Zooplankton biomass and metabolism through image analysis systems: from the development and testing of metabolic equations to the assessment of carbon fluxes

! ! ! UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA Facultad de Ciencias del Mar D. MELCHOR GONZÁLEZ DÁVILA DECANO DE LA FACULTAD DE CIENCIAS DEL MAR DE LA UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA, CERTIFICA, Que el Consejo de Doctores de la Facultad en sesión permanente tomó el acuerdo de dar el consentimiento para su tramitación a la tesis doctoral titulada “Zooplankton biomass and metabolism through image analysis systems. From the development and testing of metabolic equations to the assessment of carbon fluxes” presentada por el doctorando D. Juan Carlos Garijo López y dirigida por el Doctor D. Santiago Hernández León. Y para que así conste, y a efectos de lo previsto en el Art nº 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 28 de julio de 2016. Fdo. : D. Melchor González Dávila Facultad de Ciencias del Mar Universidad de Las Palmas de Gran Canaria ! ! UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA DOCTORAL THESIS Zooplankton biomass and metabolism through image analysis systems From the development and testing of metabolic equations to the assessment of carbon fluxes Author Supervisor Juan Carlos Dr. Santiago GARIJO LÓPEZ HERNÁNDEZ LEÓN Programa de Doctorado en Oceanografía Facultad de Ciencias del Mar Instituto de Oceanografía y Cambio Global Las Palmas de Gran Canaria July 2016 ! ! ! vii! UNIVERSIDAD DE LAS PALMAS DE GRAN CANARIA Zooplankton biomass and metabolism through image analysis systems Abstract Zooplankton plays an important role in the biogeochemical cycles in the ocean. Due to their central position in the ocean’s food web, they recycle and redistribute energy and matter, not only at different levels of the trophic web, but also horizontally and vertically in the water column. Understanding the role of zooplankton in the biological pump and the ocean carbon cycle requires accurate estimates of community biomass and metabolism at large spatial scales. Measurements of the former are relatively affordable. However, traditional methods to estimate metabolism are highly timeconsuming processes, preventing its use over large study regions. Similarly, enzymatic methods, although useful, show uncertainties to approach metabolic rates from enzymatic activity. In this thesis we developed and tested alternative tools to assess zooplankton biomass and metabolic fluxes at large spatial and temporal scales. Thus, we examined the suitability of image-based systems (IBS), combined with empirical relationships and metabolic equations, to estimate biomass and metabolism of zooplankton on subtropical regions. We observed that these procedures could be as reliable as traditional and enzymatic methods when equations fitted the conditions of the subtropical region, although the former resulted in a faster and inexpensive process, among other advantages. Nevertheless, enzymatic methods seemed to be a better alternative to assess specific metabolic rates on particular studies, such as those on temporal-series. The reliability of IBS to study zooplankton was also ascertained along physical structures in a region with a high mesoscale activity near the Canary Islands. Here, we observed a physical-biological coupling, since the ! ! viii! distribution of biomass and metabolism of zooplankton matched the threedimensional signature of the oceanographic structures. Then, based on the results achieved using the IBS on subtropical waters, we developed specific equations for growth and respiration of zooplankton, fitted to the temperature ranges of the main regions of the global ocean. Thus, comparing equations and methods along the ~40ºN-40ºS latitudinal band, we observed that estimates using these temperature-specific equations were comparable to those from enzymatic methods, and probably more accurate than using existing generalist relationships for the global ocean. Given their usefulness, we finally applied these temperature-specific metabolic equations to study active fluxes through zooplankton in the tropical and subtropical Atlantic (10ºS-25ºN). We highlight the contribution of zooplankton export to the biological pump, and the importance of addressing studies assessing active fluxes through mortality, but also due to egestion and ammonia excretion processes. Additionally, we illustrate the future possibilities of predicting zooplankton export using remote sensing data. ! ! xv! Contents Abstract vii Resumen ix Agradecimientos xi Contents xv List of Figures xvii List of Tables xxi Symbols and Abbreviations xxiii Introduction I. Background 3 I.1 The role of zooplankton in the biological pump and carbon cycle…...…. 3 I.2 Methodological constraints of biomass and metabolism…………….…... 6 I.3 Image-based analysis systems (IBS) ….....................................………...…... 8 I.4 Remote sensing and zooplankton….….….….….….….….….….….…..... 11 II. Thesis objectives and outline 12 Results 1. The use of an image-based approach for the assessment of zooplankton physiological rates: a comparison with enzymatic methods 17 2. The influence of mesoscale structures on the biomass and metabolism of zooplankton in the Coastal Transition Zone off Northwest Africa during the weak upwelling season 51 3. Large-scale zooplankton metabolism inferred through an image based system: a comparison of methods 79 4. Zooplankton active flux in the warm ocean: relationship with chlorophyll and temperature 105 Contents! ! ! xvi! Synthesis and future research I. General discussion 141 I.1 IBS as alternative methodology....................................................................... 141 I.2 Physical-biological coupling on mesoscale structures.................................. 143 I.3 Specific metabolic equations for specific regions......................................... 144 I.4 Contribution of zooplankton to the biological pump.................................. 146 I.5 Exploring zooplankton by remote sensing.................................................... 148 II. Conclusions 150 III. Future research 152 Resumen en español I. Introducción general 157 I.1 Antecedentes….….….….….….….….….….….….….….….….….….…. 157 I.2 Objetivos y planteamiento de la investigación….….….….….….….….…. 167 II. Material y métodos generales 169 III. Principales resultados y discusión 178 III.1 El uso de un sistema basado en el análisis de imágenes para estudiar las tasas fisiológicas del zooplancton: comparativa con métodos enzimáticos….….….….….….….….…..….….….….….….….….… 178 III.2 La influencia de las estructuras mesoescalares en la biomasa y el metabolismo del zooplancton en la Zona de Transición Costera del Noroeste de África durante la estación de afloramiento debilitado….….............................................................................................. 181 III.3 Estimación del metabolismo del zooplancton a gran escala mediante un sistema de análisis de imágenes: comparativa entre métodos…………………………………...….….….….….….…..... 184 III.4 Flujo activo del zooplancton en el océano cálido: relaciones con la clorofila y la temperatura………..….….….….….….….….….….….. 188 ! IV. Síntesis y trabajo futuro 193 IV.1 Discusión general….….….….….….….….….….….….….….….….…. 193 IV.2 Conclusiones….….….….….….….….….….….….….….….….….…... 203 IV.3 Investigación futura….….….….….….….….….….….….….….….…... 205 Bibliography 209 ! ! xvii! List of Figures I.1 Schematic of the carbon pump in the ocean…………………………... 4! I.2 Digital images of mesozooplankton for further analysis through an image-based system (IBS).................…………………………………...!10! I.3!Schematic of EXPORTS scientific program from the NASA.....................!11! ! 1.1 Situation of the Canary Islands in the Atlantic Ocean and the sampling stations during the Lucifer II cruise …………………………………...!25! 1.2 Average temperature, chlorophyll a and body weight of zooplankton along the Lucifer II cruise.................................................................................!37! 1.3 Comparison of community biomass between estimates using the imagebased system and the Lovegrove method (Lovegrove, 1966).....................!38! 1.4 Specific growth and community production according to the AARS method and using the IBS in combination with the equations by Hirst et al. (Hirst et al., 2003), Hirst and Bunker (Hirst and Bunker, 2003), Zhou et al. (Zhou et al., 2010), and the one developed in this study for subtropical waters...............................................................................................!40! 1.5 Specific and community respiration from the ETS method and using the IBS in combination with the Ikeda (Ikeda, 1985) and the equation developed in this study for subtropical waters..............................................!41! 1.6 Relationship between body weight and gut content for mesozooplankton living in subtropical waters, and the same relationship at a higher scale......................................................................................................................!43! 1.7 Specific and community egestion rates from the gut fluorescence method (pigmented food) and using the IBS (pigmented plus non-pigmented food) in combination with predictive equations developed in this study.....................................................................................................................!46! 1.8 Specific and community ingestion rates from proxies of physiological rates, using the IBS in combination with predictive equations and according to respiration estimates...................................................................!47! List!of!Figures! ! ! xviii! ! 2.1 Situation of the Canary Islands and the sampling stations along the ConAfrica cruise, with surface temperature and chlorophyll a, and indicating the main mesoscale structures!..................................................................!58! 2.2 Horizontal distribution of temperature at 20 m depth with the location of the main mesoscale structures superimposed…………….…..……………..63 2.3 Vertical profiles of temperature, salinity, chlorophyll a and zooplankton community biomass along Transects 1 and 2 of the ConAfrica cruise!............!65! 2.4!Vertical profiles of temperature, salinity, chlorophyll a and community biomass of zooplankton along Transect 3 of the ConAfrica cruise.!.................!67! 2.5!Horizontal distribution (at 50 m isobath) of chlorophyll a, zooplankton community biomass, and specific respiration and growth of this community along the study region of the ConAfrica cruise!................................!70! 2.6!Abundance of zooplankton according to three size classes (200-500, 5001000 and >1000 µm) along Transects 1, 2 and 3 of the ConAfrica cruise!...................................................................................................................................!74! 2.7 Vertical profiles of zooplankton specific respiration and growth along Transects 1, 2 and 3 of the ConAfrica cruise!...........................................................!76! 3.1 Sampling stations used for metabolic assessments along the Malaspina cruise, with the average global distribution of primary production for 2010, and chlorophyll a and temperature along the circumnavigation................................................................................................!86! 3.2 Epipelagic zooplankton specific and community respiration along the Malaspina cruise from the ETS method and the IBS, in combination with the temperature-specific equations developed in this study and the Ikeda equation(Ikeda, 2014).............................................................................!94! 3.3 Mesoand bathypelagic zooplankton specific and community respiration rates along the Malaspina cruise from the ETS method and the IBS, in combination with the temperature-specific equations developed in this study and the Ikeda equation (Ikeda, 2014).................................................. 96 3.4 Epipelagic zooplankton specific growth and community production along the Malaspina cruise, according to the AARS method and using the IBS in combination with generalist equations given by Hirst et al. (Hirst et al., 2003) and Hirst and Bunker (Hirst and Bunker, 2003), and using the temperature-specific equations developed in this study.............!98! ! List!of!Figures! ! ! xix! 4.1 Sampling stations along the Mafia cruise with the average primary production during April 2015, and surface temperature and fluorescence along the cruise!.....................................................................................!112! 4.2!Vertical profiles of temperature, salinity and chlorophyll along the Mafia cruise.!...............................................................................................................................!118! 4.3!Vertical profiles of zooplankton community biomass during day and nighttime periods, as well as migrant biomass along the Mafia cruise!...........!120! 4.4 Vertical profiles of specific respiration, mortality, egestion and ammonia excretion rates for zooplankton individuals >1000 µm along the Mafia cruise!................................................................................................................................!122! 4.5!Zooplankton migrant biomass and active carbon flux through respiration, mortality and egestion processes, as well as nitrogen export according to zooplankton ammonia excretion along the Mafia cruise. Surface chlorophyll concentration is also represented!.......................................!123! 4.6!Contribution of major taxonomic groups and size classes to total active flux through zooplankton along the Mafia cruise!................................................!124! 4.7!Correlations between surface chlorophyll concentration and estimates of zooplankton migrant biomass, and active flux through respiratory, mortality, egestion, and ammonia excretion processes along the Mafia cruise!................................................................................................................................!127! ! I.1 Esquema de la bomba oceánica de carbono!.............................................................!158! I.2 Imágenes de mesozooplancton tomadas con cámara digital para análisis mediante un IBS!...........................................................................................................!163! I.3 Esquema del proyecto científico EXPORTS de la NASA!...................................!165! ! ! ! ! ! xxi! List of Tables 1.1 Empirical relationships between body area and dry weight to estimate zooplankton biomass with the image-based system (IBS)!...................................!27! 1.2 Predictive equations for the global ocean to estimate growth of zooplankton according to Hirst and Bunker (Hirst and Bunker, 2003) and Zhou et al. (Zhou et al., 2010)!.............................................................................!30! 1.3 Predictive equations for the global ocean used to estimate zooplankton growth according to Hirst et al. (Hirst et al., 2003), and the specific equations developed in this study for subtropical regions!...................................!32! 1.4 Predictive equation for respiration of zooplankton given by Ikeda (Ikeda, 1985) for the global ocean, and the specific equation developed in this study for subtropical regions!........................................................................................!33! 1.5 Linear regressions between estimates of specific growth and community production from enzymatic measurements and using the IBS in combination with predictive equations!......................................................................!45! 1.6 Linear regressions between specific and community ingestion rates from proxies of physiological rates, using the IBS in combination with predictive equations, and according to respiration estimates!..............................!48! ! 2.1!Predictive equations used to estimate zooplankton specific growth, respiration and ammonia excretion according to Hirst and Bunker (Hirst and Bunker, 2003), Garijo and Hernández-León (Garijo and Hernández-León, 2015) and Ikeda (Ikeda, 2014), respectively!...........................!62! 2.2 Average primary production and zooplankton community production, respiration, ammonia excretion and ingestion rates along mesoscale structures and the oligotrophic region during the ConAfrica cruise!.................!71! ! 3.1 Average temperature of samples used to compare zooplankton growth and respiration rates according to enzymatic methods and predictive equations along the Malaspina cruise!.........................................................................!89! List!of!Tables! ! ! xxii! 3.2 Predictive equations to estimate zooplankton specific-respiration developed in this study according to the temperature ranges of the main ocean regions and depth layers!....................................................................................!90! 