A Dam-Good Model for Salmon Population Dynamics, using @RISK

US National Marine Fisheries Service uses @RISK for Salmon Population Models
Scientists with the National Oceanic and Atmospheric Administration’s (NOAA’s) National Marine Fisheries Service used @RISK to model the effects of dams on the population viability of Atlantic salmon in the Penobscot River watershed in Maine. Their results showed that abundance and distribution upstream increased when dams in the primary downstream segments of the river, also called “mainstem dams," were removed, or fish passage survival was increased. The findings were recently published online in the ICES Journal of Marine Science.

Julie Nieland, Timothy Sheehan, and Rory Saunders, researchers with NOAA, assessed the interactions of Atlantic salmon with 15 Federal Energy Regulatory Commission (FERC)-licensed hydroelectric dams in the system. Their objective was to estimate the relative change in Atlantic salmon abundance and distribution throughout the Penobscot River system due to varying levels of fish passage at these 15 hydroelectric dams. They also assessed the change in the abundance of this population under varying levels of indirect latent mortality, increased marine and freshwater survival rates, and no hatchery supplementation.

"@RISK enabled us to quickly and efficiently run thousands of iterations and get both summary statistics and individual iteration data from our model runs," she continues. "Finally, as an add-on to Microsoft Excel, @RISK allowed us to have the familiarity of the Excel user-interface, which was an important feature for demonstrating our model to colleagues."

 » Read the full case study  

@RISK@RISK was beneficial for our project
     in many ways. For example, we
     wanted to build a stochastic model
     rather than a deterministic model,
     and we were able to easily incorporate
     the variation in our model inputs
     by making a random draw from input
     distributions for each iteration.
Trial Versions

                                  Dr. Julie Nieland, Researcher
                                  ICES Journal of Marine Science

New features, and new software, in DecisionTools Suite v7
Version 7 of the DecisionTools Suite offers efficient frontiers, copulas, and graphical model sharing in @RISK.

And it adds an entirely new Excel add-in: BigPicture—Palisade's new five-star reviewed mind-mapping and data exploration tool! Unlike other mind mapping software, BigPicture combines the power of visuals and spreadsheets directly in Excel. All content in your spreadsheet cells can be linked to your BigPicture charts, reports, and maps. When you update your numbers or formulas in Excel, your visual diagrams automatically update as well. The integration is seamless.

As part of the DecisionTools Suite 7, BigPicture integrates directly with PrecisionTree, converting decision trees to visual maps. It enables you to convert your most complex data and results into intuitive and flexible diagrams—making reports more compelling and accessible to stakeholders and decision makers.

Version 7 of StatTools features many new graphs, including dendrograms as part of the Cluster Analysis report, score plots in the Principal Component Analysis report, and categorical bar charts.

  » See the new features in version 7 of the DecisionTools Suite

Licensing Options

There are three easy ways to get the DecisionTools Suite for your students:


Course Licenses available from Palisade. These are economical, annually renewing bulk licenses for both
network lab and laptop installation. Palisade offers a complimentary Live Web Training session to all instructors who order a Course License.
» Learn more

Student Versions from Palisade. These are 12-month versions available for purchase individually by students through either the Palisade web site or the school store.
» Learn more

Textbook Editions available bundled with textbooks.
These are time- and model-limited licenses.
» Learn more

Get DecisionTools Suite 7 for your course by contacting:

AddressPalisade Academic Sales    
800-432-7475 / 607-277-8000

Teaching Tips & Examples

The DecisionTools Suite is a great way to present quantitative techniques in a straightforward, easy-to-understand way for students.

Capacity Planning for an Electric Utility
Excerpted from Decision Making Under Uncertainty with RISKOptimizer, by Wayne Winston, published by Palisade

In this era of deregulation, power companies have many complex decisions to make. For example, should a power company sell some of their power plants and buy power from other producers? The problem with this is that on hot days when power usage is high it can be very expensive to purchase power. If we choose to never purchase power from other producers, however, we incur high daily fixed costs for operating our plants. The following example shows how RISKOptimizer and Solver can be used to determine which power plants a power company should be operating and which should be closed.

» Learn more and download the associated example files

Tech Tip of the Month

Latin Hypercube Versus Monte Carlo Sampling

The @RISK and RISKOptimizer manuals state, "We recommend using Latin Hypercube, the default sampling type setting, unless your modeling situation specifically calls for Monte Carlo sampling." But what's the actual difference?

Monte Carlo sampling refers to the traditional technique for using random or pseudo-random numbers to sample from a probability distribution. Monte Carlo sampling techniques are entirely random in principle — that is, any given sample value may fall anywhere within the range of the input distribution. With enough iterations, Monte Carlo sampling recreates the input distributions through sampling. A problem of clustering, however, arises when a small number of iterations are performed.

By contrast, Latin Hypercube sampling stratifies the input probability distributions. With this sampling type, @RISK or RISKOptimizer divides the cumulative curve into equal intervals on the cumulative probability scale, then takes a random value from each interval of the input distribution.

» Read the complete explanation here
» View other Techniques and Tips here

Featured White Paper

A comparison of methods for assessing the relative importance of input variables in artificial neural networks
by O.M. Ibrahim, Field Crops Research Department,
National Research Centre, Giza, Egypt.

Artificial neural networks are considering powerful statistical modeling technique in the agricultural sciences; however, they provide little information about the contributions of the independent variables in the prediction process. The goal of relative importance analysis is to partition explained variance among multiple predictors to better understand the role played by each predictor.

Dr. Omar Maghawri Ibrahim Associate professor at City of Scientific Research and Technological Applications , Alexandria, Egypt, uses the DecisionTools Suite to compute the relative importance of input variables in artificial neural networks. According to Dr. Maghawri, the DecisionTools Suite gave accurate results compared to the other methods he used which he explains in a paper he published in the Journal of Applied Sciences Research.

Palisade  Read the full paper here


The Academic Global Leader in
Risk and Decision Analysis
Palisade is the world's leading provider of risk and decision analysis software and solutions. Founded in 1984, its products @RISK and the DecisionTools Suite are used by over 95% of the Fortune 100, in nearly every industry around the world. Palisade is headquartered in New York State, and has offices in London, Sydney, Rio de Janeiro, and Tokyo.

What is The DecisionTools Suite?
The DecisionTools Suite is the world’s only integrated set of risk and decision analysis programs. The Suite includes @RISK for Monte Carlo simulation, BigPicture for mind mapping and data exploration, PrecisionTree for decision trees, and TopRank for “what if” sensitivity analysis. In addition, DecisionTools Suite comes with StatTools for statistical analysis and forecasting, NeuralTools for predictive neural networks, and Evolver and RISKOptimizer for optimization. All programs work together seamlessly, and all integrate completely with Microsoft Excel for ease of use and maximum flexibility.

The Suite is offered in three affordable and flexible licensing options for those in academia: Student Versions, Course Licenses and Full Academic Versions.

» Schools using Palisade software
Textbooks using Palisade software

In This Issue

» US National Marine
   Fisheries Service uses
   @RISK for Salmon
   Population Models

» New features, and
   new software, in
   DecisionTools Suite v7

» Licensing Options

» Teaching Tips &

   Capacity Planning for
   an Electric Utility

» Tech Tip
   Latin Hypercube Versus
   Monte Carlo Sampling

» Featured White Paper
   A comparison of methods
   for assessing the relative
   importance of input
   variables in artificial
   neural networks

» About Palisade and the
   DecisionTools Suite


Keep in Touch

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