Reversing extinction trends: new uses of (old) herbarium specimens to accelerate conservation action on threatened species

Abstract

Summary Although often not collected specifically for the purposes of conservation, herbarium specimens offer sufficient information to reconstruct parameters that are needed to designate a species as ‘at‐risk’ of extinction. While such designations should prompt quick and efficient legal action towards species recovery, such action often lags far behind and is mired in bureaucratic procedure. The increase in online digitization of natural history collections has now led to a surge in the number new studies on the uses of machine learning. These repositories of species occurrences are now equipped with advances that allow for the identification of rare species. The increase in attention devoted to estimating the scope and severity of the threats that lead to the decline of such species will increase our ability to mitigate these threats and reverse the declines, overcoming a current barrier to the recovery of many threatened plant species. Thus far, collected specimens have been used to fill gaps in systematics, range extent, and past genetic diversity. We find that they also offer material with which it is possible to foster species recovery, ecosystem restoration, and de‐extinction, and these elements should be used in conjunction with machine learning and citizen science initiatives to mobilize as large a force as possible to counter current extinction trends.

Evidence-based guidelines for automated conservation assessments of plant species

“…Growing recognition of the imperative to accelerate extinction risk assessments (Albani Rocchetti et al, 2020;Bachman et al, 2019), advances in digitisation of natural history collections (Paton et al, 2020), and widening availability of biodiversity data have stimulated development of AA methods. Early examples of method development focused on relatively small groups: Krupnick et al (2009) calibrated a rule-based method on 1,192 Hawaiian plants, Bland et al (2015) compared how different machine learning algorithms predicted the conservation status of 637 terrestrial mammals, and Darrah et al (2017) explored the use of coarse-scale distribution data to predict conservation status for 6,439 bulbous monocots.…”

Citizen Science Contributions to Address Biodiversity Loss and Conservation Planning in a Rapidly Developing Region

“…The rapid loss of biodiversity represents a wicked problem in conservation and demands innovative ways to quickly document populations of rare taxa and areas with high conservation value to provide protection before populations are lost and taxa become extinct [1]. Biodiversity is being lost before it is described [2], and an estimated 39% of plant taxa alone are at risk of extinction [3], with only 10% of all plant taxa assessed using the IUCN Red List guidelines [4].…”

“…Compilation of biodiversity data supports conservation action to address the problem of rapid biodiversity loss, and both historic and current biodiversity records provide valuable spatial and temporal information to those applying resources to land acquisition, conservation planning, ecological restoration, environmental review/regulation, and other on-the-ground conservation actions [1]. Specifically, biodiversity data guide the application of conservation resources (funding, labor, and education/outreach efforts) toward the highest priority sites, species, and ecosystems [2].…”

Prediction of temperature tolerance in Lilium based on distribution and climate data

“…There are plenty publications providing guidance for resistant taxa selection by studying either morphological or physiological traits in experimental researches ( Ji et al., 2020 ; Ma and Fan, 2014 ). However, the number of experimental taxa is often restricted by the time-consuming fieldwork, compounded by the possibility that practitioners would be interested in just a small proportion of the resources ( Albani Rocchetti et al., 2021 ; Watkins et al, 2020 ). With ever-improving digital technologies enhancing our ability to access information, using digitized collections data to predict the abiotic stress tolerance of plants is an alternative strategy to complement the experimental resource lists ( James et al., 2018 ).…”