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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.
finbif_occurrence(filter = c(country = "Finland"))
#> Records downloaded: 10
#> Records available: 44691386
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84
#> 1 …JX.1594385#3 Sciurus vulgaris Li… 1 60.23584 25.05693
#> 2 …KE.176/64895825d5de884fa20e297d#Unit1 Heracleum persicum … NA 61.08302 22.38983
#> 3 …JX.1594382#9 Hirundo rustica Lin… NA 64.12716 23.99111
#> 4 …JX.1594382#37 Pica pica (Linnaeus… NA 64.12716 23.99111
#> 5 …JX.1594382#49 Muscicapa striata (… NA 64.12716 23.99111
#> 6 …JX.1594382#39 Larus canus Linnaeu… NA 64.12716 23.99111
#> 7 …JX.1594382#5 Emberiza citrinella… NA 64.12716 23.99111
#> 8 …JX.1594382#31 Ficedula hypoleuca … NA 64.12716 23.99111
#> 9 …JX.1594382#41 Alauda arvensis Lin… NA 64.12716 23.99111
#> 10 …JX.1594382#21 Numenius arquata (L… NA 64.12716 23.99111
#> ...with 0 more record and 7 more variables:
#> date_time, coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualityOr by a set of coordinates.
finbif_occurrence(
filter = list(coordinates = list(c(60, 68), c(20, 30), "wgs84"))
)
#> Records downloaded: 10
#> Records available: 37318868
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84
#> 1 …JX.1594385#3 Sciurus vulgaris Li… 1 60.23584 25.05693
#> 2 …KE.176/64895825d5de884fa20e297d#Unit1 Heracleum persicum … NA 61.08302 22.38983
#> 3 …JX.1594382#9 Hirundo rustica Lin… NA 64.12716 23.99111
#> 4 …JX.1594382#37 Pica pica (Linnaeus… NA 64.12716 23.99111
#> 5 …JX.1594382#49 Muscicapa striata (… NA 64.12716 23.99111
#> 6 …JX.1594382#39 Larus canus Linnaeu… NA 64.12716 23.99111
#> 7 …JX.1594382#5 Emberiza citrinella… NA 64.12716 23.99111
#> 8 …JX.1594382#31 Ficedula hypoleuca … NA 64.12716 23.99111
#> 9 …JX.1594382#41 Alauda arvensis Lin… NA 64.12716 23.99111
#> 10 …JX.1594382#21 Numenius arquata (L… NA 64.12716 23.99111
#> ...with 0 more record and 7 more variables:
#> date_time, coordinates_uncertainty, any_issues, requires_verification, requires_identification,
#> record_reliability, record_qualitySee ?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,
#> Records downloaded: 10
#> Records available: 23847
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …107 Pica pica (Linnaeus… 31 65.0027 25.49381 2020-12-31 10:20:00
#> 2 …45 Larus argentatus Po… 1 65.0027 25.49381 2020-12-31 10:20:00
#> 3 …153 Emberiza citrinella… 2 65.0027 25.49381 2020-12-31 10:20:00
#> 4 …49 Columba livia domes… 33 65.0027 25.49381 2020-12-31 10:20:00
#> 5 …117 Corvus corax Linnae… 1 65.0027 25.49381 2020-12-31 10:20:00
#> 6 …111 Corvus monedula Lin… 7 65.0027 25.49381 2020-12-31 10:20:00
#> 7 …161 Sciurus vulgaris Li… 1 65.0027 25.49381 2020-12-31 10:20:00
#> 8 …123 Passer montanus (Li… 28 65.0027 25.49381 2020-12-31 10:20:00
#> 9 …149 Pyrrhula pyrrhula (… 1 65.0027 25.49381 2020-12-31 10:20:00
#> 10 …77 Turdus pilaris Linn… 1 65.0027 25.49381 2020-12-31 10:20:00
#> ...with 0 more record 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.
