1. Jeppe Kolding and Åsmund Skålevik
Pasgear 2
Version 2.3 (Build )
Jeppe Kolding and Åsmund Skålevik

2. What is Pasgear 2 ? ‘Database’ Extract Analysis
+ Series of ready made analyses for quick exploration and overview of the data

3. Philosophy Raw data never touched !
Data stored at raw level (as sampled)
Keep automatic track of ‘effort’
Extract information
Visualize
Condense and group

4. 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..)

5. Nice Graphs…
Length frequencies corrected for gear selectivity by the SELECT method

6. Relative biomass-size distributions

7. 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

8. How does it keep automatic track of effort?
“2 stage” sampling in one record
How many samples are here? 1 2 3 4
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.
‘Physical’ data combined with the biological..

9. Id–tables: codes or values

10. Biological data - standard
Level of information
Species
Number
Length
Weight
Sex/
gonads
Individual X 1
X or 0
LFQ N
Catch
N or 0
No catch
Other columns can be added – also physical

11. No catch = empty setting
A single record with only physical values species = 0

12. 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).

13. Standardized catch per unit effort
= kg · 100m-1 · 12hrs-1 net set
100 m or m2
# Samples
12 hrs
# Nets
# hrs
# Nets
# m or m2
# Nets

14. Standardize CPUE 1 2

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

16. 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’

17. Expression builder: These two expressions are doing the same thing !
What this expression does:
Lookup field ‘Date’ in Data table
Return the Month of the date (1..12)
If Month is between 2 to 5 or 9 to 11 then result = true else result = false

18. Analysis

19. 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

20. Analysis: Groups

21. Analysis: Variables

22. Analysis: Run.. = F5 or

23. Modify analysis
Double click

24. Export Analysis

25. Analysis: 2 D – rows, columns
Column groups
Rows
Columns R3 R2 R1 C4 C3 C2 C1 H
Row variables

26. Analysis: 2 D – rows, columns
Column variables R3 R2 R1 C4 C3 C2 C1 H
Row groups

27. Analysis: 2 D – the page variable
Column groups R3 R2 R1 C4 C3 C2 C1 H
Row groups
Page variables

28. Analysis: 3 D – the page concept
Column groups
Page groups R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H
Row variables
Page variables

29. Analysis: 3 D groups Column variables Page groups Row groupsH R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H
Row groups
Page variables

30. Analysis: 3 D groups + variables
Column groups
Column variables
Pages C8 C7 C6 C5 R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H R3 R2 R1 C4 C3 C2 C1 H
Row groups
Page variables R6 R5 R4
Row variables

31. Diagrams and charts Control pane Y- series Diagram area Chart area
Plot area
Z - series
Options pane
Zoom and
scale pane

32. Diagrams and charts
Check off and write 1

33. Diagrams and charts
Reset to default
Invert colors

34. Making a chart
For a variable
For a table

35. Making a chart - example

36. Gear Selectivity
All fishing or sampling gears are more or less selective

37. What is selectivity?
Sample this population with 2 gillnets of different mesh sizes

38. 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).

39. 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

40. 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)

41. 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

42. Gear Selectivity – 5 models
Normal location shift
Normal scale shift
Lognormal
Gamma
Bimodal normal scale shift
μi = mean size (length) of fish caught in mesh size i = k1mi
σi = standard deviation of the size of fish in mesh i = k2mi or αmi
Lj = mean size of fish in size (length) class j

43. Gear Selectivity – 5 models
Only means are proportional to mesh size, spread is constant.
Normal location shift
Normal scale shift
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).
Lognormal
Means and spread are proportional to mesh size but with asymmetrical retention modes (i.e. skewed distributions).
Gamma
Bimodal normal scale shift
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

44. Gear Selectivity – Step 1
Find the linear part of the mesh size range
Exclude

45. 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.

46. Gear Selectivity – Step 3
Estimate selection curve
Probability
= less than 1
Sum of all selection curves standardized to 1
Cut off level

47. Gear Selectivity – Step 4
Correct observed catches
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

48. 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

49. Connect a selectivity file
Catches by groups are now corrected for estimated selectivity

50. Correcting for gear selectivity

51. Correcting for gear selectivity
Growth ?

52. Export to FiSAT