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Article

Temperature Effects Explain Continental ScaleDistribution of Cyanobacterial Toxins

Evanthia Mantzouki 1 Miquel Luumlrling 23 ID Jutta Fastner 4 Lisette de Senerpont Domis 23Elzbieta Wilk-Wozniak 5 Judita Koreiviene 6 Laura Seelen 23 ID Sven Teurlincx 3 ID Yvon Verstijnen 2 Wojciech Krzton 5 Edward Walusiak 5 Jurate Karosiene 6Jurate Kasperoviciene 6 Ksenija Savadova 6 Irma Vitonyte 6 Carmen Cillero-Castro 7 ID Agnieszka Budzynska 8 Ryszard Goldyn 8 ID Anna Kozak 8 ID Joanna Rosinska 8 ID Elzbieta Szelag-Wasielewska 8 Piotr Domek 8 Natalia Jakubowska-Krepska 8 ID Kinga Kwasizur 9 ID Beata Messyasz 9 Aleksandra Pełechata 9 Mariusz Pełechaty 9Mikolaj Kokocinski 9 Ana Garciacutea-Murcia 10 Monserrat Real 10 Elvira Romans 10Jordi Noguero-Ribes 10 David Parrentildeo Duque 10 Eliacutesabeth Fernaacutendez-Moraacuten 10 ID Nusret Karakaya 11 Kerstin Haumlggqvist 12 Nilsun Demir 13 ID Meryem Beklioglu 14 Nur Filiz 14Eti E Levi 14 ID Ugur Iskin 14 Gizem Bezirci 14 Uumllkuuml Nihan Tavsanoglu 14 Koray Oumlzhan 15 ID Spyros Gkelis 16 ID Manthos Panou 16 Oumlzden Fakioglu 17 Christos Avagianos 18Triantafyllos Kaloudis 18 ID Kemal Ccedilelik 19 Mete Yilmaz 20 Rafael Marceacute 21Nuria Catalaacuten 2122 ID Andrea G Bravo 22 ID Moritz Buck 22 William Colom-Montero 23Kristiina Mustonen 23 Don Pierson 23 Yang Yang 23 Pedro M Raposeiro 24 Viacutetor Gonccedilalves 24 ID Maria G Antoniou 25 Nikoletta Tsiarta 25 Valerie McCarthy 26 Victor C Perello 26Totildenu Feldmann 27 Alo Laas 27 ID Kristel Panksep 27 Lea Tuvikene 27 Ilona Gagala 28Joana Mankiewicz-Boczek 28 ID Meral Apaydın Yagcı 29 ID Sakir Ccedilınar 29 Kadir Ccedilapkın 29Abdulkadir Yagcı 29 Mehmet Cesur 29 Fuat Bilgin 29 Cafer Bulut 29 Rahmi Uysal 29Ulrike Obertegger 30 ID Adriano Boscaini 30 Giovanna Flaim 30 ID Nico Salmaso 30 ID Leonardo Cerasino 30 Jessica Richardson 31 Petra M Visser 32 Jolanda M H Verspagen 32Tuumlnay Karan 33 Elif Neyran Soylu 34 ID Faruk Maraslıoglu 35 ID Agnieszka Napioacuterkowska-Krzebietke 36 ID Agnieszka Ochocka 37 Agnieszka Pasztaleniec 37Ana M Antatildeo-Geraldes 38 ID Vitor Vasconcelos 39 ID Joatildeo Morais 39 ID Micaela Vale 39 ID Latife Koumlker 40 Reyhan Akccedilaalan 40 ID Mericcedil Albay 40 Dubravka Špoljaric Maronic 41Filip Stevic 41 Tanja Žuna Pfeiffer 41 Jeremy Fonvielle 42 Dietmar Straile 43 ID Karl-Otto Rothhaupt 43 Lars-Anders Hansson 44 Pablo Urrutia-Cordero 2244 ID Ludek Blaacuteha 45Rodan Geriš 46 Markeacuteta Fraacutenkovaacute 47 Mehmet Ali Turan Koccediler 48 Mehmet Tahir Alp 49Spela Remec-Rekar 50 Tina Elersek 51 Theodoros Triantis 52 ID Sevasti-Kiriaki Zervou 52 ID Anastasia Hiskia 52 Sigrid Haande 53 Birger Skjelbred 53 Beata Madrecka 54 Hana Nemova 55Iveta Drastichova 55 Lucia Chomova 55 Christine Edwards 56 Tugba Ongun Sevindik 57Hatice Tunca 57 Burccedilin Oumlnem 57 Boris Aleksovski 58 Svetislav Krstic 58 Itana Bokan Vucelic 59Lidia Nawrocka 60 Pauliina Salmi 61 Danielle Machado-Vieira 62 Alinne Gurjatildeo de Oliveira 62Jordi Delgado-Martiacuten 63 David Garciacutea 63 Jose Luiacutes Cereijo 63 Joan Gomagrave 64 ID Mari Carmen Trapote 64 Teresa Vegas-Vilarruacutebia 64 ID Biel Obrador 64 Magdalena Grabowska 65Maciej Karpowicz 65 ID Damian Chmura 66 Baacuterbara Uacutebeda 67 Joseacute Aacutengel Gaacutelvez 67 ID Arda Oumlzen 68 ID Kirsten Seestern Christoffersen 69 ID Trine Perlt Warming 69 Justyna Kobos 70Hanna Mazur-Marzec 70 Carmen Peacuterez-Martiacutenez 71 ID Eloiacutesa Ramos-Rodriacuteguez 71 ID Lauri Arvola 72 Pablo Alcaraz-Paacuterraga 73 Magdalena Toporowska 74Barbara Pawlik-Skowronska 74 Michał Niedzwiecki 74 Wojciech Peczuła 74 Manel Leira 75 ID Armand Hernaacutendez 76 ID Enrique Moreno-Ostos 77 Joseacute Mariacutea Blanco 77 Valeriano Rodriacuteguez 77Jorge Juan Montes-Peacuterez 77 Roberto L Palomino 77 Estela Rodriacuteguez-Peacuterez 77Rafael Carballeira 78 Antonio Camacho 79 ID Antonio Picazo 79 Carlos Rochera 79 ID Anna C Santamans 79 Carmen Ferriol 79 Susana Romo 80 Juan Miguel Soria 80 ID

Toxins 2018 10 156 doi103390toxins10040156 wwwmdpicomjournaltoxins

Toxins 2018 10 156 2 of 24

Julita Dunalska 81 Justyna Sienska 81 Daniel Szymanski 81 Marek Kruk 82 ID Iwona Kostrzewska-Szlakowska 83 Iwona Jasser 84 Petar Žutinic 85 Marija Gligora Udovic 85Anđelka Plenkovic-Moraj 85 Magdalena Frak 86 Agnieszka Bankowska-Sobczak 87Michał Wasilewicz 87 Korhan Oumlzkan 88 Valentini Maliaka 28990 ID Kersti Kangro 2791Hans-Peter Grossart 4292 ID Hans W Paerl 93 ID Cayelan C Carey 94 ID and Bas W Ibelings 1

1 Department F-A Forel for Environmental and Aquatic Sciences University of Geneva 1205 GenevaSwitzerland BastiaanIbelingsunigech

2 Department of Environmental Sciences Wageningen University amp Research 6700 WageningenThe Netherlands miquellurlingwurnl (ML) ldesenerpontdomisniooknawnl (LdSD)lseelenniooknawnl (LS) yvonverstijnenhotmailcom (YV) valentinimaliakagmailcom (VM)

3 Department of Aquatic Ecology Netherlands Institute of Ecology (NIOO-KNAW) 6700 WageningenThe Netherlands steurlincxniooknawnl

4 German Environment Agency Unit Drinking Water Resources and Water Treatment Corrensplatz 114195 Berlin Germany juttafastnerubade

5 Institute of Nature Conservation Polish Academy of Sciences 31-120 Krakow Polandwilkiopkrakowpl (EW-W) krztoniopkrakowpl (WK) walusiakiopkrakowpl (EW)

6 Institute of Botany Nature Research Centre Vilnius 08412 Lithuania juditakoreivienegmailcom (JK)juratekarosienebotanikalt (JK) juratekasperovicienebotanikalt (JK)ksenijasavadovagamtostyrimailt (KS) irmavitonytegmailcom (IV)

7 RampD Department Environmental Engineering 3edata 27004 Lugo Spain carmencillero3edataes8 Department of Water Protection Adam Mickiewicz University 61614 Poznan Poland

agbudzynskagmailcom (AB) rgoldamuedupl (RG) akozakamuedupl (AK)rosinskaasiagmailcom (JR) eszelagamuedupl (ES-W) domekpamuedupl (PD)jakubowskangmailcom (NJ-K)

9 Department of Hydrobiology Adam Mickiewicz University 61614 Poznan Polandkingakwasizurwppl (KK) messyaszamuedupl (BM) olapamuedupl (AP)marpelamuedupl (MP) kokamuedupl (MK)

10 Department of Limnology and Water Quality AECOM URS 08036 Barcelona Spainanagarciaaecomcom (AG-M) montserratrealaecomcom (MR) elviraromansaecomcom (ER)jordinogueroaecomcom (JN-R) parra1404hotmailcom (DPD) elifdzmogmailcom (EF-M)

11 Department of Environmental Engineering Abant Izzet Baysal University 14280 Bolu Turkeykarakaya_nibuedutr

12 Department of Science and Engineering Aringbo Akademi University 20520 Aringbo Finlandkerstinhaggqvistgmailcom

13 Department of Fisheries and Aquaculture Ankara University 6100 Ankara Turkeynilsundemir2gmailcom

14 Department of biology Middle East Technical University 6800 Ankara Turkey meryemmetuedutr (MB)nrflzstergmailcom (NF) etitheleviscom (EEL) iskinugurgmailcom (UI)gizbezircigmailcom (GB) unyazgangmailcom (UumlNT)

15 Institute of Marine Sciences Department of Oceanography Middle East Technical University 06800 AnkaraTurkey korayimsmetuedutr

16 Department of Botany Aristotle University of Thessaloniki 54124 Thessaloniki Greecesgkelisbioauthgr (SG) mattpanobioauthgr (MP)

17 Department of Basic Science Ataturk University 25240 Erzurum Turkey ozdenfakiogluatauniedutr18 Water Quality Department Athens Water Supply and Sewerage Company 11146 Athens Greece

cavagianosicloudcom (CA) t_kaloudisathforthnetgr (TK)19 Department of Biology Balikesir University 10145 Balikesir Turkey kcelikbalikesiredutr20 Department of Bioengineering Bursa Technical University 16310 Bursa Turkey meteyilmazbtuedutr21 Catalan Institute for Water Research (ICRA) 17003 Girona Spain rmarceicracat (RM)

ncatalangarciagmailcom (NC)22 Department of Ecology and Genetics Limnology Uppsala University 75236 Uppsala Sweden

jandriugarciagmailcom (AGB) moritzbuckebcuuse (MB) pablourrutia_corderobiolluse (PU-C)23 Department of Ecology and Genetics Erken Laboratory Uppsala University 76173 Norrtalje Sweden

williamcolomebcuuse (WC-M) kristiinamustonenebcuuse (KM) donpiersonebcuuse (DP)yangyanghotmailse (YY)

Toxins 2018 10 156 3 of 24

24 Research Center in Biodiversity and Genetic Resources (CIBIO-Azores) InBIO Associated LaboratoryFaculty of Sciences and Technology University of the Azores 9501-801 Ponta Delgada Portugalpedromvraposeirouacpt (PMR) vitormcgoncalvesuacpt (VG)

25 Department of Environmental Science and Technology Cyprus University of Technology 3036 LemesosCyprus mariaantonioucutaccy (MGA) tsiartanikolettagmailcom (NT)

26 Centre for Freshwater and Environmental Studies Dundalk Institute of Technology A91 K584 DundalkIreland valeriemccarthydkitie (VM) victorcperellodkitie (VCP)

27 Institute of Agricultural and Environmental Sciences Estonian University of Life Sciences 51014 TartuEstonia tonufeldmannemuee (TF) alolaasemuee (AL) kristelpanksepemuee (KP)leatuvikeneemuee (LT) kitiutee (KK)

28 European Regional Centre for Ecohydrology of the Polish Academy of Sciences 90364 Lodz Polandilonagagalaerceunescolodzpl (IG) jmankiewiczerceunescolodzpl (JM-B)

