This research was funded in part from support received from the U.S. Environmental Protection Agency's Targeted Watersheds Grants Program (Project # WS97704801), the National Science Foundation's Dynamics of Coupled Natural and Human Systems (Project #DEB1010259-CARD-KLIN), and the U.S. Department of Agriculture-National Institute of Foodand Agriculture's Coordinated Agricultural Project (Project # 20116800230190-CARD-).

1. Prepare Watershed Model and Provide Input Data for Optimization
<ol>
	<li>Create an i_SWAT database.
	<ol>
		<li>Using a program called &#34;rotator&#34;, build the database from multiple input databases including soils, weather, management and fertilizer.</li>
		<li>Alternatively, an existing SWAT run (possibly created with ArcSWAT or AVSWAT) can be imported with i_SWAT.exe. In this case, the program &#34;swat_rewrite&#34; can be used to replace management or other HRU information based on field-level data.</li>
		<li>Calibration and validation of the SWAT model should be performed at this point. The SWAT (version 2005) model incorporated within this Raccoon River Watershed EA modeling framework was originally calibrated and validated for a Total Maximum Daily Load (TMDL) study as described by Jha et al. (2010). Further calibration and validation of the SWAT model was conducted in support of the development of a Raccoon River Watershed Master Plan, as described in Agren, Inc. (2011), which is the SWAT model that was used for this study.</li>
		<li>Use a modified version of SWAT2005.exe, called SWAT2005GA.exe.</li>
	</ol>
	</li>
	<li>Prepare file &#34;watershed presets.csv&#34; - This is a text file storing many of the settings specific to the Raccoon watershed. It is read by GeneticiSWAT and MapSWAT below to set multiple controls and data fields for the watershed with one click.</li>
	<li>Prepare the costs of conservation practice elements. For this example, these are stored in table [Practice Costs - Raccoon by County] in a database &#34;Practice costs by subbasin Josh.mdb&#34;.</li>
</ol>
The total cost of a candidate solution is the sum of costs of conservation practices applied to watershed units (&#34;hydrologic response units&#34;, or HRUs). The optimization program considers an optimal assignment of a single conservation practice from a particular set of conservation practices in every cropland HRU in the watershed. The sets of possible conservation practices assigned to an HRU are called allele sets.
<ol start="4">
	<li>Create SWAT folders. For this run, 16 CPUs were used, meaning 16 copies of SWAT2005GA.exe were run in 16 separate folders (the same applies for systems with fewer CPUs, e.g. 4 folders should be created for a &#34;quad-core&#34; processor).</li>
</ol>
2. Select Optimization Parameters
<ol>
	<li>Optimization is controlled by the program called &#34;GeneticISWAT&#34;. To perform optimization, open GeneticISWAT.exe.
	<ol>
		<li>Go to &#34;File&#34;, then &#34;Open&#34; and select the i_SWAT database &#34;Raccoon GA.mdb&#34;.</li>
		<li>Go to &#34;File&#34;, then &#34;Configuration&#34; to assign the paths to SWAT model executables (SWAT2005GA.exe).</li>
		<li>Go to &#34;Execute&#34;, then &#34;Select Allele Set&#34;. This step determines the combinations of conservation practices used in optimization. For this run, allele set #14 was used, which has 23 combinations of conservation practices. Available allele sets are stored in the configuration file &#34;Alleles.csv&#34;.</li>
		<li>Go to &#34;Execute&#34;, then select &#34;SPEA2 Archive Baseline Aware Subset&#34; to perform multiobjective optimization using the SPEA2 evolutionary algorithm.</li>
	</ol>
	</li>
</ol>
<img alt="Figure 1" src="/files/ftp_upload/4009/4009fig1.jpg" /> 
Figure 1. Setting optimization objectives and parameters.
Optimization parameters to be selected:
Preset: Select the watershed to be optimized. Clicking &#34;Apply&#34; selects entries from the presets file &#34;watershed presets.csv&#34; to fill control values on this screen.
Output Variable: Select the environmental objectives for optimization. As selected (N Outlet, P Outlet), this defines a 3-dimensional objective function: Nitrogen (Organic N + NO3 + NH4 + NO2) averaged for 5 years at the outlet, Phosphorus (Organic P + Mineral P) averaged for 5 years at the outlet, and the total cost of conservation practices. Note that this will create a 3-dimensional tradeoff frontier. Alternative output variables can be selected, where the multiobjective program is to minimize ({Output Variable}, Total Cost).
Population size: Set initial population size. This determines the initial number of candidate solutions. When &#34;Seed with each allele&#34; option is selected, candidate solutions representing a uniform application of each conservation practice specified in the allele set to all cropland HRUs in the watershed are created first. The remaining candidate solutions are created by a random assignment of conservation practices from the allele set to cropland HRUs. When selecting the &#34;Seed with each allele&#34; option, one needs to make sure that the initial population size is at least as large as the number of alleles in an allele set (23 in this demonstration).
Number of generations: Set the desired number of generations (iterations) for the optimization run (note that the run can be restarted).
Crossover probability: When two candidate solutions are selected for creating new candidate solutions, crossover probability specifies the probability that distinct new solutions are created (set to 1 for this demonstration).
