Reducing the number of Data Deficient plant species

Abstract

Although most plant species have yet to have their extinction risk evaluated for the IUCN Red List, current knowledge of plant diversity suggests that tens of thousands (8%–38% of species) are likely to remain assessed as Data Deficient (DD). This impacts evaluations of the overall proportion of threatened species, as well as on setting appropriate priorities for biodiversity conservation. One of the principle causes of Data Deficiency is taxonomic uncertainty: data permitting an IUCN Red List assessment is often lacking, because species that are poorly known are also taxonomically uncertain. Establishing the taxonomic status of DD species thus assists in determining whether confusion over this is causing their evaluation as DD. In this paper, three separate numerical methods – hierarchical clustering, ordination and phylogenetic ordination – were applied to three independent datasets of standardized traits for plant species from China in order to identify, for each DD species, morphologically similar Non-DD species. Semi-automated analysis of morphological disparity quickly identifies which species are in need of further taxonomic research, as shared traits can indicate either potential synonymy or, otherwise, successful adaptations of distinct species to similar ecological conditions. Extinction risk assessments of morphologically and phylogenetically distinct DD species could then be informed by data on habitat preferences, geographic range or population size from Non-DD species with the most similar traits. Ultimately, determination of the morphological distance between DD species and their most similar Non-DD species, and therefore of differences in the trait space occupied by DD and Non-DD species, could be the basis of future research for improved inference of conservation priorities, and help distinct DD species with similar ecological traits as other taxonomically accepted, threatened species to be identified as being threatened themselves.