Predicted distribution of Michelia lacei


for 1970–2000 to predict current distribution and for 2021–2040, 2041–2060, 2061–2080 and 2081–2100 to predict future distribution. Shared Socioeconomic Pathways can be used to generate different climate change scenarios to assess the impacts of policies and actions on future climate change (Weng et al., 2020). We modelled each time period using four Pathways (SSP126, SSP245, SSP370, SSP585) under the second-generation Earth system model of the Centre National de Recherches Météorologiques (CNRM-ESM 2-1; Fick & Hijmans, 2017). We chose this model, which was developed for the sixth phase of the Coupled Model Intercomparison Project, because in a number of experi- ments it has shown a more significant response to the ex- ternal environment than other models as it includes interactive Earth system components such as the carbon cycle, aerosols and atmospheric chemistry (Seferian et al., 2019). The four Shared Socioeconomic Pathways represent the net radiative forcings at the end of 2100 (2.6, 4.5, 7.0 and 8.5 W/m2) and simulate global warming trends in the absence of climate policy intervention. As the forcing value increases, so does the radiative capacity, representing higher levels of greenhouse gas emissions that therefore have a stronger impact on environmental changes (Fick & Hijmans, 2017;O’Neill et al., 2017; Riahi et al., 2017). To reduce analytical interference from strong correlations


between the bioclimatic variables, we extracted the 19 biocli- matic variables for each of the 18 locations of M. lacei.We ran correlation analyses of these data in R, and we analysed the contributions of the 19 bioclimatic variables to the species distribution using MaxEnt 3.4.3 (Phillips et al., 2006). When screening bioclimatic variables we first retained that with the largest contribution, and then removed variables that had a correlation of r.0.8 with the variable retained, and then retained the remaining variables. We repeated this step until no more variables could be removed. Finally, we used the remaining variables to predict the potential distribution area of M. lacei (Dormann et al., 2013;Yietal., 2016;Wang et al., 2018).


Model description


We divided the species location information into two sub- sets and used 75% of the location information as training data to build the distribution model. Weused the remaining 25% to validate the model, with 10 repetitions and the rep- licate run type set to bootstrap (Efron, 1979; Khanal et al., 2022; Soilhi et al., 2022).Weset the output format to cloglog, which estimates the probability of presence between 0 and 1, as recent studies have suggested that cloglog has greater theoretical support than one of the alternative output for- mats, logistic (Phillips et al., 2017). We left the remaining parameters as the MaxEnt 3.4.3 defaults (Phillips & Dudik, 2008). To demonstrate the reliability of the gene- rated model, we calculated the area under the curve (AUC)


of the receiver operating characteristic curve (ROC). The AUC value is related to the accuracy of the model, and the closer the AUC value is to 1, the better the test data fit the model (Swets, 1988; Coban et al., 2020).


Suitable habitat classification and distribution changes


We used SDM_Toolbox 2.5 (Brown et al., 2017)in ArcGIS to convert the resulting MaxEnt ASCII format files to raster files, and created a map of the potential distribution of M. lacei. Cloglog values range from 0 to 1, with 1 being areas most suitable for the species. We then used the Reclassify (Spatial Analyst) tool in ArcGIS (Brown et al., 2017) to categorize habitat into four grades based on pre- vious studies of native Chinese species: unsuitable areas (0–0.05), areas of low suitability (0.06–0.33), moderately suitable areas (0.34–0.66) and highly suitable areas (0.67–1.00; He & Zhou, 2011; Gong et al., 2022). Finally, we calculated the area and proportion of each habitat suit- ability class using ArcGIS. To analyse changes in potential distribution over time, we


examined only those areas with suitable habitat (i.e. with a cloglog value .0.05). We used SDM_Toolbox to compare potential distribution in each future period with the current potential area of distribution (Brown et al., 2017), identifying areas that will become more suitable, less suitable or will be unchanged. We viewed this output in ArcGIS and graded


habitat as: −1 (the future habitat is more suitable than it is at present), 0 (the habitat is suitable neither in the future nor at present), 1 (the habitat is suitable during both periods) or 2 (the habitat is suitable at present but will not be suitable in the future; Mkala et al., 2022).


Results Population status survey


During our surveys we recorded 10 populations and 53 indi- viduals of M. lacei, comprising 50 mature individuals and three seedlings in five counties. Seven individuals were found in Daweishan National Nature Reserve, the remain- der were outside protected areas, in villages, along roadsides, on farmland and elsewhere. The extent of occurrence and area of occupancy of M. lacei in China are 3,881 km2 and 52 km2, respectively. The height of 90%of the M. lacei individuals was 6–30m


(Fig. 2a); 85% had a diameter at breast height of 11–90 cm (Fig. 2b). One tree had a diameter at breast height of 192 cm, which is the largest M. lacei individual on record (Plate 1a). Five institutions run ex situ conservation programmes


for M. lacei in China: Kunming Botanical Garden, South China Botanical Garden, Wuhan Botanic Garden, Guilin Botanical Garden and Fairy Lake Botanical Garden (Xian


Oryx, 2024, 58(5), 631–640 © The Author(s), 2024. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605323001783


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