Size of temporary population: This determines the number of new candidate solutions created. Processor resources are used most efficiently when this value is an integer multiple of the number of processor threads (16 in this demonstration).
Mutation probability: Specify the probability of random change in HRU assignment to another conservation practice from the allele set. (It is set to 0.03 for this demonstration).
Number of threads: Select the number of processors or threads used. 16 is used in this demonstration.
Curve No. calibration factor: This is provided from the SWAT model calibration.
Save Population in text file: This is important to select if one wishes to restart the optimization run after the specified number of iterations is completed. Checking this option produces a text file with the allele values of every HRU in every surviving candidate solution (individual). This can be read back in to restart and continue a run.
Secondary optimization parameters
First Year: Must be set to a year after start of historical weather information, and no later than 7 years before end of this data.
Price of Corn: Used with the yield loss equation to estimate the cost of fertilizer reductions.
Scoring Method: SPEA2 Archive. Scoring determines how likely a surviving individual is to be selected for crossover.
Purge Method: Dominated. Individuals which are worse in all 3 dimensions are dominated and purged.
HUC Source: Set to &#34;Specified Location&#34;, meaning the value &#34;7100006&#34; from the following field &#34;Watershed HUC&#34; is used to find a row in the HUC Zone table. The value &#34;07100006&#34; is the eight-digit HUC code for the Raccoon watershed.
Cost Source: Set to &#34;County (HRU Location Code)&#34; to indicate that costs other than CRP will be determined by county FIPS codes in the practice costs table above.
Cost Source CRP: Set to &#34;1 Location&#34; to indicate that CRP cost will be determined by county FIPS codes in the practice costs table above.
SWAT version: SWAT2005
3. Representative Results
GeneticiSWAT.exe produces a log file showing the settings and results for all candidate solutions (individuals), as well as a &#34;save&#34; file which encodes the results from the final algorithm iteration and which can be used to restart the optimization run.
At this point, one can visualize the entire set of Pareto-efficient solutions (the tradeoff frontier) by following the steps below:
<ol>
	<li>Run GeneticiSWAT;
	<ol>
		<li>Go to &#34;File&#34;, then &#34;Open&#34; to open the i_SWAT database &#34;Raccoon GA.mdb&#34;.</li>
		<li>Go to &#34;Execute&#34;, then &#34;Export HRU List&#34;. Save file as &#34;Raccoon Allele HRU.txt&#34;.</li>
	</ol>
	</li>
	<li>Produce an animation by running Mapswat.exe, selecting &#34;Execute&#34; and then &#34;3d Animation&#34;.</li>
</ol>
<img alt="Figure 2" src="/files/ftp_upload/4009/4009fig2.jpg" /> 
Figure 2. Screenshot for creating &#34;snapshots&#34; for 3-dimensional frontier visualization.
Output is a series of files which can be rendered all at once into image files by using POV-RAY program and selecting &#34;Render&#34;, then &#34;File Queue&#34;. The images can be used on their own or combined into a movie showing the algorithm progression.
<img alt="Figure 3" src="/files/ftp_upload/4009/4009fig3.jpg" /> 
Figure 3. Static visualization of the tradeoff frontier.
If desired, a movie showing the algorithm progression can be created by running &#34;Framescanner.exe&#34; and following these steps:
<ol>
	<li>Go to &#34;File&#34;, then &#34;New&#34;, then &#34;File&#34;, then &#34;Import&#34;, then &#34;PNG Files&#34;. Select the static images.</li>
	<li>To create a movie, go to &#34;File&#34;, then &#34;Export&#34;, then &#34;AVI&#34;.</li>
	<li>Select codec &#34;DIB&#34; to create AVI files from batches of image files.</li>
</ol>
Each point in the frontier represents a watershed configuration (a specific assignment of conservation practices on a landscape). Maps of these configurations can be seen for the entire frontier by following these steps:
<ol>
	<li>Run Mapswat.exe, select &#34;Execute&#34;, then &#34;Map Animation&#34;.</li>
	<li>Select &#34;Raccoon&#34; from preset list and click &#34;Apply&#34;.</li>
	<li>Select &#34;Layout 7 (Raccoon)&#34; from Map Layout list, then click &#34;OK&#34;.</li>
</ol>
<img alt="Figure 4" src="/files/ftp_upload/4009/4009fig4.jpg" /> 
Figure 4. Screenshot of creating a map of each individual in the final frontier.
Exporting specific watershed configurations (individuals) of interest. 
Often a question of interest is to select specific watershed configurations (individuals) achieving particular water quality objectives. For example, one may wish to find an individual in the frontier which reduces Nitrogen by 30% and Phosphorus by 20% relative to baseline loadings. MapSWAT allows one to search the frontier for the individual with the smallest Euclidean distance to the specified objective. This can be done by doing the following:
<ol>
	<li>Open MapSWAT.exe. Select &#34;Execute&#34; | &#34;Search&#34;.</li>
</ol>
<img alt="Figure 5" src="/files/ftp_upload/4009/4009fig5.jpg" /> 
Figure 5. Screenshot of searching for a specific individual in the frontier based on water quality objectives.
<ol start="2">
