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rebird: wrapper to the eBird API

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Project Status: Active – The project has reached a stable, usable
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rebird is a package to interface with the eBird webservices.

eBird is a real-time, online bird checklist program. For more information, visit their website: https://ebird.org/home

The API for the eBird webservices can be accessed here: https://documenter.getpostman.com/view/664302/S1ENwy59?version=latest

Install

You can install the stable version from CRAN

install.packages("rebird")

Or the development version from Github

install.packages("devtools")
devtools::install_github("ropensci/rebird")

Direct use of rebird

Load the package:

library("rebird")

The eBird API server requires users to provide an API key, which is linked to your eBird user account. You can pass it to the ‘key’ argument in rebird functions, but we highly recommend storing it as an environment variable called EBIRD_KEY in your .Renviron file. If you don’t have a key, you can obtain one from https://ebird.org/api/keygen.

You can keep your .Renviron file in your global R home directory (R.home()), your user’s home directory (Sys.getenv("HOME")), or your current working directory (getwd()). Remember that .Renviron is loaded once when you start R, so if you add your API key to the file you will have to restart your R session. See ?Startup for more information on R’s startup files.

Furthermore, functions now use species codes, rather than scientific names, for species-specific requests. We’ve made the switch easy by providing the species_code function, which converts a scientific name to its species code:

species_code('sula variegata')
#> Peruvian Booby (Sula variegata): perboo1
#> [1] "perboo1"

The species_code function can be called within other rebird functions, or the species code can be specified directly.

eBird Taxonomy

The eBird taxonomy is internally stored in rebird and can be called using

rebird::tax
#> # A tibble: 17,064 × 15
#>    sciName     comName speciesCode category taxonOrder bandingCodes comNameCodes
#>    <chr>       <chr>   <chr>       <chr>         <dbl> <chr>        <chr>       
#>  1 Struthio c… Common… ostric2     species           2 <NA>         COOS        
#>  2 Struthio m… Somali… ostric3     species           7 <NA>         SOOS        
#>  3 Struthio c… Common… y00934      slash             8 <NA>         SOOS,COOS   
#>  4 Rhea ameri… Greate… grerhe1     species          10 <NA>         GRRH        
#>  5 Rhea penna… Lesser… lesrhe2     species          16 <NA>         LERH        
#>  6 Rhea penna… Lesser… lesrhe4     issf             17 <NA>         LERH        
#>  7 Rhea penna… Lesser… lesrhe3     issf             20 <NA>         LERH        
#>  8 Nothocercu… Tawny-… tabtin1     species          22 <NA>         TBTI        
#>  9 Nothocercu… Highla… higtin1     species          23 HITI         <NA>        
#> 10 Nothocercu… Highla… higtin2     issf             24 <NA>         HITI        
#> # ℹ 17,054 more rows
#> # ℹ 8 more variables: sciNameCodes <chr>, order <chr>, familyCode <chr>,
#> #   familyComName <chr>, familySciName <chr>, reportAs <chr>, extinct <lgl>,
#> #   extinctYear <int>

While the internal taxonomy is kept up to date with each package release, it could be outdated if a new taxonomy is made available before the package is updated. You can obtain the latest eBird taxonomy by

new_tax <- ebirdtaxonomy()

Sightings at location determined by latitude/longitude

Search for bird occurrences by latitude and longitude point

ebirdgeo(species = species_code('spinus tristis'), lat = 42, lng = -76)
#> American Goldfinch (Spinus tristis): amegfi
#> # A tibble: 31 × 13
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 amegfi      America… Spinus… L144… Heart … 2024…      10  41.8 -75.8 TRUE    
#>  2 amegfi      America… Spinus… L982… Hazel … 2024…       1  42.1 -76.1 TRUE    
#>  3 amegfi      America… Spinus… L243… 71 Wil… 2024…      12  42.2 -75.9 TRUE    
#>  4 amegfi      America… Spinus… L124… Happy … 2024…       7  42.1 -75.9 TRUE    
#>  5 amegfi      America… Spinus… L197… esther… 2024…       4  42.1 -75.9 TRUE    
#>  6 amegfi      America… Spinus… L299… 184 Sw… 2024…       4  42.2 -76.0 TRUE    
#>  7 amegfi      America… Spinus… L300… 194 Sw… 2024…       6  42.2 -75.9 TRUE    
#>  8 amegfi      America… Spinus… L210… 1099 P… 2024…       2  42.1 -76.0 TRUE    
#>  9 amegfi      America… Spinus… L131… Vestal  2024…       1  42.1 -76.1 TRUE    
#> 10 amegfi      America… Spinus… L447… Bingha… 2024…       2  42.1 -76.0 TRUE    
#> # ℹ 21 more rows
#> # ℹ 3 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>

