Name: visualization of productivity zones based on nitrogen mass balance model in narragansett bay, rhode island
Uploaded: 2023-07-14T13:40:00.000+00:00
Duration: 5 min 4 s
Description: Watch this Scientific Journal Video about Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island at JoVE.com

This study 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 also would like to thank the Rhode Island School of Design for developing the Vis-A-Thon project and this visualization.

1. Applying the N-mass balance model
<ol>
	<li>Download the dissolved inorganic nitrogen (DIN) data from the US Environmental Protection Agency (USEPA) for 166 stations in Narragansett Bay from 1990 to 2015. 
	NOTE: In this study, the sum of ammonium (NH4+), nitrite (NO2-), and nitrate (NO3-) concentrations were considered as the DIN concentration.</li>
	<li>Split the Narragansett Bay into fifteen boxes along its axis modified from the previous study14 using Adobe Illustrator to divide the Bay in the map (Figure 1).</li>
	<li>Apply the N-mass balance model to calculate the mean concentration of DIN at each box. 
	NOTE: In this study, the N-mass balance model, consisting of DIN input and output terms, was modified from previous studies2,15 and applied to each box (1-15) of the Narragansett Bay as Equation 1. 
	<img alt="Equation 7" src="/files/ftp_upload/65728/65728eq07v2.jpg" style="margin: auto;" />&#160; Eq. (1) 
	Table 1 shows the definitions of each term and unit used in this model of Narragansett Bay. The model calculates the mean DIN concentration by determining the difference in each box of Narragansett Bay, representing the net DIN removal by biological production. Detailed information on the N-mass balance model is shown in the previous studies2,15. The detailed values used in the model of this study were derived from the previous studies14.</li>
	<li>Calculate the potential primary production (PPP) rate based on the N-mass balance model results by converting the net DIN removal to carbon units using the Redfield ratio (C: N = 106: 16, molar ratio) in a spreadsheet file.</li>
</ol>
2. Visualizing three zones in the map of Narragansett Bay
<ol>
	<li>Plot the identified three zones in the map of Narragansett Bay as a contour plot using the Ocean Data View software.
	<ol>
		<li>Save the PPP rate data of each box as a text file (.txt) from the spreadsheet file. 
		NOTE: The .txt file also includes the location of each box number as latitude and longitude. Put the longitude as a negative value. The PPP rate data is labeled as PPP [gC&#183;m-2&#183;day-1].</li>
		<li>Load the PPP rate data into the Ocean Data View software.
		<ol>
			<li>Go to open in the File menu.</li>
			<li>Click Associate Variables Box, Latitude, Longitude with Station, latitude [degrees_north], and Longitude [degrees_east], in the Metadata Variable Association window, then click the OK button.</li>
			<li>Click the OK button in the Import window.</li>
		</ol>
		</li>
		<li>Draw the contour plot to show the PPP ranges in the map of Narragansett Bay.
		<ol>
			<li>Right-click on the map, click Zoom, drag the red box to zoom into the data area of the map, and then click on Enter.</li>
			<li>Click the 1 SCATTER window of the Layout Templates in the View menu.</li>
			<li>Right-click in the Sample panel and select Derived Variables.</li>
			<li>Click the Add button after selecting Latitude under Metadata from the list of Choices panel. Do the same thing for Longitude and then click the OK button.</li>
			<li>Select drvd: Longitude [degrees_East] as X-Variable by right-clicking on the scatter window.</li>
			<li>Select drvd: Latitude [degrees_North] as Y-Variable by right-clicking on the scatter window.</li>
			<li>Select PPP [gC&#183;m-2&#183;day -1] as Z-Variable by right-clicking on the scatter window.</li>
			<li>Select Properties by right-clicking on the scatter window and go to the Display Style option.
			<ol>
				<li>Select the Gridded field.</li>
				<li>Go to the Contours option and click the &#60;&#60; button to make 0, 0.1, and 2 values only remain in the Already Defined Panes of the left.</li>
				<li>Click the OK button.</li>
			</ol>
			</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Based on the contour plot from the Ocean Data View Software, define the edge of the brown, green, and blue zones in Narraganset Bay, and visualize the zones using Adobe Illustrator to plot three zones in the map. 
	NOTE: Following the previous study15, the PPP rate of the brown zone was over 2 gC&#183;m-2&#183;day-1, the green zone was between 0.1-2 gC&#183;m-2&#183;day-1, and the blue zone was less than 0.1 gC&#183;m-2&#183;day-1, respectively.</li>
</ol>
3. Converting the contour plot of three zones into the three-dimensional (3D) frame with LED light
<ol>
	<li>Etch three acrylic panels as 5.5&#39;&#39; x 8&#39;&#39; with a laser cutter to show the boundary of each zone.</li>
	<li>Stack three acrylic panels in an illuminated frame.Overlap each acrylic panel showing the blue, green, and brown zones. Place a panel showing green zones on top of the blue zones panel and a brown zones panel on top of that.</li>
	<li>For the second physical model, etch four acrylic sheets as 5.5&#39;&#39; x 8&#39;&#39; with a laser cutter, with the UV printed three boundaries of zones and one panel to represent the entire Narragansett Bay (as per steps 3.1-3.2).</li>
	<li>Change the color of each zone into brown, green, and blue using the LEDs placed at the bottom of the frame.</li>
</ol>

