inst/markdown/01.snowcrab.md

In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.

Snow crab assessment data

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Survey data

Prepare the data environment.

• Ensure the year.assessment is correct

• make sure your passwords are correct in the externally stored password file.

• sequence is important .. do not skip steps

  require(aegis)  # basic helper tools

  year.assessment = 2024  # change this as appropriate

  p = bio.snowcrab::load.environment( year.assessment=year.assessment )  # set up initial settings

First, ensure data have been added to the back-end storage relational databases (ISSDB). Currently this is loaded manually by Brent Cameron.

{Brent, please add a short description here, with links to your work, when you can}

After load, we download the whole data system locally to manipulate and error check. There are several ways to proceed.

• Run in an R-session in Linux: This is challenging as ROracle does not compile easily.

• Run in an R-session in MSWindows with minimal dependencies (DBI, ROracle). See:

  • bio.snowcrab/inst/scripts/99_data_extrqaction_jae.R

• Run in an R-session in MSWindows: This requires you have bio.snowcrab and supporting libraries installed. Just run the following:

  # choose appropriate years
  yrs = 1996:year.assessment # redo all years
  yrs = p$year.assessment  # redo just last year

  snowcrab.db( DS="set.rawdata.redo", yrs=yrs ) # set data 
  snowcrab.db( DS="det.rawdata.redo", yrs=yrs ) # determined data (individual sex, size, mat, etc)
  snowcrab.db( DS="cat.rawdata.redo", yrs=yrs ) # aggregated weights and counts by set

At-sea-observed fishery data

The storage is in the same data base system as the survey data: ISSDB

  # bring in raw data from ISSDB backend as annual snapshots
  observer.db( DS="rawdata.redo", yrs=yrs )

  # compute items of interest
  observer.db( DS="bycatch.redo", yrs=yrs )  
  observer.db( DS="odb.redo", p=p ) # 3 minutes
  observer.db( DS="bycatch_clean_data.redo", p=p, yrs=p$yrs ) # 3 minutes

Fishery logbook data

Produce base data files from bio.snowcrab logbook database (marfis) and historical data

  # bring in raw data from back-end MARFIS databases as annual snapshots
  logbook.db(  DS="rawdata.logbook.redo", yrs=yrs )  
  logbook.db(  DS="rawdata.licence.redo" )  
  logbook.db(  DS="rawdata.areas.redo" )  

  # clean up and format
  logbook.db( DS="logbook.redo", p=p )
  logbook.db( DS="logbook.filtered.positions.redo", p=p )

  # fishing ground are used for determination of contraints for interpolation (no longer used?)
  logbook.db( DS="fishing.grounds.redo",  p=p )
  logbook.db( DS="logbook.gridded.redo", p=p )

Create base set level information with fixes for year-specific issues, etc

This initial set-level information is required to sanity check net mensuration estimates:

  snowcrab.db( DS="setInitial.redo", p=p ) # this is required by the seabird.db (but not minilog and netmind)

  # few sanity checks on the setInitial data pulled from the raw tables
  problems = data.quality.check( type="stations", p=p)  # duplicates
  problems = data.quality.check( type="count.stations", p=p)
  problems = data.quality.check( type="position", p=p) #MG try checking the end position of the tow, if there is an error
  problems = data.quality.check( type="position.difference", p=p)

Net mensuration

Net configuration (and temperature, depth) has been recorded using several systems over the years. Currently, the following now tracks only net configuration. Temperature anddepth are processess separately.

At the end of each survey year, a data folder for the recent survey needs to be placed in:

• bio.data/bio.snowcrab/data/seabird
• bio.data/bio.snowcrab/data/minilog
• bio.data/bio.snowcrab/data/esonar

Ideally, "click/touch" (manual touchdown / lift off) has already been completed externally with annual results appended to:

• C:/bio.data/bio.snowcrab/data/touchdown/results/clicktouchdown_all.csv

Any stations without touchdown / liftoff will prompt for manual input when running code below:

{Brent, add links to your system here and a short description}.

  # TODO:: this section needs to be moved to a static database system with incremental additions each year 

  if (updating.year.assessment) {
  # if just updating a single year, run the following, else all years will be run by default
    p$seabird.yToload = p$year.assessment
    p$minilog.yToload = p$year.assessment
    p$netmind.yToload = p$year.assessment
    p$esonar.yToload  = p$year.assessment
  }

  # Only do this line at the beginning to create the netmind format. Set 
  # redo.marp... to TRUE to convert the SDS csv file to a usable table 
  # which takes a long time. Only do once!!
  # convert.marport.sds_csv2netmind(yr=yrs, redo.marport_conversion = FALSE )

  seabird.db( DS="load", Y=p$seabird.yToload ) # this begins 2012;duplicates are often due to seabird files not being restarted each morning

  minilog.db( DS="load", Y=p$minilog.yToload ) # minilog data series "begins" in 1999 -- 60 min?

