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.
Records can be filtered by the name of a location or by a set of coordinates.
filter1 <- c(country = "Finland")
filter2 <- list(coordinates = list(c(60, 68), c(20, 30), "wgs84"))
par(mfcol = 1:2)
plot(finbif_occurrence(filter = filter1, n = 1000), main = "Name")
plot(finbif_occurrence(filter = filter2, n = 1000), main = "Coordinates")
See ?filters section “Location” for more details
The event or import date of records can be used to filter occurrence data from FinBIF. The date filters can be a single year, month or date,
finbif_occurrence(filter = list(date_range_ym = c("2019-12")))
#> Records downloaded: 10
#> Records available: 9305
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 mus.utu.fi/MY.73114… Nowellia curvifolia 1 60.38972 22.52086 2019-12-31 12:00:00
#> 2 mus.utu.fi/MY.72933… Viscum album 1 60.4638 22.3343 2019-12-31 12:00:00
#> 3 tun.fi/HR.3211/3713… Bombycilla garrulus 1 60.1732 24.9521 2019-12-31 12:00:00
#> 4 tun.fi/HR.3211/3712… Columba livia domes… 1 60.16758 24.95355 2019-12-31 12:00:00
#> 5 tun.fi/HR.3211/3712… Columba livia domes… 1 60.16748 24.94759 2019-12-31 12:00:00
#> 6 tun.fi/HR.3211/3713… Cyanistes caeruleus 1 60.16763 24.94723 2019-12-31 12:00:00
#> 7 tun.fi/HR.3211/3712… Passer domesticus 1 60.16751 24.95001 2019-12-31 12:00:00
#> 8 tun.fi/HR.3211/3712… Passer domesticus 1 60.16958 24.95622 2019-12-31 12:00:00
#> 9 tun.fi/HR.3211/3712… Passer domesticus 1 60.168 24.9527 2019-12-31 12:00:00
#> 10 tun.fi/HR.3211/3713… Perca fluviatilis 1 66.04649 26.90589 2019-12-31 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_quality
, or for record events, a range as a character vector or an Interval object.
finbif_occurrence(
filter = list(date_range_ymd = c("2019-06-01", "2019-12-31"))
)
#> Records downloaded: 10
#> Records available: 408296
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 mus.utu.fi/MY.73114… Nowellia curvifolia 1 60.38972 22.52086 2019-12-31 12:00:00
#> 2 mus.utu.fi/MY.72933… Viscum album 1 60.4638 22.3343 2019-12-31 12:00:00
#> 3 tun.fi/HR.3211/3713… Bombycilla garrulus 1 60.1732 24.9521 2019-12-31 12:00:00
#> 4 tun.fi/HR.3211/3712… Columba livia domes… 1 60.16758 24.95355 2019-12-31 12:00:00
#> 5 tun.fi/HR.3211/3712… Columba livia domes… 1 60.16748 24.94759 2019-12-31 12:00:00
#> 6 tun.fi/HR.3211/3713… Cyanistes caeruleus 1 60.16763 24.94723 2019-12-31 12:00:00
#> 7 tun.fi/HR.3211/3712… Passer domesticus 1 60.16751 24.95001 2019-12-31 12:00:00
#> 8 tun.fi/HR.3211/3712… Passer domesticus 1 60.16958 24.95622 2019-12-31 12:00:00
#> 9 tun.fi/HR.3211/3712… Passer domesticus 1 60.168 24.9527 2019-12-31 12:00:00
#> 10 tun.fi/HR.3211/3713… Perca fluviatilis 1 66.04649 26.90589 2019-12-31 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityRecords for a specific season or time-span across all years can also be requested.