Integrated Visualization of Biogeochemical Data for Nutrient Management

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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=Eutrophication+of+Chesapeake+Bay:+historical+trends+and+ecological+interactions.">Eutrophication of Chesapeake Bay: historical trends and ecological interactions.</a> Marine Ecology Progress Series. 303, 1-29 (2005).</li><li>Brush, M. J., Malone, T. C., Malej, A., Faganeli, F., 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=.">.</a> Coastal Ecosystems in Transition: A Comparative Analysis of the Northern Adriatic and Chesapeake Bay. Chapter 5, (2021).</li><li>Howarth, R. W., Marino, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Nitrogen+as+the+limiting+nutrient+for+eutrophication+in+coastal+marine+ecosystems:+Evolving+views+over+three+decades.">Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: Evolving views over three decades.</a> Limnology and Oceanography. 51 (1 part 2), 364-376 (2006).</li><li>Paerl, H. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Controlling+eutrophication+along+the+freshwater-marine+continuum:+Dual+nutrient+(N+and+P)+reductions+are+essential.">Controlling eutrophication along the freshwater-marine continuum: Dual nutrient (N and P) reductions are essential.</a> Estuaries and Coasts. 32, 593-601 (2009).</li><li>Kim, J. S., Chapman, P., Rowe, G., DiMarco, S. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Categorizing+zonal+productivity+on+the+continental+shelf+with+nutrient-salinity+ratios.">Categorizing zonal productivity on the continental shelf with nutrient-salinity ratios.</a> Journal of Marine Systems. 206, 103336 (2020).</li><li>Rowe, G. T., Chapman, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Continental+shelf+hypoxia:+Some+nagging+questions.">Continental shelf hypoxia: Some nagging questions.</a> Gulf of Mexico Science. 20 (2), 153-160 (2002).</li><li>Nixon, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Eutrophication+and+the+macroscope.">Eutrophication and the macroscope.</a> Hydrobiologia. 629, 5-19 (2009).</li><li>Barbier, E. B., 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+value+of+estuarine+and+coastal+ecosystem+services.">The value of estuarine and coastal ecosystem services.</a> Ecological Monographs. 81 (2), 169-193 (2011).</li><li>Cloern, J. E., Foster, S. Q., Kleckner, A. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phytoplankton+primary+production+in+the+world's+estuarinecoastal+ecosystem.">Phytoplankton primary production in the world's estuarinecoastal ecosystem.</a> Biogeosciences. 11 (9), 2477-2501 (2014).</li><li>Codiga, D. L., Stoffel, H. E., Oviatt, C. A., Schmidt, C. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Managed+nitrogen+load+decrease+reduces+chlorophyll+and+hypoxia+in+warming+temperate+urban+estuary.">Managed nitrogen load decrease reduces chlorophyll and hypoxia in warming temperate urban estuary.</a> Frontiers in Marine Science. 9, 930347 (2022).</li><li>Sigman, D. M., Hain, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+biological+productivity+of+the+ocean.">The biological productivity of the ocean.</a> Nature Education Knowledge. 3 (10), 21 (2012).</li><li>Kremer, J. N., 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=Simulating+property+exchange+in+estuarine+ecosystem+models+at+ecologically+appropriate+scales.">Simulating property exchange in estuarine ecosystem models at ecologically appropriate scales.</a> Ecological Modelling. 221 (7), 1080-1088 (2010).</li><li>Kim, J. S., Chapman, P., Rowe, G., DiMarco, S. F., Thornton, D. C. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Implications+of+different+nitrogen+input+sources+for+potential+production+and+carbon+flux+estimates+in+the+coastal+Gulf+of+Mexico+(GOM)+and+Korean+Peninsula+coastal+waters.">Implications of different nitrogen input sources for potential production and carbon flux estimates in the coastal Gulf of Mexico (GOM) and Korean Peninsula coastal waters.</a> Ocean Science. 16, 45-63 (2020).</li><li>Lake, S. J., Brush, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+contribution+of+microphytobenthos+to+total+productivity+in+upper+Narragansett+Bay,+Rhode+Island.">The contribution of microphytobenthos to total productivity in upper Narragansett Bay, Rhode Island.</a> Estuarine, Coastal and Shelf Science. 95 (2-3), 289-297 (2011).</li><li>Brush, M. J., Nixon, S. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+the+role+of+macroalgae+in+a+shallow+sub-estuary+of+Narragansett+Bay,+RI+(USA).">Modeling the role of macroalgae in a shallow sub-estuary of Narragansett Bay, RI (USA).</a> Ecological Modelling. 221 (7), 1065-1079 (2010).</li><li>Deacutis, C. F., Murray, D., Prell, W., Saarman, E., Korhun, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hypoxia+in+the+upper+half+of+Narragansett+Bay,+RI,+during+August+2001+and+2002.">Hypoxia in the upper half of Narragansett Bay, RI, during August 2001 and 2002.</a> Northeastern Naturalist. 13 (Special Issue 4), 173-198 (2006).</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>Boesch, D. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Barriers+and+bridges+in+abating+coastal+eutrophication.">Barriers and bridges in abating coastal eutrophication.</a> Frontiers in Marine Science. 6, 123 (2019).</li><li>Oviatt, C. A., Keller, A. A., Reed, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Annual+primary+production+in+Narragansett+Bay+with+no+bay-wide+winter-spring+phytoplankton+bloom.">Annual primary production in Narragansett Bay with no bay-wide winter-spring phytoplankton bloom.</a> Estuarine, Coastal and Shelf Science. 54, 1013-1026 (2002).</li></ol>