#> Records downloaded: 10
#> Records available: 911735
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84
#> 1 …KE.921/LGE.627772/1470480 Pteromys volans (Li… NA 61.81362 25.75756
#> 2 …JX.1054648#107 Pica pica (Linnaeus… 3 65.30543 25.70355
#> 3 …JX.1054648#85 Poecile montanus (C… 1 65.30543 25.70355
#> 4 …JX.1054648#103 Garrulus glandarius… 3 65.30543 25.70355
#> 5 …JX.1054648#123 Passer montanus (Li… 3 65.30543 25.70355
#> 6 …JX.1054648#149 Pyrrhula pyrrhula (… 1 65.30543 25.70355
#> 7 …JX.1054648#93 Cyanistes caeruleus… 9 65.30543 25.70355
#> 8 …JX.1054648#95 Parus major Linnaeu… 35 65.30543 25.70355
#> 9 …JX.1054648#137 Carduelis flammea (… 2 65.30543 25.70355
#> 10 …JX.1056695#107 Pica pica (Linnaeus… 6 62.7154 23.0893
#> date_time
#> 1 2019-12-31 12:00:00
#> 2 2019-12-31 10:20:00
#> 3 2019-12-31 10:20:00
#> 4 2019-12-31 10:20:00
#> 5 2019-12-31 10:20:00
#> 6 2019-12-31 10:20:00
#> 7 2019-12-31 10:20:00
#> 8 2019-12-31 10:20:00
#> 9 2019-12-31 10:20:00
#> 10 2019-12-31 10:15:00
#> ...with 0 more record 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: 1486845
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …433443#318 Accipiter nisus (Li… 1 64.8162 25.32106 2023-02-20 15:00:00
#> 2 …531663#107 Pica pica (Linnaeus… 10 62.9199 27.71032 2023-02-20 07:40:00
#> 3 …530610#107 Pica pica (Linnaeus… 21 65.78623 24.49119 2023-02-20 09:15:00
#> 4 …530449#107 Pica pica (Linnaeus… 4 65.74652 24.62216 2023-02-20 08:20:00
#> 5 …531663#153 Emberiza citrinella… 12 62.9199 27.71032 2023-02-20 07:40:00
#> 6 …531663#49 Columba livia domes… 10 62.9199 27.71032 2023-02-20 07:40:00
#> 7 …530610#49 Columba livia domes… 2 65.78623 24.49119 2023-02-20 09:15:00
#> 8 …530610#117 Corvus corax Linnae… 1 65.78623 24.49119 2023-02-20 09:15:00
#> 9 …531663#61 Dendrocopos major (… 6 62.9199 27.71032 2023-02-20 07:40:00
#> 10 …531663#111 Corvus monedula Lin… 7 62.9199 27.71032 2023-02-20 07:40:00
#> ...with 0 more record 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(
wild_status = c("wild", "non_wild", "wild_unknown"),
record_quality = c(
"expert_verified", "community_verified", "unassessed", "uncertain",
"erroneous"
),
abundance_min = 0
)
c(
strict = finbif_occurrence(filter = strict, count_only = TRUE),
permissive = finbif_occurrence(filter = permissive, count_only = TRUE)
)
#> strict permissive
#> 52654 51733557The 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.
You can filter occurrence records based on informal taxonomic groups
such as Birds or Mammals.
#> Records downloaded: 10
#> Records available: 22116048
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …5#3 Sciurus vulgaris Li… 1 60.23584 25.05693 2023-06-14 08:56:00
#> 2 …2#9 Hirundo rustica Lin… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 3 …2#37 Pica pica (Linnaeus… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 4 …2#49 Muscicapa striata (… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 5 …2#39 Larus canus Linnaeu… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 6 …2#5 Emberiza citrinella… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 7 …2#31 Ficedula hypoleuca … NA 64.12716 23.99111 2023-06-14 08:48:00
#> 8 …2#41 Alauda arvensis Lin… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 9 …2#21 Numenius arquata (L… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 10 …2#29 Dendrocopos major (… NA 64.12716 23.99111 2023-06-14 08:48:00
#> ...with 0 more record 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:
Many records in the FinBIF database include taxa that have one or
another regulatory statuses. See
finbif_metadata("regulatory_status") for a list of
regulatory statuses and short-codes.
