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Here we present 'APCalign', an R package and accompanying browser-sourced application to align and update scientific names for Australian vascular plants to the most likely currently accepted name in the Australian Plant Census (APC) or a name in the Australian Plant Names Index (APNI). Scientific names are the label assigned to unique taxon concepts by the scientific community, but this common terminology is most useful if a taxon concept is consistently referred to by the same name. These links can be broken because of either spelling mistakes or taxonomic changes. Automated tools are required to resolve taxon lists, aligning and updating long lists of possibly erroneous scientific names to the most likely currently accepted names. It is essential that tools specific to the APC/APNI be developed, because these lists specify an endorsed national-level nomenclature used in government legislation and include the uniquely Australian concept of phrase names, absent in global taxonomic datasets. To align input names to names within the APC or APNI, 'APCalign' works progressively through a sequence of checks that combine different permutations of the input name, exact versus fuzzy matches, matches that consider the entire name input versus a subset of words, and character strings that indicate a name can be resolved only to a genus or family. The aligned names are then, when possible, updated to a currently accepted taxon concept within the APC. This package should facilitate all research outputs that require diverse scientific name lists to be merged or outdated lists to be updated. To facilitate communication, research projects, biodiversity assessments and nurseries across Australia should use the scientific names accepted by the Australian Plant Census (APC), Australia's national taxonomic standard for vascular plants. Here we present 'APCalign', an R package and browser-sourced application to efficiently align and update lists of scientific names for Australian vascular plants to the most likely currently accepted name. This package will be useful for all research outputs that require diverse scientific name lists to be updated. Image by Elizabeth Wenk. [ABSTRACT FROM AUTHOR]

Many citizen scientists are highly motivated to help address the current extinction crisis. Their work is making valuable contributions to protecting species by raising awareness, identifying species occurrences, assessing population trends, and informing direct management actions, such as captive breeding. However, clear guidance is lacking about how to use existing citizen science data sets and how to design effective citizen science programs that directly inform extinction risk assessments and resulting conservation actions based on the International Union for Conservation of Nature (IUCN) Red List criteria. This may be because of a mismatch between what citizen science can deliver to address extinction risk and the reality of what is needed to inform threatened species listing based on IUCN criteria. To overcome this problem, we examined each IUCN Red List criterion (A–E) relative to the five major types of citizen science outputs relevant to IUCN assessments (occurrence data, presence–absence observations, structured surveys, physical samples, and narratives) to recommend which outputs are most suited to use when applying the IUCN extinction risk assessment process. We explored real‐world examples of citizen science projects on amphibians and fungi that have delivered valuable data and knowledge for IUCN assessments. We found that although occurrence data are routinely used in the assessment process, simply adding more observations of occurrence from citizen science information may not be as valuable as inclusion of more nuanced data types, such as presence–absence data or information on threats from structured surveys. We then explored the characteristics of citizen science projects that have already delivered valuable data to support assessments. These projects were led by recognized experts who champion and validate citizen science data, thereby giving greater confidence in its accuracy. We urge increased recognition of the value of citizen science data within the assessment process. [ABSTRACT FROM AUTHOR]