Search Results (26)

Plantago asiatica L. is an important medicinal and edible plant in China, whose wild resources have declined sharply due to overharvesting. Accurately assessing its potential suitable habitats and identifying the dominant environmental factors are of great scientific significance for germplasm conservation. In this study, we focused on Jiangxi Province and, based on 79 field occurrence records and 29 environmental variables, employed a MaxEnt model optimized using the kuenm package (version 1.1.9) (AUC = 0.806) to simulate the potential distribution of this species under current and three future Shared Socioeconomic Pathway (SSP) scenarios (SSP126, SSP370, and SSP585). The results indicated that static non-climatic factors at the regional scale—specifically, soil clay content (contribution rate: 56.7%)—were the dominant force determining the distribution of Plantago asiatica, with an influence far exceeding that of climatic and topographic factors. Under all future SSP scenarios, its distribution range exhibited notable spatiotemporal conservatism, and the centroid showed no long-distance migration. This study provides a scientific reference for optimizing the conservation framework of Plantago asiatica germplasm resources, identifying priority cultivation areas, and formulating regional adaptive management strategies. Full article

Species in Gentiana sect. Cruciata (Gentianaceae) are usually used as a bulk medicinal herb with multiple clinical therapeutic functions, yet wild populations have declined drastically due to overharvesting and environmental changes, prompting urgent conservation demands. This study collected occurrence records of eight sect. Cruciata species and integrated forty-two environmental variables. MaxEnt models were optimized using the “kuenm” R package v. 3.6.3 to predict their potential suitable areas under current and future climate scenarios (SSP126 and SSP585), followed by analyses of spatial pattern changes and centroid migration. Results showed that all optimized models achieved average AUC > 0.9, indicating high prediction accuracy. Elevation, annual precipitation, human footprint index, and temperature seasonality dominantly affected the species distribution, with distinct limiting factor combinations revealing interspecific niche differentiation. Based on the prediction, the eight gentian species exhibited three divergent climate response types: climate-sensitive G. macrophylla suffered severe, irreversible habitat loss with long-distance centroid migration; five stable-adaptive species (G. straminea, G. crassicaulis, G. tibetica, G. robusta, and G. siphonantha) maintained stable core ranges and partial expansion capacity; G. dahurica and G. officinalis followed a “contraction-then-recovery” trajectory. The eastern Qinghai–Tibetan Plateau, Hengduan Mountains, Qinling Mountains, and the Gansu–Qinghai–Sichuan border represented core conservation zones for the sect. Cruciata species. Highly adaptive G. crassicaulis, G. tibetica, and G. straminea were prioritized for conservation and artificial propagation. This study revealed the differentiated responses and adaptation potentials of medicinal plants of Gentiana sect. Cruciata to climate change, providing a scientific basis for precise germplasm conservation and rational cultivation layout for sustainable medicinal resource utilization. Full article

Climate change may redistribute understory medicinal plants, but national predictions are useful when model uncertainty and field constraints are explicit. We aimed to identify the current and future suitable habitat of Sceptridium ternatum and translate predictions into survey and conservation priorities. We compiled 187 occurrences, screened 104 candidate predictors to 9, calibrated MaxEnt with kuenm, and evaluated it using partial ROC, omission rate, AICc, random and spatial-block cross-validation, and the Boyce index. Future suitability was projected with two GCMs under SSP1-2.6 and SSP5-8.5. October precipitation had the highest permutation importance, and growing-season thermal accumulation was another major contribution. NDVI had high percent contribution but no unique permutation importance. The selected model achieved mean random-fold and spatial-block test AUCs of 0.9217 and 0.8575, respectively, and a Boyce index of 0.8211. Late-century SSP5-8.5 reduced total and highly suitable habitat by 45.06% and 89.96%, respectively. Protected areas covered only 14.77% of highly suitable habitat. The resulting framework separates regional climatic suitability from habitat compatibility and management feasibility, a distinction relevant to other data-limited understory medicinal plants. It distinguishes sites for population verification, germplasm collection, and long-term monitoring, and plot-scale decisions require measurements of canopy, moisture, substrate, population state, and mycorrhizal context. Full article

