diff --git a/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_3.csv b/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_3.csv index e8d29b4..b77bee8 100644 --- a/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_3.csv +++ b/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_3.csv @@ -1,20 +1,20 @@ -"MSFD habitat code","Extent of habitat (10^3 km^2)","Landings 10^3 tonnes","Value 10^6 euro","Number of grid cells","Swept area 10^3 km^2","Proportion of grid cells fished (indicator 2)","Proportion of area fished (indicator 3)","Fishing intensity per year (indicator 1)","Average impact (indicator 6)","Proportion of habitat fished with 90% of effort (indicator 4)" -"Offshore circalittoral sand",241.37069705701,14149,286.415875570461,344.218259702843,727.105556029528,0.784719768181497,0.473463376779653,3.14877637042527,0.163995908972028,0.218955403208707 -"Offshore circalittoral mud",107.196482961054,6387,190.379945391252,271.215786448225,620.090950217225,0.948489118521998,0.669075573038092,5.93575882809196,0.270271945748211,0.318615938625333 -"Offshore circalittoral coarse sediment",75.5720927300617,4098,90.2151375826207,195.057843031283,285.831171465063,0.827232796486091,0.520474721584984,3.88117065390339,0.1948545229254,0.25402635431918 -"Circalittoral sand",68.1836914681247,3784,96.5845905634031,133.218652124798,143.412252035544,0.865750528541226,0.604919266688638,2.12911898019587,0.189766171880414,0.397198731501057 -"Circalittoral coarse sediment",29.7505152422457,1585,33.0817695462118,53.6182185216676,49.359705039811,0.648580441640379,0.326944114710546,1.61297084295869,0.105090530742186,0.174132492113565 -"Infralittoral sand",13.4574160840727,754,15.6866245460428,16.9644583339983,18.7763973979087,0.587533156498674,0.390081112642398,1.35739198089168,0.120122644086986,0.248010610079576 -"Offshore circalittoral mixed sediment",7.66454048644963,433,6.26525658401567,17.6400748992016,21.6235989045518,0.815242494226328,0.534310732206869,2.87568142813373,0.200154419403664,0.304849884526559 -"Unknown",7.31804129727414,439,3.47199794937401,8.92573625197827,7.91774144993476,0.300683371298405,0.223213281982935,1.00203765409852,0.086859231089329,0.129840546697039 -"Circalittoral mud",6.39682085648969,355,5.40390829525995,14.5713328289008,11.0531259297755,0.763380281690141,0.481024712809163,1.70478352805534,0.161635584259823,0.312676056338028 -"Circalittoral mixed sediment",4.58905063209022,260,3.42045038640966,7.50322720120095,6.03267738966686,0.634615384615385,0.38999883015155,1.37401514076365,0.156603392168347,0.265384615384615 -"Offshore circalittoral rock and biogenic reef",3.53351527774003,217,0.79060523249967,2.36503495167096,3.73297671877029,0.377880184331797,0.209330444837114,1.00184873981825,0.0709096230074694,0.124423963133641 -"Infralittoral coarse sediment",2.96163334994027,155,4.21198003158393,6.90194541489091,2.74158856089347,0.787096774193548,0.379259216092783,0.930005519535722,0.0868962694330944,0.303225806451613 -"Circalittoral rock and biogenic reef",2.3480995693336,132,0.185640619732853,0.594566664280182,0.455670499165755,0.424242424242424,0.105651306530456,0.19343032394855,0.0185894232127939,0.121212121212121 -"Upper bathyal sediment",1.8812917759086,116,3.39852207440547,7.18334803423829,30.5301194298036,0.974137931034483,0.954865199541924,16.2665825760727,0.737667518206592,0.646551724137931 -"Infralittoral mud",1.74021937746325,98,0.55775905812222,1.61929420029423,1.98999722537487,0.295918367346939,0.235577707683348,1.03612712330698,0.0828420118411506,0.142857142857143 -"Infralittoral rock and biogenic reef",1.16855404104268,66,0.293631018817539,0.766315300260074,0.391949846306114,0.46969696969697,0.114188297297476,0.320891365745846,0.0291876134508008,0.0909090909090909 -"Infralittoral mixed sediment",1.15933484757459,68,0.0421025201094486,0.131987363200011,0.239899366177935,0.235294117647059,0.0960938314811794,0.206053297414614,0.0224565186360571,0.102941176470588 -"Upper bathyal sediment or Upper bathyal rock and biogenic reef",0.849609493800335,55,0.0034857789810644,0,0.167661968878211,0.0545454545454545,0.0455200435677406,0.18717228284788,0.016342547089964,0.0363636363636364 -"Upper bathyal rock and biogenic reef",0.774116908753437,51,0,0,0,0,0,0,0,NA +"MSFD habitat code","Extent of habitat (10^3 km^2)","Number of grid cells","Landings 10^3 tonnes","Value 10^6 euro","Swept area 10^3 km^2","Proportion of grid cells fished (indicator 2)","Proportion of area fished (indicator 3)","Fishing intensity per year (indicator 1)","Average impact (indicator 6)","Proportion of habitat fished with 90% of effort (indicator 4)" +"Offshore circalittoral sand",241.4,14149,286.4,344.2,727.1,0.8,0.5,3.1,0.2,0.2 +"Offshore circalittoral mud",107.2,6387,190.4,271.2,620.1,0.9,0.7,5.9,0.3,0.3 +"Offshore circalittoral coarse sediment",75.6,4098,90.2,195.1,285.8,0.8,0.5,3.9,0.2,0.3 +"Circalittoral sand",68.2,3784,96.6,133.2,143.4,0.9,0.6,2.1,0.2,0.4 +"Circalittoral coarse sediment",29.8,1585,33.1,53.6,49.4,0.6,0.3,1.6,0.1,0.2 +"Infralittoral sand",13.5,754,15.7,17,18.8,0.6,0.4,1.4,0.1,0.2 +"Offshore circalittoral mixed sediment",7.7,433,6.3,17.6,21.6,0.8,0.5,2.9,0.2,0.3 +"Unknown",7.3,439,3.5,8.9,7.9,0.3,0.2,1,0.1,0.1 +"Circalittoral mud",6.4,355,5.4,14.6,11.1,0.8,0.5,1.7,0.2,0.3 +"Circalittoral mixed sediment",4.6,260,3.4,7.5,6,0.6,0.4,1.4,0.2,0.3 +"Offshore circalittoral rock and biogenic reef",3.5,217,0.8,2.4,3.7,0.4,0.2,1,0.1,0.1 +"Infralittoral coarse sediment",3,155,4.2,6.9,2.7,0.8,0.4,0.9,0.1,0.3 +"Circalittoral rock and biogenic reef",2.3,132,0.2,0.6,0.5,0.4,0.1,0.2,0,0.1 +"Upper bathyal sediment",1.9,116,3.4,7.2,30.5,1,1,16.3,0.7,0.6 +"Infralittoral mud",1.7,98,0.6,1.6,2,0.3,0.2,1,0.1,0.1 +"Infralittoral rock and biogenic reef",1.2,66,0.3,0.8,0.4,0.5,0.1,0.3,0,0.1 +"Infralittoral mixed sediment",1.2,68,0,0.1,0.2,0.2,0.1,0.2,0,0.1 +"Upper bathyal sediment or Upper bathyal rock and biogenic reef",0.8,55,0,0,0.2,0.1,0,0.2,0,0 +"Upper bathyal rock and biogenic reef",0.8,51,0,0,0,0,0,0,0,NA diff --git a/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_4.csv b/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_4.csv index 3a4dbf4..4e9f1bf 100644 --- a/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_4.csv +++ b/5 - Output/Greater North Sea/2019/Greater North Sea_2019_Table_4.csv @@ -1,9 +1,9 @@ "Metier","OT_CRU","OT_REST","TBB_ALL" -"Area fished (10^3 km^2)",152.133713023926,1242.68178945138,155.321990803283 -"Intensity (indicator 1)",1.33432952994549,3.46508522597945,0.78012485580126 -"Aggregation of fishing pressure, smallest prop of grid cells with 90% effort (indicator 4)",0.184470310888463,0.259191260843622,0.415646571560055 -"average impact (indicator 6)",0.100913919567823,0.163212764755374,0.1236313502574 -"Landings 10^3 tonnes",37.903942635178,542.638206414891,74.1556525483033 -"Value 10^6 euro",165.964209696098,418.95891899016,296.533213781264 -"Average impact/landings ratio (10^-2)",0.266235944210634,0.0300776397286307,0.1667187139603 -"Average impact/value ratio (10^-2)",0.0608046275474752,0.0389567466778784,0.0416922437392108 +"Area fished (10^3 km^2)",152.1,1242.7,155.3 +"Intensity (indicator 1)",1.3,3.5,0.8 +"Aggregation of fishing pressure smallest prop of grid cells with 90% effort (indicator 4)",0.2,0.3,0.4 +"average impact (indicator 6)",0.1,0.2,0.1 +"Landings 10^3 tonnes",37.9,542.6,74.2 +"Value 10^6 euro",166,419,296.5 +"Average impact/landings ratio (10^-2)",0.3,0,0.2 +"Average impact/value ratio (10^-2)",0.1,0,0 diff --git a/5 - Output/Greater North Sea/2019/Greater North Sea_2019_figureA1.pdf b/5 - Output/Greater North Sea/2019/Greater North Sea_2019_figureA1.pdf index 82b00e4..acc8646 100644 Binary files a/5 - Output/Greater North Sea/2019/Greater North 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layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.dbf new file mode 100644 index 0000000..fb22db3 Binary files /dev/null and b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.dbf differ diff --git a/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.prj b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.prj new file mode 100644 index 0000000..f45cbad --- /dev/null +++ b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.prj @@ -0,0 +1 @@ +GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]] \ No newline at end of file diff --git a/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.sbn b/TAF - ICES 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0000000..9b12359 Binary files /dev/null and b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shp differ diff --git a/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shp.xml b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shp.xml new file mode 100644 index 0000000..2d978d2 --- /dev/null +++ b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shp.xml @@ -0,0 +1,94 @@ + +20141015112441001.0INSPIRE Metadata DirectiveFALSE1500000005000001eez_land_v2file://\\fs\shared\datac\geo\marbound\Union_land_eez\based on v8\Topology.mdbLocal Area NetworkGeographicGCS_WGS_1984Angular Unit: Degree (0.017453)<GeographicCoordinateSystem xsi:type='typens:GeographicCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/10.1'><WKT>GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433],AUTHORITY[&quot;EPSG&quot;,4326]]</WKT><XOrigin>-400</XOrigin><YOrigin>-400</YOrigin><XYScale>999999999.99999988</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>8.983152841195215e-009</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><LeftLongitude>-180</LeftLongitude><WKID>4326</WKID><LatestWKID>4326</LatestWKID></GeographicCoordinateSystem>FeatureClassToFeatureClass "G:\marbound\Union_land_eez\based on v8\EEZ_land_v2.shp" "G:\marbound\Union_land_eez\based on v8\Topology.mdb\topologie" eez_land_v2 # "ISO_3digit "ISO_3digit" true true false 5 Text 0 0 ,First,#,G:\marbound\Union_land_eez\based on v8\EEZ_land_v2.shp,ISO_3digit,-1,-1;Country "Country" true true false 100 Text 0 0 ,First,#,G:\marbound\Union_land_eez\based on v8\EEZ_land_v2.shp,Country,-1,-1;Changes "Changes" true true false 50 Text 0 0 ,First,#,G:\marbound\Union_land_eez\based on v8\EEZ_land_v2.shp,Changes,-1,-1" #INSPIRE20141015090438002014101509043800To create sovereign areas for each country.