3.3!Predictive equations to estimate zooplankton specific-growth developed in this study according to the main taxonomic groups, and the temperature ranges of the main ocean regions and depth layers!........................!92! 3.4!Linear regressions between estimates of zooplankton specific and community respiration derived from the ETS method, and using the IBS in combination with predictive equations!.....................................................!100! 3.5!Linear regressions between estimates of zooplankton specific-growth and community production derived from the AARS method, and using the IBS in combination with predictive equations!.....................................................!102! ! 4.1 Predictive equations to estimate zooplankton respiration, egestion and ammonia excretion, according to Garijo et al. (Chapter 3), Garijo and Hernández-León (Garijo and Hernández-León, 2015) and Ikeda (Ikeda, 2014), respectively .................................................................................................. 117 4.2 Comparisons among studies measuring zooplankton migrant biomass and active carbon fluxes!.............................................................................................!132! 4.3 Comparisons among studies addressing zooplankton migrant biomass, in terms of nitrogen, and ammonia export to the mesopelagic layer!..................!134! 4.4 Multiple linear regressions between surface chlorophyll and temperature, and zooplankton migrant biomass and active fluxes to the mesopelagic layer through respiration, mortality, egestion and ammonia excretion processes along the warm Atlantic!..........................................................................!136! ! ! ! xxiii! Symbols and Abbreviations AARS AminoAcyl-tRNA-Synthetase method ANOVA Analysis of Variance ATC Aspartate-TransCarbamylase method BW Body Weight CC Canary Current Chla Chlorophyll a CTD Conductivity Temperature and Depth (Profiler) CTZ Coastal Transition Zone DCM Deep Chlorophyll Maximum DIC Dissolved Inorganic Carbon DOC Dissolved Organic Carbon DOM Dissolved Organic Matter DVM Diel Vertical Migration DW Dry Weight EBUS Eastern Boundary Upwelling System ESD Equivalent Spherical Diameter ETS Electron Transport System POM Particulate Organic Matter PP Primary Production PPi Pyrophosphate SST Sea Surface Temperature ! ! Introduction! ! ! 7! In turn, classical methods to study zooplankton physiology, such as incubations, are highly time-consuming and labor-intensive processes (see Basedow et al., 2014), precluding their application along large spatial scales and resulting impractical to match physical and chemical measurements in oceanography. Moreover, they show other limitations, in relation to the number of individuals incubated to ensure optimal conditions, stress of captured animals, bacterial growth or starvation (Ikeda et al., 2000). The low concentration of organisms below the euphotic region (Yebra et al., 2005b) and the scarce possibilities to obtain healthy organisms for experiments also highlight the difficulty of assessing metabolism at depth. Many other methodologies have been developed during the last decades as, for instance, the egg production method to assess zooplankton growth rates (see Basedow et al., 2014) and, more recently, biochemical methods and enzymatic procedures. The former use the rate of nucleic acids (RNA/DNA), while the latter are based on measurements of cellular activities to approach respiration and growth of zooplankton. In this case, individuals are immediately frozen after sampling, allowing the assessment of the enzymatic activity afterwards in the laboratory, and therefore enabling to cover larger regions than previously through incubation methods. Moreover, the enzyme activity can be measured in organisms inhabiting at deep layers, resulting helpful to study vertical profiles of zooplankton metabolism distribution (see Packard et al., 1985). This is the case of the electron transfer system method (ETS, Packard, 1971) to estimate respiration or aspartate transcarbamylase (ATC, Biegala and Bergeron, 1998) and aminoacyl-tRNA-synthetases methods (AARS, Yebra and Hernández-León, 2004) for assessing growth. However, despite these proxies for metabolism were a breakthrough over standard methods, they still show uncertainties in the relationship between the enzyme activity and the metabolic rate (Hernández-León et al., 1995; Hernández-León and Gómez, 1996). For instance, ETS activities are measured under substrate-saturated conditions, something that is not always observed in nature, probably leading to the overestimation of respiration. In this sense, measurements of ETS activities actually represent the potential Background! ! ! 8! respiration of an organism. In contrast, a ratio between respiration and ETS (R/ETS) of 0.5 could underestimate metabolic rates when substrates are not limited in vivo (Hernández-León and Gómez, 1996; Hernández-León and Torres, 1997; Putzeys et al., 2005). Consequently, the search for accurate ratios, fitted to specific environmental conditions, is still a bottleneck for the use of enzymatic methods. I.3 Image-based analysis systems (IBS) The conception of the recently developed semi-automated image-based systems (IBS) stems from the need to address (1) the constraints of existing methods, and (2) the demands of current oceanography to understand the contribution of zooplankton in the ocean carbon cycle. These systems enable the estimation of taxonomic and size class abundances according to a semi-automated process, resulting in a faster procedure than using traditional methodology. Consequently, spatial scales may be notably increased (Grosjean et al., 2004; Benfield et al., 2007; Gislason and Silva, 2009; MacLeod et al., 2010; Gorsky et al., 2010; Bachiller et al., 2012). Additionally, using empirical relationships based on data from parameters accurately measured through the IBS, such as the equivalent spherical diameter (ESD) or the body area and the prosome length of individuals (e.g., Nakata et al., 2001; Hernández-León and Montero, 2006; Lehette and Hernández-León, 2009; Viñas et al., 2010), these systems also allow assessing individual body mass in terms of dry weight, biovolume, etc. in a faster and non-destructive process (see Fig. I.2). In turn, assessments of individual biomass through IBS allow the application of predictive equations to each organism, relating temperature and body weight, and metabolic rates of respiration (Ikeda, 1985), growth (Ikeda and Motoda, 1978; Hirst and Lampitt, 1998; Hirst et al., 2003) and mortality (Peterson and Wroblewski, 1984; Hidaka et al., 2001). Additionally, this kind of equations may also incorporate Chla as a proxy of the food availability (Hirst and Bunker, 2003; Zhou et al., 2010), while Ikeda (2014) also considered the habitat depth and the taxonomic group of individuals as Introduction! ! ! 9! predicting variables to obtain estimates of respiration and ammonia excretion rates. Reasonable assessments have been already ascertained using this kind of equations (Peterson et al., 2002; Basedow et al., 2014; Stock et al., 2014). However, it still remains unclear the effect of food availability on zooplankton metabolism (e.g., Webber and Roff, 1995; Richardson and Verheye, 1999). Concerning the relative contribution of the different variables to the overall variability of metabolic rates, Ikeda (2014) has recently observed that respiration and ammonia excretion rates are primarily determined by body weight and temperature, while depth and taxonomy are suggested as factors of lesser importance. I.4 Remote sensing and zooplankton Understanding the contribution of zooplankton to the oceanic carbon budget requires the assessment of fluxes mediated by these communities at the global scale. However, this is something unaffordable in terms of time, resources and effort through the methodology currently employed. Due to several factors (e.g., weather conditions, distance to land, etc.), massive regions of the ocean remain unexplored, while oceanographic cruises usually repeat and concentrate along similar areas (Reid et al., 2003; Isla et al., 2004; San Martin et al., 2006). Alternatively, global-scale ecosystem models could be useful to study vast and remote regions of the ocean, approaching carbon fluxes of the different communities according to macroecological functions, body size or food-web ecology (López-Urrutia et al. 2006, Jennings et al. 2009). Recent advances on satellite remote sensing could be also valued as alternative tools to provide information of the ocean's status at a planetary scale. Thus, satellites regularly provide data of key parameters of the surface ocean, such as chlorophyll, height anomalies, primary production or temperature. In this regard, many researchers hypothesize that data characterizing the ocean’s surface, provided by satellites and autonomous tools (gliders, floats, etc.), could be employed to feed models predicting the magnitude of carbon fluxes in the ocean (e.g., EXPORTS, Background! ! ! 10! http://exports.oceancolor.ucsb.edu, see Fig. I.3). Hence, understanding possible correlations between zooplankton and environmental data could help to develop models, through which continuously ascertain the contribution of these communities to the biological pump, including remote and challenging regions, otherwise too expensive to be directly sampled (Stock et al., 2014). ! Figure I.2. Digital images of mesozooplankton for further analysis through an image-based system (IBS). In this case, individuals were stained using Rose Bengal and digitized at a resolution of 1850 dpi. We used a Nikon D800 digital camera (36 Megapixel) and a Macro Lens (Micro Nikkor 60mm f/2.8G ED). A white LED illumination provided a uniform background light. Introduction! ! ! 11! Nevertheless, modelers still need in situ measurements of biomass and fluxes mediated by zooplankton over large areas to develop global models. This will require the use of the available technology (e.g., image systems) and reliable equations to predict the metabolism of these organisms. Specific work will be necessary on particular regions demanding special attention, as it is the case of upwelling and coastal waters or oxygen minimum zones (OMZs), where models based on data from the ocean’s surface could introduce considerable bias. For instance, organisms inhabiting the OMZs show a reduced metabolism as a consequence of the low oxygen concentration within the water column (Kiko et al., 2015a, 2015b), and therefore influencing the magnitude of zooplankton-mediated fluxes. ! Figure I.3.!Schematic of EXPORTS scientific program from the National Aeronautics and Space Administration (NASA). It hypothesizes that the magnitude of organic matter transferred to deeper layers might be predicted through the state of the surface ecosystem. Models will use data from remote sensing and autonomous tools (gliders, floats, etc.), calibrated through in situ data from cruises, to continuously ascertain carbon export. Thesis!objectives!and!outline! ! ! 12! II. Thesis objectives and outline Traditional methods employed to estimate metabolism of zooplankton are highly time-consuming processes, preventing its use on large study regions. Likewise, enzymatic methods, although useful, show many uncertainties to approach metabolic rates. In this regard, semi-automated image-based systems (IBS), combined with metabolic equations, could result a suitable alternative to predict zooplankton-mediated fluxes along large spatial scales. On the other hand, relationships between zooplankton and data from environmental variables obtained from remote sensing might eventually help to develop models exploring carbon fluxes mediated by these communities. Understanding the role of zooplankton in the biological pump in the ocean requires accurate estimates of community biomass and metabolism. The aim of this thesis was to develop and test alternative tools to assess zooplankton biomass and metabolic fluxes at large spatial and temporal scales. The specific objectives of this work are now outlined in order to answer the following questions: Are image-based systems (IBS) a valid alternative to study zooplankton biomass and metabolism? Chapter 1 We addressed this question through a comparative analysis between estimates of biomass and growth, respiration, egestion and ingestion rates using an IBS, and those approached from enzymatic and traditional methods. This study was carried out along a time-series in subtropical waters. Is there a physical-biological coupling on regions with high mesoscale activity? Are IBS useful to study zooplankton on these particular regions? Chapter 2 We answered this double question by applying the IBS and metabolic equations previously developed in this thesis for the subtropical region to the transition zone between the coastal upwelling off NW Africa and the Canary Islands waters. The distribution of biomass and zooplankton Introduction! ! ! 13! metabolic fluxes was studied along numerous physical structures: filaments, island-induced eddies, jets and fronts. Does it increase the accuracy of zooplankton metabolic estimates when predictive equations fit to the specific conditions of each region? Chapter 3 To answer this question we developed a set of equations predicting growth and respiration of zooplankton, fitted to the temperature ranges found along the main ocean regions and depth layers. Then, estimates using these temperature-specific equations, and those from existing (global) relationships, were compared with measurements from enzymatic methods along the circumnavigation Malaspina-2010 (~40ºN-40ºS, up to 2000 m depth). What is the magnitude of zooplankton active fluxes in the warm Atlantic? Are they correlated with environmental data available from remote sensing? Chapter 4 These questions were addressed by applying the temperature-specific equations to predict metabolism previously developed and tested in this thesis. Using an IBS, we studied the contribution of zooplankton active fluxes to the biological pump through respiration, mortality, egestion and ammonia excretion on a latitudinal transect along the tropical and subtropical Atlantic (10ºS-25ºN). The possible correlation between active flux and environmental data easily available through remote sensing was also investigated. ! ! ! ! 15! Results ! ! Chapter!1! ! ! 23! half used for growth (Kiørboe et al., 1985; Lenz et al., 1993; Båmstedt et al., 1999; Hernández-León et al., 2001, 2002, 2004). Likewise, another extended procedure assumes respiration to be 40% of ingestion (Ikeda and Motoda, 1978). Nevertheless, both approaches require the determination of the physiological parameters, at least respiration, with the constraints listed above. Moreover, when using respiration from ETS to estimate ingestion rates, results may be widely influenced by the R/ETS ratio adopted. Since the first estimations of the gut fluorescence (GF) in zooplankton (Nemoto, 1968), grazing upon phytoplankton from measurements of this parameter and gut evacuation rates (r) has become a methodology widely accepted in marine zooplankton over the last decades (e.g., Irigoien, 1998). However, despite GF results a relatively simple method, the determination of r requires to analyze a higher number of samples and many timeconsuming experiments. In any case, this methodology was a breakthrough compared with standard procedures, since it resulted in a faster method suitable for organisms captured at depth. To solve the problem of estimating r, Irigoien (Irigoien, 1998) made a review to demonstrate, as previously done by Kiørboe et al. (Kiørboe et al., 1982), that r was temperature-dependent and related both parameters in one equation. Such relationships have been commonly accepted (e.g., Landry et al., 2009). Nevertheless, there is a controversy about the variability of r in relation to food concentration (Pasternak, 1994) and initial gut content (GC) (e.g., Perissinotto and Pakhomov, 1996). Besides, GF also suffers from the uncertainty of the pigment degradation in the gut (e.g., Conover et al., 1986). Thus, alternative in vivo methods, following a non-destructive process, such as pulse amplitude modulated (PAM) fluorometry or laser imaging techniques were proposed to improve grazing assessments (Karaköylü et al., 2009, 2012; Sastri et al., 2011). The development of imaging systems to obtain body weight (HernándezLeón and Montero, 2006) allows the application of physiological models to each individual, obtaining reliable estimates for taxonomic groups and size classes when the habitat temperature is known. Thus, in this study, a total of Testing!IBS!to!estimate!biomass!and!metabolism! ! ! 24! 