29 Republic of Turkey Ministry of Food Agriculture Fisheries Research Institute 32500 Egirdir Isparta Turkeymeralyagcigmailcom (MAY) sakircinargmailcom (SCcedil) kadircapkin32hotmailcom (KCcedil)akyagci58gmailcom (AY) mehmetcesur3232gmailcom (MC) fuatbilgingmailcom (FB)caferbulutgmailcom (CB) uysalrahmihotmailcom (RU)

30 Department of Sustainable Ecosystems and Bioresources Fondazione Edmund Mach 38010 San MicheleallrsquoAdige Italy ulrikeoberteggerfmachit (UO) adrianoboscainifmachit (AB)giovannaflaimfmachit (GF) nicosalmasofmachit (NS) leonardocerasinofmachit (LC)

31 Department of Biological and Environmental Sciences University of Stirling Stirling FK9 4LA UKjerich15cehacuk

32 Department of Freshwater and Marine Ecology University of Amsterdam 1090 GE AmsterdamThe Netherlands pmvisseruvanl (PMV) jmhverspagenuvanl (JMHV)

33 Department of Molecular Biology and Genetics Gaziosmanpasa University 60250 Merkez Turkeybiyo_tunayhotmailcom

34 Department of Biology Giresun University 28100 Giresun Turkey elifneyransoylugiresunedutr35 Department of Biology Hitit University 19040 Corum Turkey farukmarasliogluhititedutr36 Department of Icthyology Hydrobiology and Aquatic Ecology Inland Fisheries Institute 10719 Olsztyn

Poland anapiorkowska-krzebietkeinfishcompl37 Department of Freshwater Protection Institute of Environmental Protection- National Research Institute

01-692 Warsaw Poland aochockaiosedupl (AO) pasztaiosedupl (AP)38 Centro de Investigaccedilatildeo da Montanha Instituto Politeacutecnico de Braganccedila Campus de Santa Apoloacutenia

5300-253 Braganccedila Portugal geraldesipbpt39 Interdisciplinary Centre of Marine and Environmental Research (CIIMAR) and University of Porto 4450-208

Matosinhos Portugal vmvasconfcuppt (VV) joaopmoraisgmailcom (JM)micaciimargmailcom (MV)

40 Department of Freshwater Resource and Management Faculty of Aquatic Sciences Istanbul University34134 Istanbul Turkey latifekokergmailcom (LK) rakcaalangmailcom (RA)albayhermanogmailcom (MA)

41 Department of Biology Josip Juraj Strossmayer University of Osijek 31000 Osijek Croatiadspoljaricbiologijaunioshr (DŠM) fstevicbiologijaunioshr (FS) tzunabiologijaunioshr (TŽP)

42 Department of Experimental Limnology Leibniz Institute of Freshwater Ecology and Inland Fisheries 16775Stechlin Germany fonvielleigb-berlinde (JF) hgrossartigb-berlinde (H-PG)

43 Department of Biology Limnological Institute University of Konstanz 78464 Konstanz Germanydietmarstraileuni-konstanzde (DS) karlrothhauptuni-konstanzde (K-OR)

44 Department of Biology Lund University 22362 Lund Sweden lars-andershanssonbiolluse45 RECETOX Faculty of Science Masaryk University 62500 Brno Czech Republic blaharecetoxmunicz46 Department of Hydrobiology Morava Board Authority 60200 Brno Czech Republic gerispmocz47 Laboratory of Paleoecology Institute of Botany The Czech Academy of Sciences 60200 Brno Czech

Republic marketkakozakovaseznamcz48 Department of Environment and Resource Management Mediterranean Fisheries Research Production and

Training Institute 7090 Antalya Turkey matkocerhotmailcom49 Faculty of Aquaculture Mersin University 33160 Mersin Turkey talpmersinedutr50 Department of Water Quality Slovenian Environmental Agency 1000 Ljubljana Slovenia

spelaremec-rekargovsi

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51 Department of Genetic Toxicology and Cancer Biology National Institute of Biology 1000 LjubljanaSlovenia tinaelerseknibsi

52 Institute of Nanoscience and Nanotechnology National Center for Scientific Research laquoDEMOKRITOSraquo15341 Attiki Greece ttriantisinndemokritosgr (TT) szervouinndemokritosgr (S-KZ)ahiskiainndemokritosgr (AH)

53 Department of Freshwater Ecology Norwegian Institute for Water Research 0349 Oslo Norwaysigridhaandenivano (SH) birgerskjelbrednivano (BS)

54 Institute of Environmental Engineering Poznan University of Technology 60965 Poznan Polandbeatamadreckaputpoznanpl

55 National Reference Center for Hydrobiology Public Health Authority of the Slovak Republic 82645Bratislava Slovakia hananemovauvzsrsk (HN) ivetadrastichovauvzsrsk (ID)luciachomovauvzsrsk (LC)

56 School of Pharmacy and Life Sciences Robert Gordon University Aberdeen AB10 7GJ UKcedwardsrguacuk

57 Department of Biology Sakarya University 54187 Sakarya Turkey tsevindiksakaryaedutr (TOS)htuncasakaryaedutr (HT) burcinonem1ogrsakaryaedutr (BOuml)

58 Faculty of Natural Sciences and Mathematics SS Cyril and Methodius University 1000 Skopje Macedoniaborisaleksovskipmfukimmk (BA) skrsticpmfukimmk (SK)

59 Department for Ecotoxicology Teaching Institute of Public Health of Primorje-Gorski Kotar County 51000Rijeka Croatia itanabokanzzjzpgzhr

60 Institute of Technology The State University of Applied Sciences 82300 Elblag Polandnawrockalidiaoppl

61 Department of Biological and Environmental Science University of Jyvaumlskylauml 40014 Jyvaumlskylauml Finlandpauliinaumsalmijyufi

62 Departamento de Sistemaacutetica e Ecologia Universidade Federal da Paraiacuteba 58059-970 Paraiacuteba Brasildaniellemachadovieira86gmailcom (DM-V) alinnegurjaogmailcom (AGdO)

63 Department of Civil Engineering University of A Coruntildea 15192 A Coruntildea Spainjdelgadoudces (JD-M) davidmorrondoudces (DG) josecereijoudces (JLC)

64 Department of Evolutionary Biology Ecology and Environmental Sciences University of Barcelona 08028Barcelona Spain jgomaubedu (JG) mctrapote84gmailcom (MCT) tvegasubedu (TV-V)obradorubedu (BO)

65 Department of Hydrobiology University of Bialystok 15245 Bialystok Poland magrauwbedupl (MG)mkarpowiczuwbedupl (MK)

66 Institute of Environmental Protection and Engineering University of Bielsko-Biala 43309 Bielsko-BialaPoland dachmuragmailcom

67 Department of Biology University of Caacutediz 11510 Puerto Real Caacutediz Spain barbaraubedaucaes (BUacute)joseangelgalvezucaes (JAacuteG)

68 Department of Forest Engineering University of Cankiri Karatekin 18200 Cankiri Turkeyardaozengmailcom

69 Freshwater Biological Laboratory Department of Biology University of Copenhagen 2100 CopenhagenDenmark kchristoffersenbiokudk (KSC) twperltbiokudk (TPW)

70 Department of Marine Biotechnology University of Gdansk 81378 Gdynia Polandjustynakobosugedupl (JK) biohmugedupl (HM-M)

71 Department of Ecology University of Granada 18071 Granada Spain cperezmugres (CP-M)eloisaugres (ER-R)

72 Lammi Biological Station University of Helsinki 16900 Lammi Finland lauriarvolahelsinkifi73 Department of Animal Biology Plant Biology and Ecology University of Jaen 23701 Jaen Spain

pabloalcarazparragagmailcom74 Department of Hydrobiology and Protection of Ecosystems University of Life Sciences in Lublin 20262

Lublin Poland magdawisinteriapl (MT) barbarapawlikuplublinpl (BP-S)michalniedzwieckiuplublinpl (MN) wojciechpeczulauplublinpl (WP)

75 Instituto Dom Luiz University of Lisbon 1749016 Lisbon Portugal mleirafculpt76 Institute of Earth Sciences Jaume Almera ICTJA CSIC 08028 Barcelona Spain ahernandezictjacsices77 Department of Ecology University of Malaga 29071 Malaga Spain quiqueumaes (EM-O)

jmblancoumaes (JMB) valerianoumaes (VR) montesperezjjgmailcom (JJM-P)rlpalominoumaes (RLP) stelax_rphotmailcom (ER-P)

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78 Centro de Investigacioacutens Cientificas Avanzadas (CICA) Facultade de Ciencias Universidade da Coruntildea15071 A Coruntildea Spain rcarballeiraudces

79 Cavanilles Institute of Biodiversity and Evolutionary Biology University of Valencia 46980 Paterna ValenciaSpain antoniocamachouves (AC) antoniopicazo-mozouves (AP) carlosrocherauves (CR)annasantamansgmailcom (ACS) carmenferrioluves (CF)

80 Department of Microbiology and Ecology University of Valencia 46100 Burjassot Spainsusanaromouves (SR) juansoriauves (JMS)

81 Department of Water Protection Engineering University of Warmia and Mazury 10-720 Olsztyn Polandjulitaduwmedupl (JD) justynasienskauwmedupl (JS) danielszymanskiuwmedupl (DS)

82 Department of Tourism Recreation and Ecology University of Warmia and Mazury 10-720 Olsztyn Polandmkrukuwmedupl

83 Faculty of Biology University of Warsaw 02-096 Warsaw Poland iwonaksbioluwedupl84 Department of Plant Ecology and Environmental Conservation Faculty of Biology University of Warsaw

02-089 Warsaw Poland jasseriwonabioluwedupl85 Department of Biology Faculty of Science University of Zagreb 10000 Zagreb Croatia

petarzutinicbiolpmfhr (PŽ) marijagligoraudovicbiolpmfhr (MGU)andjelkaplenkovic-morajbiolpmfhr (AP-M)

86 Department of Environmental Improvement Faculty of Civil and Environmental Engineering WarsawUniversity of Life SciencesmdashSGGW 02-787 Warsaw Poland magdalena_fraksggwpl

87 Department of Hydraulic Engineering Faculty of Civil and Environmental Engineering Warsaw Universityof Life SciencesmdashSGGW 02-787 Warsaw Poland agnieszka_bankowskasggwpl (AB-S)michal_wasilewiczsggwpl (MW)

88 Institute of Marine Sciences Marine Biology and Fisheries Middle East Technical University 06800 AnkaraTurkey okorhanmetuedutr

89 Society for the Protection of Prespa 53077 Agios Germanos Greece90 Institute for Water and Wetland Research Department of Aquatic Ecology and Environmental Biology

Radboud University Nijmegen 6525 AJ Nijmegen The Netherlands91 Tartu Observatory Faculty of Science and Technology University of Tartu 61602 Tartu Estonia92 Institute of Biochemistry and Biology Potsdam University 14469 Potsdam Germany93 Institute of Marine Sciences University of North Carolina at Chapel Hill Chapel Hill NC 28557 USA

hpaerlemailuncedu94 Department of Biological Sciences Virginia Tech Blacksburg VA 24061 USA cayelanvtedu Correspondence EvanthiaMantzoukiunigech

Received 15 February 2018 Accepted 29 March 2018 Published 13 April 2018

Abstract Insight into how environmental change determines the production and distribution ofcyanobacterial toxins is necessary for risk assessment Management guidelines currently focus onhepatotoxins (microcystins) Increasing attention is given to other classes such as neurotoxins (eganatoxin-a) and cytotoxins (eg cylindrospermopsin) due to their potency Most studies examinethe relationship between individual toxin variants and environmental factors such as nutrientstemperature and light In summer 2015 we collected samples across Europe to investigate the effectof nutrient and temperature gradients on the variability of toxin production at a continental scaleDirect and indirect effects of temperature were the main drivers of the spatial distribution in the toxinsproduced by the cyanobacterial community the toxin concentrations and toxin quota Generalizedlinear models showed that a Toxin Diversity Index (TDI) increased with latitude while it decreasedwith water stability Increases in TDI were explained through a significant increase in toxin variantssuch as MC-YR anatoxin and cylindrospermopsin accompanied by a decreasing presence of MC-LRWhile global warming continues the direct and indirect effects of increased lake temperatures willdrive changes in the distribution of cyanobacterial toxins in Europe potentially promoting selectionof a few highly toxic species or strains

