Lagrangian studies of phytoplankton growth and grazing relationships in a coastal upwelling ecosystem off Southern California

Abstract

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Temporal and spatial patterns of microbial community biomass and composition in the Southern California Current Ecosystem

“…Although AC:Chl a is rarely measured, both its values and its variability are important for estimating carbon flows from pigmentbased experimental rate determinations (e.g., Landry et al, 2009;Stukel et al 2013), for modeling of ocean ecosystem dynamics (Morel, 1988;Taylor et al, 1991;Sathyendranath et al, 2009;Wang et al, 2009) and for interpreting biomass and production distributions with remote sensing techniques (Eppley et al, 1985;Falkowski, 1994;Antoine et al, 1996;Behrenfled et al, 2005). Using CCE-LTER data, for example, Li et al (2010) have successfully parameterized models that effectively capture observed spatial and depth variability in phytoplankton biomass, AC:Chl a and growth rates across inshore and offshore regions of the southern CCE.…”

Phytoplankton community dynamics during late spring coccolithophore blooms at the continental margin of the Celtic Sea (North East Atlantic, 2006–2008)

“…), mixed layer depth (MLD). Nutrient concentration data (collected every 10 m, see below) were used to compute the depth of the nitracline, a proxy of nutrient supply to the upper mixed layer of the ocean, which was operationally defined as the shallowest depth at which nitrate + nitrite concentration (NO x ) exceeded 1.00 lmol l À1 (Cermeño et al, 2008;Landry et al, 2009). …”

On database biases and hypothesis testing with dilution experiments: Response to comment by Latasa

“…Oceanogr., 59(3), 2014, 1095-1096 E 2014, by the Association for the Sciences of Limnology andOceanography, Inc. doi:10.4319/lo.2014.59.3.1095 not shed useful light on natural rates and relationships, regardless how good the regression statistics. On a more promising note, however, some dilution results, done in large enough numbers to average over many experiments, have been shown to mesh well with complementary and independent measurements of primary production, net ambient phytoplankton growth, mesozooplankton grazing, and export flux in describing how natural systems work and in testing explicit hypotheses about process interactions and relationships (Landry et al 2009(Landry et al , 2011aStukel et al 2011). It seems to me that this is the level of hypothesis testing to which all process-estimating tools need to be applied in aquatic field studies.…”

“…While it is quite true that regression slopes are more difficult to distinguish statistically from zero when rates are inherently low, experimentalists can readily compensate by running their incubations proportionately longer, 2 or 3 d instead of 1 d, to amplify the differences in net biomass change that occur in the different dilution treatments (Landry et al 2002;Sherr et al 2009). This assumes that containment artifacts scale with temperature effects on metabolism or growth, such that they would be no worse after longer incubations of polar samples compared with 1 d experiments in warmwater systems.…”

“…Viewed in this way, negative grazing rate estimates are, in fact, statistically possible and might be considered a valid experimental outcome under some circumstances. For example, Landry et al (2011b) included negative estimates in the mean grazing rate calculations assuming that they counterbalanced other experiments in which the rates may have been overestimated.…”