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.

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

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 ...

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Research

JoVE Journal

Environment

1.1K 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.

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

Optimize Flue Gas Settings to Promote Microalgae Growth in Photobioreactors via Computer Simulations

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently than plants3, but also synthesize advanced biofuels2-4. Generally, atmospheric CO2 is not a sufficient source for supporting maximal algal growth5. On the other hand, the high concentrations of CO2 in industrial exhaust gases have adverse effects on algal physiology. Consequently, both cultivation conditions (such as nutrients and light) and the control of the flue gas flow into the photo-bioreactors are important to develop an efficient &ldquo;flue gas to algae&rdquo; system. Researchers have proposed different photobioreactor configurations4,6 and cultivation strategies7,8 with flue gas. Here, we present a protocol that demonstrates how to use models to predict the microalgal growth in response to flue gas settings. We perform both experimental illustration and model simulations to determine the favorable conditions for algal growth with flue gas. We develop a Monod-based model coupled with mass transfer and light intensity equations to simulate the microalgal growth in a homogenous photo-bioreactor. The model simulation compares algal growth and flue gas consumptions under different flue-gas settings. The model illustrates: 1) how algal growth is influenced by different volumetric mass transfer coefficients of CO2; 2) how we can find optimal CO2 concentration for algal growth via the dynamic optimization approach (DOA); 3) how we can design a rectangular on-off flue gas pulse to promote algal biomass growth and to reduce the usage of flue gas. On the experimental side, we present a protocol for growing Chlorella under the flue gas (generated by natural gas combustion). The experimental results qualitatively validate the model predictions that the high frequency flue gas pulses can significantly improve algal cultivation.

Flue gas from power plants is a cheap CO2 source for algal growth. We have built prototype &#34;flue gas to algal cultivation&#34; systems and described how to scale up the algal cultivation process. We have demonstrated the use of a mass-transfer bio-reaction model to simulate and to design the optimal operation of flue gas for the growth of Chlorella sp. in algal photo-bioreactors.

Flue gas from power plants can promote algal cultivation and reduce greenhouse gas emissions1. Microalgae not only capture solar energy more efficiently ...

A Small Volume Bioassay to Assess Bacterial/Phytoplankton Co-culture Using WATER-Pulse-Amplitude-Modulated (WATER-PAM) Fluorometry

Conventional methods for experimental manipulation of microalgae have employed large volumes of culture (20 ml to 5 L), so that the culture can be subsampled throughout the experiment1&#8211;7. Subsampling of large volumes can be problematic for several reasons: 1) it causes variation in the total volume and the surface area:volume ratio of the culture during the experiment; 2) pseudo-replication (i.e., replicate samples from the same treatment flask8) is often employed rather than true replicates (i.e., sampling from replicate treatments); 3) the duration of the experiment is limited by the total volume; and 4) axenic cultures or the usual bacterial microbiota are difficult to maintain during long-term experiments as contamination commonly occurs during subsampling.
The use of microtiter plates enables 1 ml culture volumes to be used for each replicate, with up to 48 separate treatments within a 12.65 x 8.5 x 2.2 cm plate, thereby decreasing the experimental volume and allowing for extensive replication without subsampling any treatment. Additionally, this technique can be modified to fit a variety of experimental formats including: bacterial-algal co-cultures, algal physiology tests, and toxin screening9&#8211;11. Individual wells with an alga, bacterium and/or co-cultures can be sampled for numerous laboratory procedures including, but not limited to: WATER-Pulse-Amplitude-Modulated (WATER-PAM) fluorometry, microscopy, bacterial colony forming unit (cfu) counts and flow cytometry. The combination of the microtiter plate format and WATER-PAM fluorometry allows for multiple rapid measurements of photochemical yield and other photochemical parameters with low variability between samples, high reproducibility and avoids the many pitfalls of subsampling a carboy or conical flask over the course of an experiment.

A Small Volume Bioassay to Assess Bacterial/Phytoplankton Co-culture Using WATER-Pulse-Amplitude-Modulated WATER-PAM Fluorometry

The goal of this procedure is to demonstrate the reproducibility and adaptability of using a microtiter plate format for microalgal screening. This rapid screen combines WATER-Pulse-Amplitude-Modulated (WATER-PAM) fluorometry to measure photosynthetic yield as an indicator of Photosystem II (PSII) health with small volume bacterial-algal co-cultures.

