We thank the numerous field and laboratory helpers that were involved in various aspects of this work, especially Donna Hartman, Aaron Maxwell, Eric Miller, and Alison Anderson. Funding for this study was provided by the US Geological Survey through support from US Environmental Protection Agency (EPA) Region III. This study was partially developed under the Science To Achieve Results Fellowship Assistance Agreement number FP-91766601-0 awarded by the US EPA. Although the research described in this article has been funded by the US EPA, it has not been subjected to the agency&#39;s required peer and policy review and, therefore, does not necessarily reflect the views of the agency, and no official endorsement should be inferred.

1. Target Sites for Inclusion in Watershed Assessment
<ol>
	<li>Identify the dominant land use activities within the target 8-digit hydrologic unit code (HUC) watershed that are impacting physicochemical and biological condition3, 7. 
	Note: This methodology assumes pre-existing knowledge of important stressors within the watershed of interest. However, consulting regulatory agencies or watershed groups familiar with the system can aid in this effort.</li>
	<li>Select landscape-based measures of dominant land use activities [e.g., 2011 National Land Cover Database (NLCD)] 3, 7.
	<ol>
		<li>Consult published literature to help identify the best landscape-based measures for each land use activity10. Contact natural resource agencies to identify and obtain region-specific landscape datasets that are available for use. However, it may be necessary to create new landscape variables or datasets.</li>
	</ol>
	</li>
	<li>Tabulate land cover and use attributes to the 1:24,000 or 1:100,000 national hydrography dataset (NHD) catchments using Geographic Information (GIS) software.
	<ol>
		<li>Ensure each 1:24,000 or 1:100,000 catchment has a unique identifier. Use any user-defined numeric or categorical identifier as the unique identifier.</li>
		<li>Tabulate vector data (e.g., points or lines) falling within each catchment.
		<ol>
			<li>Summarize all vector features within each catchment using the Tabulate Intersection tool within the Statistics toolset of the Analysis toolbox. Select the NHD catchment layer as the Input Zone Feature, the catchment unique identifier as the Zone Field, and the vector dataset of interest as the Input Class Feature.</li>
			<li>Join the tabulated landscape attributes to the catchment layer. Right click on the catchments layer in the table of contents and select Joins and Relates from the dropdown menu and Join from the subsequent menu. Select the unique identifier as the field that the join will be based on, the output table from 1.3.2.1 as the table to be joined, and the unique identifier as the field in the table that the join will be based on.</li>
		</ol>
		</li>
		<li>Tabulate raster data using the Tabulate Area tool located within the Zonal toolset of the Spatial Analyst toolbox.
		<ol>
			<li>Load the Spatial Analyst extension. Select Extensions from the Customize menu. In the Extensions dialog box, check the box that corresponds to the Spatial Analyst extension.</li>
			<li>In the Tabulate Area dialog box, select the NHD catchment shapefile as the input raster or feature zone data, the unique identifier (e.g., FEATUREID) as the zone field, and the land cover dataset (e.g., NLCD) as the input raster or feature class data.</li>
			<li>Join the tabulated landscape attributes to the catchment layer following protocols in step 1.3.2.2, with the tabulate area results table as the join table.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Accumulate landscape attributes for all NHD catchments.
	<ol>
		<li>Download the NHDPlusV2 Catchment Attribute Allocation and Accumulation Tool (CA3TV2) at http://www2.epa.gov/waterdata/nhdplus-tools. Use the accumulation function of CA3TV2 for accumulation of attributes for 1:100,000 NHD catchments 11. 
		Note: We used custom written code that accumulates landscape attributes for 1:24,000 scale NHD watersheds12. Detailed instructions for using CA3TV2 are integrated into the tool and can be accessed via the Help function.</li>
	</ol>
	</li>
	<li>Select NHD catchments as study sites based on accumulated landscape attributes.
	<ol>
		<li>Create a scatter plot of all NHD catchments with respect to accumulated values of major land use activities (Figure 1A).</li>
		<li>Select study sites (approximately 40 sites per 8-digit HUC watershed) to represent the full range of influence from dominant land use activities found within the target watershed (Figure 1B). Select sites within independent stressor gradients (i.e., influenced by a single land use activity) and stressor combinations (i.e., influenced by multiple land use activities) (Figure 1B).</li>
		<li>Ensure that study sites are spatially distributed throughout the target watershed and independent of one another with respect to downstream drainage. Ensure that sites falling within each individual and combined stressor gradient also have similar average basin areas.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/54095/54095fig1.jpg" /> 
Figure 1. Hypothetical scatter plot of NHD catchments with respect to influence from 2 land use activities. Magnitude of influence of 2 land use activities across all NHD catchments within the hypothetical watershed (n=4,229) (A). Selected study sites (n=40) that represent the full range of observed conditions within the watershed with respect to independent and combined stressor gradients (B). <a href="https://www.jove.com/files/ftp_upload/54095/54095fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Field Protocols for Collection of Physicochemical and Biological Data
Note: All data for each site should be collected during the same site visit at normal base flow conditions. Protocols presented herein represent standard operating procedures for the West Virginia Department of Environmental Protection (WVDEP)13. It may be more appropriate to use state or federally recognized procedures for the specific watershed being assessed.
<ol>
	<li>Delineate the sampling reach for each site as 40&#215; active channel width (ACW), with maximum and minimum lengths of 150 and 300 m3, 7.</li>
	<li>Sample water quality attributes from locations with moving water that are characteristic of the entire sampling site (e.g., not directly influenced by tributary or drainage pipe inputs).
	<ol>
		<li>Obtain instantaneous measures of dissolved oxygen, specific conductivity, temperature, and pH using handheld sensors. Calibrate sensors prior to each sampling event following manufacturer instructions.</li>
		<li>Rinse filtration equipment with deionized water prior to water sample collection.</li>
		<li>Filter 250 ml of water (mixed cellulose ester membrane filter, 0.45 &#181;m pore size) for analysis of dissolved metals. Fix to a pH&#60;2 to ensure metals remain dissolved in solution. 