Keywords microcystin anatoxin cylindrospermopsin temperature direct effects indirect effectsspatial distribution European Multi Lake Survey

Toxins 2018 10 156 6 of 24

Key Contribution This manuscript describes the outcomes of the European Multi Lake Survey(EMLS) in which the effects of temperature and nutrients on the variability of production of multipleclasses of cyanobacterial toxins were assessed At this continental scale temperature throughdirect and indirect effects was found to be the main driver of cyanobacterial toxin distributionand toxin quota

1 Introduction

As a consequence of human population growth along with associated agricultural urban andindustrial activities harmful algal blooms worldwide are on the increase [1] Eutrophication onemajor outcome of anthropogenic activities in the catchments of aquatic ecosystems is consistentlyrecognized as the main driver of cyanobacterial blooms [23] In addition damage to ecosystems andloss of natural resources (eg in Lake Taihu China [4] and references within [5]) are also attributed toon-going climatic change [6] A synergistic interaction between increased nutrients and climate-relatedchanges is predicted [7] based on experimental [8] and field studies [9] potentially further exacerbatingthe occurrence of cyanobacterial blooms

The long history of cyanobacterial adaptation to a wide range of environmental conditionsincluding extremes [10] supports their successful occurrence in a variety of lake ecosystems Theseadaptations come in the form of functional traits such as phosphorus storage buoyancy regulationnitrogen fixation and the formation of akinetes (resting spores) Extensive research has linked theprevalence of species with specific functional traits to certain sets of environmental conditions [1112]For example Microcystis aeruginosa can rapidly float up in the illuminated near surface layers underconditions of enhanced water column stability [13] and through buoyancy regulation gain access toboth nutrients at deeper layers and light at the surface [14]

Toxin production by the production of hepatotoxins (eg microcystins (MCs) and nodularin(NOD)) cytotoxins (cylindrospermopsins (CYN)) and neurotoxins (eg anatoxins (ATX)) is anothercommon trait of a large number of cyanobacterial species Although numerous studies have elucidatedthe chemical properties biosynthesis and genetics of the most well-known toxins [15ndash17] stilllittle is known why toxins are produced and what determines their presence in field populationsThere is evidence that production of these secondary metabolites might provide a competitiveadvantage for example through providing resistance against grazing [18ndash20] or a physiologicalbenefit eg in enhancing nutrient uptake or offering protection against oxidative stress (referenceswithin [21]) The abundance of toxins during blooms depends on the presence of toxic strains withinthe cyanobacterial population [22] Different species have been shown to produce specific toxins oreven variants [23ndash25] However in toxic strains the environmental factors controlling expressionof the toxin synthetase genes are still a contentious issue [26] Lack of consistency in experimentalfindings along with a lack of standardization in surveying and sampling design in field studies so farhas hindered a coherent understanding of how environmental stressors are linked to cyanobacterialtoxin production and toxin quota (toxin concentration per cell or unit algal biomass)

Various studies have shown contradictory responses of toxin producing taxa to similarenvironmental parameters For example experiments with MC-producing Planktothrix agardhii showedthat high nitrogen concentrations (one factor in the study to vary among others like phosphorustemperature pH light) were correlated to high MC production in batch cultures [27] However inexperiments with Microcystis aeruginosa N-limited chemostat experiments triggered an increase inMC content with smaller and faster growing cells being mostly promoted as a response to favourablegrowing conditions [28] Similarly an experiment with ATX producers showed that temperaturedependent optimal growth conditions did not necessarily result in higher toxin concentration [29] Inthis study while Aphanizomenon cultures proliferated at 30 C Dolichospermum (formerly Anabaena)cultures suffered at this high temperature [29] Nevertheless ATX production was reduced by both

Toxins 2018 10 156 7 of 24

tested species [29] A subsequent study on ATX or MC producing strains of Dolichospermum indicatedstrain-specific responses to temperature and light limited conditions [30] Consequently there is noeasy way to deduce from environmental conditions which toxins will come to dominate in a bloomnor whether toxin concentrations will be high

Microcystins (MCs) as the largest best described and most diverse group of cyanobacterialtoxins [26] have been the focus of management and mitigation guidelines For drinking water theWorld Health Organisation (WHO) has set a provisional guideline value-maintained at the same levelin the EU Drinking Water Directive-of 1 microgL for MC-LR a value that is accepted in most countriesIn recreational waters however there is less consistency even for MC-LR and national authoritiesuse a variety of risk assessment schemes and criteria to inform management decisionspractice [31]MC-LR is the best studied MC variant [3233] yet other variants such as MC-YR [34] -LW and -LF [35]can also be highly toxic and may contribute significantly to the total MCs in a lake MC-RR althoughreported to be ten times less toxic than MC-LR after intraperitoneal injection in mice (LD50) is oneof the most frequently reported toxin in the lakes along with MC-LR and MC-YR [36] and may bemore harmful to aquatic biota than MC-LR [37] Good toxicity data are lacking for the vast majority ofMC variants (presently more than 250 [23]) Good toxicological data on other toxins such as CYN andATX are necessary to include a full spectrum of cyanotoxins in human and ecosystem risk assessment(reviewed in [3638])

Although a fair number of toxin surveys at the national level have been carried out [39] studieshave rarely investigated the spatial distribution of different classes of cyanotoxins at larger geographicalscales encompassing lakes of widely different characteristics and environmental diversity Accordingto studies [4041] the climate in Europe is already shifting north (eg central European countries willexperience longer hot summers similar to presently in Mediterranean countries) and as such a studyon cyanotoxins over a large latitudinal gradient may offer insights into their future distribution Inthe European Multi Lake Survey (EMLS) lakes across the continent were sampled once in a snapshotapproach for physical chemical and biological parameters during the summer of 2015 Standardizedand commonly practiced field protocols along with centralized laboratory analyses for all parametersother than microscopy were undertaken for all samples avoiding inconsistencies between data inthis large dataset In addition we addressed our research questions minimizing confounding effectsof seasonality by sampling all lakes during the locally two warmest weeks of summer based on atleast 10-year air temperature records In this study we investigated how the distribution of toxinconcentration and toxin quota were defined by environmental parameters We hypothesize that in anunusually warm summermdashlike 2015mdashwas in parts of Europe [42]mdashtemperature either through direct(surface temperature epilimnetic temperature) or indirect (water stability expressed as maximumbuoyancy frequency) effects strongly influences the distribution of toxin concentrations and toxinquota Additionally we hypothesize that under high temperature stress the stringent selection ofspecific well-adapted strains of cyanobacteria reduces toxin diversity potentially promoting dominanceby a few highly toxic variants

2 Results

21 Toxin Distribution on a Continental Scale

In the subset of the 137 EMLS lakes used in our analysis all seven toxins analysed were detectedin samples from only three lakes which shows that it is possible but rare to have such a diverse numberof toxins present in one lake at one moment in time The presence of four five or six toxins was foundin 34 26 and 25 lakes respectively Finally 18 lakes had two toxins and 13 lakes only one toxin

MC variants were found in 93 of the 137 EMLS lakes (Table 1) MC-YR was the most common ofthe five MCs (in 82 of the subset) Although the variant MC-dmRR was the rarest variant encounteredit had the highest concentrations compared to any other toxin variant (1489 microgL in Polish LakeSyczynskie) The MC variants MC-LW MC-LF and MC-LY and the toxin nodularin (NOD) were

Toxins 2018 10 156 8 of 24

also analysed but they were not included in the analysis as they were too scarce (see materials andmethods) The MC-LF was present in two Spanish reservoirs (Abegondo and As Forcadas) whileMC-LW and MC-LY were not found in any of the EMLS lakes Nodularin was found only in twoSpanish reservoirs (As Forcadas and Valdecanas) in concentrations close to the limit of quantification(0007 microgL)

Table 1 Summary of toxin variants (Total microcystin MC-Tot microcystin YR MC-YRmicrocystin dmLR MC-dmLR microcystin LR MC-LR microcystin RR MC-RR anatoxin ATXcylindrospermopsin CYN microcystin dmRR MC-dmRR) ordered by decreasing number of presencein the investigated 137 EMLS lakes

Toxin Variant Present(n Lakes)

ConcentrationRange(microgL)

Limit ofQuantification 1

(microgL)Mean Stdv

MC-Tot 127 0ndash1718 120 270MC-YR 113 0ndash492 00050 014 056

MC-dmLR 108 0ndash316 00054 015 050MC-LR 93 0ndash397 00086 020 055MC-RR 67 0ndash331 00358 020 050

ATX 54 0ndash133 00004 003 012CYN 53 0ndash201 00004 005 020

MC-dmRR 52 0ndash1489 00489 052 1831 limit of quantification (LOQ) of the LC-MSMS method measured for an average filtered volume = 250 mL

CYN was detected in 39 of the 137 EMLS lakes (Table 1) One German Lake (GrosserDabelowsee) three Polish Lakes (Bninskie Lusowskie and Probarskie) and two Turkish lakes(Caycoren and Mollakoy) (Figure 1a) had solely CYN in relatively low concentrations (lt005 microgLsupplemental material) ATX was found in 39 of the EMLS lakes (Table 1) out of which only onePolish Lake (Dziekanowskie) (Figure 1a) produced the specific toxin exclusively albeit at very lowconcentrations 0002 microgL

Figure 1 Cont

Toxins 2018 10 156 9 of 24

Figure 1 Percentages of (a) toxin concentrations (microgL) and (b) toxin quota (microg toxinmicrog chlorophyll-a)of each toxin of the 137 EMLS lakes used in the analyses Blue shades correspond to the five microcystinvariants (MC-YR MC-dmLR MC-LR MC-RR MC-dmRR) yellow to cylindrospermospin (CYN) andred to anatoxin (ATX) The radius of the pie charts corresponds to (a) the total toxin concentrations and(b) to the total toxin quota

22 Multivariate Multiple Regression Analysis

The ordination analysis showed a clear delineation among toxin variants in the EMLS lakes(Figure 2) Lakes with MC-dmLR and MC-dmRR were clustered on the negative-value side of thecanonical axis 1 with MC-dmLR occupying the positive-value side of axis 2 and MC-dmRR thenegative side of the second axis Lakes with MC-LR and MC-RR were grouped on the positive-valueside of axis 1 and on the positive- and negative-value side of axis 2 respectively MC-YR demonstratedthe highest positive correlation with the canonical axis 2 (r = 024) ATX correlated significantly withthe negative-value side of axis 2 while CYN correlated negatively with the canonical axis 1 Thepermutation test confirmed the significance of the canonical analysis (p = 0001 for axis 1 p = 003 foraxis 2)

Toxins 2018 10 156 10 of 24

Figure 2 RDA biplot of the toxin quota (toxin microgchlorophyll-a microg Hellinger transformed dueto many zeros) of the five microcystin variants (MC-YR MC-dmLR MC-LR MC-RR MC-dmRR)cylindrospermopsin (CYN) and anatoxin (ATX) The vectors represent the environmental variablesepilimnetic temperature (T_Epi) surface temperature (T_Surf) and the log transformed Secchi depth(Secchi) and maximum buoyancy frequency (BuoyFreq) Length and direction of vectors indicate thestrength and direction of the relationship

The redundancy analysis for toxin concentration and toxin quota data yielded the same results(Table 2) Both the distribution of toxin concentrations and toxin quota were defined by epilimnetictemperature (T_Epi) surface temperature (T_Surf) buoyancy frequency (BuoyFreq) and Secchi depth(Secchi) Therefore in Figure 2 we present only the biplot for the toxin quota since the plot for thetoxin concentration was almost identical Marginal tests showed that T_Epi T_Surf BuoyFreq andSecchi were all significant in determining the toxin variant ordination (Table 2) T_Epi correlatedclosely with the negative-value side of the axis 1 (r = minus062) T_Surf and BuoyFreq had a strongercorrelation with the axis 2 (r = minus055 and r = minus054) The positive-value side of the second axiscorrelated strongly with Secchi (r = 073) Variance partitioning showed that T_Epi explained 73T_Surf 25 and BuoyFreq 1 of the variance (Figure S1a) while the Venn diagram on T_Epi T_Surfand Secchi demonstrated 11 7 and 1 of variance explained respectively (Figure S1b)

