Search Results (20)

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

The Dendrolimus houi, a phytophagous pest, displays a broad range of adaptations and often inflicts localized damage to its hosts. Cupressus funebris, an indigenous timber species in China, is significantly impacted by D. houi. Investigating the suitable habitat distribution and changes in D. houi and its host plant, C. funebris, within the context of climate warming, is essential for understanding D. houi’s development and providing novel insights for managing D. houi and conserving C. funebris resources. In this study, MaxEnt was employed to simulate the distribution of D. houi and its host plant, C. funebris, in their suitable habitats, evaluating the influence of environmental factors on their distribution and determining changes under a warming scenario. MaxEnt model parameters were adjusted using the Kuenm data package based on available distribution and climatic data. The minimum temperature of the coldest month emerged as the primary environmental factor influencing the distribution of a suitable habitat for D. houi and C. funebris, with a percentage contribution of environmental factors over 60%. There was a substantial similarity in the suitable habitat distributions of D. houi and C. funebris, with varying degrees of expansion in the total habitat area under different temporal and climatic scenarios. Intersection analysis results indicated that the 2041–2060 period, especially under low (SSP1-2.6) and high (SSP5-8.5) emission scenarios, is a critical phase for D. houi control. The habitat expansion of D. houi and C. funebris due to climate change was observed, with the distribution center of D. houi shifting northeast and that of C. funebris shifting northwest. Full article

Camellia luteoflora Li ex Chang is an endangered plant endemic to the East Asian flora with high ornamental value as well as phylogenetic and floristic research value. Predicting the impact of climate change on its distribution and suitable habitat is crucial until scientific conservation measures are implemented. Based on seven environmental variables and 17 occurrence records, this study optimized the MaxEnt model using the kuenm data package to obtain the optimal parameter combinations (RM = 1.3, FC = LPT) and predicted the potential distribution pattern of C. luteoflora in various future periods. The results revealed that the mean diurnal range, temperature annual range, and precipitation of the wettest month were the influential factors determining the distribution pattern of C. luteoflora, contributing 60.2%, 14.4%, and 12.3% of the variability in the data, respectively. Under the current conditions, the area of suitable habitats for C. luteoflora was only about 21.9 × 10⁴ km². Overall, the suitable area around the C. luteoflora distribution points will shrink in a circular pattern in response to future global warming, but some potentially suitable distribution areas will expand and migrate to higher latitudes and the Hengduan Mountains region, representing a survival strategy for coping with climate change. It is hypothesized that the future climate refugia will be the highly suitable area and the Hengduan Mountains region. Furthermore, a retrospective validation method was employed to assess the reliability of the predictions and estimate the model’s predictive performance in the future. This study proposes a survival strategy and adaptation measures for C. luteoflora in response to climate change, and the proposed measures can be generalized for application in conservation planning and restoration processes. We also recommend that future studies incorporate factors such as the anthropogenic disturbances and associated socio-economic activities related to C. luteoflora into the model and to further predict the distribution pattern for C. luteoflora in response to historical climatic changes, tracing the evolutionary history of its population. Full article

Dioscorea remotiflora, a perennial climbing herbaceous plant native to Mexico, produces tubers with great nutritional and ethnobotanical value. However, most ecological aspects of this plant remain unknown, which limits its cultivation and use. This is why the objective of this research was to characterize the ecogeography of D. remotiflora as a source to determine its edaphoclimatic adaptability and current and potential distribution. A comprehensive database encompassing 480 geo-referenced accessions was assembled from different data sources. Using the Agroclimatic Information System for México and Central America (SIAMEXCA), 42 environmental variables were formulated. The MaxEnt model within the Kuenm R package was employed to predict the species distribution. The findings reveal a greater presence of D. remotiflora in harsh environments, characterized by arid to semiarid conditions, poor soils, and hot climates with long dry periods. Niche modeling revealed that seven key variables determine the geographical distribution of D. remotiflora: precipitation of the warmest quarter, precipitation of the driest month, minimum temperature of the coldest month, November–April solar radiation, annual mean relative humidity, annual moisture availability index, and May–October mean temperature. The current potential distribution of D. remotiflora is 428,747.68 km². Favorable regions for D. remotiflora coincide with its current presence sites, while other suitable areas, such as the Yucatán Peninsula, northeast region, and Gulf of Mexico, offer potential expansion opportunities for the species distribution. The comprehensive characterization of Dioscorea remotiflora, encompassing aspects such as its soil habitats and climate adaptation, becomes essential not only for understanding its ecology but also for maximizing its economic potential. This will enable not only its sustainable use but also the exploration of commercial applications in sectors such as the pharmaceutical and food industries, thus providing a broader approach for its conservation and optimal utilization in the near future. Full article