  #netmind.db( DS='esonar2netmind.conversion',Y=p$esonar.yToload ) #may no longer need to be run BZ
  netmind.db( DS="load", Y=p$netmind.yToload) # netmind data series "begins" in 1998 -- 60 min?
  #JC note: 1998:2002 have about 60 files with no data, just a short header

  p$netmensuration.problems = c() 
  # add troublesome id's to the list above .. eventually move into their respective functions

  seabird.db (DS="stats.redo", Y=p$seabird.yToload )
  minilog.db (DS="stats.redo", Y=p$minilog.yToload )  # note no depth in minilog any more (since 2014 ..  useful for temperature only)
  netmind.db (DS="stats.redo", Y=p$netmind.yToload )

Merging data and compute aggregate variables

Merge in netmind, minilog, seabird, esonar data and do some sanity checks.

NOTE: There are many historical data elements that require manual fixes to the raw data.

  snowcrab.db( DS="set.clean.redo", p=p ) 

  # Identify any issues with set.clean
  # problems = data.quality.check( type="minilog.mismatches", p=p )
  problems = data.quality.check( type="minilog.load", p=p)
  problems = data.quality.check( type="minilog.dateproblems", p=p) #track down why ~all sets are giving mismatches
  problems = data.quality.check( type="minilog", p=p)   # Check for duplicate timestamps
  problems = data.quality.check( type="netmind.load", p=p)
  problems = data.quality.check( type="netmind.mismatches", p=p )
  problems = data.quality.check( type="tow.duration", p=p)
  problems = data.quality.check( type="tow.distance", p=p)
  problems = data.quality.check( type="seabird.mismatches", p=p )
  problems = data.quality.check( type="seabird.load", p=p)
  problems = data.quality.check( type="netmind.timestamp" , p=p)


  # QA/QC of morphology and catches
  # sanity check  morphology 
  # identifies morphology errors (they are written to logs), 
  # fix them if found and re-run .. if no morphology errors exist, you will get an error message. Confirm legitimacy.

  snowcrab.db( DS="det.initial.redo", p=p ) 

  # sanity check catches
  snowcrab.db( DS="cat.initial.redo", p=p )

Prepare external data for lookups and modelling

Local empirical lookup tables are required for the steps that follow. Most do not need to be re-run, UNLESS you want reset the base data for each project.

Polygon data base

If this your first time using this system, you will need to see if the polygons defined are good enough for your use. If you need/want to (re)-initialize polygons, they can be found in:

• aegis.polygons/inst/scripts/01_polygons.md

• aegis.coastline/inst/scripts/01_coastline.md

NOTE: If you over-write them, then you will need to make sure that all the other polygon-based methods are consistent. Beware.

Bathymetry

The model solutions for bathymetry comes from stmv smoothed solutions. There is no need to re-run the carstm solutions unless you want to replace the lookup system with them. The empirical lookup of aggregated data however is useful and should be updated reasonably frequently. Every ~3-5 years? See the bathymetry project for more details:

• aegis.bathymetry/inst/scripts/01_bathymetry.r

  pC = bio.snowcrab::snowcrab_parameters( project_class="carstm", yrs=1999:year.assessment, areal_units_type="tesselation" )

  pB = aegis.bathymetry::bathymetry_parameters( p=parameters_reset(pC), project_class="carstm"  )

  M = aegis.bathymetry::bathymetry_db( p=pB, DS="aggregated_data" , redo=TRUE ) #this step can take ~20 minutes

Substrate grainsize

The model solutions for substrate grain size comes from stmv smoothed solutions. There is no need to re-run the carstm solutions unless you want to replace the lookup system with them. The empirical lookup of aggregated data however is useful and should be updated reasonably frequently. Every ~3-5 years? See the substrate project for more details:

• aegis.substrate/inst/scripts/01_substrate_data.R

      pS = substrate_parameters( p=parameters_reset(pC), project_class="carstm"  )
      M = aegis.substrate::substrate_db( p=pS, DS="aggregated_data" , redo=TRUE )

Bottom temperature

Base data comes from many sources. Historical data was assimilated by OSD and copied here. Recent data are now coming from a separate project and stored in its' own relational database (lead by Amy Glass).

{Amy, add a short description and link here}

After loading, we download annual time slices here for processing. See for more details:

• aegis.temperature/inst/scripts/01_temperature_data.md

The following is only for data assimilation. Modelling requires step 3. Bottom temperature modelling should however be re-run annually after assimilating new year's data. Currently, we use:

• aegis.temperature/inst/scripts/03_temperature_carstm.md.

      pT = temperature_parameters( p=parameters_reset(pC), project_class="carstm"  )

      temperature_db( DS="bottom.annual.rawdata.redo", p=p, yr=1970:year.assessment )  # brent and amy's new db view

      o = temperature_db( DS="bottom.annual.redo", p=p,   yr=1970:year.assessment ) # use all years to improve spatial resolution 

      o = aegis.temperature::temperature_db( p=pT, DS="aggregated_data" , redo=TRUE )

Data from other surveys

Namely groundfish surveys provide supplemental data for species composition analysis:

• aegis.survey/inst/scripts/01_survey_data.md.