Table 2 Redundancy analysis showing results of marginal tests for toxin concentrations followedby toxin quota (both Hellinger transformed) based on F-model and 9999 permutations Epilimnetictemperature (T_Epi) surface temperature (T_Surf) maximum buoyancy frequency (BuoyFreq) andSecchi depth (Secchi) were the predictors that were selected (stepwise elimination) for the constrainedanalysis The Adjusted R2 (AdjR2) estimates the relative quality of the two models Statisticallysignificant effects are shown in bold

RDA AdjR2 Predictor Variance F p

ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24

23 Toxin Diversity Index and Environmental Parameters

The environmental parameters maximum depth (DMax) latitude (Latitude) epilimnetictemperature (T_Epi) maximum buoyancy frequency (BuoyFreq) and Secchi depth (Secchi) wereselected (stepwise selection) as the best explanatory variables in the final model for both the TDIand Richness The negative binomial generalized linear model showed a significant positive effect oflatitude and a significant negative effect of maximum buoyancy frequency in defining the TDI on acontinental scale (Table 3) In the case of Richness the model showed again a significant positive effectof latitude and a significant negative effect of maximum buoyancy frequency Additionally epilimnetictemperature (T_Epi) had also a significant positive effect while Secchi depth had a significant negativeeffect in determining Richness Both of these factors however explained less variance than latitudeand buoyancy frequency (X2 Table 3)

Table 3 Summary of the Generalized Linear Model for the Toxin Diversity Index (TDI) and ToxinRichness of toxin quota Stepwise elimination selected for final model with predictors maximum depth(DMax) latitude (Latitude) epilimnetic temperature (T_Epi) maximum buoyancy frequency (BuoyFreq)and Secchi depth (Secchi) Statistical significant variables are shown in bold

Index GLM Family = NegativeBinomial Predictor X2 p

TDIquota

minus193 + 0003 DMax + 003Latitude + 003 T_Epiminus242 BuoyFreq minus 006Secchi

Latitude 121 0004BuoyFreq 075 002

DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota

minus016 + 0002 DMax + 002Latitude + 004 T_Epi minus263 BuoyFreq minus 009Secchi


DMax 041 015T_Epi 113 002Secchi 140 0007

Each toxin quota was tested separately against the TDI to reveal responses in individual toxins tochanges in overall toxin diversity (Table 4) The negative binomial generalized linear model showedthat the variants MC-YR and MC-dmLR increased significantly (p lt 005) with increases in TDI Theresponse of CYN and ATX to increases in the TDI were positive and highly significant (p lt 001)Positive trends in total microcystin MC-RR and MC-dmRR were also determined but without anystatistical significance (p gt 005) MC-LR was the only toxin variant that showed a negative trend(red arrow) to increases in toxin diversity although lacking statistical significance Similarly all toxinquota were tested against Toxin Richness In this case all toxin quota increased significantly withtoxin richness apart from MC-dmLR and MC-LR that although showing a positive trend were notsignificant (Table 4)

Toxins 2018 10 156 12 of 24

Table 4 Statistical results of the negative binomial generalized linear model showing the response ofthe toxin quota (MC-YR MC-dmLR MC-LR MC-RR MC-dmRR ATX and CYN over chlorophyll-a)to increases in Toxin Diversity Index (TDI) and Toxin Richness Black upward arrows correspondto increases of the toxin variant to increases in the TDI and Toxin Richness red downward arrowscorrespond to decreases of the toxin variant when TDI increases Statistically significant effects areshown in bold (p lt 005)

Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p

MC-YR uarr 010 002 uarr 010 001MC-dmLR uarr 010 002 uarr 006 006

MC-LR darr 009 054 uarr 03 02MC-RR uarr 044 006 uarr 090 0003

ATX uarr 015 00002 uarr 019 lt00001CYN uarr 038 0007 uarr 056 00009

MC-dmRR uarr 02 085 uarr 041 002

highly significant results are marked with ldquordquo for p lt 001 and ldquordquo for p lt 0001

3 Discussion

Our study shows that MCs were by far the most abundant cyanotoxins across the Europeanlakes in our dataset being detected at greater frequency than either CYN or ATX (Table 1) Howeverit is important to note that we analysed only the intracellular toxin content on filter samples whichmight have resulted in an underestimation of CYN or ATX concentrations as they can be largelyextracellular [36] We found that among the microcystins MC-LR was only the third most abundantmicrocystin variant after MC-YR and MC-dmLR (Table 1) MC-dmRR was the least common toxinin the EMLS lakes but showed the highest concentrations (up to 14 microgL) Furthermore CYN wasdetected less frequently but in several cases it was the only toxin detected that could indicate thatCYN producers might have a potential to exclude the producers of other toxin variants (Figure 1)CYN can be present over extended period in aquatic systems since it can be produced by a successionof different bloom species For example in Lake Albano (Italy) a succession in CYN productionby Cylindrospermopsis raciborskii to Aphanizomenon ovalisporum lead to the toxin being present in thesystem from early summer until early autumn [43] ATX also occurred as a single toxin ie notin complex mixtures with other cyanotoxins albeit at lower concentrations (Figure 1) A concreteexample is the ongoing substitution of P rubescens (mainly a MC-dmRR producer) by Tychonemabourrellyi (ATX-producer) in Lake Garda (Italy) Over the last decade a shift in dominance betweenthese two species caused an increase in ATX at the expense of MC-dmRR [44] However and perhapsmore typically there are studies that highlight ATX dominance during short periods of time only mostlikely because MC producing taxa take over after a short period of dominance by ATX producers [45]These results indicate that risk assessment should be broader and address other toxin variants thanjust the well-known MC-LR variant A similar conclusion has been reached by other studies withrelevance for human risk assessment but equally for ecosystem functioning [37]

In the cyanobacterial literature it is entirely customary to discuss data on presence of toxins indirect relation to the cyanobacterial taxa that produce them However here we only present dataon toxins no information on the taxonomic composition of the phytoplankton communities of theEMLS lakes is given Why is this the case To begin with this goes back to one of the key principlesunderlying the EMLS one of complete data integration As explained in the methodsrsquo nutrients HPLCpigments or toxins come from one instrument operated by one person Samples for microscopy weretaken but each participating laboratory counted these locally using different quality microscopesand varying levels of taxonomic expertise Given a recent discussion [46] on problems with thetrustworthiness even at the genus level of a long-term phytoplankton dataset in which all sampleswere counted by a small team of experts supervised by the same person over the years we couldnot trustfully use microscopic counts from so many different labs in our study Moreover in recent

Toxins 2018 10 156 13 of 24

studies there is a strong tendency to focus on key functional traits as the focal point of phytoplanktonecology (eg [47]) Cyanobacterial key traits rather than taxonomic relatedness are also the basis forsuccessful management of cyanobacterial blooms [48] For the purpose of this study we examinedtoxins as functional traits and aimed to study how much we can understand about the spatial driversof toxin abundance by just focusing on the traits themselves This trait-centred view may be furthersupported by the fact that all countries base their assessment of the risks of toxic cyanobacteria forthe consumption of drinking water or food directly on toxin concentrations and not on taxonomicinformation [31] A further EMLS paper is in preparation where we compare traits-pigments fromHPLC and size related traits from flowcytometry-with phylogenetic information (16s rRNA) andfunctional genes (toxin synthetases) to better understand the occurrence of cyanobacteria at thecontinental scale

Functional trait diversity of cyanobacteria explains the coexistence or succession of the differentcyanobacteria species under diverse environmental conditions or lake settings [11] Howeverspecies-specific toxin production is rarely attributed to environmental factors with certainty Clustersof genes encoding different cyanotoxin classes can be selectively present in different cyanobacteriaspecies and strains but mutations can also turn a toxic genotype into non-toxic under conditions thatare not exhaustively studied [49] Phylogenetic analysis on the evolutionary age of the MCNODsynthesis pathways implied that all cyanobacteria are potential MC producers [50] Reported datafrom Lake Great Prespa (GreecendashMacedonia) showed that although certain isolated cyanobacteriaspecies produced more specific MC variants they all had the potential to produce all the analyzedMC variants just in smaller quantities Toxin concentrations are the result of (i) species abundances(ii) the abundance of potentially toxigenic genotypes (iii) the type of toxins that can be produced bythose strains (iv) the cellular quota of the toxins and finally (v) how all these levels are controlledby environmental settings In short the product of toxigenic cyanobacterial biomass x cellular quotadetermines the toxin concentration in the lake In terms of environmental drivers there are thosethat control cyanobacterial growth and losses (growth ndash losses = biomass) and most of these are wellstudied in particular phytoplankton resources like phosphorus nitrogen and light [51] Moreovercyanobacterial growth is strongly temperature dependent [52] Population losses are driven by factorslike lysis grazing and parasitism [53] Many of the factors that determine biomass have also beenfound to have an effect on toxin quota (eg [285455])

Indeed in line with the overlap in environmental control of biomass and quota from the literaturethe distribution of toxin concentrations and quota in EMLS were explained by the same set ofenvironmental predictors Previous field studies showed elevated MC concentrations in lakes withboth low or high cell abundances [56] MC concentration per unit biomass can vary considerably fromone bloom to another or even within the same bloom On the other hand stable toxin quota havealso been observed [16] Toxin quota can vary greatly within a toxin producing species eg rangingfrom 0ndash5 microgmg of dry cyanobacteria biomass [57] From a management point of view understandingwhat drives low toxin quota during high cyanobacterial biomass or high toxin quota during lowcyanobacterial biomass versus simply looking at the overall toxin concentrations would be helpfulto better understand variation in toxin concentrations and the risks they pose for use of the watersystems [58] Even in oligotrophic lakes that typically have low cell densities the cyanobacterialbiomass may accumulate at surface and form scums at leeward shores [59] potentially leading tohighly localized toxin concentrations especially when cells possess considerable toxin quota Incontrast the influence of environmental factors on strain composition is hardly understood

The ordination model showed that temperature effects were mostly responsible for the distributionof the different toxins at a continental scale (Table 2) Interestingly a significant grouping of lakeswith MC-LR and MC-RR on the one hand and lakes with MC-dmLR and MC-dmRR on the otherhand was found (Figure 2) According to these results lakes with high MC-LR contents wouldbe more likely to have the MC-RR variant as well while lakes with MC-dmLR are likely to alsoproduce MC-dmRR (Figure 2) Epilimnetic temperature accounted for the delineation of lakes with

Toxins 2018 10 156 14 of 24

MC-LR and MC-RR while lakes with MC-dmLR and MC-dmRR were positively associated withincreased buoyancy frequency (Figure 2) This division in lake and toxin groupings indicates that lakescharacterized by frequent wind mixing (low buoyancy frequency) and elevated temperature supportproducers of MC-RR and MC-LR [60] An example of this type of lake and conditions is Lake Taihu(China) dominated by Microcystis [4] Conversely in deeper and more stable stratified waters theco-dominance of MC-dmRR and MC-dmLR may be attributed to buoyant species that can accuratelyregulate their position in the water column providing them a stable position in the metalimnion likein particular Planktothrix rubescens (eg [6162]) See below for more in-depth discussion

Our results did not indicate that either total phosphorus or total nitrogen concentrations had asignificant impact on the distribution of toxin concentrations or toxin quota (Table 2) As discussedearlier studies have shown that increased nutrients are linked to increased growth rates and toxinproduction (references within [26] and results of [8]) However there is also contradictory evidencethat for example nitrogen availability promoted cell growth but it did not directly influence toxinproduction [63] Hence there is no consistent evidence supporting a causative relationship betweennutrient supported growth and toxin production In our analyses at the continental scale nutrients insharp contrast to temperature effects did not emerge as control factors We could argue that nutrientswould potentially play a role in the occurrence of the individual toxin variants through supporting highcyanobacterial biomasses or toxin quota but according to the results from our study nutrients wouldnot be the predictor that would select among the different toxin variants or affect toxin diversity

