The authors acknowledge the Water Quality Analysis Laboratory at the University of New Hampshire for assistance with sample analysis. The authors also thank two anonymous reviewers whose comments have helped to improve the manuscript. This work is funded by the National Science Foundation (DEB-1556603). Partial funding was also provided by the EPSCoR Ecosystems and Society Project (NSF EPS-1101245), New Hampshire Agricultural Experiment Station (Scientific Contribution #2662, USDA National Institute of Food and Agriculture (McIntire-Stennis) Project (1006760), the University of New Hampshire Graduate School, and the New Hampshire Water Resources Research Center.

1. Identifying and Characterizing the Ideal Experimental Stream Reach
<ol>
	<li>Ensure that experimental stream reaches are long enough to promote complete mixing of solutes11 and long enough where biological uptake can occur. Reach lengths can vary among streams and experiments. In small first-order headwater streams, reach length may vary from 20-150 m (or longer if the system requires it) depending on discharge and other physical properties of the stream.
	<ol>
		<li>Exclude large pools from experimental reaches, as they retard the downstream movement of solutes, minimal flow sections, and tributaries that dilute the added solution. Times of low discharge may require shortening the reach length while higher discharge may require a longer reach.</li>
		<li>Identify a location at the top of the experimental stream reach above a riffle to facilitate mixing of the added solutes. This will be the addition site. At the bottom of the experimental stream reach, identify a location where flow is constricted and representative of about 90% of the total flow (Figure 1). This will be the sample collection site.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/54704/54704fig1.jpg" /> 
Figure 1: Example of Downstream Sampling Site. An ideal sampling site is where the majority of flow is constricted and easily accessible without disturbance of the stream channel and benthos. Here a fallen piece of wood debris has created this sampling point in a small first-order headwater stream. <a href="https://www.jove.com/files/ftp_upload/54704/54704fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Obtain discharge measurement and background nutrient concentrations of the solutes of interest prior to experiments in order to calculate the mass of solutes needed for the manipulations. Please see calculations in step 2.2.1.
	<ol>
		<li>Obtain background concentration data for the target solute of manipulation (e.g. NO3-) and chloride (Cl-) which is often used as the conservative tracer. Use the conservative tracer in the context of these experiments, to track changes in conductivity, which indicate the arrival of the nutrient pulse at the sampling station and the rate in which the pulse is passing through. Conductivity, or specific conductance, is a surrogate for in-situ changes in concentration of the conservative tracer.</li>
		<li>Characterize the physio-chemical properties of the experimental reach by collecting ancillary data such as reach width and depth, temperature, pH and dissolved oxygen.
		<ol>
			<li>Perform measurements that cannot be made with the use of an environmental probe (e.g. width and depth), the day before or immediately after the experiment in order to minimize any benthic or chemical disturbance within the stream channel. Divide the experimental reach in equidistant transects (e.g. every 10 m) where width and at least 3 depth measurements can be assessed (e.g. right bank, thalweg, and left bank). These data are valuable to connect the physical properties of a stream to biogeochemical measurements and if the researchers are also interested in calculating nutrient uptake kinetics and parameters6.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
2. Preparation for Experiment
<ol>
	<li>Determine the mass (kg) of solute required for manipulation using the outlined equations below. 
	Note: The example below applies to a nitrate-based experiment with NO3- in the form of sodium nitrate (NaNO3) and assumes a targeted increase of 3x above background (equations are based on those of Kilpatrick and Cobb12). In this example the following assumptions have been made with respect to background conditions: discharge = 10 L/sec; [Cl] = 10 mg/L; [NO3-] = 50 &#181;g N/L. Due to variation among experiments, adjust required input data.
