FMSP stock assessment tools
Training Workshop
LFDA Practical Session
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
L∞
K
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)
•
Results: PROJMAT Analysis with TUTOR.LF5
L∞
K
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
1.298
162.977
-0.666
-0.176
PROJMAT Maximisation
K=0.7-1.5 (21)
L∞=130-250 (13)
Estimation of Growth Parameters (6/10)
•
Results: ELEFAN Analysis with TUTOR.LF5
L∞
K
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
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.
• The toggling out of points here is quite a subjective
process but your results should look something like
this.
Distribution
Z
1
1.35
2
3
1.40
1.51
4
5
6
7
8
1.45
1.29
1.26
1.31
1.66
9
10
1.34 1.54
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)
• As for the LCCC method we get an estimate for each
distribution as shown below.
• The mean Z here is 0.991,
Distribution
1
Z
1.93
2
1.00
3
0.78
4
5
6
7
8
0.69
0.52
1.97
1.13
0.72
9
10
0.65 0.52
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.