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Introduction and manual

1. FRO M Use to scale axis categories M Show values M Show zeroes Inside axis Outside axis High Axis axis layout General Vaule category scale __ r Axis label M Default min value Default max value M Default label Axis min fo Nojset Axis max 4500 Position M Default major and minor intervals 9 Ticks Y axis crosses X axis at Major tick length 10 le Min C Max major intervals minor per major Axis direction East C West 5 C North C South V Defaultdecimals jo Interval gap width fo gt M Use 1000 separator Ticks Major tick length 10 Major alignment Outside C Inside C Cross us ae Axis direction major intervals minor per major 5 Worth C South Max Center Min Outside C Inside Minor alignment i Cross Help Cance OK Max Center Min Major alignment r Minor alignment Outside Outside C Inside C Inside Cross C Cross Additional category series S ei I Use to scale axis categories F Show values F Showzeroes Inside axis Outside axis C High Help Cancel OK Here you can set The range Min and max value of both categories and values if plot type is scatter or the axis 1s continuous The axis label and position Max Min or Center Th
2. eeg el If the variable is a row or column variable only the first column in the external set will be Category All Returns the data obtained from an external connected text file of matching dimension Connect by pressing on link Link Kafue work No Species xt I No ranking rank code 0 Statistics None Percentbased on F Cumulative Calculate by None C Mean C Min Max C Standard deviation C Standard error C Coefficient of vanation Conf limits Low High Variable label No Species Reportlayout Alignment C Lett Right Center Show Zeros F Zeros inred V Use default Decimals Font Cell color n M Use 1000 separator M Make Ad Hoc chart External data sets have the following format 1p used read However if the variable is a page variable matrix all rows and all columns will be added to the table Group names rows and column headers with same value or name as found in the data table will be grouped with these whereas if the external data set contains groups or column headers that are new these will be added as new groups to the table Variable restrictions Depending on the chosen variable they can be directly restricted without connecting a query when reading from the Data such as e Exclude zero values e No ranking only cho
3. This will give you a short summary of the date record number where that day starts the number of settings of each mesh size and optionally the total number of fish caught in each mesh size relative effort etc Effort summary properties Effort Summary can be run on various levels of group resolution and on various time intervals date week month and year It can also give you a summary of selected variables within each defined group E Lx Properties for Effort summary Display Query General Gr Variables O Individuals Length 0 LJ Individuals Length gt 0 cores Weight g Day C Week and year Month and year eai The empty setting For proper calculation of frequency of occurrence and catch per unit effort it is very important that all empty settings are included in the data base An empty setting is a setting or hauls in which one or some of the mesh panels lines or traps caught no fish It is entered as a single record where all the physical fields are filled but where the biological fields the species most importantly number length weight and maturity are all set to empty values 1 e 0 or X or the values that are defined as being empty see column properties An empty setting will be displayed as a unit of absolute effort but with no catch Individuals 0 see mesh size 178 mm in the first date below which is an empty setting 59 Effort summary 11
4. The Zoom is like an ordinary zoom where you can zoom in and out of the whole diagram You can also use Hide to remove the tree view It can be reinstalled by right click in the diagram Show control pane Add Trend On the series property page there is a frame for configuration of a fitted trend 103 ax Fitting method r erative options Linear analytic iterations 20000 C Non linear iterative Precision 5E 6 Trend options Convergence 0 005 Power range jo to fO 4 M Lntransformed sum ofsquares Function type Bi pita for outliers 20 for y TAT rend filter a Min observations per point fo Polynomial order Exclude zero Y values M Fixed asymptote l Fixed intercept a M Use trend filter a Trend legend M Function type M Caption M Coefficients P p value fo IV N M r T N outliers Position M Default Let C Right Eree f Top Bottom Left 00 Top 00 Add Edit Remove i Help Cancel OK There are two different fitting methods 1 Linear analytic 2 Non linear iterative The linear is based on linear transformation of the data depending on the trend type and least squares regression The non linear are based on an iterative numerical search of the minimum sum of squares observed predicted The non linear search algorithm Fletchers me
5. Checking and cleaning records is a tedious job but very important if you want good and reliable results of your data analysis Remember that in all computer programs rubbish in is equal to rubbish out Estimate missing weights If the records have length measurements but weight measurements are missing or wrongly measured and set to 0 and the length weight coefficients of the species are entered into the Species table then weights can be automatically estimated and inserted in the data base by the module Estimate weight Right click on data gt Select and gt Estimate weight a gt Tigerfish A Count matches maja gt From Ta M Allinsearch range 133 133 1 New value C Length Value Weight 360 20 El M Use expected W Defaultrank Record Rec no 133 Hydrocynus vittatus Calculation r Coefficients Expected length cm Expected weight g Deviation length Additional restrictions V Length gt 0 V Species lt gt 0 M Weight 0 l Number 1 W Rank 0 Close In such case the Rank value is set by default to 20 or user defined Pasgear 2 calculates missing weights in two different ways 1 if the individual lengths are recorded in mm and each record contains one fish only then the weight is estimated from the normal length weight relationship weight a lenght Where weight is in grams
6. Gear selectivity C Pasgear 2 Tiger selectivity gse File View Options Help Salg Gear selectivity E al Chart aK Hydrocynus vittatus Tigerfish 1 cm intervals Et y N 3634 oi Mean i Mean length atz cm m N LN 70 yore rrr npr nnn opr rnp pone eee eg 60 i ane een en cane paints ba Sains thins als Sane aes nial ones i Pies ania pene ada emeininneis es 50 H 5 5 7 4 4 4 4 fF 444 Uae opn Canaan naana M Show title M Show subtitle 1 Show subtitle 2 OShow diagram leg 4 Bho wo a oOo Ga i i l Ey A fa i a i lz 0 X margin Y margin 38 51 64 76 89 102 114 127 140 152 165 178 190 203 MESH size Axis length 26 26 26 26 2 26 2 26 2 26 2 25 0 0 Set Objects Zoom Hide Properties MESHrange 2 38 to AERE C Explore mean length SD and skewness C Estimate selectivity by model Normalscale Select the mesh size range where the mean length is reasonably linear and the number of fish is not too small Gear selectivity C Pasgear 2 Tiger selectivity gse File View Options Help eag Gear selectivity 2 yl Chart HiX Et Y of Mean i Mean length atz BN oN Hydrocynus vittatus Tigerfish 1 cm intervals Show title Show subtitle 1 Show subtitle 2 Show diagram leg X margin 102 114 MESH size Monee 26 0 26 0 26 0 26 0 25 0 26 0 26 0 Set Objects Zoom Hide Properties MESH
7. Scatter series Series Add Edit Remove Clear Plot axis Y Help Cancel OK You can choose between 6 types of charts Category single Y axis Category double Y axis Category multiple Y axis Scatter single Y axis Scatter double Y axis Scatter multiple Y axis The preview next to the combo box will give you an idea of how each are looking AnNRWNS Then you must choose your X axis from the table dimension rows columns or pages 93 If you have a multiple Y axis chart you must also choose your primary Y axis from one of the remaining table dimensions In this case the data will be plotted against a series of Z axes which is lying parallel to the X axis but has no scale see The Diagram tree view and options for display for how to get the Z scale on top of the plot area Charttype setup Chart layout General Chart type Category multiple Y axis d4 es A dimension on chart Y dimension on chart Mesh mm Rows Length Scatter series aes A If you have a category plot you can now choose your series and 1f you have a scatter plot you can choose the X values and the Y values series 1 Category plot Add your series by clicking Add under the series pane 94 gt Plot series ES Total Pages Year Total Help cance OK Choose the variable you want as a series and where on the Y dimension of the ta
8. Gear Station Set type and Stratum can be empty or renamed and used for other purposes or for the two latter even deleted For the biological fields only the Species code and number field needs to be specified Species code 0 by default means no catch empty 14 setting or haul Fields with no information entered will be stored with default values for unknown or empty see Database tables Fields You can define the number of fields to be displayed visually shown in the data table properties 2 stage sampling design in one record Rec No Date Station Species Gear Mesh Setting Type Number Length em Weigh g Sex Gonadal Stage Stratum Rank 12 1 75 F 140 2 1 28 1 775 000 F 1 13 175 F 140 2 6 700 000 F 1 14 175 F 140 2 750 000 M 1 15 175 F 140 2 1 23 3 575 000 F 1 16 175 F 140 2 1 29 4 1000 000 F 1 17 1 75 F 140 2 1 21 5 425 000 M 1 Sampling of biological organisms normally consists of two stages or sample levels 1 The physical capture or sampling stage where you set the gear 2 The biological recording stage where you count and measure various parameters in the catch The capture or physical sampling stage defines the primary sample unit PSU and is characterised by physical attributes eg Date Station Stratum Set Type Gear Mesh size etc that are unique for each primary sample unit A primary sample unit in fisheries is typically labelled as a set
9. NO g 1 00 0 0 set 9 0 0 NO set 10 1 1 0 0 0 1 L cm NO 11 33 8 47 13 1 3 SD L cm NO 12 135 7 142 3 9 53 W g NO 13 189 3 192 53 10 5 SD W g NO 14 180 12 1 193 5 3 15 9 El Gear Mesh 15 95 72 3 170 4 7 20 5 NO 16 46 149 5 200 55 26 0 CUM NO 17 20 182 2 204 5 6 31 6 Wig NO 12 6 130 9 145 40 35 6 CVW NO 19 41 g 1 154 42 39 9 etait 20 5 73 53 1 132 36 43 5 21 5 61 98 2 1 171 47 48 2 E E Pages NA 2 8 310 5 1 158 43 52 6 NO 23 3 1 134 9 2 164 45 57 1 icharts 24 3 8 16 2 1 175 48 61 9 Diagrams 25 5 8 0 4 2 1 162 45 66 3 26 1 3 8 52 147 40 70 4 27 3 9 50 6 164 45 749 This macro gives length frequencies by mesh or gear size in the nets or sampling gear or any other chosen column grouping Mean weight per length group with CV 1s displayed Mean retention lengths cm and weight g for each mesh size with standard deviations This macro is the standard programme for exporting the results into the tool Estimate gear selectivity in order to correct the length frequencies for gear selectivity see Correction for gear selectivity The catch curve diagram can be manipulated into various transformations such as In transformation of the length frequencies or calculation of relative biomass CPUE in grams set in order to plot biomass size distributions Hydrocynus vittatus Length cm fs g Atepe cal fucataain ph i ea Eed e Wis Gaui btm a Raina A wi em E E te eevee lpn S E E
10. Meee ocidero enn one duase ous saeneesau aca seeds cau csadaceonapaestanengceanoeneccedaasnimcuetacesaeareeetoens 104 TOUS seasons cio ouib ada saab E E ade Wanda agin EN 106 Confidence Hmi S srsarss eas sasteesncuayscat E E A E A ANR 106 Thedroguency CISEM DUG ON nce ena a a A E E A E uammnceasteaases 107 Options for calculating confidence intervals nnsssenrna aT AEE E R 107 ESHMAIES based ON he sample TeaM rirnan eTa T E E EEA OTSR 107 Estimates of the mean based on log normal theory The Pennington estimator cccccceeeseeeeeeees 108 The modited PenninetomestiinatO siisi Eo EO OEE R NEEE S Enno 110 Bootstrap COMMENCES IMCCHV AlS yescs sasiaoek ivoren Eno sor beens EEE EE rO EENET 111 EE E a i AEA EE A EE N A EA NEEN E A RA 113 Howtouse GearsclectiVii yacine nonn EEn E EOE EE EE EOE ESTEER 116 Save estimated sear SCICCHVILY osons ennonn EEO EEES EEN AAN ENES 121 References ainnir iran A NA AS EE OAA E A EAN EN EA AEA AE aN EAEN EA 122 Overview NV Tif C lr ase A 2 Oe Si EEE T secuesmateednaaahabendbainabnlbsynnnaieatnssaaumaeneiaGoneos T E 7 Multiple document interface MDI cccccececccccccceccceeeeeeeeeeseseeeeeeeeeeeeeeeeeeeeeseseeeeeeseeeeeeeeeeeeeeeeeeeaaaaaasessseeeeeeeeeeeeeeeeeaaas 8 The organization of PASGEAR ou cccccssssssssssseeeecccecceceensaaaaesssssseseeeeeeeeeeseessaaaeessssseeeeeeeceeeseesesaaaggssaseseeeeeeeeeeseeeqaaas 8 PVA AGG r a r tas dacuesnaguatnasasamandadndesscagusseacaaaessiesgesssasseaeagetaeeesea
11. Run analysis hy E B Columns Mor Reset fonts to standard E NO 13 30 mcum Nc 4 amp Pasgear confidence intervals 30 ElW g iNO 15 38 cvwno Iie Estimate gear selectivity 37 ElBiomass c 17 ASAC 25 B Pages Year 49 17 EINO yCharts 19 1 Diagrams 20 9 21 8 22 7 In the default chart the length frequencies are plotted on a time axis either year by year or all years combined to visually examine the various dynamics and or modal progression which can be interpreted as growth or size specific migration over time Length cm ee eee ee ee Hydrocynus vittatus 60 4 eee Geen eee eee teen Sete tet Dneee bene tee nenen Seeee 50 40 30 20 10 0 Maturity by length geeeenssesssssnsesen s Table Maturity ogive diagram Reccesccccneccvencoees Rows Length E Columns Gonadal Stage Length em CUM NO 6 AMAT 7 O BJ Pages Sex 8 NO 9 Charts 10 yDiagrams 11 _ co amp MM MH W a a b Males 1 2 3 4 5 6 Total CUM NO 0 1 1 5 1 7 3 1 5 5 8 3 10 8 14 1 18 5 22 7 27 3 33 4 41 0 49 0 58 3 65 8 MAT _ awos omnoc eoOoemi e nv Nov Jan Date 1993 Females 2 3 4 5 6 Total CUM NO MAT 2 0 3 0 1 0 4 0 2 0 7 0 3 1 2 0 7 2 2 14 6 3 1 33 13 49 31 25 8 6 24 25 12 2 48 4 18 2 68 40 24 0 88 44 30 4 91 76 41 4 84 1 85 53 8 92 2 70 64 0 96 IN N Dm Ww ODUN _ D Ww AQ 83 Th
12. as well as accessing the properties of these components objects The organization of PASGEAR Pasgear 2 is both a database and a tool for data exploration and analysis A project is a file pg2 that Links the database files tables to an unlimited set of specified analyses Describes and defines the contents and units of the database and tables 3 Stores the layout of the defined queries expressions analyses charts etc that connects with the database i Pasgear II Demo project pg2 File Edit View Insert Project Data Tools Window Help Cah amp x f ka EE ELI 4 gt CREA MLECI BPG2 Demo a Database Data _ EBSpecies Gear _ amp Station _ Setting Typ _ Stratum _ Rank amp My Id table _ Queries Effort sumn Database The database is a set of files that stores the data records in binary format bds These data can be viewed in tables see Database Tables The main file is Data which stores all the primary information in records see the data structure in PASGEAR Some of this information is stored as codes so called foreign keys e g Species gear station etc in order to save space Therefore there is a number of additional reference or Id files that link these codes by primary key fields to further information such as e g the generic name of the key field code When the data is tabulated in the Analysis section all key field codes will be translated into co
13. default 0 These options should be explored in order to see what fit gives the best results Set lower limit for exclusion Double click on the series in the Diagram control pane and choose the Maturity ogive tab Properties for Males Trend filter _ _____ W Lower limit for exclusion 25 Lower limit 5 for outliers 25 Shortcutto configure the default trend filter used in Trend setthe lower exclusion limit y direction above which to exclude points if subsequent lengths have a lower 4 mature value Check off if you want to configure the trend filter in the x direction else this point will getthe default value of lower limit See Help This tab is used as a shortcut to configure the optional default trend filter when estimating a maturity ogive Since the value of mature is based on the number of observed specimens and this number can be low in the small or large length classes there is sometimes noise in the lower part of the logistic curve where a a high percentage of mature individuals can be caused one or two erroneous entries Many small positive maturity values intercepted by O values may also be interpreted as noise However if used they may tend to lower the slope of the ogive and thus over estimate the Lso By applying the default trend filter these will be excluded in order to improve the curve fit The algorithm works as follows When setting t
14. have been created and entered and you have the Info Validation view checked on you can optionally enter data while the names corresponding to the codes of species stations setting types etc are simultaneously displayed This will help you to remember and check the codes while entering the data Ids that are not defined in the corresponding Id tables will be given a Warning Not defined Species in Latin or Local This is for display of corresponding species names to the Id codes in either local language or in Latin Length weight control Similarly if the Id Table for Species has been created and the species specific length weight coefficients see Analysis Length weight relationship have been entered in the table you can optionally enter data while the corresponding length and weight data are simultaneously controlled for mutual consistency see also Queries Length weight validation This will help you to avoid punching in obvious mistakes and or disclose major errors in the field observations Default level of warning is more than 20 deviance between observed and expected length Weight Unit Specify the unit of the weight field all data will by default be converted and stored in grams 30 e Punch fields This for shortening the data entries by excluding fields that are not changed often Fields that are not checked will be jumped when pressing Enter next field in the data entry dialogue Edit previously punched records
15. 00 Total 7484 100 0 3012 532 100 0 1085 350 0 10697 100 0 1 652 0 56 This analysis gives total catch composition in numbers and weight kg as well as frequency of occurrence FRQ in the fleet or mesh size settings i e whether the species was present or not irrespective of the abundance Each of these values is also given in percentage of total not that the percentage frequency of occurrence FRQ does not add to 100 as the total is the total number of settings As a measure of relative abundance or commonness of each species i in the catch composition an index of relative importance RI Kolding 1989 is used y 76 e E gt W N F j l 100 where W and N is percentage weight and number of each species of total catch F is percentage frequency of occurrence of each species in total number of settings and S is total number of species This index was originally graphical and combines and show simultaneously the relative numeric abundance N the average size W and the commonness F of a species IRI N W F Pinkas et al 1971 see also Caddy amp Sharp 1986 displayed as a rectangle see figure RI gives the relative area of this rectangle in percentage to all the other species present After running the programme there is an option for illustrating the IRI graphically instead of in percentages see figure Shannon s diversity index The IRI programme also calculates Shannon s diversity ind
16. 1 Ei 1 tT 1 1 1 1 i 11 Fish 3 7 31 94 29 23 12 1 11 14 1 1l 269 Effort Summary see Database Effort summary is a module that quickly gives you an overview of the data by various date intervals and specifically lets you check the chronology and the number of primary samples units see Effort or primary sample unit It also has an option for inserting empty gear settings see empty settings into the Data file if these have not been recorded An empty setting 1s a gear setting which did not catch any fish This module should always be run after creating the database and prior to run the analyses in order to secure that the effort and number of primary sample units are correctly counted Analysis The Analysis see Analysis part of Pasgear consists of two parts e General analysis e Predefined analyses An analysis is generally built as a grouping condensing of data fields in one to three dimensions rows columns and pages with a number of variables statistics see Analysis variables associated with the groups Table 1992 Total Species F M Total F M Total E nom A Qacha i y ee vittatus a PADiagrams Ostehodus shenga Labeo alivels Labeo congoro Labeo cylinarcus Schilbe mysius Ganas ganvepinus Total 11 The outputs off all analyses can be standardized to a given standard effort see Effort mode in properties for tables and the result table can be sorted in various ways Results can also be shown g
17. 24 226 Range 3 00 P25 897 200 P50 0 971 P975 10 50 Me Mean 971 SE mean 0 39 150 twalue 1 960 meantSE 6 96 mean tSE 10 4 125 100 75 50 25 8 24 8 74 9 24 9 74 10 24 10 74 11 24 Mean Wikg set Class Interval 0 100 30 Bootstrap confidence intervals can be constructed on both the sample mean and on Pennington s estimator and all four estimators can be displayed simultaneously E Pasgear confidence intervals plot i m Options V Arithmetic estimator Wikq set V Pennington estimator v Arithmetic Bootstrap V Pennington Bootstrap V Boxplot V Confidence Intervals Jv Number of observations Statistics mean and SE Set cut level Pennington geosesoeocsosososecscsecsoscsococsesy n Rescale Y axix V Show Plot Legend Jv Show Group Legend Vertical X Legend Zoom from right gt zoom from left 4 gt x axis stretch 4 gt 0 Species Total E Arithmetic mean SE mean t 95 5E number sample size Pennington s estimator SE T mean t 95 SE number M Obs gt 0 or cut Font gt Background Arithmetic Bootstrap 5E 95 percentiles number bootstrap runs Pennington Bootstrap SE 95 percentiles number bootstrap runs Copy Print Save J Close OSA Sb IY castes peta eects cee A A ng an E ene ae cence ves Sem ape sae AA S A E A E 113 Indirect estimation of gear selectiv
18. A A E TEE 104 EREE e e a E EE E EAE E EA EEEE A errr err EEE 104 There are almost endless possibilities for designing your diagrams and charts in terms of layout and orientation Below only a brief description is given on how to access the different property pages and their configuration Basic concepts e A diagram is a frame shown in cyan in the example below that can display one to several charts or other diagrams as well as title two subtitles and a free positioned legend e A chart consist of two parts gt The chart area in green in example below consisting of a title axes legends and a plot area gt The plot area in yellow in example below which plot one to several series on 2 or 3 axes e An axis X Y or Z can hold one to several series and can be formatted in various ways e A series is a set of data connected to an axis that can be plotted with various layouts e A trend is a curve that can be fitted to a series and added to the plot with an optional legend e A graph element is any item in a diagram or chart that has graphic properties such as color line style fills and for some fonts 92 Making a chart A diagram and chart can be created either automatically or by the user When charts are created automatically default charts they are ad hoc or non persistent charts which mean that they will be re created on every run of the analysis These charts can however be modified and saved as persi
19. EN ctrl a G8 Station ig Select and 9 Q Find pais ii Rank ims Sort a Replace Cti H _ EMy Id ti X Delete records Export i CaQueries gy 5 Ctrl G ma BE ffort A is OS de Check length weight a Analysis 9 Erst T Estimate weight F 91 Diagrams and charts Dior a a E E crasersca o ue nee vac ect crt tee cer ehv ota Bataige ealeneenan bh nb otna sb ua soupeckak oumeoets 91 PLO e E AE E E E E E E E E nie 91 D a a E E E E E E errr rer rt Terre errs 92 EE a o E E E E E O A axons A A E E NE PE A E A T E E EA 93 Pe Oe E E ros aate Beane dt we scontiel sec Pielusenteciaaeaen aan padi cue aaeauascnes 94 Linkime achat toa cll ose 110 ss E sy a Ea rA rer ttnt ERRAK a iaaa reer E eE EE EESE 94 ITU charin one Magra ssi once ascianoaennactinancntaeit EEA Erna eaea 95 PPO PPL raa T a E se susus tsetse sasewaiaeaetsaneees 95 Properne Tre D aS ec rnman ere E EE E E E TEE E 96 Pone eS a a a E A arrrtreerr rrr 97 Popo ae Or Sa a E E N E E 98 Layout of Diagrams Charts Axes and SSM eS cccxevesssanssanecacrescnanamstotassaeadesanaiadobaceenstesieaumabhsadvencedepeuvaebioawenccestasaanetoantsa 99 The Diagram tree view and options for display ccccccccccccccecenseeseeseeeeeeeeeeeeeaaeseeseeeeeeeeeeeeeeesaeseeeesseeeeeeeeeeeeeaeaaaaeaes 100 PO e a NG EEE EEEE ON E E A A EEA A A A N EEE E T 102 NY e e EE EE EE E E AEE EE A EON E E E E E S 103 A Ts CR E E E A a E A oo ace A A A E A