92 samples from the subtropical waters off the Canary Islands region were analyzed to compare assessments of zooplankton biomass, physiological rates and carbon fluxes. The objectives were (i) to validate IBS using empirical relationships to estimate biomass comparing with the standard methodology, (ii) to compare physiological rates assessed from predictive equations in combination with an IBS respect to enzymatic methodologies, (iii) to develop a suitable set of predictive equations to estimate growth and respiration of zooplankton in subtropical waters, (iv) to adjust a relationship between bw and GC in zooplankton living in subtropical regions, and (v) to test the potential of the IBS for the appraisal of community carbon fluxes of zooplankton at large spatial and temporal scales. 1.2 Methods 1.2.1 Sampling Samples used in this study were obtained from the Lucifer II sampling program. We sampled a single transect of four stations, 10 nautical miles apart, north of Gran Canaria Island (Fig. 1.1) in an oligotrophic region considered undisturbed by the high mesoscale activity of the islands (Barton et al., 1998). This sampling was carried out on board the R/V “Atlantic Explorer” from 22nd November 2010 to 2nd June 2011 completing a timeseries of 23 weekly samplings. Zooplankton was sampled in vertical hauls from 200 m to the surface using a double WP-2 net (UNESCO, 1968) with a 100 µm mesh. One of the zooplankton samples from the WP-2 net was size fractionated (100-200, 200-500, 500-1000 and >1000 µm) and immediately frozen in liquid nitrogen on board for subsequent analysis of AARS, ETS and GF. Chapter!1! ! ! 25! ! Figure 1.1.!Map showing the Canary Islands in the Atlantic Ocean and the study region inside the black box (A). Location of the four stations north of Gran Canaria Island (B). The other sample was preserved in formaldehyde (1%) and splitted into two parts in the laboratory the next day, using one-half for dry weight measurements (Lovegrove, 1966) and preserving the other half in formaldehyde (4%) for the estimation of biomass and abundance using an IBS. Temperature and conductivity were recorded down to 300 m depth using a SeaBird SBE 25 plus CTD mounted on a General Oceanics rosette Testing!IBS!to!estimate!biomass!and!metabolism! ! ! 26! sampler, equipped with Niskin bottles. Phytoplankton chlorophyll was derived from depth profiles of in situ fluorescence measured with a Turner Scufa fluorometer, calibrated with samples collected within the upper 200 m of the water column in accordance with the JGOFS recommendations (UNESCO, 1994). 1.2.2 Biomass estimates To compare biomass using the dry weight method (BDW) and an IBS (BIBS), samples were analyzed in two different ways. In the first case, samples were size fractionated into 100-200, 200-500, 500-1000 and >1000 µm size classes and organisms were dried at 60ºC for 24 h according to Lovegrove (1966), allowing the samples to reach room temperature, avoiding humidity, and then weighed on a microbalance. In the case of BIBS samples were, first of all, sieved into two size fractions using a 1000 µm mesh to analyze separately large and small zooplankton. These size-separated fractions were introduced in a flask where organisms were homogeneously distributed and, depending on the zooplankton density, aliquots of 5 mL were taken with a Hensen pipette and poured onto polystyrene plates (90 x 130 mm). This procedure ensures the best representation of the zooplankton diversity of each sample with minimum overlap of the animals. Subsamples were then digitized using an Epson Perfection 4990 PHOTO scanner (with VueScan Professional Edition 8.4.77 software) at a resolution of 1200 dpi and processed afterwards with the software ZooImage 1 version 1.2-1 (http://www.sciviews.org/zooimage) according to Grosjean and Denis (2007). Organisms were enumerated, measured, weighed and classified into five taxonomic groups: copepods, chaetognaths, euphausiid-like, gelatinous and other zooplankton. A manual training set from 24 samples chosen to represent the whole diversity of the studied area was set up to help the software to establish classification patterns. A total of 1685 digitized organisms were manually sorted into the above mentioned categories. The Random Forest algorithm was selected to build the classifier according to Grosjean et al. (2004) as we Chapter!1! ! ! 27! checked that it provided the best results. To minimize the global error in this step, mysids, euphausiids and decapods larvae (brachyura) were included into the euphausiid-like group, as well as thaliaceans, siphonophores and ctenophores into gelatinous. The other zooplankton group embraced some scarce organisms such as amphipods, cladocerans, appendicularians, ostracods, polychaets, isopods, pteropods, other larvae or some unidentified organisms. Marine snow, fibers, bubbles, shadows and many other inorganic components of the samples were assembled into an extra group in order to be discarded when determining biomass or abundances. With these conditions the final global error achieved in the automated classification was estimated to be as low as 4.7%. Estimates of BIBS were based on relationships between body area and dry weight. The software estimated the area of the individuals from their silhouette and then automatically transformed it into an ellipse of equivalent area. Then, we applied the empirical relationships between body area and dw given by Hernández-León and Montero (2006) and improved by Lehette and Hernández-León (2009), using a different equation for each taxonomic group (Table 1.1). Measurements of biomass were expressed in terms of carbon content according to Kiørboe (2013) stressing the different body composition of individuals according to their taxonomic group. Table 1.1. Empirical relationships between body area and dw given by Lehette and Hernández-León (2009) to estimate biomass with the IBS. Conversion factors were extracted from Kiørboe (2013). Categories Dry weight (dw) Conversion factor (C:dw) Copepods dw = 43.97· S1.52 r = 0.972 n =315 0.480 Chaetognaths dw = 23.45· S1.19 r = 0.840 n =33 0.361 Euphausiid-like dw = 49.58· S1.48 r = 0.987 n =88 0.419 Gelatinous dw = 43.17· S1.02 r = 0.916 n =9 0.051 Other zooplankton dw = 43.38· S1.54 r = 0.947 n =227 0.435 Testing!IBS!to!estimate!biomass!and!metabolism! ! ! 28! 1.2.3 Growth and AARS activity Growth rates from measurements of AARS activity (GAARS) were compared with the results of four different sets of predictive equations (GIBS) to test their reliability determining growth rates and production: Hirst et al. (Hirst et al., 2003), Hirst and Bunker (Hirst and Bunker, 2003), Zhou et al. (Zhou et al., 2010) and an additional set of equations developed in this study (see below). AARS activity was also measured during the sampling program and it is reported elsewhere (M. L. Torreblanca et al., in revision). Briefly, frozen samples were homogenized in Tris-HCl buffer (20 mM, pH 7.8) and centrifuged (10 min, 0°C) before the AARS enzymes specific activity was assayed following the method of Yebra and Hernández-León (Yebra and Hernández-León, 2004), slightly modified by Herrera et al. (2012). AARS activity was then corrected for the in situ temperature by applying an activation energy of 8.57 kcal mol−1 (Yebra et al., 2005a) to the Arrhenius equation in order to obtain the in situ activity normalized to protein content of samples. A fraction of the initial homogenate was analyzed following the Lowry et al. (Lowry et al., 1951) method, adapted for micro-assay by Rutter (1967), using Bovin Serum Albumin as standard (A-4503, from Sigma). Specific activity (spAARS) was then converted to specific growth (GAARS) using the equation given by Hernández-León et al. (in preparation): GAARS (d-1) = -0.0117 + 0.0038 · spAARS (r2 = 0.738; p<0.001) (1.1) where spAARS was expressed in terms of nmol PPi mg prot-1 h-1, and PPi is pyrophosphate. Regarding predictive equations, the purely empirical estimates by Hirst and Bunker (2003) and the combined theoretical-empirical estimates by Zhou et al. (2010) used body weight, temperature and Chla as a food quantity proxy to determine the growth of the community through a single equation (Table 1.2). Zhou et al. (2010) derived a semi-empirical relationship by combining the empirical equations of Hirst and Bunker (Hirst and Bunker, 2003) with the theoretical definitions of growth by Huntley and Boyd (1984), and with theoretical and empirical considerations in relation to clearance rates. In the Chapter!1! ! ! 29! case of Zhou et al. (2010) we adopted a food saturation concentration of 38 mg C m-3 at 19ºC (as originally given in their manuscript, equation 17) as well as ratios C:Chla of 50 (e.g., Reigstad et al., 2008) and 100 (Almeda et al., 2010). Additional food saturation concentrations of 28.5 and 19 mg C m-3 at 19ºC (25% and 50% lower) were also tested. In turn, Hirst et al. (2003) developed a set of predictive equations for epipelagic metazoan zooplankton from a vast compilation of experiments from polar to equatorial waters and from upwelling to more oligotrophic regions. They considered growth rates as a function of temperature and body weight and provided different equations according to taxonomic groups (Table 1.3). In contrast, the set of equations developed in this study was fitted to the physical conditions given in the subtropical waters of the Canary Islands region (16-26ºC) (Table 1.3), and handling the data contained in Hirst et al. (2003) (their Appendices 1 and 2). Regressions were developed using a linear regression program in SYSTAT version 13. All individual estimates of growth from predictive equations used body weight from the IBS. We obtained the production of the community (mg C m-2 d-1) from enzymatic measurements (PAARS) using estimates of specific GAARS and the community biomass obtained with the traditional method (Lovegrove, 1966). On the other hand, we used the community biomass from the IBS and estimates of specific GIBS to obtain production using predictive equations (PIBS). 1.2.4 Respiration and ETS activity Respiration from measurements of ETS activity (RETS) were compared with estimates using the generalist equation for the global ocean given by Ikeda (Ikeda, 1985) as well as an equation developed in this study according to the physical conditions of our subtropical waters (Table 1.4). It was calculated using a linear regression program in SYSTAT version 13 with data compiled by Hernández-León and Ikeda (2005). An aliquot of the homogenate used Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 30! Table 1.2. Predictive equations used to estimate growth of zooplankton according to Hirst and Bunker (Hirst and Bunker, 2003) and Zhou et al. (Zhou et al., 2010). Copepods Multiple linear regression a b c d r2 Hirst & Bunker (2003) log10 G (d-1) = a[T] + b[log10 bw] + c[log10 Ca] + d 0.0186 -0.288 0.417 -1.209 0.289 Zhou et al. (2010) G (d-1) = 0.033 · [Ca / (Ca + 205 · eaT] · eb T· bw-c -0.125 0.090 -0.060 - - Chapter(1( ( ! 31! for AARS activity measurements was also analyzed for ETS activity based on the method of Packard (Packard, 1971) for zooplankton. Details of the procedure are given in Hernández-León and Gómez (1996). ETS activity was corrected for in situ temperature using the Arrhenius equation and an activation energy of 15 Kcal mol-1 (Packard et al., 1975) and was finally normalized to the protein content of samples (µl O2 mg prot-1 h-1). ETS activity was then approximated to potential respiration rates (d-1) of the community assuming a ratio C:dw = 0.48 (Kiørboe, 2013) and using the relationship between dry weight (dw) and proteins given by Hernández-León et al. (2001) for the Canary Islands waters: dw = 1.445 + 4.283 · prot (r2 = 0.900; n = 306; p<0.001) (1.2) In both, our equation and the one given by Ikeda (Ikeda, 1985), individual respiration rates (µl O2 ind-1 h-1) were assessed using the body weight (dw content) estimated with the IBS. In the case of Ikeda (Ikeda, 1985), dry weight was converted into N according to Kiørboe (2013), as this equation provided the best results. As occurred with growth, specific respiration from the enzymatic method was converted into community RETS using the biomass of the community obtained from the traditional method (Lovegrove 1966), and assuming R/ETS ratios of 0.5 and 1.0 (Hernández-León and Gómez, 1996). Likewise, we used the biomass obtained from the IBS to estimate community respiration from predictive equations. In all cases the oxygen consumption was converted into C units using a respiratory quotient of 0.97 (Omori and Ikeda, 1984). Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 32! Table 1.3. Log-transformed zooplankton growth equations given by Hirst et al. (Hirst et al., 2003) for the global ocean and the equations developed in this study for subtropical regions as a function of body weight (bw, µg C ind-1) and temperature (T, ºC). We used the crustaceans equations to estimate growth of our defined euphausiid-like and other zooplankton groups. Source: Hirst et al. (2003). Copepods Chaetognaths Crustaceans Gelatinous Hirst et al. (2003) This study Hirst et al. (2003) This study Hirst et al. (2003) This study Hirst et al. (2003) This study Multiple linear regression ------------- log10 g (d-1) a[T]+b[log10bw]+c a+b[log10T]+c[log10bw] a+b[T] a+b[log10T] a[T]+b[log10bw]+c a+b[log10T] a[T]+b[log10bw]+c a+b[log10T]+c[log10bw] Variables bw, T bw, T T T bw, T T bw, T bw, T Data points 2232 503 87 32 253 24 88 91 T range (ºC) (-)2.3-29.5 16-26 1.8-31 16-26 0.5-25.6 17-25.6 11-26.5 16.26 bw range (µg C ind-1) 0.006-3620 0.5-240 0.9-650.2 0.9-238.4 2.54-64172 3.3-1639.5 4-34868.4 4-34868.4 a 0.035 -2.102 -1.851 -6.078 0.026 -14.840 0.065 -4.299 b -0.128 1.020 0.037 3.920 -0.327 10.192 0.138 2.489 c -1.529 -0.227 - - -0.919 - -2.070 0.153 r2 0.297 0.338 0.323 0.389 0.447 0.732 0.280 0.494 Chapter(1( ( ! 39! case lower estimates than ETS along the time-series (Fig. 1.5a). Nevertheless, ETS measurements showed higher variability than specific respiration from both equations along the sampling period. On the other hand, community RETS and both equations followed a similar pattern, although differences on the magnitudes were observed (Fig. 1.5b). Thus, community respiration using the Ikeda (Ikeda, 1985) equation and RETS assuming a R/ETS ratio of 1 showed no significant differences (ANOVA, p>0.05), while the former was around two-fold higher when this ratio was 0.5 (Fig. 1.5b). However, community respiration from our equation ranged between estimates of RETS assuming ratios of 0.5 and 1.0. The relationship between bw and GC for organisms living in subtropical waters showed a rather high coefficient of determination (log10 GC = 0.852 · log10 bw – 1.160; r2 = 0.769; n = 208; p<0.05). It was, indeed, close to the one observed when literature data from other ocean regions were added (Fig. 1.6). As expected, specific and community estimates of EGF were significantly lower (ANOVA, p<0.05) than those of EIBS (Fig. 1.7a,b). As observed for growth and respiration, specific EGF showed higher variability than EIBS within the sampling period (Fig. 1.7a). Specific ingestion from Ibiochem (and also from IS&C) showed the highest variability within the sampling period (Fig. 1.8a). Nevertheless, average community ingestion from IIBS showed no significant differences (ANOVA, p>0.05) respect to Ibiochem and IS&C, although estimates from IIBS were slightly higher (Fig. 1.8b and Table 1.6). However, specific and community ingestion from II&M were significantly lower (ANOVA, p<0.05) than estimates from Ibiochem (Fig. 1.8a, b and Table 1.6). Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 40! ! Figure 1.4. Specific growth (A) and community production (B) estimates according to the AARS method and using the IBS in combination with the equations by Hirst et al. (Hirst et al., 2003), Hirst and Bunker (Hirst and Bunker, 2003), Zhou et al. (Zhou et al., 2010), and the one developed in this study. Vertical bars denote standard deviations. Chapter(1( ( ! 41! ! Figure 1.5. Specific respiration estimates from ETS and IBS in combination with the Ikeda (Ikeda, 1985) and the equation developed in this study for subtropical waters (A), and community respiration fluxes from these two equations and from ETS, assuming in this case R/ETS ratios of 0.5 and 1.0 (B). Vertical bars denote standard deviation. Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 42! 