Name: visualizing oceanographic data to depict long-term changes in phytoplankton
Uploaded: 2023-07-28T13:40:00.000+00:00
Duration: 8 min 15 s
Description: Watch this Scientific Journal Video about Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression at JoVE.com

This research was supported by the National Science Foundation (OIA-1655221, OCE-1655686) and Rhode Island Sea Grant (NA22-OAR4170123, RISG22-R/2223-95-5-U). We thank the multiple captains for providing field assistance and the many students and researchers who collected data since 1970. We thank Stewart Copeland and Georgia Rhodes for developing the Vis-A-Thon project that produced the plankton mural as well as Rafael Attias from the Rhode Island School of Design for his artistic guidance during project development.

1. Converting phytoplankton images into vector graphics
<ol>
	<li>Select phytoplankton microscopic images taken from the Narragansett Bay Long Term Plankton Time Series (NBPTS) as either .JPG, .PNG, or .PDF files (Figure 1A). 
	NOTE: Taxa include Thalassiosira nordenskioeldii, Thalassionema nitzschioides, Tripos spp., Odontella aurita, Skeletonema species complex, Chaetoceros diadema, Eucampia zodiacus, Dinophysis spp., and Pseudo-nitzschia spp. Images were taken with a light microscope.</li>
	<li>Open specific software vector graphics editor or the illustrator used for this study (see Table of Materials). Vector graphic software has been mentioned as illustrator further in the manuscript.</li>
	<li>Place a .JPG or .PNG microscopic image into the illustrator workspace by opening a file from the computer or drag and dropping it into a new workspace.</li>
	<li>Go to View &#62; Show Transparency Grid to reveal the checkerboard background that indicates transparency.</li>
	<li>Click on Window &#62; Image Trace in the dropdown menu to open the Image Trace window.</li>
	<li>Click on the Selection tool (black arrow) in the toolbar and click on the phytoplankton image.</li>
	<li>Click on Object &#62; Expand in the dropdown menu.</li>
	<li>Use the Direct Selection tool (white arrow) in the toolbar to click and select the background parts of the image to get rid of around the phytoplankton. Press delete.</li>
	<li>Repeat for each background region of the image.</li>
	<li>Click on File &#62; Export to save the file as .PNG file. Ensure the Background Transparent box is selected.</li>
	<li>Place .PNG microscopic image with background removed into a new workspace in the illustrator by opening the file from the computer or drag and drop it into a new workspace.</li>
	<li>Click on Window &#62; Image Trace in the dropdown menu to open the Image Trace window.</li>
	<li>Under the Image Trace options, click Preset &#62; Black and White Logo and Mode &#62; Black and White.</li>
	<li>Use the Threshold as well as the Advanced options (i.e., Paths, Corners, and Noise) to refine the image.</li>
	<li>Under Properties, select Expand to vectorize it.</li>
	<li>Select View &#62; Show Transparency Grid.</li>
	<li>Click on the vector image then right click and select Ungroup.</li>
	<li>Choose the Direct Selection tool (white arrow) in the toolbar. Drag and draw a box around the whitespace only. Press delete to remove it.</li>
	<li>Repeat until all the whitespace is removed.</li>
	<li>Click File &#62; Save As and select .EPS to save as a vector graphic.</li>
	<li>Repeat for the phytoplankton taxa from 1.1 (Figure 1B).</li>
</ol>
2. Creating phytoplankton patterns
<ol>
	<li>Utilize phytoplankton count data from the NBPTS dataset to determine the average abundance of each taxon from 1970-1979 (1970s), 1990-1999 (1990s), and 2010-2019 (2010s).</li>
	<li>Calculate mean &#177; standard deviation for each phytoplankton taxa for each decade in a statistical software program by clicking on or typing &#39;mean() and sd()&#39;.</li>
	<li>Click-on or type &#39;aov() &#39; to use an ANOVA to test for significant differences among decades in a statistical software program. 
	NOTE: Some species (e.g., Tripos spp., Chaetoceros diadema) do not have large enough sample sizes in the 1990s. In this case, click-on or type &#39;t.test()&#39; in a statistical software program to compare mean abundances in the 1970s to the 2010s.</li>
	<li>Use the &#39;Artboard tool &#39; (square) in the toolbar to click and create a new Artboard in a new workspace of the specific illustrator used in this study.</li>
	<li>Make three identical Artboards of the same size. Adjust the size within Properties &#62; Transform. 
	NOTE: For this project, Artboards for the phytoplankton images were 1224 px by 545 px.</li>
	<li>Drag and drop the .EPS files of the different phytoplankton taxa onto the three Artboards.</li>
	<li>Color the phytoplankton with different colors representative of the decade by using the Direct Selection tool&#160;(white arrow) to draw a box around an individual phytoplankton.</li>
	<li>Under Properties, select Fill and then click on the desired color from the color palette. Press enter to fill the vector.</li>
	<li>Use the Selection Tool&#160;(black arrow) to highlight a particular phytoplankton then select Edit &#62; Copy and Edit &#62; Paste.</li>
	<li>Paste each phytoplankton vector qualitatively based on the relative proportions of each taxa in the dataset as determined in 2.2 for each of the three decades (Figure 1C). 
	NOTE: The abundance of phytoplankton on each panel is a qualitative representation of Table 1. For example, if a higher abundance of Pseudo-nitzschia spp. is observed in the 2010s than in the 1990s then copy more Pseudo-nitzschia graphics onto the 2010 artboard than on the 1990 artboard.</li>
