Pinus Yunnanensis Research Articles

Phylogeography and introgression between Pinus kesiya and Pinus yunnanensis in Southeast Asia

AbstractSoutheast Asia (SEA) has seen strong climatic oscillations and fluctuations in sea levels during the Quaternary. The impact of past climate changes on the evolution and distribution of local flora in SEA is still poorly understood. Here we aim to infer how the Quaternary climate change affects the evolutionary process and range shifts in two pine species. We investigated the population genetic structure and diversity using cytoplasmic DNA markers, and performed ecological niche modeling to reconstruct the species past distribution and to project range shift under future climates. We found substantial gene flow across the continuous distribution of the subtropical Pinus yunnanensis. In contrast, the tropical Pinus kesiya showed a strong population structure in accordance with its disjunct distribution across montane islands in Indochina and the Philippines. A broad hybrid zone of the two species occurs in southern Yunnan. Asymmetric introgression from the two species was detected in this zone with dominant mitochondrial gene flow from P. yunnanensis and chloroplast gene flow from P. kesiya. The observed population structure suggests a typical postglaciation expansion in P. yunnanensis, and a glacial expansion and interglacial contraction in P. kesiya. Ecological niche modeling supports the inferred demographic history and predicts a decrease in range size for P. kesiya under future climates. Our results suggest that tropical pine species in SEA have undergone evolutionary trajectories different from high latitude species related to their Quaternary climate histories. We also illustrate the need for urgent conservation actions in this fragmented landscape.

Comparison of the diversity and structure of the rhizosphere microbial community between the straight and twisted trunk types of Pinus yunnanensis.

Pinus yunnanensis is a major silvicultural species in Southwest China. Currently, large areas of twisted-trunk Pinus yunnanensis stands severely restrict its productivity. Different categories of rhizosphere microbes evolve alongside plants and environments and play an important role in the growth and ecological fitness of their host plant. However, the diversity and structure of the rhizosphere microbial communities between P. yunnanensis with two different trunk types-straight and twisted-remain unclear. We collected the rhizosphere soil of 5 trees with the straight and 5 trees with the twisted trunk type in each of three sites in Yunnan province. We assessed and compared the diversity and structure of the rhizosphere microbial communities between P. yunnanensis with two different trunk types by Illumina sequencing of 16S rRNA genes and internal transcribed spacer (ITS) regions. The available phosphorus in soil differed significantly between P. yunnanensis with straight and twisted trunks. Available potassium had a significant effect on fungi. Chloroflexi dominated the rhizosphere soils of the straight trunk type, while Proteobacteria was predominant in the rhizosphere soils of the twisted trunk type. Trunk types significantly explained 6.79% of the variance in bacterial communities. This study revealed the composition and diversity of bacterial and fungal groups in the rhizosphere soil of P. yunnanensis with straight and twisted trunk types, providing proper microbial information for different plant phenotypes.

Deep learning for crown profile modelling of Pinus yunnanensis secondary forests in Southwest China.

Accurate information concerning crown profile is critical in analyzing biological processes and providing a more accurate estimate of carbon balance, which is conducive to sustainable forest management and planning. The similarities between the types of data addressed with LSTM algorithms and crown profile data make a compelling argument for the integration of deep learning into the crown profile modeling. Thus, the aim was to study the application of deep learning method LSTM and its variant algorithms in the crown profile modeling, using the crown profile database from Pinus yunnanensis secondary forests in Yunnan province, in southwest China. Furthermore, the SHAP (SHapley Additive exPlanations) was used to interpret the predictions of ensemble or deep learning models. The results showed that LSTM's variant algorithms was competitive with traditional Vanila LSTM, but substantially outperformed ensemble learning model LightGBM. Specifically, the proposed Hybrid LSTM-LightGBM and Integrated LSTM-LightGBM have achieved a best forecasting performance on training set and testing set respectively. Furthermore, the feature importance analysis of LightGBM and Vanila LSTM presented that there were more factors that contribute significantly to Vanila LSTM model compared to LightGBM model. This phenomenon can explain why deep learning outperforms ensemble learning when there are more interrelated features.

Variation in seed number/mass trade-off for Pinus yunnanensis along a natural climate gradient in the mountains of Southwest China

Variation in seed number/mass trade-off for Pinus yunnanensis along a natural climate gradient in the mountains of Southwest China

Spatial Distribution Pattern and Sampling Plans for Two Sympatric Tomicus Species Infesting Pinus yunnanensis during the Shoot-Feeding Phase.

