-
Notifications
You must be signed in to change notification settings - Fork 0
/
Script#5_OVERSEE_v5.R
537 lines (444 loc) · 20.6 KB
/
Script#5_OVERSEE_v5.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
##### 04/05/2018: R Script to remove the zooplankton occurrences coming from net tows going deeper than 500m and/or those who exceed MLD © Fabio Benedetti, ETH Zürich, IBP, UP Group.
### For each large zooplankton group, aims to:
# - remove any occurrence coming from a net tow that went deeper than 500m
# - load MLD monthly climatologies (De Boyer Montégut - 2°x2°) and apply MLD criterion
# - report number of lost occurrences on excel sheet at each step
module load R/3.4.3/ # To load latest R version on kryo
### Latest update: 15/05/2018
library("rgeos")
library("raster")
library("maptools")
library("rgdal")
library("dplyr")
library("tidyr")
library("stringr")
library("reshape2")
library("geosphere")
library("ncdf4")
### ----------------------------------------------------------------------------------------------------------------------------
##### 1°) 500m depth criterion (v5.1v3.1 & v5.1v3.2)
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.1/Copepoda_OBIS_03_05_18/")
files <- dir()
# f <- files[217] # For testing
### Apply in a for loop for
for(f in files) {
# Load the data
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.1/Copepoda_OBIS_03_05_18/")
message(paste(f, sep = ""))
data <- get(load(f))
spname <- unique(data$species)
# summary(data$depth)
# Sometimes you still have negative depths for some reason... -> absolute
data$depth <- abs(data$depth)
# Remove obs with depth > 500
data2 <- data[!(data$depth > 500),]
# summary(data2$depth)
# dim(data2)
# Go to v3.1 to save if you have point left
if( nrow(data2) > 0) {
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.1/Copepoda_OBIS_04_05_18/")
save(data2, file = paste(spname, "_OBIS_04_05_18.Rdata", sep = ""))
# Clean
rm(data, data2, spname)
gc()
} else {
# Clean
rm(data, data2, spname)
gc()
}
} # eo for loop
### Check the resulting number of obs
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.1/Copepoda_OBIS_04_05_18/")
files <- dir() ; files
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
#data <- read.csv(f, sep = "\t")
return(data[,c("decimalLongitude","decimalLatitude","depth")])
}) # eo lapply
# Check
d <- do.call(rbind, res)
dim(d) # 863'292
summary(d)
rm(res,d)
gc()
### Do the same for the other datasets
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.1/")
dir()
data <- get(load("Copepoda_GBIF_03_05_18.Rdata"))
dim(data)
colnames(data)
summary(data$depth)
### Since depth has the same colname in both OBIS and GBIF files, apply depth criterion in a for loop
files <- dir()[c(1:4,6:34)]
for(f in files) {
# Get/ load/ message
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.1/")
message(paste("Doing ",f, sep = ""))
data <- get(load(f))
# summary(data$depth)
# Sometimes you still have negative depths for some reason... -> absolute
data$depth <- abs(data$depth)
# Remove obs with depth > 500
data2 <- data[!(data$depth > 500),]
# summary(data2$depth)
# dim(data2)
message(paste("DIM = ", nrow(data2), sep = ""))
# Go to v5.1v3.1 to save if you have point left
if( nrow(data2) > 0) {
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.1/")
require("stringr")
save(data2, file = paste(str_replace_all(string = f, pattern = "_03_05", replacement = "_04_05"), sep = ""))
# Clean
rm(data, data2, spname)
gc()
} else {
# Clean
rm(data, data2, spname)
gc()
}
} # eo for loop
### OKay, check some results
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.1/")
dir()
files <- dir()[c(1:4,6:34)]
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
#data <- read.csv(f, sep = "\t")
return(data.frame(data[,c("depth")]))
}) # eo lapply
# Check
d <- do.call(rbind, res)
dim(d) #
summary(d) # Ok gut
### ----------------------------------------------------------------------------------------------------------------------------
##### Do the same as above but for the v3.2 (dist2coast) data
### First, Copepoda OBIS
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/Copepoda_OBIS_04_05_18/")
