Search Results (190)

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

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article

Luprops orientalis (Motschulsky, 1868) is an economically important pest in traditional Chinese medicines, widely distributed in East Asia. However, the primary limiting factors affecting its distribution, potential suitable areas, as well as its response to global warming, remain largely unknown. Utilizing 295 filtered distribution points and 10 environmental variables (9 climate variables and 1 land cover type), this study uses the MaxEnt model to predict the potential distribution of L. orientalis under near-current and future environmental change scenarios. The results indicated that precipitation of the warmest quarter (bio18), temperature seasonality (bio04), and precipitation of the wettest month (bio13) were the most significant environmental variables affecting the distribution of suitable habitats for L. orientalis, while the contribution of average variation in daytime temperature (bio2) was the smallest. Under the near-current climate, the areas of low, moderate, and high suitability for L. orientalis are approximately 1.02 × 10⁶ km², 1.65 × 10⁶ km², and 8.22 × 10⁵ km², respectively. The suitable areas are primarily located in North China, Central China, the Korean Peninsula, and Central and Southern Japan. Under future climate conditions, the potential suitable areas are expected to expand significantly, especially in Central China. However, the high-suitability areas in North China are predicted to experience a slight reduction. With the increase in carbon emission concentrations, the suitable area shows an increasing trend in the 2050s, followed by a declining trend in the 2090s. The centroids of suitable areas will shift to the northeast in the future. These findings enhance our understanding of how climate change affects the distribution of L. orientalis and will assist governments in formulating effective pest control strategies, including widespread monitoring and stringent quarantine measures. Full article

The composition of the gut microbiome, defined by environmental factors, significantly affects research outcomes, with variations observed across animal facilities. Efforts to standardize led to the definition of the ‘Altered Schaedler flora’ (ASF), comprising eight bacterial groups. Our data highlights the variability of ASF under pathogen contact. Feces from two wild-type strains (C57Bl/6J and Balb/c mice) with and without proven infection was collected in two different animal facilities and analyzed. The data show a significant difference in the quantity (either reduction or increase) of the eight ASF bacterial groups when comparing infected and non-infected mice across different housing areas (SPF-specific pathogen-free, quarantine, and conventional-experimental areas) within a facility, as well as in comparison to another facility. Furthermore, strain-specific differences are also evident, with certain ASF groups showing a reduction in quantity at one facility but an increase at the other, comparing the same housing area. Comparative studies across facilities confirmed the necessity of baseline determination for accurate ASF analysis. Performing ASF analysis, facilitated by in-house qPCR (quantitative polymerase chain reaction) kits, offers prompt and precise microbiome profiling, enhancing experimental accuracy and health monitoring in animal research settings. Full article

Pine wilt disease (PWD) is a severe forest disease caused by the infestation of pine wood nematodes. Due to its short disease cycle and strong transmission ability, it has caused significant damage to China’s forestry resources. To achieve large-scale monitoring of PWD, this study utilized machine learning/deep learning algorithms with Sentinel-1/2 images in the Google Earth Engine cloud platform to implement province-wide PWD monitoring in Anhui Province, China. The study also analyzed the spatial distribution of PWD in Anhui Province from two perspectives—spatiotemporal patterns and influencing factors—aiming to investigate the spatiotemporal evolution patterns and the impact of influencing factors on the occurrence of PWD. The results show that (1) the random forest model exhibited the strongest performance, followed by the CNN model, while the DNN model performed the worst. Using the RF model to monitor PWD and calculate the affected area in Anhui Province from 2019 to 2024 yielded errors within 30% compared to official statistics. (2) PWD in Anhui Province showed a clear clustering trend, with global Moran’s indices all exceeding 0.79 from 2019 to 2024. The LISA map revealed a spread pattern from south to north and from west to east. (3) Topographic and temperature factors had the greatest influence on PWD distribution. SHAP analysis indicated that topographic and climatic factors were the primary drivers of PWD-affected areas, with slope and temperature being the two most significant contributing factors. This study helps to rapidly and accurately identify outbreak areas during epidemics and enables precise quarantine measures and targeted control efforts. Full article

