Commit bda666b5 authored by pfreon's avatar pfreon
Browse files

Cosmetic changes in output text

In particular HLP and MODEL files
parent 0edd320f
......@@ -52,7 +52,7 @@ public class Global {
static int stock_divise; // Is the single stock subdivided into various geographical sub-stocks (all must be exploited by the fleet)
static int unite_standardisee; // Is the fishing effort unit standardized and is the CPUE proportional to abundance
static int effet_delais_abundance_negligeable; // Do time-lags and deviations from the stable age structure have negligible effects on production rate
//static String pdf$ = "resources/Freon_et_al_FAO_Manuel_Climprod_1993.pdf"; // Name of the pdf file of Help menu
/**
* ******* Stabilite de la modélisation *****
*/
......
......@@ -13,7 +13,7 @@ import javax.swing.UnsupportedLookAndFeelException;
public class Main {
boolean packFrame = false;
private void show() {
CadreMain frame = new CadreMain();
if (packFrame) {
......
......@@ -1257,7 +1257,7 @@ public class Modele {
Global.fF = Global.fF(Global.coeff_determination, (Global.nbre_param - 1), getNbDataRetenue());
//System.out.println("fF Model = " + Global.fF);
data$[nbre_par + 4][0] = "Fisher test : F(" + (Global.nbre_param -1) + "; " + (getNbDataRetenue() - Global.nbre_param) +")"; // Contrairement à p dans fF de Global.java dernière methode, nbre_param inclus la constante (a en général). D'où la soustraction de 1.
data$[nbre_par + 4][0] = "Fisher test: F(" + (Global.nbre_param -1) + "; " + (getNbDataRetenue() - Global.nbre_param) +")"; // Contrairement à p dans fF de Global.java dernière methode, nbre_param inclus la constante (a en général). D'où la soustraction de 1.
// F(x,y) Donne entre parenthèses les valeurs du nombre d'observation et de paramètres du modèle pour le test de Fisher sur R2
if ((Global.fF < 0.001 && Global.fF > 0) || Global.fF> 999.999) data$[nbre_par + 4][1] = numFormat.format(Global.fF);
else data$[nbre_par + 4][1] = nf.format( Global.fF);
......@@ -1304,7 +1304,7 @@ public class Modele {
if (!Global.modelisationOk) {
return null;
}
String[] title$ = {"Years", "Observed Cpue", "Fitted Cpue", "Residuals"};
String[] title$ = {"Year/season", "Observed CPUE", "Fitted CPUE", "Residuals"};
Object[][] data$ = new String[nim + 1][4];
DecimalFormat nf = new DecimalFormat(" 0.000000;-0.000000");
DecimalFormat nf0 = new DecimalFormat("0");
......
......@@ -202,8 +202,8 @@ public class PlotPreferences extends JDialog
txtCut[0] = new JTextField("" + graOld.getXcutYat());
txtCut[1] = new JTextField("" + graOld.getYcutXat());
JLabel lblCutX = new JLabel("X cut Y at: ", JLabel.RIGHT);
JLabel lblCutY = new JLabel("Y cut X at: ", JLabel.RIGHT);
JLabel lblCutX = new JLabel("X cuts Y at: ", JLabel.RIGHT);
JLabel lblCutY = new JLabel("Y cuts X at: ", JLabel.RIGHT);
cutPanel.add(lblCutX);
cutPanel.add(txtCut[0]);
cutPanel.add(lblCutY);
......@@ -439,7 +439,7 @@ class buildPatternPanel {
int nb = plot.nbSerie;
listeSeries = plot.getListeSeries();
for (int i = 0; i < nb; i++) {
modele.addElement("Serie " + (i + 1));
modele.addElement("Series " + (i + 1));
}
lstSeries.setModel(modele);
for (int i = 0; i < line$.length; i++) {
......@@ -546,7 +546,7 @@ class buildLegendPanel extends JPanel
int nb = plot.nbSerie;
listeSeries = plot.getListeSeries();
for (int i = 0; i < nb; i++) {
modele.addElement("Serie " + (i + 1));
modele.addElement("Series " + (i + 1));
}
lstSeries.setModel(modele);
this.setLayout(gridBagLayout1);
......
......@@ -256,17 +256,17 @@ public static void ClickWarning() {
case 2: // Suite à réorganisation ordre des questions posées. Modif. 2020.
Global.changement_exploitation = index;
if(Global.changement_exploitation==3)
throw new OnError("It is compulsory to know the answer to this question. \nPlease note that you can answer 'Yes' if \nthe used fishing effort takes into account these changes. \nPlease have a look at the help file using the 'Help (H)' button if necessary.");
throw new OnError("It is compulsory to know the answer to this question. \nPlease note that you can answer 'Yes' if the used \nfishing effort takes into account these changes. \nPlease have a look at the help file using the 'Help (H)' \nbutton if necessary.");
break;
case 3: // Suite à réorganisation ordre des questions posées. Modif. 2020.