finbif_occurrence(
filter = list(
date_range_md = c(begin = "12-21", end = "12-31"),
date_range_md = c(begin = "01-01", end = "02-20")
)
)
#> Records downloaded: 10
#> Records available: 1495416
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 KE.176/5e6d4dea2d0e… Cygnus cygnus 1 61.437 23.031 2020-02-20 12:00:00
#> 2 HR.3211/38879545-U Arctoparmelia 1 63.66888 22.54659 2020-02-20 12:00:00
#> 3 HR.3211/38883010-U Arctostaphylos uva-… 1 63.66254 22.54102 2020-02-20 12:00:00
#> 4 HR.3211/38879685-U Diploschistes scrup… 1 63.6679 22.54609 2020-02-20 12:00:00
#> 5 HR.3211/38883029-U Empetrum nigrum 1 63.66272 22.54134 2020-02-20 12:00:00
#> 6 HR.3211/38880672-U Juniperus communis 1 63.66396 22.55074 2020-02-20 12:00:00
#> 7 HR.3211/38879433-U Lecanoromycetes 1 63.66931 22.54812 2020-02-20 12:00:00
#> 8 HR.3211/38879561-U Lecanoromycetes 1 63.66892 22.54648 2020-02-20 12:00:00
#> 9 HR.3211/38879560-U Lecanoromycetes 1 63.66892 22.54653 2020-02-20 12:00:00
#> 10 HR.3211/38879691-U Lecanoromycetes 1 63.66788 22.54607 2020-02-20 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityYou can filter occurrence records by indicators of data quality. See ?filters section “Quality” for details.
strict <- c(
collection_quality = "professional", coordinates_uncertainty_max = 1,
record_quality = "expert_verified"
)
permissive <- list(
quality_issues = "both",
record_reliability = c("reliable", "unassessed", "unreliable"),
record_quality = c(
"expert_verified", "community_verified", "unassessed", "uncertain",
"erroneous"
)
)
c(
strict = finbif_occurrence(filter = strict, count_only = TRUE),
permissive = finbif_occurrence(filter = permissive, count_only = TRUE)
)
#> strict permissive
#> 14 36095576The FinBIF database consists of a number of constituent collections. You can filter by collection with either the collection or not_collection filters. Use finbif_collections() to see metadata on the FinBIF collections.
finbif_occurrence(
filter = c(collection = "iNaturalist Suomi Finland"), count_only = TRUE
)
#> [1] 204408
finbif_occurrence(
filter = c(collection = "Notebook, general observations"), count_only = TRUE
)
#> [1] 898820You 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: 18004003
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 KE.176/5e9079b42d0e… Turdus iliacus 1 65.5677 24.44429 2020-04-21 12:00:00
#> 2 KE.176/5e9079b42d0e… Falco tinnunculus 1 65.5677 24.44429 2020-04-21 12:00:00
#> 3 JX.1110587#148 Phasianus colchicus 1 60.4088 22.20608 2020-04-21 12:00:00
#> 4 JX.1110587#82 Pica pica 7 60.4088 22.20608 2020-04-21 12:00:00
#> 5 JX.1110587#121 Ardea cinerea 1 60.4088 22.20608 2020-04-21 12:00:00
#> 6 JX.1110587#112 Larus argentatus 3 60.4088 22.20608 2020-04-21 12:00:00
#> 7 JX.1110587#115 Picus canus 1 60.4088 22.20608 2020-04-21 12:00:00
#> 8 JX.1110587#133 Linaria cannabina 2 60.4088 22.20608 2020-04-21 12:00:00
#> 9 JX.1110587#106 Mergus merganser 2 60.4088 22.20608 2020-04-21 12:00:00
#> 10 JX.1110587#151 Larus canus 2 60.4088 22.20608 2020-04-21 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualitySee 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, puffballsMany 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: 437
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 JX.1045316#34 Alopochen aegyptiaca 3 52.16081 4.485534 2019-10-23 13:00:00
#> 2 JX.138840#123 Alopochen aegyptiaca 4 53.36759 6.191796 2018-10-26 11:15:00
#> 3 JX.139978#214 Alopochen aegyptiaca 6 53.37574 6.207861 2018-10-23 08:30:00
#> 4 JX.139710#17 Alopochen aegyptiaca 30 52.3399 5.069133 2018-10-22 10:45:00
#> 5 JX.139645#57 Alopochen aegyptiaca 36 51.74641 4.535283 2018-10-21 13:00:00
#> 6 JX.139645#10 Alopochen aegyptiaca 3 51.74641 4.535283 2018-10-21 13:00:00
