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T E S E D E D O U T O R A D O
Mixotrofia do fitoplâncton emum gradiente de luz e
nutrientes
Mariana Rodrigues Amaral da CostaUFRN
U N I V E R S I D A D E F E D E R A L D O R I O G R A N D E D O N O R T E
P R O G R A M A D E P Ó S - G R A D U A Ç Ã O E M E C O L O G I A
Mixotrofia do fitoplâncton em um gradientede luz e nutrientes
Phytoplankton mixotrophy across nutrient and lightgradients
Mariana Rodrigues Amaral da Costa
NATAL - RN - BRASIL - 2019
Tese de doutorado apresentada ao
programa de Pós-Graduação em
Ecologia da Universidade Federal do
Rio Grande do Norte para obtenção
do título de Doutora em Ecologia.
Orientação: Vanessa Becker
Co-orientação: Hugo Sarmento
Orientação doutorado sanduíche:
Fernando Unrein
Mariana Rodrigues Amaral da Costa
Mixotrofia do fitoplâncton em um gradientede luz e nutrientes
Phytoplankton mixotrophy across nutrient and lightgradients
NATAL - RN - BRASIL - 2019
O presente trabalho foi realizado com apoio da Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) -
Código de Financiamento 001
Costa, Mariana Rodrigues Amaral da. Mixotrofia do fitoplâncton em um gradiente de luz enutrientes / Mariana Rodrigues Amaral da Costa. - Natal, 2019. 94 f.: il.
Tese (Doutorado) - Universidade Federal do Rio Grande doNorte. Centro de Biociências. Programa de Pós-graduação emEcologia. Orientadora: Profa. Dra. Vanessa Becker. Coorientador: Prof. Dr. Hugo Sarmento.
1. Seca extrema - Tese. 2. Grazing - Tese. 3. Cianobactérias- Tese. 4. Disponibilidade de luz - Tese. 5. Citometria de fluxo- Tese. 6. Traços funcionais - Tese. I. Becker, Vanessa. II.Sarmento, Hugo. III. Universidade Federal do Rio Grande doNorte. IV. Título.
RN/UF/BSE-CB CDU 551.577.5
Universidade Federal do Rio Grande do Norte - UFRNSistema de Bibliotecas - SISBI
Catalogação de Publicação na Fonte. UFRN - Biblioteca Setorial Prof. Leopoldo Nelson - Centro de Biociências - CB
Elaborado por KATIA REJANE DA SILVA - CRB-15/351
Dedico esta tese à minha família,meu porto seguro
“Não é possível refazer este país, democratizá-lo,humanizá-lo, torná-lo sério, com adolescentes brincando
de matar gente, ofendendo a vida, destruindo o sonho,inviabilizando o amor. Se a educação sozinha não
transformar a sociedade, sem ela tampouco a sociedademuda.”
Paulo Freire
Crédito para imagem: Robert Fisher
1
AGRADECIMENTOS
Agradeço ao programa de pós-graduação em Ecologia da UFRN e seus
excelentes docentes. Ao programa de pós-graduação de Ecologia e Recursos Naturais da
UFSCar – São Carlos –SP, por ter me acolhido durante minha estadia em São Carlos. E
ao Instituto de Investigações Biotecnológicas (IIB – INTECH) de Chascomús –
província de Buenos Aires, Argentina.
Às agências financiadoras: Coordenação de Aperfeiçoamento de Pessoal de
Nível Superior (CAPES) pela concessão de bolsa de doutorado e pela concessão da
bolsa na modalidade doutorado sanduíche – edital PDSE 2016. Ao Conselho Nacional
de Desenvolvimento Científico e Tecnológico (CNPq) pelo financiamento da pesquisa
resultando no primeiro capítulo da tese. À Fundação de Pesquisa de São Paulo
(FAPESP) pelo financiamento nos projetos que resultaram no segundo e terceiro
capítulo da tese. Gostaria de ressaltar a importantíssima contribuição que essas agências
têm para pesquisa, ciência e tecnologia. Fortalecer nossas agências é garantir o
desenvolvimento e soberania do nosso país.
A todos os laboratórios envolvidos para o desenvolvimento dessa pesquisa:
Laboratório de Recursos Hídricos e Saneamento Ambiental (LARHISA – UFRN) e seu
grupo de pesquisa ELISA (Estudos em Limnologia do Semiárido), Laboratório de
Ecologia Aquática (LEA – UFRN), Laboratório de Microbiologia Aquática (LAMAq –
UFRN), Laboratório de Limnologia (DOL – UFRN), Laboratório de Processos e
Biodiversidade Microbiana (LMPB – UFSCar), Laboratório de Ficologia (UFSCar) e ao
Laboratório de Ecologia e Fotobiologia Aquática (LEFA – IIB- INTECH, Chascomús).
À minha orientadora Vanessa Becker, a quem devo muita gratidão durante todos
esses anos que estamos juntas na academia e na vida. Vanessa é meu espelho.
Inspiração, rigidez, amabilidade e bom humor. O que sou e o que me inspiro a ser na
academia vem da força dessa mulher. Meu muito obrigada por essa parceria que
ultrapassa os muros da universidade.
Ao meu co-orientador, Hugo Sarmento. Muito obrigada por todos os
ensinamentos, pela paciência e todo o apoio durante todo o processo da tese. Participar
de seu laboratório e de seu grupo de pesquisa foi muito importante no meu crescimento
acadêmico e pessoal. Mas gostaria de ressaltar minha gratidão pelo seu convite e por me
2
acolher em São Carlos em um momento tão importante e divisor de águas na minha
vida.
Ao meu co-orientador no exterior, Fernando Unrein. Meus agradecimentos por
me acolher em Chascomús e no seu grupo de pesquisa. Viver em Chascomús foi uma
experiência transformadora. Obrigada pelos ensinamentos e pela paciência. Um brinde
ao choripan e ao churrasco argentino.
Á Inessa Bagatini, por me receber em seu laboratório de Ficologia da UFSCar
com toda sua delicadeza e dedicação e por todas as ajudas no que foi necessário para
melhorar esses trabalhos. Suas contribuições foram valiosas.
Uma tese de doutorado é um produto que não foi feito somente pelas minhas
mãos, existe toda uma equipe por trás como já mencionado. São programas de pós-
graduação de excelência, agências financiadoras, universidades públicas de alta
qualidade, laboratórios equipados e seus grupos de pesquisas, professores e orientações
excelentes. Nada se faz só. Mas essa tese de doutorado vai além disso, existe toda uma
rede apoio, essa rede é a base fortalecedora que me sustenta no grande peso que é fazer
ciência no Brasil.
Dedico e agradeço aos meus pais, Moab e Solange, meu porto seguro. Muitas (e
muitas) vezes senti medo e pensei em não continuar mas segui em frente pois sabia que
nunca ficaria desamparada. Ao meu irmão, Rodolfo, minha mão amiga e braço forte.
Estaremos sempre juntos no caminhar e desafios da vida. À minha família,
especialmente a minha avó Rosa, pelo amor, atenção e por ter segurado a nossa barra
tantas e tantas vezes. Tia Lilian, meu exemplo inspirador, tia Simone e tio Júnior. Minha
família é uma família de mulheres fortes e de garra. Ao meu avô João Batista em
memória, ainda lembro do senhor e sua lembrança me deixa mais feliz, obrigada por
inserir a poesia na minha vida.
Agradeço a Gentil Santos que esteve presente em todo esse processo e com esse
será o quarto título que ele esteve ao meu lado, você foi muito importante na minha rede
de apoio.
Existem pessoas que foram diretamente e ativamente importantes na construção
desse trabalho e na minha vida pessoal. Esse doutorado me proporcionou três casas,
uma casa para cada capítulo:
3
Gostaria de agradecer a minha primeira casa, Natal – RN, UFRN, aos amigos de
laboratório. Meu núcleo: Fabiana Araújo, Gabi Moura, Maria Marcolina, Pablo Rubim e
Danyhelton, vocês estiveram presentes em todo esse processo desde quando eu era IC
até chegar aqui. Carrego vocês no meu coração. Obrigada por tornar o trabalho um
ambiente leve e divertido. Aos amigos GREMLINS: Ewaldo, Anízio, Fabíola, Letícia
Quesado, Radmila, Iagê, Maiara Menezes, Leo Teixeira, Gustavo Girão, Camila Cabral,
Laissa, Regina e Carol Medeiros. É tão importante ter um grupo como o nosso, onde são
todos amigos, nos ajudando em qualquer momento e sem competição. Não é todo lugar
que encontramos essa parceria.
Agradeço aos LIMNIONS: Carlos Júnior, Gabi Trigueiro, Jéssica Leite, Hérika
Calvalcante, Jade e Jéssica Pápera. Vocês são braço forte. Obrigada.
A todos os responsáveis pelo trabalho de campo e laboratório que resultaram no
capítulo 1 da tese, especialmente a Rosemberg Menezes, coordenador do projeto, além
de Seu Edson, Anísio, Cleto, Lenice, Isaac Falcão, Juliana Leroy, Bruno Wanderley,
Regina Nobre, Pedro Junger e Barbara Bezerra.
Gostaria de agradecer a minha segunda casa, Chascomús na Argentina,
especialmente Juliana Ospina e Marión Perez, se levo grandes amizades desse momento
são vocês. Obrigada por abrirem as portas da sua casa e das suas vidas. Agradeço muito
também a Victoria Quiroga, por todo o apoio na citometria e todas as trocas de
conhecimentos. Aos amigos do LEFA: Juli, Paula Huber, Mariana Odriozola, Manuel
Castro, Pepe, Sebastian Metz, Nadia Diovisalvi, María Eugenia Llames, Leonardo
Lagomarsino, Paulina Fermani, Marcela Ferraro e Horacio Zagarese. E a todos os
amigos que fiz em Chascomús: Lisset Ruiz, Melisa Alberti, Estefanía Vásquez, Rosario
Lastra e a todos que participaram dos asaditos e por compartilhar o vinho.
Se eu pudesse voltar no tempo não mudaria a decisão de ir para lá. Foi uma
experiência transformadora e de autoconhecimento. Obrigada a todos que fizeram parte
dessa minha história.
Quero agradecer minha terceira casa, São Carlos – SP. São Carlos foi um marco
na minha vida. Meu muito obrigada a República Potilombia: Pedro e Laura e os
agregados (Mika, Paulita e Helena). Nesse apartamento da rua episcopal fui muito feliz.
4
Agradeço especialmente ao Pedro, pela convivência diária tanto em casa como
no laboratório, você faz parte tanto do GREMLINS, como do LMBP, das coletas do
doutorado, da potilombia e da minha vida diária. Muito obrigada meu irmão.
Agradeço a Michaela Ladeira (Milkinha), parceira de laboratório, de
comidinhas, vinhos e discos, viagens e da vida. Você é inspiração.
Agradeço a Helena Vieira, minha experiência em São Carlos não teria sido tão
extraordinária sem a sua companhia. Muito obrigada por me inserir na sua vida, na vida
de seus amigos, no Natal com sua família. Que bom que cruzamos o mesmo caminho
em momentos tão importantes de nossas vidas.
Ao LMBP: Pedro, Mika, Erick (e Sofia) e Henrique. Especialmente ao Henrique
que foi meu braço forte e com a ajuda dele saiu os experimentos do terceiro capítulo.
Meu melhor IC <3. Aos amigos do laboratório de Ficologia da UFSCar: Inessa, Nathan,
Marcelo, Guilherme e Naiara.
À rede de apoio dos amigos de São Carlos, amigos de café e cerveja, almoços e
lanches. Principalmente os cafés das marocas: Helena, Popinho, Luiza e Carol. A todos
que cruzaram meu caminho. Cada um é especial na minha vida. Fui muito feliz com
vocês. Obrigada por transformar um ano em cinco.
Por fim, mas não menos especial, minha rede de apoio das amizades que estão
sempre comigo, aquelas amigas de sempre e para sempre: Franzinha, Larinha e Jaci;
Lara, Katy e Juliana. Não existe Maricota sem elas e não existe elas sem Maricota.
Ao decidir fazer doutorado e me dedicar à pesquisa, eu nutria o desejo que com
o meu trabalho eu poderia mudar o mundo. Mal sabia eu que o doutorado mudaria a
mim. Somos resultados de nossas experiências de vida e o doutorado me proporcionou
muitas vivências. Mahalo.
5
Sumário Agradecimentos ............................................................................................................................. 1
Lista de Figuras ............................................................................................................................. 6
Lista de Tabelas ............................................................................................................................. 9
Apresentação da Tese .................................................................................................................. 10
Fluxograma da Tese .................................................................................................................... 12
Resumo ........................................................................................................................................ 13
Abstract ......................................................................................................................................15
Introdução Geral ........................................................................................................................16
Fitoplâncton e abordagens funcionais ......................................................................................... 16
Eventos climáticos extremos e a dinâmica fitoplanctônica ......................................................... 17
Mixotrofia como importante traço funcional do fitoplâncton ..................................................... 20
Capítulo 1 .................................................................................................................................... 31
Capítulo 2 .................................................................................................................................... 45
Capítulo 3 .................................................................................................................................... 63
Considerações Finais ................................................................................................................... 81
Material Suplementar .................................................................................................................. 84
6
Lista de Figuras
Introdução geral
Fig. 1 Esquema do modelo teórico da mudança da composição da comunidade fitoplanctônica
em resposta ao grau de eutrofização e concentração da turbidez inorgânica devido à redução da
capacidade máxima de volume de reservatórios e lagos rasos. O azul representa um ambiente
com baixa disponibilidade de nutrientes e alta disponibilidade de luz, a área verde representa um
ambiente eutrofizado com alta biomassa de cianobactérias e a área marrom representa um
ambiente com baixa disponibilidade de luz e alta turbidez inorgânica de material em suspensão
particulado devido a ressuspensão do sedimento para a coluna d’água. A linha vermelha
pontilhada representa um nível crítico de nível de água dos reservatórios e lagos
rasos..............................................................................................................................................20
Capítulo 1
Fig. 1 Study area of the Piranhas-Açu River watershed showing the 16 reservoirs
distributed in the two sub-basins, Piancó sub-basin (PB) and Seridó sub-basin (SB).
