Wolfram Research

Function Repository Resource:

INaturalistSearch

Source Notebook

Search for iNaturalist observations using the iNaturalist API

Contributed by: Jofre Espigule-Pons

ResourceFunction["INaturalistSearch"][query]

gives a dataset with taxa occurrences of the specified query.

ResourceFunction["INaturalistSearch"][query,prop]

gives the property prop of the specified query.

Details and Options

ResourceFunction["INaturalistSearch"] utilizes iNaturalist's API to find observations of species and taxa.
The query can be specified using a taxon (as a string, Entity or an ExternalIdentifier for a Wikidata ID) or by All to include all possible taxa in the search.
ResourceFunction["INaturalistSearch"][query] is equivalent to ResourceFunction["INaturalistSearch"][query,"Dataset"].
Supported properties for ResourceFunction["INaturalistSearch"] include:
"Count"total count of taxa observations of the specified query
"Dataset"dataset with taxa observations of the specified query
"SimplifiedDataset"simplified dataset with taxa observations of the specified query
"RawData"raw data of taxa observations of the specified query
ResourceFunction["INaturalistSearch"] accepts the same options as Dataset for the "SimplifiedDataset" property.
Other options supported by ResourceFunction["INaturalistSearch"] include:
MaxItems200number of observations to return
Page1page of the observations to return
ObservationGeoRange{{-90, 90}, {-180, 180}}geographic range of the observations
ObservationDateRange{DateObject[{1700}],Today}date range of the observations
The following "QualityGrade" option specifications can be given:
"Research"observations with research grade
"NeedsID"observations that need identification
"Casual"casual observations
The following "HasImage" option specifications can be given:
Trueobservations with images
Falseobservations without images
The following "HasAudio" option specifications can be given:
Trueobservations with audio
Falseobservations without audio
The following "LifeStage" option specifications can be given:
"Egg"observations of eggs
"Adult"observations of adult specimens
"Teneral"observations of teneral specimens
"Pupa"observations of pupa specimens
"Nymph"observations of nymph specimens
"Larva"observations of larva specimens
"Subimago"observations of subimago specimens
"Juvenile"observations of juvenile specimens
The following "Sex" option specifications can be given:
"Male"observations of male specimens
"Female"observations of female specimens
The following "PlantPhenology" option specifications can be given:
"Flowering"observations of flowering plant specimens
"Fruiting"observations of fruiting plant specimens
"FlowerBudding"observations of flower budding plant specimens
"NoEvidenceOfFlowering"observations without evidence of flowering plant specimens
The following "Alive" option specifications can be given:
Trueobservations of alive specimens
Falseobservations of inert specimens
"CannotBeDetermined"observations of unconfirmed alive specimens
The following "Threatened" option specifications can be given:
Trueobservations of threatened taxa
Falseobservations without threatened taxa
The following "Endemic" option specifications can be given:
Trueobservations of endemic taxa
Falseobservations without endemic taxa
The following "Native" option specifications can be given:
Trueobservations of native taxa
Falseobservations without native taxa
The following "Introduced" option specifications can be given:
Trueobservations of introduced taxa
Falseobservations without introduced taxa
The following "Captive" option specifications can be given:
Trueobservations of captive taxa
Falseobservations without captive taxa
ResourceFunction["INaturalistSearch"] does not include observations with missing geographic coordinates.

Examples

Basic Examples (6) 

Obtain a dataset of threatened species observed in Yellowstone National Park in a span of one week:

In[1]:=
data = ResourceFunction["INaturalistSearch"][All, "Threatened" -> True, "ObservationGeoRange" -> GeoBounds[Entity["Park", "YellowstoneNationalPark::zc6x9"]], "ObservationDateRange" -> {DateObject[{2020, 7, 19}], DateObject[{2020, 7, 26}]}]
Out[1]=
In[2]:=
data = ResourceFunction["INaturalistSearch"][All, "ObservationGeoRange" -> GeoBounds[Entity["Park", "YellowstoneNationalPark::zc6x9"]], "ObservationDateRange" -> {Yesterday, Today}]
Out[2]=

Get the total count of wolf observations in Yellowstone National Park:

In[3]:=
ResourceFunction["INaturalistSearch"][
 ExternalIdentifier["WikidataID", "Q18498", <|"Label" -> "wolf", "Description" -> "species of mammal"|>], "Count", "ObservationGeoRange" -> GeoBounds[Entity["Park", "YellowstoneNationalPark::zc6x9"]]]
Out[3]=

Obtain a dataset of all observations of different types of eggs from a specific region:

In[4]:=
ResourceFunction["INaturalistSearch"][ All , "ObservationGeoRange" -> GeoBounds[Entity["Park", "YellowstoneNationalPark::zc6x9"]], "LifeStage" -> "Egg" ]
Out[4]=

Obtain observations of a specific user containing audio recordings:

In[5]:=
audioData = ResourceFunction["INaturalistSearch"][ All, "UserLogin" -> "jofree", "HasAudio" -> True  ]
Out[5]=

Import the audio file from the previous observation:

In[6]:=
Import[First@Normal@audioData[All, "AudioURL"]]
Out[6]=

Obtain a dataset of observations from Canada containing audio recordings:

In[7]:=
ResourceFunction["INaturalistSearch"][ All, "ObservationGeoRange" -> GeoBounds[Entity["Country", "Canada"]], "ObservationDateRange" -> {DateObject[{2019, 7, 19}], DateObject[{2019, 7, 21}]}, "HasAudio" -> True  ]
Out[7]=

Obtain a dataset of a specific taxon with observations of dead specimens only:

In[8]:=
ResourceFunction["INaturalistSearch"][ Entity["Species", "Species:NezaraViridula"] , "SimplifiedDataset" , "Alive" -> False]
Out[8]=

Options (1) 

Options from Dataset can be directly specified in INaturalistSearch for the "SimplifiedDataset" property:

In[9]:=
ResourceFunction["INaturalistSearch"][ Entity["Species", "Species:CanisLupus"], "SimplifiedDataset", "ObservationGeoRange" -> GeoBounds[Entity["Park", "YellowstoneNationalPark::zc6x9"]], "ObservationDateRange" -> {DateObject[{2020, 07, 20}], Today}, Background -> LightBrown ]
Out[9]=

Neat Examples (5) 

Create a classifier to automatically identify observations of nymphs and adults of brown marmorated stink bugs:

In[10]:=
classes = {"Nymph", "Adult"};

Import the images to create a training set and a test set:

In[11]:=
nymphImages = Flatten@Table[
     Map[Import, Normal[ResourceFunction["INaturalistSearch"][ "Halyomorpha halys", "QualityGrade" -> "Research"  , "LifeStage" -> "Nymph", "Page" -> i][All, "SquareImageURL"]]], {i, 1, 3}] // Quiet;
In[12]:=
adultImages = Flatten@Table[
     Map[Import, Normal[ResourceFunction["INaturalistSearch"][ "Halyomorpha halys", "QualityGrade" -> "Research"  , "LifeStage" -> "Adult", "Page" -> i][All, "SquareImageURL"]]], {i, 1, 3}] // Quiet;
In[13]:=
dataset = RandomSample@
   Flatten[Thread /@ Thread[{nymphImages, adultImages} -> classes]];
In[14]:=
trainingSet = dataset[[;; 700]];
testSet = dataset[[701 ;;]];
In[15]:=
c = Classify[trainingSet, PerformanceGoal -> "Quality"]
Out[15]=
In[16]:=
cm = ClassifierMeasurements[c, testSet, "ConfusionMatrixPlot"]
Out[16]=

Test the trained classifier with two specific test examples:

In[17]:=
testSet[[8]]
Out[17]=
In[18]:=
c[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJw9muePY2ty3gUbBvzRH2zAtpxWkm1pd2+Y2DnnPDndyTOdmMNhJk/k4cmR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"], {{0, 75.}, {75., 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{75., 75.},
PlotRange->{{0, 75.}, {0, 75.}}]\)]
Out[18]=
In[19]:=
testSet[[5]]
Out[19]=
In[20]:=
c[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJwtewdzJId5peuurspX9tmyJEqUyF1u3sUuFljkHAaYASbnnHPOsSfnnAcZ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"], {{0, 75.}, {75., 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{75., 75.},
PlotRange->{{0, 75.}, {0, 75.}}]\)]
Out[20]=

Check unconfirmed observations:

In[21]:=
nymphUnconfirmedImages = Map[Import, Normal[ResourceFunction["INaturalistSearch"][ "Halyomorpha halys", "QualityGrade" -> "NeedsID"  , "Page" -> 1, MaxItems -> 5][All, "SquareImageURL"]]] // Quiet
Out[21]=
In[22]:=
c[\!\(\*
GraphicsBox[
TagBox[RasterBox[CompressedData["
1:eJxlfAl7W9W5bs+9tz1AEhIIEBJC4iQeJdmSB8mDZFueZcnzIE/xGE+ZQxIg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"], {{0, 75.}, {75., 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag[
     "Byte", ColorSpace -> ColorProfileData[CompressedData["
1:eJx1kbFLQlEUxj+tMMqgqMGhwZZoeJVokEuDGogRJM+ErOn5fD4FfT7eeyFG
W7SG0H+QQXPQkEQYtDQ0BFFDRG1NTgUtJbdz1VCJDpx7f/fjO4fDuVPJgpi2
AzAH6RClYsAwpFJslB5xzcyqmpKKaJaiKoY/aSPRxb2wuyRdz/UT5TXLEMNB
90Zi0+14gw3cRCHJph6IRlc5/9698fnY8t7P8l71RuXAvx++zp9dru08ueb/
+ntiKKWYMt3flB5ZNyzAJhBHi5bOeZd4wqChiMuc1RYfc062+LzpWRdDxLfE
bjkjpYjrxEKyS1e7OJ/bltsz8OmdihaP8T6Uk1hGFiZ05CChBDei8P3jX2j6
QyiQuwSD6lRkYFFNgBTeQSGOQIOMOQjEXngovXzP7f09tPcndLS9V2Cpxhi7
6GgrNeB0kVZW7WgzfmBsGLip6pIhNaU+/q3pNPB+AowkgPE7qtky0z5va3pn
EBh4YexjGnAcAo0yY19HjDUqVPwMXGk/e+x3Lw==
"], "RGB", "XYZ"], Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSizeRaw->{75., 75.},
PlotRange->{{0, 75.}, {0, 75.}}]\)]
Out[22]=

Get the observation link in order to annotate the "LifeStage" of the specimen and hence contribute to a research-grade observation:

In[23]:=
Normal[ResourceFunction["INaturalistSearch"][ "Halyomorpha halys", "SimplifiedDataset", "QualityGrade" -> "NeedsID"  , "Page" -> 1, MaxItems -> 1][All, "ObservationURL"]] // Quiet
Out[23]=

Publisher

Jofre Espigule-Pons

Version History

  • 1.1.0 – 13 June 2022
  • 1.0.0 – 17 August 2020

Related Resources

License Information