Short-term variability and trophic interactions within planktonic community in subtropical waters of he Canary Islands (Northeast atlantic)

D. JOSÉ MANUEL VERGARA MARTÍN SECRETARIO DEL DEPARTAMENTO DE BIOLOGÍA DE LA UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA, CERTIFICA, Que el Consejo de Doctores del Departamento en su sesión de fecha ............................. tomó el acuerdo de dar el consentimiento para su tramitación, a la tesis doctoral titulada “Short-term variability and trophic interactions within the planktonic community in subtropical waters of the Canary Islands (Northeast Atlantic)” presentada por la doctoranda Dª. Gara Franchy Gil y dirigida por el Doctor D. Santiago Hernández León. Y para que así conste, y a efectos de lo previsto en el Artº 6 del Reglamento para la elaboración, defensa, tribunal y evaluación de tesis doctorales de la Universidad de Las Palmas de Gran Canaria, firmo la presente en Las Palmas de Gran Canaria, a ........ de Septiembre de 2014. Fdo. : D. José Manuel Vergara Martín Departamento de Biología Universidad de Las Palmas de Gran Canaria TESIS DOCTORAL Programa de Doctorado en Oceanografía SHORT-TERM VARIABILITY AND TROPHIC INTERACTIONS WITHIN THE PLANKTONIC COMMUNITY IN SUBTROPICAL WATERS OF THE CANARY ISLANDS (NORTHEAST ATLANTIC) (VARIABILIDAD A CORTO PLAZO E INTERACCIONES TRÓFICAS DE LA COMUNIDAD PLANCTÓNICA EN AGUAS SUBTROPICALES DE LAS ISLAS CANARIAS, NORESTE ATLÁNTICO) Tesis doctoral presentada por Dª. Gara Franchy Gil para la obtención del grado de Doctora en Oceanografía por la Universidad de Las Palmas de Gran Canaria. Esta tesis doctoral ha sido dirigida por el Dr. D. Santiago Hernández León. La Doctoranda El Director Fdo.: Dª. Gara Franchy Gil Fdo.: Dr. D. Santiago Hernández León En Las Palmas de Gran Canaria, a , de Septiembre, de 2014. RESUMEN Los giros subtropicales abarcan grandes áreas del océano donde la productividad del ecosistema se sostiene a través del reciclado de materia y energía. En estas aguas, la interacción entre la disponibilidad de recursos y la presión de los niveles tróficos superiores determina la dinámica de la comunidad planctónica. Sin embargo, en aguas subtropicales, el conocimiento de la variabilidad temporal o el papel de los diferentes componentes de la comunidad dentro de la red trófica es bastante limitado. En esta tesis se evalúa la variabilidad a corto plazo de los diferentes componentes de la comunidad planctónica. El picoplancton dominó la comunidad salvo durante la época productiva, en la que los organismos autótrofos de mayor tamaño desempeñaron un papel destacado. Nuestros resultados muestran como la variabilidad estacional está relacionada con fuerzas “bottom-up”, mientras que los procesos “top-down” dominan a una escala de tiempo más corta. Encontramos que el microzooplancton ejerce un gran impacto sobre la comunidad microbiana, en organismos tanto autótrofos como heterótrofos. Además, observamos un acoplamiento muy estrecho entre estos consumidores y sus presas. Otro mecanismo que regula la estructura planctónica es la depredación de los migradores verticales sobre el zooplancton. Así, la variabilidad del mesozooplancton epipelágico está controlada por un ciclo de depredación vinculado a la iluminación de la luna. En este trabajo realizamos una simulación de esta variabilidad con la que se obtuvieron valores de mortalidad comunitaria de los que derivamos el flujo de carbono activo hacia la zona mesopelágica. Estos valores calculados de transporte activo de carbono son del mismo orden de magnitud que el flujo gravitacional en aguas subtropicales. En el Atlántico noreste la comunidad marina también podría estar influenciada por las tormentas de polvo sahariano que ocurren con gran frecuencia en la zona. En este sentido, se estudió la respuesta de la comunidad planctónica en un período de deposición de polvo atmosférico de gran intensidad, en el año 2010, sin observar una clara respuesta en términos de producción primaria. Por el contrario, la biomasa de diatomeas y mesozooplancton sí se vio aumentada en gran medida tras el paso de una fuerte tormenta de polvo del Sáhara, mientras que los organismos autótrofos de menor tamaño se vieron afectados negativamente. Los resultados de esta tesis suponen una contribución importante para entender la dinámica planctónica tan compleja en los ecosistemas subtropicales, y además, pone de manifiesto la necesidad de llevar a cabo muestreos oceanográficos a escalas de tiempo más cortas. ÍNDICE DE TABLAS / LIST OF TABLES Table I.1. Monthly average of autotrophic:heterotrophic biomass and primary production:autotrophic biomass ratios at the mixed layer during the period studied ____________________________ 48 Table I.2. Correlation (Spearman’s rank, r) between abundance (cells mL-1) of microbial components and average temperature in the mixed layer ____________________________________________ 52 Table II.1. Monthly average of autotrophic:heterotrophic biomass ratio at the mixed layer from February to June 2010 ___________________________________________________________________ 74 Table III.1. Date and environmental conditions: temperature (ºC) and Chl a concentration (μg L-1) at the mixed layer for each experiment _________________________________________________ 94 Table III.2. Mortality and growth rates (d-1) of autotrophic organisms ______________________________ 97 Table III.3. Mortality and growth rates (d-1) of heterotrophic organisms _____________________________ 98 Table III.4. Nutrient limitation index calculated from growth rates without and adding nutrients _________ 100 Table IV.1. Experiments carried out to measure microzooplankton grazing rates, comparing the Frost and dilution methods and performing time-series inside the incubators ______________________ 118 Table IV.2. Grazing rates (d-1) upon Synechococcus, Prochlorococcus, autotrophic picoeukaryotes and total phytoplankton measured according to Frost (1972) to the north of the Canary Islands and the Namibian upwelling _______________________________________________________ 125 Table V.1. Average daily community mortality (mmolC m-2 d-1) estimated from simulated mesozooplankton biomass and mortality __________________________________________ 146 Table V.2. Comparison between gravitational and active carbon fluxes (mmolC m-2 d-1) in subtropical waters _____________________________________________________________________ 149 GENERAL INTRODUCTION INTRODUCCIÓN GENERAL    GENERAL INTRODUCTION  19 I. INTRODUCTION I.1. BASIC CONCEPTS ABOUT PLANKTON The term plankton comes from the Greek word planktos that means errant or drifter, alluding to the limited swimming ability of the organisms belonging to this community. It is a heterogeneous group of organisms including a wide variety of sizes, morphologies and types of feeding. The most widespread classification is from Sieburth et al. (1978), a general division in several categories according to size and feeding behavior (Fig. 1). Thus, four categories are distinguished according to size: picoplankton (0.2-2 μm), nanoplankton (2-20 μm), microplankton (20-200 μm) and mesoplankton (200-20000 μm). Two other size groups are frequently used for smaller or larger organisms: femtoplankton (<0.2 μm) and macroplankton (>20 mm). Among these categories we found two types of feeding: autotrophy and heterotrophy. Autotrophic organisms, called phytoplankton, obtain their energy requirements through the photosynthesis metabolism, while heterotrophs, called zooplankton, feed on other organisms (autoor heterotrophic) to cover their metabolic needs. Although not included in the classification of Sieburth, it is now widely known that there are organisms, called mixotrophs, that are able to use both types of feeding. That is, they ingest other prey but they can also photosynthesize organic matter. Picoplankton include autotrophic picoeukaryotes, two types of cyanobacteria (Prochloroccocus and Synechoccocus) and heterotrophic bacteria. Nanoplankton is mainly composed by unicellular flagellates, both autotrophic and heterotrophic. The major autotrophic components of microplankton are diatoms, dinoflagellates and coccolithophorids, although some species of dinoflagellates are now well recognized as mixotrophic (Sherr & Sherr 2002). Within microzooplankton, the principal components are protozoan organisms: ciliates, foraminiferans, radiolarians and acantharids; although metazoans as copepod nauplii, copepodites or meroplanktonic larvae are also included in this group. Mesoplankton is usually conformed by heterotrophs, although some autotrophic cells such as large dinoglagellates or diatom chains could be larger than 200 μm. Copepods are the main group within mesozooplankton, although other metazoans as pteropods, salps, chaetognaths, cnidarians or fish larvae are also important components. G ENERAL I  20  Figure 1. Cl a from Siebur t I.2. T HE C The Canar y (Fig. 2). T h 1973, Brau upwelling ( Canaries, w and the isl a The ocean i strong stra nutrients i n permanent resulting fr o promotes t h During the I NTRODUC T a ssification o t h et al. (197 C ANARY C U y Current S h is system i n 1980), w h ( Barton et w here cycl o a nds (Aríst e i c area, un a tification o f n to the pho t seasonal t o m the slig h h e so-calle d winter bloo T ION o f plankton c 8). U RRENT S Y S ystem is l o s characte r h ilst to the e al. 1998). o nic and an t e gui et al. 1 9 a ffected by f the water t ic layer (D e hermocline h t sea-surf a d “late wint e m, maximu c omponents Y STEM o cated in t h r ized by oli g e ast, we fin Furthermo r t icyclonic e 9 94, Barto n the African column d u e León & B is only ero a ce cooling e r bloom” ( D m values o by size and f h e eastern b g otrophic w d the prod u r e, producti ddies are f o n et al. 200 4 upwelling a u ring most raun 1973, ded in Jan u . At that ti m D e León & B f chloroph y f eeding beh a b ranch of t h w aters in th e u ctive wate r ve pulses o rmed by t h 4 ). a nd the ed d of the yea r Braun 19 8 u ary-March m e, the nut r B raun 197 3 y ll a concen t a vior from S c h e North A e oceanic a r s produce d are freque h e interacti o d y influenc e r that precl 8 0, Barton e because o r ient entran c 3 , Braun 19 8 t ration and c hmoker (2 0 tlantic subt a rea (De L e d by the Afr i nt at the s o n betwee n e , is charac udes the i n e t al. 1998) . o f the winte r c e into the 8 0, Barton e primary pr o 0 11), redraw n ropical gyr e e ón & Brau n i can coast a s outh of th e n the cur r en terized by a n put of ne w . The quasi r convectio n photic laye e t al. 1998 ) o duction ar e  n e n a l e t a w - n r ) . e GENERAL INTRODUCTION  21 reached in surface waters (De León & Braun 1973, Braun 1980, Arístegui et al. 2001), coinciding with the maximum mixed layer depth and nutrient availability (Cianca et al. 2007). During this productive period, the enhanced phytoplankton growth is followed by an increase of mesozooplankton biomass (Arístegui et al. 2001, Hernández-León et al. 2004, Hernández-León et al. 2007). Figure 2. Scheme of surface circulation of the Canary Basin from Mason et al. (2011). AzC, Azores Current; CanC, Canary Current; CanUC, Canary Upwelling Current; EBC, Eastern Boundary Current; MC, Mauritania Current; NEC, North Equatorial Current; NECC, North Equatorial Countercurrent; PC, Portugal Current; MAR, Mid-Atlantic Ridge; LP, Lanzarote Passage. GENERAL INTRODUCTION   22  I.3. PLANKTONIC COMPOSITION AND VARIABILITY IN SUBTROPICAL WATERS OF THE CANARY ISLANDS A common feature of subtropical gyres is that primary production is mainly supported by nutrient recycling and the flow of energy and matter is controlled by the microbial loop (Pomeroy 1974, Azam et al. 1983, Longhurst 1998). That is, bacteria and small autotrophic cells (<2 μm) are consumed by nanoand microheterotrophic organisms, which in turn excrete dissolved organic matter used by the former for their metabolic requirements (Fig. 3). This is the case in oligotrophic waters of the Canary Current System, where phytoplankton is mostly composed by picoautotrophs, which are largely consumed by micrograzers (nanoand microheterotrophs), that controls more than 80% of primary production (Arístegui et al. 2001, Marañón et al. 2007). Figure 3. Diagram from Pomeroy et al. (2007) showing the principal role of the microbial components (green and yellow boxes) in the trophic web. Major fluxes of energy and matter are represented by continuous lines. However, the seasonal variability of the planktonic community composition or the role of different planktonic groups throughout the year is poorly known in subtropical gyres, and also in the northeast Atlantic. To our knowledge, there are only two detailed studies about the temporal variability of the planktonic composition in the Canary Islands (Schmoker et al. 2012, Schmoker & Hernández-León 2013). These authors described a seasonal pattern for picophytoplankton consisting of the dominance of eukaryotic cells and Synechococcus during winter-spring while Prochlorococcus reached their maximum abundance in summer-fall. This seasonal variability has GENERAL INTRODUCTION  23 also been found in subtropical gyres at the northwest Atlantic and in the north Pacific (DuRand et al. 2001, Giovannoni & Vergin 2012). On the contrary, any seasonality has been observed for heterotrophic bacteria in the northeast Atlantic (Schmoker & Hernández-León 2013), whilst in the northwest the biomass of bacteria was the highest after the winter-spring productive period (Steinberg et al. 2001). A seasonal increase has also reported for nanoand microphytoplankton during the bloom period in the Canaries (Schmoker et al. 2012, Schmoker & Hernández-León 2013). However, a clear signal has not been found for all microplanktonic groups. For example, in the case of diatoms, they bloom occasionally in winter-spring or summer. Finally, a seasonal increment of the mesozooplankton stock during the productive season has also been described in these waters (Hernández-León et al. 2004, 2007). I.4. TOP-DOWN PROCESSES REGULATING PLANKTONIC COMMUNITIES Planktonic variability in subtropical waters is not only driven by physical conditions (light, temperature, stratification, etc.) or nutrient availability (bottom-up processes), but also top-down forces, namely grazing and predation, play an important role regulating planktonic populations. Nanoand microzooplankton (heterotrophic organisms ranging 2-200 μm, hereinafter microzooplankton) are key components in tropical and subtropical areas where they account for 7276% of the daily primary production grazed (Calbet & Landry 2004). Additionally, heterotrophic consumption of microzooplankton seems to be common, especially in subtropical waters because of the complexity of the food web (Calbet & Saiz 2013). Heterotrophic nanoflagellates are known to be effective bacterivores and they could be in turn preyed by ciliates (Azam et al. 1983, Guillou et al. 2001, Sherr & Sherr 2002, Pomeroy et al. 2007). Despite their importance, the knowledge about the feeding impact of microzooplankton is limited in subtropical gyres (Schmoker et al. 2013). In the subtropical waters of the Canary Current direct measurements are practically non-existent to our knowledge, and only some studies in the south-east of Azores (Gaul et al. 1999, StelfoxWiddicombe et al. 2000, Quevedo & Anadón 2001) and in the south of the Canary Islands (Gutiérrez-Rodríguez et al. 2011) have assessed the microzooplankton grazing impact on primary production. The measured impact on productivity is highly variable according to these studies ranging between 37 and >100% of the daily production consumed by micrograzers. Furthermore, potential differences of the feeding impact upon the diverse autotrophic community inhabiting these waters have only been evaluated by Quevedo & Anadón (2001) and Gutiérrez-Rodríguez et al. (2011). GENERAL INTRODUCTION   24  On the other hand, microzooplankton support the predator pressure exerted by mesozooplankton. Actually, microzooplankton contribute up to 50% to the daily copepod diet in oligotrophic waters (Calbet 2008). Mesozooplankton stocks, in turn, are also controlled and shaped by predation. In the Canary Islands waters, variability of the epipelagic mesozooplankton (non-migrant organisms inhabiting the upper water column) has been related to predation by diel vertical migrants (DVM) (Hernández-León 1998, Hernández-León et al. 2001a, 2002, 2004, 2010). Vertical migration of zooplankton and micronekton has been explained as a strategy to avoid predation (Stich & Lampert 1981, Ringelberg 2010). They keep at deeper and unlit waters between 200-1000 meters during the day, and rise to shallower layers for feeding during the night (Moore 1950, Uda 1956, Angel 1989, Longhurst et al. 1989), when they are less visible to their visual predators (Fig. 4). The magnitude of this process is partly determined by the lunar illumination (Uda 1956, Benoit-Bird et al. 2009), as these animals do not reach the upper layers when maximum illumination takes place (full moon) to avoid predation. In fact, the influence of the moonlight on the DVM behavior has been proposed as the mechanism that explains the variability of the epipelagic mesozooplankton biomass observed in the Canaries (Hernández-León 1998, Hernández-León et al. 2001a, 2002, 2004, 2010). Furthermore, this predatory cycle affects not only the planktonic dynamic in the upper water column, but significantly contributes to the active carbon export to the mesopelagic realm (Longhurst & Harrison 1988, Longhurst et al. 1989). Figure 4. Diel vertical migration observed in an echogram (image by A. Ariza). Migrants descended from the surface to the deep scattering layer (600 m) at dawn (06:00 local time), and they moved up to upper layers at dusk (18:00 local time). Short-term variability of planktonic composition in subtropical waters off the Canary Islands (Northeast Atlantic) CAPÍTULO I CHAPTER I     SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  33 ABSTRACT The planktonic community composition in oligotrophic waters off the Canary Islands was studied from November 2010 to June 2011 carrying out a weekly sampling and covering the productive period in these waters. The characteristic late winter bloom took place from February to April 2011, when the cooling of surface waters promoted the deepening of the mixed layer, and chlorophyll a concentration and primary production increased at the surface. Planktonic community was dominated by picoplankton, especially Prochlorococcus and heterotrophic prokaryotes, except during the productive period, when Synechococcus and picoeukaryotes dominated the picophytoplankton. During the bloom, diatoms were the major contributors to total autotrophic biomass and they were probably responsible for the highest rates of primary production. The planktonic variability was driven by bottom-up forces at a seasonal scale through the nutrient enrichment during winter. Additionally, a short-scale variability of phytoplankton biomass and productivity was observed even during stratified conditions. Short-term variability would also result from top-down processes such as feeding and grazing by nanoand microheterotrophs. In this sense, a significant correlation was found between the latter and their potential preys: heterotrohic prokaryotes and autotrophic picoeukaryotes and nanoflagellates. These results showed the need to sample at short-time scales to account for the total variability of the planktonic communities in subtropical waters. As an example, the striking and ephemeral diatom peak observed here, which would be probably not detected in a monthly sampling. CHAPTER I  34 I. INTRODUCTION Subtropical gyres comprise large oligotrophic areas of the world ocean where, despite the low nutrient conditions, complex trophic webs are common (Longhurst 1998) and annual production rates could be as high as in temperate ecosystems (Menzel & Ryther 