241
Views
7
CrossRef citations to date
0
Altmetric
Articles

Modeling the drivers of interannual variability in cyanobacterial bloom severity using self-organizing maps and high-frequency data

ORCID Icon, , &

References

  • Andersen T, Carstensen J, Hernández-García E, Duarte CM. 2009. Ecological thresholds and regime shifts: approaches to identification. Trends Ecol Evol. 24:49–57.
  • Arhonditsis GB, Brett MT. 2004. Evaluation of the current state of mechanistic aquatic biogeochemical modeling. Mar Ecol Prog Ser. 271:13–26.
  • Barreto GA. 2007. Time series prediction with the self organizing map: a review. Berlin Heidelberg: Springer. Perspectives of Neural-Symbolic Integration; p. 135–158.
  • Bonissone PP. 1997. Soft computing: the convergence of emerging reasoning technologies. Soft Comput. 1:6–18.
  • Brauer VS, Stomp M, Huisman J. 2012. The nutrient-load hypothesis: patterns of resource limitation and community structure driven by competition for nutrients and light. Am Nat. 179:721–740.
  • Brooks BW, Lazorchak JM, Howard MDA, Johnson MV, Morton SL, Perkins DAK, Reavie ED, Scott GI, Smith SA, Steevens JA. 2015. Are harmful algal blooms becoming the greatest inland water quality threat to public health and aquatic ecosystems? Environ Toxicol Chem. 35:6–13.
  • Burger DF, Hamilton DP, Pilditch CA. 2008. Modelling the relative importance of internal and external nutrient loads on water column nutrient concentrations and phytoplankton biomass in a shallow polymictic lake. Ecol Model. 211:411–423.
  • Butterwick C, Heaney SI, Talling JF. 2005. Diversity in the influence of temperature on the growth rates of freshwater algae, and its ecological relevance. Freshw Biol. 50:291–300.
  • Carey CC, Hanson PC, Lathrop RC, St Amand AL. 2016. Using wavelet analyses to examine variability in phytoplankton seasonal succession and annual periodicity. J Plankton Res. 38:27–40.
  • Carey CC, Ibelings BW, Hoffmann EP, Hamilton DP, Brookes JD. 2012. Eco-physiological adaptations that favour freshwater cyanobacteria in a changing climate. Water Research. 46:1394–1407.
  • Coloso JJ, Cole JJ, Hanson PC, Pace ML. 2008. Depth-integrated, continuous estimates of metabolism in a clear-water lake. Can J Fish Aquat Sci. 65:712–722.
  • Coloso JJ, Cole JJ, Pace ML. 2011. Difficulty in discerning drivers of lake ecosystem metabolism with high-frequency data. Ecosystems. 14:935–948.
  • Costa JA, Netto ML. 1999. Estimating the number of clusters in multivariate data by self-organizing maps. Int J Neural Syst. 9:195–202.
  • Crusius J, Wanninkhof R. 2003. Gas transfer velocities measured at low wind speed over a lake. Limnol Oceanogr. 48:1010–1017.
  • Faassen EJ, Veraart AJ, van Nes EH, Dakos D, Lurling M, Scheffer M. 2015. Hysteresis in an experimental phytoplankton population. Oikos. 124:1617–1623.
  • Giles CD, Isles PDF, Manley T, Xu Y, Druschel GK, Schroth AW. 2016. The mobility of phosphorus, iron, and manganese through the sediment–water continuum of a shallow eutrophic freshwater lake under stratified and mixed water-column conditions. Biogeochemistry. 127:15–34.
  • Heisler J, Glibert PM, Burkholder JM, Anderson DM, Cochlan W, Dennison WC, Dortch Q, Gobler CJ, Heil CA, Humphries E, Lewitus A, et al. 2008. Eutrophication and harmful algal blooms: a scientific consensus. Harmful Algae. 8:3–13.
  • Hipsey MR, Hamilton DP, Hanson PC, Carey CC, Coletti JZ, Read JS, Ibelings BW, Valesini FJ, Brookes JD. 2015. Predicting the resilience and recovery of aquatic systems: a framework for model evolution within environmental observatories. Water Resour Res. 51:7023–7043.
  • Huber V, Adrian R, Gerten D. 2008. Phytoplankton response to climate warming modified by trophic state. Limnol Oceanogr. 53:1–13.
  • Huber V, Wagner C, Gerten D, Adrian R. 2012. To bloom or not to bloom: contrasting responses of cyanobacteria to recent heat waves explained by critical thresholds of abiotic drivers. Oecologia. 169:245–256.
  • Huisman J, Sharples J, Stroom JM, Visser PM, Kardinaal WEA, Verspagen JMH, Sommeijer B. 2004. Changes in turbulent mixing shift competition for light between phytoplankton species. Ecology. 85:2960–2970.
