Limnol. Oceanogr. 66, 2021, 4314–4333
© 2021 The Authors. Limnology and Oceanography published by Wiley Periodicals LLC on
behalf of Association for the Sciences of Limnology and Oceanography.
doi: 10.1002/lno.11963
Stratification strength and light climate explain variation in chlorophyll
a at the continental scale in a European multilake survey in a heatwave
summer
Daphne Donis ,1* Evanthia Mantzouki,1 Daniel F. McGinnis,1 Dominic Vachon,1,2 Irene Gallego,1,a
Hans-Peter Grossart,3,4 Lisette N. de Senerpont Domis,5,7 Sven Teurlincx,5 Laura Seelen,5,7
Miquel Lürling,5,6 Yvon Verstijnen,6 Valentini Maliaka,6,8,9 Jeremy Fonvielle,3 Petra M. Visser,10
Jolanda Verspagen,10 Maria van Herk,10 Maria G. Antoniou,11 Nikoletta Tsiarta,11 Valerie McCarthy,12
Victor C. Perello,12 Danielle Machado-Vieira,13 Alinne Gurj~ao de Oliveira,13 Dubravka Špoljaric Maronic,14
Filip Stevic,14 Tanja Žuna Pfeiffer,14 Itana Bokan Vucelic,15 Petar Žutinic,16 Marija Gligora Udovicˇ,16
Anđelka Plenkovic-Moraj,16 Ludeˇk Blaha,17 Rodan Geriš,18 Markéta Frankova,19
Kirsten Seestern Christoffersen,20 Trine Perlt Warming,20 Tõnu Feldmann,21 Alo Laas,21 Kristel Panksep,21
Lea Tuvikene,21 Kersti Kangro,21,22 Judita Koreivien _e,23 Jurat _e Karosien _e,23 Jurat _e Kasperovicˇien _e,23
Ksenija Savadova-Ratkus,23 Irma Vitonyt _e,23 Kerstin Häggqvist,24 Pauliina Salmi,25 Lauri Arvola,26
Karl Rothhaupt,27 Christos Avagianos,28 Triantafyllos Kaloudis,28 Spyros Gkelis,29 Manthos Panou,29
Theodoros Triantis,30 Sevasti-Kiriaki Zervou,30 Anastasia Hiskia,30 Ulrike Obertegger,31
Adriano Boscaini,31 Giovanna Flaim,31 Nico Salmaso,31 Leonardo Cerasino,31 Sigrid Haande,32
Birger Skjelbred,32 Magdalena Grabowska,33 Maciej Karpowicz,33 Damian Chmura,34 Lidia Nawrocka,35
Justyna Kobos,36 Hanna Mazur-Marzec,36 Pablo Alcaraz-Parraga,37 Elżbieta Wilk-Wozniak,38
Wojciech Krzton,38 Edward Walusiak,38 Ilona Gagala-Borowska,39 Joana Mankiewicz-Boczek,39
Magdalena Toporowska,40 Barbara Pawlik-Skowronska,40 Michał Niedzwiecki,40 Wojciech Pęczuła,40
Agnieszka Napiorkowska-Krzebietke,41 Julita Dunalska,42 Justyna Sienska,42 Daniel Szymanski,42
Marek Kruk,43 Agnieszka Budzynska,44 Ryszard Goldyn,44 Anna Kozak,44 Joanna Rosinska,44
Elżbieta Szeląg-Wasielewska,44 Piotr Domek,44 Natalia Jakubowska-Krepska,44 Kinga Kwasizur,45
Beata Messyasz,45 Aleksandra Pełechata,45 Mariusz Pełechaty,45 Mikolaj Kokocinski,45
Beata Madrecka-Witkowska,46 Iwona Kostrzewska-Szlakowska,47 Magdalena Frąk,48
Agnieszka Bankowska-Sobczak,49 Michał Wasilewicz,49 Agnieszka Ochocka,50 Agnieszka Pasztaleniec,50
Iwona Jasser,51 Ana M. Ant~ao-Geraldes,52 Manel Leira,53 Vitor Vasconcelos,54 Joao Morais,54
Micaela Vale,54 Pedro M. Raposeiro,55 Vítor Gonçalves,55 Boris Aleksovski,56 Svetislav Krstic,56
Hana Nemova,57 Iveta Drastichova,57 Lucia Chomova,57 Spela Remec-Rekar,58 Tina Elersek,59
Lars-Anders Hansson,60 Pablo Urrutia-Cordero,60,61 Andrea G. Bravo,61 Moritz Buck,61
William Colom-Montero,62 Kristiina Mustonen,62 Don Pierson,62 Yang Yang,62 Jessica Richardson,63
Christine Edwards,64 Hannah Cromie,65 Jordi Delgado-Martín,66 David García,66 Jose Luís Cereijo,66
Joan Gomà,67 Mari Carmen Trapote,67 Teresa Vegas-Vilarrúbia,67 Biel Obrador,67 Ana García-Murcia,68
Monserrat Real,68 Elvira Romans,68 Jordi Noguero-Ribes,68 David Parreño Duque,68
*Correspondence: daphne.donis@unige.ch
This is an open access article under the terms of the Creative Commons
Attribution-NonCommercial-NoDerivs License, which permits use and dis-
tribution in any medium, provided the original work is properly cited, the
use is non-commercial and no modifications or adaptations are made.
Additional Supporting Information may be found in the online version of
this article.
aPresent address: Department Aquatic Ecology, Eawag Überlandstrasse,
Dübendorf, Switzerland
Author Contribution Statement: D.D. analyzed and worked on data
visualization, coordinated feedback from coauthors, and wrote the manu-
script. E.M. coordinated the EMLS, collected data, curated the dataset,
analyzed the data, and contributed to writing the manuscript.
B.I. conceived the idea for the EMLS, contributed to discussions through-
out the study and to the writing of the manuscript. D.M., D.V., I.G., H.-P.
G., L.N.d.S.D., S.T., L.S., N.C., A.G.B., M.B., P.V., and C.C. assisted in ana-
lyzing and interpreting the dataset. The rest of the coauthors were
responsible for finalizing the sampling protocols, organizing the local sur-
veys, collecting data in their respective countries, and providing invalu-
able feedback on the manuscript and data analysis.
4314
Elísabeth Fernandez-Moran,68 Barbara Úbeda,69 José Angel Galvez,69 Núria Catalan,70
Carmen Pérez-Martínez,71 Eloísa Ramos-Rodríguez,71 Carmen Cillero-Castro,72 Enrique Moreno-Ostos,73
José María Blanco,73 Valeriano Rodríguez,73 Jorge Juan Montes-Pérez,73 Roberto L. Palomino,73
Estela Rodríguez-Pérez,73 Armand Hernandez,74 Rafael Carballeira,75 Antonio Camacho,76
Antonio Picazo,76 Carlos Rochera,76 Anna C. Santamans,76 Carmen Ferriol,76 Susana Romo,77
Juan Miguel Soria,77 Arda Özen,78 Tünay Karan,79 Nilsun Demir,80 Meryem Beklioglu,81 Nur Filiz,81
Eti Levi,81 Ugur Iskin,81 Gizem Bezirci,81 Ülkü Nihan Tavs¸anoglu,81 Kemal Çelik,82 Koray Ozhan,83
Nusret Karakaya,84 Mehmet Ali Turan Koçer,85 Mete Yilmaz,86 Faruk Maras¸lıoglu,87 Özden Fakioglu,88
Elif Neyran Soylu,89 Meral Apaydın Yagcı,90 S¸akir Çınar,90 Kadir Çapkın,90 Abdulkadir Yagcı,90
Mehmet Cesur,90 Fuat Bilgin,90 Cafer Bulut,90 Rahmi Uysal,90 Köker Latife,91 Reyhan Akçaalan,91
Meriç Albay,91 Mehmet Tahir Alp,92 Korhan Özkan,93 Tugba Ongun Sevindik,94 Hatice Tunca,94
Burçin Önem,94 Hans Paerl,95 Cayelan C. Carey,96 Bastiaan W. Ibelings1
1Department F.-A. Forel for Environmental and Aquatic Sciences and Institute for Environmental Sciences, University of Geneva,
Geneva, Switzerland
2Department of Ecology and Environmental Sciences, Umeå University, Umeå, Sweden
3Department of Experimental Limnology, Leibniz Institute of Freshwater Ecology and Inland Fisheries, Stechlin, Germany
4Institute of Biochemistry and Biology, Potsdam University, Potsdam, Germany
5Department of Aquatic Ecology, Netherlands Institute of Ecology (NIOO-KNAW), Wageningen, The Netherlands
6Department of Environmental Sciences, Wageningen University & Research, Wageningen, The Netherlands
7Department of Environmental Sciences, Aquatic Ecology and Water Quality Management group, Wageningen University,
Wageningen, 6708 PB, The Netherlands
8Society for the Protection of Prespa, Agios Germanos, Greece
9Department of Aquatic Ecology and Environmental Biology, Institute for Water and Wetland Research, Radboud University
Nijmegen, Nijmegen, The Netherlands
10Department of Freshwater and Marine Ecology, Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam,
Amsterdam, The Netherlands
11Department of Chemical Engineering, Cyprus University of Technology, Lemesos, Cyprus
12Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, Dundalk, Ireland
13Departamento de Sistematica e Ecologia, Universidade Federal da Paraíba, Paraíba, Brazil
14Department of Biology, Josip Juraj Strossmayer University of Osijek, Osijek, Croatia
15Department for Ecotoxicology, Teaching Institute of Public Health of Primorje-Gorski Kotar County, Rijeka, Croatia
16Department of Biology, Faculty of Science, University of Zagreb, Zagreb, Croatia
17RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
18Department of Hydrobiology, Morava Board Authority, Brno, Czech Republic
19Department of Paleoecology, Institute of Botany, The Czech Academy of Sciences, Brno, Czech Republic
20Freshwater Biological Laboratory, Department of Biology, University of Copenhagen, Copenhagen, Denmark
21Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Tartu, Estonia
22Tartu Observatory, Faculty of Science and Technology, University of Tartu, Tartu, Estonia
23Institute of Botany, Nature Research Centre, Vilnius, Lithuania
24Department of Science and Engineering, Åbo Akademi University, Åbo, Finland
25Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland
26Lammi Biological Station, University of Helsinki, Lammi, Finland
27Department of Biology, Limnological Institute, University of Konstanz, Konstanz, Germany
28Water Quality Department, Athens Water Supply and Sewerage Company, Athens, Greece
29Department of Botany, School of Biology, Aristotle University of Thessaloniki, Thessaloniki, Greece
30Institute of Nanoscience and Nanotechnology, National Center for Scientific Research «DEMOKRITOS», Agia Paraskevi, Attiki,
Greece
31Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, 38010, Italy
32Department of Freshwater Ecology, Norwegian Institute for Water Research, Oslo, Norway
33Department of Hydrobiology, University of Bialystok, Bialystok, Poland
34Institute of Environmental Protection and Engineering, University of Bielsko-Biala, Bielsko-Biala, Poland
35Institute of Technology, The State University of Applied Sciences, Elblag, Poland
36Department of Marine Biotechnology, University of Gdansk, Gdynia, Poland
37Department of Animal Biology, Plant Biology and Ecology, University of Jaen, Jaen, Spain
38Institute of Nature Conservation, Polish Academy of Sciences, Krakow, Poland
Donis et al. European lake survey: summer Chl-a drivers
4315
39European Regional Centre for Ecohydrology of the Polish Academy of Sciences, Lodz, Poland
40Department of Hydrobiology and Protection of Ecosystems, University of Life Sciences in Lublin, Lublin, Poland
41Department of Ichthyology, Hydrobiology and Aquatic Ecology, S. Sakowicz Inland Fisheries Institute, Olsztyn, 10-719, Poland
42Department of Water Protection Engineering, University of Warmia and Mazury, Olsztyn, Poland
43Department of Applied Computer Science and Mathematical Modelling, University of Warmia and Mazury, Olsztyn, 10-710,
Poland
44Department of Water Protection, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland
45Department of Hydrobiology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland
46Institute of Environmental Engineering and Building Installations, Faculty of Environmental Engineering and Energy, Poznan
University of Technology, Poznan, 60965, Poland
47Faculty of Biology, University of Warsaw, Warsaw, Poland
48Department of Remote Sensing and Environmental Assessment, Institute of Environmental Engineering, Warsaw University of
Life Sciences - SGGW, Nowoursynowska Str. 166, Warsaw, 02-787, Poland
49Department of Water Engineering and Applied Geology, Faculty of Civil and Environmental Engineering, Warsaw University of
Life Sciences – SGGW, Warsaw, 02-787, Poland
50Department of Freshwater Protection, Institute of Environmental Protection - National Research Institute, Warsaw, Poland
51Department of Plant Ecology and Environmental Conservation, Faculty of Biology, University of Warsaw, Warsaw, 02-089,
Poland
