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Within food webs, vectors of plant pathogens interact with individuals of other species across multiple trophic levels, including predators, competitors, and mutualists. These interactions may in turn affect vector-borne pathogens by altering vector fitness and behavior. Predators, for example, consume vectors and reduce their abundance, but often spur movement of vectors as they seek to avoid predation. However, a general framework to predict how species interactions affect vectors of plant pathogens, and the resulting spread of vector-borne pathogens, is lacking. Here we developed a mathematical model to assess whether interactions such as predation, competition, and mutualism affected the spread of vector-borne plant pathogens with nonpersistent or persistent transmission modes. We considered transmission mode because interactions affecting vector–host encounter rates were expected to most strongly affect nonpersistent pathogens that are transmitted with short feeding bouts; interactions that affect vector feeding duration were expected to most strongly affect persistent pathogens that require long feeding bouts for transmission. Our results show that interactions that affected vector behavior (feeding duration, vector–host encounter rates) substantially altered rates of spread for vector-borne plant pathogens, whereas those affecting vector fitness (births, deaths) had relatively small effects. These effects of species interactions were largely independent of transmission mode, except when interactions affected vector–host encounter rates, where effects were strongest for nonpersistent pathogens. Our results suggest that a better understanding of how vectors interact with other species within food webs could enhance our understanding of disease ecology.

1. Landscape composition affects local arthropod biodiversity, including herbivorous insects and their predators, yet to date landscape effects on insect-vectored plant diseases have received little attention. Here, we examine how landscape composition affects the prevalence of a viral pathogen in host plants, and the role the arthropod vector assemblage plays in mediating landscape effects. 2. We measured the effect of landscape composition (measured as percentage of cropland and unmanaged land) on the plant virus Potato virus Y (PVY), its aphid vectors, and their coccinellid predators during the 2012 and 2013 field seasons at 19-21 farms. 3. In both years, we found a positive relationship between final virus prevalence and percentage of cropland within 500, 1000 and 1500 m surrounding study sites. Percentage of cropland also had a significant negative effect on aphid species richness, and the aphid community composition in turn affected PVY prevalence. By contrast, landscape composition had no measurable effect on coccinellid abundance or species richness in this study. 4. Synthesis and applications. Our work demonstrates that landscape composition plays an important role in vector-borne pathogen spread, and that pathogen spread appears to be mediated by the effects of the landscape on the insect vector community. The small spatial scale (≤1500 m) of the effects seen in our study indicates that on-farm management practices have the potential to reduce virus prevalence on small-scale farms. Farmers may be able to reduce Potato virus Y prevalence by on-farm diversification, by isolating potato fields from other agricultural crops, and by not using saved potato seed.

Climatic, landscape, and host features are critical components in shaping outbreaks of vector-borne diseases. However, the relationship between the outbreaks of vectorborne pathogens and their environmental drivers is typically complicated, nonlinear, and may vary by taxonomic units below the species level (e.g., strain or serotype). Here, we aim to untangle how these complex forces shape the risk of outbreaks of Bluetongue virus (BTV); a vector-borne pathogen that is continuously emerging and re-emerging across Europe, with severe economic implications. We tested if the ecological predictors of BTV outbreak risk were serotype-specific by examining the most prevalent serotypes recorded in Europe (1, 4, and 8). We used a robust machine learning (ML) pipeline and 23 relevant environmental features to fit predictive models to 24,245 outbreaks reported in 25 European countries between 2000 and 2019. Our ML models demonstrated high predictive performance for all BTV serotypes (accuracies > 0.87) and revealed strong nonlinear relationships between BTV outbreak risk and environmental and host features. Serotype-specific analysis suggests, however, that each of the major serotypes (1, 4, and 8) had a unique outbreak risk profile. For example, temperature and midge abundance were as the most important characteristics shaping serotype 1, whereas for serotype 4 goat density and temperature were more important. We were also able to identify strong interactive effects between environmental and host characteristics that were also serotype specific. Our ML pipeline was able to reveal more in-depth insights into the complex epidemiology of BTVs and can guide policymakers in intervention strategies to help reduce the economic implications and social cost of this important pathogen.