Isohyets show the differences in annual precipitation. The reservoirs in SB are located
in the drier region, within the isohyets of 500 mm, and those in PB are located within
the isohyets of 700 mm. ………………………………………………………………..35
Fig. 2 Box plots of some of the environmental variables a) maximum volume storage
(Vol %), b) maximum depth (Zmax), c) Secchi disk depth, d) water:evaporation ratio, e)
total phosphorous, and f) chlorophyll-a in the two regions (PB and SB) and in the two
sampling periods: dry and extremely dry (ED). The dotted lines separate the studied
periods. The box plot shows the median, minimum, maximum, first, and third quartiles.
The results of the two-way ANOVA are shown for each variable.
………………………………………………………………………………………….37
Fig. 3 Box plots of the total phytoplankton biomass in log scale (a) and species richness
(b) in the two regions (PB and SB) and in the two sampling periods: dry and extremely
dry (ED). The dotted lines separate the studied periods. The box plot shows the median,
minimum, maximum, first, and third quartiles. The results of the two-way ANOVA are
shown for each variable…………………………………………………………………38
Fig. 4 Relative biomass of the phytoplankton morphological functional traits (A) and
the physiological functional traits (B) between the two regions (PB and SB) and the two
study periods (dry and extremely dry) ........................................................……………38
Fig. 5 Box plot of the significant functional traits: large round/ovoid shape (A),
nitrogen-fixing (B) and potential mixotrophs (C) between the two regions (PB and SB)
and the two studied period: dry and extremely dry (ED). The box plot shows the
median, minimum, maximum, first, and third quartiles. The results of the two-way
ANOVA are shown for each variable. ……………………………………………….39
Fig. 6 Linear regression between the biomass of the potential mixotrophic algae and the
source of turbidity: (a) inorganic suspended solids (ISS) and (b) organic suspended
solids (OSS). Data represent the two regions PB (black circles) and SB (black triangles)
together during the extremely dry period. …..…………………………………………39
7
Fig. 7 RDA ordination of the phytoplankton functional traits and the environmental
variables in the studied period. Dark squares and circles represent the dry period. White
squares and circles represent the extremely dry period (ED). Squares represent the
Piancó sub-basin (PB), and circles denote the Seridó sub-basin (SB). Volume (vol),
euphotic zone (Zeu), turbidity (Turb), total phosphorous (TP), conductivity (Cond),
dissolved oxygen (DO), chlorophyll-a (Chla), small round/ovoid (small), large
round/ovoid (large), coenobial (Coe), filamentous (filam), complex shapes (shapes),
colonies (colonial), nitrogen fixing (Nfix), potential for mixotrophy (mix.), high light
requirement (HighLight), and low light requirement (LowLight).
………………………………………………………………………………………….40
Fig. 8 Schematic diagram of the effect of water volume reduction altering the
phytoplankton functional traits groups. HL = high light acquisition, Nfix = nitrogen
fixation. Dashed lines imply the critical threshold for the column depth. ……………..41
Capítulo 2
Fig. 1 Experimental design illustrating unialgal and mixed cultures under three distinct
light regimes. High light (HL) consist in direct incident light in the C. obovata
monoculture, in M. aeruginosa monoculture and in the mixed cultures (C. obovata + M.
aeruginosa, C+M). Low light (LL) consisted in a physical barrier (culture tissue flask
with sediment diluted in medium WC) above the single and co-cultures. Low light +
sediment (Sed) consisted in the same light intensity from LL treatment but with the
sediment inside the treatment flasks. This experimental design was performed with
axenic and non-axenic (bacteria-added) cultures.……………………………………..50
Fig 2. Trends in Cryptomonas obovata (a) and Microcystis aeruginosa (b) carbon
biomass through experimental period (days) for the axenic experiment in High light
(HL), Low Light (LL) and Sediment (SED) treatments in the single and co-cultures.
Error bars (n=3) are standard deviations.
………………………………………………………………………………………….52
Fig 3. Trends in Cryptomonas obovata (a) and Microcystis aeruginosa (b) carbon
biomass relative to the initial time through experimental period (days) for the non-
axenic experiment in High light (HL), Low Light (LL) and Sediment (SED) treatments
observed in the single and co-cultures. Error bars (n=3) are standard
deviations……………………………………………………………………………….52
Fig. 4 Growth Rates (day-1) for Cryptomonas ovata and Microcystis aeruginosa in
single and co-cultures in experiment (a) without bacteria (axenic) and (b) with bacteria
(non-axenic). HL= high light treatment, LL = low light treatment and SED = sediment
treatment. Bars describe the standard deviation. Two-way ANOVA results for
competition and effect of light limitation with F and p-value are shown. (a) and (b) are
the results from Tukey’s post-hoc test..………………………………………………..53
8
Capítulo 3
Fig 1 – The 3x3 factorial experimental design for ingestion and food vacuoles staining
experiments …………………………………………………………………………….67
Fig. 2 Cell-specific grazing rates (CSGR, bact.ind-1.h-1) from the short-term grazing
experiments for C. marssonii measured by flow cytometry (A) and by epifluorescence
microscope (B) and for O. tuberculata measured by flow cytometry (C) and by
epifluorescence microscope (D) between the distinct nutrient treatments: high nutrient
(HN), medium nutrient (MN) and low nutrient (LN); and between the distinct light
treatments: high light (HL), low light (LL) and total darkness (D). Bars are the standard
deviation (n=3), p is the p-values from the two-way ANOVA, L = light and N =
nutrient. Letters “a” and “b”, denotes differences by pots-hoc test for the nutrient
treatments and letters “c” and “d” denotes differences by the post-hoc test for the light
treatments. ……………………………………………………………………………………..71
Fig 3 – General model of the type II linear regression comparing specific grazing rates (CSGR)
obtained by flow cytometry and epifluorescence microscopy in short-term ingestion
experiments with C. marsonii and O. tuberculata. Dashed line indicates a 1:1 ratio and solid
dark line is the linear regression for data points. (R2 = 0.50, p-value <
0.001).…………………………………………………………………………………………...72
Fig 4 - Cytograms of food vacuoles experiment using Lyso-Tracker (LyT) for Chlamydomonas
sp. in the nine treatments combinations. HN = high nutrient, MN = medium nutrient, LN = low
nutrient, HL = high light, LL = low light and D = total darkness. Red dots are cells before the
addition of LyT and black dots are cells after the addition of LyT. ……………………………73
Fig 5 – Cytograms of food vacuoles experiment using Lyso-Tracker (LyT) for C. marsonii in
the nine treatments combinations. HN = high nutrient, MN = medium nutrient, LN = low
nutrient, HL = high light, LL = low light and D = total darkness. Blue dots are cells before the
addition of LyT and black dots are cells after the addition of LyT. ……………………………73
Fig 6 – Cytograms of food vacuoles experiment using Lyso-Tracker (LyT) for O. tuberculata
in the nine treatments combinations. HN = high nutrient, MN = medium nutrient, LN = low
nutrient, HL = high light, LL = low light and D = total darkness. Red dots are cells before the
addition of LyT and black dots are cells after the addition of LyT. ……………………………74
9
Lista de Tabelas
Introdução geral
Tabela 1 – Metodologias propostas para estudar grazing em protistas ..........................22
Capítulo 1
Table 1 - Groups of the phytoplankton functional traits identified in the study ………36
Capítulo 2
Table 1 – Two-way ANOVA showing differences in C. obovata and M. aeruginosa ..54
Capítulo 3
Table 1 - Summary of the experiments performed ………………………………........67
Table 2 – Two-way ANOVA for LyT experiment between treatments. Asterisks (*)
indicate significant or marginally significant values. ………………………………….74
Table 3 – Comparison of study methodologies and significant results related to
treatments, n.s. indicate no significant differences between treatments (light and
nutrients). HN = High nutrient treatment, LN = Low nutrient treatment and LL = low
light treatment. …………………………………………………………………………75
10
Apresentação da Tese
Este trabalho foi realizado na Universidade Federal do Rio Grande do Norte,
Programa de Pós-Graduação em Ecologia, sob a orientação da professora Dra.
Vanessa Becker (UFRN), e na Universidade Federal de São Carlos (UFSCar) sob a
coorientação do professor Dr. Hugo Sarmento (UFSCar). Além disso, conta com a
parceria do professor Dr. Fernando Unrein, do Instituto de Investigaciones
Biotecnológicas (IIB-INTECH), CONICET, Chascomús, Argentina, supervisor do
doutorado sanduíche realizado de maio de 2017 a janeiro de 2018.
A tese de doutorado está estruturada pela composição de três capítulos na
forma de artigos científicos. O primeiro capítulo intitulado “Extreme drought favors
potential mixotrophic organisms in tropical semi-arid reservoirs” foi publicado na
edição especial – Phytoplankton & Biotic Interactions da Hydrobiologia, em março de
2019. Este capítulo avaliou o efeito de duas áreas distintas em um gradiente de
precipitação pluviométrica no semiárido brasileiro sobre a estrutura e dinâmica
fitoplanctônica utilizando uma abordagem de traços funcionais. O estudo teve como
objetivo testar a hipótese que a redução do nível da água favorece a dominância de
cianobactérias em condições normais de seca, porém em eventos de seca extrema, as
algas com potencial de mixotrofia serão favorecidas devido à limitação de luz causada
pelo aumento da ressuspensão do sedimento. Este estudo contou com a parceria do Dr.
Rosemberg Fernandes Menezes, da Universidade Federal da Paraíba, coordenador do
projeto intitulado “Impactos da redução da precipitação pluviométrica sobre a
qualidade da água e biodiversidade aquática de ecossistemas lacustres da Caatinga”.
O segundo capítulo da tese intitulado “Effects of inorganic turbidity and light
availability in the competition of cyanobacteria and mixotrophic algae” teve como
objetivo simular, em laboratório, o cenário observado no primeiro capítulo onde a
baixa disponibilidade de luz, devido à alta turbidez inorgânica causada pela
ressuspensão de sedimento, leva ao sucesso competitivo de algas mixotróficas em
detrimento de cianobactérias. No estudo encontramos taxas de crescimento maiores
para a espécie mixotrófica no tratamento onde foi adicionado sedimento e com a
presença de bactérias. Os experimentos desse capítulo foram realizados na
Universidade Federal de São Carlos (UFSCar) sob a supervisão do professor Dr. Hugo
Sarmento, com parceria da professora Dra. Inessa Bagatini Lacativa do laboratório de
Ficologia da UFSCar, a ser submetido para a revista Journal of Plankton Research.
11
No terceiro manuscrito da tese intitulado “Effects of light and nutrients on
phytoplankton phagotrophy: a comparison of different methods for estimating
mixotrophy”, teve como objetivo quantificar taxas de grazing sob diferentes condições
de luz e nutrientes, comparando diferentes métodos para estimar a mixotrofia. O
estudo foi realizado durante o estágio de doutorado sanduíche na Argentina, no
Instituto de Pesquisas Biotecnológicas (IIB-INTECH) durante o período de maio de
2017 a janeiro de 2018, sob a coordenação do professor Dr. Fernando Unrein. Neste
capítulo foram realizados experimentos de grazing de duas espécies de algas
flageladas (Ochromonas tuberculata e Cryptomonas marsonii), apontadas na literatura
como espécies de metabolismo mixotrófico, na predação de dois tipos de presas
distintas (bactérias coradas com fluorescência - FLB, fluorescent label bacteria; e
microesferas sintéticas - beads). Nesses experimentos, foram manipulados diferentes
níveis de luz e nutrientes, recursos que podem afetar o metabolismo mixotrófico das
algas. Além disso, também foram realizados experimentos marcando vacúolos
digestivos das algas, utilizando Lyso-Tracker, um corante fluorescente verde que tinge
compartimentos ácidos de células vivas, sendo utilizados como um proxy para
identificar atividade mixotrófica das algas. Os objetivos desse capítulo foram
determinar taxas de bacterivoria por algas flageladas em distintas condições de luz e
nutrientes, e testar novas metodologias para estimar mixotrofia, comparando com
metodologias clássicas, com a finalidade de facilitar a quantificação das taxas de
ingestão. Este trabalho será submetido para Limnology and Oceanography Methods.
O presente trabalho foi realizado com apoio da Coordenação de
Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Código de
Financiamento 001.
12
Fluxograma da Tese
Fluxograma – Sequência de capítulos e seus respectivos objetivos.
13
Resumo Eventos de precipitação e flutuações do nível de água são fatores ambientais que afetam
de modo determinante o funcionamento de ecossistemas aquáticos, influenciando a
dinâmica fitoplanctônica e seus recursos. O cenário climático futuro prevê um aumento
na frequência e intensidade das secas nas regiões semiáridas. Eventos de seca podem
levar à redução do nível de água e consequente aumento da disponibilidade de
nutrientes, turbidez e condutividade, favorecendo a dominância de cianobactérias.
Porém, estudos recentes demonstram que algas mixotróficas possam ser melhores
competidoras em condições mais extremas, como eventos de secas prolongadas.
Organismos mixotróficos desempenham funções importantes, como produtores e
consumidores, e isto é refletido na estrutura da teia trófica. Nesse trabalho avaliamos o
efeito do gradiente de precipitação pluviométrico na região semiárida sobre a estrutura
da comunidade fitoplanctônica, baseada na abordagem dos traços funcionais.