	<ol>
		<li>Calculate the Targeted increase (Equation 1): 
		Targeted [NO3- &#956;g N/L] increase = expected background [NO3- &#956;g N/L] * targeted increase 
		150 &#956;g N/L = 50 &#956;g N/L * 3</li>
		<li>Calculate the total atomic mass flux (Equation 2): 
		Total atomic mass flux (NO3- &#956;g N) = 30 min * 60 sec * Q(L/sec) * targeted [NO3- &#956;g N/L] increase 
		Where 30 minutes is the assumed duration of solute peak12 and Q is discharge 
		2 700 000 &#956;g N = 30 min * 60 sec * 10 L/s * 150 &#956;g N/L</li>
		<li>Calculate the total molecular mass flux (Equation 3): 
		Total molecular mass flux (NO3- &#956;g N) = total atomic mass flux (NO3- &#956;g N)/atomic mass (14) * molecular weight (85) 
		Where atomic mass refers to N and molecular weight refers to NaNO3. 
		16,392,857.14 &#956;g N = 2,700,000 &#956;g N/(14 * 85)&#160;</li>
		<li>Calculate the mass to add (Equation 4): 
		Mass to add (g) = total molecular mass flux (NO3- &#956;g N)/1,000,000 g/&#956;g 
		16.39 g NaNO3 = 16,392,857.14 &#956;g N/1,000,000 g/&#956;g 
		Note: Follow the above calculations for any other solutes including the conservative tracer (e.g. sodium chloride). Make sure to adjust the atomic and molecular masses for the solute of interest.</li>
	</ol>
	</li>
	<li>Prepare all solutes one day prior to field experiments. Weigh out enough solutes to raise the ambient concentration of both the biological tracer and the conservative tracer three times (or desired amount) above background. It is important that the quantity of added solutes causes a measureable change above background concentration that is sufficient to create a wide dynamic range in added nutrient concentration.
	<ol>
		<li>Weigh solutes using analytical scales and subsequently store in clean acid-washed high-density polyethylene bottles with appropriate labels. Examples of biological tracers include: NO3-: sodium nitrate (NaNO3); NH4+: ammonium chloride (NH4Cl); PO4-3: potassium phosphate (K2HPO4). However, the choice of biological tracer will be a function of the biogeochemical question being asked. Options for conservative tracers include sodium chloride (NaCl) and sodium bromide (NaBr).</li>
	</ol>
	</li>
	<li>Collect remaining materials: field book, labeling tape and pen, field measuring tape, cooler, conductivity meter, ~ 20 L bucket and large stirring rod (e.g. beer paddle, rebar, large stick), approximately 50 clean and acid-washed 125 ml high-density polyethylene bottles. Label the 125 ml bottles #1-50. 
	Note: Less than 50 samples can be taken per experiment and background samples are included in the 50 total bottles.</li>
	<li>Optional: Depending on the numbers of field personnel, perform sample filtering on-site (see section #5). If this option is chosen, bring 50 clean, pre-labeled and acid-washed 60 ml high-density polyethylene bottles into the field. Label the 60 ml bottles #1-50 to match the 125 ml collection bottles.</li>
</ol>
3. Day of Set Up
<ol>
	<li>Deploy the field conductivity meter at the collection site. Place the instrument upstream (approximately 0.5-1.0 m) of where samples will be taken so sample collection does not interfere with instrument readings. The meter will remain in place throughout the experiment. A field conductivity meter is best as it provides real-time conductivity readings, which are required to determine the sampling rate (see step 5.2) and filtration and analysis order (steps 5.3 and 6.1).</li>
	<li>Collect 125 ml background samples in triplicate at the addition site and at the collection site of the experimental reach prior to the addition of the solution. These data will be used to verify day-of ambient concentration and to determine variation in solute concentration along the stream reach. These data are also valuable to connect ambient stream chemistry (e.g. DOC: NO3- ratios13) to the biogeochemical measurements of interest.</li>
	<li>Record the time and conductivity of background samples collected.</li>
	<li>Record the background conductivity of stream prior to addition of solutions.</li>
</ol>
4. Adding Solutes
<ol>
