ceda practical session fmsp stock assessment tools training workshop mangalore college of fisheries...

CEDA Practical Session

FMSP Stock Assessment Tools

Training Workshop

Mangalore College of Fisheries

20th -24th September 2004

CEDA Practical Session

• During the session we will look in detail at;

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

• Then using the pre-prepared CEDA tutorial to investigate some example catch and effort data analysis with the pre-prepared example datasets.

• You will be able to use your own catch and effort data with CEDA later on in the course.

CEDA Example Datasets

• During the course of the CEDA tutorial we will use two different datasets to illustrate different fisheries and how CEDA can be used to analyse them.

• The first (SQUID.CD3) is a dataset from an annual squid fishery (Illex argentius, 1989). We have one season’s data showing the gradual removal of catch from the population.

• The second dataset (XTUNA.CD3) is a long time series of catch and effort data for a Yellowfin Tuna (Thunnus abacares) fishery.

CEDA Tutorial Contents

• Introduction

• Loading data into CEDA

• Analysis of tutorial dataset 1 – “SQUID.CD3”

• Analysis of tutorial dataset 2 – “XTUNA.CD3”

• Making projections

see help files ‘Tutorial’ section

Starting CEDA

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

• Double-click on the CEDA icon.

• Start > Programs > MRAG Ltd > CEDA3

• Open up Windows Explorer. Find the program “CEDA3.EXE” and double-click.

Loading and Inspecting Data

• There are three ways of loading and entering into CEDA.

1. Import of data from a text file

2. Loading a previously created CEDA file .CD*…1

3. Manual data entry into CEDA.

1 NB: CEDA version 3.0 will not import files from CEDA v1.0 only 2.0 and above

Manual Data Entry

• It is possible to create a CEDA .CD3 file and manually enter data from scratch (try on Friday?)

• However, we will be using pre-generated datasets during this tutorial session for CEDA …..

Loading the Squid Dataset

• The squid dataset is initially in a text file called “SQUID.TXT”. This data is real catch and effort data for the Falkland Island squid fishery taken from Rosenberg et al (1990).

• During the loading we will;

• Load the file• Allocate the columns• Check computed columns• Save the file as a CEDA .CD3 file.

Analysing the Squid Data (1/17)

• The squid dataset is now loaded:

CEDA Data Requirements (1/5)

• Total Catch (in weight) - Total catch in weight taken in the fishery by all gears during the time period.

• Total Catch (in numbers) - Total catch in numbers taken in the fishery by all gears during the time period.

• Effort - In a fishery where only a single gear is employed, the effort column should include all effort for the fishery. In the case where several different gears are used, the effort and partial catch (see below) for the type of gear that most closely match the assumptions of the model or that are considered to be the most reliable should be used.


• Mean Weight - The mean weight of individual fish in the time period. This is used to convert catch in weight to numbers for various models.

• (Partial) Catch in weight – The effort column in CEDA may contain effort for a single gear type rather than the whole fishery. In this case, it is necessary to specify a partial catch column; i.e. a catch corresponding to the specified effort.

• (Partial) Catch in numbers -The catch corresponding to the effort column where the specified effort is not for all gear types.

• Recruitment Index - This is an index whose value is proportional to the number of new recruits. The recruitment index model requires such an index to be specified for each time period.


• Abundance Index (in Weight Only) - As an alternative to supplying a catch and partial effort series, CEDA allows you to specify an Abundance Index. This is an index whose value is proportional to the biomass of the population. CEDA then converts this internally to catch and effort columns.

• Variance of Abundance Index - If estimates of variances of each abundance index value are available, these can be entered into CEDA and used to weight the fitting of models; i.e. indices with smaller variances are given greater weight than those with bigger variances.

Data Requirements (4/5)

• Timing - The Time column on the left-hand side is very important. In order to cater for the various temporal data that you may wish to analyse (e.g. monthly, weekly, annual), CEDA does not fix the time units for you. All datasets being imported in must have associated time units, although these can be of your own choice. The only constraint is that time periods must be denoted by integers and these must be consecutive.


