nceas / ca-mpa Goto Github PK

We decided to produce our own gridded product of maximum Kelp extent for habitat analysis.

I found this data set on EDI: https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-sbc.74.17

Note:

• the data do not have gridded dimensions as normally in a netcdf file
• the dimensions are time and stations
• The station locations are stored in a different grid

Here is the workflow attempted so far:

1. Import the grid as a data frame using tidync package; this data frame will have time x stations nrow
2. group_by station taking the max value since it is what we are interested in (max biomass); this results in a data frame having one row per station
3. transform the station location grid into a data frame
4. join the two data frames on station
5. create a point sf object
6. rasterize to any grid we want

Here is the script: https://github.com/NCEAS/ca-mpa/blob/main/analyses/7habitat/code/kelplter_data.R

Goal: Compute biomass estimates from fish length

Data conversion

We have several main cases:

• We have the total length (TL):
  • We have the right unit for a and b: g and cm => done
  • We have the wrong units for a and b => need to transform using formulas
• We have another type of length:
  • We need to first transform the length into the total one using the formula in column "LL_Equation_for_WL"

Main formula: W = a * L^b

Unit transformation (source: Fishbase)

a'(cm, g) = a (mm, g)*10^b
a'(cm, g) = a (cm, kg)*1000
a'(cm, g) = a (mm, mg)*10^b/1000
a'(cm, g) = a (mm, kg)10^b1000

Relationship reference: https://www.fishbase.de/manual/fishbasethe_length_weight_table.htm

code: https://github.com/NCEAS/ca-mpa/blob/main/analyses/1performance_eco/code/biomass_reference_file_prep.R

We want to download this data set:

https://maps.nrel.gov/marine-energy-atlas/data-library/layers

API information:
https://registry.opendata.aws/wpto-pds-us-wave/
https://developer.nrel.gov/docs/solar/nsrdb/guide/

References using this data:
https://www.sciencedirect.com/science/article/pii/S0964569122002186#fig2
https://doi.org/10.1371/journal.pbio.3001195

Define areas with similar characteristics to MPAs to test hypotheses about the attractivity of MPAs.

• Need to compile the layers needed
• Compute distance from those assets (need to choose raster or vector approach)
• Need to combine those layers and extract Characteristics for each MPA
• Need to compute areas that are similar to MPAs characteristics but are not MPA

I need to consolidate our data management folder on Google Drive to enable PIs to share their data. Probably a subfolder under the working group shared folder.

• Set up Folder on GD
• Set up rclone on Aurora for that folder
• Check the Datalog to track the provenance of the data sets