Metacarcinus magister
Dungeness crab
The Dungeness crab is a species of crab in the family Cancridae. It makes up one of the most important seafood industries along the west coast of North America.W CalCOFI holds 15 observations of it in 1 dataset between 1984 and 2014, as megalopa.R
- 15
- observations
- 526
- records
- 526
- sampling events
- 1984–2014
- years
- 1
- datasets
- megalopa
- life stages
Observed ini
One row per dataset, one cell per year, darker with more observations — the same strip the front
door draws per dataset. Each row ends with the name that dataset uses (dataset_taxon);
the accepted name is written once, in the title above. A quiet pill marks a name the authority does not accept.
i
One row per dataset, one cell per year, darker with more observations — the same strip the front door draws per dataset. Each row ends with the name that dataset uses (dataset_taxon); the accepted name is written once, in the title above. A quiet pill marks a name the authority does not accept.
-
name used: Metacarcinus magister · code
metacarcinus_magister
How big is iti
Every mark is a measured length with its own source — hover one to read it.
The reference objects are _data/size_reference.csv: two of them,
the 505 µm bongo mesh and the net's ring, are CalCOFI's own sampling gear.
i
Every mark is a measured length with its own source — hover one to read it. The reference objects are _data/size_reference.csv: two of them, the 505 µm bongo mesh and the net's ring, are CalCOFI's own sampling gear.
Ways ini
Read from taxa.json of release
v2026.09.11, written at release
by calcofi4db::build_taxa_catalog(). The catalog's index is
/species/.
Explorerdb-queryi
Read from taxa.json of release v2026.09.11, written at release by calcofi4db::build_taxa_catalog(). The catalog's index is /species/.
library(calcofi4r) con <- cc_get_db() # the promoted release tbl(con, "obs_bio") |> filter(taxon_key == "worms:440388")
import calcofi4py as cc
con = cc.cc_get_db()
con.sql("SELECT * FROM obs_bio WHERE taxon_key = 'worms:440388'").df()