Confirmamos nossa hipótese que em períodos de seca os principais traços funcionais
fitoplanctônicos recrutados são algas fixadoras de nitrogênio e com formas filamentosas
e/ou coloniais, esses traços são relacionados com dominância de cianobactérias, porém
em evento extremo de seca, com volumes hídricos críticos, há um colapso das
cianobactérias e a substituição de dominância de algas com potencial mixotrófico. Para
confirmar que a redução da disponibilidade de luz, causada pela ressuspensão do
sedimento era o principal fator que altera esse padrão de substituição na comunidade
fitoplanctônica, realizamos em laboratório experimentos de competição entre uma
espécie de cianobactéria (Microcystis aeruginosa) e uma alga mixotrófica
(Cryptomonas obovata) manipulando a luz e adição ou não de sedimento, em ambientes
com altos níveis de nutrientes. Além disso, realizamos experimentos de grazing para
estimar taxas de bacterivoria por algas flageladas mixotróficas (Cryptomonas marsonii e
Ochromonas tuberculata) em distintas condições de luz e nutrientes, a fim de propor
novas metodologias para facilitar a quantificação dessas taxas. Observamos que as algas
mixotróficas obtiveram maiores taxas de crescimento nos tratamentos com adição de
sedimento somente quando bactérias estavam presentes e calculamos maiores taxas de
grazing via citometria de fluxo do que através da microscopia de epifluorescência e essa
relação foi forte e significativa. Além disso, observamos que a técnica de marcação de
vacúolos digestivos das algas mixotróficas demonstrou ser uma boa metodologia para
estimar mixotrofia. Nosso estudo demonstra a importância das algas mixotróficas em
ambientes eutrofizados, como os sistemas do semiárido afetados pela baixa
14
disponibilidade hídrica, e compara metodologias que facilitam a quantificação de taxas
de bacterivoria permitindo entender um melhor sobre essa forma mista de nutrição.
Portanto, o estudo da mixotrofia implica em melhor compreender seu papel na estrutura
e funcionamento das teias tróficas aquáticas.
Palavras-chave: Seca extrema, Grazing, Cianobactérias, Cryptophyceae,
disponibilidade de luz, Citometria de Fluxo, Traços Funcionais.
15
Abstract Precipitation events and water level fluctuations are environmental factors that affects
aquatic ecosystem functioning influencing phytoplankton dynamic and their resources.
Nutrient availability and trophic state of arid and semi-arid regions are controlled by
quantity and rain periodicity. Future climate scenario predicts an increase in intensity
and frequency of droughts in semi-arid regions. Drought leads to water level reduction
and consequently increase nutrients concentrations, turbidity, salinity and conductivity,
favoring cyanobacteria blooms. However, recent studies shows that mixotrophic algae
can be better competitors under more extreme conditions, such as prolonged periods of
droughts. Mixotrophic organisms play important role as producers and consumers
reflecting in the structure of food webs. In this work, we evaluate the effect of
precipitation gradient in semi-arid region on the structure of phytoplankton community
based on a trait-based approach. We confirm the hypothesis that in dry period the main
phytoplankton traits are related to a high cyanobacteria biomass (nitrogen fixation,
filaments, coloniality), however, in extremely drought periods with critical water level,
cyanobacteria collapse and shifts the dominance to mixotrophic algae. To confirm that
the reduction on light availability caused by sediment resuspension was the main factor
on phytoplankton pattern, we performed laboratory experiments with competition
between cyanobacteria and a mixotrophic species, manipulating light and sediment
addition in systems with high levels of nutrients. Besides this, we also performed
grazing experiments to estimate bacterivory by flagellate algae in distinct light and
nutrients conditions and propose new methodologies to facilitate ingestion rates
quantification. Our study shows the importance of mixotrophic algae in eutrophic
environments, such as semi-arid systems affected by hydric deficit, and compare
methodologies in order to facilitate bacterivory rates quantification, allowing a better
knowledge about this kind of mixed nutrition. Therefore, research about mixotrophy
implies in paradigmatic changes in how we understand aquatic food webs nowadays, in
particular this is even more critic when it links to shifts in environmental conditions in a
changing climatic world.
Key-words: Extreme drought, Grazing, Cyanobacteria, Cryptophyceae, light
availability, flow cytometry, functional traits.
16
Introdução Geral
Fitoplâncton e abordagens funcionais
O fitoplâncton é um grupo de microrganismos polifiléticos amplamente
distribuídos nos ecossistemas aquáticos, com representantes de vários grupos de algas e
bactérias fotossintetizantes que respondem às condições ambientais com diferentes
estratégias adaptativas para seu crescimento, sobrevivência e reprodução (Reynolds,
2006). São amplamente, mas não exclusivamente foto-autotróficos, portanto, seu
crescimento depende da captação de energia luminosa suficiente para sustentar a
fixação de carbono via fotossíntese. Juntamente com as macrófitas e o perifíton são
considerados produtores primários e sustentam a base da cadeia trófica aquática.
Autotrofia também requer absorção de nutrientes inorgânicos disponíveis no meio
aquático, sendo então luz e nutrientes recursos importantes para o fitoplâncton
(Reynolds, 2006).
Desta forma, a composição da comunidade fitoplanctônica afeta as teias tróficas
aquáticas e o ciclo biogeoquímico de muitos elementos, como o carbono, nitrogênio e
fósforo, devido aos diferentes requerimentos e modos de aquisição desses elementos
pelos principais grupos fitoplanctônicos (Falkowski et al. 2004, Litchman &
Klausmeier, 2008), por exemplo, alguns grupos de cianobactérias possuem a capacidade
de fixar nitrogênio e estocar fósforo (Padisák, 1997) e diatomáceas possuem grande
eficiência de sequestro de carbono (Smetacek, 1999). Outro fator importante na
estrutura trófica aquática é a palatabilidade e estratégia nutricional das espécies
fitoplanctônicas, que influenciam diretamente no fluxo de energia para os demais níveis
tróficos (Sterner & Elser, 2002).
Há um crescente interesse na ecologia de comunidades em trabalhar com os
papéis funcionais e adaptações estruturais das espécies, de forma a compreender melhor
como os sistemas são organizados, e distinguir diferenças no fluxo de energia e na
estrutura das comunidades. As abordagens funcionais trabalham com o conceito de
nicho ecológico das espécies (Salmaso & Padisák, 2007, Reynolds et al., 2012,
Litchman & Klausmeier 2008). O nicho da comunidade fitoplanctônica é bem definido
de acordo com os processos fisiológicos, morfológicos e ecológicos e estão distribuídos
em três principais eixos: i) aquisição de recursos, ii) crescimento e iii) evitar a predação
(Litchman & Klausmeier 2008).
17
Uma abordagem bastante utilizada na ecologia de comunidades baseada na
teoria do nicho ecológico é a de traços funcionais, que trabalha as relações entre traços,
gradientes ambientais, interações entre espécies e performances de desempenho (McGill
et al., 2006). O fitoplâncton é ideal para implantar estudos baseado nessa abordagem
devido aos múltiplos traços já bem definidos, rápido tempo de geração e por ser
excelente para modelagem (Litchman et al., 2007). Abordagens baseadas em traços
funcionais da comunidade fitoplanctônica são projetadas para um grupo de espécies
com propriedades fisiológicas, morfológicas e ecológicas similares, indicando, assim, as
estratégias ecológicas ideais para certas condições de habitat (Reynolds, 2006; Naselli-
Flores & Barone, 2012).
Classificações morfo-funcional do fitoplâncton são cada vez mais utilizadas em
estudos ecológicos de diferentes ecossistemas aquáticos (Reynolds, 1994, Reynolds et
al., 2002; Salmaso & Padisák, 2007; Litchman & Klausmeier, 2008; Kruk et al., 2010).
A abordagem de traços funcionais apresentado por Litchman & Klausmeier, 2008,
classifica os traços do fitoplâncton por função ecológica (reprodução, aquisição de
recursos, evitar predadores) e por tipo (morfológico, fisiológico, comportamental e
história de vida). Esses traços variam em um gradiente ambiental e sofrem vários
mecanismos estruturadores da comunidade (Litchman & Klausmeier, 2008).
Classificações baseadas em características morfológicas e funcionais são poderosos
preditores da dinâmica ecossistêmica (Reynolds & Irish, 1997; McGill et al. 2006;
Brasil & Huszar, 2011).
Eventos climáticos extremos e a dinâmica fitoplanctônica
Eventos hidrológicos alteram as condições físicas e químicas da água, sendo
reconhecidos como direcionadores da estrutura e dinâmica fitoplanctônica (Huszar &
Reynolds 1997; Naselli-Flores & Barone, 2000, Medeiros et al., 2015). Apesar de
características hidrológicas distintas entre reservatórios e lagos naturais, os mecanismos
seletivos do fitoplâncton não são, de fato, diferentes nas respostas à flutuação de
disponibilidade de recursos (Reynolds, 1999). Nestes sistemas, há uma marcada
heterogeneidade espacial na produtividade fitoplanctônica devido aos gradientes
longitudinais da morfologia da bacia, velocidade de fluxo, tempo de residência, sólidos
em suspensão, luz e disponibilidade de nutrientes (Thornton et al., 1990). As variações
verticais devido à presença ou não de estratificação da coluna d’água também
18
interferem na disponibilidade destes recursos (luz e nutrientes) e, consequentemente,
nos atributos da comunidade fitoplanctônica (Becker et al., 2010).
Devido às mudanças climáticas, há uma forte necessidade em se conhecer como
alterações no clima e nas condições ambientais podem afetar a estrutura trófica e a função
dos ecossistemas. Alterações na temperatura, aumento do dióxido de carbono atmosférico
e aumento na frequência e intensidade de eventos extremos (seca e inundações) são
considerados os principais estressores que contribuem para as mudanças climáticas
(Woodward et al., 2010). De acordo com o Painel Intergovernamental sobre Mudanças
Climáticas (IPCC), as regiões semiáridas tropicais estão entre as que mais sofrerão com o
aquecimento global da Terra (IPCC, 2014), onde as previsões mostram um aumento na
frequência e intensidade das secas (Marengo et al., 2009; Roland et al., 2012). Estas
regiões são caracterizadas por altas temperaturas e precipitação anual inferior à
evapotranspiração potencial, levando a um déficit hídrico durante a maior parte do ano, o
que influencia diretamente na disponibilidade e a qualidade da água da região (Barbosa et
al., 2012; Figueiredo & Becker, 2018).
A comunidade fitoplanctônica desenvolveu várias estratégias adaptativas
referentes a mudanças nas condições ambientais. Como o fitoplâncton responde às
mudanças climáticas depende de sua plasticidade fenotípica e a adaptação de
organismos dentro das populações (Schimidt et al., 2018). Muitas espécies podem
tolerar mudanças nas concentrações de carbono (Raven et al., 2011), outras como
dinoflagelados, crisofíceas e criptofíceas, podem mudar suas estratégias nutritivas para
mixotrofia dependente de recursos limitantes (Ward et al., 2011, Unrein et al., 2014).
Quantificar o papel que cada resposta tem na adaptação do fitoplâncton é um campo
crescente em estudos experimentais.
O grau de eutrofização dos lagos e reservatórios também constitui relevância
para o fitoplâncton, quanto maior o grau de trofia maior a biomassa fitoplanctônica,
principalmente de grupos de cianobactérias potencialmente tóxicas e formadora de
florações (Naselli-Flores et al., 2007, Paerl & Otten, 2013). A eutrofização é
caracterizada como um processo de enriquecimento dos ecossistemas aquáticos por
aumento nas concentrações de nutrientes, principalmente nitrogênio e fósforo (Dodds et
al., 2009), porém o grau de eutrofização pode ser agravado por atividades antrópicas,
como por exemplo, poluição pontual e difusa (Dodds et al., 2009, Paerl & Otten, 2013).
Além disso, alterações nos padrões de temperatura e precipitação podem ter
consequências diretas e indiretas no processo de eutrofização e na comunidade
19
fitoplanctônica, como redução da diversidade de espécies e floração de cianobactérias
potencialmente tóxicas que afetam a qualidade da água (Jeppesen et al., 2015).
Estudos sobre a dinâmica fitoplanctônica em regiões áridas e semiáridas
mostram a relevante importância das condições ambientais e dos fatores físicos para a
estrutura da comunidade (Naselli-Flores & Barone, 2005; Zohary et al., 2010, Costa et
al., 2018). O regime de luz possui profundo impacto sobre a dinâmica do fitoplâncton e
a dominância de espécies depende de fatores chave como intensidade de luz,
profundidade da coluna d’água e regime de mistura (Huisman & Weissing 1994, Zohary
et al., 2010).
Eventos de seca podem levar à redução no nível da água, afetando os principais
recursos para o fitoplâncton, concentrando nutrientes em um menor volume de água e
reduzindo a transparência da água, seja pelo auto sombreamento da biomassa de algas
e/ou cianobactérias ou pelo aumento da turbidez inorgânica de material particulado,
devido à ressuspensão de sedimento para a coluna d’água (Costa et al., 2016). A
literatura mundial aponta que essas condições ambientais são favoráveis para a
dominância de cianobactérias (Moss et al., 2001; Soares et al., 2013, Jeppesen et al.,
2015, Brasil et al., 2016). Porém um novo cenário vem sendo observado na região
semiárida brasileira nos eventos de seca prolongada, o nível crítico hídrico desfavorece
o grupo de cianobactérias, havendo maior contribuição de algas eucariontes adaptadas a
baixa luz e com metabolismo mixotrófico (Medeiros et al., 2015, Costa et al., 2016,
2019).