	<li>Pour all reagents (16.39 g NaNO3 and 1483 g NaCl) into a large container (e.g. 20 L bucket) and add enough stream water to completely dissolve the solutes. Rinse reagent vessels thrice with additional stream water and pour rinse into solution container. Keep track of amount of water added.
	<ol>
		<li>For example, use a 500 ml bottle to pour stream water into the container. Stir solution until all reagents have been completely dissolved.</li>
	</ol>
	</li>
	<li>Collect 60 ml aliquot of the addition solution. Keep this highly concentrated sample separate (e.g. zip-lock bag) from all other samples to minimize cross contamination. Such samples are important if calculating nutrient uptake kinetics6 is an additional goal of the research project as these samples can be used to determine the exact mass of solutes added.</li>
	<li>Pour solution into addition site. Do this by pouring the solution in a smooth and quick motion to minimize travel lag time and splashing that could reduce the amount of reagents added. Rinse the container and the stir stick three times in the stream immediately after the addition to guarantee all reagents have been added to the stream.
	<ol>
		<li>Record the time the solution was added: hr:min:sec.</li>
		<li>Record the masses of tracers added (e.g. NaNO3 and NaCl).</li>
		<li>After the solution has been added, do not disturb the stream. Make sure that all travel along the stream occurs on the banks to ensure that the stream benthos and the solution itself are not disturbed.</li>
	</ol>
	</li>
</ol>
5. Field Sampling
<ol>
	<li>Order sampling bottles in ascending order while waiting for solution to arrive to the sampling location. Travel time will be a function of discharge and reach length and can be determined ahead of time (one-day prior) either with a NaCl-only injection or rhodamine dye (which can be used to establish travel time14). 
	Note: If working on a DON-themed project, avoid using rhodamine dye as it is a type of DON and will therefore alter the ambient DON pool if any remains in the study reach.</li>
</ol>
<img alt="Figure 2" src="/files/ftp_upload/54704/54704fig2.jpg" /> 
Figure 2: Example Schematic of Solute Breakthrough Curve (BTC). A BTC represents changes in solute concentration over time and can be used to explain the transit and biogeochemical cycling of a tracer in a stream. Grab samples should be taken across the BTC with a frequency that gives equal representation to both the ascending and descending limbs of the BTC. <a href="https://www.jove.com/files/ftp_upload/54704/54704fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<ol start="2">
	<li>Sample Collection 
	Note: The over-arching objective of sample collection, is to adequately represent changes in solute concentration along both the rising and falling limbs of the break through curve (BTC) (Figure 2).
	<ol>
		<li>Upon arrival of the solution (detected via an increase in conductivity), collect samples in 125 ml bottles throughout the BTC by holding a 125 ml bottle into the main flow of water at the sampling point. Quickly rinse the bottle with stream water and discard rinse downstream and then take sample. Cap sample and place into cooler.</li>
		<li>Record the time (hr:min:sec) and conductivity of each sample taken along the BTC into a field book (Table 1).</li>
		<li>Collect samples based on time (e.g. 1 min intervals) or based on the rate at which conductivity changes. For example, if conductivity is changing quickly, sample every 30-60 sec until changes in conductivity slow, at which time samples can be taken every 5-10 min. For intervals based on conductivity, take samples every 15-30 units depending on the rate at which conductivity is changing.</li>
		<li>Sample until conductivity returns to background or within 5 &#181;S/cm of background conditions. Intervals of sample collection can be adjusted during the experiment as long as the BTC is well represented in the grab samples.</li>
	</ol>
	</li>
</ol>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Bottle #</td>
			<td>Specific Conductance</td>
			<td>Time</td>
			<td>Notes</td>
		</tr>
		<tr>
			<td>1</td>
			<td></td>
			<td>hr:min:sec</td>
			<td>e.g. background (downstream)</td>
		</tr>
		<tr>
			<td>2</td>
			<td></td>
			<td></td>
			<td>e.g. background (downstream)</td>
		</tr>
		<tr>