1960). The seasonal and interannual production cycle in north subtropical gyres is fairly known from the Ocean Long-Term Time-Series Stations in the Pacific (HOT: Hawaii Ocean Time-Series) and the western Atlantic (BATS: Bermuda Atlantic Time-Series). The productive cycle in these subtropical waters, where light is not a limiting factor, is mainly supported by nutrient recycling, as the input of new nutrients into the photic layer only occurs during winter (Menzel & Ryther 1960, Karl et al. 1996, Cianca et al. 2007). Sea surface cooling during this season produces a deepening of the mixed layer, reaching the highest surface nutrient concentration and promoting the highest annual production rate. The microbial loop (Pomeroy 1974, Azam et al. 1983) controls the flow of energy and matter in subtropical gyres (Pomeroy 1974, Longhurst 1998). It is now well known that picophytoplankton (<2 μm cells) accounts for most of the primary productivity (Li et al. 1983), which is mostly consumed by nanoand micrograzers (Calbet & Landry 2004). However, the seasonal variability of the planktonic community composition or the role of different planktonic groups throughout the year remains poorly known. Regarding to picophytoplankton composition, a seasonal pattern has been found in the Sargasso Sea and the HOT station (DuRand et al. 2001, Giovannoni & Vergin 2012). Eukaryotic cells and Synechococcus dominated in winter-spring while Prochlorococcus reached their maximum values in late summer and fall. This seasonal variability was also observed in the eastern subtropical North Atlantic gyre (Schmoker et al. 2012, Schmoker & Hernández-León 2013). These authors also described a seasonal pattern in nanoand microphytoplankton that increased their biomass during the bloom period. Within autotrophic microplankton, a seasonal variability has been observed for coccolithophores at BATS (Steinberg et al. 2001), where the highest concentrations were found during the productive period (January-March). However, other groups do not show a clear pattern, as in the case of diatoms, which bloom occasionally in winter-spring or summer at both eastern (Schmoker et al. 2014) and western north Atlantic gyre (Steinberg et al. 2001). Any seasonality has been observed for heterotrophic prokaryotes or nanoflagellates in the northeast Atlantic (Schmoker & Hernández-León 2013), whilst in the northwest the biomass of the former was the highest after the winter-spring productive period (Steinberg et al. 2001). Finally, a seasonal increase of the mesozooplankton stock during the productive season has been also described at both edges of the north Atlantic gyre (Madin et al. 2001, Hernández-León et al. 2004, 2007). The Canary Current System is located in the eastern branch of the North Atlantic subtropical gyre. This system is characterized by oligotrophic waters in the oceanic area (De León & Braun 1973, Braun 1980), and like other subtropical areas, is characterized by a strong stratification of the water SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  35 column during most of the year. The seasonal thermocline is only eroded in winter because of the slight sea surface cooling, allowing a nutrient input into the photic layer and promoting the so-called late winter bloom (De León & Braun 1973, Braun 1980, Barton et al. 1998). During the winter bloom (January-March), maximum values of chlorophyll a concentration and primary production are reached in surface waters (De León & Braun 1973, Braun 1980, Arístegui et al. 2001), coinciding with the maximum mixed layer depth and nutrient availability (Cianca et al. 2007). Phytoplankton, mostly composed by picoautotrophs, are largely consumed by micrograzers that controls more than 80% of primary production in these waters (Arístegui et al. 2001, Marañón et al. 2007). Microplankton in turn are controlled by mesozooplankton (Schmoker & Hernández-León 2013), which mainly prey on non-pigmented organisms (Arístegui et al. 2001, Hernández-León et al. 2004) and are influenced by the predator pressure of diel vertical migrants (Moore 1950, Uda 1956, Angel 1989). Hence, all these trophic interactions modulate the planktonic variability and composition. In this sense, Schmoker et al. (2012) described how the interplay between different planktonic groups resulted in a succession of biomass peaks during the late winter bloom in the Canaries. The cycle, observed several times during the bloom, consisted in the increase of autotrophic picoeukaryotes, followed by heterotrophic nanoflagellates, microplankton and mesozooplankton. In order to better understand the complex trophic interactions and successions described for subtropical waters, we studied the planktonic community composition in oligotrophic waters of the Canary Islands. For that, a weekly sampling covering the productive period was carried out during 8 months. Thus, differences between bloom and non-bloom conditions were assessed, as well as the short-term variability in terms of abundance, biomass and primary productivity. II. MATERIAL AND METHODS A weekly sampling was carried out in the Canary Islands waters to the north of Gran Canaria Island from November 2010 to June 2011, on-board the R/V Atlantic Explorer. Four stations 10 nautical miles equidistant (Fig. I.1) were sampled from surface to 300 m depth. Pressure, salinity, temperature and fluorescence were measured using a SBE25 CTD and a Turner Scufa Fluorometer coupled to an oceanographic rosette equipped with six 4-L Niskin bottles. Seawater samples were taken at the mixed layer (20 m) and at the deep chlorophyll maximum (DCM) depth to characterize the pico-, nanoand microplankton communities. A SBE19 plus CTD was eventually used during some cruises because of the main CTD failure. From temperature data, the mixed layer depth (MLD) was calculated as the level with a temperature difference of 0.5ºC from the 10 m depth (Cianca et al. 2007). CHAPTER I  36 Figure I.1. Location of the four oceanographic stations (St) at the north of Gran Canaria Island (Canary Islands). Chlorophyll a (Chl a) was measured by filtering 500 mL of seawater through a 25 mm Whatman GF/F filter and freezing it at -20ºC until its analysis at the laboratory. The extraction procedure consisted in placing the filter in 90% acetone at -20ºC in the dark, during at least 20 hours, and following the acidification method by Strickland & Parsons (1972b). Pigments were measured on a Turner Design 10A Fluorometer, previously calibrated with pure Chl a (Yentsch & Menzel 1963). From these data at 20 m, fluorescence in the whole profile was converted to Chl a (Chl a = 0.004 + 0.29 · fluorescence, r2=0.54, p<0.001 for the SBE25 and Chl a = 0.034 + 15.78 · fluorescence, r2=0.45, p<0.001 for the SBE19 plus) as an indicator of phytoplankton biomass. Size-fractionated Chl a (<2 μm, 2-20 μm, >20 μm) was obtained filtering additional 500 mL samples serially through 20 and 2 μm polycarbonate filters (25 mm). The smallest fraction was obtained subtracting the sum of 2-20 and >20 μm fractions from total chlorophyll values. Picoplanktonic organisms (0.2-2 μm) were sampled in 1.6 mL tubes, fixed immediately with 100 μL of 20% paraformaldehyde, incubated at 4ºC during half an hour, frozen in liquid nitrogen, and finally kept at -80ºC until further analysis. Samples were analyzed later by flow cytometry using a FACScalibur Cytometer (Beckton and Dickinson). Side scatter (SSC) and fluorescence parameters SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  37 were obtained to distinguish between autotrophic picoeukaryotes (APE), cyanobacteria (Prochlorococcus, Pro, and Synechococcus, Syn) and heterotrophic prokaryotes (HP). Samples were run until 10000 events were reached or after 2 minutes at high speed to measure phototrophic organisms (APE, Pro and Syn), and at low speed for HP samples which were pre-stained with SYTO-13. Autoand heterotrophic nanoflagellates (ANF and HNF) were fixed using 540 μL of 25% glutaraldehyde in a tube containing 45 mL of seawater. Samples were kept at 4ºC in dark until the sample was filtered onto a 0.6 μm black polycarbonate filter placed over a backing filter and stained by diamidino-2-phenylindole (DAPI) for 5 minutes. The filter was immediately mounted on a microscope slide with low-fluorescence immersion oil and kept at -20ºC. Finally, it was analyzed by epifluorescence microscopy with a Zeiss Axiovert 35 microscope (Haas 1982). Only samples from 20 m were analyzed. Microplanktonic organisms were kept in 500 mL dark bottles fixed with 1.5 mL of acid lugol and analyzed afterwards by the Utermöhl technique. A 100 mL subsample was settled for 48 h in a composite chamber. The bottom chamber was then examined by an inverted Zeiss Axiovert 35 microscope to identify the main microplanktonic groups: diatoms (Dia), dinoflagellates (Din), ciliates (Cil), and copepod nauplii or copepodites. Only samples from station 3 at 20 m were analyzed. The abundance of organisms obtained by flow cytometry was converted to biomass using the carbon conversion factor of: 17 fgC cell-1 for HP (Bode et al. 2001), 29 fgC cell-1 for Pro, 100 fgC cell-1 for Syn (Zubkov et al. 2000) and 1500 fgC cell-1 for APE (Zubkov et al. 1998). After biovolume estimation by microscopy, nanoflagellates were converted to carbon using the factor of 220 fgC μm3 for HNF (Borsheim & Bratbak 1987) and the equation 0.433(BV)0.863 pgC cell-1 for ANF (Verity et al. 1992). Finally, microplanktonic abundance was converted to biomass from total biovolume data obtained directly by microscopy for the most abundant cells or from previous measurements in these waters (A. Ojeda, unpublished data). If these data were not available, an average size from the literature for every organism or group (Tomas 1997, Horner 2002, Ojeda 2006, Ojeda 2011) was assumed, fitting them to the suitable shape following Hillebrand et al. (1999). The corresponding parameters were used for Dia (log a = -0.541, b = 0.811 for V<3000 μm3; log a = 0.933, b = 0.881 for V>3000 μm3), Din (log a = -0.353, b = 0.864) and Cil (log a = -0.639, b = 0.984 for aloricate ciliates; log a = -0.168, b = 0.841 for tintinnids) to obtain the biovolume to biomass conversion factor (Menden-Deuer & Lessard 2000) (log pgC cell-1 = log a + b · log V). Some species were not taken into account to calculate the microbial biomass because of the impossibility of finding their size range or biovolume in the literature, but these species were always practically negligible in terms of abundance. Average Chl a concentration, abundance and biomass were calculated without station CHAPTER I  38 1 as a significant difference (Kruskal-Wallis test, p<0.05) was found between this coastal station and the oceanic stations 2, 3 and 4 for APE. Primary production (PP) was measured using the 13C method (Hama et al. 1983) in station 2 at 20 m and DCM depth. Water samples were transferred to 2L polycarbonate bottles previously rinsed with 10% HCl and distilled-deionized water and NaH13CO3 was added at about 10% of total inorganic carbon in the ambient water. Samples were incubated in a tank on-deck for between 3 and 7 hours depending on the cruise. Running surface seawater and appropriate meshes simulated in situ temperature and light intensity. Initial and final particulate organic carbon (POC) and particulate material used for isotope analysis were filtered through precombusted GF/F filters (5 h, 450ºC). These filters were frozen and stored at -20ºC until analysis. POC was measured using a CHN analyzer (Carlo Erba EA 1108) and isotopes in a mass spectrometer equipped with an elemental analyzer (Flash EA 11 ThermoFinnigan with Deltaplus). III. RESULTS The characteristic late winter bloom was developed from February to April 2011 when the cooling of surface waters occurred and a minimum temperature of 19ºC was measured in the mixed layer (Fig. I.2A). At that time the average depth of the mixed layer (ML) reached maximum values (around 140 m), promoting the shoaling of the 26.4 kg m-3 isopycnal surface (Fig. I.2B) and a modest increase in the Chl a concentration at the ML reaching around 0.3 mg m-3 (Fig. I.2C). Before February and after April higher surface temperatures were observed (Fig. I.2A) and the seasonal thermocline was well developed resulting in a thinner ML, especially during May and June (above 60 m). Within this period a deep chlorophyll maximum appeared between 70 and 100 m (Fig. I.2C). Integrated Chl a concentration increased in February and March (Fig. I.3A), reaching average values of 34 and 50 mg m-2, respectively. These maxima matched the deepest ML and a positive correlation between them was observed (Spearman’s rank r=0.38, p<0.01). Relatively high values were also maintained during April, when the maximum chlorophyll concentration was found at the DCM (Fig. I.2C). SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  39 Figure I.2. Temporal variability of temperature (2A, ºC), potential density (2B, kg m-3) and Chl a (2C, mg m-3) from surface to 200 db. ML depth is drawn in the upper panel (black solid line). Data correspond to average from stations 2, 3 and 4; from November 2010 to June 2011. Figure I.3. Average (±SD) integrated Chl a concentration (mg m-2) and MLD (m) from November 2010 to June 2011. |D|J|F|M|A|M|J 15 20 25 30 35 40 45 50 55 60 Integrated Chl a (mg m-2) 0 20 40 60 80 100 120 140 160 180 MLD (m) CHAPTER I  40 |D|J |F|M |A |M |J 0 5 10 15 20 25 30 35 40 45 50 PP (mgC m -3 d -1 ) 0.0 0.1 0.2 0.3 0.4 0.5 Chl a (mg m -3 ) A FI MI AI MIJ 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45 0.50 Chl a (mg m -3 ) >20 μm 2-20 μm <2 μm B Figure I.4. Average (±SD) primary production (PP, mgC m-3 d-1) and Chl a concentration (mg m-3) measured in the ML during the period studied (4A); and average fractionated Chl a (mg m-3) measured in the ML from February to June 2010 (4B). SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  47 N|D|J|F|M|A|M|J 0 2 4 6 8 10 Biomass (mgC m -3 ) Cil HNF HP C 0 2 4 6 8 10 Biomass (mgC m -3 ) ANF Pro Syn APE A 0 10 20 30 40 90 100 110 120 130 Biomass (mgC m -3 ) APE+Syn+Pro ANF Dia Din B Figure I.9. Monthly average biomass (mgC m-3) of Pro, Syn, APE, ANF (9A), Dia, Din (9B), HP, HNF and Cil (9C) in the ML from November 2010 to June 2011. CHAPTER I  48 Month APico,ANF:HHP,HNF A:H P:B P:B+Din Nov 0.79 (±0.00) 0.89 (±0.00) 0.34 (±0.00) 0.45 (±0.00) Dec 0.78 (±0.22) 1.03 (±0.13) 0.16 (±0.00) 0.31 (±0.00) Jan 0.72 (±0.06) 0.82 (±0.14) Feb 0.60 (±0.23) 5.79 (±4.74) 0.08 (±0.00) 0.11 (±0.00) Mar 0.63 (±0.10) 1.58 (±0.44) 0.20 (±0.00) 0.21 (±0.00) Apr 0.62 (±0.24) 0.78 (±0.25) 0.52 (±0.20) 0.74 (±0.30) May 0.31 (±0.14) 0.43 (±0.16) 0.51 (±0.07) 0.90 (±0.13) Jun 0.23 (±0.04) 0.57 (±0.29) Table I.1. Monthly average (±SD) of autotrophic:heterotrophic biomass (A:H) and primary production:autotrophic biomass (P:B) ratios at the ML during the period studied. A:H ratios are shown for picoand nanoplankton (APico,ANF:HHP,HNF) and including microplankton (Dia, Cil, Din) considering 50% Din as autotrophs and 50% as heterotrophs (A:H). P:B ratios (d-1) are shown without (P:B) or with 50% of Din (P:B+Din) in computing autotrophic biomass. Biomass in mgC m-3 and primary production in mgC m-3 d-1. IV. DISCUSSION The characteristic late winter bloom previously described for subtropical gyres (Menzel & Ryther 1960, Karl et al. 1996) and also for the Canary Islands waters (De León & Braun 1973, Braun 1980, Arístegui et al. 2001) was observed to the north of the Canaries from February to April 2011. The cooling of surface waters promoted the deepening of the ML and the consequent input of new nutrients into the euphotic zone. Although nutrients were not measured here, it is well known that nutrient concentration significantly increases at a potential density of 26.4 kg m-3 in the eastern North Atlantic subtropical waters (Cianca et al. 2007). Thus, the shoaling of the 26.4 kg m-3 isopycnal surface (Fig. I.2B) indicates that nutrient-enriched deeper waters reached the surface, especially during March and April. The availability of new nutrients promoted the increase of Chl a concentration and the enhancement of PP at the ML during those productive months (Figs. I.2C, I.3 and I.4). In fact, both parameters were positively correlated to the ML depth (Chl a: Spearman’s rank r=0.42, p<0.001; PP: Spearman’s rank r=0.55, p<0.01). After the bloom, when surface waters became warmer, autotrophic biomass decreased in the ML and increased at the DCM (Fig. I.3C). The composition of autotrophs in this layer, as at the surface, was dominated by small cells (<2 μm). SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  49 The magnitude of the winter-spring bloom was similar to previous studies in these waters in terms of Chl a (Schmoker & Hernández-León 2013). These authors found maximum Chl a values of 0.25 mg m-3 at the mixed layer in 2007. They observed a decrease in Chl a within the bloom period as winter temperatures increased from year 2005 to 2007. Actually, the characteristic bloom did not take place during 2010, when winter temperatures above 19ºC were measured and Chl a remained below 0.1 mg m-3 in the ML (see Chapter II). In spite of low Chl a concentration (up to 0.3 mg m-3), PP showed quite high values not only within the bloom, but during the whole period studied. Average productivity rates measured here (3.731.1 mgC m-3 d-1) were higher than rates measured at the BATS station during the same period, which ranged 2.4-15.5 mgC m-3 d-1 (BATS interactive data access, http://bats.bios.edu/index.html, last access: July 2013). Absolute rates of PP could be overestimated because of the 13C method used here. However, a rather good agreement between both 13C and 14C isotopic methods has been referred in the literature (Slawyk et al. 1977, Hama et al. 1983). Furthermore, values and variability of the P:B ratios shown here are similar to other measurements in the area (Arístegui et al. 2001). On the other hand, a higher variability of our data may be unveiled by the frequency of the sampling, as we carried out a weekly sampling whereas data for the time-series station at the Sargasso Sea were obtained once a month. Additionally, the enhancement of PP could be caused by the fertilization effect of Saharan dust storms, frequent in the area (Goudie & Middleton 2001, Viana et al. 2002, Sarthou et al. 2007). A dust deposition event occurred in mid-January (data not shown), matching the peak of PP within that month. However, any dust event took place in December, when high rates were also measured. Nevertheless, relatively high values of PP and Chl a even when stratification conditions would preclude the entrance of nutrient-rich deeper waters, point out the existence of other short-scale events apart from winter convection that would enhance primary productivity in the ML. The planktonic community at the ML was dominated by picoplankton, especially autotrophic and heterotrophic prokaryotes, as expected in subtropical waters (Buck et al. 1996, Longhurst 1998). Prochlorococcus dominated picophytoplankton abundance and biomass (Figs. I.6 and I.9A), except during the productive period, when Syn and APE abundance increased and comprised the majority of the autotrophic biomass. The seasonality observed for cyanobacteria and picoeukaryotes has already been described for these waters (Baltar et al. 2009b, Schmoker et al. 2012, Schmoker & Hernández-León 2013) and it is a common feature of subtropical gyres (Zubkov et al. 2000, DuRand et al. 2001, Giovannoni & Vergin 2012). HP also reached the highest abundance and biomass during the productive period, maintaining the predominance over picophytoplankton even at that time (APico,ANF:HHP,HNF<1, Table I.1). The heterotrophy of the picoand nanoplankton community in the Canary Islands waters during both spring and summer seasons has also been reported by other authors (Arístegui & Montero 2005, Baltar et al. 2009a). By opposite, the dominance of