Get BioShifts Range Shifts - Start Here.
get_shifts.RdGet species' range shift values from the BioShifts database, filtered by taxon, study type or geography. BioShifts includes range shift observations of over 31,000 taxa within studies conducted around the world, published between
Usage
get_shifts(
group = "All",
realm = "All",
continent = "All",
type = c("LAT", "ELE")
)Arguments
- group
Rough taxonomic subgroups for which to pull bioshifts data. Options are Algae, Birds, Fish, Fungi, Mammals, Marine Invertebrates, Nonvascular Plants, Reptiles and Amphibians, Terrestrial Invertebrates, and Vascular Plants, or All (default). This shortcut is meant to provide a coarse subsetting for data exploration, but for more precise taxonomic filtering, see add_taxo().
- realm
Subset of study realms for which to uplaod range shift data. Options are "Mar" (marine), "Ter" (terrestrial), or "All" (default).
- continent
Continent of studies for which to upload BioShifts data. Options include "North America", "South America", "Africa", "Europe", "Asia", "Oceania", "High Seas", or "All".
- type
Gradient over which to extract range shifts. Options are "ELE" for elevational shifts, or "LAT" for latitudinal shifts.
Examples
get_shifts()
#> # A tibble: 31,759 × 13
#> id article_id poly_id method_id eco type param sp_name_publication
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 A001_P1_E… A001 P1 M01 Ter ELE O Aegithalos_caudatus
#> 2 A001_P1_E… A001 P1 M01 Ter ELE O Certhia_familiaris
#> 3 A001_P1_E… A001 P1 M01 Ter ELE O Dendrocopos_major
#> 4 A001_P1_E… A001 P1 M01 Ter ELE O Dryocopus_martius
#> 5 A001_P1_E… A001 P1 M01 Ter ELE O Erithacus_rubecula
#> 6 A001_P1_E… A001 P1 M01 Ter ELE O Fringilla_coelebs
#> 7 A001_P1_E… A001 P1 M01 Ter ELE O Garrulus_glandarius
#> 8 A001_P1_E… A001 P1 M01 Ter ELE O Nucifraga_caryocat…
#> 9 A001_P1_E… A001 P1 M01 Ter ELE O Parus_ater
#> 10 A001_P1_E… A001 P1 M01 Ter ELE O Parus_caeruleus
#> # ℹ 31,749 more rows
#> # ℹ 5 more variables: sp_name_checked <chr>, subsp_or_pop <chr>,
#> # calc_rate <dbl>, calc_unit <chr>, direction <chr>
get_shifts(group = "Birds", continent = "Asia")
#> # A tibble: 80 × 13
#> id article_id poly_id method_id eco type param sp_name_publication
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 A048_P1_E… A048 P1 M01 Ter ELE LE Eurylaimus_ochroma…
#> 2 A048_P1_E… A048 P1 M01 Ter ELE LE Micropternus_brach…
#> 3 A048_P1_E… A048 P1 M01 Ter ELE LE Oriolus_cruentus
#> 4 A048_P1_E… A048 P1 M01 Ter ELE LE Rhipidura_albicoll…
#> 5 A048_P1_E… A048 P1 M10 Ter ELE LE Pellorneum_pyrroge…
#> 6 A048_P1_E… A048 P1 M11 Ter ELE LE Phaenicophaeus_cur…
#> 7 A048_P1_E… A048 P1 M12 Ter ELE LE Phylloscopus_trivi…
#> 8 A048_P1_E… A048 P1 M13 Ter ELE LE Pycnonotus_flavesc…
#> 9 A048_P1_E… A048 P1 M14 Ter ELE LE Reinwardtipicus_va…
#> 10 A048_P1_E… A048 P1 M02 Ter ELE LE Alophoixus_ochrace…
#> # ℹ 70 more rows
#> # ℹ 5 more variables: sp_name_checked <chr>, subsp_or_pop <chr>,
#> # calc_rate <dbl>, calc_unit <chr>, direction <chr>
get_shifts(continent = c("North America","South America"), type = "ELE")
#> # A tibble: 7,475 × 13
#> id article_id poly_id method_id eco type param sp_name_publication
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 A011_P1_E… A011 P1 M01 Ter ELE O Abies_concolor
#> 2 A011_P1_E… A011 P1 M01 Ter ELE O Abies_magnifica
#> 3 A011_P1_E… A011 P1 M01 Ter ELE O Adenostoma_fascicu…
#> 4 A011_P1_E… A011 P1 M01 Ter ELE O Aesculus_californi…
#> 5 A011_P1_E… A011 P1 M01 Ter ELE O Amelanchier_alnifo…
#> 6 A011_P1_E… A011 P1 M01 Ter ELE O Arbutus_menziesii
#> 7 A011_P1_E… A011 P1 M01 Ter ELE O Arctostaphylos_gla…
#> 8 A011_P1_E… A011 P1 M01 Ter ELE O Arctostaphylos_nev…
#> 9 A011_P1_E… A011 P1 M01 Ter ELE O Arctostaphylos_vis…
#> 10 A011_P1_E… A011 P1 M01 Ter ELE O Artemisia_tridenta…
#> # ℹ 7,465 more rows
#> # ℹ 5 more variables: sp_name_checked <chr>, subsp_or_pop <chr>,
#> # calc_rate <dbl>, calc_unit <chr>, direction <chr>