Recent observations at a region

Search for bird occurrences by region and species name

ebirdregion(loc = 'US', species = 'btbwar')
#> # A tibble: 15 × 13
#>    speciesCode comName sciName locId locName obsDt howMany   lat    lng obsValid
#>    <chr>       <chr>   <chr>   <chr> <chr>   <chr>   <int> <dbl>  <dbl> <lgl>   
#>  1 btbwar      Black-… Setoph… L129… Cave C… 2024…       1  31.9 -109.  TRUE    
#>  2 btbwar      Black-… Setoph… L584… Treasu… 2024…       1  27.8  -80.4 TRUE    
#>  3 btbwar      Black-… Setoph… L300… 11300 … 2024…       1  27.8  -80.4 TRUE    
#>  4 btbwar      Black-… Setoph… L127… Lettuc… 2024…       1  28.1  -82.4 TRUE    
#>  5 btbwar      Black-… Setoph… L195… 1 My H… 2024…       1  27.0  -80.1 TRUE    
#>  6 btbwar      Black-… Setoph… L582… Southe… 2024…       1  25.4  -80.6 TRUE    
#>  7 btbwar      Black-… Setoph… L661… Memori… 2024…       1  29.6  -95.6 TRUE    
#>  8 btbwar      Black-… Setoph… L214… Saxapa… 2024…       1  35.9  -79.3 TRUE    
#>  9 btbwar      Black-… Setoph… L149… Jackso… 2024…       1  30.3  -81.5 TRUE    
#> 10 btbwar      Black-… Setoph… L127… Key We… 2024…       2  24.6  -81.7 TRUE    
#> 11 btbwar      Black-… Setoph… L127… Key La… 2024…       1  25.2  -80.4 TRUE    
#> 12 btbwar      Black-… Setoph… L385… My Yard 2024…       1  25.7  -80.3 TRUE    
#> 13 btbwar      Black-… Setoph… L823… J. N. … 2024…       1  26.5  -82.1 TRUE    
#> 14 btbwar      Black-… Setoph… L682… Univer… 2024…       1  25.7  -80.3 TRUE    
#> 15 btbwar      Black-… Setoph… L130… Castle… 2024…       1  29.6  -81.2 TRUE    
#> # ℹ 3 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>

Recent observations at hotspots

Search for bird occurrences by a given hotspot

ebirdregion(loc = 'L99381')
#> # A tibble: 62 × 14
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 gadwal      Gadwall  Mareca… L993… Stewar… 2024…       6  42.5 -76.5 TRUE    
#>  2 amewig      America… Mareca… L993… Stewar… 2024…       2  42.5 -76.5 TRUE    
#>  3 mallar3     Mallard  Anas p… L993… Stewar… 2024…      25  42.5 -76.5 TRUE    
#>  4 ambduc      America… Anas r… L993… Stewar… 2024…       2  42.5 -76.5 TRUE    
#>  5 commer      Common … Mergus… L993… Stewar… 2024…       6  42.5 -76.5 TRUE    
#>  6 killde      Killdeer Charad… L993… Stewar… 2024…       2  42.5 -76.5 TRUE    
#>  7 ribgul      Ring-bi… Larus … L993… Stewar… 2024…      NA  42.5 -76.5 TRUE    
#>  8 gbbgul      Great B… Larus … L993… Stewar… 2024…      NA  42.5 -76.5 TRUE    
#>  9 doccor      Double-… Nannop… L993… Stewar… 2024…       2  42.5 -76.5 TRUE    
#> 10 amecro      America… Corvus… L993… Stewar… 2024…       4  42.5 -76.5 TRUE    
#> # ℹ 52 more rows
#> # ℹ 4 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>,
#> #   exoticCategory <chr>