Vector‐borne bacterial pathogens cause devastating plant diseases that cost billions of dollars in crop losses worldwide. These pathogens have evolved to be host‐ and vector‐dependent, resulting in a reduced genome size compared to their free‐living relatives. All known vector‐borne bacterial plant pathogens belong to four different genera: ‘Candidatus Liberibacter’, ‘Candidatus Phytoplasma’, Spiroplasma and Xylella. To protect themselves against pathogens, plants have evolved pattern recognition receptors that can detect conserved pathogen features as non‐self and mount an immune response. To gain an understanding of how vector‐borne pathogen features are perceived in plants, we investigated three proteinaceous features derived from cold shock protein (csp22), flagellin (flg22) and elongation factor Tu (elf18) from vector‐borne bacterial pathogens as well as their closest free‐living relatives. In general, vector‐borne pathogens have fewer copies of genes encoding flagellin and cold shock protein compared to their closest free‐living relatives. Furthermore, epitopes from vector‐borne pathogens were less likely to be immunogenic compared to their free‐living counterparts. Most Liberibacter csp22 and elf18 epitopes do not trigger plant immune responses in tomato or Arabidopsis. Interestingly, csp22 from the citrus pathogen ‘Candidatus Liberibacter asiaticus’ triggers immune responses in solanaceous plants, while csp22 from the solanaceous pathogen ‘Candidatus Liberibacter solanacearum’ does not. Our findings suggest that vector‐borne plant pathogenic bacteria evolved to evade host recognition. Vector‐borne bacterial plant pathogens generally exhibit fewer immunogenic proteinaceous features compared to their free‐living relatives.

Bacterial infections account for a large proportion of zoonoses. Our current understanding of zoonotic spillover, however, is largely based on studies from viral systems. Small mammals such as rodents and their ectoparasites present a unique system for studying several bacterial pathogens and mapping their spillover pathways. Using Bartonella spp. (a Gram‐negative bacteria) as a model system within a rainforest human‐use landscape, we investigated (1) ecological correlates of Bartonella prevalence in small mammal hosts and (2) evolutionary relationships between Bartonella spp. and various hosts and ectoparasites to gain insight into pathogen movement pathways within ecological communities. We detected Bartonella in five out of eight small mammal species and in 86 (40.56%) out of 212 individuals, but prevalence varied widely among species (0%–75.8%). Seven of the ten ectoparasite species found on these small mammals were positive for Bartonella. Interestingly, while Bartonella genotypes (15) in small mammals were host‐specific, ectoparasites had nonspecific associations, suggesting the possibility for vector‐mediated cross‐species transmission. We also found that Bartonella prevalence in hosts was positively correlated with their aggregated ectoparasite loads, further emphasizing the crucial role that ectoparasites may play in these transmission pathways. Our cophylogenetic analysis and ancestral trait (host) reconstruction revealed incongruence between small mammal and Bartonella phylogenies, indicating historic host shifts and validating the potential for contemporary spillover events. We found that small mammal hosts in this fragmented landscape often move across habitat boundaries, creating a transmission pathway (via shared ectoparasites) to novel hosts, which may include synanthropic species like Rattus rattus. Our results highlight the necessity to disentangle the complex relationship among hosts, ectoparasites, and bacterial pathogens to understand the implications of undetected spillover events. This study focuses on Bartonella spp. (a Gram‐negative bacteria) in small mammals and their ectoparasites within a rainforest human‐use landscape. We found that a high proportion (40.56%) of the tested small mammals across five species and two sites carry Bartonella, with prevalence being positively correlated with aggregated ectoparasite load. Despite the occurrence of host‐specific genotypes, we observed incongruence in small mammal and Bartonella phylogenies, indicating historic host shifts within the community, and the potential for cross‐species transmissions in the future mediated by ectoparasites.

