CPUE Data Wrangle
Fish Team!
7/26/2017
## Background
The Southeast Area Monitoring and Assessment Program (SEAMAP) is a fisheries-independent data collection program that in the Gulf of Mexico was initiated in 1981 and includes the continental shelf of the U.S. waters. Sampling off Florida started later (2009). Our study examines data from the Groundfish Bottom Trawl surveys.
##Import Data
#cache=TRUE
setwd("~/oss/Synthesis/Seamap")
invrec <-read.table("INVREC.txt", header=TRUE,sep=",")
starec <-read.table("STAREC.txt", header=TRUE,sep=",")
bgsrec <-read.table("BGSREC.txt", header=TRUE,sep=",")
glfrec<- read.table("GLFREC.txt", header=TRUE,sep=",")
head(invrec)## INVRECID STATIONID CRUISEID VESSEL CRUISE_NO P_STA_NO GEAR_SIZE
## 1 1 4 581 4 256 4 40
## 2 2 5 581 4 256 5 40
## 3 3 6 581 4 256 6 40
## 4 4 7 581 4 256 7 40
## 5 5 8 581 4 256 8 40
## 6 6 9 581 4 256 9 40
## GEAR_TYPE MESH_SIZE OP MIN_FISH WBCOLOR BOT_TYPE BOT_REG TOT_LIVE
## 1 ST 1.63 38 150.1
## 2 ST 1.63 12 31.4
## 3 ST 1.63 10 27.2
## 4 ST 1.63 25 14.7
## 5 ST 1.63 15 24.1
## 6 ST 1.63 12 7.9
## FIN_CATCH CRUS_CATCH OTHR_CATCH T_SAMPLEWT T_SELECTWT FIN_SMP_WT
## 1 149.0 0.4 0.7 14.65 2.77 14.54
## 2 29.6 0.4 1.4 14.98 1.87 14.23
## 3 26.8 0.3 0.2 12.45 0.16 12.30
## 4 12.1 1.9 0.8 0.00 14.74 0.00
## 5 23.5 0.4 0.3 11.57 0.37 11.28
## 6 7.3 0.3 0.3 0.00 7.89 0.00
## FIN_SEL_WT CRU_SMP_WT CRU_SEL_WT OTH_SMP_WT OTH_SEL_WT COMBIO
## 1 2.75 0.04 0.02 0.06 0.00
## 2 1.48 0.03 0.38 0.71 0.00
## 3 0.00 0.04 0.16 0.10 0.00
## 4 12.08 0.00 1.86 0.00 0.78
## 5 0.29 0.14 0.07 0.14 0.00
## 6 7.26 0.00 0.34 0.00 0.28head(starec)## STATIONID CRUISEID VESSEL CRUISE_NO P_STA_NO TIME_ZN TIME_MIL S_LATD
## 1 1 581 4 256 1 8 249 26
## 2 2 581 4 256 2 8 619 26
## 3 3 581 4 256 3 8 1029 25
## 4 4 581 4 256 4 8 1337 26
## 5 5 581 4 256 5 8 1519 26
## 6 6 581 4 256 6 8 1659 26
## S_LATM S_LATH S_LOND S_LONM S_LONH DEPTH_SSTA S_STA_NO
## 1 29.67 N 96 30.29 W 147.2 B239
## 2 2.92 N 96 28.26 W 119.4 B316
## 3 59.33 N 96 59.07 W 51.8 B36
## 4 5.15 N 97 5.63 W 18.3 TD06
## 5 7.27 N 97 8.41 W 13.4 TD03
## 6 13.29 N 97 9.30 W 13.5 TD02
## MO_DAY_YR TIME_EMIL E_LATD E_LATM E_LATH E_LOND E_LONM E_LONH
## 1 10/11/2003 0:00:00 257 26 29.43 N 96 30.16 W
## 2 10/11/2003 0:00:00 628 26 2.91 N 96 27.99 W
## 3 10/11/2003 0:00:00 1034 25 59.27 N 96 58.92 W
## 4 10/11/2003 0:00:00 1415 26 6.43 N 97 4.29 W
## 5 10/11/2003 0:00:00 1531 26 7.68 N 97 8.11 W
## 6 10/11/2003 0:00:00 1709 26 13.48 N 97 8.87 W
## DEPTH_ESTA