Geographical Distribution Research Articles

V4RIN: visual analysis of regional industry network with domain knowledge.

The regional industry network (RIN) is a type of financial network derived from industry networks that possess the capability to describe the connections between specific industries within a particular region. For most investors and financial analysts lacking extensive experience, the decision-support information provided by industry networks may be too vague. Conversely, RINs express more detailed and specific industry connections both within and outside the region. As RIN analysis is domain-specific and current financial network analysis tools are designed for generalized analytical tasks and cannot be directly applied to RINs, new visual analysis approaches are needed to enhance information exploration efficiency. In this study, we collaborated with domain experts and proposed V4RIN, an interactive visualization analysis system that integrates predefined domain knowledge and data processing methods to support users in uploading custom data. Through multiple views in the system panel, users can comprehensively explore the structure, geographical distribution, and spatiotemporal variations of the RIN. Two case studies were conducted and a set of expert interviews with five domain experts to validate the usability and reliability of our system.

New records of Dipsas welborni Arteaga & Batista, 2023, Gelanesaurus flavogularis (Altamirano-Benavides, Zaher, Lobo, Grazziotin, Sales-Nunes & Rodrigues, 2013), and Synophis insulomontanus Torres-Carvajal, Echevarría, Venegas, Chávez & Camper, 2015 in the Cordillera de Colán, Peru

A herpetological survey during recent expeditions to the foothills of the Cordillera de Colán in the Amazonas region of Peru resulted in new records and extensions of the geographical distribution of three poorly known species of reptiles: Dipsas welborni (family Colubridae), Gelanesaurus flavogularis (family Gymnophthalmidae), and Synophis insulomontanus (family Colubridae).

Species boundaries of the whiptail catfish Rineloricaria (Siluriformes: Loricariidae) from the Paraíba do Sul River drainage, southeastern Brazil, with species redescriptions and description of a new species.

Species of the catfish genus Rineloricaria are common in the Paraíba do Sul River basin, in southeastern Brazil; here we present a revision of the taxonomic diversity and geographic distribution of the species of the genus inhabiting the basin, based on novel morphologic and molecular data. Five species delimitation methods based on cytochrome C oxidase subunit 1 nucleotide sequences yielded comparable molecular operational taxonomic units. The automatic barcode gap discovery, assemble species by automatic partitioning, barcode index number, and Bayesian implementation of the Poisson tree process methods supported the recognition of five evolutionary lineages. These taxonomic units were assigned to the previously described Rineloricaria nigricauda, Rineloricaria steindachneri, Rineloricaria zawadzkii, and Rineloricaria nudipectoris, and an additional undescribed species. R. zawadzkii was further divided into two intraspecific geographically structured lineages using the generalized mixed Yule coalescent delimitation method. A maximum likelihood phylogenetic analysis revealed that the five lineages from the Paraíba do Sul have closer relationships to different species from southern and southeastern Brazil (Ribeira de Iguape, Lagoa dos Patos, Uruguay, Paraguay, and Parana river basins) than to each other. Based on the analysis of lectotypes, recently collected material, and specimens from ichthyological collections, the poorly described R. nigricauda and R. steindachneri are redescribed following current descriptive standards. The undescribed species from the middle and upper Paraíba do Sul River basin is formally described. The description of a new species, along with the description of species boundaries in R. nigricauda and R. steindachneri, contributes to the knowledge of the ichthyofauna of the Paraíba do Sul River basin and adjacent coastal drainages of southeastern Brazil. An identification key for the species of Rineloricaria occurring in the Paraíba do Sul River basin is provided.