Model No Recruitment

Indexed

Recruitment

Constant

Recruitment

Schaefer Fox Pella-Tomlinson

Total Catch (weight) * * * R R R

Total catch (Numbers) R R R * * *

Mean Weight * * * * * *

Effort R R R R R R

Partial Catch (weight) * * * R R R

Partial Catch (Numbers) R R R * * *

Recruitment Index R

Abundance Index * * * * * *

Variance Abundance Index

* * * * * *


• Now back to our squid data set:


• The most suitable method of analysis for this type of data from a squid fishery is a depletion model with no recruitment. This method of analysis is available in CEDA.

• You are now ready to analyse the data.


• Select Fit | New Fit from the CEDA menu.

• Select the “No Recruitment” model.

• Mortality Rate = 0.05

• Error model – “Least Squares (unweighted)”

• What outputs do we get?


• We now have a fit.

• Next question to ask ourselves is “Is this a good fit?”

• We need to look at the residuals relating to this fit.

• Under the Graph menu you will see two residual plots for the residual catches vs time and expected catches.


Look at the two residual plots.

What is wrong with them?

Triangular in shape, not the best result for a residual plot.

>> Saving the Fit <<

• Before carrying on with the analysis you should save the work you have done so far. This is done by adding the fit to the Fit Manager. Each fit is logged separately within the CEDA file and saved automatically.

• Select Fit | Fit Manager from the menu. This brings up the fit manager, a tool which allows you to save, delete and reload different models you have run on your dataset. We are going to add the current model to the fit manager. Press the Add Current Fit button. You are then prompted to enter a description to identify your work.


• We can now take a look at fitting a different model to the data, or excluding some of the data points.

• In the case of the squid data we know from experience that the first few points of catch and effort data collected are not representative of the fishery as a whole as the squid are still migrating into the area. Therefore we have a valid reason to omit these three points from the analysis.

• Remember you should not exclude any data points from your analysis unless you have a very good reason to!


• Re-run the fit without these three points.

• Check the fit against the residuals.

• Are the residual plots better than the previous fit?

• Save it and then compare the fit against the first fit.


Comparison of the two fits we have done:

Fit Model Error R2 N1 q

Illex1 No recruit LSq 0.888 5.96E+08 1.31E-05

Illex2

(as above but 1st 3 points excluded)

No recruit LSq 0.915 4.88E+08 2.26E-05


• We have now investigated the “Least Squares (Unweighted) Model”.

• Now try doing the same process with the other two models, “Log Transform” and “Gamma”.

• Run through the same process. Running the model at M=0.05. Check the fit, the residuals and outputs. Check for outliers. Save Log Transform fit as Illex 3, and Gamma fit as Illex 4.


Comparison of the fits we have now done:Fit Model Error R2 N1 q

Illex1 No recruit LSq 0.888 5.96E+08 1.31E-05

Illex2

(as above but 1st 3 points excluded)

No recruit LSq 0.915 4.88E+08 2.26E-05

Illex3

(1st 3 points excluded)

No recruit Log 0.968 4.67E+08 2.79E-05

Illex4

(1st 3 points excluded)

No recruit Gamma 0.955 5.22E+08 1.89E-05


• We have throughout the analysis so far used M=0.05. However, what if this is not right?

• You should always test for the sensitivity of a model to key input parameters such as M. Try running the model with different (but likely) values of M in the range (use the Gamma Error model):

0.01 – 0.10logging the fits as you go (call e.g. M0.01), so you can compare them to each other.

• Investigate the changes to N1 and q.


Example results of sensitivity analysis for M (Gamma Error)

M R2 N1 q

0.01 0.954 4.04E+08 2.32E-05

0.03 0.954 4.57E+08 2.10E-05

0.05 0.955 5.22E+08 1.89E-05

0.07 0.955 6.04E+08 1.67E-05

0.09 0.955 7.12E+08 1.45E-05


Example results of sensitivity analysis for inclusion / exclusion of outliers (Gamma Error Model)

Excluded R2 N1 q

1st 3 points only 0.955 5.22E+08 1.89E-05

1st 3 points + week 16

0.967 5.11E+08 2.09E-05

1st 3 points + week 17

0.971 5.13E+08 2.07E-05

1st 3 points + weeks 16 + 17

0.990 4.98E+08 2.40E-05


• All the analysis so far has given us point estimates.

