Jeppe Kolding and Åsmund Skålevik Pasgear 2 Version 2.3 (Build 02.12.2009) Jeppe Kolding and Åsmund Skålevik www.cdcf.no/data/pasgear

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

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

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? “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..

Id–tables: codes or values

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

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 = kg · 100m-1 · 12hrs-1 net set 100 m or m2 # Samples 12 hrs # Nets # hrs # Nets # m or m2 # Nets

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: 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

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 Column groups Rows Columns R3 R2 R1 C4 C3 C2 C1 H Row variables

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

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

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

Analysis: 3 D groups Column variables Page groups Row groups 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 groups Page variables

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

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

Diagrams and charts Check off and write 1

Diagrams and charts Reset to default Invert colors

Making a chart For 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 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

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

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 Probability = less than 1 Sum of all selection curves standardized to 1 Cut off level

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

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