1.4 Discussion Our results confirm the usefulness and reliability of IBS in combination with adequate empirical relationships (Lehette and Hernández-León, 2009) to determine zooplankton biomass, since estimates using this method showed no significant differences (ANOVA, p>0.05) to those from the standard, well-accepted dry weight methodology (Lovegrove, 1966) (Fig. 1.3). In addition to the bias introduced by scarce remnants of molts, which could be retained by the mesh and measured by the latter methodology, slight differences between both methods could be mainly explained by physical effects of the preservative on the organisms. Early studies suggested that preservation with formaldehyde results in a weight loss of planktonic organisms (e.g., Omori, 1970; Durbin and Durbin, 1978; Landry, 1978). However, results regarding possible shrinkage effects in Daphnia (Black and Dodoson, 2003) and crustaceans (Landry, 1978; Durbin and Durbin, 1978; Viitasalo et al., 1995) caused by formaldehyde indicated that this effect, if present, is minor. Moreover, Pollupüü (2007) observed no significant change in the body length of living and preserved organisms either in the case of adults or nauplii. The relationships we managed (Lehette and HernándezLeón, 2009) were free of these formaldehyde-effects, since they only preserved their organisms at -20ºC before drying. Therefore, assuming no shrinkage effect, we can confirm the validity and, perhaps, the greater accuracy of the estimates from the IBS in comparison with standard procedures. The accuracy (r2) of the equations developed in this study to estimate growth in subtropical regions was significantly greater compared with those of Hirst et al. (Hirst et al., 2003) (Table 1.3). Besides, specific growth and production from the enzymatic method and our equations showed no significant differences (ANOVA, p>0.05) and were in agreement with Hernández-León et al. (2002) in the Canary Island waters (their Table 3, transects 5 and 6). However, estimates using the Hirst et al. (Hirst et al., 2003), Hirst and Bunker (Hirst and Bunker, 2003) and the Zhou et al. (Zhou et al, 2010) equations were significantly lower (ANOVA, p<0.05) (Fig. 1.4a, b and Table Chapter(1( ( ! 43! 1.5). In this sense, we consider these equations could underestimate growth and production in subtropical waters. As growth decreases with body weight and the opposite for temperature (e.g., Hirst and Lampitt, 1998; Hirst et al., 2003; Hirst and Bunker, 2003), the equations given by Hirst et al. (Hirst et al., 2003) could be influenced by data from polar waters (-2.3ºC) and individuals up to two orders of magnitude larger than currently found in subtropical regions (Table 1.3). Likewise, Hirst and Bunker (Hirst and Bunker, 2003) and Zhou et al. (Zhou et al., 2010) managed an average temperature 4-5ºC lower than usually given in our region (Fig. 1.2). ! Figure 1.6. Log-transformed relationship between body weight and gut content for mesozooplankton living in subtropical waters (dashed line, n = 208, source: HernándezLeón et al. (2004); this study), and the same relationship including also data from other regions (solid line, n = 296, source: Morales et al., 1991; Huskin et al., 2001; López and Anadón, 2008; Lee et al., 2011; Hernández-León et al, 2004; this study). Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 44! Moreover, these two equations could be also more appropriated for more productive waters, since the values of chlorophyll a in Hirst and Bunker (2003) (their Table 5) were up to two orders of magnitude higher than usually found in oligotrophic regions. Even more, chlorophyll a was the main variable influencing growth in this case (Fig. 1.2). Therefore, global models to estimate production using the Hirst and Bunker (Hirst and Bunker, 2003) or the Zhou et al. (Zhou et al., 2010) equations (e.g., Stock et al., 2014) should be cautious since they could underestimate production in oligotrophic regions, where the importance of pigmented food in the diet of zooplankton is normally reduced (e.g., Hernández-León et al., 2004). For all that, we claim for the need to set up more taxonomically-specified equations, adapted to each particular region in order to increase accuracy. The differences between community production from AARS and the four equations analyzed here were lower than those observed for specific growth (Fig. 1.4b). This was in agreement with Huntley and Lopez (1992), who suggested biomass as the main factor influencing production estimates, since the variability of biomass measurements greatly exceeds that of growth rates (one to three orders of magnitude). Consequently, efforts should also focus on accurate determinations of biomass. The analysis of variance in specific growth rates from the AARS measurements revealed that more than 80% of the total variance was due to intrinsic variability of the method. This fact was also reflected by the standard deviations of the AARS measurements within each weekly sampling (Fig. 1.4a). In this sense, despite the enzymatic method has uncertainties, it seemed more sensitive to the variability occurred along the sampling period. Therefore, this method could represent a better alternative when assessing specific growth along a time-series, while the IBS could result more suitable for production and flux estimates when combined with adequate equations (Fig. 1.4b). The average R/ETS ratio of 1.2 observed using the equation of Ikeda (Ikeda, 1985) was out of the range given by Hernández-León and Gómez (1996), who observed that R/ETS varied from 0.5 to 1.0, as indeed occurred using our equation (R/ETS = 0.8). Moreover, specific respiration from the Chapter(1( ( ! 45! latter equation was very close to averaged values obtained by HernándezLeón et al. (2001, 2002) and Yebra et al. (2005b) in the same region, as well as to the results of Hernández-León and Ikeda (2005) for this latitude. However, specific respiration using the equation of Ikeda (Ikeda, 1985) was considerably higher. This could be due to several reasons. First, the results could be influenced by the large amount of data from high and low latitudes and the wide range of temperature used to configure his equation. Moreover, the fact that oligotrophic waters are normally dominated by small zooplankton, with higher weight-specific respiration rates (e.g., Ikeda and Mitchell, 1982), could introduce some bias when the size of organisms is not properly considered. In this case, specific respiration rates of the 100-500 µm individuals, which were 90% of total abundance, would determine the averaged rates for the community. Table 1.5. Linear regressions (type II) between estimates of specific growth (G) and community production (P) from enzymatic measurements (AARS) and using the imagebased system (IBS) in combination with empirical equations (n = 84). Sp growth (G) (d-1) Community production (P) (mg C m-2 d-1) Hirst et al. (2003) GIBS = 0.636· GAARS + 0.009 PIBS = 0.617· PAARS + 2.583 Hirst and Bunker (2003) GIBS = 0.324· GAARS + 0.004 PIBS = 0.311· PAARS + 1.267 Zhou et al. (2010) GIBS = 0.636· GAARS + 0.006 PIBS = 0.482· PAARS + 2.190 This study GIBS = 0.946· GAARS + 0.013 PIBS = 0.919· PAARS + 3.837 Concerning community respiration, our equation seemed more suitable for subtropical and oligotrophic regions since our results were in the range of Hernández-León et al. (2002) in the same region and agreed with Hernández-León and Gómez (1996), were they observed that R/ETS was close to 1 only during the Late Winter Bloom in the Canary Island waters. Therefore, specific equations fitted to each region could be more suitable to estimate respiration. Nevertheless, we suggest the ETS method as the main alternative to estimate the effect of physical structures or time-series (Fig. Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 46! 1.5a), while IBS may result equally accurate to determine community respiration (Fig. 1.5b). ! Figure 1.7. Specific egestion rates (A) and community egestion fluxes (B) from gut fluorescence (EGF; pigmented food) and using the IBS (EIBS; pigmented plus non-pigmented food). Vertical bars denote standard deviations. Chapter(1( ( ! 47! ! Figure 1.8. Specific ingestion rates (A) and community ingestion fluxes (B) estimated from proxies of physiological rates (Ibiochem), using the IBS with equations developed in this study for subtropical waters (IIBS), and also according to respiration estimates from the IBS and Ikeda and Motoda (II&M; Ikeda and Motoda, 1978). Besides, community ingestion from IIBS was compared with estimates using the equation given by Saiz and Calbet (Saiz and Calbet, 2007) (IS&C) (see Methods section) (C). Vertical bars denote standard deviations. Testing(IBS(to(estimate(biomass(and(metabolism( ( ! 48! Table 1.6. Linear regressions (type II) between specific and community ingestion estimated from proxies of physiological rates (Ibiochem), from the IBS in combination with our developed equations for subtropical regions (IIBS) and using the equation given by Saiz and Calbet (Saiz and Calbet, 2007) (IS&C), and from respiration estimates (II&M, Ikeda and Motoda 1978) (see Methods section, n = 84). Sp Ingestion (d-1) Community Ingestion (mg C m-2 d-1) IngestionIBS IIBS = 1.042· Ibiochem + 0.042 IIBS = 1.117· Ibiochem + 6.792 IngestionI&M II&M = 0.520· Ibiochem + 0.020 II&M = 0.551· Ibiochem + 3.502 IngestionS&C IS&C = 0.979· Ibiochem + 0.019 IS&C = 0.960· Ibiochem + 5.694 Our developed relationship between body weight and gut content for organisms in subtropical waters used to assess specific and community EIBS showed a rather high coefficient of determination. Besides, this relationship was also close to the one observed when literature data from other ocean regions was added (see Fig. 1.6). Moreover, our estimated gut evacuation rates derived from the equation given by Irigoien (1998) agreed with the value of 0.056 min-1 observed by Hernández-León et al. (2002) in the Canary Island waters. Average values of gut content (converted to carbon) in relation to body mass could be a valid option since gut fullness in nature is not constant over the time (see Simard et al, 1985). Both, community EGF and EIBS were within the range observed by Hernández-León et al. (2004, 2007) in the same region. However, as expected, specific and community estimates from EIBS were significantly higher (ANOVA, p<0.05) than EGF since the latter only referred to pigmented material. In this regard, mesozooplankton in subtropical waters are mainly omnivores, with a reduced importance of pigmented food in the diet (e.g., Saiz et al., 1999; Hernández-León et al., 2001, 2002). As observed in estimates of growth and respiration, biomass was the main factor influencing community egestion as well. Ibiochem and IS&C are suggested as the main alternative to measure the variability of specific ingestion rates along a sampling period (Fig. 1.8b).!On the other hand, Ibiochem and IIBS could be used indistinctly to estimate Chapter(2( ( ! 55! zone (Arístegui et al., 1997). In turn, anticyclonic eddies show a convergent effect, entraining adjacent surface waters and carrying phytoand bacterioplankton into deeper layers (Arístegui et al., 1997). Eddies in the region south of the Canary Islands are suggested to propagate westward for periods over six months while traveling more than 2000 km (Sangrá et al., 2009). In this sense, these mesoscale structures might influence the plankton distribution in oligotrophic regions beyond the Archipelago. However, the overall significance and mechanisms of upwelling filaments and islandinduced eddies to export zooplankton into oligotrophic waters is still poorly known. The process depends on the interaction between the different structures, and also on the strength and seasonality of the upwelling events (Álvarez-Salgado et al., 2001). Studies addressing this topic are very scarce, with most of the work carried out in relation to density, retention and transport of fish larvae on filaments (i.e., Rodríguez et al., 2004; Bécognée et al., 2006, 2009; Moyano et al., 2009, 2014). Moreover, the vertical distribution of zooplankton along mesoscale structures still remains unexplored. The influence of eddies and filaments on trophic interactions and zooplankton energy fluxes is still unclear due to the limited number of studies performed on this subject. However, there is interest to examine if individuals are simply advected offshore by filaments or, by contrast, production is increased along these structures. Smith and Lane (1991) showed increased egg production of the copepod Eucalanus californicus in a filament in the CTZ off the California Current. In contrast, studies carried out in the CTZ off NW Africa (Hernández-León et al., 2001, 2002) showed no clear patterns of zooplankton respiration and growth, with results primarily dependent on size classes. For instance, indices of respiration gradually decreased to typical values of the oceanic region along a filament shed from the upwelling area, while growth proxies increased in the frontal zone between filaments and island-induced eddies. Within the latter, respiration clearly depended on the kind of structure (cyclonic or anticyclonic). Physiological rates in these studies were derived from enzyme activity proxies: aspartate transcarbamylase (ATC, Bergeron and Buestel, Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 56! 1979; Biegala and Bergeron, 1998) as a proxy for growth, and electron transport system (ETS, Packard, 1971) for respiration. These methods constituted, at that moment, the main alternative to map the different components of the energy budget of the zooplankton community at the mesoscale level. However, these methods suffer from the uncertainty of the relation between the enzyme activity and growth (Hernández-León et al., 1995) and respiration (Hernández-León and Gómez, 1996). Nevertheless, biomass has been suggested as the main factor influencing zooplankton-mediated fluxes (Huntley and Lopez, 1992), with a rather low importance of specific metabolism in the overall results of community production and respiration. In this sense, semi-automated image-based systems (IBS), using empirical relationships and predictive equations, although showing uncertainties as well, produced comparable estimates of zooplankton community biomass and carbon fluxes to those obtained through standard and enzymatic procedures (Garijo and Hernández-León, 2015), although in a faster and inexpensive process. In this paper, we explored the CTZ off NW Africa during the weak upwelling season (winter-spring). Our objective was to better understand the influence of persistently-generated hydrodynamic structures (fronts, filaments and eddies) on the biomass, size distribution and energy fluxes of the zooplanktonic community in this oligotrophic region. We used an IBS to estimate community biomass according to empirical relationships between body area and body weight (bw). Likewise, specific rates and community fluxes of respiration, ammonia excretion, growth and production, as well as ingestion of zooplankton were assessed using predictive equations, relating physiological rates, bw and temperature. This study aimed to continue the previous work carried out by Hernández-León et al. (2002) in the same region during the strong upwelling season, providing contrast data from a rather different scenario. We based on the previous studies carried out by Benítez-Barrios et al. (2011) and Moyano et al. (2014) addressing, respectively, the physics and the larval fish distribution in the region along the same cruise. Chapter(2( ( ! 57! 2.2 Material and methods 2.2.1 Hydrographic surveys During the ConAfrica cruise (22 March-7 April 2006) on board the R/V “Hespérides”, 78 stations were sampled continuously during day and night from the coastal upwelling waters off NW Africa to the offshore waters of the Canary Islands (Fig. 2.1a). This grid covered different mesoscale structures (coastal upwelling, filaments, jets and eddies) observed after processing SST and Chla images (4 km resolution) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Aqua (EOS PM) satellite (http://oceancolor.gsfc.nasa.gov/cms/data/aqua) from the NASA (Fig. 2.1). Vertical profiles of temperature, conductivity and fluorescence were recorded down to 2000 m depth (when bathymetry permitted) using a SeaBird 911 plus CTD system, mounted on a General Oceanics rosette sampler, equipped with twenty-four 12 l Niskin bottles. Phytoplankton chlorophyll was derived from depth profiles of in situ fluorescence measured with a Seapoint Chla fluorometer, calibrated with samples collected within the upper 200 m of the water column, in accordance with the JGOFS recommendations (UNESCO, 1994). Primary production (PP) data was obtained from the Ocean Productivity website (http://www.science.oregonstate.edu/ocean.productivity/index.php), according to the Vertically Generalized Production Model (VGPM, Behrenfeld and Falkowski, 1997), which is based on estimates of euphotic zone depth and MODIS Chla and temperature data. Primary production data was averaged every eight days, according to a very detailed coverage (2160 x 4320 grid size; 9.2 km coverage at Equator). According to the distribution of the physical structures identified by BenítezBarrios et al. (2011) along the same cruise, we examined the effects of mesoscale structures upon zooplankton according to three sections (Fig. 2.1b). Transect T1 was perpendicular to the African coast, from the upwelling (U) to the southwest of Gran Canaria Island, covering the filament F2, the cyclonic eddy C1, as well as the upwelling jet and a quasipermanent anticyclonic eddy to the east of Gran Canaria (A2). In turn, T2 Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 58! helped to examine the three-dimensional of the A2 eddy, while T3 was parallel to the coast (about 80 km distance), sampling the filament F1 and both anticyclonic (A1) and cyclonic (C1) eddies. Moreover, the horizontal exchange and distribution of zooplankton along the region was studied according to the reference isobath of 50 m depth, where we roughly found the deep chlorophyll maximum (DCM) and the horizontal influence of mesoscale structures was more clearly detected. Figure 2.1. Map showing the Canary Islands and the study region (dashed black box) with satellite SST (ºC) for March 29 (A). Coastal transition zone off NW Africa, with satellite Chla (mg m-3) for 7 April, showing the vertical profiles examined (T1, T2 and T3), as well as anticyclonic (A1 and A2) and cyclonic (C1) eddies, the upwelling jet and two upwelling filaments (F1 and F2) (B); black dots within both panels indicate sampling stations. Chapter(2( ( ! 