	<li>Enter minimum and maximum reduction targets Tmin and Tmax as well as an interval Tint. Also enter a specific percent reduction in Nitrogen (Nspec) from baseline in the &#34;% reduction&#34; space next to N Baseline, and percent reduction in Phosphorus (Pspec) in the &#34;% reduction&#34; next to Phosphorus baseline. The program produces output in a popup screen:</li>
</ol>
<img alt="Figure 6" src="/files/ftp_upload/4009/4009fig6.jpg" /> 
Figure 6. Screenshot of search output
<ol start="3">
	<li>Click &#34;Copy Text&#34; and paste into a spreadsheet. Three tables are produced. In the first are individuals nearest to N and P targets of the same percent reduction, which ranges from Tmin to Tmax by Tint. Just below this the closest single individual to the target (Nspec, Pspec) appears. Second, a table giving nearest individuals where the P target ranges from Tmin to Tmax while N is held constant near Nspec. Third, a table giving individuals nearest N targets ranging from Tmin to Tmax while P is held constant near Pspec. In this case, the closest individual to a 30% N reduction was ID 8423 with an N value of 14,637,279.60. Here is the map showing the spatial distribution of conservation practices and the location of this watershed configuration in the tradeoff frontier:</li>
</ol>
<img alt="Figure 7" src="/files/ftp_upload/4009/4009fig7.jpg" /> 
Figure 7. Screenshot of a sample map describing the selected individual in the frontier. <a href="https://www.jove.com/files/ftp_upload/4009/4009fig7large.jpg" target="_blank"> Click here to view larger figure</a>.
Exporting map data for further analysis is possible by following these steps:
<ol>
	<li>Run Mapswat.exe, select &#34;Execute&#34;, then &#34;Export Map Data&#34;.</li>
	<li>Select &#34;Raccoon&#34; from preset list and click &#34;Apply&#34;.</li>
	<li>Enter the ID of a watershed configuration (individual) (8423 shown), check &#34;Show Allele Properties&#34; and &#34;Show Costs&#34;, and then click &#34;OK&#34;. This data can be used to create custom maps of the selected watershed configuration using a GIS program.</li>
</ol>
<table border="1">
	<tbody>
		<tr>
			<td>Name of Program</td>
			<td>Source</td>
			<td>Description</td>
		</tr>
		<tr>
			<td>Rotator</td>
			<td>CARD</td>
			<td>Creates and fills an i_SWAT database with soil, weather, and management data for a watershed.</td>
		</tr>
		<tr>
			<td>Swat2005GA.exe</td>
			<td>USDA Grassland, Soil &#38; Water Research Laboratory</td>
			<td>Watershed simulation model</td>
		</tr>
		<tr>
			<td>i_SWAT.exe</td>
			<td>CARD</td>
			<td>SWAT database interface</td>
		</tr>
		<tr>
			<td>GeneticISWAT.exe</td>
			<td>CARD</td>
			<td>Evolutionary algorithm SWAT controller. Incorporates GALib from <a href="http://lancet.mit.edu/ga/" target="_blank">http://lancet.mit.edu/ga/</a>.</td>
		</tr>
		<tr>
			<td>MapSWAT.exe</td>
			<td>CARD</td>
			<td>Reads i_SWAT databases and shapefiles, produces images of generations and individuals.</td>
		</tr>
		<tr>
			<td>POV-Ray</td>
			<td>Povray.org</td>
			<td>Persistence of Vision raytracer.</td>
		</tr>
		<tr>
			<td>Framescanner.exe</td>
			<td>Todd Campbell</td>
			<td>PNG image to AVI converter</td>
		</tr>
		<tr>
			<td>Windows Live Movie Maker</td>
			<td>Microsoft</td>
			<td>Used to compress AVI to WMV</td>
		</tr>
	</tbody>
</table>
Table 1. Table of programs required.
<table border="1">
	<tbody>
		<tr>
			<td>Name of File</td>
			<td>Type</td>
			<td>Description</td>
		</tr>
		<tr>
			<td>Raccoon GA.mdb</td>
			<td>Access database</td>
			<td>Structure and management descriptions of Raccoon watershed. Read by GeneticiSWAT and MapSWAT.</td>
		</tr>
		<tr>
			<td>watershed presets.csv</td>
			<td>Text</td>
			<td>Setting presets for GeneticiSWAT.exe and MapSWAT</td>
		</tr>
		<tr>
			<td>Alleles.csv</td>
			<td>Text</td>
			<td>List of allele sets for evolutionary algorithm.</td>
		</tr>
		<tr>
			<td>Raccoon Allele HRU.txt</td>
			<td>Text</td>
			<td>File created by GeneticISWAT listing those alleles determined to be cropland. Read by MapSWAT.</td>
		</tr>
		<tr>
			<td>Practice costs by subbasin Josh.mdb</td>
			<td>Access Database</td>
			<td>Costs by management practice and county.</td>
		</tr>
		<tr>
			<td>Terrace Zones.mdb</td>
			<td>Access Database</td>
			<td>Table [HUC Data] holds the terrace and yield zone numbers for the watershed.</td>
		</tr>
		<tr>
			<td>NRI Budgets.mdb</td>
			<td>Access Database</td>
			<td>Read by GeneticISWAT.exe for crop &#38; machine tables which are not used in this run.</td>
		</tr>
		<tr>
			<td>phucrp 2008-12-15.dat</td>
			<td>Text</td>
			<td>Plant Heat Unit lookup table, not used in this run.</td>
		</tr>
		<tr>
			<td>Management.mdb</td>
			<td>Access Database</td>
			<td>Rotation lookup table, not used in this run.</td>
		</tr>
		<tr>
			<td>Raccoon GA 2011-09-28 1313.log, Raccoon GA 2011-09-29 0732.log, Raccoon GA 2011-10-07 0644.log</td>
			<td>Text</td>
			<td>Log files of GeneticISWAT run.</td>
		</tr>
		<tr>
			<td><a href="https://www.jove.com/files/ftp_upload/4009/Raccoon_GA.wmv">Raccoon GA.wmv</a></td>
			<td>Animation</td>
			<td>3d display of individuals by generation</td>
		</tr>
		<tr>
			<td><a href="https://www.jove.com/4009/Subbasin.shp" target="_blank">Subbasin.shp</a></td>
			<td>ESRI Shapefile</td>
			<td>Outlines of subbasins in the watershed.</td>
		</tr>
		<tr>
			<td><a href="https://www.jove.com/files/ftp_upload/4009/Raccoon_Map.wmv" target="_blank">Raccoon Map.wmv</a></td>
			<td>Animation</td>
			<td>Display of dominant alleles for each subbasin for each individual on the frontier.</td>
		</tr>
	</tbody>
</table>
Table 2. Table of sample files required.