		Note: The correct volume of acid can be added to the water sample following sample collection. Alternatively, the correct volume can be added to the bottle prior to the sampling event. The volume required to fix to a pH&#60;2 is dependent upon acid strength.
		<ol>
			<li>For the study described here, collect a single filtered sample from each site and fix with nitric acid for determination of dissolved Al, Ca, Fe, Mg, Mn, Na, Zn, K, Ba, Cd, Cr, Ni, and Se3, 7. 
			Note: Selection of analytes should be guided by watershed-specific land use activities.</li>
		</ol>
		</li>
		<li>Collect 250 ml unfiltered sample(s) by completely submerging the sample bottle in the water column. Gently squeeze the bottle to expunge any remaining air and simultaneously place the cap on the sample bottle. Fix the sample(s) to a pH &#60;2 if necessary (e.g., prevent biological activity from affecting nutrients).
		<ol>
			<li>For the study described here, collect two unfiltered samples from each site. Fix the first with sulfuric acid for determination of NO2 and NO3 and total P. Do not fix the second unfiltered sample and use it to determine total and bicarbonate alkalinity, Cl, SO4, and total dissolved solids3, 7. 
			Note: Selection of analytes should be guided by watershed-specific land use activities.</li>
		</ol>
		</li>
		<li>Obtain a field blank for each fixative used during each sampling event. Obtain field blanks by following all protocols for sample collection (i.e., rinsing, filtering, fixing) using deionized water as the final sample. 
		Note: Field blanks are used to identify contamination in sample collection and analysis.</li>
		<li>Store all water samples at 4 &#176;C until all analyses are completed. Ensure all analytes are measured within their specified holding time14.</li>
	</ol>
	</li>
	<li>Measure discharge at each sample site.
	<ol>
		<li>Divide the wetted stream width into equal-sized increments.</li>
		<li>Measure depth and average current velocity at the mid-point of each section.
		<ol>
			<li>Using a depth gauge rod, measure depth as the distance from the stream bed to the water&#39;s surface.</li>
			<li>Using a current meter, measure water velocity at 60% water depth.</li>
		</ol>
		</li>
		<li>Calculate discharge as the sum of the product of velocity, depth, and width across all sections.</li>
	</ol>
	</li>
	<li>Sample the macroinvertebrate community at each site.
	<ol>
		<li>Obtain kick samples (net dimensions 335 &#215; 508 mm2 with 500-&#181;m mesh) from 4 separate representative riffles distributed throughout the entire length of the sampling reach.
		<ol>
			<li>At each kick location, place the kick net perpendicular to stream flow and disturb a 0.50 &#215; 0.50 m2 (i.e., 0.25 m2) area of the stream bed immediately upstream. Ensure all organisms and debris flow downstream into the kick net.</li>
			<li>Combine organisms and debris from the 4 kick samples into a single composite sample (representing 1.00 m2 of the stream bed) and immediately preserve with 95% ethanol.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Measure physical habitat quality and complexity throughout the stream reach.
	<ol>
		<li>Take measurements of water depth, hydraulic channel-unit type, sediment class, and distance to fish cover object at evenly spaced points along the thalweg (portion of the stream through which the main or most rapid flow occurs). Take measurements every 1 ACW for streams &#60;5 m wide and every 0.5 ACW for streams &#62;5 m wide15.
		<ol>
			<li>Classify the channel unit within which each thalweg location is located (e.g., riffle, run, pool, or glide)16.</li>
			<li>Using a depth gauge rod, measure depth as the distance from the stream bed to the water&#39;s surface.</li>
			<li>Randomly identify a piece of sediment and determine its Wentworth size classification (silt, sand, gravel, cobble, boulder)17.</li>
			<li>Estimate the distance from each thalweg point to the nearest cover object. 
			Note: Fish cover is defined as any structure in the active channel capable of concealing a 20.32 cm (8 in) fish18.</li>
		</ol>
		</li>
		<li>Count all pieces of large woody debris within the active channel.</li>
		<li>Estimate habitat quality with US Environmental Protection Agency (EPA) rapid visual habitat assessments (RVHA) protocols19.</li>
	</ol>
	</li>
	<li>Obtain duplicate measurements and samples from a randomly selected 10% of study sites. Duplicate measures are used to estimate sampling and laboratory analysis precision.</li>
</ol>
3. Laboratory Protocols for Physicochemical and Biological Data
Note: Describing laboratory protocols for quantifying water chemistry attributes is outside the scope of this manuscript. However, the current study used standard chemical methods for water and wastes14.
<ol>
	<li>Subsample organisms contained within each macroinvertebrate sample (collected using protocols in section 2.4) to obtain a representative subsample of the macroinvertebrate community at each site.
	<ol>
		<li>Place the entire composite macroinvertebrate sample into a 100 in2 gridded sorting (measuring 5 &#215; 20 in2). Randomly assign each 1 in2 grid a number from 1 to 100.</li>
		<li>Use a stereo microscope to count and identify all organisms within randomly selected 1 in2 grids until the total number of sorted individuals is 200 &#177; 20%. Identify organisms to genus using macroinvertebrate keys, such as those published by Merritt and Cummins20.</li>
		<li>Compile genus-level abundance data into community metrics [e.g., total richness and % Ephemeroptera, Plecoptera, and Trichoptera (EPT)] for use as response variables in statistical models and subsequent scenario analysis3, 7.</li>
	</ol>
	</li>
</ol>
4. Statistical and Scenario Analyses
<ol>
	<li>Construct generalized linear models for predicting in-stream physical, chemical, and biological conditions from landscape-based indicators of dominant land use activities. 
	Note: Protocols and analyses were performed in the R language and environment for statistical computing (version 3.2.1)21.
	<ol>
		<li>Test for normality using Shapiro-Wilk [shaprio.test() function in R package stats21] tests and transform variables to meet assumptions of parametric analyses and linearize relationships.</li>
		<li>Fit initial maximal models specifying 2-way interactions among all land use predictors [glm() function in R package stats21].</li>
		<li>Apply a backward deletion to identify the minimum adequate model3, 7, 22.
		<ol>
			<li>Identify the least significant (i.e., explains the least amount of variation) variable in the maximal model [summary() function in R package stats21] and fit a new model with this variable excluded [glm () function in R package stats21].</li>
			<li>Continue removing variables until all remaining predictors are significantly different from 0 and explanatory power does not differ significantly from the maximal model for each response variable using analysis of deviance tables and likelihood ratio tests [lrtest() function in R package lmtest23].</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Predict current conditions.
