In pbs-assess/PACea: An R package of Pacific ecosystem information to help facilitate an ecosystem approach to fisheries management
BCCM
British Columbia Continental Margin (BCCM) model
Physical biogeochemical oceanographic model
-
Regional Ocean Modelling Systen (ROMS)
-
Curvilinear grid at 3km x 3km resolution
-
We interpolate to regular grids
\
Model output provided by Angelica Peña at the Institute of Ocean Sciences (Fisheries and
Oceans Canada).
\
\footnotesize
Peña, M.A., Fine, I. and Callendar, W. 2019. Interannual variability in primary production and shelf-offshore transport of nutrients along the northeast Pacific Ocean margin. Deep-Sea Research II, doi:10.1016/j.dsr2.2019.104637.
\normalsize
Mapping to grids
Original mapping:
-
clipped to Canada's Pacific Exclusive Economic Zone
-
Inshore (2 km x 2 km) and offshore (6 km x 6 km)
-
40,480 cells
-
Monthly from 1993-2019
New additional mapping:
-
r colorize("covers full domain (into US waters)", "red")
-
r colorize("2 km x 2km grid throughout", "red")
-
r colorize("161,025 cells", "red")
-
Monthly from 1993-2019
-
r colorize("New grid also used for HOTSSea outputs, and has interpolated
depths", "red")
Available variables
The variables are:
- dissolved oxygen concentration
- pH
- salinity
- temperature
- depth-integrated phytoplankton
- depth-integrated primary production.
For applicable variables these are given for
- sea surface
- 0-40 m integration
- 40-100 m integration
- 100 m to the sea bottom
- sea bottom.
List of available variables (original just Canada)
bccm_data for available variables
bccm_data
List of available variables (full domain)
bccm_data_full for available variables
bccm_data_full
Downloading data locally
The data are saved as sf (simple feature) objects in a separate GitHub repository pacea-data (https://github.com/pbs-assess/pacea-data).
\
One-time download to local machine (will take several minutes):
bccm_all_variables()
Downloading all BCCM data may take many minutes. Files will be downloaded to pacea_cache directory:
C:\Users\TAIT\AppData\Local/pacea/Cache
Would you like to continue?
1: Yes
2: No
Selection:
Example information
Average of pH from 100 m to sea bottom:
> bccm_avg100mtoBot_ph()
Simple feature collection with 33642 features and 324 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 164671.6 ymin: 163875 xmax: 1097612 ymax: 1103653
Projected CRS: NAD83 / BC Albers
# A tibble: 33,642 × 325
`1993_1` `1993_2` `1993_3` `1993_4` ... geometry
<dbl> <dbl> <dbl> <dbl> ... <POLYGON [m]>
1 7.83 7.87 7.88 7.90 ... ((575612.5 1103653, 577612.5 ~
2 7.83 7.87 7.88 7.90 ... ((577612.5 1103653, 579612.5 ~
3 7.83 7.87 7.88 7.90 ... ((579612.5 1103653, 581612.5 ~
4 7.83 7.87 7.88 7.89 ... ((581612.5 1103653, 583612.5 ~
5 7.79 7.82 7.84 7.82 ... ((619612.5 1103653, 621612.5 ~
6 7.79 7.82 7.83 7.82 ... ((621612.5 1103653, 623612.5 ~
# i 33,636 more rows
# i 320 more variables: ...
```{asis, echo = more_details}
Help file
```r
?bccm_surface_oxygen
BCCM environmental variable data
Description
Loading and/or downloading British Columbia continental margin (BCCM) model data for the BC Pacific region to local drive.
To download all BCCM data, use the function 'bccm_all_variables()'. See help page for details ('?bccm_all_variables').
Usage
bccm_bottom_oxygen(update = FALSE, ask = interactive(), force = FALSE)
bccm_bottom_ph(update = FALSE, ask = interactive(), force = FALSE)
bccm_bottom_salinity(update = FALSE, ask = interactive(), force = FALSE)
...
Arguments
update Logical. Would you like to check for a newer version of the layer?
ask Logical. Should the user be asked before downloading the data to local cache? Defaults to the value of interactive().
force Logical. Should download of data be forced? Overrides 'ask' argument if TRUE.
Format
A simple features dataframe.
...
Built in plotting
plot(bccm_surface_temperature(), months.plot = c("april", "july"), years.plot = c(2013, 2016, 2019))
Built in plotting
plot(bccm_surface_ph())
```{asis, echo = long_talk}
Extras -- climatology function
Built in functions to calculate 30-year climatology
```r
?calc_clim
Description
Function for calculating climatology of a pacea data object.
