“How can we standardize the methods used to incorporate point data into distribution range polygons? How can we accelerate the collection of observation (point) data”? These questions were posed to an international group of taxonomists during a workshop that was held in conjunction with the IUCN Red List Gulf of Mexico Fishes Assessment Workshop, which was held at the Harte Research Institute for Gulf of Mexico Studies on the campus of Texas A&M University-Corpus Christi in January 2014. Table 1 lists attendees and their affiliations. This paper seeks to review the discussions that occurred during the workshop, to review existing protocols and to suggest possible changes that might enhance the current protocol and encourage inclusion of new and different data streams.

The goal of this workshop was to discuss a methodology for community-based recording of observations of marine species. These data, if collected in a repeatable and consistent manner over a long period of time, will become a valuable reference for distribution mapping for marine species ranges (adapted from Insley et al. (2008)). During this four-hour workshop several topics were discussed, including current Red List mapping standards, Biodiversity of the Gulf of Mexico (BioGoMx) database (Moretzsohn et al. 2011), species data and maps, and combining different types of geographic data, such as range maps, museum specimen records, field observations and other point data. A need for accelerating the collection and acceptance of point data, and facilitating expert review of species distribution and ranges is evident. Inclusion of alternate sources of data, such as fisheries data and that generated with crowdsourced applications was discussed. Ensuring the quality of new point data from a variety of sources was discussed, and should include considering how close to current range data the new points lay, how current the data is, and the possibility of establishing an ‘Index of Reliability’. The importance of including the costs of curating the specimens, spatial and attribute data in grants and funding requests was recognized. An additional topic of discussion during the workshop was whether the Red List spatial data can be useful in response to catastrophic events, such as the Deepwater Horizon oil spill in 2010, and what tools would need to be developed to do so. Discussion evolved around the ability to map species data for a given region using an online mapping portal, and to perform advanced spatial analysis on species distribution polygons and intersecting data sets. The purpose of these distribution polygons is to provide the current known distribution of the taxon within its native range (IUCN 2013), and the spatial information is subject to change between update intervals, usually at regional evaluation workshop intervals, and publication. The limits of distribution are determined using known occurrences of the taxon, and knowledge of its habitat preferences, remaining suitable habitat, elevation (depth) limits. Other parameters should be considered, such as time periods or seasons when a species might aggregate in a particular region, the effects of trophic structure, migration patterns and known threats. Other caveats include that, although the 2013 IUCN (Global) Red List contains assessments for more than 70,000 species, spatial data exists for only about 43,000 species, leaving 27,000 that would be omitted from a spatial query that was dependent upon them (IUCN 2014b). Of this total of 70,000 species, the IUCN Global Marine Species Assessment, led by the Species Survival Commission, estimates 20,000 species are classified as 'marine' (IUCN 2015). Participants discussed the inclusion of additional ‘crowd-sourced’ point-based observation data. In addition, participants suggested that habitats and habitat preferences should be considered in mapping ranges. Specifically, depth range intervals for certain species or species groups should be considered and noted. Mating or spawning aggregations, nesting areas, nursery habitats, areas of particular abundance, and areas of habitat preference should be considered when mapping. Areas which are vulnerable to threats, such as coral reefs or Essential Fish Habitats should be included in mapping efforts. These considerations are included in the existing IUCN mapping and evaluation protocols, which are discussed below.