Ciliate symbionts of bivalves with notes on their worldwide geographic distribution

The shells, mantle cavities and various organs of mollusks serve as suitable habitats for symbiotic ciliates, as commensal, epibiotic and parasitic. With about 80,000 species, molluscans are distributed in freshwater, marine and terrestrial habitats; symbiotic ciliates have been recorded in bivalves, gastropods and polyplacophorans; however these records have not been integrated in comprehensive revisions. The goal of this work is to provide an updated checklist of the ciliates involved as symbionts of bivalve molluscs worldwide. Available records of symbiotic species of ciliates were compiled and checked, along with their molluscan hosts and localities. We obtained data for 165 species of ciliates as symbionts of 158 bivalve hosts, distributed in 31 countries, regions and seas. This is the first comprehensive study to review the biodiversity of ciliates associated with bivalves and show that only a small fraction of this class of molluscs has been studied in terms of their symbiotic relationships with ciliates.

Incorporating physiological knowledge into correlative species distribution models minimizes bias introduced by the choice of calibration area

AbstractCorrelative species distribution models (SDMs) are important tools to estimate species’ geographic distribution across space and time, but their reliability heavily relies on the availability and quality of occurrence data. Estimations can be biased when occurrences do not fully represent the environmental requirement of a species. We tested to what extent species’ physiological knowledge might influence SDM estimations. Focusing on the Japanese sea cucumber Apostichopus japonicus within the coastal ocean of East Asia, we compiled a comprehensive dataset of occurrence records. We then explored the importance of incorporating physiological knowledge into SDMs by calibrating two types of correlative SDMs: a naïve model that solely depends on environmental correlates, and a physiologically informed model that further incorporates physiological information as priors. We further tested the models’ sensitivity to calibration area choices by fitting them with different buffered areas around known presences. Compared with naïve models, the physiologically informed models successfully captured the negative influence of high temperature on A. japonicus and were less sensitive to the choice of calibration area. The naïve models resulted in more optimistic prediction of the changes of potential distributions under climate change (i.e., larger range expansion and less contraction) than the physiologically informed models. Our findings highlight benefits from incorporating physiological information into correlative SDMs, namely mitigating the uncertainties associated with the choice of calibration area. Given these promising features, we encourage future SDM studies to consider species physiological information where available.

A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering

AbstractArtificial neural networks (ANN), machine learning (ML), deep learning (DL), and ensemble learning (EL) are four outstanding approaches that enable algorithms to extract information from data and make predictions or decisions autonomously without the need for direct instructions. ANN, ML, DL, and EL models have found extensive application in predicting geotechnical and geoenvironmental parameters. This research aims to provide a comprehensive assessment of the applications of ANN, ML, DL, and EL in addressing forecasting within the field related to geotechnical engineering, including soil mechanics, foundation engineering, rock mechanics, environmental geotechnics, and transportation geotechnics. Previous studies have not collectively examined all four algorithms—ANN, ML, DL, and EL—and have not explored their advantages and disadvantages in the field of geotechnical engineering. This research aims to categorize and address this gap in the existing literature systematically. An extensive dataset of relevant research studies was gathered from the Web of Science and subjected to an analysis based on their approach, primary focus and objectives, year of publication, geographical distribution, and results. Additionally, this study included a co-occurrence keyword analysis that covered ANN, ML, DL, and EL techniques, systematic reviews, geotechnical engineering, and review articles that the data, sourced from the Scopus database through the Elsevier Journal, were then visualized using VOS Viewer for further examination. The results demonstrated that ANN is widely utilized despite the proven potential of ML, DL, and EL methods in geotechnical engineering due to the need for real-world laboratory data that civil and geotechnical engineers often encounter. However, when it comes to predicting behavior in geotechnical scenarios, EL techniques outperform all three other methods. Additionally, the techniques discussed here assist geotechnical engineering in understanding the benefits and disadvantages of ANN, ML, DL, and EL within the geo techniques area. This understanding enables geotechnical practitioners to select the most suitable techniques for creating a certainty and resilient ecosystem.

The influence of manual segmentation strategies and different phases selection on machine learning-based computed tomography in renal tumors: a systematic review and meta-analysis.