files <- dir()
# f <- files[217] # For testing
### Apply in a for loop for
for(f in files) {
# Load the data
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/Copepoda_OBIS_04_05_18/")
message(paste(f, sep = ""))
data <- get(load(f))
spname <- unique(data$species)
# summary(data$depth)
# Sometimes you still have negative depths for some reason... -> absolute
data$depth <- abs(data$depth)
# Remove obs with depth > 500
data2 <- data[!(data$depth > 500),]
# summary(data2$depth)
# dim(data2)
# Go to v3.1 to save if you have point left
if( nrow(data2) > 0) {
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.2/Copepoda_OBIS_04_05_18/")
save(data2, file = paste(spname, "_OBIS_04_05_18.Rdata", sep = ""))
# Clean
rm(data, data2, spname)
gc()
} else {
# Clean
rm(data, data2, spname)
gc()
}
} # eo for loop
### Check the resulting number of obs
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.2/Copepoda_OBIS_04_05_18/")
files <- dir() ; files
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
#data <- read.csv(f, sep = "\t")
return(data[,c("decimalLongitude","decimalLatitude","depth")])
}) # eo lapply
# Check
d <- do.call(rbind, res)
dim(d) # 1344909
summary(d) # ok gut gut
rm(res,d)
gc()
### Do the same for the other datasets
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/")
dir()
### Since depth has the same colname in both OBIS and GBIF files, apply depth criterion in a for loop
files <- dir()[c(1:4,6:34)]
for(f in files) {
# Get/ load/ message
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/")
message(paste("Doing ",f, sep = ""))
data <- get(load(f))
# summary(data$depth)
# Sometimes you still have negative depths for some reason... -> absolute
data$depth <- abs(data$depth)
# Remove obs with depth > 500
data2 <- data[!(data$depth > 500),]
# summary(data2$depth)
# dim(data2)
message(paste("DIM = ", nrow(data2), sep = ""))
# Go to v5.1v3.1 to save if you have point left
if( nrow(data2) > 0) {
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.2/")
require("stringr")
save(data2, file = paste(str_replace_all(string = f, pattern = "_03_05", replacement = "_04_05"), sep = ""))
# Clean
rm(data, data2, spname)
gc()
} else {
# Clean
rm(data, data2, spname)
gc()
}
} # eo for loop
### OKay, check some results
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.1v3.2/")
dir()
files <- dir()[c(1:4,6:34)]
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
#data <- read.csv(f, sep = "\t")
return(data.frame(data[,c("depth")]))
}) # eo lapply
# Check
d <- do.call(rbind, res)
dim(d) #
summary(d) # Ok gut
### ----------------------------------------------------------------------------------------------------------------------------
##### 2°) MLD criterion (v5.2v3.1 & v3.2)
### Read netcdf file containing the MLD monthly climatologies and turn them into rasters
nc <- nc_open("mld_DT02_c1m_reg2.0.nc")
nc
names(nc$var)
# mld_da_mean = average mld based on density algorithm
# mld_dt_mean = average mld based on temperature algorithm
# print(nc)
# Extract data
lons <- ncvar_get(nc, "lon")
dim(lons) # 180
lons # Need to - 180
lons2 <- lons - 180
lons2
lats <- ncvar_get(nc, "lat")
dim(lats) # 90
head(lats)
lats # This is OK
mld <- ncvar_get(nc, "mld")
dim(mld) # 180 90 12 -> lon/ lat / months
class(mld) # 3D array
length(mld) # 12*180*90
dimnames(mld)
dimnames(mld)[[1]] <- as.character(lons2)
dimnames(mld)[[2]] <- as.character(lats)
dimnames(mld)[[3]] <- as.character(c(1:12))
# Melt it and rename columns
mlds <- melt(mld)
dim(mlds)
head(mlds)
colnames(mlds)[1:4] <- c("x","y","Month","MLD")
summary(mlds) #
# missing_value: -9999
# min_value: 10.2676773071289
# max_value: 772.370483398438
# mask_value: 1e+09
# Change mask value to NA
mlds$MLD[mlds$MLD == 1e+09] <- NA
summary(mlds) # Gut gut
str(mlds)
mlds$Month <- as.factor(mlds$Month)
### Convert a monthly climatology to raster for a try
ras6 <- mlds[which(mlds$Month == 12),c("x","y","MLD")]
colnames(ras6)[3] <- c("z")
coordinates(ras6) <- ~ x + y
# create an empty raster object to the extent of the points
rast <- raster(ext = extent(ras6), resolution = 2)
rasOut <- rasterize(ras6, rast, ras6$z, fun = mean)