	<li>Select Object &#62; Pattern &#62; Make to create a color swatch phytoplankton pattern for each of the three decades.</li>
</ol>
3. Incorporating phytoplankton biomass and temperature data
<ol>
	<li>Click-on or type &#39;mean()&#39; to calculate the average chlorophyll a (chl a, proxy for phytoplankton biomass) for each week of every decade in a statistical software program.</li>
	<li>Click-on or type &#39;plot()&#39; in a statistical software program to graph the mean decadal biomass (dependent variable) by each week (independent variable) and click on Save the graph as a .JPG or .PNG.</li>
	<li>Place a .JPG or .PNG of the chl a decadal biomass figure into the illustrator workspace by opening the file from the computer or drag and drop&#160;it into a new workspace.</li>
	<li>Repeat steps 1.3 to 1.8 to vectorize each of the three chl a seasonal cycles.
	<ol>
		<li>Go to View &#62; Show Transparency Grid to reveal the checkerboard background that indicates transparency.</li>
		<li>Click on Window &#62; Image Trace in the dropdown menu to open the Image Trace window.</li>
		<li>Click on the Selection tool&#160;(black arrow) in the toolbar and click on the image.</li>
		<li>Click on Object &#62; Expand in the dropdown menu.</li>
		<li>Use the Direct Selection tool&#160;(white arrow) in the toolbar to click and select the background parts of the image to get rid of around the line indicating the seasonal cycle. Press delete. Repeat for each background region of the figure.</li>
		<li>Click on File &#62; Export to save the file as .PNG file. Ensure the Background Transparent box is selected.</li>
		<li>Place .PNG figure with background removed into a new workspace of the specific illustrator used by opening the file from the computer or drag and dropping it into a new workspace.</li>
		<li>Click on Window &#62; Image Trace in the dropdown menu to open the Image Trace window.</li>
		<li>Under Properties, select Expand to vectorize it.</li>
		<li>Select View &#62; Show Transparency Grid.</li>
		<li>Click on the vector image then right click and select Ungroup.</li>
		<li>Choose the Direct Selection tool (white arrow) in the toolbar. Drag and draw a box around the whitespace only. Press delete to remove it.</li>
		<li>Repeat until all the whitespace is removed for each of the lines from the 1970s, 1990s, and 2010s.</li>
		<li>Click File &#62; Save As and select .EPS to save each line as a separate vector graphic.</li>
	</ol>
	</li>
	<li>Use the &#39;Artboard tool&#39; (square) in the toolbar to click drag, and create a new Artboard in a new illustrator workspace.</li>
	<li>Make three identical Artboards of the same size. Adjust the size within Properties &#62; Transform. 
	NOTE: For this project, dimensions were 1224 px by 3456 px.</li>
	<li>Drag and drop one of the chl a .EPS files onto one of the three Artboards, respectively.</li>
	<li>Create a new Layer by clicking on the&#160;&#39;sticky note icon&#39;.</li>
	<li>Create a rectangle within the new layer with the Rectangle tool from the toolbar.</li>
	<li>Fill the rectangle with a light blue gradient using the Gradient tool from the toolbar.</li>
	<li>Copy the vectorized trend line and add it to the layer with the rectangle in it.</li>
	<li>Use the &#39;Line Segment&#39; tool from the toolbar to create a box attached to the trend line. Hold shift to make the lines straight and aligned.</li>
	<li>Press the control key and select all the components, including the lines, rectangle, and trend line within the layer.</li>
	<li>Select Object &#62; Clipping Mask &#62; Make. This will remove the top fill of the shape.</li>
	<li>Fill the shape with the phytoplankton pattern saved as a color swatch from 2.11.</li>
	<li>Repeat this process for each of the three decades.</li>
	<li>Repeat steps 3.9 &#38; 3.10 to create a rectangle colored with a red to blue color gradient to represent warming water temperature across the three decadal panels.</li>
	<li>Right click on the object and move it back behind the phytoplankton patterns.</li>
</ol>
4. Adding detail to phytoplankton panels
<ol>
	<li>To add images of photographed phytoplankton onto the phytoplankton patterns, select Open and click on the image file to open it in the illustrator used here.</li>
	<li>Create a circle with the Ellipse tool from the toolbar and overlay it on top of the phytoplankton image.</li>
	<li>Hold the shift key to select both the shape and the image, then in the menu click on Object &#62; Clipping Mask &#62; Make to fill the shape with the image.</li>
	<li>Repeat for select phytoplankton images and distribute across the three decades to look like a magnifying glass zooming in on the illustrative phytoplankton (Figure 1D). 
	NOTE: Steps 1.3 to 1.8 can be repeated to add artistic elements of boats and birds to the panels to make the chl a seasonal cycles look like ocean waves.</li>
	<li>Use the &#39;Rectangle tool&#39; from the toolbar to create a textbox on each of the decade artboards.</li>
	<li>Use the &#39;Type tool&#39; (T) to click and type informational text about each decade. Add text at the top of each decade with the name of the decade and add the names of the corresponding seasons at the bottom of each of the three panels.</li>
	<li>Save the workspace in the illustrator.</li>
</ol>
5. Mural production
<ol>
	<li>Import the saved illustrator file and select to only import the three completed decades. Select all and export as a .PDF file.</li>
	<li>Open the plankton pattern .PDF file with a Large-format Plotter to scale the three decadal artboards into 96 inch by 34 inch panels.</li>
	<li>Print panels on Heavyweight Matter Paper and install with hanging hardware.</li>
</ol>

