The support from the National Key Research and Development Program of China (No. 2016YFC1401601), the Postgraduate Research &#38; Practice Innovation Program of Jiangsu Province (No. SJKY19_0415) supported by the Fundamental Research Funds for the Central Universities (No. 2019B62814), the National Natural Science Foundation of China (Nos. 41890805, 41806026 and 41730536) and Joint Advanced Marine and Ecological Studies in the Bay of Bengal and the eastern equatorial Indian Ocean were greatly acknowledged. The authors appreciate the provision of data from sources including the National Aeronautics and Space Administration (NASA), the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine and Environment Monitoring Service (CMEMS) and the National Oceanic and Atmospheric Administration (NOAA).

1. Dataset acquisition
<ol>
	<li>SST and CHL
	<ol>
		<li>Download a dataset of satellite observations for SST and CHL from MODIS-Aqua (podaac-tools.jpl.nasa.gov/), where the spatial resolution of both datasets is approximately&#160;4.5 km at daily intervals. 
		NOTE: Structure the directories&#160;and data following the example scripts folder available in the Supplemental Files. Store the .nc files of the satellite data in the &#39;Data&#39; folder. Add the path to the NetCDF toolbox&#160;in the analysis software (i.e., MATLAB). Select Add with subfolders to enclose the paths of the &#39;UTILITIES&#39; folder and its subfolders.</li>
		<li>Determine the time span. To maintain consistency among different datasets, use the same time span for all parameters. Adjust the time span based on the temporal coverage and use the longest observation period among different datasets. For this protocol, download 15 years of data from October 2002 to September 2017.</li>
		<li>Determine the spatial coverage. 
		NOTE: The designed study region is between 105&#176;E and 123&#176;E and between 0&#176; and 25&#176;N.</li>
		<li>Check preprocessing instructions. Read instructions in the .nc files regarding the preprocessing requirements of the SST and CHL data (e.g., whether scaling is needed). 
		NOTE: The downloaded dataset already exclude&#160;data over land and within 5 km of the coastline, as well as those contaminated by clouds.</li>
		<li>Load SST and CHL data into the analysis software. Type Read_MODIS_SST in the command window to read the SST data. Similarly, type Read_MODIS_CHL in the command window to read the CHL data. Transform the CHL data logarithmically because they have a log-normal distribution31. 
		NOTE: Loaded variables include SST and CHL in three dimensions, representing meridional location, zonal location, and time in days, respectively. The range of SSTs is between -2 and 44, and the range of CHL is between 0.01 and 20.</li>
	</ol>
	</li>
	<li>Sea level anomaly (SLA)
	<ol>
		<li>Download daily SLA data with a 25 km spatial resolution from 2002&#8211;201732. 
		NOTE: SLAs&#160;describe&#160;the difference between observed sea surface heights and the mean sea surface height over 20 years (1993&#8722;2012) for a&#160;corresponding pixel. The SLA data are processed by SSALTO/DUACS and distributed by Archiving, Validation, and Interpretation of Satellite Oceanographic Data (AVISO, https://www.aviso.altimetry.fr).</li>
		<li>Load data into the analysis software. Load single-day SLA data by typing Read_SLA in the command window. 
		NOTE: The &#39;Data&#39; folder in the Supplemental Files only includes one sample datum in the script for illustration.</li>
	</ol>
	</li>
	<li>Wind speed
	<ol>
		<li>Obtain the wind information from an ERA-Interim reanalysis product, which is a global atmospheric reanalysis dataset developed by the European Center for Medium-Range Weather Forecasts (ECMWF)33. Download wind data for the same period (October 2002&#8211;September 2017) to maintain consistency with the CHL and SST data. 
		NOTE: The wind dataset has a spatial resolution of approximately 25 km and was interpolated from the original dataset with a spatial resolution of approximately 0.7&#176;.</li>
		<li>Load data into the analysis software. Type Read_WindVector in the command window to read the one-month wind data. Calculate the monthly mean by averaging the original data, which is at 6 h intervals.</li>
	</ol>
	</li>
	<li>Topography
	<ol>
		<li>Download the high-resolution topography data from the National Centers for Environmental Information website (NCEI, https://maps.ngdc.noaa.gov/viewers/wcs-client/). The spatial resolution is ~2 km. Obtain the ETOPO1 data for bedrock in XYZ format for the selected study region.</li>
		<li>Load data into the analysis software. Type Read_topography in the command window to load the topography data into the analysis software.</li>
	</ol>
	</li>
</ol>
2. Data preprocessing
<ol>
	<li>Temporal average
	<ol>
		<li>Due to the large cloud coverage in the SST and CHL data, replace the original data with 3-day average data. To do this, after running the Read_MODIS_SST.m and Read_MODIS_CHL.m scripts (step 1.1.5), type Temporal_average in the command window to run the script.</li>
	</ol>
	</li>
	<li>Interpolation into the same grid
	<ol>
		<li>Because the spatial resolution is not consistent for different datasets, interpolate the SST and CHL data into a spatial grid that is the same as the wind and SLA spatial grid before making comparisons. After running the Temporal_average.m and Read_WindVector.m scripts, type Interpolation_grid in the command window to run the script.</li>
	</ol>
	</li>
	<li>Wind stress and wind stress curl
	<ol>
		<li>Type Wind_stress_curl in the command window to calculate the wind stress (WS) and wind stress curl (WSC) using the following equations: 
		<img alt="Equation 1" src="/files/ftp_upload/61172/61172equ01.jpg" /> 
		<img alt="Equation 2" src="/files/ftp_upload/61172/61172equ02.jpg" /> 