The authors have no conflicts of interest to declare.

This study estimated the extent of nutrient impacts from riverine inputs in Narraganset Bay (NB) based on the N-mass balance model by defining the three theoretical zones. Historically, hypoxic zones appeared near the Providence River, the western side of Greenwich Bay, and Mount Hope Bay during the summer period18, which were defined as brown zones in this study. Moreover, the zonation of NB is comparable to the results of a previous study19, which examined nutrient concentration and primary production in NB. Both highlight the importance of nutrient reduction efforts. In addition, the boundaries of each zone in this study were similar to results from a previous study19, indicating that hypoxia in the upper bay of&#160;NB may be controlled by the advection of organic matter from the Providence River, yielding elevated respiration with high productivity over 2.6 gC&#183;m-2&#183;day-1. These results in the upper bay of NB were represented by the brown zone in this study. In addition, productivity continued to decrease towards the ocean, indicated by the green and blue zones.
In contrast, during the summer season, Mt. Hope Bay (box 10) was defined as the brown zone in this study, showing higher primary productivity over 2 gC&#183;m-2&#183;day-1 compared to the previous study19. This increased productivity indicates that other nutrient input sources, in addition to the riverine nitrogen input, might affect this region and should be considered as another DIN input term in N-mass balance models. The zonation in this study is expected to inform better management efforts for nutrients in NB aimed at reducing riverine nitrogen discharge as well as atmospheric nitrogen deposition, which has been highlighted in other estuarine systems, including the Chesapeake Bay2,20. Oviatt et al. (2002) found that mixing rate and light penetration influenced PPP21, but future work is needed to better quantify these factors attributed to high PPP in the brown zones.
Finally, by representing the three theoretical zones of NB as two physical frameworks, an improved understanding of the extent of riverine or other nutrient inputs to the coastal&#160;area is visually achieved. While frameworks can have fixed boundaries for each zone, in our framework, flexibility is additionally shown to inform that the three theoretical zones can change from month to month according to the nutrient concentrations of freshwater, mixing rate, and river flow, as pointed out from the previous applications of the N-mass balance model2,15. For example, several boxes in Figure 3 and Figure 4 were represented as mixed zones because they were categorized as different zones monthly during summer periods based on N-mass balance model results. The frameworks show the effect of riverine nutrients in NB by providing an integrated visualization of scientific biogeochemical data through an art form, which is useful for nutrient management in the coastal area and for scientific communication.

