The past, present, and future distribution of Calanthe graciliflora: implications for conservation and phylogeography

21 The past, present, and future distribution of Calanthe graciliflora: implications for conservation and phylogeography Pingting Guo1, Aixian Lu2, Jiahao Zheng1, Lunyan Chen3, Shasha Wu1, Chao Hu4, Muyang Li1, Weichang Huang4, Junwen Zhai1 1 Key Laboratory of National Forestry and Grassland Administration for Orchid Conservation and Utilization at College of Landscape Architecture and Art, Fujian Agriculture and Forestry University, Fuzhou 350002, China 2 Guangxi Modern Polytechnic College, Hechi 547000, China 3 Pinglongshan Forest Centre, Guangxi 538021, China 4 Eastern China Conservation Centre for Wild Endangered Plant Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China Corresponding author: Weichang Huang ([email protected]); Junwen Zhai ([email protected]) Copyright: © Pingting Guo et al. This is an open access article distributed under terms of the Creative Commons Attribution License (Attribution 4.0 International – CC BY 4.0). Research Article Abstract Calanthe graciliflora, an orchid species endemic to China, is one of the most widely distributed members of the genus Calanthe, occupying the highest latitudinal range and exhibiting strong cold tolerance. These traits suggest key adaptations to diverse and extreme environments, making it an ideal model for studying plant responses to climate variability. Ecological niche models (ENMs) are powerful tools for simulating species’ potential distributions across different time periods, thereby aiding biodiversity conservation. In this study, 75 filtered occurrence records of C. graciliflora and 19 climatic variables, derived from field surveys and herbarium records in China, were used to model the species’ potential distribution across 6 periods (Last Interglacial, Last Glacial Maximum, Middle Holocene, Current, Future 2050s, and Future 2070s). Research findings indicate that key environmental factors influencing its distribution include mean diurnal temperature range (bio2), mean temperature of the warmest quarter (bio10), annual precipitation (bio12), and precipitation of the driest month (bio14). Historically, suitable habitats for C. graciliflora were primarily concentrated south of the Qinling-Huaihe River region, closely associated with the Qinling, Luoxiao, Nanling, and Mount Wuyi ranges. During the Last Glacial Maximum, extensive suitable habitats existed in southwestern China, subsequently contracting to refugia in the Qinling and Mount Wuyi areas, underscoring these regions as refugia for C. graciliflora. Future projections indicate an overall decline in suitable habitat, highlighting the significant impacts of global warming on its long-term survival. Notably, this study represents the first application of the MaxEnt model to infer historical refugia of C. graciliflora while simultaneously integrating analyses of its future distribution shifts. This work fills the gap in long-term climate response research for this species and evaluates the impacts of climate change on its distribution, providing valuable insights for its phylogeography and conservation practice. By further identifying core habitats and clarifying their climate sensitivity, the findings provide a basis for developing targeted conservation strategies that prioritize key ecological areas and mitigate the risk of habitat loss. Key words: Climate change, geographical distribution, MaxEnt model Academic editor: Muhammad Rais Received: 23 April 2025 Accepted: 13 September 2025 Published: 10 October 2025 ZooBank: https://zoobank. org/9D6A40C8-97B9-4D81-B8A1A7C6A6236A94 Citation: Guo P, Lu A, Zheng J, Chen L, Wu S, Hu C, Li M, Huang W, Zhai J (2025) The past, present, and future distribution of Calanthe graciliflora: implications for conservation and phylogeography. Nature Conservation 60: 21–38. https://doi.org/10.3897/ natureconservation.60.156661 Nature Conservation 60: 21–38 (2025) DOI: 10.3897/natureconservation.60.156661 22 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora Introduction Evidence is mounting that the distribution patterns of species are influenced by rapid temperature changes, including shifts in temperature, precipitation, and other climatic factors (Corlett and Westcott 2013; Du et al. 2024; Zhu et al. 2024). In particular, decreases in temperature typically cause species to retreat to lower latitudes and altitudes, whereas warming promotes expansion toward higher latitudes and altitudes (Spence and Tingley 2020). During the Last Glacial Maximum (LGM), the high-altitude mountains of the Qinghai–Tibet Plateau acted as a barrier to the eastward expansion of high-latitude glaciers in Asia (Deng and Ding 2015; Zhou et al. 2017). This allowed many high-altitude regions in China, including the Qinghai–Tibet Plateau, Daba Mountains, Wushan Mountains, Dalou Mountains, Wuling Mountains, Shennongjia Forestry District, Nanling Mountains, Mount Wuyi, and the mountains of Taiwan, to serve as glacial refugia for plants (Chen et al. 2011). During this period, species either migrated to refugia or evolved in situ through genetic variation to cope with temperature fluctuations. However, if migration or adaptation could not keep up with climate change, species would face population decline, range contraction, or extinction (Webb 1997; Wiens and Graham 2005). Furthermore, human activities have exacerbated habitat fragmentation, posing increasing threats to plant