Search Results (40)

The pepper weevil Anthonomus eugenii is a devastating pest native to Central America that can cause severe damage to over 35 pepper varieties. Global trade in peppers has significantly increased the risk of its spread and expansion. Moreover, future climate change may add more uncertainty to its distribution, resulting in considerable ecological and economic damage globally. Therefore, we employed an ensemble model combining Random Forests and CLIMEX to predict the potential global distribution of A. eugenii in historical and future climate scenarios. The results indicated that the maximum temperature of the warmest month is an important variable affecting global A. eugenii distribution. Under the historical climate scenario, the potential global distribution of A. eugenii is concentrated in the Midwestern and Southern United States, Central America, the La Plata Plain, parts of the Brazilian Plateau, the Mediterranean and Black Sea coasts, sub-Saharan Africa, Northern and Southern China, Southern India, Indochina Peninsula, and coastal area in Eastern Australia. Under future climate scenarios, suitable areas in the Northern Hemisphere, including North America, Europe, and China, are projected to expand toward higher latitudes. In China, the number of highly suitable areas is expected to increase significantly, mainly in the south and north. Contrastingly, suitable areas in Central America, northern South America, the Brazilian Plateau, India, and the Indochina Peninsula will become less suitable. The total land area suitable for A. eugenii under historical and future low- and high-emission climate scenarios accounted for 73.12, 66.82, and 75.97% of the global land area (except for Antarctica), respectively. The high-suitability areas identified by both models decreased by 19.05 and 35.02% under low- and high-emission scenarios, respectively. Building on these findings, we inferred the future expansion trends of A. eugenii globally. Furthermore, we provide early warning of A. eugenii invasion and a scientific basis for its spread and outbreak, facilitating the development of effective quarantine and control measures. Full article

Climate change significantly impacts species distribution and survival, particularly for habitat specialists with limited dispersal abilities. This study investigates the current and future distribution of Tylonycteris pachypus, one of the world’s smallest bats specialized in bamboo-dwelling, using ensemble modeling approaches. Based on comprehensive occurrence data and seven environmental variables, we developed an ensemble model using the Biomod2 platform, achieving high predictive accuracy (AUC: 0.981, TSS: 0.877). Three environmental variables were identified as crucial determinants: minimum temperature of the coldest month (40.90% contribution), maximum temperature of the warmest month (38.38%), and precipitation of the wettest quarter (11.09%). Currently, highly suitable habitats (291.893 × 10⁴ km²) are concentrated in three main regions: southern China and Indochina Peninsula, Myanmar–Bangladesh–northeastern India, and isolated areas in southwest India and Thailand. Under future climate scenarios, particularly SSP585, suitable habitats are projected to decrease substantially (64.4% reduction by 2090s), with a notable northward shift in distribution. However, the species’ limited dispersal ability, specific habitat requirements, and geographical barriers may constrain its capacity to track these climate-driven changes. Our findings highlight the vulnerability of T. pachypus to climate change and emphasize the need for targeted conservation strategies, including protecting climate-resilient habitats and maintaining bamboo forest corridors. This study provides a comprehensive framework for monitoring and conserving this specialized species under climate change, while considering its unique ecological constraints and dispersal limitations. Full article

The purpose of this study is to solve the problem of low load factor and profit margin in the point-to-point transportation of international freight trains through the assembly transportation organization mode. A bi-objective location-routing optimization model is constructed to optimize problems, such as the location of the assembly center, route of freight assembly, frequency of international freight trains, and number of formations. The objectives are to minimize the total comprehensive cost and maximize the average satisfaction of the shippers. Considering the impact of blockchain technology, the proportion of customs clearance time reduction after blockchain implementation, the proportion of customs clearance fee reduction after blockchain implementation, and the cost of blockchain technology are introduced into the model. The case study is based on railroad transportation data for 2022. In this case, 43 stations in the Indo-China Peninsula are selected as origin stations, and two Chinese stations are designated terminal stations. An improved NSGA-II algorithm (ANSGAII-OD) is proposed to resolve the location-routing optimization model. This algorithm is based on opposition-based learning and its dominant strength. The case study indicates that assembly transportation is advantageous compared with direct transportation. Moreover, the comprehensive cost is reduced by 19.77%. Furthermore, blockchain technology can effectively reduce costs and improve transportation efficiency. After the implementation of blockchain technology, the comprehensive cost is reduced by 8.10%, whereas the average satisfaction of shippers is increased by 10.35%. Full article