	<ol>
		<li>Use final models to predict physicochemical and biological condition given current landscape characteristics within all un-sampled NHD catchments throughout the target watershed [predict() function in R package stats21].</li>
		<li>Visualize predictions in GIS software.
		<ol>
			<li>Join predictions to NHD catchments. Right click on the catchments layer in the table of contents and select Joins and Relates from the dropdown menu and Join from the subsequent menu. Select the unique identifier as the field that the join will be based on, the predictions file as the table to be joined, and the unique identifier as the field in the table that the join will be based on</li>
			<li>Right click on the catchments layer and select Properties. In the Layer Properties dialog box, click on the Symbology tab and select Quantities. Select the predicted value of interest as the Value field and click Apply. 
			Note: Range values can be manually changed to match recognized ecological criteria using the Classify button.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Conduct scenario analyses to compare predicted changes in aquatic conditions under various land use scenarios.
	<ol>
		<li>Update the current landscape dataset to simulate plausible future development or mitigation scenarios. For the study described here, manually update accumulated landscape values for the catchment of interest within the attribute table (e.g., change 10 acres of forested to mining land cover).
		<ol>
			<li>Select the catchment of interest using the Select by Attribute function located within the Selection drop down menu. In the Select by Attribute dialog box, choose the NHD catchments as the Layer. Double click on the unique identifier attribute, select =, and then type the identifier for the catchment of interest in the equation box.</li>
			<li>Open the NHD catchment attribute table by right clicking the catchments layer in the table of contents and selecting Open Attribute Table from the drop down menu. Choose to show only selected catchments.</li>
			<li>With only selected catchments showing, right click on the column of interest and select Field Calculator and input the new simulated value. Note: Multiple catchments can be altered to simulate multiple spatially explicit development or management activities occurring across large spatial scales. 
			Note: Alternatively, original vector and raster datasets can be updated by digitizing new features or altering and removing original features to simulate new land use activity or management of a pre-existing land use impact24. This can be accomplished using the Editor Toolbar.</li>
		</ol>
		</li>
		<li>Re-allocate and re-accumulate landscape attributes for all NHD catchments using protocols presented in steps 1.3-1.4.</li>
		<li>Predict physicochemical and biological condition as a function of the updated landscape dataset [predict() function in R package stats21].</li>
		<li>Visualize predicted conditions under alternative land use scenarios using protocols presented in step 4.2.2.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Allan, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Landscapes+and+riverscapes:+the+influence+of+land+use+on+stream+ecosystems.">Landscapes and riverscapes: the influence of land use on stream ecosystems.</a> Annu. Rev. Ecol. Evol. Syst. 35, 257-284 (2004).</li><li>Merovich, G. T., Petty, J. T., Strager, M. P., Fulton, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hierarchical+classification+of+stream+condition:+a+house-neighborhood+framework+for+establishing+conservartion+priorities+in+complex+riverscapes.">Hierarchical classification of stream condition: a house-neighborhood framework for establishing conservartion priorities in complex riverscapes.</a> Freshwater Science. 32, 874-891 (2013).</li><li>Merriam, E. R., Petty, J. T., Strager, M. P., Maxwell, A. E., Ziemkiewicz, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scenario+analysis+predicts+context-dependent+stream+response+to+land+use+change+in+a+heavily+mined+central+Appalachian+watershed.">Scenario analysis predicts context-dependent stream response to land use change in a heavily mined central Appalachian watershed.</a> Freshwater Science. 32, 1246-1259 (2013).</li><li>Petty, J. T., Fulton, J. B., Strager, M. P., Merovich, G. T., Stiles, J. M., Ziemkiewicz, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Landscape+indicators+and+thresholds+of+stream+ecological+impairment+in+an+intensively+mined+Appalachian+watershed.">Landscape indicators and thresholds of stream ecological impairment in an intensively mined Appalachian watershed.</a> J. N. Am. Benthol. Soc. 29, 1292-1309 (2010).</li><li>Seitz, N. E., Westbrook, C. J., Noble, B. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bringing+science+into+river+systems+cumulative+effects+assessment+practice.">Bringing science into river systems cumulative effects assessment practice.</a> Environ. Impact Asses. 31, 172-179 (2011).</li><li>Duinker, P. N., Greig, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+importance+of+cumulative+effects+assessment+in+Canada:+ailments+and+ideas+for+redeployment.">The importance of cumulative effects assessment in Canada: ailments and ideas for redeployment.</a> Environ. Manage. 37, 153-161 (2006).</li><li>Merriam, E. R., Petty, J. T., Strager, M. P., Maxwell, A. E., Ziemkiewicz, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Landscape-based+cumulative+effects+models+for+predicting+stream+response+to+mountaintop+mining+in+multistressor+Appalachian+watersheds.">Landscape-based cumulative effects models for predicting stream response to mountaintop mining in multistressor Appalachian watersheds.</a> Freshwater Science. 34, 1006-1019 (2015).</li><li>Duinker, P. N., Greig, L. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scenario+analysis+in+environmental+impact+assessment:+improving+explorations+of+the+future.">Scenario analysis in environmental impact assessment: improving explorations of the future.</a> Environ. Impact Asses. 27, 206-219 (2007).</li><li>Kepner, W. G., Ramsey, M. M., Brown, E. S., Jarchow, M. E., Dickinson, K. J. M., Mark, A. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Hydrologic+futures:+using+scenario+analysis+to+evaluate+impacts+of+forecasted+land+use+change+on+hydrologic+services.">Hydrologic futures: using scenario analysis to evaluate impacts of forecasted land use change on hydrologic services.</a> Ecosphere. 3, 1-25 (2012).</li><li>Gergel, S. E., Turner, M. G., Miller, J. R., Melack, J. M., Stanley, E. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Landscape+indicators+of+human+impacts+to+riverine+systems.">Landscape indicators of human impacts to riverine systems.</a> Aquat. Sci. 64, 118-128 (2002).</li><li>McKay, L., Bondelid, T., Dewald, T., Johnston, J., Moore, R., Rea, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> NHDPlus Version 2: User Guide. , (2012).