		where <img alt="Equation 10" src="/files/ftp_upload/61172/icon01.jpg" /> is the wind speed vector; <img alt="Equation 10" src="/files/ftp_upload/61172/icon02.jpg" /> is the WS in the same direction as the wind vector; <img alt="Equation 10" src="/files/ftp_upload/61172/icon03.jpg" /> and <img alt="Equation 10" src="/files/ftp_upload/61172/icon04.jpg" /> are the WS in the east and north directions, respectively; <img alt="Equation 10" src="/files/ftp_upload/61172/icon05.jpg" /> is the air density (equal to 1.2 kg/m3); and C is the drag coefficient (a value of 0.0015 is used) under neutral stability conditions34.</li>
	</ol>
	</li>
	<li>Monthly averages
	<ol>
		<li>Calculate the monthly SST, wind, and SLA time series as 30-day averages in each pixel by typing Monthly_average to run the script. Due to the high cloud coverage rate, use a 60-day average as the monthly time series for CHL, including 30 days before to 30 days after the 15th day of the month.</li>
	</ol>
	</li>
</ol>
3. SST front detection
<ol>
	<li>Spatial smoothing
	<ol>
		<li>Type Spatial_smoothing to run the script to average the three-day SST data in each pixel. 
		NOTE: A large amount of noise was identified in the SST data. Thus, the data were&#160;smoothed with a 3 x 3 spatial average. When no data were available in the original 3-day averaged data, the spatial averaged data were set as unavailable.</li>
	</ol>
	</li>
	<li>SST gradient
	<ol>
		<li>Type SST_gradient to run the script to calculate the zonal and meridional SST gradients (i.e., Gx and Gy, respectively) as the SST difference between the nearest two pixels divided by the corresponding distance via equation (3). Use the obtained gradient vector to calculate the total gradient, G, as a scalar following equation (4). 
		<img alt="Equation 3" src="/files/ftp_upload/61172/61172equ03.jpg" /> 
		<img alt="Equation 4" src="/files/ftp_upload/61172/61172equ04.jpg" /></li>
	</ol>
	</li>
	<li>Local maximum
	<ol>
		<li>Identify a front by testing a&#160;SST gradient value: label a&#160;pixel as a potential frontal pixel if the value is larger than a designated threshold. Only maintain the local maximum pixel in the same direction perpendicular to the gradient direction if there are connected pixels with values larger than the threshold. Here, define the threshold as 0.035 &#176;C/km following former studies10,28. 
		NOTE: The corresponding script &#8216;Local_maximum.m&#8217; is available in the&#160;Supplemental Files.</li>
	</ol>
	</li>
	<li>Monthly frontal probability (FP) 
	NOTE: The frontal probability (FP) describes the probability of observing a front.
	<ol>
		<li>Calculate the FP for a certain time span (in this case, a&#160;monthly interval), by typing Monthly_FP to run the script. Divide the occurrence of fronts at each pixel during a time window by the number of days that are&#160;free of clouds.</li>
	</ol>
	</li>
</ol>
4. Spatial and temporal variability
<ol>
	<li>Seasonal cycle
	<ol>
		<li>Calculate the seasonal cycles of different factors as the averages of different seasons. Define the seasons as follows: winter is from December to February, spring is from March to May, summer is from June to August, and fall is from September to November. 
		NOTE: The seasonal cycle is not shown in this study; the following method is used to explain the spatial and temporal variability instead.</li>
	</ol>
	</li>
	<li>Empirical orthogonal function (EOF)
	<ol>
		<li>Remove the temporal average and unavailable pixels. Before conducting the EOF, subtract the overall mean at each pixel and exclude the locations where missing observations exceed 20% because of cloud coverage. Load data by typing load(&#39;Monthly_data_for_EOF.mat&#39;) in the command window.</li>
		<li>Apply an EOF to describe the spatial and temporal variabilities of&#160;different parameters. Type Empirical_orthogonal_function.m to run the script to calculate the magnitude (Mag), eigenvalues (Eig), and amplitude (Amp) of the EOFs for the dataset (i.e., time series of monthly averaged SST, wind stress, wind stress curl, CHL, and FP). 
		NOTE: The function decomposes the monthly time series into different modes, which are composed of spatial and temporal patterns and the variance explained by each mode decreases with increasing mode number.</li>
	</ol>
	</li>
</ol>
5. Intercorrelation
<ol>
	<li>Correlation at the seasonal scale
	<ol>
		<li>Calculate the correlations between two factors using their time series at each pixel by typing Seasonal_correlation to run the script. Because the seasonal cycle is not removed, check the significance of the correlation for all correlations.</li>
	</ol>
	</li>
	<li>Correlation of an anomalous field
	<ol>
		<li>Calculate the correlations between the monthly CHL anomalies and other factors, such as SST, WS, fronts, and SLAs. Obtain the monthly anomalies (i.e., the deviation from the mean status) by subtracting the overall average for a&#160;corresponding month from the monthly time series. Type Anomalous_correlation to run the script and obtain the correlations.</li>
	</ol>
	</li>
</ol>
6. Displaying information and calculating relationships
<ol>
	<li>Display satellite information.
	<ol>
		<li>Type Sat_SCS_Fig3457 to run the script to generate a showcase of satellite information, including SST, CHL, and frontal distributions. Set the current folder as &#8216;scripts&#8217; where the data &#8216;Sat_SCS_data.mat&#8217; are located. 
		NOTE: Figure 1, Figure 2, Figure 3, and Figure 4 show SST, CHL, fronts, wind, and topography for the selected date as an example.</li>
	</ol>
	</li>
	<li>Display the EOF result by typing Sat_SCS_Fig890.m to run the script. 
	NOTE: Figure 5, Figure 6, and Figure 7 describe the spatial magnitude, monthly average, and time series of first two modes for CHL, SST, and fronts, respectively.</li>
	<li>Calculate the relationship between CHL&#160;and other factors at seasonal timescales and for anomalous fields by typing Sat_SCS_Fig1112.m to run the script. Obtain the correlation map for seasonal variabilities&#160;(Figure 8) and anomalies&#160;(Figure 9).</li>
</ol>