rasOut
crs(rasOut) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
plot(rasOut) # Seemed to have worked out
### Now, in a for loop, perform this and save 12 monthly reasters of MLD
### NOTE: to be ran on personal computer of you want to see the plots appearing...
for(i in unique(mlds$Month)) {
# Message
message(paste("Doing MLD raster for month ", i, sep = ""))
ras <- mlds[which(mlds$Month == i),c("x","y","MLD")]
colnames(ras)[3] <- c("z")
coordinates(ras) <- ~ x + y
# create an empty raster object to the extent of the points
rast <- raster(ext = extent(ras), resolution = 2)
rasOut <- rasterize(ras, rast, ras$z, fun = mean)
#rasOut
crs(rasOut) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
quartz()
plot(rasOut) # Seemed to have worked out
# Save
save(rasOut, file = paste("raster_mld_2d_",i,"_05_05_18.Rdata", sep = ""))
} # eo for loop
### OK
### ----------------------------------------------------------------------------------------------------------------------------
##### 07/05/18: Apply MLD crieria to v4v3.1 and v4v3.2 data --> v5.2v3.1 & v5.2v3.2
### First, get load the MLD 1° monthly climatologies from DR
stack <- get(load("deBoyerMontegut_MLD_RasterStack_V1.RData"))
class(stack)
stack
ras1 <- subset(stack, 1)
ras2 <- subset(stack, 2)
ras3 <- subset(stack, 3)
ras4 <- subset(stack, 4)
ras5 <- subset(stack, 5)
ras6 <- subset(stack, 6)
ras7 <- subset(stack, 7)
ras8 <- subset(stack, 8)
ras9 <- subset(stack, 9)
ras10 <- subset(stack, 10)
ras11 <- subset(stack, 11)
ras12 <- subset(stack, 12)
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/Copepoda_OBIS_04_05_18/")
files <- dir()
# f <- files[558] # For testing
### Apply in a for loop for
for(f in files) {
# Load the data
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/Copepoda_OBIS_04_05_18/")
message(paste(f, sep = ""))
data <- get(load(f))
spname <- unique(data$species)
# Get month from (and year while you're at it) eventDate and 'lubridate'
require("lubridate")
data$month <- lubridate::month(data$eventDate)
data$year <- lubridate::year(data$eventDate)
data <- data %>% drop_na(depth) # Remove depth NAs (if some are left for SOME STRANGE reason)
data$depth <- abs(data$depth)
# Create salinity vector
data$MLD <- NA
# Fill it according to month
### NOTE: IMPORTANT to specify the extract fun from the 'raster' package
data[which(data$month == 1),"MLD"] <- raster::extract(x = ras1, y = data[which(data$month == 1),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 2),"MLD"] <- raster::extract(x = ras2, y = data[which(data$month == 2),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 3),"MLD"] <- raster::extract(x = ras3, y = data[which(data$month == 3),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 4),"MLD"] <- raster::extract(x = ras4, y = data[which(data$month == 4),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 5),"MLD"] <- raster::extract(x = ras5, y = data[which(data$month == 5),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 6),"MLD"] <- raster::extract(x = ras6, y = data[which(data$month == 6),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 7),"MLD"] <- raster::extract(x = ras7, y = data[which(data$month == 7),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 8),"MLD"] <- raster::extract(x = ras8, y = data[which(data$month == 8),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 9),"MLD"] <- raster::extract(x = ras9, y = data[which(data$month == 9),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 10),"MLD"] <- raster::extract(x = ras10, y = data[which(data$month == 10),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 11),"MLD"] <- raster::extract(x = ras11, y = data[which(data$month == 11),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 12),"MLD"] <- raster::extract(x = ras12, y = data[which(data$month == 12),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
# Check
# summary(data)
# Remove obs based on mld
data2 <- data[-which(data$depth > (data$MLD+50)),] # Remove all obs whose net tows went deeper than the average monthly MLD + 50m
# summary(data2$MLD)
data3 <- data2[!(data2$depth > 150 & data2$MLD == NA),] # If some MLD have NAs, then just stick to a rather strick depth threshold of 150m depth
#data3 <- data2 %>% drop_na(MLD) # Remove NAs
# dim(data3)
# summary(data3) ### Strange, sometimes NAs are created...use drop_na() to remove those
data3 <- data3 %>% drop_na(year)
# Go to v3.1 to save if you have point left
if( nrow(data3) > 0) {
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.2v3.2/Copepoda_OBIS_15_05_18/")
save(data3, file = paste(spname, "_OBIS_15_05_18.Rdata", sep = ""))
# Clean
rm(data, data2, data3, spname)
gc()
} else {
# Clean
rm(data, data2, data3, spname)
gc()
}
} # eo for loop
### Check the resulting number of obs
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.2v3.2/Copepoda_OBIS_15_05_18/")
files <- dir()
res <- lapply(files, function(f) {
message(paste(f, sep = ""))
data <- get(load(f))
#data <- read.csv(f, sep = "\t")
return(data[,c("decimalLongitude","decimalLatitude","species","depth","MLD","salinityWOA13")])
}) # eo lapply
# Check
d <- do.call(rbind, res)
dim(d) # 622206 ; 1072621