Delineating the region/volume of interest (ROI/VOI) and selecting the phases are of importance in developing machine learning (ML). The results will change when choosing different methods of drawing the ROI/VOI and selecting different phases. However, there is no related standard for delineating the ROI/VOI and selecting the phases in renal tumors to develop ML based on computed tomography (CT). The PubMed and Web of Science were searched for related studies published until March 1, 2023. Inclusion criteria were studies that developed ML models in renal tumors from CT images. And the binary diagnostic accuracy data were extracted to obtain the outcomes, such as sensitivity (SE), specificity (SP), accuracy (ACC), and area under the curve (AUC). Twenty-three papers were included in the meta-analysis with a pooled SE of 87% (95% CI 85-88%), SP of 82% (95% CI 79-85%), and AUC of 91% (95% CI 89-93%) in phases; a pooled SE of 82% (95% CI 80-84%), SP of 85% (95% CI 83-86%), and AUC of 90% (95% CI 88-93%) in phases combined with delineating strategies, respectively. In all different combinations, the contour-focused and single phase produce the highest AUC of 93% (95% CI 90-95%). In subgroup analyses (sample size, year of publication, and geographical distribution), the performance was acceptable on phases and phases combined strategies. To explore the effect of manual segmentation strategies and different phases selection on ML-based CT, we find that the method of single phase (CMP or NP) combined with contour-focused was considered a better strategy compared to the other strategies.

Assessing the conservation and restoration potential of biotopes in a central European region

AbstractChanging environmental conditions and land use are threatening biodiversity on a large scale, making successful conservation and restoration essential for maintaining biodiversity. Planning of such efforts profits from information about where conditions are suitable for biotopes, to evaluate how likely successful conservation or restoration is at these sites. This study uses the distribution model Maxent to identify varying levels of conservation and restoration potential for 29 different biotopes in the central European region of Bavaria, Germany, by comparing the environmentally suitable areas identified by models with the current distribution of each biotope. We identified a conservation potential when a biotope occurred under suitable environmental conditions and a restoration potential when suitable environmental conditions were present at a site where the biotope was not observed. We found that 69.57% of biotope observations occurred under suitable environmental conditions representing a large conservation potential. Also, 22 biotopes showed more restoration potential than their current distribution and both conservation and restoration potential showed a similar geographical distribution. The approach used in this study can provide valuable insights for conservation and restoration decision-making by suggesting priority areas for the conservation and restoration of multiple biotopes. Further, it could be applied in other regions globally and by incorporating future climate projections it could identify particularly resilient locations for biotope conservation or restoration.

Risk Assessment of Spodoptera exempta against Food Security: Estimating the Potential Global Overlapping Areas of Wheat, Maize, and Rice under Climate Change

Spodoptera exempta, known as the black armyworm, has been extensively documented as an invasive agricultural pest prevalent across various crop planting regions globally. However, the potential geographical distribution and the threat it poses to host crops of remains unknown at present. Therefore, we used an optimized MaxEnt model based on 841 occurrence records and 19 bioclimatic variables to predict the potential suitable areas of S. exempta under current and future climatic conditions, and the overlap with wheat, rice, and maize planting areas was assessed. The optimized model was highly reliable in predicting potential suitable areas for this pest. The results showed that high-risk distribution areas for S. exempta were mainly in developing countries, including Latin America, central South America, central Africa, and southern Asia. Moreover, for the three major global food crops, S. exempta posed the greatest risk to maize planting areas (510.78 × 104 km2), followed by rice and wheat planting areas. Under future climate scenarios, global warming will limit the distribution of S. exempta. Overall, S. exempta had the strongest effect on global maize production areas and the least on global wheat planting areas. Our study offers a scientific basis for global prevention of S. exempta and protection of agricultural crops.