Tomicus minor (Hartig) and Tomicus yunnanensis Kirkendall and Faccoli are two sympatric species that infest Pinus yunnanensis (Franchet) in southwest China, contributing to growth losses. Accurate sampling plans are needed to make informed control decisions for these species. We investigated three pine forests within experimental sites in Yuxi, Yunnan province, China from 2016 to 2018. The spatial distribution patterns of two pine shoot beetles during the shoot-feeding phase were determined using Taylor's power law. The optimum sample sizes and stop lines for precision levels of 0.25 and 0.10 were calculated. The model was validated using an additional 15 and 17 independent field datasets ranging in density from 0.06 to 1.90 beetles per tree. T. minor and T. yunnanensis adults showed aggregated spatial distributions. For T. minor, sample sizes of 41 and 259 trees were adequate for a D of 0.25 and 0.10, respectively, while for T. yunnanensis, a mean density of one individual per tree required sample sizes of 33 plants (D = 0.25) and 208 plants (D = 0.10). The software simulations of this sampling plan showed precision levels close to the desired levels. At a fixed-precision level of 0.25, sampling is easily achievable. This sampling program is useful for the integrated pest management (IPM) of two sympatric Tomicus species.

Geographical variation of needles phenotypic and anatomic traits between populations of <i>Pinus yunnanensis</i> var. <i>tenuifolia</i> and its environmental interpretation

Geographical variation of needles phenotypic and anatomic traits between populations of <i>Pinus yunnanensis</i> var. <i>tenuifolia</i> and its environmental interpretation

Damage Diagnosis of Pinus yunnanensis Canopies Attacked by Tomicus Using UAV Hyperspectral Images

It remains challenging to control Tomicus spp., a pest with fast spreading capability, leading to the death of large numbers of Pinus yunnanensis (Franch.) and posing a severe threat to ecological security in southwest China. Therefore, it is crucial to effectively and accurately monitor the damage degree for Pinus yunnanensis attacked by Tomicus spp. at large geographical scales. Airborne hyperspectral remote sensing is an effective, accurate means to detect forest pests and diseases. In this study, we propose an innovative and precise classification framework to monitor the damage degree of Pinus yunnanensis infected by Tomicus spp. using hyperspectral UAV (unmanned aerial vehicle) imagery with machine learning algorithms. First, we revealed the hyperspectral characteristics of Pinus yunnanensis from a UAV-based hyperspectral platform. We obtained 22 vegetation indices (VIs), 4 principal components, and 16 continuous wavelet transform (CWT) features as the damage degree sensitive features. We classified the damage degree of Pinus yunnanensis canopies infected by Tomicus spp. via three methods, i.e., discriminant analysis (DA), support vector machine (SVM), and backpropagation (BP) neural network. The results showed that the damage degree detected from the BP neural network, combined with 16 CWT features, achieved the best performance (training accuracy: 94.05%; validation accuracy: 94.44%).

Cold Tolerance and Cold-Resistant Substances in Two Tomicus Species during Critical Transferring Periods

The pine shoot beetles Tomicus minor and Tomicus yunnanensis are important stem borers of Pinus yunnanensis in southwestern China. To determine strategies for cold resistance and changes in major cold-resistant substances in adults of two Tomicus species during two critical transferring periods, “shoot-to-trunk” and “trunk-to-shoot”, the insects’ supercooling point (SCP), freezing point (FP), and antifreeze protective substances were determined. The SCP and FP did not differ between female and male adults in the shoot-to-trunk phase, but were significantly lower in females in the trunk-to-shoot period. Although there was no difference in the SCP and FP between the two Tomicus species adults, both indexes were significantly lower in the shoot-to-trunk period than in the trunk-to-shoot period. The trehalose content in females of two Tomicus species was significantly lower than that in males in the trunk-to-shoot period, and the protein, glycerol, glycogen, fat, and sorbitol contents were different between the species in the same period. The protein and water content in adults of both species were significantly lower in the shoot-to-trunk period than in the trunk-to-shoot period, but the content of glycerol, trehalose, water, sorbitol, glycogen, and fat content were significantly higher in the shoot-to-trunk. Different types of cold-resistant substances regulating sex, species, and developmental stages were found, and the most abundant were cold-resistant substances regulating developmental stages.