Usage
calc_clim(
data,
clim_years = c(1991:2020),
clim_time = "month",
time_period_return = "all"
)
Arguments
data pacea data object: BCCM, OISST, buoy_sst data only
clim_years climatology period years
clim_time time units (e.g. month) to summarize climatologies
time_period_return vector of value(s) for the specific time units to estimate climatologies (e.g. '4' for week 4 or April). Set to equal 'all' for all time units.
...
```{asis, echo = long_talk}
Extras -- climatology function
Output with each climatology time unit (e.g. week, month)
```r
calc_clim(bccm_surface_temperature())
Simple feature collection with 486960 features and 4 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 164671.6 ymin: 163875 xmax: 1097612 ymax: 1103653
Projected CRS: NAD83 / BC Albers
# A tibble: 486,960 x 5
month clim_value clim_sd clim_n geometry
<dbl> <dbl> <dbl> <int> <POLYGON [m]>
1 1 5.80 0.721 27 ((575612.5 1103653, 5776~
2 2 5.47 0.610 27 ((575612.5 1103653, 5776~
3 3 5.64 0.627 27 ((575612.5 1103653, 5776~
4 4 6.61 0.623 27 ((575612.5 1103653, 5776~
5 5 8.49 0.732 27 ((575612.5 1103653, 5776~
6 6 10.5 0.662 27 ((575612.5 1103653, 5776~
# i 486,954 more rows
# i Use `print(n = ...)` to see more rows
```{asis, echo = long_talk}
\
And we can also use this with the OISST and buoy data
```r
calc_clim(oisst_7day)
calc_clim(buoy_sst)
```{asis, echo = long_talk}
Extras -- anomaly function
Built in functions to calculate anomaly to the 30 year climatology
```r
?calc_anom
Description
Function for calculating climatology of a pacea data object.
Usage
calc_anom(
data,
clim_years = c(1991:2020),
clim_time = "month",
time_period_return = "all",
years_return
)
Arguments
data pacea data object: BCCM, OISST, buoy_sst data only
clim_year climatology period years
clim_time time units (e.g. month) to summarize climatologies
time_period_return vector of value(s) for the specific time units to estimate climatologies (e.g. '4' for week 4 or April). Set to equal 'all' for all time units.
years_return vector of value(s) to return the years of interest. Defaults to all years in input data
...
```{asis, echo = long_talk}
Extras -- anomaly function
```r
if(!exists("anom_dat")){
anom_dat <- calc_anom(bccm_surface_temperature(), time_period_return = "April", years_return = c(2010, 2015))
}
```{asis, echo = long_talk}
Specify year(s) and time period (e.g. week, month) to return -- otherwise all.
```r
anom_dat <- calc_anom(bccm_surface_temperature(), time_period_return = "April", years_return = c(2010, 2015))
Simple feature collection with 40580 features and 2 fields
Geometry type: POLYGON
Dimension: XY
Bounding box: xmin: 164671.6 ymin: 163875 xmax: 1097612 ymax: 1103653
Projected CRS: NAD83 / BC Albers
# A tibble: 40,580 x 3
`2010_4` `2015_4` geometry
<dbl> <dbl> <POLYGON [m]>
1 0.114 1.09 ((575612.5 1103653, 577612.5 1103653, 577612.5 1101653, 575612.5 1101653, 575~
2 0.124 1.08 ((577612.5 1103653, 579612.5 1103653, 579612.5 1101653, 577612.5 1101653, 577~
3 0.150 1.07 ((579612.5 1103653, 581612.5 1103653, 581612.5 1101653, 579612.5 1101653, 579~
4 0.204 1.08 ((581612.5 1103653, 583612.5 1103653, 583612.5 1101653, 581612.5 1101653, 581~
5 0.204 1.07 ((583612.5 1103653, 585612.5 1103653, 585612.5 1101653, 583612.5 1101653, 583~
6 0.244 1.08 ((585612.5 1103653, 587612.5 1103653, 587612.5 1101653, 585612.5 1101653, 585~
# i 40,574 more rows
# i Use `print(n = ...)` to see more rows
Extras -- anomaly plot
plot(anom_dat, months.plot = "April", years.plot = c(2010, 2015))
```{asis, echo = long_talk}
Extras -- other tidbits in vignettes
Geospatial functions in vignettes
-
area_mean() estimates inshore and offshore spatial mean (200m isobath)
-
Masking/clipping specific areas (e.g. fishing regions of interest)
-
Various other data visualisation examples
```
pbs-assess/PACea documentation built on April 17, 2025, 11:36 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
BCCM
British Columbia Continental Margin (BCCM) model
Physical biogeochemical oceanographic model
-
Regional Ocean Modelling Systen (ROMS)
-
Curvilinear grid at 3km x 3km resolution
-
We interpolate to regular grids
\ Model output provided by Angelica Peña at the Institute of Ocean Sciences (Fisheries and Oceans Canada).