summary(d)
rm(res, d)
gc()
##### And do the same for the other datasets
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/")
dir()
### Since depth has the same colname in both OBIS and GBIF files, apply depth criterion in a for loop
files <- dir()[c(1:4,6:34)] # Remove Copepoda_OBIS which you just did
# f <- "Sagittoidea_OBIS_03_05_18.Rdata"
for(f in files) {
# Get/ load/ message
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v4v3.2/")
message(paste("Doing ",f, sep = ""))
data <- get(load(f))
# Sometimes you still have negative depths for some reason... -> absolute
data$depth <- abs(data$depth)
data <- data %>% drop_na(depth) # Remove depth NAs
# If OBIS type of file -> re-provide year and month from eventDate and use decimalLongitude and decimalLatitude
if( grepl("OBIS", f) ) {
require("lubridate")
data$month <- lubridate::month(data$eventDate)
data$year <- lubridate::year(data$eventDate)
### Fill it according to month
data$MLD <- NA
# NOTE: IMPORTANT to specify the extract fun from the 'raster' package
# require("raster")
data[which(data$month == 1),"MLD"] <- raster::extract(x = ras1, y = data[which(data$month == 1),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 2),"MLD"] <- raster::extract(x = ras2, y = data[which(data$month == 2),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 3),"MLD"] <- raster::extract(x = ras3, y = data[which(data$month == 3),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 4),"MLD"] <- raster::extract(x = ras4, y = data[which(data$month == 4),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 5),"MLD"] <- raster::extract(x = ras5, y = data[which(data$month == 5),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 6),"MLD"] <- raster::extract(x = ras6, y = data[which(data$month == 6),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 7),"MLD"] <- raster::extract(x = ras7, y = data[which(data$month == 7),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 8),"MLD"] <- raster::extract(x = ras8, y = data[which(data$month == 8),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 9),"MLD"] <- raster::extract(x = ras9, y = data[which(data$month == 9),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 10),"MLD"] <- raster::extract(x = ras10, y = data[which(data$month == 10),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 11),"MLD"] <- raster::extract(x = ras11, y = data[which(data$month == 11),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
data[which(data$month == 12),"MLD"] <- raster::extract(x = ras12, y = data[which(data$month == 12),c("decimalLongitude","decimalLatitude")], method = 'bilinear')
} else {
### Fill it according to month
data$MLD <- NA
# require("raster")
data[which(data$month == 1),"MLD"] <- raster::extract(x = ras1, y = data[which(data$month == 1),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 2),"MLD"] <- raster::extract(x = ras2, y = data[which(data$month == 2),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 3),"MLD"] <- raster::extract(x = ras3, y = data[which(data$month == 3),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 4),"MLD"] <- raster::extract(x = ras4, y = data[which(data$month == 4),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 5),"MLD"] <- raster::extract(x = ras5, y = data[which(data$month == 5),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 6),"MLD"] <- raster::extract(x = ras6, y = data[which(data$month == 6),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 7),"MLD"] <- raster::extract(x = ras7, y = data[which(data$month == 7),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 8),"MLD"] <- raster::extract(x = ras8, y = data[which(data$month == 8),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 9),"MLD"] <- raster::extract(x = ras9, y = data[which(data$month == 9),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 10),"MLD"] <- raster::extract(x = ras10, y = data[which(data$month == 10),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 11),"MLD"] <- raster::extract(x = ras11, y = data[which(data$month == 11),c("decimallongitude","decimallatitude")], method = 'bilinear')
data[which(data$month == 12),"MLD"] <- raster::extract(x = ras12, y = data[which(data$month == 12),c("decimallongitude","decimallatitude")], method = 'bilinear')
}
# Check
# summary(data$MLD)
# summary(data$depth)
# Remove obs with SSS <= 20
data2 <- data[-which(data$depth > (data$MLD+50)),] # Remove all obs whose net tows went deeper than the average monthly MLD + 50m
# summary(data2$MLD)
data3 <- data2[!(data$depth > 150 & data2$MLD == NA),] # If some MLD have NAs, then just stick to a rather strick depth threshold of 150m depth
# dim(data3)
data3 <- data3 %>% drop_na(year)
message(paste("DIM = ", nrow(data3), sep = ""))
# Go to v5.1v3.1 to save if you have point left
if( nrow(data3) > 0 ) {
setwd("/UP_home/fabioben/Desktop/OVERSEE/data/biology/occurence_data_groups/v5.2v3.2/")
require("stringr")
save(data3, file = paste(str_replace_all(string = f, pattern = "_04_05", replacement = "_15_05"), sep = ""))
# Clean
rm(data, data2, data3, spname)
gc()
} else {
# Clean
rm(data, data2, data3, spname)
gc()
}
} # eo for loop
### Check results