Improved Georeferencing: Three essential guiding documents

Abstract

To understand biological and geological events and the history of collected samples, it is essential to determine and communicate location information accurately. The accuracy of a georeference depends upon the circumstances of the event. Historical collections depend on having clear verbatim locality descriptions, the correct interpretation of data written on labels, and on the availability of gazetteers and maps of suitable scale and time. Observation and tracking data localities depend on GPS (Global Positiioning System) accuracy, and on presence or absence of nearby obstructions such as buildings, forest cover, cliffs, etc. Marine data depend on the accurate determination of the surface location and the techniques to position a dive event from that location and to determine its depth and extent. Many people are using smartphones or maps such as Google Earth and Google Maps to determine their georeferences – but are they suitable and accurate enough to determine locations and elevations? New editions of the Georeferencing Best Practices (Chapman and Wieczorek 2020), the Georeferencing Quick Reference Guide (Zermoglio et al. 2020), and the Georeferencing Calculator Manual (Bloom et al. 2020), were published earlier this year and address all the issues listed above and many more. These documents were based on earlier versions but have been updated and improved considerably – adding information for marine biomes, caves, lithographic stratifications, transects, and the use of elevation, as well as including many more illustrations and examples. The expansion of an extensive georeferencing glossary adds to consistency in the use of terms The trio of documents now provides consistent guidance about how to georeference diverse locality types and detailed instructions on how to calculate uncertainty using many different coordinate reference systems and datums (horizontal and vertical) along with the importance of recording this information. Finally, they provide guidance on how to set up a georeferencing project and how to relate the results to the Darwin Core Standard (Darwin Core Task Group 2009). For the last decade, Darwin Core (Wieczorek et al. 2012) has been one of the preferred standards for sharing biodiversity data, including associated location information. Darwin Core currently has 44 terms in its Location class, allowing sharing from administrative divisions, to elevations and depths, coordinates in different formats, and georeference metadata, among others. Although Darwin Core provides definitions for each of its terms, their correct use is sometimes poorly understood, resulting in information being captured incorrectly, or not captured, documented or shared at all. We will re-introduce these documents, discuss their content, importance, and differences from previously published versions. The newly revised documents provide guidance on capturing and documenting georeferences, clarifying the georeferencing process and showing how to capture information using Darwin Core appropriately. They will improve the location data associated with biological events and our understanding of these events.

Cabo Verde’s Poaceae Flora: A Reservoir of Crop Wild Relatives Diversity for Crop Improvement

“…The distribution of Poaceae CWR in Cabo Verde was estimated with occurrence data collected from previous fieldwork, studied specimens housed in worldwide herbaria and information available at the GBIF website ( GBIF.org, 2020 ). Specimens without geographical coordinates were georeferenced following the Guide to Best Practices for Georeferencing ( Chapman and Wieczorek, 2006 ) and using Google Earth Pro 7.3.2.5491 ( Serea, 2018 ). Duplicate records, i.e., with the same collector and the same number of collection, were excluded.…”

Including imprecisely georeferenced specimens improves accuracy of species distribution models and estimates of niche breadth

“…Owing to advances in collection practices, mapping, and GPS (Wieczorek et al 2004;Wieczorek & Chapman 2020), imprecise records may tend to be older than precise records. If this is the case, two opposing considerations should also inform the decision over whether to include imprecise records.…”

“…Regardless of the decisions researchers make about which records to include in their analyses, it is important to clearly document the criteria and steps used to clean specimen data prior to conducting environmental niche modelling (Chapman 2005;Wieczorek & Chapman 2020).…”

“…We classified records as "precise" if they had spatial uncertainty small enough to enable them to be matched with confidence to environmental data, and "imprecise" if they could not. We emphasize that our definition of an imprecise record does not include records with locations appearing to be outside the range of the species (i.e., geographic outliers; Feeley & Silman 2010) or specimens that do not pass quality-assurance checks (Chapman 2005). We then compared estimates of range size and climatic niche breadth of each species when using only precisely georeferenced records to estimates using precise and imprecise records together.…”

Guide francophone pour la modélisation de niches écologiques

“…Par exemple, la localisation de l'occurrence peut être plus ou moins précise selon la qualité et la source de l'information: elle peut se résumer à une indication vague, comme le nom de la commune ou du pays de collecte, ou à l'inverse être associée à une position géoréférencée par GPS. Dans ce cas, il est par exemple conseillé d'associer à chaque occurrence une notion d'incertitude (Wieczorek et al, 2004;Chapman et al, 2020), puis de ne garder que les occurrences se situant en dessous d'un seuil prédéfini (que l'on peut fixer par exemple à la résolution des données environnementales). Il est également fréquent que les coordonnées de localisation comportent des erreurs, comme une inversion de la latitude/longitude, ou encore une incohérence écologique de la localisation (cas d'occurrences situées dans un océan pour une espèce terrestre ou occurrence isolée très éloignée du reste de la répartition géographique; Sillero et al, 2021; Figure 7).…”