This study employed machine learning, specifically deep neural networks (DNNs) and long short-term memory (LSTM) networks, to build a model for estimating acid rain pH levels. The Yangming monitoring station in the Taipei metropolitan area was selected as the research site. Based on pollutant sources from the air mass back trajectory (AMBT) of the HY-SPLIT model, three possible source regions were identified: mainland China and the Japanese islands under the northeast monsoon system (Region C), the Philippines and Indochina Peninsula under the southwest monsoon system (Region R), and the Pacific Ocean under the western Pacific high-pressure system (Region S). Data for these regions were used to build the ANN_AMBT model. The AMBT model provided air mass origin information at different altitudes, leading to models for 50 m, 500 m, and 1000 m (ANN_AMBT_50m, ANN_AMBT_500m, and ANN_AMBT_1000m, respectively). Additionally, an ANN model based only on ground station attributes, without AMBT information (LSTM_No_AMBT), served as a benchmark. Due to the northeast monsoon, Taiwan is prone to severe acid rain events in winter, often carrying external pollutants. Results from these events showed that the LSTM_AMBT_500m model achieved the highest percentages of model improvement rate (MIR), ranging from 17.96% to 36.53% (average 27.92%), followed by the LSTM_AMBT_50m model (MIR 12.94% to 26.42%, average 21.70%), while the LSTM_AMBT_1000m model had the lowest MIR (2.64% to 12.26%, average 6.79%). These findings indicate that the LSTM_AMBT_50m and LSTM_AMBT_500m models better capture pH variation trends, reduce prediction errors, and improve accuracy in forecasting pH levels during severe acid rain events. Full article

Data on particulate matter, gaseous pollutants, and AQI values from three cities (Haikou, Sanya, and Danzhou) between January 2018 and December 2022 were obtained in order to analyze the spatiotemporal distribution characteristics of air pollution, the correlation between pollutants with meteorological conditions, and the potential sources in Hainan Island. The spatiotemporal distribution’s characteristics demonstrated that the annual mean concentrations of SO₂, NO₂, CO, O₃, PM₁₀ and PM_2.5 were 4.34 ± 1.11 μg m⁻³, 9.87 ± 1.87 μg m⁻³, 0.51 ± 0.06 mg m⁻³, 73.04 ± 6.36 μg m⁻³, 27.31 ± 3.63 μg m⁻³, and 14.01 ± 2.02 μg m⁻³, respectively. The yearly mean concentrations were trending downward in the past few years and were below the National Ambient Air Quality Standard (NAAQS) Grade II. Summer was the season with the lowest concentrations of all pollutants (3.84 μg m⁻³, 7.34 μg m⁻³, 0.42 mg m⁻³, 52.80 μg m⁻³, 18.67 μg m⁻³ and 8.67 μg m⁻³ for SO₂, NO₂, CO, O₃, PM₁₀ and PM_2.5, respectively), and afternoons were the time with the lowest concentrations of pollutants (except for 78.04 μg m⁻³ for O₃). The influence of meteorological conditions on pollutants was examined: there was a prominent positive correlation between temperature and O₃ in summer, and relative humidity largely influenced the concentrations of PM. The pollution in Hainan was affected more by regional transport; according to the backward trajectory results, Hainan is susceptible to air masses from Guangdong and Fujian to the northeast, the Indochina Peninsula to the southwest, and the South China Sea to the southeast. The results of PSCF and CWT analyses indicated that Guangdong, Jiangxi, Hunan, and Fujian were the primary potential sources of PM_2.5 and O₃. Full article