Diante desse cenário de mudanças climáticas, propomos um modelo teórico
onde a redução volume dos reservatórios e lagos rasos aumenta o grau de trofia (i.e
oligotrófico/mesotrófico para eutrófico/hipereutrófico), e a concentração da turbidez
inorgânica e material suspenso particulado alteram a composição da comunidade
fitoplanctônica. Em maiores volumes de água e menor concentração de nutrientes, os
grupos fitoplanctônicos favorecidos seriam as algas verdes (clorofíceas) e algas
mixotróficas (e.g. crisofíceas) que são adaptadas à limitação por nutrientes e possuem
uma maior demanda por luz. A redução do nível de água, com eventos regulares de
seca, aumenta os níveis de eutrofização tornando um ambiente propício para o
estabelecimento de florações de cianobactérias potencialmente tóxicas
(Cylindrospermopsis raciborskii, Microcystis aeruginosa) afetando a biodiversidade
aquática desses ambientes. Porém, inferimos que existe um nível crítico de volume de
água em eventos extremos de seca, como seca prolongada, aumentando a turbidez
20
inorgânica do sistema, com redução brusca da disponibilidade de luz. Neste caso,
favorecendo grupos das diatomáceas e de espécies flageladas com metabolismo
mixotrófico (criptofíceas), apesar da alta concentração de nutrientes. O mecanismo por
trás disso provavelmente é o aumento da biomassa fitoplanctônica sedimentada como
fonte de carbono para a comunidade de bactérias heterotróficas, que por sua vez são
consumidas pelas espécies fitoplanctônicas que podem mudar a estratégia nutritiva de
autotrofia para heterotrofia via fagocitose (i.e. mixotrofia) (Fig. 1).
Fig. 1 Esquema do modelo teórico da mudança da composição da comunidade fitoplanctônica
em resposta ao grau de eutrofização e concentração da turbidez inorgânica devido à redução da
capacidade máxima de volume de reservatórios e lagos rasos. O azul representa um ambiente
com baixa disponibilidade de nutrientes e alta disponibilidade de luz, a área verde representa um
ambiente eutrofizado com alta biomassa de cianobactérias e a área marrom representa um
ambiente com baixa disponibilidade de luz e alta turbidez inorgânica de material em suspensão
particulado devido a ressuspensão do sedimento para a coluna d’água. A linha vermelha
pontilhada representa um nível crítico de nível de água dos reservatórios e lagos rasos.
Mixotrofia como importante traço funcional do fitoplâncton
A mixotrofia é uma estratégia nutricional que combina autotrofia e heterotrofia
em um mesmo organismo, envolvendo o uso de luz para produzir energia química e a
capacidade de ingestão e digestão de compostos orgânicos (Sanders, 1991, Jones, 1997).
Esta estratégia nutricional é um importante traço funcional fitoplanctônico no qual
representa uma vantagem adaptativa quando as condições ambientais são limitantes (i.e.
baixa concentração de nutrientes e/ou baixa disponibilidade de luz) (Rothhaupt, 1996).
Antigamente se acreditava que as algas eram autotróficas obrigatórias, porém estudos
21
recentes mostram que muitas espécies de fitoplâncton possuem metabolismo
mixotrófico e argumentam que esta estratégia pode ser mais regra do que exceção tanto
em ambientes marinhos como em ecossistemas continentais (Mitra et al., 2014; Ward &
Follows, 2016; Stoecker et al., 2017; Gerea et al., 2018), podendo contabilizar de 50% a
95% da bacterivoria total nos oceanos (Unrein et al., 2007, 2014; Hartmann et al., 2012,
Mitra et al., 2013).
A combinação de estratégias nutricionais mistas faz com que esses organismos
possuam papéis de produtores e consumidores, e isto é refletido em efeitos na estrutura
e dinâmica trófica aquática, no fluxo de carbono e nutrientes se relacionando com
processos ecossistêmicos e manutenção da biodiversidade (Sinistro et al., 2006, Beisner
et al., 2019). Portanto, é relevante incorporar a mixotrofia em modelos biogeoquímicos,
Os principais fatores que influenciam a contribuição relativa da nutrição autotrófica
versus heterotrófica nos organismos mixotróficos é a fisiologia da própria espécie e as
condições ambientais (Hansen, 2011). Apesar de ser uma estratégia adaptativa que
confere uma vantagem competitiva, ela representa um custo energético alto aos
organismos.
A mixotrofia é frequentemente observada em criptofíceas, crisofíceas,
primnesiofíceas e dinoflagelados, mas raramente é observada em clorofíceas, e devido
a esses grupos apresentarem pelo menos um flagelo em suas estruturas, podemos nos
referir a eles como fitoflagelados mixotróficos (Selosse et al., 2017). Fitoflagelados
mixotróficos juntamente com nanoflagelados heterotróficos representam uma fração
muito relevante na bacterivoria total nos ecossistemas aquáticos (Unrein et al., 2007;
Gerea et al., 2018), e possuem uma ampla gama de comportamentos e habilidades na
aquisição de carbono para energia ou crescimento celular (Stoecker et al., 2017).
Fitoflagelados estão amplamente distribuídos em ambientes aquáticos, e nos últimos 30
anos vem sendo amplamente estudado em ecossistemas continentais, confirmando sua
atividade heterotrófica com a utilização de presas fluorescentes ingeridas como um
método para estimar mixotrofia (Floder et al., 2006; Saad et al., 2016; Anderson et al.,
2017).
Estudos com o tema da mixotrofia vêm crescendo e se tornando um hot topic na
ecologia aquática devido sua importância nos processos e funções ecossistêmicas.
Porém, apesar da mixotrofia ser reconhecida atualmente como comum, estando presente
em todos os ambientes e numa grande diversidade de espécies planctônicas (Selosse et
al., 2017) ainda é difícil quantificar seus efeitos e contribuição para os ecossistemas
22
aquáticos devido à falta de informações (Ward & Follows, 2016; Stoecker et al. 2017,
Beisner et al., 2019). Uma razão para isso é a dificuldade de medir quais células estão
ingerindo e acessar qual a contribuição da heterotrofia desses organismos mixotróficos.
São necessárias estimativas através do tempo e espaço para avaliar quais as
condições ambientais e/ou habitats que podem favorecer a mixotrofia. Existem algumas
metodologias propostas para se estudar o grazing de protistas com a finalidade de
quantificar o impacto de protistas na comunidade bacteriana. Dentre elas podemos citar
estudos manipulando a comunidade por métodos de filtrações, inibição e diluição,
utilizando presas marcadas fluorescentemente para quantificação de taxas de ingestão e
estimativas da atividade mixotrófica (Tabela 1).
Os métodos utilizados em pesquisas com mixotrofia têm suas origens em
abordagens que foram desenvolvidas para estudar e distinguir a heterotrofia e a
autotrofia (Beisner et al., 2019). Devido a essa variedade de metodologias os resultados
de vários estudos não podem ser diretamente comparáveis, porém pode-se fazer
observações gerais sobre o impacto do grazing de protistas na comunidade bacteriana
(Medina et al., 2017).
Tabela 1 – Metodologias propostas para estudar grazing em protistas
Método Presas Quantificação Referência
Diluição
FLB, FLA CF Landry et al. 1995
Fracionamento de tamanho BH ME Wright and Coffin,
1984
Inibidores metabólicos
BH ME Sherr et al., 1986
Ingestão de
presas lábeis fluorescentemente
FLA, FLB,
RLB, FMP
ME e CF Vazquez-Dominguez
et al., 1999,
Hollibaugh et al.,
1980)
Marcadores de vacúolos digestivos FLA, FLB,
RLB, FMP
ME e CF Sintes & del Giorgio,
2010,
Anderson et al., 2017
FLB = bactérias lábeis fluorescentes, FLA = autótrofos lábeis fluorescentes, BH =
bactérias heterotróficas, RLB = bactérias lábeis radioativas, FMP = beads de látex. CF =
citometria de fluxo, ME = microscopia de epifluorescência.
O método mais comum para se quantificar grazing é o uso de microscópio de
epifluorescência para observar presas (naturais ou artificiais) marcadas
23
fluorescentemente ingeridas pelos protistas (Unrein et al., 2007; McKie-Krisberg et al.,
2014; Saad et al., 2016), o uso de presas marcadas permite o cálculo de taxas de
ingestão específica por cada protista em incubações de curto tempo de duração ou de
longo tempo por desaparecimento utilizando medidas em microscópio de
epifluorescência. Além disso, pode ser usado para identificar características
morfológicas das espécies (e.g. posição dos cloroplastos, forma geral, presença e forma
da lórica). Apesar de comumente usada em estudos de ingestão de presas, essa
metodologia requer um longo tempo em microscópio e treinamento de pessoas
qualificadas.
Portanto, é necessário incluir e combinar novas metodologias que permitam
obtenção de resultados acurados mais ágeis, desta maneira mais experimentos e mais
amostras ambientais poderiam ser analisadas promovendo mais informações em um
menor intervalo de tempo. Entretanto, não existem ainda protocolos consolidados.
Sugerimos que a citometria de fluxo pode ser uma alternativa para quantificar
mixotrofia rotineiramente devido sua alta capacidade de quantificar e distinguir
milhares células em minutos através da fluorescência de pigmentos das células dos
organismos, heterótrofos podem ser identificados usando corantes de DNA
(fluorescência verde) e autótrofos através da autofluorescência de seus pigmentos
próprios.
Outra abordagem que pode ser utilizada para estudar a atividade mixotrófica é a
identificação de vacúolos digestivos de protistas que estão atuando heterotroficamente.
Essa metodologia consiste em marcar os vacúolos digestivos utilizando fluorocromos,
os mais usuais são LysoSensor Blue DND-167 (Carvalho & Granéli, 2006) e
LysoTracker Green (DND-26) (Sintes & del Giorgio, 2010; Anderson et al., 2017). Esse
método depende da acidificação dos vacúolos digestivos (lisossomos) dos protistas para
a digestão de presas ingeridas.
Já estão sendo proposta novas alternativas como a análise de estudos de
genômica e metagenômica para indicar o potencial para mixotrofia em protistas.
Estudos genômicos são capazes de identificar mudanças na expressão de genes durante
a alternância de autotrofia para a heterotrofia (Liu et al., 2015). Métodos futuros vão se
beneficiar dos avanços que vêm sendo feitos em nanotecnologia, micromanipulação e
microscopia combinada com isótopos estáveis e estudos genômicos (Beisner et al.,
2019).
24
É importante compilar dados para observar o que vêm sendo realizado em
estudos sobre mixotrofia em protistas, e analisar as lacunas de conhecimentos de forma
a preenchê-las. Nesse sentido, teremos mais dados efetivos que nos permita entender
melhor sobre essa forma mista de nutrição, e promover novas metodologias capazes de
facilitar os estudos nessa área, melhores estimativas vão permitir modelos mais
confiáveis para predizer mudanças nos ambientes aquáticos.
O estudo da mixotrofia implica em mudanças paradigmáticas da forma de como
se compreende a ecologia das teias tróficas aquáticas, em particular em ecossistemas
aquáticos continentais eutrofizados, isso é ainda mais crítico quando conectamos às
alterações das condições ambientais em um mundo que enfrenta rápidas mudanças
climáticas.
25
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31
CAPÍTULO 1
EXTREME DROUGHT FAVORS POTENTIAL MIXOTROPHIC ORGANISMS
IN TROPICAL SEMI-ARID RESERVOIRS
Mariana R. A. Costa1, Rosemberg F. Menezes 2,3, Hugo Sarmento4, José L. Attayde2, Leonel da S.L.
Sternberg5 & Vanessa Becker1,6
1Universidade Federal do Rio Grande do Norte (UFRN), Programa de Pós-Graduação em Ecologia,
Natal, RN, Brazil.
2Universidade Federal do Rio Grande do Norte (UFRN), Departamento de Ecologia, Natal, RN, Brazil.
3 Universidade Federal da Paraíba (UFPB), Campus II, Departamento de Fitotecnia e Ciências
Ambientais, Areia, PB, Brazil.
4Universidade Federal de São Carlos (UFSCar), Departamento de Hidrobiologia, São Carlos – SP,
Brazil.
5Department of Biology, University of Miami, Coral Gables, Florida, USA
6Universidade Federal do Rio Grande do Norte (UFRN), Departamento de Engenharia Civil, Natal, RN,
Brazil.
Corresponding author: E-mail: [email protected]
Publicado na edição especial da Hydrobiologia – Phytoplankton & Biotic
Interactions
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CAPÍTULO 2
EFFECTS OF INORGANIC TURBIDITY AND LIGHT IN THE
COMPETITION BETWEEN A BLOOM-FORMING
CYANOBACTERIA AND A MIXOTROPHIC PHYTOFLAGELLATE
Mariana R. A. Costa1, Henrique Miceli Gonçalves2, Inessa Lacativa Bagatini3
Fernando Unrein4, Vanessa Becker1,5 & Hugo Sarmento2.
1Universidade Federal do Rio Grande do Norte (UFRN), Programa de Pós-Graduação em
Ecologia, Natal, RN, Brazil.
2Universidade Federal de São Carlos (UFSCar), Departamento de Hidrobiologia, São Carlos
– SP, Brazil.
3 Universidade Federal de São Carlos (UFSCar), Departamento de Botânica, São Carlos – SP,
Brazil.
4 Instituto de Investigaciones Biotecnológicas (IIB-INTECH) – Instituto Tecnológico de
Chascomús – CONICET. Chascomús, Argentina.
1,5 Universidade Federal do Rio Grande do Norte (UFRN), Departamento de Engenharia Civil,
Natal, RN, Brazil
Corresponding author: E-mail: [email protected]
A ser submetido para Journal of Plankton Research
46
Abstract
Climate change is altering hydrological regimes, increasing water scarcity of lakes and
reservoirs, especially in semi-arid regions. The drastic reduction of water volume
sometimes reaches a critical level, enabling sediment resuspension, which increases
inorganic turbidity and reduces light availability in the water column. In this context,
the ecosystem dynamics is altered, affecting biological communities, mainly primary
producers that depend on light availability. Recent studies indicate that eutrophic
environments usually dominated by cyanobacteria, may shift towards a domination of
mixotrophic algae if a drastic reduction of water volume occurs, as a response to the
decrease in light availability. The aim of this study was to evaluate the effect of
inorganic turbidity on the competition between a mixotrophic phytoflagellate and bllom
forming cyanobacteria in laboratory experiments. To do so, cultures of cyanobacteria
Microcystis aeruginosa Kützing and mixotrophic Cryptomonas obovata Czosnowski
were grown axenically and non-axenically, separately and in co-culture under different
conditions of turbidity and light. We found that in treatments with sediment (high
turbidity), the mixotrophic species had higher growth rates than the cyanobacteria, but
only in treatments containing heterotrophic bacteria. This may be because Cryptomonas
can change its nutritive strategy from autotrophy to heterotrophy due light limiting
conditions. Given the increasing water scarcity already observed in most semi-arid
regions, this work seeks to understand its effect on competition within phytoplankton
community.