Global.unite_standardisee = index;
if(Global.unite_standardisee==3)
throw new OnError("It is compulsory to know the answer to this question. \nPlease have a look at the help file using the 'Help (H)' button if necessary.");
throw new OnError("It is compulsory to know the answer to this question. \nPlease have a look at the help file using the 'Help (H)' \nbutton if necessary.");
break;
case 4:
Global.effet_delais_abundance_negligeable = index;
if(Global.effet_delais_abundance_negligeable==3)
throw new OnError("It is compulsory to know the answer to this question. \nPlease have a look at the help file using the 'Help (H)' button if necessary.");
throw new OnError("It is compulsory to know the answer to this question. \nPlease have a look at the help file using the 'Help (H)' \nbutton if necessary.");
break;
case 5:
Global.stock_unique = index;
......@@ -283,7 +283,7 @@ public static void ClickWarning() {
case 10:
Global.under_and_optimaly = index;
if(Global.under_and_optimaly==3)
throw new OnError("It is compulsory to know the answer to this question. \nPlease have a look at the help file using the 'Help (H)' button if necessary.");
throw new OnError("It is compulsory to know the answer to this question. \nPlease have a look at the help file using the 'Help (H)' \nbutton if necessary.");
break;
case 11:
Global.statistiques_anormales = index;
......
......@@ -54,8 +54,6 @@ System.out.println("RapportHtml Ligne 33 FlagNewHtmlFolder = " + Global.FlagNewH
cboFile.addItem(dd[i].getName());
}
}
}
try {
......@@ -66,7 +64,6 @@ System.out.println("RapportHtml Ligne 33 FlagNewHtmlFolder = " + Global.FlagNewH
} catch (Exception e) {
e.printStackTrace();
}
}
private void initWindow() throws Exception {
......@@ -74,11 +71,11 @@ System.out.println("RapportHtml Ligne 33 FlagNewHtmlFolder = " + Global.FlagNewH
this.getContentPane().setLayout(new BorderLayout());
jPanCombo.setBorder(new TitledBorder(BorderFactory.createEtchedBorder(Color.white, new Color(142, 142, 142)), "Select or create a new directory"));
jPanChk.setBorder(new TitledBorder(BorderFactory.createEtchedBorder(Color.white, new Color(142, 142, 142)), "Checked element are added in the folder"));
jPanChk.setBorder(new TitledBorder(BorderFactory.createEtchedBorder(Color.white, new Color(142, 142, 142)), "Checked element are added in the directory"));
cboFile.setEditable(true);
jTextInfo.setBackground(new Color(204, 204, 204));
jTextInfo.setText("If you select an existing directory\nall HTML folder files of \nthis directory will be overwritten\nalthough this is not always\nreflected in the file date.");
jTextInfo.setText("If you select an existing directory\nall HTML directory files of \nthis directory will be overwritten\nalthough this is not always\nreflected in the file date.");
jTextInfo.setFont(new java.awt.Font("Dialog", 2, 13));
this.getContentPane().add(jPan, BorderLayout.CENTER);
......@@ -96,7 +93,7 @@ System.out.println("RapportHtml Ligne 33 FlagNewHtmlFolder = " + Global.FlagNewH
bEnable[i] = (Global.nom_fichier != null);
}
bEnable[5] = bEnable[0] && Global.modelisationOk;
bEnable[6] = bEnable[5];
bEnable[6] = bEnable[5]; // Residuals CPUEs vs E and vs V
bEnable[8] = bEnable[5];
bEnable[7] = bEnable[0] && Global.validationOk; // Jackknife plot
bEnable[9] = Global.validationOk && (((Global.numero_modele > 5) || (Global.numero_modele < 2)) && (Global.numero_modele != 20) && (Global.numero_modele != 33));
......@@ -180,7 +177,7 @@ System.out.println("RapportHtml Ligne 33 FlagNewHtmlFolder = " + Global.FlagNewH
"MS_Plot.html", "Model.html", "Validation.html","QuestionsAnswers.html", "index.html"};
String[] caption = {"Main Results", "Data", "Time graphs", "Histogram graphs",
"Bivariate plots", "Observed-fitted & residual CPUE graphs", "Residual CPUE vs E & V plots", "Jackknife bar graphs", "CPUE=f() & Y=() tri-variate graphs",
"MSY & MSE vs V graphs", "Modelisation", "Validation", "Questions, answers & warnings"};
"MSY & MSE vs V graphs", "Modelization", "Validation", "Questions, answers & warnings"};
String folderName = "";
Hashtable listFile = new Hashtable();
for (int i = 0; i < fileName.length; i++) {
......@@ -368,7 +365,7 @@ System.out.println("Ligne 194 FlagNewHtmlFolder = " + Global.FlagNewHtmlFolder)
}
folderList = new String[vList.size()];
vList.copyInto(folderList);
MsgDialogBox msg = new MsgDialogBox(0, "Folder successfully saved in the directory\n" + folderPath, 1, this.parent);
MsgDialogBox msg = new MsgDialogBox(0, "Files successfully saved in the directory\n" + folderPath, 1, this.parent);
choice = 1;
cancel();
}
......@@ -402,7 +399,7 @@ System.out.println("Ligne 194 FlagNewHtmlFolder = " + Global.FlagNewHtmlFolder)
if (Global.warningDic.containsKey(i)){
//System.out.println("Flag if (Global.warningDic.containsKey(i)) dans RapportHtml.java ligne 400, i = " + i + " Global.warningDic.containsKey(i) = " + Global.warningDic.containsKey(i)); // Modif. 2020
if (!Global.warningDic.get(i).equals("")){
resultHTML += "<i style='color:red;'> (Warning:"+Global.warningDic.get(i)+")</i>";
resultHTML += "<i style='color:red;'>. Warning: "+Global.warningDic.get(i)+"</i>";
}
}
......
