Canary Current LME robis demo

Dependencies

library(robis)
library(dplyr)
library(ggplot2)
library(sf)
library(rnaturalearth)

Fetching occurrences

The OBIS database has a number of geographic areas against which the occurrence data are indexed. This includes LMEs. Use the areas() function to get a list of LME area identifiers.

areas <- area() %>%
  filter(type == "lme")

areas %>% rmarkdown::paged_table()

Then use the Canary Current LME area ID as a filter in the occurrence() function.

occ <- occurrence(areaid = 40027)
occ

## # A tibble: 98,765 × 197
##    country    date_year scientificNameID superorder year  scientificName absence
##    <chr>          <int> <chr>            <chr>      <chr> <chr>          <lgl>  
##  1 FRANCE          1971 urn:lsid:marine… Peracarida 1971  Hyperiidea     FALSE  
##  2 <NA>              NA urn:lsid:marine… <NA>       <NA>  Katsuwonus pe… FALSE  
##  3 <NA>            1921 urn:lsid:marine… Elopomorp… 1921  Anguilla       FALSE  
##  4 <NA>            1975 urn:lsid:marine… <NA>       1975  Paragloborota… FALSE  
##  5 <NA>            1996 urn:lsid:marine… <NA>       1996  Reophax biloc… FALSE  
##  6 <NA>            1984 urn:lsid:marine… Peracarida 1984  Leucon (Epile… FALSE  
##  7 FRANCE          1975 urn:lsid:marine… <NA>       1975  Biota          FALSE  
##  8 <NA>              NA urn:lsid:marine… <NA>       <NA>  Katsuwonus pe… FALSE  
##  9 <NA>            1981 urn:lsid:marine… <NA>       1981  <NA>           FALSE  
## 10 SOVIET UN…      1970 urn:lsid:marine… Podoplea   1970  Microsetella … FALSE  
## # … with 98,755 more rows, and 190 more variables: dropped <lgl>,
## #   superorderid <int>, aphiaID <int>, decimalLatitude <dbl>, subclassid <int>,
## #   originalScientificName <chr>, marine <lgl>, minimumDepthInMeters <dbl>,
## #   phylumid <int>, subphylumid <int>, occurrenceStatus <chr>,
## #   basisOfRecord <chr>, superclass <chr>, date_mid <dbl>,
## #   maximumDepthInMeters <dbl>, id <chr>, class <chr>, order <chr>,
## #   suborder <chr>, superclassid <int>, orderid <int>, dataset_id <chr>, …

Let’s create a map:

land <- ne_coastline(returnclass = "sf", scale = "large")

ggplot() +
  geom_sf(data = land, size = 0.5) +
  geom_sf(data = occ %>% st_as_sf(coords = c("decimalLongitude", "decimalLatitude"), crs = 4326), size = 0.1, color = "#45add6") +
  coord_sf(xlim = c(-26, -3), ylim = c(10, 38), default_crs = sf::st_crs(4326)) +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

WRiMS taxa

While it’s possible to filter the dataframe we downloaded earlier based on value in the wrims column, the occurrence() function also has a wrims filter which restricts results to species listed in the World Register of Introduced Marine Species (WRiMS). Note that this currently does not take into account native versus introduced ranges.

occ_wrims <- occurrence(areaid = 40027, wrims = TRUE)
occ_wrims

## # A tibble: 2,428 × 151
##    infraphylum   date_year scientificNameID         year  scientificName dropped
##    <chr>             <int> <chr>                    <chr> <chr>          <lgl>  
##  1 Gnathostomata      2003 urn:lsid:marinespecies.… 2003  Chromis limba… FALSE  
##  2 Gnathostomata      2003 urn:lsid:marinespecies.… 2003  Chromis limba… FALSE  
##  3 Gnathostomata        NA urn:lsid:marinespecies.… <NA>  Microchirus a… FALSE  
##  4 <NA>               1971 urn:lsid:marinespecies.… 1971  Acteocina kno… FALSE  
##  5 Gnathostomata      2004 urn:lsid:marinespecies.… 2004  Chromis limba… FALSE  
##  6 <NA>               1966 urn:lsid:marinespecies.… 1966  Pulleniatina … FALSE  
##  7 Gnathostomata        NA urn:lsid:marinespecies.… <NA>  Seriola lalan… FALSE  
##  8 <NA>               2014 urn:lsid:marinespecies.… 2014  Palaemon eleg… FALSE  
##  9 Gnathostomata        NA urn:lsid:marinespecies.… <NA>  Conger conger  FALSE  
## 10 Gnathostomata        NA 127240                   <NA>  Pontinus kuhl… FALSE  
## # … with 2,418 more rows, and 145 more variables: gigaclassid <int>,
## #   aphiaID <int>, language <chr>, decimalLatitude <dbl>, subclassid <int>,
## #   gigaclass <chr>, infraphylumid <int>, phylumid <int>, familyid <int>,
## #   catalogNumber <chr>, occurrenceStatus <chr>, basisOfRecord <chr>,
## #   terrestrial <lgl>, modified <chr>, maximumDepthInMeters <dbl>, id <chr>,
## #   day <chr>, parvphylum <chr>, order <chr>, dataset_id <chr>,
## #   decimalLongitude <dbl>, collectionCode <chr>, date_end <dbl>, …

ggplot() +
  geom_sf(data = land, size = 0.5) +
  geom_sf(data = occ_wrims %>% st_as_sf(coords = c("decimalLongitude", "decimalLatitude"), crs = 4326), size = 0.1, color = "#45add6") +
  coord_sf(xlim = c(-26, -3), ylim = c(10, 38), default_crs = sf::st_crs(4326)) +
  theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank())

Let’s take a look at the most common WRiMS species for the CCLME:

occ_wrims %>%
  filter(!is.na(species)) %>%
  group_by(class, species) %>%
  summarize(records = n()) %>%
  arrange(desc(records)) %>%
  rmarkdown::paged_table()