• These are dangerous to use as we all know fisheries are highly variable.

• To include some of this variability in our analysis we need to add confidence intervals to our point estimates.

• This can be done to any fit by selecting Fit | Generate Confidence Intervals or pressing the button on the Parameter Estimates dialog box. Try this for a few fits.


• You will see a new box added to the Parameter Estimates dialog box.

• This shows the 95% CI of N1 and q.

• You can also view the results of the bootstrapping by selecting the graphs from the Graphs menu.

• Again, you should now recalculate the CIs for sensitivity of different values of M.

Summary – Squid Analysis (1/2)

• The no recruitment model with gamma error gives both satisfactory and useful fits to these data.

• The diagnostic plots (residual/percentile plots) do not highlight any major problems and the confidence intervals are narrow.

• The remaining possible outliers suggests that there may still be some problems with the model or the data, but the lack of sensitivity of the parameter estimates to these data points indicates the utility of the model.

Summary – Squid Analysis (2/2)

• One important conclusion is that the parameter estimates are very sensitive to the value used for the natural mortality rate M, and that the data yield very little information about M.

• Given this sensitivity, a sensible course for management might be to consider how to improve the estimate of M.

Analysis of Tuna Data (1/12)

• We now move on to the more complex tuna dataset.

• The “XTUNA.CD3” dataset only comprises four columns, for time (year), total catch (wt), effort and catch (wt).

• With these data columns only the three production models, Fox, Schaefer and Pella-Tomlinson are allowed.

• We will start off trying to fit a Pella-Tomlinson model to the data.


We need to set the initial parameters for the model:

Initial Proportion: The degree of exploitation of the stock before the start of the dataset. Here the default is 1.

Z shape parameter: Shape of the curve, again assume the default of 1.

Time: This is the time lag between a biomass creating recruitment and this recruitment becoming evident in the population. Enter 0 for preliminary examination.


• As with the squid data we are looking at which error model best fits the data.

• Check the residuals for the fit for Least Squares and then repeat for the Log Transform and Gamma error models, using the same parameters. Remember to save each fit to allow later comparison.

(call Tuna1, Tuna2 and Tuna3 respectively)

• Gamma model – will need to reduce parameter tolerance to 0.001.


• Residuals show better fit for the log transform and gamma models than for least squares. Which is best?

• Still have two outliers for 1951 and 1953. What do we do with these?


• Outliers will occur at 95% confidence levels 5% of the time.

• These two outliers are very close to 0 and 1 though, suggesting a problem with the data or the model.

• We have no good reason to exclude them, so we must leave them in the analysis.

• Neither error model seems to fit the data well.


• We need to test the sensitivity of the model to the different outliers.

• Run both error models through excluding 1951, 1953 and both years. Look for the differences in K, q and r and if excluding the outliers makes a significant difference to the fit (try Gamma and Log Transform models).


Sensitivity of K to various fits:

Fit Gamma Log

All data 1.39E+6 1.88E+6

Without 1951 1.07E+6 1.42E+6

Without 1953 2.45E+6 2.60E+6

Without 1951 and 1953 1.70E+6 1.85E+6


Then repeat the analysis for sensitivity to the initial parameters (time lag, initial proportion and Z the shape parameter of the Pella-Tomlinson production model – using just the Gamma model.

• Examine sensitivity to Initial Proportion over range 0.8 – 1.0.

• Examine sensitivity to the Time Lag over the range 0 – 4• Examine sensitivity to the Z-parameter over the range

0.5 – 2.0


Sensitivity to Initial Proportion over range 0.8 – 1.0:

Initial Proportion

R2 K q r

0.8 0.815 1.16E+6 9.63E-6 0.56

0.9 0.821 1.27E+6 8.68E-6 0.51

1.0 0.825 1.38E+6 7.85E-6 0.46


Sensitivity to Time Lag (in range 0 – 4)