Nearest observations of a species

Search for a species’ occurrences near a given latitude and longitude

nearestobs(species_code('branta canadensis'), 42, -76)
#> Canada Goose (Branta canadensis): cangoo
#> # A tibble: 67 × 13
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 cangoo      Canada … Branta… L179… "Bingh… 2024…       2  42.1 -75.9 TRUE    
#>  2 cangoo      Canada … Branta… L144… "Heart… 2024…       2  41.8 -75.8 TRUE    
#>  3 cangoo      Canada … Branta… L116… "Lake … 2024…       2  41.8 -75.9 TRUE    
#>  4 cangoo      Canada … Branta… L465… "Bingh… 2024…       8  42.1 -75.9 TRUE    
#>  5 cangoo      Canada … Branta… L300… "Our L… 2024…       2  42.1 -75.9 TRUE    
#>  6 cangoo      Canada … Branta… L145… "Lake … 2024…       2  41.8 -75.8 TRUE    
#>  7 cangoo      Canada … Branta… L204… "McDon… 2024…       4  41.8 -75.9 TRUE    
#>  8 cangoo      Canada … Branta… L936… "Bolan… 2024…       6  42.2 -75.9 TRUE    
#>  9 cangoo      Canada … Branta… L186… "Otsin… 2024…      10  42.1 -75.9 TRUE    
#> 10 cangoo      Canada … Branta… L717… "PA-SQ… 2024…       2  41.9 -75.9 TRUE    
#> # ℹ 57 more rows
#> # ℹ 3 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>
<!-- ## Frequency of observations at hotspots or regions Obtain historical frequencies of bird occurrences by hotspot or region ```r # ebirdfreq(loctype = 'hotspots', loc = 'L196159') ``` -->

Recent notable sightings

Search for notable sightings at a given latitude and longitude

ebirdnotable(lat = 42, lng = -70)
#> # A tibble: 3,200 × 14
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 osprey      Osprey   Pandio… L300… 100 Gr… 2024…       1  41.7 -72.6 FALSE   
#>  2 fiespa      Field S… Spizel… L514… Pinnac… 2024…       1  43.1 -72.4 FALSE   
#>  3 chispa      Chippin… Spizel… L586… High S… 2024…       2  42.9 -70.8 FALSE   
#>  4 palwar3     Palm Wa… Setoph… L250… Hampto… 2024…       1  42.9 -70.8 FALSE   
#>  5 bkhgul      Black-h… Chroic… L829… Dowses… 2024…       5  41.6 -70.4 FALSE   
#>  6 rosgoo      Ross's … Anser … L279… Hardar… 2024…       1  42.9 -70.8 FALSE   
#>  7 osprey      Osprey   Pandio… L675… Longme… 2024…       2  42.1 -72.6 FALSE   
#>  8 yebsap      Yellow-… Sphyra… L458… Waldob… 2024…       1  44.1 -69.4 FALSE   
#>  9 yebsap      Yellow-… Sphyra… L458… Waldob… 2024…       1  44.1 -69.4 FALSE   
#> 10 baleag      Bald Ea… Haliae… L226… W Basi… 2024…       1  41.4 -70.8 FALSE   
#> # ℹ 3,190 more rows
#> # ℹ 4 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>,
#> #   exoticCategory <chr>

or a region

ebirdnotable(locID = 'US-NY-109')
#> # A tibble: 84 × 13
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 veggul1     Herring… Larus … L212… Steven… 2024…       1  42.4 -76.4 FALSE   
#>  2 sancra      Sandhil… Antigo… L212… Steven… 2024…       9  42.4 -76.4 FALSE   
#>  3 sancra      Sandhil… Antigo… L212… Steven… 2024…       9  42.4 -76.4 FALSE   
#>  4 veggul1     Herring… Larus … L212… Steven… 2024…       1  42.4 -76.4 FALSE   
#>  5 veggul1     Herring… Larus … L212… Steven… 2024…       1  42.4 -76.4 FALSE   
#>  6 veggul1     Herring… Larus … L212… Steven… 2024…       1  42.4 -76.4 FALSE   
#>  7 eastow1     Eastern… Pipilo… L117… Ringwo… 2024…       1  42.4 -76.4 FALSE   
#>  8 eastow1     Eastern… Pipilo… L117… Ringwo… 2024…       1  42.4 -76.4 FALSE   
#>  9 eastow1     Eastern… Pipilo… L117… Ringwo… 2024…       1  42.4 -76.4 FALSE   
#> 10 eastow1     Eastern… Pipilo… L117… Ringwo… 2024…       1  42.4 -76.4 FALSE   
#> # ℹ 74 more rows
#> # ℹ 3 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>