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

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The authors have no conflicts of interest to declare.

Critical steps of the protocol include obtaining microscopic images of phytoplankton and converting them into vector graphics. Making the images of phytoplankton, which are not noticeable to the naked eye, large enough to be seen without a magnifying glass on the mural, helps to bring them to life for the viewer. To accomplish this mural as not only a work of art but also a data visualization method, it is important to incorporate observed data into the project. In the case of the phytoplankton mural, the chlorophyll a (chl a) annual cycles that were averaged by decade represent the actual data and show how chl a declined by decade on the different panels. For phytoplankton abundance, the average concentrations of certain taxa varied among the decades, therefore, a higher abundance of a taxa observed in a particular decade would have more graphics of that taxa copied onto the decadal panel compared to another panel with lower mean abundance. Using observed data to inform the artistic elements, such as a gradient of color from blue to red to represent temperature increases, also aid in visualizing these scientific data.
Modifications of the method could include obtaining microscopic images of phytoplankton from open-access image repositories as well as utilizing other phytoplankton imaging systems for photographic images besides a microscope (e.g., Imaging Flow-Cytobot). Further, microscopic images and scientific data could include daily phytoplankton counts and images, rather than decades, for shorter time series datasets, as well as zooplankton images to reveal food web interactions. Finally, average temperatures recorded for each decade could be included on the panels to quantify changes in temperature, or a trend line drawn near the bottom of the panels, in addition to the illustrative changes shown through the gradient background. Limitations include troubleshooting these scientific data to scale within the confines of the physical art piece as well as obtaining instrumentation for printing on large panels. It is also important to ensure that the background color is transparent enough to reveal the changes in phytoplankton abundance distinctly over time, which can be difficult to distinguish until printed. Finally, Adobe Illustrator is a proprietary software, which may limit accessibility for certain users, but free software illustration programs are available (e.g., Inkscape, GIMP, Vectr, Vectornator). Adapting the protocol to produce the phytoplankton murals on these free programs represents useful future work to increase accessibility.
Given that phytoplankton represent the base of the food web in almost all marine systems, communicating their importance is critical; However, most studies in phytoplankton ecology only present results as data graphs reducing their accessibility to general audiences. The protocol presented here of developing the phytoplankton mural shows the impact of visualizing scientific data through an artistic lens17. Through analysis of this mural, the viewer can see that phytoplankton biomass has decreased in Narragansett Bay (NBay) since the 1970s.&#160;This&#160;decline occurs over a period when there have been long-term increases in seawater temperature in NBay13. Similar shifts in plankton communities (i.e., zooplankton) with warming seawater temperatures were also observed in San Francisco Bay Estuary, which, like NBay, supports a large human population18,19. This approach represents a visualization method that can be utilized for many other plankton time series, like San Francisco Bay Estuary, throughout the world.
At a far glance, the shape and color of the panels change over time. Viewing the panels more closely, the patterns of phytoplankton are reflective of shifts in the abundance and biomass of different taxa. This is where the worlds of art and science collide in that the scientific patterns are the literal patterns shown on the mural. It is evident that there is much more to NBay than what appears on the water&#39;s surface by visualizing phytoplankton data through art.

