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.
library(finbif)
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")))
Click to show/hide output.
Records downloaded: 10
Records available: 7150
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Pica pica 6 62.7154 23.0893 2019-12-31 08:15:00
2 Poecile montanus 1 62.7154 23.0893 2019-12-31 08:15:00
3 Dendrocopos major 2 62.7154 23.0893 2019-12-31 08:15:00
4 Corvus monedula 8 62.7154 23.0893 2019-12-31 08:15:00
5 Passer montanus 11 62.7154 23.0893 2019-12-31 08:15:00
6 Cyanistes caeruleus 5 62.7154 23.0893 2019-12-31 08:15:00
7 Parus major 20 62.7154 23.0893 2019-12-31 08:15:00
8 Acanthis flammea 12 62.7154 23.0893 2019-12-31 08:15:00
9 Passer domesticus 22 62.7154 23.0893 2019-12-31 08:15:00
10 Chloris chloris 6 62.7154 23.0893 2019-12-31 08:15:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
, 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: 227693
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Pica pica 6 62.7154 23.0893 2019-12-31 08:15:00
2 Poecile montanus 1 62.7154 23.0893 2019-12-31 08:15:00
3 Dendrocopos major 2 62.7154 23.0893 2019-12-31 08:15:00
4 Corvus monedula 8 62.7154 23.0893 2019-12-31 08:15:00
5 Passer montanus 11 62.7154 23.0893 2019-12-31 08:15:00
6 Cyanistes caeruleus 5 62.7154 23.0893 2019-12-31 08:15:00
7 Parus major 20 62.7154 23.0893 2019-12-31 08:15:00
8 Acanthis flammea 12 62.7154 23.0893 2019-12-31 08:15:00
9 Passer domesticus 22 62.7154 23.0893 2019-12-31 08:15:00
10 Chloris chloris 6 62.7154 23.0893 2019-12-31 08:15:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
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: 1471374
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 <NA> 1 60.66508 23.60832 2020-01-14 08:47:00
2 <NA> 1 60.66508 23.60832 2020-01-14 08:47:00
3 Fomitopsis pinicola 1 64.97964 25.52697 2020-01-13 22:00:00
4 Basidioradulum radu… 1 64.97964 25.52697 2020-01-13 22:00:00
5 Trichaptum abietinum 1 64.97964 25.52697 2020-01-13 22:00:00
6 Inonotus obliquus 1 64.97964 25.52697 2020-01-13 22:00:00
7 Dendrocopos major 2 60.42794 22.20052 2020-01-13 22:00:00
8 Turdus merula 1 60.42794 22.20052 2020-01-13 22:00:00
9 Pyrrhula pyrrhula 1 60.42794 22.20052 2020-01-13 22:00:00
10 Carduelis carduelis 1 60.42794 22.20052 2020-01-13 22:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
You can filter occurrence records by indicators of data quality. See ?filters
section “Quality” for details.
strict <- c(
collection_reliability = 5, coordinates_uncertainty_max = 1,
taxon_reliability = "reliable"
)
permissive <- list(
collection_reliability = 1:5, coordinates_uncertainty_max = 10000,
quality_issues = "both"
)
c(
strict = finbif_occurrence(filter = strict, count_only = TRUE),
permissive = finbif_occurrence(filter = permissive, count_only = TRUE)
)
#> strict permissive
#> 300819 31096863
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"), count_only = TRUE
)
#> [1] 15716
finbif_occurrence(
filter = c(collection = "Notebook, general observations"), count_only = TRUE
)
#> [1] 585342
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: 17895168
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Bucephala clangula 6 61.03523 26.13556 2020-01-07 22:00:00
2 Cygnus cygnus 30 61.03529 26.13553 2020-01-07 22:00:00
3 Apodemus flavicollis 1 60.43997 22.37462 2020-01-07 22:00:00
4 Sitta europaea 1 61.04351 26.17732 2020-01-07 22:00:00
5 Spinus spinus 2 60.88937 26.44874 2020-01-06 22:00:00
6 Cygnus cygnus 26 60.93560 26.37433 2020-01-06 22:00:00
7 Mergus merganser 70 60.96558 26.42339 2020-01-06 22:00:00
8 Melanitta nigra 1 60.96561 26.42340 2020-01-06 22:00:00
9 Fringilla montifrin… 46 60.94323 26.43148 2020-01-06 22:00:00
10 Surnia ulula 1 60.99228 26.43129 2020-01-06 22:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
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
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: 437
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Alopochen aegyptiaca 3 52.16081 4.485534 2019-10-22 22:00:00
2 Alopochen aegyptiaca 4 53.36759 6.191796 2018-10-26 08:15:00
3 Alopochen aegyptiaca 6 53.37574 6.207861 2018-10-23 05:30:00
4 Alopochen aegyptiaca 30 52.33990 5.069133 2018-10-22 07:45:00
5 Alopochen aegyptiaca 36 51.74641 4.535283 2018-10-21 10:00:00
6 Alopochen aegyptiaca 3 51.74641 4.535283 2018-10-21 10:00:00
7 Alopochen aegyptiaca 2 51.90871 4.532580 2018-10-20 09:10:00
8 Alopochen aegyptiaca 2 53.19242 5.437417 2017-10-24 08:06:00
9 Alopochen aegyptiaca 20 53.32081 6.192341 2017-10-23 09:15:00
10 Alopochen aegyptiaca 5 53.32081 6.192341 2017-10-23 09:15:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
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: 1645