52Centro de Investigaç~ao da Montanha (CIMO), Instituto Politécnico de Bragança, Bragança, Portugal
53BioCost Research Group, Faculty of Science and Centro de Investigacions Científicas Avanzadas (CICA), Department of Biology,
Faculty of Science, University of A Coruña, A Coruña, 15071, Spain
54Interdisciplinary Centre of Marine and Environmental Research (CIIMAR/CIMAR), University of Porto, Terminal de Cruzeiros
do Porto de Leixões, Matosinhos, 4450-208, Portugal
55Research Center in Biodiversity and Genetic Resources (CIBIO-Azores), InBIO Associated Laboratory, Faculty of Sciences and
Technology, University of the Azores, Ponta Delgada, 9500-321, Portugal
56Faculty of Natural Sciences and Mathematics, SS Cyril and Methodius University, Skopje, Macedonia
57National Reference Center for Hydrobiology, Public Health Authority of the Slovak Republic, Bratislava, Slovakia
58Department of Water Quality, Slovenian Environmental Agency, Ljubljana, Slovenia
59Department of Genetic Toxicology and Cancer Biology, National Institute of Biology, Ljubljana, Slovenia
60Department of Biology, Lund University, Lund, Sweden
61Department of Ecology and Genetics, Limnology, Uppsala University, Uppsala, Sweden
62Department of Ecology and Genetics, Erken Laboratory, Uppsala University, Norrtalje, Sweden
63Department of Biological and Environmental Sciences, University of Stirling, Stirling, UK
64School of Pharmacy and Life Sciences, Robert Gordon University, Aberdeen, UK
65Agri-Food & Biosciences Institute, Belfast, UK
66Department of Civil Engineering, University of A Coruña, A Coruña, Spain
67Department of Evolutionary Biology, Ecology, and Environmental Sciences, University of Barcelona, Barcelona, Spain
68Department of Limnology and Water Quality, AECOM U.R.S., Barcelona, Spain
69Department of Biology, INMAR Marine Research Institute, University of Cadiz, Cadiz, 11510 Puerto Real, Spain
70Catalan Institute for Water Research (ICRA), Girona, Spain
71Department of Ecology and Institute of Water Research, University of Granada, Granada, Spain
72R&D Department Environmental Engineering, 3edata, Lugo, Spain
73Department of Ecology, University of Malaga, Malaga, Spain
74Institute of Earth Sciences Jaume Almera, ICTJA, CSIC, Barcelona, Spain
75Centro de Investigacions Cientificas Avanzadas (CICA), Facultade de Ciencias, Universidade da Coruña, A Coruña, Spain
76Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, Valencia, Spain
77Department of Microbiology and Ecology, University of Valencia, Burjassot, Spain
78Department of Forest Engineering, University of Cankiri Karatekin, Cankiri, Turkey
79Department of Animal Nutrition and Zootechnics, Faculty of Veterinary Medicine, Yozgat Bozok University, Yozgat, Turkey
80Department of Fisheries and Aquaculture Engineering, Ankara University, Ankara, 06110, Turkey
81Department of Biological Sciences, Limnology Laboratory, Middle East Technical University, Ankara, Turkey
82Department of Biology, Balikesir University, Balikesir, Turkey
83Department of Oceanography, Institute of Marine Sciences, Middle East Technical University, Ankara, Turkey
84Department of Environmental Engineering, Abant Izzet Baysal University, Bolu, Turkey
85Department of Environment and Resource Management, Mediterranean Fisheries Research Production and Training Institute,
Antalya, Turkey
86Department of Bioengineering, Bursa Technical University, Bursa, Turkey
Donis et al. European lake survey: summer Chl-a drivers
4316
87Department of Biology, Hitit University, Corum, Turkey
88Department of Basic Science, Ataturk University, Erzurum, Turkey
89Department of Biology, Giresun University, Giresun, Turkey
90Republic of Turkey Ministry of Food Agriculture, Fisheries Research Institute, Isparta, Turkey
91Department of Freshwater Resource and Management, Faculty of Aquatic Sciences, Istanbul University, Istanbul, Turkey
92Faculty of Aquaculture, Mersin University, Mersin, Turkey
93Institute of Marine Sciences, Marine Biology and Fisheries, Middle East Technical University, Mersin, Turkey
94Department of Biology, Sakarya University, Sakarya, Turkey
95Institute of Marine Sciences, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
96Department of Biological Sciences, Virginia Tech, Blacksburg, Virginia
Abstract
To determine the drivers of phytoplankton biomass, we collected standardized morphometric, physical, and
biological data in 230 lakes across the Mediterranean, Continental, and Boreal climatic zones of the European
continent. Multilinear regression models tested on this snapshot of mostly eutrophic lakes (median total phos-
phorus [TP] = 0.06 and total nitrogen [TN] = 0.7 mg L
1), and its subsets (2 depth types and 3 climatic zones),
show that light climate and stratification strength were the most significant explanatory variables for chloro-
phyll a (Chl a) variance. TN was a significant predictor for phytoplankton biomass for shallow and continental
lakes, while TP never appeared as an explanatory variable, suggesting that under high TP, light, which partially
controls stratification strength, becomes limiting for phytoplankton development. Mediterranean lakes were the
warmest yet most weakly stratified and had significantly less Chl a than Boreal lakes, where the temperature
anomaly from the long-term average, during a summer heatwave was the highest (+4C) and showed a signifi-
cant, exponential relationship with stratification strength. This European survey represents a summer snapshot
of phytoplankton biomass and its drivers, and lends support that light and stratification metrics, which are both
affected by climate change, are better predictors for phytoplankton biomass in nutrient-rich lakes than nutrient
concentrations and surface temperature.
Globally, temperature, light, and nutrients are key drivers
of phytoplankton blooms, but their relative importance in
determining algal biomass strongly depends on the role of
thermal stratification, that is, water column stability
(Sverdrup 1953; Cloern 1996; Ptacnik et al. 2003; Carvalho
et al. 2016). As a matter of fact, the relative importance of
these drivers and interactive mechanisms between them can-
not be fully resolved without including thermal stability
(Winslow et al. 2017). This is particularly relevant under
global processes of eutrophication and climate warming
(Sinha et al. 2017) as some research foresees an allied impact
of eutrophication and climate change effects in promoting
harmful cyanobacterial blooms (Moss et al. 2011).
Stratification suppresses the exchange of heat and dissolved
substances between the epi- and hypolimnion by reducing tur-
bulent motions that otherwise would facilitate transport
(Wüest and Lorke 2003). While the vertical structure of the
water column constitutes the first response to temperature fluc-
tuations (Sahoo et al. 2016), it also regulates the development
of phytoplankton biomass by affecting light and nutrient avail-
ability (Yang et al. 2016), as well as phytoplankton settling, and
therefore exerts a strong control on lake ecosystem functioning
(Scheffer et al. 2001; Bartosiewicz et al. 2015).
Especially when nutrients are not limiting (e.g., in eutro-
phic lakes), light climate and stratification strength likely play
dominant roles in regulating phytoplankton biomass (Fig. 1),
and this role of light as a limiting resource has been suggested
since the early days of eutrophication research (Mur
et al. 1977). In general, by controlling light and nutrient avail-
ability, the underwater light climate and stratification strength
determine phytoplankton growth conditions. When stratifica-
tion is strong, thus suppressing fluxes from the deeper layers,
mixing is restricted to the surface layer. Under such condi-
tions, phytoplankton is constantly maintained within the
euphotic zone, promoting algal growth until nutrients are
depleted or other factors as grazing and sedimentation take
over in controlling phytoplankton biomass (Fig. 1a; Cam-
acho 2006; Reynolds 2006; Yankova et al. 2017). When strati-
fication is weak, water column mixing can reach deep and
nutrient rich waters, however potentially taking the algal com-
munities beyond the euphotic zone that would limit their
growth (Ibelings et al. 1994; Fig. 1b). One other ecological
consequence of a strongly stratified lake is that phytoplankton
may have reduced access to nutrients that remain locked in
the hypolimnion (Nürnberg 1984; Posch et al. 2012; Sal-
maso et al. 2020). Yet, while the strength of stratification is
determined primarily by light climate and heat exchange,
other factors too can affect the extent and duration of the
stratification, such as lake morphology (i.e., basin geome-
try, maximum depth and surface area) (Thompson and
Schmidt 2005; Kirillin and Shatwell 2016; Magee and
Wu 2017) as well as the dissolved organic and inorganic
carbon content of the water, wind orientation and shelter-
ing. Dissolved organic matter in general can have a huge
Donis et al. European lake survey: summer Chl-a drivers
4317
impact on stratification by influencing light penetration, and
consequently surface heating, as seen in humic boreal lakes
(Heiskanen et al. 2014). Wind and convection, acting on the sur-
face mixed layer (SML), control a lake’s interior diffusive fluxes
regulating the physical environment experienced by phyto-
plankton. Important properties of the SML, such as its depth,
vary widely among lakes as the result of a specific balance
between factors that strengthen stratification (surface warming),
and factors that disrupt or deepen the layer, such as wind shear
and surface cooling (Imberger 1985; Imboden and Wüest 1995;
Boehrer and Schultze 2008).
Stratification of lakes is changing under the impact of
eutrophication, re-oligotrophication and climate warming
(Flaim et al. 2016). For instance, in recent decades, the
strength of stratification of lakes in northeastern North Amer-
ica has clearly increased (Richardson et al. 2017); a phenome-
non that might be further enhanced by a trend of
atmospheric stilling (Woolway and Merchant 2019). Analyses
of the 2007 National Lake Assessment, NLA dataset (Pollard
et al. 2018) showed that synergistic interactions between
nutrients and temperature promoting algal or cyanobacterial
developments are probable, especially in the eutrophic and
hypereutrophic subsets of NLA lakes (Rigosi et al. 2014).
Kosten et al. (2012) provided more support for synergistic
interactions between nutrients and temperature in determin-
ing chlorophyll a (Chl a) and cyanobacterial dominance in a
multilake survey along a latitudinal gradient stretching from
the tip of South America to the equator. However, no lake
physical variables other than surface temperature, such as den-
sity gradient or stratification strength, were included in these
large-scale studies on drivers of algal biomass.