20. Right click on the table choose Import gt Text This will bring you into the Import Wizard with 4 steps Step 1 Import text to Data wizard Step Ic 3 x Define the source location and format for import See Help Get started for details Import from Clipboard File eee Format f Flat single record per line C Aggregated multiple records per line 2Back Finish Define the source origin e Clipboard or e File If file must be a text file then open the file by pressing ial Define the format Flat or aggregated e A flat file is one record with various fields per line e An aggregated file is a matrix with rows and columns typically a length frequency file with length intervals in rows and e g dates or mesh sizes in columns Press Next for next step 25 Step 2 Import text to Data wizard Step 2 0of4 Preview ofthe source and destination specifications Define delimiters date format start line end line etc Destination H _ r Source C Replace all Delimiter Insert after rec no Tab Other Overwrite 552 Comma Merge and Mask Delimiter Source DMY E i Erom line tobe o MU 2 fC Overwrite existing keys C Preserve existing keys E Date format Source preview ptt F 2005 J i 06 10 2005 CHACH36 06 10 2005 CHACH36 06 10 2005 SCASCO5 E 06 10 2005 SCASCO5 06 10 2005 SCASCO5 Help Cancel lt Back F
21. a fleet or the relative sampling effort e g fraction of sampled fish of total catch in a primary sampling unit or number of settings effort for the total catch in a sample To illustrate standardized catch per unit effort CPUE in PASGEAR is calculated as n CPUE gt We where i e yis the absolute effort and n is number of primary sample units thus when effort is not a variable then y n e W is the catch weight or numbers of sample unit i e SU is the standard value of one absolute effort unit i e 100 or standard effort e g area of a gillnet e U 1s the relative effort value or weight of sample unit 7 given in e g the RELATIVE EFFORT field i e 50 percent if only half the catch was measured or the actual area of the net used PASGEAR can thus easily work on sampled catch and effort and thus sampled CPUE even if only a fraction of the catch in one fishing operation PSU is actually recorded or if the recorded catch in a sample is aggregated from several effort units e g nets Both absolute y and relative U effort can be stored in PASGEAR but there are 2 different mode or kind options for absolute effort within a PSU depending on whether the number of settings or absolute effort units in a primary sample is a variable y n or not y n 1 e whether the biological information in a primary sample unit is obtained from one or a variable number of effort units nets hauls et
22. after the first record data entry they will remain unchanged in the subsequent new records by just pressing Enter on the field Only fields that are actually entered with new Id s or values have their contents changed This feature makes it easy and fast to enter records as most fields do not change very often Data entry is fastest using the numeric keyboard therefore the Sex fields F M J or X unknown can be entered by corresponding numbers 1 2 4 or 3 You can also set up the punching module in Options to jump over fields that are not changed often they will simply inherit their value from the previous record or connect the fields with the Id Tables to give warnings when unknown codes and or unrealistic combinations of length and weights are entered Once a record has been entered you append it to the data base by pressing Append record or just press Enter when you reach the bottom of the field array and you are ready for the next record entry Data entry options In addition there is a series of Options for Data entry which can be accessed by pressing Options 29 F View columns Species in M Format Latin M Info Validation Local Validation __ Weight unit W Length Weight Control g Max deviation in 20 Punch fields check active View Columns Check these if you want these columns to appear in the data entry dialogue If the Id Tables see database tables
23. and codes in a PASGEAR record sssnn nennseesssssssseeeeerressssssssssssseeererrerosresssssssssssseeereresssessse 13 Valid records and Missing INIOrma Ossian E OE E E N 13 2 Blage sampine desio min ONE TCCORC ssia E ee ataunsiaa sa shantedeemnamuerenwiee 14 Physical data the pimarysampline Unii arenie n EE ened 15 Biological data the secondary sampling leve sissies sesh ovat id EN 15 Types Of DielOgical dilasa aceasta icaanns nidconatei uaa eiae andes mania a 15 ER ore detinition dand Samiple raising MOdeS uena eat iatant eid Sun seteseaadiiaencntelotsued N 16 PE TNO EC TNO Cs sien ae ee ta ta cen le eats cetacean clea lat cease td osaram ed naan an aaon E 16 SAMIPIC TalS 11S ena sa deaadav sa tnouestancoeaen R oct ovesaas cdadeuseae gee aces ssestumensensmeadecumenoes 19 Record PAIS ING enone n cre daaseoxicestacesauconeme cu edu cuddaceonuanceaadesseeaanonuccesaa eoreeucaaceauecnanueriees 20 Get started ares saz retactacsoassasgarssastiasesaane meseotscsusasseanscstaasaauane mmesoate A cause esuemsaasaceene cacmesoetee ZA Get Started wilh Paso Cai 2 cacti ct he ssa cece aiads ede ot auccuuei N 21 Crede ai Mew PLO CU ssc esate a ccs aa ea cates dane eden tea Nac oaued caaccaaadecdceaccte E 21 Import Trom Passear IDOS Verion einn aE E A E AE 22 Import fron Clipboard ortext Tes eccna E E A 24 DLS DN PA ros EAEE A EE T E E A E E E S O E E aaamenaneueeaavirans 24 SEM Z Aa 25 SED aa A 26 IOP Aeae R E EE A E uuaameenmatedavrane 21 EFoterd
24. and weight where you can toggle between different units by choosing from the Unit combobox 47 Unit Conversion The Conversion field will then give you the multiplication factor between the displayed unit and the value as stored in the database Data are preferably stored at the lowest resolution possible so that for weight for example the stored unit is grams so that the conversion factor between grams and kg the optional displayed unit will be 0 001 You can add any units of your own by pressing which will open the Unit editor Press New to define a new unit and corresponding conversion factor Renaming fields and any othe object Almost any object in a Pasgear Project can be renamed according to your preferences If the property page has a tab called General usually located as the last tab on any property page then by opening this you can rename the object your are addressing Thus if you want to rename any of the fields tables queries analyses charts etc then open property page and change the name in the Caption edit field Similarly you can add any personal comments memos etc to any of the objects in the Comments pane Properties for Station l a x Column General Comments Editto give new user defined name ofthe object Add any comments you like in this pane Help Cancel OK 48 Data table GiProject 1 Uj Database 5 Data A Rec No
25. are 5 choices of selection curves all are of unit height L k m y A Normal location shift expl S o where only the modes maximum retention length is changing with mesh size mi Spread is constant 115 L k m y B Normal scale shift exp 2 k m where both the modes and the spreads of the selection curves are increasing with mesh size 1 e the principle of geometric similarity The following three models all include asymmetrical retention modes i e skewed distributions The bimodal curve is appropriate if the fish are caught by different mechanisms e g both wedged by the gills and entangled in the mesh sizes j e U ea 2 a oO m C Log Normal exp oe i 20 L e L D Gamma ox a 1 k m k m L k m y L k m E Bi modal on Gemy w on Gzim 2 k m 2 k m where u mean size length of fish caught in mesh size i k m Standard deviation of the size of fish in mesh 7 k gt m or a m L mean size of fish in size length class j In all cases of the above considerations and models one can use the Poisson distribution of Nj to apply maximum likelihood for purposes of statistical inferences and estimation fits The code and implementation of a general non linear maximum likelihood optimizer and the five optional selection curves into PASGEAR was done by Ren Holst ConStat Denmark In order to explore the possible shapes of t
26. around O the red line and have a value smaller than 2 white lines Residuals with an absolute value bigger than 2 are represented in yellow Under assumption that the deviance is A distributed which is not always the case a p value is given with degrees of freedom Lii parameters The p values will in general always be small so the best measure of goodness of fit is looking at the deviance The total relative selectivity catch or retention probability of the gear is computed as S gt S j max j where each estimated mesh specific selectivity curve si J is weighed by the number of settings effort of that net The total relative selectivity is used to estimate the corrected catch frequency in each size class N from the observed catches computed as N N j a where S 1 if S jj lt 0 1 or optionally chosen value ij The total relative selectivity by length groups S ij can be stored on a file ASCII and used for selectivity corrections in the Analyses The default name of the ASCII file with selectivity probabilities will consist of a combination of the species Id the length interval used and the length unit with the extension sel so that several selectivity files can be stored and accessed by the Analyses programs How to use Gear selectivity 1 First you must specify the range of mesh sizes you want to include in the analysis This is best done by a visual examination of the first diagram 117
27. be used if for example different proportions of the catch is sampled and recorded within one PSU Get started Gel sared wiih Me A cates tated cet rie dedi E S 21 Ceea FAL OCC a E inca ationhenatwigeasincetacasebsentase 21 Import from Pasgear 1 DOS VersiOm ipceiivatisaihiwsintavedssednonticecuaucsvedvennbiattbaiiantesurabieliievenbiensdintebecieciaess 22 Import from Clipboard or t xt files aida stietiosdscctatweesadanabasartiledal youd eicbedenanndacmbaienbves SaaS 24 RL E PEE E T E E I EEE E A A E E E E ET E E E E E ET E EA 24 E1 AREE E A E TETE AEA A A A E din anderen OA 23 BS ER PIO AATE E TF ena E ence AE A O OET E E aden tos 26 E a A E E E sito E E ei antedacaioarsee sane nigaioncaactevatadecammencteacen 27 Enter data directhy mt0 Pas seat 2 assis taniiseceasencicdeniadacebnensnedvegediausdinst sdonaedacesnandeedengndscuseeauietenardiceseendeatens 2 Data STE OPONSE issoria R A dam treaccaniseee EATER 28 Edit previously punched PECOTS ca tandssecvaneesdeevesadacubnendawtendacanssensssdeuasdecsnandeateniadsceoteassedeaasdicusnendeveees 30 Get started with Pasgear 2 For those not acquainted with Pasgear DOS they should first read the Overview to understand the structure of Pasgear 2 The following points show how to e Create a new project in Pasgear 2 and migrate from old Pasgear 1 files e Import data from the clipboard text stream or a text file e Punch and edit data directly in Pasgear 2 Create a new Project File Edit View I
28. box Category by e All e Basic number length weight frequency of occurrence etc taken from the Data table e Effort absolute relative duration or conversion factor for standardizing samples e Date various ways of grouping or expressing dates e Derived special such as Index of relative importance Fulton s condition factor percent mature Shannon s diversity index etc e External reads the variables from an external file of rows and columns The info pane under the combo box with listed variables will tell what each chosen variable will return External Data sets This 1s a powerful feature that enables the user to import external data to the report table You can also export a report table to e g Excel do your own calculations and then re import the new variable to the table or for use in the plots Variables can be imported from outside of Pasgear by connecting an external dataset from a file in text format Choose the variable External dataset and link the file by pressing on the Link edit line Properties for variable Definition and layout m Define variable Variable Relative effort Conversion factor Condition factor Biomass Relative biomass Index of relative importance Maturity percentage Investigation percentage External dataset Table column m Restriction FE Exclude zero values I Number 7 F Within gonadal range F Within mature range Help Cancel OK
29. database in project tree and choose query type from the sub menu Available queries are 1 Selection 2 Validation Once a query has been created defined and optionally named it will be stored in the pg2 file of the project and available on all subsequent entries Queries can also be cut copied pasted edited deleted or if in text mode be added to Expression library Connecting a query You can connect a query to a table or analysis in two ways 1 Right click on the table or the analysis Choose Connect query and select one of the available queries that have been created 53 File Edit View Insert Project Data Tools Window Help Oe OSX e em me A K 4 gt JEA 0 HS B Bf amp IPG2 Demo a Database G a MSpecies View Gear ima Connect query uae Tyee Tiger a Stratum iImpot gt Tiger or bream E oe 21 Sort Feb May Sep Nov 2 Goi into properties Alt 4 Return choose the Query tab and select an available query or create a new by clicking a Properties for Data i x Effort mode Display File Information Query General No query connected f No query connected Bream Feb May ep Nov To disconnect a query then choose No query connected in the combo box or Right click on the table or the analysis Disconnect Query name When a query is connected to a table the all matching records those satisfying the selec
30. given by an n m 1 gt mn m 2 a n m _ varch vare 2s zem of 2 ye where s is the variance of the non transformed values less than the cut level and c and var c are the equations above but with m bigger than the cut level instead of 0 111 Setting the cut level There is no single objective criterion upon which to define a cut level bigger than zero Basically the logged Delta distribution should be viewed in the plot module see above figure in order to determine 1f it is skewed to the left and or contains isolated small catches As a rule of thump Pennington pers com the cut level should be set as either e Thump rule 1 R1 2x x respectively of the log transformed values greater than 0 where x and x are the mean and the largest value max max e Thump rule 2 R2 y of x where default y 5 but the fraction of the mean value can be changed by the user In the plotting module of conflim exe there is an option for setting the cut level bigger than 0 by pressing T You can then either set your own cut level by entering a number or automatically apply one of the two the rules of thump by entering RI or R2 It is always advisable to view the Delta distribution to see the actual cut level When viewing the Delta distribution it is also possible to change the cut level up and down by toggling the arrow keys gt and to see the effect on the Pennington s modif
31. i B Date gt Station q Species Gear Mesh E Rel effort I Duration 4 Setting Ty LJ Number J 0 x peene cee en eret nt feat penta nag gem Re l aj a 2 r E NA JE 55 800 1700 000 0 00 le 3 i 28 100 775 000F 25 600 700 000F 25 000 750 000M _ 23 300 575 000 F 29 400 1000 000F l Weight o ll 12 115 1 Sex E e 115 gt IGonads 14 115 Stratum 15 115 Lg Rank 16 7 115 r on en vn anh ahs mm ae gt AARAA Mm Mw Nw PH anh ab ah ab ab ao N w ao h h b aud ee UMA E G Oe C0 140 NA NA Fields columns In a standard Pasgear data base there are the following fields by default see also table 1 e Record number automatically calculated e Date Uses local short date format In MS Windows choose Control Panel Regional options Date NB A date is mandatory for the record to be accepted as valid see_2 stage sampling design in one record e Station Numeric station Id Corresponds to Station table Id e Species String Id 7 chars Corresponds to Species table Id Unknown default 0 NB If Species Id is known the record is considered as a valid secondary sample record SSU or valid biological record see_2 stage sampling design in one record e Gear String Id 2 chars Corresponds to Gear table Gear Id e Mesh Numeric Id Corresponds to Gear ta
32. nome pen etererere eevee enon Veneers B J Penn enone Benne One Seenet Benet Ohne Senne Rennn Bnnne Senne een A assssdpisssacnin pennas gleam ba E paei l 3 2 i a Aa 0O 35 51 6 amp 6 76 89 102 114 127 140 152 165 178 Mesh 26 25 26 276 25 26 275 25 25 265 25 25 set Hydrocynus vittatus NO Biomass set A aa 240 oh aaa lat alta calla el al ae ae 210 180 180 150 150 120 120 90 90 60 60 30 30 0 0 6 11 14 17 20 23 26 29 32 35 38 41 44 47 50 53 56 59 62 Length Length by time Table ILFa diagram Catch curve diagram 1992 Panis Length cm Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Total NO CL NO set 8 1 1 0 0 L cm NO 9 SD L cm NO 110 1 1 0 0 EW g NO ania mo Ia 7 0 8 4 6 2 10 47 13 E Columns Month 12 1 1 2 19 2 18 16 24 7 17 12 142 39 NO 13 1 3 15 2 15 11 30 32 38 22 192 53 CUM NO 114 1 9 19 11 14 30 23 59 27 193 53 EI g NO 15 2 1 1 9 WW 21 7 38 31 25 24 170 47 pa a 16 11 18 3 17 18 37 28 46 22 200 55 El B Pages Year 17 21 6 4 9 8 11 9 25 40 48 23 204 56 NO 18 4 3 7 2 3 4 8 2 17 2 35 27 145 40 ichars 19 14 7 6 8 5 4 2 3 13 22 38 22 154 42 a Depams 20 8 13 22 6 9 4 4 1 9 11 28 17 132 36 21 10 10 32 30 2 2 4 3 8 10 18 18 17 47 22 9 12 33 22 29 6 6 4 7 3 14 13 158 43 23 5 7 42 29 39 11 4 8 3 5 6 5 164 45 The macro gives length frequencies by various chosen time intervals If you set the pages years to NA the results will be pooled by time interval over all the y
33. of zero catches often observed and the occasional occurrence of very large catches Development of confidence intervals is complicated by the asymmetric distribution and the occurrence of zero catches confounds an effective normalization transformation Logarithmic transformation will stabilize the variance but data will still not be normally distributed and interpretation of re transformed means is difficult 109 One way to generate more precise estimates of the mean and more accurate confidence statements for skewed catch data is to base the estimators on the log normal Delta distribution Pennington 1983 1996 Conquest et al 1996 in which catches are divided into zero and non zero units followed by transformation of the non zero values to natural logarithms When it is found that the transformed non zero data are approximated by a log normal distribution i e the logged values are normally distributed then a more efficient estimator of mean CPUE 1s given by Pennington 1983 1996 c exp X G s7 2 where n m is the number of sample values greater than 0 xand s are the mean and variance respectively of the logged values of non zero catches G f is an infinite series function of m and f for example f s 2 in above equation which is used to correct for bias in re transformation from log to arithmetic scale and is defined by m 1 aie ial Ga E T E TET The variance of c is given by iroa al 2 62 var c e
34. or haul and often represent a unit of absolute effort see Effort definition The recording of the biological catch defines the secondary sample unit SSU and consist of biological attributes typically Species Number Length Weight Sex Gonads The number of biological records within one primary sample will vary with the size of the catch and the aggregated level of the biological recording see below under Types of Catch data One record in Pasgear consist of both physical PSU and biological SSU information indicated by red and green color respectively in Table 1 The minimum of physical information required for a record to be valid is date and mesh gear whereas no biological information is required One record with only physical values but no biological simply means an empty setting 1 e the sampling was done but no catch obtained When the records are properly sorted chronologically see sorting data this unique system enables Pasgear to automatically keep track of the number of primary sample units This is a great advantage because Pasgear itself keeps track of the number of 15 sample units PSU in calculations e g CPUE see Calculating CPUE irrespective of the number of records within one sample Physical data the primary sampling unit PASGEAR will automatically count a new PSU setting fishing operation or sample each time one of the fixed physical fields Date Gear or Mesh change value
35. or optionally also if any of the other physical fields change value see Database tables Thus each time a physical PSU field changes its value a new primary sample is considered regardless of the number of biological records SSU within each PSU You can define which optional physical fields should be used to demarcate a PSU by checking these as sample separators under column properties Each time any one of these fields change its numerical value compared to the previous record PASGEAR will consider it a new primary sample and add the number of sample units n with 1 In other words when just one of these fields changes its value it simply means that it is now a different fishing operation i e a new sample Properties for Station 2 x Column General Field name Field Id l Sample level Station 3 Primary physical ype ize in bytes W Sample separator Integer C Secondary biological Id key Too taka Teea Format Id alias label lt No alas pee ates Color ld relationship with table 0 Font station Station Id Calculated expression Biological data the secondary sampling level Types of biological data The biological data or catch data secondary sample unit can be from 3 different levels of information 16 a The individual level where one record is one organism fish and all biological fields have a value referring to this single organism In this case the NUMBER
36. own Si thod ciinetfeet 3 i gm Gonads and maturity Depends nit em bi Gonads and maturity 1 5 erar E er on this data field exist G Measurement type gt Gonadal stages of defined Lowest gonadal stage FTotiengh 1 i fo stages allowed in data field Lowest default mature stage gt Weight gonads NB To change the displayed value for columns fields entered as Lowest gonadal stage onadal sta lowest codes then open the properties on the key column ofthe stage allowed in data field corresponding Id table and set the Id alias field property For example To change between latin or local name for Species d codes then change the alias ofthe Species Id in the Species gona S Lowest default mature stage Lowest mature stage for mature individuals Used by the analysis variable Mature Help Cancel o percentage This property can be overridden at the analysis See Analysis properties Length Defines the length unit used in groupings and display This is a short cut to the unit table Expressions x Summary Definitions Effort mode Expressions Table Layout Default chart layout Name Body syntax ShiftUnit Col Val U1 U2 Val UnitFac Col U1 UnitFac Converts Val from unit U1 to unit CollnUnit Col U ShiftUnit Col Col Unit Col U Converts Column value from cur InColUnit Col Val U ShiftUnit C
37. particularly useful for test fishing data from multi mesh gillnets such as Lundgren nets or the Drottningholm method e g Degerman et al 1989 Fyalling amp F rst 1991 but can also be used for many other sampling methods such as lines trawls or Underwater Visual Census UVC Labrosse et al 2002 Although PASGEAR has many of the features of an advanced database it is intended for users with little or no experience in computerized data bases or with limited access to sophisticated computer equipment Emphasis has been put on easy data input editing and manipulation and on procedures for checking and cleaning data records With a clean complete and accurate data base you are ensured that the calculated results are reliable at least from a computational point of view All data can be explored tabulated analysed and displayed graphically in the analysis section by query selecting various groupings and calculations of a wide range of statistics A number of predefined extraction condensing and calculation programmes have been incorporated see Analysis section which give an easy overview of large data files and serve the need of the most fundamental data exploration and most common primary analyses of experimental or statistical fisheries data Among the several special features included are e Automatic estimation of weights from length weight relationships e Standardized weighed calculation of CPUE with co
38. plot If the series is a mean value of many observations then it can be shown with error bars SE SD or 95 CI and the sample size number of observations for each point You can also fit and add an optional trend see Add trend below 102 El S LFQ diagram ah LFQ Show series LJ Standard error interval Standard deviation interval 095 confidence interval OShow sample size Bar plot i Smoothed area bars Line plot Scatter plot O Trend curve Show trend legend O Trend confidence interval curve _ Show outliers Below the tree view you have options for zooming change the legend positions change axes length or diagram margins Ral show title Show subtitle 1 Show subtitle 2 LK Show diagram legend X margin 2 Y margin Axis length Obi scale 100 Zoom 100 Hide Properties The Objects zoom will simultaneously change the relative size of selected objects in the diagram and is a short cut for changing font size line width symbol size etc Changing legend positions When clicking an object in the diagram which has a legend Diagram chart or series if a trend is applied and the legend is displayed you check can off o Default position and move the legend by changing the values in the Left pos and Top pos fields Show series legend jDetault seres legend position Leftpos 54 Top pos 1 Axis length Obi scale 100 Zoom 100 Hide