</li><li>Strager, M. P., Petty, J. T., Strager, J. M., Barker-Fulton, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+spatially+explicit+framework+for+quantifying+downstream+hydrologic+conditions.">A spatially explicit framework for quantifying downstream hydrologic conditions.</a> J. Environ. Manag. 90, 1854-1861 (2009).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> WVDEP (Virginia Department of Environmental Protection). Standard operating proceedures. , (2009).</li><li>. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EPA-60014-79-020.">EPA-60014-79-020.</a> USEPA. Methods for chemical analysis of water and wastes. , (1983).</li><li>Merriam, E. R., Petty, J. T., Merovich, G. T., Fulton, J. B., Strager, 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=Additive+effects+of+mining+and+residential+development+on+stream+conditions+in+a+central+Appalachian+watershed.">Additive effects of mining and residential development on stream conditions in a central Appalachian watershed.</a> J. N. Am. Benthol. Soc. 30, 399-418 (2011).</li><li>Bisson, P. A., Nielsen, J. L., Palmason, R. A., Grove, L. E., Armentrout, N. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+system+of+naming+habitat+types+in+streams,+with+examples+of+habitat+utilization+by+salmonids+during+low+streamflow.">A system of naming habitat types in streams, with examples of habitat utilization by salmonids during low streamflow.</a> Acquisition and utilization of aquatic habitat inventory information. Proceedings of a symposium held 28-30 October, 1981. , 62-73 (1982).</li><li>Wentworth, C. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+scale+of+grade+and+class+terms+for+clastic+sediments.">A scale of grade and class terms for clastic sediments.</a> J. Geol. 30, 377-392 (1922).</li><li>Petty, J. T., Freund, J., Lamothe, P., Mazik, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantifying+instream+habitat+in+the+upper+Shavers+Fork+basin+at+multiple+spatial+scales.">Quantifying instream habitat in the upper Shavers Fork basin at multiple spatial scales.</a> Proceedings of the Annual Conference of the Southeastern Association of Fisheries and Wildlife Agencies. 55, 81-94 (2001).</li><li>Barbour, M. T., Gerritsen, J., Snyder, B. D., Stribling, J. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EPA/841-B-99-022.">EPA/841-B-99-022.</a> Rapid bioassessment protocols for use in streams and wadeable rivers: periphyton, benthic macroinvertebrates, and fish. 2nd edition. , (1999).</li><li>Merritt, R. W., Cummins, K. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> An introduction to the aquatic insects of North America. 4th edition. , (2008).</li><li>Crawley, 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=.">.</a> Statistics: an introduction using R. , (2005).</li><li>Zeileis, A., Hothorn, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Diagnostic+Checking+in+Regression+Relationships.">Diagnostic Checking in Regression Relationships.</a> R News. 2, 7-10 (2002).</li><li>Maxwell, A. E., Strager, M. P., Yuill, C., Petty, J. T., Merriam, E. R., Mazzarella, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Disturbance+mapping+and+landscape+modeling+of+mountaintop+mining+using+ArcGIS.">Disturbance mapping and landscape modeling of mountaintop mining using ArcGIS.</a> Proceedings of the ESRI International User Conference. , (2011).</li><li>Gerritsen, J., Burton, J., Barbour, M. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A stream condition index for West Virginia wadeable streams. , (2000).</li><li>Pond, G. J., Passmore, M. E., Borsuk, F. A., Reynolds, L., Rose, C. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Downstream+effects+of+mountaintop+coal+mining:+comparing+biological+conditions+using+family-+and+genus-level+macroinvertebrate+bioassessment+tools.">Downstream effects of mountaintop coal mining: comparing biological conditions using family- and genus-level macroinvertebrate bioassessment tools.</a> J. N. Am. Benthol. Soc. 27, 717-737 (2008).</li><li>Luo, Y., 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=Ecological+forecasting+and+data+assimilation+in+a+data-rich+era.">Ecological forecasting and data assimilation in a data-rich era.</a> Ecol. Appl. 21, 1429-1442 (2011).</li><li>Petty, J. T., Strager, M. P., Merriam, E. R., Ziemkiewicz, P. F., Craynon, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scenario+analysis+and+the+Watershed+Futures+Planner:+predicting+future+aquatic+condiditons+in+an+intensively+mined+Appalachian+watershed.">Scenario analysis and the Watershed Futures Planner: predicting future aquatic condiditons in an intensively mined Appalachian watershed.</a> Environmental Considerations in Energy Productions. , 5-19 (2013).</li><li>Daraio, J. A., Bales, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Effects+of+land+use+and+climate+change+on+stream+temperature+I:+daily+flow+and+stream+temperature+projections.">Effects of land use and climate change on stream temperature I: daily flow and stream temperature projections.</a> J. Am. Water Resour. As. 50, 1155-1176 (2014).</li><li>Mantyka-Pringle, C. S., Martin, T. G., Moffatt, D. B., Linke, S., Rhodes, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Understanding+and+predicting+the+combined+effects+of+climate+change+and+land-use+change+on+freshwater+macroinvertebrates+and+fish.">Understanding and predicting the combined effects of climate change and land-use change on freshwater macroinvertebrates and fish.</a> J. Appl. Ecol. 51, 572-581 (2014).</li><li>Piggott, J. J., Townsend, C. R., Matthaei, C. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Climate+warming+and+agricultural+stressors+interact+to+determine+stream+macroinvertebrate+community+dynamics.">Climate warming and agricultural stressors interact to determine stream macroinvertebrate community dynamics.</a> Glob. Change Biol. 21, 1897-1906 (2015).</li><li>Elith, J., Leathwick, J. R., Hastie, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+working+guide+to+boosted+regression+trees.">A working guide to boosted regression trees.</a> J. Anim. Ecol. 77, 802-813 (2008).</li><li>Mattson, K. M., Angermeier, P. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrating+human+impacts+and+ecological+integrity+into+a+risk-based+protocol+for+conservation+planning.">Integrating human impacts and ecological integrity into a risk-based protocol for conservation planning.</a> Environ. Manage. 39, 125-138 (2007).</li><li>(US, U. S. E. P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=EPA+841-B-11-002.">EPA 841-B-11-002.</a> USEPA. Identifying and protecting healthy watersheds. Concepts, assessments, and management approaches. , (2012).</li><li>Merriam, E. R., Petty, J. T., Strager, M. P., Maxwell, A. E., Ziemkiewicz, P. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+contaminant+mixtures+in+multi-stressor+Appalachian+riverscapes.">Complex contaminant mixtures in multi-stressor Appalachian riverscapes.</a> Environ. Toxicol. Chem. , (2015).</li></ol>