\ \footnotesize Peña, M.A., Fine, I. and Callendar, W. 2019. Interannual variability in primary production and shelf-offshore transport of nutrients along the northeast Pacific Ocean margin. Deep-Sea Research II, doi:10.1016/j.dsr2.2019.104637. \normalsize
Mapping to grids
Original mapping:
-
clipped to Canada's Pacific Exclusive Economic Zone
-
Inshore (2 km x 2 km) and offshore (6 km x 6 km)
-
40,480 cells
-
Monthly from 1993-2019
New additional mapping:
-
r colorize("covers full domain (into US waters)", "red") -
r colorize("2 km x 2km grid throughout", "red") -
r colorize("161,025 cells", "red") -
Monthly from 1993-2019
-
r colorize("New grid also used for HOTSSea outputs, and has interpolated depths", "red")
Available variables
The variables are:
- dissolved oxygen concentration
- pH
- salinity
- temperature
- depth-integrated phytoplankton
- depth-integrated primary production.
For applicable variables these are given for
- sea surface
- 0-40 m integration
- 40-100 m integration
- 100 m to the sea bottom
- sea bottom.
List of available variables (original just Canada)
bccm_data for available variables
bccm_data
List of available variables (full domain)
bccm_data_full for available variables
bccm_data_full
Downloading data locally
The data are saved as sf (simple feature) objects in a separate GitHub repository pacea-data (https://github.com/pbs-assess/pacea-data).
\
One-time download to local machine (will take several minutes):
bccm_all_variables() Downloading all BCCM data may take many minutes. Files will be downloaded to pacea_cache directory: C:\Users\TAIT\AppData\Local/pacea/Cache Would you like to continue? 1: Yes 2: No Selection:
Example information
Average of pH from 100 m to sea bottom:
> bccm_avg100mtoBot_ph() Simple feature collection with 33642 features and 324 fields Geometry type: POLYGON Dimension: XY Bounding box: xmin: 164671.6 ymin: 163875 xmax: 1097612 ymax: 1103653 Projected CRS: NAD83 / BC Albers # A tibble: 33,642 × 325 `1993_1` `1993_2` `1993_3` `1993_4` ... geometry <dbl> <dbl> <dbl> <dbl> ... <POLYGON [m]> 1 7.83 7.87 7.88 7.90 ... ((575612.5 1103653, 577612.5 ~ 2 7.83 7.87 7.88 7.90 ... ((577612.5 1103653, 579612.5 ~ 3 7.83 7.87 7.88 7.90 ... ((579612.5 1103653, 581612.5 ~ 4 7.83 7.87 7.88 7.89 ... ((581612.5 1103653, 583612.5 ~ 5 7.79 7.82 7.84 7.82 ... ((619612.5 1103653, 621612.5 ~ 6 7.79 7.82 7.83 7.82 ... ((621612.5 1103653, 623612.5 ~ # i 33,636 more rows # i 320 more variables: ...
```{asis, echo = more_details}
Help file
```r ?bccm_surface_oxygen
BCCM environmental variable data Description Loading and/or downloading British Columbia continental margin (BCCM) model data for the BC Pacific region to local drive. To download all BCCM data, use the function 'bccm_all_variables()'. See help page for details ('?bccm_all_variables'). Usage bccm_bottom_oxygen(update = FALSE, ask = interactive(), force = FALSE) bccm_bottom_ph(update = FALSE, ask = interactive(), force = FALSE) bccm_bottom_salinity(update = FALSE, ask = interactive(), force = FALSE) ... Arguments update Logical. Would you like to check for a newer version of the layer? ask Logical. Should the user be asked before downloading the data to local cache? Defaults to the value of interactive(). force Logical. Should download of data be forced? Overrides 'ask' argument if TRUE. Format A simple features dataframe. ...