Historic Observations

Obtain a list of species reported on a specific date in a given region

ebirdhistorical(loc = 'US-VA-003', date = '2019-02-14',max = 10)
#> # A tibble: 10 × 13
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 cangoo      Canada … Branta… L139… Lickin… 2019…      30  38.1 -78.7 TRUE    
#>  2 mallar3     Mallard  Anas p… L139… Lickin… 2019…       5  38.1 -78.7 TRUE    
#>  3 gnwtea      Green-w… Anas c… L139… Lickin… 2019…       8  38.1 -78.7 TRUE    
#>  4 killde      Killdeer Charad… L139… Lickin… 2019…       1  38.1 -78.7 TRUE    
#>  5 baleag      Bald Ea… Haliae… L139… Lickin… 2019…       1  38.1 -78.7 TRUE    
#>  6 belkin1     Belted … Megace… L139… Lickin… 2019…       1  38.1 -78.7 TRUE    
#>  7 carwre      Carolin… Thryot… L139… Lickin… 2019…       1  38.1 -78.7 TRUE    
#>  8 whtspa      White-t… Zonotr… L139… Lickin… 2019…       2  38.1 -78.7 TRUE    
#>  9 norcar      Norther… Cardin… L139… Lickin… 2019…       1  38.1 -78.7 TRUE    
#> 10 canvas      Canvasb… Aythya… L331… Montic… 2019…      19  38.0 -78.5 TRUE    
#> # ℹ 3 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>

or a hotspot

ebirdhistorical(loc = 'L196159', date = '2019-02-14', fieldSet = 'full')
#> # A tibble: 14 × 27
#>    speciesCode comName  sciName locId locName obsDt howMany   lat   lng obsValid
#>    <chr>       <chr>    <chr>   <chr> <chr>   <chr>   <int> <dbl> <dbl> <lgl>   
#>  1 annhum      Anna's … Calypt… L196… Vancou… 2019…       4  49.3 -123. TRUE    
#>  2 ribgul      Ring-bi… Larus … L196… Vancou… 2019…       4  49.3 -123. TRUE    
#>  3 glwgul      Glaucou… Larus … L196… Vancou… 2019…      29  49.3 -123. TRUE    
#>  4 amecro      America… Corvus… L196… Vancou… 2019…     100  49.3 -123. TRUE    
#>  5 bkcchi      Black-c… Poecil… L196… Vancou… 2019…      16  49.3 -123. TRUE    
#>  6 bushti      Bushtit  Psaltr… L196… Vancou… 2019…      20  49.3 -123. TRUE    
#>  7 pacwre1     Pacific… Troglo… L196… Vancou… 2019…       1  49.3 -123. TRUE    
#>  8 houfin      House F… Haemor… L196… Vancou… 2019…       2  49.3 -123. TRUE    
#>  9 purfin      Purple … Haemor… L196… Vancou… 2019…       3  49.3 -123. TRUE    
#> 10 amegfi      America… Spinus… L196… Vancou… 2019…      15  49.3 -123. TRUE    
#> 11 daejun      Dark-ey… Junco … L196… Vancou… 2019…      37  49.3 -123. TRUE    
#> 12 sonspa      Song Sp… Melosp… L196… Vancou… 2019…      12  49.3 -123. TRUE    
#> 13 spotow      Spotted… Pipilo… L196… Vancou… 2019…       1  49.3 -123. TRUE    
#> 14 rewbla      Red-win… Agelai… L196… Vancou… 2019…       6  49.3 -123. TRUE    
#> # ℹ 17 more variables: obsReviewed <lgl>, locationPrivate <lgl>, subId <chr>,
#> #   subnational2Code <chr>, subnational2Name <chr>, subnational1Code <chr>,
#> #   subnational1Name <chr>, countryCode <chr>, countryName <chr>,
#> #   userDisplayName <chr>, obsId <chr>, checklistId <chr>, presenceNoted <lgl>,
#> #   hasComments <lgl>, firstName <chr>, lastName <chr>, hasRichMedia <lgl>

