The International Working Group on Taxonomic Databases (TDWG)*1 was established in 1985 to promote biodiversity data sharing and collaboration among organizations, projects, and people who manage biodiversity information. To achieve these goals TDWG develops standards for publishing biodiversity data and provides a forum for advancing biodiversity informatics. TDWG provides critical elements for developing biodiversity information standards, including: an organizational structure, processes for ratifying and maintaining standards, and the collaboration tools available to participants. TDWG's role as an open standards body creates a complementary partnership with the Global Biodiversity Information Facility (GBIF). TDWG’s organizational structure is established in its Constitution and includes an Executive Committee, a suite of standing committees for on-going operational concerns, a Technical Architecture Group (TAG), and an array of interest, task and maintenance groups that develop and maintain standards. These groups and committees provide TDWG’s administration, maintain existing standards and best practices, and support the efforts of the community to create, expand and implement data standards using current methodologies of computer and information science. TDWG’s Process prescribes how interest and task groups are established to develop standards. Interest groups typically form organically when people with a shared interest in a particular aspect of biodiversity science determine that the science can be better supported by integrating information they create and manage. An interest group is established by writing a charter that describes its purpose and scope, the way of work, and how to become involved. The group designates one or more conveners, who are then responsible for managing the group and communicating with the Executive. The conveners submit the charter to the Executive for approval, and the approved charter is posted on the TDWG website and announced via email and social media. In addition, collaboration tools, such as a GitHub repository, mailing list, and slack channel can be created to support the group’s work. Interest groups explore problems and plan the development of potential solutions. When a plan has taken shape, a task group is established within the interest group by drafting its charter using a similar template and having it approved by the Executive. The essential difference between an interest and task group is that an interest group continues as long as the community’s interest sustains it, whereas a task group works to a schedule and dissolves when the task is finished. Throughout the exploration and planning phases, an interest group should seek advice from the TAG on methods for developing the standard and the framework for documenting it. The TDWG Process also specifies how a standard gets ratified. When a draft is complete the conveners submit it to the Executive, which then appoints a review manager to shepherd the draft through review. Ratification entails both expert and public review, combining the familiar practice of scientific peer review and the internet practice of open source improvement. The review manager summarizes the review outcomes and makes a recommendation to the Executive. When a consensus indicates that the standard is usable and contains no serious problems, the Executive ratifies the standard. The standard is registered and a landing page with a standard suite of metadata is posted on the TDWG website. If the standard contains terms and definitions, URL targets are created in https://rs.tdwg.org. In many cases, a website specific to the standard is established in the TDWG domain, and guides people in how to use the standard. Finally, a maintenance group is established to receive feedback and modification proposals from the community, and ensure that the standard continues to improve. Maintenance groups use a similar, but less cumbersome process, the Vocabulary Maintenance Standard (VMS), to add, change, or deprecate elements of ratified standards. When TDWG was founded, members were focussed primarily on taxonomic compilations; basic species information, such as scientific names and synonyms, geographic distributions, and bibliographies of essential literature. During the 1990s, the number and size of primary biodiversity databases (e.g., natural history collection databases) grew exponentially, and soon it was recognized that a global data infrastructure would be needed to integrate all these data resources and support biodiversity science (Robbins 1996). GBIF was established in 2001 to meet that need, and since then TDWG and GBIF have been engaged in a complementary informal partnership. TDWG’s role in this partnership has been to establish and maintain standards for the key concepts in primary biodiversity data. GBIF's role has been to develop and maintain the technology that enables access to the world's biodiversity data as a well integrated resource. By necessity, GBIF was created through international agreement, as an organization responsive to governments, ensuring that this global data infrastructure is effective and sustained. TDWG, on the other hand, is an open standards organization that enables any member of the global community to participate in developing the data concepts required for biodiversity science. While TDWG's basic standards have helped to integrate immense quantities of primary biodiversity data, they have also imposed limitations. For example, presence-only occurrence data supports only a limited class of methods for species distribution modeling. Our more recent efforts, however, are broadening the kinds of primary data that can be brought to bear on documenting biodiversity and its change over time. These include the Humboldt extension for surveys and monitoring, and the environmental DNA standard (eDNA) for documenting occurrences through the traces of DNA left in the environment. Finally, the Darwin Core Data Package, recently developed by GBIF and currently in review by TDWG, should enable us to mobilize more kinds of data, broaden our community, and support new advances in biodiversity science.