39. selected data format Selectrange e Apply and define a region in the plotwhere the data values either included or excluded will get a separate layout defined as Selected data in the Series layout Invert colors V Apply invert colors fol Apply and define a Y value below which fill colors of bars and symbols will be inverted Scale by MV Apply scale factor fi Apply and define a constant scaling value to the series m Gap width Interval gap width fo Sl Define gap width of individual series in will be added to common gap width set on primary axis Help Cancel OK Under Series you can do 1 Selection Define and apply a region in the series that you may want to give a special layout color marker etc Click on and define the selected region Soc Select inside outside region Selectinside defined range Select outside defined range gt lt X Range fo Y Range fo Help Cancel This region will have the graph element name Selected data in the Series layout see Layout of Diagrams Charts and Series below 2 Invert colors Define a constant Y value below which the colors of filled elements bars and symbols will be automatically inverted 99 3 Scale by Scale or raise multiply all values by a constant factor 4 Gap width Give each individual series a gap width in the X direction
40. species gears mesh sizes stations settings etc in the data file match with the one specified in the Id tables All Id s found with no corresponding definition in the Id table will be selected Check the box V Check invalid codes by and select the Id tables you want to check against Properties for Validation 1 2 x Validate Select General M Invert validation Max length deviation limit in 20 W Check invalid codes by 1 Station Gear L Mesh Mesh 57 Effort summary PESO SUMMA Y stressed eect t cca eet E hen bom sueredasu seein se tnemtabott reidanoda ead wiataeiesetuentaiateoatesetuewtniaceeed ster 57 Chronology and the units of effort Settings or SAMPIES ccccceeecceceeeeeeeceeeaesseseeeeeeeeeeeeeeeeeeeeaaasasseseeeeeeeeeeeeeeeeaaaas 57 Check chronology and the number of settings Samples ssssssseeececeeeeeeceeceaeesesesseeesecceeeeeeeeeesaaasaasaeeeeeeeeeeeess 57 Enor BUN AT ORO NS aha pate cate iaritsc ai E ua io wcasmenetie aE Oa 58 SES UMM Se OI heh Steerer eee tease re Se sete a leew inn E E E E E uontac ution lo E A 58 IVS STAN SS CIN asian dee E A E AAEN someedacsonatese seiednda nese AE AAA T EATA 59 Wrong apsolute GOL ses se daiee cade dareaaedacspenct ranri anda i E E EEEa EET 59 O O a E E E E E E A E A E 59 Chronology and the units of effort settings or samples Chronology is important when analysing the data This means that is each date should prefe
41. the value 600 it means that this sample is the combined catch from 6 times the standard effort unit e g 6 settings pooled Table 3 Typical relative effort values depending on the survey methodology Sampling gear methodology Relative effort Gillnets Area or length of net panels Lines Number of hooks Underwater Visual Census Area of transect Trawl Area swept Sub sample Fraction of total The duration field can be used to store the actual duration of the catch operation and the corresponding standard value can then be used to raise your catch to standard duration When both the relative effort and duration fields are defined then your optional standardization in the analyses can be performed on each level separately or combined see standardize catch 20 Note that the relative effort units e g meter m for relative effort expressing net length and hours or minutes for duration are defined in the respective column properties Units They will appear in the Standard effort sample raising unit field when pressing Apply If information on the relative effort and or the duration is not available then click None for these under Sample raising Record raising Record raising means that the raising of calculated values will take place at each record Thus each record will be raised according to the ratio of the SU U for the U value given in the record This option set in Data table effort mode should
42. this off and enter any new value manually e Choose the validity code you want for the Rank field see below or use default e When ready press Replace Validity codes in Rank field Punching mistakes should be relatively easy to disclose and correct with the above procedure but some times it may not be possible to find the source of an error particularly if the mistake is done in the original data sheets or during the sampling itself In such case you can either decide to delete the record altogether or to change it the most sensible way Remember that if you do so you must enter a correction validity code in the Rank field see Table below Rank codes suggested ld Comments when length is assumed wrong and corrected when weight is estimated from weight length relationship when weight is estimated from mean weight in mesh size of species when weight is measured as mean weight in catch of species When the Rank field contains values different from O the record will not be used in certain of the calculation procedures such as length weight relationship mean weight condition factor etc If deleting a possible mistaken weight from a record but maintaining the length the weight 64 can be re estimated and entered by using the Estimate weights option see next section This option should only be used at the very end of a file cleaning when all the length weight coefficients entered in the species table are as precise as possible
43. wizard gives you a preview of how the data will be imported into Pasgear 2 If it is OK then press Finish otherwise go back to previous steps and adjust your settings Enter data directly into Pasgear 2 Choose Edit or Append records or press amp in the main menu Pasgear II Demo project pg2 Data File Edit View Insert Project Data Tools Window Help kaxe BOE Edit or append records GIPG2 Demo Re v Sample separation ii Database The data entry dialogue a gt Enter records Empty fields keeps value of previous record Rec No Calculated Integer T1532 1533 AutoNumber Date dd MMiyyyy 24 12 1992 Station Code Integer 1 Sampling station 1 Species Code String 26 Hydrocynus vittatus sear Code String F Gill net Mesh Code Integer 38 38 Relative effort m Float 45 Duration hour Float 0 000 Setting Type Code Integer 2 Bottom set Number Integer 1 Length cm Float Warning Dev 27 Exp 10 843 Float 25 Warning Dev 50 Exp 50 464 Input FM 1 4 F Gonadal Stage Integer 3 Stratum Code Integer 4 22 Warning Not defined Rank Code Integer 0 Calculated Summer Four seasons ma al mle S dt Append record Options Help Cancel OK Each record in the DATA table each individual or frequency of same organism contains and array of codes or Id fields foreign codes and field data values Once the values have been entered in a field i e
44. you want If you choose string you must also give the maximum number of characters that the filed can take The type General is for calculated fields Calculated fields You can choose the column to be a calculated expression In that case the type will automatically be set to General Calculated fields will take no physical space in the database You must then specify the expression optionally by using the expression builder 2 Calculated fields are very useful for making new groupings on the data If for example you want to group the species into commercial and non commercial you can do this by making a calculated field and give it the following expression If Field Species in codel code2 code3 etc commercial non commercial 45 Or you can nest several If expressions inside each other to make several groups If Field Species codel namel If Field Species code2 name2 Other You can then group your analyses on this calculated field Delete columns Of the standard default Pasgear columns you can only delete Stratum Gonadal stages Sex Setting type and Duration In addition you can delete any of the new columns you have added to a table by highlighting the column and press delete Alt Return Field column properties Right click field in project tree view and choose properties or highlight field and press Depending on the type o
45. 01 1992 1 266 FFFFFE FFF FEF EF 25 38 51 64 76 89 102 114 127 140 152 165 40 203 Total Absolute effort set 1k ttt 14a gt id 12 Individuals 39 774525 4 9 20 11 17 12 3 262 Date 25 01 1992 Record range 267 535 rer FPP PP FFF F FFF FPF F 2 25 38 51 64 76 89 102 114 127 140 152 165 178 190 203 Total Absolute effort set 111 1M iniii a 11 Individuals 37 31 4 29 23 12 16 11 14 1 1 269 gt Missing settings If a setting is missing 1 e the mesh has not been registered in a date but was found on the previous or the next date it will be displayed in red with a question mark W If the missing setting is simply an empty setting then Pasgear 2 can automatically insert this into the data table by clicking on the W right click and choose Insert gear mesh into data or press Ins Date 11 01 1992 Record range 1 266 Gear FoF F FFF Sof oe oe eE E E E E Mesh mm 25 38 51 64 76 89 1 Export to Absolute effort a a4 4 Connect query i Date 25 01 1992 gt Find next irregular F3 Record range 267 535 ae pe ee ey Run analysis Mesh mm 25 38 51 64 76 89 1 7 PUSSI esi Me plceMel le Ins Absolute effort 11113 M4 Goto Record 267 in data Date 08 02 1992 Print Record range 536 702 eni ea Properties Alt Return Mesh mm 25 38 51 64 76 89 102 114 127 140 152 165 178 190 203 Total Absolute effort 1 1111 171 1A 1 1 1 1 12 Wrong absolute effort If the data are not g
46. 1 Catch rates CPUE Dy Samlede EE e Ee ae E 78 Or he og setter CPUE D E a a E E E E E A 79 Eor A nE ee tence E E EA E E A A EEE E E ET 80 Be E e E E E ideal Leeaatoan imate accuse sanenatintaduandads 81 Correction TOF gear SClECUYILY cctcnnsesancctdeacecnnndencantedte cana deieneaetfetaanctoaseceanadetutacsoaseananeteavextenonetanachotndengnoieaanactade 82 PEAT P ect seepage saeac arene tacenabs E E E a ncadenstonsaenetnaesetetepied 82 INO D MG 1 ines auc yaa sacsceantasarc E E E EER 83 IVT Vo CII cos csiuca ce sscaciea ncharencanetceradsem sana dncaraee ction E E 86 BS E PA TAT E ceo rss ss eps ase a a nc AE PATA TEE AEE E 87 Pei AVY 0 is AOS TO os cee n tsa eeecdae ca racsarceedaerteennapentenmeqecane var saetan anette ooaoennecaetanennununsitiate animate tonoabeanantnes 88 Pasgear II Demo project pg2 File Edit View Insert Project Data Tools Window Help Dae we Xx ee me H oh gt m s i Eeg G1PG2 Demo Database g Insert analysis d General analysis es Properties Alt Return Catch composition and IRI Catch rates CPUE by species Catch rates CPUE by sample Catch rates CPUE by time Length by gear Length by time Maturity by length Maturity by time Stages by sex Length weight relationship To make an analysis either e Choose Insert on the main menu or e Right click on Analysis in project tree and insert one of the following analysis types 1 General analysis 2 Catch composition and IR
47. Charts Total 100 0 a Diagrams This macro gives the number of gonadal stages by sex as well as the mean condition factor mean length and mean weight 1 9 1 9 1 9 2 0 2 0 1 9 1 9 0 2 0 5 0 5 0 5 0 2 0 1 0 5 males Gonadal Stage NO KNO SD KNO Licm NO SD Licmy NO Wigi NO SD Wig NO 22 0 26 2 26 6 29 0 50 0 33 6 22 45 43 6 5 2 3 3 6 0 6 2 2 229 7 447 7 406 3 910 3 966 2 LETE 200 199 1 201 4 202 1 210 8 210 1 Vif 182 2 88 Hydrocynus vittatus Males NO Mean length 100 911 42 80 36 60 33 40 29 26 23 0 20 0 1 2 3 4 5 Gonadal Stage mean mean t 95 5E Length Weight relationship Hydrocynus vittatus Length Weight relationship Grams 4000 ERE P dl 3200 fm 0 974 2600 NM 2690 9400 Out 14 2000 1600 1200 600 400 0 10 20 30 40 50 60 0 cm possible outliers gt 20 0 deviation between expected and observed length This macro is used to calculated length cm weight g coefficients from the relationship weight a length of the chosen species or range of species in query and show this in a diagram plot of the length weight relationship with possible outliers indicated I also give the Condition factor as a function of length Note that only records with Rank code 0 are included by 89 default in the length weight regression as different rank codes normally are set when either the length or the weight has been corrected un
48. Effort mode The most commonly used method of estimating the relative abundance of an exploited fish stock is by using the catch per unit effort CPUE as an index of abundance The nominal fishing effort is expressed in for example the number of fishermen the number of boat days the number of gill net set the number of hooks set the number of hauls or pulls made etc However it is important that the so called catchability coefficient remains constant Catchability is defined as the relationship between the catch rate CPUE and the true population size B So the unit is fish caught per fish available per effort unit and per time unit Catchability is also called gear efficiency Hillborn and Walters 1992 or sometimes fishing power For the gear efficiency to be constant it 1s important that the relative effort of the nominal fishing effort is also constant In PASGEAR effort is considered at 2 different levels or types and with two different kinds in the primary sampling unit 17 The two different levels are 1 The absolute effort 1 e the number of gear settings hauls transects etc or sample units in a data series Each Primary sample unit setting or sample can consist of one to several absolute effort units depending on the defined effort mode on the data table 2 The relative effort within one primary sample unit This can vary between each PSU according to the respective gear size e g area of different mesh panels in
49. Estimate selectivity by model Normal scale k Iterations 4 Ui The residuals plot by mesh size 120 Estimated residuals Hydrocynus vittatus Tigerfish 1 cm intervals N 3634 Mean length E Residuals n el M Show title Show subtitle 1 Show subtitle 2 Show diagram leg 114 MESH size 26 0 26 0 26 0 i i 26 0 Set Model Normal Scale d f 165 k1 0 415 r 0 730 k2 0 075 p value 0 000000 Deviance 2642 737 l Normal scale Mean and SD Hydrocynus vittatus 1 cm intervals N 3620 Arithmetic Mean SD Arithmetic SD lt Estimated Mean from normal curve fit 10 amp Estimated SD from normal curve fit Estimated Mean by selectivity model Estimated SD by selectivity model jea B SuExplore SD no w A O N O O 38 51 64 76 89 102 114 Mesh size 38 51 64 76 89 102 114 Mesh size Model Normal Scale d f 165 k1 0 415 r 0 727 k2 0 075 p value 0 000000 Deviance 2648 865 And the corrected catch superimposed on the observed 121 amp Gear selectivity C Pasgear 2 Tiger selectivity gse i lol x File View Options Help sas Hydrocynus vittatus Tigerfish 1 cm intervals Observed catch from all MESH Frequency E Corrected catch 800 E Observed catch 700 600 500 400 300 O Show title MShow subtitle 1 Show subtitle 2 MShow diagram leg xj 6 9 12 15 18 21 24 27 30 33 36 39 42 45 48 51 54 57 60 cm Xmargin 5 all Corre
50. Field Id Station 3 i Primary physical in bytes W Sample separator Secondary biological sample level Type Si Integer F i E bel Format aliss uel decimals Color Id relationship with table fo 4 Font Station Station Id Calculated expression Unit Conversion h This means that field lt Station gt in the data table has Id relationship with the lt Station Id gt field in the Station Id table Column Genera I Field name Field Id Station Id 2 C Primary physical sample level Type Sample separator Secondary biological Integer Nol id codes Calculated expression Unit Conversion a E The primary key field lt Station Id gt in the Station Id table will be translated into the content of the lt Name gt field when running analyses Thus on the data table you can set Id relationships of the values which are in codes to a set of primary keys list of all codes used in an Id Table On the Id Table you give the field that contains the information the codes should be translated into For example 1f you want the species codes to be translated in Local name instead of Latin name you change the Id alias from the field with caption Latin name to the field with caption Local name in the species table on the Species Id Defining and adding units Numeric fields can have automatic units e g length
51. Fill elements Depending on layout property page you are on Diagram Charts or Series you can access all elements from the hierarchical level you have selected and below and apply your changes on all objects from your hierarchical level and below by checking Apply recursively to all objects Depending on the element type you can change the available layout attributes Gf some attributes are not available such as Font on a marker symbol then these will be grayed out on the property frame You should select only the style attributes line color width fill color fill style etc you want to change by checking Apply You can invert the colors on either selected element by checking o Invert colors or all colors in the layout by pressing the nvertall button Similarly you can convert all colors to grey scale monochrome by pressing the amp rey scale button You can reset the layout to the specified default see Project properties by selecting all elements and press the 8 s t _ button You can reset everything to default by entering at the highest hierarchical level Diagram check Apply recursively to all objects and press __Beset _ The Diagram tree view and options for display E3 LFOQ diagram Show axis i Continuous M4 Axis line i Major grid LI Fixed interval grid w Tick text Split series Ozero line On the diagram tree view you will see all the objects associated with the diagr
52. H Length of the individual n mm or cm Weight of the number defined in the number field of individuals in grams WEIGHT or Kg SEX Male female or not determined M F or X GONADS Gonad stage range defined in project properties integer STRATUM Free e g bottom depth or any other separator integer free field RANK Data validity codes integer codes different from 0 has special meanings Table 1 shows the standard default fields in a Pasgear record Some of these fields are fixed and you have limited options for editing their properties Two fields Date and mesh code indicated by yellow are obligatory for a record to be considered valid Some default fields can be deleted indicated by grey background and others indicated by red italics are free which means that they can be changed from physical fields red blue to biological fields green or vice versa according to the users wish Any field however or any visible object in Pasgear can have its field header renamed The user can add any number of additional fields columns to the data base either containing values or calculated from other fields see Database tables Fields Valid records and missing information You do not need to have information for all fields in a record for using Pasgear 2 For a record to be valid only 2 physical fields need to have values Date and Mesh code indicated by yellow background in Table 1 Other physical fields such as
53. I 67 3 Catch rates CPUE by species 4 Catch rates CPUE by sample 5 Catch rates CPUE by time 6 Length by gear 7 Length by time 8 Maturity by time 9 Stages by sex 10 Length Weight relationship A general analysis is built from scratch see general analysis below while the remaining 9 analyses are predefined setups macros but which all can be modified Analysis properties Properties for Length by gear 1 a x Standardize catch by r Sorttable by _ Relative effort C None Row field Y axis Duration Column X axis variable Catch fraction NO Primary sample unit Order Mesh i Ascending Descending C Gillnetfleet Absolute effort Treat unknown values as Unknown 0 Lowest maturity stage 3 C One 1 W Make default charts W Build summary ranges of data Help Cancel OK Under the properties of an analysis the analysis can be Named and given comments Tab Properties General Connected with a query Tab Properties Query or right click and Connect and already existing query Standardized to Standard effort If absolute effort is not default one per sample then there is an option for treating unknown values Result table can be sorted by row field groups or column variables Lowest maturity stage can be set for maturity analyses M Make default charts should be checked if you want the predefined graphs add
54. Introduction and Manual to Pasgear 2 Version 2 3 Build 02 12 2009 Jeppe Kolding and Asmund Skalevik www cdcf no data pasgear Jeppe Kolding Department of Biology University of Bergen N 5020 Bergen Norway December 2009 Background and acknowledgements PASGEAR was initially developed in Turbo Pascal for analyzing a series of experimental multi mesh fishing data from Lake Turkana Kolding 1989 and was at that stage never intended for anything else In 1991 however I Jeppe Kolding was assigned to computerize clean and analyze together with Lawrence Karenger the huge amount of experimental gillnet data collected by Lake Kariba Fisheries Research Institute in Zimbabwe and the old Turkana programs were dusted of and adapted to the Lake Kariba data set During this process various modules for facilitating the data entering and cleaning procedures were developed and added and the idea of developing a more general package was initiated Pasgear 1 DOS was released in various versions until 2003 by Jeppe Kolding In February 2003 Asmund Skalevik was assigned to help redesign and convert the old DOS program into Pasgear II on the Windows Platform The result so far is what you have here I am grateful to late Lawrence Karenge for patiently discovering bug after bug and proposing valuable suggestions for improvements Thanks also to various other users while developing Pasgear 1 particularly Salih El Thair Henne Tiche
55. M Relative effort Mesh mm M Duration Relative effort m Setting Type Duration hour Stratum setting Type Number secondary level Length cm Rec No lw Weight g Species Number Length Weight Sex Gonadal Stage Rank New Column Help Cancel OK In the project tree view you can see some of the properties of the fields by their icons Fields that are used as sample separators PSU fields are red other physical fields are blue while biological fields are green In addition you can see if the field is a code field with an Id relationship reference to one of the Id tables marked by a key or if it is a calculated field marked by a red F for function p Down New Column Add new fields columns You can add any number of new fields columns to a table by right click on the table and choose add column in the pop menu 44 i Da O Connect query j W Sten Select and T Spi 9 Ge Import i 4 Me 24 Sort E Rel amp Delete records EDU a4 Goto Ctrl G A Ler 4 Previous MH Last x str amp Edit or append records a Rais Insert Record Ins A o if Add column om This will open the Add column dialog Add column 2 x Field id Type characters 17 General fo a i aes pe omallint Float string Boolean Date Calculated fro Here you can give the column a name and specify the type
56. NO May 112 268 43 3 426 74 L 1 9 0 2 26 3 58 BOSD L cmy No fJun 109 103 35 247 56 19 03 26 2 65 BB Pages NA Jul 69 111 12 3 195 65 2 19 02 243 32 _ ENO Aug 62 123 32 1 278 72 1 20 02 26 7 7 6 H Charts Sep 132 170 24 1 327 60 1 19 02 21 9 65 H Diagrams Oct 152 150 29 1 5 2 39 55 4 19 0 2 933 69 Nov 299 164 23 4 3 493 39 4 19 02 235 74 Dec 124 149 42 11 5 3 2 336 63 10 18 0 3 23 9 42 Total 1502 1700 356 40 27 14 2 3635 59 4 18 02 25 1 6 6 This macro gives time interval frequencies of maturity stages for each specified species 87 or range of species and percentage of gonadal active defined by an optional stage of total investigated fish Mean Fulton s condition factor K and mean length of total fish caught with standard deviations are also given This macro can be used to determine the maximum spawning peaks and choosing the monthly sampling interval for calculating length at maturity Hydrocynus vittatus 12 10 a om amp 6 S Pi 0 Jan Mar May mean mean t 95 SE Stages by sex Jul Sep Nov Mon 2 12 2 07 2 02 1 97 1 92 1 88 1 83 1 78 1 73 1 68 Mean condition factor th Table Gonadal stages males diagram Gonadal stages females diagram Gonadal stages Total diagram Rows Gonadal Stage E B Columns NA mE NO EK NO 0 90 0 ESD K NO 1 45 EL cm NO a 24 ESD L cm NO 3 13 mE Wig NG 4 14 ESD Wig NO aa B Pages Sex H
57. OR NOT etc F Invertselection Clear Text mode Help Cancel OK In the example above the Query will only return those records containing either species or 15 and only for the months March 3 to June 6 and October 10 Validation query Properties for Validation 2 T Invert validation M Max length deviation limit in 20 Check invalid codes by A validation query is used to validate and check records It consists of 2 parts e A selection query as above e A specification of the validation to be performed o Length weight validation o Validation of the Id s used in the Data table 56 Length weight validation Check the box V Max length deviation in and set the percentage limit default 20 If the Species Id table see Data tables contains the coefficients of the species specific length weight relationship either entered manually or estimated form Pasgear see Find check and correct records then each record can be checked for deviations in the recorded lengths and weight values where the deviation criteria is more than the specified percentage limit between the observed length and the expected length as calculated form the observed weight Length weights can also be checked and directly edited by right click in the Data table and choose Select and Check length weight Validation of the Id s used in Data This is used to check if the entered Id s codes of
58. On length weight relationships Part I Computing the mean weight of the fish in a given length class Fishbyte April 1987 11 13 Cochran W G 1977 Sampling Techniques 3 rd ed Wiley New York Conquest L Burr R Donnelly J Chavarria J and Gallucci V 1996 Sampling methods for stock assessment for small scale fisheries in developing countries In V F Gallucci S B Salia D J Gustafson and B J Rothschild Editors Stock Assessment Quantitative Methods and Applications for Small Scale Fisheries p 179 225 CRC Press New York NY Degerman E Nyberg P and Appelberg M 1989 Estimating the number of species and relative abundance of fish in oligotrophic Swedish lakes using multi mesh gillnets Nordic J Freshw Res 64 91 100 Caddy J F and G D Sharp 1986 An ecological framework for marine fishery investigations FAO Fish Tech Pap No 283 FAO Rome 151 p Efron B and Tibshirani R J 1986 Bootstrap methods for standard errors confidence intervals and other measures of statistical accuracy Statistical science 2 54 77 Efron B and Tibshirani R J 1993 An Introduction to the Bootstrap Monographs on Statistics and Applied Probability 57 Chapmann amp Hall London 436 pp Fyalling A and Furst M 1991 Introduction of standardized test fishing methods to Zambian water Fisheries Development Series 53 ISSN 0280 5375 41 p Gayanilo F C Jr Soriano M and Pauly D 1989 A draft guide to
