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center <- -160
boundary <- center + 180
target_crs <- paste0("+proj=robin +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +over +lon_0=", center)
# target_crs <- paste0("+proj=eqearth +lon_0=", center)
# target_crs <- paste0("+proj=igh_o +lon_0=", center)

worldmap <- ne_countries(scale = 'small',
                         type = 'map_units',
                         returnclass = 'sf')

worldmap <- worldmap %>% st_break_antimeridian(lon_0 = center)
worldmap_trans <- st_transform(worldmap, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans)

coastline <- ne_coastline(scale = 'small', returnclass = "sf")
coastline <- st_break_antimeridian(coastline, lon_0 = 200)
coastline_trans <- st_transform(coastline, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans)


bbox <- st_bbox(c(xmin = -180, xmax = 180, ymax = 65, ymin = -78), crs = st_crs(4326))
bbox <- st_as_sfc(bbox)
bbox_trans <- st_break_antimeridian(bbox, lon_0 = center)

bbox_graticules <- st_graticule(
  x = bbox_trans,
  crs = st_crs(bbox_trans),
  datum = st_crs(bbox_trans),
  lon = c(20, 20.001),
  lat = c(-78,65),
  ndiscr = 1e3,
  margin = 0.001
)

bbox_graticules_trans <- st_transform(bbox_graticules, crs = target_crs)
rm(worldmap, coastline, bbox, bbox_trans)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col="grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans)

lat_lim <- ext(bbox_graticules_trans)[c(3,4)]*1.002
lon_lim <- ext(bbox_graticules_trans)[c(1,2)]*1.005

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey90", col = "grey90") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans, linewidth = 1) +
#   coord_sf(crs = target_crs,
#            ylim = lat_lim,
#            xlim = lon_lim,
#            expand = FALSE) +
#   theme(
#     panel.border = element_blank(),
#     axis.text = element_blank(),
#     axis.ticks = element_blank()
#   )

latitude_graticules <- st_graticule(
  x = bbox_graticules,
  crs = st_crs(bbox_graticules),
  datum = st_crs(bbox_graticules),
  lon = c(20, 20.001),
  lat = c(-60,-30,0,30,60),
  ndiscr = 1e3,
  margin = 0.001
)

latitude_graticules_trans <- st_transform(latitude_graticules, crs = target_crs)

latitude_labels <- data.frame(lat_label = c("60°N","30°N","Eq.","30°S","60°S"),
                 lat = c(60,30,0,-30,-60)-4, lon = c(35)-c(0,2,4,2,0))

latitude_labels <- st_as_sf(x = latitude_labels,
               coords = c("lon", "lat"),
               crs = "+proj=longlat")

latitude_labels_trans <- st_transform(latitude_labels, crs = target_crs)

# ggplot() +
#   geom_sf(data = worldmap_trans, fill = "grey", col = "grey") +
#   geom_sf(data = coastline_trans) +
#   geom_sf(data = bbox_graticules_trans) +
#   geom_sf(data = latitude_graticules_trans,
#           col = "grey60",
#           linewidth = 0.2) +
#   geom_sf_text(data = latitude_labels_trans,
#                aes(label = lat_label),
#                size = 3,
#                col = "grey60")

Read data

In this study, we use the biome mask from RECCAP2, a modification of the original mask developed by Fay and McKinley (2014).

path_reccap2 <-
  "/nfs/kryo/work/datasets/gridded/ocean/interior/reccap2/"
print("RECCAP2_region_masks_all_v20221025.nc")
[1] "RECCAP2_region_masks_all_v20221025.nc"
biome_mask <-
  read_ncdf(
    paste(
      path_reccap2,
      "supplementary/RECCAP2_region_masks_all_v20221025.nc",
      sep = ""
    )
  ) %>%
  as_tibble()


biome_mask <-
  biome_mask %>%
  mutate(lon = if_else(lon < 20, lon + 360, lon))

Basemap and landmask

land_mask <- biome_mask %>%
  filter(seamask == 0) %>% 
  select(lon, lat)

map <- ggplot(land_mask,
              aes(lon, lat)) +
  geom_tile(fill = "grey80") +
  scale_y_continuous(breaks = seq(-60,60,30)) +
  scale_x_continuous(breaks = seq(0,360,60)) +
  coord_quickmap(expand = 0, ylim = c(-60, 80)) +
  theme(axis.title = element_blank(),
        axis.text = element_blank(),
        axis.ticks = element_blank())

map

Version Author Date
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
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1d6b4c8 jens-daniel-mueller 2024-03-24 
map %>%
  write_rds(paste0("../data/","map.rds"))

Global ocean

Global ocean estimates are integrated or averaged across the following areas.