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O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Consideration+of+off-site+impacts+in+targeting+soil+conservation+programs.">Consideration of off-site impacts in targeting soil conservation programs.</a> Land Econ. 62, 402-411 (1986).</li><li>Ribaudo, M. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Targeting+the+conservation+reserve+program+to+maximize+water+quality+benefits.">Targeting the conservation reserve program to maximize water quality benefits.</a> Land Econ. 65, 320-332 (1989).</li><li>Srivastava, P., Hamlett, J. M., Robillard, P. D., Day, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Watershed+optimization+of+best+management+practices+using+AnnAGNPS+and+a+genetic+algorithm.">Watershed optimization of best management practices using AnnAGNPS and a genetic algorithm.</a> Water Resour. Res. 38 (3), 1-14 (2002).</li><li>Veith, T. L., Wolfe, M. L., Heatwole, 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=Development+of+optimization+procedure+for+cost-effective+BMP+placement.">Development of optimization procedure for cost-effective BMP placement.</a> J. Am. Water Resour. Assoc. 39 (6), 1331-1343 (2003).</li><li>Whittaker, G., Confesor, R., Griffith, S. M., F&#228;re, R., Grosskopf, S., Steiner, J. J., Mueller-Warrant, G. W., Banowetz, G. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+Hybrid+Genetic+Algorithm+for+Multiobjective+Problems+with+Activity+Analysis-based+Local+Search.">A Hybrid Genetic Algorithm for Multiobjective Problems with Activity Analysis-based Local Search.</a> Eur. J. Operat. Res. , (2007).</li><li>Zitzler, E., Laumanns, M., Thiele, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SPEA2:+Improving+the+Strength+Pareto+Evolutionary+Algorithm+for+Multiobjective+Optimization.">SPEA2: Improving the Strength Pareto Evolutionary Algorithm for Multiobjective Optimization.</a> CIMNE. , 95-100 (2002).</li></ol>