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Rangifer tarandus f… 4 64.42648 29.11431 2019-10-19 21:00:00
2 Rangifer tarandus f… 1 64.09919 29.40356 2019-09-22 21:00:00
3 Rangifer tarandus f… 1 63.79309 29.51080 2019-09-12 21:00:00
4 Rangifer tarandus f… 1 63.93649 29.59252 2019-07-25 21:00:00
5 Rangifer tarandus f… 2 63.27123 25.35634 2019-06-27 21:00:00
6 Rangifer tarandus f… 1 63.26554 25.36645 2019-06-27 21:00:00
7 Rangifer tarandus f… 4 63.03293 24.32905 2019-06-12 21:00:00
8 Rangifer tarandus f… 1 64.32293 26.69975 2019-05-26 21:00:00
9 Rangifer tarandus f… 1 63.54897 24.54795 2019-05-17 21:00:00
10 Rangifer tarandus f… 7 64.43140 29.11998 2019-04-27 21:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
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_types") 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_types"))
#> habitat_name habitat_code
#> 1 Forests M
#> 2 Heath forests MK
#> 3 Sub-xeric, xeric and barren heath fores… MKK
#> 4 Mesic and herb-rich heath forests MKT
#> 5 Herb-rich forests (also spruce-dominate… 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: 19443255
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Apodemus flavicollis 1 60.43997 22.37462 2020-01-07 22:00:00
2 Sitta europaea 1 61.04351 26.17732 2020-01-07 22:00:00
3 Acanthosoma haemorr… 1 60.80671 26.74877 2020-01-07 22:00:00
4 Anthocoris confusus 1 60.80671 26.74877 2020-01-07 22:00:00
5 Scolopostethus thom… 1 60.80671 26.74877 2020-01-07 22:00:00
6 Antrodia sinuosa 1 63.15110 21.66860 2020-01-06 22:00:00
7 Phyllotopsis nidula… 1 63.15150 21.67020 2020-01-06 22:00:00
8 Trichaptum abietinum 1 63.15260 21.67000 2020-01-06 22:00:00
9 Trichaptum fuscovio… 1 63.15150 21.67020 2020-01-06 22:00:00
10 Panellus mitis 1 63.15260 21.67000 2020-01-06 22:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
You may further refine habitat based searching using a specific habitat type
qualifier such as “sun-exposed” or “shady”. Use
finbif_metadata("habitat_qualifiers") 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_qualifiers")[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: 105
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Pammene fasciana 1 60.00217 23.43591 2019-06-21 21:00:00
2 Pammene fasciana 1 60.50396 27.72823 2019-05-29 21:00:00
3 Pammene fasciana 3 60.00217 23.43591 2019-05-07 21:00:00
4 Pammene fasciana 1 59.96020 20.99521 2017-09-19 21:00:00
5 Pammene fasciana 2 60.35244 19.83238 2017-08-02 21:00:00
6 Pammene fasciana 1 60.35244 19.83238 2017-08-02 21:00:00
7 Pammene fasciana 1 60.35244 19.83238 2017-08-01 21:00:00
8 Pammene fasciana 1 60.21166 24.90204 2017-07-30 21:00:00
9 Pammene fasciana 1 60.35244 19.83238 2017-07-30 21:00:00
10 Pammene fasciana 1 60.35244 19.83238 2017-07-29 21:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
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: 256919
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Dromius quadrimacul… 1 60.40483 22.42259 2020-01-10 22:00:00
2 Calodromius spilotus 1 60.20746 25.63819 2020-01-08 22:00:00
3 Tillus elongatus 1 60.27955 22.20464 2020-01-08 22:00:00
4 Dromius quadrimacul… 1 60.40462 22.42190 2020-01-07 22:00:00
5 Psammoecus bipuncta… 1 60.80671 26.74877 2020-01-07 22:00:00
6 Calodromius spilotus 1 60.05714 24.23511 2020-01-04 22:00:00
7 Dromius quadrimacul… 1 60.05083 24.26637 2020-01-04 22:00:00
8 Dromius quadrimacul… 1 60.05535 24.27343 2020-01-04 22:00:00
9 Acalles echinatus 3 60.26386 24.06557 2020-01-03 22:00:00
10 Myrmechixenus subte… 1 60.26386 24.06557 2020-01-03 22:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
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: 17732629
A data.frame [10 x 30]
scientific_name abundance lat_wgs84 lon_wgs84 date_time
1 Turdus merula 5 60.24014 24.98273 2020-01-12 22:00:00
2 Passer domesticus 1 60.24014 24.98273 2020-01-12 22:00:00
3 Spinus spinus 1 60.96301 21.44838 2020-01-11 22:00:00
4 Anas platyrhynchos 1 61.12859 21.52177 2020-01-11 22:00:00
5 Turdus pilaris 1 61.41560 22.62856 2020-01-11 22:00:00
6 Dryocopus martius 1 61.41560 22.62856 2020-01-11 22:00:00
7 Parus major 1 61.41560 22.62856 2020-01-11 22:00:00
8 Pica pica 1 61.41560 22.62856 2020-01-11 22:00:00
9 Dendrocopos major 1 61.41934 22.62869 2020-01-11 22:00:00
10 Corvus monedula 3 61.41560 22.62856 2020-01-11 22:00:00
...with 0 more records and 25 more variables:
taxon_rank, country, province, municipality, date_start, date_end,
hour_start, hour_end, minute_start, minute_end, record_id,
individual_id, event_id, collection_id, any_issues, record_issue,
record_reliable, taxon_reliability, document_issue,
collection_reliability, coordinates_uncertainty, event_issue,
location_issue, time_issue, duration
See finbif_metadata("finnish_occurrence_status") for a full list of statuses
and their descriptions.