Primary productivity, the production of organic compounds by phytoplankton, fuels ecosystem food webs, and is important for understanding the system&#39;s function in response to environmental changes1,2. Estuarine primary productivity is also closely linked to eutrophication which is defined as excessive nutrients in the ecosystem1, causing several harmful consequences in the coastal regions, such as an overgrowth of phytoplankton leading to large algal blooms and subsequent hypoxia3,4. Importantly, primary productivity in estuaries is highly dependent on the riverine nutrient loading, particularly nitrogen concentrations, which are the typical limiting nutrient in most temperate ocean ecosystems5,6. However, an estimation of the extent of riverine nitrogen impacts in coastal areas remains challenging.
To estimate the estuarine primary productivity, a nitrogen (N) mass balance model is a useful tool to calculate nitrogen fluxes2. The N-mass balance model also provides an understanding of biological mechanisms beyond data observations, revealing information at the edges of different primary productivity zones7. Three different zones8, defined as brown, green, and blue zones, are particularly useful for predicting the impact of nutrient loading in hypoxic regions. The brown zone, defined as the nearest region of a river mouth, represents a high physical process, the green zone has high biological productivity, and the blue zone represents low biological process. The boundary of each zone depends on river flow, nutrient concentrations, and mixing rates8.
Narragansett Bay (NB) is a coastal, temperate estuary in Rhode Island, USA, supporting economic and ecological services and goods9,10,11, in which hypoxia has been consistently occurring. These hypoxic events, defined as the period of low dissolved oxygen (i.e., less than 2-3 mg of oxygen per liter), are particularly prevalent in July and August and are heavily impacted by riverine nitrogen loading during these months12. With an increase in primary production and hypoxia due to anthropogenic emissions of nutrients13, understanding the nitrogen inputs into NB is critical to managing and addressing coastal issues such as eutrophication and hypoxia. Thus, in this study, the rate of primary production in NB is calculated from the N-mass balance model using historically observed nutrient data, especially dissolved inorganic nitrogen (DIN). Based on the results of the N-mass balance model by converting to carbon units using the Redfield ratio, three different primary productivity zones were identified to visualize the extent of nitrogen influence from the river in NB. The model was then recreated into a 3D representation to better visualize the different zones. The products produced from this study can better inform nutrient management in NB in response to hypoxia and eutrophication. Further, results from this study are applicable to other coastal regions to visualize the effects of riverine transport on nutrients and primary productivity.

<table><tbody><tr><td>Adobe Illustrator&amp;nbsp;</td><td>Adobe</td><td>version 27.6.1</td><td>https://www.adobe.com/products/illustrator.html</td></tr><tr><td>Ampersand Gessobord Uncradled 1/8&quot; Profile 8&quot; x 8&quot;</td><td>Risdstore</td><td>70731053088</td><td>https://www.risdstore.com/ampersand-gessobord-8x8-flat-1-8-profile.html</td></tr><tr><td>Ocean Data View software</td><td></td><td></td><td>https://odv.awi.de/en/software/download/</td></tr><tr><td>W-Series (Wide) Flexible LED Strip Light - Ultra Bright (18 LEDs/foot)</td><td>aspectLED</td><td>SKU AL-SL-W-U</td><td>https://www.aspectled.com/products/w-wide-5050-ultra-bright?gclid=CjwKCAjwm4ukBhAuEiwA0z&lt;br/&gt; QxkyqisRPqBcHvXEW8KcJE-bK0d2cvGtqlOxXWJI_&lt;br/&gt; E2rd6DzttPR0FLRoCgfkQAvD_BwE</td></tr></tbody></table>

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.

