Epipelagic mesozooplankton communities in the northeastern Indian Ocean off Myanmar during the winter monsoon

The northern Andaman Sea off Myanmar is one of the relatively high productive regions in the Indian Ocean.The abundance,biomass and species composition of mesozooplankton and their relationships with environmental variables in the epipelagic zone(~200 m)were studied for the first time during the Sino-Myanmar joint cruise(February 2020).The mean abundance and biomass of mesozooplankton were(1916.7±1192.9)ind./m3and(17.8±7.9)mg/m3,respectively.A total of 213 species(taxa)were identified from all samples.The omnivorous Cyclopoida Oncaea venusta and Oithona spp.were the top two dominant taxa.Three mesozooplankton communities were determined via cluster analysis:the open ocean in the Andaman Sea and the Bay of Bengal(Group A),the transition zone across the Preparis Channel(Group B),and nearshore water off the Ayeyarwady Delta and along the Tanintharyi Coast(Group C).Variation partitioning analysis revealed that the interaction of physical and biological factors explained 98.8%of mesozooplankton community spatial variation,and redundancy analysis revealed that column mean chlorophyll a concentration(CMCHLA)was the most important explanatory variable(43.1%).The abundance and biomass were significantly higher in Group C,the same as CMCHLA and column mean temperature(CMT)and in contrast to salinity,and CMT was the dominant factor.Significant taxon spatial variations were controlled by CMCHLA,salinity and temperature.This study suggested that mesozooplankton spatial variation was mainly regulated by physical processes through their effects on CMCHLA.The physical processes were simultaneously affected by heat loss differences,freshwater influx,eddies and depth.

Observed characteristics of flow,water mass,and turbulent mixing in the Preparis Channel

Preparis Channel is the very important exchange path of energy and materials between the northern Bay of Bengal and Andaman Sea(AS).A set of hydrographic measurements,a microstructure profiler,and a deep mooring were used to determine the characteristics of water masses,turbulent mixing,and flows in the Preparis Channel.The unprecedented short-term mooring data reveal that a deep current in the deep narrow passage(below 400 m)of the Preparis Channel flows toward the Bay of Bengal(BoB)with a mean along-stream velocity of 25.26 cm/s at depth of 540 m;above the deep current,there are a relatively weak current flows toward the AS with a mean along-stream velocity of 15.46 cm/s between 500 m and 520 m,and another weak current flows toward the BoB between 430 m and 500 m.Thus,a sandwiched vertical structure of deep currents(below 400 m)is present in the Preparis Channel.The volume transport below 400 m is 0.06 Sv(1 Sv=106 m^(3)/s)from the AS to the BoB.In the upper layer(shallower than 300 m),the sea water of the AS is relatively warmer and fresher than that in the BoB,indicating a strong exchange through the channel.Microstructure profiler observations reveal that the turbulent diffusivity in the upper layer of the Preparis Channel reaches O(10−4 m^(2)/s),one order larger than that in the interior of the BoB and over the continental slope of the northern AS.We speculate that energetic high-mode internal tides in the Preparis Channel contribute to elevated turbulent mixing.In addition,a local“hotspot”of turbidity is identified at the deep mooring site,at depth of about 100 m,which corresponds to the location of elevated turbulent mixing in the Preparis Channel.

Application and evaluation of layering shear method in LADCP data processing

The current velocity observation of LADCP(Lowered Acoustic Doppler Current Profiler)has the advantages of a large vertical range of observation and high operability compared with traditional current measurement methods,and is being widely used in the field of ocean observation.Shear and inverse methods are now commonly used by the international marine community to process LADCP data and calculate ocean current profiles.The two methods have their advantages and shortcomings.The shear method calculates the value of current shear more accurately,while the accuracy in an absolute value of the current is lower.The inverse method calculates the absolute value of the current velocity more accurately,but the current shear is less accurate.Based on the shear method,this paper proposes a layering shear method to calculate the current velocity profile by“layering averaging”,and proposes corresponding current calculation methods according to the different types of problems in several field observation data from the western Pacific,forming an independent LADCP data processing system.The comparison results have shown that the layering shear method can achieve the same effect as the inverse method in the calculation of the absolute value of current velocity,while retaining the advantages of the shear method in the calculation of a value of the current shear.

Identify information sources with different start times in complex networks based on sparse observers

The dissemination of information across various locations is an ubiquitous occurrence,however,prevalent methodologies for multi-source identification frequently overlook the fact that sources may initiate dissemination at distinct initial moments.Although there are many research results of multi-source identification,the challenge of locating sources with varying initiation times using a limited subset of observational nodes remains unresolved.In this study,we provide the backward spread tree theorem and source centrality theorem,and develop a backward spread centrality algorithm to identify all the information sources that trigger the spread at different start times.The proposed algorithm does not require prior knowledge of the number of sources,however,it can estimate both the initial spread moment and the spread duration.The core concept of this algorithm involves inferring suspected sources through source centrality theorem and locating the source from the suspected sources with linear programming.Extensive experiments from synthetic and real network simulation corroborate the superiority of our method in terms of both efficacy and efficiency.Furthermore,we find that our method maintains robustness irrespective of the number of sources and the average degree of network.Compared with classical and state-of-the art source identification methods,our method generally improves the AUROC value by 0.1 to 0.2.