diversity. As these impacts intensify, understanding both the historical and potential future distributions of endangered species is critical for biodiversity conservation. Ecological niche models (ENMs) are powerful tools for predicting species distributions based on known occurrence records and environmental variables. These predictions are generated through algorithmic modeling and can be projected across different temporal and spatial scales (Araújo and Peterson 2012). The model can be applied to simulate the potential range of species under different periods and climatic conditions, which is crucial for understanding how species respond to diverse climatic conditions. Currently, commonly used ENMs include GARP (Elith et al. 2006), BIOCLIM (Nix 1986), DOMAIN (Carpenter et al. 1993), and the MaxEnt model (Phillips and Dudík 2008; Elith et al. 2011; Liu et al. 2021). Among them, the MaxEnt model (i.e., maximum entropy model) is widely used for predicting plant and animal distributions. It is favored for its fast processing, high accuracy, and ability to perform well with limited distribution data compared to other models (Kaky et al. 2020; Liu et al. 2021; Zhang et al. 2021a). C. graciliflora, a perennial herb, belongs to the genus Calanthe in the Orchidaceae (subfam. Epidendroideae). It is one of the most widely distributed species of the genus Calanthe, with the broadest latitudinal range and the greatest cold tolerance. It is primarily distributed in subtropical montane forests of China, mainly in the Qinling Mountains, Daba Mountains, Luoxiao Mountains, Nanling Mountains, and Mount Wuyi, all of which fall within the subtropical monsoon zone, where precipitation is abundant yet strongly seasonal (Clayton and Cribb 2013). These regions are characterized by evergreen broad-leaved forests at elevations of 600–1500 m and mixed coniferous–broadleaf forests at 1500–2500 m. The high humidity (>75%), acidic, humus-rich soils (pH 5.0–6.5), and shaded understories provide optimal microhabitats for the species (Chen et al. 1999). The species is believed to have originated on the Asian continent and is endemic to China, diverging from its ancestor in the early Pleistocene (2.5 Ma) (Chen 2020). However, the changes in its geographical distribution from the Pleistocene 23 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora to projected conditions in the 2070s, as well as the location of its potential glacial refugia in China, remain unclear. Furthermore, sympatric distribution, overlapping flowering periods, and hybridization between C. graciliflora and closely related species play key roles in maintaining species diversity and stabilizing forest ecosystems (Carpenter et al. 1993). C. graciliflora is also valued for its colorful flowers and its medicinal properties, making it highly sought after for ornamental and medicinal use. Nevertheless, human activities, combined with habitat loss and climate change, have led to significant annual declines in its natural populations. Ongoing mountain development has particularly contributed to habitat loss, causing localized population extinctions in southeastern China (Qiu et al. 2023). Currently, the species is listed as a near threatened (NT) species by the International Union for Conservation of Nature (IUCN). It is also included under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES), with international trade strictly prohibited and regulated. Although C. graciliflora has a broad geographic range, it is highly sensitive to microenvironmental changes and exhibits specific ecological niche preferences, making it a potential indicator species for assessing the impacts of climate change on montane plants. Therefore, the MaxEnt model was used to predict the potential distribution areas of C. graciliflora under 6 periods: Last Interglacial (LIG), Last Glacial Maximum (LGM), Middle Holocene (MH), Current, and Future (2050s, 2070s). The study explored the following two questions: (1) What were the potential refugia of the species during the LGM? (2) How have the spatial distribution patterns and suitable habitat areas of the species shifted from the Pleistocene to projections for the 2070s? This study provides a framework for understanding the phylogeography and guiding the conservation of C. graciliflora and other montane orchid species. Materials and methods Species distribution data collection Occurrence records of C. graciliflora were mainly obtained from the Chinese Virtual Herbarium (CVH, https://www.cvh.ac.cn/, accessed on 20 July 2022), Global Biodiversity Information Facility (GBIF, https://www.gbif.org/, accessed on 21 July 2022), and Flora of China. A total of 229 latitude and longitude record points for the species were gathered from relevant literature and fieldwork recordings. A 1 km × 1 km buffer zone was established across China using ArcGIS 10.8.1 (ESRI 2020). To reduce spatial bias from clustered sampling, redundant and misidentified records were removed, and only one occurrence point was retained per buffer zone for subsequent ecological niche modeling. Environmental variable data acquisition Nineteen bioclimatic factors in six different periods were downloaded from WorldClim (http://www.worldclim.org/), including the Last Interglacial (LIG, 120ka), Last Glacial Maximum (LGM, 22 ka), Middle Holocene (MH, 6 ka), Current, and Future (the 2050s, 2070s). The spatial resolution was 30″. Climate data were obtained from the CCSM4 model developed by the National