59. P 1987 Computer programs for fish stock assessment length based fish stock assessment for Apple I computers Fao Fish Tech Pap 101 suppl 2 Fao Rome 218 p Sparre P and Venema S 1998 Introduction to tropical fish stock assessment FAO Fish Tech Pap T306 1Rev 2 407 pp http www fao org docrep w5449e w5449e00 htm Str mme T 1992 NAN SIS Software for fishery survey data logging and analysis User s manual FAO Computerized Information Series Fisheries No 4 FAO Rome 103 pp
60. Pennington estimator Options for calculating confidence intervals There are four options for calculating confidence intervals standard parametric based on arithmetic the sample mean the Pennington estimator based on the log normal Delta distribution bootstrap on the arithmetic sample mean bootstrap on the Pennington estimator Pe Each of the methods will be elaborated below Estimates based on the sample mean The estimate of the standard error for the mean CPUE is given by 108 SE CPUE 4 S n where S is the variance of the individual observations If the sample size is large enough then the Central Limit Theorem states that there is a 95 chance that the true mean lies in the interval see Cochran 1977 pp 39 44 CPUE tt cnt SE CPUE Where tis from Students t table with n 1 degrees of freedom and a 0 025 Estimates of the mean based on log normal theory The Pennington estimator Since catch data from fisheries surveys usually have a large variance much higher than the mean and are highly skewed to the right the sample sizes are typically not large enough so that estimates based on the sample mean and standard SE 1s a valid 95 confidence interval In fact the confidence associated with the parametric confidence interval is usually much lower than 95 McConnaughey and Conquest 1992 Conquest et al 1996 Pennington 1996 A major problem to the degree of skewness is due to the high proportion
61. T EAM asec sont ssa ies E EEE E E E dare stead Gun sabe eats tea tices at sed dace edsedeuacesetessedstdete lt dsgtessetesdens 16 By AMUN SE AVG 0 sto Stee ete aot eet nao Note E Wet cia EE A A Macatee E A E E E 19 PR OG EAN SIN pete eet eg septs a ee ect ect et on oe Se em cate ent ene ct ee ured rate eae enna olen saeeten da semen dees 20 i An ignorant fish from some far away sea was asked if it believed in democracy it was put on a board proud as a lord and got lost somewhere in a length frequency Da oy d KLARKA d y i hy k i V YY YY YY Y J Sa fad Pasgear 2 demo Demo Project pg2 efx File Edit View Insert Project Data Tools Window Help De aSx we lamella gt msm m 0 j Data q E PASGEAR 2 is a customised data base E ty package primarily intended for experimental sl or artisanal fishery data It is a tool that neatly a li and quickly lets you store and analyse fishery Peo al 5 a data from various survey designs p E m KESE a What Pasgear 2 does PASGEAR is a customized data base package primarily intended for experimental or artisanal fishery data The package is developed to facilitate the entering storage and analysis of the often huge amounts of experimental fishery data or catch and effort data accumulated in the research institutes of many countries It 1s
62. This gapwidth will be added to the common gap width defined on the axis property Layout of Diagrams Charts Axes and Series Properties for Males a xl Maturity ogive Series Trend Series layout General Graph elements Category I Apply recursively to all objects l Shadow a Ali elements Preview Trend legend Object text Bar Bar interpolated Std error box Selected data Nata evclided from trend d D Eont MV Apply line style WV Apply color Solid M Apply line width iW Apply radius 1 ah V Apply fill style M Apply fill color Solid MV Apply marker I Color shade J0 M Invert colors Invert all Grey scale Selectall Reset Help Cancel OK There are 30 different graph elements that can all be given their own layout in terms of font size color line style fill etc depending on the graph element type Title Sub title Diagram legend Chart area Plot area Axis label Tick label Series legend Trend legend Mean legend Category legend Z scale legend Object text Bar std error box Marker Selected data Data excluded from trend Region excluded from trend Marker outher Major grid Fixed interval grid Axis Major tick Minor tick Marker line Trend line Confidence limits standard dev limits The various elements can be categorized and filtered under Category into 100 All elements Font elements Line elements
63. You can use the data entry dialogue to go back and check edit previously punched data Use PETE TE or the Goto button Li for navigating around in the data table With E you can insert new records anywhere in the file Project Project properties definitions and layout ccccccccccccccccccccceesesseseseeeececeeeeeeeeesaussseeeseeseeeceeeeeeeeeessauaaasassnseeseeeeeess S AE WF 5 scott E E A A A A DSTO NS E E E E TEX PCS SIONS e a E E E E E E EE A S Tao Vay E er E E E E E E NS PST CAEL NAG OU encsi EE aE Project properties definitions and layout A Pasgear project is a set of data and Id files in binary format bds and a pg2 file in XML eXtended Markup Language format The bds files contain the data and the pg2 file holds the project properties and keeps all the settings queries expressions analyses layouts and graphics When data are stored in binary format they cannot be red without using the programme Note that all editing or changes on the bds files is always done directly on the disk and cannot be undone It is therefore highly recommended to have regular backups of the bds files or the whole project by using Save as in the File menu The pg2 file however will only be saved to disk on prompt So any changes in running Pasgear 2 or changing properties will only be kept when responding Yes to saving on exit or pressing the save button i For accessing the project p
64. aaeantud ss 87 ene the Were AE Telall ONS WUD esse sevens el eaccncuneruas gudsauidwauatoiebed oaataauwilluss ped piaitaen ats Ged Saabiad toa dadaietaem arn telanae 88 PDP SEATS and Chart Sucess E E EE AN E E A I E EN 91 Basie CONC Di Sar nn E N E E AE N E 91 INVA TRAIL AGS Nare enna E E AE E A 92 Eey PO e E E A AAE E N E ting 93 ZS CAET 0 0 monera E A NE A E try Cree ee 94 Einka aC hart toa daora ean a E AE N S A A E 94 Muti ple cbans mTonediagta Mises des sumenue chanralebiaammuaieenaledionmud sauaeinuesseeia ametuer 95 Properes Tordid raS neona eerie uaee ae aanetlotuans de auamasdesiunsedaenanenlon sues ud suanale eusaedeauentsieuites 95 Properes Torc har Saa uansicis companion cenicus even sie suse ad aed loduans iad auanasdestunaiiaeaanenlon sua sua uanandeeeueanieaueyieuies 96 Properes Tobak Siras E eve sie sass ad aanetledunns ad auamasdestunaiiaenanemlonsuas tua uananieteusacieauentyieuiios 97 Properes MOR SECS l ease ae aanetleduuns ide auamasdestunaiiae anemones tua uenanieteueacieauentieuiies 98 Layour oft Didsrams Charts Axes and Sere Sannas id sane leceiaaiuacaaneilet siesta N 99 The Diagram tree view and options for Gisplay cccccccsssssssssseeeecceceeeceeeeeaaeeseesseeeeeeeeeeseeeeseaeaeaeeesnes 100 Add UIE TC inhi isc isac das use tesserae ne wa aetna a 102 Tend LY PC okioxsdietuccassncatett acdc casa somes A 103 WRT TUS eea cea sasatonenasauctacusaueneasanscas saaauerseacanesaseuadeses tenses Aaatomssausctasseateeweents 104
65. al off all the other pages summed To group in one of the dimensions then right click 1 e Rows and choose Properties at the menu E Columnst Delete B Pages Add variable E Charts Diagram Properties 4lt Return The property page for grouping dimension in this case grouped by species 70 Properties for Rows Species ax Group and interval Group by Display Species X M Show code Date format Font Month l Exclude unknown values I Condensed Interval None List by C Fixed 0 Lower interval limit F Show empty intervals in range Interval ronge C User defined M Make rest group The grouping can be done by each individual element found Interval None or by user defined fixed intervals of any positive integer gt 0 Groups can also be user defined by checking User defined and then right click in the pane and choose add alx User interval You can then give the group a label and select ranges From to You can also define your own groupings by adding a calculated field in the data table and use an expression to make a new set of groups See example Variables Pasgear II Demo project pg2 File Edit View Insert Project Data Tools Window Help Osa SXF SRMAl sa lm eo mia A 1PG2 Demo Database a Analysis Length weic Catch rates c Maturity by X Delete Length by g Add variable GiLength by
66. always be given a default label as header in the Analysis table However if checking off Use default the user can specify the given label 74 Variables can be given a font aligned given a specific number of decimals or optimally show zero values The cell color in the Analysis table can also be given a specific background color in the report table Conditional formatting If you press the buton under Report layout Report layout _ Alignment Decimals C Lett fo Right Font Center Cell color Show Zeros Zeros inred zal M Use 1000 separator You can define a selected value range inside or outside of the variable which will be given a specified color format in the report table Conditional format W Apply conditional formatting Color CE Condition A jpAR_ Range fF 3 Selectinside outside range Selectinside defined ranqe Select outside defined range gt lt Hep _ cence Making Ad hoc chart When checking this option Pasgear will automatically set up a diagram with a plot of the chosen variable This plot can be modified see Diagrams and Charts Analysis results Table CPUE diagram Rows Species E E Columns NA Samples and settings a es S SE E ET eae Mesh samples 310 A Absolute effort 310 set NOjset Absolute effort with empty catch 8 set 2 58 EISDNO set Standar
67. am The Chart or charts their respective axes X Y and Z if multiple Y axes and the series assigned to the respective axes By clicking on these you get a number of options for display depending on the object By double clicking you get into the properties frames of each object On the axes you can change configuration grid text ticks etc or even make them invisible Show axis If there are several series on the same axis these can be stacked and on the opposite axis they can be split 101 If the axis 1s continuous 1 e not a category the series values can be In transformed or given in percent If the values are In transformed there will be an additional option for converting the axes to log scale display E Diagram 1 als Chart 1 ie Mormyrops deliciosus i Mormyrops longirostris Heterobranchus longifilis Clarias gariepinus oi Hydrocynus vittatus fi Labeo congaro Lidl lv Show axis Axis line Major grid Major ticks Stack series OReverse Stack order Ozero line OLn transformation OPercentage display If the axis is a Z axis in multiple Y axes plots then you can get a scale indication by checking show z scale legend Al GJLFQ diagram E als LFQ ero line OLn transformation Percentage display aiShow z scale legen On the series you can remove these visually from the plot or change their display bar line scatter If bars these can be smoothed like an area
68. and if necessary a help text of the chosen operator or function is given Formal and actual parameters Some functions have formal parameters that must be specified to actual parameters The formal parameters are given in parenthesis after the expression function name lt name gt formal param1 formal param2 36 For example if you want to look up a value from a specific table field you use the function Lookup table column which has two formal parameters table and column To make this function operational you must the insert the actual table name choose group table choose table and double click instead of formal parameter table and the actual column name in chosen table instead of formal parameter column choose a column in second pane and double click Expression Library You can add any new expressions queries functions calculated fields etc to the Project Expression library by pressing _Ag 0 library This will open a new dialogue where you must give the expression a header name specify if there are formal parameters and optionally give an explanatory note annotation to you expression ax Expression Name with or without parameters Tiger_or_Bream_in_selected_months Expression body syntax Field Species 6 or Field Species 15 and Month Field Date In 3 6 10 Note Compile Query that returns true if species i
69. and length in cm 2 if the individual lengths are recorded in whole cm and the record represents a length frequency see project properties definitions then the weight is estimated as the mean weight in the length class 1 L L2 and calculated from Beyer 1987 1 a weight a baa l L L 1 65 where a and b are the length weight coefficients L is lower interval limit which should be the recorded length in the data base and Ly is the higher interval limit so L2 L is the length class interval in cm default interval is 1 cm but other whole cm length class intervals can be specified 66 Analysis RTM SSIS gs gta E EE asst nea E O EEE N Error Bookmark not defined PA AIS IS E Se E O O E E mug riamnbbeunred A E 67 CG La AL ysis encreire rE ENE EEE E ETEO T 68 OU NM TT SI areca eres a eE e E e E eE E TEER 69 BAI l SE E E AA E E oes oe ogee A E EEE E E E E 70 a AD E E CS E O E E E E E erie E P E A 71 pe Fe 6 ow oli 0 Le 110 eee ee ere A en eee eer ner one ener een ere eer tere rrr eer er 12 B E E E AE E E EEE E A A E E ee 73 VE Yate allem re hige 0 Ieee eee E r eR rT Tor EE crn rn rere eeTregrr set rrr E er terter err rer 73 Condon Oaa E ee A EE E ET A 74 Marma Ad oe O e E E E E E 74 P E E E T E 74 Me V0 TO pe US EE E N E A A A AS I NAE E A E 75 ae eE h o e A A E A AE E E T A A E E E ET 75 ach OTITIS OLN a Th IR eese T E EE aise Sackaieuaies 75 Cath tates CPUE SC C1 Sr a r REE E a EA IRO rE A aA 7
70. arasianenaents 69 BaT Etele eG EREE E IERTAT EE EAREN E ETA aaa dead AE PT ETTE E E P EET A ES T E TAE EET EETA T 70 Exe rmal Dalas CS Arora an cpu r o a a nace edesstiacseaceeadeonaines 71 IV TAOS TES ONS E E E E T2 VaraDE S E eerie eo e E E E 73 NAA IS VAY OU reran E EEEE 73 CondihonalTormatiN eesse soy adaantevctewader ier Sensiacsesapeedvenaanetenedevsdeasaeieccedimsns EEREN 74 Makina Ad Doo Cha rra a ae hea vettantlnded A oaseaS ea oideas a ovvatathece Massena 74 ANa SIS TE SUIS ssaScnci reek E A dees Mas teanachisced ccnnbens aouieded MM onvaineacebscuntinin NE 74 EXPO Fe SUIS caves cig ae a contends caus ces Ma beanacticced ccxabens E cca bens acute A 75 Prede nned analyse Siranee aa a E a E aed Mowbuinactiaceacomseancaens T3 Catch Composition ane IR sena a E E E E J3 Catch tates CRUE Dy SPEC CSia a a N Ti Catch tates CPUE DY Sainpl ee i sswacceowccheslochacssea a a a 78 Cateh rates CPU DY GINE a5 tise ceded e ee aO e ea E 79 Eea oa E OE EA E A EEEE E E P EEE E EE A EE T E EE E E E 80 PSTD ye U eee E r vawaied aacevaanele ee tase vied aaie 81 COmMeCUON fOr CEdT Sele CUL VIL jiisic catenins cannes E EN guded 82 PISA TD CX POD naia E boiwmcubduispedapatmeuatots bod Outl adaniuns bad piaduamn eewie oud eautaasveruicdad onetiemar van Gude 82 Matriiy By LOMO Bi acearsit rnd a bad a bed piuldcinn einen bul ooemaaual ean ded ahett emer Geta 83 Matri Dy E eea E E A 86 SLAPS SY Se onnee a E ween Ruut aaa ead ree limceatioss ted cautueveru
71. atadirectiy Ino Pas gear 2 eenornpiso ninen E eno seersgarsnenanehsetcedensacustes seaccdengasnacel selmeceesacuagas 2a Daten Op ONS aor oren EEE OE ERA OE RENEO OE 28 Edit PreviOUshy punched TECOLS scessinsnaoni renon a Eon Enna ES A OEE A EEOSE 30 PTO CCL wsesnoses basaa oE E E OE E 21 Project properties Ge MmitiOns and LAVO kisiri acorrer nE E E OEA SOE 30 SLITI a ETP E A E O PET A E T E E POT N AE N E E N A N AE 31 Pie MUTT ONE AE E E E E E AE S 31 EXPO SON enara A E A ee seaiaticte a2 TPAD IG Way OUI esciasscccarentenedeest ances E E E E A yia tau emcanuasseseaaeuiate 33 Dei ehan AY OU apn TS 34 FE LCG G1 ONS a E a OS 34 Expression DUE osn E e e aE aS 35 Fonal and acmal Parameter S nroa ra EE EE E OKE E NEEE 35 Xe SSI E a E a A E E EN T E A E EE AEE AAE A EEE EE ERE A EA EEA EAE A 36 DAD 1 O55 csp e a E E E E ieee decade conan 37 DV AEAD ASS ADCS pe O EE O 37 Add ADIO a E e r N 37 Delet ta DIES siririna e A E E 38 Tseri ccOrdS crisis d rae a a E E E A EN 38 Deleterecord Seri a E E E OS 39 EXPO ea E A E a E N 40 Prop ries TortaiDIE Siar a a a E N 41 EHO MOE eenia a r O OE E E 41 B ES 0 Fs en E A 43 Ada new TEIAS COLIN S sa bse vacteh vse vesin Cauganetsiceiunenncne a Ee aa E E a TE Er E Ee 43 Cal ukte field San A E dleGiaereaiodaa teeta vedas ove tatedeo ust 44 De VCC SC OTANI aree E E E sata dicndane sd emedecooeaue 45 Bic ld COMM PEO PCL S r Gud eu anemsweiaa end anedncatis God baudinsualnadadaieinemmncetanial 45 Pennas an
72. ault 1 but you can insert any number of records by changing the value in the Repeat field If you have many records to insert it is easier to simply append them at the end using Enter data and then sort them in to their right place in the table 39 Insert record into Data 4 x peo em Nw 25 01 1992 Mesh mm area covered m Duration hour Length cm Weight g Sex Gonadal Stage Stratum Rank Empty values means Insert After C Empty recarcl 268 l Repeat Previous record ho Help Cancel OK Delete records Choose the starting record the right click on the table and choose Delete records In the From and To fields you can then define the number of records to be deleted You can use arrow keys PgUp PgDown Home or End when specifying the number of records to be deleted Note that once you press OK the records will be deleted permanently there are no Undo or Ctrl Z options in Pasgear when working on the tables You should therefore always make sure you have a backup of the tables and project bette ata recois EE From a Allin table You can also select and delete records using a Query by right click on the Table and choose Connect query T NA 2 ua Select and amp Import 24 Sort a Replace Ctrl H Delete records Export 40 by one a range by increasing the To field or all found by checking V the box All in search range Coun
73. ault absolute effort mode is not applicable as y in the above CPUE equation is a recorded variable that can change with each record or sample where the number of samples n are still automatically separated each time any one of the defined physical fields change value In this case you can use either the RELATIVE EFFORT field or the DURATION field or any other new field to store the absolute effort variables and then specify define the used location on the property page by giving the column name in the definition field The number of settings y in the above equation will then be read from the file in the specified column instead of being automatically counted If you choose the DURATION field to store the absolute effort variable then you can still use the RELATIVE EFFORT field to give relative weight to the sample as described below under sample raising Alternatively if you use the RELATIVE EFFORT field to store the absolute effort variable then you can 19 use the DURATION field to standardize raise the calculated CPUE by time units or any other unit see sample raising Sample raising Sample raising means that the raising of calculated values number and weight will take place on each primary sample unit Thus the whole PSU will be raised according to the ratio of the SU U for the U value given in the first record of a new PSU U values in subsequent records within the same PSU are recorded but may be considered re
74. ble Mesh Id NB If Mesh is known lt gt 0 the record is considered as a valid primary sample record PSU or physical record see 2 stage sampling design in one record e Relative Effort field Relative effort type is defined in the Data property page effort mode see also Effort definition and sample raising modes or Table 3 e Duration field Duration type is defined in the Data property page effort mode see also Effort definition and sample raising modes e Setting type Numeric Id Corresponds to Id field in Setting type table e Number Number of individuals per record Unknown 0 see Biological data the secondary sampling level e Length length of individuals in record Unknown 0 e Weight Weight of individuals in record Unknown 0 e Sex The values Males M females F unknown X juvenile J is allowed here Default unknown X e Gonadal stage The gonadal stage numeric value of the individuals in the record The range of valid gonadal stages is defined in the Project Properties e Stratum Numeric Id Corresponds to Id field in Stratum table e Rank Numeric Id Corresponds to Id field in Rank table 49 Id Tables or Reference Tables The Id tables are used to store information on the Id s used in the data table such as names etc This works a bit like a relational database and the purpose is to save space in the often very large data tables All Id tables can automaticall
75. ble you find it If your X dimension is rows then the available Y dimension is columns and vice versa If you have pages then also specify which page to choose from default Total 2 Scatter plot First choose your X values by pressing i E on chart f dimension on chart Mesh mm x values Mean length Seles n fee weight Then choose your Y values by clicking Add under the series pane You can add as many series as you have on any chart type If you have double Y axis on either the category or scatter plot then assign each added series to one of the two axes by choosing plot axis Plot axis AAM If you have multiple Y axes then you can assign the series to either the primary Y axis or the additional Z axis Hint You can go into any of the chart properties in the diagrams of the different predefined analyses see Analysis to see how they have been configured All diagrams in Pasgear 2 are built from the same modules Linking a chart to a diagram When you have created and designed a new chart and pressed OK you will be prompted E Q2 Do you want to add a new diagram with a link to Chart 3 If you confirm a new diagram will automatically be created and linked with the chart If you press No the new chart will not be visible before you have linked it to an existing diagram If there are no diagrams then add one by click on the Diagram icon on the toolbar or right
76. by different mechanisms e g both wedged by the gills and entangled in the mesh sizes 3 When the model has been chosen then click estimate selectivity After successful iteration the results will be displayed in a series of diagrams The estimated selectivity curves for each mesh size In this plot you can set a lower cut off limit default 0 1 and whether to include or not the descending leg of the overall curve default not amp Gear selectivity C Pasgear 2 Tiger selectivity gse p lox File View Options Help saig Estimated selectivity curves Estimated fitted catch curves Estimated residuals Mean and SD Corrected catch all mesh sizes Corrected catch selected r KIO w Estimated selecti LI la Hydrocynus vittatus Tigerfish 1 cm intervals E audChart Probability 10 4 MESH 4 MESH 0 9 MESH 08 4 MESH 4 MESH 0 7 i MESH 06 MESH X Delete Del Copy picture to clipboard Save picture as amp Print Ctrl P Show control pane 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 38 51 64 76 89 102 114 Model Normal Scale d f 165 Combined estimated selectivity c Set lower cut off limit k1 0 415 P 0 730 s Part of the curve not used Include d dina k2 0 075 p value 0 000000 include descending leg CO N Properties Alt Return C MESH range 2 38 to 8 114 C Explore mean length SD and skewness
77. c The effort mode and standard units are defined on the data table properties 18 Properties for Data 2 x Effort mode Display File Information Query General Effort variable Definition Absolute effort Default One per sample 2 Sample raising 1 Relative effort 2 Duration Record raising Catch fraction a en el None a sss fi i Jarg am raising uni Apply Sample and record raising is done on Number and Weight data They can be applied individually if activated in analysis properties Help Cancel The two different kinds of absolute effort mode 1 The absolute effort is the same as a primary sample unit y n For most experimental fishing data the recorded catch is usually separated by each unit of effort i e settings hauls mesh size etc and each primary sample is from one such effort unit In this case which is the automatic default effort mode in PASGEAR see Data properties effort mode you do not have to store the number of gears settings or hauls within a primary sample Click on Default under absolute effort definition will then be 1 and PASGEAR will count the effort as the number of primary sampling units 2 The absolute effort number of settings or hauls within a primary sample unit is a variable y n If the catch data consists of pooled data coming from y units of effort then the automatic def