Southeast Asia is an essential region for companies carrying out large-scale land acquisitions (LSLAs). Exploring the distribution patterns and influencing factors of LSLA projects in this region is of great practical significance for summarizing the characteristics of LSLA projects in Southeast Asia, for gaining a thorough understanding of LSLA project development rules, and for formulating reasonable policies to guide local LSLA projects. This study explores the spatial distribution and influencing factors of LSLA projects in Southeast Asia using the mean center method, the kernel density estimation method, and the grey correlation method. The findings indicate the following: Firstly, the majority of LSLA projects in Southeast Asia are located in the Indo-China Peninsula, Cambodia, Myanmar, Laos, and other countries, which represent significant regions of interest for LSLA projects in this region. Secondly, the spatial distribution of LSLA intention projects and LSLA contract projects in Southeast Asia is similar, whereas LSLA production projects differ from the former two. Thirdly, the scale of LSLA projects in Southeast Asia is closely related to the host country’s natural resources, socio-economic conditions, governance, and market environment. The total GDP, per capita arable land area, net foreign direct investment inflow, and political stability have been identified as exerting a significant influence on investment corporations’ selection of LSLA host countries. Full article

The distribution of known Syzygiella taxa in Indochina was reviewed. Currently, four species are known in Indochina: S. autumnalis, S. elongella, S. nipponica, and S. securifolia. This genus is reported for the first time in the flora of Cambodia, and S. securifolia is newly recorded for Vietnam. Herein, a description of oil bodies for S. securifolia is provided for the first time. A morphological description of the species and intravital photographs, as well as line-art illustrations, are provided along with the identification key to the Syzygiella taxa known in Indochina. A comparison of the climatic parameters of the collection sites for four known species showed that three of them occupy a relatively marginal position in the flora of Indochina as a whole and are known from colder biomes on the very northern edge of the peninsula. The locations of Syzygiella securifolia are scattered not only on the geographical map of Indochina but also on the bioclimatic scatterplot; these locations are likely an underestimation of the distribution of this taxon in Indochina, although it is generally rare worldwide. A comparison of lists of liverworts across the countries of Indochina will help identify groups of taxa for further targeted searches with the purpose of obtaining more comprehensive knowledge of the biodiversity of still poorly studied Indochina countries. Full article

Due to the special nature of karst landforms, quantification of their vegetation dynamics and their underlying driving factors remains a formidable challenge. Based on the NDVI dataset, this study uses principal component analysis to extract comprehensive factors and utilizes an optimized parameter-based geographical detector and geographically weighted regression models to assess the explanatory capacity of comprehensive factors concerning the spatial differentiation of vegetation change. The results of this study revealed the following: (1) In terms of temporal and spatial vegetation changes, the Asian karst concentrated distribution area (AKC) displayed overall stability and an increasing trend between 2000 and 2020. Notably, the northern (Southwest China) karst region experienced the most substantial vegetation increase, with increased areas exceeding 70%, primarily concentrated in the provinces of Guizhou and Guangxi. In contrast, the southern (Indochina Peninsula) karst region, particularly in Cambodia, Laos, and Vietnam (CLV), exhibited a significant decreasing trend, with decreased areas exceeding 30%. (2) By analyzing the driving factors affecting vegetation change, vegetation changes exhibited distinct spatial differentiations, along with positive and negative effects. Human factors, including human activity intensity, urban economic development, and agricultural economic development (explanatory power and local R² were both greater than 0.2), exerted a more significant impact on vegetation change in the AKC than natural factors such as thermal conditions, water conditions, and soil conditions. This impact was positive in Southwest China but inhibited in the Indochina Peninsula, particularly within the CLV karst area. Notably, the interaction between natural and human factors greatly enhanced their impacts on vegetation changes. These results provide valuable insights into vegetation changes and their driving mechanisms, which are crucial for preserving the stability of delicate karst ecosystems and facilitating vegetation recovery. Full article