......@@ -187,7 +187,7 @@ public static boolean isTrue(){
case 45:
result = (Global.stock_unique!=1 && Global.metapopulation==1 && Global.sous_stock_isole!=1); // Global.metapopulation==1
if(!result){
commentaireEnCours="Your case is a borderline one either because you deal only with a sub-stock or with a full metapopulation (and the lower is the connectivity between sub-stocks, the more borderline you are).\nYou must not extrapolate your results beyond the interval of observation of the different variables (effort and/orenvironment) when using the model for predictions.\nMoreover, any surplus production model using effort will implicitly make the assumption that no variation in recruitment due to other sub-stocks will occur.";
commentaireEnCours="Your case is a borderline one either because you deal only with a sub-stock or with a full metapopulation (and the lower is the connectivity between sub-stocks, the more borderline you are).\nYou must not extrapolate your results beyond the interval of observation of the different variables (effort and/or environment) when using the model for predictions.\nMoreover, any surplus production model using effort will implicitly make the assumption that no variation in recruitment due to other sub-stocks will occur.";
}
break;
case 35:
......@@ -196,7 +196,7 @@ public static boolean isTrue(){
case 49:
result = (Global.under_and_optimaly==2); // Avant (Global.under_and_over_exploited!=1 && Global.under_and_optimaly!=1). Modif 2020.
if(Global.under_and_optimaly==1){
commentaireEnCours="Your can still go on but your case is a borderline one. Model results will be uncertain \n(particularly maximum sustainalbe production that could be underestimated) owing to \nthe lack of exploitation above MSY/MSE levels.";
commentaireEnCours="You can still go on but your case is a borderline one. Model results will be uncertain \n(particularly maximum sustainable production that could be underestimated) owing to \nthe lack of exploitation above MSY/MSE levels.";
}
break;
case 37:
......@@ -532,7 +532,7 @@ public static boolean isTrue(){
commentaireEnCours = "The model is not considered as convenient because R2 is \nlower than the threshold of 0.70";
if (Global.coeff_determination < 0.60 && Data.getNbYears() > 20)
commentaireEnCours = commentaireEnCours + ". (R2 = " + nf.format(Global.coeff_determination)
+ "\nyou can still build a history & html folder by using"
+ "\nyou can still build a history & html directory by using"
+ "\nthe corresponding sub-menu in the 'Files' menu.";
else commentaireEnCours = commentaireEnCours + ". \nBut please note that this threshold is empirical and that your case"
+ "\nmight be borderline because R2 = " + nf.format(Global.coeff_determination) + " and because the length of your"
......@@ -542,7 +542,7 @@ public static boolean isTrue(){
+ "\nstatistical results and graphs in order to make your own decision. But please "
+ "\nremember that R2 is always overestimated due to the colinearity between CPUE "
+ "\nand fishing effort."
+ "\nIn any case you can still build a history & html folder by using "
+ "\nIn any case you can still build a history & html directory by using "
+ "\nthe corresponding sub-menu in the 'Files' menu.";
}
break;
......
......@@ -646,7 +646,7 @@ static private void makePlotJack(){
}
//else
//System.out.println("No problem in Jackknife computation Nb years = " + nim + " i = " + i + " Nb_years_lower100[i] = " + Nb_years_lower100[i] + " Nb_years_greater100[i] = " + Nb_years_greater100[i]); // Test 2020);
PlotSerie ps= new PlotSerie("Years",etiq,ty[i],trjk);
PlotSerie ps= new PlotSerie("Year/season",etiq,ty[i],trjk);
ps.setFigure(3);
Global.jackknifePlot[i]=new PlotHisto();
Global.jackknifePlot[i].setValeurs(ps100);
......@@ -872,7 +872,7 @@ public static Object[][] getYearResult(){
if(!Global.validationOk) return null;
String[] title$={"Years","a (%)","b (%)","c (%)","d (%)"};
String[] title$={"Year/season","a (%)","b (%)","c (%)","d (%)"};
double[] years=Data.getYears();
Object[][] data$=new String[nim+1][nbre_par+2];
DecimalFormat nf0= new DecimalFormat("0");
......