To further our understanding of the main drivers and their
interactions on phytoplankton biomass across continental
climatic gradients, the “grassroots” European Multi Lake Sur-
vey (EMLS) was organized during summer 2015, which coin-
cided with the period of maximum stratification in most of
the examined lakes. Data from the EMLS are publicly available
(Mantzouki et al. 2018). Here, we report on the difference in
Chl a as a proxy for phytoplankton biomass between 230 of
the EMLS lakes to: (1) determine the dependency of phyto-
plankton biomass at the continental scale on a set of ecosys-
tem drivers, including growth conditions (total phosphorous
[TP], total nitrogen [TN], lake temperature, and light) and
morphophysical properties (lake depth, surface area, light cli-
mate, and stratification strength); and (2) investigate potential
interactions between these predictors that influence phyto-
plankton biomass.
Methods
EMLS organization
During the EMLS in summer 2015, 230 lakes were sam-
pled across major geographical and climatic regions in
Europe for various chemical, physical, and biological
parameters using highly standardized sampling protocols
(Mantzouki et al. 2018; Mantzouki and Ibelings 2018). All
key variables were analyzed centrally (by one person on one
machine) in dedicated laboratories to ensure data compara-
bility and a fully integrated dataset.
The lake sampling site was selected as either the historical
sampling point, for which long-term records exist, or the geo-
graphic center of the lake. The sampling period was defined as
the warmest 2-week period of the summer, based on long-term
(minimum 10 yr) air temperature data of each region. An in
situ temperature profile carried out on the sampling day was
used to identify and characterize the thermocline as the point
where there was ≥ 1C change of temperature per meter lake
depth. An integrated water sample was obtained from 0.5 m
depth to the bottom of the thermocline using a water sampler
that could effectively sample the whole volume without creat-
ing intervals. In nonstratified shallow lakes, an integrated
sample was drawn from 0.5 m below the lake surface to 0.5 m
above the lake bottom.
Nutrient analyses
Total phosphorus and nitrogen concentrations were
assessed in unfiltered samples. Sample bottles were acid
washed overnight in 1 M HCl and rinsed with demineralized
water before usage. Nutrients were measured using a Skalar
SAN+ segmented flow analyzer (Skalar Analytical BV, Breda,
the Netherlands) with UV/persulfate digestion integrated in
the system. The limit of detection was 0.02 mg L
1 for TP and
0.2 mg L
1 for TN. TP was analyzed following NEN (1986) and
TN according to NEN (1990). All nutrient analyses were per-
formed at the University of Wageningen, the Netherlands.
Fig 1. Schematic overview of how lake N2 and light climate (Zeu/Zmix)
may define phytoplankton biomass in nutrient-rich lakes. (a) A strong
stratification (> N2) allows phytoplankton to circulate well within the
euphotic zone (Zeu/Zmix ≥ 1)—promoting growth. (b) A weaker stratifica-
tion (< N2) allows deeper mixing, hence phytoplankton communities are
highly diluted—eventually below the euphotic zone (Zeu/Zmix < 1).
Donis et al. European lake survey: summer Chl-a drivers
4318
Pigment analyses
Pigment analysis, modified from the method described by
Van der Staay et al. (1992), was carried out to determine con-
centrations of Chl a and Zeaxanthin (Zea). Measurement of
Zea concentrations in the EMLS lakes were carried out with
the aim of investigating cyanobacterial biomass, alongside to
the general phytoplankton biomass estimate obtained with
Chl a. Filters (45 mm diameter GF/C or /F) were freeze-dried
for 6 h and then cut in half, placed in separate Eppendorf
tubes, and kept on ice. A number of 0.5 mm beads and 600 μL
of 90% acetone were added to each tube. To release the pig-
ments from the phytoplankton cells and increase the extrac-
tion yield, tubes were placed on a bead-beater for 1 min and
then in an ultrasonic bath for 10 min. To ensure complete
extraction of the total pigment content of the filters, the bead-
beater and ultrasonic bath steps were performed twice. To
achieve binding of the pigments during the high-performance
liquid chromatography (HPLC) analysis, 300 μL of a tributyl
ammonium acetate (1.5%) and ammonium acetate (7.7%)
mix were added to each tube. Lastly, samples were centrifuged
at 15,000 rpm and 4C for 10 min. Next, 35 μL of the superna-
tant from Eppendorf tubes were transferred into glass HPLC
sampling vials. Pigments were separated on a Thermo Scien-
tific ODS Hypersil column (250 mm  3 mm, particle size
5 μm) in a Shimadzu HPLC, using a KONTRON SPD-M2OA
diode array detector. The different pigments were identified
based on their retention time and absorption spectrum and
quantified by means of pigment standards. Pigment analysis
was performed at the University of Amsterdam, the
Netherlands.
Lake groups
Lake classification was based on climatic zone and depth
type. Predicted climatic zones based on different IPCC scenar-
ios (2000–2025; Rubel and Kottek 2010) were used to avoid
the inconsistency in available digital maps, especially for areas
such as the Alpine region (Rubel et al. 2017). The climatic
zones were defined using the Köppen-Geiger’s classification
(Köppen 1900). This classification regards the main climate of
the region (C = warm temperate, D = alpine), precipitation
levels (f = fully humid, s = summer dry), and mean tempera-
ture (a = hot summers, b = warm summers). For easier inter-
pretation and more statistical power, climatic regions that
were of the same main climate and precipitation level were
combined in three main ones: Mediterranean (Csa and Csb,
n = 54 lakes), Continental (Cfa and Cfb, n = 128 lakes), and
Boreal (Dfb and Dfc, n = 48 lakes) (Fig. 2). This way, only the
mean temperature varied within each of the combined groups,
which allowed for testing of a temperature gradient. The selec-
tion of climatic zones has a clear advantage over a latitudinal
analysis, as several lakes within the Continental region are
classified as Boreal lakes based on their climatic characteristics
rather than their position on a latitudinal gradient (see
Table S1 for list of EMLS lakes and corresponding cli-
matic zone).
The EMLS lakes were categorized into shallow (< 6 m maxi-
mum depth, n = 93 lakes) and deep (> 6 m maximum depth,
n = 137 lakes). This classification was used in previous snap-
shot surveys as an approximation for weakly or strongly ther-
mally stratified systems (Kosten et al. 2012; Beaulieu
et al. 2013).
Fig 2. Location of the 230 EMLS lakes distributed over the main climatic zones of the European continent (Rubel and Kottek 2010). The Mediterranean
region (n = 54) consists of Csa and Csb classes (C, warm temperate; s, summer dry; a, hot summer; b, warm summer), the Continental region (n = 128)
of Cfa and Cfb (f, fully humid; rest as above), and the Boreal region (n = 48) of Dfb and Dfc (D, snow; c, cool summer; rest as above).
Donis et al. European lake survey: summer Chl-a drivers
4319
Statistical analysis
To disentangle the importance of various drivers of phyto-
plankton biomass, we applied linear regression models to six
lake groups: all, deep, shallow, Mediterranean, Continental,
Boreal. We (1) assessed the quality of the statistical models
after excluding collinear and nonsignificant variables,
(2) included groups of interactions and nominal variables as
environmental predictors, and (3) discussed the three most
important predictors for each model.
Response variable and environmental predictors
The response variable of all regression models was the con-
centration of Chl a obtained from the HPLC analysis, which
was used as a proxy for total phytoplankton biomass
(Pinckney et al. 2001; Tamm et al. 2015) and tested with the
following single predictors: maximum depth (maxD), surface
area (SurfA), TN, TP, surface temperature (SurfT), average tem-
perature (AvT), Secchi disk depth (SD), light climate (Zeu/
Zmix), and maximum buoyancy frequency (stratification
strength, N2) (Table 1).
Surface and average temperatures were determined via a
water column profile with a temperature probe, taking respec-
tively the temperature of the top 0.5 m of the water column
and the average of the full profile.
Light climate was defined as the ratio of euphotic depth over
mixing depth (Zeu/Zmix), which describes the light that phyto-
plankton experience while circulating through the water col-
umn (Scheffer et al. 1997). The equation Zeu = 2  SD (Secchi
depth) was used to calculate Zeu (equation selected as an aver-
age estimate from the range of constants reported in literature,
e.g., Koenings and Edmundson 1991; Salmaso 2002; Brentrup
et al. 2018). In stratified lakes, Zmix was determined as the
depth of the steepest density gradient (Winslow et al. 2017). In
nonstratified shallow lakes, Zmix matched the maximum depth
and sampling depth. Water density was calculated according to
the combined effects of salinity (set to 0) and water temperature
based on the method of Millero and Poisson (1981).
Lake stratification is the density-induced layering of the
water column (Boehrer and Schultze 2008). Strength of water
column stratification was determined by the N2 given by the
Brunt Väisälä equation or buoyancy frequency, N (s
1).
N ¼
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

g
ρ
∂ρ
∂z
 s
;N2¼
g
ρ
∂ρ
∂z
 
ð1Þ
Buoyancy frequency is greater than zero, when a water vol-
ume (of density ρ) that is displaced vertically (z) from its initial
position without heat transfer, experiences a restoring force. If
N2 < 0 instead, the water parcel tends to be displaced away
from its initial position and the vertical water column is
locally unstable. Here, we use the symbol N2 to indicate the
maximum value over the entire water column. By suppressing
vertical turbulent eddies, density stratification determines the
water column stability so that, in general, the greater the den-
sity gradient, the slower the diffusive exchange of water con-
stituents between the hypolimnion and the epilimnion
(Boehrer and Schultze 2008).
Three groups of interactions between some of the afore-
mentioned variables, selected based on ecological theory and
previous literature, were included as additional predictors in
the models. Namely the interaction between (1) nutrients and
surface temperature (Rigosi et al. 2014), (2) stratification
strength and light climate (Graff and Behrenfeld 2018), and
(3) surface area and light climate.
Analysis of variance
Differences in mean values of the selected variables within cli-
matic zones and depth types were tested using one-way ANOVA.
Homogeneity of variance was tested using the Levene’s test from
the car R package (Fox and Weisberg 2011). In case of heteroge-
neity, a Kruskal–Wallis test was used instead of ANOVA. Post hoc
Table 1. List of lake variables with their units, range of values, means, medians, and standard deviations for the 230 EMLS lakes. Vari-
ables with * are included in the linear models.
Variable Units Range Mean  SD Median
Maximum depth maxD m 1–310 2341 10.00
Surface area* SurfA km2 0.001–580 1969 5
Total nitrogen* TN mg L
1 0.1–5 1.00.8 0.70
Total phosphorus* TP mg L
1 0.02–1 0.10.1 0.06
Surface temperature* SurfT C 14.6–33 233.4 22.4
Average temperature AvT C 13.4–33 213.5 20.6
Secchi depth SD m 0.16–10 1.81.7 1.19
Light climate* Zeu/Zmix - 0.02–11 1.01.0 0.63
Stratification strength * N2 s
2 310
5–310
2 510
3410
3 4.10
3
Chlorophyll a* Chl a μg L
1 0.03–933 44110 9.98
Zeaxanthin Zea μg L
1 0.00–90 39.7 0.68
Donis et al. European lake survey: summer Chl-a drivers
4320
pairwise comparisons for unequal sample sizes were performed
using Tukey HSD (Honest Significant Difference) or Games–
Howell test (userfriendlyscience R package; Peters et al. 2018) for
homogeneous or heterogeneous variance, respectively.