The pine wood nematode (Bursaphelenchus xylophilus, PWN) is a globally significant quarantine pest that causes severe economic and ecological damage to coniferous forests worldwide. Additionally, PWNs continue to expand into higher latitudes. However, studies on their cold tolerance remain limited. This study investigated the overwintering environment of PWNs in epidemic areas of Liaoning Province, China. It established a protocol to induce anhydrobiosis in PWNs, evaluated their low-temperature resistance, observed morphological changes during anhydrobiosis, and explored potentially involved key genes. The results showed that (1) there was no significant difference in thermal insulation between infected and healthy wood in Liaoning Province; both effectively reduced temperature fluctuation rates, providing a protective function for PWN overwintering. (2) PWNs significantly enabled their cold tolerance through anhydrobiosis, accompanied by significant morphological changes and substantial lipid droplet depletion. (3) Eleven anhydrobiosis-related genes were identified. Among these, the collagen gene family showed consistent expression patterns throughout dehydration and rehydration. This suggests a potential role in cuticle structural changes and osmoregulation during anhydrobiosis. These findings provide a theoretical basis for understanding how PWNs survive winter conditions in high-latitude regions. Additionally, they offer valuable insights for future research into PWN anhydrobiosis and the development of effective control strategies. Full article

Pantoea stewartii subsp. stewartii (Pss) is a Gram-negative bacterium causing Stewart wilt, a severe disease in maize. Native to North America, it has spread globally through the maize seed trade. Resistant maize varieties and insecticides are crucial to mitigate the disease’s economic impact. Pss is a quarantine pest, requiring phytosanitary certification for the seed trade in European countries. Accurate diagnostic tests, including real-time PCR, are fundamental to detect Pss and distinguish it from other bacteria, like Pantoea stewartii subsp. indologenes (Psi), a non-quarantine bacteria associated with maize seeds. Population genetics is a valuable tool for studying adaptation, speciation, population structure, diversity, and evolution in plant bacterial pathogens. In this study, the key activities of interlaboratory comparisons are reported to assess diagnostic sensitivity (DSE), diagnostic specificity (DSP) and accuracy (ACC) for different real-time PCR able to detect Pss in seeds. The results of complete sequencing of Italian bacterial isolates are presented. This study enhances our understanding of molecular methods for diagnosing and identifying pathogens in maize seeds, improving knowledge of Pss genomes to prevent their spread and trace possible entry routes from endemic to non-endemic areas. Full article

Biosecurity is essential in livestock farming to prevent the spread of diseases, ensure animal welfare, and maintain farm sustainability. In Portugal, small ruminant farms are predominantly extensive and small-scale, and most of them are familiar, especially in the northern and inland regions. Thus, biosecurity implementation on these farms is low due to factors such as an aging livestock farmer population, poor training, limited veterinary support, and economic constraints. This study, the first to assess biosecurity on Portuguese small ruminant farms, evaluated compliance levels and the influence of sociodemographic factors. A cross-sectional study was conducted between July 2023 and April 2024, through structured interviews with 276 farmers. A 32-question checklist covering nine biosecurity categories was used to assess compliance. The results revealed poor implementation of key biosecurity measures, particularly cleaning and disinfection, quarantine protocols, and visitor control. Inadequate premises infrastructure, including the absence of quarantine areas, isolation facilities for sick animals, and farrowing rooms, further hampered disease prevention. Compliance was influenced by farmers’ age, education level, herd size, and production. Larger farms, particularly dairy farms, demonstrated better biosecurity practices, likely due to better management and infrastructure. This study highlights the challenges of implementing biosecurity measures on small-scale, extensive farms and argues that standardized plans are ineffective. Instead, region- and farm-specific strategies are needed, considering the socioeconomic realities of farmers. Improving farmers’ education and access to veterinary services is crucial. Furthermore, public policies should provide financial incentives and educational programs to improve biosecurity without compromising farm viability. Strengthening biosecurity on small ruminant farms is vital to protecting animal and public health and ensuring the long-term sustainability of rural communities in Portugal. Full article

In this study, we predicted the global climatic suitability of Tuta absoluta, using maximum entropy (MaxEnt) modeling. We used species’ natural occurrence records in 1981–2024 and environmental variables in 1981–2010, reflecting near-current climate conditions, for modeling. The occurrence records used for modeling excluded the data from greenhouses and summer-only presence. The optimized MaxEnt models demonstrated an excellent predictive performance; Jaccard’s and Sørensen’s indices were greater than 0.8. Temperature, particularly the mean daily air temperature in February (tas2), was identified as the primary influencing factor. Projections based on five global climate models (GCMs) and four shared socioeconomic pathways (SSPs) indicated an increasing risk of T. absoluta expansion. Under SSP126, the lowest-risk period (2011–2040) exhibited a 7.08% increase in suitable areas, while SSP370 during the highest-risk period (2071–2100) projected an 18.13% increase relative to near-current conditions. Model outputs underestimated the pest’s actual distribution, underscoring its invasive potential. We recommend stringent quarantine measures, particularly for artificial facilities that support overwintering, to mitigate future invasions. These findings provide critical insights for policymakers and agricultural stakeholders to safeguard global tomato production against this invasive threat. Full article