The dried leaves of Turpinia arguta (Lindl.) Seem, a traditional Chinese medicinal herb, have been used for the treatment of tonsillitis, sore throat, throat arthralgia, and novel coronavirus pneumonia. This plant possesses significant medicinal, economic, and ecological values. Assessing its distribution patterns and its response to global climate change is critical for the conservation and sustainable use of its resources. This study used GIS technology and ENMTools v1.3 to select 247 distribution records of T. arguta and employed the kuenm R package (running on R v4.4.3, package version 2.0.1) to optimize the MaxEnt model parameters. Based on current and future climate data, this study predicted the current and future potential suitable areas of T. arguta in China during the periods of the 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100) under three SSP emission scenarios (SSP126, SSP245, and SSP585). Additionally, it identified the key environmental variables driving its distribution patterns and conducted a quality suitability regionalization analysis using sample chemical content data. The results show that under current climatic conditions, the highly suitable areas for T. arguta are mainly distributed across five provinces: Jiangxi, Guangdong, Guangxi, Fujian, and Hunan. The distribution of T. arguta is primarily influenced by precipitation and temperature. The suitable ranges of key environmental variables are as follows: average temperature in September > 26 °C (optimal range: 28–32 °C), precipitation in April 175–250 mm, precipitation in September 100–160 mm, annual mean temperature 20–30 °C (optimal range > 22.5 °C), and annual precipitation 1500–2000 mm (peak value: 1750 mm). Quality analysis reveals a positive correlation between ligustroflavone content and the mean diurnal temperature range, as well as between rhoifolin content and soil sand content. Compared with current suitable areas, the total suitable areas of T. arguta are projected to contract by varying degrees across all scenarios in the future. This study will provide a robust scientific basis for guiding the sustainable development/utilization of its resources and optimizing artificial cultivation practices. Full article

Against the backdrop of China’s “Dual Carbon” strategy (peak carbon emissions and carbon neutrality), timber forests serve the dual function of wood supply and carbon sink enhancement. In this study, we employed the Kuenm package in R to optimize Maximum Entropy model (MaxEnt) parameters. Based on the distribution data of six timber tree species in Sichuan Province and 43 environmental factors, we utilized the MaxEnt outputs and ArcGIS 10.8 software to map the geographic distribution of the suitable habitats for these species from the present day into the future (2061–2080) under different climate scenarios (SSP126 and SSP585). Furthermore, we analyzed the migration trend of their future distribution centers. The model optimization significantly improved both fit and predictive performance, with AUC values ranging from 0.8552 to 0.9637 and TSS values ranging from 0.6289 to 0.84, indicating high predictive capability and stability of the model. Analysis of environmental factors, including altitude, precipitation, and temperature, revealed that altitude plays a dominant role in species distribution. Future climate scenario simulations indicated that climate change will significantly alter the distribution of suitable habitats for these timber tree species. The suitable areas for some species contracted, with changes being particularly pronounced under the SSP585 scenario, in which the high-suitability area for Phoebe zhennan is projected to increase from 12,788 km² to 20,004 km², whereas the high-suitability area for Eucalyptus robusta is expected to contract from 8706 km² to 7715 km². The migration distances of suitable habitats for timber tree species in Sichuan range from 5 km to 101 km southwestward under different climate scenarios, and these shifts are statistically significant (p < 0.01), with shifts in elevation and precipitation patterns, reflecting species-specific responses to climate change. This study aims to predict future suitable habitats of timber tree species in Sichuan, providing scientific support for forestry planning, forest quality improvement, and climate risk mitigation. Full article

Species distribution models (SDMs) grapple with uncertainty. To address this, a parameter-optimized MaxEnt model was used to predict habitat suitability for Elsholtzia densa, a predominant agricultural weed on the Tibetan Plateau. Through multiparameter optimization with 149 occurrence points and three climate variable sets, we systematically evaluated how the three MaxEnt extrapolation approaches (Free Extrapolation, Extrapolation with Clamping, No Extrapolation) influenced model outputs. The results showed the following: (1) Model optimization using the Kuenm R package version (1.1.10) identified seven critical bioclimatic variables (Feature Combinations = LQTH, Regularization Multipliers = 2.5), with optimized models demonstrating high accuracy (Area Under Curve > 0.9). (2) Extrapolation approaches exhibited negligible effects on variable selection, though four bioclimatic variables “bio1 (annual mean temperature)”, “bio12 (annual precipitation)”, “bio2 (mean diurnal range)”, and “bio7 (temperature annual range)” predominantly drove model predictions. (3) Current high-suitability areas are clustered in the eastern and southern regions of the Tibetan Plateau, and with Free Extrapolation yielding the broadest current distribution. Climate change projections suggest habitat expansion, particularly under conditions of No Extrapolation. (4) Multivariate Environmental Similarity Surface (MESS) and Most Dissimilar Variable (MoD) are not affected by the extrapolation method, and extrapolation risk analyses indicate that future climate anomalies are mainly concentrated in the western and southern parts of the Tibetan Plateau and that future warming will further increase the unsuitability of these regions. (5) Variance analysis showed that the extrapolation methods did not significantly affect the 10-replicate results but influenced the parameter and emission scenarios, with No Extrapolation methods showing minimal variance changes. Our findings validate that multiparameter optimization improves species distribution model robustness, systematically characterizes extrapolation impacts on distribution projections, and provides a conceptual framework and early warning systems for agricultural weed management on the Tibetan Plateau. Full article