Three theoretical zones of Narragansett Bay based on the N-mass balance model 
The three theoretical zones in Narragansett Bay (NB) were defined based on the N-mass balance model results, in which the DIN data were applied to fifteen boxes of NB, and then the mean DIN in each box was converted to the PPP rates for the summer period. As shown in Figure 2, based on the mean summer (June to September) PPP rates of each box, three (brown, green, and blue) zones in NB were identified as following the criteria of the PPP rates of each zone from the previous study15. During the summer period, boxes 1, 2, 5, 6, 7, and 10, mostly located near the river mouth, were defined as brown zones with high PPP rates greater than 2 gC&#183;m-2&#183;day-1, indicating there was a strong physical process and biological process with high turbidity and light limitation. Boxes 3, 4, 8, 9, and 11 were classified as green zones, having PPP ranges from 0.1-2 gC&#183;m-2&#183;day-1, where a strong biological process occurred, showing nutrient limitation and high primary production. Due to high turbidity in the brown zone, the penetration of light was limited, which was a significant difference from the green zone. In contrast, blue zones, with low PPP rates of less than 0.1 gC&#183;m-2&#183;day-1, were identified in boxes 12, 13, 14, and 15 and were the furthest offshore, representing low biological productivity.
Visualization of three zones of Narragansett Bay using physical frameworks 
To visually implement the boundaries of three theoretical zones in NB, a 3D representation was created in which layered acrylic panels were used and etched, creating two physical frameworks as described in section 3. As shown in Figure 3, three acrylic panels were used with the LED lights on the bottom of the frame, which can be changed to show a better representation of the characteristics of each boundary. In addition, dot-matrix patterns were etched with a different degree to represent the amount of sediment turbidity in each zone. Figure 4 shows the second physical framework with four acrylic sheets containing three boundaries of each zone, UV printed, and one layer etched to show the entire NB. The images of the development stage of the second framework are shown in Figure 4A, with three sheets representing each zone and one additional sheet showing the entire three zones. In Figure 4B, the second physical framework was illuminated by the LED lights and showed the overlaps of the boundaries for each zone.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/65728/65728fig01.jpg" /> 
Figure 1: Map of Narragansett Bay. The numbered segments show the 15 boxes along the axis, which is modified from a previous study14. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65728/65728fig01large.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/65728/65728fig02.jpg" /> 
Figure 2: Extent of the three theoretical zones in Narragansett Bay. Zones were defined based on the N-mass balance model results. Each zone is divided by the mean summer (June to September) potential primary production (PPP) rates, which are converted to the N-mass balance model results defined from the previous study15. The mean summer PPP rate of brown zones is over 2 gC&#183;m-2&#183;day-1, the green zones is between 0.1-2 gC&#183;m-2&#183;day-1, and the blue zones is less than 0.1 gC&#183;m-2&#183;day-1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65728/65728fig02large.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/65728/65728fig03.jpg" /> 
Figure 3: The first physical framework of the three zones in Narragansett Bay. The physical framework uses three acrylic panels and dot-matrix patterns to represent the amount of sediment turbidity in each zone. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65728/65728fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/65728/65728fig04.jpg" /> 
Figure 4: The second physical framework of three theoretical zones in Narragansett Bay. (A) The schematic images of the entire three zones in the Bay for UV printing and stacking of the second physical framework. (B) The framework created using four acrylic sheets to show overlaps of zones&#39; boundaries. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65728/65728fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Unit</td>
			<td>Definitions</td>
		</tr>
		<tr>
			<td><img alt="Equation 1" src="/files/ftp_upload/65728/65728eq01.jpg" style="margin: auto;" /></td>
			<td>DIN flux from each river discharge</td>
		</tr>
		<tr>
			<td><img alt="Equation 2" src="/files/ftp_upload/65728/65728eq02.jpg" style="margin: auto;" /></td>
			<td>Diffusive flux from atmospheric deposition</td>
		</tr>
		<tr>
			<td><img alt="Equation 3" src="/files/ftp_upload/65728/65728eq03.jpg" style="margin: auto;" /></td>
			<td>Benthic flux from the bottom sediments</td>
		</tr>
		<tr>
			<td><img alt="Equation 4" src="/files/ftp_upload/65728/65728eq04.jpg" style="margin: auto;" /></td>
			<td>Denitrification in the water column</td>
		</tr>
		<tr>
			<td><img alt="Equation 5" src="/files/ftp_upload/65728/65728eq05.jpg" style="margin: auto;" /></td>
			<td>An advection term which calculated from the current velocity</td>
		</tr>
		<tr>
			<td><img alt="Equation 6" src="/files/ftp_upload/65728/65728eq06.jpg" style="margin: auto;" /></td>
			<td>Removal by biological production</td>
		</tr>
	</tbody>
</table>
Table 1: Definitions of each term in N mass balance model. The detailed values used in the model were derived from previous studies14,16,17.

Watch this Scientific Journal Video about Visualization of Productivity Zones Based on Nitrogen Mass Balance Model in Narragansett Bay, Rhode Island at JoVE.com

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.

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 ...

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Nanyang Technological University

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Environment

331 Views.  University of Texas - Rio Grande Valley. Deze studie schat de omvang van de stikstofeffecten van rivieren en de primaire productiviteit voor een beter nutriëntenbeheer in Narragansett Bay, het kustgebied in Rhode Island, VS. Met een verhoogde primaire productie en hypoxie als gevolg van antropogene emissies van voedingsstoffen, is het van cruciaal belang om inzicht te krijgen in de stikstoftoevoer in de baai voor het beheersen en aanpakken van kustproblemen zoals eutrofiëring en hypoxie. Het blijft echter een uitdaging om de omvan...

Video: Auteur in de schijnwerpers: inzicht in de stikstofeffecten van rivieren en primaire productiviteit voor effectief nutriëntenbeheer

The Benthic Exchange of O2, N2 and Dissolved Nutrients Using Small Core Incubations

The measurement of sediment-water exchange of gases and solutes in aquatic sediments provides data valuable for understanding the role of sediments in nutrient and gas cycles. After cores with intact sediment-water interfaces are collected, they are submerged in incubation tanks and kept under aerobic conditions at in situ temperatures. To initiate a time course of overlying water chemistry, cores are sealed without bubbles using a top cap with a suspended stirrer. Time courses of 4-7 sample points are used to determine the rate of sediment water exchange. Artificial illumination simulates day-time conditions for shallow photosynthetic sediments, and in conjunction with dark incubations can provide net exchanges on a daily basis. The net measurement of N2 is made possible by sampling a time course of dissolved gas concentrations, with high precision mass spectrometric analysis of N2:Ar ratios providing a means to measure N2 concentrations. We have successfully applied this approach to lakes, reservoirs, estuaries, wetlands and storm water ponds, and with care, this approach provides valuable information on biogeochemical balances in aquatic ecosystems.