Keywords: Interspecific competition, growth rate, inorganic turbidity, light availability,
sediment.
47
Introduction
It urges understanding how climate changes affect ecosystem’s trophic structure
and function. Temperature changes, increase of atmospheric carbon dioxide and the
increase in frequency and intensity of extreme events, such as droughts and floods, are
considered as major stressors associated with climate change (Woodward et al., 2010).
According to the Intergovernmental Panel on Climate Change (IPCC, 2007), tropical
regions are very susceptible to global warming and its effects on tropical aquatic
ecosystems is still under debate (De Senerpont Domis et al., 2013b, 2013a; Sarmento et
al., 2013).
Future climate scenario predicts more intense and frequent droughts in semi-arid
regions (Marengo et al. 2009). Droughts lead to water level reduction and consequent
increase of nutrients, turbidity, conductivity and changes in phytoplankton composition,
biomass and richness, increasing the risk of eutrophication and favoring cyanobacterial
blooms (Naselli-Flores & Barone, 2005; Jeppesen et al., 2015; Costa et al., 2016).
Cyanobacterial blooms generate serious problems in water quality and management and
became a public health problem due their potential toxicity (Paerl and Huisman, 2009,
Paerl et al., 2016). Moreover, blooms of cyanobacteria can affect food web structure
altering energy flow from producers to higher trophic levels (Heathcore et al., 2016).
Worldwide literature links cyanobacterial blooms with eutrophication (e.g. Naselli-
Flores et al., 2007, Jeppesen et al., 2015). However, a new scenario has been recently
reported for semi-arid regions during extreme drought events (Medeiros et al., 2015,
Costa et al., 2016, 2018). Water level reduction caused by high evapotranspiration rates,
leads to the collapse of cyanobacteria biomass and favor the dominance of
phytoplankton species adapted to low light and with mixotrophic metabolism (Medeiros
et al., 2015, Costa et al. 2016, Costa et al., 2019). These studies suggest that the critical
water volume and sediment resuspension increases inorganic turbidity and consequently
reduce light availability, enhancing mixotrophic algae.
Mechanisms of phytoplankton selection depend on environmental conditions and
species adaptions that allow them to survive under such conditions (Reynolds, 1998). In
this sense, species morphology and physiology are important components for resource
acquisition: size, shape and high or low light demand will influence on photosynthesis
rates, nitrogen fixation provides adaptive strategy and may sustain a high cyanobacterial
biomass (Schindler et al., 2008) and mixotrophy also represents an adaptive strategy
when nutrients or/and light are limiting (Rothhaupt, 1996).
48
One common mixotrophic genus Cryptomonas found in freshwater
demonstrated to be an important grazer on bacterioplankton (Grujcic et al., 2018), thus
presenting a big role in energy flux. During extreme droughts in semi-arid reservoirs,
this genus was also found to follow cyanobacterial blooms, as Microcystis aeruginosa
(Costa et al 2019). The bloom-forming cyanobacteria Microcystis aeruginosa is
ubiquitously distributed and often founded in eutrophic water bodies, and recognized to
have allelopathic effects on other phytoplankton species (Paerl & Otten, 2013).
However, a study with mixed co-cultures of Microcystis and Cryptomonas indicated no
allelopathic effect of the cyanobacteria, but showed that a selective grazing by
zooplankton facilitate Microcystis dominance (Leitão et al., 2018). The coexistence of
these species may depend on the proportion of cell abundance from each species in
initial conditions (B-Béres et al., 2012) and a recent study showed that the mixotrophic
flagellate can benefit from cyanotoxins under low concentrations of nitrogen and
phosphorus in culture (Princiotta et al., 2019).
Therefore, we tested in microcosm experiments whether the replacement of
cyanobacteria by mixotrophs during extreme drought in shallow waters is favored by
high inorganic turbidity and to nutritional strategy. We performed competition
experiments using the cyanobacteria Microcystis aeruginosa and the mixotrophic
cryptophyte Cryptomonas obovata manipulating inorganic turbidity, to simulate a turbid
shallow lake, in both axenic and non-axenic phytoplankton cultures. Our hypothesis was
that Cryptomonas would have competitive success due their mixotrophic metabolism
under lower light availability and in non-axenic cultures (with bacteria present).
Materials and Methods
Culture and growth conditions
Axenic strains of Cryptomonas obovata (CCMA-UFSCar 148) and Microcystis
aeruginosa (CCMA-UFSCar 666) were used in this study. Axenic cultures were
maintained in sterile WC medium (Guillard & Lorenzen, 1972), pH 7, in temperature
controlled room at 23 ± 1°C and irradiance of 50µmol photons m−2 s−1 under a
light:dark cycle of 12:12 h. Cultures were kept in exponential growth phase by
biweekly dilution into fresh medium. Flasks were gently swirled regularly to prevent
clusters of cells. The algal inocula were checked for axenic conditions before the
49
experiment in WC medium supplemented with peptone + glucose and epifluorescence
microscopy examination.
The strains of C. obovata grew as motile spheroid single cells, mean 20.39 x
14.38 µm and M. aeruginosa grew as spherical single or double cells with a mean 5.8
µm of diameter. Cell biovolume was calculated from approximated geometric models
(Hillebrand et al., 1999) and the fresh-weight unit was expressed in mass (mg L-1)
(Wetzel & Likens, 2000) and then converted to carbon biomass by multiplying cell
density and biovolume using the formulae: pgC cell−1 = 0.1204 × (µm3)1.051 (Rocha and
Duncan, 1985). Cryptomonas ovata carbon content was 393.70 pgC cell-1 and
Microcystis aeruginosa was 15.5 pgC cell-1.
Experimental design
Two experiments, one with axenic and other with non-axenic cultures, were
performed using the same experimental design at different time. The non-axenic
cultures were prepared by adding environmental bacterial inoculum as described below.
For both, axenic and non-axenic, we used monospecific cultures (i.e., control) of each
strain and mixed co-cultures (treatment, C. obovata + M. aeruginosa) under different
light conditions (high and low light) and with and without sediment addition to simulate
a natural condition of turbid shallow lakes (Fig 1).
We estimated 20 µE.m-2s-1 as “low light” based in measures from turbid shallow
lakes in a semi-arid region, using the average light in the mixed layer (expressed in
µE.m2s) according to Riley (1957). The low light treatment (LL) in our experiment had
20 µE.m-2s-1) and the high light treatment (HL) had 95 µE.m-2s-1. We used tissue culture
flasks filled with 30 ml of medium with sediment representing the same light intensity
in treatments with sediment (20 µE.m-2s-1). Therefore, LL and sediment treatments had
the same light intensity, but the LL treatment did not have the influence of inorganic
particles in suspension. The sediment was collected in the Boqueirão de Parelhas
reservoir located in the Brazilian semi-arid region. To avoid contamination, sediment
was muffled at 490º C for 5 hours and then autoclaved with culture medium.
The experiments run in triplicates in tissue culture flasks with 30 ml of the single
and co-cultures of Cryptomonas obovata and Microcystis aeruginosa under laboratory-
controlled conditions at constant temperature (23 ºC) and 12:12h dark:light cycle in a
shaker incubator (INFORS HT multitron). Flasks were randomly distributed among the
50
stirring table and were gently shaken (55 rpm) to stir up sediment. The experiment had
the duration of 6 days, and samples (0.9 ml) were taken every two days and fixed with
paraformaldehyde + glutaraldehyde (1% final concentration) to quantify phytoplankton
by flow cytometry (BD Accuri C6). At least 10 000 events were acquired for each
sample. Phytoplankton cells were detected by their signature in a plot of side scatter
(SSC, proxy for cell size) versus FL3-H (red fluorescence, proxy for phytoplankton
pigments).
The non-axenic experiment was performed the same way of the axenic, but 10%
(v/v) of a 0.7 µm-filtered water from Lobo reservoir was added to each flask as bacterial
inoculum.
The inoculated cell number of C. obovata and M. aeruginosa were, respectively,
~6.0x103 and ~2.5x105 in the axenic experiment, and ~2.0x104 for C. obovata and
~6.5x105 for M. aeruginosa in the non-axenic experiment. Microcystis cultures were
diluted in fresh medium to begin under same biomass of Cryptomonas. Relative
biomass of phytoplankton species was estimated by biovolume of each algae in their
monocultures from approximated geometric models (Hillebrand et al., 1999). Light
intensity was measured by using a photosynthetically active radiation (PAR) photometer
(Biospherical QLS-100.
Fig. 1 Experimental design illustrating unialgal and mixed cultures under three distinct
light regimes. High light (HL) consist in direct incident light in the C. obovata
monoculture, in M. aeruginosa monoculture and in the mixed cultures (C. obovata + M.
aeruginosa, C+M). Low light (LL) consisted in a physical barrier (culture tissue flask
with sediment diluted in medium WC) above the single and co-cultures. Low light +
51
sediment (Sed) consisted in the same light intensity from LL treatment but with the
sediment inside the treatment flasks. This experimental design was performed with
axenic and non-axenic (bacteria-added) cultures.
Growth rates and data analysis
Difference in phytoplankton concentration over time between controls and
treatments were evaluated by comparing temporal trends in cell density and by
comparing growth rates or changes in biomass between controls and treatments (i.e.,
single and co-cultures).
The effect of competition was assessed comparing Cryptomonas and Microcystis
growth rates over six days in single species (i.e. control) and co-cultures (i.e. treatment)
and the effect of light limitation was assessed comparing growth rates of the species
between light treatments (HL, LL and SED). Phytoplankton growth rates in controls and
treatments were estimated using the slope of the regression line between natural log
transformed of biomass concentrations and the experimental period (day-1). A two-way
ANOVA and post-hoc Tukey’s test was performed to test significant differences
between treatments to examine the effect of light limitation and to test significant
differences between mono and co-cultures in light treatments to examine the effect of
competition. ANOVA assumptions were checked for normality and homogeneity of
variance.
All analyses were performed in R Statistical Software (version 3.5.2, <www.r-
project.org>).
Results
Single cultures and co-cultures carbon biomass
Trends in phytoplankton carbon biomass in each treatment through time in
axenic experiment (Fig 2) and in non-axenic experiment (Fig 3) shows an increase in
biomass through time for all treatments and both phytoplankton species, except for C.
obovata in the treatment with sediment added. Biomass increase was bigger in single
cultures than in co-cultures for both experiments and phytoplankton species. However,
biomass decrease was observed in LL and SED treatments in axenic experiment for C.
obovata from day 4 both in single and co-cultures (Fig 2A).
When bacteria was added to cultures (non-axenic experiment), C. obovata and
M. aeruginosa carbon biomass increased through time in all treatments, both in single
and co-cultures, except for a decrease in day 4 in LL treatment (Fig3 A and B).
52
Fig 2. Trends in Cryptomonas obovata (a) and Microcystis aeruginosa (b)
carbon biomass through experimental period (days) for the axenic experiment in High
light (HL), Low Light (LL) and Sediment (SED) treatments in the single and co-
cultures. Error bars (n=3) are standard deviations.
Fig 3. Trends in Cryptomonas obovata (a) and Microcystis aeruginosa (b)
carbon biomass relative to the initial time through experimental period (days) for the
non-axenic experiment in High light (HL), Low Light (LL) and Sediment (SED)
treatments observed in the single and co-cultures. Error bars (n=3) are standard
deviations.
53
Growth rates
Positive growth rates of Cryptomonas and Microcystis were observed for single
and co-cultures in high light (HL) treatments in axenic experiment (Fig. 4 A) and in all
treatments in the experiment with bacteria (non-axenic) (Fig. 4 B). Negative growth rate
was observed for Cryptomonas when sediment was added in both cultures and in LL
only in mixed co-culture (Fig. 4 A). The two-way ANOVA showed significant
differences between light treatments (p < 0.001) and no effect of competition (p = 0.194
and p = 0.301)in axenic and non-axenic experiments, respectively. Without bacteria
SED was significantly different from HL (p < 0.001) and LL (p = 0.009) (Fig.4). With
bacteria added HL showed significant differences from LL (p = 0.005) and SED (p =
0.001), sediment and LL had no significant differences (p = 0.873) (Fig. 4).
Fig. 4 Growth Rates (day-1) for Cryptomonas ovata and Microcystis aeruginosa in
single and co-cultures in experiment (a) without bacteria (axenic) and (b) with bacteria
(non-axenic). HL= high light treatment, LL = low light treatment and SED = sediment
treatment. Bars describe the standard deviation. Two-way ANOVA results for
competition and effect of light limitation with F and p-value are shown. (a) and (b) are
the results from Tukey’s post-hoc test.
Analyzing the effect of competition (single and co-cultures) and differences in
algae growth rate among each treatment was observed significant differences between
54
C. obovata and M. aeruginosa for all treatments in both experiments, except for SED in
non-axenic experiment (Table 1). The effect of competition was observed in LL (p <
0.001) and SED (p = 0.001) in axenic experiment and in HL did not shown effect of
competition (Table 1). In non-axenic experiment the competition only had effect in SED
treatment (p = 0.002), single cultures were significant different from co-cultures and
Cryptomonas had higher growth than Microcystis when sediment and bacteria were
added (Fig. 4 B).
Table 1 – Two-way ANOVA showing differences in C. obovata and M. aeruginosa
growth rate and the effect of competition (single and co-cultures) in high light (HL),
low light (LL) and SED (sediment) treatment with the interaction of this two factors.