The authors have nothing to disclose.

In this study, the major features of marine systems are described using satellite observations. The CHL, which can be used to represent ocean production, is selected as an indicator factor. Factors related to CHL variability were investigated using monthly averaged time series, e.g., SST, WS, WSC, FP and SLA. Three critical steps are described in this study: acquiring satellite data for different parameters, describing their spatial and temporal variabilities via EOF, and determining interrelationships among different factors by calculating correlation coefficients. A detailed procedure showing the identification for daily frontal distribution, which is derived from the SST observations, is included. Two major approaches have been developed for SST front detection: the gradient method10,38 and the histogram method39,40. The histogram method is based on a similar range of values for SST, which can be used to divide the water masses into different groups. The pixels with values between different groups representing the pixel in a transitional band are defined as fronts. On the other hand, the gradient method separates several relatively uniform water bodies as pixels with large gradient values. A comparison study was conducted, and they found lower false rates using the histogram method and fewer missed fronts using the gradient method41. In this study, the gradient-based method38 was adopted following former studies10,28. The algorithm can avoid front break-up into multiple edge fragments by allowing the magnitude to decrease to a level below a smaller threshold. In addition to the dataset included here, other satellite observations, such as the aerosol index, can also be used with a similar approach.
Most of the procedures can be directly applied in other regions or datasets. Modification may take place to change the threshold of front detection. Because the SST gradient in the SCS is comparable with the Eastern Boundary Current System28, the same thresholds were implemented for the current study. A previous study revealed that the SST gradient from different datasets can vary as much as three times42, which makes the method somehow less objective. Substantial studies have investigated frontal activities around the global oceans28,43. The best approach to validate fronts is to compare them with in situ observations. Yao44 described the monthly frontal distribution for the SCS. Their results agreed well with the in situ measurements. The overall gradient should be checked and adjusted since its value may vary depending on the spatial resolution and instruments. In particular, the threshold should be updated when another SST dataset is used. A basic understanding of the regional dynamics is fundamental to understanding frontogenesis45,46,47. The front detection script can be developed by individual authors based on the description in this paper.
Satellite information offers a comprehensive understanding of surface features, and a results comparison with in situ observations can aid in evaluating credibility. However, satellite observations are limited to the ocean surface, which limits the application for understanding the vertical structure of the water column. In a recent study, satellite observations revealed that the surface CHL increased by 15 times, but the vertical integrated value only increased by 2.5 times48. This difference was because the surface value was impacted by the coeffects of phytoplankton growth and shoaling of MLD, resulting in an unrealizable value at the surface. Thus, the surface feature may not offer an accurate description for the entire water column. Additionally, the influence of cloud coverage limits the continuous observations of satellites. Thus, monthly time series are calculated for different factors over the same region and same period. This will guarantee the credibility of calculating the correlations among different factors. However, the short-period events, e.g., typhoons that last for a few days to a week, will not be resolved.
Compared with former studies, the proposed method can offer spatial information at the pixel level, which can help to evaluate the dynamics in a more detailed manner. Some former studies averaged the entire SCS as a single number and obtained a time series. They found that an unusually strong WS and high SST can induce anomalously high CHL16, which is consistent with the current result. However, the spatial variation in the relationships was not resolved. In this study, the basin-scale correlation between WS and CHL was weak in the anomalous field. A large significant correlation was only identified for certain areas, e.g., in the center of the SCS (Figure 9B). Thus, the current method offers a comprehensive description for investigating spatial variations. Similarly, observations from two Bio-Argo floats were used and revealed that WSC did not correlate with CHL variability20. However, the trajectories of the two floats are only located in certain regions. In this case, it was exactly within the band where the correlation between the CHL level and the WSC was not significant (Figure 8D). The proposed method is very helpful for resolving the spatial dependence among factors, which is a fundamental characteristic of the global ocean.
In summary, the method used here can accurately describe the spatial distribution and temporal variability in ocean surface features using satellite observations. With the increasing resolution of satellite datasets, more detailed features can be identified and investigated, which enables a general understanding of regional features, including CHL, SST, and SSH. The correlation of monthly time series among different factors can aid in understanding their dynamic relationships and potential impact on an ecosystem49. Because the correlation can largely vary at different spatial locations, the proposed method offers a detailed and comprehensive description. A similar approach can be applied to any ocean basin worldwide, which will be greatly helpful to improve the understanding of marine dynamics and ecosystems.