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/t/65571">Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton</a>. The Authors section was updated from:
Patricia S. Thibodeau1 
Jongsun Kim2 
1Graduate School of Oceanography, University of Rhode Island 
2School of Earth, Environmental and Marine Sciences, The University of Texas - Rio Grande Valley
to:
Patricia S. Thibodeau1 
Jongsun Kim2 
1School of Marine and Environmental Programs, University of New England 
2School of Earth, Environmental and Marine Sciences, The University of Texas - Rio Grande Valley

An erratum was issued for:&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/t/65571">Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton</a>. The Authors section was updated.

Erratum: Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton

Phytoplankton are primary producers representing the base of the food web across aquatic ecosystems1,2. While phytoplankton monitoring programs are key to identifying current and future changes in marine ecosystems, their support is declining over time 3. Due to their relatively short generation times and limited mobility, phytoplankton are particularly responsive to climate change, which makes them an important tool in time series monitoring. Phytoplankton time series are also important for informing ecosystem-based management of resource availability&#160;and providing context for episodic events, such as marine heatwaves4. Short-term time series, on the matter of years, give insights into phytoplankton community succession and seasonal dynamics (e.g., ref.5,6), whereas long-term time series, such as the Bermuda Atlantic Time Series (BATS) and Hawaii Ocean Times Series (HOTS) Programs, span&#160;more than two decades and enable the detection of long-term trends7,8. Such studies illustrate the benefit and importance of a highly resolved phytoplankton record for a complete understanding of long-term ecosystem change in dynamic marine environments. Further, visualizing and communicating these changes in phytoplankton, which cannot be seen by the naked eye, are more difficult to comprehend than for organisms that are large and readily visible, like fish and whales. Computer visualizations offer a technique to explore complex data sets9 and improved illustrative graphics are becoming readily available (e.g., Integration and Application Network, University of Maryland Center for Environmental Science). However, most studies in phytoplankton ecology, including many referenced here, still only present results as data graphs reducing their accessibility to general audiences. Given that phytoplankton represent the base of the food web in most marine systems, communicating their importance to the nearly 2.4 billion people who live within the coastal ocean is critical10. Here, we developed a protocol with the goal of visualizing the diversity and magnitude of phytoplankton, as collected by a phytoplankton monitoring program.
The Narragansett Bay Plankton Time Series (NBPTS) provides a long-term 60+ year (1959-Present) perspective on the effects of global change within a climate context on phytoplankton abundance, seasonality, and phenology (life history). Narragansett Bay (NBay) is a coastal estuary connected to the broader systems of the U.S. Northeast Shelf and Northwest Atlantic whose production has important implications for fisheries and human use along the coastal U.S.A.11. NBay is considered a highly seasonal system experiencing long-term (1950-2015) warming waters in the region as well as shifts in nutrients and an increase in water clarity12,13. In addition, a decline in phytoplankton biomass has occurred in upper NBay related to anthropogenic decreases in dissolved inorganic nitrogen, which is partially attributed to upgrades in wastewater treatment plants12. Shifts in phytoplankton taxa, particularly harmful algal blooms (HABs), are also occurring in NBay. Pseudo-nitzschia spp., which produces pervasive toxic blooms in upwelling regions along the U.S. west coast, led to notable shellfish closures for the first time in NBay&#39;s history&#160;in 2016 and 201714,15,16. Communicating these changes to diverse audiences is important for increasing science literacy and for promoting continued support of phytoplankton monitoring studies.
The goal of this project was to utilize microscopic images of phytoplankton from NBay, as well as data synthesized from NBPTS, to visualize the literal shifts in phytoplankton taxa and biomass that are occurring in NBay to communicate and enhance the importance of phytoplankton to general audiences. NBPTS provides 60+ years of publicly available weekly phytoplankton counts and biomass to leverage data from (https://web.uri.edu/gso/research/plankton/). The final product was a large mural of plankton patterns representative of the time series data (e.g., phytoplankton biomass and taxa, temperature) within the art piece itself. This approach represents a visualization method that can be utilized for many other plankton time series throughout the world and can be adapted for monitoring programs with short-term, seasonal data as well. The benefits of implementing this protocol include increasing efforts in data visualization, science communication, education, and engagement with local communities.