			<td>3</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>4</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>5</td>
			<td></td>
			<td></td>
			<td>e.g. sample at peak conductance</td>
		</tr>
		<tr>
			<td>.</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>.</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>.</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Highest Bottle #</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1PField book: Example Page from Lab Book and Required Information
<ol start="3">
	<li>Sample Filtering 
	Note: Filtering of samples can occur either in the field or upon returning to the laboratory.
	<ol>
		<li>Filter samples from the rising limb in order of ascending specific conductivity until the peak in specific conductivity. Wait for the experiment to be over and filter samples from the falling limb in ascending order of specific conductivity (i.e. start with the last sample and work backwards toward peak specific conductivity). 
		Note: This order of samples minimizes cross contamination between samples and allows for the same filter, syringe, and filter holder to be used as long as the filter, syringe and filter holder are appropriately rinsed in between each sample (see steps 5.3.2-5.3.4).</li>
		<li>Remove plunger from a 60 ml syringe and then close stop-cock. Pour ~10 ml of sample into syringe and return plunger to syringe. Shake syringe so that sample rinses internal walls of syringe. Attached syringe to filter-holder and open stop-cock. Push sample through filter-holder and discard rinse.</li>
		<li>Remove plunger and close stop-cock. Pour ~30 ml of sample into syringe and return plunger to syringe. Open stock-cock and expel ~10 ml through filter-holder and into 60 ml sampling bottle. Cap the bottle, swirl with filtrate and discard. Repeat this step for a total of 3 rinses. This will ensure any impurities have been removed from the 60 ml sample bottle and that the walls are coated with sample.</li>
		<li>Remove plunger and close stop-cock. Pour ~60 ml of sample into syringe and return plunger to syringe. Push the sample through the filter holder and into the 60 ml sample bottle. Fill bottles up to the shoulder to prevent cracking of bottles when frozen. Cap bottle and place into cooler.</li>
		<li>Repeat steps 5.3.2-5.3.4 for all remaining samples. Change filter between rising and falling limb samples to minimize contamination.</li>
		<li>Transport samples back to the laboratory on the same day and on ice.</li>
	</ol>
	</li>
</ol>
6. Preparation for Laboratory Analysis
<ol>
	<li>If filtering of samples is to occur in the laboratory, follow protocol as outlined in section 5.3.1. Filter samples from both the ascending and descending limbs of the BTC in order of increasing conductivity. Change the filter between rising limb and falling limb samples.</li>
	<li>Freeze filtered samples at -20 &#176;C until analysis.</li>
	<li>Ensure that analytical facilities are equipped to handle highly concentrated samples. 
	Note: Some laboratories are not equipped to run highly concentrated samples and thus care should be taken. Incorporate prepared standards that capture that higher end of expected solute concentrations. High concentration standards will help to ensure a standard curve that captures the expected range of manipulated solute concentrations.</li>
	<li>Analyze samples from low to high conductivity on all analytical instruments. Ordering samples from low to high specific conductance prevents contamination of low salt/nutrient samples by high salt/nutrient samples. This means samples from the rising and falling limbs will be mixed with respect to sequence.
	<ol>
		<li>Analyze samples for total dissolved organic carbon, total dissolved nitrogen, nitrate and ammonium, although the exact combination of solute analysis will be a function of the research question (see Wymore et al.10 for example).</li>
	</ol>
	</li>
</ol>
7. Data Analysis
<ol>
	<li>Analyze the data using simple linear regression. The independent variable is the concentrations of the added nutrient and the dependent variable is DOM concentration either as DOC or DON. Each point on the figure represents one grab sample from the breakthrough curve and that sample&#39;s nutrient and DOC/DON concentration.</li>
</ol>