autotrophic picoand nanophytoplankton was measured during the colder winter- CHAPTER I  50 spring period in 2005 (Schmoker et al. 2012). It is likely that less oligotrophic conditions in 2005 (up to 0.5 mg Chl a m-3) favored A:H ratios above 1, whereas lower Chl a concentration, as observed here, promoted the heterotrophic biomass dominance (Buck et al. 1996). However, during the bloom period, in February-March, diatoms substantially rose in abundance and dominated total autotrophic biomass (Figs. I.7 and I.9B). Moreover, only when diatoms increased in December, February and March, autotrophic biomass was higher than heterotrophic biomass (A:H>1, Table I.1). The appearance of a peak of diatoms at the beginning of the bloom period has been previously observed in Canary Islands waters (Ojeda 1998, Schmoker et al. 2014) but contrary to our results, their contribution to autotrophic biomass even at that time remained low. The large increase of diatom biomass was a striking result as higher temperatures than previous years (see Schmoker & Hernández-León 2013) would expect less mixing and nutrient availability and, thus, a less favorable scenario for these large algae. However, recent research has contradicted the traditional view that diatoms are favored by mixing and high nutrient conditions (Kemp & Villareal 2013). These authors argue that diatom species involved in diazotrophic diatom symbioses could enhance primary production in stratified and oligotrophic waters. This view would support our results, although we were not able to assess if within Chaetoceros sp, the major contributor to the high diatom biomass in February (Fig. I.10), some species would be involved in diazotrophic diatom assemblages. On the other hand, the increase in abundance and biomass of Chaetoceros sp could also be related to Saharan dust deposition events in this area (see Chapter II). In this sense, a dust storm observed in January (data not shown) could contribute to previously fertilize the ML, and thereby enhancing the availability of nutrients due to the subsequent deep water input and thus favoring the extraordinary increase of diatoms. Apart from the productive period, microplankton abundance was dominated by dinoflagellates, as it has been previously found in the area (Buck et al. 1996, Bode et al. 2001, Schmoker & HernándezLeón 2013), although we observed lower abundances. Schmoker and Hernández-León (2013) also found that Cil dominated microplanktonic biomass, whilst our results showed higher Din (1.6-21.2 mgC m-3) than Cil (0.6-1.5 mgC m-3) biomass (Fig. I.9B and C). However, higher values in December, February and June were due to the presence of only few cells of Kofoidinium velleloides, a rather large dinoflagellate. Furthermore, microplankton biomass data should be taken with caution as we did not make direct measurements of biovolumes in some cases (see Material and methods) and different conversion factors than previous years were used. SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  51 Figure I.10. Contribution of different species to diatom biomass (mgC m-3) in the ML from November 2010 to June 2011. The planktonic community dynamic was clearly controlled by bottom-up forces at a seasonal scale. Actually, the abundance of picoand nanoplankton was significantly correlated to average temperature in the ML (Table I.2). However, trophic interactions also influence the variability of the different planktonic groups at a short-term scale. HP could be consumed by HNF as a clear inverse pattern was observed between them (Fig. I.8 and Table I.2). Flagellates are known to be effective bacterivores (Azam et al. 1983) and not only upon HP, but also upon cyanobacteria. Here, a significant correlation between HNF and Syn was also measured (Table I.2). Similarly, a significant relationship between HNF and HP, as well as between HNF and Syn, has been observed in the Canaries (Arístegui & Montero 2005). An inverse relationship between autotrophic picoeukaryotes and nanoflagellates, and Din was also observed (Figs. I.6 and I.7 and Table I.2), suggesting that Din would be grazing upon APE and ANF. These results are partly supported by Schmoker & Hernández-León (2013) who found a strong correlation between microplankton and autotrophic picoplankton. They suggested that small-sized microplankton in these waters would be able to control small autotrophic cells. In any case, we are aware of the limitation to totally understand the relationships between different trophic levels of complex webs in subtropical waters through linear regressions alone. |D|J |F|M |A |M |J 0 20 40 200 Diatom biomass (mgC m -3 ) Rhizosolenia setigera Rhizosolenia fragilissima Rhizosolenia hebetata Leptocylindrus danicus Hemidiscus cuneiformis Guinardia striata Dactyliosolen mediterraneus Chaetoceros sp Coscinodiscus sp Climacodium frauenfeldianum Cerataulina pelagica Asterionellopsis glacialis CHAPTER I  52 Syn Pro APE HP ANF HNF Dia Din Cil Pro 0.083 APE 0.489*** -0.035 HP 0.541*** -0.320* 0.530*** ANF -0.218* 0.162 -0.089 -0.319* HNF -0.278* 0.089 -0.061 -0.310* 0.575*** Dia -0.122 -0.255 0.217 -0.077 0.142 0.031 Din 0.004 -0.302 -0.498* -0.063 -0.473* -0.277 -0.367 Cil 0.274 0.132 0.536** 0.272 -0.066 -0.064 0.101 -0.209 Temp -0.369** -0.720*** 0.448*** -0.636*** 0.484*** 0.307* -0.165 -0.190 -0.199 Table I.2. Correlation (Spearman’s rank, r) between abundance (cells mL-1) of Synechoccocus (Syn), Prochloroccocus (Pro), autotrophic picoeukaryotes (APE), heterotrophic prokaryotes (HP), autotrophic nanoflagellates (ANF), heterotrophic nanoflagellates (HNF), diatoms (Dia), dinoflagellates (Din) and ciliates (Cil); and average temperature in the ML (Temp). Significant correlations at p<0.05 (*), p<0.01 (**) and p<0.001 (***) levels are in bold. In conclusion, the oligotrophic waters of the Canary Current were dominated by picoplankton during the period studied, especially Prochlorococcus and heterotrophic prokaryotes. However, during the productive period Synechococcus and picoeukaryotes dominated the picophytoplankton, and diatoms became the major contributors to total autotrophic biomass. Thereby, diatoms were probably responsible for the highest rates of primary production in February and March. This variability of the phytoplanktonic community was driven by bottom-up forces at a seasonal scale, so that the availability of new nutrients during winter-spring allowed the enhancement of primary production at that period. However, other short-scale events such as Saharan dust deposition could be enhancing the primary productivity in the ML at a short-time scale even during stratified periods. We emphasize the observed and unexpected role of diatoms in these subtropical waters, which needs further research. Short-term variability would also be driven by top-down forces through feeding and grazing by nanoand microheterotrophs, as significant correlations were found between the latter and their potential preys: heterotrophic prokaryotes and autotrophic picoeukaryotes and nanoflagellates. Finally, the results presented here highlight the need to carry out oceanographic samplings at short-time scales to account for the total variability of the planktonic communities in subtropical waters. SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  53 V. ACKNOWLEDGMENTS This work was supported by the projects Lucifer (CMT2008‐03538) and Mafia (CTM2012-39587), both from the Spanish Ministry of Science and Innovation, and by a PhD fellowship to G. Franchy from the University of Las Palmas de Gran Canaria (ULPGC). We would like to thank to all the colleagues from the Lucifer project for the hard work on-board, and especially to M.L. Nieves. VI. REFERENCES Angel M (1989) Vertical profiles of pelagic communities in the vicinity of the Azores Front and their implications to deep ocean ecology. Prog Oceanogr 22:1-46 Arístegui J, Hernández-León S, Montero MF, Gómez M (2001) The seasonal planktonic cycle in coastal waters of the Canary Islands. Sci Mar 65:51-58 Arístegui J, Montero MF (2005) Temporal and spatial changes in plankton respiration and biomass in the Canary Islands region: The effect of mesoscale variability. J Marine Syst 54:65-82 Azam F, Fenchel T, Field JG, Gray JS, Meyerreil LA, Thingstad F (1983) The ecological role of water-column microbes in the sea Mar Ecol Prog Ser 10:257-263 Baltar F, Arístegui J, Gasol JA, Sintes E, Herndl GJ (2009a) Evidence of prokaryotic metabolism on suspended particulate organic matter in the dark waters of the subtropical North Atlantic. Limnol Oceanogr 54:182-193 Baltar F, Arístegui J, Montero MF, Espino M, Gasol JA, Herndl GJ (2009b) Mesoscale variability modulates seasonal changes in the trophic structure of nanoand picoplankton communities across the NW Africa-Canary Islands transition zone. Prog Oceanogr 83:180-188 Barton ED, Arístegui J, Tett P, Cantón Mand others (1998) The transition zone of the Canary Current upwelling region. Prog Oceanogr 41:455-504 Bode A, Barquero S, Varela M, Braun JG, de Armas D (2001) Pelagic bacteria and phytoplankton in oceanic waters near the Canary Islands in summer. Mar Ecol Prog Ser 209:1-17 Borsheim K, Bratbak G (1987) Cell volume to cell carbon conversion factors for a bacterivorous Monas sp. enriched from seawater. Mar Ecol Prog Ser 36:171-175 Braun JG (1980) Estudios de producción en aguas de las Islas Canarias I. Hidrografía, nutrientes y producción primaria. Bol Inst Esp Oceanogr 5:147-154 Buck K, Chavez F, Campbell L (1996) Basin-wide distributions of living carbon components and the inverted trophic pyramid of the central gyre of the North Atlantic Ocean, summer 1993. Aquat Microb Ecol 10:283-298 Calbet A, Landry MR (2004) Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol Oceanogr 49:51-57 Cianca A, Helmke P, Mouriño B, Rueda MJ, Llinás O, Neuer S (2007) Decadal analysis of hydrography and in situ nutrient budgets in the western and eastern North Atlantic subtropical gyre. J Geophys ResOceans 112:C07025 CHAPTER I  54 De León AR, Braun JG (1973) Annual cycle of primary production and its relation to nutrients in the Canary Islands waters. Bol Inst Esp Oceanogr 167:1-24 DuRand MD, Olson RJ, Chisholm SW (2001) Phytoplankton population dynamics at the Bermuda Atlantic Time-series station in the Sargasso Sea. Deep-Sea Res II 48:1983-2003 Giovannoni SJ, Vergin KL (2012) Seasonality in ocean microbial communities. Science 335:671-676 Goudie A, Middleton N (2001) Saharan dust storms: nature and consequences. Earth-Sci Rev 56:179-204 Haas L (1982) Improved epifluorescence microscopy for observing planktonic micro-organisms. Ann Inst Oceanogr 58:1-266 Hama T, Miyazaki T, Ogawa Y, Iwakuma T, Takahashi M, Otsuki A, Ichimura S (1983) Measurement of photosynthetic production of a marine phytoplankton population using a stable 13C isotope. Mar Biol 73:31-36 Hernández-León S, Almeida C, Becognée P, Yebra L, Arístegui J (2004) Zooplankton biomass and indices of grazing and metabolism during a late winter bloom in subtropical waters. Mar Biol 145:1191-1200 Hernández-León S, Gómez M, Arístegui J (2007) Mesozooplankton in the Canary Current System: The coastal-ocean transition zone. Prog Oceanogr 74:397-421 Hillebrand H, Dürselen CD, Kirschtel D, Pollingher U, Zohary T (1999) Biovolume calculation for pelagic and benthic microalgae. J Phycol 35:403-424 Horner R (2002) A Taxonomic Guide to Some Common Marine Phytoplankton, Vol. Biopress Limited, Bristol, England, UK Karl D, Christian J, Dore J, Hebel D, Letelier R, Tupas L, Winn C (1996) Seasonal and interannual variability in primary production and particle flux at Station ALOHA. Deep-Sea Res II 43:539-568 Kemp AE, Villareal TA (2013) High diatom production and export in stratified waters–A potential negative feedback to global warming. Prog Oceanogr 119:4-23 Li W, Rao S, Harrison W, Smith J, Cullen J, Irwin B, Platt T (1983) Autotrophic picoplankton in the tropical ocean. Science 219:292-295 Longhurst AR (1998) Ecological geography of the sea, Vol. Academic press Madin LP, Horgan EF, Steinberg DK (2001) Zooplankton at the Bermuda Atlantic Time-series Study (BATS) station: Diel, seasonal and interannual variation in biomass, 1994–1998. Deep-Sea Res II 48:20632082 Marañón E, Pérez V, Fernández E, Anadón Rand others (2007) Planktonic carbon budget in the eastern subtropical North Atlantic. Aquat Microb Ecol 48:261-275 Menden-Deuer S, Lessard EJ (2000) Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol Oceanogr 45:569-579 Menzel DW, Ryther JH (1960) The annual cycle of primary production in the Sargasso Sea off Bermuda. Deep-Sea Res 6:351-367 Moore HB (1950) The relation between the scattering layer and the Euphausiacea. Biol Bull 99:181-212 Ojeda A (1998) Dinoflagelados de Canarias: estudio taxonómico y ecológico. Ojeda A (2006) Dinoflagelados de Canarias: estudio taxonómico y ecológico, Vol. Instituto de Estudios Canarios, La Laguna, Tenerife, Spain SHORT-TERM VARIABILITY OF PLANKTONIC COMPOSITION  55 Ojeda A (2011) Diatomeas de las Aguas Costeras de las Islas Canarias, Vol. Instituto Canario de Ciencias Marinas (Gobierno de Canarias), Islas Canarias, Spain Pomeroy LR (1974) The Ocean's Food Web, A Changing Paradigm. Bioscience 24:499-504 Sarthou G, Baker AR, Kramer J, Laan Pand others (2007) Influence of atmospheric inputs on the iron distribution in the subtropical North-East Atlantic Ocean. Mar Chem 104:186-202 Schmoker C, Arístegui J, Hernández-León S (2012) Planktonic biomass variability during a late winter bloom in the subtropical waters off the Canary Islands. J Marine Syst 95:24-31 Schmoker C, Hernández-León S (2013) Stratification effects on the plankton of the subtropical Canary Current. Prog Oceanogr 119:24-31 Schmoker C, Ojeda A, Hernández-León S (2014) Patterns of plankton communities in subtropical waters off the Canary Islands during the late winter bloom. J Sea Res 85:155-161 Slawyk G, Collos Y, Auclair J-C (1977) The use of the 13C and 15N isotopes for the simultaneous measurement of carbon and nitrogen turnover rates in marine phytoplankton. Limnol Oceanogr 22:925-932 Steinberg DK, Carlson CA, Bates NR, Johnson RJ, Michaels AF, Knap AH (2001) Overview of the US JGOFS Bermuda Atlantic Time-series Study (BATS): a decade-scale look at ocean biology and biogeochemistry. Deep-Sea Res II 48:1405-1447 Strickland J, Parsons T (1972) A Practical Handbook of Seawater Analysis, Vol 167. Journal of the Fisheries Research Board of Canada, Otawa Tomas CR (1997) Identifying Marine Phytoplankton, Vol. Academic Press, San Diego, California, USA Uda M (1956) Researches on the fisheries grounds in relation to the scattering layer of supersonic wave. Introductory report. J Tokyo Univ Fish 42:103-111 Verity PG, Robertson CY, Tronzo CR, Andrews MG, Nelson JR, Sieracki ME (1992) Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol Oceanogr:1434-1446 Viana M, Querol X, Alastuey A, Cuevas E, Rodrıguez S (2002) Influence of African dust on the levels of atmospheric particulates in the Canary Islands air quality network. Atmos Environ 36:5861-5875 Yentsch CS, Menzel DW (1963) A method for the determination of phytoplankton chlorophyll and phaeophytin fluorescence. Deep-Sea Res I 10:221-231 Zubkov MV, Sleigh MA, Burkill PH, Leakey RJG (2000) Picoplankton community structure on the Atlantic Meridional Transect: a comparison between seasons. Prog Oceanogr 45:369-386 Zubkov MV, Sleigh MA, Tarran GA, Burkill PH, Leakey RJG (1998) Picoplanktonic community structure on an Atlantic transect from 50 degrees N to 50 degrees S. Deep-Sea Res I 45:1339-1355      PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 63 Sea surface temperature was obtained from the Deep Sea Buoy Network dataset (REDEXT, http://www.puertos.es/oceanografia_y_meteorologia/redes_de_medida/index.html, last access: April 2013) belonging to Puertos del Estado (Spanish Government). Hourly data were taken at 3 m depth by a SeaWatch buoy located to the northwest of Gran Canaria (28.20°N, 15.80°W, Fig. II.1) from February to June 2010. Chlorophyll a (Chl a) was measured by filtering 500 mL of seawater through a 25 mm Whatman GF/F filter and freezing it at -20ºC until its analysis at the laboratory. The extraction procedure consisted in placing the filter in 90% acetone at -20ºC in the dark, during at least 20 hours, and following the acidification method by Strickland and Parsons (1972a). Pigments were measured on a Turner Designs 10A Fluorometer, previously calibrated with pure Chl a (Yentsch & Menzel 1963). From these data at 20 m, fluorescence in the whole profile was converted to Chl a (Chl a = -0.001 + 26.05 · fluorescence, r2=0.65, p<0.001 for the SBE19plus and Chl a = 0.001 + 0.21 · fluorescence, r2=0.12, p>0.05 for the SBE25) as an indicator of phytoplankton biomass. The relationship between fluorescence and Chl a for the SBE25 was not significant because of the scarcity of data and the fact that all concentrations measured using that sensor were very low. However, it was the only way to obtain chlorophyll data for all cruises in May and until June, 9. Picoplanktonic organisms (0.2-2 μm) were sampled in 1.6 mL tubes, fixed immediately with 100 μL of 20% paraformaldehyde, incubated at 4ºC during half an hour, frozen in liquid nitrogen, and finally kept at -80ºC until further analysis. Samples were analyzed later by flow cytometry using a FACScalibur Cytometer (Beckton and Dickinson). Side scatter (SSC) and fluorescence parameters were obtained to distinguish between autotrophic picoeukaryotes (APE), cyanobacteria (Prochlorococcus, Pro, and Synechococcus, Syn) and heterotrophic prokaryotes (HP). Samples were run until 10000 events were reached or after 2 min at high speed to measure phototrophic organisms (APE, Pro and Syn), and at low speed for HP samples which were pre-stained with SYTO-13. Autoand heterotrophic nanoflagellates (ANF and HNF) were fixed using 540 μL of 25% glutaraldehyde in a tube containing 45 mL of seawater. Samples were kept at 4ºC in dark until the sample was filtered onto a 0.6 μm black polycarbonate filter placed over a backing filter and stained by diamidino-2-phenylindole (DAPI) for 5 min. The filter was immediately mounted on a microscope slide with low-fluorescence immersion oil and kept at -20ºC. Finally, it was analyzed by epifluorescence microscopy with a Zeiss Axiovert 35 microscope (Haas 1982). Microplanktonic organisms were kept in 500 mL dark bottles fixed with 1.5 mL of acid lugol and analyzed afterwards by the Utermöhl technique. A 100 mL subsample was settled for 48 h in a composite chamber. The bottom chamber was then examined by an inverted Zeiss Axiovert 35 CHAPTER II  64 microscope to identify the main microplanktonic groups: diatoms (Dia), dinoflagellates (Din), ciliates (Cil), and copepod nauplii or copepodites. Only samples from station 3 at 20 m were analyzed. The abundance of organisms obtained by flow cytometry was converted to biomass using the carbon conversion factor of: 17 fgC cell-1 for HP (Bode et al. 2001), 29 fgC cell-1 for Pro, 100 fgC cell-1 for Syn (Zubkov et al. 2000) and 1500 fgC cell-1 for APE (Zubkov et al. 1998). After biovolume estimation by microscopy, nanoflagellates were converted to carbon using the factor of 220 fgC μm3 for HNF (Borsheim & Bratbak 1987) and the equation 0.433(BV)0.863 pgC cell-1 for ANF (Verity et al. 1992). Finally, microplanktonic abundance was converted to biomass from total biovolume data obtained directly by microscopy for the most abundant cells or from previous measurements in these waters (A. Ojeda, unpublished data). If these data were not available, an average size from the literature for every organism or group (Tomas 1997, Horner 2002, Ojeda 2006, Ojeda 2011) was assumed, fitting them to the suitable shape following Hillebrand et al. (1999). The corresponding parameters were used for Dia (log a = -0.541, b = 0.811 for V<3000 μm3; log a = 0.933, b = 0.881 for V>3000 μm3), Din (log a = -0.353, b = 0.864) and Cil (log a = -0.639, b = 0.984 for aloricate