Noctuid moths, a group of “non-bee” pollinators, are essential but frequently underappreciated. To elucidate their roles in cross-regional pollination, this study selected the agriculturally significant species, cabbage looper (CL) Trichoplusia ni, as a representative model. From 2017 to 2021, this study was conducted on Yongxing Island, situated at the center of the South China Sea. We investigated the flower-visiting activities of CL, including its occurrence, potential host species, and geographic distribution in the surrounding areas of the South China Sea. First, the potential transoceanic migratory behavior and regional distribution of CL were systematically monitored through a comprehensive integration of the data obtained from a searchlight trap. The transoceanic migratory behavior of CL was characterized by intermittent occurrence, with the major migratory periods and the peak outbreak yearly. Furthermore, trajectory analysis confirmed the ability of CL to engage in periodic, round-trip, migratory flights between Southeast Asian countries and China. More importantly, an observation of pollen on the body surface demonstrated that 95.59% (130/136) of the migrating individuals carried pollen. The proboscis and compound eyes were identified as the primary pollen-carrying parts, with no observable gender-based differences in pollen-carrying rates. Further, identifying the pollen carried by CL using morphological and molecular methods revealed a diverse range of pollen types from at least 17 plant families and 31 species. Notably, CL predominantly visited eudicot and herbaceous plants. In conclusion, this pioneering study has not only revealed the long-distance migration activities of these noctuid moths in the East Asian region but also provided direct evidence supporting their role as potential pollinators. These findings offer a critical theoretical basis to guide the development of scientific management strategies. Full article

Influenced by historical background, regional economic development, and the frequent occurrence of armed conflict, the human–earth relationship in the Central and Southern Peninsula, which is located in a “fragmented zone”, is characteristic of the region. The Indochina Peninsula has now become an area of interest for the study of spatial changes in production–living–ecological spaces (PLES). Taking the Indochina Peninsula as the study area, this paper explores the evolution of the spatiotemporal patterns of PLES and its driving mechanism in the Indochina Peninsula, from 2010 to 2020, based on a grid scale. Methods such as the land-use transition matrix, land-use dynamics index, and geographically and temporally weighted regression (GTWR) were used in our model, which will provide the basic data and reference for sustainable development planning across the Indochina Peninsula. Our results show that, from 2010 to 2020, ecological space dominated the PLES pattern on the Indochina Peninsula, but its area gradually decreased, accompanied by a sharp increase in the areas of productive and living spaces. The area of PLES interconversion on the Indochina Peninsula in 2010–2020 was 212,818.70 km², and the intertransfer of production and ecological spaces was distributed in a networklike manner throughout the Indochina Peninsula, while the transfer of living space was distributed in a pointlike manner. The migration path of the center of gravity of PLES on the Indochina Peninsula demonstrated a significant directional difference, and the direction and extent of the standard deviation ellipse distribution of the ecological space was similar to that of the production space. The PLES’s pattern evolution was affected by the degree of multiple factors, with a significant spatial and temporal heterogeneity. The positive and negative feedback effects of the factors were distributed in different areas and in different transfer directions. Full article

Based on 35-year (1981–2015) ensemble (24 members) hindcasts of the IAP/LASG global seasonal prediction system named FGOALS-f2 V1.0 (FGOALS-f2), the tropical cyclone (TC) seasonal prediction skills over the South China Sea (SCS) during the TC peak season (July–November) are evaluated. Starting the prediction from June 20th, FGOALS-f2 can well capture the seasonal mean characteristics for both the genesis location and track of TCs over the SCS. For seasonal anomalous TC numbers, FGOALS-f2 underestimates the maximum and minimum of the TC number compared to the observation. The temporal correlation coefficients (TCCs) between FGOALS-f2 and the observation are 0.39 for the TC number and 0.51 for accumulated cyclone energy (ACE) over the SCS, respectively, which are both above the 95% significant level. Additionally, FGOALS-f2 has acceptable prediction skill for the seasonal mean number of TCs landing on three areas (coastal southeastern China, Indochina Peninsula, and Philippines) surrounding the SCS. The skillful prediction of SCS TCs could be ascribed to the well-predicted tropical anomaly of sea surface temperature (SSTA), TC and El Niño-Southern Oscillation (TC-ENSO) relations, and Genesis potential index (GPI). Full article