Center for Atmospheric Research (NCAR) under the framework of the Coupled Model Intercomparison Project Phase 5 (CMIP5). CCSM4 was chosen for its well-doc- 24 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora umented performance in simulating global and regional climate processes (Gent et al. 2011) and its demonstrated utility in ecological studies, such as predicting species–community decoupling under climate change (Thomas et al. 2023). The model’s high-resolution outputs and reduced biases in critical variables ensured robustness for biogeographical analyses. For future projections in the 2050s and 2070s, the Representative Concentration Pathway (RCP) 2.6 scenario was used. This low-emission pathway aligns with global climate mitigation efforts, provides a conservative estimate of potential climate impacts on species distributions, and allows comparability with recent studies on threatened taxa under optimistic scenarios. Subsequently, the Mask tool in ArcGIS 10.8.1 (ESRI 2020) was used to clip and extract regional climate data in China and then convert them to ASCII format for further analysis. Environmental variable data screening Multicollinearity among 19 climatic variables can lead to model overfitting, which affects the evaluation of simulation results (Graham 2003; Zhang et al. 2014). Therefore, environmental factors with a small contribution at |r| ≥ 0.8 were removed using Pearson correlation analysis. Seven environmental factors were finally identified for model prediction, including mean diurnal range (bio2), isothermality (bio3), minimum temperature of the coldest month (bio6), mean temperature of the wettest quarter (bio8), mean temperature of the warmest quarter (bio10), annual precipitation (bio12), and precipitation of the driest month (bio14). As orchids are highly sensitive to water availability and seasonal drought (Zotz and Bader 2009; Gao et al. 2025), and precipitation has been shown to be a major determinant of orchid distributions (Qiu et al. 2023; Pica et al. 2024; Tsiftsis et al. 2024), bio12 and bio14 were prioritized under collinearity to represent overall water supply and drought stress in the subtropical monsoon regions where C. graciliflora occurs. MaxEnt model building and parameter setting We collected environmental data and species occurrence points across six time periods, focusing on climate variables that significantly affect species suitability. Climate data for each period were matched with species distribution points. The environmental data (*.asc) and distribution points of C. graciliflora (*.csv) were imported into the MaxEnt program for habitat distribution modeling. We randomly selected 75% of the data as the training set and 25% as the test set for model evaluation. To improve methodological rigor and address potential uncertainties in conventional MaxEnt modeling, we complemented the core modeling process with additional evaluations of environmental variable importance and species–environment response curves. Variable contributions were quantified using jackknife tests, while response curves were analyzed to verify the plausibility of adaptive thresholds to key climatic factors. Together, these supplementary analyses allowed for a more robust identification of environmental drivers and reduced biases in model outputs. Model settings included a maximum of 1000 iterations to ensure convergence, with 10 bootstrap replicates to improve stability. The random seed option was applied, and the average output across repli- 25 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora cates was used as the final prediction. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), where values of 0.50–0.60 indicate invalid predictions, 0.60–0.70 poor, 0.70–0.80 fair, 0.80–0.90 good, and 0.90–1.00 excellent (Phillips et al. 2006; Phillips and Dudík 2008). Division of suitable regions The MaxEnt model results were imported into ArcGIS 10.8.1 for further analysis. Potential distribution maps for different time periods were classified into suitability categories and then visualized. Based on the model’s equal training sensitivity and specificity thresholds, fitness zones were categorized into four levels: unsuitability, low suitability, moderate suitability, and high suitability. Potential distribution areas with different suitability levels were obtained through area tabulation using the SDMtoolbox in ArcGIS 10.8.1 (ESRI 2020). Results Overview of verified distribution records A total of 75 occurrence records were retained after spatial filtering and data validation and were used for subsequent ecological niche modeling (Fig. 1). These occurrence points are broadly distributed across the montane regions of central, eastern, and southern China, particularly in Sichuan, Hunan, Jiangxi, Zhejiang, Fujian, Guangdong, and Guangxi provinces. The validated distribution records span a wide range of latitudes and elevations in central and southern China, with notable concentrations in subtropical mountainous areas. Screening for dominant environmental factors The environmental variables used in the MaxEnt model were selected based on their ecological relevance to C. graciliflora and their availability in the WorldClim database (Manthey and Box 2007; Fick and Hijmans 2017; Sun et al. 2020). Nineteen bioclimatic variables from the WorldClim dataset were initially assessed, and highly collinear predictors (|r| > 0.8) were excluded based on Pearson correlation analysis. Ecologically relevant