ciliates; log a = -0.168, b = 0.841 for tintinnids) to obtain the biovolume to biomass conversion factor (Menden-Deuer & Lessard 2000) (log pgC cell-1 = log a + b · log V). Some species were not taken into account to calculate the microbial biomass because of the impossibility of finding their size range or biovolume in the literature, but these species were always practically negligible in terms of abundance. Average Chl a concentration, abundance and biomass were calculated without station 1 as a significant difference (Kruskal-Wallis test, p<0.001) was found between this coastal station and the stations 2, 3 and 4 for Chl a. Primary production (PP) was measured using the 13C method (Hama et al. 1983) in station 2 at 20 m depth. Water samples were transferred to 2L polycarbonate bottles previously rinsed with 10% HCl and distilled-deionized water and NaH13CO3 was added at about 10% of total inorganic carbon in the ambient water. Samples were incubated in a tank on-deck for between 6.5 and 10 h depending on the cruise excepting on March 10, when the incubation lasted 22 h because of logistical issues. Running surface seawater and appropriate meshes simulated in situ temperature and light intensity. Initial and final particulate organic carbon (POC) and particulate material used for isotopic analysis were filtered through precombusted GF/F filters (5 h, 450ºC). These filters were frozen and stored at -20ºC until analysis. POC was measured using a CHN analyzer (Carlo Erba EA 1108) and isotopes in a mass spectrometer equipped with an elemental analyzer (Flash EA 11 ThermoFinnigan with Deltaplus). Additional PP rates from satellite data were obtained from the Ocean Productivity web page (http://www.science.oregonstate.edu/ocean.productivity/index.php, last acces: June 2014). These PP values are based on the original description of the Vertically Generalized Production Model PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 65 (VGPM) (Behrenfeld & Falkowski 1997), surface chlorophyll concentrations (MODIS), sea surface temperature data (MODIS 4), and cloud-corrected incident daily photosynthetically active radiation (PAR) (MODIS). Euphotic depths are calculated from surface chlorophyll concentrations following Morel and Berthon (1989). Average surface PP rates were obtained every 8 days from 10 locations close to the area studied (28.08ºN, 15.25ºW; 28.08ºN, 15.42ºW; 28.25ºN, 15.25ºW; 28.25ºN, 15.42ºW; 28.42ºN, 15.25ºW; 28.42ºN, 15.42ºW; 28.58ºN, 15.25ºW; 28.58ºN, 15.42ºW; 28.75ºN, 15.25ºW; 28.75ºN, 15.42ºW), from January to July 2010. Atmospheric total suspended particulate matter (TSM) was collected every 6 days from November 2009 to June 2010. A high volume sampler pumping system (MCV) was used at a flow rate of 50 m3 h-1 and onto Whatman GF/A 20 cm x 25 cm fiberglass filters. Each sampling period started at 08:00 h local time and lasted 24 h. Three collectors were located in the northeast of the island (Fig. II.1) and placed 10 m above the ground. For Fe, Al and Mn analysis, filters were treated with nitric and hydrochloric acid, according to the Beyer modified method (López-Cancio et al. 2008). These elements were determined by atomic emission spectrophotometry using an inductively coupled plasma optical emission spectrometer (Perkin Elmer 3200 DV). The zooplankton sampling procedure is explained in detail elsewhere (Herrera et al. in prep.). Briefly, organisms were captured in vertical hauls with a double WP-2 net equipped with 100 μm mesh nets. One of the samples was used for measuring biomass as dry weight using a standard protocol (Lovegrove 1966). The average zooplankton biomass was calculated from the four stations data as no statistical differences were found (Herrera et al. in prep.). III. RESULTS A highly stratified water column was characteristic during the whole period from February to June 2010. Average temperature and potential density were above 19ºC and 26.4 kg m-3, respectively, in the upper 100 m layer (Fig. II.2A and B). Average MLD ranged between 110 m in April to 38 m in June, with no differences among stations (Kruskal-Wallis test, p>0.05). Very low values of Chl a (<0.3 mg m-3) were measured within the mixed layer, and at the deep chlorophyll maximum (DCM) the highest average chorophyll concentration was 0.4 mg m-3 at the end of March and during April (Fig. II.2C). CHAPTER II  66 Figure II.2. Temporal variability of temperature (2A, ºC), potential density (2B, kg m-3) and Chl a (2C, mg m-3) from surface to 200 db. MLD is drawn in the upper panel (black solid line). Data correspond to average from station 2, 3 and 4; from February to June 2010. CTD data were available after March 17. Before that date sea surface temperature is showed since February 2 (from SeaWatch Buoy data). Saharan dust events were identified in every month from TSM data, from February to June (Fig. II.3, upper panel). The highest value (521 ± 71 μg m-3) was reached during March, coinciding with maximum concentrations of iron and aluminum (Fig. II.3, central and lower panels). Less intense TSM maxima also coincided with relative maxima in metal concentrations, but the magnitude of the increment was not proportional in all cases. In May, an intense maximum of aluminum was found without observing a similar increase of iron. In June, a rather high value of iron concentration was reached without observing a parallel increase in aluminum. Nevertheless, a significant correlation (Spearman’s rank r>0.41, p<0.001) was found for TSM and the two metals. PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 67 |FIM IAIM IJ| 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 Aluminum concentration (μg m -3 )  Figure II.3. Atmospheric total suspended matter (TSM, μg m-3) and iron and aluminum concentration (μg m-3) from February to June 2010. Data showed as average and standard deviation. 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 Iron concentration (μg m -3 ) 0 100 200 300 400 500 600 TSM (μg m -3 ) CHAPTER II  68 In spite of the quite low Chl a concentration observed at 20 m from February to June (Figs. II.2C and II.4, upper panel), unrealistic high values of PP were measured. Rates were maximal at the end of March (56.2 ± 6.9 mgC m-3 d-1) and in May and June (Fig. II.4, central panel), reaching rates up to 97.4 ± 53.3 mgC m-3 d-1. Given these unlikely rates measured, we used surface PP rates from satellite data (Fig. II.4, lower panel). Surface PP rates were rather low during the period studied, but higher rates were also observed at the end of March (414.9 ± 60.3 mgC m-2 d-1) and June (449.8 ± 60.6 mgC m-2 d-1). Furthermore, a relative increase of surface PP rates was observed almost every month from January to May after every dust event observed from TSM data (Fig. II.5). Figure II.4. Average (±SD) Chl a concentration (mg m-3) and primary production measured at the mixed layer (PP measured, mgC m-3 d-1) and from satellite data (PP satellite, mgC m-2 d-1) from February to June 2010. FI MI AI MIJ 150 200 250 300 350 400 450 500 550 PP satellite (mgC m -2 d -1 ) 0 20 40 60 80 100 120 140 160 PP measured (mgC m -3 d -1 ) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 Chlorophyll a (mg m -3 ) PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 69 Figure II.5. Primary production from satellite data (PP satellite, mgC m-2 d-1) and atmospheric total suspended matter (TSM, μg m-3) from February to June 2010. Both autotrophic and heterotrophic organisms kept quite low abundances (Fig. II.6). Average values were below 4·103, 4·104 and 0.3·103 cells mL-1 for Syn, Pro and APE respectively. Dia showed cell numbers below 1 cell mL-1, while ANF reached more than 3·102 cells mL-1. Dinoflagellates varied between 4 and 27 cells mL-1. HP were below 16·104 cells mL-1, HNF varied between 1 and 6·102 cells mL-1 and Cil were below 2 cells mL-1. Most of planktonic groups showed a low variability during the period studied and a clear signal was not observed after every dust event. However, the abundance of Syn and APE was minimal after the deposition event on 18 March, and maximal for ANF and HNF (Fig. II.6). The effect of dust upon the planktonic community was estimated from the dust deposition event observed on 18 March. For that, the difference between parameters, before (two samplings before) and after (two samplings after) this date was calculated. Negligible and non-significant changes (t test, p>0.05) in PP and Chl a were measured (Fig. II.7). The response of the different planktonic groups considered was quite different, finding an increase in abundance (Fig. II.8A) and biomass (Fig. II.8B) for nanoand microplankton, excluding Dia. The latter organisms decreased in abundance but increased significantly their biomass. In the case of picoplankton, autotrophic organisms (Syn, Pro, APE) showed a negative change in abundance and biomass (Fig. II.8A and B), while HP increased. Mesozooplankton (Meso) also showed a positive change in biomass (Fig. II.8B). However, the changes observed after the dust event were only significant (t test) in the case of Syn and APE for abundance and biomass, and for Dia biomass. The positive changes of HNF abundance and Meso biomass were also close to the level of significance (p=0.06). |J|FIMIAIMIJ| 200 250 300 350 400 450 500 550 PP satellite (mgC m -2 d -1 ) 0 100 200 300 400 500 600 TSM (μg m -3 ) CHAPTER II  70 0 2 4 6 8 10 12 14 16 18 Syn (10 3 cells mL -1 ), Pro & HP (10 4 cells mL -1 ) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 APE (10 3 cells mL -1 ) & Dia (cells mL -1 ) 0 2 4 6 8 10 ANF & HNF (10 2 cells mL -1 ) FIM IA IM IJ 0 5 10 15 20 25 30 Din (cells mL -1 ) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 Cil (cells mL -1 ) Figure II.6. Average (±SD) abundance (cells mL-1) of HP, Syn, Pro, APE, Dia, ANF, HNF, Din and Cil during the period studied in 2010. Dust deposition is marked at the top with grey arrows for relative low dust events, and a black arrow for the highest dust event in March. PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 71 Figure II.7. Comparison between average (±SD) PP (Satellite PP, mgC m-2 d-1) and Chl a (mg m-3) values before and after the highest dust event on 18 March. These changes in abundance and biomass of some planktonic groups entailed considerable relative changes (Fig. II.9). The negative effect of the dust event upon APE and Syn supposed a reduction in their abundance and biomass of 79 ± 154 and 70 ± 164 %, respectively (Fig. II.9A and B). HNF abundance increased more than 100% (Fig. II.9A) and the positive change in Meso biomass supposed an increase of 95 ± 213 % (Fig. II.9B). The highest relative change was observed for Dia biomass which supposed an increment of 1308 ± 1885 % (Fig. II.9B), despite its abundance decreased by 11 ± 68 % (Fig. II.9A). Before After 0 50 100 150 200 250 300 350 400 Satellite PP (mgC m -2 d -1 ) 0.00 0.02 0.04 0.06 0.08 0.10 0.12 Chl a (mg m -3 ) CHAPTER II  72 -20000 -10000 0 10000 20000 30000 40000 Change in Abundance (cells mL -1 ) APE Syn Pro HP ANF HNF Dia Din Cil ** 0.05 0.06 A -1012345 Change in Biomass (mgC m -3 /mg dw m -3 ) APE Syn Pro HP ANF HNF Dia Din Cil Meso ** * 0.05 0.06 B Figure II.8. Average (+SD) change in abundance (8A, cells mL-1) and biomass (8B, mgC m-3) for pico-, nanoand microplankton; mgDW m-3 for mesozooplankton). * p<0.05, ** p<0.01 and *** p<0.001. p-values below or equal to 0.06 are also shown. PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 79 organisms after a Saharan dust storm. This enhancement has been reported previously by Boyd et al. (2007) after the enrichment promoted by mesoscale iron experiments, although not in all cases. In summary, our results showed that the Canary Islands waters would be potentially affected by the Saharan dust deposition during the whole period studied. The effect of these events was strongly supported by TSM, hydrographic data and nutrient concentration in the mixed layer. These data suggest that in addition to the heavy dust event observed in March, the smaller but numerous dust events in April, May and June would potentially reinforce the effect of atmospheric deposition. Furthermore, despite the low values of PP observed at the surface, a relative short-term variability seemed to be related to the Saharan dust events identified. In any case, different responses were found in the planktonic community. On one hand, diatoms increased their biomass by more than 1000%, and mesozooplankton also showed an increment in biomass, as it has been observed before. On the other hand, picophytoplankton seemed to be negatively affected, but if this effect was directly caused by dust or indirectly by grazing losses remains unknown. This unequal effect upon autotrophs, favoring diatoms instead the small autotrophs, could also enhanced the biological pump due to a higher carbon export flux resulted from diatom sedimentation. Hence, the Saharan dust deposition would be partly fuelling the primary production in these oligotrophic waters, and could also enhance the carbon export, especially during stratified periods, when it would be the most important nutrient source. However, Saharan dust fertilization seems to promote a modest increase in productivity or it would not be detected as primary producers could be rapidly consumed. Nevertheless, further research is needed to better understand the potential influence of this process in the subtropical northeast Atlantic. In this sense, an intensive temporal sampling would help to properly quantify the timing and intensity of the response of the different planktonic groups in the field, especially in subtropical waters, given the complexity and quickness of the biological interactions. V. ACKNOWLEDGMENTS This work was supported by the projects Lucifer (CMT2008‐03538/MAR) and Mafia (CTM201239587), both from the Spanish Ministry of Science and Innovation, and by a PhD fellowship to G. Franchy from the University of Las Palmas de Gran Canaria (ULPGC). We would like to thank to all the colleagues from the Lucifer project for the hard work on-board, and especially to M.L. Nieves. We are also grateful to J. Arístegui, M. Benavides and two anonymous reviewers for their helpful comments and remarks that considerably improved this work. This study is a contribution to the international IMBER Project. CHAPTER II  80 VI. REFERENCES Alonso-González IJ, Arístegui J, Vilas JC, Hernández-Guerra A (2009) Lateral POC transport and consumption in surface and deep waters of the Canary Current region: A box model study. Global Biogeochem Cycles 23:GB2007 Arístegui J, Hernández-León S, Montero MF, Gómez M (2001) The seasonal planktonic cycle in coastal waters of the Canary Islands. Sci Mar 65:51-58 Arístegui J, Sangra P, Hernández-León S, Cantón M, Hernández-Guerra A, Kerling J (1994) Island-induced eddies in the Canary Islands. Deep-Sea Res I 41:1509-1525 Azam F, Fenchel T, Field JG, Gray JS, Meyerreil LA, Thingstad F (1983) The ecological role of water-column microbes in the sea Mar Ecol Prog Ser 10:257-263 Barton ED, Arístegui J, Tett P, Cantón Mand others (1998) The transition zone of the Canary Current upwelling region. Prog Oceanogr 41:455-504 Barton ED, Arístegui J, Tett P, Navarro-Pérez E (2004) Variability in the Canary Islands area of filament-eddy exchanges. Prog Oceanogr 62:71-94 Behrenfeld MJ, Falkowski PG (1997) Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol Oceanogr 42:1-20 Benavides M, Arístegui J, Agawin N, Cancio JL, Hernández-León S (2013) Enhancement of nitrogen fixation rates by unicellular diazotrophs vs. Trichodesmium after a dust deposition event in the Canary Islands. Limnol Oceanogr 58:267-275 Bishop JKB, Davis RE, Sherman JT (2002) Robotic observations of dust storm enhancement of carbon biomass in the North Pacific. Science 298:817-821 Bode A, Barquero S, Varela M, Braun JG, de Armas D (2001) Pelagic bacteria and phytoplankton in oceanic waters near the Canary Islands in summer. Mar Ecol Prog Ser 209:1-17 Bollens GCR, Landry M (2000) Biological response to iron fertilization in the eastern equatorial Pacific (IronEx II). II. Mesozooplankton abundance, biomass, depth distribution and grazing. Mar Ecol Prog Ser 201:43-56 Bonnet S, Guieu C, Chiaverini J, Ras J, Stock A (2005) Effect of atmospheric nutrients on the autotrophic communities in a low nutrient, low chlorophyll system. Limnol Oceanogr:1810-1819 Borsheim K, Bratbak G (1987) Cell volume to cell carbon conversion factors for a bacterivorous Monas sp. enriched from seawater. Mar Ecol Prog Ser 36:171-175 Boyd PW, Jickells T, Law C, Blain Sand others (2007) Mesoscale iron enrichment experiments 1993-2005: Synthesis and future directions. Science 315:612-617 Braun JG (1980) Estudios de producción en aguas de las Islas Canarias I. Hidrografía, nutrientes y producción primaria. Bol Inst Esp Oceanogr 5:147-154 Buck K, Chavez F, Campbell L (1996) Basin-wide distributions of living carbon components and the inverted trophic pyramid of the central gyre of the North Atlantic Ocean, summer 1993. Aquat Microb Ecol 10:283-298 Calbet A, Landry MR (2004) Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol Oceanogr 49:51-57 PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 81 Cianca A, Helmke P, Mouriño B, Rueda MJ, Llinás O, Neuer S (2007) Decadal analysis of hydrography and in situ nutrient budgets in the western and eastern North Atlantic subtropical gyre. J Geophys Res-Oceans 112:C07025 Chami M, Mallet M, Gentili B (2012) Quantitative analysis of the influence of dust sea surface forcing on the primary production of the subtropical Atlantic Ocean using a ten-year time series of satellite observations. J Geophys Res 117:C07008 De Baar HJW, Boyd PW, Coale KH, Landry MRand others (2005) Synthesis of iron fertilization experiments: from the Iron Age in the Age of Enlightenment. J Geophys Res-Oceans 110:C09S16 De León AR, Braun JG (1973) Annual cycle of primary production and its relation to nutrients in the Canary Islands waters. Bol Inst Esp Oceanogr 167:1-24 Donaghay PL, Liss PS, Duce RA, Kester DRand others (1991) The role of episodic atmospheric nutrient inputs in the chemical and biological dynamics of oceanic ecosystems. Oceanography 4:62-70 Duarte CM, Dachs J, Llabrés M, Alonso-Laita Pand others (2006) Aerosol inputs enhance new production in the subtropical northeast Atlantic. J Geophys Res 111:G04006 Duce RA, Tindale NW (1991) Atmospheric transport of iron and its deposition in the ocean. Limnol Oceanogr:1715-1726 Eker-Develi E, Kideys AE, Tugrul S (2006) Role of Saharan dust on phytoplankton dynamics in the northeastern Mediterranean. Mar Ecol Prog Ser 314:61-75 Giovagnetti V, Brunet C, Conversano F, Tramontano F, Obernosterer I, Ridame C, Guieu C (2013) Assessing the role of dust deposition on phytoplankton ecophysiology and succession in a low-nutrient lowchlorophyll ecosystem: a mesocosm experiment in the Mediterranean Sea. Biogeosciences 10:29732991 Goudie A, Middleton N (2001) Saharan dust storms: nature and consequences. Earth-Sci Rev 56:179-204 Guerzoni S, Chester R, Dulac F, Herut Band others (1999) The role of atmospheric deposition in the biogeochemistry of the Mediterranean Sea. Prog Oceanogr 44:147-190 Haas L (1982) Improved epifluorescence microscopy for observing planktonic micro-organisms. Ann Inst Oceanogr 58:1-266 Hama T, Miyazaki T, Ogawa Y, Iwakuma T, Takahashi M, Otsuki A, Ichimura S (1983) Measurement of photosynthetic production of a marine phytoplankton population using a stable 13C isotope. Mar Biol 73:31-36 Harris G (1986) Phytoplankton Ecology: structure, function and fluctuation, Vol. Chapman and Hall, LondonNew York Henjes J, Assmy P, Klaas C, Verity P, Smetacek V (2007) Response of microzooplankton (protists and small copepods) to an iron-induced phytoplankton bloom in the Southern Ocean (EisenEx). Deep-Sea Res I 54:363-384 Hernández-León S, Almeida C, Becognée P, Yebra L, Arístegui J (2004) Zooplankton biomass and indices of grazing and metabolism