A Gaussian Function Model of Mesoscale Eddy Temperature Anomalies and Research of Spatial Distribution Characteristics

Mesoscale eddies are ubiquitous oceanic phenomena and play an important role in ocean circulation, ocean dynamics, and the transport of material energy. Temperature anomalies are a crucial parameter that reflects the state of mesoscale eddies. This study proposes a Gaussian function model to fit the vertical temperature anomaly (TA) profile to facilitate the analysis of variations, and the principle of the model is based on the fact that each TA profile tends to fluctuate around one or more peaks. The model is extracted and validated using Argo profiles within cyclonic and anticyclonic eddies in the Northwest Pacific Ocean spanning over the period from 2002 to 2021. The validation demonstrates that the model can accurately recover the vertical TA profiles with a limited number of parameters. This makes it suitable for analysing the spatial distribution patterns that require a large sample count. The analysis indicates that eddies with different TA profiles have a spatial aggregation effect in geographic distribution. Eddies with lower extreme temperature anomalies, at depths of 200–300 m, are mainly distributed along two bands on the north side of the Kuroshio Extension (KE) and the North Equatorial Current. Eddies with extreme TAs at the deepest depth (500–600 m) are distributed along the KE.

Terrestriality across the primate order: A review and analysis of ground use in primates.

Terrestriality is relatively rare in the predominantly arboreal primate order. How frequently, and when, terrestriality appears in primate evolution, and the factors that influence this behavior, are not well understood. To investigate this, we compiled data describing terrestriality in 515 extant nonhuman primate taxa. We describe the geographic and phylogenetic distribution of terrestriality, including an ancestral state reconstruction estimating the frequency and timing of evolutionary transitions to terrestriality. We review hypotheses concerning the evolution of primate terrestriality and test these using data we collected pertaining to characteristics including body mass and diet, and ecological factors including forest structure, food availability, weather, and predation pressure. Using Bayesian analyses, we find body mass and normalized difference vegetation index are the most reliable predictors of terrestriality. When considering subsets of taxa, we find ecological factors such as forest height and rainfall, and not body mass, are the most reliable predictors of terrestriality for platyrrhines and lemurs.

Screening and Identification of the Biomarkers Applied for the Evaluation of Acute and Chronic Thermal Tolerance Ability in Largemouth Bass (Micropterus salmoides)

Affected by the continuously rising temperature, thermal stress leads to a delinked growth rate and resistance to stress in cultured largemouth bass (Micropterus salmoides, LMB) in China. Identification of LMB with better thermal resistance will benefit the breeding of new varieties. However, there has been limited reporting on the evaluation to identify LMB with better thermal resistance. LMB consists of the northern LMB (Micropterus salmoides salmoides, NLMB) and the Florida LMB (Micropterus salmoides floridanus, FLMB). Due to their different geographical distributions, it has been suggested that FLMB exhibit better thermal resistance compared to NLMB. In this study, NLMB and FLMB were subjected to thermal stress for 3 h (acute) and 60 d (chronic) at 33 °C, respectively. Subsequently, the variations of 12 candidate biomarkers between NLMB and FLMB were analyzed. Exposure to acute thermal stress significantly increased plasma cortisol, blood glucose, and lactate levels; activities of superoxide dismutase (SOD), glutathione peroxidase (GPX), catalase (CAT), glucose kinase (GK), pyruvate kinase (PK), lactate dehydrogenase (LDH), and glucose 6 phosphatase (G6Pase); and the expressions of hsp70 and hsp90 in both NLMB and FLMB (p < 0.05). Compared to NLMB, FLMB exhibited a lower plasma cortisol level and a higher expression of hsp90 under acute thermal stress (p < 0.05). Exposure to chronic thermal stress significantly increased plasma cortisol and blood glucose levels, as well as activities of GK, PK, LDH, and G6Pase, as well as expressions of hsp70 and hsp90 in both NLMB and FLMB (p < 0.05). Additionally, FLMB showed a lower expression of hsp70 compared to NLMB (p < 0.05). In conclusion, our results showed that LMB with lower plasma cortisol level and higher expression of hsp90 under acute thermal stress, as well as lower expression of hsp70 under chronic thermal stress were suggested to have better thermal resistance. Our study provides valuable information for identifying and breeding LMB varieties with better thermal resistance in the future.

