Report of the special session of "Chesapeake Bay Ecological Forecasting: Moving Ecosystem Modeling from Research to Operation" of the 2010 Chesapeake Modeling Symposium

Moving ecosystem modeling from research to applications and operations has direct management relevance and will be integral to achieving the water quality and living resource goals of the 2010 Chesapeake Bay Executive Order. Yet despite decades of ecosystem modeling efforts of linking climate to water quality, plankton and fish, ecological models are rarely taken to the operational phase. In an effort to promote operational ecosystem modeling and ecological forecasting in Chesapeake Bay, a meeting was convened on this topic at the 2010 Chesapeake Modeling Symposium (May, 10-11). These presentations show that tremendous progress has been made over the last five years toward the development of operational ecological forecasting models, and that efforts in Chesapeake Bay are leading the way nationally. Ecological forecasts predict the impacts of chemical, biological, and physical changes on ecosystems, ecosystem components, and people. They have great potential to educate and inform not only ecosystem management, but also the outlook and opinion of the general public, for whom we manage coastal ecosystems. In the context of the Chesapeake Bay Executive Order, ecological forecasting can be used to identify favorable restoration sites, predict which sites and species will be viable under various climate scenarios, and predict the impact of a restoration project on water quality.

Selection and application of a suitable sampling method for quantitative and qualitative evaluation of lab-lab

Milkfish and prawn pond operation in the Philippines is often associated with lab-lab culture. Lab-lab is a biological complex of blue-green algae, diatoms, bacteria and various animals which form a mat at the bottom of nursery ponds or floating patches along the margins of ponds. This complex is considered the most favorable food of milkfish in brackishwater ponds. Variations in the quantity and quality of lab-lab between and within areas of a 1,000 sq. m. pond was determined over 2 culture periods (6 month duration) and the applicability and suitability of stratified random sampling as a method of sampling lab-lab was evaluated.

Study of effect Rosmarinus officinalis oil and nisin on the growth of Streptococcus iniae in lab conditions and fillets of rainbow trout (Oncorhynchus mykiss)

Fish are an important part of a healthy diet since they contain high quality protein, but typically present a low fat percent when compared to other meats. Fish is an extremely perishable food commodity. On the other hand, food borne diseases are still a major problem in the world, even in well-developed countries. The increasing incidence of food borne diseases coupled with the resultant social and economic implications means there is a constant striving to produce safer food and to develop new antimicrobial agents concerns over the safety of some chemical preservatives and negative consumer reactions to preservatives they perceive as chemical and artificial, have prompted on increased interest in more ‘‘naturalgreen’’ alternatives for the maintenance or extension of product shelf-life. Particular interest has focused on the potential applications of plant essential oils. However, to establish the usefulness of natural antimicrobial preservatives, they must be evaluated alone and in combination with other preservation factors to determine whether there are synergistic effects and multiple hurdles can be devised. In this study, were evaluated the effects of different concentrations of Rosmarinus officinalis and nisin and storage time (15 days) on growth of Streptococcus iniae GQ850377 in a lab conditions and a food model system (fillets of rainbow trout) in 4 and 8 °C. In addition, we also studied multi factorial effects of four different concentration of rosemary, three different concentrations of nisin, two different levels of pH in 3 temperature 4,15 and 37 °C on log% of S.iniae during 43 days in BHI broth. The results on growth of S. iniae were evaluated using SPSS 20.0 statistical software and analyzed the logarithm of total count of the bacterial by Tukey Test. Results were considered statistically significant when P<0.05. MIC and MBC values of rosemary and nisin were 0.03, 0.075 % and 5, 40 μg/mL, respectively. The growth of S. iniae was effected significantly (P<0.05) by rosemary and nisin and also combination of rosemary and nisin in 4 and 8 °C. Samples treated with 0.135 and 0.405 % of rosemary showed a significant decrease on the growth of the bacteria compared with control sample(P<0.05). The most ١٤٦ inhibitory effects were seen in samples treated with 0.135 and 0.405% of rosemary until 9 days after storage. Also, the synergism effects of rosemary and nisin on the growth rate of bacteria was significant (P<0.05) compared with untreated samples and samples treated with the rosemary or nisin, only. Synergistic effects was observed at concentration of 0.405% rosemary and 0.75 μg/mL nisin in both temprature. Results of this study showed that different concentration of rosemary a significant inhibitory effect (P<0.05) on log% of S. iniae, in BHI broth in pH 5.5 and 7 in 4,15 and 37 °C during 43 days. In concentration of 0% rosemary (control) in pH 5.5 and 7 and 37°C, log% were 1.099 and 3.15, whereas in concentration of 0.015% rosemary were -4/241 and 1.454, respectively. The use of essential oils may improve food safety and overall microbial quality. If essential oils were to be more widely applied as antibacterials in foods, the organoleptic impact would be important. In addition, it is recommended to apply essential oils or their compounds as part of a hurdle system and to use it as an antimicrobial component along with other preservation techniques. Thus essential of R. officinalis with high antibacterial activity selected in this study could be a potential source for inhibitory substances against some food-borne pathogens and they may be candidates for using in foods or food-processing systems.