during a late winter bloom in subtropical waters. Mar Biol 145:1191-1200 Herrera I, López-Cancio J, Yebra L, Hernández-Léon S (in prep.) Influence of dust deposition events and the lunar cycle on epipelagic zooplankton biomass and metabolism in oligotrophic waters. In preparation CHAPTER II  82 Herut B, Zohary T, Krom M, Mantoura RFCand others (2005) Response of East Mediterranean surface water to Saharan dust: On-board microcosm experiment and field observations. Deep-Sea Res II 52:30243040 Hillebrand H, Dürselen CD, Kirschtel D, Pollingher U, Zohary T (1999) Biovolume calculation for pelagic and benthic microalgae. J Phycol 35:403-424 Horner R (2002) A Taxonomic Guide to Some Common Marine Phytoplankton, Vol. Biopress Limited, Bristol, England, UK Jickells T (1999) The inputs of dust derived elements to the Sargasso Sea; a synthesis. Mar Chem 68:5-14 Landry MR, Barber RT, Bidare RR, Chai Fand others (1997) Iron and grazing constraints on primary production in the central equatorial Pacific: an EqPac synthesis. Oceanography 42:405-418 Landry MR, Constantinou J, Latasa M, Brown SL, Bidigare RR, Ondrusek ME (2000a) Biological response to iron fertilization in the eastern equatorial Pacific (IronEx II). III. Dynamics of phytoplankton growth and microzooplankton grazing. Mar Ecol Prog Ser 201:57-72 Landry MR, Ondrusek ME, Tanner SJ, Brown SLand others (2000b) Biological response to iron fertilization in the eastern equatorial Pacific (IronEx II). I. Microplankton community abundances and biomass. Mar Ecol Prog Ser 201:17-42 Legendre L, Le Fèvre J (1995) Microbial food webs and the export of biogenic carbon in oceans. Aquat Microb Ecol 9:69-77 López-Cancio J, Castellano AV, Hernández MC, Bethencourt RG, Ortega EM (2008) Metallic species in atmospheric particulate matter in Las Palmas de Gran Canaria. J Hazard Mater 160:521-528 Lovegrove T (1966) The determination of the dry weight of plankton and the effect of various factors on the values obtained. In: Barnes H (ed) Some contemporary studies in Marine Sciences. George Allen and Unwin Ltd., London, p 429-467 Mahaffey C, Bjorkman KM, Karl DM (2012) Phytoplankton response to deep seawater nutrient addition in the North Pacific Subtropical Gyre. Mar Ecol Prog Ser 460:13-34 Marañón E, Behrenfeld MJ, González N, Mouriño B, Zubkov MV (2003) High variability of primary production in oligotrophic waters of the Atlantic Ocean: uncoupling from phytoplankton biomass and size structure. Mar Ecol Prog Ser 257:1-11 Marañón E, Fernández A, Mourino-Carballido B, Martínez-García Sand others (2010) Degree of oligotrophy controls the response of microbial plankton to Saharan dust. Limnol Oceanogr 55:2339-2352 Marañón E, Holligan PM, Varela M, Mouriño B, Bale AJ (2000) Basin-scale variability of phytoplankton biomass, production and growth in the Atlantic Ocean. Deep-Sea Res I 47:825-857 McAndrew PM, Bjorkman K, Church MJ, Morris PJ, Jachowski N, Williams P, Karl DM (2007) Metabolic response of oligotrophic plankton communities to deep water nutrient enrichment. Mar Ecol Prog Ser 332:63-75 Menden-Deuer S, Lessard EJ (2000) Carbon to volume relationships for dinoflagellates, diatoms, and other protist plankton. Limnol Oceanogr 45:569-579 Moore C, Mills M, Arrigo K, Berman-Frank Iand others (2013) Processes and patterns of oceanic nutrient limitation. Nature Geoscience 6:701-710 PLANKTON COMMUNITY RESPONSE TO SAHARAN DUST EVENTS 83 Morel A, Berthon J-F (1989) Surface pigments, algal biomass profiles, and potential production of the euphotic layer: Relationships reinvestigated in view of remote-sensing applications. Limnol Oceanogr 34:15451562 Neuer S, Cianca A, Helmke P, Freudenthal Tand others (2007) Biogeochemistry and hydrography in the eastern subtropical North Atlantic gyre. Results from the European time-series station ESTOC. Prog Oceanogr 72:1-29 Ohde T, Siegel H (2010) Biological response to coastal upwelling and dust deposition in the area off Northwest Africa. Cont Shelf Res 30:1108-1119 Ojeda A (2006) Dinoflagelados de Canarias: estudio taxonómico y ecológico, Vol. Instituto de Estudios Canarios, La Laguna, Tenerife, Spain Ojeda A (2011) Diatomeas de las Aguas Costeras de las Islas Canarias, Vol. Instituto Canario de Ciencias Marinas (Gobierno de Canarias), Islas Canarias, Spain Paytan A, Mackey KRM, Chen Y, Lima IDand others (2009) Toxicity of atmospheric aerosols on marine phytoplankton. P Natl Acad Sci 106:4601-4605 Pulido-Villena E, Rérolle V, Guieu C (2010) Transient fertilizing effect of dust in P-deficient LNLC surface ocean. Geophys Res Lett 37 Ridame C, Dekaezemacker J, Guieu C, Bonnet S, L’Helguen S, Malien F (2014) Phytoplanktonic response to contrasted Saharan dust deposition events during mesocosm experiments in LNLC environment. Biogeosciences Discussion 11:753–796 Sarthou G, Baker AR, Kramer J, Laan Pand others (2007) Influence of atmospheric inputs on the iron distribution in the subtropical North-East Atlantic Ocean. Mar Chem 104:186-202 Schmoker C, Hernández-León S (2013) Stratification effects on the plankton of the subtropical Canary Current. Prog Oceanogr 119:24-31 Slawyk G, Collos Y, Auclair J-C (1977) The use of the 13C and 15N isotopes for the simultaneous measurement of carbon and nitrogen turnover rates in marine phytoplankton. Limnol Oceanogr 22:925932 Strickland J, Parsons T (1972) A practical handbook of seawater analysis. J Fish Res Board Can 167:281 Tomas CR (1997) Identifying Marine Phytoplankton, Vol. Academic Press, San Diego, California, USA Tsuda A, Saito H, Nishioka J, Ono T (2005) Mesozooplankton responses to iron-fertilization in the western subarctic Pacific (SEEDS2001). Prog Oceanogr 64:237-251 Tsuda A, Takeda S, Saito H, Nishioka Jand others (2007) Evidence for the grazing hypothesis: Grazing reduces phytoplankton responses of the HNLC ecosystem to iron enrichment in the western subarctic Pacific (SEEDS II). J Oceanogr 63:983-994 Verity PG, Robertson CY, Tronzo CR, Andrews MG, Nelson JR, Sieracki ME (1992) Relationships between cell volume and the carbon and nitrogen content of marine photosynthetic nanoplankton. Limnol Oceanogr:1434-1446 Viana M, Querol X, Alastuey A, Cuevas E, Rodrıguez S (2002) Influence of African dust on the levels of atmospheric particulates in the Canary Islands air quality network. Atmos Environ 36:5861-5875 CHAPTER II  84 Volpe G, Banzon VF, Evans RH, Santoleri R, Mariano AJ, Sciarra R (2009) Satellite observations of the impact of dust in a low-nutrient, low-chlorophyll region: Fertilization or artifact? Global Biogeochem Cycles 23:GB3007 Wang SH, Hsu NC, Tsay SC, Lin NH, Sayer AM, Huang SJ, Lau WKM (2012) Can Asian dust trigger phytoplankton blooms in the oligotrophic northern South China Sea? Geophys Res Lett 39:L05811 Yentsch CS, Menzel DW (1963) A method for the determination of phytoplankton chlorophyll and phaeophytin fluorescence. Deep-Sea Res I 10:221-231 Zubkov MV, Sleigh MA, Burkill PH, Leakey RJG (2000) Picoplankton community structure on the Atlantic Meridional Transect: a comparison between seasons. Prog Oceanogr 45:369-386 Zubkov MV, Sleigh MA, Tarran GA, Burkill PH, Leakey RJG (1998) Picoplanktonic community structure on an Atlantic transect from 50 degrees N to 50 degrees S. Deep-Sea Res I 45:1339-1355 Microzooplankton feeding impact on the microbial community of the Canary Islands waters CAPÍTULO III CHAPTER III     MICROZOOPLANKTON FEEDING IMPACT ON THE MICROBIAL COMMUNITY  87 ABSTRACT Microzooplankton are major grazers in the ocean but the knowledge about their feeding impact in subtropical gyres is rather limited. In order to assess the grazing activity of these organisms in the subtropical waters of the Northeast Atlantic we carried out several dilution experiments in 2010 and 2011 to the north of the Canary Islands. During the period studied heterotrophic prokaryotes and Prochloroccocus were the most abundant preys and heterotrophic nanoflagellates were the dominant consumers. We found an unequal impact of microzooplankton feeding on the different microbial components. These differences were not related to temperature, chlorophyll a or community composition, whereas a significant correlation was found between microzooplankton grazing and the growth rate of their prey. We found the highest grazing rates upon large-sized cells, autotrophic nanoflagellates (2.14 ± 0.74 d-1) and diatoms (1.93 ± 0.30 d-1), which also showed the highest growth. Contrary, small autotrophic cells, Synechoccocus (0.22 ± 0.10 d-1), Prochloroccocus (0.27 ± 0.26 d-1) and autotrophic picoeukaryotes (0.89 ± 0.00 d-1), were grazed at lower rates. Heterotrophic consumption was prevalent (1.12 ± 0.55 and 1.59 ± 0.31 d-1 for HP and HNF, respectively) and could be the cause of non-significant grazing values obtained. Our results showed that more than 100% of the daily primary production was removed, although the impact on the different autotrophic organisms was unequal. Consumption over eukaryotic cells was higher than the impact on prokaryotic production. The daily procaryotic production was almost totally removed by microzooplankton (96%), and more than 100% of the daily production of heterotrophic nanoflagellates was consumed. CHAPTER III  88 I. INTRODUCTION Nanoand microzooplankton (heterotrophic organisms ranging 2-200 μm according to Sieburth et al. 1978) comprise a heterogeneous group including heterotrophic nanoflagellates, ciliates and heterotrophic dinoflagellates which are key components of the food webs in the ocean (Sherr & Sherr 2002). They are major grazers not only in tropical and subtropical areas where they account for 72-76% of the daily primary production grazed, but also in productive ecosystems where the percentage of the primary production consumed per day is also high: 60-75% (Calbet & Landry 2004). In addition to their large impact on primary production, they are also an important food resource for mesozooplankton contributing up to 50% to the daily copepod diet in oligotrophic waters (Calbet 2008). Thus, these organisms play a key role in the recycling of energy and matter within the microbial loop (Pomeroy 1974, Azam et al. 1983, Sherr & Sherr 2002) and also in the flow to higher trophic levels (Sherr & Sherr 2002). Despite their importance, the knowledge about the feeding impact of micrograzers (nanoand microheterotrophs) is limited in subtropical gyres (Schmoker et al. 2013). The dilution method (Landry & Hassett 1982) has been widely used to assess the microzooplankton feeding impact through the estimation of phytoplankton mortality rates by grazing. Furthermore, the method provides a simultaneous estimation of phytoplankton growth. This approach is based on the premise that grazing pressure is proportionally reduced with the dilution of seawater. As a result, the net growth rate of preys increases linearly with the increasing dilution of natural seawater due to the decrease of the encounter rates between preys and their grazers. Additionally, this technique entails two other assumptions: the intrinsic growth rate of preys is not density-dependent and the density of preys (P) changes with time (t) following the exponential equation Pt = P0 e(μ-m)t, where μ and m are the instantaneous rates of growth and mortality by grazing, respectively. However, these assumptions are sometimes violated in dilution experiments as non-linear responses have been frequently observed (Gallegos 1989, Strom et al. 2007, Teixeira & Figueiras 2009). This non-linear relationship has been attributed to saturated feeding of microzooplankton at low dilution levels (Gallegos 1989) or to changes in microzooplankton community among the dilution treatments (Dolan et al. 2000, Agis et al. 2007). Additionally, Teixeira & Figueiras (2009) showed that the nonlinear response could also be the result of food selectivity by micrograzers. More recently, Calbet & Saiz (2013) suggested that the presence of trophic cascades also promotes the non-linearity of dilution experiment results. Hence, heterotrophic consumption occurs during incubations besides grazing affecting the relationship between preys and grazers, the latter being also under predator pressure. This process would be particularly important in subtropical waters where trophic relationships are especially complex (Longhurst 1998). MICROZOOPLANKTON FEEDING IMPACT ON THE MICROBIAL COMMUNITY  95 0 100 200 300 400 500 600 700 800 Abundance (cells mL -1 ) B APE ANF HNF Figure III.3. Planktonic community composition at the mixed layer (20 m) in the station sampled for every experiment. Abundance (cells mL-1) of heterotrophic prokaryotes (HP), Prochlorococcus (Pro), Synechococcus (Syn) (3A), autotrophic picoeukaryotes (APE), autotrophic (ANF) and heterotrophic nanoflagellates (HNF) (3B), diatoms (Dia), dinoflagellates (Din) and ciliates (Cil) (3C). Note the different concentrations of HP, Pro and Syn; and the different scales among panels. D1 D2 D3 D4 D5 D6 D7 0 2 4 6 8 10 12 Abundance (cells mL -1 ) C Dia Din Cil 0 20 40 60 80 100 120 Abundance (103 cells mL-1/104 for HP) HP Pro Syn A CHAPTER III  96 A large number of non-significant results were found when total phytoplankton (as Chl a) or the different autotrophic groups were considered (Table III.2). Thus, the microzooplankton grazing (m) on autotrophic organisms was unequal depending on the experiment or group of organisms. In almost all cases (excepting D2), significant grazing rates were measured on at least one of the autotrophic groups despite the non-significant grazing observed from Chl a data. Regarding to heterotrophic organisms, significant results were obtained in all cases analyzed (Table III.3). Nevertheless, the microzooplankton feeding rate on heterotrophic organisms was also variable depending on the experiment. The average grazing rate upon total phytoplankton (Fig. III.4) was 0.94 ± 0.13 d-1, higher than the mean growth rate of 0.57 ± 0.43 d-1. Within the different autotrophic groups considered, the smaller cells (picoeukaryotes and cyanobacteria) showed lower mortality rates by grazing, especially Syn and Pro, which presented average rates lower than 0.5 d-1 (APE: 0.89 ± 0.00, Syn: 0.22 ± 0.10, Pro: 0.27 ± 0.26 d-1). Contrary, ANF and Dia supported higher grazing rates above 1.5 d-1 (ANF: 2.14 ± 0.74, Dia: 1.93 ± 0.30 d-1). Mortality by grazing (m) was higher than growth rate (μ) for eukaryotic cells (APE, ANF and Dia), while it was lower for autotrophic prokaryotes (Syn and Pro). Higher growth rates were observed for larger cells (ANF: 1.58 ± 0.10, Dia: 1.56 ± 0.44 d-1) but also for Pro (1.12 ± 0.95 d-1). In the case of APE and Syn their average growth rates were below 1 d-1 (APE: 0.34 ± 0.00, Syn: 0.54 ± 0.28 d-1). On average, the feeding rate upon heterotrophs was above 1 d-1 (Fig. 4), being 1.12 ± 0.55 and 1.59 ± 0.31 d-1 for HP and HNF, respectively. Din showed an average mortality rate of 0.98 ± 0.70 d-1. Average growth rate of these groups balanced mortality by consumption as values around 1 d-1 were measured (HP: 1.09 ± 0.60, HNF: 0.93 ± 0.27, Din: 1.04 ± 0.92 d-1). MICROZOOPLANKTON FEEDING IMPACT ON THE MICROBIAL COMMUNITY  97 Table III.2. Mortality (m) and growth (μ0) rates (d1 ) of total phytoplankton (Chl a), autotrophic picoeukaryotes (APE), Synechococcus (Syn), Prochlorococcus (Pro), autotrophic nanoflagellates (ANF) and diatoms (Dia) for each dilution experiment. No data were available for ANF in D2-4 and for Dia in D1-4. *p<0.05, **p<0.01, **p<0.001, n.s. not significant.   Chl a APE Syn Pro ANF Dia m μ 0 m μ 0 m μ 0 m μ 0 m μ 0 m μ 0 D1 n.s. n.s. n.s. n.s. 0.15* 0.74* 0.23* 0.63* n.s. n.s. - - D2 n.s. n.s. 0.89* 0.34* 0.29** 0.35** n.s. n.s. - - - - D3 n.s. n.s. n.s. n.s. n.s. n.s. 0.62*** 2.20*** - - - - D4 n.s. n.s. n.s. n.s. -0.33* -0.01* n.s. n.s. - - - - D5 1.03* 0.88* n.s. n.s. n.s. n.s. 0.24* 1.58* 2.67** 1.51** n.s. n.s. D6 0.85** 0.27** -0.48* -0.42* n.s. n.s. n.s. n.s. n.s. n.s. 2.14*** 1.87*** D7 n.s. n.s. n.s. n.s. -0.54** -0.25** -0.55* 0.07* 1.62* 1.65* 1.72* 1.25* CHAPTER III   98 Table III.3. Mortality (m) and growth (μ0) rates (d-1) of heterotrophic prokaryotes (HP), heterotrophic nanoflagellates (HNF) and dinoflagellates (Din) for each dilution experiment. No data were available for HNF in D2-4, and for Din in D1-4. *p<0.05, **p<0.01, **p<0.001, n.s. not significant. Figure III.4. Average (±SD) rates (d-1) of mortality (m) and growth (μ0) of total phytoplankton (Chl a, n=2), APE (n=1), Syn (n=2), Pro (n=3), ANF (n=2), Dia (n=2), Din (n=3), HP (n=7) and HNF (n=3). Abbreviations as in Fig. III.3. Negative rates were not included in average calculations. HP HNF Din m μ0 m μ0 m μ0 D1 1.81*** 1.73*** 1.26* 0.76* - - D2 1.59*** 1.32*** - - - - D3 1.32*** 1.45*** - - - - D4 0.36** -0.10** - - - - D5 0.57*** 0.87*** 1.87** 1.24** 0.34* 0.22* D6 0.76*** 0.75*** n.s. n.s. 1.73*** 2.03*** D7 1.45*** 1.54*** 1.64* 0.78* 0.86*** 0.86*** Chl a APE Syn Pro ANF Dia Din HP HNF 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Growth and mortality rates (d-1) m μ 0 MICROZOOPLANKTON FEEDING IMPACT ON THE MICROBIAL COMMUNITY  99 Growth and mortality rates did not show any relationship with neither temperature nor Chl a or initial abundance or biomass (Spearman correlation, p>0.05). Mortality rate was only significantly correlated to growth rate (Spearman’s rank r=0.62, p<0.001) considering both autotrophs and heterotrophs (Fig. III.5). This correlation was even higher when Pro were excluded (Spearman’s rank r=0.80, p<0.0001). Figure III.5. Relationship between growth (μ0) and mortality (m) rates (d-1) obtained for all planktonic groups (abbreviations as in Fig. III.3). The line represents the linear regression including all groups (y = 0.457 + 0.536 · x, r=0.62, p<0.001, n=26). Negative rates were not included. A possible effect of nutrient limitation on growth rates was assessed using the nutrient limitation index (NL = μ0 / μn). Only Syn and ANF showed average values of NL below 1 (Table III.4), indicating that the growth of these groups could be limited by nutrient availability. The mean NL value for autotrophs (from Chl a) was also below 1 although its variability was high. In fact, autotrophs were only nutrient-limited in D6, in which NL was below 1. -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Mortality rate, m (d -1 ) -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Growth rate, μ 0 (d -1 ) Chl a APE Syn Pro ANF Dia Din HP HNF CHAPTER III   100 Table III.4. Nutrient limitation index (NL) (Landry et al., 1995; Landry et al., 1998) calculated from growth rates without (μ0) and adding (μn) nutrients, NL = μ0 / μn (SD). D2 was not included because nutrients were not added to the dilution series. Abbreviations as in Table III.2. Non-significant or negative values of mortality (significant positive slopes for Syn in D4, APE in D6 and cyanobacteria in D7) were observed (Tables III.2 and III.3). To evaluate if these responses were due to changes in the microzooplankton community during incubations data on net growth rates of potential consumers were also analyzed (Fig. III.6). Unfortunately, we only obtained data on microplankton abundance in D5, D6 and D7. In these cases, the net growth rates of HNF, Din and Cil were negatively correlated to the dilution factor except in D5 for Cil and D6 for HNF. In terms of daily production (Fig. III.7A), HP and Din accounted for the highest values (HP: 3.57 ± 3.71, Din: 3.15 ± 1.89 μgC L-1 d-1) and they also supported the highest daily consumption (HP: 3.52 ± 3.43, Din: 3.10 ±1.38 μgC L-1 d-1) (Fig. III.7B). The average daily production and consumption for the other organisms were below 1 μgC L-1 d-1. Autotrophic production (from Chl a) was on average 0.37 ± 0.12 μgC L-1 d-1, while a higher value of 0.89 ± 0.79 μgC L-1 d-1 was measured for the daily feeding on autotrophic biomass. Thus, more than 100% of primary production was removed on average (Fig. III.8), although a rather high variability was observed. The impact on the different autotrophic organisms was unequal. Consumption over