HL LL SED
F p- value F p- value F p- value
Axenic
Algae growth 6.06 0.039 * 9.82 0.016 * 760.02 <0.001*
competition effect 0.55 0.477 115.55 <0.001* 20.70 0.001 **
interaction 0.47 0.510 138.7 <0.001* 24.38 0.001 **
Non-axenic
Algae growth 25.47 <0.001** 18.04 0.005** 0.62 0.454
competition effect 2.28 0.169 0.33 0.586 18.63 0.002**
interaction 0.03 0.859 0.27 0.617 6.27 0.036 *
Discussion
We evaluated the interaction between a photoautotrophic bloom-forming
cyanobacterium and a eukaryotic mixotrophic phytoflagellate manipulating light
availability and inorganic turbidity. We found out that inorganic turbidity provided by
sediment may enhance the growth rates of the mixotrophic phytoplankton over
cyanobacteria in a nutrient-rich environment, as long as bacteria (prey) are available
(non-axenic experiments).
Mixotrophic plankton is now recognized as a rule more than an exception in a
wide range of aquatic environments (Unrein et al., 2007; Flynn et al., 2012). Some
studies have documented that mixotrophic algae can account to 50% - 95% of total
bacterivory in the ocean (Unrein et al., 2007, 2014; Hartmann et al. 2012). However,
most studies involving phytoplankton mixotrophy are from marine, oligotrophic
systems and until now, little is known about mixotrophy in eutrophic and turbid
freshwater systems. Since light is crucial to phytoplankton survival and growth it is
55
important to understand how inorganic turbidity affects light availability for
phytoplankton, mainly in turbid shallow lakes.
Our results indicated that Cryptomonas biomass decreased and exhibited
negative growth rates in the treatment with sediment and without bacteria. Nevertheless,
in the presence of bacteria, we observed an increase of Cryptomonas biomass compared
to the initial time and positive growth rates but only in monocultures. This result
suggests that Cryptomonas relied on heterotrophic bacteria to enhance its growth rates.
However we cannot discard the nutrient remineralization by bacteria, that might have
contributed to algal growth (Sarmento & Gasol, 2012). Still, phagotrophy in
Cryptomonas is well described and it is probable that phagotrophy occurred in our
experiments (Urabe et al., 2000; Pålsson & Granéli, 2003; Grujcic et al., 2018). In both
cases, in the presence of heterotrophic bacteria, Cryptomonas grew faster than
Microcystis, which indicates an increase in competitive fitness for the mixotrophic
species.
During the begging of the experiment, we observed the formation of
spontaneous micro-aggregates only in the experiment containing bacteria in sediment
treatment flasks with Cryptomonas (single and co-cultures). These micro-aggregates
caused a sediment flocculation and a reduction on cells count, which is the reason why
the sediment treatment starts with a low Cryptomonas abundance. This aggregation may
be a bacterially mediated process (Simon et al., 2002) or a spontaneous complexation of
the organic compounds (Sarmento & Gasol, 2012). The interaction of bacteria with
particles has been studied and is considered a relevant process and an indication of
specialization (Smith et al., 1992, Grossart et al., 2006, Sarmento & Gasol, 2012).
Further, Cryptophyceae can produce extracellular compounds mainly polysaccharides
and have positive impact on bacteria (B-Béres et al., 2012, Giroldo et al., 2006).
Light and prey concentrations are important factors that influence
photosynthesis, growth and feeding rates of mixotrophic algae. Most studies with
mixotrophs showed that irradiance is required at least to match respiration needs, so
most mixotrophic algae cannot grow in complete darkness even if they fed (Floder et
al., 2006). Also, they will grow until a saturation irradiance, as it does with pure
phototrophs and cyanobacteria. As a consequence, mixotrophs should have higher
growth rates over photoautotrophs, due the acquisition of nutrients ingested with prey
when nutrients (mainly phosphorous) are limited (Fischer et al., 2017). However, to
simulate a eutrophic system in our study nutrients were not limiting.
56
Studies in semi-arid regions demonstrated the effect of water volume reduction
(caused by extreme droughts) altering phytoplankton dynamic and groups succession
due light penetration (Costa et al., 2016, 2019). However, it was not tested how the
inorganic turbidity affect phytoplankton until now, our findings suggests that the
mixotrophic phytoflagellate had an advantage in a turbid low light environment.
However, this was only true to monocultures, indicating that Microcystis had
advantaged in co-cultures may due allelopathic effects on the mixotrophic species.
Furthermore, colored dissolved organic matter (cDOM) may also explain this
phytoplankton pattern. “Browner” lakes seems to favor phytoplankton that adopted
mixed nutritional strategy than photoautotrophic species due reduced light penetration
(Hansson et al., 2019).
It has been argued that mixotrophs can outcompete photoautotrophic
phytoplankton under light limited conditions (Jones, 2000) and dominate in humic and
brown colored lakes (Routhaupt, 1996, Hansson et al., 2019). However, our results
emphasize that light alone does not explain it all. Sediment particles and the presence of
prey (bacteria) are key factors to understand the mechanisms behind the dominance of
mixotrophic algae in nutrient rich-turbid environments. In addition, the cyanobacteria
has physiological strategies to outcompete the mixotroph under this experimental
conditions. Considering the climatic changes are affecting aquatic systems and we are
already experiencing changings in phytoplankton dynamics, it is necessary incorporate
the mixotrophy and its consequence in to food webs knowledge, since the increase of
inorganic turbidity predicted by climate change may lead to increased mixotrophy
Acknowledge
This study was financed in part by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, CNPq/Universal,
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)
(CAPES/PNPD–Project N : 2304/2011), Fundação de Pesquisa de São Paulo
(FAPESP) (Processes: 2014/14139-3 and 2016/50494-8).
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63
CAPÍTULO 3
COMPARISON OF DIFFERENT TECHNIQUES TO ACCESS THE
EFFECT OF LIGHT AND NUTRIENTS ON PHYTOPLANKTON
PHAGOTROPHY
Mariana R. A. Costa1, Hugo Sarmento2, Vanessa Becker1,3, Inessa L. Bagatini4
& Fernando Unrein5.
1Universidade Federal do Rio Grande do Norte (UFRN), Programa de Pós-Graduação em
Ecologia, Natal, RN, Brazil.
2 Universidade Federal de São Carlos (UFSCar), Departamento de Hidrobiologia, São Carlos
– SP, Brazil.
3 Universidade Federal do Rio Grande do Norte (UFRN), Departamento de Engenharia Civil,
Natal, RN, Brazil
4 Universidade Federal de São Carlos (UFSCar), Departamento de Botânica, São Carlos – SP,
Brazil.
5 Instituto de Investigaciones Biotecnológicas (IIB-INTECH) – Instituto Tecnológico de
Chascomús – CONICET. Chascomús, Argentina.
Corresponding author: E-mail: [email protected]
64
Abstract
Mixotrophy is a nutritional strategy that combines autotrophy and heterotrophy in the
same organism and represents an adaptive strategy in limiting environmental conditions.
Although mixotrophy is recognized to be widespread, it is hard to quantify its effects
and contribution to carbon flux in aquatic food webs. Thus, it is necessary to develop
new methodologies that allow obtaining rapid and accurate results. The goal of this
study was to determine grazing rates (GRs) by phytoflagellates in distinct light and
nutrient conditions using different protocols, including epifluorescence microscopy
(EM) and flow cytometry (FC). We performed short-term grazing experiments by two
mixotrophic phytoflagellates (Ochromonas tuberculata and Cryptomonas marssonii)
using fluorescent latex beads as prey surrogates, manipulating different conditions of
light and nutrients. We also performed experiments using food vacuole markers
(LysoTracker - LyT), to investigate mixotrophic metabolism. The comparison of the
GRs between FC and EM demonstrated a significant and strong relationship (R2 = 0.50,
p < 0.001), being FC estimations higher than EM. The highest GRs by C. marssonii
were observed at high nutrient concentrations, which seems to be the most important
factor for GR by this species, whereas for O. tuberculata GR increased at low light
availability. Moreover, the use of LyT showed that both phytoflagellates were actively
feeding on preys. We conclude that, at least in culture, the use of flow cytometry is a
useful tool to estimate mixotrophy.
Keywords: Bacterivory, Flow Cytometry, Lyso-Tracker, Ochromonas, Cryptomonas.
65
Introduction
Aquatic microorganism interactions have an important role for biogeochemical
cycle and account for large amounts of carbon transfer among trophic levels (Azam et
al., 1983). Mixotrophic organisms act both as producers and consumers in aquatic
ecosystems, shaping food web structure, carbon flux and ecosystem functioning
(Stoecker et al., 2017). Limiting resources for photosynthesis (i.e. light and nutrients)
are recognized to drive mixotrophs success across different environments, although,
little is known about how environmental conditions acts for the prevalence of
mixotrophy (Edwards, 2019)(Edwards, 2019)(Edwards, 2019)(Edwards, 2019).
Moreover, mixotrophic plankton is now recognized to be widespread in a range
of aquatic environments and this mode of nutrition could be more a rule than an
exception both in marine and in freshwater ecosystems (Unrein et al., 2007; Flynn et al.,
2013; Stoecker et al., 2017). Some studies have documented that mixotrophic
phytoflagellates can account up about 50% -95% of total bacterivory at sea (Unrein et
al., 2007; Hartmann et al., 2012) and in many freshwater systems (Urabe et al. 2000,
Gerea et al., 2018). In this sense, is paramount to incorporate this nutritional strategy
into food webs and biogeochemical models.
Although mixotrophy is recognized to be widespread and relevant to ecosystem
functioning, it is hard to quantify its effects and contribution to carbon fluxes due to the
restrict amount of effective grazing rates measurements and the knowledge of which
species are actually phagotrophic (Ward & Follows, 2016; Stoecker et al., 2017). One
reason for that is the difficulty to measure ingestion rates and the contribution of
phagotrophy by mixotrophic organisms as an indicator of heterotrophic activity.
A technique usually used to quantify grazing is counting ingested fluorescent
particles in epifluorescence microscopy (Unrein et al., 2007; McKie-Krisberg et al.,
2015; Saad et al., 2016). However, it requires a long time on the microscope and
qualified personal training. Therefore, faster and more automatic techniques or methods
that allow achieving good quantification and accuracy would be useful. In this way,
more experiments and environmental samples may be analysed providing more data in a
shorter time. There is the to propose a protocol for estimating ingestion rates using flow
cytometry to detect fluorescent label preys ingested by protists present in natural
communities (González, 1999). However, there are no consolidated protocols yet
(Beisner et al., 2019). Flow cytometry can quantify thousands of cells in minutes
(Shapiro, 2003) and has been used in microbial ecology routinely to estimate abundance
66
of several aquatic organisms (Gasol & Del Giorgio, 2000; Sarmento et al., 2008;
Schiaffino et al., 2013). It detects information of cells size and complexity plus the
fluorescence emitted by natural pigments or fluorescent probes (Gasol & Morán, 2015).
Flow cytometry can also capture fluorescent probes signal inside other organisms.
Acidotropic probes, such as LyT, can be used as food vacuole markers and visualized
by flow cytometry, been a proxy for digestive activity by mixotrophic phytoplankton
(Rose et al., 2004; Sintes & del Giorgio, 2010; Anderson et al., 2017).
Future works will be benefited by the improvement in nano and genomics
technology combining with traditional methods, and better estimates on mixotrophic
activity will allow more reliable biogeochemical models to predict changes in aquatic
food webs in a changing world (Beisner et al., 2019).
The goal of this study was to evaluate the use of flow cytometry to estimate
mixotrophy comparing it to the time-consuming epifluorescence microscopy counts. For
that, using both techniques, we have quantified bacteria ingestion rates by two
mixotrophic phytoflagellates under different light and nutrient conditions.
Materials and Methods
Culture and growth conditions
Cultures of three freshwater non-axenic strains were used for the experiments,
Ochromonas tuberculata, (CCAP 933/27) and Cryptomonas marssonii (CCAP
979/64), both species reported in the literature with mixotrophic metabolism, and a
strictly photosynthetic chlorophyte, Chlamydomonas sp. CCAP. The cultures were
cultivated and maintained in sterile growth WC medium (Guillard & Lorenzen, 1972)
in temperature controlled room at 20 ± 1°C and irradiance of 50 µmol m–2 s–1 under a
light:dark cycle of 12:12 h. Cultures were maintained in exponential growth rate by
biweekly dilution into fresh medium. The strains grew as motile spheroid single cells,
mean 8.12 x 7.18 µm (O. tuberculata), 9.39 x 7.97 µm (C. marssonii) and 9.10 x 8.20
µm (Chlamydomonas sp.).
The abundance of background bacteria (mainly small bacillus), naturally in the
phytoplankton cultures, ranged from 2 to 5,0 x 106 cells ml -1 in the cultures used in
this study. A total of 250 ml of a dense phytoplankton culture (5x 105 cells ml-1) were
used as inoculum to experimental cultures.
67
Preparation of Prey
The prey used in the grazing experiments were FLB and synthetic fluorescent
yellow-green micro-spheres (beads). The FLBs were prepared from a Brevundimonas
diminuta (syn. Pseudomonas diminuta Leifson & Hugh) strain obtained from the
Spanish Type Culture Collection (Burjassot, València) and stained with a solution of
5-([4,6 dichlorotriazin-2-yl]-amino)-fluorescein (DTAF) overnight in a water bath at
60º C according to Sherr et al. (1987). Stained cells were rinsed with filtered (0.2 µm)
carbonate-bicarbonate buffer, resuspended and centrifuged to prevent the transfer of
leftover dye to the natural samples. The cell suspensions were kept frozen (-80ºC)
until use, for more details see Unrein et al. (2007). A stock solution of a concentrated
suspension of FLBs were added to the short-term grazing experiments. FLBs mean
cell size was 0.82 x 0.48 µm.