78. cectossesige sseceaaetgesce 9 OOS Te Sez aea sire create acai caosaecsban be semnthnesiens aabsancruesGaaeoausdcoe E 9 PROUD SUMMIT y coset contgnceesasasecaeuseaaian E E E E 10 A E E E E E A E beteenee 10 SEONG e EA E E E E E EEE E EEE E EE E 12 Thedata stucen MS Gr Po esas rs setins rasoniznacisotecaat a teacene oumseansastecahasotaeatdasiacansepsosanecebasenogtuassdasuecencsordaannasmaccscanterennecees 12 Field types and codes in a PASGEAR record 0 cc ccccssssssssssseseeccccceceeecsaaaaeessssssesesseeeececceesesssaaaassesssseeseseceeeeeeeeaaaas 13 Valid records and missing infOrmation cccccccccccccccecceessesseseeeeeeeeeeeeeeeeeeeaeseeseeeeeeseeeeeeeeeeeeeeeeeeasasseaseseseeeeeeeseeeeeaaas 13 2 Stage sampling design 1M ONS TOCOM sso ce ccce seat essshonedancdosedaaninit uiia aa Doancubasgosudusudenssdpaedebensdeaadeausausebbadsadeentesesdenand 14 Physical data the primary sampling unit cc ccccsessessseseeeeeeeeececeeeesaaaaessesseeseeeeeeeeeeeeessaaasssssseeeeeeeeeeeeeeeeeaaaaaaas 15 Biological data the secondary sampling level cc cc cccccsssssessseeeeeceeeeeeeeeeaaaaaeesenseeeeeeeeeeeeeeessesasaaeseesseeeeeeeeeeeeeeeeaaas 15 SEY PSOE ooo eal Ng ats osise cco deco gees E este recone deadee used ety tea ued venouedetncc amass ateleceiesenced tee anas 15 Effort definition and sample raising MOES ccccccccccccccccceaeeeseeeeeeeeeeeeeeeeeeeeeeeaeseeseeseseeeeeeeeeeeseeeeaaaaaseaseeeeeeeeeeeeeeeeaaaas 16 PE
79. click on U Diagrams folder Add diagram 95 os 6 293 260 613 Add diagram Properties H Charts Alt Return If a diagram exists then link the chart by right click the Diagram Link to choose object i Charts li Chart 1 li Chart 2 li Chart 3 Diagra S Diagrar amp View X Delete Remove link gt i Chart 2 Properties Alt Return l Chart 3 Aatm A diagram can also be linked to another diagram and any links can be removed by right click the Diagram Remove link choose object Diagram 2 Multiple charts in one diagram You can link as many charts or diagrams into one diagram as you like and format the layout by defining the number of columns If your analysis is grouped by pages then each chart will be given a default title corresponding to the page group ims io Whatever is on the Y axis jf do 0 10 20 30 40 50 60 70 80 0 10 20 30 40 50 60 70 80 O 10 20 30 40 50 60 70 80 Whatever is on the X axis Properties for diagrams Diagrams charts axes and series all have the property Tabs Layout and General in common On the General frame you can like elsewhere in Pasgear 2 give the Caption and add comments The graph Layout frame is besides the graph elements included identical for and will be described below under Layout of Diagrams Charts and series 96 Properties for Diagram 1 Diagram Diag
80. cted catch 2822 Y margin 15 al gt Observed catch 3634 Aves Gid an E R jects 100 4 Cutlevel 0 1 a Zoom 100 4 i Hide Properties Iterations 4 Save estimated gear selectivity Once you are satisfied you can save the results to a file in order to correct for estimated selectivity in your analyses or before exporting to FiSAT amp Gear selectivity C Pasgear 2 File View Options Help amp Open Ctrl 0 Save file as Save probabilities as Print Setup amp Print Properties Alt Return The default file name will consist of a combination of the species Id the length interval used and the length unit with the extension sel This will secure that selectivity curves are not applied on wrong species or with different length intervals or units in the analysis Object name Save as type Length probability file sel 122 References Bagenal T ed 1978 Methods for assessment of fish production in fresh waters IBP handbook No 3 3d edition Blackwell Sci Publ Oxford London Edinburgh Melbourne 365 pp Baranov F 1948 Theory and assessment of fishing gear In Theory of fishing with gillnets Chap 7 Pishchepromizdat Moscow Translation from Russian by Ontario Dept Of Lands Maple Ont 45 pp Begon M Harper J L and Townsend C R 1990 Ecology Individuals Populations and Communities 2nd ed Blackwell Scient Publ 945 pp Beyer J E 1987
81. d addins UTS saretan n E netlatwacholoduliadaaten sed sieieemacrein Gata iet 46 Renamins elds and any Othe OB CCL sisiccsaraceuateadenainnua outs A 47 Data table ca E sedesemaeeuniom Gelgnuat 48 Fede con a a A ner Te One NEr Seen rere er eee ere er 48 rables Or Reterence Tablisa E E E 49 CCIE 0 asia sty n A E n E AA E A A 49 EEA e o E E EE EEE E EA E A inset E E E E E E A T 49 SAONA eaa a E E E E E E 50 SD Der LAD leis AEE NT E E EE E E E EAE EE E ETT 50 SMa AD Oaa a E E E E E 51 IPS TR eA EG ah a ensrsao et anca dancin te E E E E E at 51 OUTES cscs cieeieeeiga ais E A uss a da paaaoas sata aaa anad sauah daa den anes onenas bu neasamune monnaaeame 52 COMME CLIT ano Yeh dntiyest a sass lene ale boas aa E onus suse uenaneciatnnes 52 Se ECON UET a a ie sunbele anna ed nannnlensemacas coe b ela amuse ee aanscleunenua aay tsomamaaaeatles 54 SLM ple Tan Ge MO 6 sissies sania gansesie shanti aemadntineile aun suleiiane N 54 TVET SCLC CIO Ei aee hia ceases dansaaceea ee cea madaaeunaiaencenntenneea ade eaahsdescauenanat dan san nid caanandaatanhanenannainauar 54 Advanced EEOC enr ne a E senile ausiestoenaleeaanaleusens 55 Mahdati on GUC y soen E sacuadacneenaacneose saa toxesecaucssalectae ca cuuceadsiseemmmacsuceeagassnedcetaes 55 Weneth werent validato Miena A EE 56 Valid tionof the Id s sed in Data sarera e EE 56 E HOr SUMMA Y eena cnaduccuormmacaacteasanneeedoencoce uautonsecaacesatecncee dc cau cso dlasuewmoatecseagestmuscetes 57 Chronolog
82. d effort unit 45 m set El W kg set i aaa aa a ee Pages NA Field Range found a ee ee Diagrams Rec No 1 to 7532 Date 11 01 1992 to 24 12 1992 Station Sampling station 1 Species Hippopotamyrus dischorhyncus to Synodontis zambezensis Gear Gill net Gear2 Mesh 38 to 178 Setting Type Bottom set Stratum 1to15 When the analysis has been built by groupings and variables then press F5 or the refresh icon I to run it 75 The result will be shown in the Table tab which has 2 panes The upper is a summary pane giving The number of primary sample units Mesh samples and absolute effort and 1f the Analysis is standardized then also the Standard effort unit The ranges found over the major physical fields Records date station etc and the species If some of these ranges have been restricted by a connected query this will be displayed too The lower is the results table with the chosen variables and statistics built layout and sorted into rows columns and pages By right click in this pane you have various options such as e g Export the table to clipboard can e g be pasted directly into MS Excel Word etc Export results You can export the Pasgear report table via the clipboard by right click on the Table gt Export to gt Clipboard Length by gear 1 Table LFa diagram Catch curve diagram EEE Length cm 38 51 64 76 89 102 114 127 140152 165 NO c Clipboard
83. der cleaning The length weight relationship can be regressed both linearly by logarithmic transformation of the power function default or iteratively by selecting the fitting method under Series Properties trend The default deviation for outliers 1s 20 for x i e when there is more than 20 deviation between the observed and the expected length x for a given weight y od enna needeeeeenswnnes Fitting method r Iterative options k Linear analytic iterations 20000 E is pies ae Non mae jiterative Bi guja f Trend options Convergence 0 005 Power range jo to 0 Ta z Ln transformed sum ofsquares Punchon toe M deviation for outliers 20 for X gt I r Trend filter Power X Min observations per point fo Polynomial order 2 S T Exclude zero Y values F Fixed intercept a M Use trend filter M Fixed asymptote o Trend legend Eunction type iV Coefficients T p value l SEtrend MN M r MV N outliers Position Default Horizontal Let C Right Vertical Top Bottom Add Edit Remove Help Cancel OK The coefficients a and b as well as the coefficient of determination q and the sample size N if acceptable can optionally be written automatically into the Species table for the respective species right click on the Analy
84. dited Fenmine lon ESUIMALOF seieren NE ae EE E eI EE a Eo EES 110 a E LOVE EIE I AEN A EIN A NA AIS PTEE O ITEE TOTE 111 Bootstrap conidence INLET VAIS roiuri rnor EAEE EE vows EERE TEE 111 Confidence limits This stand alone tool conflim exe of the Pasgear II package is meant of calculating alternative confidence intervals than standard parametric from a dataset such as Pennington s estimator or bootstrap see options for confidence intervals Pasgear confidence limits Pasted from clipboard Bz x File Edit Options Run Help Species Wikg set Mean SDP SEL IN Lower 95 Upper95 Min Max Bootstrap Hippopotamyrus dischorhyncus 0 06 0 21 0 01 310 0 03 0 08 0 00 1 88 Marcusenius macrolepidotus 0 03 0 12 0 01 310 0 01 0 04 0 00 1 18 Mormyrops deliciosus 0 08 0 46 0 03 310 0 03 0 13 0 00 qd Mormyrops longirostris 0 76 1 63 0 09 310 0 58 0 94 0 00 0 05 Alestes imberi 0 01 0 09 0 00 310 0 00 0 02 0 00 1 35 Hydrocynus vittatus 3 4 4 40 0 25 310 de 3 40 0 00 21 93 Distichodus shenga 0 01 0 10 0 01 310 0 00 0 02 0 00 le Labeo altivelis 0 00 0 03 0 00 310 0 00 0 00 0 00 0 50 Labeo congoro 0 03 0 35 0 02 310 0 01 0 07 0 00 5 20 Labeo cylindricus 0 00 0 04 0 00 310 0 00 0 01 0 00 0 75 Schilbe mystus 0 08 0 18 0 01 310 0 06 0 10 0 00 1 33 Clarias garepinus 0 99 1 94 0 11 310 OF 1 21 0 00 11 94 Heterobranchus longifilis 0 03 0 37 0 02 310 0 01 0 07 0 00 6 15 Oreochromis machrochir 0 00 0 06 0 00 310 0 00 0 01 0 00 0 97 Oreochro
85. dundant This option should be used if the same proportions of the catch is sampled and recorded within one PSU or the whole sample should be raised according to a given standard value E g the catch was sampled with nets of 45 m length relative effort 45 but you want your results standardised to say 100 m net lengths The two standard effort fields in a record relative effort and duration can be used depending on the setup of the Effort mode If any one of these is not used for storing absolute effort i e absolute effort is not Default then they can also be used for sample raising However you can define any new field for storing absolute and relative effort and duration The relative effort field e g gear length area or relative sample effort fraction of catch recorded of total is normally given in the RELATIVE EFFORT field Table 1 This field together with the given standard effort unit defined under effort mode is used to give relative weight to the catch within a primary sample unit If the standard effort unit for example is 100 settings nets hauls hooks fishermen pulls boats etc and the RELATIVE EFFORT field for these records is given the value 50 it means that the recorded catch within this setting or sample will be given double weight or in other words only half the catch was sampled and recorded Similarly if the standard effort unit is set to 100 and the RELATIVE EFFORT field is given
86. e 1 Otherwise you must specify in the definition field the column or expression where the relative effort and duration values are given 3 The record raising if applicable see Effort definition and sample raising modes and the column or expression where these values are given 4 The relative effort units e g m for relative effort expressing net length and hours or minutes for duration are defined 1f applicable in the respective column properties Units They will appear in the combined definition for Standard effort or sample raising unit field when pressing Apply By changing the standard values of the relative efforts all calculations will be adjusted accordingly if Standardize catch by 1s checked in the Analysis properties Analysis Query General standardize catch by W Relative Effort M Duration Catch fraction 43 If any of the raising options are set to None in the data table properties definition and standard values 1 then these will be disabled in the Analysis properties as e g Catch fraction in the example above Display Under the Display tab you can define the number of visible columns checked and for the data table you can change some of the field columns to being physical with or without PSU separation or biological see 2 stage sampling design in one record Properties for Data al x Primary level checked PSU separator Date Station Gear Mesh W
87. e axis direction if on the X axis you can turn this in all directions East West South North if on Y then North or South and if you have multiple Y axes then the data will be plotted against a Z axis that can be turned East or West The number of major intervals if not a category axis The number of minor intervals per major if not a category axis The axis values format number of decimals or using 1000 separator the format of the latter is defined in the Regional and Language options of the PC If on the Y axis you can set its position on the X axis crosses at Min or max 98 e If the axis is a category you can change the interval gap width 1 e narrowing the displayed elements This operation can also be performed on each series individually e The length of tick marks and their position on the axis Inside Outside or Cross e If on the X axis and category you can add an additional series to be displayed and even use its values to scale the X categories such as the IRI plot By changing the axis direction and position you can turn your chart in nearly all directions and if you have double Y axis these can be pointing in opposite directions one North the other South Properties for series Series have two special property frames tabs besides Series layout and General e Series e Trend see below under Add trend properties for es Series Trend Series layout General m Selection MV Apply
88. e bds Hep comet oe Here you give the name caption of the table The file name will automatically be a combination of the project name lt table caption gt bds and located on the same path as the project You can choose between two table types e Data table e Id table NB Although you can add a data table these cannot yet be accessed by the analyses so this option is for future use only The Id tables or reference tables are for creating relationships for translating codes in the data table to corresponding names and additional information Thus if you want more code fields in the data tables and associate these with a new Id Table you can create one and set the relationship on the data field property Delete tables You can delete some of the standard Pasgear Id tables Station Setting type and Stratum and any of the tables you have added yourself by highlight the table and choose delete a Ls SSS a Que se View Effe X PEGEA Insert records You can insert new records anywhere in a table by right click and choose Insert record or simply press Ins The insert record dialog consist of 2 columns where the first Default contains the field values of the last record you want to insert after This means you only have to fill out the fields in the New column where information has changed All the others will inherit their values form the Default column The number of records to be inserted is by def
89. e duration field in the Data Table Standardization of the CPUE 1 e expressing the result per standard effort units gear size or standard time is optionally achieved by activating one or both of the terms SU U and ST T in the above equation when setting the Analysis properties under Analysis Standardize catch by Analysis Query General standardize catch by W Relative Effort l Duration Catch fraction For changing the standard units see Effort definition and sample raising modes 78 All species NO set Wi kg set 16 45 14 12 i ai 3 0 10 25 8 2 0 6 0 15 i i i 10 m 05 gt _ e 0 ee ee el CCE gg Hydrocynus vittatus Schilbe mystus Marcusenius macrolepidotus Species B mean SE I mean t 95 SE B mean SE I mean t 95 SE Catch rates CPUE by sample prreseseseseseseseseeg Rows Sample El Columns Species Sample Hippopotamyrus dischorhyncus Marcusenius macrolepidotus Mormyrops EE Pages NA Zampe 00 0 0 EI W kg set Sample 2 0 0 0 0 Charts Sample 3 0 0 0 0 Diagrams Sample 4 0 0 0 0 Sample 5 0 0 0 0 Sample 6 0 0 0 0 Sample 7 0 0 0 0 Sample 8 0 2 0 0 Sample 9 0 0 0 0 Sample 10 0 7 0 5 Sample 11 0 3 0 4 Sample 12 0 0 0 0 Sample 13 1 6 0 3 Sample 14 0 3 0 0 This macro gives CPUE by each individual sample rows and can be variously grouped columns The main purpose is for exporting the results to the tool Estimate confidence intervals in
90. e value in the Gear size field will be used The Size unit gives the unit of the Gear size Station table CT ES gt Sampling station 1 stil The Station Id integer should correspond with the ones you have used in the DATA table Here you can now enter the name of the station and a short print name for graphs and the geographic position of the station Setting type table HE ai YES The Setting type Id integer should correspond with the ones you have used in the DATA table Here you can now enter the name of the particular Type of setting and the label e g Sector Strata Type etc A type of setting can be any of your choice For example Bottom set Top set Perpendicular to the shore or whatever your particular samplings programme wants to stratify by or differentiate between 5I Stratum table Stratum Jof EERE This table works like the Setting type table The Stratum Id integer should correspond with the ones you have used in the DATA table etc Rank table 10 length is assumed wrong and corrected 20 weight is estimated from weight length relationship 30 weight is estimated from mean weight in mesh size of species 30 The Rank field in Pasgear is typically used to give validity to the specific records When the Rank field contains values different from 0 the record will not be used in certain of the calculation procedures such as length weight relationship mean we
91. ear II Demo project pg2 a x File Edit View Insert Project Data Tools Window Help osaSxirlamea talm gt m en e me amp 1PG2 Demo Data oj x EE 5 a x B Database 11 01 1992 n Data rd range 1 266 Species FF FFF EF F F F F Gear 25 38 51 64 76 89 102 114 127 140 i tt ta t tT 7 Taanon 39 774525 4 9 2 1 17 amp Setting Typ amp Stratum Rank ee amp My Id table Queries a Date JH Rec Allspecies 25 01 1992 267 535 FOF FFF EF F F F F 25 38 51 64 76 89 102 114 127 140 t ta t 4 4 37 31 94 29 23 122 16 1 08 02 1992 536 702 Gear FF FFFF F F FF esh 25 38 51 64 76 89 102 114 127 140 Absolute effort set CITIT tT 7 1 1 Indintidiiale 9 12 22 IN 1n a an 2 16 GIRI by time Catch comp siclzielelelelololololololsl gt ian cosaS8SSSSSEB alain an alot nll nim on nl mln on on oe soe OC S T A E E L 38 64 89 114 140 165 Mesh 26 26 26 26 25 26 26 26 26 26 2625 set Mean Length 4 8 12 16 20 24 28 32 36 40 Length Males L50 27 37 Females L50 18 58 Pasgear 2 has a multiple document interface which means that you can show any number of tables or analyses simultaneously Data files reference tables queries and analyses are combined into a Project PasGear 2 has a project tree which lets you navigate easily among the various tables queries and analyses
92. ears in the data base 82 Correction for gear selectivity If you have estimated gear selectivity of the particular species and stored the relative probabilities on file you can optionally correct the observed length frequencies to estimated true frequencies when examining the monthly catches or total catch curve or before exporting in FiSAT format Choose the properties for the analysis and then the selectivity tab and connect the appropriate selectivity file sel where species and length intervals must match with the format of the analysis Properties for Length by time 1 Selectivity Analysis Query General Selectivity file connected Disconnect Content ee NB Correction for gear selectivity probabilities requires a saved selectivity fle created by the gear selectvity tool Make sure that the species length intervals and lenghtranges are matching Correction will only take place on the first variable should be numbers found on the dimension pages ofthe analysis table Cancel l FiSAT export This macro has also export facilities into FiSAT format Gayanilo et al 1996 Right click on table and choose Export to FiSAT Length by time 1 Table LFa diagram Catch curve diagram B BJ Rows Length 1992 NO Prija Leny Export to gt Ll an Sep EINO set amp Disconnect Tigerfish ElL cm NO 9 ESD L cmyt 10 Connect query EIW g NO 111 i 6 EI SD Wig 4 2
93. ecsadaviaasanrests eC ode o T e E A E E OA Check observed length versus Wee NG asirarpipreo pi no aE aeai AREE E ANS OE Validity codes 11 Rank CIC css ccacacasttaervnnlacamansuassxnchidasdasieuepuanannces EEE sitensauideeestidenwduuddsadeiaaaeledeube esemaeaentatas Fsimate missie CU MiSs paced ec anne AA ESEE i250 OERE KA EPER r EEE ER REE ERa EE Find check and correct records Find any specific records By creating a query and entering the search criteria any query combination you can find one or several of the codes and values in a table see Queries Alternatively press Ctrl F or right click Select and gt Find Rec No Date Station Species Gear Mesh Relative efort Duration hour Setting Type Number Len Connect query l Lv4 Select and Import gt X Delete 4 Sort a Replace Ctril H X Delete records Export 4 Goto Ctril G L Check length weight W First T Estimate weight gt Here you can connect an already existing query or create a new by clicking E No query connected F Count matches ial gt gt Find and replace any specific records Press Ctrl H or right click on table gt Select and gt Replace Find and Replace Data rec Count matches Hal a gt Replace Replace all Close The connected query will now be extended with a Replace column where you can specify the updates you want to make e g chan
94. efficients give the estimated coefficients where the number depends on the trend type e SE b or trend gives the Standard error of the slope if linear or else the trend er gives the coefficient of determination regression sum of squares total sum of squares e p value gives the probability that the trend is different from O F regression mean squares residual mean squares e N gives the number of points in the regression e N outliers give the number of points where there are more than a specified percentage deviation between the observed value and the expected value The level of deviation is given in deviation for outliers default 20 For the two smoothing trends cubic spline and moving average coefficients r and p values are not available Finally you can give the position of the trend legend Either chooses the default which is under the series legend or in one of the corners of the plot area or a Free position 106 Tools Oa ias TI AAE insu sci aca as gem E E beara a aac adel T Meets ot a E EAEE ones E E 106 The ire UC MEY dis MOU OM rsss EREA EERE aaar E SEER ESEESE 107 Options for calculating confidence intervals 0 0 0 cessseesseeeeeecceceececeaasesseeeeeeeeeeeeeeeseaaeesseseeeeeeeeeeeeeeeeeegensees 107 ESimales based OM he sample MEAN sirarite N A ag A R eater pdp teens 107 Estimates of the mean based on log normal theory The Pennington estimator cccccceccseenssenseeeeeees 108 The mo
95. entation Noumea New Caledonia Secretariat of the Pacific Community 54 p ISBN 982 203 878 X http www spc int McConnaughey R A and Conquest L L 1992 Trawl survey estimation using a comparative approach based on lognormal theory Fish Bull 91 107 118 Millar R B 1992 Estimating the size selectivity of fishing gear by conditioning on the total catch Journal of the Americam Statistical Association 87 962 968 Millar R B and Holst R 1997 Estimation of gillnet and hook selectivity using log linear models ICES Journal of Mar Sci 54 471 477 Millar R B and Fryer R J 1999 Estimating the size selection curves of towed gears traps nets and hook Rev Fish Biol and Fisheries 9 89 116 Pauly D and David N 1981 ELEFAN I a basic program for the objective extraction of growth parameters from length frequency data Meeresforschung Rep Mar Res 28 4 205 211 Pennington M 1983 Efficient estimators of abundance for fish and plankton surveys Biometrics 39 28 1 286 124 Pennington M 1996 Estimating the mean and variance from highly skewed marine data Fish Bull 94 498 505 Pinkas L Oliphant M S and I L K Iverson 1971 Food habits of albacore bluefin tuna and bonito in Californian waters Fish Bull Calif Dep Fish game 152 1 105 Rickey M H 1995 Maturity spawning and seasonal movement of Arrowtooth flounder Atherestes stomias off Washington Fish Bull 93 1 127 138 Sparre
96. ex 1 and the relative evenness J defined as S H P Ln P and J H H where H Ln S i l where P is the relative abundance 1 e the number of individuals for each species divided by the total number of individuals for all species S in each sample Begon et al 1990 p 617 All species W kg 60 Species 18 1 Hydrocynus vittatus 50 2 Oreochromis mortimeri 3 Serranochromis codringtonii 40 4 Clarias gariepinus 30 5 Mormyrops longirostris 6 Schilbe mystus 20 7 Tilapia rendalli 8 Hippopotamyrus dischorhyncus 10 9 Synodontis zambezensis 10 Marcusenius macrolepidotus 0 11 Alestes imberi 10 12 Mormyrops deliciosus 13 Distichodus shenga 20 14 Heterobranchus longifilis 15 Labeo congoro 30 16 Oreochromis machrochir 17 Labeo cylindricus 0 18 Labeo altivelis 50 60 NO 59 68 57 36 26 24 24 17 FRQ 1 2 3 5 6 7 8 9 Species 77 Catch rates CPUE by species Catch rates CPUE by species 1 Revssossrsscsssevsecss i maesa Species NO NO W kg W NO set SD NO set W kg set SD W kg set NO Hydrocynus vittatus 3635 48 6 1059 761 35 2 11 7 18 3 3 4 44 NO Oreochromis mortimeri 1386 18 5 815 372 27 1 45 6 8 2 6 4 0 beg Serranochromis codringtonii 1002 13 4 423 915 141 3 2 5 9 1 4 2 6 NO set Clarias gariepinus 239 3 2 307 119 10 2 0 8 1 5 1 0 1 9 SDNO set Mormyrops longirostris 163 22 235 650 7 0 5 1 1 0 8 1 6 fie a Tilapia rendalli 1145 15 53106 18 04 0 9 0 2 0 5 Pages NA Schi