GEARS TEMP_SSURF TEMP_BOT TEMP_SAIR
## 1 147.0 BGPNNN 27.62 20.98 25.3
## 2 120.2 BGPNNN 27.56 24.08 19.3
## 3 52.9 BGPNNN 27.27 27.21 26.5
## 4 20.3 STBG 27.32 27.00 27.7
## 5 15.2 STBG 27.28 26.84 27.7
## 6 15.4 STBG 27.37 26.69 27.8
## B_PRSSR WIND_SPD WIND_DIR WAVE_HT SEA_COND DBTYPE DATA_CODE VESSEL_SPD
## 1 1011.2 10 125 0.3 3 S US 2.0
## 2 1011.1 2 120 0.3 1 S US 1.7
## 3 1014.4 7 198 0.3 3 S US 1.8
## 4 1007.5 4 156 0.2 2 S US 2.8
## 5 1008.5 5 140 0.2 2 S US 2.6
## 6 1008.9 6 124 0.2 2 S US 2.7
## FAUN_ZONE STAT_ZONE TOW_NO NET_NO COMSTAT DECSLAT DECSLON DECELAT
## 1 7 21 NA NA 26.49 -96.50 26.49
## 2 7 21 NA NA 26.04 -96.47 26.04
## 3 7 22 NA NA 25.98 -96.98 25.98
## 4 7 21 1 1 26.08 -97.09 26.10
## 5 7 21 1 1 26.12 -97.14 26.12
## 6 7 21 1 1 26.22 -97.15 26.22
## DECELON START_DATE END_DATE HAULVALUE
## 1 -96.50 10/11/2003 2:49:00 10/11/2003 2:57:00 G
## 2 -96.46 10/11/2003 6:19:00 10/11/2003 6:28:00 G
## 3 -96.98 10/11/2003 10:29:00 10/11/2003 10:34:00 G
## 4 -97.07 10/11/2003 13:37:00 10/11/2003 14:15:00 G
## 5 -97.13 10/11/2003 15:19:00 10/11/2003 15:31:00 G
## 6 -97.14 10/11/2003 16:59:00 10/11/2003 17:09:00 Ghead(bgsrec)## BGSID CRUISEID STATIONID VESSEL CRUISE_NO P_STA_NO CATEGORY GENUS_BGS
## 1 1 581 4 4 256 4 3 RHIZOPR
## 2 2 581 4 4 256 4 3 SPHYRNA
## 3 3 581 4 4 256 4 3 HARENGU
## 4 4 581 4 4 256 4 3 OPISTHO
## 5 5 581 4 4 256 4 3 SARDINE
## 6 6 581 4 4 256 4 3 ANCHOA
## SPEC_BGS BGSCODE CNT CNTEXP SAMPLE_BGS SELECT_BGS BIO_BGS NODC_BGS
## 1 TERRAE 1 1 NA 0.71 108021802 0
## 2 TIBURO 5 5 NA 0.95 108040104 0
## 3 JAGUAN 9 91 0.15 1.55 121052004 0
## 4 OGLINU 2 20 0.04 0.49 121053002 0
## 5 AURITA 1 10 0.02 0.21 121053801 0
## 6 HEPSET 4 40 0.05 0.58 121060101 0
## IS_SAMPLE TAXONID INVRECID
## 1 N NA NA
## 2 N NA NA
## 3 Y NA NA
## 4 Y NA NA
## 5 Y NA NA
## 6 Y NA NAhead(glfrec)## GLFID CRUISEID BGSID STATIONID VESSEL CRUISE_NO P_STA_NO BIO_GLF
## 1 1 581 1 4 4 256 4 108021802
## 2 2 581 2 4 4 256 4 108040104
## 3 3 581 2 4 4 256 4 108040104
## 4 4 581 2 4 4 256 4 108040104
## 5 5 581 2 4 4 256 4 108040104
## 6 6 581 2 4 4 256 4 108040104
## NODC_GLF GENUS_GLF SPEC_GLF INDVL_WT MEASCD_GLF LEN_GLF SEX_GLF MAT_GLF
## 1 0 RHIZOPR TERRAE NA 18 526 F 1
## 2 0 SPHYRNA TIBURO NA 18 356 M 1
## 3 0 SPHYRNA TIBURO NA 18 378 NA
## 4 0 SPHYRNA TIBURO NA 18 347 NA
## 5 0 SPHYRNA TIBURO NA 18 360 NA
## 6 0 SPHYRNA TIBURO NA 18 384 NAClean Data
Fish Data
head(fishdata)## STATIONID GENUS_BGS SPEC_BGS CNTEXP