Toxin diversity and richness showed a significant increase with latitude which means thatnorthern areas exhibited a higher toxin diversity (Figure 3 Table 4) In a parallel study based on thesame lake dataset Mantzouki et al (in preparation) showed that during summer 2015 a significantlyhigher algal-and specifically cyanobacterial-biomass was found in the Boreal climatic zone comparedto lakes in Continental and Mediterranean climate The higher cyanobacterial biomass was potentiallyexplained by the heat wave that occurred mainly in northern European regions (NOAA onlinedata) The majority of the Boreal EMLS lakes (75) were sampled during a two week-period wheretemperature anomalies exceeded the local long-term average summer temperature by +5 C comparedto a much smaller temperature anomaly in the Mediterranean lakes (+18 C) Cyanobacteria growthrate steeply increases with water temperature until about 25 C and plateaus at about 28 C whiletemperature can be detrimental when it exceeds 33 C [64] In 90 of the Boreal and Continentallakes with the +5 C temperature difference epilimnetic temperatures approached but did not exceed25 C giving a higher potential to northern European strains to reach their optimum growth rateContrastingly the Southern European strains were already on the plateau of their growth curvesand a 2 C warming did not add any growth potential Although a +5 C versus a +2 C differencemay explain why cyanobacteria in the northern Europe may have caught up with those in the southit would not explain why cyanobacterial biomass in the north would actually be higher Clearlythere are differences in temperature responses both between and within cyanobacterial species [65]The rather extreme +5 C temperature anomaly could have altered community composition andorhave favoured cyanobacterial genotypes with an exceptional set of thermal reaction norms [65] Theextremely warm summer of 2015 in the north is likely to have selected genotypes which are atthe extreme upper-warm-end of the ldquoset of reaction normsrdquo that have evolved locally This couldpotentially explain why cyanobacteria in the Boreal lakes developed higher biomass

Toxins 2018 10 156 15 of 24

Figure 3 Map of the Toxin Diversity Index (TDI) of the 137 EMLS lakes calculated using the Shannonequation TDI is categorized in four classes with higher colour density (red) representing higher toxindiversity and lower colour density (white) lower toxin diversity The radius of the markers correspondsto the total toxin concentration in microgL

High buoyancy frequency as a proxy for water stability had a significantly negative effect onthe TDI and richness of the EMLS lakes (Table 3) In strongly stratified lakes in particular those withan oligo-mesotrophic state highly selective conditions arise with a strict spatial separation of lightat the surface and nutrients at depth This leads to the selection of metalimnetic species with a veryspecific set of functional traits like well-controlled buoyancy regulation and elevated phycoerythrincontent [62] Under such conditions where a single cyanobacterium monopolizes the resources wemay expect that the low cyanobacterial diversity leads to a low toxin diversity (Table 3) On the otherhand in lakes with less stringent environmental conditions like the more shallow and eutrophic lakesin the EMLS dataset the scope for co-existence of several less specialised cyanobacterial species isenhanced hence a more diverse toxin community might be established

In the EMLS dataset high MC concentrations did not exclude production of the other two toxinclasses (neurotoxins cytotoxins) but rather increased the probability of CYN and ATX occurrencetogether with MCs resulting in an increase in the TDI (Table 4) A diverse representation of toxinvariants can increase the relative toxic potential of a lake ecosystem [66] As the different toxinclasses have different modes of action and target different organs separate toxicity assessments arerequired [34] but ultimately these separate assessments need to be combined to evaluate the overalltoxicity risks The relative toxic potential of the cyanotoxin mixture is calculated as the sum of therelative abundance of each toxin variant multiplied by a defined Toxicity Equivalent Factor based onLD50 values for each toxin class separately (eg neurotoxins vs hepatotoxins) as proposed in [34]As the presence of different toxin classes increases significantly with toxin diversity (Table 4) thedifferentiated toxic potential would have ramifications for understanding the overall risks of bloomsin a lake with an elevated TDI A higher toxin diversity would potentially lead to higher stability inoverall toxicity within a bloom since if one toxin declines another may increase leading to persistencein overall toxicity [26] To make things worse it may not be sufficient to look at the sum of toxins

Toxins 2018 10 156 16 of 24

present (additive effects) since synergistic effects of cyanotoxin mixtures pose a potential risk tohumans animals and aquatic ecosystems [67ndash69]

To conclude we demonstrated that temperature effects were largely responsible for thedistribution of the different cyanotoxins on a continental scale Additionally we showed thattemperature related mechanisms lead to the selective development of well-adapted strains ofcyanobacteria that would reduce toxin diversity potentially promoting dominance by a few highlytoxic strains Further high buoyancy frequency as a proxy for water stability had a significantlynegative effect on the TDI and toxin richness Overall our study provided the - perhaps surprising- result that at this large-scale temperature rather than classic drivers of toxic blooms like nutrientsdetermines the distribution of toxins

4 Materials and Methods

41 Sampling Survey

The European Multi Lake Survey (EMLS) was organized by 26 European countries during summer2015 with each participating group using their own financial means to conduct their sampling Sincethe EMLS was a voluntary effort individual countries contributed samples from lakes that theyroutinely sampled and these were typically lakes with a history of eutrophication A total of 369 lakeswere sampled using standardized protocols for sampling processing and preserving Sampling tookplace during the two warmest weeks of the summer which was specific for each region Data collectorsidentified the correct sampling period using long-term air temperature data spanning at least 10 yearsThe sampling location was either the historically sampled location or the centre of the lake if thelake was not routinely sampled A temperature profile with a minimum required resolution of 05 msampling intervals served to define the sampling depth An integrated water column sample whichwill henceforth be referred to as epilimnetic sample was taken from 05 m below the surface until thebottom of the thermocline This was defined as the point where there was a ge1 C temperature changeper meter lake depth If the lake was shallow then the entire water column was sampled until 05 mabove the lake bottom

All data collectors constructed a simple device using a stoppered hose of the correct length inorder to acquire the epilimnetic sample The hosepipe was lowered with the bottom end open in thewater at a depth of just under the end bottom of the thermocline When the hosepipe was verticaland the water level was visible at the surface layer of the hosepipe then the stopper was insertedto create vacuum pressure The bottom end of the hosepipe was pulled to the surface to collect theepilimnetic sample in a bucket The diameter of the hosepipe was appropriate to sample the requiredwater volume (about 5ndash10 L for hypertrophic and eutrophic 15ndash30 L for mesotrophic and oligotrophiclakes) for the analyses in an acceptable number of runs The first three sampling runs served thepurpose of rinsing the hosepipe the sampling bucket and the plastic rod The subsequent runs were thewater sample taken for analysis The water sample in the bucket was mixed adequately before beingdivided into different bottles for further processing prior to analysis For pigment and toxin analysesa volume of 50ndash250 mL for hypertrophic and eutrophic lakes and 500ndash1000 mL for mesotrophic tooligotrophic lakes was filtered through 47 mm Glass fibre filters (GFC or GFF or similar) using afiltration device The filters were stored in minus20 C and in the dark until shipping For analyses of totalnutrients unfiltered water subsamples of 50 mL were stored in minus20 C until shipping All sampleswere shipped frozen using dry ice in Styrofoam boxes

All participating countries took part in a one-week training school to discuss and practice all fieldprocedures All samples were shipped to and stored at the University of Wageningen (Netherlands)until further analysis Each of the nutrients pigments and toxins analyses were done in one dedicatedlaboratory by one operator on one machine to minimize analytical errors and maximize integrationof the datasets Specifically the nutrients MCs and NOD analyses were done at the University of

Toxins 2018 10 156 17 of 24

Wageningen the pigment analysis at the University of Amsterdam and the CYN and ATX analysis atthe German Environment Agency

42 Cyanotoxin Analysis

In the laboratory frozen filters were transferred to 8 mL glass tubes and placed for twohours in a freeze-drier (Alpha 1-2 LD Martin Christ Gefriertrocknungsanlagen GmbH Osterodeam Harz Germany) The freeze-dried filters then used for the Liquid Chromatography withtandem Mass Spectrometry detection (LC-MSMS) analysis of microcystins (MCs) nodularin (NOD)cylindrospermopsin (CYN) and anatoxin (ATX) as they are described below

421 Microcystins (MCs) and Nodularin (NOD) Analysis

For the extraction of MCs and NOD 25 mL of 75 hot methanolminus25 ultrapure water (vv) wasadded to the freeze-dried filters which were then sealed with a screw cap and placed for half an hourat 60 C Subsequently the extract was transferred to a clean 8 mL glass tube This extraction procedurewas performed three times for each filter The supernatants of the repeated extraction procedurewere combined to a final volume of 75 mL and then dried in a Speedvac (Thermo Scientific SavantSPD121P Asheville NC USA) After that the extracts were reconstituted in 900 microL 100 MeOHThe reconstituted samples were transferred into 2 mL Eppendorf vials with a 022 microm cellulose-acetatefilter and centrifuged for 5 min at 16000times g (VWR Galaxy 16DH Boxmeer Netherlands) Filtrateswere transferred to amber glass vials for the analysis

The LC-MSMS analysis was performed on an Agilent 1200 LC and an Agilent 6410A QQQ(Waldbronn Germany) The extracts were separated using a 5 microm Agilent Eclipse XDB-C18 (46 mm150 mm column Agilent Technologies Waldbronn Germany) at 40 C The mobile phase consistedof Millipore water (vv eluent A) and acetonitrile (vv eluent B) both containing 01 formic acidat a flow rate of 05 mLmin with the following gradient program 0ndash2 min 30 B 6ndash12 min 90B with a linear increase of B between 2 and 6 min and a 5 min post run at 30 B (as describedin [35]) The injection volume was 10 microL Identification of the eight MC variants (MC-dmRR MC-RRMC-YR MC-dmLR MC-LR MC-LY MC-LW and MC-LF) and nodularin (NOD) was performed inthe positive Multiple Reaction Monitoring (MRM) with the following transitions MC-dmRR 5128mz [M + H]+ to 1351 quantifier MC-RR 5198 mz [M + H]+ to 1351 quantifier MC-YR 5233 mz[M + H]+ to 1351 quantifier MC-dmLR 4913 mz [M + H]+ to 8476 quantifier MC-LR 4983 mz[M + H]+ to 1351 quantifier MC-LY 8684 mz [M + H]+ to 1630 quantifier MC-LW 8915 mz [M+ H]+ to 1630 quantifier MC-LF 8525 mz [M + H]+ to 1630 quantifier and NOD 8255 mz [M +H]+ to 1351 quantifier [35] Mass spectrometric parameters are given in [70] Each MC variant wasquantified against a calibration curve The calibration curves were made using certified calibrationstandards obtained from DHI LAB Products (Hoslashrsholm Denmark) The limit of detection (LOD) for a250 mL sample was 00489 microgL for MC-dmRR 00358 microgL for MC-RR 00050 microgL for MC-YR00054 microgL for MC-dmLR 00086 microgL for MC-LR 00817 microgL for MC-LY 00531 microgL for MC-LW00206 microgL for MC-LF and 00048 microgL for NOD The limit of quantification (LOQ) for a 250 mLsample was 00489 microgL for MC-dmRR 00358 microgL for MC-RR 00050 microgL for MC-YR 00054microgL for MC-dmLR 00086 microgL for MC-LR 00817 microgL for MC-LY 00531 microgL for MC-LW 00206microgL for MC-LF and 00048 microgL for NOD

422 Cylindrospermopsin (CYN) and Anatoxin (ATX) Analysis

For the extraction of CYN and ATX 15 mL of 01 formic acid (FA) was added to the freeze-driedfilters sonicated for 10 min shaken for 1 hour and then centrifuged This extraction procedurewas repeated two more times and the combined supernatants were dried in a Speedvac (EppendorfGermany) Prior to analysis the dried extracts were re-dissolved in 1 mL 01 FA and filtered (02 micromPVDF Whatman Maidstone UK)

Toxins 2018 10 156 18 of 24

LC-MSMS analysis was carried out on an Agilent 2900 series HPLC system (Agilent TechnologiesWaldbronn Germany) coupled to a API 5500 QTrap mass spectrometer (AB Sciex Framingham MAUSA) equipped with a turbo-ion spray interface The extracts were separated using a 5 mm AtlantisC18 (21 mm 150 mm column Waters Eschborn Germany) at 30 C The mobile phase consistedof water (vv eluent A) and methanol (vv eluent A) both containing 01 formic acid and wasdelivered as a linear gradient from 1 to 25 B within 5 min at a flow rate of 025 mLmin Theinjection volume was 10 microL Identification of CYN and ATX was performed in the positive MRMmode with the following transitions CYN mz 4161 [M + H]+ to 194 (quantifier) and 4161176and ATX mz 1661 [M + H]+ to 149 1661131 166191 166143 (quantifier) Mass spectrometricparameters are given in [71] Certified reference standards were purchased from National ResearchCouncil (Ottawa ON Canada) The limit of detection (LOD) for both ATX and CYN was 00001 microgLand the limit of quantification (LOQ) was 00004 microgL for a 250 mL sample

