[Corrigendum to] Effects of small-scale turbulence on lower trophic levels under different nutrient conditions [vol 32, pg 197, 2010]

Small-scale turbulence affects the pelagic food web and energy flow in marine systems and the impact is related to nutrient conditions and the assemblage of organisms present. We generated five levels of turbulence (2*10 29 to 1*10 24 W kg 21 ) in land-based mesocosms (volume 2.6 m 3 ) with and without additional nutrients (31:16:1 Si:N:P m M) to asses the effect of small-scale turbulence on the lower part of the pelagic food web under different nutrient conditions. The ecological influence of nutrients and small-scale turbulence on lower trophic levels was quantified using multivariate statistics (RDA), where nutrients accounted for 31.8% of the observed biological variation, while 7.2% of the variation was explained by small-scale turbulence and its interaction with nutrients. Chlorophyll a, primary production rates, bacterial production rates and diatom and dinoflagellate abundance were positively correlated to turbulence, regardless of nutrient conditions. Abundance of autotrophic flagellates, total phytoplankton and bacteria were positively correlated to turbulence only when nutrients were added. Impact of small-scale turbulence was related to nutrient con- ditions, with implications for oligotrophic and eutrophic situations. The effect on community level was also different compared to single species level. Microbial processes drive biogeochemical cycles, and nutrient-controlled effects of small-scale turbulence on such processes are relevant to foresee altered carbon flow in marine systems

Bacterivory by phototrophic picoplankton and nanoplankton in Arctic waters

Author Posting. © The Author(s), 2011. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in FEMS Microbiology Ecology 82 (2012): 242–253, doi:10.1111/j.1574-6941.2011.01253.x.Mixotrophy, the combination of phototrophy and heterotrophy within the same individual, is widespread in oceanic systems. Yet, neither the presence nor ecological impact of mixotrophs has been identified in an Arctic marine environment. We quantified nano- and picoplankton during early autumn in the Beaufort Sea and Canada Basin and determined relative rates of bacterivory by heterotrophs and mixotrophs. Results confirmed previous reports of low microbial biomass for Arctic communities in autumn. The impact of bacterivory was relatively low, ranging from 0.6 x 103 to 42.8 x 103 bacteria mL-1 day-1, but it was often dominated by pico- or nano-mixotrophs. From 1-7% of the photosynthetic picoeukaryotes were bacterivorous, while mixotrophic nanoplankton abundance comprised 1-22% of the heterotrophic and 2-32% of the phototrophic nanoplankton abundance, respectively. The estimated daily grazing impact was usually < 5% of the bacterial standing stock, but impacts as high as 25% occurred. Analysis of denaturing gradient gel electrophoresis band patterns indicated that communities from different depths at the same site were appreciably different, and that there was a shift in community diversity at the midpoint of the cruise. Sequence information from DGGE bands reflected microbes related to ones from other Arctic studies, particularly from the Beaufort Sea.Funding for participation in the 2008 cruise was provided by the Woods Hole Oceanographic Institution Arctic Research Initiative, with additional support from National Science Foundation Grants OPP-0838847 (RWS) and OPP-0838955 (RJG)

Changes in phytoplankton composition in a Mediterranean coastal lagoon in the Cullera Estany (Comunitat Valenciana, Spain)

