To investigate the effects of episodic occurrence of dissolved organic carbon (DOC) in the natural environment, bacterial degradation of labile DOC was studied under laboratory-controlled conditions followed by modelling. A single labile DOC compound was periodically added to the experimental culture and its degradation by a monospecific marine bacterial strain was followed. The measured variables were DOC and bacterial biomass determined from the particulate organic carbon values. Experimental dynamics showed a repetition of 2 successive patterns after each DOC pulse: (1) substrate consumption and bacterial growth in the first few hours after substrate addition, followed by (2) bacterial reduction (organic carbon-related) and associated non-labile DOC release within the next few hours. Based on these experimental results, the Dynamic Energy Budget theory was applied for the first time to such conditions to develop a mechanistic model that comprised 7 parameters and 4 state variables in which bacterial biomass was fractionated into reserve and structure compartments. The model was constructed by accounting for a constant specific maintenance rate and comprised 2 different cell maintenance fluxes, one fuelled from cell reserves when substrate was abundant and one from reserves and cell structures when starvation occurred. This new model of bacterial degradation adequately matched experimental measurements and accurately reproduced the accumulation of non-labile DOC in the culture during the experiment. This model can easily be implemented in an aquatic biogeochemical model and could provide better understanding of the role of bacteria in carbon cycling in fluctuating environments.

Experiments were performed on juvenile sole in controlled conditions in the aim of understanding how the biology of common sole may affect the accumulation and dilution of Polychlorinated biphenyls (PCBs). The fish were raised in optimal conditions and divided into two tanks: one control tank and one PCB tank. 4 PCB congeners were added to food for 3 months in the PCB tank; the soles were subsequently fed unspiked food for 3 months. Growth (length and weight) and PCB concentrations were monitored in both tanks and juvenile sole growth was not significantly affected by PCBs in our experimental conditions. We used the Dynamic Energy Budget (DEB) theory to model sole biology and paid special attention to model calibration through the wide use of data from the literature. The model accurately reproduced fish growth in both tanks. We coupled a bioaccumulation model to reproduce the concentration dynamics of the 4 PCB congeners used. This model did not require additional calibration and was dependent solely on the growth model and PCB concentrations in food. The bioaccumulation model accurately simulated PCB accumulation in fish, but overestimated PCB concentrations in fish during the dilution phase. This may suggest that in addition to PCB dilution due to growth, PCB concentrations decreased due to other PCB elimination mechanisms. Finally, we discussed potential improvements to the model and its future applications.

Abstract. This paper assesses how considering variation in DOC availability and cell maintenance in bacterial models affects Bacterial Growth Efficiency (BGE) estimations. For this purpose, we conducted two biodegradation experiments simultaneously. In experiment one, a given amount of substrate was added to the culture at the start of the experiment whilst in experiment two, the same amount of substrate was added, but using periodic pulses over the time course of the experiment. Three bacterial models, with different levels of complexity, (the Monod, Marr-Pirt and the dynamic energy budget -DEB -models), were used and calibrated using the above experiments. BGE has been estimated using the experimental values obtained from discrete samples and from model generated data. Cell maintenance was derived experimentally, from respiration rate measurements. The results showed that the Monod model did not reproduce the experimental data accurately, whereas the Marr-Pirt and DEB models demonstrated a good level of reproducibility, probably because cell maintenance was built into their formula. Whatever estimation method was used, the BGE value was always higher in experiment two (the periodically pulsed substrate) as compared to the initially one-pulsed-substrate experiment. Moreover, BGE values estimated without considering cell maintenance (Monod model and empirical formula) were always smaller than BGE values obtained from models taking cell maintenance into account. Since BGE is commonly estimated using constant experimental systems and ignoreCorrespondence to: R. Sempéré (richard.sempere@univmed.fr) maintenance, we conclude that these typical methods underestimate BGE values. On a larger scale, and for biogeochemical cycles, this would lead to the conclusion that, for a given DOC supply rate and a given DOC consumption rate, these BGE estimation methods overestimate the role of bacterioplankton as CO 2 producers.

A Monod (1942) model was used to describe the interaction and dynamics between marine bacteria and labile-dissolved organic carbon (l-DOC) using data obtained from 36 biodegradation experiments. This model is governed by 2 state variables, DOC and bacterial biomass (BB), and 3 parameters, specific maximum assimilation rate (V max ), half-saturation constant (K S ) and bacterial growth efficiency (BGE). The calibrations were obtained from biodegradation experiments carried out in the Northeast Atlantic Ocean over different seasons and at different depths. We also conducted a sensitivity analysis to determine (1) which parameter had the greatest influence on the model, and (2) whether the model was robust with regard to experimental errors. Our results indicate that BGE is greater in surface layers than in deeper waters, with minimum values observed during winter. In contrast, the V max /K S ratio is inversely dependent on depth and does not show any seasonal trend. This reflects an increase in bacterial affinity for substrate with increasing depth (decrease of K S ) and/or better specific maximum assimilation rates (increase of V max ). The sensitivity and robustness analyses demonstrate that the model is more sensitive to the V max /K S ratio than to BGE, and that the parameters estimated are reliable. However, although the BGE values are close to those estimated experimentally, the use of a constant V max /K S ratio and BGE in a 1-dimensional model is not appropriate as these parameters should be described as variables that take depth and season into account. KEY WORDS: Bacterial growth efficiency · Monod model · Bacterial biodegradation · Northeast Atlantic OceanResale or republication not permitted without written consent of the publisher

Abstract. This paper assesses how considering variation in DOC availability and cell maintenance in bacterial models affects Bacterial Growth Efficiency (BGE) estimations. For this purpose, we conducted two biodegradation experiments simultaneously. In experiment one, a given amount of substrate was added to the culture at the start of the experiment whilst in experiment two, the same amount of substrate was added, but using periodic pulses over the time course of the experiment. Three bacterial models, with different levels of complexity, (the Monod, Marr-Pirt and the dynamic energy budget (DEB) model), were used, and calibrated using the above experiments. BGE has been estimated using the experimental values obtained from discrete samples and from model generated data. Cell maintenance was derived experimentally, from respiration rate measurements. The results showed that the Monod model did not reproduce the experimental data accurately, whereas the Marr-Pirt and DEB models demonstrated a good level of reproducibility, probably because cell maintenance was built into their formula. Whatever estimation method was used, the BGE value was always higher in experiment two (the periodically pulsed substrate) as compared to the initially one-pulsed-substrate experiment. Moreover, BGE values estimated without considering cell maintenance (Monod model and empirical formula) were always smaller than BGE values obtained from models taking cell maintenance into account. Since BGE is commonly estimated using constant experimental systems and ignore maintenance, we conclude that these typical methods underestimate BGE values. On a larger scale, and for biogeochemical cycles, this would lead to the conclusion that, for a given DOC supply rate and a given DOC consumption rate, these BGE estimation methods overestimate the role of bacterioplankton as CO2 producers.