Multiple linear regression model
All variables were log-transformed (natural logarithm) to
obtain a normal and homogeneous distribution. Stepwise
selection (backwards and forward) was used for model selec-
tion where the AIC scores were compared based on a modified
equation that corrects for unequal sample size among catego-
ries (R code provided by Statoo Consulting, Switzerland). If
the interaction term was significant (p ≤ 0.05), the lower order
terms were included in the equation. The most parsimonious
model, in which elimination or addition of any other predic-
tors would not improve the model by ΔAIC > 2, was used for
the ANOVA. The metric “lmg” of the relaimpo R package
(Gr}omping 2006) was used to decompose the overall R2 of
each final model into the absolute contributions of each pre-
dictor term and their interaction terms (similarly done in
Rigosi et al. 2014). The relative contribution of each predictor
was normalized, by forcing the sum to 100%. A bootstrapping
approach was used to replicate the observed data 9999 times
and determine if there were any clear differences between the
predictors of the interaction terms with regards their relative
contribution to the interaction term (Gr}omping 2006). If
those differences included zero, it indicated that the predictors
were not significantly different from each other, meaning that
they contributed similarly to the interaction term. When the
interaction term had a significant value of p < 0.05 and was
positive, it was interpreted as a synergistic interaction.
To avoid multicollinearity between the interactions and
their main effects, we checked the variance inflation factor
(VIF). If VIFs were exhibiting high numbers (VIF > 3, threshold
according to (Zuur et al. 2010), we centered the interaction
term with the mean of the raw variables which alleviated the
collinearity problem.
We applied multiple linear regression models to test the rel-
ative importance of the selected response variables in
explaining Chl a variance. The model applied was:
Chla¼A0þA1XSurfAþA2XN2þA3XSurfTþA4XTNþA5XZeu=Zmix
þA6XN2Zeu=Zmixþε
ð2Þ
where A0 represents the intercept term, A1–A6 are model parame-
ters for each respective predictor in the models, “*” denotes the
interaction between two terms, and ε is an error term. Two multi-
ple linear regression models were applied to the entire EMLS
group of lakes. Apart from the full set of environmental predictors,
each of these two models included the nominal variable “depth
type” or “climatic zone” (see Supplementary Material for more
Table 2. List of applied models and relative metrics. AIC does not apply correctly if number of observations is not the same, for which
we rely on R2.
Lake group Multilinear model N lakes R2 AIC
(1) All-a Chl a = 
9.06 
 0.23 (SurfA) 
 0.31 (N2) + 3.36 (SurfT)
+ 0.46 (TN) + 0.47 (Zeu/Zmix) + 0.18 (N
2*Zeu/Zmix) 

0.90 (Cont) 
 2.06 (Med)
230 35% 842.43***
(2) All-b Chl a = 
2.44 
 0.15 (SurfA) 
 0.11 (N2) + 1.12 (SurfT)
+ 0.31 (TN) + 0.45 (Zeu/Zmix) + 0.19 (N
2*Zeu/Zmix)
+ 1.17 (Shallow)
230 30% 856.65**
(3) Shallow Chl a = 0.33–0.05 (SurfA) 
 0.17 (N2) + 0.48 (SurfT)
+ 0.78 (TN) 
 0.09 (Zeu/Zmix) + 0.12 (N2*Zeu/Zmix)
93 31% Na
(4) Deep Chl a = 
0.65 
 0.14 (SurfA) + 0.07 (N2) + 0.84 (SurfT)
+ 0.01 (TN) + 1.13 (Zeu/Zmix) + 0.29 (N
2*Zeu/Zmix)
137 12% Na
(5) Med. Chl a = 
17.035 
 0.23 (SurfA) 
 0.029 (N2) + 5.26
(SurfT) + 0.40 (TN) + 0.83 (Zeu/Zmix) + 0.22 (N
2*Zeu/
Zmix)
54 45% Na
(6) Cont. Chl a = 
5.40 
 0.26 (SurfA) 
 0.33 (N2) + 1.88 (SurfT)
+ 0.47 (TN) + 0.23 (Zeu/Zmix) + 0.12 (N
2*Zeu/Zmix)
128 25% Na
(7) Bor. Chl a = 
15.65 
 0.15 (SurfA) 
 0.005 (N2) + 6.05
(SurfT) + 0.41 (TN) + 2.77 (Zeu/Zmix) + 0.62 (N
2*Zeu/
Zmix)
48 43% Na
* P ≤ 0.05.
** P ≤ 0.01.
*** P ≤ 0.001.
Donis et al. European lake survey: summer Chl-a drivers
4321
detail). These nominal variables comprehend the lake subsets to
which the same multilinear regression model that was further
applied, that is, deep, shallow, Mediterranean, Continental, Boreal
(Table 2).
Results
Response variable and environmental predictors
The EMLS lake data cover a wide range of morphological,
physical, chemical, and biological values (Table 1). The
median measured TP was 60 μg L
1, and according to Carlson
trophic state index (TSI) 85% of the lakes were classified as
eutrophic (TSI > 50). EMLS lakes were largely represented by
eutrophic conditions (70%) also when calculating the TSI on
basis of Secchi disk depth (median SD = 1.2 m), while TSI
based on Chl a concentration (median Chl a = 10 μg L
1)
leads to 54% of lakes being classified as eutrophic.
Significant collinearity was observed between maximum
depth and surface area, and between surface temperature and
average temperature (Fig. S1). VIFs of maximum depth and
average temperature were higher than 3, thus they were
removed from subsequent analyses. Secchi depth was also
removed in favor of using the light climate variable, Zeu/Zmix.
All the variables were found to be significant, except for TP,
and the interactions TN*SurfT and SurfA*Zeu/Zmix, which
therefore never appeared as Chl a variance predictors.
Lake groups: Climatic zone and depth type
Climatic zone
ANOVA was performed on the three climatic zone groups,
composed by 54 Mediterranean, 128 Continental, and
48 Boreal lakes (Fig. 3; Table S2).
Mean Chl a concentrations were significantly higher in the
Boreal lakes (mean ln  1 SD, 3  1 μg L
1) compared to
Fig 3. EMLS log-transformed response variable, (a) Chl a, and significant
predictors: (b) surface temperature, (c) total nitrogen, (d) maximum
buoyancy frequency (stratification strength), (e) light climate (Zeu/Zmix),
and (f) surface area, averaged over climatic zones. Significant differences
at the 0.05 level are marked with *. Different italic letters indicate signifi-
cant differences among categories (Tukey test; p < 0.05).
Fig 4. EMLS log-transformed response variable (a) Chl a, and significant
predictors: (b) surface temperature, (c) total nitrogen, (d) maximum
buoyancy frequency (stratification strength), (e) light climate (Zeu/Zmix),
and (f) surface area, averaged over depth type. Significant differences at
the 0.05 level are marked with *.
Donis et al. European lake survey: summer Chl-a drivers
4322
Continental (2.2  1 μg L
1) and Mediterranean
(1.7  2 μg L
1), while no significant difference was found
between Continental and Mediterranean lakes (Fig. 3a;
Table S2).
Depth type
The EMLS dataset is composed of 93 shallow and 137 deep
lakes (> 6 m). Response variable, Chl a, and all of the predic-
tors used in the statistical models of the EMLS significantly
differed between deep and shallow lakes (Fig. 4; Table S2). Chl
a, SurfT, TN, and Zeu/Zmix were all higher for shallow lakes,
whereas deep lakes showed a stronger stratification strength
(N2) and greater surface area than shallow ones.
Drivers explaining Chl a at the continental scale
The applied models significantly explain a proportion of the
variability in Chl a (p ≤ 0.001; Table 2), with the model applied
to Mediterranean lakes explaining the highest variability
(R2 = 45%), closely followed by the model applied to Boreal
lakes (R2 = 43%) with Continental lakes further behind
(R2 = 25%), while the model applied to deep lakes explained
the lowest variability (R2 = 12%), compared to R2 = 31% for
shallow lakes. Based on AIC comparison, the nominal variable
“climatic zone” is more significant than “depth type” in
explaining the variance of algal biomass (Table 2). Nevertheless,
the lake group “depth type” explained more of the overall R2
compared to “climatic zone” (37% vs. 26%; Table S3).
When the model included the nominal variable “climatic
zone” among the predictors, it resulted as the strongest predic-
tor for algal biomass with 26% of the model R2 explained,
closely followed by stratification strength (24%), and with a
significant but smaller contribution of TN (13%; Table S3).
Similarly, when “depth type” was included, it resulted as the
most significant predictor (37%); however, it was much more
important than the second most significant predictor (stratifi-
cation strength, 18%), that was closely followed by light cli-
mate (15%; Table S3).
The 230 lakes dataset allows us to carry out the same analy-
sis separately on each group of lakes corresponding to the
explanatory categories, climatic zone, and depth type, to gain
more insights on the summer drivers of phytoplankton bio-
mass for this set of lakes.
Fig 5. First two significant predictors for Chl a in lake group model 3 (shallow) and model 4 (deep). (a, b) Light climate, Zeu/Zmix, and TN explain
respectively 46% and 33% of Chl a variance in EMLS shallow lakes (model R2 = 31%). (c, d) Light climate, Zeu/Zmix, and its interaction with stratification
strength, N2*Zeu/Zmix, explain respectively 32% and 29% of Chl a variance in EMLS deep lakes (model R
2 = 12%). All variables are plotted as to the statis-
tical models, that is, natural logarithm (ln). See Table S4 for relative contribution and significance of all predictors.
Donis et al. European lake survey: summer Chl-a drivers
4323
Shallow vs. deep lakes
Light climate was the most important variable explaining
Chl a variance in both shallow and deep lake subsets (46%
and 32%, respectively; Fig. 5a,c). Stratification strength was
also a significant contributor for both lake types, either indi-
vidually (14%, shallow lakes; Table S4), or in synergistic inter-
action with light climate (29%, deep lakes; Fig. 5d). However,
for shallow lakes, TN played a more significant role than strati-
fication (33%; Fig. 5b) while not appearing as a significant pre-
dictor of algal biomass in the deep lakes subset.
Mediterranean vs. Continental vs. Boreal lakes
When applying the model to the different climatic zones,
the strength of the stratification appeared as a strong
Fig 6. First two significant predictors for Chl a in lake group model 5 (Mediterranean), model 6 (Continental), and model 7 (Boreal). (a, b) Stratification
strength, N2, and surface temperature, SurfT, explain respectively 46% and 24% of Chl a variance in EMLS Med lakes (model R2 = 45%). (c, d) TN and
N2 explain respectively 35% and 29% of Chl a variance in EMLS Cont lakes (model R2 = 25%). (e, f) Light climate, interaction with stratification strength,
N2*Zeu/Zmix, and SurfT explain respectively 34% and 21% of Chl a variance in EMLS Boreal lakes (model R
2 = 43%). All variables are plotted as to the sta-
tistical models, that is, natural logarithm (ln). See Table S5 for relative contribution and significance of all predictors.
Donis et al. European lake survey: summer Chl-a drivers
4324
predictor, either individually (Med. 46% and Cont. lakes 29%;
Fig. 6a,d) or in interaction with light climate (Boreal lakes
34%; Fig. 6e). In Mediterranean and Boreal lakes, surface tem-
perature was also a strong predictor of algal biomass (24% and
21%, respectively; Fig. 6b,f) but not for Continental lakes
(Table S5). Instead, nutrients were the most significant predic-
tors of Chl a (34%) for Continental (Fig. 6c), while being less
important for Boreal lakes (14%) and not important for Medi-
terranean lakes (Table S5).