# Search for birds on the EU invasive species list
finbif_occurrence(
filter = list(informal_groups = "Birds", regulatory_status = "EU_INVSV")
)
#> Records downloaded: 10
#> Records available: 471
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84
#> 1 …JX.1580858#3 Oxyura jamaicensis … 1 60.28687 25.0271
#> 2 …JX.1580860#3 Oxyura jamaicensis … 1 60.28671 25.02713
#> 3 …KE.176/62b1ad90d5deb0fafdc6212b#Unit1 Oxyura jamaicensis … 7 61.66207 23.57706
#> 4 …JX.1045316#34 Alopochen aegyptiac… 3 52.16081 4.485534
#> 5 …JX.138840#123 Alopochen aegyptiac… 4 53.36759 6.191796
#> 6 …JX.139978#214 Alopochen aegyptiac… 6 53.37574 6.207861
#> 7 …JX.139710#17 Alopochen aegyptiac… 30 52.3399 5.069133
#> 8 …JX.139645#57 Alopochen aegyptiac… 36 51.74641 4.535283
#> 9 …JX.139645#10 Alopochen aegyptiac… 3 51.74641 4.535283
#> 10 …JX.139442#16 Alopochen aegyptiac… 2 51.90871 4.53258
#> ...with 0 more record and 7 more variables:
#> date_time, 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_groups = "Mammals", red_list_status = "NT")
)
#> Records downloaded: 10
#> Records available: 42510
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84
#> 1 …JX.1594024#23 Rangifer tarandus f… 15 63.31266 24.43298
#> 2 …JX.1588853#1075 Rangifer tarandus f… 1 63.84551 29.8366
#> 3 …JX.1593780#3 Pusa hispida botnic… 1 65.02313 25.40505
#> 4 …HR.3211/166639315-U Rangifer tarandus f… NA 63.7 24.7
#> 5 …HR.3211/166049302-U Rangifer tarandus f… NA 64.1 26.5
#> 6 …HR.3211/165761924-U Rangifer tarandus f… NA 63.9 24.9
#> 7 …JX.1589779#105 Rangifer tarandus f… 3 63.7261 23.40827
#> 8 …KE.176/647ad84dd5de884fa20e25e6#Unit1 Rangifer tarandus f… 1 64.12869 24.73877
#> 9 …HR.3211/165005253-U Pusa hispida botnic… NA 64.2865 23.87402
#> 10 …JX.1588052#18 Rangifer tarandus f… 2 64.13286 26.26767
#> ...with 0 more record and 7 more variables:
#> date_time, 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"))
#> code name
#> MKV.habitatMt Mt alpine birch forests (excluding herb-rich alpine …
#> MKV.habitatTlk Tlk alpine calcareous rock outcrops and boulder fields
#> MKV.habitatTlr Tlr alpine gorges and canyons
#> MKV.habitatT T Alpine habitats
#> MKV.habitatTp Tp alpine heath scrubs
#> MKV.habitatTk Tk alpine heaths# 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: 26362337
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …5#3 Sciurus vulgaris Li… 1 60.23584 25.05693 2023-06-14 08:56:00
#> 2 …2#37 Pica pica (Linnaeus… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 3 …2#49 Muscicapa striata (… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 4 …2#5 Emberiza citrinella… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 5 …2#31 Ficedula hypoleuca … NA 64.12716 23.99111 2023-06-14 08:48:00
#> 6 …2#29 Dendrocopos major (… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 7 …2#15 Sylvia borin (Bodda… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 8 …2#11 Anthus trivialis (L… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 9 …2#45 Corvus monedula Lin… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 10 …2#3 Phylloscopus trochi… NA 64.12716 23.99111 2023-06-14 08:48:00
#> ...with 0 more record 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, ]
#> code name
