1. Mapping standards for IUCN Red List assessments

2. 35,000+ species with spatial data on the IUCN Red List
There are more than 35,000 species with spatial data on the IUCN Red List, and this spatial data is some of the most highly sought-after information on the Red List. Complete spatial datasets are available on the Spatial Data Download page on the IUCN Red List website, and in static maps on the website. We have also launched an interactive map browser, where species maps are overlaid with observation and protected areas data.

3. Purpose of including species maps on the Red List
Visual representation of the species’ distribution
Analysis of Red List data
To inform Red List assessments
EOO/AOO
To identify conservation priorities
First step in identifying priority areas for protection (KBAs, PAs)
Species maps are included on the Red List for several reasons. Primarily, the maps provide a visual representation of the species’ distribution, so people can see where the species is found. These data can also be used to conduct many different analyses, such as global threatened species richness for different taxonomic groups, which are then used to inform conservation planning and other decisions. The maps can help inform Red List assessments, by allowing calculations of extent of occurrence (EOO) and area of occupancy (AOO). They can also help to identify conservation priorities. For example, this data can help identify priority areas for conservation and inform conservation policy; it can help identify gaps in scientific knowledge; and it can help inform business decisions (e.g. where not to expand development).

4. Biological standards: What are we mapping?
Known or inferred limits of the species’ distribution range
Distribution depicted as polygons
Means species probably only occurs within the polygon
Does not mean species is distributed equally within the polygon or occurs everywhere in the polygon
The species distribution maps, commonly referred to as “limits of distribution” or “field guide” maps, aim to provide the current known distribution of the species within its native range. The limits of distribution are determined by using known occurrences of the species, along with knowledge of habitat preferences, remaining suitable habitat, elevation limits, and other expert knowledge of the species and its range. In most cases the range is depicted as polygons. A polygon displaying the limits of a species distribution is essentially meant to communicate that the species probably only occurs within this polygon, but it does not mean that it is distributed equally within that polygon or occurs everywhere within that polygon.

5. Mapping Protocols
The protocols differ slightly for species in different ecosystems:
Terrestrial Marine Freshwater
The mapping protocols for the IUCN Red List assessments differ slightly for species in different ecosystems.

6. Mapping terrestrial species
Plot observation / locality data points
Expand the distribution considering knowledge of habitat preferences
Remove unsuitable/unoccupied habitat based on availability of suitable habitat, elevation limits, climate/temperature restrictions, other expert knowledge
Let’s go over the mapping standards for terrestrial species. There are various ways in which a distribution map is derived but the most common method is by plotting observation records captured through GPS or other sightings, and drawing a smallest possible convex polygon or a minimum convex hull around these point. This is the same as imagining an elastic band being pulled around all the observation or locality points. This convex hull often represents extent of occurrence (EOO). (Note for more detail: There are various tools available to construct this both in open source as well as commercial software. ESRI ArcGIS has an extension called the ‘Geospatial Modelling Environment’ which includes tools for this.) Very often this convex polygon is further refined using spatially explicit habitat, elevation and climate information according to species’ preference data.

7. Mapping terrestrial species
Dark green: Tree cover, broad leaved and ever green
Light green: Tree cover, broadleaved, deciduous & closed
Let’s look at this step by step. Here are a few observation/locality points for a species found in South America. According to the species expert, this species’ preferred habitat is broadleaved, evergreen and deciduous forest. The colored areas represent two land cover types taken from the USGS Global Land Cover Characteristics database (
Dark green: Tree cover, broad leaved and ever green
Light green: Tree cover, broadleaved, deciduous and closed
Preferred habitat is broadleaved, evergreen and deciduous forest.

