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: 19546
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …KE.921/LGE.627772/… Pteromys volans 1 61.81362 25.75756 2019-12-31 12:00:00
#> 2 …JX.1054777#4 Sarcosoma globosum 1 60.28506 21.98599 2019-12-31 12:00:00
#> 3 …JX.1054554#7 Exidia glandulosa 1 60.37529 23.16411 2019-12-31 12:00:00
#> 4 …JX.1054930#7 Panellus ringens 1 63.068 21.6902 2019-12-31 12:00:00
#> 5 …JX.1054930#4 Basidioradulum radu… 1 63.068 21.6902 2019-12-31 12:00:00
#> 6 …JX.1054554#4 Hypocreopsis lichen… 10 60.37529 23.16411 2019-12-31 12:00:00
#> 7 …JX.1054621#4 Flammulina 10 60.39362 25.67044 2019-12-31 12:00:00
#> 8 …HR.3211/65241302-U Pinus sylvestris 1 68.84709 28.33712 2019-12-31 11:00:00
#> 9 …HR.3211/37131235-U Bombycilla garrulus 1 60.1732 24.9521 2019-12-31 12:00:00
#> 10 …HR.3211/37128031-U Bombycilla garrulus 1 60.16761 24.94694 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: 669879
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …KE.921/LGE.627772/… Pteromys volans 1 61.81362 25.75756 2019-12-31 12:00:00
#> 2 …JX.1054777#4 Sarcosoma globosum 1 60.28506 21.98599 2019-12-31 12:00:00
#> 3 …JX.1054554#7 Exidia glandulosa 1 60.37529 23.16411 2019-12-31 12:00:00
#> 4 …JX.1054930#7 Panellus ringens 1 63.068 21.6902 2019-12-31 12:00:00
#> 5 …JX.1054930#4 Basidioradulum radu… 1 63.068 21.6902 2019-12-31 12:00:00
#> 6 …JX.1054554#4 Hypocreopsis lichen… 10 60.37529 23.16411 2019-12-31 12:00:00
#> 7 …JX.1054621#4 Flammulina 10 60.39362 25.67044 2019-12-31 12:00:00
#> 8 …HR.3211/65241302-U Pinus sylvestris 1 68.84709 28.33712 2019-12-31 11:00:00
#> 9 …HR.3211/37131235-U Bombycilla garrulus 1 60.1732 24.9521 2019-12-31 12:00:00
#> 10 …HR.3211/37128031-U Bombycilla garrulus 1 60.16761 24.94694 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: 1598855
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …HR.3211/69860385-U Magnoliopsida 1 60.30464 25.00083 2021-02-20 12:00:00
#> 2 …JX.1223101#107 Pica pica 8 62.70713 22.20652 2021-02-20 08:20:00
#> 3 …JX.1223101#85 Poecile montanus 3 62.70713 22.20652 2021-02-20 08:20:00
#> 4 …JX.1223101#153 Emberiza citrinella 24 62.70713 22.20652 2021-02-20 08:20:00
#> 5 …JX.1223101#117 Corvus corax 4 62.70713 22.20652 2021-02-20 08:20:00
#> 6 …JX.1223101#61 Dendrocopos major 1 62.70713 22.20652 2021-02-20 08:20:00
#> 7 …JX.1223101#111 Corvus monedula 4 62.70713 22.20652 2021-02-20 08:20:00
#> 8 …JX.1223101#123 Passer montanus 3 62.70713 22.20652 2021-02-20 08:20:00
#> 9 …JX.1223101#149 Pyrrhula pyrrhula 10 62.70713 22.20652 2021-02-20 08:20:00
#> 10 …JX.1223101#93 Cyanistes caeruleus 47 62.70713 22.20652 2021-02-20 08:20:00
#> ...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
#> 37 40636542The 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] 417052
finbif_occurrence(
filter = c(collection = "Notebook, general observations"), count_only = TRUE
)
#> [1] 1344139You 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: 19946472
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …KE.176/615c0572d5d… Pteromys volans 1 61.84054 23.5467 2021-10-04 12:00:00
#> 2 …KE.176/615bd55fd5d… Martes martes 1 62.41282 25.05716 2021-10-04 12:00:00
#> 3 …JX.1315879#3 Cygnus 2 60.13003 23.75727 2021-10-04 12:00:00
#> 4 …JX.1315856#12 Clangula hyemalis 10 60.42794 22.20052 2021-10-04 12:00:00
#> 5 …JX.1315856#15 Clangula hyemalis 3 60.42794 22.20052 2021-10-04 12:00:00
#> 6 …JX.1315856#57 Pica pica 1 60.42794 22.20052 2021-10-04 12:00:00
#> 7 …JX.1315856#27 Larus argentatus 12 60.42794 22.20052 2021-10-04 12:00:00
#> 8 …JX.1315856#33 Branta canadensis 6 60.42794 22.20052 2021-10-04 12:00:00
#> 9 …JX.1315856#9 Cygnus olor 8 60.42794 22.20052 2021-10-04 12:00:00
#> 10 …JX.1315856#24 Larus marinus 4 60.42794 22.20052 2021-10-04 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: 452
#> 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#89 Alopochen aegyptiaca 5 53.32081 6.192341 2017-10-23 12:15:00
#> 10 …KE.8_1208068#101 Alopochen aegyptiaca 20 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: 1958
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …JX.1292283#3 Rangifer tarandus f… 3 63.03263 24.61494 2021-09-15 12:00:00