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The authors have nothing to disclose.

The objective of the nutrient pulse method, as presented here, is to characterize and quantify the response of the highly diverse pool of ambient stream water DOM across a dynamic range of an added inorganic nutrient. If the added solute sufficiently increases the concentration of the reactive solute, a large inferential space can be created to understand how the biogeochemical cycling of DOM is linked to nutrient concentrations. This nutrient pulse approach is ideal as it involves none of the machinery associated with p...

Dissolved organic matter (DOM) provides an important energy and nutrient source to freshwater ecosystems and is defined as organic matter that passes through a 0.7 &#181;m filter. Within aquatic ecosystems, DOM can also influence light attenuation and metal complexation. DOM is a highly diverse and heterogeneous mixture of organic compounds with various functional groups, as well as essential nutrients such as nitrogen (N) and phosphorous (P). While the term &#34;DOM&#34; describes the entire pool including its C, N and P components, its concentration is measured as dissolved organic carbon (DOC). The inherent molecular complexity of the DOM pool however, creates challenges to its study. For example, there is no direct way to measure the fraction of the total DOM pool comprised of organic nutrients such as dissolved organic nitrogen (DON) and dissolved organic phosphorus (DOP). Instead, the concentration of organic nutrients must be determined by difference (e.g. [DON] = [total dissolved nitrogen] - [dissolved inorganic nitrogen]).
Adding a realistic DOM amendment to a stream is difficult due to the diversity of the ambient DOM pool. Previous studies have added single carbon sources (e.g. glucose, urea1) or a particular source such as leaf litter leachate2 to manipulate concentrations in the field. However, these sources are not particularly representative of the ambient DOM pool. Trying to refine or concentrate ambient DOM for subsequent experimentation is also wrought with difficulties including the loss of certain fractions (e.g. highly labile components) during processing. As a result, it is difficult to understand the controls on the ambient DOM pool as we currently do not possess any method to directly manipulate the ambient DOM pool. However, since the biogeochemistry of DOM is linked to nutrients commonly found in the environment (e.g. nitrate [NO3-]3), we can add other solutes to stream ecosystems and measure the response of the DOM pool to these manipulations. By examining how the DOM pool responds to a wide range of experimentally imposed nutrient concentrations we hope to gain better insight into how DOM responds to fluctuating environmental conditions.
One method commonly used in stream biogeochemistry is the nutrient addition method. Nutrient addition experiments have traditionally been used to understand uptake kinetics or the fate of the added solute4,5,6,7. Nutrient additions can be short-term on the hr6 to day scale4, or longer-term manipulations over the course of multiple years8. Nutrient additions can also include isotopically labelled nutrients (e.g. 15N-NO3-) to trace added nutrient through biogeochemical reactions. However, isotope-based studies are often expensive and require challenging analyses (e.g. digestions) of the multiple benthic compartments where isotopically-labeled nutrients may be retained. Recent experimentation has revealed the utility of short-term nutrient pulses to elucidate the controls on non-added and ambient solutes such as DOM9,10, revealing a new way by which to examine real-time in situ biogeochemical reactions. Here we describe and demonstrate the key methodological steps to conducting short-term nutrient pulses with the objective of understanding the coupled biogeochemistry of C and N and specifically the controls on the highly diverse DOM pool. This easily reproducible method involves adding a nutrient pulse to an experimental stream reach and measuring changes in the concentration of both the manipulated solute and the response variable of interest (e.g. DOC, DON, DOP). By directly manipulating nutrient concentrations in situ we are able to indirectly alter the DOM pool and examine how DOM concentration changes across a dynamic range of nutrient concentrations10.

<table><tbody><tr><td>Sodium Nitrate</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Sodium Chloride</td><td>Any</td><td>Any</td><td>Store purchased table salt can be used as well, however, it does contain trace levels of impurities</td></tr><tr><td>Whatman GFF glass-fiber filters</td><td>Any</td><td>Any</td><td></td></tr><tr><td>BD Filtering Syringe</td><td>Any</td><td>Any</td><td></td></tr><tr><td>EMD Millipore Swinnex Filter Holders</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Syringe stop-cock</td><td>Any</td><td>Any</td><td></td></tr><tr><td>YSI Multi-parameter probe</td><td>Yellow Springs International</td><td>556-01</td><td></td></tr><tr><td>Wide mouth HDPE 125 ml bottles</td><td>Any</td><td>Any</td><td></td></tr><tr><td>60 ml HDPE bottles</td><td>Any</td><td>Any</td><td></td></tr><tr><td>20 L bucket</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Field measuring tape</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Lab labeling tape</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Stir stick</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Cooler</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Sharpie pen</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Field notebook</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Tweezers</td><td>Any</td><td>Any</td><td></td></tr><tr><td>Zip-lock bags</td><td>Any</td><td>Any</td><td></td></tr></tbody></table>

understanding dissolved organic matter biogeochemistry through in situ nutrient manipulations in stream ecosystems

Dissolved organic matter (DOM) is a highly diverse mixture of molecules providing one of the largest sources of energy and nutrients to stream ecosystems. Yet the in situ study of DOM is difficult as the molecular complexity of the DOM pool cannot be easily reproduced for experimental purposes. Nutrient additions to streams however, have been shown to repeatedly alter the in situ and ambient DOM pool. Here we demonstrate an easily replicable field-based method for manipulating the ambient pool of DOM at the ecosystem scale. During nutrient pulse experiments changes in the concentration of both dissolved organic carbon and dissolved organic nitrogen can be examined across a wide-range of nutrient concentrations. This method allows researchers to examine the controls on the DOM pool and make inferences regarding the role and function that certain fractions of the DOM pool play within ecosystems. We advocate the use of this method as a technique to help develop a deeper understanding of DOM biogeochemistry and how it interacts with nutrients. With further development this method may help elucidate the dynamics of DOM in other ecosystems.