43 Nutrient Analysis

Sample bottles were acid washed overnight in 1M HCl and rinsed with demineralized water beforeusage Nutrients were measured using a Skalar SAN+ segmented flow analyser (Skalar Analytical BVBreda NL) with UVpersulfate digestion integrated in the system Total phosphorus and nitrogenwere measured in unfiltered subsamples following Dutch standards protocols [7273] The limit ofdetection (LOD) was 002 and 02 mgL for total phosphorus and total nitrogen respectively

44 Pigment Analysis

The analysis of pigments was modified from the method described by [74] All filters were freezedried Filters (45 mm GFC and GFF) were cut in half placed in separate Eppendorf tubes andkept on ice until the end of the procedure We added 600 microL of 90 acetone to each tube alongwith a small amount of 05 mm beads To release the pigments from the phytoplankton cells filterswere placed on a bead-beater for one minute Next they were placed in an ultrasonic bath for tenminutes to increase the extraction yields This procedure was repeated twice to ensure a completeextraction of the total pigment content from the filters To achieve binding of the pigments during theHigh-Performance Liquid Chromatography (HPLC) analysis 300 microL of a Tributyl Ammonium Acetate(15) and Ammonium Acetate (77) mix were added to each tube Lastly samples were centrifugedat 15000 rpm and 4 C for ten minutes 35 microL of the supernatant from both Eppendorf tubes of afilter were transferred into a HPLC glass vials Pigments were separated on a Thermo Scientific ODSHypersil column (250 mm times 3 mm particle size 5 microm) in a Shimadzu HPLC (Shimadzu corporationKyoto Japan) and using a KONTRON SPD-M2OA diode array detector (Shimadzu corporation KyotoJapan) The different pigments were identified based on their retention time and absorption spectrumand quantified by means of pigment standards

45 Response Variables and Environmental Parameters

Our focal response variables were the toxin variants MC-dmRR MC-RR MC-YR MC-dmLRMC-LR CYN and ATX We also calculated the toxin quota as the ratio of each toxin variantconcentration (microgL) and the chlorophyll-a concentration (microgL) The latter was used as a proxyfor the total phytoplankton biomass

We used the environmental parameters latitude (Latitude) longitude (Longitude) Secchi depth(Secchi) sampling depth (DSampl) maximum depth (DMax) total phosphorus (TP) total nitrogen(TN) surface temperature (T_Surf) epilimnetic temperature (T_Epi) maximum buoyancy frequency(BuoyFreq) and light climate (ZeuZmix)

Latitude longitude secchi depth sampling depth and temperature profiles were measureddirectly in the field at all sites We interpolated all the temperature profiles at a 05 m resolution tostandardize the data as most of the profiles were obtained at a higher resolution than the requiredminimum interval of 05 m From the interpolated profiles we calculated the epilimnetic temperature

Toxins 2018 10 156 19 of 24

as the average temperature from surface until the bottom of the thermocline The surface temperaturevalue corresponded to the surface temperature

We calculated maximum buoyancy frequency (BuoyFreq) as a metric of stratification strength [75]In the rLakeAnalyzer package [76] in R 333 statistical software (R Core Team Vienna Austria)temperature profiles were used to estimate profiles of buoyancy frequency (N2) N2 is defined as theBruntndashVaumlisaumllauml equation N2 = minus(g$0) times (δ$(z)δ(z)) where g is the gravitational acceleration $0 isthe density at each depth and δ$(Z)δz is the density gradient The rlakeAnalyzer uses temperatureprofiles (in our case of 05 m resolution) to determine the density gradients applying thermodynamicequations specific to freshwater systems [77] The maximum value of buoyancy frequency generatedfrom the profile was used as an indication of depth where stratification was the strongest

The ratio (ZeuZmix) of euphotic depth (Zeu) to the mixing depth (Zmix) describes the light climatethat phytoplankton experience while circulating underwater [78] We calculated Zeu as Zeu = 2 times ZSD

(Secchi depth) As Zmix in shallow lakes we used the sampling depth when N2 was 0 or the top of themetalimnetic depth when stratification was present In deep lakes we used the top of the metalimneticdepth This was calculated as the depth where the steepest density gradient was found [76]

We used a total of 137 out of the 369 sampled lakes in order to test our hypotheses Selection ofa smaller subset was justified since in some samples several environmental variables were missingeither due to shipping issues or due to deviations from preservation protocols that could affect theintegrity of the dataset Additionally in order to build the ordination approaches described furtherdown lakes that had zero concentrations in all toxin variants needed to be excluded to build ameaningful similarity matrix of toxins Hence it was necessary to sacrifice a big number of lakes tobuild a concise dataset where all parameters could be used to select the right model and test the toxindistribution in the EMLS Samples that were below the limit of detection (LOD) ie a toxin signalwas detected qualitatively but it was too weak to quantify and was assigned a very small value ofhalf the limit of quantification (LOQ) enabling their inclusion in the analysis The toxins MC-LF andNODmdashwhich were found only in two lakesmdashand MC-LY and MC-LWmdashwhich were absent from alllakesmdashwere removed from the analysis following the approach of [79] for the most rare species in adataset Any statistical results included in this paper correspond to the subset of the 137 EMLS lakesSupplementary Material provides a table with information on the total number of lakes where toxinvariants were (a) not present (no toxin signal) (b) present and (c) missing for the 369 EMLS lakes(Table S1) All response variables and environmental parameters along with the Toxin Diversity Index(TDI) and toxin Richness of the 137-lake subset are provided in the Supplementary Material (Table S2)

46 Statistical Analysis

The geographical distribution of the toxin variants (Figure 1a) and their toxin quota (Figure 1b)were mapped with QGIS 218 Las Palmas (QGIS Development Team Girona Spain) We use pie chartsto show the percentage of each toxin variant in each sampled lake

To investigate the relationship between the toxin concentrationquota distribution and theenvironmental parameters we used canonical redundancy analysis of principal coordinates (RDA)with permutation test (9999 permutations) Analysis of variation inflation factor (VIF) allowed us touse all sampled environmental variables (as mentioned in Section 45) to test the relationship betweentoxin concentrationsquota and environmental variables Most of the environmental parameters werestandardized using log10 transformation (except for surface and epilimnetic temperature) The toxinconcentration and toxin quota matrices were standardized by Hellinger transformation [80] A stepwiseelimination of environmental predictors was applied to find the set of parameters that could bestexplain the ordination of the toxin concentrationsquota The selected environmental parameterswere surface temperature (T_Surf) epilimnetic temperature (T_Epi) maximum buoyancy frequency(BuoyFreq) and Secchi depth (Secchi) Significance of the ordination was provided performingANOVA analysis

Toxins 2018 10 156 20 of 24

We used the ShannonndashWiener index to calculate a Toxin Diversity Index (TDI) based on the EMLStoxin quota of 137 lakes The number of toxins (ldquoRichnessrdquo) per lake was also calculated based onthe same data A negative binomial generalized linear model was used to determine the effect ofsignificant environmental parameters on defining the TDI The same model was used to determine therelation of each toxin variant to TDI

For all the above analyses we selected the most significant environmental parameters usingstepwise selection based on the Akaike Information Criterion All statistical analyses were performedin R 333 [81] using mainly the vegan [82] and MASS [83] packages

Supplementary Materials The following are available online at httpwwwmdpicom2072-6651104156s1Table S1 Number of lakes where toxin variants (a) were not present (no toxin signal) (b) present (c) missingvalues in the entire EMLS dataset (n = 369 lakes) Table S2 Basic information on the study lakes their physicaldata analysed nutrients pigments toxins and calculated indices

Acknowledgments The authors acknowledge COST Action ES 1105 ldquoCYANOCOSTmdashCyanobacterialblooms and toxins in water resources Occurrence impacts and managementrdquo and COST Action ES 1201ldquoNETLAKEmdashNetworking Lake Observatories in Europerdquo for contributing to this study through networking andknowledge sharing with European experts in the field Evanthia Mantzouki was supported by a grant from theSwiss State Secretariat for Education Research and Innovation (SERI) to Bas Ibelings and by supplementaryfunding from the University of Geneva We thank Clare Ahnlund Ena Suarez and Irene Gallego for helping outwith the Swiss survey We thank Wendy Beekman and Els J Faassen for the nutrient and toxin analysis

Author Contributions Evanthia Mantzouki coordinated the EMLS collected data curated the dataset analysedthe data and wrote the manuscript Bas Ibelings conceived the idea for the EMLS contributed to discussionsthroughout the study and to the writing of the manuscript Jutta Fastner and Miquel Luumlrling performed thetoxin analyses and contributed to the writing of the manuscript Lisette deSenerpont Domis Sven Teurlincx andDamian Chmura assisted in analysing interpreting the dataset and in writing the manuscript The rest of thecoauthors were responsible for data collection in lakes in their respective countries for providing invaluablefeedback and for finalizing the manuscript

Conflicts of Interest The authors declare no conflict of interest

References

1 Codd GA Meriluoto J Metcalf JS Introduction Cyanobacteria Cyanotoxins Their Human Impact andRisk Management In Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis John Wiley amp Sons LtdHoboken NJ USA 2017

2 Huisman JM Matthijs HCP Visser PM Harmful Cyanobacteria Springer Aquatic Ecology Series 3 SpringerDordrecht The Netherlands 2005 p 243

3 Conley DJ Paerl HW Howarth RW Boesch DF Seitzinger SP Havens KE Lancelot C Likens GEControlling eutrophication Nitrogen and Phosphorus Science 2009 323 1014ndash1015 [CrossRef] [PubMed]

4 Otten TG Xu H Qin B Zhu G Paerl HW Spatiotemporal patterns and ecophysiology of toxigenicMicrocystis blooms in Lake Taihu China Implications for water quality management Environ Sci Technol2012 46 3480ndash3488 [CrossRef] [PubMed]

5 United Nations Environment Programme (UNEP) Loss and Damage The role of Ecosystem Services UnitedNations Environment Programme UNEP Nairobi Kenya 2016

6 Intergovernmental Panel on Climate Change (IPCC) Climate Change 2014 Impacts Adaptation andVulnerability Part A Global and Sectoral Aspects Contribution of Working Group II to the Fifth AssessmentReport of the Intergovernmental Panel on Climate Change Field CB Barros VR Dokken DJ Mach KJMastrandrea MD Bilir TE White LL Eds IPCC Cambridge UK New York NY USA 2014

7 Moss B Kosten S Meerhoff M Battarbee RW Jeppesen E Mazzeo N Havens K Lacerot G Liu ZDe Meester L et al Allied attack Climate change and eutrophication Inland Waters 2011 1 101ndash105[CrossRef]

8 Lurling M van Oosterhout F Faassen E Eutrophication and Warming Boost Cyanobacterial Biomass andMicrocystins Toxins 2017 9 64 [CrossRef] [PubMed]

9 Rigosi A Carey CC Ibelings BW Brookes JD The interaction between climate warmingand eutrophication to promote cyanobacteria is dependent on trophic state and varies among taxaLimnol Oceanogr 2014 59 99ndash114 [CrossRef]

Toxins 2018 10 156 21 of 24

10 Schopf JW The Fossil Record Tracing the Roots of the Cyanobacterial Lineage In The Ecology ofCyanobacteria Their Diversity in Time and Space Whitton BA Potts M Eds Springer DordrechtThe Netherlands 2002 pp 13ndash35

11 Carey CC Ibelings BW Hoffmann EP Hamilton DP Brookes JD Eco-physiological adaptations thatfavour freshwater cyanobacteria in a changing climate Water Res 2012 46 1394ndash1407 [CrossRef] [PubMed]

12 Mantzouki E Visser PM Bormans M Ibelings BW Understanding the key ecological traits ofcyanobacteria as a basis for their management and control in changing lakes Aquat Ecol 2015 50333ndash350 [CrossRef]