Tropical cyclones entering coastal areas adversely affect southern China. However, changes in the frequency of tropical cyclones affecting the west of southern China remain unclear. Our study reveals the possible impact of the thermal forcing anomaly over the southeast Tibetan Plateau (TP) on the frequency of tropical cyclones affecting Guangxi formed within the west of 120° E during boreal summer. Further analysis indicates that the cooling over the southeast TP is accompanied by local descending motions over southeastern TP and compensating ascending motions over eastern Indochina Peninsula and results in a reduced 850–200 hPa vertical wind shear over the north of 15° N in South China Sea (SCS), which is conducive to the westward development of tropical cyclones and favorable conditions for the formation of TCs affecting Guangxi over the SCS. Finally, the results from a linear baroclinic model experiment also verify that the changes in the 850–200 hPa vertical wind shear over southern SCS and compensating vertical motions over eastern Indochina Peninsula are associated with the thermal forcing anomaly over the southeast TP. Our results imply that in summer the thermal forcing anomaly over TP should be emphasized when interpreting and predicting the frequency of tropical cyclones affecting local areas in southern China. Full article

We used the Constellation Observing System for Meteorology, Ionosphere, and Climate-2 (COSMIC-2) dry temperature profile data from December 2019 to November 2021 to study the vertical wavenumber spectra of the potential energy of stratospheric gravity waves (GWs Ep) in the Asian monsoon region (15–45°N, 70–150°E). The GW Ep decreases with increasing vertical wavenumber, and the spectral slope varies with wavenumber. The spectral slope becomes smaller over a wavenumber range of 0.1–0.45 km⁻¹, and larger from 0.45–1 km⁻¹, with increasing wavenumber. The energy density distribution at middle and low latitudes shows seasonal variations. Over a wavenumber range of 0.05–0.5 km⁻¹, the energy density in winter is higher at middle latitudes than at low latitudes, and the opposite is observed in summer over a wavenumber range from 0.1 to 1 km⁻¹. Both the spectral amplitude and characteristic wavelength exhibit band-like patterns, and the large-value bands and their centers vary significantly with the season. In winter, the middle latitude spectral amplitude is larger than that at low latitudes, and the significant large-value band-like distribution is at ~40°N. In summer, the distribution is opposite, with large-value band regions over the Bay of Bengal and Indo-China Peninsula. The large-value region of the middle latitude spectral amplitude corresponds to a longer characteristic wavelength, while the large-value region of the low latitude spectral amplitude corresponds to a shorter characteristic wavelength. There is also significant seasonal variation in the distribution of spectral slopes. Over a wavenumber range of 0.1 to 0.5 km⁻¹, the slope is smaller at middle latitudes and larger at low latitudes in winter; the opposite is observed in summer. There is a significant annual cycle of spectral amplitude at middle and low latitudes, and a 4.8 month cycle at middle latitudes. Full article

Land use/cover (LUC) datasets are the basis of global change studies and cross-scale land planning. Data fusion is an important direction for correcting errors and improving the reliability of multisource LUC datasets. In this study, a new fusion method based on Bayesian fuzzy probability prediction was developed, and a case study was conducted in five countries of the Indochina Peninsula to form a fusion dataset with a resolution of 30 m in 2020 (BeyFusLUC30). After precision and uncertainty analysis, it was found that: (1) using accuracy validation information as prior knowledge and considering spatial relations can be well applied to LUC data fusion. (2) When compared to the four source datasets (LSV10, GLC_FCS30, ESRI10, and Globeland30), the accuracy indices of BeyFusLUC30 are all optimal. The average overall consistency increased by 6.42–13.61%, the overall accuracy increased by 4.84–7.11%, and the kappa coefficient increased by 4.98–7.60%. (3) The accuracy of the fusion result improved less for land types with good original accuracy (cropland, forest, water area, and built-up land), and the improved range of F1 score was at least 0.40–2.29%, and at most 6.66–9.88%. For the land types with poor original accuracy (grassland, shrubland, wetland, and bare land), the accuracy of the fusion result improved more, and the F1 score improved by at least 4.02–5.82%, and at most 14.41–48.35%. The LUC dataset fusion and quality improvement method developed in this study can be applied to other regions of the world as well. BeyFusLUC30 can provide reliable LUC data for scientific research and government applications in the peninsula. Full article