The IUCN Red List of Threatened Species™ is essentially a checklist of taxa that have undergone an extinction risk assessment using the IUCN Red List Categories and Criteria, as shown in Fig. 1. International Union for Conservation of Nature and Natural Resources (IUCN) and Global Marine Species Assessment (GMSA) have adopted the Documentation Standards and Consistency Checks for IUCN Red List Assessments and Species Accounts (IUCN 2013). These standards state that a distribution map is required as supporting information for all IUCN Red List assessments. The purpose of a distribution map is to provide the current known distribution of the taxon within its native range (IUCN 2013). The limits of distribution are determined using known occurrences of the taxon and knowledge of its habitat preferences, remaining suitable habitat, and elevation or depth limits. Further, it stipulates that a GIS shapefile (for geo-referenced polygons or point localities) is the preferable format for spatial data. Using shapefiles, an esri-developed geospatial vector data format, ensures their value for spatial data analyses, visual displays, and future functionality of the Red List website. IUCN gratefully acknowledges support from esri (Environmental Systems Research Institute) by providing GIS software and training under its Conservation Grant Program. If for some reason a shapefile cannot be created, other formats, including paper maps, text file coordinates, a pdf document, or a graphics file are also acceptable. This document also specifies a Red List standardized set of required attribute fields (Table 2) for shapefiles that will help maintain consistency within the IUCN database. Coded values for presence, origin and seasonality attributes may be found in the appendices of this document. The use of codes (see Tables 3, 4, 5) and subtypes eliminates synonyms, misspellings, and errors, and ensures that the data conforms to the schema of the Red List Species Information System (SIS) database.

The majority of assessments appearing on the IUCN Red List are carried out by members of the IUCN Species Survival Commission (SSC), appointed Red List Authorities (RLAs), Red List Partners, or participants of IUCN-led assessment projects (IUCN 2014a). However, assessments can be done by anyone and submitted to IUCN for consideration. The panels of species and taxonomic experts and authorities at IUCN workshops should be familiar with current IUCN mapping protocols, and also review Critical Habitat and Essential Fish Habitat mapping. The Guidelines for Appropriate Uses of Red List Data specifies that the maps that accompany many Red List Assessments are generalized, and should be used primarily as an orientation tool that represents the best data available at the time of review, not necessarily at time of publication, which can differ by several years. Environmental and threat level changes should also be reflected in the map review processes, as habitat conditions become either less or more desirable. The creation of Marine Protected Areas and protection of Essential Fish Habitats serve as prime examples of positive changes in habitat conditions. Negative changes such as major oil spills or hypoxic areas should also be reflected in the map review process. Changes in sea surface temperature should also be considered (Karnauskas et al. 2013). Current biotic and abiotic factors should be considered, and maps adjusted accordingly.

A detailed guidance document ‘Documentation standards and consistency checks for IUCN Red List assessments and species accounts’ (IUCN 2013) provides IUCN mapping standards for creating distribution maps to support Red List assessments. These distribution maps can be useful for identifying priority conservation areas, identifying gaps in scientific knowledge, and informing management decisions. A tutorial module is also available through the online IUCN Red List Training course ‘Module 5: IUCN Red List Mapping Standards’ (ConservationTraining 2013). This online tutorial takes approximately 1 hour to complete, and focuses on understanding the standards in place for creating species distribution maps for supporting IUCN Red List Assessments. Additional objectives for this tutorial are to aid in understanding the benefits of distribution mapping, recognize what information is being represented in the map, and know what mapping tools are available. Essential IUCN terminology, such as Extent of Occurrence (EOO) and Area of Occupancy (AOO) are explained.

Extent of Occurrence (EOO) is defined as the area contained within the shortest continuous imaginary boundary which can be drawn to encompass all the known, inferred or projected sites of present occurrence of a taxon, excluding cases of vagrancy (species far out of their typical range). This measure may exclude discontinuities or disjunctions within the overall distributions of taxa (e.g., large areas of obviously unsuitable habitat; but see 'area of occupancy'). Extent of occurrence can often be measured by a minimum convex polygon (the smallest polygon in which no internal angle exceeds 180 degrees and which contains all the sites of occurrence).

Area of Occupancy (AOO) is defined as the area within its 'Extent of Occurrence' (see definition above) which is occupied by a taxon, excluding cases of vagrancy. The measure reflects the fact that a taxon will not usually occur throughout the area of its extent of occurrence, which may, for example, contain unsuitable habitats. The area of occupancy is the smallest area essential at any stage to the survival of existing populations of a taxon (e.g. colonial nesting sites, feeding sites for migratory taxa). The size of the area of occupancy will be a function of the scale at which it is measured, and should be at a scale appropriate to relevant biological aspects of the taxon. The criteria include values in km², and thus to avoid errors in classification, the area of occupancy should be measured on grid squares (or equivalents) which are sufficiently small.