59! 2.2.2 Zooplankton sampling and biomass assessment Zooplankton samples were stratified at 10 different depths, within the upper 200 m of the water column, using a Longhurst-Hardy Plankton Recorder net (LHPR, Longhurst and Williams, 1976), equipped with a 200 µm mesh size. Oblique trawls were performed at a towing speed of ca. 3 knots, measuring the volume of filtered water with a calibrated electronic flowmeter. Samples were immediately preserved on board in 4% buffered formaldehyde. In the laboratory, larval and fish eggs were removed for further analysis. Thereafter, samples were digitized using an Epson Perfection 4990 PHOTO scanner (with VueScan Professional Edition 8.4.77 software) at a resolution of 1200 dpi. Different aliquots of samples were taken using a Hensen pipette and spilled onto 90x130 mm polystyrene plates. Images of subsamples were processed afterwards with the software ZooImage 1 version 1.2-1 (http://www.sciviews.org/zooimage), according to Grosjean and Denis (2007). As a result, organisms were automatically counted, measured and individually classified into 5 taxonomic groups: copepods, chaetognaths, euphausiid-like, gelatinous and other zooplankton. Inorganic particles were assembled into an extra non-planktonic group. The software used a manually performed training set, containing nearly 5000 images of organisms from the study region to establish patterns for automatic classification. We selected the Random Forest algorithm according to Grosjean et al. (2004), and the global error rate achieved in the classification was estimated below 7%. Once the organisms were classified, individual biomass was estimated using the empirical relationships given by Hernández-León and Montero (2006), and improved by Lehette and Hernández-León (2009), between body area and body weight, applying a different equation for each taxonomic group, as detailed in Garijo and Hernández-León (2015) (Table 1.1). We also studied the structure of communities along the study region according to three size classes: 200-500, 500-1000 and >1000 µm. Because the sampling grid was covered in a relatively short period and day and night samples needed to be pooled, a day/night ratio was used in the largest size fraction (>1000 µm) to Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 60! convert values into a day situation. This correction was important to minimize the effect of the diel variability and, in this case, the ratio was 0.66 due to the vertical migration of the interzonal fauna. 2.2.3 Estimation of metabolic rates and community fluxes Specific growth rates (G, d-1) were individually estimated according to the predictive equation given by Hirst and Bunker (2003), relating physiological rates and body weight, temperature and Chla (Table 2.1). Given the heterogeneous distribution of the latter along the region we chose this equation, rather than others not including a food proxy (e.g., Hirst et al., 2003; Garijo and Hernández-León, 2015), as it could be helpful to understand if food quantity influences growth rates (see Discussion section). Furthermore, specific respiration (R, d-1) was estimated as a function of temperature and body weight according to the equation developed by Garijo and HernándezLeón (2015), fitted to the specific temperature ranges of subtropical regions (Table 2.1), and assuming a respiratory quotient of 0.97 (Omori and Ikeda, 1984). Egestion rates of the community (E, ng C ind-1 min-1) were in turn derived using two empirical relationships. On the one hand, we approached the gut content of individuals (GC, ng Chla ind-1) (pigmented food) using the relation between this parameter and body weight, developed by Garijo and Hernández-León (2015): (log10 GC = 0.852 log10 bw – 1.160; r2 = 0.769; n = 208; p<0.05) (2.1) On the other hand, we approximated gut evacuation rates (e, min-1) from habitat temperature (T, ºC), according to the relation developed by Irigoien (1998): e = 0.0026T + 0.012 (r2 = 0.940; n = 19) (2.2) Then we assumed a C:Chla ratio of 50 (e.g., Reigstad et al., 2008) in order to estimate total gut content of individuals (pigmented + non-pigmented food), Chapter(2( ( ! 61! and egestion rates were finally obtained from gut content and estimates of gut evacuation rates. As a result, specific ingestion rates (I, d-1) were derived from previous estimates of growth, respiration and egestion as I = G + R + E (2.3) Additionally, we assessed specific rates of ammonia excretion using the equation given by Ikeda (2014) for the global ocean, according to body weight, habitat temperature and depth. Corrections made for each taxonomic group are indicated in Table 2.1. Community fluxes (mg C m-2 d1) through production, respiration, egestion, ammonia excretion and ingestion were obtained relating specific rates and community biomass from the IBS. For all calculations, body weight of zooplankton (dry mass) was converted to carbon and nitrogen units using the conversion factors given by Kiørboe (2013), indicated in Table 1.1. 2.3 Results 2.3.1 Hydrography and chlorophyll distribution Despite some sporadic downwelling-favourable winds occurred before the ConAfrica cruise at the end of February 2006, the sampling was carried out during an upwelling event, as corroborated by the time-series of wind speed and direction over the study region (Benítez-Barrios et al., 2011). In this paper, authors described in detail the hydrography of the area during the ConAfrica cruise. SST and Chla images, derived from MODIS aqua satellite (Fig. 2.1), indicated the existence of a transition zone between the cold and productive waters of the upwelling, and the warm and oligotrophic waters offshore. As a result, Benítez-Barrios et al. (2011) identified a frontal system (their Fig. 3 and 4) as a band of about 30 km width, parallel to the coast, associated to a southwestward baroclinic upwelling jet crossing the study Zooplankton)in)the)CTZ)off)NW)Africa) ) ! 62! Table 2.1.!Predictive equations used to estimate zooplankton specific growth (d-1), respiration (µl O2 ind-1 h-1) and ammonia excretion (µg N ind-1 h-1) according to Hirst and Bunker (Hirst and Bunker, 2003), Garijo and Hernández-León (Garijo and Hernández-León, 2015) and Ikeda (Ikeda, 2014), respectively. T is habitat temperature, bw is body weight, Ca is the concentration of Chla and D is the depth where the organisms were captured. Equation to estimate ammonia excretion for copepods (referred on table) was corrected for chaetognaths and gelatinous estimates according to given factors (- 0.558 and -1.397, respectively). Multiple linear regression Variables a b c d r2 Specific growth (Hirst and Bunker, 2003) log10 g (d-1)=a[T]+b[log10 bw]+c[log10 Ca]+d T (ºC) bw (µg C ind-1) Ca (mg m-3) 0.0186 -0.288 0.417 -1.209 0.289 Respiration (Garijo and Hernández-León, 2015) log10 R (µl O2 ind-1 h-1)=a+b[log10 bw]+c[log10 T] bw (mg dw ind-1) T (ºC) -0.133 0.764 0.272 - 0.844 Ammonia excretion (Ikeda, 2014) *ln N (µg N ind-1 h-1)=a+b[ln bw]+c[1000/T]+d[ln D] bw (mg dw ind-1) T (K) D (m) 15.567 0.796 -5.010 -0.115 0.897 Chapter(2( ( ! 63! region (Fig. 2.1b and 2.2), which transported approximately 1 Sv (BenítezBarrios et al., 2011). We detected the front signal in transect T2 as a deepening of isotherms and isopycnals (Fig. 2.3a, b). These figures also show the cold and less salty upwelled waters along the upper 100 m over the African shelf. Figure 2.2. Horizontal distribution of temperature (ºC) at 20 m depth. Superimposed is the location of relevant mesoscale structures: anticyclonic (A1 and A2) and cyclonic (C1) eddies, the southwestward upwelling jet and two upwelling filaments (F1 and F2). Moreover, two upwelling filaments (F1) and (F2) showing cold and high Chla waters flowed offshore due to the interaction between the CC and Capes Bojador and Juby, respectively (Fig. 2.1b). As indicated by BenítezBarrios et al. (2011), F1 and F2 were likewise associated to respective anticyclonic (A1) and cyclonic (C1) eddies (Fig. 2.1b and 2.2). These eddies were located to the boundaries of the upwelling, conforming a dipole of onshore flow in between. A1 was identified as a deepening of slightly warmer and saltier waters from the surface to deeper layers in transect T1 (Fig. 2.4a, b). By opposite, we observed a slight doming effect (mainly below 100 m depth) in T1 due to the presence of C1 (Fig. 2.4a, b). F2 interacted with the upwelling jet and extended westward as a meandering filament (Fig. 2.1b). It finally entered an anticyclonic eddy (A2) of approximately 70-85 km Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 64! diameter generated southeast off Gran Canaria Island. In this regard, sharp gradients of temperature and salinity at the interfaces eddy-filament and eddy-open ocean were observed in Fig. 2.3a and 2.3b, respectively. The eddy entrained warmer and saltier waters from the surface to deeper layers, as observed in the salinity and temperature distribution along T2 and T3 (Fig. 2.3). T2 crossed the meandering westward trajectory of F2, as indicated by the relatively colder and less salty waters observed in the upper 50 m depth (Fig. 2.3a, b). Chla distribution showed the highest values in the upper 30 m depth near the African shelf (Fig. 2.3c). It extended offshore as a tongue of relatively rich waters, gradually sinking and eventually accumulating around the frontal system and the upwelling jet (Fig. 2.5a). Beyond the front, Chla at 50 m depth sharply decreased to typical oceanic values except for the region south off Fuerteventura Island (Fig. 2.5a), probably due to the influence of the upwelling jet. Both filaments (F1, F2) also transported Chla to the oligotrophic region, through a subsurface layer around 50-75 m depth (Fig. 2.3c and 2.4c), while A1 and C1 eddies also concentrated higher Chla at this depth layer (Fig. 2.4c). Besides, higher values, similar to those found in the upwelling region, were observed at 35-75 m depth of the interface between the A2 eddy and the filament F2 (Fig. 2.3c, 2.5a). 2.3.2 Zooplankton biomass and size distribution As expected, biomass of zooplankton was highest along the upper layers over the African shelf, coinciding with the upwelling waters (Fig. 2.3d, 2.5b). It gradually decreased towards the ocean, showing a sharp decline beyond the frontal system. However, biomass in the oligotrophic region remained higher along mesoscale structures. Thus, we observed high values in the upper 100 m of both F1 and F2 filaments (Fig. 2.3d, 2.4d). Besides, higher biomass was equally observed in the deeper layers of the eastern flank of the A2 eddy, slightly below the maximum of Chla. In addition, A1 and C1 eddies accumulated similar biomass to the observed in the upwelling zone Chapter(2( ( ! 71! Remarkably, both Chla and zooplankton biomass mainly accumulated in the region of interaction between eddies (A2, C1) and filaments, giving place to a generalized coupling between Chla and zooplankton along the threedimensional of both kinds of structures. In this respect, Hernández-León et al. (2002) suggested that higher biomass of zooplankton along filaments and eddies could be explained by the combination of increasing Chla and advection processes. Particularly, the onshore flow produced in between the A1-C1 dipole, might also contribute to the enhancement of biomass observed in this region, as already described by Moyano et al. (2014) for the retention of fish larvae in the same area. Table 2.2. Average (±SD) primary production and zooplankton community production, respiration, ammonia excretion and ingestion* rates (mg C m-2 d-1) along mesoscale structures (detailed in Fig. 1) and the oligotrophic region: U=upwelling, F1-F2=filaments, A1-A2=anticyclonic eddies, C1=cyclonic eddy, O=oligotrophic zone (region offshore not influenced by mesoscale structures). Numbers in brackets represent the percentage (%) of zooplankton community ingestion respect to primary production. *Note the high influence of egestion on total ingestion flux in comparison with that of production and respiration. On average, egestion accounted for 46-57% of ingestion flux. Primary production Production Respiration Egestion Ammonia excretion Ingestion U 2746.6±1579.5 57.7±36.8 183.5±124.4 294.2±205.4 113.0±72.2 535.4±366.6 (19.5) F1 1321.9±352.4 53.2±48.2 166.5±91.2 268.3±189.1 97.1±74.7 488.0±328.5 (36.9) A1 738.2±75.2 49.4±14.4 161.2±25.0 180.0±55.9 96.7±26.4 390.6±95.3 (52.9) C1 710.1±38.2 28.3±20.9 101.9±73.1 138.8±89.1 61.7±44.5 269.0±183.1 (37.9) F2 764.0±89.5 25.0±12.3 83.3±35.7 122.7±56.1 45.5±19.9 231.0±104.1 (30.2) A2 549.3±41.5 16.3±6.9 54.6±9.7 95.7±19.8 31.0±5.4 166.6±36.4 (30.3) O 477.9±75.2 13.6±1.9 51.1±17.0 68.7±27.8 27.1±6.8 133.4±46.7 (27.9) Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 72! On the contrary, we observed a vertical uncoupling between Chla and zooplankton within the A2 eddy, where higher biomass was observed deeper than the maximum of Chla. This was probably due to the variability occurring in the upper layers of newly formed island-induced eddies (Arístegui et al., 1994). In fact, eddies generated south off the Canary Islands are normally ageostrophic, changing their vertical and horizontal structure during their development, and thus showing elliptical and asymmetric shapes (Sangrá, 1995; Arístegui et al., 1997). Nevertheless, the distribution of biomass within both anticyclonic eddies (A1, A2) highly matched the signature of the physical structures, showing a higher accumulation of zooplankton along intermediate and deeper layers. This might be attributed to the convergent effect of this kind of eddies, in agreement with Arístegui et al. (1997), who already observed the same process for phytoand bacterioplankton. Likewise, the accumulation of biomass in the upper layers of C1 (Fig. 2.4d) agreed with general patterns observed for cyclonic eddies. There, isotherms elevation and nutrient pumping into the euphotic zone could enhance biomass in their cores (Arístegui et al., 1997). Specific growth according to the Hirst and Bunker (2003) equation was probably underestimated (and consequently production fluxes as well) along the oligotrophic region, since Garijo and Hernández-León (2015) observed that this equation, using Chla as unique indicator of the feeding status of organisms, could underestimate growth rates of zooplankton in these regions. In this sense, it is known the rather low contribution of pigmented food to total ingestion of mesozooplankton in oligotrophic waters which, in contrast, feed upon an important portion of microzooplankton to fulfill their physiological demands (e.g., Saiz et al., 1999; Hernández-León et al., 2001, 2002). However, the use of this equation over a region with a highly heterogeneous distribution of Chla seemed to be more appropriated than other relationships only based on temperature and body weight, such as those developed by Hirst et al (2003) or Garijo and Hernández-León (2015). Although it still remains unclear the effect of food on growth rates of Chapter(2( ( ! 