<table><tbody><tr><td>Adobe Illustrator</td><td>Adobe</td><td>version 23.0.6</td><td>Free alternatives include: Inkscape, GIMP, Vectr, Vectornator</td></tr><tr><td>Eclipse E800</td><td>Nikon</td><td>ECLIPSE Ni/Ci Upright Microscope</td><td>Now succeeded by Eclipse Ni-U</td></tr><tr><td>Epson Large Format Printer</td><td>Epson</td><td>SCT5475SR</td><td></td></tr><tr><td>Heavy Matte Paper</td><td>Epson</td><td>S041596</td><td></td></tr><tr><td>RStudio</td><td>Rstudio, PBC</td><td>version 2022.07.1</td><td>Any statistical software tool will suffice</td></tr></tbody></table>

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.

Results document a decline in phytoplankton biomass from the 1970s to 1990s to 2010s (Figure 1). All decades exhibited a bimodal peak in chlorophyll a (chl a) concentration with the first peak occurring in winter and the second occurring in summer. The 1970s exhibited higher average chl a in winter than in summer. Conversely, the 1990s showed lower chl a in winter than in summer. The 2010s returned to a higher mean chl a concentration in winter than in summer. These results are reflected in the final product through different chl a peaks in the panels as well as with the text boxes added to emphasize different components of the chl a dataset (Figure 2).
Analysis of ecologically relevant phytoplankton taxa from Narragansett Bay revealed a wide range in abundance over time. This variation often masked any statistically significant differences in the taxa among the three decades although the HAB taxa, Dinophysis spp., and Tripos spp. (previously referred to as Ceratium) decreased (Table 1). In contrast, Thalassiosira nordenskioeldii and Skeletonema spp. increased (Table 1). Other taxa oscillated in abundance such as Eucampia zodiacus (Table 1). These results were illustrated in the final product by the increased presence of more E. zodiacus images in the 2010s compared to the 1970s and 1990s, as well as an overlayed microscopic image of E. zodiacus to bring the actual species to &#39;real-life&#39; for the audience (Figure 2 &#38; 3).
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Taxa Name</td>
			<td>Type</td>
			<td>1970-79 Mean &#177; SD (Cells L-1)</td>
			<td>1990-99 Mean &#177; SD (Cells L-1)</td>
			<td>2010-19 Mean &#177; SD (Cells L-1)</td>
			<td>p-value</td>
		</tr>
		<tr>
			<td>Pseudo-nitzschia spp.</td>
			<td>Diatom</td>
			<td>3701 &#177; 18235</td>
			<td>5123 &#177; 24396</td>
			<td>12919 &#177; 58632</td>
			<td>&#62; 0.05</td>
		</tr>
		<tr>
			<td>Thalassionema nitzschioides</td>
			<td>Diatom</td>
			<td>81797 &#177; 245710</td>
			<td>22909 &#177; 59246</td>
			<td>62656 &#177; 292940</td>
			<td>&#62; 0.05</td>
		</tr>
		<tr>
			<td>Tripos spp.</td>
			<td>Dinoflagellate</td>
			<td>1933 &#177; 703</td>
			<td>500 &#177; 706</td>
			<td>841 &#177; 353</td>
			<td>&#60; 0.001</td>
		</tr>
		<tr>
			<td>Eucampia zodiacus</td>
			<td>Diatom</td>
			<td>27266 &#177; 27675</td>
			<td>7500 &#177; 2121</td>
			<td>90764 &#177; 181415</td>
			<td>&#62; 0.05</td>
		</tr>
		<tr>
			<td>Thalassiosira nordenskioeldii</td>
			<td>Diatom</td>
			<td>76800 &#177; 150545</td>
			<td>27000 &#177; 28284</td>
			<td>362411 &#177; 376064</td>
			<td>0.008</td>
		</tr>
		<tr>
			<td>Odontella aurita</td>
			<td>Diatom</td>
			<td>5571 &#177; 8541</td>
			<td>5000 &#177; 2645</td>
			<td>17750 &#177; 23485</td>
			<td>&#62; 0.05</td>
		</tr>
		<tr>
			<td>Chaetoceros diadema</td>
			<td>Diatom</td>
			<td>103027 &#177; 239802</td>
			<td>18000 &#177; 0</td>
			<td>40402 &#177; 46128</td>
			<td>&#62; 0.05</td>
		</tr>
		<tr>
			<td>Skeletonema spp.</td>
			<td>Diatom</td>
			<td>2457847 &#177; 7814228</td>
			<td>1884674 &#177; 4888589</td>
			<td>1349184 &#177; 3732765</td>
			<td>0.003</td>
		</tr>
		<tr>
			<td>Dinophysis spp.</td>
			<td>Dinoflagellate</td>
			<td>5166 &#177; 8983</td>
			<td>1978 &#177; 1840</td>
			<td>2331 &#177; 2504</td>
			<td>&#60; 0.001</td>
		</tr>
	</tbody>
</table>
Table 1: Phytoplankton counts. Mean (cells L-1) and standard deviation of phytoplankton concentrations for each taxa for each decade. Type designates whether the phytoplankton is classified as a diatom or dinoflagellate. ANOVA or t-test was performed to test for statistically significant differences in mean abundance among the three decades (ANOVA) or two (t-test) when low sample size was present in the 1990s (i.e., Tripos spp., Eucampia zodiacus, Thalassiosira nordenskioeldii, Odontella aurita, and Chaetoceros diadema). Significant p-values determined at &#945; = 0.05 and indicated in bold.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65571/65571fig01v2.jpg" /> 
Figure 1: Schematic of methodology. A) Convert microscopic image into a vector illustrative graphic, B) Create repetitive pattern for each decade (1970s, 1990s, 2010s), C) Use decadal chlorophyll a data to inform shapes of patterns. Fill background with blue to red color scheme to represent increasing water temperature, and D) Finalize product by adding text to inform distinct features in patterns and microscopic images of phytoplankton used to create illustrative graphics. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65571/65571fig01largev2.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/65571/65571fig02.jpg" /> 
Figure 2: Completed visualization. Finalized phytoplankton visualization made in the illustrator. Taxa include Thalassiosira nordenskioeldii, Thalassionema nitzschioides, Tripos spp., Odontella aurita, Skeletonema species complex, Chaetoceros diadema, Eucampia zodiacus, Dinophysis spp., and Pseudo-nitzschia spp. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65571/65571fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/65571/65571fig03.jpg" /> 
Figure 3: Completed art piece. Finalized phytoplankton visualization made in the illustrator alongside printed version for A) the 1970s, B) the 1990s, and C) the 2010s.&#160;<a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65571/65571fig03large.jpg" style="font-size: 14px;" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Unveiling Plankton Response to Climate Change Through Time-Series Data and Artistic Expression at JoVE.com