The Benthic Exchange of O2, N2 and Dissolved Nutrients Using Small Core Incubations

Here, we present small core incubations for the measurement of sediment-water gas and solute exchange. These will provide reliable measurements of sediment-water exchange that assess the role of sediment in influencing biological and biogeochemical processes in aquatic ecosystems.

The measurement of sediment-water exchange of gases and solutes in aquatic sediments provides data valuable for understanding the role of sediments in ...

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence the oxidizing capacity of the atmosphere through interactions with ozone and hydroxyl radicals. The main sink of NOx is the formation and deposition of nitric acid, a component of acid rain and a bioavailable nutrient. NOx is emitted from a mixture of natural and anthropogenic sources, which vary in space and time. The collocation of multiple sources and the short lifetime of NOx make it challenging to quantitatively constrain the influence of different emission sources and their impacts on the environment. Nitrogen isotopes of NOx have been suggested to vary amongst different sources, representing a potentially powerful tool to understand the sources and transport of NOx. However, previous methods of collecting atmospheric NOx integrate over long (week to month) time spans and are not validated for the efficient collection of NOx in relevant, diverse field conditions. We report on a new, highly efficient field-based system that collects atmospheric NOx for isotope analysis at a time resolution between 30 min and 2 hr. This method collects gaseous NOx in solution as nitrate with 100% efficiency under a variety of conditions. Protocols are presented for collecting air in urban settings under both stationary and mobile conditions. We detail the advantages and limitations of the method and demonstrate its application in the field. Data from several deployments are shown to 1) evaluate field-based collection efficiency by comparisons with in situ NOx concentration measurements, 2) test the stability of stored solutions before processing, 3) quantify in situ reproducibility in a variety of urban settings, and 4) demonstrate the range of N isotopes of NOx detected in ambient urban air and on heavily traveled roadways.

Automated, High-resolution Mobile Collection System for the Nitrogen Isotopic Analysis of NOx

Previous work suggests that the nitrogen isotopic composition of atmospheric nitrogen oxides might distinguish the influence of different sources in the environment. We report on an automated, mobile, field-based method for the high collection efficiency of atmospheric NOx for N isotopic analysis at an hourly time resolution.

Nitrogen oxides (NOx = NO + NO2) are a family of atmospheric trace gases that have great impact on the environment. NOx concentrations directly influence ...

Comparison of Scale in a Photosynthetic Reactor System for Algal Remediation of Wastewater

An experimental methodology is presented to compare the performance of two different sized reactors designed for wastewater treatment. In this study, ammonia removal, nitrogen removal and algal growth are compared over an 8-week period in paired sets of small (100 L) and large (1,000 L) reactors designed for algal remediation of landfill wastewater. Contents of the small and large scale reactors were mixed before the beginning of each weekly testing interval to maintain equivalent initial conditions across the two scales. System characteristics, including surface area to volume ratio, retention time, biomass density, and wastewater feed concentrations, can be adjusted to better equalize conditions occurring at both scales. During the short 8-week representative time period, starting ammonia and total nitrogen concentrations ranged from 3.1-14 mg NH3-N/L, and 8.1-20.1 mg N/L, respectively. The performance of the treatment system was evaluated based on its ability to remove ammonia and total nitrogen and to produce algal biomass. Mean &#177; standard deviation of ammonia removal, total nitrogen removal and biomass growth rates were 0.95&#177;0.3 mg NH3-N/L/day, 0.89&#177;0.3 mg N/L/day, and 0.02&#177;0.03 g biomass/L/day, respectively. All vessels showed a positive relationship between the initial ammonia concentration and ammonia removal rate (R2=0.76). Comparison of process efficiencies and production values measured in reactors of different scale may be useful in determining if lab-scale experimental data is appropriate for prediction of commercial-scale production values.

An experimental methodology is presented to compare the performance of small (100 L) and large (1,000 L) scale reactors designed for algae remediation of landfill wastewater. System characteristics, including surface area to volume ratio, retention time, biomass density, and wastewater feed concentrations, can be adjusted based on application.

An experimental methodology is presented to compare the performance of two different sized reactors designed for wastewater treatment. In this study, ...