“Use cases” are “a methodology used in system analysis to identify, clarify and organize system requirements” (Brush 2022). They provide context and purpose for data concepts. The Darwin Core (Wieczorek et al. 2012) was initially designed to serve two purposes: 1) to gather data for documenting species distributions, particularly through species distribution modeling, and 2) to support the discovery of specimens in biological collections. The occurrence concept, the "existence of a dwc:Organism at a particular place at a particular time" (Darwin Core Maintenance Group 2009), was established so that both observations and specimens could be combined into a single tabular data set and used to document the distribution of a species. By 2008, the TDWG Technical Architecture Group began recommending that TDWG develop its standards in the framework of the semantic web (SW); i.e., RDF. Two efforts have contributed significantly to casting the terms of Darwin Core in RDF. Baskauf and Webb (2015) distilled nearly a decade of online discussions into the Darwin-SW. In a separate effort, but involving many of the same people, a series of workshops were convened to coordinate standards between the genomics and biodiversity communities and produced the BioCollections Ontology (BCO; Walls et al. 2014), which placed Darwin Core terms in the context of the "OBO foundry" (Smith et al. 2007) and the Basic Formal Ontology (BFO; Arp et al. 2015, but also see videos by Smith). Darwin-SW included explicit recognition of IndividualOrganism, Occurrence, Event, and Token (i.e., evidence of a dwc:MaterialSample or an observation). The simplest use case, in which an organism is collected or observed once, doesn't require that Organism, Occurrence and MaterialSample/Observation be recognized as separate entities. The relationships are one-to-one-to-one. They can be joined into a single entity with only one identifier (e.g., materialSampleID) without loss of information. Note that a specimen or observation infers the existence of an organism and its occurrence in nature. The use cases that require separating Organism and possibly Occurrence from the MaterialSample or Observation are the ones where an organism is sampled or observed, remains in nature, and is subsequently sampled or observed again; i.e., the organism is the target of more than one dwc:Event. These cases require that the organism can be reliably identified as the same organism encountered earlier; e.g., by tag, identifying marking, DNA fingerprint, or precise and fixed location for sessile organisms. The BCO (Walls et al. 2014) does not explicitly recognize something equivalent to the dwc:Occurrence class. Following Smith et al. (2007), the focus is on "realism," things and relationships in the real world, as opposed to what we want our information systems to do. At a high level, the BFO separates material things, processes, and information artifacts as fundamentally different entities. Accordingly, the BCO separates the "material sampling process" from the "observing process," as observations and material samples are fundamentally different. Again, an occurrence class is not present because it represents a union of MaterialSample and Observation; it is justified by an analytical use-case, which was not in scope. That might not be ultimately disqualifying, but it raises a caution flag. A question then emerges for original providers who practice only the simplest case: should the provider manufacture dwc:occurrenceID and dwc:organismID even if they aren’t used in the original database? If they are useful to someone outside the local context, should creating redundant identifiers be the responsibility of the provider or the aggregator? Fig. 1A shows how the concepts in Darwin-SW could be represented in an entity-relationship diagram, with the Occurrence entity used to realize the many-to-many relationship between Organism and Event. Fig. 1B shows an alternative model, in which MaterialSample realizes the relationship between Organism and Event (the Observation entity is not shown, but would parallel MaterialSample, realizing another association between Organism and Event). If there are no attributes that are most appropriately assigned to Occurrence, this representation could be viewed as simpler and sufficient. The Darwin Core Quick Reference Guide lists 25 properties of the Occurrence class. My contention is that all but a few would be more appropriately assigned to the MaterialSample or Observation. Note that the MaterialSample or Observation represents the Organism at the time of the Event, and can be viewed as the appropriate subject for properties that change over time, e.g., lifeStage and reproductiveCondition. Moreover, others have argued that even permanent features of an Organsim are more correctly represented as having been directly assessed in the MaterialSample/Observation. It allows for contradictory assessments, but accommodating and resolving contradictions are real parts of scientific research. The alternative placements of properties not assigned to MaterialSample/Observation are: occurrenceID: deprecated; recordedBy and recordedByID: move to Event georeferenceVerificationStatus: move to Location or Event. occurrenceID: deprecated; recordedBy and recordedByID: move to Event georeferenceVerificationStatus: move to Location or Event. Under the model represented (in part) by Fig. 1B, the task of forming the union between MaterialSample and Observation (for documenting species distributions) would fall to the aggregator or end user. The important point is that where these unions are created in our biodiversity pipelines is an engineering choice. The dwc:Occurrence class is a term of convenience, not necessarily a reflection of real-world things and processes.