8. Mapping terrestrial species
Extrapolation of observation records and expert knowledge suggests that the species is limited to these 2 habitat types.
Extrapolation of the distribution of observation records and the knowledge of the expert suggests that the species is limited to these two habitat types. However it doesn’t necessarily mean that it would occur in all areas coved by the forests. There are various other factors which may limit the distribution, such as elevation, temperature or even natural physical barriers.
Consider other factors that may limit the distribution (e.g. elevation, temperature, natural physical barriers)

9. Mapping terrestrial species
The purple areas on this map represents areas classed as ‘managed and cultivated land’ and has been derived once again from the Global Land Cover Characteristics database. The species is not likely to occur in agricultural areas, which means that these areas should not be part of the range. In another scenario this could potentially be areas of unsuitable elevation or climate. We would exclude this unsuitable habitat from the distribution map (that is, the purple area should not be part of the distribution map).
Purple: Managed and cultivated land
Exclude unsuitable habitat

10. Mapping terrestrial species
Inferred range ?
EOO
A convex hull around the point data would include all suitable and unsuitable habitat. The next step therefore would be to remove from the convex hull (shown in orange) the areas of unsuitable habitat.
Here we can see various measures visually represented: extent of occurrence (EOO) is represented by the orange polygon (the minimum convex polygon around all of the known and inferred data points); area of occupancy (AOO) is represented by the blue polygons (CLICK and here we can see in better detail how AOO might be calculated, by overlaying a 2x2 km grid over the point localities and summing the number of occupied cells); and extent of suitable habitat is represented by the green polygons.
Extent of suitable habitat is possibly the best representation of the range as it excludes areas where the species is not likely to occur.
In the above example the areas between the north and south populations are included in the species range as the assumption here is that even if the species has not been recorded, there is a likelihood that the species occurs there because of the suitable habitat present. This area in between the north and south populations may also be recorded as ‘Possibly present’.
AOO
Extent of
Suitable Habitat

11. Mapping terrestrial species
Inferred range ?
The shaded green area represents what the final distribution map might look like, as defined by the point localities and areas where the species is believed to likely be found, and with the unsuitable habitat excluded from the map. We have two polygons representing the species’ distribution.
Final species’ distribution map

12. Extent of Occurrence (EOO) Area of Occupancy (AOO)
Just a reminder of how extent of occurrence and area of occupancy might be estimated using mapping software. EOO is calculated by drawing the smallest possible convex polygon or a minimum convex hull using observation records captured through GPS or other sightings. This is the same as imagining an elastic band being pulled over all the points. Here you can see how this is done for multipoint input, line input and polygon input.
Again, AOO is best calculated by laying a 2x2 km grid over the distribution points or polygons and calculating the number of cells that are occupied.
Extent of Occurrence
(EOO)
Area of Occupancy
(AOO)

13. Examples of unacceptable maps
The ranges in these maps have been created by drawing a maximum convex hull around the observation points, but no considerations have been given to habitat suitability. This is not correct and will not be accepted for inclusion on the IUCN Red List.

14. If there are less than 3 points, a minimum convex polygon cannot be created.
Use habitat information to inform the map OR
If no habitat data is available and experts have no idea where the species might be found, draw a circle with a 10 km (radius) buffer as a polygon around the points.
10 km
A minimum convex hull cannot be created if there are less than three points. In those cases either habitat information is used to inform the shape and size of the map, or a circle with a 10 km (radius) buffer is drawn as a polygon around the two points.
For costal terrestrial species, the map must exclude the sea, so it must be clipped to the coastline. For small islands, we recommend mapping the entire island so the map is easier to see.
For coastal species, must exclude the sea (clip to the coastline).
For small islands, capture the whole island

15. Mapping marine species
Mapped using essentially the same process as terrestrial species.
Bathymetry can be used to delineate species ranges limited by depth (much as elevation is used for terrestrial species).
For coastal species: draw a buffer of 50 km around the coastline.
Marine species are mapped using essentially the same process as terrestrial species. Bathymetry can be used to delineate species ranges limited by depth, much as elevation is used for terrestrial species. A buffer of 50 km is drawn around the coastline to represent a species range if it’s a coastal species.

16. Mapping freshwater species
Species are mapped to catchments as they are considered to be the minimum management unit for freshwater conservation.
Add known observation/ locality points to map
Intersect points with catchment layer to identify “Extant” catchments
Use expert knowledge to identify presence codes for other catchments
Redefine range to take into account environmental factors, if necessary, and to buffer around rivers and estuaries.
When mapping freshwater species, species are mapped to catchments (or basins) because catchments are considered to be the minimum management unit for freshwater conservation. The methodology essentially involves adding a hydroshed layer and detailed river layer to your map. It is a good idea to clip these layers to your study region to reduce processing time and file size. You will also need to add your known location points and then perform an intersect with the hydroshed layer to produce the Extant (known) basins. Expert knowledge is then used to decipher and select the probably extant (inferred) basins. Further processing such as redefining the range to take into account environment factors such as elevation is possible as well as buffering around rivers and estuaries.