No conflicts of interest declared.

We build an integrated simulation-optimization framework to search for Pareto-efficient sets of watershed configurations involving lowest-cost mix and location of agricultural conservation practices to achieve a range of watershed-level nutrient reduction objectives. A conceptual diagram of the simulation-optimization system is presented in Figure 8. Watershed simulation, including simulating the water quality impacts of agricultural conservation practices are handled by the hydrologic model, SWAT2005, c...

spatial multiobjective optimization of agricultural conservation practices using a swat model and an evolutionary algorithm

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals across the landscape is of primary importance in watershed management. Traditional economics methods of finding the lowest-cost solution in the watershed context (e.g.,5,12,20) assume that off-site impacts can be accurately described as a proportion of on-site pollution generated. Such approaches are unlikely to be representative of the actual pollution process in a watershed, where the impacts of polluting sources are often determined by complex biophysical processes. The use of modern physically-based, spatially distributed hydrologic simulation models allows for a greater degree of realism in terms of process representation but requires a development of a simulation-optimization framework where the model becomes an integral part of optimization.
Evolutionary algorithms appear to be a particularly useful optimization tool, able to deal with the combinatorial nature of a watershed simulation-optimization problem and allowing the use of the full water quality model. Evolutionary algorithms treat a particular spatial allocation of conservation practices in a watershed as a candidate solution and utilize sets (populations) of candidate solutions iteratively applying stochastic operators of selection, recombination, and mutation to find improvements with respect to the optimization objectives. The optimization objectives in this case are to minimize nonpoint-source pollution in the watershed, simultaneously minimizing the cost of conservation practices. A recent and expanding set of research is attempting to use similar methods and integrates water quality models with broadly defined evolutionary optimization methods3,4,9,10,13-15,17-19,22,23,25. In this application, we demonstrate a program which follows Rabotyagov et al.'s approach and integrates a modern and commonly used SWAT water quality model7 with a multiobjective evolutionary algorithm SPEA226, and user-specified set of conservation practices and their costs to search for the complete tradeoff frontiers between costs of conservation practices and user-specified water quality objectives. The frontiers quantify the tradeoffs faced by the watershed managers by presenting the full range of costs associated with various water quality improvement goals. The program allows for a selection of watershed configurations achieving specified water quality improvement goals and a production of maps of optimized placement of conservation practices.

Watch this Scientific Journal Video about Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm at JoVE.com

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.

Finding the cost-efficient (i.e., lowest-cost) ways of targeting conservation practice investments for the achievement of specific water quality goals ...

spatial-multiobjective-optimization-agricultural-conservation

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JoVE Journal

Biology

12.9K Views.  University of Washington. This work demonstrates an integration of a water quality model with an optimization component utilizing evolutionary algorithms to solve for optimal (lowest-cost) placement of agricultural conservation practices for a specified set of water quality improvement objectives. The solutions are generated using a multi-objective approach, allowing for explicit quantification of tradeoffs.

Video: Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Charles Taylor and John Marshall explain the utility of mathematical modeling for evaluating the effectiveness of population replacement strategy.  Insight is given into how computational models can provide information on the population dynamics of mosquitoes and the spread of transposable elements through A. gambiae subspecies.  The ethical considerations of releasing genetically modified mosquitoes into the wild are discussed.

Charles Taylor and John Marshall explain the utility of mathematical modeling for evaluating the effectiveness of population replacement strategy. Insight is given into how computational models can provide information on the population dynamics of mosquitoes and the spread of transposable elements through A. gambiae subspecies. The ethical considerations of releasing genetically modified mosquitoes into the wild are discussed.

Charles Taylor and John Marshall explain the utility of mathematical modeling for evaluating the effectiveness of population replacement strategy.  ...

Non-radioactive in situ Hybridization Protocol Applicable for Norway Spruce and a Range of Plant Species

The high-throughput expression analysis technologies available today give scientists an overflow of expression profiles but their resolution in terms of tissue specific expression is limited because of problems in dissecting individual tissues. Expression data needs to be confirmed and complemented with expression patterns using e.g. in situ hybridization, a technique used to localize cell specific mRNA expression. The in situ hybridization method is laborious, time-consuming and often requires extensive optimization depending on species and tissue. In situ experiments are relatively more difficult to perform in woody species such as the conifer Norway spruce (Picea abies). Here we present a modified DIG in situ hybridization protocol, which is fast and applicable on a wide range of plant species including P. abies. With just a few adjustments, including altered RNase treatment and proteinase K concentration, we could use the protocol to study tissue specific expression of homologous genes in male reproductive organs of one gymnosperm and two angiosperm species; P. abies, Arabidopsis thaliana and Brassica napus. The protocol worked equally well for the species and genes studied. AtAP3 and BnAP3 were observed in second and third whorl floral organs in A. thaliana and B. napus and DAL13 in microsporophylls of male cones from P. abies. For P. abies the proteinase K concentration, used to permeablize the tissues, had to be increased to 3 g/ml instead of 1 g/ml, possibly due to more compact tissues and higher levels of phenolics and polysaccharides. For all species the RNase treatment was removed due to reduced signal strength without a corresponding increase in specificity. By comparing tissue specific expression patterns of homologous genes from both flowering plants and a coniferous tree we demonstrate that the DIG in situ protocol presented here, with only minute adjustments, can be applied to a wide range of plant species. Hence, the protocol avoids both extensive species specific optimization and the laborious use of radioactively labeled probes in favor of DIG labeled probes. We have chosen to illustrate the technically demanding steps of the protocol in our film.
Anna Karlgren and Jenny Carlsson contributed equally to this study.
Corresponding authors: Anna Karlgren at Anna.Karlgren@ebc.uu.se and Jens F. Sundstr&#246;m at Jens.Sundstrom@vbsg.slu.se

Non-radioactive in situ Hybridization Protocol Applicable for Norway Spruce and a Range of Plant Species

We describe a modified DIG in situ hybridization protocol, which is fast and applicable on a wide range of plant species including Norway spruce. With just a few adjustments, including altered RNase treatment and proteinase K concentration, the protocol may be used in studies of different tissues and species.

