A major challenge in understanding the emergence of infectious disease is to identify the driving factors responsible for changes in infectious disease dynamics. New infectious diseases mostly emerge in tropical regions that have undergone strong ecological and economic change (Swei et al., 2020). Tropical rainforests are terrestrial ecosystems with a high biodiversity, constituting a habitat with a diverse range of hosts and pathogens. This high host richness likely corresponds to a high pathogen richness as each host is likely to carry its own specific pathogens (Dunn et al., 2010). Pristine rainforests are subject to large scale anthropogenic land use transformation leading to increased contact among humans, wildlife, and pathogens (Keesing et al., 2010; Olival et al., 2017). Disturbed habitats often show a drastic decline in biodiversity or turnover of host species community composition, which is accompanied by an increase of species that are resilient to disturbance, so-called generalist species. How changes in community composition influence pathogen prevalence in target hosts is still unclear and a matter of scientific debate (Randolph and Dobson, 2012).

In this context, the dilution effect hypothesis postulates that a high diversity of different host species dilutes the prevalence of a specific pathogen in intact ecosystems as the density of competent hosts (hosts that contribute to pathogen transmission and maintenance) is diluted by the presence of non-competent hosts. Biodiversity loss increases the infection prevalence of this specific pathogen in the disturbance-resilient, competent host species (Ostfeld, 2017). Prerequisites for the occurrence of a dilution effect are the presence of species that differ in their susceptibility for a particular pathogen and a lower risk of extinction of competent hosts under habitat disturbance. This may then lead to a higher relative frequency of competent host species in modified habitats (Halliday et al., 2017; Joseph et al., 2013; Ostfeld and Keesing, 2012). Hence, in the case of zoonotic pathogens, the disease risk for humans is increased (Ostfeld, 2017; Gibb et al., 2020). Studies confirming the dilution effect hypothesis focused on pathogens known to cause outbreaks in humans and livestock (e.g. West Nile virus [WNV], Sin Nombre virus, and Borrelia spp.) and use generalist species as hosts (Allan et al., 2009; Clay et al., 2009; Ostfeld and Keesing, 2000; Khalil et al., 2016). However, it seems that this effect cannot be generalized and that diverse mechanisms regulate infectious disease transmission dynamics in a host-, pathogen-, and situation-dependent manner (Guo et al., 2019; Johnson et al., 2015b). Some studies observed a contrary effect for WNV, Usutu virus, and Borellia spp., where the infection rate or density of infected hosts increased with biodiversity, referred to as amplification effect (Levine et al., 2017; Ruyts et al., 2016; Roiz et al., 2019).

These conflicting results indicate that biodiversity loss has complex impacts on disease risk and effects can be heterogeneous and scale-dependent (Johnson et al., 2015b; Keesing et al., 2006; McLeish et al., 2017; Salkeld et al., 2013). Partial views on single host species or single pathogens cannot reveal general mechanisms. Existing studies either focused only on pathogen discovery aside from an ecological context or on one specific pathogen in a specific host across different habitats (Clay et al., 2009; Ostfeld and Keesing, 2000; Levine et al., 2017; Shi et al., 2016). Studies with a wider scope analyzing community composition of entire host groups and assessing the genetic diversity of their pathogens in undisturbed and disturbed habitats are lacking and would be paramount for a comprehensive understanding of biodiversity-infection relationships. To this end, analyzing species interactions is crucial for a mechanistic understanding of the factors governing emerging infectious diseases (Johnson et al., 2015a).