#> 7 JX.139442#16 Alopochen aegyptiaca 2 51.90871 4.53258 2018-10-20 12:10:00
#> 8 KE.8_1208123#15 Alopochen aegyptiaca 2 53.19242 5.437417 2017-10-24 11:06:00
#> 9 KE.8_1208068#101 Alopochen aegyptiaca 20 53.32081 6.192341 2017-10-23 12:15:00
#> 10 KE.8_1208068#89 Alopochen aegyptiaca 5 53.32081 6.192341 2017-10-23 12:15:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityFiltering 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: 1658
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 105583#7 Rangifer tarandus f… 7 63.11867 24.12289 2020-03-28 12:00:00
#> 2 102127#4 Pusa hispida botnica 1 64.66837 24.40488 2020-03-07 12:00:00
#> 3 100490#3 Rangifer tarandus f… 5 63.83774 29.16123 2020-02-29 09:18:00
#> 4 046837#113 Rangifer tarandus f… 4 64.42648 29.11431 2019-10-20 12:00:00
#> 5 055497#7 Rangifer tarandus f… 1 64.09919 29.40356 2019-09-23 12:00:00
#> 6 036556#322 Rangifer tarandus f… 1 63.79309 29.5108 2019-09-13 12:00:00
#> 7 035179#144 Rangifer tarandus f… 1 63.93649 29.59252 2019-07-26 12:00:00
#> 8 018572#4 Rangifer tarandus f… 2 63.27123 25.35634 2019-06-28 12:00:00
#> 9 018571#4 Rangifer tarandus f… 1 63.26554 25.36645 2019-06-28 12:00:00
#> 10 008788#4 Rangifer tarandus f… 4 63.03293 24.32905 2019-06-13 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityMany 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_type") 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_type"))
#> habitat_name habitat_code
#> 1 Forests M
#> 2 Heath forests MK
#> 3 Sub-xeric, xeric and barren heath forests MKK
#> 4 Mesic and herb-rich heath forests MKT
#> 5 Herb-rich forests (also spruce-dominated) 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: 19741767
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 JX.1110576#13 Aglais io 1 62.23735 27.42594 2020-04-21 12:00:00
#> 2 JX.1110576#4 Archiearis partheni… 6 62.23735 27.42594 2020-04-21 12:00:00
#> 3 JX.1110576#7 Gonepteryx rhamni 7 62.23735 27.42594 2020-04-21 12:00:00
#> 4 JX.1110576#10 Polygonia c-album 2 62.23735 27.42594 2020-04-21 12:00:00
#> 5 KE.176/5e9079b42d0e… Aglais urticae 1 65.5677 24.44429 2020-04-21 12:00:00
#> 6 KE.176/5e9079b42d0e… Turdus iliacus 1 65.5677 24.44429 2020-04-21 12:00:00
#> 7 JX.1110588#7 Aglais io 2 63.13815 21.56879 2020-04-21 12:00:00
#> 8 JX.1110588#4 Aglais urticae 6 63.13815 21.56879 2020-04-21 12:00:00
#> 9 JX.1110588#10 Nymphalis antiopa 1 63.13815 21.56879 2020-04-21 12:00:00
#> 10 KE.176/5e5bb8932d0e… Aglais urticae 1 62.94 26.794 2020-04-21 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityYou may further refine habitat based searching using a specific habitat type qualifier such as “sun-exposed” or “shady”. Use finbif_metadata("habitat_qualifier") 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_qualifier")[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: 107
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 1012832#367 Pammene fasciana 1 60.00217 23.43591 2019-06-22 12:00:00
#> 2 1098381#487 Pammene fasciana 1 60.04555 23.31692 2019-06-19 12:00:00
#> 3 1011605#97 Pammene fasciana 1 60.50396 27.72823 2019-05-30 12:00:00
#> 4 996622#130 Pammene fasciana 3 60.00217 23.43591 2019-05-08 12:00:00
#> 5 1103286#13 Pammene fasciana 1 59.90522 23.49645 2018-05-28 12:00:00
#> 6 1011998#37 Pammene fasciana 1 59.9602 20.99521 2017-09-20 12:00:00
#> 7 155516#2 Pammene fasciana 2 60.35244 19.83238 2017-08-03 12:00:00
#> 8 155516#6 Pammene fasciana 1 60.35244 19.83238 2017-08-03 12:00:00
#> 9 155515#9 Pammene fasciana 1 60.35244 19.83238 2017-08-02 12:00:00
#> 10 161553#44 Pammene fasciana 1 60.21166 24.90204 2017-07-31 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityYou can restrict the occurrence records by the status of the taxa in Finland. For example you can request records for only rare species.