Integrated Pest Management (IPM) is an approach to minimizing economic and environmental harm caused by pests, and Integrated Vector Management (IVM) uses similar methods to minimize pathogen transmission by vectors. The risk of acquiring a vector-borne infection is often quantified using the density of infected vectors. The relationship between vector numbers and risk of human infection is more or less linear when both vector numbers and pathogen prevalence in vectors are low, but the relationship is nonlinear when vector density and/or infection prevalence are high. Therefore, the density of infected vectors often does not accurately predict risk of human exposure to pathogens, and traditional estimates of the percent control often overestimate the level of protection from infection resulting from management programs. We suggest a modified estimator, percent protection, which more accurately quantifies protection against human infection resulting from a management intervention. Cost-effectiveness of a management program is critical to protection of both public health and the environment, because the more efficiently available resources and funding are used, the fewer people get sick, and well-targeted efficient management programs minimize the need for poorly targeted, expensive environmental interventions (e.g., broadscale pesticide applications) that tend to damage nontarget organisms and natural systems. Design of an efficient, cost-effective IVM program requires knowledge of the cost-effectiveness functions (the effectiveness of control methods at lowering vector bites and/or infection prevalence with different levels of application) of the various control methods to be applied. Alternative programs can be designed that optimize percent protection by integrating different control methods at different levels of investment, and environmental effects of these alternatives can be compared, allowing environmental considerations to be included explicitly in the decision process. IPM, IVM, and Adaptive Management share the characteristic that management decisions must be made with incomplete knowledge of the functioning of natural systems or the efficacies of interventions. IVM surveillance programs that assess the effects of individual control methods and of combinations of control methods on the numbers of vector bites and on infection prevalence in vectors can increase knowledge of pathogen transmission dynamics and provide information to improve program effectiveness in subsequent applications.

The bird cherry-oat aphid (Rhopalosiphum padi L.) is a global pest of wheat and vectors some of the most damaging strains of barley yellow dwarf virus (BYDV). In years of heavy R. padi infestation, R. padi and BYDV together reduce wheat yields by 30–40% in Kansas and other states of the U.S. Great Plains wheat production area. Cultivation of wheat cultivars resistant to R. padi can greatly reduce production costs and mitigate R. padi–BYDV yield losses, and increase producer profits. This study identified cultivars of hard red and soft white winter wheat with R. padi resistance that suppress R. padi populations or tolerate the effects of R. padi feeding damage. ‘Pioneer (S) 25R40,' ‘MFA (S) 2248,' ‘Pioneer (S) 25R77,' and ‘Limagrain LCS Mint' significantly reduced R. padi populations. MFA (S) 2248, Pioneer (S) 25R40, and ‘Limagrain LS Wizard’ exhibited tolerance expressed as significantly greater aboveground biomass. These findings are significant in that they have identified wheat cultivars currently available to producers, enabling the immediate improvement of tactics to manage R. padi and BYDV in heavily infested areas. Secondarily, these results identify cultivars that are good candidates for use in breeding and genetic analyses of arthropod resistance genes in wheat.

Phytoplasmas (Mollicutes, Acholeplasmataceae), vector‐borne obligate bacterial plant parasites, infect nearly 1,000 plant species and unknown numbers of insects, mainly leafhoppers (Hemiptera, Deltocephalinae), which play a key role in transmission and epidemiology. Although the plant–phytoplasma–insect association has been evolving for >300 million years, nearly all known phytoplasmas have been discovered as a result of the damage inflicted by phytoplasma diseases on crops. Few efforts have been made to study phytoplasmas occurring in noneconomically important plants in natural habitats. In this study, a subsample of leafhopper specimens preserved in a large museum biorepository was analyzed to unveil potential new associations. PCR screening for phytoplasmas performed on 227 phloem‐feeding leafhoppers collected worldwide from natural habitats revealed the presence of 6 different previously unknown phytoplasma strains. This indicates that museum collections of herbivorous insects represent a rich and largely untapped resource for discovery of new plant pathogens, that natural areas worldwide harbor a diverse but largely undiscovered diversity of phytoplasmas and potential insect vectors, and that independent epidemiological cycles occur in such habitats, posing a potential threat of disease spillover into agricultural systems. Larger‐scale future investigations will contribute to a better understanding of phytoplasma genetic diversity, insect host range, and insect‐borne phytoplasma transmission and provide an early warning for the emergence of new phytoplasma diseases across global agroecosystems. Phytoplasmas are a diverse group of obligate intracellular bacterial parasites, and diseases associated with these bacteria are among the most important problems affecting agriculture worldwide. However, most knowledge of the diversity and ecology of phytoplasmas and their hosts has been accumulated through studies of phytoplasma disease epidemiology in agroecosystems. In light of recent attention to the importance of wildlife as reservoirs of emergent diseases, we screened potential insect vectors collected in natural areas and preserved in museum biorepositories, 6 of them were positive for the presence of phytoplasmas.