Remote sensing technology offers great datasets with large spatial scales and long periods for describing marine environments. With the increasing spatial resolution of satellites, detailed features are now resolved from the regional scale to a few hundred meters1,2. An improved understanding of marine dynamics can be achieved with most updated satellite observations3.
By incorporating multiple sensors on a remote sensing platform, a comprehensive description of different parameters is possible. Sea surface temperature (SST) is the basic parameter that has been observed for more than half a century4. Recently, observations for sea surface chlorophyll-a (CHL) have become available and can be used to describe marine productivity5. Altimetry satellites are used for measuring sea surface height6,7, which is strongly related to mesoscale eddy activities in the global ocean8,9. In addition to eddies, frontal activities are also important for impacting regional dynamics and primary production10.
The major focus of the current study is to find a standard procedure to describe the spatial distribution and temporal variabilities of different ocean factors. In this method,&#160;SST, CHL, SSH, and front-data, which are derived from SST gradients, are analyzed to determine patterns. In particular, the CHL is used to represent the productivity of the ocean, and a method is introduced to investigate the relationship between CHL and other ocean parameters. To validate the method, the time period between October 2002 and September 2017 in the South China Sea was used to examine all parameters. The method can be easily used for other regions around the globe to capture major ocean patterns and explore how marine dynamics impact the ecosystem.
The South China Sea (SCS) was designated as the study region because of its relatively high coverage rate of satellite observations. The SCS is abundant in solar radiation; thus, the CHL is mainly determined by the availability of nutrients11,12. With more nutrients being transported into the euphotic layer, CHL levels can increase13. Mixing, induced by wind, can introduce nutrients into the ocean surface and enhance CHL14. The SCS is uniquely dominated by a monsoon wind system, which determines the dynamics and ecosystem in the region. The monsoon wind is strongest during winter15. In summer, the winds change direction and the wind speeds are much weaker than those in winter16,17. The wind intensity can determine the strength of vertical mixing, such that the mixed layer depth (MLD) deepens as the wind increases in winter and becomes shallower as the wind decreases in summer18. Thus, more nutrients are transported into the euphotic layer during winter when the wind is strong19 and CHL reaches its highest point of the year20,21.
In addition to the wind, the MLD can also be determined using other factors, such as SST and sea level anomalies&#160;(SLAs), which ultimately impact nutrient content and CHL22. During winter, the weak vertical gradient is associated with low temperatures at the surface20. The corresponding MLD is deep and more nutrients can be transported upward; thus, the CHL in the surface layer is high17. An increasing variation in CHL levels can be attributed to mesoscale eddies, which induce vertical transport and mixing23. Upwelling is usually found in cyclonic eddies associated with depressed SLAs8,9 and elevated CHL concentrations24. Downwelling is usually found in anticyclonic eddies associated with elevated SLAs8,9 and depressed CHL concentrations24. For other seasons, the MLD becomes shallow, and mixing becomes weak; thus, low CHL can be observed over the majority of the basin25. The seasonal cycles of CHL levels are subsequently predominant for the region26.
In addition to mixing, fronts and their associated coastal upwelling can further modulate the CHL. The front, which is defined as a boundary of different water masses, is important to determine the regional circulation and ecosystem responses27. Frontogenesis is usually associated with coastal upwelling and convergence28,29, which can induce nutrients and elevate the growth of phytoplankton30. Different algorithms have been developed to automatically identify fronts from satellite observations, including histogram and SST gradient methods. The latter approach is adopted in this study28.
The correlation of time series between CHL and different factors offers great insights for quantifying their relationship. The current study offers a comprehensive description of how to use satellite observations to reveal regional marine dynamics related to productivity. This description can be used as a guide for investigating the surface processes in any part of the ocean. The structure of this article includes a step-by-step protocol, followed by descriptive results in the text and figures. The applicability in addition to the pros and cons of the method are subsequently discussed.

