DOI: 10.5281/zenodo.8423306
title: A Standardized Review of Bat Names Across Multiple Taxonomic Authorities

description: The taxonomic boundaries of species and higher-level taxa change relatively frequently as knowledge of population structure and evolutionary relationships improves, and older hypotheses are reframed. For the order Chiroptera (bats), valid names have long been assessed by multiple authorities, but differences among the resulting classifications largely remain unharmonized amongst authorities. We collected and aligned treatments across three primary taxonomic authorities for Chiroptera to create a single dynamic and versioned name translation tool. The resulting Bat Taxonomy Alignment will expedite future taxonomic assessments. The complexities uncovered in this alignment suggest the need for more explicit tracking of taxonomic concepts and usage of names in the future, particularly since biodiversity data (including hosts of zoonotic diseases) are cataloged and tracked using taxonomic names.


Find user friendly links and more analyses here; https://jhpoelen.nl/bat-taxonomic-alignment/


The CTAF DISSCO Covid-19 Taskforce started in 2019, but has evolved into the Biodiversity Exchange on Host and Pathogen Interactions (Bi HaPI). This group formed with the goal of creating narratives framed to ready the scientific community for potential future zoonotic spillover events involving bat species. 


Bats play a crucial role in ecosystems as pollinators and insect population control. However, they are also known to carry and transmit zoonotic diseases to humans and other animals. Bats are unique among mammals due to their ability to fly. This ability requires a significant amount of energy, so bats have increased metabolism and body temperature - similar to the fever response in other mammals. This regular spike in body temperature could, theoretically, create an environment that is more challenging for viruses to survive in. Some studies also suggest that bats have evolved mechanisms to limit inflammation, reducing the damage to their own cells from their immune response. This could be another factor that allows them to coexist with viruses without developing severe disease responses. Correct species identification is crucial not just for monitoring disease transmission on a wide scale, but also because viruses tend to invade cells more readily from taxonomically similar species. However, the taxonomic boundaries of bat species and higher-level taxa are subject to frequent changes as our knowledge of their evolutionary relationships improves. These changes often result in discrepancies among taxonomic authorities, where different classifications coexist without being harmonized. 


Bats are unique among mammals due to their ability to fly. This ability requires a significant amount of energy, so bats have increased metabolism and body temperature - similar to the fever response in other mammals. This regular spike in body temperature could, theoretically, create an environment that is more challenging for viruses to survive in. Some studies also suggest that bats have evolved mechanisms to limit inflammation, reducing the damage to their own cells from their immune response. This could be another factor that allows them to coexist with viruses without developing a severe disease responses


We want to know if a bat sneezes, who could be on the receiving end of that spray. Then a network of potential pathogen transmission can be constructed. Co-roosting in bats has not been thoroughly examined since their roosting behavior is elusive, their survival can be adversely impacted as they are highly susceptible to disturbances, it is difficult to identify bat species when they are active at night, and their behaviors often change seasonally. 


Following open access and FAIR (Findable, Accessible, Interoperable, and Reusable) principles for extracting data and choosing methodologies to liberate ecological knowledge from dark data, we aim to achieve a long-term semantic foundation for evolutionary knowledge by reconciling taxonomic names. The evolution of name-to-meaning relationships in taxonomy presents a challenge for maintaining consistent communication and integration of information over time. However, information about organismal traits, genomes, and geographic distributions continuously lead to changes in taxonomic classifications, causing discrepancies in the meaning of taxonomic names over time and across different taxonomic reference material.  One approach to resolving this issue involves creating methods for monitoring taxonomic alterations that facilitate straightforward comparisons of taxonomic name treatments from one reference to another that can be shared in a fast and dynamic way. 