<img alt="Figure 3" src="/files/ftp_upload/54704/54704fig3.jpg" /> 
Figure 3: Example Results from Nitrate (NO3-) Additions with Dissolved Organic Nitrogen (DON) as the Response Variable. Analyses are linear regressions. Asterisks represent statistical significance at &#945; = 0.05. Note the dynamic range in NO3- concentration that was achieved with the nutrient pulse method. Different pane...

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Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Dissolved organic matter provides an important source of energy and nutrients to stream ecosystems. Here we demonstrate a field-based method to manipulate the ambient pool of dissolved organic matter in situ through easily replicable nutrient pulses.

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Dissolved organic matter (DOM) is a highly diverse mixture of molecules providing one of the largest sources of energy and nutrients to stream ecosystems. ...

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10.3K Views.  University of New Hampshire. Dissolved organic matter provides an important source of energy and nutrients to stream ecosystems. Here we demonstrate a field-based method to manipulate the ambient pool of dissolved organic matter in situ through easily replicable nutrient pulses.

Video: Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes and soil organic matter formation to small-scale stable-isotope biomarker probing. Coupling multiple stable isotopes such as 13C with 15N, 18O or&#160;2H has the potential to reveal even more information about complex stoichiometric relationships during biogeochemical transformations. Isotope labeled plant material has been used in various studies of litter decomposition and soil organic matter formation1-4. From these and other studies, however, it has become apparent that structural components of plant material behave differently than metabolic components (i.e. leachable low molecular weight compounds) in terms of microbial utilization and long-term carbon storage5-7. The ability to study structural and metabolic components separately provides a powerful new tool for advancing the forefront of ecosystem biogeochemical studies. Here we describe a method for producing 13C and 15N labeled plant material that is either uniformly labeled throughout the plant or differentially labeled in structural and metabolic plant components.
Here, we present the construction and operation of a continuous 13C and 15N labeling chamber that can be modified to meet various research needs. Uniformly labeled plant material is produced by continuous labeling from seedling to harvest, while differential labeling is achieved by removing the growing plants from the chamber weeks prior to harvest. Representative results from growing Andropogon gerardii Kaw demonstrate the system&#39;s ability to efficiently label plant material at the targeted levels. Through this method we have produced plant material with a 4.4 atom%13C and 6.7 atom%15N uniform plant label, or material that is differentially labeled by up to 1.29 atom%13C and 0.56 atom%15N in its metabolic and structural components (hot water extractable and hot water residual components, respectively). Challenges lie in maintaining proper temperature, humidity, CO2 concentration,&#160;and light levels in an airtight 13C-CO2 atmosphere for successful plant production. This chamber description represents a useful research tool to effectively produce uniformly or differentially multi-isotope labeled plant material for use in experiments on ecosystem biogeochemical cycling.

Design and Operation of a Continuous 13C and 15N Labeling Chamber for Uniform or Differential, Metabolic and Structural, Plant Isotope Labeling

This method explains how to build and operate a continuous 13C and 15N isotope labeling chamber for uniform or differential plant tissue labeling. Representative results from metabolic and structural labeling of Andropogon gerardii are discussed.

Tracing rare stable isotopes from plant material through the ecosystem provides the most sensitive information about ecosystem processes; from CO2 fluxes ...

Unraveling the Unseen Players in the Ocean - A Field Guide to Water Chemistry and Marine Microbiology

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling protocols include the assessment of resources available to the microbial community (dissolved organic carbon, particulate organic matter, inorganic nutrients), and a comprehensive description of the viral and bacterial communities (via direct viral and microbial counts, enumeration of autofluorescent microbes, and construction of viral and microbial metagenomes). We use a combination of methods, which represent a dispersed field of scientific disciplines comprising already established protocols and some of the most recent techniques developed. Especially metagenomic sequencing techniques used for viral and bacterial community characterization, have been established only in recent years, and are thus still subjected to constant improvement. This has led to a variety of sampling and sample processing procedures currently in use. The set of methods presented here provides an up to date approach to collect and process environmental samples. Parameters addressed with these protocols yield the minimum on information essential to characterize and understand the underlying mechanisms of viral and microbial community dynamics. It gives easy to follow guidelines to conduct comprehensive surveys and discusses critical steps and potential caveats pertinent to each technique.