<table><tbody><tr><td>Matlab</td><td>MathWorks</td><td>Matlab R2016</td><td>https://www.mathworks.com/products/matlab.html; referred to analysis software in the protocol</td></tr><tr><td>Sea surface chlorophyll</td><td>NASA</td><td>MODIS</td><td>mg/mg3 (podaac-tools.jpl.nasa.gov)</td></tr><tr><td>Sea surface height</td><td>AVISO</td><td>AVISO</td><td>meter (www.aviso.altimetry.fr)</td></tr><tr><td>Sea surface temperature</td><td>NASA</td><td>MODIS</td><td>&amp;deg;C (podaac-tools.jpl.nasa.gov)</td></tr><tr><td>Topography</td><td>NOAA</td><td>NGDC</td><td>meter (maps.ngdc.noaa.gov/viewers/wcs-client/)</td></tr><tr><td>Wind</td><td>ECMWF</td><td>ERA-interim</td><td>m/s (www.ecmwf.int/en/forecasts/datasets)</td></tr></tbody></table>

investigating the relationship between sea surface chlorophyll and major features of the south china sea with satellite information

Satellite observations offer a great approach to investigate the features of major marine parameters, including sea surface chlorophyll (CHL), sea surface temperature (SST), sea surface height (SSH), and factors derived from these parameters (e.g., fronts). This study shows a step-by-step procedure to use satellite observations to describe major parameters and their relationships in seasonal and anomalous fields. This method is illustrated using satellite datasets from 2002&#8211;2017 that were used to describe the surface features of the South China Sea (SCS). Due to cloud coverage, monthly averaged data were used in this study. The empirical orthogonal function (EOF) was applied to describe the spatial distribution and temporal variabilities of different factors. The monsoon wind dominates the variability in the basin. Thus, wind from the reanalysis dataset was used to investigate its driving force on different parameters. The seasonal variability in CHL was prominent and significantly correlated with other factors in a majority of the SCS. In winter, a strong northeast monsoon induces a deep mixed layer and high level of chlorophyll throughout the basin. Significant correlation coefficients were found among factors at the seasonal cycle. In summer, high CHL levels were mostly found in the western SCS. Instead of a seasonal dependence, the region was highly dynamic, and factors correlated significantly in anomalous fields such that unusually high CHL levels were associated with abnormally strong winds and intense frontal activities. The study presents a step-by-step procedure to use satellite observations to describe major parameters and their relationships in seasonal and anomalous fields. The method can be applied to other global oceans and will be helpful for understanding marine dynamics.

Watch this Scientific Journal Video about Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information at JoVE.com

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

Sea surface chlorophyll, temperature, sea level height, wind, and front data obtained or derived from satellite observations offer an effective way to characterize the ocean. Presented is a method for the comprehensive study of these data, including overall average, seasonal cycle, and intercorrelation analyses, to fully understand regional dynamics and&#160;ecosystems.

Satellite observations offer a great approach to investigate the features of major marine parameters, including sea surface chlorophyll (CHL), sea surface ...

investigating-relationship-between-sea-surface-chlorophyll-major

Nanyang Technological University

Research

JoVE Journal

Environment

5.7K Views.  Hohai University. Satellietwaarnemingen bieden een geweldige aanpak om de kenmerken van belangrijke mariene parameters te onderzoeken, waaronder chlorofyl en temperatuur van het zeeoppervlak, hoogte van het zeeoppervlak en de factoren die zijn afgeleid van deze parameters, zoals fronten. Onze studie laat zien hoe satellietwaarnemingen kunnen worden gebruikt om belangrijke parameters en hun relaties te beschrijven. Satellietdata science van 2002 tot 2017 werden gebruikt om de oppervlaktekenmerken van de Zuid-Ch...

Video: Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

RGB and Spectral Root Imaging for Plant Phenotyping and Physiological Research: Experimental Setup and Imaging Protocols

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop cultivars against environmental stresses. An experimental protocol is presented for RGB and hyperspectral imaging of root systems. The approach uses rhizoboxes where plants grow in natural soil over a longer time to observe fully developed root systems. Experimental settings are exemplified for assessing rhizobox plants under water stress and studying the role of roots. An RGB imaging setup is described for cheap and quick quantification of root development over time. Hyperspectral imaging improves root segmentation from the soil background compared to RGB color based thresholding. The particular strength of hyperspectral imaging is the acquisition of chemometric information on the root-soil system for functional understanding. This is demonstrated with high resolution water content mapping. Spectral imaging however is more complex in image acquisition, processing and analysis compared to the RGB approach. A combination of both methods can optimize a comprehensive assessment of the root system. Application examples integrating root and aboveground traits are given for the context of plant phenotyping and plant physiological research. Further improvement of root imaging can be obtained by optimizing RGB image quality with better illumination using different light sources and by extension of image analysis methods to infer on root zone properties from spectral data.

