Abstract
Herbarium specimens provide verifiable and citable evidence of the occurrence of particular plants at particular points in space and time, and are vital resources for assessing extinction risk in the tropics, where plant diversity and threats to plants are greatest. We reviewed approaches to assessing extinction risk in response to the Convention on Biological Diversity's Global Strategy for Plant Conservation Target 2: an assessment of the conservation status of all known plant species by 2020. We tested five alternative approaches, using herbarium-derived data for trees, shrubs and herbs in five different plant groups from temperate and tropical regions. All species were previously fully assessed for the IUCN Red List. We found significant variation in the accuracy with which different approaches classified species as threatened or not threatened. Accuracy was highest for the machine learning model (90%) but the least data-intensive approach also performed well (82%). Despite concerns about spatial, temporal and taxonomic biases and uncertainties in herbarium data, when specimens represent the best available evidence for particular species, their use as a basis for extinction risk assessment is appropriate, necessary and urgent. Resourcing herbaria to maintain, increase and disseminate their specimen data is essential to guide and focus conservation action.This article is part of the theme issue ‘Biological collections for understanding biodiversity in the Anthropocene’.
Highlights
Global species extinction risk assessments prepared through application of IUCN Red List categories and criteria are increasingly important to monitoring progress against international biodiversity targets [1], informing allocation of conservation resources [2] and guiding business decisions to mitigate biodiversity impacts [3]
Vascular plants known to science [6], the 24 057 vascular plant species assessments on the IUCN Red List [7] represent ca 6% of known vascular plant species, of which 55% are threatened with extinction
Forests—using climatic and threat data derived from species 5 ranges; (ii) rCAT—preliminary assessments by the rCAT package [47] based on species’ extent of occurrence (EOO); (iii) ConR—approximate assessments using default settings in the ConR package [48] based on IUCN Red List criteria B; (iv) US Method [49] based on specimen collection data and locality; and (v) Specimen Count—a naive approach based on classifying a species as potentially threatened if the number of specimens is lower than a threshold value
Summary
Global species extinction risk assessments prepared through application of IUCN Red List categories and criteria are increasingly important to monitoring progress against international biodiversity targets [1], informing allocation of conservation resources [2] and guiding business decisions to mitigate biodiversity impacts [3]. The first report of use of machine learning models to evaluate extinction risk at global scale for a species-rich plant group [51] relied primarily on coarse-scale species distribution data but, for a subset of assessed species for which suitable data were available, models were tested based on fine-scale range size data, including point data from herbarium specimens. Forests—using climatic and threat data derived from species 5 ranges; (ii) rCAT—preliminary assessments by the rCAT package [47] based on species’ EOO; (iii) ConR—approximate assessments using default settings in the ConR package [48] based on IUCN Red List criteria B; (iv) US Method [49] based on specimen collection data and locality; and (v) Specimen Count—a naive approach based on classifying a species as potentially threatened if the number of specimens is lower than a threshold value (table 1). Rich as possible in effort on species most likely to be endemic legumes of species of conservation of conservation concern
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