The second prey added to the experiments was synthetic fluorescent yellow-
green microspheres (latex beads). A stock solution of a concentrated suspension of 1
µm diameter beads (Polysciences Fluoresbrite) was treated with bovine serum albumin
(0.5 mg ml-1) to avoid particles aggregation. We tested two beads size (0.5 and 1 µm),
the smallest size could not be visualized properly in the flow cytometer, therefore
beads of 1 µm were chosen for the experiments.
Experimental design
Two kinds of experiments were carried out, one short-term grazing incubations
to detect mixotrophy and calculate bacterial ingestion rates and another to detect
mixotrophy by food vacuoles staining using LyT (Table 1). Experiments were
performed at separate time.
The ingestion experiments were performed using two mixotrophic
phytoflagellate strains (C. marssonii and O. tuberculata) and two kinds of prey (beads
and FLB), resulting in four combinations of mixotroph-prey in the experiments: O.
tuberculata + beads, O. tuberculata + FLB, C. marssonii + beads, C. marssonii +
FLB. The food vacuoles staining experiments were performed using three
phytoflagellates strains (Chlamydomonas sp., C. marssonii and O. tuberculata) and
identified mixotrophs by positive vacuoles staining (Table 1).
68
Table 1 – Summary of the experiments performed.
Flagellate strain Prey Experiment name
Ingestion
experiment
Cryptomonas marsonii Beads 1µm Crypto + Beads
Cryptomonas marsonii FLB Crypto + FLB
Ochromonas tuberculata Beads 1µm Ochro + Beads
Ochromonas tuberculata FLB Ochro + FLB
Flagellate strain Stain Experiment name
Food vacuoles
staning
experiment
Chlamydomonas sp. Lyso-Tracker (LyT) Chlamy-LyT
Cryptomonas marsonii Lyso-Tracker (LyT) Crypto-LyT
Ochromonas tuberculata Lyso-Tracker (LyT) Ochro-LyT
The experimental design of all experiments consisted in a 3x3 factorial design
resulting in 9 different conditions. We manipulated three levels of light: high light
(HL) 95 µmol m-2 s-1, low light (LL) 10 µmol m-2 s-1 and total darkness (D); and three
concentrations of nutrients: high nutrients (HN), medium nutrients (MN) and low
nutrients (LN). We diluted WC medium 20 times with deionized water for LN, 5 times
for MN, while HN was not diluted (Fig 1). All treatments were performed in triplicates
for the short-term ingestion experiments and two replicates for the food vacuoles
staining experiments.
To induce mixotrophy by phytoflagellates we acclimated the species for
starvation before starting the experiments. Nutrient starvation was induced by
transferring exponential-phase of each culture into each treatment-diluted (HN, MN
and LN) sterile growth medium for one week and light starvation was induced with the
same batch culture for two days. Preliminary tests (not shown) were carried out before
starting the experiments to confirm that the phytoflagellates were able to ingest prey
and to determine when the starvation phase began.
69
Fig 1 – The 3x3 factorial experimental design for ingestion and food vacuoles staining
experiments.
Ingestion experiment
Short-term ingestion experiments were carried out in 10 ml microcosms. A
working solution of each prey (FLB and beads) was immediately inoculated to a
concentration of 30-40% of in situ bacterial concentration for each experiment.
Samples were taken at initial time (T0), after 25 minutes and 45 minutes and fixed for
analysis in flow cytometry (1 mL) and for epifluorescence microscopy counts (1 mL).
The incubation time was chosen since prey ingestion reached a plateau according to
Unrein et al., 2007. Samples for flow cytometry were preserved with 1%
paraformaldehyde and 0,05% glutaraldehyde fixation solution (final concentration)
and with filtered (0.2 µm) glutaraldehyde 1% final concentration for epifluorescence
microscopy counts. Samples were stored cool and in the dark until analysis.
For epifluorescence microscopy counts, 1 mL of sample were filtered on to 0.8
µm black filters (Brand, Manufacturer) and stained for 5 min with a solution of 4′,6-
diamidino-2-phenylindole (DAPI). At least 100 flagellates were counted under a Zeiss
epifluorescence microscope at 1000x magnitude using set filters for DAPI,
phycoerythrin and chlorophyll autofluorescence. For each flagellate were quantified
the presence of prey.
70
Phytoflagellates, natural bacteria present in cultures and prey (beads and FLB)
were also counted by flow cytometry (FACSCalibur, Becton Dickinson) equipped
with a 15-mW argon-ion laser (488 nm emission). Natural bacteria and prey were
detected by their signature in a plot of side scatter (SSC; proxy for cell complexity)
versus FL1-H (green fluorescence) and phytoflagellates by SSC versus FL3-H (red-
fluorescence, chlorophyll). For heterotrophic bacteria (HB), 200 µl of sample was
added to a diluted SYTO-13 (Molecular Probes Inc., Eugene,OR, U.S.A.) stock, left
for about 10 min in the dark to complete the staining and run in the flow cytometer
(Gasol & Del Giorgio, 2000).
Grazing rates of phytoflagellates on prey (bacteria ind-1 h-1) were determined
through the uptake of fluorescent prey by each phytoflagellate and specific grazing
rate (CSGR) was calculated as:
𝐶𝑆𝐺𝑅 =(If − Ii) × (
𝑏𝑎𝑐𝑡𝑝𝑟𝑒𝑦)
t
Where “If “and “Ii” are the ingestion of prey at the final (f) and initial (i)
incubation time, “bact” is the abundance of natural populations of heterotrophic
bacteria, “prey” is the total abundance of inoculated prey (FLB or beads) and “t” is
the incubation time in hours. The cell-specific grazing rates (CSGR) were calculated
assuming that native prey and fluorescent prey were grazed upon at the same rates.
Food vacuoles staining experiment
Flagellate cells containing food vacuoles were determined using the acidotropic
probe Lyso-Tracker Green DND-26 (LyTG) (ThermoFisher Scientific) final
concentration 50 nM LyTG, according to the protocol from Sintes & del Giorgio
(2010). We used the presence of food vacuoles as a proxy of actively digestive activity
(Sintes & del Giorgio, 2010; Anderson et al., 2017).
The experimental design was the same 3x3 factorial performed at different
times; manipulating three levels of light (HL, LL, D) and three levels of nutrients
concentration (HN, MN, LN). Chlamydomonas sp. is considered a strictly authotrophic
algae, thus, it was used as a negative control for digestive activity. C. marssonii and O.
tuberculata were used to investigate a positive food vacuoles staining.
71
Cultures live samples of each strain with no added LyTG were measured for 5
minutes by flow cytometry (FACSCalibur, Becton Dickinson) using high flow rate
(103.6 µl/min) and after we added LyTG for 5 minutes more (Sintes & del Giorgio,
2010). Algae and positive food vacuoles staining were detected by their signature in a
plot of FL3-H (red fluorescence) corresponding to the chlorophyll versus FL1-H (green
fluorescence) corresponding to the LyTG. It was possible to visualize in the plot cells
that exhibited a higher green fluorescence after incubation with LyTG. Thus, these cells
were considered to have labeled food vacuoles
Data analyses
Two-way ANOVA and post-hoc Tukey’s test was performed to observe
significant differences between treatments (light and nutrient) for each experiment.
Linear regression type II was performed to plot specific grazing rate (CSGR) from flow
cytometry against microscopy epifluorescence to compare both methodologies using the
lmodel2 function and major axis (MA) method of the package ‘lmodel2’ (Legendre,
2018). Natural log transformation was made when data were not normally distributed.
All analysis was realized in R Statistical Software (version 3.5.2, <www.r-project.org>).
Results
The green fluorescence (FL1-H) signal of FLB inside food vacuoles could not
be observed properly by the flow cytometry. Consequently, it was not possible to
measure mixotrophy using this methodology with FLBs as prey. Therefore, in this
study we present only data from the two short-term grazing experiments with beads
1µm in size as well as the LyTG staining experiments.
Short-term grazing experiments
The CSGR from the short-term grazing experiments were higher when
estimated by flow cytometry than by epifluorescence microscopy (Fig. 1) and the
experiments with C. marssonii in both methodologies had higher CSGR than with O.
tuberculata strain (Fig. 1). The two-way ANOVA revealed significant differences in
the CSGR, nutrients (p<0.001, F = 65.0), as well as the interaction among light and
nutrients (p=0.012, F = 4.87), significantly affected the CSGR, respectively, when
measured by flow cytometry for Cryptomonas (Fig. 1A), and the post-hoc test showed
that HN was significantly different from MN and LN (Fig. 1A). For the experiment
72
with Cryptomonas measured by epifluorescence microscopy, CSGR was significantly
different in the light (p = 0.007, F = 6.75) and nutrient (p =0.002, F = 8.59) treatments
(Fig. 1B), the post-hoc test showed that HN was significantly different from MN and
LN, and LL was significantly different from HL and D (Fig. 1B).
The two-way ANOVA of the short-term grazing experiment with O.
tuberculata strain only showed significant differences between the light treatment (p =
0.007, F = 6.75) when CSGR were measured by flow cytometry and HL was
significantly different from LL and D (Fig. 1C). Epifluorescence microscopy with O.
tuberculata did not differ significantly between light (p = 0.069, F = 3.19) and nutrient
(p = 0.794, F = 0.23) neither showed interaction between light and nutrient (p = 0.532,
F = 0.82) (Fig. 1D).
Fig. 2 Cell-specific grazing rates (CSGR, bact.ind-1.h-1) from the short-term grazing
experiments for C. marssonii measured by flow cytometry (A) and by epifluorescence
microscope (B) and for O. tuberculata measured by flow cytometry (C) and by
epifluorescence microscope (D) between the distinct nutrient treatments: high nutrient
(HN), medium nutrient (MN) and low nutrient (LN); and between the distinct light
treatments: high light (HL), low light (LL) and total darkness (D). Bars are the standard
deviation (n=3), p is the p-values from the two-way ANOVA, L = light and N =
nutrient. Letters “a” and “b”, denotes differences by pots-hoc test for the nutrient
treatments and letters “c” and “d” denotes differences by the post-hoc test for the light
treatments.
73
The mean CSGR measured by flow cytometry were on average about 3 times
higher than the rates estimated by epifluorescence microscopy counts (appendix table
1). Cross-comparations of the mixotrophic phytoflagellates CSGR measured by flow
cytometry and microscopy epifluorescence counts indicated the results from both
methods were significantly correlated (R2 = 0.50, p < 0.001). However, CSGR
measured by flow cytometry tended to be higher than by epifluorescence microscopy
(Fig. 2).
Fig. 3 – General model of the type II linear regression comparing specific grazing rates (CSGR)
obtained by flow cytometry and epifluorescence microscopy in short-term ingestion
experiments with C. marsonii and O. tuberculata. Dashed line indicates a 1:1 ratio and solid
dark line is the linear regression for data points. (R2 = 0.50, p-value < 0.001).
Food vacuoles experiment
No cells containing food vacuoles were found for Chlamydomonas sp. in all
treatments, therefore, presumed no feeding activity (Fig. 3). C. marsonii and O.
tuberculata exhibited cells containing food vacuoles (Fig. 4 and 5). Two-way ANOVA
showed significant differences for light and nutrients for O. tuberculata but no
interaction, and marginally significant differences in light for C. marsonii and in
nutrient for Chlamydomonas (Tab. 2). Post-hoc test for O. tuberculata revealed that HL
74
were significantly different from D (p < 0.001) and from LL (p = 0.010), and that LN
were significantly different from HN (p < 0.001) and from MN (p = 0.006).
Fig 4 – Cytograms of food vacuoles experiment using Lyso-Tracker (LyT) for Chlamydomonas
sp. in the nine treatments combinations. HN = high nutrient, MN = medium nutrient, LN = low
nutrient, HL = high light, LL = low light and D = total darkness. Red dots are cells before the
addition of LyT and black dots are cells after the addition of LyT.
75
Fig 5 – Cytograms of food vacuoles experiment using Lyso-Tracker (LyT) for C. marsonii in
the nine treatments combinations. HN = high nutrient, MN = medium nutrient, LN = low
nutrient, HL = high light, LL = low light and D = total darkness. Blue dots are cells before the
addition of LyT and black dots are cells after the addition of LyT.
Fig 6 – Cytograms of food vacuoles experiment using Lyso-Tracker (LyT) for O. tuberculata
in the nine treatments combinations. HN = high nutrient, MN = medium nutrient, LN = low
nutrient, HL = high light, LL = low light and D = total darkness. Red dots are cells before the
addition of LyT and black dots are cells after the addition of LyT.
Table 2 – Two-way ANOVA for LyT experiment between treatments. Asterisks (*)
indicate significant or marginally significant values.
C. marsonii
F / p-value
O. tuberculata
F / p-value
Chlamydomonas sp.
F / p-value
Light 4.39 0.052* 0.001* 18.45 0.533 0.67
Nutrient 1.48 0.282 0.001* 15.44 0.055* 4.17
Light*Nutrient 0.29 0.875 0.098 2.92 0.976 0.11
76
In summary, experiments with Cryptomonas had significant differences in
grazing rates in HN treatment estimated by epifluorescence microscopy and by flow
cytometry; also, LL seemed to be a significant treatment when measured by flow
cytometry. However, the food vacuoles experiment did not exhibited significant
differences between treatments (Table 3). Experiments with O. tuberculata had
significant differences in grazing rates in HL treatment estimated by epifluorescence
microscopy and with food vacuoles experiment. In addition, LN treatment showed
highest number of cells containing food vacuoles stained. However, flow cytometry did
not exhibited significant differences between treatments for experiments with O.
tuberculata (Tab. 3).
Table 3 – Comparison of study methodologies and significant results related to
treatments, n.s. indicate no significant differences between treatments (light and
nutrients). HN = High nutrient treatment, LN = Low nutrient treatment and LL = low
light treatment.
Epifluorescence
Microscopy
Flow Cytometry Food vacuoles
(LyT)
C. marsonii HN HN, LL
n.s.