Relationship between stratification metrics
Within the EMLS lakes, we analyzed the relationship
between stratification strength and some of the drivers, that is,
temperature, light penetration, and lake morphology. As
already shown, Mediterranean lakes, while being on average
the warmest, did not have the highest average stratification
strength (Fig. 3d). When looking at the entire dataset (Fig. 7),
the polynomial fit between the maximum N2 and surface tem-
perature was significant (p < 0.001) but weak (R2 = 0.14;
Fig. 7a), indicating that only for a relatively small number of
the EMLS lakes, higher surface water temperatures at the sam-
pling time corresponded to a stronger stratification. An even
weaker relationship (R2 = 0.06) was observed between stratifica-
tion strength and light penetration depth (Zeu; Fig. 7b). As for
the morphological features, the relationship observed between
maximum N2 and surface area (Fig. 7c), was much weaker
(R2 = 0.08) than between maximum N2 and maximum lake
depth (R2 = 0.2). Here, the 4th-order polynomial fit followed
the effect of temperature on N2 for increasing lake depths,
reaching a plateau for lakes deeper than  20 m (Fig. 7d).
Fig 7. Relationships between N2 and (a) surface temperature, 2nd-order polynomial fit, R2 = 0.14; (b) euphotic depth, 2nd-order polynomial fit,
R2 = 0.06; (c) surface area, 3rd-order polynomial fit, R2 = 
0.08; (d) maximum depth, 4th-order polynomial fit, R2 = 0.2. All polynomial fit are signifi-
cantly better than function y = constant at the 0.05 level.
Fig 8. Eight-day average temperature anomaly at the sampling site and
sampling period in relation to the lake stratification strength for the EMLS
climatic zone subsets, Continental (gray), Mediterranean (light pink), and
Boreal (blue). All best fits are given by exponential curves, with the one
for Boreal lakes being the most significant (R2 = 0.4).
Donis et al. European lake survey: summer Chl-a drivers
4325
Air temperature anomaly
The summer of 2015 was the third warmest summer (after
2003 and 2010) since 1880 in Europe (GISTEMP, NOAA
online data). During the sampling period in 2015, 70% of
EMLS lakes experienced a positive temperature anomaly of
1.9C  3.4C (average  1 SD, based on each lake 8-d temper-
ature average compared to 10-yr average for the same 8 d).
However, when looking at each climatic zone separately, 96%
of Continental lakes and 87% of the Boreal lakes experienced
a positive temperature anomaly of 3.8C  2.6C and
3.7C  2.9C, respectively. In contrast, only 53% of the Med-
iterranean lakes experienced a positive temperature anomaly,
of 1.4C  1C. The remaining 30% of the total, 4% of Conti-
nental, 13% of Boreal and 47% of Mediterranean are lakes that
showed a negative deviation from the long term average.
Hence, at the time of sampling, the great majority of Conti-
nental and Boreal lakes experienced a strong temperature
increase compared to the long-term average levels, which was
not the case for Mediterranean lakes. Compared to the other
regions, Boreal lakes as well showed the strongest exponential
relationship between the experienced temperature anomaly
and stratification strength (Fig. 8).
Pigments analysis
Measurement of Zea concentrations in the EMLS lakes were
carried out with the aim of investigating cyanobacterial bio-
mass. A strong linear relationship was found between Zea and
Chl a (R2 = 0.6; Fig. S3) indicating that higher Chl
a concentrations systematically corresponded with higher
concentrations of Zea.
Discussion
Drivers explaining phytoplankton biomass at the
continental scale
Several studies have focused on the effects of nutrients and
warming on phytoplankton in more than one lake (table S1 in
Salmaso and Tolotti 2021). This is of particular concern for
resolving the climate warming effect on lakes and the positive
feedbacks on eutrophication of lakes (Sinha et al. 2017; Deng
et al. 2018). However, thermal stratification, which will likely
increase with climate warming (Woolway and Merchant 2019),
is an important feature governing lake ecosystems as it affects
both nutrient availability and light climate (Schwefel
et al. 2016), generating complex feedbacks for the biota
(Mesman et al. 2021). The importance of these factors may
dominate when lakes are not nutrient limited.
We have applied a set of multiple regression models to
230 European lakes (54–85% of which were eutrophic
depending on the criterion applied) to test the dependency of
Chl a on phytoplankton growth resources (nutrients, tempera-
ture, and light climate) and morphophysical lake properties
(surface area, stratification strength), including interactions
between specific predictors. Our results indicate that physical
properties of a lake, such as stratification strength and light
climate (expressed as the ratio of euphotic to mixing depth),
are the strongest ecosystem drivers for phytoplankton biomass
for this set of mostly nutrient-rich lakes, at the sampling time.
It is possible, however, that a different result would be
obtained from the same dataset in a different time of the year.
In a similar fashion to the present work, an earlier study on
1076 US lakes (Rigosi et al. 2014), showed that surface temper-
ature, nutrients, and their interaction were the main phyto-
plankton biomass predictors. Interestingly, their results
showed that the largest part of the variance in Chl a for the
subset of eutrophic and hypereutrophic lakes was explained
by a synergistic interaction between nutrients and tempera-
ture. Our study moves a step forward and highlights the fact
that additional variables need to be considered when collect-
ing lake “snapshots” at a continental scale. The analysis pres-
ented here indicates that nutrients, temperature, and light
should not be the only algal growth conditions to be consid-
ered. We show that when lake stratification metrics are
included, we can gain insights into the lake physics mecha-
nisms that promote phytoplankton biomass growth and
potentially improve the development of predictive tools.
Moreover, our statistical analysis indicates that surface tem-
perature alone should not be used as a proxy for stratification
strength. Indeed for a multilake survey, it is necessary to esti-
mate lake stability (N2) as a variable that comprises the lake
thermal “history,” and therefore gives insight into the environ-
mental conditions that the phytoplankton have experienced
during the recent past. Such information is easily attained with
a temperature profile and is extremely relevant when looking at
ecosystem functioning, as thermal structure and light penetra-
tion determine the physical constraints of the photosynthetic
biomass distribution in the water column. These constraints
also determine to what extent specific phytoplankton features
adapted to life in a stable water column, such as the pigment
composition (e.g., presence of phycoerythrin in deep chloro-
phyll maxima), and buoyancy regulation (e.g., gas vesicles,
motility, shape adaptations) may favor specific algal groups.
Shallow vs. deep lakes
In the EMLS, most of the lakes were eutrophic which may
explain the predominant importance of light climate (Zeu/
Zmix) for algal biomass variance in both shallow and deep
lakes. We therefore assume that, for nutrient-rich lakes, phyto-
plankton rather than inorganic suspended solids determine
underwater light extinction (Scheffer et al. 1997), which sub-
sequently determines phytoplankton biomass.
Although light climate was the most important factor for
both EMLS depth types, we observed a relatively greater
importance of light climate in shallow rather than deep lakes
(explaining 46% and 32% of the variation, respectively),
which may be explained by the fact that shallow lakes exist in
two clearly distinct states, clear vs. turbid. Mechanisms
directly linked to the underwater light climate, for example,
Donis et al. European lake survey: summer Chl-a drivers
4326
high cyanobacterial biomass and benthivorous fish stirring up
the sediment, provide varying degrees of resilience to the tur-
bid state (Scheffer et al. 1997). In contrast, macrophytes stabi-
lize the clear water state, and light penetration that reaches
the sediment is vital for their development (Ibelings
et al. 2007). With 72% of the shallow EMLS lakes having a
Secchi depth of less than 0.8 m, we could argue that the
majority are in a turbid state, be it stable or not. This may go
some way to explain the critical role of light in determining
biomass of algae in EMLS shallow lakes.
TN is the second-most important predictor for Chl a in
shallow lakes (33%) which, together with the general absence
of TP as significant predictor for Chl a variance, suggests that
for the 230 EMLS lakes, the commonly found linear relation-
ship between TP and Chl a does not hold true
(Vollenweider 1968). This is in line with previous studies on
nutrient-rich lakes suggesting that (1) a positive linear TP–Chl
a relationship exists only at intermediate concentrations of TP
(0.004–0.23 mg L
1; Quinlan et al. 2020) and (2) nitrogen
becomes limiting for phytoplankton under high TP, especially
over shorter temporal scales (Filstrup et al. 2014).
A eutrophic status of a lake, however, does not mean that
nutrients cannot be limiting for dense phytoplankton, with a
large demand to sustain a high biomass. Yet, the condition of
nutrient limitation (in our case nitrogen) could be seen as an
effect driven by Zeu/Zmix (first predictor). Especially for shal-
low lakes, when this ratio becomes smaller, the mixed layer
exceeds the euphotic zone and nutrients from the sediment
are likely to be resuspended. We may easily see a more direct
relationship between Chl a and light climate than with the
nutrient abundance, because light climate, by revealing the
recent mixing history, is a more integrative indicator of nutri-
ent availability than the nutrient content of a single water
sample, especially for productive shallow lakes.
For deep lakes, light climate and its synergistic interaction
with water column stability had a similarly important contri-
bution to the overall R2, explaining Chl a variance (32% and
29% for Zeu/Zmix and N
2, respectively). High algal biomass
increases turbidity, which can increase water temperature in
the surface layer through increased heat absorption (Ibelings
et al. 2003), and thus reinforce stratification (Paerl and
Huisman 2008). Reinforced stratification through increased
turbidity implies that phytoplankton is maintained within the
euphotic zone offering a potential explanation of how light
climate can interact synergistically with water column stability
(Zeu/Zmix > 1; Fig. 1a). However, in a strongly stratified lake,
nutrients may remain available in the hypolimnion even
when they are depleted in the epilimnion, so that deeper
mixing, also of short duration, enhances the likelihood that
phytoplankton gains access to this pool of nutrients. In deep,
well-stratified lakes, it is also relatively common to find algal
biomass maxima (a.k.a. deep chlorophyll maximum [DCM])
at the crossroads of light from above and nutrients from below
(Leach et al. 2018). On the other hand, if stratification is weak
and mixing can reach deeper layers, it will take the algal com-
munities beyond the euphotic zone reducing algal growth
(Zeu/Zmix < 1; Fig. 1b). A deeper mixed layer will allow light to
reach greater depths by diluting epilimnetic phytoplankton
over a larger volume of lake water, thus increasing light pene-
tration. This extended euphotic depth will likely, however,
not make up for light limitation due to a deeper mixing depth,
so the ratio Zeu/Zmix would still decrease when water column
stability decreases (Fig. 1b), exacerbating the light limitation
of phytoplankton growth.
In contrast to the shallow lakes, in EMLS deep lakes neither
TP nor TN appeared as a significant predictor of algal biomass,
possibly because of the higher likelihood of light limitation
mentioned above. Interestingly, another difference between
EMLS shallow and deep lakes was that the surface area
explained a significant 22% of the overall Chl a variance of
deep lakes, while did not explain the Chl a variance for the
shallow lakes (Table S4). This might be due to the fact that the
surface area becomes important considering its direct relation-
ship with lake wind exposure, which can influence the water
column mixing depth in deep lakes, hence the availability of
light and nutrients for phytoplankton (Fig. 1). Although wind
exposure was not included in this study, EMLS lake area corre-
lated with depth (Fig. S1a), and was therefore indirectly related
to the water column thermal structure. Indeed, EMLS lakes
with larger surface areas tended to be deeper (Fig. 4f) and more
stable (Fig. 4d), and this may have favored phytoplankton’s
access to light, in particular when nutrients are not—or less
of—a limiting factor, for example, when DCMs are formed
where phytoplankton has access to nutrients in the hypolim-
nion (Leach et al. 2018).