Digitisation and publication of museum specimen data is happening worldwide, but far from complete. Museums can start by sharing what they know about their holdings at a higher level, long before each object has its own record. Information about what is held in collections worldwide is needed by many stakeholders including collections managers, funders, researchers, policy-makers, industry, and educators. To aggregate this information from collections, the data need to be standardised (Johnston and Robinson 2002). So, the Biodiversity Information Standards (TDWG) Collection Descriptions (CD) Task Group is developing a data standard for describing collections, which gives the ability to provide: automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). automated metrics, using standardised collection descriptions and/or data derived from specimen datasets (e.g., counts of specimens) and a global registry of physical collections (i.e., digitised or non-digitised). Outputs will include a data model to underpin the new standard, and guidance and reference implementations for the practical use of the standard in institutional and collaborative data infrastructures. The Task Group employs a community-driven approach to standard development. With international participation, workshops at the Natural History Museum (London 2019) and the MOBILISE workshop (Warsaw 2020) allowed over 50 people to contribute this work. Our group organized online "barbecues" (BBQs) so that many more could contribute to standard definitions and address data model design challenges. Cloud-based tools (e.g., GitHub, Google Sheets) are used to organise and publish the group's work and make it easy to participate. A Wikibase instance is also used to test and demonstrate the model using real data. There are a range of global, regional, and national initiatives interested in the standard (see Task Group charter). Some, like GRSciColl (now at the Global Biodiversity Information Facility (GBIF)), Index Herbariorum (IH), and the iDigBio US Collections List are existing catalogues. Others, including the Consortium of European Taxonomic Facilities (CETAF) and the Distributed System of Scientific Collections (DiSSCo), include collection descriptions as a key part of their near-term development plans. As part of the EU-funded SYNTHESYS+ project, GBIF organized a virtual workshop: Advancing the Catalogue of the World's Natural History Collections to get international input for such a resource that would use this CD standard. Some major complexities present themselves in designing a standardised approach to represent collection descriptions data. It is not the first time that the natural science collections community has tried to address them (see the TDWG Natural Collections Description standard). Beyond natural sciences, the library community in particular gave thought to this (Heaney 2001, Johnston and Robinson 2002), noting significant difficulties. One hurdle is that collections may be broken down into different degrees of granularity according to different criteria, and may also overlap so that a single object can be represented in more than one collection description. Managing statistics such as numbers of objects is complex due to data gaps and variable degrees of certainty about collection contents. It also takes considerable effort from collections staff to generate structured data about their undigitised holdings. We need to support simple, high-level collection summaries as well as detailed quantitative data, and to be able to update as needed. We need a simple approach, but one that can also handle the complexities of data, scope, and social needs, for digitised and undigitised collections. The data standard itself is a defined set of classes and properties that can be used to represent groups of collection objects and their associated information. These incorporate common characteristics ('dimensions') by which we want to describe, group and break down our collections, metrics for quantifying those collections, and properties such as persistent identifiers for tracking collections and managing their digital counterparts. Existing terms from other standards (e.g. Darwin Core, ABCD) are re-used if possible. The data model (Fig. 1) underpinning the standard defines the relationships between those different classes, and ensures that the structure as well as the content are comparable across different datasets. It centres around the core concept of an 'object group', representing a set of physical objects that is defined by one or more dimensions (e.g., taxonomy and geographic origin), and linked to other entities such as the holding institution. To the object group, quantitative data about its contents are attached (e.g. counts of objects or taxa), along with more qualitative information describing the contents of the group as a whole. In this presentation, we will describe the draft standard and data model with examples of early adoption for real-world and example data. We will also discuss the vision of how the new standard may be adopted and its potential impact on collection discoverability across the collections community.