Unharmonized taxonomic treatments refer to situations where different taxonomic authorities or sources have divergent or conflicting approaches in classifying or naming taxa. It occurs when there are discrepancies in the taxonomic concepts, classifications, or naming conventions applied to the same group of organisms across different references or databases. Unharmonized treatments can include variations in species delimitation, assignment of subspecies, synonymies, elevations of taxa to higher taxonomic levels, splits, merges, or other taxonomic decisions. Resolving unharmonized taxonomic treatments involves reconciling these differences and establishing a consistent and agreed-upon taxonomy for the taxa in question


For co-roost occurrence data mining, we are extracting roosting interaction evidence from published works that span 160 years and 137 countries, so we face a specific challenge with taxonomic names. Once we resolve for taxonomic names by integrating current names and sorting out disparate taxonomic treatments, our network model becomes more representative of the frequency interactions occur between species. For example, since Tadarida cynocephala is now considered a subspecies of Tadarida brasiliensis, it drastically changes the frequency of interactions associated between this species and others. Resolving taxonomic names also allowed the software to identify links to species unidentified in our data. 


There  are multiple taxonomic authorities that researchers and research facilities may follow depending on their specific specimen collections, research interests, and historical traditions. For example, some museums follow the American Society of Mammalogists, while others may use the European Mammal Assessment or the Mammals of South-East Asia (MOSE) project. The leading authorities for the order Chiroptera include Simmons (2005) chapter in Mammal Species of the World, Bat Species of the World: A taxonomic and geographic database or batbase.org, Arizona State University housed Mammal Diversity Database, and the Handbook Mammals of the World Volume 9 Bats, edited by Wilson and Mittermeier. We present this alignment of bat taxonomic treatments and extraction of those name strings in disunity. Our results reveal ~15% of the name strings do not match across these references. 


Our team bought the HMW2019 printed text, outsourced digitizing this hefty text to Picturae, a company specialized in digitizing objects. This cost about $4500 for 5000 species treatments, so about $1 dollar per species record. To convert the OCR’ed (Optical Character Recognition), PDF into a structured XML format, we utilized Plazi's specialized GoldenGate mark-up editor. Plazi's workflow was specifically designed to adhere to Swiss copyright protection laws. Plazi, an association with the primary objective of transforming scientific literature, both in print and digital formats, into semantically enabled and enhanced documents, plays a crucial role in this process. The conversion involves transforming taxonomic literature into XML documents that are semantically enriched, allowing for both human and machine readability. Utilizing a specific version of Plazi's Treatment-XML, we converted the HMW2019 texts into a table schema specific to our needs. The resulting data was stored in JSON format, as provided by Preston, a biodiversity data tracker. The text was parsed based on treatment categories and converted to a csv format for use in a shared google sheets document. Following the manual name alignment process and interpretation, the text was further processed using mlr, a command-line tool in R commonly used for statistics and data analysis, capable of querying, shaping, and reformatting data files in various formats such as CSV, TSV, JSON, and JSON Lines. By leveraging these tools and workflows, we were able to efficiently extract, organize, and convert the Handbook Mammals of the World 2019 text into a structured and accessible format, facilitating further analysis and research.


For the name alignment, the team collected and aligned treatments across the primary taxonomic authorities for Chiroptera, the order that includes bats. The result of this effort is the Bat Taxonomy Alignment (BTA), a dynamic and versioned name translation tool. The BTA provides a standardized approach to aligning and comparing taxonomic treatments, enabling accurate integration of data and facilitating research. On the surface, this might seem like an easy task to download treatment records and sort them, but realistically took months and four different skill sets. We  reviewed 6 established taxonomic resources for bats and their published taxonomic treatments for all specific epithets under the order Chiroptera. This included 4 widely used taxonomic authorities, the species occurrence repository GBIF,  and species records from the (IUCN) Red List of Threatened Species. We evaluated the variations of taxonomic names and identified areas of incongruence and missing information by analyzing the data, rationales, author notes, and data provenance provided in each source. This BTA was created to compare taxonomic treatments and the history of each epithet in one comprehensive spreadsheet. The referenced publications were not reviewed and epithets were not challenged during this study. Discrepancies were identified and categorized, but not unpacked here. However, we have established a solid foundation as we grow our list of newly described species and phylogenetic resolutions to associations among taxa.