The Cullera Estany is a coastal lagoon located in a highly intensified agriculture and tourist area in Valencia. This coastal lagoon has connections with the sea that produce marine intrusion and generate a freshwater interface. Four sampling campaigns were carried out during 2010 in order to analyse the phytoplankton composition and its relation to nutrient content through a Redundancy Analysis. Temperature, dissolved oxygen, nitrite and salinity are the main factors controlling the dynamics of phytoplankton community. During July and October, there is water column stratification; meanwhile in March, there is a well-mixed water column. In addition, in May and July campaigns, hypoxia/anoxia conditions are detected at the bottom. The most abundant phytoplankton groups are Diatoms and Cryptophyceae. Diatoms and Cyanophyceae respond positively to temperature while Cryptophyceae, Prasinophyceae and Dinophyceae respond to high salinity and dissolved oxygen values. Furthermore, picoplankton is correlated inversely with nutrient concentrations.This research work has been supported by the Generalitat Valenciana.Pachés Giner, MAV.; Romero Gil, I.; Martínez Guijarro, MR.; Martí Insa, CM.; Ferrer Polo, J. (2014). Changes in phytoplankton composition in a Mediterranean coastal lagoon in the Cullera Estany (Comunitat Valenciana, Spain). Water and Environment Journal. 28(1):135-144. doi:10.1111/wej.12020S135144281Agencia Estatal de Meteorologia. AEMET 2011 http://www.aemet.es/es/portadaTer Braak, C. J. F. (1986). Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis. Ecology, 67(5), 1167-1179. doi:10.2307/1938672Brogueira, M. J., Oliveira, M. do R., & Cabeçadas, G. (2007). Phytoplankton community structure defined by key environmental variables in Tagus estuary, Portugal. Marine Environmental Research, 64(5), 616-628. doi:10.1016/j.marenvres.2007.06.007Caroppo, C. (2000). The contribution of picophytoplankton to community structure in a Mediterranean brackish environment. Journal of Plankton Research, 22(2), 381-397. doi:10.1093/plankt/22.2.381Cloern, J. (2001). Our evolving conceptual model of the coastal eutrophication problem. Marine Ecology Progress Series, 210, 223-253. doi:10.3354/meps210223Domingues, R. B., Barbosa, A., & Galvão, H. (2008). Constraints on the use of phytoplankton as a biological quality element within the Water Framework Directive in Portuguese waters. Marine Pollution Bulletin, 56(8), 1389-1395. doi:10.1016/j.marpolbul.2008.05.006Falco , S. 2003 Comportamiento de los nutrientes en un estuario estratificado: Caso del Delta del EbroReview Lecture - Picoplankton. (1986). Proceedings of the Royal Society of London. Series B. Biological Sciences, 228(1250), 1-30. doi:10.1098/rspb.1986.0037Havskum, H., Schlüter, L., Scharek, R., Berdalet, E., & Jacquet, S. (2004). Routine quantification of phytoplankton groups—microscopy or pigment analyses? Marine Ecology Progress Series, 273, 