  • Isles PDF, Giles CD, Gearhart TA, Xu Y, Druschel GK, Schroth AW. 2015. Dynamic internal drivers of a historically severe cyanobacteria bloom in Lake Champlain revealed through comprehensive monitoring. J Great Lakes Res. 43:818–829.
  • Jennings E, Jones S, Arvola L, Staehr PA, Gaiser E, Jones I, Weathers KC, Weyhenmeyer GA, Chiu C, De Eyto E. 2012. Effects of weather-related episodic events in lakes: an analysis based on high-frequency data. Freshw Biol. 57:589–601.
  • Jöhnk KD, Huisman J, Sharples J, Sommeijer B, Visser PM, Stroom JM. 2008. Summer heatwaves promote blooms of harmful cyanobacteria. Glob Chang Biol. 14:495–512.
  • Kalteh AM, Hjorth P, Berndtsson R. 2008. Review of the self-organizing map (SOM) approach in water resources: analysis, modelling and application. Environ Model Softw. 23:835–845.
  • Klug JL, Richardson DC, Ewing HA, Hargreaves BR, Samal NR, Vachon D, Pierson D, Lindsey A, O’Donnel DM, Effler SW, Weathers KC. 2012. Ecosystem effects of a tropical cyclone on a network of lakes in Northeastern North America. Environ Sci Technol. 46:11693–11701.
  • Kohonen T. 1990. The self-organizing map. Proc IEEE. 78:1464–1480.
  • Kosten S, Huszar VLM, Bécares E, Costa LS, Van Donk E, Hansson L, Jeppesen E, Kruk C, Lacerot G, Mazzeo N, et al. 2011. Warmer climates boost cyanobacterial dominance in shallow lakes. Glob Chang Biol. 18:118–126.
  • Kotti EP, Sylaios GK, Tsihrintizis VA. 2013. Fuzzy logic models for BOD removal prediction in free-water surface constructed wetlands. Ecol Eng. 51:66–74.
  • Kotti IP, Sylaios GK, Tsihrintzis VA. 2016. Fuzzy modeling for nitrogen and phosphorus estimation in free-water surface constructed wetlands. Environ Process. 3 (Suppl 1):65–S79.
  • Kuha J, Arvola L, Hanson PC, Huotari J, Huttula T, Juntunen J, Järvinen M, Kallio K, Ketola M, Kuoppamäki K, et al. 2016. Response of boreal lakes to episodic weather-induced events. Inland Waters. 6:523–534.
  • Laas A, Noges P, Kõiv T, Nõges T. 2012. High-frequency metabolism study in a large and shallow temperate lake reveals seasonal switching between net autotrophy and net heterotrophy. Hydrobiologia. 694:57–74.
  • Levine SN, Lini A, Ostrofsky ML, Bunting L, Burgess H, Leavitt PR, Reuter D, Lami A, Guilizzoni P, Gilles E. 2012. The eutrophication of Lake Champlain’s northeastern arm: insights from paleolimnological analyses. J Great Lakes Res. 38:35–48.
  • Maier HR, Dandy GC. 2000. Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ Model Softw. 15:101–124.
  • Mangiameli P, Chen SK, West D. 1996. A comparison of SOM neural network and hierarchical clustering methods. Eur J Operational Res. 93:402–417.
  • McCarthy MJ, Gardner WS, Lehmann MF, Guindon A, Bird DF. 2016. Benthic nitrogen regeneration, fixation, and denitrification in a temperate, eutrophic lake: effects on the nitrogen budget and cyanobacteria blooms. Limnol Oceanogr. 61:1406–1423.
  • McQuaid N, Zamyadi A, Prévost M, Bird DF, Dorner S. 2011. Use of in vivo phycocyanin fluorescence to monitor potential microcystin-producing cyanobacterial biovolume in a drinking water source. J Environ Monit. 13:455–463.
  • Melssen W, Wehrens R, Buydens L. 2006. Supervised Kohonen networks for classification problems. Chemometr Intell Lab Syst. 83:99–113.
  • Mitrovic SM, Bowling LC, Buckney RT. 2001. Vertical disentrainment of Anabaena circinalis in the turbid, freshwater Darling River, Australia: quantifying potential benefits from buoyancy. J Plankton Res. 23:47–55.
  • Mooij WM, Trolle D, Jeppesen E, Arhonditsis G, Belolipetsky PV, Chitamwebwa DBR, Degermendzhy AG, DeAngelis DL, De Senerpont Domis LN, Downing AS, et al. 2010. Challenges and opportunities for integrating lake ecosystem modelling approaches. Aquat Ecol. 44:633–667.
  • Muttil N, Chau KW. 2006. Neural network and genetic programming for modelling coastal algal blooms. Int J Environ Pollut. 28:223–238.