Time Lag R2 K q r

0 0.825 1.39E+6 7.86E-6 0.46

1 0.828 1.61E+6 6.88E-6 0.39

2 0.833 1.81E+6 6.22E-6 0.34

3 0.833 1.82E+6 6.27E-6 0.35

4 0.833 2.01E+6 5.73E-6 0.31

Analysis of Tuna Data (11/12)Sensitivity to Z parameter (over range 0.5 – 2.0)

Z R2 K q r

0.5 0.834 1.38E+6 8.13E-6 0.791

0.8 0.827 1.40E+6 7.99E-6 0.536

1.0 0.825 1.39E+6 7.82E-6 0.459

1.3 0.820 1.41E+6 7.56E-6 0.375

1.6 0.816 1.42E+8 7.34E-6 0.326

2.0 0.810 1.48E+6 6.96E-6 0.273


• Confidence Intervals: We now need to generate confidence intervals for the models, looking at the ranges for K, MSY, q.

• You should consider the sensitivity of the model to the input parameters (e.g. Z, Time Lag, Initial Proportion) in terms of these confidence intervals.

Conclusions of the Tuna Data Analysis

• Substantial discrepancies exist between the model and the data.

• The residuals do not show a particularly good fit with runs of values above and below the expected catch.

• Two outliers exist that cannot be explained.

• Wide confidence intervals are shown for MSY.

• Results are sensitive to input parameters.

Ways Forward with the Tuna Data Analysis

• The lack of “contrast” in the data means that good parameter estimates would always be difficult to obtain.

• Methods of improving the contrast in future data from the fishery might also be considered.

• Often your data will not show clear results. As frustrating as it is, this is the real world.

Projection Scenarios (1/5)

• CEDA can also be used to make predictions into the future given a set of data and a good fitting model.

• We will now build a scenario based on future effort or catch for the tuna fishery and see the effects of this on the population size.


Select Projections | Set up scenarios. We will enter our effort scenario as follows:

However, try different scenarios yourselves.

Can also set Confidence Intervals about your projections.


• This is projecting a catch of 30,000 tonnes in 1968, 25,000 in 1969 and 20,000 tonnes between 1970 - 1980. It shows the stock recovering.


Select Projections | Set up scenarios. We will enter our effort scenario as follows:

However, try different scenarios yourselves.

Can also set Confidence Intervals about your projections.

Projection Scenarios (5/5)This is projecting a constant catch of 160,000 tonnes: the stock

collapses.

TAC 130 Kt

TAC 140 Kt

TAC 140 Kt, MSYafter 1977

TAC 151 Kt (Rep Y)

TAC 161 Kt (MSY)

TAC 170 Kt

Fit

Sto

ck b

iom

ass

Year

0

500000

1000000

1500000

1930 1940 1950 1960 1970 1980 1990 2000

Comparison of management options (TACs)

MSY catch = 161 Kt, but not sustainable at current biomass

see Section 8.3, p142 for further explanation

TAC 140 Kt

Fit

Stoc

k bi

omas

s

Year

0

500000

1000000

1500000

2000000

1930 1940 1950 1960 1970 1980 1990 2000

Projections with confidence intervals

50% confidence interval

for a 140 Kt TAC,

projected from 1968

There is less than a 25%

chance that stock size

should fall below 50 Kt

after returning to the

MSY levels

But there is still some

chance that the stock

will crash in future ….

- try projecting with a 95% confidence interval

Section 8.3, p142-3

Summary

• CEDA uses Catch (Numbers or weight) and Abundance Data • Six models to choose from. We have looked at the No Recruitment

Model (Catch in numbers) and the Pella-Tomlinson model (Catch in Biomass).

• The Recruitment model gave estimates of N1, q and Final population size.

• The Pella-Tomlinson gave estimates of k, r, q and MSY• It is important to choose the correct Error Model (Use Residual plots

to do this).• Varying your input parameters may influence the estimated

parameters, so should perform sensitivity analysis on these.• Results should always be expressed in terms of ranges – use

confidence intervals.

ceda practical session fmsp stock assessment tools training workshop mangalore college of fisheries...

Documents

ceda data requirements

ceda example

ceda icon

ceda version

ceda v1

created ceda file

effort data analysis

preprepared ceda tutorial