0;-1;103;41;1;;;;;;;;;;;;;;
103;108;1;30;1;;;;;;;;;;;;;;
108;1;1;-1;1;double_click;Do you wish to see again this special warning about the double click use in CLIMPROD when you will re-open the software?; click.hlp;;;;;;;;;;;;;
108;1;1;-1;1;double_click;Do you wish to see again the special warning about the double click use in CLIMPROD next time you will open the software?; click.hlp;;;;;;;;;;;;;
1;-1;2;47;1;;;;;;;;;;;;;;
2;-1;3;42;1;changement_exploitation; Have there been changes in the fishing pattern during the period (effort allocation quota mesh-size ...)?; changes.hlp ;;;;;;;;;;;
3;4;-1;43;1;unite_standardisee;Is the fishing effort unit standardized and is the CPUE proportional to abundance?; standard.hlp ;;;;;;;;;;;
4;5;-1;44;1;effet_delais_abundance_negligeable;Do time-lags and deviations from the stable age structure have negligible effects on production rate?; lageffe1.hlp ;;;;;;;;;;;
3;4;-1;43;1;unite_standardisee;Is the fishing effort unit standardized and is the CPUE proportional to abundance?; standard.hlp ;;;;;;;;;;;
4;5;-1;44;1;effet_delais_abundance_negligeable;Do time-lags and deviations from the stable age structure have negligible effects on production rate?; lageffe1.hlp ;;;;;;;;;;;
5;9;6;-1;1;stock_unique;Does the data-set apply to a unit stock? (A 'Don't know' answer will be interpreted as a conservative 'No'); singlest.hlp ;;;;;;;;;;;
6;7;-1;46;1;metapopulation;Does the data-set applies to a metapopulation?; metapopulation.hlp ;;;;;;;;;;;
7;9;-1;45;1;sous_stock_isole;Does the data applies to the full metapopulation or at least to only one of its sub-stock with limited connectivity?; isostock.hlp ;;;;;;;;;;;
8;9;-1;34;1;;;;;;;;;;;;;;
9;11;10;35;1;under_and_over_exploited;Do you think that the data-set covers periods of both underexploitation and overexploitation?; undover.hlp ;;;;;;;;;;;
10;11;-1;49;1;under_and_optimaly;Do you think that the data-set cover periods of both underexploitation and optimal exploitation?; undopt.hlp ;;;;;;;;;;;
11;12;12;37;1;statistiques_anormales;Do you see any abnormal statistics in the statistical data table? (See the second table in the Climprod frame); abnormal.hlp ;;;;;;;;;;;
11;12;12;37;1;statistiques_anormales;Do you see any abnormal statistics in the statistical data table? (See the second table in the CLIMPROD main window); abnormal.hlp ;;;;;;;;;;;
12;13;13;36;1;unstability;Is the interannual variability too large?; unstabil.hlp ;;;;;;;;;;;
13;14;14;38;1;abnormal_points_dist;Do you see abnormal distribution in the histograms?; abnormal distrib.hlp ;;;;;;;;;;;
14;15;15;39;1;abnormal_points_scatt;Do you see outlier points?; outlier.hlp ;;;;;;;;;;;
......@@ -25,10 +25,10 @@
22;39;23;19;1;stock_deja_effondre;Did the stock already collapse or did it exhibit drastic decrease(s) in catches?; collapse.hlp ;;;;;;;;;;;
23;24;24;-1;1;lifespan;What is the life span of the species?; lifespan.hlp ;1;2;3;4;5;6;7;8;9;10;>10
24;25;25;-1;1;rapport_vie_exploitee_inferieur_deux;Is the ratio (lifespan/number of exploited year-classes) or (lifespan/seasonal-classes) if more than a single one per year lower than 2?; ratio.hlp ;;;;;;;;;;;
25;26;26;-1;1;reserves_naturelles;Are there natural protected areas for the stock or constantly inacessible adult biomass?; protect.hlp ;;;;;;;;;;;
26;27;27;-1;1;premiere_reproduction_avant_recrutement;Are there one or several non negligible spawnings before recruitment?; firstspa.hlp ;;;;;;;;;;;
25;26;26;-1;1;reserves_naturelles;Are there natural protected areas for the stock or constantly inaccessible adult biomass?; protect.hlp ;;;;;;;;;;;
26;27;27;-1;1;premiere_reproduction_avant_recrutement;Are there one or several non-negligible spawnings before recruitment?; firstspa.hlp ;;;;;;;;;;;
27;28;28;-1;1;fecondite_faible;Is the fecundity of the species very low (sharks mammals etc.)?; fecundit.hlp ;;;;;;;;;;;
28;29;29;-1;1;cpue_unstable;Is there a strong instability in the cpue time series?; pueunsta.hlp ;;;;;;;;;;;
28;29;29;-1;1;cpue_unstable;Is there a strong instability in the CPUE time series?; pueunsta.hlp ;;;;;;;;;;;
29;41;30;10;1;;;;;;;;;;;;;;
30;41;31;11;1;;;;;;;;;;;;;;
31;41;32;12;1;;;;;;;;;;;;;;