97. f the field column and table you have various options e If the table is an Id table reference table you can define the field as a primary key field by checking 4 key e If the field is a primary key field you can then choose the default Id alias Label that the key shall translate into e If the field is a code foreign key in a data table you can set the Id relationship with an Id table and the corresponding primary key see example below on how the lt station gt field in the data table is related to the lt Station Id gt field in the Station table and that the default alias is the lt name gt field e If the field is a calculated expression only applicable to new columns see add fields you must specify the expression optionally by using the expression builder e If the field has a metric unit stored unit has conversion 1 you can choose or define other units e g stored length unit mm then cm has conversion 0 1 You can also add more units to the list by pressing e You can set the default empty or unknown value for the field e Sample level For some of the standard Pasgear fields and all new fields you can define the sample level as physical with or without being a sample separator or biological see 2 stage sampling design in one record e Finally you can specify the field format decimals font and background color 46 Properties for Station a xl Column General Field name
98. field takes the value see Table 2 b The length frequency level where the length is a length group and the weight is the total weight of individuals in the length group and the NUMBER field is the number of individuals in the length group In this case the number field is normally gt 1 see Table 2 c The total catch level where the length is unknown value 0 and the weight is the summed weight of the species caught in one to several settings or a sample in this case the NUMBER field is the number of fish caught if not known it is set to 0 Thus there are 3 possibilities for entering biological information into a record Table 2 Fields Level of information species length Weight number sex gonads a Individual fish x or 0 x or 0 1 b Length frequency X x or 0 X c Catch in Number weight 0 x or 0 x or 0 where x stands for known information and 0 for unknown missing information If species is 0 and the physical fields i e date and gear fields are entered then such record will count as an empty setting PASGEAR will work on any of these levels and they can even be mixed into the same file and in the same sample e g when various proportions of a catch is sampled for individual measurements length frequencies and maybe a rest groups of only numbers and weight PASGEAR will keep track of which information is available in the individual calculation programmes Effort definition and sample raising modes
99. fields which are Id s and have corresponding Id Tables a combo box will appear from which you can choose your Ids The From record and To record fields define the part of the database that the query shall refer to All uses of a query are restricted to only apply within the defined record range If you do not enter any values in the From or To record fields the query will apply to the whole table file Invert selection At the bottom of the query dialogue there is a check box for inverting the selection When this is checked it simply means that all records that returned true i e satisfies the selection criteria now returns false and vice versa 55 Advanced text mode Queries can be constructed either by defining simple ranges combined with logical And operators or by pressing L tmode where you write the query in text like using SQL When in text mode you have unlimited possibilities of constructing a query using a range of operators such as or not in if etc see Expressions To help building a query expression press Z to enter the Expression builder Properties for Tiger or bream i 2 x Select General Table Erom record To record Data Where Advanced text mode Species 6 or Species 15 and Month Date In 3 6 10 Enter a free selection in text expression You can expand the query using mathematical expresssions together with logical operators like AND
100. ging species Id s or any other values 62 Properties for Selection 1 x Select General Erom Table Data Where Rec No Date Station Species Gear Mesh mm You can use expressions in the replace column by writing these or using the expression builder Check codes Id s See Queries V alidation of the Id s used in Data Check observed length versus weight If you have individual fish measurements you should check if the entered length and weight data of each fish and species are corresponding This is best done by first running the length weight relationship If there are inconsistencies between observed recorded length and weight then the r in the length weight relationship will be relatively low r is acceptable within roughly 0 95 1 00 and the diagram of length versus weight will show many outliers First you should try to get the best set of estimated length weight coefficients of a given species This is done iteratively 1 Run the length weight Analysis see Analysis Length weight relationship 2 Transfer coefficients to species table see Analysis Length weight relationship 3 Connect a Validation query and check the Max length deviation in 4 Invert the query see Queries This will return all records with less than the specified deviation Properties for Validation 1 l x Validate Select General Invertvalidation M Max length deviation limit in 20 Check in
101. he Lower limit for exclusion to e g 25 then all values above this will be excluded until the point on the x axis where the subsequent value is higher than the limit given by Lower limit for outliers when Y gt lower limit and Y lt Yj4 Normally the value for lower limit for exclusion the value for lower limit for outliers by default but you can configure the two values separately by checking off the checkbox The lower limit for exclusion y direction will be kept but the extent in the x direction will be defined by the first point where the subsequent x class length will have a higher mature value than the new normally lower limit given for outliers You can see the applied trend filter by checking o Show excluded region and or the data not used by checking o Data excluded from trend on the Diagram control pane under the series Minimum number of observations per point Double click on the series in the Diagram control pane and choose the Trend tab 86 Properties for Males Maturity ogive Seres Trend Series layout General f Linear analytic iterations 20000 Precision E6 Convergence oos Power range jo 4 to fo a l Ln transformed sum of squares Non linear iterative Fiting method _ Iterative options Trend options l deviation for outliers 20 for y Trend filter _ _VVVVVVYTY
102. he display connected query and the table caption under the general tab In addition to can see the file information of the table path size and last modified Effort mode If the table is a Data table it has one more frame for effort mode 42 Properties for Data l i x Effort mode Display File Information Query General Effort variable Definition Absolute effort Default One per sample zl Sample raising 1 Relative effort None Field relative effort Rl J100 2 Duration None Fiela Duration all Record raising ADAMS Catch fraction None None zl fi Absolute efort unt Standard effort sample raising unit set 100 m 12 hour set Apply Sample and record raising is done on Number and Weight data They can be applied individually if activated in analysis properties Cancel Help Here you define 1 The absolute effort value and unit see also Overview Effort definition and sample raising modes Default 1 means that the absolute effort is the same as a primary sample unit one sample one absolute effort unit If this is not the case you must specify in the definition field the column or expression where the absolute effort values are given 2 The relative effort and the duration effort used for sample raising to a given standard value If not applicable then press None and definition and standard value will both be set to None cod
103. he selection curve S and whether the principle of geometric similarity seems applicable there is an option for estimating the mean standard deviation and degree of skewness for each of the observed catch frequencies in mesh size i Fig 10 You can choose between two models or function types The standard normal function type 2 n L m Pase ee nj PN oe and a skew normal function Helser et al 1991 1994 for species that also have some degree of entanglement function type 1 and default 116 L m y L m L m 1 ON2T 20 ii o i i i where new symbols are qi skewness coefficient of the distribution of fish in mesh when qi 0 the model reduces to the standard normal distribution nij catch of fish of size class j in mesh 1 n total catch of fish in mesh i gt n J The model parameters ui 6 qi are estimated by an iterative numerical search of the minimum sum of squares observed predicted between the expected catch based on the model E nij and the observed catch nj The non linear search algorithm Fletchers method is adapted to Pascal from a QBasic program FLET supplied with Hilborn and Walters 1992 It is also possible to evaluate the statistical fit of the chosen selection curve by an examination of the residual plot or by the model deviance squared sum of residuals In the residual plot it is expected that the residuals are randomly distributed
104. hoc charts to be produced M Build summary ranges ofdata should be checked if you want a summary of the ranges found in the data table If the analysis is a length grouped analysis it can be connected to a gear selectivity file Tab Properties Selectivity 68 Pasgear II Demo project pg2 Data i File Edit View Insert Project Data Tools Window Help DSMSX SBME Nel gt mme sees a GIPG2 Demo pe ee aid Seti a i Database BB 011992 16 8 000 a fm Analysis 113 NM A Maturity by gt ka Length by View Seasons Copy Biomass si Cut CPUE by Wx Delete BIRI by tim Maturity by amp Disconnect Tiger FA taLength we Kea UE d Bream Run analysis F5 Tiger Reset fonts to standard EJ Tiger or bream Add chart Feb May Sep Nov amp Add diagram Selection 1 Pasgear confidence intervals gt NA NA 4 Estimate gear selectivity gt NA NA FE Export to FISAT Sa HA ET Properties Alt Return NA NA MA RIA All analyses can also be copied or cut and pasted within or among pasgear programs and viewed or deleted According to the grouping setup and the chosen variables the results can also be exported to e Estimate confidence intervals Pasgear tool e Estimate gear selectivity Pasgear tool e FiSAT FAO ICLARM Stock Assessment Tools Gayanilo et al 1996 General analysis Table 7 Rows Species 1992 Total e E Columns Sex Species F M Tota
105. ied estimator Bootstrap confidence intervals The bootstrap Efron and Tibshirani 1986 1993 is a computer based re sampling method for assigning measures of accuracy to statistical estimates The properties of a sample statistic e g mean CPUE are determined from the sample information itself and thus are free from the usual distributional assumptions in parametric statistics The basic idea 1s to construct a sequence of new samples individual observations which is obtained by randomly sampling N times with replacement from the original data points 1 e the original individual CPUE estimates where N is the size of the original sample i e the total number of individual CPUE estimates settings By generating a large number of independent bootstrap sample series typically 2000 each of size N then the frequency distribution of the mean of the bootstrap samples will approximate a normal distribution Fig 6 Standard 95 confidence limits can then be obtained by taking the 2 5 and 97 5 percentiles P 2 5 P 97 5 or by calculating the standard error SE and multiplying with 1 96 from the t distribution when df gt 120 and a 0 025 By calculating the CPUE 196 SE this will give the lower and upper CL at the 95 confidence level Both upper and lower CL s and P 2 5 and P 97 5 or other optional confidence levels are output in PASGEAR 112 2000 Bootstrap runs of Mean Vv kg set Total Freq 250 max 11 24 min 6
106. ight condition factor etc See Find check and correct records for possible sources of errors and the suggested codes used when correcting them The Rank Id integer should correspond with the ones you have used in the DATA table Here you can now enter the meaning Name of the Id 1 e how is this particular record ranked in terms of validity and usage The label is used in the Analysis tables and diagrams 52 Queries RSIS T cic ag secu setindse T E T E 52 OMI eA a T R 52 EERE ICI PAE E E EE E E A E E E E E A AET 54 S O A LS i l e T T T EE EE E A E E A EEE EAA T 54 eaa E a EE E T T E EE E A anata T E A A E EA A T TT 54 Advanced EEMOL oie deme ndacsonsaane setedndancenamidumndecsoesaune teaaadaceeenndnadeecndneanee wie staeantecssnces 55 E AA OESE h E EEE E E T I O O E ESSE AOE OAE E A T 55 Teme Uy CLOG valide EE EE EEE ATE 56 Validation ot the Id Fused in Dala sipeccaricentievensnsicersancandeedddpsiensersedeeuandacssnandeetceindsceetarasetoaandicesnendinteededeauevensistenendacesness 56 Pasgear II Demo project pg2 File Edit View Insert Project Data Tools Window Help De aX em La eo Se PG2 Demo a Database Data _ amp EiSpecies a Gear a 4 Station a Setting Typ Stratum a Rank s iMy Id table J Queries Effort sur ete El fil Analysis Properties Alt Return Validation Queries or selection filters can be applied to the data or Id tables or to any analysis Right click Queries under
107. ill net 102 0 Set 114 Gill net 114 0 Set i I 64 Gill net 64 0 Set i f i 50 The Gear Id 2 characters and Mesh Id integer should correspond with the ones you have used in the DATA table Under Gear name you can enter the name of the gear e g gill net and the Mesh label output format you want to be used in the Analysis Tables The Mesh Id and the Mesh label can be identical and usually are when operating with different mesh sizes However the order of Mesh labels given in the gear file will also determine the order they are shown in some of the Analysis tables calculated by PASGEAR and the order they are presented in the gear selectivity tool It is therefore recommended to arrange and enter or sort various mesh or hook sizes in ascending order The Mesh size unit field is for calculating the selectivity coefficients in the Ger selectivity tool and should always be numerical The Abs effort unit field is used to define the fishing unit Set haul etc The field Gear size field is used if for example the different mesh panels long lines or traps have different dimensions They may be identical to the Relative effort field in the DATA table but this is not critical for most calculations of standardized CPUE For calculations of standardized CPUE only the Relative effort field in the data base is used except when Gear table is chosen in Data properties Effort mode Sample raising relative effort where th
108. intsh Here you have various options on the Destination i e the Pasgear file you import to on whether to replace insert or overwrite records You must also define the format of the external source data date format field delimiter as well on which lines the import should start If there is a header line for the columns to be imported then check Has header row and the import will automatically start at line2 The source preview gives you the layout on how the data are read from the source 26 Step 3 Map the source data to the destination table fields Mark Include on fields to be Imported and map them from the preview Relative effort Duration hour Mark all Source preview on starts atrow tan CHACH36 005 CHACH36 005 SCASCO5 1005 SCASCO5 005 SCASCO5 06 10 2005 _ Help Cancel lt Back Next gt Finish Pasgear 2 will import any number of fields in any specified order from the clipboard a text file where each line is a record if not an aggregated matrix as long as they correspond to the Pasgear formats First mark V the fields you want to import if all then use Mark all Next The column number 1 n given in the preview of a particular variable field in the source file should be indicated to the right of the corresponding Pasgear 2 field This means that the source file can contain both more or less fields and in any different order than the Pasgear 2 fields and
109. is macro gives distribution of gonadal stages by length and sex as well as the percentage mature M depending on the chosen stage for individuals considered mature o Mature 100 90 60 0 60 50 40 30 20 10 4 B Males L50 27 37 Females L50 18 56 Oreochromis mortimen so Mature calculated from stage 1 36 Males Females 40 Length cm 84 Length at maturity ogive A length at maturity ogive can be estimated by fitting a logistic model Gunderson et al 1980 P gt 100 where m 1 pee Pn 1s percent mature M at length L and a and b are fitted constants The model constants are estimated see trends by an iterative numerical search of the minimum sum of squares observed predicted The non linear search algorithm Fletchers method is adapted to Pascal from a QBasic program FLET supplied with Hilborn amp Walters 1992 Lso 1s defined as the length where 50 of the fish are mature and are calculated Rickey 1995 from the estimated constants a and b a Lon b There are several options available when fitting the logistic model on each series Males or females which can be accessed by clicking on the series and choose properties or simply double click 85 Lower limit for exclusion default 25 Lower limit for outliers default 25 Minimum number of observations per point default 5 Weigh fitting with number of observations
110. isting keys Pq1 datafile eks fe record To record peee 7532 a g2 Support tables C Replace alr C insert after rec no C Overwrite 1 Merge and Preserve existing keys C Overwrite existing keys From record To record pame Pqg1 Suppor tables 5 Choose the import range From record and To record or accept defaults whole range and press OK 6 Double click Data to view cae ee in the eae a Sratum 1 ee A 45 Tl ful 2 1 0 2 1 NA NA 2 i 2 1 0 3 1 2 F 127 NA NA 2 1 48 500 1300 000 F 4 1 0 4 1 2 F 177 NA NA 2 1 55 600 1 00 000 F 3 L 0 5 1 5 F 127 WA WA 2 1 26 300 650 000 F 2 1 0 6 175 F 127 WA WA 2 1 2 500 850 000 F 3 1 0 i 175 F 177 WA WA 2 1 22 700 525 000 M 0 1 0 a 175 F 177 NA WA 2 1 24 100 600 000 F 2 1 0 9 1 76 F 127 NA WA 2 1 23 400 575 000 M 2 1 0 10 14 F 177 NA WA 2 1 56 800 2125 000 M 2 1 0 11 13 F 127 NA NA 2 1 58 800 1850 000 F 1 1 0 7 Open view Id tables species Station Setting type etc 8 Open Project properties see Project properties and set all definitions and default settings for the project This is equivalent to the old CONSTANT file in Pasgear 1 DOS 9 Open Data properties see Properties for tables and set the Effort mode 10 You are ready to build and run analyses see Analysis 24 Importing data from external sources e g Excel must be done on the Data table and or each Id Table separately 3
111. ity CUIVES cccsecscecccecccceccaeeaeseeseeeeeeeeceeesecsaessssseseeeeeeeeeeseeaaeaassseseseeeees 113 How io use Gear selechVil y serrie nny ree ener nr EASE ARSE rr tree TT rrr reciente errr rrr ert TTT 116 Saye estimated vear SCISCUVILY sixeticicncctcteceemnausedcdacacnaasetnaniuinde tbs ate enenatditatinebeateualvetwausvondeescbadianasehadeeneenaieteinern 121 Gear selectivity Indirect estimation of gear selectivity curves 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 In PASGEAR 2 gillnet hook and trap selectivity is 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 and Holst 1997 and Millar and Fryer 1999 For a given length class j the number of fish Yj that encounter gillnet 7 are assumed to be observations of independent Poisson variables Yji Po pidj where the expected count pij is the product of the abundance of length class j fish and the relative fishing intensity of gillnet 7 Fishing intensity can also be considered as a combination of fishing effort and fishing powe
112. l F M Total 7 dhs an Alestes imber Alyorocynus vittatus Orstchodus shenga Labeo alivels Labeo congoro Labeo cylindricus Schilbe mystus Canas ganepinus Total The general analysis and all predefined macros consists of 3 grouping dimension rows columns and pages like the MS Excel pivot table and a number of chosen variables or statistics can be added to each dimension by Add variable By pressing the F5 button or refresh the analysis spools over the data table and will aggregate groups se below and calculated variables up in a report table A matrix table X Y can be created by grouping rows and columns and adding one page variable An array of matrices can be made by grouping by pages e g if you want the result of an analysis repeated for each year in the data then group pages by Year 69 Grouping dimensions All data fields can be grouped by either Rows Y Columns X or Pages Z and any number of variables can be added to rows columns and pages See for example the Length by time analysis where all three dimensions are used Column groups Column variables ml 2a 3 a E C uw ii c4 R1 Row groups Wa a y m oc d p Technically the DE dimension is the 3D dimension of a cube but for the display each page will be sliced off the cube and located next to each other as pages If you are using the page dimension the last page will always be the tot
113. lbe mystus 317 42 23803 08 1 0 2 9 0 1 0 2 acharts Mormyrops deliciosus 10 0 1 23 550 0 8 0 0 0 2 0 1 0 5 MDiagrams Hippopotamyrus dischorhyncus 283 3 8 17 934 06 09 3 0 0 1 0 2 Synodontis zambezensis 85 1 1 16 965 0 6 0 3 1 0 0 1 0 2 Heterobranchus longifilis 3 0 0 8 800 0 3 0 0 0 1 0 0 0 4 Labeo congoro 2 0 0 8 650 0 3 0 0 0 1 0 0 0 4 Marcusenius macrolepidotus 1 8 7 750 0 3 0 4 2 4 0 0 0 1 Distichodus shenga 7 0 1 4 175 0 1 0 0 0 1 0 0 0 1 Alestes imberi 1 3 3 756 0 1 0 3 2 6 0 0 0 1 Oreochromis machrochir 0 0 0 975 0 0 0 0 0 1 0 0 0 1 Labeo cylindricus 0 0 0 750 0 0 0 0 0 1 0 0 0 0 Labeo altivelis 1 0 0 0 500 0 0 0 0 0 1 0 0 0 0 Total 7484 100 0 3012 532 100 0 24 1 20 9 9 7 6 7 Catch per unit effort CPUE in PASGEAR is calculated as n PUr yw U T gt where i 1 i i y absolute effort e g number of net panel or fleet settings and n number of samples NB if effort is not a variable then y n W catch in weight or numbers in set or sample SU standard relative effort unit size of a net panel defined in the data table properties Effort mode U actual relative effort unit size of net this can be given in the Relative effort field in the Data Table and or defined in the Gear Table see Effort definition and sample raising modes ST standard time unit hours or minutes of a setting defined in the data table properties Effort mode T actual time unit of setting this can be given in th
114. ler and Niklas Mattson who have discovered more bugs and or given useful comments and proposals Ren Holst ConStat Denmark has kindly provided a maximum likelihood optimizer and implemented the gear selectivity functions Lastly a great thanks to Asmund who through his impressive programming skills have turned Pasgear into a flexible relational database and a tool with almost unlimited possibilities for the user The development of Pasgear 2 has been kindly been supported by The Department of Biology University of Bergen Norway The European Commission INCO DEV programme KNOWFISH Ministry of Fisheries and Marine Resources Namibia The Norwegian Research Council WorldFish Center The Institute of Marine Research IMR Bergen Norway Table of contents Background and acknowledgements ssseeessseeeeceeeeeeeeeeeaaaaseeeeseeeeeeeeeeeeeeeeeaaasseeeeseeeeeeeeeeeeeeeeeaaaaaasaessees 2 OY CVC Warena E E E E wloneae aentneaeiens 6 Mha t Pasccar C IO CS cx inca cases peda n a E N S 7 Mulople document intertace MIDI cspern E 8 The OreanizationOr FASGEAR eeigen a E E 8 DACA ASG see stin te cestaaccnctnns anc nsnaesaabuaeadiestemenetions aod painstacleu A pretameaotoanGedeaurie 9 EIE TE PAI EE RAE E E E T E EA aed parte E EIE E E AE E E neath EE E AEP EAA AT 9 EROE S UMM Ary aier E E E E EA E E 10 AVS S poani a E E E A E E ut oe 10 TOO Eee E E E E E EEE 12 Thedata structare mn PASGEA IR ipn E E E E E E 12 Field types
115. mis mortimeri 2 63 4 00 0 25 310 2 16 3 06 0 00 31 90 Seranochromis codringtonii 1 37 2 54 0 15 310 1 08 1 66 0 00 20 40 Tilapia rendalli 0 17 0 46 0 03 310 0 12 O22 0 00 3 35 Synodontis zambezensis a we 0 01 ut ms L 0 00 1 23 0 00 34 55 AB PB Fennington 9 95 0 46 30d 10 86 285 er Arithmetic bootstrap 9 71 0 30 5 95 10 45 Fennington bootstrap 9 44 0 40 5 18 10 74 1 44 Total Sample 1 310 Obs 1 00 310 00 The program will read a general matrix from the clipboard or a text file of the following format separators can be lt tab gt or comma lt gt Line 1 General header line Line2 Group Label e g species value unit e g kg set Line 3 Sampleld_1 Sampleld_2 Groupname_1 Groupname_2 Groupname_n Line4 Val val val val val val etc Val val val val val val The two first columns lt Sample Id_1 gt and lt Sample Id_2 gt are for grouping and sorting They can contain e g the station number and the depth or any other grouping value If no information then just fill out with dummies e g numbers 1 n The remaining columns from 3 and onwards are the actual data values for each group The length of each group column does not need to be the same so for example 1f the groups are e g years and the data are samples 107 and there is an unequal sample size for each year then just fill out fields with no information with a dash or something which is not a number The frequency distribution lol