Conventional methods for experimental manipulation of microalgae have employed large volumes of culture (20 ml to 5 L), so that the culture can be ...

Experimental Protocol for Biodiesel Production with Isolation of Alkenones as Coproducts from Commercial Isochrysis Algal Biomass

The need to replace petroleum fuels with alternatives from renewable and more environmentally sustainable sources is of growing importance. Biomass-derived biofuels have gained considerable attention in this regard, however first generation biofuels from edible crops like corn ethanol or soybean biodiesel have generally fallen out of favor. There is thus great interest in the development of methods for the production of liquid fuels from domestic and superior non-edible sources. Here we describe a detailed procedure for the production of a purified biodiesel from the marine microalgae Isochrysis. Additionally, a unique suite of lipids known as polyunsaturated long-chain alkenones are isolated in parallel as potentially valuable coproducts to offset the cost of biodiesel production. Multi-kilogram quantities of Isochrysis are purchased from two commercial sources, one as a wet paste (80% water) that is first dried prior to processing, and the other a dry milled powder (95% dry). Lipids are extracted with hexanes in a Soxhlet apparatus to produce an algal oil (&#34;hexane algal oil&#34;) containing both traditional fats (i.e., triglycerides, 46-60% w/w) and alkenones (16-25% w/w). Saponification of the triglycerides in the algal oil allows for separation of the resulting free fatty acids (FFAs) from alkenone-containing neutral lipids. FFAs are then converted to biodiesel (i.e., fatty acid methyl esters, FAMEs) by acid-catalyzed esterification while alkenones are isolated and purified from the neutral lipids by crystallization. We demonstrate that biodiesel from both commercial Isochrysis biomasses have similar but not identical FAME profiles, characterized by elevated polyunsaturated fatty acid contents (approximately 40% w/w). Yields of biodiesel were consistently higher when starting from the Isochrysis wet paste (12% w/w vs. 7% w/w), which can be traced to lower amounts of hexane algal oil obtained from the powdered Isochrysis product.

Experimental Protocol for Biodiesel Production with Isolation of Alkenones as Coproducts from Commercial Isochrysis Algal Biomass

Detailed methods are presented for the production of biodiesel along with the co-isolation of alkenones as valuable coproducts from commercial Isochrysis microalgae.

The need to replace petroleum fuels with alternatives from renewable and more environmentally sustainable sources is of growing importance. ...

Methods of Soil Resampling to Monitor Changes in the Chemical Concentrations of Forest Soils

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental changes. Repeated sampling to monitor these changes in forest soils is a relatively new practice that is not well documented in the literature and has only recently been broadly embraced by the scientific community. The objective of this protocol is therefore to synthesize the latest information on methods of soil resampling in a format that can be used to design and implement a soil monitoring program. Successful monitoring of forest soils requires that a study unit be defined within an area of forested land that can be characterized with replicate sampling locations. A resampling interval of 5 years is recommended, but if monitoring is done to evaluate a specific environmental driver, the rate of change expected in that driver should be taken into consideration. Here, we show that the sampling of the profile can be done by horizon where boundaries can be clearly identified and horizons are sufficiently thick to remove soil without contamination from horizons above or below. Otherwise, sampling can be done by depth interval. Archiving of sample for future reanalysis is a key step in avoiding analytical bias and providing the opportunity for additional analyses as new questions arise.

Repeated soil sampling has recently been shown to be an effective way to monitor forest soil change over years and decades. To support its use, a protocol is presented that synthesizes the latest information on soil resampling methods to aid in the design and implementation of successful soil monitoring programs.

Recent soils research has shown that important chemical soil characteristics can change in less than a decade, often the result of broad environmental ...

Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt

The morphology and structure of plumbing systems can provide key information on the eruption rate and style of basalt lava fields. The most powerful way to study subsurface geo-bodies is to use industrial 3D&#160;reflection seismological imaging. However, strategies to image subsurface volcanoes are very different from that of oil and gas reservoirs. In this study, we process seismic data cubes from the Northern Tarim Basin, China, to illustrate how to visualize sills through opacity rendering techniques and how to image the conduits by time-slicing. In the first case, we isolated probes by the seismic horizons marking the contacts between sills and encasing strata, applying opacity rendering techniques to extract sills from the seismic cube. The resulting detailed sill morphology shows that the flow direction is from the dome center to the rim. In the second seismic cube, we use time-slices to image the conduits, which corresponds to marked discontinuities within the encasing rocks. A set of time-slices obtained at different depths show that the Tarim flood basalts erupted from central volcanoes, fed by separate pipe-like conduits.

