FishBase NEEDS YOUR HELP\!
Dear FishBase Users,FishBase needs help and I am writing to you because you either have at one point or another requested data to be extracted from FishBase for your own research purposes or have contributed your own data to FishBase.
One of the FishBase funders has had to reduce its commitment and as a result, there is a US$200,000 gap in the FishBase 2018/2019 budget, which will result in forced unpaid leave of key staff with direct consequences for the constant updating and growth of FishBase, a resource on which many of us rely.
Many of us use FishBase regularly in our work given it provides important data on distribution, traits etc. Indeed, these data are so valuable that FishBase receives over 700,000 unique visits per month and underpins key scientific breakthroughs such as the Nature paper on rates of evolution [it’s slower in the tropics!] (see Nature (see https://www.nature.com/articles/d41586-018-05575-2 and https://www.facebook.com/FishBase/posts/1885134558216592).
Key about FishBase is that it is free to everyone in the world, regardless of whether their institutions can afford journal subscriptions.
FishBase co-founder Daniel Pauly once said “sending a bibliography is like providing cookbooks in a famine” and it has been the underpinning ethos of FishBase to make information available equally, regardless of where one works.
So for nearly 30 years, FishBase (www.fishbase.org), with its team of biologists and programmers has done just that, while constantly improving and expanding this valued resource.
But FishBase needs our help now. So, when you are next online on FishBase and see the donate request pop up, please donate at least $25. That’s one bottle of good VQA wine in Canada or half a carton of decent beer in Australia, 3 packs of organic Hess avocados from Loblaws or 6 latte grandes at Starbucks. If you drink more beer, use more avocado on your toast or are caffeine dependent, please consider a larger donation. IF EVERY MARINE RESEARCHER GETS ON BOARD, we can make a major contribution to FishBase. Imagine if you had to pay to access this type of information.
It’s time to pay it forward!
Thank you for your consideration and we all look forward to a flood of world-wide support to FishBase.
Welcome to rfishbase 3.0. This package is the third rewrite of the
original rfishbase package described in Boettiger et al.
(2012).
rfishbase 3.0 queries pre-compressed tables from a static server and
employs local caching (through memoization) to provide much greater
performance and stability, particularly for dealing with large queries
involving 10s of thousands of species. The user is never expected to
deal with pagination or curl headers and timeouts.
We welcome any feedback, issues or questions that users may encounter through our issues tracker on GitHub: https://github.com/ropensci/rfishbase/issues
remotes::install_github("ropensci/rfishbase")library("rfishbase")
library("dplyr") # convenient but not requiredFishBase makes it relatively easy to look up a
lot of information on most known species of fish. However, looking up a
single bit of data, such as the estimated trophic level, for many
different species becomes tedious very soon. This is a common reason for
using rfishbase. As such, our first step is to assemble a good list of
species we are interested in.
Almost all functions in rfishbase take a list (character vector) of
species scientific names, for example:
fish <- c("Oreochromis niloticus", "Salmo trutta")You can also read in a list of names from any existing data you are working with. When providing your own species list, you should always begin by validating the names. Taxonomy is a moving target, and this well help align the scientific names you are using with the names used by FishBase, and alert you to any potential issues:
fish <- validate_names(c("Oreochromis niloticus", "Salmo trutta"))Another typical use case is in wanting to collect information about all
species in a particular taxonomic group, such as a Genus, Family or
Order. The function species_list recognizes six taxonomic levels, and
can help you generate a list of names of all species in a given group:
fish <- species_list(Genus = "Labroides")
fish[1] "Labroides dimidiatus" "Labroides bicolor"
[3] "Labroides pectoralis" "Labroides phthirophagus"
[5] "Labroides rubrolabiatus"
rfishbase also recognizes common names. When a common name refers to
multiple species, all matching species are returned:
trout <- common_to_sci("trout")
trout# A tibble: 279 x 4
Species ComName Language SpecCode
<chr> <chr> <chr> <dbl>
1 Salmo obtusirostris Adriatic trout English 6210
2 Schizothorax richardsonii Alawan snowtrout English 8705
3 Schizopyge niger Alghad snowtrout English 24454
4 Salvelinus fontinalis American brook trout English 246
5 Salmo trutta Amu-Darya trout English 238
6 Oncorhynchus apache Apache Trout English 2687
7 Oncorhynchus apache Apache trout English 2687
8 Plectropomus areolatus Apricot trout English 6082
9 Salmo trutta Aral Sea Trout English 238
10 Salmo trutta Aral trout English 238
# … with 269 more rows
Note that there is no need to validate names coming from common_to_sci
or species_list, as these will always return valid names.
With a species list in place, we are ready to query fishbase for data. Note that if you have a very long list of species, it is always a good idea to try out your intended functions with a subset of that list first to make sure everything is working.