<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>This dataset combines the boundaries of the world countries and the Exclusive Economic Zones of the world. It was created by combining the ESRI world country database and the EEZ V7 dataset.</SPAN></P></DIV></DIV></DIV>VLIZ (2012). Union of the ESRI Country shapefile and the Exclusive Economic Zones (version 1). Available online at http://www.marineregions.org/. Consulted on 2013-01-14.Marine and land zones: the union of world country boundaries and EEZ's2012-11-30T00:00:00120121130Nathalie De HauwereVLIZ (Flanders Marine Institute)Scientific assistant - GIS<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN>The Flanders Marine Institute manages Marine Regions, but is aware that it is not complete and undoubtedly contains errors. The Flanders Marine Institute cannot be made responsible for any errors or misuse of data contained in this register. Comments from our users are more than welcome, so if you come across any error or incomplete information or you are willing to contribute to this initiative please contact us.</SPAN></P><P><SPAN>The data is provided "as is", and no warranty express, implied or otherwise is offered as to the data's accuracy. The developers do not imply any opinion concerning the legal status of any country, territory or area, or concerning the delimitation of its frontiers or boundaries. The data can be used for educational, scientific or research purposes but should not be used for legal, commercial/economical (exploration of natural resources) or navigational purposes.</SPAN></P><P><SPAN>Anyone can download this data but it is for the sole use of the organisation or individual downloading the data. The geodata may not be redistributed without the permission of the Flanders Marine Institute (VLIZ). The geodata may be used in a Value-Added Software Application (like webservices), on condition that the Flanders Marine Institute is acknowledged as the source of the data. Redistribution rights are granted for hard-copy renditions or static, electronic map images (e.g. jpeg, gif, etc.) that are plotted, printed or publicly displayed with reference to the Flanders Marine Institute. For redistribution rights of derived products, please contact us. You can contact us at info@marineregions.org</SPAN></P><P><SPAN>The database can be cited as follows: </SPAN><SPAN>VLIZ</SPAN><SPAN>(Flanders Marine Institute) (2013). Marineregions.org. Accessed at http://www.marineregions.org on 2013-01-14. </SPAN></P></DIV></DIV></DIV>Microsoft Windows 7 Version 6.1 (Build 7601) Service Pack 1; Esri ArcGIS 10.1.0.3035Nathalie De HauwereVLIZ (Flanders Marine Institute)Scientific assistant - GISNathalie De Hauwerenathalie.dehauwere@vliz.be/9j/4AAQSkZJRgABAQEAAQABAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a +HBwgJC4nICIsIxwcKDcpLDAxNDQ0Hyc5PTgyPC4zNDL/2wBDAQkJCQwLDBgNDRgyIRwhMjIyMjIy +MjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjIyMjL/wAARCACFAMgDASIA +AhEBAxEB/8QAHwAAAQUBAQEBAQEAAAAAAAAAAAECAwQFBgcICQoL/8QAtRAAAgEDAwIEAwUFBAQA +AAF9AQIDAAQRBRIhMUEGE1FhByJxFDKBkaEII0KxwRVS0fAkM2JyggkKFhcYGRolJicoKSo0NTY3 +ODk6Q0RFRkdISUpTVFVWV1hZWmNkZWZnaGlqc3R1dnd4eXqDhIWGh4iJipKTlJWWl5iZmqKjpKWm +p6ipqrKztLW2t7i5usLDxMXGx8jJytLT1NXW19jZ2uHi4+Tl5ufo6erx8vP09fb3+Pn6/8QAHwEA +AwEBAQEBAQEBAQAAAAAAAAECAwQFBgcICQoL/8QAtREAAgECBAQDBAcFBAQAAQJ3AAECAxEEBSEx +BhJBUQdhcRMiMoEIFEKRobHBCSMzUvAVYnLRChYkNOEl8RcYGRomJygpKjU2Nzg5OkNERUZHSElK 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++0wf89o/++hXG8bU6RK5UTfaj/z7y/mv+NH2iQ/dgI/32A/lmoftMH/PaP8A76FH2mD/AJ7R/wDf +QqPrlbt+A+VEhed+Syx+yc/qf8KQtOwwZVA9UTB/UmmfaYP+e0f/AH0KPtMH/PaP/voVm8RXfUdk +OihihBEaKoJycDqfU1HuHnus1x5SjGwZADD6nvnNO+0wf89o/wDvoUfaYP8AntH/AN9Cs6cnGXNK +NwYM1qQPLWSTA4MTEZ7/AHsgHv3p6PIqBI40jXqMksfxHr+NM+0wf89o/wDvoUfaYP8AntH/AN9C +t5Yqre8Vb5C5USb7n/npF/37P/xVG+5/56Rf9+z/APFVH9pg/wCe0f8A30KPtMH/AD2j/wC+hUfW +K/cdkPJnbrNtP+wgA/XNIUZvvyyt/wAC2/yxTftMH/PaP/voUfaYP+e0f/fQqXWrPdsLIcEdfuTy +qPTIb+YJoMZblpZSfXeR+gwKb9pg/wCe0f8A30KPtMH/AD2j/wC+hS9pWta7CyF8of35f+/rf40e +UP78v/f1v8aT7TB/z2j/AO+hR9pg/wCe0f8A30Knnq92Gg7yh/fl/wC/rf40eUP78v8A39b/ABpv +2mD/AJ7R/wDfQo+0wf8APaP/AL6FPnq93+IWQ7y+3mS4/wCuh/xpfOkgBLfvIwM5J+Yf40z7TB/z +2j/76FMmnhaCRVlQkqQAGHPFaU61ZSWrBpGjRRRXtGQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAU +UUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRR +RQAUUUUAFFFFABRRRQAUUUUAFFFFAH//2Q==20141015Personal GeoDatabase Feature ClassdatasetEPSG7.9.40SimpleFALSE0TRUEFALSEeez_land_v2Feature Class0OBJECTIDOBJECTIDOID400Internal feature number.EsriSequential unique whole numbers that are automatically generated.ShapeShapeGeometry000Feature geometry.EsriCoordinates defining the features.ISO_3digitISO_3digitString500CountryCountryString10000ChangesChangesString5000Shape_LengthShape_LengthDouble800Length of feature in internal units.EsriPositive real numbers that are automatically generated.Shape_AreaShape_AreaDouble800Area of feature in internal units squared.EsriPositive real numbers that are automatically generated. diff --git a/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shx b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shx new file mode 100644 index 0000000..8a3df78 Binary files /dev/null and b/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/EEZ_land_union_v2_201410/EEZ_land_v2_201410.shx differ diff --git a/TAF - ICES tutorial/Step 1 Assign region of interest/Step1_region.R b/TAF - ICES tutorial/Step 1 Assign region of interest/Step1_region.R index f5b91a2..ae5f84d 100644 --- a/TAF - ICES tutorial/Step 1 Assign region of interest/Step1_region.R +++ b/TAF - ICES tutorial/Step 1 Assign region of interest/Step1_region.R @@ -2,11 +2,11 @@ # install libraries library(rgdal) library(sp) - library(vmstools) library(raster) # set folder directory - pathdir <- "C:/Users/pdvd/Online for git/FBIT/TAF - ICES tutorial/Step 1 Assign region of interest/Data layers/" + pathdir <- "C:/Users/pdvd/Online for git/FBIT/TAF - ICES tutorial" # path to tutorial folder + path_env <- paste(pathdir,"Step 1 Assign region of interest/Data layers", sep="/") # path to environmental data layers # assign area of interest gt<-(GridTopology(c(-1.975, 50.025), c(0.05, 0.05), c(200, 150))) # c(long, lat), c(cellsize long, lat), c(nb of grids long, lat) @@ -20,13 +20,14 @@ bargrid@bbox # make sure "min" is a whole number # assign c-squares + source(paste(pathdir, "Utilities/coords_to_csquare_VMStools.R",sep="/")) coord <- coordinates(bargrid) squares<-CSquare(coord[,1],coord[,2],0.05) bargrid@data$csquares <- squares # assign EEZ - shapeEEZ <- readOGR(dsn = paste(pathdir,"EEZ_land_union_v2_201410",sep="/") ,layer="EEZ_land_v2_201410") - #plot(shapeEEZ) + shapeEEZ <- readOGR(dsn = paste(path_env,"EEZ_land_union_v2_201410",sep="/") ,layer="EEZ_land_v2_201410") + plot(shapeEEZ) shapeEEZ@proj4string # check coordinates reference system shapeEEZ <- spTransform(shapeEEZ,CRS(proj4string(bargrid))) # make it similar to bargrid shapeEEZ@proj4string # check coordinates reference system again @@ -34,7 +35,7 @@ bargrid@data$EEZ <- tr$Country # assign ICES ecoregions - shapeEcReg <- readOGR(dsn = paste(pathdir,"ICES_ecoregions",sep="/") ,layer="ICES_ecoregions_20171207_erase_ESRI") + shapeEcReg <- readOGR(dsn = paste(path_env,"ICES_ecoregions",sep="/") ,layer="ICES_ecoregions_20171207_erase_ESRI") #plot(shapeEcReg) shapeEcReg@proj4string # check coordinates reference system shapeEcReg <- spTransform(shapeEcReg,CRS(proj4string(bargrid))) # make it similar to bargrid @@ -43,7 +44,7 @@ bargrid@data$EcReg <- tr$Ecoregion # assign MSFD habitats - fgdb <- paste(pathdir,"EUSM2019_EUNIS_BroadscaleModel.gdb", sep="/") + fgdb <- paste(path_env,"EUSM2019_EUNIS_BroadscaleModel.gdb", sep="/") subset(ogrDrivers(), grepl("GDB", name)) fc_list <- ogrListLayers(fgdb) print(fc_list) @@ -62,14 +63,14 @@ bargrid@data$MSFDhab<-tr$MSFD_BBHT # save bargrid - setwd(pathdir) + setwd(path_env) save(bargrid,file="region_grid.RData") # remove big file(s) rm(EUSeaMap2019) # assign OSPAR reporting units - fgdb <- paste(pathdir,"OSPAR_Reporting_Units_20180813.gdb", sep="/") + fgdb <- paste(path_env,"OSPAR_Reporting_Units_20180813.gdb", sep="/") subset(ogrDrivers(), grepl("GDB", name)) fc_list <- ogrListLayers(fgdb) print(fc_list) @@ -89,6 +90,6 @@ bargrid <- subset(bargrid,!