LFDA Practical Session

FMSP stock assessment toolsTraining Workshop

Session Overview

• How this session will run.

• What we will cover during the session.

• LFDA Tutorial Contents.

• LFDA example dataset.

• LFDA Tutorial.

• Summary.

LFDA Practical Session

• The LFDA practical session will last the rest of today.

• During the session we will look in detail at;

• File formats used in LFDA, importing and inputting data into LFDA.

• Then using the pre-prepared LFDA tutorial to investigate some example length frequency data in the example dataset.

• You will be able to use your own data with LFDA later on in the course.

LFDA Example Dataset

• The example dataset we are using is a simulated dataset.

• Data will be loaded in from a text file called “TUTOR.TXT”.

LFDA Example Dataset

• Sample Timings

- Regular sampling

• Length Frequency Classes

– Regular with a good range

• Number of individual fish measured

– Reasonably high samples

LFDA Tutorial Contents

• Introduction

• Loading and Inspecting Data

• Estimation of Non-Seasonal Growth Parameters

• Estimation of Seasonal Growth Parameters

• Estimation of the Total Mortality Rate “Z”

LFDA Tutorial – Starting LFDA

• As with most Windows software you have a number of ways of starting LFDA:

• Double-click on the LFDA icon.

• Start > Programs > MRAG Software > LFDA5

• Open up Windows Explorer. Find the program “LFDA5.EXE” and double-click.

LFDA Tutorial – Starting LFDA

• Start LFDA whichever way you want. Then close it down and try another way.

• You should get the screen below;

LFDA Help File

• There is an extensive help file that details all the functionality behind LFDA, some theory behind the analysis and the tutorial that we will be running through.

• This can be accessed by clicking on Help>Contents and Index on the menu bar.

• Clicking on the “Tutorial” section and then “Loading and Inspecting Data”, will allow us to start following the LFDA Tutorial.

Importing Data (1/2)

• Number of ways to load data.

• Commonest way is to load from an ASCII file.

• We will be loading the file “TUTOR.TXT”.

Importing Data (2/2)

• File | Open

• Select *.txt

• Select “TUTOR” from the list of files.

• The dataset will then be imported into LFDA. You can now save this as an LFDA file by using File | Save As “TUTOR.LF5”.

Inspecting and Editing Data (1/4)

• You are viewing data in a sort of “Spreadsheet Mode”.

• The data as you see them on the screen cannot be edited to avoid accidental mistakes and overwriting of data.

• To edit the data. Go to the “Edit” menu and select “Edit Mode”. This will allow you to edit data already imported into LFDA. To stop any editing you need to repeat this action by selecting “Edit | Edit Mode” again at the end.

Inspecting and Editing Data (2/4)

• When you are in “Edit Mode” you will see a number of menu choices appear that were previously not available. These are:

• Add a distribution• Erase a distribution• Combine distributions • Edit Sample Time.

• These allow you to build LFDA datasets from within the program by manually entering the data directly into the package.

Inspecting and Editing Data (3/4)

• A screen full of figures is not too clear though when you want to look at the length distributions over time.

• To view the data in a graphical format just select;

– Data | Plot Data.

• This is much clearer and makes tracing the progress of length distributions much easier.

Inspecting and Editing Data (4/4)

Estimation of Growth Parameters (1/10)

• We are going to assume that the data is non-seasonal and try to fit a standard von Bertalanffy growth curve to the data we have just imported.

• The basic idea behind estimating the growth parameters is to find the combination of parameters that maximizes a specified score function.

• A score function in LFDA is something that takes a model (e.g. von Bertalanffy) with its specific parameters (e.g. values for K and L∞), then looks at your data and gives you a number which tells you how likely it is that your data comes from a stock with that growth function.

Estimation of Growth Parameters (2/10)

• Maximisation with score grids.

• If 2x+y=28 (x and y both whole numbers >0), and our score is;

• SCORE = -1 * difference between 28 and our estimate from the pair of parameters (x and y).

• Then we have a number of maxima where 2x+y=28, where score = 0, e.g. Y=14 and x=7.

Estimation of Growth Parameters (3/10)

Different score functions in LFDA:

• Shepherd’s Length Composition Analysis

• ProjMat

• ELEFAN

Details of each are to be found in the tutorial.