ABCD (Access to Biological Collection Data, Holetschek et al. 2012) and DwC (Darwin Core, Wieczorek et al. 2012), are TDWG standards for documenting the occurrence of organisms in nature and/or collections, whether as specimens or observations (i.e., unit-level data), and are used for a wide range of applications. Since 2019, the working group has been investigating ways to enable a closer link and integration of these standards (Blum et al. 2019). This presentation will summarize the results of the September 2020 workshop of the ABCD/DwC Alignment Working Group as part of the TDWG 2020 Virtual Conference Working Sessions Week. Prior to the workshop, we will have collected use cases for the application of the standards, which will have been analysed and discussed in the workshop itself. On this basis, smaller working groups will have been formed to address technical, organisational, and sociological aspects relevant for an alignment and future maintenance of ABCD and DwC. The results of these working groups, as well as the general summary of the workshop and the planned next steps of the ABCD/DwC Alignment Working Group will be shown and discussed in this presentation.

For the last 15 years, Biodiversity Information Standards (TDWG) has recognized two competing standards for organism occurrence data, ABCD (Access to Biological Collections Data; Holetschek et al. 2012) and DarwinCore (Wieczorek et al. 2012). These two representations emerged from contrasting strategies for mobilizing information about organism occurrences (also commonly called species occurrence data). ABCD was capable of representing details of more kinds of information, but was necessarily more complicated. DarwinCore, on the other hand, was simpler but more limited in its ability to represent data of different kinds and formats. TDWG endorsed both standards because the different projects and communities that generated them remained dedicated to their different strategies and tool sets, and the Global Biodiversity Information Facility (GBIF) developed the ability to integrate data published in either standard. Since their inceptions, DarwinCore and ABCD have become more similar. DarwinCore has gotten more complicated through the addition of terms and has begun to assign terms to classes. ABCD is now expressed in RDF (Resource Description Framework), potentially enabling re-use of terms with alternative structures among classes. At the same time, methodologies for conceptual modeling and representing complex scientific data have continued to evolve. In particular, a suite of modeling and data representation methods related to linked data and the semantic web, i.e., RDF, SKOS (Simple Knowledge Organization System), and OWL (web Ontology Language), promise to make it easier for us to reconcile shared concepts among different representations or schemas. A mapping between ABCD 2.1 and DarwinCore has existed since before 2005.*1 ABCD 3.0 and DarwinCore are both now represented in RDF. In addition, the BioCollections Ontology (BCO) covers many of the shared concepts and is derived from the Basic Formal Ontology (BFO), an upper level ontology that has oriented many other biomedical ontologies. Reconciling ABCD and DarwinCore through alignment with BCO (in the OBO Foundry; Smith et al. 2007) would better connect TDWG standards to other domains in biology. We appreciate that many working scientists and data managers perceive ontologies as overly complicated. To mitigate the steep learning curve associated with ontologies, we expect to create simpler application profiles or schemas to guide and serve narrower communities of practice within the wider biodiversity domain. We also plan to integrate the current work of the Taxonomic Names and Concepts Interest Group and thereby eliminate the redundancy between DarwinCore and Taxonomic Concepts Transfer Schema (TCS; Kennedy et al. 2006). At the time of this writing, we have only agreements from the authors (i.e., conveners of relevant TDWG Interest Groups and other key stakeholders) to collaborate in pursuit of these common goals. In this presentation we will give a more detailed description of our objectives and products, the methods we are using to achieve them, and our progress to date.

Premise of the Study Herbarium specimens provide a robust record of historical plant phenology (the timing of seasonal events such as flowering or fruiting). However, the difficulty of aggregating phenological data from specimens arises from a lack of standardized scoring methods and definitions for phenological states across the collections community. Methods and Results To address this problem, we report on a consensus reached by an iDigBio working group of curators, researchers, and data standards experts regarding an efficient scoring protocol and a data‐sharing protocol for reproductive traits available from herbarium specimens of seed plants. The phenological data sets generated can be shared via Darwin Core Archives using the Extended MeasurementOrFact extension. Conclusions Our hope is that curators and others interested in collecting phenological trait data from specimens will use the recommendations presented here in current and future scoring efforts. New tools for scoring specimens are reviewed.

