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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J., Robinson, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Climate+change+and+marine+plankton.">Climate change and marine plankton.</a> Trends in Ecology &amp; Evolution. 20 (6), 337-344 (2005).</li><li>Harvey, C. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+importance+of+long-term+ecological+time+series+for+integrated+ecosystem+assessment+and+ecosystem-based+management.">The importance of long-term ecological time series for integrated ecosystem assessment and ecosystem-based management.</a> Progress in Oceanography. 188, 102418 (2020).</li><li>Leeuwe, M. 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W., Muller-Karger, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Sea+change:+Charting+the+course+for+biogeochemical+ocean+time-series+research+in+a+new+millennium.">Sea change: Charting the course for biogeochemical ocean time-series research in a new millennium.</a> Deep Sea Research Part II: Topical Studies in Oceanography. 93, 2-15 (2013).</li><li>Bates, N. R., Johnson, R. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acceleration+of+ocean+warming,+salinification,+deoxygenation+and+acidification+in+the+surface+subtropical+North+Atlantic+Ocean.">Acceleration of ocean warming, salinification, deoxygenation and acidification in the surface subtropical North Atlantic Ocean.</a> Communications Earth &amp; Environment. 1 (1), 33 (2020).</li><li>Wolanski, E., Spagnol, S., Gentien, P., Spaulding, M., Prandle, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualization+in+Marine+Science.">Visualization in Marine Science.</a> Estuarine, Coastal and Shelf Science. 50 (1), 7-9 (2000).</li><li>United Nations. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Factsheet:+People+and+Oceans+(2017).">Factsheet: People and Oceans (2017).</a> , (2017).</li><li>Oviatt, C. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+changing+ecology+of+temperate+coastal+waters+during+a+warming+trend.">The changing ecology of temperate coastal waters during a warming trend.</a> Estuaries. 27 (6), 895-904 (2004).</li><li>Oviatt, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Managed+nutrient+reduction+impacts+on+nutrient+concentrations,+water+clarity,+primary+production,+and+hypoxia+in+a+north+temperate+estuary.">Managed nutrient reduction impacts on nutrient concentrations, water clarity, primary production, and hypoxia in a north temperate estuary.</a> Estuarine, Coastal and Shelf Science. 199, 25-34 (2017).</li><li>Fulweiler, R. W., Oczkowski, A. J., Miller, K. M., Oviatt, C. A., Pilson, M. E. Q. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Whole+truths+vs.+half+truths+-+And+a+search+for+clarity+in+long-term+water+temperature+records.">Whole truths vs. half truths - And a search for clarity in long-term water temperature records.</a> Estuarine, Coastal and Shelf Science. 157, A1-A6 (2015).</li><li>Trainer, V. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Pseudo-nitzschia+physiological+ecology,+phylogeny,+toxicity,+monitoring+and+impacts+on+ecosystem+health.">Pseudo-nitzschia physiological ecology, phylogeny, toxicity, monitoring and impacts on ecosystem health.</a> Harmful Algae. 14, 271-300 (2012).</li><li>Sterling, A. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Emerging+harmful+algal+blooms+caused+by+distinct+seasonal+assemblages+of+a+toxic+diatom.">Emerging harmful algal blooms caused by distinct seasonal assemblages of a toxic diatom.</a> Limnology and Oceanography. 67 (11), 2341-2359 (2022).</li><li>Roche, K. M., Sterling, A. R., Rynearson, T. A., Bertin, M. J., Jenkins, B. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Decade+of+Time+Series+Sampling+Reveals+Thermal+Variation+and+Shifts+in+Pseudo-nitzschia+Species+Composition+That+Contribute+to+Harmful+Algal+Blooms+in+an+Eastern+US+Estuary.">A Decade of Time Series Sampling Reveals Thermal Variation and Shifts in Pseudo-nitzschia Species Composition That Contribute to Harmful Algal Blooms in an Eastern US Estuary.</a> Frontiers in Marine Science. 9, 889840 (2022).</li><li>Li, . <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Qi+Data+visualization+as+creative+art+practice.">Qi Data visualization as creative art practice.</a> Visual Communication. 17 (3), 299-2222312 (2018).</li><li>Cloern, J. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Projected+Evolution+of+California's+San+Francisco+Bay-Delta-River+System+in+a+Century+of+Climate+Change.">Projected Evolution of California's San Francisco Bay-Delta-River System in a Century of Climate Change.</a> PLoS ONE. 6 (9), e24465 (2011).</li><li>Bashevkin, S. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Five+decades+(1972-2020)+of+zooplankton+monitoring+in+the+upper+San+Francisco+Estuary.">Five decades (1972-2020) of zooplankton monitoring in the upper San Francisco Estuary.</a> PLOS ONE. 17 (3), e0265402 (2022).</li></ol>

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 <em...

An erratum was issued for:&#160;<a href="https://www.jove.com/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/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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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.