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.

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 ...

visualizing-oceanographic-data-to-depict-long-term-changes

Nanyang Technological University

Research

JoVE Journal

Environment

1.1K Views.  University of Rhode Island. Ik ben een planktonecoloog die zich richt op het karakteriseren van de effecten van snelle klimaatverandering op de ecologie en fysiologie van fytoplankton en zoöplankton. Planktonecologie vereist zowel een mix van traditionele als moderne benaderingen. Meer recent wordt planktonbeeldvormingstechnologie, naast microscopie, gebruikt om plankton te kwantificeren en te meten.Deze technologie verkort de tijd die nodig is om plankton handmatig te tellen door gebruik te maken van kunstmatig...

Video: Auteur in de schijnwerpers: onthulling van de reactie van plankton op klimaatverandering door middel van tijdreeksgegevens en artistieke expressie 

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 ...

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.

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 ...

Streamlined Sampling and Cultivation of the Pelagic Cosmopolitan Larvacean, Oikopleura dioica

Oikopleura dioica is a planktonic chordate with exceptional filter-feeding ability, rapid generation time, conserved early development, and a compact genome. For these reasons, it is considered a useful model organism for marine ecological studies, evolutionary developmental biology, and genomics. As research often requires a steady supply of animal resources, it is useful to establish a reliable, low-maintenance culture system. Here we describe a step-by-step method for establishing an O. dioica culture. We describe how to select potential sampling sites, collection methods, target animal identification, and the set-up of the culturing system. We provide troubleshooting advice based on our own experiences. We also highlight critical factors that help sustain a robust culture system. Although the culture protocol provided here is optimized for O. dioica, we hope our sampling technique and culture setup will inspire new ideas for maintaining other fragile pelagic invertebrates.

Streamlined Sampling and Cultivation of the Pelagic Cosmopolitan Larvacean, Oikopleura dioica

Oikopleura dioica is a tunicate model organism in various fields of biology. We describe sampling methods, species identification, culturing setup, and culturing protocols for the animals and algal feed. We highlight key factors that helped strengthen the culture system and discuss the possible problems and resolutions.

