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      A workflow for standardising and integrating alien species distribution data

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          Abstract

          Biodiversity data are being collected at unprecedented rates. Such data often have significant value for purposes beyond the initial reason for which they were collected, particularly when they are combined and collated with other data sources. In the field of invasion ecology, however, integrating data represents a major challenge due to the notorious lack of standardisation of terminologies and categorisations, and the application of deviating concepts of biological invasions. Here, we introduce the SInAS workflow, short for Standardising and Integrating Alien Species data. The SInAS workflow standardises terminologies following Darwin Core, location names using a proposed translation table, taxon names based on the GBIF backbone taxonomy, and dates of first records based on a set of predefined rules. The output of the SInAS workflow provides various entry points that can be used both to improve coherence among the databases and to check and correct the original data. The workflow is flexible and can be easily adapted and extended to the needs of different users. We illustrate the workflow using a case-study integrating five widely used global databases of information on biological invasions. The comparison of the standardised databases revealed a surprisingly low degree of overlap, which indicates that the amount of data may currently not be fully exploited in the original databases. We highly recommend the use and development of publicly available workflows to ensure that the integration of databases is reproducible and transparent. Workflows, such as SInAS, ultimately increase trust in data, study results, and conclusions.

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          The taxonomic name resolution service: an online tool for automated standardization of plant names

          Background The digitization of biodiversity data is leading to the widespread application of taxon names that are superfluous, ambiguous or incorrect, resulting in mismatched records and inflated species numbers. The ultimate consequences of misspelled names and bad taxonomy are erroneous scientific conclusions and faulty policy decisions. The lack of tools for correcting this ‘names problem’ has become a fundamental obstacle to integrating disparate data sources and advancing the progress of biodiversity science. Results The TNRS, or Taxonomic Name Resolution Service, is an online application for automated and user-supervised standardization of plant scientific names. The TNRS builds upon and extends existing open-source applications for name parsing and fuzzy matching. Names are standardized against multiple reference taxonomies, including the Missouri Botanical Garden's Tropicos database. Capable of processing thousands of names in a single operation, the TNRS parses and corrects misspelled names and authorities, standardizes variant spellings, and converts nomenclatural synonyms to accepted names. Family names can be included to increase match accuracy and resolve many types of homonyms. Partial matching of higher taxa combined with extraction of annotations, accession numbers and morphospecies allows the TNRS to standardize taxonomy across a broad range of active and legacy datasets. Conclusions We show how the TNRS can resolve many forms of taxonomic semantic heterogeneity, correct spelling errors and eliminate spurious names. As a result, the TNRS can aid the integration of disparate biological datasets. Although the TNRS was developed to aid in standardizing plant names, its underlying algorithms and design can be extended to all organisms and nomenclatural codes. The TNRS is accessible via a web interface at http://tnrs.iplantcollaborative.org/ and as a RESTful web service and application programming interface. Source code is available at https://github.com/iPlantCollaborativeOpenSource/TNRS/.
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            Essential biodiversity variables for mapping and monitoring species populations

            Species distributions and abundances are undergoing rapid changes worldwide. This highlights the significance of reliable, integrated information for guiding and assessing actions and policies aimed at managing and sustaining the many functions and benefits of species. Here we synthesize the types of data and approaches that are required to achieve such an integration and conceptualize 'essential biodiversity variables' (EBVs) for a unified global capture of species populations in space and time. The inherent heterogeneity and sparseness of raw biodiversity data are overcome by the use of models and remotely sensed covariates to inform predictions that are contiguous in space and time and global in extent. We define the species population EBVs as a space-time-species-gram (cube) that simultaneously addresses the distribution or abundance of multiple species, with its resolution adjusted to represent available evidence and acceptable levels of uncertainty. This essential information enables the monitoring of single or aggregate spatial or taxonomic units at scales relevant to research and decision-making. When combined with ancillary environmental or species data, this fundamental species population information directly underpins a range of biodiversity and ecosystem function indicators. The unified concept we present links disparate data to downstream uses and informs a vision for species population monitoring in which data collection is closely integrated with models and infrastructure to support effective biodiversity assessment.
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              Native invaders – challenges for science, management, policy, and society

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                Author and article information

                Journal
                NeoBiota
                NB
                Pensoft Publishers
                1314-2488
                1619-0033
                July 28 2020
                July 28 2020
                : 59
                : 39-59
                Article
                10.3897/neobiota.59.53578
                ae71b3c1-ea81-4d46-8bea-6f854335f318
                © 2020

                http://creativecommons.org/licenses/by/4.0/

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