Catalpa bungei C. A. Mey, an economically and ecologically important tree species endemic to China, exhibits notable drought resistance; however, the spatial dynamics of its habitat under future climate change have not been thoroughly investigated. We employed a parameter-optimized MaxEnt modeling framework to project current and future suitable habitats for C. bungei under two Shared Socioeconomic Pathway scenarios, SSP126 (low-emission) and SSP585 (high-emission), based on CMIP6 climate data. We incorporated 126 spatially rarefied occurrence records and 22 environmental variables into a rigorous modeling workflow that included multicollinearity assessment and systematic variable screening. Parameter optimization was performed using the kuenm package in R version 4.2.3, and the best-performing model configuration was selected (Regularization Multiplier = 2.5; Feature Combination = LQT) based on the AICc, omission rate, and evaluation metrics (AUC, TSS, and Kappa). Model validation demonstrated robust predictive accuracy. Four primary environmental predictors obtained from WorldClim version 2.1—the minimum temperature of the coldest month (Bio6), annual precipitation (Bio12), maximum temperature of the warmest month (Bio5), and elevation—collectively explained over 90% of habitat suitability. Currently, the optimal habitats are concentrated in central and eastern China. By the 2090s, the total suitable habitats are projected to increase by approximately 4.25% under SSP126 and 18.92% under SSP585, coupled with a significant northwestward shift in the habitat centroid. Conversely, extremely suitable habitats are expected to markedly decline, particularly in southern China, due to escalating climatic stress. These findings highlight the need for adaptive afforestation planning and targeted conservation strategies to enhance the climate resilience of C. bungei under future climate change. Full article

Species migration or extinction events may occur on a large scale with the intensification of climate change. Plant Species with Extremely Small Populations (PSESP) are more sensitive to climate change as compared to other plants. To date, the potential effect of climate change on Acer yangbiense and Cinnamomum chago, both of which belong to PSESP, remain unknown. In this study, we modeled the distribution dynamics of A. yangbiense and C. chago spanning from the Last Glacial Maximum (LGM) to the end of the 21st century based on the MaxEnt model, optimized using the Kuenm package. The results revealed that the parameter settings of the optimal models were RM (regularization multiplier) = 3.5, FC (feature combination) = QP, and RM = 2, FC = QPT. A. yangbiense and C. chago had AUCs of 0.982 and 0.993, respectively, indicating that the model predictions are highly accurate while effectively balancing complexity and avoiding overfitting. The distribution of A. yangbiense and C. chago was mostly influenced by the precipitation of the driest quarter (bio17) and the min temperature of the coldest month (bio6). From the LGM to the present, the total suitable areas of A. yangbiense and C. chago initially declined before showing a subsequent increase, but it is projected to experience significant reductions in the future, with decreases of 32.98%–64.99% and 63.48%–99.49%, respectively. The distribution centroids of A. yangbiense and C. chago showed a migration trend from south to north from the LGM to the present, and this trend is expected to continue. To enhance the resilience of A. yangbiense and C. chago to meet the challenges of climate change in the future, we proposed that the introduction and artificial cultivation of these species should be carried out in Baoshan, Dali, and Nujiang in the northwest of Yunnan Province, which were the areas with high heat values, so as to expand the populations gradually. Full article