## 1 4 RHIZOPR TERRAE 1
## 2 4 SPHYRNA TIBURO 5
## 3 4 HARENGU JAGUAN 91
## 4 4 OPISTHO OGLINU 20
## 5 4 SARDINE AURITA 10
## 6 4 ANCHOA HEPSET 40Location Data
head(location)## STATIONID DECSLAT DECSLON HAULVALUE year month day
## 1 1 26.49 -96.50 G 2003 10 11
## 2 2 26.04 -96.47 G 2003 10 11
## 3 3 25.98 -96.98 G 2003 10 11
## 4 4 26.08 -97.09 G 2003 10 11
## 5 5 26.12 -97.14 G 2003 10 11
## 6 6 26.22 -97.15 G 2003 10 11Gear Data
head(gear)## STATIONID GEAR_SIZE GEAR_TYPE MESH_SIZE MIN_FISH
## 1 4 40 ST 1.63 38
## 2 5 40 ST 1.63 12
## 3 6 40 ST 1.63 10
## 4 7 40 ST 1.63 25
## 5 8 40 ST 1.63 15
## 6 9 40 ST 1.63 12Size Data
head(size)## STATIONID GLFID SPEC_GLF LEN_GLF
## 1 4 56 CAMPEC 100
## 2 4 57 CAMPEC 97
## 3 4 58 CAMPEC 89
## 4 4 59 CAMPEC 99
## 5 4 60 CAMPEC 104
## 6 4 61 CAMPEC 87Select Gear Type
Number of different gear combinations
## [1] 24
gearshrimptrawl <- dplyr::filter(gear, GEAR_TYPE=="ST", GEAR_SIZE==40, MESH_SIZE==1.63)
head(gearshrimptrawl)## STATIONID GEAR_SIZE GEAR_TYPE MESH_SIZE MIN_FISH
## 1 4 40 ST 1.63 38
## 2 5 40 ST 1.63 12
## 3 6 40 ST 1.63 10
## 4 7 40 ST 1.63 25
## 5 8 40 ST 1.63 15
## 6 9 40 ST 1.63 12Are We Catching Adults or Juveniles?
length_freq<- left_join(select(size, STATIONID, LEN_GLF), select(gear, STATIONID, GEAR_TYPE, GEAR_SIZE, MESH_SIZE), by="STATIONID")
#Check freqency of each gear type
length_freq<- length_freq %>% dplyr::filter(GEAR_TYPE=="ST", GEAR_SIZE==40, MESH_SIZE==1.63, !is.na(LEN_GLF))
Lm= 230 #Length at maturity Red Snapper
#Maturity obtained at year 2, but estimate for size at year 2 is greater than 230cm
Percent Juveniles
sum(length_freq$LEN_GLF<230)/length(length_freq$LEN_GLF)*100## [1] 94.30103Join Gear and Location
Gear
head(gearshrimptrawl)## STATIONID GEAR_SIZE GEAR_TYPE MESH_SIZE MIN_FISH
## 1 4 40 ST 1.63 38
## 2 5 40 ST 1.63 12
## 3 6 40 ST 1.63 10
## 4 7 40 ST 1.63 25
## 5 8 40 ST 1.63 15
## 6 9 40 ST 1.63 12Location
head(location)## STATIONID DECSLAT DECSLON HAULVALUE year month day
## 1 1 26.49 -96.50 G 2003 10 11
## 2 2 26.04 -96.47 G 2003 10 11
## 3 3 25.98 -96.98 G 2003 10 11
## 4 4 26.08 -97.09 G 2003 10 11
## 5 5 26.12 -97.14 G 2003 10 11
## 6 6 26.22 -97.15 G 2003 10 11Gear + Location
location_gearST <- dplyr::inner_join(location,gearshrimptrawl,by="STATIONID")
head(location_gearST)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 5 26.12 -97.14 G 2003 10 11 40 ST