#> MKV.habitatSpecificTypeCA CA calcareous effect
#> MKV.habitatSpecificTypeH H esker forests, also semi-open forests
#> MKV.habitatSpecificTypeKE KE intermediate-basic rock outcrops and boulder fiel…# 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("PAK", "J"))))
#> Records downloaded: 10
#> Records available: 178
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …502812#393 Pammene fasciana (L… NA 60.45845 22.17811 2022-08-14 12:00:00
#> 2 …435062#6 Pammene fasciana (L… 1 60.20642 24.66127 2022-08-04
#> 3 …435050#9 Pammene fasciana (L… 1 60.20642 24.66127 2022-07-25
#> 4 …501598#39 Pammene fasciana (L… 1 60.08841 22.48629 2022-07-21 12:00:00
#> 5 …501387#162 Pammene fasciana (L… 1 60.08841 22.48629 2022-07-20 12:00:00
#> 6 …448030#159 Pammene fasciana (L… 1 60.08841 22.48629 2022-07-18 12:00:00
#> 7 …447556#78 Pammene fasciana (L… 1 60.08841 22.48629 2022-07-14 12:00:00
#> 8 …446841#408 Pammene fasciana (L… 1 60.08841 22.48629 2022-07-12 12:00:00
#> 9 …443339#36 Pammene fasciana (L… 1 60.08841 22.48629 2022-07-10 12:00:00
#> 10 …440849#159 Pammene fasciana (L… 2 60.08841 22.48629 2022-07-08 12:00:00
#> ...with 0 more record 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.
#> Records downloaded: 10
#> Records available: 406005
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84
#> 1 …HR.3211/167313706-U Pygaera timon (Hübn… NA 62.1281 27.45272
#> 2 …JX.1594282#21 Carterocephalus pal… 1 64.65322 24.58941
#> 3 …HR.3211/167197097-U Carterocephalus pal… NA 65.07819 25.55236
#> 4 …HR.3211/167183358-U Glaucopsyche alexis… NA 60.46226 22.76647
#> 5 …JX.1594291#3 Glaucopsyche alexis… 1 60.42692 22.20411
#> 6 …KE.176/6488c111d5de884fa20e295f#Unit1 Panemeria tenebrata… 1 61.16924 25.56036
#> 7 …JX.1593930#3 Hemaris tityus (Lin… 1 60.63969 27.29052
#> 8 …KE.176/64889455d5de884fa20e294f#Unit1 Pseudopanthera macu… 2 62.054 30.352
#> 9 …JX.1594170#199 Glaucopsyche alexis… 1 61.10098 28.68453
#> 10 …JX.1594112#3 Hemaris tityus (Lin… 1 61.25511 28.89127
#> ...with 0 more record and 7 more variables:
#> date_time, 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_groups = "birds",
finnish_occurrence_status_neg = sprintf("vagrant_%sregular", c("", "ir"))
)
)
#> Records downloaded: 10
#> Records available: 21725426
#> A data.frame [10 x 12]
#> record_id scientific_name abundance lat_wgs84 lon_wgs84 date_time
#> 1 …9 Hirundo rustica Lin… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 2 …37 Pica pica (Linnaeus… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 3 …49 Muscicapa striata (… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 4 …39 Larus canus Linnaeu… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 5 …5 Emberiza citrinella… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 6 …31 Ficedula hypoleuca … NA 64.12716 23.99111 2023-06-14 08:48:00
#> 7 …41 Alauda arvensis Lin… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 8 …21 Numenius arquata (L… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 9 …29 Dendrocopos major (… NA 64.12716 23.99111 2023-06-14 08:48:00
#> 10 …15 Sylvia borin (Bodda… NA 64.12716 23.99111 2023-06-14 08:48:00
#> ...with 0 more record 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.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.