(is.na(bargrid@data$MSFDhab))) # save bargrid - setwd(pathdir) + setwd(path_env) save(bargrid,file="region_grid.RData") diff --git a/TAF - ICES tutorial/Step 2 Estimate relationships benthic data/Step 2 Estimating longevity benthic data.R b/TAF - ICES tutorial/Step 2 Estimate relationships benthic data/Step 2 Estimating longevity benthic data.R index 8b7c499..dd9057b 100644 --- a/TAF - ICES tutorial/Step 2 Estimate relationships benthic data/Step 2 Estimating longevity benthic data.R +++ b/TAF - ICES tutorial/Step 2 Estimate relationships benthic data/Step 2 Estimating longevity benthic data.R @@ -1,5 +1,4 @@ -library(dplyr) library(lme4) # set folder directory @@ -12,21 +11,17 @@ library(lme4) # summarize benthic data per sample ID and calculate the fraction namesCol <- c("Biomass","L1","L1_3","L3_10","L10") - indexcol <- which(names(datBen) %in% namesCol) - - statdat <- datBen %>% - group_by(sample_ID) %>% - summarise_at (funs(sum(., na.rm = TRUE)), .vars= vars(indexcol)) - statdat <- as.data.frame(statdat) + statdat <- aggregate(datBen[, namesCol], by= list(datBen$sample_ID), FUN=function(x){sum(x, na.rm=T)}) statdat[,c(3:6)] <- statdat[,c(3:6)]/statdat$Biomass # now link to environmental conditions statEnv <- read.csv(file="Env_Data_tutorial.csv",header=T,sep=";") - statEnv <- cbind(statEnv,statdat[match(statEnv$ID,statdat$sample_ID), c(3:6)]) + statEnv <- cbind(statEnv,statdat[match(statEnv$ID,statdat$Group.1), c(3:6)]) # select only MSFD habitats with multiple observations table(statEnv$MSFDhab) - statEnv <- subset(statEnv,statEnv$MSFDhab %in% c("Circalittoral sand","Offshore circalittoral mud","Offshore circalittoral sand")) + statEnv <- subset(statEnv,statEnv$MSFDhab %in% + c("Circalittoral sand","Offshore circalittoral mud","Offshore circalittoral sand")) # now prepare for statistical analysis diff --git a/TAF - ICES tutorial/Step 3 Predict sensitivity/region_grid_sensitivity.RData b/TAF - ICES tutorial/Step 3 Predict sensitivity/region_grid_sensitivity.RData index 8d62acd..f9173e2 100644 Binary files a/TAF - ICES tutorial/Step 3 Predict sensitivity/region_grid_sensitivity.RData and b/TAF - ICES tutorial/Step 3 Predict sensitivity/region_grid_sensitivity.RData differ diff --git a/TAF - ICES tutorial/Step 4 Calculate impact/MBCG_2016_area.RData b/TAF - ICES tutorial/Step 4 Calculate impact/MBCG_2016_area.RData new file mode 100644 index 0000000..9e1fa62 Binary files /dev/null and b/TAF - ICES tutorial/Step 4 Calculate impact/MBCG_2016_area.RData differ diff --git a/TAF - ICES tutorial/Step 4 Calculate impact/Step 4 calculate impact.R b/TAF - ICES tutorial/Step 4 Calculate impact/Step 4 calculate impact.R index 758c8ee..5940a2c 100644 --- a/TAF - ICES tutorial/Step 4 Calculate impact/Step 4 calculate impact.R +++ b/TAF - ICES tutorial/Step 4 Calculate impact/Step 4 calculate impact.R @@ -14,26 +14,21 @@ setwd(paste(pathdir,"Step 3 Predict sensitivity",sep="/")) load("region_grid_sensitivity.RData") - # load available fishing data: The shapefile datasets for the OSPAR region are available at: https://doi.org/10.17895/ices.pub.2861 #### - workingdir<-paste(pathdir,"Step 4 Calculate impact/ICES.2017.OSPAR.Technical-Service-VMS-fishing-pressure",sep="/") + # load available fishing data: The shapefile datasets for the OSPAR region are available at: + # https://www.ices.dk/sites/pub/Publication%20Reports/Advice/2018/Special_requests/ospar.2018.14.pdf + # The example is using the data for the region of interest for 2016 - # select a year and load swept area data - Period <- 2016 - TBB <- readOGR(dsn = workingdir ,layer=paste("OSPAR_intensity_Beam_",Period[1],sep="")) - OT <- readOGR(dsn = workingdir ,layer=paste("OSPAR_intensity_Otter_",Period[1],sep="")) - TD <- readOGR(dsn = workingdir ,layer=paste("OSPAR_intensity_Dredge_",Period[1],sep="")) - Seine <- readOGR(dsn = workingdir ,layer=paste("OSPAR_intensity_Seine_",Period[1],sep="")) - + setwd(paste(pathdir,"Step 4 Calculate impact",sep="/")) + load("MBCG_2016_area.RData") # fishing data subset + # link to grid via c-squares - bargrid <- cbind(bargrid, TBB@data[match(bargrid@data$csquares,TBB@data$c_square), c(5)]) - bargrid <- cbind(bargrid, OT@data[match(bargrid@data$csquares,OT@data$c_square), c(5)]) - bargrid <- cbind(bargrid, TD@data[match(bargrid@data$csquares,TD@data$c_square), c(5)]) - bargrid <- cbind(bargrid, Seine@data[match(bargrid@data$csquares,Seine@data$c_square), c(5)]) - - # add colnames - nb <- ncol(bargrid@data) - colnames(bargrid@data)[(nb-3):nb] <- c("TBB_SurfSAR","OT_SurfSAR","TD_SurfSAR","Seine_SurfSAR") - + bargrid <- cbind(bargrid, fishing@data[match(bargrid@data$csquares,fishing@data$csquares), c(2)]) #TBB + bargrid <- cbind(bargrid, fishing@data[match(bargrid@data$csquares,fishing@data$csquares), c(3)]) #OT + bargrid <- cbind(bargrid, fishing@data[match(bargrid@data$csquares,fishing@data$csquares), c(4)]) #TD + bargrid <- cbind(bargrid, fishing@data[match(bargrid@data$csquares,fishing@data$csquares), c(5)]) #Seine + colnames(bargrid@data)[(ncol(bargrid)-3):ncol(bargrid)] <- c("TBB_SurfSAR","OT_SurfSAR","TD_SurfSAR","Seine_SurfSAR") + + # now calculate the depletion rate per c-sq Depl_TBB <- 0.14 * bargrid@data$TBB_SurfSAR ### data from Hiddink et al. PNAS 2017 Table S4 Depl_OT <- 0.06 * bargrid@data$OT_SurfSAR ### data from Hiddink et al. PNAS 2017 Table S4 Depl_TD <- 0.20 * bargrid@data$TD_SurfSAR ### data from Hiddink et al. PNAS 2017 Table S4 @@ -52,7 +47,7 @@ } bargrid@data$state <- state - + # plot state as a function of total depletion plot(bargrid@data$state~bargrid@data$Depl_tot,ylab="State (PD model)",xlab="Depletion (SAR*d)",las=1) @@ -60,23 +55,7 @@ setwd(paste(pathdir,"Utilities",sep="/")) source("map_plot.R") # warnings are okay map_plot(bargrid,"state",purples) - + # estimate state per MSFD habitat aggregate(state ~ MSFDhab, data=bargrid@data, mean) - - # make a map of swept areas, weight and value - Total <- readOGR(dsn = workingdir ,layer=paste("OSPAR_intensity_total_",Period[1],sep="")) - bargrid <- cbind(bargrid, Total@data[match(bargrid@data$csquares,Total@data$c_square), c(5)]) - bargrid <- cbind(bargrid, Total@data[match(bargrid@data$csquares,Total@data$c_square), c(6)]) - bargrid <- cbind(bargrid, Total@data[match(bargrid@data$csquares,Total@data$c_square), c(7)]) - bargrid <- cbind(bargrid, Total@data[match(bargrid@data$csquares,Total@data$c_square), c(8)]) - - # add colnames - nb <- ncol(bargrid@data) - colnames(bargrid@data)[(nb-3):nb] <- c("SurfaceSAR","Subsurface","totweight","totvalue") - - map_plot(bargrid,"SurfaceSAR",bluegreen) - map_plot(bargrid,"Subsurface",bluegreen) - map_plot(bargrid,"totweight",yellowred) - map_plot(bargrid,"totvalue",yellowred) \ No newline at end of file diff --git a/TAF - ICES tutorial/Utilities/coords_to_csquare_VMStools.R b/TAF - ICES tutorial/Utilities/coords_to_csquare_VMStools.R new file mode 100644 index 0000000..4103b90 --- /dev/null +++ b/TAF - ICES tutorial/Utilities/coords_to_csquare_VMStools.R @@ -0,0 +1,49 @@ + +# taken from VMStools + +CSquare <- function(lon,lat,degrees){ + + if(length(lon) != length(lat)) stop("length of longitude not equal to length of latitude") + if(!degrees %in% c(10,5,1,0.5,0.1,0.05,0.01)) stop("degrees specified not in range: c(10,5,1,0.5,0.1,0.05,0.01)") + + dims <- length(lon) + + quadrants <- array(NA,dim=c(4,6,dims),dimnames=list(c("globalQuadrant","intmQuadrant1","intmQuadrant2","intmQuadrant3"),c("quadrantDigit","latDigit","lonDigit","latRemain","lonRemain","code"),seq(1,dims,1))) + + quadrants["globalQuadrant","quadrantDigit",] <- 4-(((2*floor(1+(lon/200)))-1)*((2*floor(1+(lat/200)))+1)) + quadrants["globalQuadrant","latDigit",] <- floor(abs(lat)/10) + quadrants["globalQuadrant","lonDigit",] <- floor(abs(lon)/10) + quadrants["globalQuadrant","latRemain",] <- round(abs(lat)-(quadrants["globalQuadrant","latDigit",]*10),7) + quadrants["globalQuadrant","lonRemain",] <- round(abs(lon)-(quadrants["globalQuadrant","lonDigit",]*10),7) + quadrants["globalQuadrant","code",] <- quadrants["globalQuadrant","quadrantDigit",]*1000+quadrants["globalQuadrant","latDigit",]*100+quadrants["globalQuadrant","lonDigit",] + + quadrants["intmQuadrant1","quadrantDigit",] <- (2*floor(quadrants["globalQuadrant","latRemain",]*0.2))+floor(quadrants["globalQuadrant","lonRemain",]*0.2)+1 + quadrants["intmQuadrant1","latDigit",] <- floor(quadrants["globalQuadrant","latRemain",]) + quadrants["intmQuadrant1","lonDigit",] <- floor(quadrants["globalQuadrant","lonRemain",]) + quadrants["intmQuadrant1","latRemain",] <- round((quadrants["globalQuadrant","latRemain",]-quadrants["intmQuadrant1","latDigit",])*10,7) + quadrants["intmQuadrant1","lonRemain",] <- round((quadrants["globalQuadrant","lonRemain",]-quadrants["intmQuadrant1","lonDigit",])*10,7) + quadrants["intmQuadrant1","code",] <- quadrants["intmQuadrant1","quadrantDigit",]*100+quadrants["intmQuadrant1","latDigit",]*10+quadrants["intmQuadrant1","lonDigit",] + + quadrants["intmQuadrant2","quadrantDigit",] <- (2*floor(quadrants["intmQuadrant1","latRemain",]*0.2))+floor(quadrants["intmQuadrant1","lonRemain",]*0.2)+1 + quadrants["intmQuadrant2","latDigit",] <- floor(quadrants["intmQuadrant1","latRemain",]) + quadrants["intmQuadrant2","lonDigit",] <- floor(quadrants["intmQuadrant1","lonRemain",]) + quadrants["intmQuadrant2","latRemain",] <- round((quadrants["intmQuadrant1","latRemain",]-quadrants["intmQuadrant2","latDigit",])*10,7) + quadrants["intmQuadrant2","lonRemain",] <- round((quadrants["intmQuadrant1","lonRemain",]-quadrants["intmQuadrant2","lonDigit",])*10,7) + quadrants["intmQuadrant2","code",] <- quadrants["intmQuadrant2","quadrantDigit",]*100+quadrants["intmQuadrant2","latDigit",]*10+quadrants["intmQuadrant2","lonDigit",] + + quadrants["intmQuadrant3","quadrantDigit",] <- (2*floor(quadrants["intmQuadrant2","latRemain",]*0.2))+floor(quadrants["intmQuadrant2","lonRemain",]*0.2)+1 + quadrants["intmQuadrant3","latDigit",] <- floor(quadrants["intmQuadrant2","latRemain",]) + quadrants["intmQuadrant3","lonDigit",] <- floor(quadrants["intmQuadrant2","lonRemain",]) + quadrants["intmQuadrant3","latRemain",] <- round((quadrants["intmQuadrant2","latRemain",]-quadrants["intmQuadrant3","latDigit",])*10,7) + quadrants["intmQuadrant3","lonRemain",] <- round((quadrants["intmQuadrant2","lonRemain",]-quadrants["intmQuadrant3","lonDigit",])*10,7) + quadrants["intmQuadrant3","code",] <- quadrants["intmQuadrant3","quadrantDigit",]*100+quadrants["intmQuadrant3","latDigit",]*10+quadrants["intmQuadrant3","lonDigit",] + + if(degrees == 10) CSquareCodes <- quadrants["globalQuadrant","code",] + if(degrees == 5) CSquareCodes <- paste(quadrants["globalQuadrant","code",],":",quadrants["intmQuadrant1","quadrantDigit",],sep="") + if(degrees == 1) CSquareCodes <- paste(quadrants["globalQuadrant","code",],":",quadrants["intmQuadrant1","code",],sep="") + if(degrees == 0.5) CSquareCodes <- paste(quadrants["globalQuadrant","code",],":",quadrants["intmQuadrant1","code",],":",quadrants["intmQuadrant2","quadrantDigit",],sep="") + if(degrees == 0.1) CSquareCodes <- paste(quadrants["globalQuadrant","code",],":",quadrants["intmQuadrant1","code",],":",quadrants["intmQuadrant2","code",],sep="") + if(degrees == 0.05) CSquareCodes <- paste(quadrants["globalQuadrant","code",],":",quadrants["intmQuadrant1","code",],":",quadrants["intmQuadrant2","code",],":",quadrants["intmQuadrant3","quadrantDigit",],sep="") + if(degrees == 0.01) CSquareCodes <- paste(quadrants["globalQuadrant","code",],":",quadrants["intmQuadrant1","code",],":",quadrants["intmQuadrant2","code",],":",quadrants["intmQuadrant3","code",],sep="") + + return(CSquareCodes)} \ No newline at end of file diff --git a/TAF - ICES tutorial/~$Introduction to scripts and tutorial on running FBIT.pptx b/TAF - ICES tutorial/~$Introduction to scripts and tutorial on running FBIT.pptx deleted file mode 100644 index 5f3adf2..0000000 Binary files a/TAF - ICES tutorial/~$Introduction to scripts and tutorial on running FBIT.pptx and /dev/null differ diff --git a/Utilities/Output_assessment.R b/Utilities/Output_assessment.R index 4f37c08..5bda04a 100644 --- a/Utilities/Output_assessment.R +++ b/Utilities/Output_assessment.R @@ -139,12 +139,14 @@ coord_cartesian(xlim=c(coordslim[1], coordslim[2]), ylim=c(coordslim[3],coordslim[4])) sdsubsar <- sdsubsar + guides(colour = guide_legend(override.aes = list(size=5))) - pdf(paste(Assregion,AssYear,"figureA3.pdf",sep="_"),width=12,height=9) - print(grid.arrange(sar,sdsar, subsar,sdsubsar, nrow = 2)) +## only save surface abrasion + pdf(paste(Assregion,AssYear,"figureA3.pdf",sep="_"),width=12,height=5.5) + print(grid.arrange(sar,sdsar, nrow = 1)) dev.off() - jpeg(file = paste(Assregion,AssYear,"figureA3.jpeg",sep="_"), width=12, height=9,units ='in', res = 300) - print(grid.arrange(sar,sdsar, subsar,sdsubsar, nrow = 2)) +## only save surface abrasion + jpeg(file = paste(Assregion,AssYear,"figureA3.jpeg",sep="_"), width=12, height=5,units ='in', res = 300) + print(grid.arrange(sar,sdsar, nrow = 1)) dev.off() ##### Figure A.4 @@ -214,8 +216,63 @@ axis(2,c(0,0.2,0.4,0.6,0.8,1),las=1) dev.off() -# Figure A.6 -# same as Figure A1 +##### Figure A.6 + load(paste(pathdir_prodFT,"FigureA6.RData",sep="/")) + + impact <- (map_plot(figA6,"impact",AssYear,bluegreen,Assregion)) + + idx <- which(AssYear == AssPeriod) + nam <- paste("state",AssPeriod[-idx],sep="_") + #figA6[,nam][is.na(figA6[,nam])] <- 0 + figA6$dif_state <- figA6[,paste("state",AssYear,sep="_")] - apply(figA6[,nam], 1, mean) + figA6$dif_impact <- figA6$dif_state*(-1) + + minlong <- round(min(figA6$longitude)-1) + maxlong <- round(max(figA6$longitude)+1) + minlat <- round(min(figA6$latitude)-1) + maxlat <- round(max(figA6$latitude)+1) + coordslim <- c(minlong,maxlong,minlat,maxlat) + coordxmap <- round(seq(minlong,maxlong,length.out = 4)) + coordymap <- round(seq(minlat,maxlat,length.out = 4)) + + quat<-c(-300,-0.5,-0.25,-0.1,0.1,0.25,0.5,300) + figA6$cat<- as.factor(cut(figA6$dif_impact,quat,right=T)) + label_all <- c("> -0.5", "-0.5 to -0.25", "-0.25 to -0.1","-0.1 to 0.1","0.1 to 0.25","0.25 to 0.5","> 0.5") + idx <- which(!(table(figA6$cat)==0)) + label_sub <- label_all[idx] + figA6$cat[figA6$Depth< -200]<- NA + + if((length(figA6$cat[is.na(figA6$cat)])>0)){ + label_sub <- c(label_sub,"depth > 200 m") + } + + colorchoice <- blueorange[idx] + + sdimpact <- ggplot() + geom_point(data=figA6, aes(x=longitude, y=latitude, colour=factor(cat)),shape=15,size=.05,na.rm=T) + sdimpact <- sdimpact + geom_polygon(data = worldMap, aes(x = long, y = lat, group = group),color="dark grey",fill="light grey") + + scale_colour_manual(values=colorchoice,na.value = "grey50",name = "Impact (dif)", + labels=label_sub) + sdimpact <- sdimpact + theme(plot.background=element_blank(), + panel.background=element_blank(), + axis.text.y = element_text(size=16), + axis.text.x = element_text(size=16), + axis.title.y = element_text(size=16), + axis.title.x = element_text(size=16), + panel.border = element_rect(colour = "grey", size=.5,fill=NA), + legend.text = element_text(size=11), + legend.title = element_text(size=11))+ + scale_x_continuous(breaks=coordxmap)+ + scale_y_continuous(breaks=coordymap)+ + coord_cartesian(xlim=c(coordslim[1], coordslim[2]), ylim=c(coordslim[3],coordslim[4])) + sdimpact <- sdimpact + guides(colour = guide_legend(override.aes = list(size=5))) + + pdf(paste(Assregion,AssYear,"figureA6.pdf",sep="_"),width=12,height=5.5) + print(grid.arrange(impact,sdimpact, nrow = 1)) + dev.off() + + jpeg(file = paste(Assregion,AssYear,"figureA6.jpeg",sep="_"), width=12, height=5,units ='in', res = 300) + print(grid.arrange(impact,sdimpact, nrow = 1)) + dev.off() #Figure A.7 load(paste(pathdir_prodFT,"FigureA7.RData",sep="/")) @@ -404,18 +461,22 @@ # Table A3 load(paste(pathdir_prodFT,"TableA3.RData",sep="/")) - colnames(A3table) <- c("MSFD habitat code","Extent of habitat (10^3 km^2)", "Landings 10^3 tonnes", "Value 10^6 euro", - "Number of grid cells","Swept area 10^3 km^2","Proportion of grid cells fished (indicator 2)", + colnames(A3table) <- c("MSFD habitat code","Extent of habitat (10^3 km^2)", "Number of grid cells","Landings 10^3 tonnes", + "Value 10^6 euro","Swept area 10^3 km^2","Proportion of grid cells fished (indicator 2)", "Proportion of area fished (indicator 3)","Fishing intensity per year (indicator 1)", "Average impact (indicator 6)", "Proportion of habitat fished with 90% of effort (indicator 4)") + + A3table[,c(2:11)] <- round(A3table[,c(2:11)], digits = 1) + write.csv(A3table, file= paste(Assregion,AssYear,"Table_3.csv",sep="_"), row.names=FALSE) # Table A4 load(paste(pathdir_prodFT,"TableA4.RData",sep="/")) - col1 <- c("Area fished (10^3 km^2)", "Intensity (indicator 1)", "Aggregation of fishing pressure, smallest prop of grid cells with 90% effort (indicator 4)", + col1 <- c("Area fished (10^3 km^2)", "Intensity (indicator 1)", "Aggregation of fishing pressure smallest prop of grid cells with 90% effort (indicator 4)", "average impact (indicator 6)", "Landings 10^3 tonnes","Value 10^6 euro","Average impact/landings ratio (10^-2)", "Average impact/value ratio (10^-2)") A4table <- data.frame(Metier = col1, OT_CRU = A4table[,1], OT_REST = A4table[,2], TBB_ALL = A4table [,3]) + A4table[,c(2:4)] <- round(A4table[,c(2:4)], digits = 1) write.csv(A4table, file= paste(Assregion,AssYear,"Table_4.csv",sep="_"), row.names=FALSE) # Table A5 @@ -425,5 +486,6 @@ "Fraction of total value from the lowest 5% area","Area with the least value constituting 10% of the swept area", "Value of the lowest 10% area", "Fraction of total area with the least value constituting 10% of the swept area", "Fraction of total value from the lowest 10% area") + write.csv(A5table, file= paste(Assregion,AssYear,"Table_5.csv",sep="_"), row.names=FALSE) \ No newline at end of file diff --git a/Utilities/Processing_assessment.R b/Utilities/Processing_assessment.R index 8fcc0b2..bca5ea8 100644 --- a/Utilities/Processing_assessment.R +++ b/Utilities/Processing_assessment.R @@ -7,9 +7,17 @@ weight_year <- paste("total_weight",AssYear,sep="_") value_year <- paste("total_value",AssYear,sep="_") + # possibly assuming a random distribution of trawling tracks + # within grid cells (see 2017 report of WGSFD and Ellis et al., 2014, Can. J. Fish. Aquat. 71:733-746). + # total area fished assuming a random distribution of trawling tracks within the grid cell: + ##Region@data$RandomF_SSAR <- apply(Region@data[, c(SSAR_year, "area_sqkm")], 1, function (x) { + ##if(!is.na(x[SSAR_year])) sum( (1-pnbinom(q=seq(1,100,by=1), size=100, mu=x[SSAR_year]*x["area_sqkm"])), na.rm=T) /x["area_sqkm"] + ##} ) + setwd(pathdir_nogit) dir.create("Producing figures and tables") setwd(paste(pathdir_nogit,"Producing figures and tables",sep="/")) + dir.create(paste(Assregion)) setwd(paste(pathdir_nogit,"Producing figures and tables",Assregion,sep="/")) dir.create(paste(AssYear)) @@ -98,9 +106,9 @@ ################ figA3 <- Region@data nam <- paste("subsurface_sar",AssPeriod,sep="_") - figA3 <- cbind(figA3, Fisheries[match(figA1$csquares,Fisheries$csquares), c(nam)]) + figA3 <- cbind(figA3, Fisheries[match(figA3$csquares,Fisheries$csquares), c(nam)]) nam <- paste("surface_sar",AssPeriod,sep="_") - figA3 <- cbind(figA3, Fisheries[match(figA1$csquares,Fisheries$csquares), c(nam)]) + figA3 <- cbind(figA3, Fisheries[match(figA3$csquares,Fisheries$csquares), c(nam)]) save(figA3, file="FigureA3.RData") ##### @@ -268,6 +276,14 @@ save(A5dat, file="FigureA5.RData") +##### +# Figure A.6 +################# + figA6 <- Region@data + nam <- paste("state",AssPeriod,sep="_") + figA6 <- cbind(figA6, State_reg[match(figA6$csquares,State_reg$Fisheries.csquares), c(nam)]) + save(figA6, file="FigureA6.RData") + ##### # Figure A.7 ################