eukaryotic cells (APE, ANF, Dia, Din) production was higher than 100%, while the impact on prokaryotic production was lower, only 52% and 20% of the daily production of Syn and Pro, respectively. In the case of heterotrophs, daily prokaryotic production (HP) was almost totally removed by microzooplankton (96%), and more than 100% of the daily production of HNF was consumed. Phytoplankton group μ0 (d-1) μn (d-1) NL n Syn 0.74 (0.00) 0.82 (0.00) 0.90 (0.00) 1 Pro 1.12 (0.95) 0.78 (0.64) 1.43 (1.69) 4 APE - - - ANF 1.58 (0.10) 2.11 (0.18) 0.75 (0.08) 2 Dia 1.56 (0.44) 1.30 (0.65) 1.20 (0.69) 2 Chl a 0.58 (0.43) 0.62 (0.09) 0.93 (0.71) 2 MICROZOOPLANKTON FEEDING IMPACT ON THE MICROBIAL COMMUNITY  101 0.00 0.20 0.40 0.60 0.80 1.00 1.20 -2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 Net growth rate (d -1 ) D5 A HNF (r=-0.775, y=-0.037-1.872·x) Din (r=-0.516, y=0.511-0.339·x) Cil (n.s) 0.00 0.20 0.40 0.60 0.80 1.00 1.20 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Net growth rate (d -1 ) B D6 HNF (n.s.) Din (r=-0.856, y=1.364-1.733·x) Cil (r=-0.951, y=0.458-1.205·x) 0.00 0.20 0.40 0.60 0.80 1.00 1.20 Dilution factor -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 Net growth rate (d -1 ) D7 C HNF (r=-0.705, y=1.045-1.636·x) Din (r=-0.958, y=0.698-0.858·x) Cil (r=-0.819, y=0.939-1.311·x) Figure III.6. Linear regression between the net growth rates of potential consumers (HNF, Din and Cil) and the dilution factor in D5 (6A), D6 (6B) and D7 (6C). Fitting line was plotted when the relationship between variables was significant (p<0.05, n.s.: not significant). Open symbols stand for net growth rates in natural seawater treatments incubated without nutrients. Abbreviations as in Fig. III.3. CHAPTER III   102 Chl a APE Syn Pro ANF Dia Din HP HNF 0 1 2 3 4 5 6 7 8 Daily production (μgC L-1 d-1) A Chl a APE Syn Pro ANF Dia Din HP HNF 0 1 2 3 4 5 6 7 8 Daily consumption (μgC L -1 d -1 ) B Figure III.7. Average (±SD) daily production (7A) and consumption (7B) (μgC L-1 d-1) of each planktonic group (abbreviations as in Fig. III.3). Negative rates were not included in average calculations. n as in Fig. III.4. MICROZOOPLANKTON FEEDING IMPACT ON THE MICROBIAL COMMUNITY  103 Figure III.8. Average (±SD) percentage of daily production of each planktonic group (abbreviations as in Fig. III.3) removed by microzooplankton consumption. Negative rates were not included in average calculations. n as in Fig. III.4. IV. DISCUSSION We attempted to quantify the temporal variability of the microzooplankton feeding impact covering the productive and non-productive periods in the subtropical waters off the Canary Islands. However, low variability in temperature and Chl a was observed suggesting rather stable environmental conditions (Fig. III.2 and Table III.1). Neither the microbial community composition showed a clear temporal pattern, although during 2011 a higher abundance of picoplankton and a lower abundance of nanoplankton were observed (Fig. III.3). This relatively higher availability of potential preys did not result in clear differences on grazing rates between 2011 (D6 and D7) and 2010 (D1, D2, D3, D4 and D5) (Tables III.2 and III.3). The variability of grazing pressure has been previously associated to changes in the planktonic community composition (Strom et al. 2007, Calbet et al. 2008, Schmoker et al. 2013). However, we did not find any relationship between growth or mortality rates and abundance or biomass of prey or potential consumers. Probably, growth and mortality changes were not related to temperature, Chl a or community composition because of the extremely low variability of these parameters. Indeed, HP and Pro were the most abundant preys and HNF were the dominant consumers during the whole period (Fig. III.3). The numerical dominance of nanoflagellates within microzooplankton assemblages has been reported in the subtropical northeastern Atlantic (Quevedo & Anadón 2001, Chl a APE Syn Pro ANF Dia Din HP HNF 0 50 100 150 200 250 300 350 Daily production removed (%) CHAPTER III   104 Quevedo et al. 2003) and it is a common feature in oligotrophic systems where nanograzers are the major consumers of primary production (Calbet 2008). Microzooplankton grazing rate was only significantly correlated to the growth rate of preys (Fig. III.5). The linear relationship obtained between both rates indicated the close coupling between consumers and producers, with a higher grazing pressure over faster-growing cells. The coupled relationship between phytoplankton growth and microzooplankton grazing is common in the ocean (Schmoker et al. 2013) and it has also been observed in the area (Quevedo & Anadón 2001). Gaul & Antia (2001) found that grazers selectively consumed the taxa with the highest growth rates, even although they accounted for minor fractions of autotrophic biomass. Here, ANF and Dia showed the highest average growth rates and they were heavily grazed, whilst the lowest grazing rates were on Syn and APE, whose growth rates were also the lowest (Fig. III.4). Additionally, we found this close relationship for heterotrophic preys, at least for HP, as few data on HNF rates were obtained. Although a considerable variability was observed (Table III.2), the exception was the case of Pro, which showed on average the lowest mortality rate by grazing despite their high average growth rate. Gutiérrez-Rodríguez et al. (2011) also found that Pro supported the lowest grazing pressure comparing to other autotrophic groups in the northeastern Atlantic. This suggests that micrograzers would be selectively feeding not only upon faster-growing autotrophs but also on eukaryotic cells more than prokaryotes (Fig. III.4). A negative selectivity on small autotrophs has also been observed by several authors in both eutrophic (Teixeira & Figueiras 2009) and oligotrophic environments (Berninger & Wickham 2005). On the other hand, a positive selection over cyanobacteria and dinoflagellates instead of diatoms was observed in the temperate Northeast Atlantic (Gaul & Antia 2001). The microzooplankton feeding impact on every planktonic group was unequal among experiments as disparate results were obtained (Tables III.2 and III.3). First, grazing rates measured from Chl a were only significant in two experiments (D5 and D6), although significant grazing rates on autotrophic groups were found in the other experiments. Therefore, calculating microzooplankton grazing impact on primary production from Chl a data alone could be a great source or error, at least under low Chl a conditions. A detailed analysis of every autotrophic group is needed to properly quantify the actual impact of microzooplankton grazing. In addition, significant positive slopes (negative values of m) were observed (Tables III.2 and III.3). This type of response could be due to changes in microzooplankton community during incubations (Dolan et al. 2000, Agis et al. 2007). In this sense, we observed higher net growth rates of potential consumers (HNF, Din and Cil) in the more diluted treatments (Fig. III.6). The increment in the abundance of HNF in highly diluted treatments has been previously attributed to Cil mortality (Agis et al. 2007). Contrary, we found that Cil also increased in diluted samples after incubation in D6 and Discriminating between dilution, 2-point, and Frost methods to estimate grazing by microzooplankton CAPÍTULO IV CHAPTER IV     DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  113 ABSTRACT Grazing by microzooplankton is normally obtained through the dilution method. However, this technique is tedious to carry out because many treatments (dilutions) and replicates are needed for statistical confidence. Large volumes of water are also required which implies a quite heavy restriction for its use in oceanography. The method is also sometimes difficult to interpret because of non-linear effects, feeding at saturation, thresholds feeding response, and top-down effects. A commonly used alternative is the so-called 2-point method in which only the natural seawater (nsw) and one dilution level (37, 33, 20, 10, or 5% of nsw) are used to assess grazing. Saturation and threshold feeding responses can also make this approach rather difficult to interpret. Here, we discriminate between dilution, 2-point, and Frost (1972) methods in order to estimate grazing rates. We used the 5% nsw dilution to assess the intrinsic phytoplankton growth (μ) as an independent estimate in order to apply the equations by Frost. We found a close relationship between grazing rates estimated by the dilution and Frost methods from a review of published data, and experiments performed in the Canary Island waters and the Namibian upwelling. We also assessed grazing rates in oligotrophic and eutrophic zones of the Atlantic and Pacific Oceans, performing time-series experiments based on the Frost approach, and we observed an important variability at short-term intervals. CHAPTER IV   114 I. INTRODUCTION Nanoand microzooplankton (hereinafter micrograzers, organisms in the 2-200 µm size class) are a heterogeneous community mainly formed by protists with heterotrophic and mixotrophic energy intake. They are the main grazers in the ocean (Calbet & Landry 2004) and therefore play an important role in the flux of energy and matter through the marine food web. Despite their importance, their study is constrained by the difficulty to know their composition, abundance, feeding, metabolism and growth. Among this, knowledge of feeding and their impact on primary production is of paramount importance in order to understand the fate of organic carbon in the water column. Most of the information about the feeding pressure exerted by micrograzers has been obtained through the so-called dilution method (Landry & Hassett 1982). The procedure involved in this method is theoretically simple and no especial equipment is required. Briefly, a series of dilutions (bottles with increased proportion of filtered seawater in relation to the natural sample) in which grazers are diluted respect to autotrophs are carried out, promoting a reduction of encounter rates between grazers and their prey. The slope of the increased phytoplankton apparent growth as dilutions increase is the mortality rate (m) of autotrophs by grazing, and the intercept with the y-axis, the phytoplankton net growth rate (µ). Other methods (fluorescence microspheres, fluorescently labeled algae, 14C or 3H labeling …) are based on artificial or manipulated food (far from nature), are difficult to carry out, tedious to measure, and/or difficult to interpret (e.g., food of different sizes). The dilution technique is a simple manipulation of a complex food web (Gallegos 1989) and it is based on three main assumptions (see Landry & Hassett 1982). Briefly, (1) growth of prey cells is not affected by the presence or absence of other phytoplankton cells, (2) the probability of a phytoplankton cell being consumed is a direct function of the rate of encounter of consumers with prey cells, and (3) the change in the density of phytoplankton over time can be represented by an exponential equation. However, this method is tedious to carry out because at least 4-5 dilution levels (each replicated) are needed for statistical confidence. Besides, large volumes of water (filtered and unfiltered) are also required for the experimental set-up. This implies the performance of only one or two estimations per day and, therefore, the impossibility to have sufficient resolution for the oceanographic work. To interpret the results of the dilution method is sometimes difficult because of non-linear responses observed in the apparent growth rate of phytoplankton. While in low diluted treatments feeding at saturation is a common result, in highly diluted treatments thresholds feeding responses (similar apparent growth rates at the higher dilutions) are also observed (Gallegos 1989). Moreover, topdown effects of unknown consequences could lead to unexpected results (Calbet & Saiz 2013). DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  115 A simplification of this procedure is the so-called 2-point method in which the undiluted sample and a dilution level of 33 (Landry et al. 2009), 37 (Landry et al. 2011), or 10% (Lawrence & MendenDeuer 2012, Sherr et al. 2013) of nsw is used to measure m. This is a compromise between the use of this method for the oceanographic work and the expected errors due to the lack of statistical confidence or the possible non-linear responses. Therefore, possible saturation and threshold feeding responses that cannot be resolved with this modified approach make the results rather difficult to interpret. According to Frost (1972), microzooplankton grazing is estimated assuming an exponential phytoplankton growth, as in the dilution method. The apparent growth rate (k) is the result of the intrinsic growth rate (µ) and mortality by grazing (m): k = µ - m In this case, k and m are not computed from linear regression analysis between the net growth rate and the dilution factor. Instead, they are measured separately (Frost 1972), k being the apparent phytoplankton growth rate measured from the incubation of natural seawater, and μ an independent estimate of the intrinsic growth rate of phytoplankton, both estimated as: µ (h-1) = (1 / t) · ln (C1 / C0) k (h-1) = (1 / t) · ln (C1´ / C0´) where t is incubation time, C0 and C1 the initial and final phytoplankton concentration without grazers, and C0´ and C1´ the initial and final phytoplankton concentration in presence of grazers. Grazing rate, therefore, is m (h-1) = µ - k The apparent growth rate of autotrophs in presence of grazers (k) is, in fact, the result of incubating the natural sample (the undiluted treatment in the dilution method). However, the problem is to know the intrinsic rate of autotrophic growth (µ). In this sense, Gallegos & Jordan (1997) found a strong relationship between the apparent phytoplankton growth in the 5% nsw dilution (in their terminology μe) and the intrinsic phytoplankton growth, μ (see their Figure 1C). Later, Dolan et al. (2000) found high grazer mortalities at high dilutions, and the grazing rate in the 5% nsw dilution was even closer to 0 than it would be predicted for a dilution factor of 0.05. Strom & Fredrickson (2008) also carried out the 2-point method and they assumed the growth measured at the 5% nsw dilution as an estimate of the intrinsic phytoplankton growth rate (μ). They supported this assumption by a previous comparison between highly diluted treatments and μ values from dilution experiments (Strom et al. 2006). CHAPTER IV   116 Thus, it is expected that knowing µ (as µe) and k, reliable estimates of m could be obtained using the equations given by Frost (1972). Conceptually, this method measures grazing by subtracting k from a known phytoplankton growth (μ) from two different (independent) estimates, providing an error in both assessments. Conversely, the dilution method is performed to derive a value of μ (the y-axis intercept) from m, which is the slope of the linear regression between the dilution factor and the apparent phytoplankton growth at each dilution. This is also the basis for the 2-point method. The difference between the dilution and 2-point, and Frost methods is simply conceptual and they provide different but similar estimates (see Fig. IV.1). However, the latter method is not based on the assumption of a decrease in encounter rates between phytoplankton and micrograzers, but it is based on an independent measurement of μ (in this approach as µe). Figure IV.1. Conceptual scheme comparing the three approaches: dilution (dil), 2-point (2p) and Frost methods. Apparent phytoplankton growth is plotted versus the dilution level (% of natural seawater, nsw) to estimate μ and m. For the Frost method, the apparent net growth rate for the natural sample (kFrost) and 5% nsw (µFrost) is the average of replicates (see text). In the dilution method the regression between the apparent growth of phytoplankton and the dilution factor should be statistically significant. The 2-point method also theoretically needs a significant difference between the apparent growth of phytoplankton in natural seawater and the one obtained in the selected dilution (37, 33, 10 or 5% of nsw). By opposite, the method of Frost is based on subtracting k from μ from two different estimates, so both could be similar as they are independently estimated. Therefore, this approach could assess grazing values around zero (see discussion by Landry 2014, Latasa 2014). % nsw 5100 m dil m 2p μ dil μ 2p μ Frost k Frost m Frost = μ Frost -k Frost DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  117 As the dilution technique, the method of Frost is theoretically simple and still assumes that growth of individual phytoplankton is not affected by the presence or absence of other phytoplankton. However, as serial dilutions are not needed and the highly diluted treatment is not affected by grazers (Dolan et al. 2000), the other assumptions of the dilution series based on the rate of encounter of consumers with prey cells does not apply. The Frost method, therefore, has the advantage of the 2-point method for the oceanographic work as several measurements could be done per day (e.g., to observe changes along transects, day and night rates, or vertical distribution of grazing), but makes the use of only one dilution level conceptually robust. Thus, in order to test this approach, we performed a series of experiments in the Atlantic and Pacific Oceans, seeking to measure grazing rates upon picoplanktonic phytoplankton in a gradient of contrasting productivities. II. MATERIAL AND METHODS Samples for grazing experiments were obtained in oceanic waters at different oceanographic stations in the Pacific and Atlantic Oceans (Table IV.1). The former sampling was performed onboard the R/V Hespérides from April to May 2011 (Fig. IV.2A) under the Malaspina Expedition. Twelve experiments (St 84 to 124) were performed covering latitudinal and longitudinal gradients in the tropical and subtropical zones of the Pacific Ocean. Seven experiments (CTB.01 to 07) were also carried out in the subtropical Atlantic on-board the R/V Cornide de Saavedra in the western Canary Islands (Fig. IV.2B) during the Cetobaph cruise from April 6 to 18, 2012. Other two additional experiments (DG6 and DG7) were carried out to the north of Gran Canaria Island (Canary Islands, Fig. IV.2B, station LCF) in January and April 2011 on-board the R/V Atlantic Explorer. Finally, six experiments (NG1 to 6) were done in the upwelling zone off Namibia from August 23 to September 19, 2011 (Fig. IV.2C) on-board the R/V Maria S. Merian. CHAPTER IV   118 Cruise Conditions Months Year Procedure Experiments n Nutrient amendment Atlantic Ocean Lucifer Oligotrophic January and April 2011 Frost vs. dilution DG6 and 7 2 Yes Succession Eutrophic August-September 2011 Frost vs. dilution NG1 to 6 6 Yes Cetobaph Oligotrophic April 2012 Time-series CTB.01 to 07 7 No Pacific Ocean Malaspina Oligotrophic April-May 2011 Time-series St 84 to 124 12 No  Table IV.1. Experiments carried out to measure microzooplankton grazing rates, comparing the Frost and dilution methods and performing time-series inside the incubators. Replicates were used for experiments CTB.01 to 07 to compare the slope of time-series using the same sample in different incubators (see text). n is the number of experiments performed.   Figure IV.2. (2A), the su b for Cetobap h Location o f b tropical wa t h Cruise an d D I S f oceanogra p t ers off the C d LCF for sta S CRIMINATI N p hic station s C anary Islan d tion Lucifer ( N G B ETWE E s sampled f o d s (2B), and ( see text for E N D ILUTIO N o r grazing e the upwelli n details). N , 2POINT , e xperiments n g zone off N AND F ROS T along the P N amibia (2C) T M ETHOD S 11 9 P acific Ocea n . CTB stand s S 9  n s A B c, ... .. '" [ e --- '" ,. '. '. : " • . - ... ,~ • • ~ . • ..