  • Muttil N, Chau KW. 2007. Machine-learning paradigms for selecting ecologically significant input variables. Eng Appl Artif Intell. 20:735–744.
  • Obenour DR, Gronewold AD, Stow CA, Scavia D. 2014. Using a Bayesian hierarchical model to improve Lake Erie cyanobacteria bloom forecasts. Water Resour Res. 50:7847–7860.
  • Odum HT. 1956. Primary production in flowing waters. Limnol Oceanogr. 1:102–117.
  • O’Neil JM, Davis TW, Burford MA, Gobler CJ. 2012. The rise of harmful cyanobacteria blooms: the potential roles of eutrophication and climate change. Harmful Algae. 14:313–334.
  • Paerl HW, Huisman J. 2009. Climate change: a catalyst for global expansion of harmful cyanobacterial blooms. Environ Microbiol Reports. 1:27–37.
  • Park YS, Chon TS, Kwak IS, Lek S. 2004. Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks. Sci Total Environ. 327:105–122.
  • Park YS, Kwon YS, Hwang SJ, Park S. 2014. Characterizing effects of landscape and morphometric factors on water quality of reservoirs using a self-organizing map. Environ Model Softw. 55:214–221.
  • Pearce AR, Rizzo DM, Mouser PJ. 2011. Subsurface characterization of groundwater contaminated by landfill leachate using microbial community profile data and a nonparametric decision-making process. Water Resour Res. 47:W06511.
  • Pearce AR, Rizzo DM, Watzin MC, Druschel GK. 2013. Unraveling associations between cyanobacteria blooms and in-lake environmental conditions in Missisquoi Bay, Lake Champlain, USA, using a modified self-organizing map. Environ Sci Technol. 47:14267–14274.
  • Penn BS. 2005. Using self-organizing maps to visualize high-dimensional data. Comput Geosci. 31:531–544.
  • Pernica P, Wells MG, MacIntyre S. 2013. Persistent weak thermal stratification inhibits mixing in the epilimnion of north-temperate Lake Opeongo, Canada. Aquat Sci. 76:187–201.
  • Pryor SC, Barthelmie RJ, Young DT, Takle ES, Arritt RW, Flory D, Gutowski WJ, Nunes A, Roadds J. 2009. Wind speed trends over the contiguous United States. J Geophys Res. 114:D14105.
  • Pryor SC, Schoof JT, Barthelmie RJ. 2006. Winds of change?: projections of near-surface winds under climate change scenarios. Geophys Res Lett. 33:L11702.
  • Reckhow KH. 1999. Water quality prediction and probability network models. Can J Fish Aquat Sci. 56:1150–1158.
  • Recknagel F, Cao H, Kim B, Takamura N, Welk A. 2006. Unravelling and forecasting algal population dynamics in two lakes different in morphometry and eutrophication by neural and evolutionary computation. Ecol Inform. 1:133–151.
  • Recknagel F, French M, Harkonen P, Yabunaka KI. 1997. Artificial neural network approach for modelling and prediction of algal blooms. Ecol Modell. 96:11–28.
  • Reynolds CS, Oliver RL, Walsby AE. 1987. Cyanobacterial dominance: the role of buoyancy regulation in dynamic lake environments. N Z J Mar Freshwater Res. 21:379–390.
  • Rigosi A, Hanson P, Hamilton DP, Hippsey M, Rusak JA, Bois J, Sparber K, Chorus I, Watkinson AJ, Boqiang Qin, et al. 2015. Determining the probability of cyanobacterial blooms: the application of Bayesian networks in multiple lake systems. Ecol Appl. 25:186–199.
  • Rimet F, Druart JC, Anneville O. 2009. Exploring the dynamics of plankton diatom communities in Lake Geneva using emergent self-organizing maps (1974–2007). Ecol Inform. 4:99–110.
  • Robarts RD, Zohary T. 1987. Temperature effects on photosynthetic capacity, respiration, and growth rates of bloom-forming cyanobacteria. N Z J Mar Freshwater Res. 21:391–399.
  • Sass GZ, Creed IF, Bayley SE, Devito KJ. 2008. Interannual variability in trophic status of shallow lakes on the Boreal Plain: i there a climate signal? Water Resour Res. 44:W08443.
  • Smeltzer E, Shambaugh AD, Stangel P. 2012. Environmental change in Lake Champlain revealed by long-term monitoring. J Great Lakes Res. 38:6–18.
  • Smith L, Watzin MC, Druschel G. 2011. Relating sediment phosphorus mobility to seasonal and diel redox fluctuations at the sediment–water interface in a eutrophic freshwater lake. Limnol Oceanogr. 56:2251–2264.