An experimental protocol is presented for assessment of soil grown plant root systems with RGB and hyperspectral imaging. Combination of RGB image time series with chemometric information from hyperspectral scans optimizes insights into plant root dynamics.

Better understanding of plant root dynamics is essential to improve resource use efficiency of agricultural systems and increase the resistance of crop ...

An Ultra-clean Multilayer Apparatus for Collecting Size Fractionated Marine Plankton and Suspended Particles

The distributions of many trace elements in the ocean are strongly associated with the growth, death, and re-mineralization of marine plankton and those of suspended/sinking particles. Here, we present an all plastic (Polypropylene and Polycarbonate), multi-layer filtration system for collection of suspended particulate matter (SPM) at sea. This ultra-clean sampling device has been designed and developed specifically for trace element studies. Meticulous selection of all non-metallic materials and utilization of an in-line flow-through procedure minimizes any possible metal contamination during sampling. This system has been successfully tested and tweaked for determining trace metals (e.g., Fe, Al, Mn, Cd, Cu, Ni) on particles of varying size in coastal and open ocean waters. Results from the South China Sea at the South East Asia Time-Series (SEATS) station indicate that diurnal variations and spatial distribution of plankton in the euphotic zone can be easily resolved and recognized. Chemical analysis of size-fractionated particles in surface waters of the Taiwan Strait suggests that the larger particles (&#62;153 &#181;m) were mostly biologically derived, while the smaller particles (10 - 63 &#181;m) were mostly composed of inorganic matter. Apart from Cd, the concentrations of metals (Fe, Al, Mn, Cu, Ni) decreased with increasing size.

Plankton and suspended particles play a major role in the biogeochemical cycles in the ocean. Here, we provide an ultra-clean, low stress method for the collection of various sizes of particles and plankton at sea with the capability of handling large volumes of seawater.

The distributions of many trace elements in the ocean are strongly associated with the growth, death, and re-mineralization of marine plankton and those ...

A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis

Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used microscopic and toxin analyses for decades for this purpose. Molecular biological methods, such as high-throughput DNA sequencing and bacterial assemblage-based approaches, are just beginning to be introduced in Chilean HAB monitoring, and the procedures have not yet been standardized. Here, 16S rRNA and 18S rRNA metabarcoding analyses for monitoring Chilean HABs are introduced stepwise. According to a recent hypothesis, algal-bacterial mutualistic association plays a critical synergetic or antagonistic relationship accounting for bloom initiation, maintenance, and regression. Thus, monitoring HAB from algal-bacterial perspectives may provide a broader understanding of HAB mechanisms and the basis for early warning. Metabarcoding analysis is one of the best suited molecular-based tools for this purpose because it can detect massive algal-bacterial taxonomic information in a sample. The visual procedures of sampling to metabarcoding analysis herein provide specific instructions, aiming to reduce errors and collection of reliable data.

This protocol introduces steps of metabarcoding analysis, targeting 16S rRNA and 18S rRNA genes, for monitoring harmful algal blooms and their associated microbiome in seawater samples. It is a powerful molecular-based tool but requires several procedures, which are visually explained here step-by-step.

Harmful algae blooms (HABs) monitoring has been implemented worldwide, and Chile, a country famous for its fisheries and aquaculture, has intensively used ...

Exploring Long-Term Shifts in Phytoplankton by Conversion of Microscopic Images

Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton

Oceanographic time series provide&#160;an important perspective on environmental processes in ecosystems. The Narragansett Bay Long-Term Plankton Time Series (NBPTS) in Narragansett Bay, Rhode Island, USA, represents one of the longest plankton time series (1959-present) of its kind in the world and presents a unique opportunity to visualize long-term change within an aquatic ecosystem. Phytoplankton represent&#160;the base of the food web in most marine systems, including Narragansett Bay. Therefore, communicating their importance to the 2.4 billion people who live within the coastal ocean is critical. We developed a protocol with the goal of visualizing the diversity and magnitude of phytoplankton by utilizing Adobe Illustrator to convert microscopic images of phytoplankton collected from the NBPTS into vector graphics that could be conformed into repetitive visual patterns through time. Numerically abundant taxa or those that posed economic and health threats, such as the harmful algal bloom taxa, Pseudo-nitzschia spp., were selected for image conversion. Patterns of various phytoplankton images were then created based on their relative abundance for select decades of data collected (1970s, 1990s, and 2010s). Decadal patterns of phytoplankton biomass informed the outline of each decade while a background color gradient from blue to red was used to reveal a long-term temperature increase observed in Narragansett Bay. Finally, large, 96-inch by 34-inch panels were printed with repeating phytoplankton patterns to illustrate potential changes in phytoplankton abundance over time. This project enables visualization of literal shifts in phytoplankton biomass, that are typically invisible to the naked eye while leveraging real-time series data (e.g., phytoplankton biomass and abundance) within the art piece itself. It represents an approach that can be utilized for many other plankton time series for data visualization, communication, education, and outreach efforts.