O. tuberculata HL n.s. HL, LN
Discussion
In this study, we performed experiments in laboratory, manipulating light and
nutrients conditions, using flow cytometry as an alternative tool to estimate mixotrophic
activity, and comparing it to the standard time-consuming epifluorescence microscopy
counts. We found out the same trend between quantifications using both techniques.
However, differences between treatments with distinct light and nutrients conditions did
not showed a robust pattern among experiments.
The use of flow cytometry is increasing in microbial ecology due its high
capacity of analyse and quantifying microorganisms abundance. It distinguish
heterotrophs (green fluorescence) using a DNA stain from autotrophs
(autofluorescence) (Gasol & Del Giorgio, 2000; Sarmento et al., 2008; Gasol & Morán,
2015). Despite this, the most common methodology to quantify grazing rates is
epifluorescence microscopy. Nevertheless, it has been proposed the use of flow
cytometry to estimate grazing rates in natural assemblages and from beads or FLB
77
uptake experiments (Keller et al., 1993; González, 1999). However, there is no study of
our knowledge applying flow cytometry as a protocol to estimate mixotrophy activity.
We performed short-term ingestion incubations to estimate grazing rates of
phytoflagellates on two kind of prey (FLB and beads). However, the use of flow
cytometry using FLBs could not be validated since it was not possible to visualize the
FLB signal inside the phytoflagellates in any moment of the experiment. On the other
hand, it was possible to count beads of 1µm inside the phytoflagellates. Previous tests
using beads of 0.5 µm was performed (data not shown) and we were not able to detect
the prey signal. Perhaps, due beads of 0.5 µm and FLB signal is two lower to be
detected by flow cytometry inside the organism.
Flow cytometry it also has been used successfully to identify mixotrophs by
their positive food vacuoles (Sintes & del Giorgio, 2010; Anderson et al., 2017, 2018;
Beisner et al., 2019). This approach detects digestive vacuoles from protists that act
heterotrophically allowing their enumeration using acidotrophic fluorescent stains
(Sintes & del Giorgio, 2010). In our study, the use of LyT (Lyso-Tracker) as food
vacuoles marker demonstrated to be a good methodology to determine mixotrophy. The
negative control (Chlamydomonas sp.) did not shown any food vacuole marker,
confirming that the phytoflagellate was not doing phagotrophy. On the other hand, the
two species (C. marsonii and O. tuberculata) with mixotrophic metabolism confirmed
the presence of digestive vacuoles by the acidotropic probe, so we can infer that the
phytoflagellates were indeed feeding on bacteria. This method is considered simple, fast
and can be observed in natural live prey. However, we can mention some limitations of
this method as it is not possible to distinguish old vacuoles from new ones, and different
protists should have distinct digestion time been necessary studies in this perspective.
Besides the increasingly studies with mixotrophy as an important trophic
strategy in the past years, we know little about environmental conditions under which
they thrive (Edwards, 2019), mainly in eutrophic systems. Many organisms has different
adaptive strategies to survive under distinct environmental conditions. This study
demonstrate that high nutrient concentrations seem to be the most important factor for
the highest grazing rate by C. marsonii, and light seem to be most important for O.
tuberculata. Therefore, in accordance with the literature Cryptophytes and O.
tuberculata have good competitive ability to survive under nutrient-rich (Hammer et al.,
2002; Grujcic et al., 2018) and in light limiting systems (Floder et al., 2006),
78
respectively. Further, the differences in grazing rates, between the two species of
phytoflagellates, infer size-dependence ingestion. C. marsonii showed the highest
CSGR than O. tuberculata due bigger size C. marsonii may ingest more preys than O.
tuberculata.
Environmental conditions is not the only factor important to the feeding activity,
size and morphology of prey are associated with the ingestion rates and prey selection
by protists may be relevant (Gerea et al., 2018). The use of synthetic prey, as beads,
could be a limitation of the work. Several studies use beads of different sizes to estimate
ingestion rates (Schmidtke et al., 2006; Kamjunke et al., 2007). However, is known that
the use of beads may underestimate the ingestion rate due faster egestion compared to
FLB (Jurgens & DeMontt, 1995). Other studies demonstrated prey selection using
different kinds of natural preys like, picocyanobacteria and picoeukaryotes (Tarbe et al.,
2011; Izaguirre et al., 2012; Gerea et al., 2018). As mention above, beads demonstrate a
better visualization of the signal in flow cytometry. The comparison of the grazing rates,
between both techniques, demonstrated a significant and strong relationship, being flow
cytometry estimations higher than microscopy.
The study of mixotrophy implies in changes in how we understand the ecology
of food webs. It is critical to have accurate estimations on autotrophy and heterotrophy
balance by protists. Linking biodiversity to ecosystem ecology including this kind of
nutritional strategy requires a combination of different methods to estimate mixotrophic
activity, to advance more rapidly in the knowledge on aquatic food webs through both
experimental and observational approaches. If we consider that climatic changes are
already influencing and altering microbial ecology, it is essential evaluate in time and
space how environmental conditions and habitats favors the mixotrophy.
Acknowledge
This study was financed in part by the Coordenação de Aperfeiçoamento de
Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, CNPq/Universal,
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES)
(CAPES/PNPD–Project N : 2304/2011), Fundação de Pesquisa de São Paulo
(FAPESP) (Processes: 2014/14139-3 and 2016/50494-8).
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CONSIDERAÇÕES FINAIS
Este estudo teve como principal objetivo proporcionar dados e análises a fim de
preencher lacunas nesses aspectos supracitados. Para tanto, realizamos análise temporal
e espacial sobre a dinâmica do fitoplâncton em sistemas de água doce em região
vulnerável a eventos de seca extrema, assim como também realizamos experimentos em
laboratório para testar hipóteses levantadas após observações no campo. Por fim
realizamos experimentos a fim de testar novos protocolos com o intuito de facilitar e
mensurar taxas de bacterivoria, sob diferentes condições de luz e nutrientes, os quais
quantificamos taxas de grazing realizada por espécies mixotróficas.
Se considerarmos que as mudanças climáticas já estão influenciando padrões de
precipitações em áreas climáticas vulneráveis, como a região do semiárido tropical,
alterando as condições físicas e químicas, bem como o grau de trofia de lagos e
reservatórios, é relevante entender como o clima afeta a dinâmica e biodiversidade
aquática. O primeiro capítulo dessa tese trouxe informações sobre a ecologia e dinâmica
do fitoplâncton, utilizando uma abordagem de espaço em substituição ao tempo em uma
região climática bastante vulnerável a alterações no clima (i.e. região semiárida
brasileira) durante um evento de seca normal e de seca extrema. Dessa forma, podemos
fazer predições em relação ao futuro. Encontramos que em eventos de secas extremas e
na área de menor precipitação pluviométrica dentro de uma mesma região climática,
aumentou a proporção de organismos com metabolismo mixotrófico, contradizendo a
maior parte dos prognósticos mundiais que mostram que esses ambientes eutrofizados
tendem a favorecer e suportar uma maior biomassa de cianobactérias.
As principais conclusões do capítulo 1 da tese é que redução do nível da água
em reservatórios do semiárido, devido ao déficit hídrico, leva a alterações críticas nas
condições físicas e químicas da água, aumentando a concentração de nutrientes e de
sólidos inorgânicos, afetando a disponibilidade de luz para o fitoplâncton, colapsando a
biomassa de cianobactérias, favorecendo as espécies mixotróficas. Podemos inferir que
redução nas precipitações levam ao colapso de cianobactérias e aumentam a proporção
do fitoplâncton mixotrófico. Porém, a qualidade da água, tão importante para essas
regiões, continuam degradadas. Nossos dados são relevantes para ações necessárias de
políticas públicas eficazes para a gestão da qualidade da água no sertão brasileiro.
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Motivados em entender e testar o efeito da turbidez inorgânica causado pela
ressuspensão do sedimento (observados no capítulo 1 da tese) na disponibilidade da luz
para o fitoplâncton realizamos experimentos em laboratório de competição, entre uma
cianobactéria e uma alga mixotrófica. Por que os organismos mixotróficos podem
competir com seres autótrofos por nutrientes e luz, e com seres heterótrofos por presas é
importante entender as condições nos quais organismos mixotróficos generalistas
utilizam e são favorecidos por essa estratégia nutricional; como também sobre quais
condições esses organismos de múltiplas estratégias nutritivas eles podem coexistir.
Os dados obtidos neste capítulo 2 demonstram o efeito da turbidez inorgânica e
do material particulado na competição e nas taxas de crescimento das espécies
envolvidas (Microcystis–Cianobactéria; Cryptomonas– Criptofícea). Em ambientes com
alta concentração de turbidez inorgânica e baixa disponibilidade de luz a espécie de
metabolismo mixotrófico é favorecida em detrimento de uma cianobactéria, porém
somente na presença de bactérias. Essa por sua vez é uma evidência que essas espécies
podem está mudando sua estratégia nutritiva de autotrofia para heterotrofia sob
condições de baixa luz e alta turbidez em sistemas eutrofizados.
Além das observações realizadas em campo e em laboratório, é importante obter
dados de taxas de grazing de protistas mixotróficos para entender sua importância para
a bacterivoria total. Neste caso no capítulo 3, testamos o uso da citometria de fluxo para
facilitar as estimativas de taxas de ingestão no estudo da mixotrofia. Nossos dados
obtidos sugerem diferenças entre os tratamentos para as duas espécies de algas.
Mostrando que os nutrientes são mais importantes para a Cryptomonas e a luz para as
Ochromonas nas taxas de ingestão de presas por cada flagelado.
O viés metodológico do capítulo se torna bastante importante, uma vez que é
necessário incorporar ferramentas que auxiliem na obtenção de dados de forma mais
ágil e acurada. Combinações de uso de ferramentas e técnicas para estimar e estudar a
mixotrofia são recomendadas. O uso da citometria de fluxo é uma boa ferramenta para
se medir a atividade mixotrófica. Sendo assim sugerimos sua utilização em futuros
trabalhos para testar e afinar um novo protocolo. Além disso, sugerimos a utilização de
outras presas, de diferentes origens e tamanhos para testar seletividade e fluorescência
em futuros experimentos.
Nosso entendimento atual sobre a mixotrofia do fitoplâncton se baseia
principalmente em estudos realizados nos oceanos e em sistemas oligotróficos e pouco
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se sabe sobre como essa forma de nutrição pode influenciar a ecologia aquática de
ambientes de água doce eutrofizados continentais.
Além disso, várias revisões sobre mixotrofia no plâncton foram realizadas nos
últimos anos e é consenso que atualmente os maiores desafios que afetam a
interpretação dos papeis dos organismos mixotróficos na ecologia do plâncton é
entender a qualidade dos dados que existem e entender como os organismos estão sendo
favorecidos e como estão realizando a mixotrofia sob distintas condições ambientais.
Por fim, destacamos a importância de mais trabalhos sobre essa estratégia nutricional de
forma a obter mais e melhores estimativas a serem incorporadas em modelos e gerar um
maior entendimento para a ecologia aquática.
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Material Suplementar
Appendix 1 chapter 3
Comparison of mean Specific Grazing Rate (CSGR) and standard deviation (SD) measured by
two methodologies: epifluorescence microscopy and flow cytometry. Treatments are high
nutrient (HN), medium nutrient (MN), low nutrient (LN), high light (HL), low light
(LL) and dark (D).
Epifluorescence
microscopy Flow Cytometry
Treatment Phytoflagellate CSGR
(bact. ind-1h-1) and SD
CSGR
(bact. ind-1h-1) and SD
HN+HL Ochromonas 0.67 (0.25) 1.85 (0.11)
HN+LL 0.55 (0.19) 2.70 (0.51)
HN+D 1.05 (0.41) 3.10 (0.69)
MN+HL 0.29 (0.21) 1.24 (0.51)
MN+LL 1.10 (0.85) 1.21 (0.41)
MN+D 1.64 (1.54) 2.57 (1.39)
LN+HL 0.63 (0.40) 0.74 (0.10)
LN+LL 0.84 (0.28) 2.48 (0.98)
LN+D 0.98 (0.36) 3.76 (2.02)
HN+HL Cryptomonas 7.09 (2.24) 26.85 (6.34)
HN+LL 3.43 (0.43) 20.15 (0.73)
HN+D 4.93 (1.85) 21.53 (3.54)
MN+HL 3.31 (1.26) 1.24 (0.15)
MN+LL 1.53 (0.91) 2.80 (2.51)
MN+D 2.29 (0.70) 4.74 (0.22)
LN+HL 2.74 (0.64) 4.69 (0.72)
LN+LL 1.39 (0.49) 8.26 (6.8)
LN+D 2.53 (1.66) 1.67 (0.54)
85
Appendix 2 chapter 3
Fig. 2 –Model of the type II linear regression comparing specific grazing rates (CSGR) obtained
by flow cytometry and epifluorescence microscopy in short-term ingestion experiments with
(A) Cryptomonas (R2 = 0.32, p-value = 0.007) and (B) Ochromonas (R2 = 0.26, p-value =
0.017)
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Fotos dos ambientes
Reservatórios do primeiro capitulo, localizados na região semi-árida nordestina do Brasil. Fotos
superiores representam a sub-bacia do piancó (PB) e as duas fotos inferiores representam reservatórios
da sub bacia do Seridó (RN).
87
Trabalho de campo e laboratório
Instituto de investigaciones y biotecnologias
88
Formação dos agregados nos tratamentos com Cryptomonas obovata e adição de
sedimento e presença de bactérias do experimento Capítulo 2.
Fotos do experimento de competição entre Microcystis aeruginosa e Cryptomonas
obovata do Capítulo 2.
89
Foto Cryptomonas obovata (esquerda) e células de Microcystis aeruginosa (direita) em
microscópio óptico aumento de 40x.
Foto de Cryptomonas marssonii ingerindo uma esfera sintética (beads) no experimento
de grazing, capítulo 3.