Here, we provide a comprehensive analysis combining fields of community ecology and virology to understand how viral richness depends on host richness, and which role host–habitat associations play for viral abundance patterns using viruses that naturally infect mosquitoes in a natural system. Habitat alterations such as agricultural development and urbanization promote thriving of disease-transmitting mosquito species (Burkett-Cadena and Vittor, 2018; Lee et al., 2020; Perrin et al., 2022; Perrin et al., 2023; Schrama et al., 2020), but it is less clear how such changes in mosquito species disassembly affect the abundance and prevalence of viruses. From the limited amount of available studies, these mostly focused on the effects of land use change on one specific virus (Ferraguti et al., 2021; Gardner et al., 2014; Martínez-de la Puente et al., 2018). In addition to the vertebrate-pathogenic arthropod-borne viruses (arboviruses), for example, dengue virus (DENV), yellow fever virus (YFV), and Zika virus (ZIKV) (Gould et al., 2017; Weaver et al., 2018a), mosquitoes can also be infected with so-called insect-specific viruses (ISVs), which cannot infect vertebrates due to several host restriction barriers, for example, sensitivity to higher (body) temperatures and inability to replicate in vertebrate cells (Blitvich and Firth, 2015; Marklewitz et al., 2015). All arbovirus taxa are embedded in a much greater phylogenetic diversity of ISVs, which suggests that arboviruses evolved from ancestral arthropod-restricted viruses (Blitvich and Firth, 2015; Marklewitz et al., 2015; Halbach et al., 2017; Junglen, 2016). The mode of transmission of most ISVs in nature remains largely unknown but is suggested to rely on direct transmission. For some insect-specific flaviviruses, vertical transmission to the progeny was described and is considered to play an important role for virus maintenance in nature (Blitvich and Firth, 2015; Bolling et al., 2012; Cook et al., 2006; Lutomiah et al., 2007). Additional transmission mechanisms of ISVs may include shared food sources, ectoparasites, or venereal transmission (reviewed in Halbach et al., 2017; Agboli et al., 2019; Meki et al., 2021). It has been shown that at least some flavi-, mesoni, and bunyaviruses are expectorated during feeding, which may provide a route for horizontal virus transmission (Birnberg et al., 2020; Gaye et al., 2020; Newton et al., 2020; Ramírez et al., 2018). However, transmission dynamics may differ among virus species and viral families and data are lacking describing the maintenance and transmission of ISVs related to arboviruses (Meki et al., 2021). Knowledge of the natural transmission of ISVs has been mainly studied for entomopoxviruses and baculoviruses, which infect other insects than mosquitoes (Ma et al., 2021). Due to their high abundance in natural mosquito populations, ISVs can serve as model systems to study how changes in mosquito species assembly affect associated virus abundance.

In a preliminary analysis, we had demonstrated that the prevalence of three ISVs, isolated from mosquitoes sampled along an anthropogenic disturbance gradient in Côte d’Ivoire, West Africa, increased from pristine to disturbed habitat types (Junglen et al., 2009b; Zirkel et al., 2011). Subsequently, a plenitude of previously unknown RNA viruses was identified in these samples establishing novel species, genera, and even families (Marklewitz et al., 2015; Zirkel et al., 2011; Hermanns et al., 2017; Hermanns et al., 2014; Junglen et al., 2009a; Junglen et al., 2017; Kallies et al., 2014; Marklewitz et al., 2011; Marklewitz et al., 2013; Quan et al., 2010; Zirkel et al., 2013; Schuster et al., 2014). To this end, we established broad-range generic PCR assays for all viruses identified previously in these samples, as well as for all major virus taxa containing arthropod-associated viruses. With this, we aimed to assess the viral genetic diversity along with the prevalence patterns of each detected virus in an entire family of hosts (Culicidae) sampled across habitat types. We expect a higher species diversity in mosquitoes of the family Culicidae in habitats of low or no disturbance and predict a turnover in mosquito species composition along the disturbance gradient concomitant with an increase in disturbance-resilient mosquito species and their viruses. We thus hypothesize that habitat perturbation and subsequent changes in community composition influence virus abundance patterns and most likely transmission dynamics. This multi-host and multi-taxa study provides insight into common and distinct micro-evolutionary patterns of virus emergence and geographic spread at the interface of pristine and modified landscapes.

In this study, we analyzed the interplay between host community composition, habitat disturbance, and virus prevalence in a multi-host and multi-taxa approach. We characterized the genetic diversity of RNA viruses (multi-taxa) in an entire family of hosts (Culicidae; multi-host), which were sampled along an anthropogenic disturbance gradient. We subsequently studied prevalence patterns of all detected viruses per habitat type, as well as mosquito community composition per habitat type. We discovered an exceptionally high diversity of 49 distinct viruses, of which 34 were previously unknown members of seven different RNA virus families. We demonstrated that the majority of these viruses occurred at low minimum infection rates (MIRs) of 0.22–1.97 infected mosquitoes per 1000 tested mosquitoes. Nine viruses occurred more frequently across the disturbance gradient, of which five increased in prevalence from pristine to disturbed habitat types. We could show that the detection rates of these viruses corresponded to the abundance patterns of their specific mosquito host species. Importantly, the differences in virus prevalence were driven by the number of hosts present in a specific habitat type and not by changes in host infection rates. This interplay was named abundance effect. These data show that host community composition critically influences virus prevalence that has direct impact on our understanding of infectious disease emergence mechanisms.