Try fitting each of these now, following the tutorial

Estimation of Growth Parameters (4/10)

• Results: Shepherd’s Length Composition Analysis with TUTOR.LF5

K L∞ to Score

SLCA 1st run

(basic parameters)

0.700 220.000 -0.163 374.9820

SLCA Maximisation

K=0.1-1.5 (15)

L∞=150-250 (11)

0.664 225.955 -0.173 375.0811

SLCA 2nd Maximisation

K=0.5-0.8 (20)

L∞=210-250 (20)

0.664 225.893 -0.173 375.0815

Estimation of Growth Parameters (5/10)

K L∞ to Score

PROJMAT 1st run

(basic parameters)

1.3 160.00 -0.081 -0.183

PROJMAT 2nd run

K=0.7-1.5 (15)

L∞=130-250 (13)

1.33 160 -0.070 -0.177

PROJMAT 3rd run

K=0.7-1.7 (21)

L∞=130-250 (13)

1.45 150.00 -0.082 -0.177

PROJMAT Maximisation

K=0.7-1.5 (21)

L∞=130-250 (13)

1.298 162.977 -0.666 -0.176

• Results: PROJMAT Analysis with TUTOR.LF5

Estimation of Growth Parameters (6/10)

K L∞ to Score

ELEFAN 1st run

(basic parameters)

0.50 200.00 -0.65 0.453

ELEFAN Region 1 0.502 198.264 -0.670 0.470

ELEFAN Region 2 0.841 180.51 -0.160 0.466

ELEFAN Region 3 0.297 241.00 -0.120 0.450

• Results: ELEFAN Analysis with TUTOR.LF5

Estimation of Growth Parameters (7/10)

• We have looked at three different methods and have widely different results.

• Why is this? Do the models fit the data?

• Suspicion that data may be seasonal, slowing the growth at certain times of the year.

• Now try to fit a seasonal model.

Estimation of Growth Parameters (8/10)

• Two methods available for the analysis of seasonal data only (PROJMAT and ELEFAN).

• Choose the Hoenig model.

• What we are trying to do now is maximise the score function not just for two parameters (K and L∞) but for four (K, L∞, C (the strength of the seasonality) and Ts (the time the seasonal growth starts).

Estimation of Growth Parameters (9/10)PROJMAT – Hoenig Seasonal Growth Estimate

Estimation of Growth Parameters (10/10)ELEFAN – Hoenig Seasonal Growth Estimate

Return to Theory presentation

Estimation of the Total Mortality Rate (1/2)

• Three methods available for estimating “Z”

• All based on non-seasonal models.

• If you have a strongly seasonal model then you should be extremely wary about using these models.

• Most seasonal patterns are OK as they closely resemble non-seasonal patterns.

Estimation of the Total Mortality Rate (2/2)

• So which set of parameter estimates do we use?

• We have three models with non-seasonal sets of widely different values of K and L∞ and two others with closer fitting seasonal data.

• Look at the best fitting non-seasonal models for each method and see which looks the best when plotted with the data and against the seasonal model equivalent.

• ELEFAN looks pretty good. We will use this then with the values of K=0.841 and L∞ = 180.51.

Length Converted Catch Curve

• As you learnt in the theory session, this method takes the lengths and converts them into ages.

• For each distribution it then calculates the number of survivors to the next length class.

• Plotting the age against the natural log of the number of survivors gives us a slope equal to the total mortality rate “Z” for that distribution.

• Averaging overall the Z’s calculated for the 10 distributions will give us an average of Z for the year.

Length Converted Catch Curve (2/2)

• We only want the “descending arm”.

• We need to “toggle out” any points that are not part of the descending arm for each distribution.

Distribution 1 2 3 4 5 6 7 8 9 10

Z 1.35 1.40 1.51 1.45 1.29 1.26 1.31 1.66 1.34 1.54

• The toggling out of points here is quite a subjective process but your results should look something like this.

Beverton-Holt “Z” (1/2)

• The Beverton-Holt method relies on a simple algebraic relationship between the mean length in each sample, the length at first full exploitation, the von Bertalanffy growth parameters and the total mortality rate Z.

• We have not used the length at first capture here and have no information on it as this is a simulated data set.

• Therefore we will use the following parameters; K=0.84, L∞ = 180.5 and Lc =20 (our first length class).

Beverton-Holt “Z” (2/2)

Distribution 1 2 3 4 5 6 7 8 9 10

Z 1.93 1.00 0.78 0.69 0.52 1.97 1.13 0.72 0.65 0.52

• As for the LCCC method we get an estimate for each distribution as shown below.

• The mean Z here is 0.991,

Powell-Wetherall (1/2)

• Similar to the Beverton-Holt method.

• Algebraic relationship for the right hand “tail” of a length frequency distribution to calculate Z.

• Missing points that have been excluded from the calculations are where the number of fish at that length class is zero.

• The results show not Z itself but Z / K and another estimate for L∞.

Powell-Wetherall (2/2)

Results from the Powell-Wetherall estimation with the TUTOR.LF5 dataset

LFDA Practical Session - Summary

• What have we covered?

• How to use LFDA.

• Estimation of Growth Parameters from LF data.

Non-Seasonal

Seasonal

• Estimation of Total Mortality Rate “Z” from LF data.