When getting records from FinBIF there are many options for filtering the data
before it is downloaded saving bandwidth and local post-processing time. For the
full list of filtering options see ?filters.
library(finbif)
You can filter occurrence records based on informal taxonomic groups such as
Birds or Mammals.
finbif_occurrence(filter = list(informal_group = c("Birds", "Mammals")))
#> Records downloaded: 10
#> Records available: 17370561
#> A data.frame [10 x 30]
#> scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 Capreolus capreolus 1 60.82651 21.34383 2019-08-21 02:30:00
#> 2 Lepus europaeus 1 60.80257 21.38201 2019-08-21 02:30:00
#> 3 Sciurus vulgaris 1 60.79812 21.35765 2019-08-21 02:30:00
#> 4 Vulpes vulpes 1 60.82976 21.26560 2019-08-21 02:30:00
#> 5 Lepus timidus 1 60.81778 21.33762 2019-08-21 02:30:00
#> 6 Vulpes vulpes 1 60.42812 22.23448 2019-08-20 21:00:00
#> 7 Erithacus rubecula 1 60.42820 22.24195 2019-08-20 21:00:00
#> 8 Vulpes vulpes 1 60.10824 23.52158 2019-08-20 21:00:00
#> 9 Sciurus vulgaris 1 60.43755 22.24938 2019-08-21 21:00:00
#> 10 Hirundo rustica 24 63.80152 22.89729 2019-08-20 21:00:00
#> ...with 0 more records and 25 more variables:
#> taxon_rank, country, province, municipality, date_start, date_end,
#> hour_start, hour_end, minute_start, minute_end, record_id,
#> individual_id, event_id, collection_id, any_issues, record_issue,
#> record_reliable, taxon_reliability, document_issue,
#> document_reliablity, coordinate_accuracy, event_issue,
#> location_issue, time_issue, duration
See finbif_informal_groups() for the full list of groups you can filter by.
You can use the same function to see the subgroups that make up a higher
level informal group:
finbif_informal_groups("macrofungi")
#> ¦--Macrofungi
#> ¦ ¦--Agaricoid fungi
#> ¦ ¦--Aphyllophoroid fungi
#> ¦ ¦ ¦--Cantharelloid fungi
#> ¦ ¦ ¦--Clavarioid fungi
#> ¦ ¦ ¦--Corticioid fungi
#> ¦ ¦ ¦--Hydnoid fungi
#> ¦ ¦ ¦--Jelly fungi, tremelloid fungi
#> ¦ ¦ ¦--Polypores
#> ¦ ¦ °--Ramarioid fungi
#> ¦ ¦--Boletoid fungi
#> ¦ ¦--Cyphelloid fungi
#> ¦ °--Gastroid fungi, puffballs
Many records in the FinBIF database include taxa that have one or another
administrative statuses. See finbif_metadata("admin_status") for a list of
administrative statuses and short-codes.
# Search for birds on the EU invasive species list
finbif_occurrence(
filter = list(informal_group = "Birds", administrative_status = "EU_INVSV")
)
#> Records downloaded: 10
#> Records available: 425
#> A data.frame [10 x 30]
#> scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 Alopochen aegyptiaca 2 51.90871 4.532580 2018-10-20 09:10:00
#> 2 Alopochen aegyptiaca 2 53.37130 6.143095 2017-10-22 06:00:00
#> 3 Alopochen aegyptiaca 2 53.19242 5.437417 2017-10-24 08:06:00
#> 4 Alopochen aegyptiaca 3 51.74641 4.535283 2018-10-21 10:00:00
#> 5 Alopochen aegyptiaca 4 53.36759 6.191796 2018-10-26 08:15:00
#> 6 Alopochen aegyptiaca 5 53.32081 6.192341 2017-10-23 09:15:00
#> 7 Alopochen aegyptiaca 6 53.37574 6.207861 2018-10-23 05:30:00
#> 8 Alopochen aegyptiaca 20 53.32081 6.192341 2017-10-23 09:15:00
#> 9 Alopochen aegyptiaca 30 52.33990 5.069133 2018-10-22 07:45:00
#> 10 Alopochen aegyptiaca 36 51.74641 4.535283 2018-10-21 10:00:00
#> ...with 0 more records and 25 more variables:
#> taxon_rank, country, province, municipality, date_start, date_end,
#> hour_start, hour_end, minute_start, minute_end, record_id,
#> individual_id, event_id, collection_id, any_issues, record_issue,
#> record_reliable, taxon_reliability, document_issue,
#> document_reliablity, coordinate_accuracy, event_issue,
#> location_issue, time_issue, duration
Filtering can be done by IUCN red list
category. See finbif_metadata("red_list") for the IUCN red list categories and
their short-codes.