73! individuals, we observed that the latter generally followed the distribution of Chla, rather than that of temperature or body weight. In this sense, filaments and eddies could enhance growth rates of zooplankton in oligotrophic regions, although more studies are needed to clarify this question. In contrast, respiration of zooplankton seemed to be primarily controlled by temperature, in agreement with Ikeda (2014) who suggested this parameter as the main factor determining respiration, followed by body weight. In this regard, different authors, such as Ikeda and Motoda (1978), Hirst and Sheader (1997), Hirst and Lampitt (1998) and Hirst et al. (2003), observed a generalized pattern of increasing physiological rates according to higher temperatures and decreasing body size. This trend coincided with the lower respiration rates estimated along the colder waters of the coastal upwelling and the meandering filament F2 where, at the same time, the abundance of largest individuals was the highest along the region (Fig. 2.6). Moreover, Garijo (2011) reported during the same cruise a predominance of smaller individuals towards the ocean, which could also help to explain the higher respiration rates estimated in the warmer waters of the oligotrophic region. Similarly, the accumulation of smaller individuals within the C1 eddy (Fig. 2.6c) probably influenced the increased respiration rates observed within this structure (Fig. 2.7c). Strikingly, respiration matched the physical signature of the anticyclonic A2 eddy, probably due to the deepening of warmer waters from the surface (Fig. 2.7a, b). These increases of respiration rates within eddies agreed with previous observations by Hernández-León et al. (2001) in the same region, who reported enhanced indices of metabolism in the smallest individuals at the core of two cyclonic and anticyclonic eddies. Nevertheless, the influence of specific metabolic rates in the distribution and magnitude of zooplankton-mediated fluxes was scarce in comparison to community biomass. This was in agreement with Huntley and Lopez (1992) who observed that, estimating production, the variance of community biomass was one to three orders of magnitude greater than that of specific growth rates. In this sense, as occurred with the distribution of community biomass, metabolic fluxes in the upwelling zone were about 3-4-fold higher Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 74! ! Figure 2.6.!Abundance of zooplankton (ind m-3) according to three size classes (200-500, 500-1000 and >1000 µm) along Transect 1 (A), Transect 2 (B) and Transect 3 (C). It is also referred the location of the mesoscale structures studied (according to map in Fig. 2.1): U=upwelling, F1-F2=filaments, A1-A2=anticyclonic eddies, C1=cyclonic eddy and the upwelling jet crossing Transect 1. Chapter(2( ( ! 75! than those estimated in the oligotrophic region, showing a gradual decrease towards the ocean. However, as observed in Table 2.2, metabolic fluxes remained considerably high within mesoscale structures, mainly due to the accumulation of biomass. Therefore, special attention should be provided to study these physical structures when assessing carbon fluxes through zooplankton in oligotrophic areas with an intense mesoscale activity. On the other hand, the contribution of egestion to the magnitude of ingestion flux was rather higher than expected (46-57%), since the latter is normally the result of about one-third of each component: growth, respiration and egestion (Kiørboe et al., 1985; Lenz et al., 1993; Båmstedt et al., 1999; Hernández-León et al., 2001, 2002, 2004). In any case, we suggest seasonality as one of the main factors influencing zooplankton-mediated fluxes in this region. In this respect, our estimates of zooplankton production (during the weak upwelling season) along the African shelf were up to 4-fold lower than those assessed by HernándezLeón et al. (2002) in summer (strong upwelling season). The productivity of the upwelling is not constant throughout the year (Barton et al., 1998), possibly leading to a certain variability of community biomass within seasons and, consequently, influencing the magnitude of metabolic fluxes. As expected, primary production was higher in the upwelling zone and decreased towards the ocean. However, it also remained considerably higher within filaments and eddies in comparison with the oligotrophic zone (Table 2.2), highlighting the importance of mesoscale structures in the export of productive waters from the upwelling to the open ocean. Zooplankton was not able to control primary production, since community ingestion was equivalent to 20-53% of PP along the region (Table 2.2). This range of values is similar to the range of 20-37% given by Hernández-León et al. (2001) in the same area, and included the values given by Hernández-León et al. (1999) in the tropical region (46%), and Dam et al. (1995) in equatorial waters (23%). However, our estimates were lower than those given by Hernández-León et al. (2002) in the same region (47-296%) during the Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 76! ! Figure 2.7. Vertical profiles of zooplankton specific respiration (d-1) along Transects 1, 2 and 3 (A, B and C, respectively). Likewise, specific growth (d-1) of this community along Transects 1, 2 and 3 (D, E and F, respectively). A1 and A2, and C1 are two anticyclonic and cyclonic eddies, respectively. F1 and F2 are the two coastal upwelling filaments studied (See Fig. 2.1 and 2.2). Chapter(2( ( ! 77! strong upwelling season, evidencing the influence of seasonality in the magnitude of zooplankton fluxes and water productivity. Finally, zooplankton ammonia excretion could support 4-13% of primary production, which agreed with estimates given by Zhang et al. (1993), Dam et al. (1995) and Hernández-León et al (1999, 2001) who assessed a contribution of excretion of this community to primary production in the range 3-17%. In summary, our results confirm the influence of mesoscale structures on the distribution, biomass, physiology and metabolic fluxes mediated by zooplankton in the CTZ off NW Africa. Thus, we observed that biomass gradually decreased towards the ocean, although upwelling jets and filaments can transport high Chla and zooplankton biomass to the oligotrophic region. In addition, the interaction between upwelling filaments and island-induced eddies may also enhance zooplankton biomass in the latter. In this sense, eddies may also influence the vertical distribution of organisms, since zooplankton mainly distributed following the physical signature of these mesoscale structures. Advection processes and increases in Chla are suggested as a combined mechanism to explain higher biomass of zooplankton within mesoscale structures. The existence of a quasipermanent frontal system in the transition zone, between the upwelling and the oligotrophic region, acts as a natural barrier retaining Chla and zooplankton, as already observed for fish larvae. The enhancement of zooplankton-mediated fluxes along mesoscale structures in oligotrophic regions is determined by their capacity to transport and concentrate biomass. In any case, this probably depends on the productivity of the upwelling, which changes throughout the year. Therefore, seasonality seems to eventually determine the capacity of physical structures to export zooplankton to the oligotrophic region, close to the Canary Archipelago. Export of these organisms, at the basis of the oceanic food chain and a natural food resource for fish larvae, could finally influence local fisheries, although possible effects in this regard still remain unknown. Zooplankton(in(the(CTZ(off(NW(Africa( ( ! 78! Acknowledgements We would like to thank our colleagues from the ConAfrica project for the hard work on board. Our special gratitude to Walber Melo, Sandra Herms, Lidia Nieves and Acorayda González, who scanned part of the zooplankton samples. We are also grateful to Alejandro Ariza for his invaluable assistance with graphics and satellite data management. Lidia Nieves arranged the available data from the cruise. This study was funded by projects ConAfrica (CTM2004-02319/MAR) and Mafia (CTM2012-39587-C04) from the Spanish Ministry of Science and Innovation. Juan Carlos Garijo was supported by a postgraduate grant (FPU-AP2010-5080) from the Spanish Ministry of Education. ( ( ! 79! Chapter 3 Large-scale zooplankton metabolism inferred through an image based system: a comparison of methods ! ! Chapter(3( ( ! 87! software) at a resolution of 1200 dpi. Different aliquots of the samples were taken with a Hensen pipette and poured onto polystyrene plates (90x130 mm). Images were then processed with the software ZooImage 1 version 1.2-1 (http://www.sciviews.org/zooimage), following the same procedure as specified by Garijo and Hernández-León (2015), including the criteria to create and conform the different taxonomic groups. However, unlike the latter, the enormous taxonomic diversity along the main ocean regions in the present study, forced to manage a manually sorted training set of nearly 7000 images to establish patterns for automated classification. The global error rate achieved was estimated below 8.5%. Once the organisms were classified, individual biomass required to assess metabolism was achieved applying empirical relationships, between body area and body weight, for the different taxonomic groups, as specified by Garijo and Hernández-León (2015) (their Table 1). Those equations were developed by Hernández-León and Montero (2006) and improved afterwards by Lehette and Hernández-León (2009). Dry weight estimates obtained from these relationships were then converted into carbon content according to specific conversion factors for taxonomic groups given by Kiørboe (2013), indicated in Table 1.1. 3.2.3 Respiration estimates Respiration rates derived from measurements of ETS activity (RETS), according to 65 samples (34 stations) within the 0-200, 200-500, 500-1000 and 1000-2000 m depth layers (24, 17, 17 and 7 samples respectively, Fig. 3.1, Table 3.1), were compared with estimates from the IBS (RIBS) using the generalist equation for the global ocean given by Ikeda (Ikeda, 2014; his Table 4 (carbon units)) and mechanistic equations developed in the present study (Table 3.2). The latter equations were specifically fitted to the temperature ranges found at the main ocean regions and depth layers. To establish those ranges we based on the in situ records of temperature found along the Malaspina cruise (Table 3.1). On the other hand, we used the Specific(metabolic(equations(for(specific(regions( ( ! 88! equation for copepods given by Ikeda (Ikeda, 2014), and then we applied the specified correction factors for chaetognaths (-0.345), euphausiid-like (0.600) and gelatinous (0.547) respiration. We assumed a respiratory quotient of 0.97 (Omori and Ikeda, 1984) to estimate specific respiration (d-1) in all cases. In turn, ETS activity was measured from frozen samples according to the method of Packard (1971) for zooplankton, with the modifications introduced by Owens and King (1975), Kenner and Ahmed (1975) and Gómez et al. (1996). Details of the procedure are given in Hernández-León and Gómez (1996). ETS activity, normalized to protein content, was approached to potential specific respiration (d-1), assuming a respiratory quotient of 0.97 (Omori and Ikeda, 1984), as well as using a C:dw ratio of 0.48 (Kiørboe, 2013) and the relationship dw = 1.445 + 4.283· prot (r2 = 0.900; n = 306; p<0.001) (3.1) given by Hernández-León et al. (2001). Finally, potential respiration was converted to specific rates (spRETS, d-1) taking into account that the R/ETS ratio normally varies with water productivity (Hernández-León and Gómez, 1996). Hence, we assumed a ratio of 1.0 for samples on eutrophic waters (Agulhas and California Currents, Southern Australia, Equatorial upwelling in the Pacific Ocean), while 0.5 for estimates on the remaining (oligotrophic) regions. 3.2.4 Growth assessments Growth rates of zooplankton derived from measurements of AARS activity (GAARS) were compared, according to 25 samples from the epipelagic layer (Fig. 3.1a), with estimates from the IBS (GIBS) in combination with mechanistic equations developed in this study (Table 3.3), and those given by Hirst et al. (Hirst et al., 2003) (their Tables 4 and 7) and Hirst and Bunker (Hirst and Bunker, 2003) (their Table 6, all data). The latter proposed a unique equation, using Chla (food proxy) additionally to body weight and temperature, while Hirst et al. (Hirst et al., 2003) developed different Chapter(3( ( ! 89! Table 3.1. Average in situ temperature (ºC) of samples used to compare zooplankton growth and respiration rates according to enzymatic methods and predictive equations. It is specified the oceanic region and depth layer where each sample was obtained: a, b, c and d denote each depth layer (see Fig. 3.2 and 3.3), while numbers in brackets detail specific depth layers, different than the others. Moreover, ranges of body weight (µg C ind-1) according to each taxonomic group along the cruise are also indicated. Depth (m) Station 0-200 (a) 200-500 (b) 500-1000 (c) 1000-2000 (d) ATLANTIC 6 16.3 (200-300) 12.6 (500-600) 10 18.7 7.6 12 10.3 4.3 13 17.9 15 20.4 17 19.8 35 18.0 12.4 5.7 39 18.4 3.1 43 12.9 6.6 INDIAN 45 15.9 10.4 5.8 3.0 49 19.8 14.8 62 17.6 64 12.0 7.9 66 20.2 4.6 69 10.6 7.3 3.0 71 14.7 10.0 7.6 76 13.3 6.7 77 16.9 7.0 WEST PACIFIC 84 24.5 16.1 6.2 3.2 (1000-1500) 2.5 (1500-2000) 86 26.8 15.6 5.6 88 27.0 90 26.8 11.8 6.0 92 24.9 10.1 6.0 3.2 96 24.1 9.2 100 23.8 11.4 5.1 EAST PACIFIC 103 21.0 106 21.4 112 17.9 114 18.0 10.4 (300-400) 8.8 (400-500) 6.0 115 17.6 117 19.1 119 16.8 121 17.1 bw range (µg C ind-1) copepods chaetognaths euphausiid-like gelatinous other zooplankton 0.8-172.1 4.2-235.4 38.6-1246.1 4.7-20.1 0.2-110.2 Specific(metabolic(equations(for(specific(regions( ( ! 90! equations according to the main taxonomic groups. Unlike our equations, these relationships disregarded the heterogeneity of temperature given on the main oceanic environments, and authors developed generalist equations for the global ocean pooling data from e.g., polar and equatorial waters. In contrast, as occurred in equations for respiration, our mechanistic relationships fitted to specific temperature ranges, using data from Hirst et al. (2003) (their Appendices 1 and 2). We developed different equations for the main taxonomic groups.Regarding AARS measurements, specific enzyme activity was assayed following the method of Yebra and HernándezLeón (2004), slightly modified by Herrera et al. (2012). Specific activity was then converted to specific growth rates (d-1) using the equation given by Hernández-León et al. (in preparation): GAARS (d-1) = -0.0117 + 0.0038· spAARS (r2 = 0.738, p<0.001) (3.2) where spAARS was expressed in terms of nmol PPi mg prot-1 h-1, and PPi is pyrophosphate. Table 3.1. Log-transformed equations to estimate zooplankton specific-respiration (µl O2 ind-1 h-1), and their Q10, developed in this study according to the temperature ranges (ºC) of the main ocean regions and depth layers (epi-, mesoand bathypelagic). Body weight ranges (mg dw ind-1) are also indicated. Community production (PAARS and PIBS) and respiration (RETS and RIBS) (mg C m-2 d-1) of zooplankton were eventually assessed using the community biomass (mg C m-2) obtained from the IBS and specific metabolic rates (d-1) derived from both, predictive equations and enzymatic methods. T range (ºC) bw range (mg dw ind-1) Multiple linear regression (log10R (µl O2 ind-1 h-1)) Variables n a b c Q10 r2 1-8 0.007-15.0 a+b[log10 bw]+c· T bw, T 45 -0.734 0.753 0.039 2.45 0.933 8-16 0.003-71.1 a+b[log10 bw]+c· T bw, T 327 -0.461 0.727 0.024 1.72 0.667 16-28.5 0.002-117.2 a+b[log10 bw]+c· T bw, T 236 -0.936 0.651 0.043 2.69 0.713 Chapter(3( ( ! 91! 3.2.5 Development of predictive equations For both growth and respiration, we developed mechanistic equations using a linear regression program in SYSTAT, version 13, as well as the software SPSS-IBM, version 22, according to the stepwise multiple linear regression method. The best fit was achieved according to logarithm of metabolism (log10 G, log10 R) and body weight (log10 bw), and temperature (T). The different Q10 were obtained from the parameter c of equations, according to the following: c = log10 (Q10^(1/10)) (3.3) In the case of equations for growth we used data given by Hirst et al. (2003) (their Appendices 1 and 2), available for the main taxonomic groups of zooplankton, while we managed the data compiled by Ikeda and HernándezLeón (2005) to develop equations for respiration. In both cases, we only employed data satisfying the established ranges of body weight (mesozooplankton) and temperature (ocean regions and epi-, mesoand bathypelagic layers) (see Table 3.1 and Discussion section below). 3.2.6 Statistics Linear regressions relating assessments of respiration and growth from the different methods (enzymatic procedures and using the IBS combined with predictive equations) (see Tables 3.4 and 3.5) were estimated using the reduced major axis (RMA) regression model (linear regression type II). It is assumed that the different variables were evenly measured with a certain error (view Smith 2009, and literature therein). Specific(metabolic(equations(for(specific(regions( ( ! 