31-42. doi:10.3354/meps273031Johnson, P. W., & Sieburth, J. M. (1979). Chroococcoid cyanobacteria in the sea: A ubiquitous and diverse phototrophic biomass1. Limnology and Oceanography, 24(5), 928-935. doi:10.4319/lo.1979.24.5.0928Kirkwood , D. Aminot , A. Pertilla , M. 1991 Report on the Results of the Fourth Intercomparison Exercise for Nutrients in Sea Water ICES Cooperative Research Report 174Lidón, A., Ramos, C., & Rodrigo, A. (1999). Comparison of drainage estimation methods in irrigated citrus orchards. Irrigation Science, 19(1), 25-36. doi:10.1007/s002710050068Martí , C.M. 2010 Caracterización ecológica y establecimiento de los criterios para determinar el potencial ecológico en las salinas de la Comunidad ValencianaMösso, C., Sierra, J. P., Rodilla, M., Romero, I., Falco, S., González del Río, J., & Sánchez-Arcilla, A. (2007). High Vertical Resolution Sampling in Density Interfaces of Estuaries and River Plumes. Estuaries and Coasts, 31(2), 258-268. doi:10.1007/s12237-007-9009-4Novarino, G. (2003). A companion to the identification of cryptomonad flagellates (Cryptophyceae = Cryptomonadea). Hydrobiologia, 502(1-3), 225-270. doi:10.1023/b:hydr.0000004284.12535.25Pachés, M., Romero, I., Hermosilla, Z., & Martinez-Guijarro, R. (2012). PHYMED: An ecological classification system for the Water Framework Directive based on phytoplankton community composition. Ecological Indicators, 19, 15-23. doi:10.1016/j.ecolind.2011.07.003Pérez-Ruzafa, A., Gilabert, J., Gutiérrez, J. M., Fernández, A. I., Marcos, C., & Sabah, S. (2002). Hydrobiologia, 475/476, 359-369. doi:10.1023/a:1020343510060Puigserver, M., Ramon, G., Moyà, G., & Martínez-Taberner, A. (2002). Hydrobiologia, 475/476, 493-504. doi:10.1023/a:1020368215511Ramos, C., Agut, A., & Lidón, A. . (2002). Nitrate leaching in important crops of the Valencian Community region (Spain). Environmental Pollution, 118(2), 215-223. doi:10.1016/s0269-7491(01)00314-1Ramos, M. C. (2001). Rainfall distribution patterns and their change over time in a Mediterranean area. Theoretical and Applied Climatology, 69(3-4), 163-170. doi:10.1007/s007040170022Reynolds, C. S. (2006). The Ecology of Phytoplankton. doi:10.1017/cbo9780511542145Rojo , C. Miracle , R. 1984 Fluctuación estacional de las poblaciones fitoplanctónicas del Estany de Cullera (Valencia) Anales de biología, 2 (Sección especial, 2) Universidad de Murcia (Spain) 161 168Suikkanen, S., Laamanen, M., & Huttunen, M. (2007). Long-term changes in summer phytoplankton communities of the open northern Baltic Sea. Estuarine, Coastal and Shelf Science, 71(3-4), 580-592. doi:10.1016/j.ecss.2006.09.004Vargo , G.A. 1978 Using a Fluorescence Microscope. Phytoplankton Manual Monographs on Oceanography Methodology UNESCO 108 112Vicente , E. Miracle , M.R. 1988 Estructura y función de los procariotas en dos ecosistemas lagunares costeros: L'albufera de Valencia y l'Estany de Cullera Actas del Congreso de Biologia Ambiental. II Congreso Mundial Vasco 79 108 ISBN 84-7585-146-