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

Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton

I am a plankton ecologist focused on characterizing the effects of rapid climate change on phytoplankton and zooplankton ecology and physiology. Plankton ecology requires both a mix of traditional and modern approaches. More recently, plankton imaging technology, in addition to microscopy, is being used to quantify and measure plankton.This technology decreases the time it takes to manually count plankton by utilizing artificial intelligence to count and classify plankton for us. This research visualizes one of the longest plankton time series in the world. These data sets are critical for providing context to understanding shifts in plankton communities and food webs in regions affected by rapid climate change.My research has helped to visualize the mechanisms underpinning those shifts. Given the microscopic nature of plankton, it can be difficult, particularly for scientists to visualize in communicate how plankton communities are changing over time. My protocol aims to provide a clear method for visualizing shifts in plankton communities, leveraging scientific data within an artistic framework.This protocol will hopefully encourage the other scientists to think more creatively about how they visualize and present their data, particularly to non-sized audience. There is open gap between lab result and how they are interpreted and present in society. So, I hope this protocol will help reach this gap.My lab will continue to leverage plankton time series data sets to identify the environmental factors affecting long-term changes in plankton communities, as well as conduct physiological experiments in the lab to understand the physiologic mechanisms underpinning these community ships in the northeast United States and beyond.

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 th...

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

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 ...

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Converting Phytoplankton Images into Vector Graphics and Creating Phytoplankton Patterns

To begin, select phytoplankton microscopic images taken from the Narragansett Bay Long-Term Plankton Time Series as either JPEG, PNG, or PDF files. Place JPEG or PNG microscopic image into the Illustrator workspace. Go to View, then click Show Transparency Grid to reveal the checkerboard background indicating transparent areas.Next, click on Window, followed by Image Trace in the dropdown menu to open the Image Trace window. Under the Image Trace options, click Preset, then Black and White Logo, followed by Mode and Black and White. Then under Properties, select Expand to vectorize it.Choose the direct selection tool and click on the white space. Delete it, and then click File and Save As, and select EPS To save as a vector graphic. To create phytoplankton patterns, first, create a new artboard in a new workspace by clicking the square Artboard tool in the toolbar.Make three identical artboards of the same size by adjusting the size within Properties. Drag and drop the EPS files of the different phytoplankton taxa onto the artboard. Use the direct selection tool to draw a box around an individual phytoplankton.Under Properties, select Embed, then Fill, and color the phytoplankton with the desired color from the color palette, which is representative of the decade. Then use the black arrow selection tool to highlight a particular phytoplankton. Select Edit, then Copy, followed by Edit, and click on Paste.Paste each phytoplankton vector qualitatively based on the relative proportions of each taxa in the dataset for each of the three decades. Select Object, then Pattern, and Make to create a color swatch phytoplankton pattern for each of the three decades.

Creating Phytoplankton Panels from Biomass and Temperature Data

Begin by opening a JPEG or PNG figure of the chlorophyll a decadal biomass into the Illustrator workspace. Then vectorize each of the three chlorophyll a seasonal cycles by removing the background and saving the image as an EPS file. Then create a new art board in a new Illustrator workspace by clicking and dragging the square art board tool in the toolbar.Adjust the size by clicking on properties and transform to make three identical art boards of the same size. Drag and drop the chlorophyll a EPS files onto the three art boards. Go to properties and embed to add it to the new art board.Create a new layer by selecting the sticky note icon. Next, create a rectangle within the new layer with the rectangle tool from the toolbar. Fill the rectangle with a light blue gradient using the gradient tool from the toolbar.Use the line segment tool from the toolbar to create a box attached to the trend line. Make the line straight and aligned by holding the Shift button, then press the Ctrl key and select all the components, including the lines and trend line, within the layer. Then select object, followed by path and join.Repeat creating a rectangle and fill it with a blue to red color gradient to represent warming water temperature across the three decadal panels. Create a third rectangle of equal size and fill it with the phytoplankton swatch pattern corresponding to that decade. Place the grouped line segments within the layer of rectangles.Next, select the two smaller rectangles and the grouped line segments. Select object, then clipping mask, followed by make. This will remove the top fill of the shape.Repeat creating a rectangle and fill it with a red to blue color gradient to represent a warming water temperature across the three decadal panels. To add images of microscopic phytoplankton onto the phytoplankton patterns, select open and click on the image file to open it in the Illustrator. Then create a circle with the ellipse tool from the toolbar and overlay it on top of the phytoplankton image.Hold the Shift key to select both the shape and the image. Then in the menu, click on object, then clipping mask, followed by make to fill the shape with the image. Repeat for selected phytoplankton images, and distribute across the three decades to look like a magnifying glass zooming in on the illustrative phytoplankton.Now use the rectangle tool from the toolbar to create a text box on each of the decade art boards. Use the type tool to click and type information 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.Import the saved Illustrator file and select to only import the three completed decades. Select all and export as a PDF file. Print the panels on heavyweight matter paper and install with hanging hardware.Visualization of phytoplankton data showed a decline in phytoplankton biomass from the 1970s to 1990s to 2010s. All decades exhibited a bimodal peak in chlorophyll a concentration with the first peak occurring in winter and the second occurring in summer. Also, Thalassiosira and Skeletonema species increased from the 1970s to 1990s.Other taxa, such as Eucampia, oscillated in abundance, as shown by the increased presence of more Eucampia zodiacus images in the 2010s compared to the 1970s and 1990s.

865 Views.  University of Rhode Island.  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.

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

Summary