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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: 57360604
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …21 Polytrichum juniper… NA 60.17967 24.914629
#> 2 …25 Polytrichum juniper… NA 60.373472 24.993816
#> 3 …29 Polytrichum juniper… NA 61.612783 21.44191
#> 4 …33 Polytrichum juniper… NA 61.322069 23.513515
#> 5 …37 Polytrichum juniper… NA 61.249458 25.040691
#> 6 …41 Polytrichum juniper… NA 62.605448 25.925676
#> 7 …45 Polytrichum juniper… NA 62.22789 30.629365
#> 8 …49 Polytrichum juniper… NA 66.004079 28.202282
#> 9 …53 Polytrichum juniper… NA 69.049179 20.812003
#> 10 …57 Polytrichum pilifer… NA 60.373472 24.993816
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusOr by a set of coordinates.
finbif_occurrence(
filter = list(coordinates = list(c(60, 68), c(20, 30), "wgs84"))
)
#> Records downloaded: 10
#> Records available: 48564194
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …21 Polytrichum juniper… NA 60.17967 24.914629
#> 2 …25 Polytrichum juniper… NA 60.373472 24.993816
#> 3 …29 Polytrichum juniper… NA 61.612783 21.44191
#> 4 …33 Polytrichum juniper… NA 61.322069 23.513515
#> 5 …37 Polytrichum juniper… NA 61.249458 25.040691
#> 6 …41 Polytrichum juniper… NA 62.605448 25.925676
#> 7 …49 Polytrichum juniper… NA 66.004079 28.202282
#> 8 …57 Polytrichum pilifer… NA 60.373472 24.993816
#> 9 …61 Polytrichum pilifer… NA 61.599004 21.434943
#> 10 …65 Polytrichum pilifer… NA 61.452593 24.099408
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusSee ?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: 30157
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude
#> 1 …herb.oulu.fi/MY.10184972 Orthotrichum anomal… NA 61.603872
#> 2 …herb.oulu.fi/MY.10185111 Schistidium submuti… NA 61.603833
#> 3 …herb.oulu.fi/MY.10313974 Skeletocutis bigutt… NA 60.234691
#> 4 …herb.oulu.fi/MY.10314039 Oxyporus populinus … NA 61.467278
#> 5 …herb.oulu.fi/MY.10314043 Stereum hirsutum (W… NA 61.467278
#> 6 …herb.oulu.fi/MY.10314116 Stereum sanguinolen… NA 60.201527
#> 7 …herb.oulu.fi/MY.10745815 Eurhynchium angusti… NA 61.467746
#> 8 …luomus.fi/MY.10204437 Dendrocopos leucoto… NA 61.6077
#> 9 …luomus.fi/MY.10221155 Allophylaria macros… NA 60.378251
#> 10 …luomus.fi/MY.10221158 Host: Chamaenerion … NA 60.378251
#> ...with 0 more records and 8 more variables:
#> decimalLongitude, eventDateTime, coordinateUncertaintyInMeters, hasIssues,
#> requiresVerification, requiresIdentification, occurrenceReliability,
#> identificationVerificationStatus
, or for record events, a range as a character vector.