Mediterranean vs. Continental vs. Boreal lakes
When EMLS lakes were clustered by climatic zone, stratifi-
cation strength appeared as a strong predictor for Chl a, either
individually (Mediterranean 46% and Continental lakes 29%
variation explained) or in synergistic interaction with light cli-
mate (Boreal lakes 34%). Stratification strength was thus a
dominant factor promoting phytoplankton optimal growth
conditions, interacting with the availability of nutrients and
light, as discussed. Light climate interaction with water col-
umn stratification was a strong factor for Boreal lakes phyto-
plankton growth (Fig. 6e) possibly due to their tendency to be
richer in humic substances and consequently darker (Kutser
et al. 2005; Kelly et al. 2018).
Phytoplankton biomass in Continental lakes seemed to
exhibit a higher degree of nitrogen dependency (Fig. 6c); how-
ever, we cannot exclude that those lakes in other regions were
in a similar state, since as discussed above, predictors like light
climate can possibly encompass nutrient limitation. On the
other hand, the comparatively lower Chl a content of Medi-
terranean lakes (Fig. 3a) seems to indicate that, at the time of
sampling, these lakes were experiencing a better nutrient–
phytoplankton balance than Continental lakes.
Donis et al. European lake survey: summer Chl-a drivers
4327
The best predictor for algal biomass: Stratification strength
or lake depth?
Stratification strength decreased in importance when split-
ting the dataset into depth types, which may indicate that
depth-type itself explained algal biomass variance. This is
also suggested by the fact that all predictors were signifi-
cantly different between deep and shallow lakes (Fig. 4), and
some important environmental factors have a different effect
on these two clusters. Wind has generally a larger effect on
temperature structure and stability of shallow lakes, because
the wind-induced mixing allows heat to be transferred
throughout the entire water column (Nõges et al. 2011). Fur-
thermore, shallow lakes respond more directly to short-term
weather variations (Arvola et al. 2009; Deng et al. 2018). For
deep lakes that have a higher heat retention and potential
energy, greater wind speeds are required to drive mixing dur-
ing the summer months, resulting in greater stability
(Boehrer and Schultze 2008). Fetch and dominant wind
direction and intensity are also important in determining
stratification strength in deep lakes (Wetzel 2001), although
these data were not collected for this study. However, given
the consistently higher N2 observed for EMLS deep lakes
(Fig. 4d), we can assume that sufficiently strong and long-
lasting winds were not present at each sampling site during—
or shortly prior to—the sampling period to modify the afore-
mentioned scenario of deep lakes that are more strongly
stratified than shallow lakes.
Depth and N2 may therefore be confounding variables
because, at least for this dataset, lake depth can explain most
of the variation in stratification trends. Nevertheless, whether
lake maximum depth or stratification strength is actually the
most significant predictor of Chl a in the overall EMLS dataset,
the message remains unchanged: lake morphophysical proper-
ties are essential when investigating phytoplankton biomass
responses to environmental changes.
Relationship between stratification metrics
Given the importance of stratification strength as a predic-
tor of Chl a variance at the European continental scale, we
analyzed the relationship between this variable, represented
by maximum N2, and the environmental and morphological
characteristics that act on the density gradient of a lake (sur-
face temperature, light penetration depth, maximum depth,
and surface area).
Surface temperature
Stratification strength responds directly to changes in water
temperature, yet each lake will need a certain number of warm
days with relatively low wind speed to develop stratification,
which also depends on lake morphological factors. The reason
for the weak correlation observed between maximum N2 and
surface temperature (Fig. 7a) may be that deep and shallow
lakes are equally represented, and while deep lakes are more
strongly stratified, the shallow lakes had the highest surface
temperature (Fig. 4b,d). The absence of a strong correlation
between stratification strength and surface temperature is fur-
ther confirmed by the absence of any trend between stratifica-
tion strength and climatic zone (Fig. 4d).
Moreover, the fact that shallow and Mediterranean lakes
had the highest surface temperature, but the weakest stratifica-
tion confirms that surface temperature can be a misleading
indicator for stratification strength, especially for snapshot
surveys as shown in previous studies on large datasets (Read
et al. 2014; Winslow et al. 2017).
Light penetration depth
Changes in light absorption by the dissolved and
suspended content of a lake affect the vertical distribution of
heat and resulting stratification (Andrew et al. 2008; Rinke
et al. 2010). We did not observe, however, a distinct rela-
tionship between stratification strength and light penetra-
tion (Fig. 7b). The reason why this relationship is not
stronger may be that the effect of light on stratification is
more evident in time series than in spatial gradients. This is
because light-induced heat diffusion in the water column
and its temporal variability has a stronger effect on the dura-
tion of the stratification than on its absolute value. Indeed,
more transparent lakes (Secchi transparency > 5 m) tend to
maintain a seasonal thermal stratification for a longer dura-
tion than more turbid ones (Richardson et al. 2017), there-
fore remaining stable longer. Assessing whether this is the
case is not possible with a summer snapshot sampling
design, although it was observed that light penetration can
drive the depth of the mixed layer. This is suggested for the
EMLS dataset by a moderate linear relationship (R2 = 0.35)
between the depth of the epilimnion, or mixed layer, and
the euphotic depth (Fig. S2).
Maximum depth and surface area
We observed a relationship between N2 and both the lake
maximum depth and lake surface area (Fig. 7c,d). The shape of
the polynomial fit shows that the linearity of stratification
strength with lake maximum depth holds until lake depths
of  20 m, because of the physical limit dictated by the ther-
mal diffusivity of water. This relationship may confirm that
N2 and depth are interdependent in determining resource
availability for the algal communities.
EMLS lakes with a greater area were on average deeper and
had a more stable water column (Fig. 4d,f). Therefore, surface
area of the EMLS dataset was directly correlated with maxi-
mum depth and was used as the only morphological variable
in the statistical models. However, the relationship between
surface area and N2 was not strong (Fig. 7c), possibly because a
larger surface area does not necessarily mean a greater wind
exposure, which is largely determined by the lake’s orienta-
tion toward the dominant wind direction and lake topogra-
phy. Clearly, as for the underwater light regime, analyzing the
effect of wind exposure on the thermal structure is not
Donis et al. European lake survey: summer Chl-a drivers
4328
possible with a single observation, but would require a water
column temperature time series.
Temperature anomaly: 2015, an unusually hot summer
As expected, Mediterranean lakes had higher surface tem-
peratures than Boreal ones (Fig. 3a,b). However, Boreal lakes
exhibited a significantly higher Chl a concentration than
Mediterranean lakes, while lakes in both climatic zones had
comparable nutrient concentrations (Table S2). This seems to
confirm the importance of factors other than temperature
(lower in Boreal lakes) or nutrients (similar) driving phyto-
plankton biomass, especially water column stability in relation
to the light climate. Indeed, while Boreal lakes are known to
stratify intermittently during summer (Kirillin and Shat-
well 2016; Woolway and Merchant 2019), the heat wave likely
intensified the stratification strength in the Boreal lakes more
strongly than in other regions, given that the region experi-
enced the largest temperature anomaly (Fig. 8). This may have
favored the conditions shown in Fig. 1a and supported by our
model results, that is, the interaction between light climate
and stratification strength is the main Chl a driver for Boreal
lakes (Fig. 6e).
As several studies addressed the relationship between light,
nutrients, and temperature effects on primary producers in
Boreal regions (Zwart et al. 2016; Bergstrom and Karlsson 2019),
alternative explanations may apply too. Although it is not pos-
sible to generalize, such observations are crucial to generate
ideas and stimulate future research. It is possible that a higher
abundance of mixotrophs in Boreal lakes may help to explain
the higher Chl a in that region, since Hansson et al. (2019)
demonstrated that the success of mixotrophs is correlated with
the elevated colored dissolved organic matter content of Boreal
lakes. It is also interesting to note that Mantzouki et al. (2018)
found that for the same EMLS dataset, the variety of toxins pro-
duced by cyanobacteria increased with latitude, which possibly
may have reduced the grazing pressure in Boreal lakes, contrib-
uting to higher Chl a.
Cyanobacteria like it warmer?
Zea is frequently used as a pigment to indicate cyanobacterial
biomass (Bianchi et al. 2000; Glibert et al. 2004; Przytulska
et al. 2017; Ewing et al. 2020), although it is found both in cya-
nobacteria and in chlorophytes (Deshpande et al. 2014; Ibelings
et al. 2016). In this study, we do not provide microscopy results
to confirm the correspondence between cyanobacteria and Zea;
therefore, the following discussion is presented with a degree of
caution, and mainly serves to stimulate further ideas, eventually
contributing to a deeper understanding of the worldwide
increase in cyanobacterial blooms.
The strong correlation between Zea and Chl a EMLS
(Fig. S3) indicated that higher Chl a concentrations systemati-
cally corresponded with higher concentrations of Zea, which
may suggest that water column stratification and light climate
are the main drivers for cyanobacterial growth in eutrophic
lakes, as they are for overall phytoplankton. Moreover, cyano-
bacteria have evolved specific traits like buoyancy and acces-
sory pigments that renders them specifically well adapted to
stably stratified conditions (Huisman et al. 2018). Conse-
quently, the fact that light climate was the main driver for
both lake depth types (Fig. 4) may confirm that at high nutri-
ent levels, light becomes limiting for cyanobacterial develop-
ment (Ganf and Oliver 1982; Bouterfas et al. 2002; Huisman
et al. 2004).
Considering the high temperature anomaly experienced in
Boreal regions at the sampling time, the significance of a posi-
tive interaction between water column stability and light cli-
mate in promoting cyanobacteria in the Boreal lakes during a
record hot summer supports the general observation that
“blooms like it hot” (Paerl and Huisman 2008). In the context
of climate change—and rapid warming at high latitudes—
perhaps a more appropriate rephrasing is “blooms like it
warmer than usual,” since the Boreal lakes were still cooler
than the Mediterranean lakes, yet the temperature anomaly
was higher as were Zea levels. Among the EMLS subset of lakes
with detectable Zea (which are 172 over the total 230 of this
study), almost all (95%) of the Boreal lakes experienced a
higher positive T-anomaly ( 4C  2.5C). Evidently, more
detailed integrated lab-field studies, including both ecological
and evolutionary aspects, are needed to resolve this issue.
Future scenario and management strategies
Among the Chl a predictors of this study, lake surface tem-
perature and water column stratification are expected to have
the strongest impact on lake ecosystems in a warming future
(O’Reilly et al. 2015; Kraemer et al. 2017). Both variables were
significant drivers for trends in phytoplankton biomass across
climatic gradients in Europe. Thus, since lake water column
stability will likely increase with a warming climate (Oleksy
and Richardson 2021), bloom-forming cyanobacteria in partic-
ular will be further promoted given their typical dependence
on buoyancy that makes them particularly well adapted to a
stable water column (Steinberg and Hartmann 1988; Paerl and
Paul 2012).