......@@ -47,18 +47,18 @@
44;101;101;52;2;;;;;;;;;;;;;;
45;95;46;6;2;;;;;;;;;;;;;;
46;48;48;-1;1;environmental_influence;Does the environment influence:; Influenc.hlp ;abundance;catchability;both;;;;;;;;
48;104;49;17;1;linear_relationship;Does this plot look linear; linear.hlp ;;;;;;;;;;;
48;104;49;17;1;linear_relationship;Does this plot look linear?; linear.hlp ;;;;;;;;;;;
49;104;104;-1;1;monotonic_relationship;Does this plot look monotonic?; monotoni.hlp ;;;;;;;;;;;
104;54;51;59;1;;;;;;;;;;;;;;
51;52;52;-1;1;recruitment_age;Age at recruitment (expressed in number of past year-classes, being them seasonal or annual); Agerec.hlp ;1;2;3;4;5;6;7;8;>8;;
52;53;53;-1;1;begin_influence_period;Age at the begining of environmental influence (expressed in number of past year-classes, being them seasonal or annual); begin.hlp ;0;1;2;3;4;5;6;7;8;>8;
52;53;53;-1;1;begin_influence_period;Age at the beginning of environmental influence (expressed in number of past year-classes, being them seasonal or annual); begin.hlp ;0;1;2;3;4;5;6;7;8;>8;
53;102;102;-1;1;end_influence_period;Age at the end of environmental influence (expressed in number of past year-classes, being them seasonal or annual); end.hlp ;0;1;2;3;4;5;6;7;8;>8;
102;47;54;56;1;;;;;;;;;;;;;;
47;54;54;57;1;cpue_sous_sur_production;May the stock present large fluctuations in abundance due to the environment when overexploited?; additif.hlp ;;;;;;;;;;;
54;95;55;4;2;;;;;;;;;;;;;;
55;95;-3;5;2;;;;;;;;;;;;;;
56;57;57;-1;1;recruitment_age;Age at recruitment (expressed in number of past year-classes, being them seasonal or annual); Agerec.hlp ;1;2;3;4;5;6;7;8;9;10;>10
57;58;58;-1;1;begin_influence_period;Age at the begining of environmental influence (expressed in number of past year-classes, being them seasonal or annual); begin.hlp ;0;1;2;3;4;5;6;7;8;>8;
57;58;58;-1;1;begin_influence_period;Age at the beginning of environmental influence (expressed in number of past year-classes, being them seasonal or annual); begin.hlp ;0;1;2;3;4;5;6;7;8;>8;
58;59;59;-1;1;end_influence_period;Age at the end of environmental influence (expressed in number of past year-classes, being them seasonal or annual); end.hlp ;0;1;2;3;4;5;6;7;8;>8;
59;62;60;27;1;linear_relationship;Does_this_plot_look_linear?; linear.hlp ;;;;;;;;;;;
60;62;63;-1;1;monotonic_relationship;Does this plot look monotonic?; monotoni.hlp ;;;;;;;;;;;
......@@ -76,7 +76,7 @@
73;74;74;-1;1;reserves_naturelles;Are there natural protected areas for the stock or constantly inacessible adult biomass?; protect.hlp ;;;;;;;;;;;
74;75;75;-1;1;premiere_reproduction_avant_recrutement;Are there one or several non negligible spawnings before recruitment?; firstspa.hlp ;;;;;;;;;;;
75;76;76;-1;1;fecondite_faible;Is the fecundity of the species very low (sharks mammals etc.)?; fecundit.hlp ;;;;;;;;;;;
76;77;77;-1;1;cpue_unstable;Is there a strong instability in the cpue time series?; unstabilCPUE.hlp ;;;;;;;;;;;
76;77;77;-1;1;cpue_unstable;Is there a strong instability in the CPUE time series?; unstabilCPUE.hlp ;;;;;;;;;;;
77;89;78;10;1;;;;;;;;;;;;;;
78;89;79;11;1;;;;;;;;;;;;;;
79;89;80;12;1;;;;;;;;;;;;;;
......@@ -101,7 +101,7 @@
94;95;-3;8;2;;;;;;;;;;;;;;
95;96;-4;0;2;good_results;Is this an acceptable model?; acceptab.hlp ;;;;;;;;;;;
96;97;-4;15;3;trend_residuals;Are there a good fit and no trend or strong autocorrelation in residuals?; fitresid.hlp ;;;;;;;;;;;
97;98;-4;1;3;coeff_determination_instable;Do you validate the model from the graphical and statistical results that appear in the Jackknife plots window (p values of t-ratios of the parameters, R2 values, p value of F)? (See help file for suggestions); valide.hlp ;;;;;;;;;;;
97;98;-4;1;3;coeff_determination_instable;Do you validate the model from the graphical and statistical results that appear in the jackknife plots window (p values of t-ratios of the parameters, R2 values, p value of F)? (See help file for suggestions); valide.hlp ;;;;;;;;;;;
98;100;99;58;3;;;;;;;;;;;;;;
99;100;-4;55;3;acceptable_graphs;Are the shapes, maximum values and confidence intervals of the MSY and MSE graphs acceptable?; MSY_MSE_GRAPHS.hlp;;;;;;;;;;;
100;-5;-5;3;3;;;;;;;;;;;;;;
......