As a prominent forest pest on international quarantine lists, pine wilt disease (PWD) is characterized by its ease of transmission, rapid onset, high mortality rate, and the complexity of its prevention and control. The disease inflicts devastating damage on pine forest ecosystems and biodiversity in affected regions, resulting in substantial losses of ecological and economic value. This study uses 40 years of county-level data on PWD occurrences in China to investigate the historical spatiotemporal distribution patterns, the spreading process, and the impact of PWD on forest ecosystems. We divided the spread of PWD in China into three stages based on the changes in the number of affected areas. We used SaTScan spatial scanning to analyze the spatiotemporal distribution patterns and regional characteristics of the disease in each stage. Based on the spatial relationships of the affected areas, we identified two types of spread, namely continuous spread and leapfrogging spread, and conducted ecological models of the two spreading processes to describe the spread of PWD over the past 40 years. The results indicate that PWD has two major expansion periods in China. They show a diffusion pattern spreading from points to areas, ultimately forming four clusters with regional characteristics. Driving factors were selected for model construction based on the biological characteristics and spatiotemporal distribution patterns of PWD. The Susceptible (SIS) model and Random Forest (RF) model achieve good results in simulating continuous and leapfrog spread. By integrating the models of the two spreading processes, we can clearly quantify the 40-year spread of PWD in China. The long-term dynamic ecological modeling of PWD, based on historical dissemination characteristics, facilitates the development of disaster prediction models and the maintenance of forest ecosystems while also providing case studies for the invasion and spread of forest pests and pathogens. Full article

Xylella fastidiosa (Xf) is a quarantine pathogen heavily affecting economically important crops worldwide. Different sequence types (STs) belonging to Xf subspecies are present in various areas of Spain, including the Balearic Islands, and cause the almond leaf scorch disease (ALSD) in Prunus spp. The increased demand for rapid tests for early detection of the pathogen should enforce strict containment measures. Molecular detection through isothermal amplification reactions enables simplified instrumentation and the use of raw nucleic acid extracts. Colorimetric loop-mediated isothermal amplification (cLAMP) was applied to rapidly detect Xf in naturally infected almonds on Mallorca Island (Spain), using a quick crude sap extraction without DNA purification. Following tissue homogenization, an alkaline treatment for target DNA extraction was conducted before the cLAMP test. The cLAMP assay was able to detect up to 100 CFU/mL of the Xf bacterial suspension diluted in healthy almond sap. The same crude extracts used in the cLAMP test were also tested by qPCR. An overall positive agreement of about 47% was observed between the results of the two techniques, while a decrease in cLAMP sensitivity was evident as the bacterial titer declined in infected plants over Cq > 26–27. This study shows the potential of the cLAMP application as a rapid and low-cost point-of-care diagnostic method for the timely monitoring of Xf directly in the field. Full article

Xylella fastidiosa subsp. pauca (Xfp), a quarantine pathogen in the European Union, severely threatens Mediterranean olive production, especially in southern Italy, where Olive Quick Decline Syndrome (OQDS) has devastated Apulian olive groves. This study addresses the urgent need to identify resistant olive genotypes by monitoring 16 potentially tolerant genotypes over six years, assessing symptom severity and bacterial load. These genotypes, which survived in heavily infected areas, showed varied responses to Xfp; some maintained low symptom severity with minimal bacterial presence (high or undetectable Cq values), while others exhibited increased bacterial loads yet remained asymptomatic or showed limited canopy desiccation. SSR markers were used to investigate the genetic relationships among these genotypes and other widespread Mediterranean cultivars, showing genetic similarity with the resistant ones such as the Albanian Kalinjot and the Greek Leucocarpa, as well as with local Apulian cultivars, highlighting the potential of local and Mediterranean olive germplasm for Xfp resistance. This study integrates phenotypic responses with genetic knowledge to support the development of conservation strategies that will enhance the genetic diversity of Apulian olive cultivars. In addition, by focusing on the resilience of the different olive genotypes, this research aims to protect the traditional cultivars from the emerging threats, thus preserving the ecological and cultural heritage of the olive biodiversity of the Mediterranean region. Full article