Lyophyllum pallidofumosum sp. nov. (Lyophyllaceae, Agaricales), from southwestern China

We describe a new species of Lyophyllum based on the morphology and molecular phylogenetic analyses. The species is widely distributed above ca. 2000 m in mixed forests dominated by Pinus yunnanensis, Pinus armandi, and Quercus fabrei in Yunnan Province. It can be distinguished by its greyish orange pileus surface, margin with waves when old; globose to subglobose spores (4.5–5.6 × 4.3–5.4 µm), clavate basidia (25–34 × 7–9 µm). Moreover, the growth rate of cultured mycelia of the species in the YPD medium is higher than that of L. decastes complex species under the same conditions. Phylogenetic analyses based on the internal transcribed spacer (ITS) and large ribosomal subunit (LSU) regions of the nuclear ribosomal DNA (nrDNA) indicated that L. pallidofumosum is a distinct taxon closely related to L. fumosum and L. shimeji.

Seven diterpenoids from the resin of Pinus yunnanensis Franch and their anti-inflammatory activity

Phytochemical investigation of the 95% ethanol extract from Pinus yunnanensis Franch resin induced the isolation of six previously unreported diterpenoids pinuyunnanacids K − N, P − Q, a nor-diterpenoid with a novel skeleton pinuyunnanacid O and six known analogues. Their structures were elucidated by spectroscopic analysis and computational methods, including nuclear magnetic resonance (NMR) spectroscopy, mass spectrometry, calculated NMR chemical shifts method and electronic circular dichroic (ECD) spectra. All the compounds were analyzed for anti-inflammatory activity through western blotting and cell viability, compounds 2, 10 and 12 significantly downregulated the protein expression of iNOS at the concentration of 40 μM. At the same time, compounds 10 and 12 decreased the expression of COX-2 in LPS-treated RAW264.7 (leukemia cells in mouse macrophage) cells at the concentration of 40 μM.

Forest foliage fuel load estimation from multi-sensor spatiotemporal features

• Spatiotemporal features of spaceborne SAR and optical data were combined first time. • The addition of spatiotemporal features greatly improves the estimation of FFL. • Temporal features seem more important than spatial features. • Both spatial and temporal changes in FFL can indicate forest fire risk. Foliage fuel is the most flammable component in crown fires. Spatiotemporal dynamics of foliage fuel load (FFL) are important for fire managers to assess fire risk. Here, we integrated optical data from the Landsat 8 Operational Land Imager (OLI) with synthetic aperture radar (SAR) data from Sentinel-1 to estimate FFL. We first reconstructed seamless time series from the Landsat 8 and Sentinel-1 imagery by accounting for unequal time intervals between image observations and outliers. We then extracted temporal features that are proxies of the intra- and inter-annual dynamics from these time series. In addition, we derived spatial features from the imagery that quantify spatial context and therefore used varying window sizes. The random forest regression was implemented to assess the importance of the spatiotemporal features, reduce errors, and derive robust FFL estimates. The satellite estimates were validated against 96 field measurements from Pinus yunnanensis forests in the Liangshan Yi Autonomous Prefecture, Sichuan Province, China. Both the spatiotemporal features of SAR and optical data importantly contributed to FFL estimation. When only optical data was used, the model achieved a R 2 of 0.75 (relative Root Mean Squared Error (rRMSE) = 25.3 %), while when only SAR data was used the R 2 was 0.76 (rRMSE = 25.6 %). However, when optical and SAR data were combined, the R 2 increased to 0.81 (rRMSE = 23.2 %). We also found that temporal features were more important predictors of FFL than features that captured spatial context. We demonstrated our FFL mapping method by a case study in the Chinese Sichuan Province, in relation to the occurrence of a fire. Our method needs additional validation over different tree species and forest types, yet has potential for mapping forest fuel loads and fire risk.