finbif_occurrence(filter = c(finnish_occurrence_status = "rare"))
#> Records downloaded: 10
#> Records available: 261222
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 JX.1110435#22 Conistra rubiginosa 8 60.05748 22.49088 2020-04-21 12:00:00
#> 2 JX.1110435#4 Eriogaster lanestris 2 60.05748 22.49088 2020-04-21 12:00:00
#> 3 JX.1110311#52 Acleris umbrana 1 63.00538 28.04969 2020-04-20 12:00:00
#> 4 JX.1110294#25 Stigmella aceris 1 60.21154 24.9021 2020-04-19 12:00:00
#> 5 JX.1110347#4 Archiearis notha 2 60.25712 24.73908 2020-04-19 12:00:00
#> 6 JX.1110183#7 Rhigognostis schmal… 1 61.51447 24.0233 2020-04-19 12:00:00
#> 7 JX.1110208#4 Ethmia bipunctella 1 60.44842 26.02492 2020-04-19 22:00:00
#> 8 JX.1110178#4 Eriogaster lanestris 1 60.05748 22.49088 2020-04-19 12:00:00
#> 9 JX.1109943#13 Grynocharis oblonga 1 59.98883 24.43653 2020-04-13 12:00:00
#> 10 HR.3211/41981726-U Otiorhynchus singul… 1 60.22261 24.96595 2020-04-12 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_quality
Or, by using the negation of occurrence status, you can request records of birds excluding those considered vagrants.
finbif_occurrence(
filter = list(
informal_group = "birds",
finnish_occurrence_status_neg = sprintf("vagrant_%sregular", c("", "ir"))
)
)
#> Records downloaded: 10
#> Records available: 17823645
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 KE.176/5e9079b42d0e… Turdus iliacus 1 65.5677 24.44429 2020-04-21 12:00:00
#> 2 KE.176/5e9079b42d0e… Falco tinnunculus 1 65.5677 24.44429 2020-04-21 12:00:00
#> 3 JX.1110587#148 Phasianus colchicus 1 60.4088 22.20608 2020-04-21 12:00:00
#> 4 JX.1110587#82 Pica pica 7 60.4088 22.20608 2020-04-21 12:00:00
#> 5 JX.1110587#121 Ardea cinerea 1 60.4088 22.20608 2020-04-21 12:00:00
#> 6 JX.1110587#112 Larus argentatus 3 60.4088 22.20608 2020-04-21 12:00:00
#> 7 JX.1110587#115 Picus canus 1 60.4088 22.20608 2020-04-21 12:00:00
#> 8 JX.1110587#133 Linaria cannabina 2 60.4088 22.20608 2020-04-21 12:00:00
#> 9 JX.1110587#106 Mergus merganser 2 60.4088 22.20608 2020-04-21 12:00:00
#> 10 JX.1110587#151 Larus canus 2 60.4088 22.20608 2020-04-21 12:00:00
#> ...with 0 more records and 6 more variables:
#> coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualitySee finbif_metadata("finnish_occurrence_status") for a full list of statuses and their descriptions.