92! Table 3.2.!Log-transformed equations to estimate zooplankton specific-growth, and their Q10, developed in this study according to the main taxonomic groups and the temperature (ºC) ranges of the main ocean regions and depth layers (epi-, mesoand bathypelagic). Body weight (µg C ind-1) ranges are also indicated. We employed the crustaceans equations to estimate growth of our defined euphausiid-like group, while other zooplankton rates were estimated using copepods equations. T range (ºC) bw range (µg C ind-1) Multiple linear regression (log10 g (d-1)) Variables n a b c Q10 r2 Copepods 1-8 2.7-164.3 a + b[log10 bw] + c· T bw, T 39 -1.742 -0.308 0.048 3.02 0.272 8-16 0.5-240 a + b[log10 bw] + c· T bw, T 81 -1.586 -0.324 0.043 2.69 0.275 16-28 0.01-39 a + b[log10 bw] + c· T bw, T 611 -1.473 -0.156 0.041 2.57 0.367 Chaetognaths 1-16 20.8-650.2 a + b[log10 bw] + c· T bw, T 51 -1.672 -0.132 0.041 2.57 0.404 16-26 0.9-238.4 a + b[log10 bw] + c· T bw, T 28 -1.373 -0.273 0.034 2.19 0.344 Crustaceans 1-8 6.0-64172 a + b[log10 bw] + c· T bw, T 116 -1.046 -0.333 0.044 2.75 0.581 8-16 38.7-14262 a + b[log10 bw] + c· T bw, T 118 -1.015 -0.400 0.044 2.75 0.609 16-26 2.8-15955.2 a + b[log10 bw] + c· T bw, T 55 -0.918 -0.372 0.028 1.91 0.569 Gelatinous 8-16 0.1-76581 a + b[log10 bw] + c· T bw, T 50 -1.128 -0.190 0.047 2.92 0.500 16-28 3.9-34868.4 a + b[log10 bw] + c· T bw, T 84 -0.967 -0.211 0.029 1.93 0.476 3.3 Results The sampling program of the Malaspina-2010 circumnavigation expedition roughly covered the main ocean regions of the ocean (~40ºN-40ºS, Fig. 3.1a). As a result, sea surface temperature ranged from approximately 15ºC in the Southern Australia to nearly 30ºC in the Equatorial Pacific (Fig. 3.1b). Moreover, temperature dropped to levels as low as 2.5ºC at deeper layers (Table 3.1, 1500-2000 m). Similarly, surface Chla and PP were highly heterogeneously distributed along the cruise: from the lower levels of the vast oligotrophic gyres to the high productivity of mesotrophic and eutrophic regions, such as the Agulhas and California Currents, the region Chapter(3( ( ! 93! off Southern Australia or the Equatorial upwelling in the Pacific Ocean (Fig. 3.1a, b). The temperature and body weight ranges of our specific equations (Tables 3.2 and 3.3) were defined according to measurements of both parameters along the main ocean regions during the Malaspina cruise (Fig. 3.1b, Table 3.1). The overall range of temperature covered by our specific equations for respiration (1.0–28.5ºC) mainly matched that of Ikeda (Ikeda, 2014) (-2.0– 30ºC), while similarly high coefficients of determination (r2) were also observed for all equations (Table 3.2). As also observed on this table, estimates of Q10 ranged from 1.72 to 2.69. Using our temperature-specific equations, estimates of specific and community respiration were not significantly different (ANOVA, p>0.05) than RETS (Fig. 3.2 and 3.3). However, estimates using the Ikeda equation (Ikeda, 2014) were significantly lower (ANOVA, p<0.05) than the latter, despite measurements from both methods followed a similar pattern of distribution along the cruise (Fig. 3.2 and 3.3). As a result, specific and community respiration using our equations were better correlated to RETS estimates than using the Ikeda equation (Ikeda, 2014) (Table 3.4). Generally, we observed that RETS assessments were higher than RIBS using both, Ikeda (Ikeda, 2014) and our equations, on samples from the epipelagic layer of lower latitudes, where temperatures were higher (Fig. 3.2; Table 3.1). In contrast, estimates from the ETS method and equations were comparable in samples along deeper layers (Fig. 3.3). Nevertheless, differences in the epipelagic layer decreased in relation to community respiration, due to the effect of community biomass (Fig. 3.2). As occurred with respiration, the range of temperatures covered by our specific equations for growth (1–28ºC) roughly matched that of generalist equations given by Hirst et al. (Hirst et al., 2003) and Hirst and Bunker (Hirst and Bunker, 2003) (-2.3–31ºC) for the global ocean. As observed on Table 3.3, Q10 values ranged from 1.91 to 3.02, with increasing effect of temperature on equations for colder waters. Furthermore, our equations showed higher coefficients of determination (r2) than those observed for previous generalist equations for the global ocean (Table 3.3), and mainly in Specific(metabolic(equations(for(specific(regions( ( ! 94! ! Figure 3.2. Epipelagic zooplankton specific-respiration (A) and community respiration fluxes (B) along the Malaspina cruise, according to estimates from the ETS* method and the IBS, in combination with the equation given by Ikeda (Ikeda, 2014)** and using the temperature-specific equations developed in this study. a, next to each sample number indicate 0-200 m depth layer (see Table 3.1). Vertical bars denote standard deviation. *We assumed a R/ETS ratio of 0.5 for oligotrophic regions, and a ratio of 1.0 for samples from productive zones, in order to adopt suitable ratios for each region according to productivity (Hernández-León and Gómez, 1996) (see Methods section). **We applied correction factors given by Ikeda (Ikeda, 2014) to estimate respiration of chaetognaths (- 0.345), euphausiid-like (0.600) and gelatinous (0.547) from the generalized equation for copepods (his Table 4). Chapter(3( ( ! 95! the case of crustaceans and gelatinous zooplankton. Although the accuracy of our equations for copepods was still rather low, it increased respect to that of generalist equations from Hirst et al. (Hirst et al., 2003) and Hirst and Bunker (Hirst and Bunker, 2003), mainly in the equation for higher temperatures (16–28ºC). Consequently, estimates of specific growth and community production using our temperature-specific equations showed a higher correlation with GAARS and PAARS estimates than those using generalist equations (Fig. 3.4a, b). In fact, estimates using our equations were not significantly different (ANOVA, p>0.05) than GAARS and PAARS assessments. As observed for respiration, higher agreement between all methods and equations was observed when community biomass was applied (Fig. 3.4b). However, estimates of community production using the equations given by Hirst et al. (Hirst et al., 2003) and Hirst and Bunker (Hirst and Bunker, 2003) were nevertheless significantly lower (ANOVA, p<0.05) than assessments of PAARS. Similarly to respiration, we also observed a higher agreement between estimates of GIBS using our equations and those of GAARS along colder waters, such as the Southern Australia region (Fig. 3.4a). Conversely, differences increased with increasing rates, as it was on the warmer waters of the West Pacific. Nevertheless, specific growth and community production from the AARS method and estimates using the different equations followed, in general terms, a similar pattern (Fig. 3.4a, b). 3.4 Discussion The present study is based on the previous results shown by Garijo and Hernández-León (2015) on subtropical waters. These authors observed that IBS might result as reliable as enzymatic methods to estimate zooplanktonmediated fluxes when using temperature-specific equations, fitted to the conditions of the subtropical waters. Hence, we tested the suitability of a new set of metabolic equations for further regions, with the aim to be Specific(metabolic(equations(for(specific(regions( ( ! 96! Figure 3.3. Mesoand bathypelagic zooplankton specific-respiration (A) and community fluxes (B) along the Malaspina cruise, according to estimates from the ETS* method and the IBS, in combination with the equation given by Ikeda (Ikeda, 2014)** and using the temperature-specific equations developed in this study. b, c and d, next to each sample number indicate 200-500, 500-1000 and 1000-2000 m depth layers, respectively (see Table 3.1). Vertical bars denote standard deviation. *We assumed a R/ETS ratio of 0.5 for oligotrophic regions, and 1.0 for productive zones (Hernández-León and Gómez, 1996) (see Methods section). **We applied correction factors given by Ikeda (Ikeda, 2014) for chaetognaths (-0.345), euphausiid-like (0.600) and gelatinous (0.547) from the generalized equation for copepods (his Table 4). Chapter(3( ( ! 103! temperature ranges, and comparisons were ascertained in a quasi-global scenario using samples from the circumnavigation Malaspina-2010, according to a wide range of temperatures (2.5–30ºC) and productivity (oligo-, mesoand eutrophic waters). Our metabolic estimates were comparable to those using enzymatic methods, and no significant differences were observed. By opposite, metabolism from previous equations for the global ocean was significantly lower than measurements from enzymatic methods. According to these results, although both generalist and specific equations could be useful, the latter are expected to increase the accuracy of estimates of zooplankton metabolic fluxes using an IBS. Nevertheless, the use of predictive equations still shows limitations on particular studies and regions, and therefore specific work is still required. In addition, new equations for more taxonomic levels, and including all significant variables, should be developed in order to increase the accuracy of carbon fluxes mediated by zooplankton at the large-scale. Acknowledgements This research was conducted by the Malaspina 2010 Expedition project, funded by the Spanish Ministry of Economy and Competitiveness (Consolider-Ingenio 2010, CSD2008-00077). Thanks are due to all those who contributed to the success of the expedition, specially those colleagues who made a successful and hard work on board, coordinating groups and obtaining samples and data. Juan Carlos Garijo was supported by a postgraduate grant (FPU-AP2010-5080) from the Spanish Ministry of Education. ! ! ( ! 105! Chapter 4 Zooplankton active flux in the warm ocean: relationship with chlorophyll and temperature ( ! Chapter(4( ( ! 107! Zooplankton active flux in the warm ocean: relationship with chlorophyll and temperature Juan Carlos Garijo, Maria Luz Fernández de Puelles and Santiago Hernández-León Abstract Zooplankton migrant biomass and active fluxes of carbon (respiratory, mortality and egestion) and nitrogen (ammonia excretion) were assessed through a latitudinal transect along the tropical and subtropical Atlantic Ocean (10ºS-25ºN). Metabolic estimates were derived from an image-based system in combination to recently developed predictive equations. Our results showed that large copepods (>1000 µm) were by far the major contributors (47% on average) to total active flux. Moreover, we observed a relationship between surface chlorophyll concentration and migrant biomass, which was identified as the main factor determining the magnitude of active fluxes through zooplankton. Sampling was performed along a wide latitudinal gradient, and our estimates covered the entire range of zooplankton active fluxes and migrant biomass found in the literature. Hence, the latter ranged from 35.4 mg C m-2 along the oligotrophic region south the Equator to 1649.4 mg C m-2 within the rich waters of the Cape Blanc oceanic upwelling, while total active flux through zooplankton followed the same distribution ranging from 1.4 to 81.7 mg C m-2 d-1. Mortality flux was higher than respiratory, while gut flux was on average 2– 3-fold lower than the former. Simple models were developed to assess zooplankton downward export through data easily available from remote sensing: sea surface temperature and chlorophyll concentration. The strong correlations observed could indicate the possibility of using satellite data to assess active fluxes along vast regions of the ocean in a near future. Active(flux(in(the(warm(ocean( ( ! 108! 4.1 Introduction The biological pump constitutes one of the major pathways of vertical transport of organic and inorganic matter in the water column (Ducklow et al., 2001; Fasham, 2003). Thus, understanding its functioning is of paramount importance in developing accurate oceanic global models (Usbeck et al., 2003). Much effort has been dedicated to the so-called gravitational flux, which refers to the sinking of particulate organic matter to deeper layers (Fowler and Knauer, 1986; Buesseler et al., 2007). However, a handful of studies performed during the last decades showed that active fluxes of dissolved carbon and nitrogen through migrant zooplankton, may also account for an important fraction (in the range of the passive flux in some cases) of the total export (e.g., Longhurst et al., 1990; Steinberg et al., 2000; Al-Mutairi and Landry, 2001; Hernández-León et al., 2001; Hidaka et al., 2001; Steinberg et al., 2002, 2008; Yebra et al., 2005b; Kobari et al., 2013; Stukel et al., 2013; Isla et al., 2015). Organisms migrate every day to feed in the epipelagic zone at night and return to deeper layers at dawn (Lampert, 1989), where they excrete nitrogen and release carbon by respiration, excretion and egestion. Community fluxes mediated by zooplankton through respiration or production are primarily dependent on community biomass, since the variability of the latter parameter may be one to three orders of magnitude greater than specific metabolic rates (Huntley and Lopez, 1992). In turn, diel vertical migration (DVM) of zooplankton occurs in all marine regions and it is probably one of the most important movements of biomass in the ocean. Consequently, the amount of matter exported downwards through migrant zooplankton must be forcedly considerable at a global scale, with subsequent implications for the biological pump. Estimates of zooplankton migrant biomass are relatively affordable. However, metabolic rates at depth are difficult to assess due to the low concentration of organisms in the mesopelagic zone (Yebra et al., 2005b) and constraints related to incubation methods traditionally used, as e.g., the Chapter(4( ( ! 109! number of individuals to ensure optimal conditions, stress of animals captured, bacterial growth or starvation (Ikeda et al., 2000). Indirect indices, such as the activity of the electron transfer system (ETS; Packard, 1971) constitute an alternative, although this method suffers from the uncertainty of the relation between the enzyme activity and respiration (HernándezLeón and Gómez, 1996). On the other hand, the use of image-based systems (IBS) in combination with empirical models, based on temperature and body weight, may also result helpful to approach metabolic rates of each individual. On this subject, Garijo and Hernández-León (2015) observed that results using an IBS were comparable to those derived from enzymatic methods, although in a faster and inexpensive way. The use of predictive equations, fitted to the specific ranges of temperature given at each depth layer, seem to be a reliable alternative to assess growth and respiration rates of zooplankton at depth (Garijo et al., Chapter 3), and therefore they could be useful to ascertain metabolic profiles in the water column. Similarly, Peterson and Wroblewski (1984) developed a weight-specific mortality model based on the biomass of individuals, which was modified afterwards by Hidaka et al. (2001). Most of the effort has been dedicated to respiratory flux, and mortality in a lesser extent, while little attention has received the so-called gut flux (egestion flux): carbon exported to the mesopelagic layer as non-assimilated food in the gut of migrants, releasing fecal pellets during daytime (Angel, 1989). In this respect, some authors have pointed out that organisms are able to transport a significant amount of pigments in their guts (Lampitt et al., 1993; Hernández-León et al., 2001; Yebra et al., 2005b; Putzeys et al., 2011). Estimates of gut flux according to the gut fluorescence (GF) method are biased, since authors are forced to assume (1) an herbivorous diet of organisms, (2) the remaining of pigments in the guts of animals during downward migration or (3) that they do not feed on pigmented material at depth. Nevertheless, this kind of estimates should be considered as conservative for omnivorous organisms, and therefore it is expected that gut flux might also contribute to enhance total export through zooplankton. Active(flux(in(the(warm(ocean( ( ! 