Methods for Image-based Surveys of Benthic Macroinvertebrates and Their Habitat Exemplified by the Drop Camera Survey for the Atlantic Sea Scallop

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the approach more practical than in the past. Image-based surveys allow for initial samples to be revisited and are non-invasive compared to traditional survey methods that typically involve nets or dredges. Protocols for image-based surveys can vary greatly but should be driven by target species behavior and survey objectives. To demonstrate this, we describe our most recent methods for an Atlantic sea scallop (Placopecten magellanicus) drop camera survey to provide a procedural example and representative results. The procedure is divided into three critical steps that include survey design, data collection, and data products. The influence of scallop behavior and the survey goal of providing an independent assessment of the U.S. sea scallop resource on the survey procedure are then discussed in the context of generalizing the method. Overall, the broad applicability and flexibility of the University of Massachusetts Dartmouth School for Marine Science and Technology (SMAST) drop camera survey demonstrates the method could be generalized and applied to a variety of sessile invertebrates or habitat focused research.

Image based surveying is an increasingly practical, non-invasive method to sample the marine environment. We present the protocol of a drop camera survey that estimates the abundance and distribution of the Atlantic sea scallop (Placopecten magellanicus). We discuss how this protocol can be generalized for application to other benthic macroinvertebrates.

Underwater imaging has long been used in the field of marine ecology but decreasing costs of high-resolution cameras and data storage have made the ...

Design and Use of an Apparatus for Quantifying Bivalve Suspension Feeding at Sea

As shellfish aquaculture moves from coastal embayments and estuaries to offshore locations, the need to quantify ecosystem interactions of farmed bivalves (i.e., mussels, oysters, and clams) presents new challenges. Quantitative data on the feeding behavior of suspension-feeding mollusks is necessary to determine important ecosystem interactions of offshore shellfish farms, including their carrying capacity, the competition with the zooplankton community, the availability of trophic resources at different depths, and the deposition to the benthos. The biodeposition method is used to quantify feeding variables in suspension-feeding bivalves in a natural setting and represents a more realistic proxy than laboratory experiments. This method, however, relies upon a stable platform to satisfy the requirements that water flow rates supplied to the shellfish remain constant and the bivalves are undisturbed. A flow-through device and process for using the biodeposition method to quantify the feeding of bivalve mollusks were modified from a land-based format for shipboard use by building a two-dimensional gimbal table around the device. Planimeter data reveal a minimal pitch and yaw of the chambers containing the test shellfish despite boat motion, the flow rates within the chambers remain constant, and operators are able to collect the biodeposits (feces and pseudofeces) with sufficient consistency to obtain accurate measurements of the bivalve clearance, filtration, selection, ingestion, rejection, and absorption at offshore shellfish aquaculture sites.

A flow-through device for using the biodeposition method to quantify filtration and feeding behavior of bivalve mollusks was modified for shipboard use. A two-dimensional gimbal table built around the device isolates the apparatus from boat motion, thereby allowing the accurate quantification of bivalve filtration variables at offshore shellfish aquaculture sites.

As shellfish aquaculture moves from coastal embayments and estuaries to offshore locations, the need to quantify ecosystem interactions of farmed bivalves ...

An Optimized Rhizobox Protocol to Visualize Root Growth and Responsiveness to Localized Nutrients

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive harvest or expensive equipment. We present a customizable and affordable rhizobox method that allows the non-destructive visualization of root growth over time and is particularly well-suited to studying root plasticity in response to localized resource patches. The method was validated by assessing maize genotypic variation in plasticity responses to patches containing 15N-labeled legume residue. Methods are described to obtain representative developmental measurements over time, measure root length density in resource-containing and control patches, calculate root growth rates, and determine 15N recovery by plant roots and shoots. Advantages, caveats, and potential future applications of the method are also discussed. Although care must be taken to ensure that experimental conditions do not bias root growth data, the rhizobox protocol presented here yields reliable results if carried out with sufficient attention to detail.

Visualizing and measuring root growth in situ is extremely challenging. We present a customizable rhizobox method to track root development and proliferation over time in response to nutrient enrichment. This method is used to analyze maize genotypic differences in root plasticity in response to an organic nitrogen source.

Roots are notoriously difficult to study. Soil is both a visual and mechanical barrier, making it difficult to track roots in situ without destructive ...

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

This protocol is an example of utilizing the eddy covariance (EC) technique to investigate spatially and temporally averaged net CO2 fluxes (net ecosystem production, NEP), in non-typical ecosystems, on a currently reforested windthrow area in Poland. After a tornado event, a relatively narrow &#8220;corridor&#8221; was created within surviving forest stands, which complicates such kind of experiments. The application of other measuring techniques, such as the chamber method, is even more difficult under these circumstances, because especially at the beginning, fallen trees and in general great heterogeneity of the site provide a challenging platform to perform flux measurements and then to properly upscale obtained results. In comparison with standard EC measurements carried out in untouched forests, the case of windthrow areas requires special consideration when it comes to the site location and data analysis in order to ensure their representativeness. Therefore, here we present a protocol of real-time, continuous CO2 flux measurements at a dynamically changing, non-ideal EC site, which includes (1) site location and instrumentation setup, (2) flux computation, (3) rigorous data filtering and quality control, and (4) gap filling and net fluxes partitioning into CO2 respiration and absorption. The main advantage of the described methodology is that it provides a detailed description of the experimental setup and measurement performance from scratch, which can be applied to other spatially limited ecosystems. It can also be viewed as a list of recommendations on how to deal with unconventional site operation, providing a description for non-specialists. Obtained quality-checked, gap filled, half-hour values of net CO2, as well as absorption and respiration fluxes, can be finally aggregated into daily, monthly, seasonal or annual totals.