Distribution and genetic diversity of cobitid species in Iran (Teleostei: Cobitidae)

Several species of Cobitis have been reported within Iran, but only four are recognized as valid. The geographical distribution and genetic diversity of these species are not well-known. Two new populations of C. saniae and C. avicennae were reported herein based on their morphological and phylogenetic characters from the Mahabad River, a tributary of the Urmia Lake basin, and the Razavar River, a tributary of the Karkheh drainage, respectively. The genetic diversity and genetic structure of populations of C. saniae, C. avicennae, and C. faridpaki were analysed using the DNA sequences of the mitochondrial COI gene. COI sequences from various watersheds in Iran, Azerbaijan, and Georgia were used. A moderate to high level haplotype diversity and low nucleotide diversity were found in most of the populations of all three species. The AMOVA test and pairwise FST comparison revealed significant genetic structure and genetic distance between the Urmia Lake basin and other C. saniae populations. The results highlight the importance of conserving these species and their habitats, particularly in the Urmia Lake basin, where significant genetic differentiation was observed.

Protocol for the crowdsourced image-based morbidity hotspot surveillance for neglected tropical diseases (CIMS-NTDs).

Efficient NTDs elimination strategies require effective surveillance and targeted interventions. Traditional methods are costly and time-consuming, often failing to cover entire populations in case of movement restrictions. To address these challenges, a morbidity image-based surveillance system is being developed. This innovative approach which leverages the smartphone technology aims at simultaneous surveillance of multiple NTDs, enhancing cost-efficiency, reliability, and community involvement, particularly in areas with movement constraints. Moreover, it holds promise for post-elimination surveillance. The pilot of this method will be conducted across three states in southern Nigeria. It will target people affected by Neglected Tropical Diseases and members of their communities. The new surveillance method will be introduced to target communities in the selected states through community stakeholder's advocacy meetings and awareness campaigns. The pilot which is set to span eighteen months, entails sensitizing NTDs-affected individuals and community members using signposts, posters, and handbills, to capture photos of NTDs manifestations upon notice using smartphones. These images, along with pertinent demographic information, will be transmitted to a dedicated server through WhatsApp or Telegram accounts. The received images will be reviewed and organized at backend and then forwarded to a panel of experts for identification and annotation to specific NTDs. Data generated, along with geocoordinate information, will be used to create NTDs morbidity hotspot maps using ArcGIS. Accompanying metadata will be used to generate geographic and demographic distributions of various NTDs identified. To protect privacy, people will be encouraged to send manifestation photos of the affected body part only without any identifiable features. NTDs prevalence data obtained using conventional surveillance methods from both the pilot and selected control states during the pilot period will be compared with data from the CIMS-NTDs method to determine its effectiveness. It is expected that an effective, privacy-conscious, population inclusive new method for NTDs surveillance, with the potential to yield real-time data for the identification of morbidity hotspots and distribution patterns of NTDs will be established. The results will provide insights into the effectiveness of the new surveillance method in comparison to traditional approaches, potentially advancing NTDs elimination strategies.

Re-discovery and taxonomic clarification of Oreocharis leveilleana (Gesneriaceae) in Guizhou, China, over 100 years

Oreocharis leveilleana Fedde was collected in Ta-pin in 1910 and published in 1911. The collected location was verified within western Luodian County, Guizhou Province, China. However, there have been no records of the species’ collection for more than 100 years since then. After extensive investigations by our research team on the type locality and its surrounding areas, we found that it is widely distributed in western Luodian County and eastern Wangmo County, Guizhou Province, China. During further research on the original literature, type specimens and type locality of O. leveilleana, the taxonomic position of O. leveilleana, which was once treated as a synonym of O. auricula (S.Moore) C.B.Clarke, was found to have a taxonomic problem. Through morphological research combined with geographical distribution analysis, it has been determined that it should belong to the genus Petrocodon Hance and it is the same species as P. coccineus (C.Y.Wu ex H.W.Li) Yin Z.Wang. According to the regulations and suggestions of the 2018 "International Code of Nomenclature for Algae, Fungi, and Plants (Shenzhen Code)", we propose and confirm a new combination – Petrocodon leveilleanus (Fedde) X.X.Bai & F.Wen and treat P. coccineus as a synonym of the new combination. Due to its unique bright red flowers within Petrocodon, its original Chinese name has been retained.

