Conservation status of Buddlejas Buddleja sessilifolia B.S. Sun ex S.Y. Pao is a perennial
shrub with sessile leaves that usually grows in dense clusters beneath Himalayan hemlock Tsuga dumosa forests or at the edges of humid valleys. The species was described in 1983 as being conspecific with B. colvilei, a plant distributed throughout the Gaoligong Mountains at 2,600–3,200 m (Ge et al., 2018). In 2018, B. sessilifolia was delineated as a distinct species (Ge et al., 2018), suggesting that the disparity in the morphological and molecular characters of these two species was caused by differences in ploidy levels or speci- ation processes during the uplift of the Himalayas (Yang et al., 2023a). Buddleja delavayi Gagnepain is a perennial shrub charac-
terized by two distinct colour polymorphisms that exhibit purple or white flowers (Chen et al., 2015). This species is distributed in sparse montane forests and moist broad- leaved forests on hillsides in Dali, Yunnan, at 2,000–3,000 m. Buddleja yunnanensis Gagnepain, which is a narrowly
endemic species, is limited to Pu’er and Xishuangbanna, Yunnan, China. It is distributed on the edges of shrub for- ests on mountains or hillsides at 1,000–2,500 m (Li & Leeuwenberg, 1996). Data-driven analyses can illuminate the interplay of vari-
ables that affect a species’ conservation status, but a lack of reliable data, obsolete information and inconsistencies can limit the accuracy and reliability of conservation assess- ments (Zizka et al., 2021). Automated assessments have the potential to mitigate these issues by creating standar- dized evaluations or by prioritizing species for reassessment (Cazalis et al., 2022; de Caetano et al., 2022). Two ap- proaches that have been used for automated conservation assessment are: (1) the criteria-explicit approach, which
uses automatic calculation of the IUCN Red List parameters from distribution data, using tools such as GeoCAT and rCAT (Bachman et al., 2011) or packages such as red (Cardoso, 2017), ConR (Dauby et al., 2017) and Rapid Least Concern (Bachman et al., 2020)in R (R Core Team, 2023), and (2) the category-predictive approach, which em- ploys machine learning such as random forests (Pelletier et al., 2018) or neural networks (Zizka et al., 2022) and gen- eralized linear models (Böhm et al., 2016). Here we assess the conservation status of the four Buddleja species (Plate 1) as a case study, comparing traditional assessments based on field studies with automated assessments.
Methods
Our study area encompasses the trans-Himalayan region, spanning the eastern Himalayas to south-western China (Fig. 1), including eastern Nepal and western China (Yunnan and Tibet). We first collected species occurrence records from the Flora of China (Li & Leeuwenberg, 1996), Herbarium KUN at the Kunming Institute of Botany, Chinese Academy of Sciences, the Chinese Virtual Herbarium (2023), GBIF (2023) and iPlant (2023). Using the distribution information from these sources we selected 12 locations that together represent almost all known sites of the four species. We conducted field studies over 13 years (2010–2022) to obtain comprehensive data on the four spe- cies, following the recommendations for field investigations of Plant Species with Extremely Small Populations (Yang & Sun, 2017; Yang et al., 2020). Our field study did not include the populations of B. colvilei in Bhutan and India, mainly because of geographical barriers and feasibility constraints.
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PLATE 1 (a) Buddleja colvilei, (b) Buddleja sessilifolia, (c) Buddleja delavayi, (d) Buddleja yunnanensis, (e) mature B. colvilei cut down in Ilam, Nepal, (f) loss of habitat of B. sessilifolia caused by debris flow and mudslides in the Gaoligong Mountains, Yunnan, China, (g) B. sessilifolia plantlets in Kunming Botanical Garden, and (h) living collection of B. delavayi in Kunming Botanical Garden. Photos: Fengmao Yang (a,c); Jia Ge (b,d–h).
Oryx, 2024, 58(5), 618–626 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323001503
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