Bluetongue virus (BTV) and epizootic hemorrhagic disease virus (EHDV) are vector-borne orbiviruses that pose an emerging threat to livestock, including cattle and sheep. This review summarizes the global distribution, genetic diversity, and key factors driving their spread along with the existing knowledge gaps and recommendations to mitigate their impact. Both viruses cause hemorrhagic disease in susceptible ruminants and are commonly reported in tropical and subtropical regions including North America, Asia, Africa, Oceania, and some parts of Europe. The geographical distribution of these viruses, encompassing 27 BTV and 7 EHDV serotypes, has shifted, particularly with the recent invasion of BTV-3, 4, and 8 and EHDV-8 serotypes in Europe. Several factors contribute to the recent spread of these viruses such as the distribution of virulent strains by the movement of temperature-dependent Culicoides vectors into new areas due to rapid climate change, the reassortment of viral strains during mixed infections, and unrestricted global trade. These diseases cause significant economic impacts including morbidity, mortality, reduced production, high management costs, and the disruption of international trade. Effective prevention and control strategies are paramount and rely on vaccination, vector control using insecticides, and the destruction of breeding sites, husbandry practices including the isolation and quarantine of infected hosts, restriction of animal movement, prompt diagnosis and identification of circulating strains, and effective surveillance and monitoring plans such as the pre-export and post-import screening of semen used for artificial insemination. However, challenges remain with intercontinental virus spread, live vaccines, and the failure of inactivated vaccines to produce protective immunity against dissimilar strains. Significant knowledge gaps highlight the need for a better scientific understanding and a strategic plan to ensure healthy livestock and global food security. Full article

Movements of plant pathogenic microorganisms in uncontaminated areas occur today at an alarming rate, driven mainly by global trade and climate change. These invaders can trigger new disease outbreaks able to impact the biodiversity and economies of vast territories and affect a variety of ecosystem services. National and supranational regulatory deficiencies, such as inadequate quarantine measures and ineffective early pathogen detection at ports of entry, exacerbate the issue. Thus, there is an urgent need for accurate and rapid diagnostic tools to intercept invasive and nonindigenous plant pathogens. The LAMP (Loop-mediated isothermal AMPlification) technique is a robust, flexible tool representing a significant advance in point-of-care (POC) diagnostics. Its user-friendliness and sensitivity offer a breakthrough in phytosanitary checks at points of entry (harbors and airports), for disease and pest surveillance at vulnerable sites (e.g., nurseries and wood-processing and storage facilities), and for territorial monitoring of new disease outbreaks. This review highlights the strengths and weaknesses of LAMP, emphasizing its potential to revolutionize modern plant disease diagnostics. Full article

The mealybug can severely threaten agricultural and horticultural crops and has a widespread distribution in tropical regions, particularly in high-risk invasion areas such as Hainan, which is an important trade port with superior geographical conditions. Traditional morphological methods can no longer meet the requirements for the rapid and precise identification of different insect stages or debris. DNA barcoding has been used to establish efficient molecular identification tools. In this study, a multiplex polymerase chain reaction (mPCR) assay based on the cytochrome c oxidase subunit I (COI) gene was successfully constructed for the rapid identification of mealybugs. The 5′ end COI gene fragments of 12 mealybug species were amplified and sequenced. Furthermore, an mPCR assay was established to identify three common mealybug species in Hainan, namely Dysmicoccus neobrevipes, Maconellicoccus hirsutus, and Paracoccus marginatus. Condition optimization, sensitivity detection, and field sample testing results prove that the assay can identify the three target species through a single PCR amplification. A sample DNA concentration of as low as 0.1–1 ng/μL can be detected. Additionally, the assay in conjunction with barcode sequencing can identify mealybugs collected in the field, clarifying the distribution and host plants of 12 mealybug species commonly found in Hainan. Thus, the rapid identification of important mealybug species is realized. The establishment of this technology provides an economical and efficient molecular tool for the quarantine and monitoring of mealybugs in Hainan and other regions, which are essential for the detection, monitoring, and early warning of invasive organisms. Full article