## 3 6 26.22 -97.15 G 2003 10 11 40 ST
## 4 7 26.11 -96.90 G 2003 10 11 40 ST
## 5 8 26.29 -97.03 G 2003 10 11 40 ST
## 6 9 26.35 -96.96 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH
## 1 1.63 38
## 2 1.63 12
## 3 1.63 10
## 4 1.63 25
## 5 1.63 15
## 6 1.63 12Join Fish Data
Gear + Location
location_gearST <- dplyr::inner_join(location,gearshrimptrawl,by="STATIONID")
head(location_gearST)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 5 26.12 -97.14 G 2003 10 11 40 ST
## 3 6 26.22 -97.15 G 2003 10 11 40 ST
## 4 7 26.11 -96.90 G 2003 10 11 40 ST
## 5 8 26.29 -97.03 G 2003 10 11 40 ST
## 6 9 26.35 -96.96 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH
## 1 1.63 38
## 2 1.63 12
## 3 1.63 10
## 4 1.63 25
## 5 1.63 15
## 6 1.63 12Gear + Location + Fish
location_gearST_fish <- dplyr::left_join(location_gearST,fishdata,by="STATIONID")
head(location_gearST_fish)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 4 26.08 -97.09 G 2003 10 11 40 ST
## 3 4 26.08 -97.09 G 2003 10 11 40 ST
## 4 4 26.08 -97.09 G 2003 10 11 40 ST
## 5 4 26.08 -97.09 G 2003 10 11 40 ST
## 6 4 26.08 -97.09 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP
## 1 1.63 38 RHIZOPR TERRAE 1
## 2 1.63 38 SPHYRNA TIBURO 5
## 3 1.63 38 HARENGU JAGUAN 91
## 4 1.63 38 OPISTHO OGLINU 20
## 5 1.63 38 SARDINE AURITA 10
## 6 1.63 38 ANCHOA HEPSET 40Get sites where Red Snapper data was present
Filter Fish Data for Red Snapper
redsnapper <- dplyr::filter(fishdata,GENUS_BGS=="LUTJANU",SPEC_BGS=="CAMPEC")
head(redsnapper)## STATIONID GENUS_BGS SPEC_BGS CNTEXP
## 1 4 LUTJANU CAMPEC 26
## 2 5 LUTJANU CAMPEC 1
## 3 7 LUTJANU CAMPEC 21
## 4 8 LUTJANU CAMPEC 22
## 5 9 LUTJANU CAMPEC 17
## 6 11 LUTJANU CAMPEC 23Join Red Snapper
Gear + Location + Fish +Red Snapper
location_gearST_redsnapper <- dplyr::left_join(location_gearST,redsnapper,by="STATIONID")
head(location_gearST_redsnapper)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 5 26.12 -97.14 G 2003 10 11 40 ST
## 3 6 26.22 -97.15 G 2003 10 11 40 ST
## 4 7 26.11 -96.90 G 2003 10 11 40 ST
## 5 8 26.29 -97.03 G 2003 10 11 40 ST
## 6 9 26.35 -96.96 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP
## 1 1.63 38 LUTJANU CAMPEC 26
## 2 1.63 12 LUTJANU CAMPEC 1
## 3 1.63 10 <NA> <NA> NA
## 4 1.63 25 LUTJANU CAMPEC 21
## 5 1.63 15 LUTJANU CAMPEC 22
## 6 1.63 12 LUTJANU CAMPEC 17Why didn’t we filter for Red Snapper earlier in Fish Data?
To detect true absence
NA’s represent areas where shrimp trawl was used but red snapper not caught
head(location_gearST_redsnapper)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 5 26.12 -97.14 G 2003 10 11 40 ST