17. Mapping freshwater species
Base layer: WWF Hydrosheds (average basin size of 100 km2)
To look at this in more detail… First, you would import the Global Hydrosheds layer and detailed river dataset (clip to local area).

18. Mapping freshwater species
1. Plot known observation/location point data on the basins.
You then plot the known observation or location point data on the basins. (Note for more detail: You can easily convert points in a spreadsheet to a point layer by using the Add XY Data option in the Tools menu. Simply select your excel spreadsheet and the X and Y columns.)

19. Mapping freshwater species
2. Select those basins as “native extant”.
Select the hydrosheds that intersect with the points – these will be labelled as catchments where the species is “native extant” (shown here in green). (Details: use select by location from the selection menu. Export the selected hydrosheds to a shapefile by right clicking on the hydroshed layer and using the Export Data option.)

20. Mapping freshwater species
3. Use expert opinion and published data to define probably extant (inferred) basins.
Use expert opinion and published data to define the catchments where the species is inferred to be found – these will be labelled “probably extant” (shown here in pink). (Details: use the select tool to select the hydrosheds then export using Export Data.) If the information is available, the basins can be further defined based on altitude, stream order, habitat, etc.

21. Mapping freshwater species
For species found only in a main river channel and not in the tributaries: use a 10 km buffer (20 km wide).
For species found only in a main river channel and not in the tributaries, we use a 10km buffer (20km in width) to emphasize the distribution (use the buffer tool in ArcMap).

22. Mapping freshwater species
For species only found in brackish water, estuaries and costal lagoons: add a 10 km buffer (10 km in width).
For species only found in brackish water, estuaries and costal lagoons, we add a 10km buffer (10 km in width), again to make the range more visible.

23. Terrestrial Freshwater Marine
Mammals
Polygons
Polygons informed by basins
Birds
Amphibians
n/a
Reptiles
Fishes
Basins
Molluscs
Odonata (& ecological equivalents)
Crustaceans
All other inverts --
Plants
Polygons and points
Fungi ?
Here is a summary of the accepted format for various taxonomic groups and ecosystems. (Note to trainers: Give the participants a moment to look this over and find their taxonomic group/ecosystem.)

24. Species with sensitive spatial data
Collect accurate spatial data (e.g. for analyses), but either:
Do not publish a map on the Red List website
“Data_sens” attribute
Publish a vague “public” map that hides details of where the species is actually found
Some species will have sensitive spatial data that we don’t wish to make publically available. The species may be found in only a few localities, for example, and may be collected, hunted or captured for the pet trade. Publishing a map that shows where that species is found in any detail could point collectors or hunters directly to the species, which would be contrary to conservation efforts. For these species, we still want to record and store accurate distribution information, as this information is important for analyses, informing conservation efforts, etc. For the public Red List website, there are two options: [Click] (1) Simply do not publish spatial data on the Red List website. Filling in the optional attribute “Data_sens” with “Y” (for yes) excludes the map from the Red List website. [Click] (2) We can publish a vague map that indicates generally where the species is found (e.g. shifting or enlarging the range, or mapping to country) but does not show the species’ specific distribution. In this example, the species might only be known from two small islands in Fiji, but the public map shows all of Fiji.
(Note to Trainers: Assessors generally decide which of these options is preferable. If anyone is assessing a species with sensitive spatial data, they should contact the Red List Unit to discuss how best to map and store the sensitive data.)