Pterygodermatites (Mesopectines) whartoni (Nematoda: Rictulariidae) encysted larvae in invasive Cuban treefrogs (Osteopilus septentrionalis) from Florida, United States

Species of Pterygodermatites are spirurid nematodes that have expanded their geographic distribution worldwide. They infect a variety of mammalian definitive hosts with few reports of potential paratenic infections in amphibian and reptile hosts. In this study, we report Pterygodermatites sp. larvae identified in free-ranging, invasive Cuban treefrogs (Osteopilus septentrionalis), from central Florida, United States. Encysted larvae were recovered from the skeletal muscle and/or the coelomic cavity of three frogs; molecular characterization of the small subunit (18S) ribosomal RNA and cytochrome oxidase I genes of the parasites matched reported sequences of Pterygodermatites (Mesopectines) whartoni (Tubangui, 1931). This is a parasite native to Southeastern Asia and to the best of the authors’ knowledge, it is the first report of the species in the New World. The recovery of invasive Pterygodermatites from invasive Cuban treefrogs in North America highlights the growing concern regarding the potential impact non-native parasites and invasive species may have on native wildlife populations.

Extension of the geographical range of the primary burrowing crayfish Cambarus strigosus Hobbs, 1981 (Decapoda: Astacidea: Cambaridae) in South Carolina

Abstract Knowledge of the geographical distribution for many primary burrowing crayfishes is often limited to information presented in species descriptions or field notes associated with epigean crayfish surveys. Cambarus strigosusHobbs, 1981, a rare primary burrowing crayfish, was collected as part of a survey of the Sumter National Forest, South Carolina (SC), USA. Prior to this discovery, C. strigosus appeared to be endemic to mid-reaches of the Broad River Drainage in Georgia (GA). We conducted additional field sampling in SC and GA and used mitochondrial DNA (mtDNA) to compare the relative genetic distance between individuals across all populations in an effort to determine the identity of the specimens collected. Genetic data indicated that the crayfish collected in SC was C. strigosus, with one shared haplotype between SC and GA populations. Our genetic results exhibited geographic structuring and indicated that SC populations have a specific haplotype unique to them, providing strong evidence that SC populations are not introduced. Two additional SC records of C. strigosus from 1983 housed in the US National Museum were examined and determined to be C. strigosus. With these records, we hypothesized that like in GA, the distribution of C. strigosus in SC is limited to riparian habitats associated with the Savannah River Drainage. While the range of this species has been extended, it is still limited to small portions of this drainage. Thus, further sampling to determine the true extent of C. strigosus distribution within SC along with conservation efforts are needed to protect this globally rare species.

Research on power generation prediction of hydropower in river basin based on multi-head attention graph convolutional neural network

For the power generation prediction of traditional hydropower stations, most of them only use time series prediction and neglect to study the spatial topological relationship of hydropower stations in the river basin, so that it is difficult to fully explore the characteristic relationship of space power stations. In this paper, a research method for power generation prediction of hydropower in river basin hydropower stations based on multi-head attention map convolutional neural network is proposed. This method establishes a first-level node neighborhood feature map based on the spatial geographic distribution information of hydropower stations in the basin, and uses the method of graph convolution to carry out node feature mining and feature learning, so as to transform the power generation capacity evaluation problem of the hydropower station in the basin into the node prediction problem in the graph, which is different from the global normalization rule. the multi-head attention mechanism introduced further improves the information aggregation quality of the graph node, and uses the historical temperature, power generation, electricity price, unit status and other data of each hydropower station in the basin for training. The reasoning results show that the proposed method achieves higher accuracy than other schemes, and the power prediction method is conducive to the formulation of power plans of hydropower stations in the basin, and can also play a positive role in guiding the site selection of hydropower plants.