#> Records downloaded: 10
#> Records available: 1167813
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …244 Aneura pinguis (L.)… NA 61.86848 24.042062
#> 2 …248 Sphenolobus saxicol… NA 61.790156 24.739934
#> 3 …264 Barbilophozia hatch… NA 62.172979 23.166974
#> 4 …268 Barbilophozia hatch… NA 61.733229 23.557042
#> 5 …276 Barbilophozia hatch… NA 62.341331 23.821755
#> 6 …280 Blepharostoma trich… NA 61.817804 23.156312
#> 7 …288 Calypogeia integris… NA 61.787466 24.740131
#> 8 …296 Calypogeia muelleri… NA 61.787466 24.740131
#> 9 …317 Cephalozia bicuspid… NA 61.768742 23.877258
#> 10 …388 Lophocolea heteroph… NA 61.4436 24.119939
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusRecords 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: 1793259
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …079640 Pohlia nutans (Hedw… NA 60.321276 24.109857
#> 2 …184972 Orthotrichum anomal… NA 61.603872 24.227601
#> 3 …185111 Schistidium submuti… NA 61.603833 24.225719
#> 4 …225336 Fuscocephaloziopsis… NA 61.510093 24.343093
#> 5 …225392 Lophocolea minor Ne… NA 61.65722 24.658173
#> 6 …225400 Obtusifolium obtusu… NA 61.458209 23.658786
#> 7 …225403 Plagiomnium cuspida… NA 61.458209 23.658786
#> 8 …281093 Pseudanomodon atten… NA 61.374279 24.197437
#> 9 …281097 Pseudanomodon atten… NA 61.571317 24.305839
#> 10 …281100 Syntrichia ruralis … NA 61.571317 24.305839
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusYou 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
#> 82631 61265240The 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: 29626954
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude
#> 1 …herb.oulu.fi/MY.17469610 Asio flammeus (Pont… NA 67.718422
#> 2 …herb.oulu.fi/MY.17483993 Picoides tridactylu… NA 67.47766
#> 3 …luomus.fi/MY.10042206 Strix uralensis Pal… NA 63.811181
#> 4 …luomus.fi/MY.10042210 Strix uralensis Pal… NA 60.360916
#> 5 …luomus.fi/MY.10042213 Bubo bubo (Linnaeus… NA 60.930307
#> 6 …luomus.fi/MY.10042218 Bubo bubo (Linnaeus… NA 63.681831
#> 7 …luomus.fi/MY.10042223 Bubo bubo (Linnaeus… NA 60.135148
#> 8 …luomus.fi/MY.10042226 Strix uralensis Pal… NA 62.250643
#> 9 …luomus.fi/MY.10042231 Astur gentilis (Lin… NA 63.811181
#> 10 …luomus.fi/MY.10042238 Accipiter nisus (Li… NA 60.2522
#> ...with 0 more records and 8 more variables:
#> decimalLongitude, eventDateTime, coordinateUncertaintyInMeters, hasIssues,
#> requiresVerification, requiresIdentification, occurrenceReliability,
#> identificationVerificationStatusSee 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 the highest level informal groups:
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: 507
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …7350700 Pycnonotus cafer su… 3 NA NA
#> 2 …7351780 Corvus splendens su… 2 NA NA
#> 3 …7351784 Corvus splendens su… 2 NA NA
#> 4 …7351788 Corvus splendens su… 2 NA NA
#> 5 …7351792 Corvus splendens su… 1 NA NA
#> 6 …7352178 Oxyura jamaicensis … 7 NA NA
#> 7 …7352182 Oxyura jamaicensis … 8 NA NA
#> 8 …7355894 Oxyura jamaicensis … 8 NA NA
#> 9 …7430682 Corvus splendens su… 3 NA NA
#> 10 …8449767 Alopochen aegyptiac… NA NA NA
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusFiltering 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: 82019
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …223201 Microtus arvalis (P… NA NA NA
#> 2 …223245 Microtus arvalis (P… NA NA NA
#> 3 …223249 Microtus arvalis (P… NA NA NA
#> 4 …223253 Microtus arvalis (P… NA NA NA
#> 5 …223565 Microtus arvalis (P… NA NA NA
#> 6 …223659 Castor fiber Linnae… NA NA NA
#> 7 …223878 Castor fiber Linnae… NA NA NA
#> 8 …580236 Castor fiber Linnae… NA NA NA
#> 9 …580240 Castor fiber Linnae… NA NA NA
#> 10 …580398 Ursus arctos Linnae… NA 67.05 29.25
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusMany 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