Three-dimensional (3D) reflection seismology is a powerful method for imaging subsurface volcanoes. By using industrial 3D seismological data from the Tarim Basin, we illustrate how to extract the sills and the conduits of subsurface volcanoes from seismic data cubes.

The morphology and structure of plumbing systems can provide key information on the eruption rate and style of basalt lava fields. The most powerful way ...

Rearing and Long-Term Maintenance of Eristalis tenax Hoverflies for Research Studies

With an estimated 6000 species worldwide, hoverflies are ecologically important as alternative pollinators to domesticated honeybees. However, they are also a useful scientific model to study motion vision and flight dynamics in a controlled laboratory setting. As the larvae develop in organically polluted water, they are useful models for investigating investment in microbial immunity. While large scale commercial breeding for agriculture already occurs, there are no standardized protocols for maintaining captive populations for scientific studies. This is important as commercial captive breeding programs focusing on mass output during peak pollination periods may fail to provide a population that is consistent, stable and robust throughout the year, as is often needed for other research purposes. Therefore, a method to establish, maintain and refresh a captive research population is required. Here, we describe the utilization of an artificial hibernation cycle, in addition to the nutritional and housing requirements, for long term maintenance of Eristalis tenax. Using these methods, we have significantly increased the health and longevity of captive populations of E. tenax compared to previous reports. We furthermore discuss small scale rearing methods and options for optimizing yields and manipulating population demographics.

Rearing and Long-Term Maintenance of Eristalis tenax Hoverflies for Research Studies

The overall goal of these procedures is to establish, maintain and refresh a captive population of Eristalis tenax in a research setting.

With an estimated 6000 species worldwide, hoverflies are ecologically important as alternative pollinators to domesticated honeybees. However, they are ...

Visualizing Efficacy of Pesticides Against Disease Vector Mosquitoes in the Field

Efficacy of public health pesticides targeting nuisance and disease-vector insects such as mosquitoes, sand flies, and filth-breeding flies is not uniform across ecological zones. To best protect public and veterinary health from these insects, the environmental limitations of pesticides need to be investigated to inform effective use of the most appropriate pesticide formulations and techniques. We have developed a research program to evaluate combinations of pesticides, pesticide application equipment, and application techniques in hot-arid desert, hot-humid tropical, warm and cool temperate, and urban locations to derive pesticide use guidelines specific to target insect and environment. To these ends we designed a system of protocols to support efficient, cost-effective, portable, and standardized evaluation of a diverse range of pesticides and equipment across multiple environments. At the core of these protocols is the use of an array of small cages with colony-reared sentinel mosquitoes (adults and immatures) and sand flies (adults), strategically arranged in natural habitats and exposed to pesticide spray. Spatial and temporal patterns of pesticide efficacy are derived from percent mortality in sentinel cages, then mapped and visualized in a geographic information system. Maps of sentinel mortality data may be statistically compared to evaluate relative efficacy of a pesticide across multiple environments, or to study multiple pesticides in a single environment. Protocols may be modified to accommodate a variety of scenarios, including, for example, the vertical orientation of sentinels in canopy habitats or simultaneous testing of ground and aerial application methods.

The efficacy of public health pesticides targeting nuisance and disease-vector insects is not uniform across different ecological zones. Here we present a system of techniques using captive vector insects as sentinels for pesticide efficacy to derive electronic maps supporting the standard evaluation of pesticides across multiple environments.

Efficacy of public health pesticides targeting nuisance and disease-vector insects such as mosquitoes, sand flies, and filth-breeding flies is not uniform ...

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster (Nephrops norvegicus)

We present a protocol related to a video-tracking technique based on the background subtraction and image thresholding that makes it possible to individually track cohoused animals. We tested the tracking routine with four cohoused Norway lobsters (Nephrops norvegicus) under light-darkness conditions for 5 days. The lobsters had been individually tagged. The experimental setup and the tracking techniques used are entirely based on the open source software. The comparison of the tracking output with a manual detection indicates that the lobsters were correctly detected 69% of the times. Among the correctly detected lobsters, their individual tags were correctly identified 89.5% of the times. Considering the frame rate used in the protocol and the movement rate of lobsters, the performance of the video tracking has a good quality, and the representative results support the validity of the protocol in producing valuable data for research needs (individual space occupancy or locomotor activity patterns). The protocol presented here can be easily customized and is, hence, transferable to other species where the individual tracking of specimens in a group can be valuable for answering research questions.