Here, we present a comprehensive protocol to assess the organic and inorganic nutrient availability and the abundance and structure of microbial and viral communities in remote marine environments.

Here we introduce a series of thoroughly tested and well standardized research protocols adapted for use in remote marine environments. The sampling ...

Chemotactic Response of Marine Micro-Organisms to Micro-Scale Nutrient Layers

The degree to which planktonic microbes can exploit microscale resource patches will have considerable implications for oceanic trophodynamics and biogeochemical flux. However, to take advantage of nutrient patches in the ocean, swimming microbes must overcome the influences of physical forces including molecular diffusion and turbulent shear, which will limit the availability of patches and the ability of bacteria to locate them. Until recently, methodological limitations have precluded direct examinations of microbial behaviour within patchy habitats and realistic small-scale flow conditions. Hence, much of our current knowledge regarding microbial behaviour in the ocean has been procured from theoretical predictions. To obtain new information on microbial foraging behaviour in the ocean we have applied soft lithographic fabrication techniques to develop 2 microfluidic devices, which we have used to create (i) microscale nutrient patches with dimensions and diffusive characteristics relevant to oceanic processes and (ii) microscale vortices, with shear rates corresponding to those expected in the ocean. These microfluidic devices have permitted a first direct examination of microbial swimming and chemotactic behaviour within a heterogeneous and dynamic seascape. The combined use of epifluorescence and phase contrast microscopy allow direct examinations of the physical dimensions and diffusive characteristics of nutrient patches, while observing the population-level aggregative response, in addition to the swimming behaviour of individual microbes. These experiments have revealed that some species of phytoplankton, heterotrophic bacteria and phagotrophic protists are adept at locating and exploiting diffusing microscale resource patches within very short time frames. We have also shown that up to moderate shear rates, marine bacteria are able to  fight the flow  and swim through their environment at their own accord. However, beyond a threshold high shear level, bacteria are aligned in the shear flow and are less capable of swimming without disturbance from the flow. Microfluidics represents a novel and inexpensive approach for studying aquatic microbial ecology, and due to its suitability for accurately creating realistic flow fields and substrate gradients at the microscale, is ideally applicable to examinations of microbial behaviour at the smallest scales of interaction.  We therefore suggest that microfluidics represents a valuable tool for obtaining a better understanding of the ecology of microorganisms in the ocean.

The fabrication of microfluidic channels and their implementation in experiments for studying the chemotactic foraging behaviour of marine microbes within a patchy nutrient seascape and the swimming behaviour of bacteria within shear flow are described.

The degree to which planktonic microbes can exploit microscale resource patches will have considerable implications for oceanic trophodynamics and ...

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 ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

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 ...

Dissolved Solute Sampling Across an Oxic-Anoxic Soil-Water Interface Using Microdialysis Profilers

Biogeochemical processes shift rapidly in both spatial (millimeter scale) and temporal (hour scale to day scale) dimensions at the oxic-anoxic interface in response to disturbances. Deciphering the rapid biogeochemical changes requires in situ, minimally invasive tools with high spatial and temporal sampling resolution. However, the available passive sampling devices are not very useful in many cases either due to their disposable nature or the complexity and extensive workload for sample preparation.
To address this problem, a microdialysis profiler with 33 individual polyethersulfone nanomembrane tubes (semipermeable, &#60;20 nm pore size) integrated into the one-dimensional skeleton (60 mm) was established to iteratively sample the dissolved compounds in porewater across the soil-water interface at a high resolution of 1.8 mm (outer diameter plus one spacing, i.e., 0.1 mm between probes). The sampling mechanism is based on the principle of concentration gradient diffusion. The automatic loading of degassed water allows minimal disturbance to the chemical species across the oxic-anoxic interface.
This paper describes the procedures of device setup and continuous porewater sampling across the soil-water interface on a daily basis. Concentration-depth profiles were selectively measured before (on Day 6) and after (on Day 7) disturbances induced by irrigation. The results showed that concentration-depth profiles were undergoing rapid changes, especially for redox-sensitive elements (i.e., iron and arsenic). These protocols can help investigate the biogeochemical responses across the soil-water interface under various disturbances caused by physical, chemical, and biological factors. The paper thoroughly discusses the advantages and disadvantages of this method for potential use in the environmental sciences.