The species() function returns a table containing much (but not all)
of the information found on the summary or homepage for a species on
fishbase.org. rfishbase functions always return
tidy data tables: rows are
observations (e.g. a species, individual samples from a species) and
columns are variables (fields).
species(trout$Species)# A tibble: 279 x 98
SpecCode Species SpeciesRefNo Author FBname PicPreferredName
<dbl> <chr> <dbl> <chr> <chr> <chr>
1 6210 Salmo … 59043 (Heck… Adria… Saobt_u0.jpg
2 8705 Schizo… 4832 (Gray… Snowt… Scric_u1.jpg
3 24454 Schizo… 4832 (Heck… Algha… <NA>
4 246 Salvel… 5723 (Mitc… Brook… Safon_u4.jpg
5 238 Salmo … 4779 Linna… Sea t… Satru_u2.jpg
6 2687 Oncorh… 5723 (Mill… Apach… Onapa_u0.jpg
7 2687 Oncorh… 5723 (Mill… Apach… Onapa_u0.jpg
8 6082 Plectr… 5222 (R<fc… Squar… Plare_u4.jpg
9 238 Salmo … 4779 Linna… Sea t… Satru_u2.jpg
10 238 Salmo … 4779 Linna… Sea t… Satru_u2.jpg
# … with 269 more rows, and 92 more variables: PicPreferredNameM <chr>,
# PicPreferredNameF <chr>, PicPreferredNameJ <chr>, FamCode <dbl>,
# Subfamily <chr>, GenCode <dbl>, SubGenCode <dbl>, BodyShapeI <chr>,
# Source <chr>, AuthorRef <lgl>, Remark <chr>, TaxIssue <dbl>,
# Fresh <dbl>, Brack <dbl>, Saltwater <dbl>, DemersPelag <chr>,
# AnaCat <chr>, MigratRef <dbl>, DepthRangeShallow <dbl>,
# DepthRangeDeep <dbl>, DepthRangeRef <dbl>, DepthRangeComShallow <dbl>,
# DepthRangeComDeep <dbl>, DepthComRef <dbl>, LongevityWild <dbl>,
# LongevityWildRef <dbl>, LongevityCaptive <dbl>, LongevityCapRef <dbl>,
# Vulnerability <dbl>, Length <dbl>, LTypeMaxM <chr>,
# LengthFemale <dbl>, LTypeMaxF <chr>, MaxLengthRef <dbl>,
# CommonLength <dbl>, LTypeComM <chr>, CommonLengthF <dbl>,
# LTypeComF <chr>, CommonLengthRef <dbl>, Weight <dbl>,
# WeightFemale <dbl>, MaxWeightRef <dbl>, Pic <chr>,
# PictureFemale <chr>, LarvaPic <chr>, EggPic <chr>,
# ImportanceRef <dbl>, Importance <chr>, PriceCateg <chr>,
# PriceReliability <chr>, Remarks7 <chr>, LandingStatistics <chr>,
# Landings <chr>, MainCatchingMethod <chr>, II <chr>, MSeines <dbl>,
# MGillnets <dbl>, MCastnets <dbl>, MTraps <dbl>, MSpears <dbl>,
# MTrawls <dbl>, MDredges <dbl>, MLiftnets <dbl>, MHooksLines <dbl>,
# MOther <dbl>, UsedforAquaculture <chr>, LifeCycle <chr>,
# AquacultureRef <dbl>, UsedasBait <chr>, BaitRef <dbl>, Aquarium <chr>,
# AquariumFishII <chr>, AquariumRef <dbl>, GameFish <dbl>,
# GameRef <dbl>, Dangerous <chr>, DangerousRef <dbl>,
# Electrogenic <chr>, ElectroRef <dbl>, Complete <lgl>,
# GoogleImage <dbl>, Comments <chr>, Profile <chr>, PD50 <dbl>,
# Emblematic <dbl>, Entered <dbl>, DateEntered <dttm>, Modified <dbl>,
# DateModified <dttm>, Expert <dbl>, DateChecked <dttm>, TS <lgl>
Most tables contain many fields. To avoid overly cluttering the screen,
rfishbase displays tables as “tibbles” from the dplyr package. These
act just like the familiar data.frames of base R except that they
print to the screen in a more tidy fashion. Note that columns that
cannot fit easily in the display are summarized below the table. This
gives us an easy way to see what fields are available in a given table.