biome_mask <- biome_mask %>%
  filter(seamask == 1) %>% 
  select(lon, lat, atlantic:southern) %>% 
  pivot_longer(atlantic:southern,
               names_to = "region",
               values_to = "biome") %>%
  mutate(biome = as.character(biome))

biome_mask <- biome_mask %>%
  filter(biome != "0")

biome_mask <- biome_mask %>%
  mutate(biome = paste(region, biome, sep = "_"))

biome_mask <- biome_mask  %>% 
  mutate(biome = case_when(
    biome == "atlantic_1" ~ "NA-SPSS",
    biome == "atlantic_2" ~ "NA-STSS",
    biome == "atlantic_3" ~ "NA-STPS",
    biome == "atlantic_4" ~ "AEQU",
    biome == "atlantic_5" ~ "SA-STPS",
    # biome == "atlantic_6" ~ "MED",
    biome == "pacific_1" ~ "NP-SPSS",
    biome == "pacific_2" ~ "NP-STSS",
    biome == "pacific_3" ~ "NP-STPS",
    biome == "pacific_4" ~ "PEQU-W",
    biome == "pacific_5" ~ "PEQU-E",
    biome == "pacific_6" ~ "SP-STPS",
    # biome == "indian_1" ~ "Arabian Sea",
    # biome == "indian_2" ~ "Bay of Bengal",
    biome == "indian_1" ~ "Equatorial Indian",
    biome == "indian_2" ~ "Equatorial Indian",
    biome == "indian_3" ~ "Equatorial Indian",
    biome == "indian_4" ~ "Southern Indian",
    # biome == "arctic_1" ~ "ARCTIC-ICE",
    # biome == "arctic_2" ~ "NP-ICE",
    # biome == "arctic_3" ~ "NA-ICE",
    # biome == "arctic_4" ~ "Barents",
    str_detect(biome, "arctic") ~ "Arctic",
    biome == "southern_1" ~ "SO-STSS",
    biome == "southern_2" ~ "SO-SPSS",
    biome == "southern_3" ~ "SO-ICE",
    TRUE ~ "other"
  ))

biome_mask <-
  biome_mask %>%
  filter(biome != "other")

map +
  geom_tile(data = biome_mask,
            aes(lon, lat, fill = region)) +
  labs(title = "Considered ocean regions") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

Version Author Date
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
0a7394b jens-daniel-mueller 2024-06-11
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1d6b4c8 jens-daniel-mueller 2024-03-24
78465dc jens-daniel-mueller 2024-03-24 
biome_mask %>%
  distinct(region, biome) %>% 
  write_rds("../data/region_biomes.rds")

Biomes

All biomes

In the following, all individual biomes are plotted by ocean region.

biome_mask %>%
  group_split(region) %>%
  # head(1) %>%
  map( ~ map +
         geom_tile(data = .x,
                   aes(lon, lat, fill = biome)) +
         labs(title = paste("Region:", .x$region)) +
         scale_fill_okabeito())
[[1]]

Version Author Date
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
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[[2]]

Version Author Date
e15a0e8 jens-daniel-mueller 2024-06-25
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
0a7394b jens-daniel-mueller 2024-06-11
009791f jens-daniel-mueller 2024-05-14
dfcf790 jens-daniel-mueller 2024-04-11
d5075c5 jens-daniel-mueller 2024-04-11
78465dc jens-daniel-mueller 2024-03-24 

[[3]]

Version Author Date
8220f80 jens-daniel-mueller 2024-06-25
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
0a7394b jens-daniel-mueller 2024-06-11
009791f jens-daniel-mueller 2024-05-14
dfcf790 jens-daniel-mueller 2024-04-11
d5075c5 jens-daniel-mueller 2024-04-11
78465dc jens-daniel-mueller 2024-03-24 

[[4]]

Version Author Date
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
0a7394b jens-daniel-mueller 2024-06-11
5f7453c jens-daniel-mueller 2024-05-25
009791f jens-daniel-mueller 2024-05-14
dfcf790 jens-daniel-mueller 2024-04-11
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78465dc jens-daniel-mueller 2024-03-24 

[[5]]

Key biomes

Following key biomes are highlighted throughout the analysis.

key_biomes <- c("NA-SPSS",
                "NA-STPS",
                "NP-SPSS",
                "PEQU-E")

key_biomes %>% 
  write_rds("../data/key_biomes.rds")

map +
  geom_tile(data = biome_mask %>% filter(biome %in% key_biomes),
            aes(lon, lat, fill = biome)) +
  labs(title = "Selected biomes to highlight") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

Version Author Date
a60be97 jens-daniel-mueller 2024-06-12
de65385 jens-daniel-mueller 2024-06-12
03c415f jens-daniel-mueller 2024-06-11
0a7394b jens-daniel-mueller 2024-06-11
009791f jens-daniel-mueller 2024-05-14
dfcf790 jens-daniel-mueller 2024-04-11
d5075c5 jens-daniel-mueller 2024-04-11
78465dc jens-daniel-mueller 2024-03-24 
biome_mask %>%
  select(-biome) %>% 
  write_rds("../data/region_mask.rds")

biome_mask <-
  biome_mask %>%
  select(-region)

biome_mask %>%
  write_rds("../data/biome_mask.rds")