The high-throughput expression analysis technologies available today give scientists an overflow of expression profiles but their resolution in terms of ...

An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies

Fopius arisanus (Sonan) is an important parasitoid of Tephritid fruit flies for at least two reasons. First, it is the one of only three opiine parasitoids known to infect the host during the egg stage1. Second, it has a wide range of potential fruit fly hosts. Perhaps due to its life history, F. arisanus has been a successfully used for biological control of fruit flies in multiple tropical regions2-4. One impediment to the wide use of F. arisanus for fruit fly control is that it is difficult to establish a stable laboratory colony5-9. Despite this difficulty, in the 1990s USDA researchers developed a reliable method to maintain laboratory populations of F. arisanus10-12. There is significant interest in F. arisanus biology13,14, especially regarding its ability to colonize a wide variety of Tephritid hosts14-17; interest is especially driven by the alarming spread of Bactrocera fruit fly pests to new continents in the last decade18. Further research on F. arisanus and additional deployments of this species as a biological control agent will benefit from optimizations and improvements of rearing methods. In this protocol and associated video article we describe an optimized method for rearing F. arisanus based on a previously described approach12. The method we describe here allows rearing of F. arisanus in a small scale without the use of fruit, using materials available in tropical regions around the world and with relatively low manual labor requirements.

An Optimized Protocol for Rearing Fopius arisanus, a Parasitoid of Tephritid Fruit Flies

Fopius arisanus is an egg-larval parasitoid of Tephritid fruit flies that is successfully used in biological control of these important tropical pests. We describe here an optimized protocol for rearing F. arisanus on a small scale using readily available materials.

Fopius arisanus (Sonan) is an important parasitoid of Tephritid fruit flies for at least two reasons. First, it is the one of only three opiine ...

Ablation of a Single Cell From Eight-cell Embryos of the Amphipod Crustacean Parhyale hawaiensis

The amphipod Parhyale hawaiensis is a small crustacean found in intertidal marine habitats worldwide. Over the past decade, Parhyale has emerged as a promising model organism for laboratory studies of development, providing a useful outgroup comparison to the well studied arthropod model organism Drosophila melanogaster. In contrast to the syncytial cleavages of Drosophila, the early cleavages of Parhyale are holoblastic. Fate mapping using tracer dyes injected into early blastomeres have shown that all three germ layers and the germ line are established by the eight-cell stage. At this stage, three blastomeres are fated to give rise to the ectoderm, three are fated to give rise to the mesoderm, and the remaining two blastomeres are the precursors of the endoderm and germ line respectively. However, blastomere ablation experiments have shown that Parhyale embryos also possess significant regulatory capabilities, such that the fates of blastomeres ablated at the eight-cell stage can be taken over by the descendants of some of the remaining blastomeres. Blastomere ablation has previously been described by one of two methods: injection and subsequent activation of phototoxic dyes or manual ablation. However, photoablation kills blastomeres but does not remove the dead cell body from the embryo. Complete physical removal of specific blastomeres may therefore be a preferred method of ablation for some applications. Here we present a protocol for manual removal of single blastomeres from the eight-cell stage of Parhyale embryos, illustrating the instruments and manual procedures necessary for complete removal of the cell body while keeping the remaining blastomeres alive and intact. This protocol can be applied to any Parhyale cell at the eight-cell stage, or to blastomeres of other early cleavage stages. In addition, in principle this protocol could be applicable to early cleavage stage embryos of other holoblastically cleaving marine invertebrates.

Ablation of a Single Cell From Eight-cell Embryos of the Amphipod Crustacean Parhyale hawaiensis

The amphipod Parhyale hawaiensis is a promising model organism for studies of crustacean embryology and comparative arthropod development and evolution. This protocol describes a method for manual removal of single blastomeres from early cleavage stage embryos of Parhyale.

The amphipod Parhyale hawaiensis is a small crustacean found in intertidal marine habitats worldwide. Over the past decade, Parhyale has emerged as a ...

Measuring Spatial and Temporal Ca2+ Signals in Arabidopsis Plants

Developmental and environmental cues induce Ca2+ fluctuations in plant cells. Stimulus-specific spatial-temporal Ca2+ patterns are sensed by cellular Ca2+ binding proteins that initiate Ca2+ signaling cascades. However, we still know little about how stimulus specific Ca2+ signals are generated. The specificity of a Ca2+ signal may be attributed to the sophisticated regulation of the activities of Ca2+ channels and/or transporters in response to a given stimulus. To identify these cellular components and understand their functions, it is crucial to use systems that allow a sensitive and robust recording of Ca2+ signals at both the tissue and cellular levels. Genetically encoded Ca2+ indicators that are targeted to different cellular compartments have provided a platform for live cell confocal imaging of cellular Ca2+ signals. Here we describe instructions for the use of two Ca2+ detection systems: aequorin based FAS (film adhesive seedlings) luminescence Ca2+ imaging and case12 based live cell confocal fluorescence Ca2+ imaging. Luminescence imaging using the FAS system provides a simple, robust and sensitive detection of spatial and temporal Ca2+ signals at the tissue level, while live cell confocal imaging using Case12 provides simultaneous detection of cytosolic and nuclear Ca2+ signals at a high resolution.