- ,., - - O' •• --- '" .. ' CHAPTER IV   120 Frost vs. dilution experiments The Canary Islands and Namibian experiments were designed to compare the method of Frost (1972) and the dilution method of Landry & Hassett (1982). In the Namibian cruise, various 20 L Niskin bottles coupled to the oceanographic rosette were used to sample at the depth of 25% of surface Photosynthetically Active Radiation (PAR) at different times along daylight hours. Sampling at station LCF was done at the mixed layer (20 m depth) around noon and using a 30 L Niskin bottle. Once on-board, water was filtered by gravity through 0.2 µm (Whatman capsule filter) to produce the dilution treatments into the 2.5 L polycarbonate (Nalgene) bottles used for experiments. In all cases, bottles were cleaned with 10% HCl, rinsed with deionized and sample water.The bottles were then slowly filled with natural seawater. The dilution method was carried out using four different seawater dilution levels: 100, 70, 40 and 5% of nsw. Nutrients (0.5 and 0.03 μM of NH4 and PO4, respectively) were added to the eight experiments. An additional bottle of natural seawater was used to measure the initial phytoplankton concentration (t0). After filled, bottles were incubated on-deck using continuous surface seawater to keep temperature constant. A net was also used to simulate light conditions at the sampling depth. We measured chlorophyll a and cell abundance at t0 and after 24 h to estimate the apparent phytoplankton growth. Time-series experiments Seawater was sampled at the surface layer (3 m depth) during the morning in the Malaspina and Cetobaph cruises using a 30 L Niskin bottle. Time-course experiments followed the same procedure to obtain the diluted treatments as the dilution experiments explained above. These experiments were also designed using the same polycarbonate bottles but slightly modified in order to sample at different intervals without producing bubbles inside the incubators (see Fig. IV.3 for details). Highly diluted (5% nsw) and natural samples were incubated on-deck simulating in situ temperature and light conditions as explained for dilution experiments. Samples for determination of initial abundances were obtained after the filling of the polycarbonate bottles. In order to study the cell variability inside bottles and to have statistical confidence, timeseries of abundance converted to biomass (see below) were obtained at different intervals during 30 h, every 2 h for the first 12-14 h and at 6 h intervals thereafter. No nutrients were added in any of the experiments carried out to study the time-course of cell abundance inside the incubators.The seven experiments carried out during the Cetobaph cruise (around the Canary Islands) were performed replicating the natural and 5% nsw dilution treatments with the aim to test the possible variability among incubation bottles. DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  127 Figure IV.7. Grazing rates (d-1) obtained along transects from New Zealand to Hawaii and Hawaii to Panama. (7A) Grazing rates upon the three picoplanktonic groups. (7B) Grazing rates upon picoeukaryotes, Synechococcus, and Procholorococcus. -1.2 -0.8 -0.4 0 0.4 0.8 1.2 84 86 90 92 94 96 98 116 118 120 122 124 m (d -1 ) All picoplankton A -1.5 -1 -0.5 0 0.5 1 1.5 2 2.5 3 3.5 4 84 86 90 92 94 96 98 116 118 120 122 124 m (d -1 ) Picoeukaryotes Synechococcus Prochlorococcus B CHAPTER IV   128 Figure IV.8. Example of short-term grazing rates along the 30 h incubation in experiments performed at similar latitudes in the Pacific Ocean (stations 116, 118, 120, 122). (8A) Biomass of Prochlorococcus. Observe the parallel trend in natural and diluted (5% nsw dilution) samples and the increase during night (shaded area, from 8 to 20 h). (8B) Short-term variability of autotrophic growth (µ) and apparent autotrophic growth (k). (8C) Time-series of grazing (d-1). Observe the higher values by day, at the beginning of the experiment, and during night. 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 µ or k (d -1 ) B µ (d -1 ) k (d -1 ) 24681012141618202224262830 Time (h) -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 m F (d -1 ) C 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 Log Prochloroccus biomass A St 116 St 118 St 120 St 122 DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  129 In experiments carried out in a similar latitudinal band (st 116, 118, 120, and 122), showing similar oceanographic conditions, time-series of picoplankton inside the bottles showed a rather similar trend for the most abundant cells (Prochlorococcus, Fig. IV.8A). Cells in the four experiments increased in both natural and diluted incubations during the night. We estimated the autotrophic growth using the 0.05 dilution factor (µe), apparent rate of autotroph growth k (Fig. IV.8B), and grazing m (Fig. IV.8C) for short intervals using the average for the 4 stations. As observed, after quite variable rates at the start of incubation, grazing rates showed the higher values after only 4 h, decreasing thereafter and slightly increasing again during night. After 18 h, grazing became almost zero. IV. DISCUSSION In the present work, we made an exercise to discriminate between the concepts used in the dilution, 2-point, and Frost methods. Then, we have compared the dilution and Frost methods using a high dilution to obtain the intrinsic autotrophic growth (µ), and the measurement of the apparent autotrophic growth in the natural sample (k). We reviewed the scarce data in which dilution experiments used the 5% nsw dilution and a rather high correlation was obtained between grazing estimated by the dilution technique using the whole dilution series, and by the Frost method using only the undiluted (k) and the 5% nsw (µ) samples. This result corroborated the agreement between values of µ and µe observed by Gallegos & Jordan (1997). We also tested this approach in experiments performed in the oligotrophic and eutrophic waters off the Canary Islands and the upwelling zone off Namibia, respectively. Pooling experiments taken from the literature and those presented here showed a rather good agreement (mF = 0.027 + 0.996 · mdil, r2=0.872, p<0.001, n=33, regression model II). However, the large errors sometimes achieved (Table IV.2) encourage to look into the sources of this variability by measuring the time-course of cells. In order to study the phytoplankton trends inside incubators, we performed time-series sampling inside the bottles as a better approach to explain processes taking place along the experiment (e.g., feeding at saturation in the natural sample). Besides, this procedure should give more statistical confidence than measuring at t0 and after 24 h. The use of the 5% nsw dilution as µ still adopts one of the assumptions of the dilution series, the effect of the presence or absence of other phytoplankton on the growth of individual phytoplankton. However, as the highest diluted treatment is not affected by grazers (Dolan et al. 2000), the assumption based on the rate of encounter of consumers with prey cells does not apply. In time-series experiments, the true change in phytoplankton concentration was measured in the 5% nsw dilution at short intervals, estimating the actual growth. We observed the increase of the intrinsic and apparent autotrophic growth during the CHAPTER IV   130 night in a number of incubations (see Fig. IV.8A and B as an example). Thus, true exponential changes in density of phytoplankton, although expected, will not be longer assumed. In fact, this could be a source of underestimation of µ and k in 24 h experiments. The parallel increase during night observed in both the natural (where nutrient regeneration by micrograzers is a fact) and diluted incubations indicate a rather low importance of nutrients at least during the first 12-15 h of the experiment. Nutrient levels are not expected to change appreciably and to have a large impact during this time interval, but this will require further research. In any case, this unamended treatment will avoid the loss of certain grazers observed in dilution series (Gifford 1988) and should give a better picture of grazing in nature. We also observed high grazing rates at the beginning of the experiment (Fig. IV.8C), similar to the finding normally observed in copepod grazing experiments (Mullen 1963, Hargrave & Geen 1970), and similar to the response of micrograzers to a pulse of prey (Schmoker et al. 2011, Calbet et al. 2013). The high grazing rate during the day is also in agreement with the circadian cycles found by Jakobsen & Strom (2004). In general, they measured higher growth and feeding rates of microzooplankton during the light period, when phytoplankton are photosynthetically active. We do not seek to go far into explaining our short-term estimations as further research is needed. However, it seems that grazing is not constant during incubations and feeding at saturation could occur as envisaged in our methodological approach. In this sense, experiments performed to study time-series of cells inside bottles (Calvo-Díaz et al. 2011) showed an important variability depending on the different organisms counted. These authors observed a marked decrease in Prochlorococcus and picoeukaryotes biomass during the first 6-12 h of incubation, remaining low thereafter. This seems a fingerprint, jointly with our results, to think about the variability normally observed in replicates and the suitability of large (24 h) incubations. Moreover, time-series carried out in bottles of quite different volumes (70 to 1000 mL) gave similar results (Calvo-Díaz et al. 2011), also arising the convenience or not of using rather large volumes. Another remarkable result was the finding of impossible negative values in the grazing estimations. As we only measured grazing upon picoplankton, those results could be explained as no selection upon specific groups or organisms. In fact, we observed negative values in the most productive zones where large cells of phytoplankton are expected to grow and nutrients to be non-limiting (e.g., the Equatorial Pacific). As predators and prey in nature preferentially select large particles as observed in mesozooplankton (Mullen 1963, Richman & Rogers 1969, Frost 1972), it is clear the need for measurements of grazing upon other groups such as autotrophic nanoflagellates, dinoflagellates and diatoms in order to have the real picture of grazing in the water column. Although the problem of negative values of grazing is out of the scope of this work and needs DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  131 further research, these results suggest top-down effects in nature, releasing primary production from grazing (see Calbet & Saiz 2013). The time-series approach presented here based on the concept of the Frost method could be fully automated using flow cytometry and image processing. Continuous measurements of cells inside different bottles using the appropriate manifold and software is nowadays possible. New technology will provide the necessary tools to automatically measure cell variability inside incubators in quite short intervals. Thus, the method should be suitable for oceanographic work as several measurements could be made per day (e.g., to observe changes along transects, day and night rates, or vertical distribution of grazing). Finally, as we unveil here, there is a need for knowing the changes produced inside the incubators in order to explain the variability in grazing experiments. The approach presented here is still a simple manipulation of a complex food web (Gallegos 1989), but continuous measurements of cell variability along experiments could promote a better understanding of interactions between grazers and their prey in the ocean. V. ACKNOWLEDGMENTS This study was funded, in part, by projects Mafia (CTM2012-39587), Malaspina (CSD2008-00077), Lucifer (CTM2008-03538), and Cetobaph (CGL2009-13112) from the Ministry of Science and Innovation of Spain, and Succession from the German Research Foundation. The authors are indebted to Dr. L. Postel for his invitation to participate in the Succession Cruise. C. Schmoker was granted from a post-doc fellowship from the Government of the Canary Islands (project EXMAR), and G. Franchy from a Ph.D. grant from the University of Las Palmas de Gran Canaria. The authors also acknowledge the critical review of the manuscript by Dr. S. Neuer and the hard work at sea and laboratory assistance of M.L. Nieves. This study is a contribution to the international IMBER project. VI. REFERENCES Calbet A, Isari S, Martínez RA, Saiz Eand others (2013) Adaptations to feast and famine in different strains of the marine heterotrophic dinoflagellates Gyrodinium dominans and Oxyrrhis marina. Mar Ecol Prog Ser 483:67-84 Calbet A, Landry MR (2004) Phytoplankton growth, microzooplankton grazing, and carbon cycling in marine systems. Limnol Oceanogr 49:51-57 CHAPTER IV   132 Calbet A, Saiz E (2013) Effects of trophic cascades in dilution grazing experiments: from artificial saturated feeding responses to positive slopes. J Plankton Res 35:1183-1191 Calvo-Díaz A, Díaz-Pérez L, Suárez LÁ, Morán XAG, Teira E, Marañón E (2011) Decrease in the autotrophicto-heterotrophic biomass ratio of picoplankton in oligotrophic marine waters due to bottle enclosure. Appl Environ Microbiol 77:5739-5746 Campbell L, Liu H, Nolla HA, Vaulot D (1997) Annual variability of phytoplankton and bacteria in the subtropical North Pacific Ocean at Station ALOHA during the 1991–1994 ENSO event. Deep-Sea Res I 44:167-192 Christian JR, Karl DM (1994) Microbial community structure at the U.S.-Joint Global Ocean Flux Study Station ALOHA: Inverse metods for estimating biochemical indicator ratios. J Geophys Res-Oceans 99:14269-14276 Dolan JR, Gallegos CL, Moigis A (2000) Dilution effects on microzooplankton in dilution grazing experiments. Mar Ecol Prog Ser 200:127-139 Frost BW (1972) Effects of size and concentration of food particles on the feeding behavior of the marine planktonic copepod Calanus pacificus. Limnol Oceanogr 17:805-815 Gallegos CL (1989) Microzooplankton grazing on phytoplankton in the Rhode River, Maryland: Nonlinear feeding kinetics. Mar Ecol Prog Ser 57:23-33 Gallegos CL, Jordan TE (1997) Seasonal progression of factors limiting phytoplankton pigment biomass in the Rhode River estuary, Maryland (USA). I. Controls on phytoplankton growth. Mar Ecol Prog Ser 47:249–258 Gallegos CL, Vant W, Safi K (1996) Microzooplankton grazing of phytoplankton in Manukau Harbour, New Zealand. N Z J Mar Freshw Res 30:423-434 Gifford D (1988) Impact of grazing by microzooplankton in the northwest arm of Halifax Harbour, Nova Scotia. Mar Ecol Prog Ser 47:249-258 Hargrave BT, Geen GH (1970) Effects of copepod grazing on two natural phytoplankton populations. J Fish Res Board Can 27:1395-1403 Jakobsen HH, Strom SL (2004) Circadian cycles in growth and feeding rates of heterotrophic protist plankton. Limnol Oceanogr 49:1915-1922 Kana TM, Glibert PM (1987) Effect of irradiances up to 2000 μE m−2s−1 on marine Synechococcus WH7803— I. Growth, pigmentation, and cell composition. Deep-Sea Res 34:479-495 Landry MR (2014) On database biases and hypothesis testing with dilution experiments: Response to comment by Latasa. Limnol Oceanogr 59:1095-1096 Landry MR, Hassett RP (1982) Estimating the grazing impact of marine micro-zooplankton. Mar Biol 67:283288 Landry MR, Ohman MD, Goericke R, Stukel MR, Tsyrklevich K (2009) Lagrangian studies of phytoplankton growth and grazing relationships in a coastal upwelling ecosystem off Southern California. Prog Oceanogr 83:208-216 Landry MR, Selph KE, Taylor AG, Décima M, Balch WM, Bidigare RR (2011) Phytoplankton growth, grazing and production balances in the HNLC equatorial Pacific. Deep-Sea Res II 58:524-535 DISCRIMINATING BETWEEN DILUTION, 2-POINT, AND FROST METHODS  133 Latasa M (2014) Comment: A potential bias in the databases of phytoplankton growth and microzooplankton grazing rates because of the improper formulation of the null hypothesis in dilution experiments. Limnol Oceanogr 59:1092-1094 Lawrence C, Menden-Deuer S (2012) Drivers of protistan grazing pressure: seasonal signals of plankton community composition and environmental conditions. Mar Ecol Prog Ser 459:39-52 Li W, Dickie P, Irwin B, Wood A (1992) Biomass of bacteria, cyanobacteria, prochlorophytes and photosynthetic eukaryotes in the Sargasso Sea. Deep-Sea Res 39:501-519 Modigh M, Franzè G (2009) Changes in phytoplankton and microzooplankton populations during grazing experiments at a Mediterranean coastal site. J Plankton Res 31:853-864 Mullen MM (1963) Some factors affecting the feeding of marine copepods of the genus Calanus Limnol Oceanogr 8:239-250 Redden AM, Sanderson BG, Rissik D (2002) Extending the analysis of the dilution method to obtain the phytoplankton concentration at which microzooplankton grazing becomes saturated. Mar Ecol Prog Ser 226:27-33 Richman S, Rogers JN (1969) The feeding of Calanus helgolandicus on synchronously growing populations of the marine diatom Ditylum brightwellii. Limnol Oceanogr 14:701-709 Schmoker C, Thor P, Hernández-León S, Hansen BW (2011) Feeding, growth and metabolism of the marine heterotrophic dinoflagellate Gyrodinium dominans. Aquat Microb Ecol 65:65-73 Sherr EB, Sherr BF, Ross C (2013) Microzooplankton grazing impact in the Bering Sea during spring sea ice conditions. Deep-Sea Res II 94:57-67 Sieracki ME, Haugen EM, Cucci TL (1995) Overestimation of heterotrophic bacteria in the Sargasso Sea: direct evidence by flow and imaging cytometry. Deep-Sea Res I 42:1399-1409 Strickland J, Parsons T (1972) A Practical Handbook of Seawater Analysis, Vol 167. Journal of the Fisheries Research Board of Canada, Otawa Strom SL, Fredrickson KA (2008) Intense stratification leads to phytoplankton nutrient limitation and reduced microzooplankton grazing in the southeastern Bering Sea. Deep-Sea Res II 55:1761-1774 Strom SL, Olson M, Macri EL, Mordy CW (2006) Cross-shelf gradients in phytoplankton community structure, nutrient utilization, and growth rate in the coastal Gulf of Alaska. Mar Ecol Prog Ser 328:75 Yentsch CS, Menzel DW (1963) A method for the determination of phytoplankton chlorophyll and phaeophytin fluorescence. Deep-Sea Res I 10:221-231 Zubkov MV, Sleigh MA, Burkill PH, Leakey RJG (2000) Picoplankton community structure on the Atlantic Meridional Transect: a comparison between seasons. Prog Oceanogr 45:369-386   Active carbon flux estimations in relation to zooplankton lunar cycles in subtropical waters CAPÍTULO V CHAPTER V   ACTIVE CARBON FLUX ESTIMATIONS IN RELATION TO ZOOPLANKTON LUNAR CYCLES  143 JIFIMIAIMIJIJIA|S|OIN|D|J|F|M|A|M|J | J|A|S|O|NIDI JIFIMIA|M 2005 2006 2007 0 1000 2000 3000 4000 5000 Mesozooplankton biomass (mg dw m -2 ) 0.0 0.2 0.4 0.6 0.8 1.0 Lunar illumination A IMIAIMIJ |D|J|F|M|A|M| 2010 2011 0 200 400 600 800 1000 1200 1400 1600 1800 2000 Mesozooplankton biomass (mg dw m -2 ) 0.0 0.2 0.4 0.6 0.8 1.0 Lunar illumination B Figure V.2. Average epipelagic mesozooplankton biomass (mgDW m-2) and lunar illumination from October 2005 to June 2006 (2A, ConAfrica cruise), from February to June 2010 and from November 2010 to June 2011 (2B, Lucifer cruise). CHAPTER V   144 Figure V.3. Standardized biomass (maximum value of biomass in each lunar cycle converted to 100%) during ConAfrica (3A), Lucifer (3B) and data from 2006 during ConAfrica (3C). Open and full grey circles stand for full and new moon, respectively. Average data (±SE). 