The use and misuse of herbarium specimens in evaluating plant extinction risks

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’.

Automated conservation assessment of the orchid family using deep learning

“…Multiple index-based methods exist (Bachman et al 2011;Schmidt et al 2017;Cardoso 2017;Dauby et al 2017) and they can be used either to support the IUCN RL assessment process, or with additional assumption on habitat destruction and threat, to stand-alone as preliminary assessments (Schmidt et al 2017;Cosiaux et al 2018;Zizka et al 2020c). 2) prediction-based methods that use existing IUCN RL assessments together with species traits to predict the conservation status of unevaluated or Data Deficient species (Bland et al 2015;Pelletier et al 2018;González-del-Pliego et al 2019;Lughadha et al 2019), including the use of machine learning algorithms. Prediction-based methods may use the same indices on species' ranges as index-based methods, but also incorporate additional traits such as climatic niche, biomes, human footprint index, geographic region, or traits related to species morphology or physiology (Bland et al 2015;Di Marco & Santini 2015).…”

“…While existing AA methods can separate threatened (IUCN RL categories Critically Endangered, Endangered, and Vulnerable) from non-threatened (Near Threatened and Least Concern) species with an accuracy between 80 and 95% in animals (Bland et al 2015), a recent study suggests lower performance for Orchids, with an accuracy of 51-84% in a taxonomically and geographically limited sample of 116 species from New-Guinea, (Lughadha et al 2019). A known issue with index-based AA, their dependency on data availability cause them to overestimate the extinction risk of species with few occurrence records available (Rivers et al 2011).…”

“…Specifically, we address three questions: 1) How threatened are Orchids and where are the global centers of extinction risk for Orchid species? Based on the IUCN RL, we expect~30% of orchids to be identified as Possibly Threatened (www.iucn.org; Lughadha et al 2019).…”

“…2) How accurate are different AA methods? Based on the existing smaller-scale studies we expect an accuracy between 60-90% (Lughadha et al 2019) and a susceptibility to over-estimate the threat status of narrow-ranged species.…”

An enigmatic genus on an enigmatic island: the re‐discovery of Kalappia on Sulawesi

“…The multiple threats listed above, and our alternative EOO and AOO, indicate that a future conservation status of Endangered for K. celebica may be appropriate when further data become available. Kalappia celebica is typical of many tropical trees for which IUCN threat category assessments are fraught with difficulties posed by lack of data about the extant status of historical collections and lack of data for surrounding, as yet unexplored, and under-collected habitat areas (Nic Lughadha et al 2018). Identification of extant populations of K. celebica was only possible through funding for travel to remote areas where the risk of failure to collect data is high due to the innate difficulties with accessibility.…”