Measuring Spatial and Temporal Ca2+ Signals in Arabidopsis Plants

Ca2+ signaling regulates diverse biological processes in plants. Here we present approaches for monitoring abiotic stress induced spatial and temporal Ca2+ signals in Arabidopsis cells and tissues using the genetically encoded Ca2+ indicators Aequorin or Case12.

Developmental and environmental cues induce Ca2+ fluctuations in plant cells. Stimulus-specific spatial-temporal Ca2+ patterns are sensed by cellular Ca2+ ...

Observation and Quantification of Mating Behavior in the Pinewood Nematode, Bursaphelenchus xylophilus

A method for observing and quantifying the mating behavior of the pinewood nematode, Bursaphelenchus xylophilus, was established under a stereomicroscope. To improve the mating efficiency of B. Xylophilus and to increase the chances of mating observation, virgin adults were cultured and used for the investigation. Eggs were obtained by keeping the nematodes in water and allowing the females to lay eggs for 10 min. The second-stage juveniles (J2) were synchronized by incubating the eggs for 24 h at 25 &#176;C in the dark, and the early J4 were obtained by culturing the J2 with grey mold, Botrytis cinerea, for another 52 h. At this time point, most J4 nematodes could be clearly distinguished as being male or female using their genital morphology. The male and female J4 were collected and cultured separately in two different Petri dishes for 24 h to get virgin adult nematodes. A virgin male and a virgin female were paired in a drop of water in the well of a concave slide. The mating behavior was filmed with a video recorder under a stereomicroscope. The whole period of the mating process was 82.8 &#177;3.91 min (mean &#177;SE) and could be divided into 4 different phases: searching, contacting, copulating, and lingering. The mean minutes of duration were 21.8 &#177; 2.0, 28.0 &#177; 1.9, 25.8 &#177; 0.7 and 7.2 &#177; 0.5, respectively. Eleven sub-behaviors were described: cruising, approaching, encountering, touching, hooping, locating, attaching, ejaculating, separating, quiescence, and roaming. Interestingly, obvious intra-sexual competition was observed when one female was grouped with 3 males or one male with 3 females. This protocol is useful and valuable, not only in investigating the mating behavior of B. xylophilus, but also in acting as a reference for ethological studies of other nematodes.

Observation and Quantification of Mating Behavior in the Pinewood Nematode, Bursaphelenchus xylophilus

A protocol for investigating the mating behavior of the pinewood nematode, Bursaphelenchus xylophilus, is presented. Behavioral features of B. xylophilus are described in the mating process.

A method for observing and quantifying the mating behavior of the pinewood nematode, Bursaphelenchus xylophilus, was established under a stereomicroscope. ...

Transient Expression and Cellular Localization of Recombinant Proteins in Cultured Insect Cells

Heterologous protein expression systems are used for the production of recombinant proteins, the interpretation of cellular trafficking/localization, and the determination of the biochemical function of proteins at the sub-organismal level. Although baculovirus expression systems are increasingly used for protein production in numerous biotechnological, pharmaceutical, and industrial applications, nonlytic systems that do not involve viral infection have clear benefits but are often overlooked and underutilized. Here, we describe a method for generating nonlytic expression vectors and transient recombinant protein expression. This protocol allows for the efficient cellular localization of recombinant proteins and can be used to rapidly discern protein trafficking within the cell. We show the expression of four&#160;recombinant proteins in a commercially available insect cell line, including two aquaporin proteins from the insect Bemisia tabaci, as well as subcellular marker proteins specific for the cell plasma membrane and for intracellular lysosomes. All recombinant proteins were produced as chimeras with fluorescent protein markers at their carboxyl termini, which allows for the direct detection of the recombinant proteins. The double transfection of cells with plasmids harboring constructs for the genes of interest and a known subcellular marker allows for live cell imaging and improved validation of cellular protein localization.

Nonlytic insect cell expression systems are underutilized for production, cellular trafficking/localization, and recombinant protein functional analysis. Here, we describe methods to generate expression vectors and subsequent transient protein expression in commercially available lepidopteran cell lines. The co-localization of Bemisia tabaci aquaporins with subcellular fluorescent marker proteins is also presented.

Heterologous protein expression systems are used for the production of recombinant proteins, the interpretation of cellular trafficking/localization, and ...