......@@ -3,7 +3,7 @@
37;Modelization will not be reliable owing to the data-series structure.
38;Your case is a borderline case for using these models because your data-set ;probably contains a few outlier points which may strongly ;force the structure of any model. The jackknife results will probably ;confirm that the fit of the model (if any selected by the software) is poor.
39;Check your data to confirm that the outlier point(s) is (are) not an error. ;If not this (these) point(s) may hinder the analysis, ;especially if the program has to use the concerned point(s) when applying the ;past-averaging approach to solve the problem of transitional situation. ;This comment does not apply in the case of short-lived species where the effort and environment only concern one year of a single year-class.
40;If the two explanatory variables, environment and fishing effort, are not independent ;(co-linearity or shaped relationship), it will be difficult to recognize ;the difference between the influence of these two variables on the CPUE, ;and therefore the model will be imprecise. ;If the mentioned relationship seems very important, it may be recommended to use a model with a single dependent variable.
40;If the two explanatory variables, environment and fishing effort, are not independent (co-linearity or shaped relationship), it will be difficult to disentangle the effects of these two variables on the CPUE, and therefore the model will be imprecise. ;If the mentioned relationship seems very important, it may be recommended to use a model with a single dependent variable.
41;Your data-set is too short for using these models. ;At least 12 years of observation are required in order to limit the problem ;of the low degrees of freedom when fitting a model (providing that after ;taking into account possible lags and the number of parameters ;of the retained model will not reduce it further down). ;Sorry, I stop the model selection routine here.
42;Your data-set is not appropriate for using these models except ;if the used fishing effort takes into account these changes.;Sorry, I stop the model selection routine here.
43;Your data-set is not appropriate for using these models because ;you have first to standardize your fishing effort unit.;Sorry, I stop the model selection routine here.
......@@ -13,6 +13,6 @@
47;Your data-set is not appropriate for using surplus production models using ;fishing effort because the relative range of fishing effort variation, as it ;appears in the "Current known facts" panel, is lower than 100% (factor x 2).;If you think that environment is the main variable driving CPUE variation ;and if you suppose that fishing effort will continue to stay at the same level, ;you may apply a simple regression using only the environment ;as an independent variable. ;For doing this, please choose the "fit_a_model_directly" menu or answer NO ;to the question: "Is the influence of effort on CPUE more important than ;the environmental influence?".;Please note that this regression is not a model and that any prediction ;will be poor since the fishing effort level may change without ;influence on the regression.
48;There is a contradiction between your answers, or your data-set is not appropriate ;for use with surplus production models because the relationship ;between CPUE (or CPUE residuals) and effort is expected to be obvioulsy decreasing. ;This means that fishing effort was probably not the key variable ;driving the stock during the period of observation.;If you think that environment is the main variable driving CPUE variations ;and if you suppose that fishing effort will continue to stay at the same level, ;you may apply a simple regression using only environment ;as independent variable (choose the "fit_a_model_directly" menu ;or answer NO to the question: "Is the influence of effort ;on CPUE more important than the environmental influence?". ;Note that this regression is not a model and that any prediction ;will be poor since the fishing effort level may change without influence on the regression.;Sorry, I stop the model selection routine here.
49;Model results would be inappropriate for stock assessment ;owing to the low range of exploitation levels.;Sorry, I stop the model selection routine here.
50;Maximum sustainable yield(s) will be overestimated owing to the dynamics of exploitation: ;when the effort is constantly increasing, the equilibrium state is not respected, ;and owing to the past-effort-averaging method which was retained in ;transitional state cases MSY is overestimaded (see Appendix B of the manual for discussion).
50;Maximum sustainable yield(s) will be overestimated owing to the dynamics of exploitation: ;when the effort is constantly increasing, the equilibrium state is not respected, ;and owing to the past-effort-averaging method which was retained in ;transitional state cases MSY is overestimated (see the Refernce Guide for discussion).
57;There is no available model fully appropriate to your case. ;This is due to your positive answer to the question: 'May the stock ;present large fluctuations in CPUE when overexploited?' and ;to your negative answer regarding the linearity of the relationship between residual CPUE ;and environment.
58;MSY and MS-E graphs do not make sense for this model where CPUE=f(V). Please continue.
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Does the data applies to the full metapopulation or at least to one of its sub-stocks
with limited connectivity?
The degree of connectivity between sub-stocks of the metapopulation is to be assessed
to answer this question. Connectivity patterns range from a well-mixed larval pool
(maximal connectivity) at one extreme to a collection of closed self-sustaining populations
(minimal connectivity) at the other. However, most situations are intermediate to these
two extremes (MSC, 2020).