40 50 60 70 80 90 100 Standarized biomass (%) A 40 50 60 70 80 90 100 Standarized biomass (%) B 1st quarter 2nd quarter 3rd quarter 4rd quarter 40 50 60 70 80 90 100 Standarized biomass (%) C ACTIVE CARBON FLUX ESTIMATIONS IN RELATION TO ZOOPLANKTON LUNAR CYCLES  145 JIFIMIAIMIJIJIA|S|OIN|D|J|F|M|A|M|J|J|A|S|O|NIDIJIFIMIA|M 2005 2006 2007 0 20 40 60 80 100 120 140 160 180 Mesozooplanton biomass (mmolC m -2 ) 0.0 0.2 0.4 0.6 0.8 1.0 Lunar illumination A IMIAIMIJ |D|J|F|M|A|M| 2010 2011 0 10 20 30 40 50 Mesozooplanton biomass (mmolC m -2 ) 0.0 0.2 0.4 0.6 0.8 1.0 Lunar illumination B Figure V.4. Measured (dots and dashed line) and simulated (solid line) mesozooplankton biomass (mmolC m-2) from October 2005 to June 2006 (4A, ConAfrica cruise), from February to June 2010 and from November 2010 to June 2011 (4B, Lucifer cruise). Both growth and mortality rates were set as a function of the lunar illumination with different values for every peak (see text). CHAPTER V   146 Table V.1. Average (minimum-maximum) daily community mortality (mmolC m-2 d-1) estimated from simulated mesozooplankton biomass and mortality. Average maximum rates of growth (gmax) and mortality (mmax) used for the simulation are shown. IV. DISCUSSION A lunar cycle pattern was observed in the mesozooplankton biomass during the productive period and also within non-bloom conditions (Figs. V.2, V.3 and V.4), as observed by previous works in the area (Hernández-León et al. 2002, 2004). However, although biomass was always higher during the illuminated phases of the moon (2nd and 3rd quarters), the increases were not always linked to the same lunar phase (Fig. V.2 and V.4). Biomass maxima were centred near the full moon from November 2005 to March 2006 and during 2011. During 2005 and 2006 a clear pattern was not observed, whereas maximal values of biomass were located around waning moon in 2010. Hernández-León et al. (2004) found the maximum biomass near the waning moon and they explained this pattern as the effect of high growth rates of zooplankton counteracting mortality until the latter surpassed the former as darkness progressed through the lunar cycle. Thus, the interplay between both rates would promote the biomass to peak around the full moon. In fact, our results suggest that growth is also following the lunar cycle, as a better agreement was found between real and predicted biomass when this rate was simulated in function of the lunar illumination. As mortality is assumed to be always maximal with minimum moonlight conditions (new moon) and vice versa, the timing of biomass maxima is determined by the variability of maximum growth. That is, the biomass maxima matched maximal moonlight levels (full moon) when gmax was set ten days before the full moon (2006), while the maxima occurred near waning moon when gmax was set within the full moon or ten days after (2010). Year Conditions gmax (d-1) mmax (d-1) r p Daily community mortality (mmolC m-2 d-1) 2005 Bloom 0.23 0.21 0.19 n.s. 6.1 (0.6-15.9) 2006 0.18 0.16 0.75 <0.001 4.2 (0.4-14.6) 2007 0.27 0.22 0.91 <0.01 9.9 (0.2-47.4) 2006 Non-bloom 0.19 0.19 0.75 <0.001 2.0 (0.3-4.5) 2010 0.23 0.16 0.89 <0.001 1.7 (0.2-6.0) 2011 0.13 0.12 0.70 <0.001 1.1 (0.2-2.4) ACTIVE CARBON FLUX ESTIMATIONS IN RELATION TO ZOOPLANKTON LUNAR CYCLES  147 The existence of a lunar rhythm in the reproductive cycle of epipelagic mesozooplankton seems to be the more plausible explanation for the possible influence of the lunar illumination on the growth rate of these organisms. In fact, potential growth rates of the epipelagic mesozooplankton, measured as enzymatic AARS activity, (Herrera, unpublished data) followed the lunar pattern described here during 2010 and 2011. That is, the potential growth rate was maximal around ten days before full moon as we set for simulating the biomass variability observed in 2011. In this sense, it has been recently observed (Mercier et al. 2011) that the reproductive cycle of various deep-sea species of invertebrates was synchronized to the lunar cycle. This evidence demonstrates that the influence of the moon is not only due to the tidal effect, as it occurs for coral reef species. However, the latter authors could not explain the nature of this lunar pattern. The mesozooplankton biomass simulated with the simple equations used in this work showed high correlation values between the measured and simulated biomass in the whole period except during 2005 (Table V.1). An intense Saharan dust deposition event occurred just before the first maximum in 2005 probably promoting the high values of biomass, as it has been described in these waters before (Hernández-León et al. 2004). Given the higher values at the beginning of the bloom, this first maximum could not be included in the simulation and, thus, less data were available for correlation. This would likely be the reason of lower correlation values compared to other years. Using our approach, we were able to assess mortality during the different mesozooplankton lunar cycles. The variability of the daily community mortality was considerably higher during bloom conditions ranging up to two orders of magnitude (Table V.1). The lower values observed during non-bloom conditions were not unexpected as the zooplankton biomass was lower as well as values of mmax. These results would be in agreement with previous works that observed higher values of carbon transport by DVMs in more productive waters (Yebra et al. 2005, Putzeys 2013). The relative low values (1.1-2.0 mmolC m-2 d-1) during non-bloom conditions were comparable to previous estimations (Hernández-León et al. 2002, 2004) obtained north of the Canary Islands, which gave average values of 1.9 and 2.9 mmolC m-2 d-1 for non-bloom and bloom conditions, respectively. They used the same approach to estimate the community mortality, although they observed a lower variability along different conditions, which could be the result of their shorter period of study. Mean community mortality was assumed to be the average active carbon flux, considering that the former is mainly induced by consumption of DVMs. However, as it has been pointed out by Ohman (2012), there are other causes of mortality such as parasitism, unfavourable environmental conditions (e.g. temperature or pH) or starvation. Therefore, although predation often accounts for a large fraction of zooplankton losses, assuming community mortality equivalent to active flux could be a source of overestimation. CHAPTER V   148 The average active flux estimated during the non-bloom period (1.6 ± 0.4 mmolC m-2 d-1) was of the same order of magnitude than the average value measured for gravitational flux in these waters and other subtropical regions considered (Table V.2). In the oceanic zone of the Canary Current, north of the Canaries, the average gravitational flux is 0.4 mmolC m-2 d-1 (Neuer et al. 2007, Helmke et al. 2010), whereas in Bermuda is 3.5 mmolC m-2 d-1 (Karl et al. 2001, Helmke et al. 2010), and 2.8 mmolC m-2 d-1 in Hawaii (Karl et al. 1996). Thus, our estimation of the active flux during the nonbloom period was higher than the gravitational flux in these waters (Canary Current), but slightly below the average gravitational flux in Hawaii and Bermuda. The average active flux including the bloom period (4.2 ± 3.4 mmolC m-2 d-1) was above gravitational fluxes in these three oligotrophic locations (Table V.2), but especially in the Canary Current, where it was 10-fold greater than the average values of gravitational flux. Moreover, our average value during bloom conditions (6.7 ± 2.9 mmolC m-2 d-1) was 5-fold greater than the highest value of gravitational flux (~1.3 mmolC m-2 d-1) recorded in the Canary Current by Neuer et al. (2007), but in the same order than the highest records of gravitational flux observed in Bermuda and Hawaii (Karl et al. 2001) of about 5-6 mmolC m-2 d-1. To compare our estimations with other active flux measurements in subtropical waters, it has to be noted that these latter have been mainly obtained from migrant biomass and metabolic estimations, both accounting for a considerable level of uncertainty. Migrant biomass assessments often include only mesozooplankton organisms as it is usually calculated from the difference between night and day mesozooplankton stocks in the epipelagic zone. Regarding to metabolism, the effectiveness of the active carbon pump has been especially discussed in terms of the gut flux efficiency. Some authors suggested that given the time needed to migrate downward after feeding on surface, migrants would empty their guts before reaching the mesopelagic zone (Angel 1989, Longhurst & Glen Harrison 1989, Longhurst 1991). Actually, Dagg et al. (1989) found that only a 10% of pigments consumed during the day by migrant copepods remained at their guts when they reached deeper waters. On the other hand, low evacuation rates or longer gut residence times have been measured in diel migrant copepods compared to non-migrant species (Flint et al. 1991, Morales et al. 1993, Atkinson et al. 1996, 1999), being the time of the gut passage long enough for an effective downward transport. Moreover, gut clearance rates in micronekton were observed to be long enough for the downward migration to have been completed before evacuation occurs (Merrett & Roe 1974, Gorelova & Kobylyanskiy 1985, Angel 1989). In addition, faecal matter of mesopelagic fish show fast sinking rates (average of 1028 m d-1), much higher than copepod or euphausiid faecal pellets (Robison & Bailey 1981). The latter authors also observed that the release of dissolved organic compounds is low and does not represent a significant output during sinking. ACTIVE CARBON FLUX ESTIMATIONS IN RELATION TO ZOOPLANKTON LUNAR CYCLES  149 a Estimations from metabolic calculations and migrant biomass (see Discussion). b Estimations from mortality losses of the epipelagic zooplankton biomass. Table V.2. Comparison between gravitational and active carbon fluxes (mmolC m-2 d-1) in subtropical waters at the western north Atlantic gyre (BATS: Bermuda Atlantic Time Series Study), the eastern north Pacific gyre (HOT: Hawaii Ocean Time Series) and the eastern north Atlantic gyre (Canary Current). The role of the large micronektonic fauna has been scarcely considered, giving rise to an important underestimation of the active flux that could partly explain the higher magnitude of our estimations (Table V.2). The average value obtained in this work (4.2 ± 3.4 mmolC m-2 d-1) is 4 and 9-fold higher than the average active flux estimated from metabolic calculations in BATS (Dam et al. 1995b, Zhang & Dam 1997, Steinberg et al. 2000) and HOT (Al-Mutairi & Landry 2001), respectively, and 7-fold higher in these waters (Hernández-León et al. 2001b, Putzeys et al. 2011). On the other hand, our results are in agreement with parallel estimations of active flux obtained from measurements of migrant biota, both zooplankton and micronekton, within the same cruise in 2011 (Ariza, unpublished data). These authors obtained an average value of 0.8 mmolC m-2 d -1 comparable to the average value of 1.1 mmolC m-2 d-1 that we estimated in 2011. In any case, it is also important to note that none of these studies included all contributing fluxes (respiration, excretion and mortality) to total active flux, while the majority is based on the respiratory flux alone. Location Gravitational Flux (mmolC m-2 d-1) References Active Flux (mmolC m-2 d-1) References BATS 0.7 – 6.3 1.3 – 2.1 Karl et al., 2001 Helmke et al., 2010 0.5 – 3.4 a 0.6 – 1.1 a 0.0 – 0.8 a Dam et al., 1995 Zhang & Dam, 1997 Steinberg et al., 2000 HOT 0.9 – 4.7 Karl et al., 1996 0.1 – 0.8 a Al-Mutairi & Landry, 2001 Canary Current 0.4 – 0.7 0.2 – 0.5 Neuer et al., 2007 Helmke et al., 2010 0.8 – 1.0 a 1.9 b 2.9 b 1.6 – 6.3 b 0.2 – 0.4 a 1.1 - 9.9 Hernández-León et al., 2001 Hernández-León et al., 2002 Hernández-León et al., 2004 Hernández-León et al., 2010 Putzeys et al., 2011 This work CHAPTER V   150 In summary, we showed that the contribution of the active flux to the downward transport of organic carbon produced in the euphotic layer could be of the same order or higher than the gravitational sinking in subtropical waters. As it has been suggested before (Steinberg et al. 2000), these results could shed some light on the uncoupling between primary production and the particle export flux in the ocean (Michaels et al. 1994, Karl et al. 1996). In addition, this active flux could explain the unaccounted downward organic flux promoting the carbon demands of bacteria and zooplankton in the mesopelagic zone (Steinberg et al. 2008). Thus, our results suggest a pivotal role of epipelagic zooplankton and DVMs in the downward carbon flux of paramount importance for the assessment of the role of the biological pump in the ocean. In any case, the lunar cycle-linked active flux described here for subtropical oligotrophic waters represents an important and unaccounted flux of carbon to the mesopelagic zone and deserves further research. V. ACKNOWLEDGMENTS This work was supported by the projects ConAfrica (CMT2004‐02319), Lucifer (CMT2008‐03538) and Mafia (CTM2012-39587), from the Spanish Ministry of Science and Innovation, and by a PhD fellowship to G. Franchy from the University of Las Palmas de Gran Canaria (ULPGC). We would like to thank to all the colleagues from all projects for the hard work on-board, and especially to M.L. Nieves. VI. REFERENCES Al-Mutairi H, Landry MR (2001) Active export of carbon and nitrogen at Station ALOHA by diel migrant zooplankton. Deep-Sea Res II 48:2083-2103 Angel M (1989) Vertical profiles of pelagic communities in the vicinity of the Azores Front and their implications to deep ocean ecology. Prog Oceanogr 22:1-46 Atkinson A, Ward P, Murphy E (1996) Diel periodicity of Subantarctic copepods: relationships between vertical migration, gut fullness and gut evacuation rate. J Plankton Res 18:1387-1405 Benoit-Bird KJ, Au WWL, Wisdom DW (2009) Nocturnal light and lunar cycle effects on diel migration of micronekton. Limnol Oceanogr 54:1789-1800 Dagg M, Frost B, Walser Jr W (1989) Copepod diel migration, feeding, and the vertical flux of pheopigments. Limnol Oceanogr 34:1062-1071 Dam HG, Roman MR, Youngbluth MJ (1995a) Downward export of respiratory carbon and dissolved inorganic nitrogen by diel-migrant mesozooplankton at the JGOFS Bermuda time-series station. Deep-Sea Res I 42:1187-1197 ACTIVE CARBON FLUX ESTIMATIONS IN RELATION TO ZOOPLANKTON LUNAR CYCLES  151 Dam HG, Zhang XS, Butler M, Roman MR (1995b) Mesozooplankton grazing and metabolism at the equator in the central Pacific: Implications for carbon and nitrogen fluxes. Deep-Sea Res II 42:735-756 Davison P, Checkley Jr D, Koslow J, Barlow J (2013) Carbon export mediated by mesopelagic fishes in the northeast Pacific Ocean. Prog Oceanogr 116:14-30 Dodson S (1990) Predicting diel vertical migration of zooplankton. Limnol Oceanogr 35:1195-1200 Ducklow HW, Steinberg DK, Buesseler KO (2001) Upper ocean carbon export and the biological pump. Oceanography 14:50-58 Flint M, Drits A, Pasternak A (1991) Characteristic features of body composition and metabolism in some interzonal copepods. Mar Biol 111:199-205 Gorelova T, Kobylyanskiy S (1985) Feeding of deepsea fishes of the family Bathylagidae. J Ichthyol 25:89100 Helmke P, Neuer S, Lomas MW, Conte M, Freudenthal T (2010) Cross-basin differences in particulate organic carbon export and flux attenuation in the subtropical North Atlantic gyre. Deep-Sea Res I 57:213-227 Hernández-León S (1998) Annual cycle of epiplanktonic copepods in Canary Island waters. Fish Oceanogr 7:252-257 Hernández-León S, Almeida C, Becognée P, Yebra L, Arístegui J (2004) Zooplankton biomass and indices of grazing and metabolism during a late winter bloom in subtropical waters. Mar Biol 145:1191-1200 Hernández-León S, Almeida C, Yebra L, Aristeguí J (2002) Lunar cycle of zooplankton biomass in subtropical waters: biogeochemical implications. J Plankton Res 24:935-939 Hernández-León S, Almeida C, Yebra L, Arístegui J, Fernández de Puelles M, García-Braun J (2001a) Zooplankton abundance in subtropical waters: is there a lunar cycle? Sci Mar 65:59-64 Hernández-León S, Franchy G, Moyano M, Menéndez I, Schmoker C, Putzeys S (2010) Carbon sequestration and zooplankton lunar cycles: Could we be missing a major component of the biological pump? Limnol Oceanogr 55:2503-2512 Hernández-León S, Gómez M, Pagazaurtundua Ma, Portillo-Hahnefeld An, Montero I, Almeida C (2001b) Vertical distribution of zooplankton in Canary Island waters: implications for export flux. Deep-Sea Res I 48:1071-1092 Herrera I, López-Cancio J, Yebra L, Hernández-Léon S The effect of a strong warm year on subtropical mesozooplankton biomass and metabolism. In preparation Hidaka K, Kawaguchi K, Murakami M, Takahashi M (2001) Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep-Sea Res I 48:1923-1939 Hirst A, Kiørboe T (2002) Mortality of marine planktonic copepods: global rates and patterns. Mar Ecol Prog Ser 230:195-209 Hirst AG, Lampitt RS (1998) Towards a global model of in situ weight-specific growth in marine planktonic copepods. Mar Biol 132:247-257 Karl D, Christian J, Dore J, Hebel D, Letelier R, Tupas L, Winn C (1996) Seasonal and interannual variability in primary production and particle flux at Station ALOHA. Deep-Sea Res II 43:539-568 CHAPTER V   152 Karl DM, Bidigare RR, Letelier RM (2001) Long-term changes in plankton community structure and productivity in the North Pacific Subtropical Gyre: The domain shift hypothesis. Deep-Sea Res II 48:1449-1470 Longhurst A, Bedo A, Harrison W, Head E, Horne E, Irwin B, Morales C (1989) NFLUX: a test of vertical nitrogen flux by diel migrant biota. Deep-Sea Res 36:1705-1719 Longhurst A, Bedo A, Harrison W, Head E, Sameoto D (1990) Vertical flux of respiratory carbon by oceanic diel migrant biota. Deep-Sea Res 37:685-694 Longhurst AR (1991) Role of the marine biosphere in the global carbon cycle. Limnol Oceanogr 36:1507-1526 Longhurst AR, Glen Harrison W (1989) The biological pump: profiles of plankton production and consumption in the upper ocean. Prog Oceanogr 22:47-123 Longhurst AR, Harrison W (1988) Vertical nitrogen flux from the oceanic photic zone by diel migrant zooplankton and nekton. Deep-Sea Res 35:881-889 Lovegrove T (1966) The determination of the dry weight of plankton and the effect of various factors on the values obtained. In: Barnes H (ed) Some contemporary studies in Marine Sciences. George Allen and Unwin Ltd., London, p 429-467 Mercier A, Sun Z, Baillon S, Hamel J-F (2011) Lunar rhythms in the deep sea: evidence from the reproductive periodicity of several marine invertebrates. J Biol Rhythms 26:82-86 Merrett N, Roe H (1974) Patterns and selectivity in the feeding of certain mesopelagic fishes. Mar Biol 28:115126 Michaels AF, Bates NR, Buesseler KO, Carlson CA, Knap AH (1994) Carbon-cycle imbalances in the Sargasso Sea. Nature 372:537-540 Moore HB (1950) The relation between the scattering layer and the Euphausiacea. Biol Bull 99:181-212 Morales CE (1999) Carbon and nitrogen fluxes in the oceans: the contribution by zooplankton migrants to active transport in the North Atlantic during the Joint Global Ocean Flux Study. J Plankton Res 21:1799-1808 Morales CE, Harris RP, Head RN, Tranter PR (1993) Copepod grazing in the oceanic northeast Atlantic during a 6 week drifting station: the contribution of size classes and vertical migrants. J Plankton Res 15:185-212 Neuer S, Cianca A, Helmke P, Freudenthal Tand others (2007) Biogeochemistry and hydrography in the eastern subtropical North Atlantic gyre. Results from the European time-series station ESTOC. Prog Oceanogr 72:1-29 Ohman MD (2012) Estimation of mortality for stage-structured zooplankton populations: What is to be done? J Marine Syst 93:4-10 Putzeys S (2013) Carbon active fluxes in the Northeast Atlantic subtropical gyre. Universidad de Las Palmas de Gran Canaria Putzeys S, Yebra L, Almeida C, Bécognée P, Hernández-León S (2011) Influence of the late winter bloom on migrant zooplankton metabolism and its implications on export fluxes. J Marine Syst 88:553-562 Ringelberg J (2010) Diel vertical migration of zooplankton in lakes and oceans: causal explanations and adaptive significances, Vol. Springer Robison B, Bailey T (1981) Sinking rates and dissolution of midwater fish fecal matter. Mar Biol 65:135-142