#> MKV.habitatTlk Tlk alpine calcareous rock outcrops and boulder …
#> 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: 34008277
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …21 Polytrichum juniper… NA 60.17967 24.914629
#> 2 …25 Polytrichum juniper… NA 60.373472 24.993816
#> 3 …29 Polytrichum juniper… NA 61.612783 21.44191
#> 4 …33 Polytrichum juniper… NA 61.322069 23.513515
#> 5 …37 Polytrichum juniper… NA 61.249458 25.040691
#> 6 …41 Polytrichum juniper… NA 62.605448 25.925676
#> 7 …45 Polytrichum juniper… NA 62.22789 30.629365
#> 8 …49 Polytrichum juniper… NA 66.004079 28.202282
#> 9 …53 Polytrichum juniper… NA 69.049179 20.812003
#> 10 …57 Polytrichum pilifer… NA 60.373472 24.993816
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatusYou 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.habitatSpecificTypeLK LK fishless ponds# 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: 218
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude
#> 1 …id.luomus.fi/MY.19077695 Pammene fasciana (L… 1 60.188362
#> 2 …tun.fi/HR.3211/53817755-U Pammene fasciana (L… NA 59.90452
#> 3 …tun.fi/JX.1011605#97 Pammene fasciana (L… 1 60.50396
#> 4 …tun.fi/JX.1011998#37 Pammene fasciana (L… 1 59.960224
#> 5 …tun.fi/JX.1012832#367 Pammene fasciana (L… 1 60.002166
#> 6 …tun.fi/JX.1038248#475 Pammene fasciana (L… NA 59.934164
#> 7 …tun.fi/JX.1098381#487 Pammene fasciana (L… NA 60.045579
#> 8 …tun.fi/JX.1103286#13 Pammene fasciana (L… 1 59.90522
#> 9 …tun.fi/JX.1134471#4 Pammene fasciana (L… 2 61.549842
#> 10 …tun.fi/JX.1143718#265 Pammene fasciana (L… NA 60.37543
#> ...with 0 more records and 8 more variables:
#> decimalLongitude, eventDateTime, coordinateUncertaintyInMeters, hasIssues,
#> requiresVerification, requiresIdentification, occurrenceReliability,
#> identificationVerificationStatusYou 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: 499022
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude decimalLongitude
#> 1 …3990009 Amanita coryli Nevi… NA 68.058832 24.058368
#> 2 …3990121 Amanita flavescens … NA 64.756355 26.197092
#> 3 …3993454 Amanita flavescens … 2 66.65863 27.482198
#> 4 …3993458 Amanita coryli Nevi… 3 66.371875 27.409632
#> 5 …4018238 Hydnum jussii Niska… NA 64.368844 28.004874
#> 6 …4018310 Lamelloclavaria pet… NA 64.421905 27.677572
#> 7 …4018314 Lamelloclavaria pet… NA 64.323694 28.044601
#> 8 …8016027 Amanita coryli Nevi… 5 66.168299 25.765382
#> 9 …8016087 Amanita coryli Nevi… 3 69.007453 20.930394
#> 10 …8016091 Amanita coryli Nevi… 2 68.185593 23.992918
#> ...with 0 more records and 7 more variables:
#> eventDateTime, coordinateUncertaintyInMeters, hasIssues, requiresVerification,
#> requiresIdentification, occurrenceReliability, identificationVerificationStatus
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: 29048611
#> A data.frame [10 x 12]
#> occurrenceID scientificName individualCount decimalLatitude
#> 1 …herb.oulu.fi/MY.17469610 Asio flammeus (Pont… NA 67.718422
#> 2 …herb.oulu.fi/MY.17483993 Picoides tridactylu… NA 67.47766
#> 3 …luomus.fi/MY.10042206 Strix uralensis Pal… NA 63.811181
#> 4 …luomus.fi/MY.10042210 Strix uralensis Pal… NA 60.360916
#> 5 …luomus.fi/MY.10042213 Bubo bubo (Linnaeus… NA 60.930307
#> 6 …luomus.fi/MY.10042218 Bubo bubo (Linnaeus… NA 63.681831
#> 7 …luomus.fi/MY.10042223 Bubo bubo (Linnaeus… NA 60.135148
#> 8 …luomus.fi/MY.10042226 Strix uralensis Pal… NA 62.250643
#> 9 …luomus.fi/MY.10042231 Astur gentilis (Lin… NA 63.811181
#> 10 …luomus.fi/MY.10042238 Accipiter nisus (Li… NA 60.2522
#> ...with 0 more records and 8 more variables:
#> decimalLongitude, eventDateTime, coordinateUncertaintyInMeters, hasIssues,
#> requiresVerification, requiresIdentification, occurrenceReliability,
#> identificationVerificationStatusSee 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.
Health stats visible at Monitor.