COX-2 and iNOS inhibitory abietane diterpenoids from Pinus yunnanensis exudates

Pinuskesiols A–F (1–6), six new structurally diverse abietane diterpenoids were isolated from Pinus yunnanensis resins. Their structures including absolute configurations were characterized by using spectroscopic and computational methods. All the compounds bear a carbonyl functionality at C-4 with 1 and 2 behaving as a lactone thereof. The isopropyl group is attached to C-13 via O-atom in 3. In addition, the presence of a Δ5(6) double bond and a ketone at C-7 makes 2 a large conjugated system. Biological evaluation revealed that 1, 2, and 4 could concentration-dependently inhibit iNOS and COX-2 expression in lipopolysaccharide-induced RAW 264.7 cells with 2 to be the most active toward COX-2 inhibition.

Simulation the potential distribution of Dendrolimus houi and its hosts, Pinus yunnanensis and Cryptomeria fortunei, under climate change in China.

Due to climate change, it is significant to explore the impact of rising temperatures on the distribution of Dendrolimus houi Lajonquiere (Lepidoptera) and its host plants, Pinus yunnanensis and Cryptomeria fortunei, and to simulate their suitable future distribution areas in order to provide a theoretical basis for the monitoring of, and early warning about, D. houi and the formulation of effective prevention and control policies. Based on the known distribution areas of, and relevant climate data for, D. houi, P. yunnanensis, and C. fortunei, their suitable habitat in China was predicted using the ENMeval data package in order to adjust the maximum entropy (MaxEnt) model parameters. The results showed that the regularization multiplier was 0.5 when the feature combination was LQHPT, with a MaxEnt model of lowest complexity and excellent prediction accuracy. The main climate variable affecting the geographical distribution of D. houi, P. yunnanensis, and C. fortunei is temperature, specifically including isothermality, temperature seasonality, maximum temperature of warmest month, minimum temperature of warmest month, average temperature of coldest quarter. The potential suitable distribution areas for P. yunnanensis and D. houi were similar under climate change, mainly distributed in southwest China, while C. fortunei was mainly distributed in southeast China. Under different future-climate scenarios, the areas suitable for the three species will increase, except for P. yunnanensis in the 2070s under Shared Socioeconomic Pathway 5-8.5. With climate change, all three species were found to have a tendency to migrate to higher latitudes and higher altitudes. The centroids of the areas suitable for P. yunnanensis and D. houi will migrate to the northwest and the centroids of the areas suitable for C. fortunei will migrate to the northeast.

Forest characteristics, population structure and growth trends of threatened relict Pseudotsuga forrestii in China

Pseudotsuga forrestii is a relict evergreen coniferous tree species in Pinaceae endemic to China. P. forrestii tree numbers have greatly decreased due to deforestation, over-utilization and habitat degradation. Here we clarify P. forrestii community types, structure, species diversity, seedling recruitments and growth trends. We identified four P. forrestii community types: (1) Pseudotsuga forrestii - Quercus guyavifolia - Acer davidii evergreen coniferous and broad-leaved mixed forest; (2) Pseudotsuga forrestii - Pinus yunnanensis - Quercus guyavifolia evergreen coniferous and broad-leaved mixed forest; (3) Pseudotsuga forrestii evergreen coniferous forest; (4) Pseudotsuga forrestii - Abies georgei var. smithii evergreen coniferous forest. P. forrestii forests are characterized by both warm temperate and temperate affinities. Simpson diversity, Pielou evenness, Shannon–Wiener diversity indices ranged from 0.75 to 0.76, 0.74–0.81, and 1.62–1.93, respectively, with no significant differences among the four forest types. The forest stratification was multilayered. The canopy layer was generally 10–25 m tall, with the emergent layer reached 25–42 m. DBH and age structures of P. forrestii showed multimodal distributions. Its maximum age P. forrestii was 570 years with a DBH of 143 cm. The growth of annual ring width of P. forrestii was slow, and generally decreased with age, whereas the basal area at the breast height increased with age. Established seedlings/saplings were mainly found in unstable micro-habitats. Regeneration of P. forrestii depends on moderate natural disturbances. Finally, we provide recommendations for P. forrestii conservation.

Targeted Metabolic and Transcriptomic Analysis of Pinus yunnanensis var. pygmaea with Loss of Apical Dominance.