Long-term Video Tracking of Cohoused Aquatic Animals: A Case Study of the Daily Locomotor Activity of the Norway Lobster Nephrops norvegicus

Here we present a protocol to individually track animals over a long period of time. It uses computer vision methods to identify a set of manually constructed tags by using a group of lobsters as case study, simultaneously providing information on how to house, manipulate, and mark the lobsters.

We present a protocol related to a video-tracking technique based on the background subtraction and image thresholding that makes it possible to ...

BtM, a Low-cost Open-source Datalogger to Estimate the Water Content of Nonvascular Cryptogams

Communities of nonvascular cryptogams, such as mosses or lichens, are an important part of the Earth's biodiversity, contributing to the regulation of the carbon and nitrogen cycles in many ecosystems. Being poikilohydric organisms, they do not actively control their internal water content and need a humid environment to activate their metabolism. Therefore, studying water relationships of nonvascular cryptogams is crucial to understand both their diversity patterns and their functions in the ecosystems. We present the BtM datalogger, a low-cost open-source platform for the study of the water content of nonvascular cryptogams. The datalogger is designed to measure ambient temperature, humidity, and conductance from up to eight samples simultaneously. We provide a design for a printed circuit board (PCB), a detailed protocol to assemble the components, and the required source code. All this makes the assembly of the BtM datalogger accessible to any research group, even to those without previous specialized knowledge. Therefore, the design presented here has the potential to help popularize the use of this type of device among ecologists and field biologists.

We present a simple and cost-effective method to build an open-source datalogger that measures the conductance of nonvascular cryptogams together with the environmental temperature and humidity. We describe the hardware design of the datalogger and provide step-by-step assembly instructions, the list of required open-source logging software, the code to run the datalogger, and a calibration protocol.

Communities of nonvascular cryptogams, such as mosses or lichens, are an important part of the Earth's biodiversity, contributing to the regulation of the ...

Composition and Distribution Analysis of Bioaerosols Under Different Environmental Conditions

Variable microorganisms in particulate matter (PM) under different environmental conditions may have significant impacts on human health. In this study, we described a protocol for multiple analyses of the biological compositions in environmental PM. Five experiments are presented: (1) PM number monitoring by using a laser particle counter; (2) PM collection by using a cyclonic aerosol sampler; (3) PM collection by using a high-volume air sampler with filters; (4) culturable microbes collection by the Andersen six-stage sampler; and (5) detection of biological composition of environmental PM by bacterial 16SrDNA and fungal ITS region sequencing. We selected hazy days and a livestock farm as two typical examples of the application in this protocol. In this study, these two sampling methods, cyclonic aerosol sampler and filter sampler, showed different sampling efficiency. The cyclonic aerosol sampler performed much better in terms of collecting bacteria, while these two methods showed the same efficiency in collecting fungi. Filter samplers can work under low temperature conditions while cyclonic aerosol samplers have a sampling limitation for temperature. A solid impacting sampler, such as an Andersen six-stage sampler, can be used to sample bioaerosols directly into the culture medium, which increases the survival rate of culturable microorganisms. However, this method mainly relies on culture while more than 99% of microbes cannot be cultured. DNA extracted from the culturable bacteria collected by the Andersen six-stage sampler and samples collected by cyclonic aerosol sampler and filter sampler were detected by bacterial 16S rDNA and fungal ITS region sequencing.All the methods above may have wide application in many fields of study, such as environmental monitoring and airborne pathogen detection. From these results, we can conclude that these methods can be used under different conditions and may help other researchers further explore the health impacts of environmental bioaerosols.

Understanding the biological composition of environmental particulate matter is important for the study of its significant impacts on human health and disease spread. Here, we used three types of bioaerosol sampling methods and a biological analysis of airborne microbes to better explore airborne microbial communities under different environmental conditions.

Variable microorganisms in particulate matter (PM) under different environmental conditions may have significant impacts on human health. In this study, ...