Super biomes

In addition to biomes, we focus our analysis on following combined super biomes.

super_biome_mask <- biome_mask  %>%
  mutate(
    biome = case_when(
      str_detect(biome, "NA-") ~ "North Atlantic",
      str_detect(biome, "NP-") ~ "North Pacific",
      str_detect(biome, "SO-") ~ "Southern Ocean",
      TRUE ~ "other"
    )
  )

super_biome_mask <-
  super_biome_mask %>%
  filter(biome != "other")

map +
  geom_tile(data = super_biome_mask,
            aes(lon, lat, fill = biome)) +
  labs(title = "Selected super biomes") +
  scale_fill_muted() +
  theme(legend.title = element_blank())

super_biomes <-
  super_biome_mask %>%
  distinct(biome) %>%
  pull()

super_biomes %>%
  write_rds("../data/super_biomes.rds")

super_biome_mask %>%
  write_rds("../data/super_biome_mask.rds")

sessionInfo()
R version 4.2.2 (2022-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: openSUSE Leap 15.5

Matrix products: default
BLAS:   /usr/local/R-4.2.2/lib64/R/lib/libRblas.so
LAPACK: /usr/local/R-4.2.2/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] khroma_1.9.0        stars_0.6-0         abind_1.4-5        
 [4] terra_1.7-65        sf_1.0-9            rnaturalearth_0.1.0
 [7] geomtextpath_0.1.1  colorspace_2.0-3    marelac_2.1.10     
[10] shape_1.4.6         ggforce_0.4.1       metR_0.13.0        
[13] scico_1.3.1         patchwork_1.1.2     collapse_1.8.9     
[16] forcats_0.5.2       stringr_1.5.0       dplyr_1.1.3        
[19] purrr_1.0.2         readr_2.1.3         tidyr_1.3.0        
[22] tibble_3.2.1        ggplot2_3.4.4       tidyverse_1.3.2    
[25] workflowr_1.7.0    

loaded via a namespace (and not attached):
  [1] googledrive_2.0.0       ellipsis_0.3.2          class_7.3-20           
  [4] rprojroot_2.0.3         fs_1.5.2                rstudioapi_0.15.0      
  [7] proxy_0.4-27            farver_2.1.1            bit64_4.0.5            
 [10] fansi_1.0.3             lubridate_1.9.0         xml2_1.3.3             
 [13] codetools_0.2-18        cachem_1.0.6            knitr_1.41             
 [16] polyclip_1.10-4         jsonlite_1.8.3          gsw_1.1-1              
 [19] broom_1.0.5             dbplyr_2.2.1            compiler_4.2.2         
 [22] httr_1.4.4              backports_1.4.1         assertthat_0.2.1       
 [25] fastmap_1.1.0           gargle_1.2.1            cli_3.6.1              
 [28] later_1.3.0             tweenr_2.0.2            htmltools_0.5.3        
 [31] tools_4.2.2             rnaturalearthdata_0.1.0 gtable_0.3.1           
 [34] glue_1.6.2              Rcpp_1.0.11             RNetCDF_2.6-1          
 [37] cellranger_1.1.0        jquerylib_0.1.4         vctrs_0.6.4            
 [40] lwgeom_0.2-10           xfun_0.35               ps_1.7.2               
 [43] rvest_1.0.3             ncmeta_0.3.5            timechange_0.1.1       
 [46] lifecycle_1.0.3         googlesheets4_1.0.1     oce_1.7-10             
 [49] getPass_0.2-2           MASS_7.3-58.1           scales_1.2.1           
 [52] vroom_1.6.0             hms_1.1.2               promises_1.2.0.1       
 [55] parallel_4.2.2          yaml_2.3.6              memoise_2.0.1          
 [58] sass_0.4.4              stringi_1.7.8           highr_0.9              
 [61] e1071_1.7-12            checkmate_2.1.0         rlang_1.1.1            
 [64] pkgconfig_2.0.3         systemfonts_1.0.4       evaluate_0.18          
 [67] lattice_0.20-45         SolveSAPHE_2.1.0        bit_4.0.5              
 [70] processx_3.8.0          tidyselect_1.2.0        seacarb_3.3.1          
 [73] magrittr_2.0.3          R6_2.5.1                generics_0.1.3         
 [76] DBI_1.1.3               pillar_1.9.0            haven_2.5.1            
 [79] whisker_0.4             withr_2.5.0             units_0.8-0            
 [82] sp_1.5-1                modelr_0.1.10           crayon_1.5.2           
 [85] KernSmooth_2.23-20      utf8_1.2.2              tzdb_0.3.0             
 [88] rmarkdown_2.18          grid_4.2.2              readxl_1.4.1           
 [91] data.table_1.14.6       callr_3.7.3             git2r_0.30.1           
 [94] reprex_2.0.2            digest_0.6.30           classInt_0.4-8         
 [97] httpuv_1.6.6            textshaping_0.3.6       munsell_0.5.0          
[100] bslib_0.4.1