116. mn properties Show tool tips with code description This option enables you to directly see the lookup alias of codes from the related Id Table Mark redundant data as NA If checked then illegal records or data not used in analyses will be marked as NA default in table or user defined layout 34 Default chart layout Properties for Project 1 2 x Pasgear has an inbuilt default for the Summary Definitions Expressions Table Layout Default chart layout m Graph elements Category I Apply recursively to all objects All elements Preview ategory legen Z scale legend Object text Bar interpolated Std error box et V Apply line style Solid M Shadow Si Eont V Apply color layout of all graph elements objects This however can be modified by the user All newly created charts and diagrams will follow the default setup Graph elements can be categorized into All elements e Font elements e Fill elements e Line elements Each element can be individually designed size color fill style etc MV Apply line width WV Apply radius Po O IV Apply fill style Solid WV Apply marker IV Apply fill color I Color shade vi 10 Selectall Reset Help Cancel OK T Invert colors Invert all Grey scale Expressions Expression builder Formal and actual parameters Expression Library Expressions Pasgear 2 has an expression co
117. mpiler that enables you to solve a large range of mathematical or logical expressions see Project properties expressions Expressions are used as queries functions mathematical calculations or data base operations such as e g lookup codes in Id tables or replacement of values By using expressions on added calculated columns in the data table or in the queries the user has virtually unlimited possibilities for selecting grouping and analyzing the data see example in Database tables Calculated fields Expressions are compiled in Pasgear and therefore needs to be constructed with the correct syntax for the compiler to deal with it For complicated sequential expression the use of parentheses is therefore important to divide the operations into smaller parts Expressions can be any combinations of the following 1 A number float i e a simple floating value like 2 2 1 or 2e 2 2 A variable a quoted expression for an object representing a value var1 3 A binary operation an arithmetic or logical operation with 2 operands DIV MOD lt etc 4 A unary operation an arithmetic or logical operation with 1 operands LN LOGIO EXP ABS etc 5 A function a freely defined function with an optional parameter list like FuncName Argl Arg2 ArgN 35 6 Another expression identified by a name and stored in the Expression library Expression builder By using the expression builder Z you can
118. ndow Help Dae hex BOE 41 i amp Estimate confidence intervals GPG2 Demo a fj Database There are two stand alone tools associated with Pasgear 2 These programs can be executed independently from Pasgear 2 and are using their own file system but can be directly accessed from Pasgear itself The two tools are e Estimation of confidence intervals conflim exe e Estimation of gear selectivity gearsel exe The confidence interval tools contains procedures for getting confidence intervals from data that are distributed both normally and highly skewed such as Pennington s estimator based on the Delta distribution Pennington 1983 1996 Conquest et al 1996 and bootstrap Efron amp Tibshirani 1986 1993 The gear selectivity tool is for indirect estimation of gillnet hook and trap selectivity 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 amp Holst 1997 and Millar and Fryer 1999 The data structure in PASGEAR PASGEAR is basically a flat data base one type of records which can be used to store and analyze fisheries data on various levels from individual fish with biological measurements to aggregated catch just numbers and or weights and effort data Each record Table 1 is consisting of 3 parts 1 the physical
119. nfidence limits e Calculation of different types of confidence limits such as arithmetic Pennington estimator and bootstrap e Non linear maximum likelihood estimation of gillnet hook and trap selectivity probabilities e Gear selectivity corrected length frequencies and catch curves e Non linear least squares estimation of maturity ogives and size at 50 maturity e Raising of mixed gear catches CPUE by length groups for cohort analysis and T amp B The data base or parts of it any combination of selected records and fields can be converted into ASCII format for export to spreadsheets other data bases or statistical packages Similarly data can be easily imported from these through the import wizard All extracts and tables can be copied and pasted in eg Excel or Word Length frequencies by time intervals or mesh sizes are exportable in FiSAT format for further analysis in ELEFAN Gayanilo et al 1989 or FiSAT Gayanilo et al 1996 Also total weighted catches by length groups are exportable in FiSAT format as input to length based cohort analysis Sparre and Venema 1998 Pasgear 2 has a powerful graphical interface which lets you plot variables and statistics in almost any combination as well as fitting a number of different trend curves The fitting of trends can be done both analytically linear least square regression method or by a non linear iterative numerical solution Multiple document interface MDI Pasg
120. nsert Project Data Tools Window Help kj New Project Ctr N ia ee Open gt ya 1 Choose File New project or press Ctrl N 21 Create and save new project a x Save in pasfiles x Oris i MWERU E E BANGWEUL Namibia My Recent cfri Niklas eee Ethiopia ONWA Itezhi OKAVANGO Kafue OPASGEAR Kyle Sudan LAOS tanganik LISE temp CO lkfri TURKANA eisini merron VICTORIA milton vietnam PE MUSANDO My Computer Save as type Pasgear2 project pg2 v Cancel 2 Select location enter Object name and Save Li Depending on the data source there are now 3 options e Import data from old Pasgear 1 DOS files e Import data from other sources e g Excel or text files e Punch data directly into Pasgear 2 Import from Pasgear 1 DOS version oF Projamia 2 See Ictatinn IGnaries lear II a T Import d Pasgear 1 DOS a Properties Alt Return H pasfiles H mt PASGEAR HG Pasgear2 HG Pasgear 2 old ES PPT H Program Files a r projects HG Adm H D Dev H 1 FasGearl a i H Forms H E help H Include H Output H a Proiert Cancel ZZ 23 4 Choose the location of your Pasgear 1 DOS files Select the extension and press OK Import project from Pas a x a Data table C Replace all Insert after rec no C Overwrite 7532 Merge and C Preserve existing keys C Overwrite ex
121. o Min observations per point E Polynomial order z Fixed intercept Inside Inside i Fixed asymptote Trend legend i Function type M Caption M Coefficients p value SEtrend w N W r7 N outliers Position M Default e Let Right Free GB Top E Bottom Leftisa 855 Top cafes Help Cancel OK This filter is used to define the minimum number of observations required in a length group for the data point to be included in the curve fit Here you can also deactivate the default trend filter described above by checking off o Use trend filter Weigh fitting with number of observations Choose the Trend tab Iterative options Power range This option is used to give more weight to length x class intervals with a high number of observations The weighing is done by multiplying the sum of squares with the number of observations raised to the power of the entered weight value which can range from 0 to 10 ie SumSQ observed predicted obs Thus with weight value 0 there is no weighing If you choose a range of power values the fitted trend for each value will be displayed simoultaneously Maturity by time 2 3 4 5 Total INV MAT KINO SDK NO Licmi NO SD Licm NO Jan 88 92 43 5 4 5 237 63 9 18 02 25 1 6 1 Feb 62 87 12 131 166 63 5 17 03 26 6 52 7 Mar 175 168 39 9 2 393 55 5 17 03 26 4 5 0 ESD KINO Apr 118 115 2 3 258 54 gt i 02 26 8 55 EI L cm
122. ol Val U Unit Col Converts Val in unit U to current Fulton L W 100 W L 3 Fulton s condition factor K W in Length_Interval 0 Set the length interval used for t Est_Length W a b W a 1 b Length estimation in cm from W i Weight_from_Length N N a L b Point weight estimation from len Beyer N L a b I N 1 l a b 1 L 1 b Eestimates mean weight in leng Data_Est_Length InColUnit Length Est_Length C Length estimation from weight u Data_Est_Weight InColUnit W eight if 0 lt Length_l Return estimated Weight using List of available user defined expressions Add View Remove Help Cancel OK 32 definitions in the length column see column properties Check length in cm intervals if the data consist of length frequencies instead of individually measured organisms see Types of biological data under Overview Weight Defines the weight unit used in groupings and display This is a short cut to the unit definitions in the Weight column see column properties To change a coded column alias e g species name in Latin or local see column properties Give the expressions library of the project An expression is any function query or constant that can be called and used in Queries Analyses Calculated fields or for replacement of values in the data base An expression consists of a name with or without ingoing parameters a body which is
123. order to see the frequency distribution of the catch rates and or for various ways of calculating alternative confidence intervals There are 4 options for calculating confidence intervals 1 Standard parametric based on the arithmetic sample mean 2 The Pennington estimator based on the log normal Delta distribution 3 Bootstrap on the arithmetic sample mean 4 Bootstrap on the Pennington estimator 79 All species Wi kg set ii a aide etree nde eae etter dicanaeinaa anand MQ wnnnn nana nnn nnnn nnn nnnene nnn c ene n ence nnc enn enne ence nenenncnnnnnncnnnenenannnnennsnensne enenanncascaes BE E dD Banscanscnsnes 30 25 20 15 10 Recnsesesenseseseneees B Rows Month bcp Date 38 51 64 76 89 102 114 127 140152 165 178 Total 6 NO 40 263 393 225 73 25 05 03 8 6 Jan Feb El L cm NO 6 3 27 8 67 5 373 145 78 15 0 3 13 6 SD L cm NO Mar Apr EI W 9 NO o 4 4 4 4 4 4 4 4 84 4 4 4 amp El SD W g NO 29 8 20 8 424 252 104 42 12 0 4 0 2 11 2 mamare ein 5 5 5 6 5 5 65 5 5 5 Ff w EINO ESD NO set Jul Aug 27 8 26 3 20 5 15 3 55 63 13 03 0 3 8 6 EI W 9 NO o 4 4 4 4 4 4 4 4 44 4 4 48 EI CV W NO 55 0 53 5 25 5 15 8 90 60 13 05 14 2 E B Pages NA Sep Oct EINOjset 4 4 4 4 4 4 4 4 4 4 4 3 47 El set 50 4 582 240 136 100 64 22 04 06 13 8 yCharts Nov Dec 60 Diagrams 29 7 358 363 214 96 55 13 02 01 0 1 O2 11 7 Total 310 This macro is used for calculating and exploring changes in standardized CPUE by different
124. ose records with rank code 0 73 e Number only choose records where number field 1 e Within gonadal range as defined in Project properties e Within mature range only records with gonadal stages defined as mature Variable statistics Depending on the chosen variable they can be expressed in various statistical terms such as e Count e Sum e Percent based on totals in either table page rows or columns e Min or Max value observed e Mean and standard spread statistics based on various chosen denominators in Calculate by o Standard deviation o Standard error of mean o Coefficient of variation o 95 confidence intervals Standard deviation In case where the estimated mean e g CPUE is the ratio of two variables then SD s are calculated from the Taylor series approximation by the following formula Cochran 1977 Krebs 1989 1 x 2R xy RY y sp t Zx 2RE w R SY here E y n i y However 1f the denominator y is equal to the sample size n then Each y 1 y ysn 1 AVF And the above ratio formula reduces to the normal standard deviation formula of sp Zo SD n n i SD Standard error is calculated as SE JN Coefficient of variation is calculated as CV SD 100 X 95 Confidence intervals are calculated as Chs x t SE where tis from Students t table with n 1 degrees of freedom and a 0 025 n 1 Variable layout The chosen variable will
125. primary sample fields date station gear gear size or relative effort duration set or sample type depth etc 2 the biological secondary sample fields species length weight sex gonads and 3 the Rank field which PASGEAR uses for internal data control and validation Data can be either punched directly see Get started Enter data or imported from Pasgear 1 DOS other sources in text or clipboard format see Get started import from Pasgear 1 or import from text 13 Field types and codes in a PASGEAR record Table 1 The entry fields of one standard default record in PASGEAR The type indicates whether the field is physical biological or free For further details see Database tables data table Fields Type Comments RECORD NO NA Number in file automatically set DATE The format depends on the default format chosen for your PC STATION Free Station or location entered as a code integer free field Name of species or group of species entered as a code 7 characters SPECIES letters or number GEAR CODE The code of fishing gear used two letters MESH CODE The mesh hook trap size or code used integer The relative sample effort or the relative gear effort e g length area or RELATIVE EFFORT other unit DURATION Time duration of effort in hours or minutes SET TYPE Free What kind of setting sample type or other separator integer free field NUMBER Number of individuals the record represents integer LENGT
126. r Millar 1992 We denote the relative selectivity catch or retention probability of length class j fish in gillnet i by si j The number of length j fish caught in gillnet 7 is then Poisson distributed Millar and Holst 1997 Nji Po pidj Sj j Without loss of generality it can be assumed that the selection curves si for each net have unit height because any differences in fishing powers is modeled through the relative fishing intensities p This is the full general model In practice the researcher will have to make assumptions about the form of pj j and s Options to be considered include Millar and Holst 1997 1 If the nets are fished with equal effort should the relative fishing intensities p be assumed equal or some function of mesh size see Hamley 1975 In PASGEAR p is simply considered equal with standardized effort 1 e number of settings of standardized panel area and time set 2 Is it reasonable to postulate a form of the population length distribution by specifying j In PASGEAR the form of the population length distribution is not assumed 3 Is the selection curve si normal log normal gamma or perhaps bimodal shaped Does the principle of geometric similarity apply i e length of maximum retention and spread of selection curve are both proportional to mesh size Baranov 1948 In PASGEAR the user can explore this feature and make assumptions about the selection curve 114 There
127. rably follow chronologically and all fish caught in the same setting gear or sample should follow each other see also sort data below The reason is that the calculation and tabulating programmes will automatically assume that when any of the physical fields that define a primary sample unit see Overview 2 stage sampling design in one record such as either the date or the station or the mesh hook trap size or the setting type changes in the record series then it is considered as a new setting or a new sample You can see the separation of settings samples in colours in the Data table by checking on Sample separation Pasgear II Demo project pg2 Data E File Edit View Insert Project Data Tools Window Help D amp S X 3m amp amp Edit or append GIPG2 Demo 4 Rec No De v Database Al Rec No Check chronology and the number of settings samples In order to check if all the settings or samples are correctly entered you should run the module Effort summary under Database in the tree view 58 Pasgear II Demo project pg2 Data File Edit View Insert Project Data Tools Window Help OSHSX S SOB 4 a gt mm S Ba ol co a GIPG2 Demo Rec No Date__ Station Species Gear Me a Database _ amp Data _ EiSpecies Gear _ Station _ Setting Type Stratum _ Rank _ amp EBMy Id table amp Queries E Effort summary a Analysis
128. ram layout General Title M Visible M Use default Al species m Subtitle 1 Postion J Visible North C South C East C vest Subtitle 2 Postion T Visible North South C East C West m Margins in Diagram legend Left B T Visible pm pa Top 3 Right B Bottom 5 V Default diagram columns af Help Cancel The diagram properties has Title Subtitle 1 position north south east west Subtitle 2 position north south east west Margins in percent of area A legend free position A number of columns VVVVV V The number of columns defines the relative position of the 1 N linked objects If you use default the number of columns will be round square root objects Thus two charts will be positioned next to each other Four will be positioned two by two etc If you want all object on top of each other then choose column If you want all next to each other then columns objects Properties for charts Is described above under Making a chart 97 Properties for axes gt Properties torx Te Axis axis layout General Vaule category scale r Axis label NM Default min value M Default max value M Default label Axis min 1 Hydrocynus vittatus Species Axis max 18 Labeo altivelis z Position M Default major and minor intervals 18 Additional category series
129. range 2 38 gt to Ea gt C Explore mean length SD and skewness C Estimate selectivity by model Normal scale 5 5 2 Then choose Explore mean length SD and skewness 118 Gear selectivity workfile gse File View Options Help oS ig Mean SD and skewness for Mesh size 38 114 Mesh 38 Mesh 51 Mesh 64 Mesh 76 Mesh 89 Mesh 102 Mesh 114 Mean SD and skewness for Mesh size 38 114 E aulExplore FEE Hydrocynus vittatus 1 cm intervals ior Lx ERY Arithme Estima 2 AlExplore SD PAX a 7ialExplore skew LX BLY 38 51 64 76 89 102 114 Mesh size 38 51 64 76 89 102 114 Mesh size Skewness 0 30 0 10 0 10 X margin 3 a a oa or slenath 4 38 51 64 76 89 102 114 Mesh size Obi scale 100 N Zoom 100 4 Hide Prope tties Mesh range 1 38 tof 7 114 gt o timate mean SD and skewness of frequency distributions in each gear size This plot will assist in deciding which selectivity model to choose e Normal location where only the modes maximum retention length is changing with mesh size Spread SD is constant 119 e Normal scale where both the modes and the spreads of the selection curves are increasing with mesh size i e the principle of geometric similarity Or the following three models which all include asymmetrical retention modes i e skewed distributions e Log Normal e Gamma e Bi modal The bimodal curve is appropriate if the fish are caught
130. raphically by using the vast range of opportunities in the charts and diagrams objects see Diagram and chart Properties for My analysis ax Analysis Query General standardize catch by r Sorttable by F Relative Effort C None Row field Y axis m Duration C Column X axis variable P Gatch fraction L ie Primary sample unit Order _ f Mesh e Ascending Descending C Fleet Treatunknown values as Gg Unknown 0 Lowest maturity stage E C One i W Build summary ranges of data Help Cancel OK Pasgear 2 contains a number of predefined analyses macros that can be added inserted in a project Absolute effort f General analysis Catch composition and IRI Catch rates CPUE by species Catch rates CPUE by sample Catch rates CPUE by time Length by gear Length by time Maturity by length Maturity by time Stages by sex Length weight relationship Each of the 10 predefined analyses are special cases of a general analysis but have been developed to serve the need for the most often used calculations They all also have predefined diagrams for showing the results Both the predefined analyses and their diagrams can all be edited or changed nearly unlimited according to the users wishes The predefined analyses are all described in more details under Analysis 12 Tools Pasgear II Demo project pg2 File Edit View Insert Project Data Tools Wi
131. roperties then Right click the Project and choose properties from the popup menu File Edit View Insert Project Data Tools Wir Dedek ek eq is Project a Datab ee Analy Properties Alt Return My analys The project properties consist of a series of Tabs each explained more fully below that set up the configuration and properties of a Project e Summary 31 e Definitions e Expressions e Table layout e Default chart layout Summary Properties for Project 1 ax Title Pr Oj ect title Summary Definitions Effort mode Expressions Table Layout Default chart layout Subj ect Project subject Title J J Proen Comments Project comments Subject Demonstration data file for PASGEAR package Kolding 1993 Data source period The first Comments and last records date of data Contains partly modified gillnet data from Lake kariba Zimbabw table Tables List of connected data Data source period First record 11 01 1992 Last record 24 12 1992 Number of table records and Id tables their names files C Pasgear 2 Test 28 09 05 Data b and records C Pasgear 2 Test 28 09 05 Specie C Pasgear 2 Test 23 09 05 Gear b C Pasgear 2 Test 28 09 05 Station C Pasgear 2 Test 28 09 05 Setting C Pasgear 2 Test 28 09 05 Stratu C Pasgear 2 Test 23 09 05 Rank b Help Cancel OK Definitions ax Survey method Choose sampling method or define your
132. rouped sorted by the primary physical sample separation fields then the number of primary sample units PSU and the absolute effort might be wrong Pasgear assumes that the number of gear samples within a date station setting type etc should be constant Thus if the same gear and mesh size is found at several places within a grouped time interval and the absolute effort is not constant then these are shown in pink If the reason is that the data have not been properly sorted then sort the data Sort the data If the data have been entered or imported haphazardly and the dates and other physical fields are out of order you can run the sorting module which will sort the records Right click on table and choose Sort or press 2 NB Sorting can be done on any table 60 x Sot by columns Hf 1 Date Up _Down i 3 Gear H 4 Mesh mm Group H 5 Setting Type H 6 Stratum H 7 Relative effort m H 3 Duration hour l 9 Species f 10 Number f 11 Length cm 12 Weight g 13 Sex l 14 Gonadal Stage 15 Rank Direction ee Ascending Descending i Sorting with index From M All To fi 500 500 The sorting will be done hierarchically down to the last item checked You can define any sorting hierarchy by highlighting a field and move it up or down the list by the Up or Down buttons By choosing Group the hierarchy will set up automatically by firs
133. rresponding names from the Id tables Note that all entering and editing in the database files bds is done directly on the disk file not in memory so that any changes will be directly and permanently written to the file All changes on the project file pg2 however will only be saved on user request either by pressing save or press yes to save when closing the project Queries i Effort sur Insert query J Selection Validation Analysis Properties Alt Return pe There are two types of queries e Selection e Validation Selection queries see Database Queries or selection filters can be added defined and applied to any database tables and selection queries on the Data table can also be connected with any analysis item or expression 10 Validation queries see Find and correct records are used to check whether there are undefined codes in the Data file 1 e there are codes which have not been defined with key field codes in the corresponding support tables Validation queries can also be used to find and correct records where the observed length is deviating from the expected length of a fish calculated from the length weight relationship by a defined limit see Check length weight and Length weight relationship Effort Summary Date 25 01 1992 Record range 267 535 Gear F F F F F F F F F F F F F F F Mesh 25 38 51 64 76 89 102 114 127 140 152 165 176 190 203 Total Absolute effort 1 1i 1
134. s Tiger or Bream and months are Mars to June or October Help _ cance OK When pressing OK the expression will be saved to the library and you can recall it anytime you need it by just referring to the name For example if you want to use it inside other expressions Expressions added to the library will be displayed and can be retrieved in the expression builder under Functions Library To delete or edit a user defined expression you must use the Edit or Remove option in Project properties Expressions If you change or edit the syntax you can check if it compiles without errors by pressing Compile 37 Database DY AU A is AOI cece cera gesin oases sarod E hago tatp atone E E E E te ete 37 PA MDS i orctee aces ssa Vesta dio ser thx sacs vats Vs hs ance tives ae Gatun pa nate a asia vs sas Sto are E E a easton 37 acerca esate wre ths saath vse ces esse cc san ods e panes sae oe eo cow E tute gata S tne vos geet acta a R 38 ET O ssa Vedat ee west a cc stesso owe cc stn ode putes sate eo E tote gala sua tnt vos ge cat aetna getGanen reas toae 38 MU ONG tra aa eet ea tt pte nates sacar ic ates de tutes owes A aetna bo gotta E nateevanesetcaviareustone 39 POO Gl A TE ATE ET TETE AAA O N AA E E A A 40 Prope ri os or CAO NSS e e Aa E E EE a tonendecnsnees 41 EEO e A E E A E E S E E AEA E E taeemtncsasees 41 VIS Ay e E E E E etme dectvecddeauee eases emendaeesnees 43 Addnew WES CGO GNIS oeeaaeaii ear E E EES EAE E E EEEE AEE OET EE 43 C S a E
135. see the available operators and functions and their syntax as well as the available tables and fields to select values variables from Expression builder q a x Field Species 6 or Field Species 15 and l Write or build your expression f Month Field Date In 3 6 10 Add to library hie gt lt e And Or Not In cL Ln Operators Functions Math Log10 El Tables Logical Abs ees Date Time Round Saar Database IsNan Foin Library Day Setting Type if Stratum AutoNumber Rank Unit Field column Returns the current field value of column in table you are applying the expression to The expression builder has an edit field where you can directly type in the expression Alternatively you can add operators or parentheses by using the speed buttons gt gt And Or Not Below the speed buttons you can select among the main expressions groups Operators Functions or Tables in the left pane The middle pane will then give you the group categories e Operators All or Arithmetic Comparison Logical and set e Functions All or Mathematical Logical Date time Database and Library e Tables The fields columns of the selected table If you have chosen operators or functions you can then select and insert double click the chosen operator or function in the expression pane Below the pane the syntax is shown