Estimating population abundance is a key objective of surveillance programmes, particularly for vector species of public health interest. For mosquitos, which are vectors of human pathogens, established methods to measure absolute population abundance such as mark‐release‐recapture are difficult to implement and usually spatially limited. Typically, regional monitoring schemes assess species relative abundance (counting captured individuals) to prioritize control efforts and study species distribution. However, assessing absolute abundance is crucial when the focus is on pathogen transmission by contacts between vectors and hosts. Here, we applied the N‐mixture model approach to estimate mosquito abundance from standard monitoring data. We extended the N‐mixture model approach in a Bayesian framework by considering a beta‐binomial distribution for the detection process. We ran a simulation study to explore model performance under a low detection probability, a time‐varying population and different sets of independent variables. When informative priors were used and the model was well specified, estimates by N‐mixture model well correlated (>0.9) with synthetic data and had a mean absolute deviation of about 20%. Correlation decreased and biased increased with uninformative priors or model misspecification. When fed with field monitoring data to estimate the absolute abundance of the mosquito arbovirus vector Aedes albopictus within the metropolitan city of Rome (Italy), the N‐mixture model showed higher population size in residential neighbourhoods than in large green areas and revealed that traps located adjacent to vegetated sites have a higher probability of capturing mosquitoes. Synthesis and applications. Our results show that, if supported by a good knowledge of the target species biology and by informative priors (e.g. from previous studies of capture rates), the N‐mixture model represents a valuable tool to exploit field monitoring data to estimate absolute abundance of disease vectors and to assess vector‐related health risk on a wide spatial and temporal scale. For mosquitoes specifically, it is also valuable to invest in increased efficiency of trapping devices to improve estimates of absolute abundance from the models. Our results show that, if supported by a good knowledge of the target species biology and by informative priors (e.g. from previous studies of capture rates), the N‐mixture model represents a valuable tool to exploit field monitoring data to estimate absolute abundance of disease vectors and to assess vector‐related health risk on a wide spatial and temporal scale. For mosquitoes specifically, it is also valuable to invest in increased efficiency of trapping devices to improve estimates of absolute abundance from the models.

Lyme disease, the most widespread tick‐borne disease in North America, is caused by the bacterium Borrelia burgdorferi (Bb). Approximately 10–15% of infections result in neuroborreliosis, common symptoms of which include headaches, facial palsy, and long‐term cognitive impairment. Previous studies of Bb dissemination focus on assessing Bb transmigration at static time points rather than analyzing the complex dynamic process of extravasation. Furthermore, current in vitro models lack crucial physiological factors such as flow, demonstrating a need for more robust models for studying Bb dissemination to understand its dynamics and mechanisms. Here, a 3D tissue‐engineered microvessel model is used and fluorescently‐labeled Bb is perfused to model vascular dissemination in non‐tissue‐specific (iEC) and brain‐specific (iBMEC) microvessels while acquiring time‐lapse images in real time. In iECs, extravasation involves two steps: adhesion to the endothelium and transmigration into the extracellular matrix, which can be modulated through glycocalyx degradation or inflammation. In contrast, Bb extravasation in iBMECs is an extremely rare event regardless of glycocalyx degradation or inflammation. In addition, circulating Bb do not induce endothelial activation in iECs or iBMECs, but induces barrier dysfunction in iECs. These findings provide a further understanding of Bb vascular dissemination. Lyme disease is caused by vascular dissemination of the bacteria Borrelia. In a 3D tissue‐engineered microvessel model, Borrelia extravasation across the blood–brain barrier is extremely rare, implying that neuroborreliosis is not caused via direct cytotoxicity. In contrast, Borrelia extravasate across non‐tissue specific microvessels which involves two steps: adhesion and transmigration, and can be modulated through glycocalyx degradation or inflammation.