Responses of coastal osmotrophic planktonic communities to simulated events of turbulence and nutrient load throughout a year

A year-long series of monthly experiments with laboratory enclosures were conducted with water from Blanes Bay (NW Mediterranean) to analyse the change in the short-time response of the osmotrophic planktonic community to simulated turbulence and nutrient input events. Both experimental factors triggered a relative increase of biomass in the enclosures, in terms of chlorophyll a, bacteria and particulate organic matter. Ratios of particulate organic nitrogen to phosphorus became lower in the water than in the sediment, although turbulence partially smoothed out this difference. Initial physico-chemical conditions significantly influenced the short-time responses to experimental forcing. The response to turbulence, in terms of chlorophyll a, was maximum in spring. The response to nutrient enrichment was found to be seasonal, and was correlated with photoperiod and temperature, and also in situ nitrate and silicate concentrations and Secchi depth, which are proxies of recent inputs of nutrients resulting from episodes of resuspension and river discharge. This study shows robust qualitative regularities in the response of the osmotrophic planktonic community to episodes of turbulence and nutrient enrichment, with quantitative variability throughout the year, depending mostly on the recent record of hydrodynamic forcing

Rising nutrient-pulse frequency and high UVR strengthen microbial interactions

Solar radiation and nutrient pulses regulate the ecosystem’s functioning. However, little is known about how a greater frequency of pulsed nutrients under high ultraviolet radiation (UVR) levels, as expected in the near future, could alter the responses and interaction between primary producers and decomposers. In this report, we demonstrate through a mesocosm study in lake La Caldera (Spain) that a repeated (press) compared to a one-time (pulse) schedule under UVR prompted higher increases in primary (PP) than in bacterial production (BP) coupled with a replacement of photoautotrophs by mixotrophic nanoflagellates (MNFs). The mechanism underlying these amplified phytoplanktonic responses was a dual control by MNFs on bacteria through the excretion of organic carbon and an increased top-down control by bacterivory. We also show across a 6-year whole-lake study that the changes from photoautotrophs to MNFs were related mainly to the frequency of pulsed nutrients (e.g. desert dust inputs). Our results underscore how an improved understanding of the interaction between chronic and stochastic environmental factors is critical for predicting ongoing changes in ecosystem functioning and its responses to climatically driven changes.This study was supported by the Ministerio de Economía y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) (CGL2011-23681 and CGL2015-67682-R to PC), Ministerio de Medio Ambiente, Rural, y Marino (PN2009/067 to PC) and Junta de Andalucía (Excelencia projects P09-RNM-5376 and P12-RNM-327 to PC and JMMS, respectively). M.J.C. was supported by the Spanish Government “Formación de Profesorado Universitario” PhD grant (FPU12/01243) and I.D.-G. by the Junta de Andalucía “Personal Investigador en Formación” PhD grant (FPI RNM-5376). This work is in partial fulfillment of the Ph. D. thesis of M.J.C

Defining Planktonic Protist Functional Groups on Mechanisms for Energy and Nutrient Acquisition: Incorporation of Diverse Mixotrophic Strategies

Arranging organisms into functional groups aids ecological research by grouping organisms (irrespective of phylogenetic origin) that interact with environmental factors in similar ways. Planktonic protists traditionally have been split between photoautotrophic “phytoplankton” and phagotrophic “microzoo-plankton”. However, there is a growing recognition of the importance of mixotrophy in euphotic aquatic systems, where many protists often combine photoautotrophic and phagotrophic modes of nutrition. Such organisms do not align with the traditional dichotomy of phytoplankton and microzooplankton. To reflect this understanding,we propose a new functional grouping of planktonic protists in an eco- physiological context: (i) phagoheterotrophs lacking phototrophic capacity, (ii) photoautotrophs lacking phagotrophic capacity,(iii) constitutive mixotrophs (CMs) as phagotrophs with an inherent capacity for phototrophy, and (iv) non-constitutive mixotrophs (NCMs) that acquire their phototrophic capacity by ingesting specific (SNCM) or general non-specific (GNCM) prey. For the first time, we incorporate these functional groups within a foodweb structure and show, using model outputs, that there is scope for significant changes in trophic dynamics depending on the protist functional type description. Accord- ingly, to better reflect the role of mixotrophy, we recommend that as important tools for explanatory and predictive research, aquatic food-web and biogeochemical models need to redefine the protist groups within their frameworks

Modeling Plankton Mixotrophy: A Mechanistic Model Consistent with the Shuter-Type Biochemical Approach

Mixotrophy, i.e., the ability to combine phototrophy and phagotrophy in one organism, is now recognized to be widespread among photic-zone protists and to potentially modify the structure and functioning of planktonic ecosystems. However, few biogeochemical/ecological models explicitly include this mode of nutrition, owing to the large diversity of observed mixotrophic types, the few data allowing the parameterization of physiological processes, and the need to make the addition of mixotrophy into existing ecosystem models as simple as possible. We here propose and discuss a flexible model that depicts the main observed behaviors of mixotrophy in microplankton. A first model version describes constitutive mixotrophy (the organism photosynthesizes by use of its own chloroplasts). This model version offers two possible configurations, allowing the description of constitutive mixotrophs (CMs) that favor either phototrophy or heterotrophy. A second version describes non-constitutive mixotrophy (the organism performs phototrophy by use of chloroplasts acquired from its prey). The model variants were described so as to be consistent with a plankton conceptualization in which the biomass is divided into separate components on the basis of their biochemical function (Shuter-approach; Shuter, 1979). The two model variants of mixotrophy can easily be implemented in ecological models that adopt the Shuter-approach, such as the MIRO model (Lancelot et al., 2005), and address the challenges associated with modeling mixotrophy