The BTA was created with taxonomists, researchers, data miners, bioinformaticians, ecologists, and museum staff in mind. The first section of the dataset is solely an extraction of the taxonomic names that might not share an epithet according to different authorities, but are referring to the same species. This list can also serve as a quick reference to valid species according to any one authority.  Our objective was to minimize interpretation and provide objective information. When encountering taxonomic names that did not match across authorities, we collected the corresponding equivalent names or relationships (such as synonym, nomen dubia, subspecies, missing altogether, etc.) and the accompanying taxonomic notes from each source. This compilation aims to present a convenient reference for those examining taxonomic concepts in the future. Additionally, we hope to encourage data aggregators to extract the citations for the taxonomic reference list, which is highly valuable and in demand. The total number of disagreements were then summed and later assigned Agreement Index values to compare authorities rates of harmonized treatments.


We also extracted synonyms and subspecies for future taxonomic revisions. The rest of the dataset includes the GBIF name status (accepted, synonymized, raised to a higher-level taxon, etc.) and the complete taxonomic records obtained from online versioned datasets. In the case of Mammal Species of the World, the data was shared directly by the author. 


We have chosen Zenodo as a platform for our dataset, because versioning is critical for large data sets that are regularly updated or revised. Since starting this project in November, an updated version of batnames was released, identified as BatNames 2023. As new versions of now online databases are released, this BTA can act as a foundation to track changes and provenance over time. This initial version of the dataset incorporates extraction errors, intentionally retained for the purpose of gathering informative feedback for those involved in the extraction process. Additionally, it contains unedited author notes that were compiled prior to publication, which contain errors. Published in 2005, Mammal Species of the World identified a total of 1,113 bat species. However, our understanding of bat species diversity has since expanded, and we now recognize approximately 1,458 valid species. 


The Handbook Mammals of the World, released in 2019, marked a significant milestone as the first fixed entity widely accepted as a taxonomic reference since the publication of Mammal Species of the World in 2005. However, despite its recent publication, it is already outdated. Two dynamic and continuously updated living databases, BatNames and Mammal Diversity Database, have already released multiple new versions of their taxonomic records since the initiation of this project in November of last year. To close the gap between their respective species accounts, one author from each project is actively engaged in this collaborative effort. As of writing this in May, the number of unharmonized names has been reduced from 138 to 28, and by the time you read this, it is expected there will be none.


This interdisciplinary collaboration has bridged a gap between taxonomic experts and the big data community to tackle a long-standing obstacle in the research community. A standardized alignment for comparing taxonomic authorities can help to resolve the name-to-meaning ambiguity by providing a consistent reference for how taxonomic name usages are related to each other. This will not only help improve the accuracy and precision of scientific communication, but it will also allow for more seamless collaboration and integration of disparate data sets. This effort will be a crucial resource in mapping bat co-roosting behavior and other relational datasets. Our hope is that it will also be useful in resolving bat taxonomy at a global level.  By providing a unique and persistent identifier for each taxonomic name, metadata, citations, and provenance information, we are able to facilitate the integration of data from different sources, making it possible to build a more complete and accurate picture of the taxonomy of a given organism in on a versionable, digital platform. 


This study represents an important step towards creating a more accurate and reliable taxonomy for the order Chiroptera. However, there are still several areas that could benefit from further research. One important next step would be to create a searchable repository for all names associated with valid taxonomic epithets, including nomen dubia, common names, misspellings, synonyms, and any other synonymized moniker. This would help to ensure that researchers have access to a comprehensive and up-to-date name list to search from. Another potential initiative could be to develop a comprehensive and open-access database that integrates taxonomic information with ecological and behavioral data for bat species worldwide. Developing a machine learning algorithm to automate the taxonomic alignment process would involve training a model on a large dataset of taxonomic records to recognize patterns and deviations in species names across different authorities. The model could then be used to automatically align and reconcile conflicting names, assisting with manual curation and improving the speed and accuracy of taxonomic assessments. We have already started the process of reverse engineering the alignment methods in order to ingest more records from additional authorities.


Link to iDigBio Digital Data Conference presentation;  https://drive.google.com/file/d/1wOFqMKgkOTLPeQ1I09eCsOA_N9QCTUE-/view?usp=sharing


For questions, interact with us through https://github.com/zenodo/zenodo/issues.
description: This is a draft for internal review.