Integrated plant diversity hotspots and long-term stable conservation strategies in the unique karst area of southern China under global climate change

Biodiversity hotspots and conservation efficiency of a large drainage basin: Distribution patterns of species richness and conservation gaps analysis in the Yangtze River Basin, China

“…Our study also highlighted the important role of species complementarity and evolutionary processes (spatial phylogenetics) in hotspots identification, casting new light on understanding distribution patterns of hotspots. The 14 plant diversity hotspot areas identified largely coincided with hotspot areas identified in previous studies, for example, the Hengduan Mountains, Bashan‐Wushan Mountains, Dalou Mountains and eastern parts of Guizhou (Xie, 2003; Xu et al, 2010; Yang et al, 2021; Zhang et al, 2014). However, three new hotspots were identified: the Three‐Rivers Headwater Region, as well as Wumeng Mountains, and the source area of Ganjiang River (Figure 2).…”

“…More hotspot grid cells will likely see reduced suitable habitat areas in the future in the center and western parts of the YRB (Figure 3c,d). In addition to considering suitable habitat areas, we also consided the land use situation which was often overlooked by previous studies (Xie, 2003; Xu et al, 2010; Yang et al, 2021). The land use changes caused strong habitat loss leading to biodiversity loss (Powers & Jetz, 2019; Winkler et al, 2021), therefore, land use needs to be fully integrated in future protection planning.…”

“…Here, we selected 385 threatened plant species as prediction objects (Table S9), to evaluate the impact of climate change on their current and future suitable habitat areas. We employed the maximum entropy method (MaxEnt, version 3.4.1) as predicating tool for current and future suitable habitat areas prediction analysis, which has been considered as one of the most reliable methods for modeling species distribution with presence—only data (Pearson et al, 2007; Tang et al, 2018; Yan et al, 2020) and has been widely applied to predict species range and vegetation shifts under climate change (Abolmaali et al, 2018; Tang et al, 2018; Warren et al, 2013; Yang et al, 2021).…”

“…We accessed a set of 19 bioclimate variables with a resolution of 10 arc min for current climate (1950-2000WorldClim, version 1.3) and future climate data (2100, 2 Â CO 2 climate scenarios, CCM3 model from the divagis databases www.diva-gis.org/climate) (Govindasamy et al, 2003), which has been used in many studies (Huang et al, 2019;Jiang et al, 2015;Rodrıguez-Nunez et al, 2021;Yang et al, 2021). To minimize overfitting of the models, we used SPSS 13.0 to calculate the intercorrelations among 19 bioclimatic variables and removed one of the two variables in case a correlation coefficient ≥.85 was obtained.…”

“…Therefore, when identifying biodiversity hotspots based on only one single group or index (Xie, 2003; Xu et al, 2010; Zhang & Ma, 2008), other important aspects of biodiversity, such as species complementarity and spatial phylogenetics may be ignored (Chi et al, 2017; Huang et al, 2012; Luo et al, 2015). Given these shortfalls, comprehensive biodiversity indicators are highly important for the determination of biodiversity hotspots, providing better baseline information and achieving higher representativeness for the planning of protected areas, the adjustment of protected area systems and the formulation of protection policies (Chi et al, 2017; Huang et al, 2016; Mittermeier et al, 2011; Xu et al, 2019; Yang et al, 2021).…”