  • Søndergaard M, Jensen JP, Jeppesen E. 1999. Internal phosphorus loading in shallow Danish lakes. Hydrobiologia. 408:145–152.
  • Søndergaard M, Larsen SE, Johansson LS, Lauridsen TL, Jeppesen E. 2015. Ecological classification of lakes: uncertainty and the influence of year-to-year variability. Ecol Indic. 68:248–257.
  • Staehr PA, Bade D, Van de Bogert MC, Koch GR, Williamson C, Hanson P, Cole JJ, Kratz T. 2010. Lake metabolism and the diel oxygen technique: state of the science. Limnol Oceanogr-Meth. 8:628–644.
  • Staehr PA, Sand-Jensen K. 2007. Temporal dynamics and regulation of lake metabolism. Limnol Oceanogr. 52:108–120.
  • Staehr PA, Testa PM, Kemp WM, Cole JJ, Sand-Jensen K, Smith SV. 2011. The metabolism of aquatic ecosystems: history, applications, and future challenges. Aquat Sci. 74:15–29.
  • Strock KE, Saros JE, Nelson SJ, Birkel SD, Kahl JS, McDowell WH. 2016. Extreme weather years drive episodic changes in lake chemistry: implications for recovery from sulfate deposition and long-term trends in dissolved organic carbon. Biogeochemistry. 127:353–365.
  • Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR. 1999. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci. 96:2907–2912.
  • Tran LT, Knight CG, O’Neill RV, Smith ER, O’Connell M. 2003. Self-organizing maps for integrated environmental assessment of the mid-Atlantic region. Environ Manage. 31:822–835.
  • [USEPA] United States Environmental Protection Agency. 2014a. Method 350.1, Rev. 2.0. 1–15.
  • [USEPA] United States Environmental Protection Agency. 2014b. Method 365.1, Rev. 2.0. 1–17.
  • Vesanto J, Alhoniemi E. 2000. Clustering of the self-organizing map. IEEE Trans Neural Netw. 11:586–600.
  • Villmann T, Merényi E. 2002. Extensions and modifications of the Kohonen-SOM and applications in remote sensing image analysis. Stud Fuzzy Soft Comput. 78:121–144.
  • Voigt B, Lees L, Erickson J. 2015. An assessment of the economic value of clean water in Lake Champlain. Lake Champlain Basin Program Technical Report no. 81. Available from: http://www.lcbp.org/media-center/publications-library/technical-reports/
  • Wehrens R, Buydens LMC. 2007. Self- and super-organizing maps in R: the kohonen package. J Stat Softw. 21:1–19.
  • Wei T. 2013. Corrplot: Visualization of a correlation matrix. R Package version 0.73. Available from: http://CRAN.R-project.org/package=corrplot
  • Winslow L, Zwart JA, Batt R, and others. 2015. LakeMetabolizer: tools for the analysis of ecosystem metabolism. R Package version. 1:3.
  • Wojtal-frankiewicz A, Kruk A, Frankiewicz P, Oleksinska Z, Katarzyna I. 2015. Long-term patterns in the population dynamics of Daphnia longispina, Leptodora kindtii and cyanobacteria in a shallow reservoir: a self-organizing map (SOM) appraoch. PLoS ONE. 10:e0144109.
  • Woolway RI, Jones ID, Maberly SC, French JR, Livingstone DM, Monteith DT, Simpson GL, Thackeray SJ, Andersen MR, Battarbee RW, et al. 2016. Diel surface temperature range scales with lake size. PLoS ONE. 11:e0152466.
  • Wu CL, Chau KW, Li YS. 2009. Methods to improve neural network performance in daily flows prediction. J Hydrol. 372:80–93.
  • Zamyadi A, McQuaid N, Prévost M, Dorner S. 2012. Monitoring of potentially toxic cyanobacteria using an online multi-probe in drinking water sources. J Environ Monit. 14:579–588.
  • Zia A, Bomblies A, Schroth AW, Koliba C, Isles PDF, Tsai Y, Mohammed I, Bucini G, Clemins P, Turnbull S, et al. 2016. Coupled impacts of climate and land use change across a river–lake continuum: insights from an integrated assessment model for Lake Champlain’s Missisquoi Basin, 2000–2040. Environ Res Lett. 11:114026.
  • Zilius M, Bartoli M, Bresciani M, Katarzyte M, Ruginis T, Jolita P, Lubiene I, Giardino C, Bukaveckas PA, de Wit R, Razinkovas-Baziukas A. 2013. Feedback mechanisms between cyanobacterial blooms, transient hypoxia, and benthic phosphorus regeneration in shallow coastal environments. Estuar Coasts. 37:680–694.
  • Zuur AF, Ieno EN, Elphick CS. 2009. A protocol for data exploration to avoid common statistical problems. Meth Ecol Evol. 1:3–14.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.