Most rfishbase functions will let the user subset these fields by
listing them in the fields argument, for
instance:
dat <- species(trout$Species, fields=c("Species", "PriceCateg", "Vulnerability"))
dat# A tibble: 279 x 3
Species PriceCateg Vulnerability
<chr> <chr> <dbl>
1 Salmo obtusirostris very high 47.0
2 Schizothorax richardsonii unknown 34.8
3 Schizopyge niger unknown 46.8
4 Salvelinus fontinalis very high 43.4
5 Salmo trutta very high 60.0
6 Oncorhynchus apache very high 53.8
7 Oncorhynchus apache very high 53.8
8 Plectropomus areolatus very high 57.0
9 Salmo trutta very high 60.0
10 Salmo trutta very high 60.0
# … with 269 more rows
Alternatively, just subset the table using the standard column selection
in base R ([[) or dplyr::select.
Unfortunately identifying what fields come from which tables is often a
challenge. Each summary page on fishbase.org includes a list of
additional tables with more information about species ecology, diet,
occurrences, and many other things. rfishbase provides functions that
correspond to most of these tables.
Because rfishbase accesses the back end database, it does not always
line up with the web display. Frequently rfishbase functions will
return more information than is available on the web versions of the
these tables. Some information found on the summary homepage for a
species is not available from the species summary function, but must
be extracted from a different table. For instance, the species
Resilience information is not one of the fields in the species
summary table, despite appearing on the species homepage of
fishbase.org. To discover which table this information is in, we can use
the special rfishbase function list_fields, which will list all
tables with a field matching the query string:
list_fields("Resilience")# A tibble: 1 x 1
table
<chr>
1 stocks
This shows us that this information appears on the stocks table. We
can then request this data from the stocks table:
stocks(trout$Species, fields=c("Species", "Resilience", "StockDefs"))# A tibble: 380 x 3
Species Resilience StockDefs
<chr> <chr> <chr>
1 Salmo obtusirost… Medium Europe: Adriatic basin in Krka, Jardo, Vr…
2 Schizothorax ric… Medium Asia: Himalayan region of India, Sikkim a…
3 Schizopyge niger Medium Asia: Kashmir Valley in India and Azad Ka…
4 Salvelinus fonti… Medium North America: most of eastern Canada fro…
5 Salmo trutta High Europe and Asia: Atlantic, North, White a…
6 Salmo trutta <NA> <i>Salmo trutta aralensis</i>: Asia: end…
7 Salmo trutta Medium <i>Salmo trutta fario</i>: Northeast Atl…
8 Salmo trutta Low "<i>Salmo trutta lacustris</i>\t: Europe:…
9 Salmo trutta <NA> "<i>Salmo trutta oxianus</i>\t: Asia: Am…
10 Salmo trutta <NA> <i>Salmo trutta aralensis</i>: Asia: Ara…
# … with 370 more rows
rfishbase relies on periodic cache releases. The current database
release is 17.07 (i.e. dating from July 2017). Set the version of
FishBase you wish to access by setting the environmental variable:
Sys.setenv(FISHBASE_VERSION="17.07")Note that the same version number applies to both the fishbase and
sealifebase data. Stay tuned for new releases.
SeaLifeBase.org is maintained by the same organization and largely
parallels the database structure of Fishbase. As such, almost all
rfishbase functions can instead be instructed to address the
We can begin by getting the taxa table for sealifebase:
sealife <- load_taxa(server="sealifebase")(Note: running load_taxa() at the beginning of any session, for either
fishbase or sealifebase is a good way to “warm up” rfishbase by loading
in taxonomic data it will need. This information is cached throughout
your session and will make all subsequent commands run faster. But no
worries if you skip this step, rfishbase will peform it for you on the
first time it is needed, and will cache these results thereafter.)