13 Ibelings BW Mur LR Kinsman R Walsby AE Microcystis changes its buoyancy in response to theaverage irradiance in the surface mixed layer Archiv fuumlr Hydrobiologie 1991 120 385ndash401

14 Granf GG Oliver RL Vertical separation of light and available nutrients as a factor causing replacementof green-algae by blue-green algae in the plankton of a stratified lake J Ecol 1982 70 829ndash844 [CrossRef]

15 Arnaud C Bernard C Spoof L Bruno M Microcystins and Nodularins In Handbook of CyanobacterialMonitoring and Cyanotoxin Analysis Meriluoto J Codd GA Eds John Wiley amp Sons Ltd Hoboken NJUSA 2017

16 Hubert JF Fastner J Ecology of Cyanobacteria In Handbook of Cyanobacterial Monitoring and CyanotoxinAnalysis John Wiley amp Sons Ltd Hoboken NJ USA 2017

17 Kokocinski M Cameaacuten AM Carmeli S Guzmaacuten-Guilleacuten R Jos Aacute Mankiewicz-Boczek J Metcalf JSMoreno IM Prieto AI et al Cylindrospermopsin and Congeners In Handbook of Cyanobacterial Monitoringand Cyanotoxin Analysis Meriluoto LSJ Codd GA Eds John Wiley amp Sons Ltd Hoboken NJ USA 2017

18 Nogueira IC Saker ML Pflugmacher S Wiegand C Vasconcelos VM Toxicity of the cyanobacteriumCylindrospermopsis raciborskii to Daphnia magna Environ Toxicol 2004 19 453ndash459 [CrossRef] [PubMed]

19 Gilbert JJ Effect of food availability on the response of planktonic rotifers to a toxic strain of thecyanobacterium Anabaena flos-aquae Limnol Oceanogr 1996 41 1565ndash1572 [CrossRef]

20 DeMott WR Zhang QX Carmichael WW Effects of toxic cyanobacteria and purified toxins on thesurvival and feeding of a copepod and three species of Daphnia Limnol Oceanogr 1991 36 1346ndash1357[CrossRef]

21 Holland A Kinnear S Interpreting the possible ecological role(s) of cyanotoxins Compounds forcompetitive advantage andor physiological aide Mar Drugs 2013 11 2239ndash2258 [CrossRef] [PubMed]

22 Capelli C Ballot A Cerasino L Papini A Salmaso N Biogeography of bloom-forming microcystinproducing and non-toxigenic populations of Dolichospermum lemmermannii (Cyanobacteria) Harmful Algae2017 67 1ndash12 [CrossRef] [PubMed]

23 Meriluoto JL Spoof L Codd G Handbook of Cyanobacterial Monitoring and Cyanotoxin Analysis WileyHoboken NJ USA 2017

24 Bortoli S Oliveira-Silva D Kruumlger T Doumlrr FA Copelicolo P Volmer DA Pinto E Growth andmicrocystin production of a Brazilian Microcystis aeruginosa strain (LTPNA 02) under different nutrientconditions Revista Brasileira de Farmacognosia 2014 24 389ndash398 [CrossRef]

25 Cerasino L Capelli C Salmaso N A comparative study of the metabolic profiles of common nuisancecyanobacteria in southern perialpine lakes Adv Oceanogr Limnol 2017 8 [CrossRef]

26 Neilan BA Pearson LA Muenchhoff J Moffitt MC Dittmann E Environmental conditions thatinfluence toxin biosynthesis in cyanobacteria Environ Microbiol 2013 15 1239ndash1253 [CrossRef] [PubMed]

27 Sivonen K Effects of light temperature nitrate orthophosphate and bacteria on growth of hepatotoxinproduction by Oscillatoria agardhii strains Appl Environ Microbiol 1990 56 2658ndash2666 [PubMed]

28 Long BM Jones GJ Orr PT Cellular microcystin content in N-Limited Microcystis aeruginosa can bepredicted from growth rate Appl Environ Microbiol 2001 67 278ndash283 [CrossRef] [PubMed]

29 Rapala J Sivonen K Luukkainen R Niemelauml SI Anatoxin-a concentration in Anabaena andAphanizomenon under different environmental conditions and comparison of growth by toxic and non-toxicAnabaena-strains A laboratory study J Appl Phycol 1993 5 581ndash591 [CrossRef]

30 Rapala J Sivonen K Assessment of environmental conditions that favor hepatotoxic and neurotoxicAnabaena spp strains cultured under light limitation and different temperatures Microb Ecol 2008 36181ndash192 [CrossRef]

31 Ibelings BW Backerb LCW Kardinaa EA Chorus I Current approaches to cyanotoxin risk assessmentand risk management around the globe Harmful Algae 2014 40 63ndash74 [CrossRef] [PubMed]

Toxins 2018 10 156 22 of 24

32 Falconer IR Choice A Hosja W Toxicity of edible mussels (Mytilus edulis) growing naturally in anestuary during a water bloom of the blue-green alga Nodularia spumigena Environmental Toxicology andWater Quality Environ Toxicol 1992 7 119ndash123

33 Fawell J Toxins from Blue-Green Algae Toxicological Assessment of Microcystin-LR WR Centre MedmenhamUK 1993 pp 1ndash259

34 Wolf HU Frank C Toxicity assessment of cyanobacterial toxin mixtures Environ Toxicol 2002 17 395ndash399[CrossRef] [PubMed]

35 Faassen EJ Lurling M Occurrence of the microcystins MC-LW and MC-LF in Dutch surface waters andtheir contribution to total microcystin toxicity Mar Drugs 2013 11 2643ndash2654 [CrossRef] [PubMed]

36 Chorus I Bartram J Toxic Cyanobacteria in WatermdashA Guide to Their Public Health Consequences Monitoringand Management Spon Press London UK 1999

37 Ibelings B Havens K Cyanobacterial toxins A qualitative meta-analysis of concentrations dosage andeffects in freshwater estuarine and marine biota Adv Exp Med Biol 2008 619 675ndash732 [PubMed]

38 Loftin KA Graham JL Hilborn E Lehmann S Meyer MT Dietze JE Griffith C Cyanotoxins ininland lakes of the United States Occurrence and potential recreational health risks in the EPA NationalLakes Assessment 2007 Harmful Algae 2016 56 77ndash90 [CrossRef] [PubMed]

39 Chorus I Current Approaches to Cyanotoxin Risk Assessment Risk Management and Regulations inDifferent Countries Chorus I Ed Federal Environmental Agency (Umweltbundesamt) Dessau-RoszliglauGermany 2005

40 Beniston M Stephenson DB Christensen OB Ferro CAT Frei C Goyette S Halsnaes K Holt TJylhauml K Koffi B et al Future extreme events in European climate An exploration of regional climatemodel projections Clim Chang 2007 81 71ndash95 [CrossRef]

41 Vautard R Gobiet A Sobolowski S Kjellstroumlm E Stegehuis A Watkiss P Mendlik T Landgren ONikulin G Teichmann C et al The European climate under a 2 C global warming Environ Res Lett2014 9 034006 [CrossRef]

42 Hoy A Haumlnsel S Skalak P Ustrnul Z Bochnicek O The extreme European summer of 2015 in a secularperspective Int J Climatol 2016 37 1ndash34

43 Messineo V Melchiorre S Di Corcia A Gallo P Bruno M Seasonal succession of Cylindrospermopsisraciborskii and Aphanizomenon ovalisporum blooms with cylindrospermopsin occurrence in the volcanicLake Albano Central Italy Environ Toxicol 2010 25 18ndash27 [PubMed]

44 Salmaso N Cerasino L Boscaini A Capelli C Planktic Tychonema (Cyanobacteria) in the large lakessouth of the Alps Phylogenetic assessment and toxigenic potential FEMS Microbiol Ecol 2016 92 fiw155[CrossRef] [PubMed]

45 Pawlik-Skowronska B Kalinowska R Skowronski T Cyanotoxin diversity and food web bioaccumulationin a reservoir with decreasing phosphorus concentrations and perennial cyanobacterial bloomsHarmful Algae 2013 28 118ndash125 [CrossRef]

46 Straile D Jochimsen MC Kuumlmmerlin R The use of long-term monitoring data for studies of planktonicdiversity A cautionary tale from Swiss lakes Freshw Biol 2013 58 1292ndash1301 [CrossRef]

47 Litchman E Klausmeier CA Trait-Based Community Ecology of Phytoplankton Ann Rev EcolEvolut Syst 2008 39 615ndash639 [CrossRef]

48 Loftin KA Dietze JE Meyer MT Graham JL Maksimowicz MM Toyne KD TotalCylindrospermopsins MicrocystinsNodularins and Saxitoxins Data for the 2007 United States EnvrionmnetalProtection Agency National Lake Assessment Kansas Water Science Center US Geological Survey LawrenceKS USA 2016

49 Kurmayer R Sivonen K Salmaso N Introduction In Molecular Tools for the Detection and Quantification ofToxigenic Cyanobacteria Kurmayer R Sivonen K Wilmotte A Salmaso N Eds John Wiley amp Sons LtdHoboken NJ USA 2017

50 Rantala A Fewer DP Hisbergues M Rouhiainen L Vaitomaa J Boumlrner T Sivonen K Phylogeneticevidence for the early evolution of microcystin synthesis PNAS 2003 101 568ndash573 [CrossRef] [PubMed]

51 Paerl HW Otten TG Harmful cyanobacterial blooms Causes consequences and controls Microb Ecol2013 65 995ndash1010 [CrossRef] [PubMed]

52 Paerl HW Huisman J Blooms like it hot Climate 2008 320 57ndash58

Toxins 2018 10 156 23 of 24

53 Visser PM Ibelings BW Mur LR Walsby AE The ecophysiology of the harmful cyanobacteriumMicrocystis In Harmful Cyanobacteria Springer Dordrecht The Netherlands 2005

54 Jang MH Jung JM Takamura N Changes in microcystin production in cyanobacteria exposed tozooplankton at different population densities and infochemical concentrations Limnol Oceanogr 2007 521454ndash1466 [CrossRef]

55 Wiedner C Visser PM Fastner J Metcalf JS Codd GA Mur LR Effects of Light on the MicrocystinContent of Microcystis Strain PCC 7806 Appl Environ Microbiol 2003 69 1475ndash1481 [CrossRef] [PubMed]

56 Wood SA Maier MY Puddick J Pochon X Zaiko A Dietrich DR Hamilton DP Trophic state andgeographic gradients influence planktonic cyanobacterial diversity and distribution in New Zealand lakesFEMS Microbiol Ecol 2016 93 fiw234 [CrossRef] [PubMed]

57 Sivonen K Jones G Toxic Cyanobacteria in Water A Guide to Their Public Health Consequences Monitoring andManagement Chorus I Bertram J Eds E amp FN Spon London UK 1999 pp 41ndash111

58 Horst GP Sarnelle O White JD Hamilton SK Kaul RB Bressie JD Nitrogen availability increases thetoxin quota of a harmful cyanobacterium Microcystis aeruginosa Water Res 2014 54 188ndash198 [CrossRef][PubMed]

59 Nimptsch J Woelfl S Osorio S Valenzuela J Moreira C Ramos V Castelo-Branco R Leatildeo PNVasconcelos V First record of toxins associated with cyanobacterial blooms in oligotrophic North Patagonianlakes of Chile- a genomic approach Int Rev Hydrobiol 2016 101 57ndash68 [CrossRef]

60 Mischke U Cyanobacteria associations in shallow polytrophic lakes Influence of environmental factorsActa Oecol 2003 24 S11ndashS23 [CrossRef]

61 Anneville O Souissi S Ibanez F Ginot V Druant JC Angeli N Temporal mapping ofphytoplankton assemblages in Lake Geneva Annual and interannual changes in their patterns of successionLimnol Oceanogr 2002 47 1355ndash1366 [CrossRef]

62 Anneville O Souissi S Gammeter S Straile D Seasonal and inter-annual scales of variability inphytoplankton assemblages Comparison of phytoplankton dynamics in three peri-alpine lakes over aperiod of 28 years Freshw Biol 2004 49 98ndash115 [CrossRef]

63 Sevilla E Martin-Luna B Vela L Bes MT Peleato ML Fillat MF Microcystin-LR synthesis as responseto nitrogen Transcriptional analysis of the mcyD gene in Microcystis aeruginosa PCC7806 Ecotoxicology2010 19 1167ndash1173 [CrossRef] [PubMed]

