Pasgear 2

Version 2.3 (Build 02.12.2009)

Jeppe Kolding and Åsmund Skålevik

www.cdcf.no/data/pasgear

What is Pasgear 2 ?

• ‘Database’

• Analysis + Series of

ready made analyses for quick exploration and overview of the data

• Extract

Philosophy• Data stored at raw

level (as sampled)

• Keep automatic track of ‘effort’

• Extract information

Condense and groupVisualize

Raw data never touched !

Philosophy cont…

• Easy data entry (punch or import)

• Easy data export (raw or grouped)

• Perform standard ‘fisheries’ analyses by click and go (inbuilt library of ‘macros’)

• Make almost any kind of ‘your own’ analyses by powerful queries and grouping techniques

• Standardize output (CPUE, correct for gear selectivity (s) or catchability (q))

• Make nice graphs (almost endless possibilities)

• Interface with other software (Excel, FiSAT..)

Nice Graphs…

Length frequencies corrected for gear selectivity by the SELECT method

Relative biomass-size distributions

Special features

• Automatic estimation of weights from length-weight relationships.

• Standardized (weighed) calculation of CPUE with confidence limits.

• Calculation of different types of confidence limits (arithmetic, Pennington estimator, and bootstrap).

• Non-linear maximum likelihood estimation of gillnet, hook and trap selectivity probabilities (SELECT)

• Gear selectivity corrected length frequencies and catch curves

• Non-linear least squares estimation of maturity ogives and size at 50% maturity

How does it keep automatic track of effort?

• No matter how you extract the data, the sample size will always be known

• even if there are ‘no fish’ in the sample as biological and physical info is counted separately.

1

2

3

4

“2 stage” sampling in one record How many samples are here?

‘Physical’ data combined with the biological..

Id–tables: codes or values

Other columns can be added – also physical

Biological data - standard

Level of information

Species Number Length WeightSex/gonads

Individual X 1 X X or 0 X or 0

LFQ X N X X or 0 0

Catch X N or 0 0 X 0

No catch 0 0 0 0 0

No catch = empty setting

• A single record with only physical values species = 0

Standardized catch per unit effort

• y = absolute effort, e.g. number of net panel (or fleet) settings

• n = number of samples (if effort is not a variable then y = n).

• Wi = catch (in weight or numbers) in set i or sample i,

• SU = standard relative effort unit (size) of a net panel

• Ui = actual relative effort unit (size) of net i (this can be given in the Relative effort field in the Data Table)

• ST = standard time unit (hours or minutes) of a setting (defined in the data table properties/Effort mode),

• Ti = actual time unit of setting i (this can be given in the duration field in the Data Table).

Standardized catch per unit effort

# Nets

# Samples

100 m or m2

12 hrs

# Nets# m or m2

# Nets# hrs

= kg · 100m-1 · 12hrs-1 net set

Standardize CPUE

1

2

Query = Filter

You can change the name (caption) of any object in Pasgear using the ‘general’ tab + adding comments if desirable

Query text mode = compiled script

In text mode you can write any advanced query or expression using the compiler syntax.

To see and understand the syntax see the ‘Expression builder’

Expression builder:

What this expression does:

1) Lookup field ‘Date’ in Data table

2) Return the Month of the date (1..12)

3) If Month is between 2 to 5 or 9 to 11 then result = true else result = false

These two expressions are doing the same thing !

Analysis

Analysis: Groups and variables

• You can group in 3 dimensions (rows, columns and pages)