Author Spotlight: Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression 

Here, we present a protocol for converting phytoplankton microscopic images into vector graphics and repetitive patterns to enable visualization of shifts in phytoplankton taxa and biomass over 60 years. This protocol represents an approach that can be utilized for other plankton time series and datasets globally.

Oceanographic time series provide&#160;an important perspective on environmental processes in ecosystems. The Narragansett Bay Long-Term Plankton Time ...

Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the status, trends, and dynamics of organisms living in subtidal habitats. By creating photo-realistic digital twins for ex situ analyses, these approaches allow small field teams to collect substantially more data than was previously possible. Here we present a four-step, large-area imaging survey pipeline and analysis methodology, including image collection, model construction, ecological analysis, and data curation, that has been developed and refined through experimentation over the past decade. Each step described has a consistent focus on the unique value of the original source imagery. While types of data extracted from large-area image surveys are vast, we include here workflows to extract ecological data for structural complexity, community composition, and demographic analyses valuable for monitoring and hypothesis-driven efforts. We additionally include recommendations for metadata standards, which complement the collection of large-area imaging data and support archival efforts facilitating transparency and collaboration between research groups.

This protocol covers a four-step large-area imaging survey methodology used to extract metrics of structural complexity, community composition, and population demographics for coral reef communities. The quality of imagery collected and integrated access to the source imagery are prioritized within each step of the protocol.

Digital imaging and processing technology have evolved to facilitate the expansion of large-area imaging surveys, which increase our capacity to study the ...

Surface Mapping of Earth-like Exoplanets using Single Point Light Curves

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate goal of this protocol is to determine whether these planetary worlds harbor geological features and/or climate systems. We present a method of extracting information from multi-wavelength single-point light curves and retrieving surface maps. It uses singular value decomposition (SVD) to separate sources that contribute to light curve variations and infer the existence of partially cloudy climate systems. Through analysis of the time series obtained from SVD, physical attributions of principal components (PCs) could be inferred without assumptions of any spectral properties. Combining with viewing geometry, it is feasible to reconstruct surface maps if one of the PCs are found to contain surface information. Degeneracy originated from convolution of the pixel geometry and spectrum information determines the quality of reconstructed surface maps, which requires the introduction of regularization. For the purpose of demonstrating the protocol, multi-wavelength light curves of Earth, which serves as a proxy exoplanet, are analyzed. Comparison between the results and the ground truth is presented to show the performance and limitation of the protocol. This work provides a benchmark for future generalization of exoplanet applications.

The protocol extracts information from light curves of exoplanets and constructs their surface maps. It uses light curves of Earth, which serves as a proxy exoplanet, to demonstrate the approach.

Spatially resolving exoplanet features from single-point observations is essential for evaluating the potential habitability of exoplanets. The ultimate ...

Engineering

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of cyanobacteria and related cyanotoxins in water samples and in an organic matrix, such as bivalve extracts. FDS combines remote/proximal sensing techniques with analytical/bioinformatics analyses. Sampling spots are chosen through multi-disciplinary, multi-scale, and multi-parametric monitoring in a three-dimensional physical space, including remote sensing. Microscopic observation and taxonomic analysis of the samples are performed in the laboratory setting, which allows for the identification of cyanobacterial species. Samples are then extracted with organic solvents and processed with LC-MS/MS. Data obtained by MS/MS are analyzed using a bioinformatic approach using the online platform Global Natural Products Social (GNPS) to create a network of molecules. These networks are analyzed to detect and identify toxins, comparing data of the fragmentation spectra obtained by mass spectrometry with the GNPS library. This allows for the detection of known toxins and unknown analogues that appear related in the same molecular network.

A fast-multidisciplinarystrategy for early detection of cyanobacterial blooms and associated cyanotoxins is described here. It allows for the detection of cyanobacteria and related cyanotoxins in water samples and in organic matrices, such as bivalve samples, in 24 h.

Fast detection of cyanobacteria and cyanotoxins is achieved using a Fast Detection Strategy (FDS). Only 24 h are needed to unravel the presence of ...