This report describes the outcomes of a recent workshop, building on a series of workshops from the last three years with the goal if integrating genomics and biodiversity research, with a more specific goal here to express terms in Darwin Core and Audubon Core, where class constructs have been historically underspecified, into a Biological Collections Ontology (BCO) framework. For the purposes of this workshop, the BCO provided the context for fully defining classes as well as object and data properties, including domain and range information, for both the Darwin Core and Audubon Core. In addition, the workshop participants reviewed technical specifications and approaches for annotating instance data with BCO terms. Finally, we laid out proposed activities for the next 3 to 18 months to continue this work.

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

The purpose of our Perspective was to raise awareness about an issue that will increase in prevalence as the global biodiversity crisis unfolds: Absent a reliable estimate of population size, is it prudent and ethical to collect a newly observed individual of a species so rare it was thought extinct

The workshop-hackathon was convened by the Global Biodiversity Information Facility (GBIF) at its secretariat in Copenhagen over 22–24 May 2013 with additional support from several projects (RCN4GSC, EAGER, VertNet, BiSciCol, GGBN, and Micro B3). It assembled a team of experts to address the challenge of adapting the Darwin Core standard for a wide variety of sample data. Topics addressed in the workshop included 1) a review of outstanding issues in the Darwin Core standard, 2) issues relating to publishing of biodiversity data through Darwin Core Archives, 3) use of Darwin Core Archives for publishing sample and monitoring data, 4) the case for modifying the Darwin Core Text Guide specification to support many-to-many relations, and 5) the generalization of the Darwin Core Archive to a “Biodiversity Data Archive”. A wide variety of use cases were assembled and discussed in order to inform further developments.

The time is ripe for a comprehensive mission to explore and document Earth's species. This calls for a campaign to educate and inspire the next generation of professional and citizen species explorers, investments in cyber-infrastructure and collections to meet the unique needs of the producers and consumers of taxonomic information, and the formation and coordination of a multi-institutional, international, transdisciplinary community of researchers, scholars and engineers with the shared objective of creating a comprehensive inventory of species and detailed map of the biosphere. We conclude that an ambitious goal to describe 10 million species in less than 50 years is attainable based on the strength of 250 years of progress, worldwide collections, existing experts, technological innovation and collaborative teamwork. Existing digitization projects are overcoming obstacles of the past, facilitating collaboration and mobilizing literature, data, images and specimens through cyber technologies. Charting the biosphere is enormously complex, yet necessary expertise can be found through partnerships with engineers, information scientists, sociologists, ecologists, climate scientists, conservation biologists, industrial project managers and taxon specialists, from agrostologists to zoophytologists. Benefits to society of the proposed mission would be profound, immediate and enduring, from detection of early responses of flora and fauna to climate change to opening access to evolutionary designs for solutions to countless practical problems. The impacts on the biodiversity, environmental and evolutionary sciences would be transformative, from ecosystem models calibrated in detail to comprehensive understanding of the origin and evolution of life over its 3.8 billion year history. The resultant cyber-enabled taxonomy, or cybertaxonomy, would open access to biodiversity data to developing nations, assure access to reliable data about species, and change how scientists and citizens alike access, use and think about biological diversity information.

Following up on efforts from two earlier workshops, a meeting was convened in San Diego to (a) establish working connections between experts in the use of the Darwin Core and the GSC MIxS standards, (b) conduct mutual briefings to promote knowledge exchange and to increase the understanding of the two communities' approaches, constraints, community goals, subtleties, etc., (c) perform an element-by-element comparison of the two standards, assessing the compatibility and complementarity of the two approaches, (d) propose and consider possible use cases and test beds in which a joint annotation approach might be tried, to useful scientific effect, and (e) propose additional action items necessary to continue the development of this joint effort. Several focused working teams were identified to continue the work after the meeting ended.