The authors have nothing to disclose.

We provide a framework for assessing and managing cumulative effects of multiple land use activities in heavily impacted watersheds. The approach described herein addresses previously identified limitations associated with managing aquatic systems in heavily impacted watersheds5-6. Most notably, the targeted watershed assessment design (i.e., sampling along individual and combined stressor axes) produces data that are well suited for quantifying complex cumulative effects at relevant spatial scales (<...

Anthropogenic alteration of natural landscapes is among the greatest current threats to aquatic ecosystems throughout the world1. In many regions, continued degradation at current rates will result in irreparable damage to aquatic resources, ultimately limiting their capacity to provide invaluable and irreplaceable ecosystem services. Thus, there is a critical need for tools and methodologies capable of managing aquatic systems within developing watersheds2-3. This is particularly important given that managers are often tasked with conserving aquatic resources in the face of socioeconomic and political pressures to continue development activities.
Management of aquatic systems within actively developing regions requires an ability to predict likely effects of proposed development activities within the context of pre-existing natural and anthropogenic landscape attributes3, 4. A major challenge to aquatic resource management within heavily degraded watersheds is the ability to quantify and manage complex (i.e., additive or interactive) cumulative effects of multiple land use stressors at relevant spatial scales2, 5. Despite current challenges, however, cumulative effects assessments are being incorporated into regulatory guidelines throughout the world5-6.
Targeted watershed assessments designed to sample the full range of conditions with respect to multiple land use stressors can produce data capable of modeling complex cumulative effects7. Moreover, incorporating such models within a scenario analysis framework [predicting ecological changes under a range of realistic or proposed development or watershed management (restoration and mitigation) scenarios] has the potential to greatly improve aquatic resource management within heavily impacted watersheds3, 5, 8-9. Most notably, scenario analysis provides a framework for adding objectivity and transparency to management decisions by incorporating scientific information (ecological relationships and statistical models), regulatory goals, and stakeholder needs into a single decision-making framework3, 9.
We present a methodology for assessing and managing cumulative effects of multiple land use activities within a scenario analysis framework. We first describe how to appropriately target sites for inclusion within the watershed assessment based on known land use stressors. We describe field and laboratory techniques for obtaining data on the ecological effects of multiple land use activities. We briefly describe modeling techniques for producing landscape-based cumulative effects models. Lastly, we discuss how to incorporate cumulative effects models within a scenario analysis framework and demonstrate the utility of this methodology in aiding regulatory decisions (e.g., permitting and restoration) within an intensively mined watershed in southern West Virginia.

<table border="0" cellpadding="0" cellspacing="0" width="1173">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:436px;">Slack Invert Sampling Kit</td>
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			<td height="21" style="height:21px;">HDPE Square Jars</td>
			<td>US Plastic Corp</td>
			<td>66188</td>
			<td>32oz./for storing fixed, composite invertebrate samples</td>
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		<tr height="21">
			<td height="21" style="height:21px;">Ethyl Alcohol 190 Proof</td>
			<td>PHARMCO-AAPER</td>
			<td>111000190</td>
			<td>For fixing and storing invertebrate samples</td>
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		<tr height="21">
			<td height="21" style="height:21px;">5in. by 20in. Macroinvertebrate sub-samplilng grid</td>
			<td>N/A</td>
			<td>N/A</td>
			<td>This item cannot be purchased and must be made in house</td>
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			<td height="21" style="height:21px;">Stereomicroscope Stemi 2000 with stand C LED</td>
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			<td>000000-1106-133</td>
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			<td height="21" style="height:21px;width:436px;">650 Multi-parameter display, standard memory</td>
			<td style="width:167px;">Fondriest Environmental</td>
			<td style="width:123px;">650-01</td>
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			<td height="21" style="height:21px;">600XL Sonde with temperature/conductivity sensor</td>
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			<td height="21" style="height:21px;">pH calibration buffer pack</td>
			<td>Fondriest Environmental</td>
			<td>603824</td>
			<td>2 pints each of pH 4, 7, &#38; 10</td>
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			<td height="21" style="height:21px;">conductivity standard</td>
			<td>Fondriest Environmental</td>
			<td>065270</td>
			<td>1 quart, 1000 uS</td>
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			<td height="21" style="height:21px;">Flo-Mate 2000</td>
			<td>TTT Environmental</td>
			<td>2000-11</td>
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			<td height="21" style="height:21px;">Keson English/Metric Open Reel Fiberglass Tape</td>
			<td>Forestry Suppliers</td>
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			<td height="21" style="height:21px;">ArcGIS 10.3.1</td>
			<td>ESRI</td>
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		</tr>
	</tbody>
</table>