Information on a given region or hotspot

Obtain detailed information on any valid eBird region

ebirdregioninfo("CA-BC-GV")
#> # A tibble: 1 × 5
#>   region                                     minX  maxX  minY  maxY
#>   <chr>                                     <dbl> <dbl> <dbl> <dbl>
#> 1 Metro Vancouver, British Columbia, Canada -123. -122.  49.0  49.6

or hotspot

ebirdregioninfo("L196159")
#> # A tibble: 1 × 16
#>   locId   name       latitude longitude countryCode countryName subnational1Name
#>   <chr>   <chr>         <dbl>     <dbl> <chr>       <chr>       <chr>           
#> 1 L196159 Vancouver…     49.3     -123. CA          Canada      British Columbia
#> # ℹ 9 more variables: subnational1Code <chr>, subnational2Code <chr>,
#> #   subnational2Name <chr>, isHotspot <lgl>, locName <chr>, lat <dbl>,
#> #   lng <dbl>, hierarchicalName <chr>, locID <chr>

Obtain a list of eBird species codes for all species recorded in a region

ebirdregionspecies("GB-ENG-LND")
#> # A tibble: 383 × 1
#>    speciesCode
#>    <chr>      
#>  1 wfwduc1    
#>  2 fuwduc     
#>  3 bahgoo     
#>  4 empgoo     
#>  5 snogoo     
#>  6 rosgoo     
#>  7 gragoo     
#>  8 swagoo1    
#>  9 gwfgoo     
#> 10 lwfgoo     
#> # ℹ 373 more rows

or a hotspot

ebirdregionspecies("L5803024")
#> # A tibble: 181 × 1
#>    speciesCode
#>    <chr>      
#>  1 gragoo     
#>  2 gwfgoo     
#>  3 pifgoo     
#>  4 bargoo     
#>  5 cangoo     
#>  6 x00758     
#>  7 mutswa     
#>  8 whoswa     
#>  9 egygoo     
#> 10 comshe     
#> # ℹ 171 more rows

Obtain a list of all subregions within an eBird region

ebirdsubregionlist("subnational1","US")
#> # A tibble: 51 × 2
#>    code  name                
#>    <chr> <chr>               
#>  1 US-AL Alabama             
#>  2 US-AK Alaska              
#>  3 US-AZ Arizona             
#>  4 US-AR Arkansas            
#>  5 US-CA California          
#>  6 US-CO Colorado            
#>  7 US-CT Connecticut         
#>  8 US-DE Delaware            
#>  9 US-DC District of Columbia
#> 10 US-FL Florida             
#> # ℹ 41 more rows

Checklist Feed

Obtain a list of checklists submitted on a given date at a region or hotspot

ebirdchecklistfeed(loc = "L207391", date = "2020-03-24", max = 5)
#> # A tibble: 5 × 9
#>   locId   subId  userDisplayName numSpecies obsDt obsTime isoObsDate subID loc  
#>   <chr>   <chr>  <chr>                <int> <chr> <chr>   <chr>      <chr> <chr>
#> 1 L207391 S6617… David Wood              10 24 M… 14:47   2020-03-2… S661… L207…
#> 2 L207391 S6617… Sofia Prado-Ir…         15 24 M… 14:31   2020-03-2… S661… L207…
#> 3 L207391 S6619… Jeffrey Gantz           19 24 M… 13:30   2020-03-2… S661… L207…
#> 4 L207391 S6617… Ann Gurka               21 24 M… 13:00   2020-03-2… S661… L207…
#> 5 L207391 S7098… Barbara Olson           20 24 M… 10:30   2020-03-2… S709… L207…

View Checklist

Obtain all information on a specific checklist

ebirdchecklist("S139153079")
#> # A tibble: 99 × 24
#>    subId      protocolId locId groupId   durationHrs allObsReported subComments 
#>    <chr>      <chr>      <chr> <chr>           <dbl> <lgl>          <chr>       
#>  1 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  2 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  3 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  4 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  5 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  6 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  7 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  8 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#>  9 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#> 10 S139153079 P22        L17   G10310841        11.2 TRUE           "There are …
#> # ℹ 89 more rows
#> # ℹ 17 more variables: creationDt <chr>, lastEditedDt <chr>, obsDt <chr>,
#> #   obsTimeValid <lgl>, checklistId <chr>, numObservers <int>,
#> #   effortDistanceKm <dbl>, effortDistanceEnteredUnit <chr>,
#> #   subnational1Code <chr>, userDisplayName <chr>, numSpecies <int>,
#> #   speciesCode <chr>, obsId <chr>, howManyStr <chr>, obsComments <chr>,
#> #   photoCounts <int>, videoCounts <int>