Biodiversity data derive from myriad sources stored in various formats on many distinct hardware and software platforms. An essential step towards understanding global patterns of biodiversity is to provide a standardized view of these heterogeneous data sources to improve interoperability. Fundamental to this advance are definitions of common terms. This paper describes the evolution and development of Darwin Core, a data standard for publishing and integrating biodiversity information. We focus on the categories of terms that define the standard, differences between simple and relational Darwin Core, how the standard has been implemented, and the community processes that are essential for maintenance and growth of the standard. We present case-study extensions of the Darwin Core into new research communities, including metagenomics and genetic resources. We close by showing how Darwin Core records are integrated to create new knowledge products documenting species distributions and changes due to environmental perturbations.

Building on the planning efforts of the RCN4GSC project, a workshop was convened in San Diego to bring together experts from genomics and metagenomics, biodiversity, ecology, and bioinformatics with the charge to identify potential for positive interactions and progress, especially building on successes at establishing data standards by the GSC and by the biodiversity and ecological communities. Until recently, the contribution of microbial life to the biomass and biodiversity of the biosphere was largely overlooked (because it was resistant to systematic study). Now, emerging genomic and metagenomic tools are making investigation possible. Initial research findings suggest that major advances are in the offing. Although different research communities share some overlapping concepts and traditions, they differ significantly in sampling approaches, vocabularies and workflows. Likewise, their definitions of 'fitness for use' for data differ significantly, as this concept stems from the specific research questions of most importance in the different fields. Nevertheless, there is little doubt that there is much to be gained from greater coordination and integration. As a first step toward interoperability of the information systems used by the different communities, participants agreed to conduct a case study on two of the leading data standards from the two formerly disparate fields: (a) GSC's standard checklists for genomics and metagenomics and (b) TDWG's Darwin Core standard, used primarily in taxonomy and systematic biology.

Taxonomy is a fundamental science that provides the scaffolding for biology. But the true value of taxonomic data remains unrealized because basic biodiversity information remains fragmented and unevenly accessible. Taxonomy helps us recognize species and map their distributions by generating text descriptions, images, and records of when and where they have been observed. Current rates of species extinction, habitat loss, and climate change mean that taxonomy has never been more relevant. Biodiversity is one of the most information-rich fields of human knowledge, but advances in basic cybertaxonomic infrastructure have only recently provided the tools to organize biodiversity information in ways that respond to a wide range of user groups, including ecologists, land managers, and interested citizens, not to mention the benefits of readily accessible information to the global taxonomic community. The call to revitalize taxonomy by embracing the internet has been sounded for more than a decade. The time is ripe to significantly increase the volume of taxonomic information freely available online. But simply posting information online will not automatically reinvigorate taxonomy. There are myriad online sites dedicated to particular taxa or projects. These are useful to users interested in questions within the site’s domains. But the greater potential lies in mechanisms for aggregating primary source data in ways that allow users to filter and recombine data easily and flexibly for whatever purposes they imagine.

We address a poorly understood aspect of ecological niche modeling: its sensitivity to different levels of geographic uncertainty in organism occurrence data. Our primary interest was to assess how accuracy degrades under increasing uncertainty, with performance measured indirectly through model consistency. We used Monte Carlo simulations and a similarity measure to assess model sensitivity across three variables: locality accuracy, niche modeling method, and species. Randomly generated data sets with known levels of locality uncertainty were compared to an original prediction using Fuzzy Kappa. Data sets where locality uncertainty is low were expected to produce similar distribution maps to the original. In contrast, data sets where locality uncertainty is high were expected to produce less similar maps. BIOCLIM, DOMAIN, Maxent and GARP were used to predict the distributions for 1200 simulated datasets (3 species x 4 buffer sizes x 100 randomized data sets). Thus, our experimental design produced a total of 4800 similarity measures, with each of the simulated distributions compared to the prediction of the original data set and corresponding modeling method. A general linear model (GLM) analysis was performed which enables us to simultaneously measure the effect of buffer size, modeling method, and species, as well as interactions among all variables. Our results show that modeling method has the largest effect on similarity scores and uniquely accounts for 40% of the total variance in the model. The second most important factor was buffer size, but it uniquely accounts for only 3% of the variation in the model. The newer and currently more popular methods, GARP and Maxent, were shown to produce more inconsistent predictions than the earlier and simpler methods, BIOCLIM and DOMAIN. Understanding the performance of different niche modeling methods under varying levels of geographic uncertainty is an important step toward more productive applications of historical biodiversity collections.