The definitions above are taken directly from: http://www.iucnredlist.org/static/categories_criteria_2_3#definitions.

Distribution maps display a polygon intended to communicate that a species probably only occurs within its extent, which is based on known occurrences, knowledge of habitat preferences, remaining suitable habitats, elevation (or depth) limitations, and other expert knowledge. Point data, which can include line-based data from transects, polygon data from a defined area, such as a national park, and grid data (observations or survey records from a regular grid) from which these polygons are derived is obtained from published peer-reviewed literature, ‘grey’ literature (academic or government literature that is not formally published), field observations, biodiversity and taxonomic databases such as the Global Biodiversity Information Facility (GBIF) and Ocean Biodiversity Information System (OBIS), museum and other curated collections, or from taxonomic expert knowledge. There is a wide variety in the quality and quantity of these data. There are also online utilities, such as GeoCAT (Bachman et al. 2011) (http://geocat.kew.org/) which will display point data from a limited variety of sources. It is also capable of importing user-supplied point data. GeoCAT will calculate and display the EOO and/or AOO, and allow the selection and download of point data from a selected region or area.

The existing IUCN Red List mapping protocol for marine species differs from that for terrestrial species, primarily in that bathymetry may be used to delineate species range limits, much like elevation limitations may be used to limit the ranges of terrestrial species. It also differs from the mapping protocol for freshwater fishes, where drainage basins are typically used for determining and delineating range extents. The IUCN protocol for converting marine observation point data into distribution polygons involves a three-step process: Step One: Plot Observation Points, Step Two: Expand the Range, and Step Three: Refine the Range.

In Step One, point observation data are plotted. Since data often come from a diverse range of formats and sources, methods for plotting data points will vary. All data should be plotted in the Geographic Coordinate System, WGS-1984.

In Step Two of this protocol, the range is extrapolated based on the extent of suitable habitat (ESH) in the area and expert knowledge of the species and its requirements. Surrounding areas of similar habitat may be included. For terrestrial species, there are various other factors such as elevation, temperature, and even natural physical barriers, such as oceans. Marine species range may be affected by depth, water temperature gradients, salinity ranges, photic zone depths, and O₂ concentrations. Often, this extrapolation is accomplished by buffering the point data, and then creating a convex polygon that surrounds the available points.

In Step Three, areas that are deemed unsuitable for a species are removed from the extrapolated habitat polygon(s). Note that this extrapolation and elimination of areas may result in discontinuous or non-contiguous polygons. This may result in different Extent of Occurrence and Area of Occupancy, and results in the best representation of the species’ likely occurrence or distribution based on currently available information. The Area Of Occupancy (AOO) will reflect influences from both biotic and abiotic factors.

A ‘Best Practices’ section of the tutorial offers several ‘rules of thumb’ to go by:

The distribution map produced with this protocol represents the taxon’s distribution within its overall range for communication and/or conservation planning purposes; it may not equate to either the spread of extinction risk (Extent Of Occurrence) or the occupied range area (Area Of Occupancy) as defined by the IUCN Red List Categories and Criteria, but can be used to support these measurements.

There are several areas where questions may exist that this protocol doesn’t address such as: what point data sources should be included, what areas should be eliminated, how should seasonality by represented (separate GIS file, separate polygon within the same GIS file…).

Much of the workshop discussion focused on bringing in additional data and data sources. With the advent of numerous portable electronic devices, including Smartphones, with different applications and interfaces and GPS/mapping capabilities, new and exciting sources of species and species/location data are available, which could be included with current datasets. Crowdsourcing, commonly known as ‘citizen science’, is a manner of collecting data and observations in which collaborators who may lack credentials and formal institutional affiliation can contribute to the work of taxonomists and scientists. For example, rather than requiring a master’s degree in ichthyology, a citizen science project might ask if a candidate can learn to identify a particular species of fish using a dichotomous key (Lauro et al. 2014). There are a variety of field guides and keys available for Smartphones, particularly for Android and iPhone systems. For example, the Audubon Society puts nature at your fingertips with fieldguides to butterflies, birds, trees, wildflowers, mammals, fishes, mushrooms, insects and spiders. In all, approximately twenty Audubon ‘apps’ are available at http://www.greenmountaindigital.com/audubon-guides.html.