Species composition

Species composition is a covariate in the areal model for snow crab. To obtain a predictive surface, the following needs to be run:

• aegis.speciescomposition/inst/scripts/01_speciescomposition_carstm.R.

Finalize the data sets

These are the data sets used for analysis.

  snowcrab.db( DS="det.georeferenced.redo", p=p )
  snowcrab.db( DS="cat.georeferenced.redo", p=p )
  snowcrab.db( DS="set.biologicals.redo", p=p )
  snowcrab.db( DS="set.complete.redo", p=p ) # note depth is log transformed here

  # this one is may be phased out as it is for quick generic plots... not used any more?
  snowcrab.db( DS="data.transforms.redo", p=p) # update a database of simple transformation ranges, etc.. for plotting range, etc.

  # create some simple/crude timeseries by each CFA using set.complete 
  snowcrab.timeseries.db( DS="observer.redo", p=p )
  snowcrab.timeseries.db( DS="biologicals.redo", p=p )

Polygon for analysis: sppoly

Create a polygon for abundance estimation and size frequency analysis. Annually updated, but not required to.

# create areal_units (polygons) for biomass and size structure

  ps = snowcrab_parameters(
    project_class="carstm",
    yrs=1999:year.assessment,   
    areal_units_type="tesselation",
    carstm_model_label=  paste( "default", "fb", sep="_" )  # default for fb (fishable biomass)
  )

  xydata = snowcrab.db( p=ps, DS="areal_units_input", redo=TRUE )
  # xydata = snowcrab.db( p=ps, DS="areal_units_input" )


  additional_features = snowcrab_mapping_features(ps, redo=FALSE )  # for mapping (background) .. redo=TRUE if resetting colours etc


  # create constrained polygons with neighbourhood as an attribute
  sppoly = areal_units( p=ps, xydata=xydata, spbuffer=3, n_iter_drop=0, redo=TRUE, verbose=TRUE )  # this needs to match carstm related parameters in snowcrab_parameters

  # sppoly=areal_units( p=ps )

      plot(sppoly["AUID"])

      sppoly$dummyvar = ""
      xydata = st_as_sf( xydata, coords=c("lon","lat") )
      st_crs(xydata) = st_crs( projection_proj4string("lonlat_wgs84") )

      tmap_mode("plot")

      plt = 
        tm_shape(sppoly) +
          tm_borders(col = "slategray", alpha = 0.5, lwd = 0.5) + 
          tm_shape( xydata ) + tm_sf() +
          additional_features[["tmap"]] +
          tm_compass(position = c("right", "TOP"), size = 1.5) +
          tm_scale_bar(position = c("RIGHT", "BOTTOM"), width =0.1, text.size = 0.5) +
          tm_layout(frame = FALSE, scale = 2) +
          tm_shape( st_transform(polygons_rnaturalearth(), st_crs(sppoly) )) + 
          tm_borders(col = "slategray", alpha = 0.5, lwd = 0.5)

      dev.new(width=14, height=8, pointsize=20)
      plt

Size-frequency distributions of snow crab cw from trawl data, broken down by maturity classes

  sppoly = areal_units(  p=ps )  # it uses sppoly
  xrange = c(10, 150)  # size range (CW)
  dx = 2 # width of carapace with discretization to produce "cwd"

  M = size_distributions(p=p, toget="base_data", pg=sppoly, xrange=xrange, dx=dx, redo=TRUE)

  # tabulate... non-zero counts ... must use add_zeros=TRUE to add them, on the fly
  M = size_distributions(p=p, toget="tabulated_data", xrange=xrange, dx=dx, redo=TRUE)

  years = as.character( c(-9:0) + year.assessment )
  M = size_distributions(p=p, toget="simple_direct", xrange=xrange, dx=dx, Y=years, redo=TRUE)

Deprecated

Unused but stored in case.

  snowcrab.timeseries.db( DS="groundfish.t.redo", p=p )  # deprecated to be removed shortly
  snowcrab.timeseries.db( DS="biologicals.2014.redo" )  # reduced subset that matches 2014 station id's .. # deprecated

  # example: to get a quick view of a few vars of interest, region of interest ... no saving to file, but does return the data for viewing
  snowcrab.timeseries.db( DS="biologicals.direct", p=p, regions='cfa4x', vn=c('R0.mass'), trim=0 )  

jae0/snowcrab documentation built on Nov. 6, 2024, 10:13 p.m.