25. Technical standards: Requirements for all maps
Standard & preferred format = ESRI shapefiles
Other acceptable formats = Mapinfo, Googlemap, KML, other open source formats that can be converted
Files are named by the scientific name: genus_species
All polygons should be smoothed and checked for irregularities before being submitted.
GIS data required in Geographic coordinates [WGS84]
Attributes are required with spatial data.
Includes codes for presence, origin and seasonality
There are a few basic technical requirements for all maps:
Data is required in GIS format, and we prefer ESRI shapefiles (though other formats that can be converted to shapefiles are accepted).
Files are always named by the scientific name, using “genus_species” format
All polygons should be smoothed and checked for irregularities before being submitted
GIS data is required in geographic coordinates (WGS84, specifically)
Finally, data attributes are absolutely required with spatial data. This includes codes for presence, origin and seasonality – we’ll take a look at these in a minute.

26. Data attributes: Required
Field
ESRI Field Type
Description
ID_NO
Integer
Internal Record ID (must match the corresponding field in SIS)
BINOMIAL
String
Scientific name (must match the corresponding field in SIS)
BASIN_ID (freshwater species only)
River catchment ID number. This must match the corresponding BASIN_ID or HYDRO_ID in the hydroshed/catchment layer.
PRESENCE
ShortInt
Is/Was the species in this area, codes listed below
ORIGIN
Why/ How the species is in this area, codes listed below
SEASONAL
What is the seasonal presence of the species in the area, codes listed below
COMPILER
Name of the individual/s or institution responsible for generating the polygon, if not IUCN.
YEAR
Year in which the polygon was mapped or compiled, or modified
CITATION
Individual/s or institution responsible for providing the data
Here is a list of the data attributes required for all maps. Don’t worry about reading this in detail now – you’ll get a copy of this presentation, and this information (and much, much more) is also available on the IUCN spatial data wiki ( This is just to highlight that there are specific attribute requirements that you should be aware of.

27. Data attributes: Other attributes
Field
ESRI Field
Type
Requirement
Description
DIST_COMM
String
Optional
Distribution comments that refer directly to the polygon.
ISLAND
Name of the island the polygon is on
SUBSPECIES
Required (if relevant)
Subspecies name
SUBPOP
Subpopulation name
TAX_COMMEN
Taxonomic comments that refer directly to the polygon. Includes notes on polygons pertaining to subspecies or subpopulations.
Data_sens
Y/N
Flags up whether the polygon distribution is sensitive. Most likely to be the case if the polygon(s) effectively correspond to individual localities.
Sens_comm
Required (if Data_sens is “Y”)
Comments on why the data are considered sensitive.
LEGEND
Central*
Corresponds to the legend code resulting from combinations of the presence, origin and seasonality fields , and determines how the map will be displayed on the IUCN Red List website.
Other attributes are either optional or required under certain circumstances. More details on these can also be found on the spatial data wiki.
(NOTE: The “Island” attribute only applies to small island species located on islands that are too small to appear on a global map. Assessors should use the “islands” base layer available from IUCN to map the species. This base layer only shows the shorelines of the islands, but does not give the names of the islands – for this reason, IUCN requires assessors to record the name of the island(s) under the islands attribute.)

28. Coded values for Presence, Origin & Seasonality
There are specific numeric codes for presence origin and seasonality. Let’s look at these more closely. (Note to Trainers: you don’t have to go into detail on what these various definitions mean, especially in a general workshop (it will take too much time); if the workshop is specifically focused on mapping, it might be worth doing so, but otherwise it is enough to draw attention to these codes and highlight that they coincide with the codes in SIS)

29. Presence
Extant – The species is known or thought very likely to occur presently in the area, usually encompassing current or recent (post 1980) localities where suitable habitat at appropriate altitudes remains.
Probably Extant – The species’ presence is considered probable, either based on extrapolations of known records, or realistic inferences (e.g., based on distribution of suitable habitat at appropriate altitudes and proximity to areas where it is known or thought very likely to remain Extant). ‘Probably Extant’ ranges often extend beyond areas where the species is Extant, or may fall between them.
Possibly Extant: The species may possibly occur, and should be searched for, but there are no known records and less than probably occurrence. ‘Possibly Extant’ ranges often extend beyond areas where the species is Extant (q.v.) or Probably Extant (q.v.), or may fall between them.
Possibly Extinct – The species was formerly known or thought very likely to occur in the area, but it is most likely now extirpated from the area because habitat loss/other threats are thought likely to have extirpated the species and/or owing to a lack of records in the last 30 years.
Extinct – The species was formerly known or thought very likely to occur in the area, but there have been no records in the last 30 years and it is almost certain that the species no longer occurs, and/or habitat loss/other threats have almost certainly extirpated the species.
Presence Uncertain – The species was formerly known or thought very likely to occur in the area but it is no longer known whether it still occurs (usually because there have been no recent surveys).
Notes:
These codes are mutually exclusive; a polygon coded as “Extant” cannot also be coded as “Extinct”.
To obtain the total historical range of a species, one would sum polygons for Extant, Probably Extant, Possibly Extinct, Extinct and Presence Uncertain, but not Possibly Extant.
Again, we’re not going to go over this in detail, but I want to highlight the six presence codes. Presence refers to whether the species is extant, probably extant, extinct, etc.