# Search for near threatened mammals
finbif_occurrence(
filter = list(informal_group = "Mammals", red_list_status = "NT")
)
#> Records downloaded: 10
#> Records available: 568
#> A data.frame [10 x 30]
#> scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 Rangifer tarandus f… 1 63.26554 25.36645 2019-06-27 21:00:00
#> 2 Rangifer tarandus f… 1 64.32293 26.69975 2019-05-26 21:00:00
#> 3 Ursus arctos 1 62.31940 24.58560 2018-12-31 22:00:00
#> 4 Ursus arctos 1 62.31940 24.58560 2018-12-31 22:00:00
#> 5 Castor fiber 1 61.70314 21.79521 2019-04-20 21:00:00
#> 6 Rangifer tarandus f… 2 63.27123 25.35634 2019-06-27 21:00:00
#> 7 Rangifer tarandus f… 3 63.13691 22.92659 2019-03-05 22:00:00
#> 8 Rangifer tarandus f… 4 63.03293 24.32905 2019-06-12 21:00:00
#> 9 Rangifer tarandus f… 4 60.07458 22.76416 2019-04-10 21:00:00
#> 10 Rangifer tarandus f… 7 63.13690 22.92677 2019-03-06 22:00:00
#> ...with 0 more records and 25 more variables:
#> taxon_rank, country, province, municipality, date_start, date_end,
#> hour_start, hour_end, minute_start, minute_end, record_id,
#> individual_id, event_id, collection_id, any_issues, record_issue,
#> record_reliable, taxon_reliability, document_issue,
#> document_reliablity, coordinate_accuracy, event_issue,
#> location_issue, time_issue, duration
Many taxa are associated with one or more primary or secondary habitat types
(e.g., forest) or subtypes (e.g., herb-rich alpine birch forests). Use
finbif_metadata("habitat_types") to see the habitat types in FinBIF. You can
filter occurrence records based on primary (or primary/secondary) habitat type
or subtype codes. Note that filtering based on habitat is on taxa not on the
location (i.e., filtering records with primary_habitat = "M" will only return
records of taxa considered to primarily inhabit forests, yet the locations of
those records may encompass habitats other than forests).
head(finbif_metadata("habitat_types"))
#> habitat_name habitat_code
#> 1 Forests M
#> 2 Heath forests MK
#> 3 Sub-xeric, xeric and barren heath fo… MKK
#> 4 Mesic and herb-rich heath forests MKT
#> 5 Herb-rich forests (also spruce-domin… ML
#> 6 Dry and mesic herb-rich forests MLT
# Search records of taxa for which forests are their primary or secondary
# habitat type
finbif_occurrence(filter = c(primary_secondary_habitat = "M"))
#> Records downloaded: 10
#> Records available: 19243360
#> A data.frame [10 x 30]
#> scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 Pica pica 2 65.70064 24.64242 2019-11-06 05:58:00
#> 2 Poecile montanus 1 65.70064 24.64242 2019-11-06 05:58:00
#> 3 Emberiza citrinella 5 65.70064 24.64242 2019-11-06 05:58:00
#> 4 Capreolus capreolus 1 65.70064 24.64242 2019-11-06 05:58:00
#> 5 Pyrrhula pyrrhula 15 65.70064 24.64242 2019-11-06 05:58:00
#> 6 Cyanistes caeruleus 24 65.70064 24.64242 2019-11-06 05:58:00
#> 7 Parus major 18 65.70064 24.64242 2019-11-06 05:58:00
#> 8 Bombycilla garrulus 2 65.70064 24.64242 2019-11-06 05:58:00
#> 9 Acanthis flammea 12 65.70064 24.64242 2019-11-06 05:58:00
#> 10 Corvus corone 9 65.70064 24.64242 2019-11-06 05:58:00
#> ...with 0 more records and 25 more variables:
#> taxon_rank, country, province, municipality, date_start, date_end,
#> hour_start, hour_end, minute_start, minute_end, record_id,
#> individual_id, event_id, collection_id, any_issues, record_issue,
#> record_reliable, taxon_reliability, document_issue,
#> document_reliablity, coordinates_uncertainty, event_issue,
#> location_issue, time_issue, duration
You may further refine habitat based searching using a specific habitat type
qualifier such as “sun-exposed” or “shady”. Use
finbif_metadata("habitat_qualifiers") to see the qualifiers available. To
specify qualifiers use a named list of character vectors where the names are
habitat types or subtypes and the elements of the character vectors are the
qualifier codes.
finbif_metadata("habitat_qualifiers")[4:6, ]
#> qualifier_name qualifier_code
#> 4 Broadleaved deciduous trees present J
#> 5 Sun-exposed PA
#> 6 Shady VA
# Search records of taxa for which forests with sun-exposure and broadleaved
# deciduous trees are their primary habitat type
finbif_occurrence(filter = list(primary_habitat = list(M = c("PA", "J"))))
#> Records downloaded: 10
#> Records available: 105
#> A data.frame [10 x 30]
#> scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 Pammene fasciana 1 60.00217 23.43591 2019-06-21 21:00:00
#> 2 Pammene fasciana 1 60.50396 27.72823 2019-05-29 21:00:00
#> 3 Pammene fasciana 3 60.00217 23.43591 2019-05-07 21:00:00
#> 4 Pammene fasciana 1 59.96020 20.99521 2017-09-19 21:00:00
#> 5 Pammene fasciana 2 60.35244 19.83238 2017-08-02 21:00:00
#> 6 Pammene fasciana 1 60.35244 19.83238 2017-08-02 21:00:00
#> 7 Pammene fasciana 1 60.35244 19.83238 2017-08-01 21:00:00
#> 8 Pammene fasciana 1 60.21166 24.90204 2017-07-30 21:00:00
#> 9 Pammene fasciana 1 60.35244 19.83238 2017-07-30 21:00:00
#> 10 Pammene fasciana 1 60.35244 19.83238 2017-07-29 21:00:00
#> ...with 0 more records and 25 more variables:
#> taxon_rank, country, province, municipality, date_start, date_end,
#> hour_start, hour_end, minute_start, minute_end, record_id,
#> individual_id, event_id, collection_id, any_issues, record_issue,
#> record_reliable, taxon_reliability, document_issue,
#> document_reliablity, coordinates_uncertainty, event_issue,
#> location_issue, time_issue, duration