Pinus yunnanensis var. pygmaea demonstrates obvious loss of apical dominance, inconspicuous main trunk, which can be used as an ideal material for dwarfing rootstocks. In order to find out the reasons for the lack of apical dominance of P. pygmaea, endogenous phytohormone content determination by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and comparative transcriptomes were performed on the shoot apical meristem and root apical meristem of three pine species (P. massoniana, P. pygmaea, and P. elliottii). The results showed that the lack of CK and the massive accumulation of ABA and GA-related hormones may be the reasons for the loss of shoot apical dominance and the formation of multi-branching, the abnormal synthesis of diterpenoid biosynthesis may lead to the influence of GA-related synthesis, and the high expression of GA 2-oxidase (GA2ox) gene may be the cause of dwarfing. Weighted correlation network analysis (WGCNA) screened some modules that were highly expressed in the shoot apical meristem of P. pygmaea. These findings provided valuable information for identifying the network regulation of shoot apical dominance loss in P. pygmaea and enhanced the understanding of the molecular mechanism of shoot apical dominance growth differences among Pinus species.

The Stable Carbon Isotope Composition of Pinus yunnanensis Pollen and Leaf in Northwestern Yunnan, China

The basis for the δ13C values of plant tissues used to infer past ecological environments is their relationships with modern ecological conditions. A total of 71 pairs of pollen and leaf samples were collected from Pinus yunnanensis, an endemic species of the Yunnan plateau, in northwestern Yunnan, China. Their carbon isotopic composition was examined to investigate the two tissues’ difference of stable carbon isotopes, possible factors affecting their δ13C values, and their distinctiveness within Pinus and Pinaceae. Our study showed that pollen δ13C values range from −32.92 to −26.34‰ with an average of −30.88‰, whereas leaf δ13C values vary between −33.79 and −28.96‰ with an average value of −31.2‰, suggesting an isotope fractionation between the two tissues. A statistically significant negative correlation between the pollen δ13C values and altitudes of the sampling sites as well as no significant correlation between the leaf δ13C values and altitudes suggested that pollen may be more sensitive to some climatic parameters than leaf. A comparison of the pollen and leaf δ13C values from Pinus yunnanensis with the available data from other Pinus species and other genus species of Pinaceae indicated that the pollen and leaf δ13C values of Pinus yunnanensis are the lowest, partially due to the effects of water availability.

A forest type-specific threshold method for improving forest disturbance and agent attribution mapping

ABSTRACT Detecting forest disturbances and attributing their contributing agents with Landsat time series (LTS) images has advanced substantially in recent years; however, whether different forest types require individual disturbance indices or specific disturbance thresholds to accurately map forest disturbances and their causes over a vast region remains limited known. This study investigated the effectiveness of six spectral indices (SIs) and two threshold methods (a forest-specific threshold and a common threshold) for detecting forest disturbances among four forest types, namely, evergreen broad-leaved forests (EBFs), cold-temperate evergreen needle-leaved forests (CENFs), and subtropical evergreen needle-leaved forests mainly dominated by Pinus yunnanensis (SENF1) or Pinus kesiya (SENF2), across Yunnan Province with the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) algorithm and yearly Landsat time series (LTS) recorded from 1990 to 2020. A random forest (RF) model was applied to classify the forest disturbance agents from aggregated patches of disturbed forest pixels. The results indicated that the normalized burn ratio (NBR) outperformed the five other SIs and achieved consistently high overall accuracies (OAs; 93.04%±0.17% to 96.09%±0.28%) when mapping forest disturbances across all four forest types. The forest-specific NBR disturbance thresholds led to considerable increases in overall (0.14–3.92%), producer (0.25–13.47%) and user accuracies (0.88–3.01%). The total mapped area of disturbed forest was 9831.48 km2 (5.31%), of which approximately 79% occurred in the EBF and SENF1 distribution areas. Forest disturbances were predominantly caused by wildfires in CENF and SENF1 and by commodity-driven plantations in EBF and SENF2; these two agents together contributed approximately 93.15% of the forest disturbances in Yunnan province. These findings highlight that the optimal selection of SIs and forest-specific disturbance thresholds can significantly improve forest disturbance detection performance.

Two new forest pathogens in Phaeolus (Polyporales, Basidiomycota) on Chinese coniferous trees were confirmed by molecular phylogeny.

