Werner Hoffmeister

Globally, vector‐borne diseases are increasing in distribution and frequency, affecting humans, domestic animals, and wildlife. Science‐based management and prevention of these diseases requires a sound understanding of the distribution and environmental requirements of the vectors and hosts involved in disease transmission. Integrated Species Distribution Models (ISDM) account for diverse data types through hierarchical modeling and represent a significant advancement in species distribution modeling. We assessed the distribution of the soft tick subspecies Ornithodoros turicata americanus. This tick species is a potential vector of African swine fever virus (ASFV), a pathogen responsible for an ongoing global epizootic that threatens agroindustry worldwide. Given the novelty of this method, we compared the results to a conventional Maxent SDM and validated the results through data partitioning. Our input for the model consisted of systematically collected detection data from 591 sampled field sites and 12 historical species records, as well as four variables describing climatic and soil characteristics. We found that a combination of climatic variables describing seasonality and temperature extremes, along with the amount of sand in the soil, determined the predicted intensity of occurrence of this tick species. When projected in geographic space, this distribution model predicted 62% of Florida as suitable habitat for this tick species. The ISDM presented a higher TSS and AUC than the Maxent conventional model, while sensitivity was similar between both models. Our case example shows the utility of ISDMs in disease ecology studies and highlights the broad range of geographic suitability for this important disease vector. These results provide important foundational information to inform future risk assessment work for tick‐borne relapsing fever surveillance and potential ASF introduction and maintenance in the United States.

The genus Rhodiola L., an integral part of traditional Chinese medicine and Tibetan medicine in China, exhibits a broad spectrum of applications. This genus contains key compounds such as ginsenosides, polysaccharides, and flavonoids, which possess anti-inflammatory, antioxidant, hypoglycaemic, immune-enhancing, and anti-hypoxic properties. As a vital raw material, Rhodiola L. contributes to twenty-four kinds of Chinese patent medicines and 481 health food products in China, finding extensive application in the health food sector. Recently, polysaccharides have emerged as a focal point in natural product research, with applications spanning the medicine, food, and materials sectors. Despite this, a comprehensive and systematic review of polysaccharides from the genus Rhodiola L. polysaccharides (TGRPs) is warranted. This study undertakes a systematic review of both domestic and international literature, assessing the research advancements and chemical functional values of polysaccharides derived from Rhodiola rosea. It involves the isolation, purification, and identification of a variety of homogeneous polysaccharides, followed by a detailed analysis of their chemical structures, pharmacological activities, and molecular mechanisms, structure-activity relationship (SAR) of TGRPs. The discussion includes the influence of molecular weight, monosaccharide composition, and glycosidic bonds on their biological activities, such as sulfation and carboxymethylation et al. Such analyses are crucial for deepening the understanding of Rhodiola rosea and for fostering the development and exploitation of TGRPs, offering a reference point for further investigations into TGRPs and their resource utilization.

Background The recent discovery of emerging relapsing fever group Borrelia (RFGB) species, such as Borrelia miyamotoi, poses a growing threat to public health. However, the global distribution and associated risk burden of these species remain uncertain. We aimed to map the diversity, distribution, and potential infection risk of RFGB.MethodsWe searched PubMed, Web of Science, GenBank, CNKI, and eLibrary from Jan 1, 1874, to Dec 31, 2022, for published articles without language restriction to extract distribution data for RFGB detection in vectors, animals, and humans, and clinical information about human patients. Only articles documenting RFGB infection events were included in this study, and data for RFGB detection in vectors, animals, or humans were composed into a dataset. We used three machine learning algorithms (boosted regression trees, random forest, and least absolute shrinkage and selection operator logistic regression) to assess the environmental, ecoclimatic, biological, and socioeconomic factors associated with the occurrence of four major RFGB species: Borrelia miyamotoi, Borrelia lonestari, Borrelia crocidurae, and Borrelia hermsii; and mapped their worldwide risk level.FindingsWe retrieved 13 959 unique studies, among which 697 met the selection criteria and were used for data extraction. 29 RFGB species have been recorded worldwide, of which 27 have been identified from 63 tick species, 12 from 61 wild animals, and ten from domestic animals. 16 RFGB species caused human infection, with a cumulative count of 26 583 cases reported from Jan 1, 1874, to Dec 31, 2022. Borrelia recurrentis (17 084 cases) and Borrelia persica (2045 cases) accounted for the highest proportion of human infection. B miyamotoi showed the widest distribution among all RFGB, with a predicted environmentally suitable area of 6·92 million km2, followed by B lonestari (1·69 million km2), B crocidurae (1·67 million km2), and B hermsii (1·48 million km2). The habitat suitability index of vector ticks and climatic factors, such as the annual mean temperature, have the most significant effect among all predictive models for the geographical distribution of the four major RFGB species.InterpretationThe predicted high-risk regions are considerably larger than in previous reports. Identification, surveillance, and diagnosis of RFGB infections should be prioritised in high-risk areas, especially within low-income regions.FundingNational Key Research and Development Program of China.

In this study, we analyzed the distribution and the spatial pattern of species diversity of vascular plants in Nepal. The aim was to identify and evaluate the occurrence and status of species‐rich areas in Nepal using ecological and environmental drivers. We used 52,973 georeferenced herbarium specimen records, representing 2650 species collected from Nepal. Altogether, 41 environmental variables were used for model development and validation. We used MaxEnt to predict the distribution pattern. All the significant species distribution predictions were then used to develop a species richness and endemism pattern in Nepal. The High Mountain and Himalaya, particularly east and central Nepal, were found to be species diverse and endemically rich areas, whereas western Nepal had lower species richness. We observed that isothermality, slope, rugosity, potential evapotranspiration, precipitation of humid months, temperature annual range, mean diurnal range, and normalized difference in vegetation index of humid months were the most influential environmental and climatic variables. We observed that about 60% of the areas, which had highest richness and endemism values, are still not included in protected areas in Nepal. We quantitatively analyzed the species richness and endemicity patterns of Nepal and were able to identify 19 areas of high species diversity and endemicity, six of which are newly identified.

Aims: Species distribution models (SDMs) are often used to forecast potential distributions of important invasive or rare species. However, situations where models could be the most valuable ecologically or economically, such as for predicting invasion risk, often pose the greatest challenges to SDM…