The majority of the detected viruses grouped with ISVs in phylogenetic analyses, suggesting that vertebrates do not participate in their amplification and maintenance cycles. However, the novel viruses Cimo peribunyavirus I and II formed a monophyletic clade that shared a most recent common ancestor with viruses of the genera Orthobunyavirus and Pacuvirus. Orthobunyaviruses are arboviruses that infect a great variety of vertebrates including humans (Hughes et al., 2020). Pacuviruses were isolated from rodents and phlebotomine sandflies in Brazil (Aitken et al., 1975; Rodrigues et al., 2014). Thus, the amplification cycle of Cimo peribunyavirus I and II may involve vertebrates and may be more complex than that of the other detected viruses. Further research is necessary to assess the host range of Cimo peribunyavirus I and II. The ISVs detected here were previously unknown, and it is thus not known whether they are viral symbionts of mosquitoes or pathogenic viruses. Since these represent previously unknown viruses, no study investigating whether they induce symptoms of disease and/or influence life history traits of mosquitoes has been conducted. Either way, the study provides data for a better understanding of factors influencing the viral community composition of mosquitoes. Although the microbiome of invertebrates is widely unexplored, it has been shown that the microbiome of mosquitoes can have a negative effect on its vector competence (the ability of mosquitoes to transmit human- and livestock pathogenic viruses). In consequence, even if the viruses detected in this study would be symbionts of mosquitoes, they may affect the transmission efficiency of pathogenic viruses and affect spillover events of pathogenic arboviruses from sylvatic to urban cycles. Nevertheless, studying abundance patterns and geographic spread of ISVs in the light of habitat disturbance and shifts in host community composition can provide valuable insight into infectious disease dynamics, at least for directly transmitted viruses. Mosquito-borne viruses are among the major global health concerns, and the recent spread and epidemics caused by Zika and chikungunya viruses have exemplified that there is an urgent need to understand viral emergence processes (Weaver et al., 2018a). Using mosquito-specific viruses as model viruses has many advantages as these viruses are more frequently found than arboviruses and no vertebrate host is required for virus maintenance and amplification, which makes links between host community composition and virus abundance patterns easier to identify. Mosquitoes and their viruses are an ideal system to study such effects as this allows studying infectious disease dynamics in an entire family of hosts, which can be tested in high numbers.

Our observed virus prevalence patterns are in agreement with several studies stating a heterogeneous effect of biodiversity on pathogen prevalence or disease risk (Randolph and Dobson, 2012; Salkeld et al., 2013). Biodiversity can influence disease risk by different mechanisms like host regulation, changes in encounter rates or transmission rates, leading to an amplifying or diluting net effect (Keesing et al., 2006). Areas with a high host richness likely harbor a high pathogen richness that might act as a source pool of novel diseases upon habitat change and increased contact to humans (Dunn et al., 2010; Keesing et al., 2010). We observed the highest virus richness in intermediately disturbed and primary rainforest habitats supporting that biodiverse habitats are also rich in pathogens. However, highest viral prevalence rates were observed in villages and at camp sites, further supporting that intact or intermediately disturbed ecosystems also contain well-balanced spectra of viruses.