110! Similarly, despite some studies showed that excretion of nitrogen through migrant zooplankton ranged or even exceeded the passive flux (Dam et al., 1995; Steinberg et al., 2002; Stuckel et al., 2013), this mechanism has been scarcely addressed. In addition, most of the results are derived from incubations (e.g., Steinberg et al., 2002) with the associated limitations that these methods present at deeper layers (see above). Alternatively, Ikeda et al. (2001) developed a nitrogen excretion model based on temperature and body weight, which seemed to provide reliable results (Stuckel et al., 2013). On the other hand, Irigoien et al. (2014) recently pointed out that biomass of mesopelagic fish strongly depends on primary production at a global scale. In this regard, it seems that understanding how productivity could influence zooplankton-mediated fluxes might be a key factor to determine the contribution of these communities to the biological pump in the ocean. However, as occurs for biomass and metabolic assessments, vast and remote regions of the ocean still remain unexplored in terms of active fluxes, while most repeated cruises concentrate along established navigation routes (Reid et al., 2003; Isla et al., 2004; San Martin et al., 2006). In this sense, many researchers hypothesize that carbon export and the state of the biological pump might be predicted through characteristics of the ocean’s surface (e.g., EXPORTS, http://exports.oceancolor.ucsb.edu). This builds on recent advances in satellite remote sensing and autonomous tools (gliders, floats, etc.), which regularly provide information on oceanic parameters such as chlorophyll a (Chla), primary production (PP), oxygen concentration or sea surface temperature (SST). Zooplankton models based on these parameters could stand as a valuable tool to continuously examine the role of these communities in oceanic carbon budgets, including environments that otherwise could result expensive and challenging to sample directly (LópezUrrutia et al., 2006; Jennings et al., 2009; Stock et al., 2014). In this paper, we examined the vertical distribution of zooplankton biomass and metabolic profiles of respiration, mortality (assessed through growth assuming the community was in steady-state), egestion and ammonia excretion rates, in order to assess carbon and nitrogen active fluxes due to Chapter(4( ( ! 111! vertical migration along the tropical and subtropical Atlantic Ocean (10ºS25ºN). Our estimates of metabolism were derived from an IBS in combination with recently developed predictive equations, based on temperature and body weight, fitted to the temperature ranges given at each depth layer. Our results from this highly heterogeneous region were compared to surface temperature and chlorophyll concentration. 4.2 Materials and methods 4.2.1 Hydrographic surveys From 3rd to 29th April 2015, 11 stations along a latitudinal transect in the Atlantic Ocean (10ºS-25ºN) were sampled during the Mafia cruise on board the R/V “Hespérides” (Fig. 4.1a). Vertical profiles of temperature, salinity and fluorescence were recorded down to 800 m depth using a SeaBird 911 plus CTD system, mounted on a General Oceanics rosette sampler, equipped with twenty-four 12 l Niskin bottles. Phytoplankton chlorophyll was derived from depth profiles of in situ fluorescence measured with a Turner Scufa fluorometer, calibrated with samples collected within the upper 200 m of the water column in accordance with the JGOFS recommendations (UNESCO, 1994). In addition, continuous underway measurements of surface (~2 m) temperature and fluorescence were recorded every minute using a Seabird thermosalinometer and fluorometer. Data was represented every nautical mile and checked by comparison with near-surface values from the CTD records. 4.2.2 Zooplankton sampling and biomass assessments Paired day-night zooplankton samples at 8 discrete depth intervals (0-50, 50100, 100-200, 200-300, 300-400, 400-500, 500-600 and 600-800 m) were taken at each station with a 1 m2, 200 µm mesh, Multiple Opening and Closing Net and Environmental Sensing System (MOCNESS). Oblique trawls were performed at a towing speed of ca. 3 knots, measuring the volume of filtered Active(flux(in(the(warm(ocean( ( ! 112! ! Figure 4.1. Zooplankton sampling stations* with the average primary production (mg C m-2 d-1) during April 2015 superimposed (A), and in situ sea surface temperature (ºC) and fluorescence (volts) along the cruise (B). The ship’s thermosalinometer and fluorometer were fed from the ship's continuous seawater supply and recorded data every minute, although it was represented every nautical mile. *White points indicate stations that were not sampled for zooplankton. Primary production was obtained from the Ocean Productivity website (http://www.science.oregonstate.edu/ocean.productivity/index.php), according to the Vertically Generalized Production Model (VGPM, Behrenfeld and Falkowski, 1997) and data from sensor MODIS aboard the Aqua satellite (http://oceancolor.gsfc.nasa.gov/cms/data/aqua). Chapter'4' ' ! 119! 4.3.2 Vertical distribution of zooplankton and migrant biomass Vertical distribution of zooplankton community biomass showed a clear pattern during both day and night samplings (Fig. 4.3a, b), with higher values above 200 m depth. We also found a deeper layer of biomass around 300500 m depth, which intensified at night in the proximities of the weak EU and mostly in the CBU regions. In these areas, community biomass (day and night) in the water column was much higher than in the oligotrophic zones. Moreover, we observed a general pattern of increasing biomass at night within the epipelagic zone due to the migrating fraction (Fig. 4.3a, b), resulting in negative values in the day-minus-night profiles in the surface layers (Fig. 4.3c). By opposite, slightly positive values were generally observed below 200 m depth, indicating increases of biomass at depth during daytime. Migrant biomass in the euphotic zone increased at both upwelling regions (Fig. 4.3c), ranging from 513.3 to 682.6 mg C m-2 in the weak EU and approximated to 1649.9 mg C m-2 in the CBU (Table 4.2). On the other hand, lower and heterogeneous values were observed along the oligotrophic regions (35.4-693.8 mg C m-2). Migrant biomass in terms of nitrogen was directly estimated from migrant values in carbon units and therefore followed the same pattern than the latter parameter (Table 4.3), ranging from 3.6 to 168.3 mg N m-2 along the whole transect. 4.3.3 Metabolic rates Daytime specific metabolic rates (d-1) of largest organisms (>1000 µm) followed a similar pattern, showing higher values in the upper 200 m and decreasing with depth (Fig. 4.4a, b, c, d). Horizontally, specific rates slightly increased in the epipelagic zone south the equator, as a consequence of the higher SST in this region. Nevertheless, none of the metabolic parameters (R, M, G, N) showed significant differences (ANOVA, p>0.05) between stations in the mesopelagic layer along the cruise. On the other hand, egestion rates within the 300-500 m layer were significantly lower (ANOVA, Active'flux'in'the'warm'ocean' ' ! 120! p<0.05) than estimates of respiration, mortality and ammonia excretion. This is the layer where most of the migrant organisms were observed to reside during daytime (Fig. 4.3a), releasing most of the carbon and ammonia exported downwards. ! Figure 4.3.!Vertical profiles of zooplankton community biomass (mg C m-3) during daytime (A) and nighttime (B), as well as migrant biomass (C, mg C m-3) at the sampling stations. The latter was the result of day-minus-night vertical profiles of biomass. Chapter'4' ' ! 121! 4.3.4 Active flux Carbon fluxes through respiration, mortality and egestion of zooplankton followed a similar pattern along the whole transect, matching the distribution of migrant biomass (Fig. 4.5a), since metabolic rates presented a homogeneous distribution along the mesopelagic zone (Fig. 4.4a, b, c). A similar scenario was observed for ammonia excretion flux (Fig. 4.5b). Thus, migrant biomass and active fluxes generally increased from south to north along the cruise (Fig. 4.5a, b), highlighting the intensification around the weak EU, and more sharply in the region of the CBU (see ranges in Tables 4.2 and 4.3). Contribution of mortality flux, derived from differences in the magnitude of specific metabolic rates, was higher than respiratory or egestion fluxes, and differences expanded at regions with higher export (Fig. 4.5a). On average, about 48% and 32% of the total carbon exported corresponded to mortality and respiratory flux respectively, while gut flux accounted for about 20% along the cruise. As their distribution was mainly influenced by migrant biomass (MB), we observed a high correlation (linear regression type II) between the latter parameter and total carbon flux (TF, respiratory + mortality + egestion) (r2 = 0.960; n = 11; p<0.05), both following a similar distribution (TF = 0.051· MB – 3.054) (Fig. 4.5c). Furthermore, total carbon flux was also correlated with surface chlorophyll concentration (see Section 4.3.5), showing a coupled distribution, with a remarkable intensification around the CBU (Fig. 4.5c). Large copepods (>1000 µm) were by far the major contributors (47% on average) to total active flux along the transect (Fig. 4.6). Except in the proximities of the Equator and within the region of the CBU, where euphausiids were dominant, large copepods and chaetognaths accounted on average for 76% of the total flux. Strikingly, smallest organisms (<500 µm) represented about 25% of the migratory flux within the EU region. Active'flux'in'the'warm'ocean' ' ! 122! ! Figure 4.4. Vertical profiles of specific respiration (A), mortality (B), egestion (C) and ammonia excretion (D) rates (d-1) for zooplankton individuals >1000 µm along the study region. Chapter'4' ' ! 123! ! Figure 4.5.!Zooplankton migrant biomass (mg C m-2) and active carbon flux (mg C m-2 d-1) into the mesopelagic layer (>200 m depth) through respiration, mortality and egestion processes (A), zooplankton migrant biomass in terms of nitrogen (mg N m-2) and ammonia excretion active flux (mg N m-2 d-1) (B), and zooplankton migrant biomass (mg C m-2) and total carbon export (R+M+G) (mg C m-2 d-1), jointly with the distribution of in situ surface chlorophyll concentration (mg m-3) (C) along the cruise. Vertical bars denote standard errors. See Methods section for calculations of migrant biomass and active fluxes. Active'flux'in'the'warm'ocean' ' ! 124! ! Figure 4.6.!Contribution of major taxonomic groups (%), as well as the three size classes (200-500, 500-1000 and >1000 µm), to total active flux (below 200 m depth) through zooplankton along the cruise. 4.3.5 Active flux and remote sensing Migrant biomass of zooplankton was correlated with in situ surface chlorophyll concentration along the cruise (r2 = 0.500; n = 11; p<0.05) (Fig. 4.7a). Furthermore, as migratory biomass mainly determined the distribution and magnitude of active fluxes (see above), the different mechanisms (and consequently total carbon flux) were also significantly correlated with surface Chla (Fig. 4.7b, c, d, e). A similar observation was made for ammonia excretion fluxes (Fig. 4.7f). Likewise, we found that migrant biomass was highly (negatively) correlated with in situ measurements of SST (r2 = 0.661; n = 11; p<0.05). As a consequence, multiple linear regressions relating Chapter'4' ' ! 125! zooplankton export and surface Chla and temperature showed rather high correlation coefficients (Table 4.4). 4.4 Discussion 4.4.1 Methodological constraints Migrant biomass was assumed to be the increment of biomass at the euphotic zone during nighttime. This probably led to a biomass overestimation (and consequently of active flux) due to daytime avoidance of nets by large organisms in the upper sunlit layers (Ianson et al., 2004). Another source of overestimation relies on the assumption that migrant biomass, estimated at night in the euphotic zone, is not predated and reaches the mesopelagic layer to reside during the day. However, it has been suggested that about 1-4% of zooplankton biomass is removed by mesopelagic fish in the upper 200 m depth (Hopkins and Gartner, 1992; Watanabe et al., 2002; Hudson et al., 2014), although it remains unclear which fraction of the migrant zooplankton is impacted by this consumption. By opposite, migrant biomass and export could be underestimated if assessments are based on daytime increments under 200 m depth, since it is known that organisms may detect the nets even at deeper layers during the day (Ianson et al., 2004). In addition, individuals migrating to the surface layers during nighttime and residing deeper than 800 m during the day (not sampled) would be also unaccounted for. Avoidance of and attraction to nets due to bioluminescence or pressure changes are additional source of bias (Ianson et al., 2004). Concerning carbon and nitrogen export, we assumed the metabolic rates of individuals in the 300-500 m layer as the average metabolism of the migrant community, since most individuals distributed at this depth during daytime (Fig. 4.3), and therefore releasing most of the carbon and nitrogen within this layer. In this regard, despite individuals were also found at other layers, metabolic rates were not significantly different (ANOVA, p>0.05) within the whole mesopelagic region, and therefore this was not considered as a Active'flux'in'the'warm'ocean' ' ! 126! significant source of uncertainty (Fig. 4.4). Moreover, the influence of taxonomy on zooplankton metabolism is known to be lower than other factors such as body weight and temperature, or even habitat depth (Bode et al., 2013; Ikeda, 2014). In any case, this effect was presumably minimized for estimates of growth rates and ammonia excretion, as different equations and corrections were applied for each group. Predictive equations, relating metabolism and body weight and temperature, are suggested as a reliable alternative to assess zooplankton community fluxes when they fit to the specific conditions of the study region (Garijo and Hernández-León, 2015; Garijo et al., Chapter 3). In these studies, authors observed that estimates using temperature-specific equations were at least comparable to assessments from enzymatic methods, and probably more accurate than using existing relationships for the global ocean, such as those from Ikeda (1985), Hirst et al. (2003), Hirst and Bunker (2003), Zhou et al. (2010) and Ikeda (2014). 4.4.2 Active flux: the importance of migrant biomass The pattern of vertical distribution of zooplankton community biomass showed two main layers during the day: one in the upper 200 m depth, and another in the 300-500 m layer. The latter coincided with the position of the so-called deep scattering layer (DSL) (see Dietz, 1948 or Boden and Kampa, 1967), regularly observed through acoustic sampling. At night, part of these organisms migrated to the surface layers, since nightly biomass in the upper 200 m was always higher than during daytime. However, mesopelagic biomass also increased during the night. This could be partly attributed to a second migration, coming from deeper layers, in agreement to the hypothesis by Vinogradov (1970), or merely due to the possible increase of effectiveness of hauls by night (Hernández-León et al., 2001). Chapter'4' ' ! 127! ! Figure 4.7.!Correlations (log10-log10) between surface Chla (in situ) and estimates of zooplankton migrant biomass (A), and assessments of the respiratory (B), mortality (C), egestion (D), total (E) and ammonia excretion (F) active fluxes through zooplankton to the mesopelagic layer (below 200 m depth). Active'flux'in'the'warm'ocean' ' ! 128! Active fluxes through zooplankton (mortality, respiratory, egestion and ammonia excretion) matched the distribution of migrant biomass, rather than specific metabolic rates, in agreement with Huntley and Lopez (1992). These authors observed that, estimating community production of zooplankton, the variation of community biomass was one to three orders of magnitude larger than specific metabolic rates. In our case, this is attributed to the scarce variability observed on estimates of specific metabolism within the mesopelagic layer, homogeneously distributed along the cruise. In this respect, metabolic rates derived from predictive equations depended on body weight and temperature, both parameters showing little variability at depth during the cruise. Therefore, we highlight the importance of precisely estimate the community biomass of zooplankton, since it eventually determines the accuracy of active fluxes through these communities. Larger organisms (>1000 µm) represented on average more than 93% of total migrant biomass along the cruise (Fig. 4.6), highlighting the relevance of this size class on DVM (e.g., Hernández-León, 2001; Ariza et al., 2015; Isla et al., 2015). On the other hand, some authors argue that active fluxes are mainly driven by euphausiids (Stuckel et al., 2013; Isla et al., 2015). However, these organisms only dominated DVM around the equatorial and the CBU regions (Fig. 4.6). In fact, euphausiids were not certainly abundant along the cruise while copepods, which represented more than 80% of abundance, were the main drivers for export fluxes. It has been suggested elsewhere that predatory pressure (Stuart and Verheye, 1991; Frost and Bollens, 1992), especially from chaetognaths (Ohman, 1990; Irigoien et al., 2004; Isla et al., 2015), could induce this migrant behavior on copepods. 4.4.3 Carbon and nitrogen export: comparisons among studies As active fluxes were directly dependent on the magnitude of migrant biomass, and the latter increased around the productive waters of both upwelling zones (mostly in the very productive waters of the CBU), downward export of zooplankton also increased in these zones in