Hotspots in a region or nearby coordinates

Obtain a list of hotspots within a region

ebirdhotspotlist("CA-NS-HL")
#> # A tibble: 290 × 9
#>    locId     locName   countryCode subnational1Code subnational2Code   lat   lng
#>    <chr>     <chr>     <chr>       <chr>            <chr>            <dbl> <dbl>
#>  1 L2334369  Abraham … CA          CA-NS            CA-NS-HL          45.2 -62.6
#>  2 L7003818  Admiral … CA          CA-NS            CA-NS-HL          44.7 -63.7
#>  3 L1765807  Admiral … CA          CA-NS            CA-NS-HL          44.8 -63.1
#>  4 L12227034 Armdale-… CA          CA-NS            CA-NS-HL          44.6 -63.6
#>  5 L12690538 Armdale-… CA          CA-NS            CA-NS-HL          44.6 -63.6
#>  6 L2390509  Bald Roc… CA          CA-NS            CA-NS-HL          44.5 -63.6
#>  7 L7598385  Bayers L… CA          CA-NS            CA-NS-HL          44.6 -63.7
#>  8 L11019120 Beaver B… CA          CA-NS            CA-NS-HL          44.8 -63.7
#>  9 L1872934  Bedford … CA          CA-NS            CA-NS-HL          44.7 -63.7
#> 10 L12134597 Bedford-… CA          CA-NS            CA-NS-HL          44.7 -63.7
#> # ℹ 280 more rows
#> # ℹ 2 more variables: latestObsDt <chr>, numSpeciesAllTime <int>

or within a radius of up to 50 kilometers, from a given set of coordinates.

ebirdhotspotlist(lat = 30, lng = -90, dist = 10)
#> No region code provided, locating hotspots using lat/lng
#> # A tibble: 54 × 9
#>    locId    locName    countryCode subnational1Code subnational2Code   lat   lng
#>    <chr>    <chr>      <chr>       <chr>            <chr>            <dbl> <dbl>
#>  1 L6025517 Algiers P… US          US-LA            US-LA-071         30.0 -90.1
#>  2 L3886471 Armstrong… US          US-LA            US-LA-071         30.0 -90.1
#>  3 L727179  Audubon L… US          US-LA            US-LA-071         30.0 -90.0
#>  4 L6665071 BAEA Nest… US          US-LA            US-LA-087         30.0 -90.0
#>  5 L6666949 BAEA Nest… US          US-LA            US-LA-071         29.9 -90.0
#>  6 L2423926 Bayou Bie… US          US-LA            US-LA-071         30.0 -90.0
#>  7 L725034  Bayou Sau… US          US-LA            US-LA-071         30.1 -89.9
#>  8 L727232  Chalmette… US          US-LA            US-LA-087         29.9 -90.0
#>  9 L453412  City Park… US          US-LA            US-LA-071         30.0 -90.1
#> 10 L5229399 City Park… US          US-LA            US-LA-071         30.0 -90.1
#> # ℹ 44 more rows
#> # ℹ 2 more variables: latestObsDt <chr>, numSpeciesAllTime <int>

rebird and other packages

How to use rebird

This package is part of a richer suite called spocc - Species Occurrence Data, along with several other packages, that provide access to occurrence records from multiple databases. We recommend using spocc as the primary R interface to rebird unless your needs are limited to this single source.

auk vs. rebird

Those interested in eBird data may also want to consider auk, an R package that helps extracting and processing the whole eBird dataset. The functions in rebird are faster but mostly limited to accessing recent (i.e. within the last 30 days) observations, although ebirdfreq() does provide historical frequency of observation data. In contrast, auk gives access to the full set of ~ 500 million eBird observations. For most ecological applications, users will require auk; however, for some use cases, e.g. building tools for birders, rebird provides a quicker and easier way to access data. rebird and auk are both part of the rOpenSci project.

API requests covered by rebird

The 2.0 APIs have considerably been expanded from the previous version, and rebird only covers some of them. The webservices covered are listed below; if you’d like to contribute wrappers to APIs not yet covered by this package, feel free to submit a pull request!

data/obs

product

ref/geo

ref/hotspot

ref/taxonomy

ref/region

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