Although we concur with Ibelings et al. (2016) that any
sustainable approach controlling cyanobacterial blooms has to
be rooted in nutrient reduction, our present analysis under-
lines the potential effectiveness of additional measures that
weaken the future strengthening of lake stratification, which
is demonstrated here to play such a critical role in determin-
ing differences in lake phytoplankton and cyanobacterial bio-
mass. It may be essential, for instance, to include measures
like artificial lake mixing (Visser et al., 2016) to mitigate algal
(and cyanobacterial) blooms.
Conclusions
Nutrients and light are the fundamental resources for phy-
toplanktonic biomass, even in nutrient rich lakes, such as the
Donis et al. European lake survey: summer Chl-a drivers
4329
ones represented in this study; however, results from the
EMLS analysis show that Chl a variance is better predicted by
light climate and stability metrics. These predictors are also
strong indicators of the epilimnetic nutrient load and of the
light experienced by the algal biomass prior to sampling. This
explains why in this nutrient-rich lake dataset, light climate
was the most important variable explaining Chl a variance in
both shallow and deep lakes, with the difference that, only for
deep lakes the optimum condition for photosynthetic biomass
was obtained when stratification operated in a synergistic
interaction with light climate. The dominance of light climate
and the absence of TP as significant predictor for Chl
a variance confirms that: (1) when TP levels are high as in the
average EMLS, light and nitrogen become limiting resources
for phytoplankton and (2) light climate, as metric for the
recent history of water column mixing, is a powerful indicator
for nutrient availability, and needs to be included in similar
studies.
Furthermore, our analysis of this pan-European dataset
shows that shallow and Mediterranean lakes exhibit the
highest surface temperature, although the weakest stratifica-
tion, confirming that lake surface temperature does not neces-
sarily correlate with lake stratification strength. Consequently,
especially for snapshot surveys, a lake temperature profile
should be preferred over surface temperature data as it is a
more sensible indicator for stratification strength and ecologi-
cal response to warming.
Finally, among the 230 European lakes, we observed a signif-
icant exponential correlation between temperature anomaly
and the stratification strength only for Boreal lakes that, inci-
dentally, had the highest Chl a concentrations; a notion deserv-
ing further attention in light of most rapid increases in
warming taking place at high latitude regions. This, coupled
with the fact that, for mildly to hyper-eutrophic lakes, light cli-
mate and water column stratification are the most important
drivers determining phytoplankton biomass, may serve to bet-
ter plan and implement lake management and mitigation
strategies.
References
Andrew, J. T., and others. 2008. Cooling lakes while the world
warms: Effects of forest regrowth and increased dissolved
organic matter on the thermal regime of a temperate, urban
lake. Limnol. Oceanogr. 53: 404–410.
Arvola, L., and others. 2009, 2010, p. 85–101. In D. G. George
[ed.], The impact of the changing climate on the thermal
characteristics of lakes. Springer Science+Business Media
B.V.
Bartosiewicz, M., I. Laurion, and S. MacIntyre. 2015. Green-
house gas emission and storage in a small shallow lake.
Hydrobiologia 757: 101–115.
Beaulieu, M., F. Pick, and I. Gregory-Eaves. 2013. Nutrients
and water temperature are significant predictors of
cyanobacterial biomass in a 1147 lakes data set. Limnol.
Oceanogr. 58: 1736–1746.
Bergstrom, A. K., and J. Karlsson. 2019. Light and nutrient
control phytoplankton biomass responses to global
change in northern lakes. Glob. Chang. Biol. 25: 2021–
2029.
Bianchi, T. S., E. Engelhaupt, P. Westman, T. Andren, C. Rolff,
and R. Elmgren. 2000. Cyanobacterial blooms in the Baltic
Sea: Natural or human-induced? Limnol. Oceanogr. 45:
716–726.
Boehrer, B., and M. Schultze. 2008. Stratification of lakes. Rev.
Geophys. 46: 1–27.
Bouterfas, R., M. Belkoura, and A. Dauta. 2002. Light and tem-
perature effects on the growth rate of three freshwater algae
isolated from a eutrophic lake. Hydrobiologia 489: 207–217.
Brentrup, J. A., and others. 2018. The potential of high-
frequency profiling to assess vertical and seasonal patterns
of phytoplankton dynamics in lakes: An extension of the
Plankton Ecology Group (PEG) model. Inland Waters 6:
565–580.
Camacho, A. 2006. On the occurrence and ecological features
of deep chlorophyll maxima (DCM) in Spanish stratified
lakes. Limnetica 25: 453–478.
Carvalho, F., J. Kohut, M. J. Oliver, R. M. Sherrell, and O.
Schofield. 2016. Mixing and phytoplankton dynamics in a
submarine canyon in the West Antarctic Peninsula.
J. Geophys. Res. Oceans 121: 5069–5083.
Cloern, J. E. 1996. Phytoplankton bloom dynamics in coastal
ecosystems: A review with some general lessons from
sustained investigation of San Francisco Bay, California.
Rev. Geophys. 34: 127–168.
Deng, J., and others. 2018. Climatically-modulated decline in
wind speed may strongly affect eutrophication in shallow
lakes. Sci. Total Environ. 645: 1361–1370.
Deshpande, B. N., R. Tremblay, R. Pienitz, and W. F. Vincent.
2014. Sedimentary pigments as indicators of cyanobacterial
dynamics in a hypereutrophic lake. J. Paleolimnol. 52:
171–184.
Ewing, H. A., and others. 2020. “New” cyanobacterial blooms
are not new: Two centuries of lake production are related to
ice cover and land use. Ecosphere 11(6): e03173.
Filstrup, C. T., and others. 2014. Regional variability among
nonlinear chlorophyll-phosphorus relationships in lakes.
Limnol. Oceanogr. 59: 1691–1703.
Flaim, G., E. Eccel, A. Zeileis, G. Toller, L. Cerasino, and U.
Obertegger. 2016. Effects of re-oligotrophication and cli-
mate change on lake thermal structure. Freshw. Biol. 61:
1802–1814.
Fox, J., and S. Weisberg. 2019. An R Companion to Applied
Regression, Third edition. Sage, Thousand Oaks CA.
Ganf, G. G., and R. L. Oliver. 1982. Vertical separation of light
and available nutrients as a factor causing replacement of
green-algae by blue-green-algae in the plankton of a strati-
fied lake. J. Ecol. 70: 829–844.
Donis et al. European lake survey: summer Chl-a drivers
4330
Glibert, P. M., and others. 2004. Evidence for dissolved
organic nitrogen and phosphorus uptake during a
cyanobacterial bloom in Florida Bay. Mar. Ecol. Prog. Ser.
280: 73–83.
Graff, J. R., and M. J. Behrenfeld. 2018. Photoacclimation
responses in subarctic Atlantic phytoplankton following a
natural mixing-restratification event. Front. Mar. Sci. 5:
209. doi:10.3389/fmars.2018.00209
Gr}omping, U. 2006. Relative Importance for Linear Regression
in R: The Package relaimpo. Journal of Statistical Software,
Foundation for Open Access Statistics vol. 17(i01).
Hansson, T. H., H.-P. Grossart, P. A. del Giorgio, N. F. St-
Gelais, and B. E. Beisner. 2019. Environmental drivers of
mixotrophs in boreal lakes. Limnol. Oceanogr. 64: 1688–
1705.
Heiskanen, J. J., and others. 2014. Effects of cooling and inter-
nal wave motions on gas transfer coefficients in a boreal
lake. Tellus B 66: 1–16.
Huisman, J., G. A. Codd, H. W. Paerl, B. W. Ibelings, J. M. H.
Verspagen, and P. M. Visser. 2018. Cyanobacterial blooms.
Nat. Rev. Microbiol. 16: 471–483. doi:10.1038/s41579-018-
0040-1
Huisman, J., and others. 2004. Changes in turbulent mixing
shift competition for light between phytoplankton species.
Ecology 85: 2960–2970.
Ibelings, B. W., M. Bormans, J. Fastner, and P. M. Visser. 2016.
CYANOCOST special issue on cyanobacterial blooms:
Synopsis—a critical review of the management options for
their prevention, control and mitigation. Aquat. Ecol. 50:
595–605.
Ibelings, B. W., B. M. A. Kroon, and L. R. Mur. 1994. Acclima-
tion of photosystem II in a cyanobacterium and a eukary-
otic green alga to high and fluctuating photosynthetic
photon flux densities, simulating light regimes induced by
mixing in lakes. New Phytol. 128: 407–424.
Ibelings, B. W., M. Vonk, H. F. J. Los, D. T. van der Molen,
and W. M. Mooij. 2003. Fuzzy modeling of cyanobacterial
surface waterblooms: Validation with NOAA-AVHRR satel-
lite images. Ecol. Appl. 13: 1456–1472.
Ibelings, B. W., and others. 2007. Resilience of alternative stable
states during the recovery of shallow lakes from eutrophica-
tion: Lake Veluwe as a case study. Ecosystems 10: 4–16.
Imberger, J. 1985. The diurnal mixed layer. Limnol. Oceanogr.
30: 737–770.
Imboden, D. M., and A. Wüest. 1995. Mixing mechanisms in
lakes. In Physics and chemistry of lakes. Springer-Verlag.
Kelly, P. T., C. T. Solomon, J. A. Zwart, and S. E. Jones. 2018. A
framework for understanding variation in pelagic gross pri-
mary production of lake ecosystems. Ecosystems 21: 1364–
1376.
Kirillin, G., and T. Shatwell. 2016. Generalized scaling of sea-
sonal thermal stratification in lakes. Earth Sci. Rev. 161:
179–190.
Koenings, J. P., and J. A. Edmundson. 1991. Secchi disk and
photometer estimates of light regimes in Alaskan lakes -
effects of yellow color and turbidity. Limnol. Oceanogr. 36:
91–105.
Kosten, S., and others. 2012. Warmer climates boost
cyanobacterial dominance in shallow lakes. Glob. Chang.
Biol. 18: 118–126.
Kraemer, B. M., T. Mehner, and R. Adrian. 2017. Reconciling
the opposing effects of warming on phytoplankton bio-
mass in 188 large lakes. Sci. Rep. 7: 1–7.
Köppen, W. 1900. Versuch einer klassifikation der klimate,
vorzugsweise nach ihren beziehungen zur pflanzenwelt.
Geographische Zeitschrif 6: 657–679.
Kutser, T., D. C. Pierson, L. Tranvik, A. Reinart, S. Sobek, and
K. Kallio. 2005. Using satellite remote sensing to estimate
the colored dissolved organic matter absorption coefficient
in lakes. Ecosystems 8: 709–720.
Leach, T. H., and others. 2018. Patterns and drivers of deep
chlorophyll maxima structure in 100 lakes: The relative
importance of light and thermal stratification. Limnol.
Oceanogr. 63: 628–646.
Magee, M. R., and C. H. Wu. 2017. Response of water tempera-
tures and stratification to changing climate in three lakes
with different morphometry. Hydrol. Earth Syst. Sci. 21:
6253–6274.
Mantzouki, E., and B. W. Ibelings. 2018. The principle and
value of the European Multi Lake Survey. Limnol. Ocean-
ogr. Bull. 27: 82–86.
Mantzouki, E., and others. 2018. A European Multi Lake Sur-
vey dataset of environmental variables, phytoplankton pig-
ments and cyanotoxins. Sci. Data 5: 180226.
Mesman, J. P., and others. 2021. The role of internal feedbacks
in shifting deep lake mixing regimes under a warming cli-
mate. Freshw. Biol. 66: 1021–1035.