30. Origin Native – The species is/was native to the area
Reintroduced - The species is/was reintroduced through either direct or indirect human activity.
Introduced – The species is/was introduced outside of its historical distribution range through either direct or indirect human activity.
Vagrant – The species is/was recorded once or sporadically, but it is known not to be native to the area.
Origin Uncertain - The species’ provenance in an area is not known (it may be native, reintroduced or introduced)
Notes:
These codes are mutually exclusive; a polygon coded as “Native” cannot also be coded as “Introduced”.
Origin refers to whether the species is native, introduced, reintroduced, etc., and there are numeric codes for each definition.

31. Seasonality
Resident – the species is/was known or thought very likely to be resident throughout the year
Breeding Season – The species is/was known or thought very likely to occur regularly during the breeding season and to breed.
Non-breeding Season – The species is/was known or thought very likely to occur regularly during the non-breeding season. In the Eurasian and North American contexts, this encompasses ‘winter’.
Passage – The species is/was known or thought very likely to occur regularly during a relatively short period(s) of the year on migration between breeding and non-breeding ranges.
Seasonal Occurrence Uncertain – The species is/was present, but it is not known if it is present during part or all of the year.
Finally, seasonality refers to when the species is present in the area. That is, is it resident throughout the year, present in the breeding season, non-breeding season, etc.

32. Data attributes requirements: Point data
There is an Excel worksheet available with the IUCN standard attributes for point locality data; this is relevant for plants, specifically, for which point data is accepted in place of (or in addition to) a generalized range map.

33. Options for creating maps:
ESRI products (ArcView GIS, Arc Editor, ArcInfo)
ESRI training courses (available through IUCN)
Extensions (Spatial Analyst, Geostatistical Analyst)
qGIS
Googlemaps (create polygons in Google Earth)
Hand-drawn maps (absolute last resort!!)
Spatial data wiki:
IUCN range maps can be created in any of a number of different programs. IUCN can provide licenses for ESRI software to the Global Species Programme and SSC network. We also have access to training courses to learn how to use this software, and ESRI toolbox extensions that are very useful for mapping. Maps can also be created using open source software, such as qGIS, and even by using Googlemaps. We are also working on tools which will allow maps to be drawn online. Hand-drawn maps are occasionally accepted as an absolute last resort, as we recognize that not all experts have GIS knowledge or access to GIS software or others with this knowledge. Please do check the spatial data wiki regularly, as much more information on the mapping protocols and resources available to help with this is available there.

34. Tools & resources available
ArcGIS scripts and tools
Additional tools to help manage data and expedite map generation (X tools, ET, Hawths, Python, etc.)
Tools specially developed to facilitate mapping in ArcGIS
Consistency checking and validating data
Many different scripts and extensions (e.g. customized extension which creates empty file with req fields; consistency checks to ensure no req fields are empty)
Spatial data wiki:
Basedata, WGS84, etc.
All of these tools and much more information can be found on the spatial data wiki. There you can find:
various base layers
tools that assessors can find and use to help manage data and expedite map generation (X tools, ET, Hawths, Python, etc.)
additional tools that have been specially developed to facilitate Red List mapping in ArcGIS, to conduct consistency checks and validate the data
and more...
This should be the first place you look when you begin mapping your species, as the site is regularly updated and contains a wealth of information.