Oikopleura dioica is a planktonic chordate with exceptional filter-feeding ability, rapid generation time, conserved early development, and a compact ...

Using Generative Art to Convey Past and Future Climate Transitions

The ability to understand modern day climate relies on a foundational understanding of past climate variability and the ways in which the planet is stabilized by interconnected feedbacks. This article presents a unique method for translating records of past climate transitions preserved in deep-sea sediments to broad audiences through an immersive visualization. This visualization is a multimedia installation that incorporates geochemical records of glacial and interglacial transitions and model predictions for future anthropogenic warming to create an immersive experience for viewers, inviting them to engage with and reflect on the subtle, nuanced differences between subsets of Earth&#39;s history. This work showcases five intervals of time, beginning with the inception of modern glacial-interglacial cyclicity (~one million years ago), comparing past climate with model results for projected future anthropogenic warming (until 2099). The installation consists of several experimental projections, one for each subset of time, displayed on different surfaces in a room. As viewers move through the space, the projections slowly cycle through different climatic transitions, using animation methods like speed, color, layering, and repetition, all generated through site-specific data to convey the planet&#39;s unique behavior as it relates to global climate. This work provides a framework for unique scientific data visualization, with generative animations created using a Perlin Noise algorithm at the center of the installation. Research variables, like sea surface temperature, nutrient dynamics, and the rate of climate change, impact formal outcomes like color, scale, and animation speed, which are all easy to manipulate and connect to specific data. This approach also allows the possibility of publishing data online and provides a mechanism for scaling visual parameters to a wide variety of quantitative and qualitative data.

Here, a protocol is presented to visualize climate data as generative art.

The ability to understand modern day climate relies on a foundational understanding of past climate variability and the ways in which the planet is ...

Integrated Visualization of Biogeochemical Data for Nutrient Management

Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island

Primary productivity in the coastal regions, linked to eutrophication and hypoxia, provides a critical understanding of ecosystem function. Although primary productivity largely depends on riverine nutrient inputs, estimation of the extent of riverine nutrient influences in the coastal regions is challenging. A nitrogen mass balance model is a practical tool to evaluate coastal ocean productivity to understand biological mechanisms beyond data observations. This study visualizes the biological production zones in Narragansett Bay, Rhode Island, USA, where hypoxia frequently occurs, by applying a nitrogen mass balance model. The Bay is divided into three zones - brown, green, and blue zones - based on primary productivity, which are defined by the mass balance model results. Brown, green, and blue zones represent a high physical process, a high biological process, and a low biological process zone, depending on river flow, nutrient concentrations, and mixing rates. The results of this study can better inform nutrient management in the coastal ocean in response to hypoxia and eutrophication.

Author Spotlight: Understanding Riverine Nitrogen Impacts and Primary Productivity for Effective Nutrient Management

Here, we aim to visualize the zonation of biological productivity in Narragansett Bay, Rhode Island, based on the nitrogen mass balance model. The results will inform nutrient management in the coastal regions to reduce hypoxia and eutrophication.

Primary productivity in the coastal regions, linked to eutrophication and hypoxia, provides a critical understanding of ecosystem function. Although ...

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

Concentration of Metabolites from Low-density Planktonic Communities for Environmental Metabolomics using Nuclear Magnetic Resonance Spectroscopy

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each other at the biochemical level1. Nuclear magnetic resonance (NMR) spectroscopy is one of several technologies, including gas chromatography&#x2013;mass spectrometry (GC-MS), with considerable promise for such studies. Advantages of NMR are that it is suitable for untargeted analyses, provides structural information and spectra can be queried in quantitative and statistical manners against recently available databases of individual metabolite spectra2,3. In addition, NMR spectral data can be combined with data from other omics levels (e.g. transcriptomics, genomics) to provide a more comprehensive understanding of the physiological responses of taxa to each other and the environment4,5,6. However, NMR is less sensitive than other metabolomic techniques, making it difficult to apply to natural microbial systems where sample populations can be low-density and metabolite concentrations low compared to metabolites from well-defined and readily extractable sources such as whole tissues, biofluids or cell-cultures. Consequently, the few direct environmental metabolomic studies of microbes performed to date have been limited to culture-based or easily defined high-density ecosystems such as host-symbiont systems, constructed co-cultures or manipulations of the gut environment where stable isotope labeling can be additionally used to enhance NMR signals7,8,9,10,11,12. Methods that facilitate the concentration and collection of environmental metabolites at concentrations suitable for NMR are lacking. Since recent attention has been given to the environmental metabolomics of organisms within the aquatic environment, where much of the energy and material flow is mediated by the planktonic community13,14, we have developed a method for the concentration and extraction of whole-community metabolites from planktonic microbial systems by filtration. Commercially available hydrophilic poly-1,1-difluoroethene (PVDF) filters are specially treated to completely remove extractables, which can otherwise appear as contaminants in subsequent analyses. These treated filters are then used to filter environmental or experimental samples of interest. Filters containing the wet sample material are lyophilized and aqueous-soluble metabolites are extracted directly for conventional NMR spectroscopy using a standardized potassium phosphate extraction buffer2. Data derived from these methods can be analyzed statistically to identify meaningful patterns, or integrated with other omics levels for comprehensive understanding of community and ecosystem function.

