Last updated: 2024-01-25

Checks: 7 0

Knit directory: WP1/

This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.


Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

The command set.seed(20210216) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 81a911a. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .httr-oauth
    Ignored:    code/.Rhistory
    Ignored:    data/analyses/clean_all.RData
    Ignored:    data/analyses/clean_all_clean.RData
    Ignored:    data/analyses/ice_4km_proc.RData
    Ignored:    data/full_data/
    Ignored:    data/sst_trom.RData
    Ignored:    metadata/globalfishingwatch_API_key.RData
    Ignored:    metadata/is_gfw_database.RData
    Ignored:    metadata/pangaea_parameters.tab
    Ignored:    metadata/pg_EU_ref_meta.csv
    Ignored:    mhw-definition_1_orig.xcf
    Ignored:    poster/SSC_2021_landscape_files/paged-0.15/
    Ignored:    presentations/.Rhistory
    Ignored:    presentations/2023_Ilico.html
    Ignored:    presentations/2023_LOV.html
    Ignored:    presentations/2023_results_day.html
    Ignored:    presentations/2023_seminar.html
    Ignored:    presentations/2023_summary.html
    Ignored:    presentations/2024_36_month.html
    Ignored:    presentations/ASSW_2023.html
    Ignored:    presentations/ASSW_side_2023.html
    Ignored:    presentations/Session5_SCHLEGEL.html
    Ignored:    shiny/dataAccess/coastline_hi_sub.csv
    Ignored:    shiny/kongCTD/.httr-oauth
    Ignored:    shiny/kongCTD/credentials.RData
    Ignored:    shiny/kongCTD/data/
    Ignored:    shiny/test_data/

Unstaged changes:
    Modified:   analysis/_site.yml

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.


These are the previous versions of the repository in which changes were made to the R Markdown (analysis/species_data.Rmd) and HTML (docs/species_data.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
html 9228f25 Robert William Schlegel 2023-12-20 Build site.
html 0926ac4 Robert William Schlegel 2023-12-12 Build site.
html 7ad8a78 Robert William Schlegel 2023-12-12 Build site.
html 77b2a61 Robert 2023-12-04 Build site.
html 610ff35 Robert 2023-08-30 Build site.
html 6ad5b79 Robert William Schlegel 2023-08-24 Build site.
html bd4819b Robert 2023-08-23 Build site.
html 4ddbb12 robwschlegel 2023-07-24 Build site.
html e8997de Robert 2023-05-30 Build site.
html 2ea419c Robert 2023-05-30 Build site.
html 1422923 Eccalin 2023-05-22 Species summary update
Rmd 33161ad Eccalin 2023-05-17 Species summary
html 33161ad Eccalin 2023-05-17 Species summary
Rmd 49a94f6 Eccalin 2023-05-17 Species summary
html 49a94f6 Eccalin 2023-05-17 Species summary
Rmd b336e80 Eccalin 2023-05-17 New species figures
Rmd ad00660 Eccalin 2023-05-17 Summary of species data
html ad00660 Eccalin 2023-05-17 Summary of species data
Rmd 29107e6 Eccalin 2023-05-16 data species
html 29107e6 Eccalin 2023-05-16 data species
html d73422a Eccalin 2023-05-16 Poster
html 73cd309 Eccalin 2023-05-12 Build site.
Rmd cfa3aca Eccalin 2023-05-12 web site test
html cfa3aca Eccalin 2023-05-12 web site test
Rmd 4ce558b Eccalin 2023-05-12 Web site
html 4ce558b Eccalin 2023-05-12 Web site

Overview

This document outlines the process of collecting, amalgamating, and analyzing species presence data from the FACE-IT Arctic Fjords study sites.


Methods

Determine which data is needed

To meet the requirements of the FACE-IT project, the data must meet certain criteria. The data must be from one of the seven study sites of the project, in Svalbard, Greenland or Norway. Data must include species biomass (presence data will also be collected). The data must concern marine species such as birds, fish, mammals, zooplankton or phytoplankton.

Searching for sources

To ensure data quality, it is essential to have reliable sources. For this, research and academic sites were used, in particular those of MOSJ, GEM, NPI.

Data collection and identification

Once the sources are found, it is important to collect the datasets that can be used for the project. Sometimes it is necessary to log in to certain websites to access the data. In order to use this data to its highest potential, it is important to determine what useful and necessary information in each dataset should be preserved. Careful consideration must be given to each set to ensure the quality of the information.

Modeling the sets

The data collected will be added to the ones already present on the FACE-IT project, they will have to follow the same format and respect the order by filling the following columns:

  • the date of access to the data,

  • the URL where to find the set,

  • the citation,

  • the type of data,

  • the site (kong, nuup, svalbard, is, disko, …),

  • the category (bio, cryo, social, …),

  • the driver (category details.) ),

  • the variable,

  • the longitude of each data,

  • the latitude of each data,

  • the date of collection of the data,

  • the depth of water of each data,

  • the value

Variable naming

All the variables follow a precise naming system. This allows to save their main information. For each variable we find:

Species type code

For an easier use, the different species studied have been divided in several categories each distinguished by a three letters code: Birds, Poisons |FIS|, Marine mammal |MAM|, Zooplankton |ZOO| and Phytoplankton |PHY|.

For the birds, subcategories have been added. The goal of the project being the study of marine species and the data collected concerning different types of birds. A distinction was made between marine birds |SBI|, land birds (non marine) |NBI| and species not yet sorted |BIR|.

Assembling the sets

Once the data are formatted, they are combined by geographic area, saved and added to the website.

Analyze the sets

Once the sets are complete, an analysis of the data recovered can be made. This allows us to see the information collected and to highlight certain patterns.


Svalbard


figure 1 - a) Svalbard data count by year, b) Svalbard species biomass over year

Kongsfjorden


figure 2 - a) Kongsfjorden data count by year, b) Kongsfjorden species biomass over year

Isfjorden


figure 3 - a) Isfjorden data count by year, b) Isfjorden species biomass over year

Barents sea


Greenland


Young Sound


Nuup Kangerlua



sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3;  LAPACK version 3.9.0

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Paris
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.1

loaded via a namespace (and not attached):
 [1] vctrs_0.6.5       httr_1.4.7        cli_3.6.2         knitr_1.45       
 [5] rlang_1.1.3       xfun_0.41         stringi_1.8.3     processx_3.8.3   
 [9] promises_1.2.1    jsonlite_1.8.8    glue_1.7.0        rprojroot_2.0.4  
[13] git2r_0.33.0      htmltools_0.5.7   httpuv_1.6.13     ps_1.7.6         
[17] sass_0.4.8        fansi_1.0.6       rmarkdown_2.25    jquerylib_0.1.4  
[21] tibble_3.2.1      evaluate_0.23     fastmap_1.1.1     yaml_2.3.8       
[25] lifecycle_1.0.4   whisker_0.4.1     stringr_1.5.1     compiler_4.3.2   
[29] fs_1.6.3          pkgconfig_2.0.3   Rcpp_1.0.12       rstudioapi_0.15.0
[33] later_1.3.2       digest_0.6.34     R6_2.5.1          utf8_1.2.4       
[37] pillar_1.9.0      callr_3.7.3       magrittr_2.0.3    bslib_0.6.1      
[41] tools_4.3.2       cachem_1.0.8      getPass_0.2-4