The Field Studies Council and Burkmar, R. 2014) has published a paper discussing the changes in biological identification brought on by the rapid develoment of information technology. An entire section is devoted to decribing the various types of keys and field guides used for biologic identification.

To illustrate crowdsourcing, consider several examples:

This small sampling of crowd-sourced data collection applications emphasizes the need to achieve a consensus on whether information collected in this manner can be used to enhance the current point and polygon observation data used to determine range and distribution extent information.

Crowd-sourced data using some of the ‘apps’ mentioned above would require a minimum of data fields be filled; other attributes should be added from existing and expert knowledge or specimen voucher information. The scientific name (binomial), the name of the compiler or submitter, and the citation (organization or app name) should be collected and added to the geo-tagged image information when available. Many of the apps and website entry points currently available fail to generate useable data, because they do not conform to taxonomic standards, or lack georeferencing. Database curators and developers now have access to several 'toolkits', such as that available from Indicia (2015) that will ensure that data collected conforms to standards such as the World Register of Marine Species (WoRMS). Compiled data sets should have appropriate metadata, formatted to international standards such as the Darwin Core. GBIF has an online tool, Darwin Core Archive Validator, as an aid to the validation process.

The Museum of Vertebrate Zoology (MVZ) at Berkley publishes a guide (Anonymous 2005) that describes and gives examples of a complete set of locality information, including a descriptive locality, to be used even when geographic coordinates are available. Elevation data, coordinates, datum, GPS accuracy, extent, references including a map name or GPS model, and a source for elevation data are required. Shah (2014) discovered that certain fields, such as scientificName, recordedBy, eventDate, country and stateProvince were more commonly recorded correctly than others such as scientificNameAuthorship and verbatimLocation, county and locality, which might be misunderstood or confused. The use of drop-down field values and auto-completing text enhances the accuracy of crowd-sourcing information, according to Shah (2014). It is possible that an app could be adapted or developed to require the minimal data fields be filled in for submission, and employ drop-down fields and autocomplete functions to eliminate a certain amount of error. For example, projects in iNaturalist can set some fields as required, limit the geographic distribution to a certain area, or the taxonomic scope to one taxon, before new observation can be submitted. Submissions should still be evaluated by regional and/or global evaluation teams, who would determine if the data is usable. Table 2 shows the fields required for an IUCN-accepted shapefile; automated filling of many of these fields should be programmed into a data collection app. When accumulted points are assembled into a single shapefile, other fields (such as 'compiler') should be completed. Shi offers a list of Best Practices about Species Mapping that is also helpful (Shi 2011).

Chamberlain (2014) takes crowdsourcing to another level, by introducing 'groupsourcing', defined as: completing a task using a group of intrinsically motivated people of varying expertise connected through a social network. A 'group' is a feature of social networking in which a small set of users communicate through a shared message system, and perform one of three types of tasks. Chamberlain defines the first of these tasks as a Task Request (TR): groups that encourage members to perform tasks such as marine creature identification. This paper goes on to discuss the accuracy of various methods, processing methodologies, ways of aggregating the collected data, and some of the limitations inherent in social networking and crowd/groupsourcing. Chamberlain concludes that groupsourcing offers a high accuracy compared to 'other methods of crowdsourcing', as well as being data-driven and low cost. Chamberlain also suggests that citizens, species experts and enthiusiasts can form these groups on social networking sites rather easily, which can allow citizen scientists to participate without much in the way of specialized equipment. Helping identify marine species in video and images from Remotely Operated Vehicles (ROVs) allows pareticipation without ever leaving your desk.