Effects of Environment Change Scenarios on the Potential Geographical Distribution of Cunninghamia lanceolata (Lamb.) Hook. in China

Changes in climate and environmental conditions have aggravated the severity and unpredictability of plant survival and growth. Cunninghamia lanceolata (Lamb.) Hook. is an economically important timber tree. Exploring its potential distribution and dynamic changes and identifying the leading environmental variables affecting it will help to adjust the planting range reasonably according to the habits and climate change, thus contributing to its survival and growth. Based on the MaxEnt model and ArcGIS tool, climate, soil, terrain, human activities, variable environment layers, and 395 C. lanceolata distribution points were used to simulate and analyze the geographical distribution characteristics of C. lanceolata in the current and future periods (the 2050s and 2070s) under RCP2.6, RCP4.5, RCP6.0, and RCP8.5. The results showed that C. lanceolata was suitable to grow in a subtropical monsoon climate with warm, humid, abundant rainfall and a relatively gentle topography. Additionally, using percent contribution, permutation importance, and the knife-cutting test, we noted that the annual precipitation (Bio12), human activities (Hfp), minimum temperature of the coldest month (Bio6), mean temperature of the coldest quarter (Bio11), precipitation of coldest quarter (Bio19), annual temperature range (Bio7), and elevation were the leading environmental factors affecting the geographical distribution of C. lanceolata. Among them, it should be noted that the impact of human activities was negatively correlated with suitable habitat areas of C. lanceolata and led to the degeneration of suitable habitats and fragmentized distribution. In addition, predictions have shown that the areas of habitats under other scenarios will be characterized by an increasing and then decreasing trend by the 2050s and 2070s, except for the RCP2.6 scenario, under which the suitable habitats area of C. lanceolata will increase continuously. The core distributional shifts showed that the suitable habitats of C. lanceolata will gradually shift and migrate to high-latitude areas due to global warming. This study focused on the characteristics of suitable habitats of C. lanceolata under different climatic scenarios using more environmental factors and scenarios than before, aiming to provide a theoretical basis and guidance for the management and utilization of forest resources, the planning of suitable planting areas, and germplasm protection.

Seroprevalence and molecular characterization of viral hepatitis and HIV co-infection in the Central African Republic.

The Central African Republic (CAR) is one of the countries with the highest prevalence of viral hepatitis infection in the world. Coinfection with HIV increases the morbidity and mortality beyond that of mono-infection with either hepatitis or HIV. The present study describes the geographic distribution of viral hepatitis infections and molecular characterization of these viruses in the CAR. Out of 12,599 persons enrolled during the fourth Multiple Indicator Cluster Survey of 2010 in the CAR, 10,621 Dried Blood Spot (DBS) samples were obtained and stored at -20°C. Of these DBS, 4,317 samples were randomly selected to represent all regions of the CAR. Serological tests for hepatitis B, D, and C viruses were performed using the ELISA technique. Molecular characterization was performed to identify strains. Of the 4,317 samples included, 53.2% were from men and 46.8% from women. The HBsAg prevalence among participants was 12.9% and that HBc-Ab was 19.7%. The overall prevalence of HCV was 0.6%. Co-infection of HIV/HBV was 1.1% and that of HBV/HDV was 16.6%. A total of 77 HBV, 6 HIV, and 6 HDV strains were successfully sequenced, with 72 HBV (93.5%) strains belonging to genotype E and 5 (6.5%) strains belonging to genotype D. The 6 HDV strains all belonged to clade 1, while 4 recombinants subtype were identified among the 6 strains of HIV. Our study found a high prevalence of HBV, HBV/HDV and HBV/HIV co-infection, but a low prevalence of HCV. CAR remains an area of high HBV endemicity. This study's data and analyses would be useful for establishing an integrated viral hepatitis and HIV surveillance program in the CAR.