Let’s look at some Gastropods:
sealife %>% filter(Class == "Gastropoda")# A tibble: 19,473 x 9
SpecCode Species Genus Subfamily Family Order Class Phylum Kingdom
<dbl> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 57 Salinator… Salin… <NA> Amphib… Pulmo… Gast… Mollu… Animal…
2 58 Tasmaphen… Tasma… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
3 59 Tasmaphen… Tasma… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
4 60 Torresiro… Torre… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
5 61 Victaphan… Victa… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
6 62 Victaphan… Victa… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
7 63 Victaphan… Victa… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
8 64 Victaphan… Victa… <NA> Rhytid… Pulmo… Gast… Mollu… Animal…
9 65 Anoglypta… Anogl… <NA> Caryod… Pulmo… Gast… Mollu… Animal…
10 66 Brazieres… Brazi… <NA> Caryod… Pulmo… Gast… Mollu… Animal…
# … with 19,463 more rows
All other tables can also take an argument to server:
species(server="sealifebase")# A tibble: 119,074 x 104
SpecCode Species SpeciesRefNo Author AuthorRef FBname PicPreferredName
<dbl> <chr> <dbl> <chr> <dbl> <chr> <chr>
1 1 Phoron… 1 Wrigh… NA <NA> <NA>
2 2 Phoron… 997 Wrigh… NA <NA> <NA>
3 3 Phoron… 1 Oka, … NA <NA> <NA>
4 4 Phoron… 1 Haswe… NA <NA> Phaus_u0.jpg
5 5 Phoron… 997 Selys… NA <NA> <NA>
6 6 Phoron… 1 Cori,… NA phoro… <NA>
7 7 Phoron… 1 (Schn… NA <NA> <NA>
8 8 Phoron… 1 Gilch… NA <NA> <NA>
9 9 Phoron… 1 Pixel… NA <NA> <NA>
10 10 Phoron… 1 Hilto… NA Calif… <NA>
# … with 119,064 more rows, and 97 more variables:
# PicPreferredNameM <chr>, PicPreferredNameF <chr>,
# PicPreferredNameJ <chr>, FamCode <dbl>, Subfamily <chr>, Source <chr>,
# Remark <chr>, TaxIssue <dbl>, Fresh <dbl>, Brack <dbl>,
# Saltwater <dbl>, Land <dbl>, DemersPelag <chr>, AnaCat <chr>,
# MigratRef <dbl>, DepthRangeShallow <dbl>, DepthRangeDeep <dbl>,
# DepthRangeRef <dbl>, DepthRangeComShallow <dbl>,
# DepthRangeComDeep <dbl>, DepthComRef <dbl>, LongevityWild <dbl>,
# LongevityWildRef <dbl>, LongevityCaptive <lgl>, LongevityCapRef <lgl>,
# Vulnerability <dbl>, Length <dbl>, LTypeMaxM <chr>,
# LengthFemale <dbl>, LTypeMaxF <chr>, MaxLengthRef <dbl>,
# CommonLength <dbl>, LTypeComM <chr>, CommonLengthF <dbl>,
# LTypeComF <chr>, CommonLengthRef <dbl>, Weight <dbl>,
# WeightFemale <dbl>, MaxWeightRef <dbl>, Pic <lgl>,
# PictureFemale <lgl>, LarvaPic <lgl>, EggPic <lgl>,
# ImportanceRef <dbl>, Importance <chr>, Remarks7 <chr>,
# PriceCateg <chr>, PriceReliability <chr>, LandingStatistics <chr>,
# Landings <chr>, MainCatchingMethod <chr>, II <chr>, MSeines <dbl>,
# MGillnets <dbl>, MCastnets <dbl>, MTraps <dbl>, MSpears <dbl>,
# MTrawls <dbl>, MDredges <dbl>, MLiftnets <dbl>, MHooksLines <dbl>,
# MOther <dbl>, UsedforAquaculture <chr>, LifeCycle <chr>,
# AquacultureRef <dbl>, UsedasBait <chr>, BaitRef <dbl>, Aquarium <chr>,
# AquariumFishII <chr>, AquariumRef <dbl>, GameFish <dbl>,
# GameRef <lgl>, Dangerous <chr>, DangerousRef <dbl>,
# Electrogenic <chr>, ElectroRef <dbl>, Complete <lgl>, ASFA <lgl>,
# GoogleImage <dbl>, Entered <dbl>, DateEntered <dttm>, Modified <dbl>,
# DateModified <dttm>, Expert <dbl>, DateChecked <dttm>, Synopsis <lgl>,
# DateSynopsis <lgl>, Flag <lgl>, Comments <chr>,
# VancouverAquarium <dbl>, Profile <lgl>, Sp2000_NameCode <chr>,
# Sp2000_HierarchyCode <chr>, Sp2000_AuthorRefNumber <chr>,
# E_Append <dbl>, E_DateAppend <date>, TS <dttm>
CAUTION: if switching between fishbase and sealifebase in a single R
session, we strongly advise you always set server explicitly in your
function calls. Otherwise you may confuse the caching system.
rfishbase 3.0 tries to maintain as much backwards compatibility as
possible with rfishbase 2.0. However, there are cases in which the
rfishbase 2.0 behavior was not desirable – such as throwing errors when
a introducing simple NAs for missing data would be more appropriate,
or returning vectors where data.frames were needed to include all the
context.
-
Argument names have been retained where possible to maximize backwards compatibility. Using previous arguments that are no longer relevant (such as
limitfor the maximum number of records) will not now introduce errors, but nor will they have any effect (they are simply consumed by the...). There are no longer any limits in return sizes. -
You can still specify server using the rfishbase
2.xformat of providing a URL argument for server, e.g."http://fishbase.ropensci.org/sealifebase"orSys.setenv(FISHBASE_API = "http://fishbase.ropensci.org/sealifebase"), or simplySys.setenv("FISHBASE_API" = "sealifebase")if you prefer. Also recall that environmental variables can always be set in an.Renvironfile.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.