64 Paerl HW Mitigating harmful cyanobacterial blooms in a human- and climatically-impacted world Life2014 4 988ndash1012 [CrossRef] [PubMed]

65 Thomas MK Litchman E Effects of temperature and nitrogen availability on the growth of invasive andnative cyanobacteria Hydrobiologia 2016 763 357ndash369 [CrossRef]

66 Cerasino L Salmaso N Diversity and distribution of cyanobacterial toxins in the Italian subalpine lacustrinedistrict Oceanol Hydrobiol Stud 2012 41 54ndash63 [CrossRef]

67 Pires LM Sarpe D Brehm M Ibelings BW Potential synergistic effects of microcystins and bacteriallipopolysaccharides on life history traits of Daphnia galeata raised on low and high food levels Aquat Toxicol2011 104 230ndash242 [CrossRef] [PubMed]

68 Freitas EC Pinheiro C Rocha O Loureiro S Can mixtures of cyanotoxins represent a risk to thezooplankton The case study of Daphnia magna Straus exposed to hepatotoxic and neurotoxic cyanobacterialextracts Harmful Algae 2014 31 143ndash152 [CrossRef] [PubMed]

69 Rzymski P Poniedzialek B In search of environmental role of cylindrospermopsin A review on globaldistribution and ecology of its producers Water Res 2014 66 320ndash337 [CrossRef] [PubMed]

70 Luumlrling M Faassen EJ Dog Poisonings associated with Microcystis aeruginosa Bloom in the NetherlandsToxins 2013 5 556ndash567 [CrossRef] [PubMed]

71 Fastner J Beulker C Geiser B Hoffmann A Kroumlger R Teske K Hoppe J Mundhenk L Neurath HSagebiel D et al Fatal Neurotoxicosis in Dogs Associated with Tychoplanktic Anatoxin-a ProducingTychonema sp in Mesotrophic Lake Tegel Berlin Toxins 2018 10 60 [CrossRef] [PubMed]

72 Netherlands Normalization Institute (NEN) Water Photometric Determination of the Content of DissolvedOrthophosphate and the Total Content of Phosphorous Compounds by Continuous Flow Analysis NEN 6663The Netherlands Normalization Institute Delft The Netherlands 1986 (In Dutch)

Toxins 2018 10 156 24 of 24

73 Netherlands Normalization Institute (NEN) Water Photometric Determination of the Content of AmmoniumNitrogen and the Sum of the Contents of Ammoniacal and Organically Bound Nitrogen According to Kjeldahl byContinuous Flow Analysis NEN-6646 Netherlands Normalization Institute Delft The Netherlands 1990

74 Van der Staay GWM Brouwer A Baard RL van Mourik F Separation of photosystems I and II from theoxychlorobacterium (prochlorophyte) Prochlorothrix hollandica and association of Chlb binding antennaewith PS II Biochim Biophys Acta 1992 1102 220ndash228 [CrossRef]

75 Leach TH Beisner BE Carey CC Pernica P Rose KC Huot Y Brentrup JA Domaizon IGrossart H-P Ibelings BW et al Patterns and drivers of deep chlorophyll maxima structure in 100 lakesThe relative importance of light and thermal stratification Limnol Oceanogr 2017 63 628ndash646 [CrossRef]

76 Winslow LA Read JS Woolway RI Brentrup JA Leach TH Zwart JA rLakeAnalyzer Lake PhysicsTools Zenodo Available online httpszenodoorgrecord1003169Ws82F9NubEY (accessed on 6October 2017)

77 Chen CTA Millero FJ Thermodynamic properties for natural waters covering only the limnologicalrange Limnol Oceanogr 1986 31 657ndash662 [CrossRef]

78 Scheffer M Rinaldi S Gragnani A Mur LR van Nes EH On the Dominance of FilamentousCyanobacteria in Shallow Turbid Lakes Ecology 1997 78 272ndash282 [CrossRef]

79 Legendre P Legendre L Numerical Ecology 3rd ed Elsevier Amsterdam The Netherlands 201280 Legendre P Gallagher ED Ecologically meaningful transformations for ordination of species data

Oecologia 2001 129 271ndash280 [CrossRef] [PubMed]81 R Development Core Team R A Language and Environment for Statistical Computing R Foundation for

Statistical Computing Vienna Austria 200982 Oksanen J Kindt R Legendre P OrsquoHara B Henry M Stevens H The vegan package

Community Ecol Package 2007 10 631ndash63783 Venables WN Ripley BD Modern Applied Statistics with S-Plus 4th ed Springer New York NY USA 2002

copy 2018 by the authors Licensee MDPI Basel Switzerland This article is an open accessarticle distributed under the terms and conditions of the Creative Commons Attribution(CC BY) license (httpcreativecommonsorglicensesby40)

Introduction

Results

Toxin Distribution on a Continental Scale

Multivariate Multiple Regression Analysis

Toxin Diversity Index and Environmental Parameters

Discussion

Materials and Methods

Sampling Survey

Cyanotoxin Analysis

Microcystins (MCs) and Nodularin (NOD) Analysis

Cylindrospermopsin (CYN) and Anatoxin (ATX) Analysis

Nutrient Analysis

Pigment Analysis

Response Variables and Environmental Parameters

Statistical Analysis

References

Toxins 2018 10 156 2 of 24

Julita Dunalska 81 Justyna Sienska 81 Daniel Szymanski 81 Marek Kruk 82 ID Iwona Kostrzewska-Szlakowska 83 Iwona Jasser 84 Petar Žutinic 85 Marija Gligora Udovic 85Anđelka Plenkovic-Moraj 85 Magdalena Frak 86 Agnieszka Bankowska-Sobczak 87Michał Wasilewicz 87 Korhan Oumlzkan 88 Valentini Maliaka 28990 ID Kersti Kangro 2791Hans-Peter Grossart 4292 ID Hans W Paerl 93 ID Cayelan C Carey 94 ID and Bas W Ibelings 1

1 Department F-A Forel for Environmental and Aquatic Sciences University of Geneva 1205 GenevaSwitzerland BastiaanIbelingsunigech

2 Department of Environmental Sciences Wageningen University amp Research 6700 WageningenThe Netherlands miquellurlingwurnl (ML) ldesenerpontdomisniooknawnl (LdSD)lseelenniooknawnl (LS) yvonverstijnenhotmailcom (YV) valentinimaliakagmailcom (VM)

3 Department of Aquatic Ecology Netherlands Institute of Ecology (NIOO-KNAW) 6700 WageningenThe Netherlands steurlincxniooknawnl

4 German Environment Agency Unit Drinking Water Resources and Water Treatment Corrensplatz 114195 Berlin Germany juttafastnerubade

5 Institute of Nature Conservation Polish Academy of Sciences 31-120 Krakow Polandwilkiopkrakowpl (EW-W) krztoniopkrakowpl (WK) walusiakiopkrakowpl (EW)

6 Institute of Botany Nature Research Centre Vilnius 08412 Lithuania juditakoreivienegmailcom (JK)juratekarosienebotanikalt (JK) juratekasperovicienebotanikalt (JK)ksenijasavadovagamtostyrimailt (KS) irmavitonytegmailcom (IV)

7 RampD Department Environmental Engineering 3edata 27004 Lugo Spain carmencillero3edataes8 Department of Water Protection Adam Mickiewicz University 61614 Poznan Poland

agbudzynskagmailcom (AB) rgoldamuedupl (RG) akozakamuedupl (AK)rosinskaasiagmailcom (JR) eszelagamuedupl (ES-W) domekpamuedupl (PD)jakubowskangmailcom (NJ-K)

9 Department of Hydrobiology Adam Mickiewicz University 61614 Poznan Polandkingakwasizurwppl (KK) messyaszamuedupl (BM) olapamuedupl (AP)marpelamuedupl (MP) kokamuedupl (MK)

10 Department of Limnology and Water Quality AECOM URS 08036 Barcelona Spainanagarciaaecomcom (AG-M) montserratrealaecomcom (MR) elviraromansaecomcom (ER)jordinogueroaecomcom (JN-R) parra1404hotmailcom (DPD) elifdzmogmailcom (EF-M)

11 Department of Environmental Engineering Abant Izzet Baysal University 14280 Bolu Turkeykarakaya_nibuedutr

12 Department of Science and Engineering Aringbo Akademi University 20520 Aringbo Finlandkerstinhaggqvistgmailcom

13 Department of Fisheries and Aquaculture Ankara University 6100 Ankara Turkeynilsundemir2gmailcom

14 Department of biology Middle East Technical University 6800 Ankara Turkey meryemmetuedutr (MB)nrflzstergmailcom (NF) etitheleviscom (EEL) iskinugurgmailcom (UI)gizbezircigmailcom (GB) unyazgangmailcom (UumlNT)

15 Institute of Marine Sciences Department of Oceanography Middle East Technical University 06800 AnkaraTurkey korayimsmetuedutr

16 Department of Botany Aristotle University of Thessaloniki 54124 Thessaloniki Greecesgkelisbioauthgr (SG) mattpanobioauthgr (MP)

17 Department of Basic Science Ataturk University 25240 Erzurum Turkey ozdenfakiogluatauniedutr18 Water Quality Department Athens Water Supply and Sewerage Company 11146 Athens Greece

cavagianosicloudcom (CA) t_kaloudisathforthnetgr (TK)19 Department of Biology Balikesir University 10145 Balikesir Turkey kcelikbalikesiredutr20 Department of Bioengineering Bursa Technical University 16310 Bursa Turkey meteyilmazbtuedutr21 Catalan Institute for Water Research (ICRA) 17003 Girona Spain rmarceicracat (RM)

ncatalangarciagmailcom (NC)22 Department of Ecology and Genetics Limnology Uppsala University 75236 Uppsala Sweden

jandriugarciagmailcom (AGB) moritzbuckebcuuse (MB) pablourrutia_corderobiolluse (PU-C)23 Department of Ecology and Genetics Erken Laboratory Uppsala University 76173 Norrtalje Sweden

williamcolomebcuuse (WC-M) kristiinamustonenebcuuse (KM) donpiersonebcuuse (DP)yangyanghotmailse (YY)

Toxins 2018 10 156 3 of 24
























Toxins 2018 10 156 4 of 24



























Toxins 2018 10 156 5 of 24


















Toxins 2018 10 156 6 of 24


1 Introduction





Toxins 2018 10 156 7 of 24




2 Results




Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 3 of 24
























Toxins 2018 10 156 4 of 24



























Toxins 2018 10 156 5 of 24


















Toxins 2018 10 156 6 of 24


1 Introduction





Toxins 2018 10 156 7 of 24




2 Results




Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 4 of 24



























Toxins 2018 10 156 5 of 24


















Toxins 2018 10 156 6 of 24


1 Introduction





Toxins 2018 10 156 7 of 24




2 Results




Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 5 of 24


















Toxins 2018 10 156 6 of 24


1 Introduction





Toxins 2018 10 156 7 of 24




2 Results




Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 6 of 24


1 Introduction





Toxins 2018 10 156 7 of 24




2 Results




Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 7 of 24




2 Results




Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 8 of 24






(microgL)Mean Stdv






Figure 1 Cont

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 9 of 24




Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 10 of 24





ToxinConcentrations

014

T_Epi 005 1322 0001T_Surf 002 493 0002

BuoyFreq 001 317 001Secchi 001 287 001

ToxinQuota 014

T_Epi 005 1322 0001T_Surf 002 493 0003

BuoyFreq 001 317 002Secchi 001 287 002

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 11 of 24





TDIquota



DMax 008 08T_Epi 024 02Secchi 030 015

Richnessquota



DMax 041 015T_Epi 113 002Secchi 140 0007


Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 12 of 24


Toxin QuotaResponse

When TDIuarr

X2 pResponse When

Richnessuarr

X2 p






3 Discussion



Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 13 of 24





Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 14 of 24




Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 15 of 24




Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 16 of 24




41 Sampling Survey




Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 17 of 24









Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 18 of 24




44 Pigment Analysis






Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 19 of 24









Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 20 of 24







References










Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 21 of 24























Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 22 of 24






















Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 23 of 24





















Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

Toxins 2018 10 156 24 of 24












Introduction

Results




Discussion


Sampling Survey

Cyanotoxin Analysis



Nutrient Analysis

Pigment Analysis



References

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