Asparagus (Asparagus officinalis L.) has high health and nutritional values, but the lack of scientific and rational cultivation planning has resulted in a decline in asparagus quality and yield. Important soil, climatic, anthropogenic, and topographic environmental factors influencing the distribution of asparagus cultivation were chosen for this study. The Kuenm package in the R language (v4.2.1) was employed to optimize the maximum entropy model (MaxEnt). Pearson’s correlation analysis, optimized MaxEnt, and geographic information spatial technology were then utilized to identify the main environmental factors that influence suitable habitats for asparagus in China. Potential distribution patterns, migration, and changes in trends concerning the suitability of asparagus in China under various historical and future climate scenarios were modeled and projected. Human activities and climate factors were found to be the primary environmental factors that influence the suitability distribution of asparagus cultivation in China, followed by soil and topographic factors. Historical suitable habitats covered 345.6 × 10⁵ km², accounting for 36% of China. These habitats are projected to expand considerably under future climatic conditions. This research offers a basis for the rational planning and sustainable development of asparagus cultivation. Full article

Climate change restricts and alters the distribution range of plant species. Predicting potential distribution and population dynamics is crucial to understanding species’ geographical distribution characteristics to harness their economic and ecological benefits. This study uses Dendrocalamus brandisii as the research subject, aiming to accurately reveal the impact of climate change on this plant. The findings offer important insights for developing practical conservation and utilization strategies, and guidance for future introduction and cultivation. The MaxEnt model was optimized using regularization multiplier (RM) and feature combination (FC) from the ‘Kuenm’ package in R language, coupled with ArcGIS for modeling 142 distribution points and 29 environmental factors of D. brandisii. This article explored the key environmental factors influencing the potential suitable regions for D. brandisii, and predicted trends in habitat changes under SSPs2.6 and SSPs8.5 climate scenarios for the current era, the 2050s, 2070s, and 2090s. (1) The results show that when FC = QPH and RM = 1, the AUC = 0.989, indicating that the model prediction is accurate with the lowest complexity and overfitting. The key environmental factors affecting its primary suitable distribution, determined by jackknife training gain and single-factor response curve, are the precipitation of warmest quarter (bio18), the temperature seasonality (bio4), the minimum average monthly radiation (uvb-4), and elevation (Elev), contributing 93.6% collectively. It was established that the optimal range for D. brandisii is precipitation of warmest quarter of between 657 and 999 mm, temperature seasonality from 351% to 442%, minimum average monthly radiation from 2420 to 2786 J/m²/day, at elevation from 1099 to 2217 m. (2) The current potential habitat distribution is somewhat fragmented, covering an area of 92.17 × 10⁴ km², mainly located in southwest, south, and southeast China, central Nepal, southern Bhutan, eastern India, northwestern Myanmar, northern Laos, and northern Vietnam. (3) In future periods, under different climate scenario models, the potential habitat of D. brandisii will change in varying degrees to become more fragmented, with its distribution center generally shifting westward. The SSP8.5 scenario is not as favorable for the growth of D. brandisii as the SSPs2.6. Central Nepal, southern Bhutan, and the southeastern coastal areas of China have the potential to become another significant cultivation region for D. brandisii. The results provide a scientific basis for the planning of priority planting locations for potential introduction of D. brandisii in consideration of its cultivation ranges. Full article

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland. Full article

Carposina coreana is an important pest of Cornus officinalis, distributed in China, Korea, and Japan. In recent years, its damage to C. officinalis has become increasingly serious, causing enormous economic losses in China. This study and prediction of current and future suitable habitats for C. coreana in China can provide an important reference for the monitoring, early warning, prevention, and control of the pest. In this study, the potential distributions of C. coreana in China under current climate and future climate models were predicted using the maximum entropy (MaxEnt) model with ArcGIS software. The distribution point data of C. coreana were screened using the buffer screening method. Nineteen environmental variables were screened using the knife-cut method and variable correlation analysis. The parameters of the MaxEnt model were optimized using the kuenm package in R software. The MaxEnt model, combined with key environmental variables, was used to predict the distribution range of the suitable area for C. coreana under the current (1971–2000) and four future scenarios. The buffer screening method screened data from 41 distribution points that could be used for modeling. The main factors affecting the distribution of C. coreana were precipitation in the driest month (Bio14), precipitation in the warmest quarter (Bio18), precipitation in the coldest quarter (Bio19), the standard deviation of seasonal variation of temperature (Bio4), minimum temperature in the coldest month (Bio6), and average temperature in the coldest quarter (Bio11). The feature class (FC) after the kuenm package optimization was a Q-quadratic T-threshold combination, and the regularization multiplier (RM) was 0.8. The suitable areas for C. coreana under the current climate model were mainly distributed in central China, and the highly suitable areas were distributed in southern Shaanxi, southwestern Henan, and northwestern Hubei. The lowest temperature in the coldest month (Bio6), the average temperature in the coldest quarter (Bio11), and the precipitation in the warmest quarter (Bio18) all had good predictive ability. In future climate scenarios, the boundary of the suitable area for C. coreana in China is expected to shift northward, and thus, most of the future climate scenarios would shift northward. Full article