## 3 6 26.22 -97.15 G 2003 10 11 40 ST
## 4 7 26.11 -96.90 G 2003 10 11 40 ST
## 5 8 26.29 -97.03 G 2003 10 11 40 ST
## 6 9 26.35 -96.96 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP
## 1 1.63 38 LUTJANU CAMPEC 26
## 2 1.63 12 LUTJANU CAMPEC 1
## 3 1.63 10 <NA> <NA> NA
## 4 1.63 25 LUTJANU CAMPEC 21
## 5 1.63 15 LUTJANU CAMPEC 22
## 6 1.63 12 LUTJANU CAMPEC 17How many absences?
sum(is.na(location_gearST_redsnapper$SPEC_BGS))## [1] 15556Add zeros and genus/species
location_gearST_redsnapper$GENUS_BGS <-"LUTJANU"
location_gearST_redsnapper$SPEC_BGS <-"CAMPEC"
location_gearST_redsnapper[is.na(location_gearST_redsnapper$CNTEXP),"CNTEXP"] <-0
head(location_gearST_redsnapper)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 5 26.12 -97.14 G 2003 10 11 40 ST
## 3 6 26.22 -97.15 G 2003 10 11 40 ST
## 4 7 26.11 -96.90 G 2003 10 11 40 ST
## 5 8 26.29 -97.03 G 2003 10 11 40 ST
## 6 9 26.35 -96.96 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP
## 1 1.63 38 LUTJANU CAMPEC 26
## 2 1.63 12 LUTJANU CAMPEC 1
## 3 1.63 10 LUTJANU CAMPEC 0
## 4 1.63 25 LUTJANU CAMPEC 21
## 5 1.63 15 LUTJANU CAMPEC 22
## 6 1.63 12 LUTJANU CAMPEC 17Remove Bad Hauls
location_gearST_redsnapper <- dplyr::filter(location_gearST_redsnapper,HAULVALUE!="B")
summary(location_gearST_redsnapper$HAULVALUE)## B G
## 6411 0 18513Check for and remove missing data
colSums(is.na(location_gearST_redsnapper))## STATIONID DECSLAT DECSLON HAULVALUE year month day
## 0 0 0 0 0 0 0
## GEAR_SIZE GEAR_TYPE MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP
## 0 0 0 3 0 0 0location_gearST_redsnapper<- na.omit(location_gearST_redsnapper)Calculate Catch Per Unit Effort (CPUE)
CPUE=Count/Minutes Trawled
location_gearST_redsnapper<- location_gearST_redsnapper %>% dplyr::mutate(CPUE=CNTEXP/MIN_FISH)
head(location_gearST_redsnapper)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 4 26.08 -97.09 G 2003 10 11 40 ST
## 2 5 26.12 -97.14 G 2003 10 11 40 ST
## 3 6 26.22 -97.15 G 2003 10 11 40 ST
## 4 7 26.11 -96.90 G 2003 10 11 40 ST
## 5 8 26.29 -97.03 G 2003 10 11 40 ST
## 6 9 26.35 -96.96 G 2003 10 11 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP CPUE
## 1 1.63 38 LUTJANU CAMPEC 26 0.68421053
## 2 1.63 12 LUTJANU CAMPEC 1 0.08333333
## 3 1.63 10 LUTJANU CAMPEC 0 0.00000000
## 4 1.63 25 LUTJANU CAMPEC 21 0.84000000
## 5 1.63 15 LUTJANU CAMPEC 22 1.46666667
## 6 1.63 12 LUTJANU CAMPEC 17 1.41666667Which months were the Red Snapper collected the most?
counts<-table(location_gearST_redsnapper$month)
barplot(counts, xlab="Month", ylab = "Sampling Effort", main="Red Snapper Sampling Effort vs Month")
June-July and October-November
Select Data in those windows
location_gearST_redsnapper_sumfall<- location_gearST_redsnapper %>% dplyr::filter(month==6:11)
head(location_gearST_redsnapper_sumfall)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 8 26.29 -97.03 G 2003 10 11 40 ST
## 2 14 26.39 -97.00 G 2003 10 12 40 ST
## 3 20 26.86 -97.06 G 2003 10 13 40 ST
## 4 26 26.83 -96.64 G 2003 10 13 40 ST
## 5 32 27.13 -96.59 G 2003 10 14 40 ST
## 6 38 27.23 -97.32 G 2003 10 14 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP CPUE
## 1 1.63 15 LUTJANU CAMPEC 22 1.466667
## 2 1.63 28 LUTJANU CAMPEC 0 0.000000
## 3 1.63 25 LUTJANU CAMPEC 37 1.480000
## 4 1.63 55 LUTJANU CAMPEC 0 0.000000
## 5 1.63 50 LUTJANU CAMPEC 0 0.000000
## 6 1.63 10 LUTJANU CAMPEC 0 0.000000Select Summer Data
location_gearST_redsnapper_summer<- location_gearST_redsnapper %>% dplyr::filter(month==c(6,7))
head(location_gearST_redsnapper_summer)## STATIONID DECSLAT DECSLON HAULVALUE year month day GEAR_SIZE GEAR_TYPE
## 1 505 30.18 -88.31 G 1997 6 4 40 ST
## 2 507 30.03 -88.46 G 1997 6 4 40 ST
## 3 509 30.00 -88.25 G 1997 6 4 40 ST
## 4 511 30.10 -88.09 G 1997 6 4 40 ST
## 5 2493 30.18 -88.05 G 1991 6 3 40 ST
## 6 2495 30.22 -88.28 G 1991 6 3 40 ST
## MESH_SIZE MIN_FISH GENUS_BGS SPEC_BGS CNTEXP CPUE
## 1 1.63 18 LUTJANU CAMPEC 0 0
## 2 1.63 14 LUTJANU CAMPEC 0 0
## 3 1.63 10 LUTJANU CAMPEC 0 0
## 4 1.63 17 LUTJANU CAMPEC 0 0
## 5 1.63 14 LUTJANU CAMPEC 0 0
## 6 1.63 10 LUTJANU CAMPEC 0 0SEAMAP Effort
The total sampling effort between 1882 and 2017 was 11,000 hours. 
