:731–739

    Article  Google Scholar 

  60. 60.

    Wang G, Johnson ZI (2009) Impact of Parasitic fungi on the diversity and functional ecology of marine phytoplankton. In: Kersey WT, Murgen SP (eds) Marine phytoplankton, 211–228

  61. 61.

    Warwick RM (2006) Environmental impact studies on marine communities: pragmatical considerations. Aust J Ecol 18(1):63–80

    Article  Google Scholar 

  62. 62.

    Weber T, Deutsch C (2012) Oceanic nitrogen reservoir regulated by plankton diversity and ocean circulation. Nature 489:419–424

    Article  Google Scholar 

  63. 63.

    Wirtz KW (2012) Who is eating whom? Morphology and feeding type determine the size relation between planktonic predators and their ideal prey. Mar Ecol Prog Ser 445:1–12

    Article  Google Scholar 

  64. 64.

    Zu W, Li D, He D, Wang J, Ma D, Li F (2010) A remote wireless system for water quality online monitoring in intensive fish culture. Comput Electron Agric 71S:S3–S9

    Google Scholar 

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The authors are grateful to the Brazilian Navy for logistical support and collaboration. We also thank Petróleo Brasileiro S.A. for participation in the cross action (ref. 2095/04 MCT/FINEP/PETROBRAS) in the early stages of this project and the Coordination of Higher Education Personnel Improvement (CAPES) for the financial support during the 2009 Postdoctoral National Program. We also thank the anonymous reviewers for their contributions. The authors declare that they have no competing interests.

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Correspondence to G. C. Pereira.

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Pereira, G.C., Oliveira, M.M.F., Andrade, L.P. et al. SiMoCo: the viability of a prototype platform for a coastal monitoring system: a case study. J Mar Sci Technol 21, 651–662 (2016).

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  • Real-time monitoring
  • In situ scanning flow cytometry
  • Ecological networks
  • Coastal zone management