Millero, F. J., and A. Poisson. 1981. International one-
atmosphere equation of state of seawater. Deep-Sea Res. A
Oceanogr. Res. Pap. 28: 625–629.
Moss, B., and others. 2011. Allied attack: Climate change and
eutrophication. Inland Waters 1: 101–105.
Mur, L. R., H. J. Gons, and L. Van Liere. 1977. Some experi-
ments on the competition between green algae and blue
green bacteria in light-limited environments. FEMS
Micobiol. Lett. 1: 335–338.
Nõges, P., T. Nõges, M. Ghiani, B. Paracchini, J. Pinto Grande,
and F. Sena. 2011. Morphometry and trophic state modify
the thermal response of lakes to meteorological forcing.
Hydrobiologia 667: 241–254.
Nürnberg, G. K. 1984. The prediction of internal phosphorus
load in lakes with anoxic hypolimnial. Limnol. Oceanogr.
29: 111–124.
Oleksy, I. A., and D. C. Richardson. 2021. Climate change and
teleconnections amplify lake stratification with differential
local controls of surface water warming and deep water
Donis et al. European lake survey: summer Chl-a drivers
4331
cooling. Geophys. Res. Lett. 48: e2020GL090959. doi:10.
1029/2020GL090959
O’Reilly, C. M., and others. 2015. Rapid and highly variable
warming of lake surface waters around the globe. Geophys.
Res. Lett. 42: 10773–10781.
Paerl, H. W., and J. Huisman. 2008. Climate. Blooms like it
hot. Science 320: 57–58.
Paerl, H. W., and V. J. Paul. 2012. Climate change: Links to
global expansion of harmful cyanobacteria. Water Res. 46:
1349–1363.
Peters, G. J. Y. 2018. Userfriendlyscience: Quantitative analysis
made accessible (R package version 0.7-1071). doi:10.
17605/osf.io/txequ
Pinckney, J. L., T. L. Richardson, D. F. Millie, and H. W. Paerl.
2001. Application of photopigment biomarkers for quanti-
fying microalgal community composition and in situ
growth rates. Org. Geochem. 32: 585–595.
Pollard, A. I., S. E. Hampton, and D. M. Leech. 2018. The
promise and potential of continental-scale limnology using
the U.S. Environmental Protection Agency’s National Lakes
Assessment. Limnol. Oceanogr. Bull. 27: 36–41.
Posch, T., O. Köster, M. M. Salcher, and J. Pernthaler. 2012.
Harmful filamentous cyanobacteria favoured by reduced
water turnover with lake warming. Nat. Clim. Change 2:
809–813.
Przytulska, A., M. Bartosiewicz, and W. F. Vincent. 2017.
Increased risk of cyanobacterial blooms in northern high-
latitude lakes through climate warming and phosphorus
enrichment. Freshw. Biol. 62: 1986–1996.
Ptacnik, R., S. Diehl, and S. Berger. 2003. Performance of sink-
ing and nonsinking phytoplankton taxa in a gradient of
mixing depths. Limnol. Oceanogr. 48: 1903–1912.
Quinlan, R., and others. 2020. Relationships of total phospho-
rus and chlorophyll in lakes worldwide. Limnol. Oceanogr.
66: 392–404.
Read, J. S., and others. 2014. Simulating 2368 temperate lakes
reveals weak coherence in stratification phenology. Ecol.
Model. 291: 142–150.
Reynolds, C. S. 2006. The ecology of phytoplankton.
Cambridge Univ. Press.
Richardson, D. C., and others. 2017. Transparency, geomor-
phology and mixing regime explain variability in trends in
lake temperature and stratification across northeastern
North America (1975-2014). Water 9: 442.
Rigosi, A., C. C. Carey, B. W. Ibelings, and J. D. Brookes. 2014.
The interaction between climate warming and eutrophica-
tion to promote cyanobacteria is dependent on trophic state
and varies among taxa. Limnol. Oceanogr. 59: 99–114.
Rinke, K., P. Yeates, and K. O. Rothhaupt. 2010. A simulation
study of the feedback of phytoplankton on thermal struc-
ture via light extinction. Freshw. Biol. 55: 1674–1693.
Rubel, F., K. Brugger, K. Haslinger, and I. Auer. 2017. The cli-
mate of the European Alps: Shift of very high resolution
Köppen-Geiger climate zones 1800–2100. Meteorol. Z. 26:
115–125.
Rubel, F., and M. Kottek. 2010. Observed and projected cli-
mate shifts 1901-2100 depicted by world maps of the
Köppen-Geiger climate classification. Meteorol. Z. 19:
135–141.
Sahoo, G. B., A. L. Forrest, S. G. Schladow, J. E. Reuter, R.
Coats, and M. Dettinger. 2016. Climate change impacts on
lake thermal dynamics and ecosystem vulnerabilities.
Limnol. Oceanogr. 61: 496–507.
Salmaso, N. 2002. Ecological patterns of phytoplankton
assemblages in Lake Garda: Seasonal, spatial and historical
features. J. Limnol. 61: 95–115.
Salmaso, N., and M. Tolotti. 2021. Phytoplankton and anthro-
pogenic changes in pelagic environments. Hydrobiologia
848: 251–284.
Salmaso, N., and others. 2020. Responses to local and global
stressors in the large southern perialpine lakes: Present sta-
tus and challenges for research and management. J. Great
Lakes Res. 46: 752–766.
Scheffer, M., S. Carpenter, J. A. Foley, C. Folke, and B. Walker.
2001. Catastrophic shifts in ecosystems. Nature 413:
591–596.
Scheffer, M., S. Rinaldi, A. Gragnani, L. R. Mur, and E. H. van
Nes. 1997. On the dominance of filamentous cyanobacteria
in shallow, turbid lakes. Ecology 78: 272–282.
Schwefel, R., A. Gaudard, A. Wüest, and D. Bouffard. 2016.
Effects of climate change on deepwater oxygen and winter
mixing in a deep lake (Lake Geneva): Comparing observa-
tional findings and modeling. Water Resour. Res. 52: 8811–
8826.
Sinha, E., A. Michalak, and V. Balaji. 2017. Eutrophication will
increase during the 21st century as a result of precipitation
changes. Science 357: 405–408.
Steinberg, C. E. W., and H. M. Hartmann. 1988. Planktonic
bloom-forming cyanobacteria and the eutrophication of
lakes and rivers. Freshw. Biol. 20: 279–287.
Sverdrup, H. U. 1953. On conditions for the vernal blooming
of phytoplankton. ICES J. Mar. Sci. 18: 287–295.
Tamm, M., R. Freiberg, I. Tonno, P. Noges, and T. Noges.
2015. Pigment-based chemotaxonomy—a quick alternative
to determine algal assemblages in large shallow eutrophic
lake? PLoS One 10: e0122526.
Thompson, R. K. C., and R. Schmidt. 2005. Ultra-sensitive
alpine lakes and climate change. J. Limnol. 64: 139–152.
Van der Staay, G. W. M., A. Brouwer, R. L. Baard, and F.
van Mourik. 1992. Separation of photosystems I and II
from the oxychlorobacterium (prochlorophyte)
Prochlorothrix hollandica and association of Chlb binding
antennae with PS II. Biochim Biochim. Biophys. Acta
1102: 220–228.
Visser, P. M., B. W. Ibelings, M. Bormans, and J. Huisman.
2016. Artificial mixing to control cyanobacterial blooms: A
Donis et al. European lake survey: summer Chl-a drivers
4332
review. Aquatic Ecology 50: 423–441. doi:10.1007/s10452-
015-9537-0
Vollenweider, R. A. 1968. Scientific fundamentals of the eutro-
phication of lakes and flowing waters with particular refer-
ence to nitrogen and phosphorus as factors in
eutrophication. OECD. Paris. Tech. Report DA5/SCII/68 27,
250 p.
Wetzel, R. G. 2001. Water movements. Limnology (Third Edi-
tion). Academic Press; 93–128. doi:10.1016/B978-0-08-
057439-4.50011-3
Winslow, L. A., J. S. Read, G. J. A. Hansen, K. C. Rose, and
D. M. Robertson. 2017. Seasonality of change: Summer
warming rates do not fully represent effects of climate
change on lake temperatures. Limnol. Oceanogr. 62: 2168–
2178.
Woolway, R. I., and C. J. Merchant. 2019. Worldwide alter-
ation of lake mixing regimes in response to climate change.
Nat. Geosci. 12: 271–276.
Wüest, A., and A. Lorke. 2003. Small-Scalehydrodynamics
Inlakes. Annu. Rev. Fluid Mech. 35: 373–412.
Yang, Y., W. Colom, D. Pierson, and K. Pettersson. 2016.
Water column stability and summer phytoplankton
dynamics in a temperate lake (Lake Erken, Sweden). Inland
Waters 6: 499–508.
Yankova, Y., S. Neuenschwander, O. Koster, and T. Posch.
2017. Abrupt stop of deep water turnover with lake
warming: Drastic consequences for algal primary producers.
Sci. Rep. 7: 13770.
Zuur, A. F., E. N. Ieno, and C. S. Elphick. 2010. A protocol for
data exploration to avoid common statistical problems.
Methods in Ecology and Evolution, 1: 3–14.
Zwart, J. A., and others. 2016. Metabolic and physiochemical
responses to a whole-lake experimental increase in dis-
solved organic carbon in a north-temperate lake. Limnol.
Oceanogr. 61: 723–734.
Acknowledgments
The authors acknowledge COST Action ES 1105 “CYANOCOST –
Cyanobacterial blooms and toxins in water resources: Occurrence impacts
and management” and COST Action Global Change Biology ES 1201
NETLAKE – Networking Lake Observatories in Europe” for contributing to
this study through networking and knowledge sharing with European
experts in the field. We acknowledge the members of the Global Lake
Ecological Observatory Network (GLEON) for their collaborative spirit and
enthusiasm that inspired the grassroots effort of the EMLS. E.M. was
supported by a grant from the Swiss State Secretariat for Education,
Research and Innovation to Bas Ibelings and by supplementary funding
from University of Geneva. We thank Wendy Beekman for the nutrient
analysis. We thank Pieter Slot for assisting with the pigment analysis. We
thank Dr. Ian Jones for valuable feedback on an earlier version of the man-
uscript. We thank the Leibniz Institute of Freshwater Ecology and the
Aquatic Microbial Ecology Group for logistic and technical support of
J. Fonvielle and H.-P. Grossart, and the Leibniz Association for financial
support. H.P. was supported by the US National Science Foundation
(1840715, 1831096). A.C.’s work was funded by the Spanish Agencia
Estatal de Investigacion and EU funds through the project CLIMAWET
(CGL2015-69557-R). The collection of data for Lough Erne and Lough
Neagh were funded by the Department of Agriculture, Environment and
Rural Affairs, Northern Ireland. We are grateful to Kristiina Vuorio from the
Freshwater Centre of the Finnish Environment institute for her help in
organizing, collecting and analysing samples by the University of Jyväskylä
and to Gerald Dörflinger from the Water Development Department of
Cyprus for his assistance with the sampling in Cyprus and for granting the
CUT team permission to use WDD’s equipment. Finally, we would like to
thank the numerous other assistants that helped realizing each local
survey. Open access funding provided by Universite de Geneve.
Conflict of Interest
None declared.
Submitted 24 September 2020
Revised 01 July 2021
Accepted 08 October 2021
Associate editor: Catherine M. O’Reilly
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