#> 2 …HR.3211/91544814-U Rangifer tarandus f… 1 63.3 24.7 2021-08-17 12:00:00
#> 3 …JX.1266980#3 Rangifer tarandus f… 4 64.15017 23.88253 2021-08-12 12:00:00
#> 4 …HR.3211/87570966-U Castor fiber 1 NA NA 2021-07-15 15:00:00
#> 5 …JX.1258628#3 Rangifer tarandus f… 1 63.8164 26.12318 2021-07-14 12:00:00
#> 6 …JX.1257513#3 Rangifer tarandus f… 2 63.74909 24.12696 2021-07-11 12:00:00
#> 7 …KE.176/60e9c3b8d5d… Rangifer tarandus f… 2 63.868 26.131 2021-07-10 12:00:00
#> 8 …HR.3211/86130026-U Rangifer tarandus f… 1 63.3 24.7 2021-07-07 12:00:00
#> 9 …HR.3211/85932628-U Rangifer tarandus f… 1 63.3 24.9 2021-07-06 12:00:00
#> 10 …HR.3211/85820527-U Rangifer tarandus f… 1 64.3 28.1 2021-06-26 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: 22695106
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …5966#9 Trichaptum fuscovio… 1 65.08865 25.45157 2021-10-05 12:00:00
#> 2 …5966#3 Lycogala epidendrum 1 65.08865 25.45157 2021-10-05 12:00:00
#> 3 …5966#6 Stereum sanguinolen… 1 65.08865 25.45157 2021-10-05 12:00:00
#> 4 …5969#3 Rana temporaria 1 62.48525 21.75467 2021-10-05 12:00:00
#> 5 …5950#3 Exechiopsis fimbria… 1 65.01504 25.52607 2021-10-05 12:00:00
#> 6 …5929#9 Epirrita autumnata 1 62.92172 27.63335 2021-10-05 12:00:00
#> 7 …5929#6 Poecilocampa populi 1 62.92172 27.63335 2021-10-05 12:00:00
#> 8 …5929#3 Xestia c-nigrum 1 62.92172 27.63335 2021-10-05 12:00:00
#> 9 …6006#3 Tricholoma equestre 1 61.59036 21.46508 2021-10-04 12:00:00
#> 10 …6004#12 Agriopis aurantiaria 2 60.14694 24.7522 2021-10-04 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: 123
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …JX.1263703#60 Pammene fasciana 1 60.08841 22.48629 2021-06-22 12:00:00
#> 2 …JX.1256040#165 Pammene fasciana 1 60.08841 22.48629 2021-06-19 12:00:00
#> 3 …HR.3211/53817755-U Pammene fasciana 1 59.90452 23.72726 2020-07-21 12:00:00
#> 4 …JX.1134471#4 Pammene fasciana 2 61.54984 29.50158 2020-06-21 12:00:00
#> 5 …JX.1143718#265 Pammene fasciana 1 60.3754 22.37212 2020-06-10 12:00:00
#> 6 …JX.1012832#367 Pammene fasciana 1 60.00217 23.43591 2019-06-22 12:00:00
#> 7 …JX.1098381#487 Pammene fasciana 1 60.04555 23.31692 2019-06-19 12:00:00
#> 8 …JX.1011605#97 Pammene fasciana 1 60.50396 27.72823 2019-05-30 12:00:00
#> 9 …JX.996622#130 Pammene fasciana 3 60.00217 23.43591 2019-05-08 12:00:00
#> 10 …JX.1103286#13 Pammene fasciana 1 59.90522 23.49645 2018-05-28 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: 318316
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …JX.1315617#3 Stigmella ruficapit… 1 61.51447 24.0233 2021-10-03 12:00:00
#> 2 …JX.1315617#6 Stigmella ruficapit… 5 61.51447 24.0233 2021-10-03 12:00:00
#> 3 …JX.1315625#3 Ectoedemia turbidel… 10 60.21493 24.86162 2021-10-03 12:00:00
#> 4 …JX.1315620#3 Ectoedemia intimella 1 62.0223 25.6044 2021-10-03 12:00:00
#> 5 …JX.1315390#3 Stigmella assimilel… 1 61.51447 24.0233 2021-10-02 12:00:00
#> 6 …JX.1314937#9 Stigmella glutinosae 1 61.51447 24.0233 2021-10-01 12:00:00
#> 7 …JX.1315138#3 Stigmella filipendu… 3 62.64272 29.63881 2021-10-01 12:00:00
#> 8 …JX.1305550#3 Stigmella myrtillel… 1 61.51447 24.0233 2021-09-30 12:00:00
#> 9 …JX.1315169#3 Ectoedemia intimella 2 61.58965 27.65204 2021-09-30 12:00:00
#> 10 …HR.3211/96782798-U Lyonetia prunifolie… 1 60.35792 24.78564 2021-09-30 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: 19675504
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …79#3 Cygnus 2 60.13003 23.75727 2021-10-04 12:00:00
#> 2 …56#12 Clangula hyemalis 10 60.42794 22.20052 2021-10-04 12:00:00
#> 3 …56#15 Clangula hyemalis 3 60.42794 22.20052 2021-10-04 12:00:00
#> 4 …56#57 Pica pica 1 60.42794 22.20052 2021-10-04 12:00:00
#> 5 …56#27 Larus argentatus 12 60.42794 22.20052 2021-10-04 12:00:00
#> 6 …56#33 Branta canadensis 6 60.42794 22.20052 2021-10-04 12:00:00
#> 7 …56#9 Cygnus olor 8 60.42794 22.20052 2021-10-04 12:00:00
#> 8 …56#24 Larus marinus 4 60.42794 22.20052 2021-10-04 12:00:00
#> 9 …56#3 Phalacrocorax carbo 32 60.42794 22.20052 2021-10-04 12:00:00
#> 10 …56#45 Corvus monedula 6 60.42794 22.20052 2021-10-04 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.