Add baseline temperatures of species- or study-area polygons for each range shift

Usage

add_baselines(
  data,
  type = "SA",
  stat = c("mean"),
  res = c(LAT = "25km", ELE = "1km"),
  suffix = FALSE
)

Arguments

data: input data from get_shifts()
type: Choice of baseline temperatures from study area (SA) or species area (SP) polygons.
stat: Statistic of the given variable. Choices are "mean" and "sd".
res: Calculation resolution. Baseline temperatures in each species/study area were calculated with environmental raster layers at up to four resolutions: 1km, 25km, 50km, 110km, resulting in slightly different values. Choose a specific resolution with one of: "1km", "25km", "50km", "110km".
suffix: Binary choice to add the resolution on to baseline variable columns. Use this if you plan to add multiple baseline resolutions to the same dataset.

Value

Shifts database supplemented with selected temperature baseline values within the study area or species-specific study area.

Examples

get_shifts() |> add_baselines()
#> # A tibble: 31,761 × 15
#>    id         article_id poly_id method_id eco   type  param sp_name_publication
#>    <chr>      <chr>      <chr>   <chr>     <chr> <chr> <chr> <chr>              
#>  1 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Ambloplites_rupest…
#>  2 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Ameiurus_nebulosus 
#>  3 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Chrosomus_eos      
#>  4 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Lepomis_gibbosus   
#>  5 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Lepomis_macrochirus
#>  6 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Luxilus_cornutus   
#>  7 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Micropterus_dolomi…
#>  8 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Micropterus_salmoi…
#>  9 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Notemigonus_crysol…
#> 10 A002_P1_L… A002       P1      M01       Ter   LAT   LE    Notropis_atherinoi…
#> # ℹ 31,751 more rows
#> # ℹ 7 more variables: sp_name_checked <chr>, subsp <chr>, calc_rate <dbl>,
#> #   calc_unit <chr>, direction <chr>, baseline_temp_mean <dbl>,
#> #   baseline_res <chr>
get_shifts(eco = "Mar") |> add_baselines(res = "25km")
#> Warning: Note: Marine realm includes intertidal species, to differentiate, further filtering is required.
#> # A tibble: 1,636 × 15
#>    id         article_id poly_id method_id eco   type  param sp_name_publication
#>    <chr>      <chr>      <chr>   <chr>     <chr> <chr> <chr> <chr>              
#>  1 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Cellana_nigrolinea…
#>  2 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Conus_ebraeus      
#>  3 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Diloma_suavis      
#>  4 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Echininus_cumingii 
#>  5 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Eugina_mendicaria  
#>  6 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Hormomya_mutabilis 
#>  7 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Modiolus_auriculat…
#>  8 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Montfortula_picta  
#>  9 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Morula_granulata   
#> 10 A042_P1_L… A042       P1      M01       Mar   LAT   LE    Nerita_albicilla   
#> # ℹ 1,626 more rows
#> # ℹ 7 more variables: sp_name_checked <chr>, subsp <chr>, calc_rate <dbl>,
#> #   calc_unit <chr>, direction <chr>, baseline_temp_mean <dbl>,
#> #   baseline_res <chr>