variables, particularly precipitation-related factors, were prioritized due to the species’ association with distinct dry and wet seasons. The results of the effects of environmental factors on species distribution in the MaxEnt model showed that the largest contribution rate was the precipitation of the driest month (bio14). This was followed by bio12, bio10, and bio2 (Table 1). The cumulative contribution of these four factors accounted for 92%. These findings indicate that the distribution of C. graciliflora is strongly influenced by these environmental conditions. The results of the variable-importance jackknife test showed relatively high gain values for bio2 and bio12 when only variables were used (Fig. 2). This indicated that these environmental factors contributed more strongly to the distribution of C. graciliflora. When a single variable was ignored, bio10 showed the greatest drop in model gain, implying that this variable contained information that other variables did not. 26 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora Figure 1. Geospatial distribution of effective occurrence records of C. graciliflora. Note: The color of each effective point indicates the province where it is located, and the purple lines indicate major mountains mapped using the Digital Mountain Map of China (Nan et al. 2022). Figure 2. Impact of bioclimatic variables on the predictive performance of C. graciliflora distribution. 27 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora In summary, the environmental factors having significant effects on the geographic distribution of C. graciliflora were bio2, bio10, bio12, and bio14. These variables were selected because they represent critical climatic thresholds for plant survival and growth, particularly in subtropical and montane regions where C. graciliflora is predominantly found. The response curve results of the four main climate factors are shown in Fig. 3. When the presence probability was greater than 0.5, the range of response values for the mean diurnal range was 6.3–8 °C, the range for the mean temperature of the warmest quarter was 21.5–25 °C, the range for the annual precipitation was 1,500–2,800 mm, and the range for the precipitation of the driest month was 30–170 mm. These ranges align with the known ecological preferences of C. graciliflora, further validating the selection of these variables. MaxEnt model accuracy testing The average AUC of the prediction model for the habitat suitability of C. graciliflora under current climate conditions was 0.989, with a standard deviation of 0.001. This value markedly exceeded the simulated random prediction distribution value of 0.5, demonstrating high accuracy and reliability of the prediction results (Fig. 4). Prediction of suitable regions As shown in Fig. 5, the suitable regions of C. graciliflora in six different periods were primarily distributed in the subtropical evergreen broad-leaved forests south of the Qinling–Huaihe line and were closely related to the Hengduan Mountains, Qinling Mountains, Luoxiao Mountains, Nanling Mountains, Mount Wuyi, and Taiwan Mountains. The highly suitable regions were stably distributed in Mount Wuyi and the Luoxiao Mountains. The moderately and poorly suitable regions were mainly concentrated in the Qinling Mountains, Nanling Mountains, and Taiwan Mountains, as well as surrounding the highly suitable regions. Prediction of suitable regions in the past period During the LIG (Fig. 5a), the suitable habitats for C. graciliflora were primarily distributed across subtropical evergreen broad-leaved forests in the present-day provinces of Chongqing, Yunnan, Guizhou, Hainan, Fujian, and Zhejiang. Table 1. Contribution rate of seven bioclimatic variables to C. graciliflora distribution based on the MaxEnt model. Symbol Environment variable Percent of contribution bio14 Precipitation of the driest month (mm) 30.6 bio12 Annual precipitation (mm) 27.0 bio10 The mean temperature of the warmest quarter (°C) 25.9 bio2 Mean diurnal range (°C) 8.5 bio3 Isothermality (bio2/bio7) (×100) 4.6 bio8 The mean temperature of the wettest quarter (°C) 2.4 bio6 Minimum temperature of the coldest month (°C) 1.0 28 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora Figure 3. Response curves of C. graciliflora presence probability to key environmental variables. Note: The red curves show the average over 10 replicate runs; the blue bands show the standard deviation (SD) calculated over 10 replicates. Figure 4. MaxEnt model achieves an outstanding prediction of C. graciliflora distribution (AUC = 0.989). Note: AUC values range from 0 to 1, where a value closer to 1 indicates better classifier performance, while a value near 0 suggests poor performance. 29 Nature Conservation 60: 21–38 (2025), DOI: 10.3897/natureconservation.60.156661 Pingting Guo et al.: Distribution of Calanthe graciliflora These regions exhibited a discontinuous distribution from west to east, closely linked to the Hengduan Mountains, Mount Wuyi, and the Taiwan Mountains. Notably, a wide range of suitable regions was present in the southern section of the Hengduan Mountains during this period. However, during the LGM, the suitable regions in the southern Hengduan Mountains disappeared (Fig. 5b), while suitable regions expanded to the Qinling Mountains, Luoxiao Mountains, Figure 5. Habitat suitability maps showing the occurrence of C. graciliflora in six different periods. Note: a. 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