Phaeolus schweinitzii (Fr.) Pat. was originally described in Europe and is considered a common forest pathogen on conifers in the Northern Hemisphere. Our molecular phylogeny based on samples from China, Europe, and North America confirms that P. schweinitzii is a species complex, including six taxa. P. schweinitzii sensu stricto has a distribution in Eurasia; the samples from Northeast and Southwest China are distantly related to P. schweinitzii sensu stricto, and two new species are described after morphological, phylogenetic, and geographical analyses. The species growing on Larix, Picea, and Pinus in Northeast China is described as Phaeolus asiae-orientalis. Another species mostly occurring on Pinus yunnanensis in Southwest China is Phaeolus yunnanensis. In addition, three taxa distributed in North America differ from P. schweinitzii sensu stricto. Phaeolus tabulaeformis (Berk.) Pat. is in Southeast North America, “P. schweinitzii-1” in Northeast North America, and “P. schweinitzii-2” in western North America.

Enhancing Aboveground Biomass Estimation for Three Pinus Forests in Yunnan, SW China, Using Landsat 8

The estimation of forest aboveground biomass (AGB) using Landsat 8 operational land imagery (OLI) images has been extensively studied, but forest aboveground biomass (AGB) is often difficult to estimate accurately, in part due to the multi-level structure of forests, the heterogeneity of stands, and the diversity of tree species. In this study, a habitat dataset describing the distribution environment of forests, Landsat 8 OLI image data of spectral reflectance information, as well as a combination of the two datasets were employed to estimate the AGB of the three common pine forests (Pinus yunnanensis forests, Pinus densata forests, and Pinus kesiya forests) in Yunnan Province using a parametric model, stepwise linear regression model (SLR), and a non-parametric model, such as random forest (RF) and support vector machine (SVM). Based on the results, the following conclusions can be drawn. (1) As compared with the parametric model (SLR), the non-parametric models (RF and SVM) have a better fitting performance for estimating the AGB of the three pine forests, especially in the AGB segment of 40 to 200 Mg/ha. The non-parametric model is more sensitive to the number of data samples. In the case of the Pinus densata forest with a sample size greater than 100, RF fitting provides better fitting performance than SVM fitting, and the SVM fitting model is better suited to the AGB estimation of the Pinus yunnanensis forest with a sample size of less than 100. (2) Landsat 8 OLI images exhibit superior accuracy in estimating the AGB of the three pine forests using a single dataset. Variables, such as texture and vegetation index variables, which can reflect the comprehensive reflection information of ground objects, play a significant role in estimating AGBs, especially the texture variables. (3) By incorporating the combined dataset with characteristics of tree species distribution and ground object reflectance spectrum, the accuracy and stability of AGB estimation of the three pine forests can be improved. Moreover, the employment of a combined dataset is also effective in reducing the number of estimation errors in cases with AGB less than 100 Mg/ha or exceeding 150 Mg/ha.

Similar Pattern of Potential Distribution of Pinus yunnanensis Franch and Tomicusyunnanensis Kirkendall under Climate Change in China

Tomicus yunnanensis Kirkendall (Coleoptera: Scolytinae) is a stem-boring pest that endangers Pinus yunnanensis Franch (Pinales:Pinoideae), which seriously affects the ecological environment safety in southwest China. In order to understand the potential distribution pattern and change in the potential distribution of P. yunnanensis and T. yunnanensis, this study used the maximum entropy model to predict the distribution of potentially suitable areas for P. yunnanensis and T. yunnanensis and explored the relationships between their different spatiotemporal distributions based on change analysis. The experimental results show that altitude is the main factor restricting the current distribution of P. yunnanensis. The current suitable areas of P. yunnanensis are mainly distributed in Yunnan, Sichuan and Guizhou. The minimum temperature of the coldest month is the main factor affecting the current distribution of T. yunnanensis. The current suitable areas of T. yunnanensis are mainly distributed in Yunnan, Sichuan and Tibet. Under future climate scenarios, the total suitable areas of P. yunnanensis and T. yunnanensis are expected to increase. The suitable areas tend to move to higher altitudes in the west and higher latitudes in the north. At the same time, this study finds that there is an obvious bottleneck of expansion to northeastern Sichuan near the Daba Mountains. The results of intersection analysis showed that, with future climate change, P. yunnanensis and T. yunnanensis mainly showed lowly suitable (or unsuitable)—lowly suitable (or unsuitable) to moderately (or highly) suitable—and moderate (or high) variation patterns of suitable areas under the SSP1-2.6 climate scenario. These results will provide an important basis for the breeding of P. yunnanensis and controlling T. yunnanensis.