Measurements of CO2 Fluxes at Non-Ideal Eddy Covariance Sites

The presented protocol uses the eddy covariance method at non-typical locations, applicable to all types of short-canopy ecosystems with limited area, on a currently reforested windthrow site in Poland. Details of measuring site setup rules, flux calculations and quality control, and final result analysis, are described.

This protocol is an example of utilizing the eddy covariance (EC) technique to investigate spatially and temporally averaged net CO2 fluxes (net ecosystem ...

Measurement of the Potential Rates of Dissimilatory Nitrate Reduction to Ammonium Based on 14NH4+/15NH4+ Analyses via Sequential Conversion to N2O

The importance of understanding the fate of nitrate (NO3&#8722;), which is the dominant N species transferred from terrestrial to aquatic ecosystems, has been increasing because global nitrogen loads have dramatically increased following industrialization. Dissimilatory nitrate reduction to ammonium (DNRA) and denitrification are both microbial processes that use NO3&#8722; for respiration. Compared to denitrification, quantitative determinations of the DNRA activity have been carried out only to a limited extent. This has led to an insufficient understanding of the importance of DNRA in NO3&#8722; transformations and the regulating factors of this process. The objective of this paper is to provide a detailed procedure for the measurement of the potential DNRA rate in environmental samples. In brief, the potential DNRA rate can be calculated from the 15N-labeled ammonium (15NH4+) accumulation rate in 15NO3&#8722; added incubation. The determination of the 14NH4+ and 15NH4+ concentrations described in this paper is comprised of the following steps. First, the NH4+ in the sample is extracted and trapped on an acidified glass filter as ammonium salt. Second, the trapped ammonium is eluted and oxidized to NO3&#8722; via persulfate oxidation. Third, the NO3&#8722; is converted to N2O via an N2O reductase deficient denitrifier. Finally, the converted N2O is analyzed using a previously developed quadrupole gas chromatography&#8211;mass spectrometry system. We applied this method to salt marsh sediments and calculated their potential DNRA rates, demonstrating that the proposed procedures allow a simple and more rapid determination compared to previously described methods.

Measurement of the Potential Rates of Dissimilatory Nitrate Reduction to Ammonium Based on 14NH4+/15NH4+ Analyses via Sequential Conversion to N2O

A series of methods to determine the potential DNRA rate based on 14NH4+/15NH4+ analyses is provided in detail. NH4+ is converted into N2O via several steps and analyzed using quadrupole gas chromatography&#8211;mass spectrometry.

The importance of understanding the fate of nitrate (NO3&#8722;), which is the dominant N species transferred from terrestrial to aquatic ecosystems, has ...

A Low-Cost Method of Measuring the In Situ Primary Productivity of Periphyton Communities of Lentic Waters

Measuring the in situ primary productivity of periphyton during the growing season gradient can elucidate the quantitative effect of environmental drivers (mainly phosphorus concentration and light intensity) and species composition on primary productivity. Primary productivity is mainly driven by light intensity, temperature, availability of nutrients, and distribution of the ionic species of the carbonate system in the respective depths of the euphotic zone. It is a complex system that is very difficult to simulate in the laboratory. This cheap, transportable, and easy-to-build floating barge allows measuring the primary productivity accurately-directly under the actual natural conditions. The methodology is based on measuring the primary productivity in real time using noninvasive oxygen sensors integrated into tightly sealed glass jars, enabling online oxygen flux monitoring and providing new insights into metabolic activities. Detailed seasonal in situ measurements of gross primary productivity of microbial mats (or other benthic organisms) can improve current knowledge of the processes controlling primary productivity dynamics in lentic waters.

A Low-Cost Method of Measuring the In Situ Primary Productivity of Periphyton Communities of Lentic Waters

Presented here is a cost-effective and transportable method/facility for measuring the primary productivity of microbial mats under actual in situ environmental temperature and light conditions. The experimental setup is based on widely available materials and can be used under various conditions while offering the advantages of laboratory-based models.

Measuring the in situ primary productivity of periphyton during the growing season gradient can elucidate the quantitative effect of environmental drivers ...

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

Visualizing Oceanographic Data to Depict Long-term Changes in Phytoplankton

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

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

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

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

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

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