watershed planning within a quantitative scenario analysis framework

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall short as a result of an inability to quantify and predict complex cumulative effects of current and future land use scenarios at relevant spatial scales. The goal of this manuscript is to provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework. Sites are first selected for inclusion within the watershed assessment by identifying sites that fall along independent gradients and combinations of known stressors. Field and laboratory techniques are then used to obtain data on the physical, chemical, and biological effects of multiple land use activities. Multiple linear regression analysis is then used to produce landscape-based cumulative effects models for predicting aquatic conditions. Lastly, methods for incorporating cumulative effects models within a scenario analysis framework for guiding management and regulatory decisions (e.g., permitting and mitigation) within actively developing watersheds are discussed and demonstrated for 2 sub-watersheds within the mountaintop mining region of central Appalachia. The watershed assessment and management approach provided herein enables resource managers to facilitate economic and development activity while protecting aquatic resources and producing opportunity for net ecological benefits through targeted remediation.

Forty 1:24,000 NHD catchments were selected as study sites within the Coal River, West Virginia (Figure 2). Study sites were selected to span a range influence from surface mining (% land area24), residential development [structure density (no./km2)], and underground mining [national pollution discharge elimination system (NPDES) permit density (no./km2)] such that each major land use activity occurred both in isolation and in combination ...

Watch this Scientific Journal Video about Watershed Planning within a Quantitative Scenario Analysis Framework at JoVE.com

Watershed Planning within a Quantitative Scenario Analysis Framework

There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.

There is a critical need for tools and methodologies capable of managing aquatic systems within heavily impacted watersheds. Current efforts often fall ...

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7.9K Views.  West Virginia University. There is a critical need for tools and methodologies capable of managing aquatic systems in the face of uncertain future conditions. We provide methods for conducting a targeted watershed assessment that enables resource managers to produce landscape-based cumulative effects models for use within a scenario analysis management framework.

Video: Watershed Planning within a Quantitative Scenario Analysis Framework

Analysis of Fatty Acid Content and Composition in Microalgae

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of microalgal biomass. These fatty acids can be present in different acyl-lipid classes. Especially the fatty acids present in triacylglycerol (TAG) are of commercial interest, because they can be used for production of transportation fuels, bulk chemicals, nutraceuticals (&omega;-3 fatty acids), and food commodities. To develop commercial applications, reliable analytical methods for quantification of fatty acid content and composition are needed. Microalgae are single cells surrounded by a rigid cell wall. A fatty acid analysis method should provide sufficient cell disruption to liberate all acyl lipids and the extraction procedure used should be able to extract all acyl lipid classes.
With the method presented here all fatty acids present in microalgae can be accurately and reproducibly identified and quantified using small amounts of sample (5 mg) independent of their chain length, degree of unsaturation, or the lipid class they are part of.
This method does not provide information about the relative abundance of different lipid classes, but can be extended to separate lipid classes from each other.
The method is based on a sequence of mechanical cell disruption, solvent based lipid extraction, transesterification of fatty acids to fatty acid methyl esters (FAMEs), and quantification and identification of FAMEs using gas chromatography (GC-FID). A TAG internal standard (tripentadecanoin) is added prior to the analytical procedure to correct for losses during extraction and incomplete transesterification.

A method for the determination of fatty acid content and composition in microalgae based on mechanical cell disruption, solvent based lipid extraction, transesterification, and quantification and identification of fatty acids using gas chromatography is described. A tripentadecanoin internal standard is used to compensate for the possible losses during extraction and incomplete transesterification.

A method to determine the content and composition of total fatty acids present in microalgae is described. Fatty acids are a major constituent of ...

Technique for Studying Arthropod and Microbial Communities within Tree Tissues

Phloem tissues of pine are habitats for many thousands of organisms. Arthropods and microbes use phloem and cambium tissues to seek mates, lay eggs, rear young, feed, or hide from natural enemies or harsh environmental conditions outside of the tree. Organisms that persist within the phloem habitat are difficult to observe given their location under bark. We provide a technique to preserve intact phloem and prepare it for experimentation with invertebrates and microorganisms. The apparatus is called a &#8216;phloem sandwich&#8217; and allows for the introduction and observation of arthropods, microbes, and other organisms. This technique has resulted in a better understanding of the feeding behaviors, life-history traits, reproduction, development, and interactions of organisms within tree phloem. The strengths of this technique include the use of inexpensive materials, variability in sandwich size, flexibility to re-open the sandwich or introduce multiple organisms through drilled holes, and the preservation and maintenance of phloem integrity. The phloem sandwich is an excellent educational tool for scientific discovery in both K-12 science courses and university research laboratories.

We provide a technique to preserve intact tree phloem and prepare it for observation. We create an apparatus called a phloem sandwich that allows for the introduction and observation of arthropods, microbes, and other organisms that inhabit phloem tissues.

Phloem tissues of pine are habitats for many thousands of organisms.  Arthropods and microbes use phloem and cambium tissues to seek mates, lay eggs, rear ...

A Protocol for Conducting Rainfall Simulation to Study Soil Runoff

Rainfall is a driving force for the transport of environmental contaminants from agricultural soils to surficial water bodies via surface runoff. The objective of this study was to characterize the effects of antecedent soil moisture content on the fate and transport of surface applied commercial urea, a common form of nitrogen (N) fertilizer, following a rainfall event that occurs within 24&#160;hr&#160;after fertilizer application. Although urea is assumed to be readily hydrolyzed to ammonium and therefore not often available for transport, recent studies suggest that urea can be transported from agricultural soils to coastal waters where it is implicated in harmful algal blooms. A rainfall simulator was used to apply a consistent rate of uniform rainfall across packed soil boxes that had been prewetted to different soil moisture contents. By controlling rainfall and soil physical characteristics, the effects of antecedent soil moisture on urea loss were isolated. Wetter soils exhibited shorter time from rainfall initiation to runoff initiation, greater total volume of runoff, higher urea concentrations in runoff, and greater mass loadings of urea in runoff. These results also demonstrate the importance of controlling for antecedent soil moisture content in studies designed to isolate other variables, such as soil physical or chemical characteristics, slope, soil cover, management, or rainfall characteristics. Because rainfall simulators are designed to deliver raindrops of similar size and velocity as natural rainfall, studies conducted under a standardized protocol can yield valuable data that, in turn, can be used to develop models for predicting the fate and transport of pollutants in runoff.

A rainfall simulator was used to apply a consistent rate of uniform rainfall to packed soil boxes in a study of the fate and transport of urea, a nonpoint source environmental contaminant. Under uniform soil and rainfall conditions, antecedent soil moisture content exerted strong control over urea loss in surface runoff.

Rainfall is a driving force for the transport of environmental contaminants from agricultural soils to surficial water bodies via surface runoff. The ...

Measurement of Greenhouse Gas Flux from Agricultural Soils Using Static Chambers

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the biogeochemical drivers of climate change and to the development and evaluation of GHG mitigation strategies based on modulation of landscape management practices. The static chamber-based method described here is based on trapping gases emitted from the soil surface within a chamber and collecting samples from the chamber headspace at regular intervals for analysis by gas chromatography. Change in gas concentration over time is used to calculate flux. This method can be utilized to measure landscape-based flux of carbon dioxide, nitrous oxide, and methane, and to estimate differences between treatments or explore system dynamics over seasons or years. Infrastructure requirements are modest, but a comprehensive experimental design is essential. This method is easily deployed in the field, conforms to established guidelines, and produces data suitable to large-scale GHG emissions studies.

This article showcases the static chamber-based method for measurement of greenhouse gas flux from soil systems. With relatively modest infrastructure investments, measurements may be obtained from multiple treatments/locations and over timeframes ranging from hours to years.

Measurement of greenhouse gas (GHG) fluxes between the soil and the atmosphere, in both managed and unmanaged ecosystems, is critical to understanding the ...

Use of Chironomidae (Diptera) Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. Unfortunately, traditional benthic larval sampling methods, such as the dip-net, can be time-consuming and expensive. An alternative protocol involves collection of Chironomidae surface-floating pupal exuviae (SFPE). Chironomidae is a species-rich family of flies (Diptera) whose immature stages typically occur in aquatic habitats. Adult chironomids emerge from the water, leaving their pupal skins, or exuviae, floating on the water&#8217;s surface. Exuviae often accumulate along banks or behind obstructions by action of the wind or water current, where they can be collected to assess chironomid diversity and richness. Chironomids can be used as important biological indicators, since some species are more tolerant to pollution than others. Therefore, the relative abundance and species composition of collected SFPE reflect changes in water quality. Here, methods associated with field collection, laboratory processing, slide mounting, and identification of chironomid SFPE are described in detail. Advantages of the SFPE method include minimal disturbance at a sampling area, efficient and economical sample collection and laboratory processing, ease of identification, applicability in nearly all aquatic environments, and a potentially more sensitive measure of ecosystem stress. Limitations include the inability to determine larval microhabitat use and inability to identify pupal exuviae to species if they have not been associated with adult males.

Use of Chironomidae Diptera Surface-Floating Pupal Exuviae as a Rapid Bioassessment Protocol for Water Bodies

Rapid bioassessment protocols using benthic macroinvertebrates are often used to monitor and assess water quality. An efficient protocol involves collections of Chironomidae surface-floating pupal exuviae (SFPE). Here, techniques for field collection, laboratory processing, slide mounting, and identification of Chironomidae SFPE are described.

Rapid bioassessment protocols using benthic macroinvertebrate assemblages have been successfully used to assess human impacts on water quality. ...

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and metabolism. Although powerful, metagenomics alone can only elucidate functional potential. On the other hand, metaproteomics enables the description of the expressed in situ metabolism and function of a community. Here we describe a protocol for cell lysis, protein and DNA isolation, as well as peptide digestion and extraction from marine microbial cells collected on a cartridge filter unit (such as the Sterivex filter unit) and preserved in an RNA stabilization solution (like RNAlater). In mass spectrometry-based proteomics studies, the identification of peptides and proteins is performed by comparing peptide tandem mass spectra to a database of translated nucleotide sequences. Including the metagenome of a sample in the search database increases the number of peptides and proteins that can be identified from the mass spectra. Hence, in this protocol DNA is isolated from the same filter, which can be used subsequently for metagenomic analysis.

This protocol is for the extraction and concentration of protein and DNA from microbial biomass collected from seawater, followed by the generation of tryptic peptides suitable for tandem mass spectrometry-based proteomic analysis.

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and ...