Ambrosia trifida is an invasive alien plant species, which has very high reproductive and environmental adaptability. Through strong resource acquisition ability and allelopathy, it could inhibit the growth and reproduction of surrounding plants and destroy the stability of an invasive ecosystem. It is very important to predict the change of suitable distribution area of A. trifida with climate change before implementing scientific control measures. Based on 106 A. trifida distribution data and 14 points of environmental data, the optimal parameter combination (RM = 0.1, FC = LQ) was obtained using the MaxEnt (version 3.4.1) model optimized by Kuenm package, and thus the potential suitable areas of A. trifida in Northeast China under three different climate scenarios (RCP2.6, RCP4.5, RCP8.5) with different emission intensities in the future (2050, 2070) were predicted. The changes of A. trifida suitable area in Northeast China under three climate scenarios were compared, and the relationship between the change of suitable area and emission intensity was analyzed. In general, the suitable area of A. trifida in Northeast China will expand gradually in the future, and the area of its highly suitable area will also increase with the increasing emission intensity, which is unfavorable to the control of A. trifida. Full article

The impact of climate change on the distribution of native species in the Neotropics remains uncertain for most species. Prosthechea mariae is an endemic epiphytic orchid in Mexico, categorized as threatened. The objective of this study was to assess the effect of climate change on the natural distribution of P. mariae and the capacity of protected areas (PAs) to safeguard optimal environmental conditions for the species in the future. Historical records were obtained from herbaria collections and through field surveys. We utilized climate variables from WorldClim for the baseline scenario and for the 2050 period, using the general circulation models CCSM4 and CNRM-CM5 (RCP 4.5). Three sets of climate data were created for the distribution models, and multiple models were evaluated using the kuenm package. We found that the species is restricted to the eastern region of the country. The projections of future scenarios predict not only a substantial reduction in habitat but also an increase in habitat fragmentation. Ten PAs were found within the current distribution area of the species; in the future, the species could lose between 36% and 48% of its available habitat within these PAs. The results allowed for the identification of locations where climate change will have the most severe effects, and proposals for long-term conservation are addressed. Full article

Global warming significantly affects forest ecosystems in the Northern Hemisphere’s mid-to-high latitudes, altering tree growth, productivity, and spatial distribution. Additionally, spatial and temporal heterogeneity exists in the responses of different tree species to climate change. This research focuses on two key species in China’s Greater Khingan Range: Larix gmelinii (Rupr.) Kuzen. (Pinaceae) and Quercus mongolica Fisch. ex Ledeb. (Fagaceae). We utilized a Maxent model optimized by the kuenm R package to predict the species’ potential habitats under various future climate scenarios (2050s and 2070s) considering three distinct Shared Socioeconomic Pathways: SSP1-2.6, SSP2-4.5, and SSP5-8.5. We analyzed 313 distribution records and 15 environmental variables and employed geospatial analysis to assess habitat requirements and migration strategies. The Maxent model demonstrated high predictive accuracy, with Area Under the Curve (AUC) values of 0.921 for Quercus mongolica and 0.985 for Larix gmelinii. The high accuracy was achieved by adjusting the regularization multipliers and feature combinations. Key factors influencing the habitat of Larix gmelinii included the mean temperature of the coldest season (BIO11), mean temperature of the warmest season (BIO10), and precipitation of the driest quarter (BIO17). Conversely, Quercus mongolica’s habitat suitability was largely affected by annual mean temperature (BIO1), elevation, and annual precipitation (BIO12). These results indicate divergent adaptive responses to climate change. Quercus mongolica’s habitable area generally increased in all scenarios, especially under SSP5-8.5, whereas Larix gmelinii experienced more complex habitat changes. Both species’ distribution centroids are expected to shift northwestward. Our study provides insights into the divergent responses of coniferous and broadleaf species in the Greater Khingan Range to climate change, contributing scientific information vital to conserving and managing the area’s forest ecosystems. Full article