The traditional sources of biodiversity data include but certainly aren't limited to museum collections, taxonomic monographs, and biodiversity databases, which obtain much of their data from the first two sources. Individual specimen and observations within these collections come from a variety of sources, including published and unpublished (grey) literature, amatuer naturalists, and volunteer recorders (Issac et al. 2013). Among the largest biodiversity databases include the Global Biodiversity Information Facility (GBIF) with over 500 million data points, Ocean Biogeography Information System (OBIS) with over 40 million points of marine data, and HERPNET (soon to be replaced by VertNet), with over 14 million records of vertebrates. BioGoMx is a smaller but comprehensive database focused on the Gulf of Mexico, with over 15,000 species. Population distributions in BioGoMx are represented using an octant structure, dividing the Gulf of Mexico into eight octants. Each octant is further divided into six bathymetric depth ranges, thus resulting in a total of 48 polygons in the Gulf (Moretzsohn et al. 2011). The presence (or absence) of each species is determined for each octant (from published, peer-reviewed sources), and further discriminated into depth ranges, based on the depth distribution reported for each species by the taxonomic expert in the comprehensive biotic inventory of the Gulf of Mexico (Felder and Camp 2009). Integrated Digitized Biocollections (iDigBio) is digitizing information about existing, vouchered natural history collections, and is funded by the National Science Foundation as part of the Advanced Digitization of Biodiversity Collections (ADBC) program. This program has digitized over 24 million specimen records and over 4 million media records in 361 record sets. Images comprise only 286,000 of these records. Data and images for millions of biologic specimens are being made available in electronic format for the research community, government agencies, students, educators, and the general public. Approximately 4.5 million of these records are dated within the last 30 years. Kroupa and Remsen (2010) estimate that more than 100,000 scientific biological records, observations and specimens are recorded daily, through this initiative and others. They stress that the use of electronic tools and software can facilitate the recording of species datasets and minimize the number of errors, which can be within the geo-referencing domain as well as the taxonomic domain.

In addition to biodiversity databases, surveys, often conducted by state and federal fisheries agencies, are another source of biodiversity data. The Texas Parks and Wildlife Department has been conducting seine, gill net and trawl surveys since the 1970’s. The Louisiana Department of Wildlife and Fisheries has been collecting fishery independent data since 1988, from programs utilizing various gear and sampling techniques. The Florida Fisheries-Independent Monitoring program began the same year. The Southeast Area Monitoring and Assessment Program (SEAMAP) Gulf of Mexico component has been operational since 1981, planning, coordinating and conducting surveys for the Gulf States Marine Fisheries Commission. The Secretary of Agriculture in Mexico and the Instituto Nacional de Pesca (National Fisheries Institute) coordinate and conduct scientific and technological research on fisheries.

Biodiversity databases and literature contain vast amounts of distribution and taxonomic information, however the quality, scope and scale of data varies. To address this potential problem, data should be verified and vetted by species experts and other knowledgeable workers before the information can be incorporated into Red List assessments. Taxonomic information can be verified in authoritative taxonomic databases such as the Integrated Taxonomic Information System (ITIS), the World Register of Marine Species (WoRMS), and other initiatives, which count on the assistance of taxonomic experts to keep the information as current as possible.

Biodiversity databases such as GBIF, OBIS, and Red List usually rely on multiple biodoversity and taxonomic databases to keep information current. It is recommended that any change in taxonomy be fully documented and linked to the source of the taxonomic authority. Similarly, information on potential mis-identifications should be provided to avoid potential problems.

Distributional data can have several sources of errors including incomplete or vague locality descriptions, wrong information from original source, transcription of data from hand-written labels and field log books, transposition of latitude and longitude, and GPS or other instrument error or calibration problems. Therefore, biodiversity databases should have fields for accuracy and data confidence, ideally reviewed by staff or an expert. If the data point is considered problematic, it should be flagged as such so that users can evaluate its usefulness. There are numerous database validation tools available.

This paper is not intended to present all the possible combinations of crowdsourcing data or species evaluations, but instead should serve as a starting point for further discussion.

The review process:

Crowd-sourced data: