Yes. Blue Matter has run several openMSE training courses in the past, including topics suitable for a general audience of fisheries stakeholders (e.g, MSE concepts and philosophy) and more technical topics including operating model conditioning, design of custom management procedures, and conducting MSE for multi-stock and multi-fleet fisheries.

We can also develop customized training courses that are tailored to suit your particular needs.

Please Contact Us to learn about upcoming courses or to discuss a training course for your organization.

Yes. Blue Matter is a consulting firm that specializes in conditioning operating models, developing bespoke management procedures, and conducting MSE to identity management approaches that are robust to uncertainty.

Please visit the Blue Matter website for more details, or Contact Us to discuss how we can work with you on your fishery problem.

Due to the problems with approximating fine-scale temporal dynamics with an annual model it is not advised to use openMSE with annual time-steps for very short lived stocks (i.e., species with a longevity of 5 years or less).

See Can the model be run with sub-year time-steps? for more information.

Possible, but not for the faint-hearted!

You can divide all temporal parameters (e.g., OM@M and OM@K) by a sub-year resolution. Be aware that all other temporal parameters (e.g., OM@t0, OM@interval, etc) will also need to be scaled to the same time-step. The Data object will be updated at this finer-scale, and you need to design Custom Management Procedures that operate correctly on your time-scale (e.g., in-season management, or at the end of each year).

openMSE assumes that, with the exception of the stock-recruitment relationship, there is no density-dependent compensation in the population dynamics, and fish growth, maturity, and mortality does not change directly in response to changes in stock size.

Options to include density-dependence in growth, mortality, etc, may be added at some time in the future.

Yes. The standard openMSE model is a single-sex, single-fleet model (i.e, models all fleets in aggregate and assumes females and males are identical).

The multiMSE feature provides the option to model multiple stocks and fleets, and test fleet-specific management procedures.

Growth is modeled using the von Bertalanffy growth curve. While this is the most commonly applied model to describe fish growth, it may not be the preferred growth model for some species. The consequences of assuming the von Bertalanffy growth model should be considered when using the model for species with alternative growth patterns.

Alternative models for describing length-at-age can be added using the Custom Parameters feature.

In some cases during the MSE, a Management Procedure may not be able to successfully calculate a management recommendation from the simulated data.

For example, a catch-curve may used to estimate $Z$, and $F$ is calculated as $F=Z-M$. Because of process and observation error, it is possible that the estimated $F$ is negative, in which case the MP may fail to calculate a recommended catch limit.

The Management Procedures have been designed to return NA if they fail to calculate a management recommendation for any reason. In this case, the management recommendations from the previous year are used in the simulation,e.g., $ \text{TAC}_y = \text{TAC}_{y-1}$.

By default, the model uses a two-box spatial model and assumes that growth and other life-history characteristics do not vary across the two spatial areas.

See the Custom Parameters section for details on modeling more than two areas.

Although this site attempts to address the most common issues and questions with openMSE, undoubtedly there will be times where you have problems with your R code. R has a somewhat annoying habit of returning cryptic error messages, that are sometimes indecipherable, especially to those who are new to the software.

Most coding problems with the R language are the result of a missing parenthesis, an extra or missing comma or quotation mark, or some other minor typo that stops your code from running.

There are a number of resources available on the Internet that are devoted to dealing with questions and problems with R programming. StackOverflow is great place to start searching for answers to your R-related problems.

There is a high chance that in the past someone has posted the exact question that you are dealing with, and one or several kind souls have provided helpful solutions. If not, you can post your own question. But be aware, the StackOverflow community is made up entirely of people who volunteer their time to help others, and they sometimes have little patience for questions that don’t demonstrate a proper search for already posted answers to the problem.

The core objects in the openMSE packages are S4 Classes. Many R users may not have worked with S4 objects and methods before.

R has three different object oriented (OO) systems, the most common of which is known as S3. S3 is known as a generic-function OO, and is a casual system with no formal definition of classes. S4 works similar to S3, but is more formal and uses classes with a more rigid definition.

It is not essential to understand the difference between S3 and S4, or why one is preferred over the other, to use the openMSE packages. The most important thing that you need to know how to access the information in S4 classes.

If you have work with R in the past, you are probably familiar with using the $ symbol to access elements in a data frame or list. S4 classes contain a named list of slots which are analogous to a standard R list. However, the slots in a S4 class differ in two important ways:

1. The type of content in each slot (e.g., character, numeric, matrix) is determined in the class definition, and cannot be changed. In other words, you are not able to put content of class character into a slot that is expecting information of class numeric. This is what is meant by the S4 system being more strict than S3.
2. The slots are accessed with the @ symbol. This is essentially the same as the $ symbol in S3 classes. You will see examples of this throughout the User Guide.

The main thing to note here is that when you see the @ symbol being used, it refers to some particular information (a slot) being accessed from a larger collection of data (the object).

For further information on the S4 systems see Advanced R.