Answer YES if the considered stock exploited by the fleet(s) is either:
1) a full metapopulation with reasonably high connectivity. Note that according to
your previous answers, all these eventual sub-stocks must be exploitedby the fleet(s)
corresponding to your data-set. This case is not in contradiction with the fact that
you answer YES to the question "Does the data-set apply to a unit stock";
2) or if it is only one of its substocks with limited connectivity with
the rest of the metapopulation.
MSC (2020) MSC Guidance to the Fisheries Certification Process v2.2. MSC Document: 89 pp.
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Did you see any abnormal statistics in the previous table?
Do you see any abnormal statistics in the statistical data table? (See the second table in the CLIMPROD main window)
This is a general question regarding the table of statistical results of
the following variables: production, effort, catch per unit of effort
(CPUE) and environment (V).
Answer YES if the distribution of one or more variable seems very far from
a normal distribution. Graphical help will be available later and you will
Answer YES if the distribution of one or more variable seems very far from
a normal distribution. Graphical help will be available later and you will
again be allowed to mention outlier points in the distributions.
Such abnormal data are not suitable for the use of production models.
......
Do you wish to see again this special warning about the double click use in CLIMPROD when you will re-open the software?
Do you wish to see again the special warning about the double click use in CLIMPROD next time you will open the software?
Answer No if you already read the message about the use of the double click in CLIMPROD and that you are sure that you will remind it.
......
......@@ -5,4 +5,8 @@ Answer Yes if it is assumed that the total catch and stock production in
weight are independant of age, and that time lags in recruitment or in
density-dependant growth, natural mortality and reproduction do not occur
(Fox, 1974).
Reference
Fox W.W., 1974. An overview of production modeling. ICCAT workshop on tuna
population dynamics, Nantes, France 1974, Rec. Doc. Scient. CICTA, 3, 142-156.

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Are the shapes, maximum vaules and confidence intervals of the MSY and MSE graphs acceptable?
For the selected model to be validated the shape of the plots of MSY vs V and
MSY vs V, which are displayed within the range of observed V values, must show
an expected shape in agreement with your answer to the questions on whether the
relationship between V and CPUE (or CPUE residuals) was either linear, monotonic
MSY vs V, which are displayed within the range of observed V values, must show
an expected shape in agreement with your answer to the questions on whether the
relationship between V and CPUE (or CPUE residuals) was either linear, monotonic
or quadratic (parabola). If not, (for instance monotonic shape of the MSY graph
when the relationship was supposed to be quadratic and the environmental effect
was supposed to influence the abundance) you have to use another model with
was supposed to influence the abundance) you have to use another model with
less parameters.
Please note that the expected shape can be seen only on the MSE graph for all
models where the environment influences the catchability (MSY is constant).
Conversely, the expected shape can be seen only on the MSY graph for the exponential
models where the environment influences the catchability (MSY is constant).
conversely, the expected shape can be seen only on the MSY graph for the exponential
multiplicative models where the environment influences the abundance (MSE is
constant), except for the models CPUE = aV^b exp(cV^d) E and CPUE=a.exp(b.E)+c.V+d
constant), except for the models CPUE=a.V^b.exp(c.V^d).E and CPUE=a.exp(b.E)+c.V+d
(but please remember for this last model the MSY graph is not shown). When the
environment influences both the abundance and the catchability, the expected shape
can be seen on both MSY and MSE graphs, but not necessarily with a variation in the
same direction.
Please remember that the model with the best fit (highest R²) is not necessarily
Please remember that the model with the best fit (highest R²) is not necessarily
the most convenient model for describing and predicting CPUEs and catches. Simply
the model with the best fit is often overparameterized. The comparison of the Akaike
Information Criterion (AIC) and the Bayesian Information Criterion (BIC) of different
models constitute one of the element of decision for choosing between two models.
These criteria work to balance the trade-offs between the complexity of a given model
and its goodness of fit. The preferred model in terms of relative quality will be the
the model with the best fit is often overparameterized. The comparison of the Akaike
Information Criterion (AIC) and the Bayesian Information Criterion (BIC) of different
models constitute one of the element of decision for choosing between two models.
These criteria work to balance the trade-offs between the complexity of a given model
and its goodness of fit. The preferred model in terms of relative quality will be the
model with the minimum AIC and/or BIC values (see results in the main CLIMPROD window,
frame: Current known facts (or below if you fitted a model directly)).

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Do you validate the model from the graphical and statistical results that appear in the Jackknife plots window titles (p values of t-ratios of the parameters, R² values, p value of F)?
Do you validate the model from the graphical and statistical results that appear in the jackknife plots window titles (p values of t-ratios of the parameters, R2 values, p value of F)?
You reached this step because the coefficient of determination R2 of the retained model is over the empirical threshold of 0.70 if the software selected a mixed model CPUE=f(E,V), or 0.90 if a simple model CPUE=f(E) or CPUE=f(V) was selected. These threshold values are extremely conservative, even when the number of degrees of freedom of the fit is low. The reasons for this choice are as follows:
1) regardless of the type of model, all the time-series involved (CPUE, E and V) use to be autocorrelated, which artificially increase the R2 value.