A method for metabolite extraction from microbial planktonic communities is presented. Whole community sampling is achieved by filtration onto specially prepared filters. After lyophilization, aqueous-soluble metabolites are extracted. This approach allows for application of environmental metabolomics to trans-omics investigations of natural or experimental microbial communities.

Environmental metabolomics is an emerging field that is promoting new understanding in how organisms respond to and interact with the environment and each ...

Biology

Multicellular Spheroid Formation for FLIM-Based Metabolic and Hypoxia Analysis

Production and Multi-Parameter Live Cell Fluorescence Lifetime Imaging Microscopy (FLIM) of Multicellular Spheroids

Multicellular tumor spheroids are a popular 3D tissue microaggregate&#160;model for reproducing tumor microenvironment, testing and optimizing drug therapies&#160;and using bio- and nanosensors in a 3D context. Their ease of production, predictable size, growth, and observed nutrient and metabolite gradients are important to recapitulate the 3D niche-like&#160;cell microenvironment. However, spheroid heterogeneity and variability of their production methods can influence overall cell metabolism, viability, and drug response. This makes it difficult to choose the most appropriate&#160;methodology, considering the requirements in size, variability, needs of biofabrication, and use as in vitro 3D tissue models in stem and cancer cell biology. In particular, spheroid production can influence their compatibility with quantitative live microscopies, such as optical metabolic imaging, fluorescence lifetime imaging microscopy (FLIM), monitoring of spheroid&#160;hypoxia with nanosensors, or viability. Here, a number of conventional spheroid formation protocols are presented, highlighting their compatibility with the live widefield, confocal, and two-photon microscopies. The follow-up imaging to analysis&#160;pipeline with multiplexed autofluorescence FLIM and, using various types of cancer and stem cell spheroids, is also presented.

Author Spotlight: Standardizing Spheroid Formation Methods for Metabolic and Oxygenation Analysis Using Fluorescence Lifetime Imaging Microscopy

Here, we describe different multicellular spheroid formation methods to perform follow-up multi-parameter live cell microscopy. Using fluorescence lifetime imaging microscopy (FLIM), cellular autofluorescence, staining dyes, and nanoparticles, the approach for analysis of cell metabolism, hypoxia, and cell death in live three-dimensional (3D) cancer and stem cell-derived spheroids is demonstrated.

Multicellular tumor spheroids are a popular 3D tissue microaggregate&#160;model for reproducing tumor microenvironment, testing and optimizing drug ...

Bioengineering

ODELAY: A Large-scale Method for Multi-parameter Quantification of Yeast Growth

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, log-phase, and stationary-phase. Each growth phase can reveal different aspects of fitness that are related to various environmental and genetic conditions. High-resolution and quantitative measurements of all 3 phases of growth are generally difficult to obtain. Here we present a detailed method to characterize all 3 growth phases on solid media using an assay called One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). ODELAY quantifies growth phenotypes of individual cells growing into colonies on solid media using time-lapse microscopy. This method can directly observe population heterogeneity with each growth parameter in genetically identical cells growing into colonies. This population heterogeneity offers a unique perspective for understanding genetic and epigenetic regulation, and responses to genetic and environmental perturbations. While the ODELAY method is demonstrated using yeast, it can be utilized on any colony forming microorganism that is visible by bright field microscopy.

We present a method for quantifying growth phenotypes of individual yeast cells as they grow into colonies on solid media using time-lapse microscopy termed, One-cell Doubling Evaluation of Living Arrays of Yeast (ODELAY). Population heterogeneity of genetically identical cells growing into colonies can be directly observed and quantified.

Growth phenotypes of microorganisms are a strong indicator of their underlying genetic fitness and can be segregated into 3 growth regimes: lag-phase, ...

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 ...

Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton

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