Dioscorea composita is a plant with historical recognition for the production of secondary metabolites of pharmaceutical importance, including diosgenin, and with great nutritional and ethnobotanical value in its center of origin (México and Central America). Furthermore, it is considered a promising therapeutic agent against cancer. Currently, México is one of the two most important countries producing this yam; however, climate change is altering the environmental conditions of its natural habits, threatening its preservation and productivity. This is why this research was focused on characterizing the eco-geography of D. composita and predicting its potential geographic distribution under climate change scenarios in México-Central America. A collection of 408 geo-referenced accessions was used to determine its climatic adaptation, ecological descriptors, and the current and future potential geographic distribution, which was modeled with the MaxEnt model through the Kuenm R-package. For future climate scenarios, an ensemble of the GCMs HadGEM-ES and CCSM4 was used. Results showed that D. composita adapts to warm and humid and very humid agro-climates and, the most contributing variables for its presence are annual and seasonal moisture availability indices, the seasonal photoperiod, annual thermal range, and Bio14 and Bio11. The current potential distribution (692,123 km²) of D. composita might decrease by the year 2050 RCP4.5 (365,680 km²) and might increase by 2050 under the scenario RCP8.5 (763,589 km²), showing this plant could be a good crop option for this climate change scenario. The findings obtained provide valuable information that will allow for the effective utilization of this plant, both in terms of developing new pharmaceutical products and implementing appropriate conservation strategies. Full article

As global warming intensifies, plant carbon sequestration is becoming increasingly important. Studies have shown that Ginkgo biloba L. is a kind of tree with a high carbon sequestration capacity and is one of the dominant tree species suitable for urban ecosystems. Predicting the changes in the potential suitable areas, carbon sequestration amount and carbon sink value of G. biloba under current and future climate change will provide some references for the resource utilization of urban green tree species and the realization of carbon sink value. In this study, we used ‘kuenm’, an R package that uses MaxEnt, as a modeling algorithm to predict the potential suitable areas of G. biloba under nine climate scenarios, and the carbon sequestration and the carbon sink values under the fair-value measurement model were calculated. We found that: (1) the optimized MaxEnt model improved the prediction accuracy (AUC = 0.97 ± 0.004) significantly. (2) The total current potential suitable area of G. biloba was 175.11 × 10⁴-km², representing 21.63% of China’s total territorial area, and the highly suitable area was 26.86 × 10⁴-km², accounting for 2.83% of China’s total territorial area, concentrated primarily in the regions of Sichuan and Chongqing, southern Jiangsu, and Zhejiang Province. (3) The eight future climate scenarios predict that the suitable area of G. biloba will initially decrease and then increase, and the newly expanded area will be distributed primarily in the middle and upper reaches of the Yangtze River as well as near the estuary of the Yangtze River, while the region suffering losses will move from Sichuan and Chongqing to Hunan, Jiangxi and Zhejiang Province as well. Hf and bio2 are the major factors affecting the distribution of G. biloba. (4) The total annual carbon sequestration of G. biloba was 1.60 × 10⁶-t, 1.50 × 10⁶-t and 1.57 × 10⁶-t under different environmental conditions in the present, the 2050s and the 2090s, respectively. Over these three time periods, we obtained total carbon sequestration values of 5.63 × 10⁷ CNY, 5.28 × 10⁷ CNY and 5.53 × 10⁷ CNY. Full article

In America, the presence of Rhipicephalus sanguineus sensu stricto and Rhipicephalus linnaei has been confirmed. Both species are found in sympatry in the southern United States, northern Mexico, southern Brazil, and Argentina. The objective of this work is to evaluate the projection of the potential distribution of the ecological niche of Rhipicephalus sanguineus sensu lato in two climate change scenarios in Mexico and the border with Central America and the United States. Initially, a database of personal collections of the authors, GBIF, Institute of Epidemiological Diagnosis and Reference, and scientific articles was built. The ENMs were projected for the current period and two future scenarios: RCP and SSP used for the kuenm R package, the ecological niche of R. sanguineus s.l. It is distributed throughout the Mexico and Texas (United States), along with the border areas between Central America, Mexico, and the United States. Finally, it is observed that the ecological niche of R. sanguineus s.l. in the current period coincides in three degrees with the routes of human migration. Based on this information, and mainly on the flow of migrants from Central America to the United States, the risk of a greater gene flow in this area increases, so the risk relating to this border is a latent point that must be analyzed. Full article