136. set NO set 9 Licm NO 10 Disconnect Tigerfish eas PAS familih i114 The content of the clipboard can then be pasted in other applications e g Excel Word etc Predefined analyses Catch composition and IRI Catch composition and IRI 1 Table RI diagram ee Species NO NO W kg WFRQ FRQ IRI IRI HO J E HENO Hydrocynus vittatus 3635 48 6 1059 761 35 2 183 590 4944 46 2 0 351 0 12 NO Oreochromis mortimeri 1386 18 5 815 372 27 1 212 684 3117 29 1 0 312 0 11 miha Serranochromis codringtonii 1002 13 4 423 915 14 1 176 56 8 1559 14 6 0 269 0 09 FRQ Clarias gariepinus 239 3 2 307 119 10 2 111 35 8 479 4 5 0 110 0 04 FRQ Mormyrops longirostris 163 2 2 235 650 78 82 26 5 265 2 5 0 083 0 03 ni Schilbe mystus 317 4 2 23 803 0 8 75 24 2 1 1 0 134 0 05 H Tilapia rendalli 115 15 53 106 18 73 235 78 0 7 0 064 0 02 J Hippopotamyrus dischorhyncus 283 3 8 17 934 06 54 17 4 0 7 0 124 0 04 ac a Synodontis zambezensis 85 11 16965 06 13 5 0 2 0 051 0 02 yDiagrams Marcusenius macrolepidotus 1 8 7 750 0 3 9 7 0 2 0 072 0 02 Alestes imberi 1 3 3 756 0 1 a1 0 1 0 058 0 02 Mormyrops deliciosus 0 1 23 550 08 3 2 0 0 0 009 0 00 Distichodus shenga 0 1 4 175 0 1 2 3 0 0 0 007 0 00 Heterobranchus longifilis 0 0 8 800 0 3 1 0 0 0 0 003 0 00 Labeo congoro 0 0 8 650 0 3 0 6 0 0 0 002 0 00 Oreochromis machrochir 0 0 0 975 0 0 0 3 0 0 0 001 0 00 Labeo cylindricus 0 0 0 750 0 0 0 3 0 0 0 001 0 00 Labeo altivelis 0 0 0 500 0 0 1 0 3 0 0 0 001 0
137. sis and choose Transfer coefficients to species table Pasgear II Demo project pg2 File Edit View Insert Project Data Tools Window Help Hg S BOE eh 4 r mae 1 a1 m ee r Demo Length weight relationship 1 1 8 Database Length We Fulton a Analysis sa Maturity by Hydrocynus vittatus EiLength by g Length Weight relationship Length by ti Grams S Seasons I y eaa Biomass siz 3600 CPUE by we oa BIRI by time Maturity by J400 a a a a a anauaeds g Length weic DF TEE Copy picture to clipboard lal Save picture as amp Print Hide control pane rt Transfer coefficients to species table Alt Return The length weight coefficients are used when running validation queries on observed 90 length versus weight in the database in order to find the outliers or when running the modules Check length weight or Estimate weight on the data table see also Find clean and correct records and missing weights Properties for Validation 1 Validate Select General Invert validation M Max length deviation limitin jo M Check invalid codes by Pasgear II Demo project pg2 Data E File Edit View Insert Project Data Tools Window Help DSHSxX Semeall twas gt mae 1 e eee aPG2 Demo pee ee ee 8 Database gt 11 01 1992 16 F Er 4 0 oh 8 Species View _ Gear Connect query F t
138. stent if desirable right click in the diagram and choose Keep diagram from the pop up menu If an ad hoc diagram or chart has been made persistent and the analysis still is asked to make default charts then these will be added to the persistent charts Automatic charts are created in two ways 1 All predefined analyses will make automatic default charts if the M Make default charts on the analysis property frame is checked 2 Any single variable can also be rendered graphically if the Make Ad Hoc chart on the variable property frame is checked User created charts are created by either click on the cart icon l on the toolbar or right click on the Charts folder Jin the Analysis tree view gt Add chart My analysis Table HJ Rows Specie E Columns Sex Species i a n ae Hydrocynus v tatus Heterobranchus longiiis Diag ME Raig Properties Alt Return Mormyrops fongirostis This will open the Chart property page Chart type setup Properties for Chart 1 a x Chart type setup Chart layout General Chart type Category single Y axis Series legend L Default position Left Top i i 0 0 0 0 xX dimension on chart Y dimension on chart Category single Y axis Category double Y axis Category multiple Y axis Scatter single Y axis Scatter double Y axis Scatter multiple Y axis Rows
139. t PSU fields red the other physical fields Blue and lastly biological fields green Check the Sorting with index in order to keep groups of data that do not need sorting 1 e the catch in a mesh size in the original order they were entered this will facilitate the retrieval of records under cleaning and correction procedures as they will have the same order as the original sheets Lastly define the record range of the sorting operation in the From To edit boxes or choose all by checking Al Once the data are correctly entered sorted chronologically and all the settings are correctly displayed in the effort summary you can start to clean the data base for recording or punching mistakes as best as possible see Find check and correct records Hint If you want to create an index so that you always can retrieve the original data order irrespective of later sorting then do the following 1 Add new column of type integer and name it e g Original Index 2 Right click Original Index choose Replace from column gt Rec No Replace from column gt ng i Date Properties Alt Return Find check and correct TECOPS secre ca cocesdwariewadewntivansnasacectebaelesnvedsaweieiaacelactacluadiateanddesadssudu ntact LEEENA NEEE ENa ALAKAN EE DE E EEE A A T E AE scans E T E E E E Find and replace amy specific record a veesayscenconteteneretevonsasinennncasksededcndansdensavevsbesintidehumenosetsanonssaaaqendel
140. t matches Searched from 1 to 7532 Found from 2 to 7358 KURALI Match 89 of 7357 1 0 From To Allin search range Close Export data Data can be selected and exported from any table by right click on table Select and gt Export Rec No Date Station Species Gear ech Relative efon Duration hour Seting Type Number Len La i 45 0 000 2 1 Connect query wed Select and Import 21 Sort Delete records 4 Goto Ctrl G L Check length weight Md First al Estimate weight Choose Format Table format f Pasqear C Text Clipboard If you choose text or clipboard then you must define the field separator and whether you want to include the column headers when exporting check Include headers on T Include headers Help Cancel 41 Finally specify which record range to export in From record To record and the Fields by checking V Included on off optionally the field ranges you want to export If you don t make any specifications or restrictions the whole data table will be exported ax Table From record To record Duration hour Setting Type Number Length cm Sex T Invert selection Clear Text mode Help Cancel i Properties for tables Right click table in project tree view and choose properties or highlight table and press Each table has a property frame where you can define t
141. that fields in the source file which are not to be imported are just skipped You can also enter a default value for fields where you don t have information in the source file such as e g the gear code or any other field with constant values It is only important that the values in the source file are within the range and format accepted by Pasgear 2 see Table 1 The only exception is the date field which will accept a range of optional input values in Step 2 Lastly you should give the units where applicable to the imported fields If some field already have defined units see Database tables column properties then these will appear in combo boxes Note that for a Pasgear record to be recognised as valid then 3 fields are 2 obligatory Date gear and mesh indicated in red If you do not have import values for any one of these fields then give a constant default value Step 4 Preview of the imported data in Pasgear before accepting the import Go back to previuos steps if not OK SAP 06 1 TTS 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 06 10 2005 CHACHSE CHACHS36 SCASCO5 SCASCO5 SCASCO5 HOLMY05 HOLMY05 HOLMY05 LUTMAQ1 ACAACS1 ACAACS1 ACAACS1 ACAAC 25 SCASC2 ACANAQ ACANAQ ACANAQ h h h h fff h h fees fesse fees The last step in the Import
142. the Compleat ELEFAN JCLARM Software 2 ISSN 0116 6964 ICLARM Manila 70 p Gayanilo F C Jr Sparre P and Pauly D 1996 The FAO ICLARM Stock Assessment Tools FiSAT User s Guide FAO Computerized Information Series Fisheries No 8 Rome FAO 266 p 123 Gunderson D R Callahan P and Goiney B 1980 Maturation and fecundity of four species of Sebastes Mar Fish Rev 42 3 4 74 79 Hamley J M 1975 Review of gillnet selectivity J Fish Bd Can 32 1943 1969 Helser T E Geaghan J amp Condrey R E 1991 A new method of estimating gillnet selectivity with an example for spotted seatrout Cynosion nebulosus Can J Fish Aquat Sci 48 487 492 Helser T E Geaghan J and Condrey R E 1994 Estimating size composition and associated variances of a fish population from gillnet selectivity with an example for spotted seatrout Cynosion nebulosus Fish Res 19 65 86 Hilborn R and Walters C J 1992 Quantitative Fisheries Stock Assessment Choice Dynamics and Uncertainty Chapman and Hall New York 570 p Kolding J 1989 The fish resources of Lake Turkana and their environment Thesis for the Cand Scient degree in Fisheries Biology and Final Report of KEN 043 Trial Fishery 1986 1987 University of Bergen 262 pp Krebs C J 1989 Ecological Methodology Harper Collins New York 654 p Labrosse P M Kulbicki and J Ferraris 2002 Underwater Visual Fish Census proper use and implem
143. the syntax that can be compiled and a note describing what the expression does Some of the expressions are predefined and cannot be edited or removed But the user can define any number of expressions and add to the library by pressing Add on the property form or using Add to library on the expression builder User defined expressions can be edited or removed Table layout al Summary Definitions Expressions Table Layout Default chart layout Standard Project font Query match color a MV Color separation of rows by primary samples Color Shadow in 10 l Color separation of column by sample level l Show tooltips with code descriptions V Mark redundant data as NA Help Cancel OK 33 The Table layout gives options for how the tables should be displayed The Standard font Sets the default font used on all tables and new objects Query match color Sets the text color used for records that matches the connected query Color separation of rows by primary samples If checked the primary sample units PSU will be separated visually in the data table by a color or a shade depending on the choice If you have colors on the columns see column properties you should use shadow Color separation of columns by sample level This option will automatically color the columns depending on their defined sample level PSU red physical blue or biological green See colu
144. thod is adapted to Pascal from a QBasic program FLET supplied with Hilborn amp Walters 1992 Trend type Depending on the chosen fitting method there are number of trends available For the linear method there are 1 Linear 2 Logarithmic 3 Power 4 Exponential 5 Polynomial 6 Cubic Spline 7 Moving average For the non linear method you have the same except the 2 smoothers spline and moving avarage plus in addition 8 Logistic 9 Standard normal 10 Skewed normal e For the polynomial you can set the polynomial order to anything less than the number of points 2 104 e For the linear exponential and the polynomial up to order of 2 you can fix and set the intercept e For the Logistic you can fix and set the asymptote e For the moving average you can set the Period elements included and whether the curve should be centered within the period or at the end There are a number of options available for the experienced user for the iterative method If the series is a mean or a percentage and the number of observations behind each point is not the same then you can give weight to the fit by the number of observations by using the power option The weighing is done by multiplying the sum of squares with the number of observations raised to the power of the entered value which can range from 0 to 10 Le SumSQ observed predicted obs Thus with power value 0 there is no weighing Note that yo
145. ti Stages by s Seasons Biomass siz Catch rates BIRI by time iLength by g E My Analysis Wa A My Analysis Diagrams Variables can be added to rows columns or matrix pages by right clicking on the dimension and choose Add variable or click on the amp speed button on the main menu The property 71 page for a variable is rather big and can best be studied by examining the hints on the dialog or the status bar Properties for variable 2x Definition and layout Define variable Statistics Category Sum All C Percent based on 7 Variable M Cumulative Frequency of occurance A Biological records C Mean Min C Max Date C Standard deviation Day of year Standard error Day of month C Coefficient of variation Day of week Conf limits Low High eee Calculate by None Year Individuals v Returns the number of individual Variable label Vv Use default biological organisms NO Report layout Alignment Decimals C Left fo gt j Pight m Restriction C Center Font l Exclude zero values Cell color I No ranking rank code 0 _ Show Zeros 2 2 Zeros inred M Number 1 e nae z Within gonadal range Z separator M Within mature range M Make Ad Hoc chart Help Cancel The list of available variables can be categorised and listed open the combo
146. time intervals and within these time intervals by mesh size or any other groupings It can also be used to explore changes in mean weight length or condition factor against the other variables In the diagrams the values can optionally be seen with 95 confidence limits The confidence limits are calculated from CPUE t _ SE CPUE n 1 where is Students t value at the 95 confidence limit and n sample size number of observations The standard error SE is the SD divided by the square root of n and SD s are calculated the standard way or from the Taylor series approximation depending on effort 1s a variable or not If n is small then it may be better to calculate confidence limits based on bootstrapping as explained under Catch rates CPUE by sample 80 This macro is particularly useful for data exploration and getting a quick overview of patterns or regularities in your experimental fishing or sampling design It can also be used to calculate stratified mean catch per unit effort with variance depending on your sampling design All species NO set 24 47 21 45 60 16 60 15 47 49 12 9 6 3 0 Jan Feb Mar Apr May Jun JulAug Sep Oct Nov Dec Month M mean 5E 7 mean t 95 SE number sample size Length by gear pttteeeeeneenennenneeyy iTable LFa diagram Catch curve diagram E B Rows Length Length cm 38 51 64 76 89 102 114 127 140 152 165 178 Total NO CUM NO 1
147. tion criteria see below will be displayed in a different color default red but can be changed in Project properties Tables layout am ee fee fede oe fe ef feed 11 01199 16 War All matching records can be found successively by using the on the tool bar or using accelerator keys W First Shift Home 4 Previous Shift F3 Next F3 WH Last Shift End 54 Selection query Simple range mode Properties for Selection 1 ax Select General Table Erom record To record Data ki Where Simple range mode Field Op From o J Station Species 3 Mormyri 4 Mormyr 5 Alestes Relative effort m Duration hour Setting Type Number Length cm amp Labeo a Weight q SEX Gonadal Stage Stratum Rank Season Invert selection Clear Text mode Cancel OK For those fields you want to select on then enter field filter criteria Operator equal or lt gt different from and the From value To value on the table fields If you do not enter any values in the From or To columns the field is not considered in the filter If you only enter a value in either the From or in the To column the other empty column will be considered to take the either the maximum or the minimum value of the field respectively Each field filtered are combined with logical And operator all filter criteria must be satisfied at the same time for the record to be accepted For those
148. u can see the effect of different power values by giving a range A curve will then be drawn for each power value in the range Trend filter You can customize the number of data points included in the trend by applying a trend filter There are 3 options e If the series is a mean or a percentage you can exclude all point where the underlying number of observation is below a given limit by setting Min observations per point e You can simply exclude all 0 Y values e Or you can select a number of regions to include or exclude by Use trend filter and Add x select inside outside region _ f Selectinside defined ranqe Select outside defined ranqe gt lt x Range fo 70 Y Range Jo 450 Several of the predefined analyses in Pasgear is using a default trend filter such as Catch curve diagram and Maturity diagram In such case there will be a check mark for Use default but this can be overridden or changed 1f desired If a trend filter is used there will be an option on the series display for o Show excluded region Trend legend You can customize the amount of information you want in the trend legend Trend legend M Functiontype M Caption W Coefficients p value SEb WN Mr N outliers Position J M Default e Let Right Free Top Bottom Left 0 0 Top 74 0 0 105 e Function type writes the chosen trend type e Caption includes the series caption e Co
149. valid codes by 5 Proceed with step 1 and 2 until there are no more outliers or r is acceptable Now with a good set of length weight coefficients in the species table you are able to go back to the original records to check the outliers for punching or recording mistakes Right click in data table Select and Check length weight 63 Length weight check 4 x Count matches Searched from 1 to 7532 Found from 131 to 7437 M a gt w Match 14 of 7307 0 0 Replace From To Allin search range 131 131 a New value Length pau Rank M Useexpected Defaultrank Record Rec no 131 Hydrocynus vittatus Caleulaten 2 _H_ _ Coefficients Expected length cm 247 a 0 024 Expected weight q 142 b 2913 Deviation length 50cm 20 2 0 974 Additional restrictions l Length gt 0 M Species lt gt 0 l Weight 0 W Length deviation bigger than M Number 1 20 W Rank 0 Close Here you can now find each outlier successively and decide whether the records need correction e g punching mistake Note that higher deviations are acceptable and expected between observed and expected weight as weight is a much more variable parameter than length e You can choose whether the length or the weight value should be updated under New value e You can replace using the expected calculated value check Use expected or check
150. was nie oss stom oor se tesa set E EOE dase oeemtaseamee 44 B Ye Ki Wg 610 110 e En E ee eee ee eee 45 FENG OEM EO Pe 1 N N E 45 Renaming fields and any other OD ect sccandes ti sessveducnaseonsedeaetlsnseauesssteiwebsieveczenes ea a a EA Ese 46 DO E E T A E E E ee ee 48 PE Cer 110 a E EEE E EE E E ee eee 48 eME e aeae ea E e otc E E E E I AAE AA E OAA ee 49 PS CM a T E E A A A E SEEE E S E TAEA E A T 49 e CAS E EE A E E AE A E E T 49 OT AS AEEA N IE AE AE PAA E SE P ENE RE E A AE A E A 50 INS a OE R E AA EE E A E E E E SE O A A T tance 50 SAO eaae E EE NAET EN EE EE E oaevetetpedicattont 51 WA E E PAA E EEA PE EA PE A EA E EPEA E E EAT 51 Database tables A standard default Pasgear database has one table for data and a series of 6 Id tables or reference tables for translating codes foreign keys in the data table to corresponding names and additional information for species gear station set type stratum and rank The unique Id field or primary key in the Id tables corresponds to an Id field foreign key in the data table 1 e Species Id Gear Id etc Add tables You can add more tables to the project by right click on Database and choose Add table DECT tT Databa a Data Add table amp Spec Er Properties Alt Return This will open the Add table dialog 38 Add table a x Caption My Id table Type i Data table dtable Filename in project C Pasgear 2 Demo project My Id tabl
151. x Options Relati le Wikq set frequencies Hydrocynus vittatus Relative scale Freq OBS 310 Number of observations 150 max 21 93 Numbers in Percent xmin 000 F Grid Range 21 93 Find outliers obs No 125 Mean 341 Setclass Interval 5D 4 40 SetNo of classes SEmean 025 iScrollx cass m Bo me Contidence intervals 75 E Mean T SE or F CV Display T Change confidence interna Set cutlevel Inf Scroll cutlevel i b 100 x axis stretch 4 gt 0 0 00 3 65 731 10 96 14 62 18 27 21 93 Wkg set Show Statistics panel Class Interval 0 731 30 Y Eont gt Background E3 Copy amp Print Due to the behaviour of fish and often clumped contagious distribution the frequency distributions of catch rates CPUE in most fisheries are highly skewed to the right log normal or negative binomial This has consequences for using parametric statistical tests when sample sizes are small or if there are occasionally very large catches as parametrical Statistical tests assume that data are normally distributed In order to examine the actual frequency distribution of a particular data set there is an option for viewing the frequency distribution graphically see figure above This option shows both the original value and the logarithmic transformations x In x 1 or the Delta distribution x In x if x gt 0 see below under the
152. xpt GS 2j m De 2 s and the standard error 1s SE 4j var c 110 The modified Pennington estimator Log transformed Wikq set frequencies Total OBS 302 men Cut level Delta distribution ne Thurnb rule 2 Thump rule 1 s max 3 69 0 7 0 36 min Sohn Range 624 E Mean 0 SDO 300 24 T SE mean 17 20 Pennington estimator 20 S Ln 045 M Xe1 44 285 Gm S4 2 1 25 15 Cimean 9 95 SE C 046 10 Lrifcut 0 36 5 0 1 61 0 73 0 16 1 04 1 92 280 369 Lrtikgl set Class Interval 0 176 30 In contrast to estimates based on the arithmetic sample mean which are highly sensitive to a single or a few isolated high catch rates that may account for more than 50 of the total catch Pennington s estimator is sensitive to low catch rates which contribute little to the total catch but when log transformed may give large negative values resulting in a distribution becoming skewed to the left In such a case a more precise estimator of mean CPUE is given by modified by Michael Pennington from Pennington 1983 1996 n m _ m _ 2 f exp X G 5 2 where m is the number of sample values greater than a defined cut level rather than 0 y denotes the arithmetic mean of the non transformed values less than the cut level x and s are the mean and variance respectively of the logged values of catches greater than the cut level The variance of Z is
153. y and the units of effort settings or SAMPIES cseeesseeeeeccceeeeceeeeeeeeeseeseeeeeeeeeeeeeeeeeeaaseeeeeees 57 Check chronology and the number of settings Samples ccsssseeeececeeceeceeeaeeeeeeeseeeeeeeceeeeeeeeeeeaas 57 Eforte SUIMIMALY POPE NICS moeren E E E A 58 Thie empo setinin E 58 INIT S SASS CUTS S eer A 59 Wiona abSOINIe c HOn sc sae ices a aes seasons a aanecahasnasad paeanncanes ened A ae emereaenen 59 SOM TNL A la sscawcinas sewn aavacaveauasceutdacesoaccaansentewacas vanenesaausaeiussuwawsaaisbsenkauisadancboeveaadnewsanseoesbencncatvansnoouatnnenans 59 Pind check and correct TECOLS ci bicd aceussansvedsrouadensaowanebs pidadentauwnvas bocdededlacwapalsateedesswauated soansdersnswasaldadiedevsnonarent 61 Find anyes PECTIC Bee OLUS esotas aso EE OEE OEO OEO E E AOE OE 61 Pind aig te plaCe any SpeCiliG TECOM Soon sneinen ene oE Seowadatce wlaswases Soenodauwosad ANTEE ESINES 61 CHECK codes AGS istem brodecmasntionnd E E bnscbdabseucares Sastarebeswonel ENEE 62 CHECK observed Iens Ll VETSUS Wel SIN sc soi orsin oropa a Eer EENE A bred ocondenas OEEC A OEN 62 W andit codes 111 RINK MEIC oecon EE EREE AE ONNO ENEE ESAE 63 Esmar AMIS SIS WES MiS urea E S A A a ar 64 EIER 1 EAEAP E A ise EE EAE A T OE AEA TE OEE EEE AE OE EE T ETT EEE 61 INTIAL GIS POPC Miles sancext ances cncen sae E A A ST 67 Ce Me Gala ysis s eae dete atten er E r A A E T 68 SITOU PINS dimension anea nea igeaasasuae ccuastaiente Auten A
154. y be filled out with Id s from the Data table by right click and choose Build from table on the pop up menu and choose the table to build from This will add all the codes foreign keys found in the chosen table Standard Pasgear Id tables are the following Species table Hu ans Hippopotamyrus TETEE Parrot fish Marcusenius macrolepidotus Bulldog 0 012 3 070 0 000 Mormyrops deliciosus Cornish jack 0 029 2 690 0 000 Mormyrops longirostris Bottlenose 0 032 2 720 0 000 Alestes imberi Imberi 0 255 2 150 0 000 Aydrocynus vittatus Tigerfish 0 024 2 911 0 000 Distichodus shenga Chessa 0 006 3 500 0 000 Labeo altivelis Rednose labeo 0 025 3 050 0 000 The Species Id 7 character text string should correspond with the ones you have used in the species field in the DATA table Here you can now enter the Latin name well as the common or local name The a and b fields are the length weight coefficients for the particular species and the r and N fields are statistics describing their estimates from the data if applicable The length weight coefficients can be entered manually values for most species can be found in FishBase http www fishbase org search php or automatically when calculated from your own data see Analysis Length weight relationship Gear table Rec No Seals eas name es ota Mesh size nA ot nt Sessa 25 Gill net 38 Gill net 38 0 Set 51 Gill net 51 0 Set 76 Gill net 76 0 Set 89 Gill net 89 0 Set 102 G

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