Please read the instructions for each of the three features below (each correspond to the three tabs above) then select your inputs in the left column for the Operating Model, Performance Metrics/Risk Thresholds, and Available Data Types.
Once the inputs are populated, you can review the results by selecting the tabs above for Simulation Testing (MSE), Method Application, and Value of Information. You can change the inputs at any time and the results under each tab will update based on your new selections.
Use the drop-down menus to select a Stock Type and a Fleet Type from the list provided.
Note that these stock and fleet types are examples for demonstration purposes. The DLMtool allows complete customization of stock and fleet types, as well the observation model for generating the data.
Use the drop-down menus to select a Biological Performance Metric and a Yield Performance Metric from the list provided.
Note that these are example performance metrics for demonstration purposes. Additional performance metrics are available in the full version of the DLMtool, and all performance metrics are fully customizable.
Use the slider bars to set the risk thresholds (probabilities and relative performance) for the Performance Metrics. The risk thresholds determine the minimum performance criteria.
Any methods that do not meet the minimum performance criteria (i.e., probability of meeting performance metric is lower than risk threshold) are not marked as not acceptable.
Use the checkboxes to specify what data-types are available. The available data determines which methods are available.
Note that these are example data types. The DLMtool includes a wide range of data types
Select the Simulation Testing (MSE) tab to see the output of the MSE
The graphs will automatically update if you change the Stock, Fleet, Performance Metrics, Risk Thresholds, or Available Data.
The DLMtool has a range of plotting and summary functions to examine the output of the MSE, and users can customize their own plots.
Select the Method Application tab to see the (example) management recommendations from the available and acceptable methods determined by the MSE.
Note that the data in these examples are entirely fictitious. The DLMtool uses a data object that includes all data from the fishery.
Select the Value of Information tab to learn from the MSE results which operating model and observation parameters are most important.
Click here for a glossary of the terms used in this demo.
This plot shows the relative performance of the data-limited management methods with respect to the Biological and Yield performance metrics.
Each point represents a different management method. Click a point for details of the management procedure. Note that this is a subset of the methods in the DLMtool, and users can develop and add their own management procedures.
The MSE results can be used to determines the conditions that influence the performance of a method. For example, some methods perform well (i.e., maintain stock at healthy levels) only if the stock is in a healthy initial state (i.e., biomass is at or greater than BMSY).
The DLMtool includes many plotting functions to examine the performance of data-limited methods. These plots compare the performance of two Data-Limited Management Methods with respect to the relative fishing mortality and stock biomass.
Use the drop-down menu to select management methods to compare:
Important note: this is only a demonstration using example data; download the full version of the Toolkit to input real fisheries data, apply management procedures, and obtain recommended levels of input or output controls for your fishery.
The MSE results can be used to examine which operating model and observation parameters are most influential in determining the performance of a data-limited management method. This information can used to decide on research priorities for improving the management of the fishery.
This plot shows how the yield is influenced by variability or uncertainty in various parameters for the top four performing methods. The black lines show how the expected yield is influenced by each parameter. A steep slope indicates that yield is strongly determined by the variability or range in the parameter. A flat line suggests that the yield is not greatly influenced by variability in the parameter.
The Operating Model parameters show which life-history or population parameters most influence the performance of a management method, and can be used to determine the situations where methods are likeky to be most useful. The Observation parameters show how bias and error in the data sources influence the expected yield. This information can be used to identify the data sources that are most important in the performance of a method, and assist in prioritizing future research efforts.
Green names indicate methods that are Available and Acceptable. Black names are Acceptable but Not Available and Grey names are Not Acceptable.
Note: not all management methods use data and may not appear in the 'Observation Parameters' plot.