Reproductive Techniques for Ovarian Monitoring and Control in Amphibians

Ovarian control and monitoring in amphibians require a multi-faceted approach. There are several applications that can successfully induce reproductive behaviors and the acquisition of gametes and embryos for physiological or molecular research. Amphibians contribute to one-quarter to one-third of vertebrate research, and of interest in this context is their contribution to the scientific community&#39;s knowledge of reproductive processes and embryological development. However, most of this knowledge is derived from a small number of species. In recent times, the decimation of amphibians across the globe has required increasing intervention by conservationists. The captive recovery and assurance colonies that continue to emerge in response to the extinction risk make existing research and clinical applications invaluable to the survival and reproduction of amphibians held under human care. The success of any captive population is founded on its health and reproduction and the ability to develop viable offspring that carry forward the most diverse genetic representation of their species. For researchers and veterinarians, the ability to monitor and control ovarian development and health is, therefore, imperative. The focus of this article is to highlight the different assisted reproductive techniques that can be used to monitor and, where appropriate or necessary, control ovarian function in amphibians. Ideally, any reproductive and health issues should be reduced through proper captive husbandry, but, as with any animal, issues of health and reproductive pathologies are inevitable. Non-invasive techniques include behavioral assessments, visual inspection and palpation and morphometric measurements for the calculation of body condition indices and ultrasound. Invasive techniques include hormonal injections, blood sampling, and surgery. Ovarian control can be exercised in a number of ways depending on the application required and species of interest.

The study of amphibian biology provides valuable information on the reproductive, physiological, embryological and developmental processes that drive organisms of many taxonomic groups. Here, we present a comprehensive guide on different methodologies that can be used to study ovarian control and monitoring in amphibians.

Ovarian control and monitoring in amphibians require a multi-faceted approach. There are several applications that can successfully induce reproductive ...

Using a Thermal Camera to Measure Heat Loss Through Bird Feather Coats

Feathers are essential to insulation, and therefore to the cost of thermoregulation,&#160;in birds. There is robust literature on the energetic cost of thermoregulation in birds across a variety of ecological circumstances. However, few studies characterize the contribution of the feathers alone to thermoregulation. Several previous studies have established methods for measuring the insulation value of animal pelts, but they require destructive sampling methods that are problematic for birds, whose feathers are not distributed evenly across the skin. More information is needed about 1) how the contribution of feathers to thermoregulation varies both across and within species and 2) how feather coats may change over space and time. Reported here is a method for rapidly and directly measuring the thermal performance of feather coats and the skin using dried whole skin specimens, without the need to destroy the skin specimen. This method isolates and measures the thermal gradient across a feather coat in a way that measurements of heat loss and metabolic cost in live birds, which use behavioral and physiological strategies to thermoregulate, cannot. The method employs a thermal camera, which allows the rapid collection of quantitative thermal data to measure heat loss from a stable source through the skin. This protocol can easily be applied to various research questions, is applicable to any avian taxa, and does not require destruction of the skin specimen. Finally, it will further the understanding of the importance of passive thermoregulation in birds by simplifying and accelerating the collection of quantitative data.

This protocol describes the quantification of heat transmission through a flat skinned avian specimen using a thermal camera and hot water bath. The method allows obtaining of quantitative, comparative data about the thermal performance of feather coats across species using dried flat skin specimens.

Feathers are essential to insulation, and therefore to the cost of thermoregulation,&#160;in birds. There is robust literature on the energetic cost of ...

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A)

Studying the structure and the dynamics of kinetochores and centromeres is important in understanding chromosomal instability (CIN) and cancer progression. How the chromosomal location and function of a centromere (i.e., centromere identity) are determined and participate in accurate chromosome segregation is a fundamental question. CENP-A is proposed to be the non-DNA indicator (epigenetic mark) of centromere identity, and CENP-A ubiquitylation is required for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability.
Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-CENP-A K124R mutant suggesting that ubiquitylation at a different lysine is induced because of the EYFP tagging in the CENP-A K124R mutant protein. Lysine 306 (K306) ubiquitylation in EYFP-CENP-A K124R was successfully identified, which corresponds to lysine 56 (K56) in CENP-A through mass spectrometry analysis. A caveat is discussed in the use of GFP/EYFP or the tagging of high molecular weight protein as a tool to analyze the function of a protein. Current technical limit is also discussed for the detection of ubiquitylated bands, identification of site-specific ubiquitylation(s), and visualization of ubiquitylation in living cells or a specific single cell during the whole cell cycle.
The method of mass spectrometry analysis presented here can be applied to human CENP-A protein with different tags and other centromere-kinetochore proteins. These combinatory methods consisting of several assays/analyses could be recommended for researchers who are interested in identifying functional roles of ubiquitylation.

Mass Spectrometry Analysis to Identify Ubiquitylation of EYFP-tagged CENP-A EYFP-CENP-A

CENP-A ubiquitylation is an important requirement for CENP-A deposition at the centromere, inherited through dimerization between cell division, and indispensable to cell viability. Here we describe mass spectrometry analysis to identify ubiquitylation of EYFP-tagged CENP-A (EYFP-CENP-A) protein.