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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.

Location

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

Time

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")))
Click to show/hide output.

#> 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"))
)
Click to show/hide output.

#> 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_quality


Records 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")
  )
)
Click to show/hide output.

#> 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_quality


Data Quality

You 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   40636542

Collection

The 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] 1344139

Informal taxonomic groups

You can filter occurrence records based on informal taxonomic groups such as Birds or Mammals.

finbif_occurrence(filter = list(informal_group = c("Birds", "Mammals")))
Click to show/hide output.

#> 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_quality


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

Administrative status

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")
)
Click to show/hide output.

#> 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_quality


IUCN red list

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")
)
Click to show/hide output.

#> 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_quality


Habitat type

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_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"))
Click to show/hide output.

#> 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_quality


You 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"))))
Click to show/hide output.

#> 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_quality


Status of taxa in Finland

You 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"))
Click to show/hide output.

#> 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"))
  )
)
Click to show/hide output.

#> 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_quality


See finbif_metadata("finnish_occurrence_status") for a full list of statuses and their descriptions.