Chemistry

Evaluation of Photosynthetic Behaviors by Simultaneous Measurements of Leaf Reflectance and Chlorophyll Fluorescence Analyses

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many parameters that efficiently measure photosynthesis. Leaf reflectance analysis provides several vegetation indices in ecology and agriculture, including the photochemical reflectance index (PRI), which can be used as an indicator of thermal energy dissipation during photosynthesis because it correlates with non-photochemical quenching (NPQ). However, since NPQ is a composite parameter, its validation is required to understand the nature of the PRI parameter. To obtain physiological evidence for evaluation of the PRI parameter, we simultaneously measured chlorophyll fluorescence and leaf reflectance in xanthophyll cycle defective mutant (npq1) and wild-type Arabidopsis plants. Additionally, the qZ parameter, which likely reflects the xanthophyll cycle, was extracted from the results of chlorophyll fluorescence analysis by monitoring relaxation kinetics of NPQ after switching the light off. These simultaneous measurements were carried out using a pulse-amplitude modulation (PAM) chlorophyll fluorometer and a spectral radiometer. The fiber probes from both instruments were positioned close to each other to detect signals from the same leaf position. An external light source was used to activate photosynthesis, and the measuring lights and saturated light were provided from the PAM instrument. This experimental system enabled us to monitor light-dependent PRI in the intact plant and revealed that light-dependent changes in PRI differ significantly between the wild type and npq1 mutant. Furthermore, PRI was strongly correlated with qZ, meaning that qZ reflects the xanthophyll cycle. Together, these measurements demonstrated that simultaneous measurement of leaf reflectance and chlorophyll fluorescence is a valid approach for parameter evaluation.

We describe a new technical approach to study photosynthetic responses in higher plants involving simultaneous measurements of chlorophyll a fluorescence and leaf reflectance using a PAM and a spectral radiometer for the detection of signals from the same leaf area in Arabidopsis.

Chlorophyll a fluorescence analysis is widely used to measure photosynthetic behaviors in intact plants, and has resulted in the development of many ...

Bioengineering

A Combined Micro-Device System for Promoting and Monitoring Coral Growth

An Integrated Micro-Device System for Coral Growth and Monitoring

Corals are fundamental organisms in marine and coastal ecosystems. With the advancement of coral protection research in recent years, precise control of the coral culture environment is highly in demand for coral conservation and study. Here, we developed a semi-closed coral culture micro-device system as a multi-functional platform, which can provide accurate and programmable temperature control, a sterile initial environment, long-term stable water quality, an adjustable dissolved oxygen concentration, and a customized light spectrum for corals. Owing to the modular design, the coral culture system can be upgraded or modified by installing desirable new modules or removing existing ones. Currently, under appropriate conditions and with proper system maintenance, the sample corals can survive for at least 30 days in a healthy state. Furthermore, due to the controllable and sterile initial environment, this coral culture system can support research into the symbiotic relationship between corals and associated microorganisms. Therefore, this micro-device system can be applied to monitor and investigate sea corals in a relatively quantitative manner.

Author Spotlight: Advancing Coral Culture - Creating a Semi-Quantitatively Controlled Microenvironment System to Counter Current Limitations 

This protocol describes the development of a modular controllable micro-device system that can be applied for the long-term culturing and monitoring of sea corals.

Corals are fundamental organisms in marine and coastal ecosystems. With the advancement of coral protection research in recent years, precise control of ...

Measuring Photophysiology of Attached Stage of Colacium sp. by a Cuvette-Type Fast Repetition Rate Fluorometer

Fast repetition rate fluorometer (FRRf) is a beneficial method for measuring photosystem II (PSII) photophysiology and primary productivity. Although FRRf can measure PSII absorption cross-section (&#963;PSII), maximum photochemical efficiency (Fv/Fm), effective photochemical efficiency (Fq&#8242;/Fm&#8242;), and non-photochemical quenching (NPQNSV) for various eukaryotic algae and cyanobacteria, almost all FRRf studies to date have focused on phytoplankton. Here, the protocol describes how to measure PSII photophysiology of an epizoic alga Colacium sp.&#160;Ehrenberg 1834 (Euglenophyta), in its attached stage (attached to zooplankton), using cuvette-type FRRf. First, we estimated the effects of substrate zooplankton (Scapholeberis mucronata O.F. M&#252;ller 1776, Cladocera, Daphniidae) on baseline fluorescence and &#963;PSII, Fv/Fm, Fq&#8242;/Fm&#8242;, and NPQNSV of planktonic Colacium sp. To validate this methodology, we recorded photophysiology measurements of attached Colacium sp. on S. mucronata and compared these results with its planktonic stage. Representative results showed how the protocol could determine the effects of calcium (Ca) and manganese (Mn) on Colacium sp. photophysiology and identify the various effects of Mn enrichment between attached and planktonic stages. Finally, we discuss the adaptability of this protocol to other periphytic algae.

Measuring Photophysiology of Attached Stage of Colacium sp. by a Cuvette-Type Fast Repetition Rate Fluorometer

Fast repetition rate fluorometer (FRRf) is a beneficial method for measuring photosystem II photophysiology and primary productivity. Here we describe a protocol to measure PSII photophysiology of epizoic alga, Colacium sp. on substrate zooplankton using cuvette-type FRRf.

Fast repetition rate fluorometer (FRRf) is a beneficial method for measuring photosystem II (PSII) photophysiology and primary productivity. Although FRRf ...

Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information

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