2) regarding mixed models CPUE=f(E,V) and simple models CPUE=f(E), by construction the dependent variable CPUE and the independent variable E are correlated (CPUE=Catches/E), which also artificially increase the R² value.
2) regarding mixed models CPUE=f(E,V) and simple models CPUE=f(E), by construction the dependent variable CPUE and the independent variable E are correlated (CPUE=Catches/E), which also artificially increase the R2 value.
3) regarding simple models CPUE=f(E) or CPUE=f(V) the R² threshold value was even more conservative than for mixed models for different reasons. In the first case it is because the single independent variable E is not independent from CPUE (reason 2 above), whereas in the case of mixed model variable V is supposed to be independent from CPUE. In the second case it is because CPUE=f(V) is not a surplus production model but an empirical equation that can be used for prediction only if the R² value is high and if E does not vary significantly or remains at an extremely low value.
3) regarding simple models CPUE=f(E) or CPUE=f(V) the R2 threshold value was even more conservative than for mixed models for different reasons. In the first case it is because the single independent variable E is not independent from CPUE (reason 2 above), whereas in the case of mixed model variable V is supposed to be independent from CPUE. In the second case it is because CPUE=f(V) is not a surplus production model but an empirical equation that can be used for prediction only if the R2 value is high and if E does not vary significantly or remains at an extremely low value.
Despite the need of using empirical thresholds of conventional R² values to select a convenient model, we feel useful to provide the values of the jackknife coefficient R², the adjusted R² and the p value of the Fisher test (F). Indeed the conventional R2 has problems that the jackknife coefficient R² and the adjusted R2 are designed to address (predicted R² is another option with a principle of resampling similar to jackknife):
Despite the need of using empirical thresholds of conventional R2 values to select a convenient model, we feel useful to provide the values of the jackknife coefficient R2, the adjusted R2 and the p value of the Fisher test (F). Indeed the conventional R2 has problems that the jackknife coefficient R2 and the adjusted R2 are designed to address (predicted R2 is another option with a principle of resampling similar to jackknife):
1) If a model has too many predictors, too many parameters or higher order polynomials, it begins to model the random noise in the data. This condition is known as overfitting the model and it produces misleadingly high R-squared values and a lessened ability to make predictions.
2) Every time you add a predictor to a model, the R-squared increases, even if due to chance alone. It never decreases. Consequently, a model with more terms may appear to have a better fit simply because it has more terms.
But please note that the use of any R² expression is controversed in multiple regression which is the case of all mixed model CPUE = f(E,V).
But please note that the use of any R2 expression is controverted in multiple regression which is the case of all mixed model CPUE = f(E,V).
In conclusion there is no way to validate the fit of a surplus production model from statistical results only. We can only recommand to validate the model if:
In conclusion there is no way to validate the fit of a surplus production model from statistical results only. We can only recommend to validate the model if:
a) the jackknife graphics show moderate variations around 100% and a fair distribution above and below this value;
b) the p values of t-ratios of the parameters are at least <0.05 (with a possible exception for the value of the intercept of the function, usually parameter: a);
c) the the adjusted R² and the jackknife coefficient R2 are respectively above 0.65 and 0.60;
c) the adjusted R2 and the jackknife coefficient R2 are respectively above 0.65 and 0.60;
d) the p value of F is at least <0.05 (p<0.01 recommended).
If you are too far from the empirical thresholds indicated in b), c) and d) a warning will be displayed.
If your results present too far departures from the empirical thresholds indicated in b), c) and d), a warning will be displayed.

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......@@ -10,8 +10,7 @@ In contrast to most of the other CPUE=f(E,V) exponential models presented
in CLIMPROD in which MSE is constant, here MSE and MSY vary according to
V values. This is due the fact that before reparametrization in this model
it is considered that the environmental influence acts independently on
parameters k and B∞ of the fully parameterized Fox model. This property is
shared with the model
parameters k and B∞ of the fully parameterized Fox model.
Note that the equation of this model is the same (at least under
equilibrium conditions, without averaging V*) as that of model #34
......
MODEL #2 CPUE = (a + bE)^(1/(c-1))
This is the conventional generalized surplus production model, usually
named "Pella and Tomlinson model". It is more flexible than the linear or
exponential models but presents an additional parameter (c).
named "Pella and Tomlinson model", in its simplified version due to the
re-parameterization of Fox (1975). Because CLIMPROD makes use of the Fox's
equilibrium approximation approach for transitional states by weighting the
past fishing efforts, this model and its fitting are strictly equivalent
to those of the PRODFIT model Fox (1975). It is more flexible than the
linear or exponential models but presents an additional parameter (c).
If c = 2, this model is strictly identical to the linear Schaefer model:
......@@ -27,4 +31,8 @@ criterion value is the most suitable.
This model does not take into account any environmental influence on the
production (white noise).
Reference:
FOX, W:W., 1975.- Fitting the generalized stock production model by least squares
and eqUllibnum approximation. Fish. Bull. (U.s.), 73 (1): 23-36.

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