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.

Estimating seasonal habitat suitability for migratory species in the Bohai Sea and Yellow Sea:A case study of Tanaka's snailfish(Liparis tanakae)

Acquiring a comprehensive and accurate understanding of habitat preference is essential for species conservation and fishery management,especially for mobile species that migrate seasonally.Presence and absence data from field surveys are recommended when available due to their high reliability.Using field survey data,we investigated seasonal habitat suitability requirements for Tanaka's snailfish(Liparis tanakae)in the Bohai Sea and Yellow Sea(BSYS)via a machine-learning method,random forests(RFs).Five environmental and biologically relevant variables(bottom temperature,bottom salinity,current velocity,depth and distance to shore)were used to build the ecological niches between the presence/absence data and suitable habitat.In addition,the degree to which false absence data might impact model performance was evaluated.Our results indicated that RFs provided accurate predictions,with seasonal habitat suitability maps of L.tanakae differing substantially.Bottom temperature and salinity were identified as important factors influencing the distribution of L.tanakae.False absence data were found to have negative effects on model performance and the decrease in evaluation metrics was usually significant(P<0.05)after 30%or more errors were added to the absence data.Through identifying highly suitable areas within its geographic range,our study provides a baseline for L.tanakae that can be further applied in ecosystem modelling and fishery management in the BSYS.