A microdialysis profiler is described to sample dissolved porewater solutes across an oxic-anoxic soil-water interface in situ with minimal disturbance. This device is designed to capture rapid changes in concentration-depth profiles induced by disturbances at the soil-water interface and beyond.

Biogeochemical processes shift rapidly in both spatial (millimeter scale) and temporal (hour scale to day scale) dimensions at the oxic-anoxic interface ...

Integrated Visualization of Biogeochemical Data for Nutrient Management

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

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

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

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

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

Visualizing Methane-Cycling Microbial Dynamics in Coastal Wetlands

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus playing a critical role in regulating the methane cycle. The wetlands in South Texas, which are subject to frequent weather events, fluctuating salinity levels, and anthropogenic activities due to climate change, influence methane cycling. Despite the ecological importance of these processes, methane cycling in South Texas coastal wetlands remains insufficiently explored. To address this gap, we developed and optimized a method for detecting genes related to methanogens and methanotrophs, including mcrA as a biomarker for methanogens and pmoA1, pmoA2, and mmoX as biomarkers for methanotrophs. Additionally, this study aimed to visualize the spatial and temporal distribution patterns of methanogen and methanotroph abundance utilizing the geographic information system (GIS) software ArcGIS Pro. The integration of these molecular techniques with advanced geospatial visualization provided critical insights into the spatial and temporal distribution of methanogen and methanotroph communities across South Texas wetlands. Thus, the methodology established in this study offers a robust framework for mapping microbial dynamics in wetlands, enhancing our understanding of methane cycling under varying environmental conditions, and supporting broader ecological and environmental change studies.

The protocol detects key methane-cycling genes in South Texas coastal wetlands and visualizes their spatial distribution to enhance understanding of methane regulation and its environmental impacts in these dynamic ecosystems.

Coastal wetlands are the largest biotic source of methane, where methanogens convert organic matter into methane and methanotrophs oxidize methane, thus ...

Linking Predation Risk, Herbivore Physiological Stress and Microbial Decomposition of Plant Litter

The quantity and quality of detritus entering the soil determines the rate of decomposition by microbial communities as well as recycle rates of nitrogen (N) and carbon (C) sequestration1,2. Plant litter comprises the majority of detritus3, and so it is assumed that decomposition is only marginally influenced by biomass inputs from animals such as herbivores and carnivores4,5. However, carnivores may influence microbial decomposition of plant litter via a chain of interactions in which predation risk alters the physiology of their herbivore prey that in turn alters soil microbial functioning when the herbivore carcasses are decomposed6. A physiological stress response by herbivores to the risk of predation can change the C:N elemental composition of herbivore biomass7,8,9 because stress from predation risk increases herbivore basal energy demands that in nutrient-limited systems forces herbivores to shift their consumption from N-rich resources to support growth and reproduction to C-rich carbohydrate resources to support heightened metabolism6. Herbivores have limited ability to store excess nutrients, so stressed herbivores excrete N as they increase carbohydrate-C consumption7. Ultimately, prey stressed by predation risk increase their body C:N ratio7,10, making them poorer quality resources for the soil microbial pool likely due to lower availability of labile N for microbial enzyme production6. Thus, decomposition of carcasses of stressed herbivores has a priming effect on the functioning of microbial communities that decreases subsequent ability to of microbes to decompose plant litter6,10,11.
We present the methodology to evaluate linkages between predation risk and litter decomposition by soil microbes. We describe how to: induce stress in herbivores from predation risk; measure those stress responses, and measure the consequences on microbial decomposition. We use insights from a model grassland ecosystem comprising the hunting spider predator (Pisuarina mira), a dominant grasshopper herbivore (Melanoplus femurrubrum),and a variety of grass and forb plants9.

We present methods to evaluate how predation risk can alter the chemical quality of herbivore prey by inducing dietary changes to meet demands of heightened stress, and how the decomposition of carcasses from these stressed herbivores slows subsequent plant litter decomposition by soil microbes.

Understanding Dissolved Organic Matter Biogeochemistry Through In Situ Nutrient Manipulations in Stream Ecosystems

Summary

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