Built in plotting
plot(bccm_surface_temperature(), months.plot = c("april", "july"), years.plot = c(2013, 2016, 2019))
Built in plotting
plot(bccm_surface_ph())
```{asis, echo = long_talk}
Extras -- climatology function
Built in functions to calculate 30-year climatology
```r ?calc_clim
Description Function for calculating climatology of a pacea data object. Usage calc_clim( data, clim_years = c(1991:2020), clim_time = "month", time_period_return = "all" ) Arguments data pacea data object: BCCM, OISST, buoy_sst data only clim_years climatology period years clim_time time units (e.g. month) to summarize climatologies time_period_return vector of value(s) for the specific time units to estimate climatologies (e.g. '4' for week 4 or April). Set to equal 'all' for all time units. ...
```{asis, echo = long_talk}
Extras -- climatology function
Output with each climatology time unit (e.g. week, month)
```r calc_clim(bccm_surface_temperature())
Simple feature collection with 486960 features and 4 fields Geometry type: POLYGON Dimension: XY Bounding box: xmin: 164671.6 ymin: 163875 xmax: 1097612 ymax: 1103653 Projected CRS: NAD83 / BC Albers # A tibble: 486,960 x 5 month clim_value clim_sd clim_n geometry <dbl> <dbl> <dbl> <int> <POLYGON [m]> 1 1 5.80 0.721 27 ((575612.5 1103653, 5776~ 2 2 5.47 0.610 27 ((575612.5 1103653, 5776~ 3 3 5.64 0.627 27 ((575612.5 1103653, 5776~ 4 4 6.61 0.623 27 ((575612.5 1103653, 5776~ 5 5 8.49 0.732 27 ((575612.5 1103653, 5776~ 6 6 10.5 0.662 27 ((575612.5 1103653, 5776~ # i 486,954 more rows # i Use `print(n = ...)` to see more rows
```{asis, echo = long_talk} \ And we can also use this with the OISST and buoy data
```r calc_clim(oisst_7day) calc_clim(buoy_sst)
```{asis, echo = long_talk}
Extras -- anomaly function
Built in functions to calculate anomaly to the 30 year climatology
```r ?calc_anom
Description Function for calculating climatology of a pacea data object. Usage calc_anom( data, clim_years = c(1991:2020), clim_time = "month", time_period_return = "all", years_return ) Arguments data pacea data object: BCCM, OISST, buoy_sst data only clim_year climatology period years clim_time time units (e.g. month) to summarize climatologies time_period_return vector of value(s) for the specific time units to estimate climatologies (e.g. '4' for week 4 or April). Set to equal 'all' for all time units. years_return vector of value(s) to return the years of interest. Defaults to all years in input data ...
```{asis, echo = long_talk}
Extras -- anomaly function
```r if(!exists("anom_dat")){ anom_dat <- calc_anom(bccm_surface_temperature(), time_period_return = "April", years_return = c(2010, 2015)) }
```{asis, echo = long_talk} Specify year(s) and time period (e.g. week, month) to return -- otherwise all.
```r anom_dat <- calc_anom(bccm_surface_temperature(), time_period_return = "April", years_return = c(2010, 2015)) Simple feature collection with 40580 features and 2 fields Geometry type: POLYGON Dimension: XY Bounding box: xmin: 164671.6 ymin: 163875 xmax: 1097612 ymax: 1103653 Projected CRS: NAD83 / BC Albers # A tibble: 40,580 x 3 `2010_4` `2015_4` geometry <dbl> <dbl> <POLYGON [m]> 1 0.114 1.09 ((575612.5 1103653, 577612.5 1103653, 577612.5 1101653, 575612.5 1101653, 575~ 2 0.124 1.08 ((577612.5 1103653, 579612.5 1103653, 579612.5 1101653, 577612.5 1101653, 577~ 3 0.150 1.07 ((579612.5 1103653, 581612.5 1103653, 581612.5 1101653, 579612.5 1101653, 579~ 4 0.204 1.08 ((581612.5 1103653, 583612.5 1103653, 583612.5 1101653, 581612.5 1101653, 581~ 5 0.204 1.07 ((583612.5 1103653, 585612.5 1103653, 585612.5 1101653, 583612.5 1101653, 583~ 6 0.244 1.08 ((585612.5 1103653, 587612.5 1103653, 587612.5 1101653, 585612.5 1101653, 585~ # i 40,574 more rows # i Use `print(n = ...)` to see more rows
Extras -- anomaly plot
plot(anom_dat, months.plot = "April", years.plot = c(2010, 2015))
```{asis, echo = long_talk}
Extras -- other tidbits in vignettes
Geospatial functions in vignettes
-
area_mean()estimates inshore and offshore spatial mean (200m isobath) -
Masking/clipping specific areas (e.g. fishing regions of interest)
-
Various other data visualisation examples ```
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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