• Grouping is done based on the field columns in the data table

• You can add a ‘variable’ to any of the 3 dimensions

• A variable is a count, a mean, etc. i.e. various calculated values

Analysis: Groups

Analysis: Variables

Analysis: Run.. = F5 or

Modify analysis

Double click

Export Analysis

Analysis: 2 D – rows, columns

Rows

Columns

R3

R2

R1

C4C3C2C1H

Column groupsR

ow

var

iab

les

Analysis: 2 D – rows, columns

R3

R2

R1

C4C3C2C1H

Column variablesR

ow

gro

up

s

Analysis: 2 D – the page variableColumn groups

Ro

w g

rou

ps

R3

R2

R1

C4C3C2C1H

Page variables

Analysis: 3 D – the page concept

R3

R2

R1

C4C3C2C1H

Column groupsR

ow

var

iab

les

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

Page groups

Page variables

Analysis: 3 D groups

R3

R2

R1

C4C3C2C1H

Column variablesR

ow

gro

up

s

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

Page groups

Page variables

Analysis: 3 D groups + variables

R3

R2

R1

C4C3C2C1H

Ro

w g

rou

ps

Column groups

R6

R5

R4

C8C7C6C5

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

R3

R2

R1

C4C3C2C1H

Page variables

Column variablesR

ow

va

ria

ble

s

Pages

Diagrams and charts

Diagram area

Chart area

Plot area

Control pane

Y- series

Z - series

Options pane

Zoom and scale pane

Diagrams and charts

Check off and write 1

Diagrams and charts

Invert colors

Reset to default

Making a chartFor a variable For a table

Making a chart - example

Gear Selectivity

All fishing or sampling gears are more or less selective

What is selectivity?

Sample this population with 2 gillnets of different mesh sizes

Gear Selectivity

• The fish retained in a gear is usually only an unknown proportion of the various size classes available in the fished population.

• Selectivity is a quantitative expression of this proportion and represented as a probability of capture of a certain size of fish in a certain size of mesh (or hook).

Gear Selectivity

• From observed catches one can calculate the selection curves, which are the probabilities that a certain length is caught in a certain mesh size

Gear Selectivity

• Gillnet, hook, and trap selectivity can be indirectly estimated from comparative data of observed catch frequencies across a series of mesh or hook sizes.

• The general statistical model (SELECT) is described in Millar (1992), and the specific application on gillnets and hooks is described in Millar & Holst (1997) and Millar and Fryer (1999)

Gear Selectivity• The principle of geometric similarity:

Length of maximum retention (mean length) and spread of selection curve (SD) are both proportional to mesh size (Baranov 1948)

With increasing mesh size there is a proportional increase in mean length and SD of the fish caught

Gear Selectivity – 5 modelsexp

( k m )

2j i

2

2

L

exp( k m )

2 (k m )j 1 i

2

2 i2

L

1exp log

m

m 2

log ( ) logmm

2j1

i

1

2 j 1i

1

2

2L

L

L Lj

i

1

j

i( 1 )k mexp 1

k m

Normal location shift

Normal scale shift

Lognormal

Gamma

μi = mean size (length) of fish caught in mesh size i = k1mi

σi = standard deviation of the size of fish in mesh i = k2mi or αmi

Lj = mean size of fish in size (length) class j

exp( k m )

2 (k m )exp

( k m )

2 (k m )j 1 i

2

2 i2

j 3 i2

4 i2

Lw

LBimodal normal scale shift

Gear Selectivity – 5 models

Normal location shift

Normal scale shift

Lognormal

Gamma

Bimodal normal scale shift

Only means are proportional to mesh size, spread is constant.

Means and spread are proportional to mesh size (principle of geometric similarity).

Means and spread are proportional to mesh size but with asymmetrical retention modes (i.e. skewed distributions).

Means and spread are proportional to mesh size but with asymmetrical retention modes (i.e. skewed distributions).

Means and spread are proportional to mesh size but 2 different capture modes, i.e. fish wedged by the gills and entangled in the mesh sizes

Gear Selectivity – Step 1• Find the linear part of the mesh size range

Exclude

Gear Selectivity – Step 2• Evaluate appropriate model

These plots assist in evaluating whether the mean and SD spread increase with mesh size, and what the degree of skewness is.

Gear Selectivity – Step 3• Estimate selection curve

Sum of all selection curves standardized to 1Probability

= less than 1Cut off level

Gear Selectivity – Step 4

Correcting for gear selectivity can have significant effect when calculating total mortality or growth from length frequency data (FiSAT).

With no correction mortality may be underestimated

• Correct observed catches

Gear Selectivity – Step 5• Save probabilities

This is a default name that ensures that Pasgear will check on the species and the length interval to accept the selectivity file:It mean species = 6 (only)And length interval = 1 cm

Connect a selectivity file

Catches by groups are now corrected for estimated selectivity

Correcting for gear selectivity

Correcting for gear selectivity

Growth ?

Export to FiSAT