Extracting Metrics for Three-dimensional Root Systems: Volume and Surface Analysis from In-soil X-ray Computed Tomography Data

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to climate change and crop management. Quantitative size information on roots in their native environment is invaluable for studying root growth and environmental processes involving plants. X-ray computed tomography (XCT) has been demonstrated to be an effective tool for in situ root scanning and analysis. We aimed to develop a costless and efficient tool that approximates the surface and volume of the root regardless of its shape from three-dimensional (3D) tomography data. The root structure of a Prairie dropseed (Sporobolus heterolepis) specimen was imaged using XCT. The root was reconstructed, and the primary root structure was extracted from the data using a combination of licensed and open-source software. An isosurface polygonal mesh was then created for ease of analysis. We have developed the standalone application imeshJ, generated in MATLAB1, to calculate root volume and surface area from the mesh. The outputs of imeshJ are surface area (in mm2) and the volume (in mm3). The process, utilizing a unique combination of tools from imaging to quantitative root analysis, is described. A combination of XCT and open-source software proved to be a powerful combination to noninvasively image plant root samples, segment root data, and extract quantitative information from the 3D data. This methodology of processing 3D data should be applicable to other material/sample systems where there is connectivity between components of similar X-ray attenuation and difficulties arise with segmentation.

A methodology for obtaining visual and quantitative root structure information from X-ray computed tomography data acquired in-soil is presented.

Plant roots play a critical role in plant-soil-microbe interactions that occur in the rhizosphere, as well as processes with important implications to ...

A Protocol for Collecting and Constructing Soil Core Lysimeters

Leaching of nutrients from land applied fertilizers and manure used in agriculture can lead to accelerated eutrophication of surface water. Because the landscape has complex and varied soil morphology, an accompanying disparity in flow paths for leachate through the soil macropore and matrix structure is present. The rate of flow through these paths is further affected by antecedent soil moisture. Lysimeters are used to quantify flow rate, volume of water and concentration of nutrients leaching downward through soils. While many lysimeter designs exist, accurately determining the volume of water and mass balance of nutrients is best accomplished with bounded lysimeters that leave the natural soil structure intact. 
Here we present a detailed method for the extraction and construction of soil core lysimeters equipped with soil moisture sensors at 5 cm and 25 cm depths. Lysimeters from four different Coastal Plain soils (Bojac, Evesboro, Quindocqua and Sassafras) were collected on the Delmarva Peninsula and moved to an indoor climate controlled facility. Soils were irrigated once weekly with the equivalent of 2 cm of rainfall to draw down soil nitrate-N concentrations. At the end of the draw down period, poultry litter was applied (162 kg TN ha-1) and leaching was resumed for an additional five weeks. Total recovery of applied irrigation water varied from 71% to 85%. Nitrate-N concentration varied over the course of the study from an average of 27.1 mg L-1 before litter application to 40.3 mg L-1 following litter application. While greatest flux of nutrients was measured in soils dominated by coarse sand (Sassafras) the greatest immediate flux occurred from the finest textured soil with pronounced macropore development (Quindocqua).

A detailed method for extraction and assembly of intact soil core lysimeters and their use for study of leachate and associated loss of nutrients from surface applied poultry litter is demonstrated.

Leaching of nutrients from land applied fertilizers and manure used in agriculture can lead to accelerated eutrophication of surface water. Because the ...

Clean Sampling and Analysis of River and Estuarine Waters for Trace Metal Studies

Most of the trace metal concentrations in ambient waters obtained a few decades ago have been considered unreliable owing to the lack of contamination control. Developments of some techniques aiming to reduce trace metal contamination in the last couple of decades have resulted in concentrations reported now being orders of magnitude lower than those in the past. These low concentrations often necessitate preconcentration of water samples prior to instrumental analysis of samples. Since contamination can appear in all phases of trace metal analyses, including sample collection (and during preparation of sampling containers), storage and handling, pretreatments, and instrumental analysis, specific care needs to be taken in order to reduce contamination levels at all steps. The effort to develop and utilize "clean techniques" in trace metal studies allows scientists to investigate trace metal distributions and chemical and biological behavior in greater details. This advancement also provides the required accuracy and precision of trace metal data allowing for environmental conditions to be related to trace metal concentrations in aquatic environments.
This protocol that is presented here details needed materials for sample preparation, sample collection, sample pretreatment including preconcentration, and instrumental analysis. By reducing contamination throughout all phases mentioned above for trace metal analysis, much lower detection limits and thus accuracy can be achieved. The effectiveness of "clean techniques" is further demonstrated using low field blanks and good recoveries for standard reference material. The data quality that can be obtained thus enables the assessment of trace metal distributions and their relationships to environmental parameters.

Special care using "clean techniques" is required to properly collect and process water samples for trace metal studies in aquatic environments. A protocol for sampling, processing, and analytical procedures with the aim of obtaining reliable environmental monitoring data and results with high sensitivity for detailed trace metal studies is presented.

Most of the trace metal concentrations in ambient waters obtained a few decades ago have been considered unreliable owing to the lack of contamination ...

Modification and Application of a Leaf Blower-vac for Field Sampling of Arthropods

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropod populations in rice. When used in combination with an enclosure, application of this sampling device provides absolute estimates of the populations of arthropods as numbers per standardized sampling area. The sampling efficiency depends critically on the sampling duration. In a mature rice crop, a two-minute sampling in an enclosure of 0.13 m2 yields more than 90% of the arthropod population. The device also allows sampling of arthropods dwelling on the water surface or the soil in rice paddies, but it is not suitable for sampling fast flying insects, such as predatory Odonata or larger hymenopterous parasitoids. The modified blower-vac is simple to construct, and cheaper and easier to handle than traditional suction sampling devices, such as D-vac. The low cost makes the modified blower-vac also accessible to researchers in developing countries.

Assessment of arthropod abundance in crops is critical for investigating population dynamics and species interactions. Here we describe the modification and application of a leaf blower-vac for suction sampling of arthropods in rice.

Rice fields host a large diversity of arthropods, but investigating their population dynamics and interactions is challenging. Here we describe the ...