For those arboviruses that are transmitted by a generalist mosquito species and use a competent host profiting from disturbance, a dilution effect can occur at local scales (Halliday et al., 2017; Ostfeld and Keesing, 2012). Several studies observed a dilution effect for WNV with either increasing non-passerine or total bird diversity (Allan et al., 2009; Ezenwa et al., 2006; Swaddle and Calos, 2008) while others reported no protective effect of avian species richness on WNV prevalence (Levine et al., 2017; Loss et al., 2009). For tick-borne encephalitis virus, a dilution effect with increasing density of incompetent deer hosts was observed at local scale (Cagnacci et al., 2012). Likewise, the prevalence of the directly transmitted hantavirus sin nombre virus is reduced at sites with higher rodent diversity as the persistence of the main host species (deer mouse) is reduced at diverse sites (Clay et al., 2009). A similar pattern was observed in our sampling for the five viruses GOLV, HEBV, FERV, Spilikins virus, and CAVV belonging to four different families (Phenuiviridae, Peribunyaviridae, Phasmaviridae, and Mesoniviridae). All these viruses used mosquitoes of the species C. nebulosus as primary host, which seemed to profit from disturbance and increased in abundance in disturbed versus pristine habitats (38% vs. 3%).

The ecological mechanisms leading to changes in mosquito community composition due to anthropogenic disturbance are presently poorly understood. Environmental changes, including land use changes, but also changes in the host species composition have been suggested to act as drivers of vector-borne diseases, including important arboviral diseases like dengue (Patz et al., 2000; Takken and Verhulst, 2013; Young et al., 2017). Yet, very few studies have focused on C. nebulosus. It has been reported as a sylvatic species in Nigeria, Benin, and Mozambique (Abílio et al., 2020; Agwu et al., 2016; Lingenfelser et al., 2010), but to the best of our knowledge this is the first study showing an increasing abundance of C. nebulosus in modified ecosystems. The possible reasons for the varying mosquito community compositions in the different habitat types might be the availability of suitable larval habitat sites and food sources, the abundance of predators, as well as differences in temperature and humidity (Ferraguti et al., 2016; Roiz et al., 2015; Thongsripong et al., 2013).

The prevalence of the five viruses associated with C. nebulosus was reduced in the diverse habitat types where the abundance of incompetent mosquito hosts increased and C. nebulosus was rarely found. It is striking that this effect was observed for five taxonomically different viruses infecting the same host species. This effect was not observed for closely related viruses using other mosquito species as host that did not increase in abundance in disturbed habitats. These data suggest that the adaptability of mosquito hosts to changed environments plays a more important role for the increase in prevalence of associated viruses than the phenotypic or genetic characteristics of these viruses. A similar observation was made by another study, which showed that the virome composition of three mosquito species captured along a rural to urban disturbance gradient in Thailand was defined primarily by the mosquito host species rather than the geographic location and that ISVs display a relatively narrow host range (Thongsripong et al., 2021).

Contrary effects can be caused by scale-dependent effects or depending on additive or substitutive community assemblies (Johnson et al., 2015b). Additionally, the most competent host can either increase or decline with disturbance, leading either to a dilution or amplification effect, respectively (Halliday et al., 2017). In our investigated sample set, two viruses (Cimo phenuivirus II, family Phenuiviridae, and Cimo flavivirus I, family Flaviviridae) with higher prevalence in pristine than in disturbed habitats were detected. As observed for the five viruses mentioned above (GOLV, HEBV, FERV, Spilikins virus, and CAVV), virus prevalence rates were closely linked to abundance rates of host mosquito species. Cimo phenuivirus II and Cimo flavivirus I were associated with Uranotaenia and Coquillettidia mosquitoes that declined in abundance in disturbed habitat types. An explanation of why such an effect is rarely found could be that model pathogens selected in the field of disease ecology are usually zoonotic pathogens that infect humans in disturbed habitats and are known to cause outbreaks. Thus, there may exist a bias towards those pathogens that thrive in disturbed habitat types. Pathogens that are rarely found in disturbed habitats may never be selected for disease ecology studies. Notably, the vast majority of detected virus species showed low infection rates without any evidence for effects of mosquito community changes on their abundance. These findings for 40 (out of 49) distinct viruses infecting different mosquito species suggest that host community disassembly does only influence a small fraction of the pathogen population. These examples underline why an unbiased multi-host and multi-taxa approach is crucial to uncover host community effects on pathogen prevalence patterns.

In other studies focusing on plant, vector-borne, indirectly and directly transmitted pathogens, the composition of the host community rather than total biodiversity was a predictor for pathogen distribution (Randolph and Dobson, 2012; McLeish et al., 2017). For example, (i) a low diversity of grassland plant species diversity was associated with a higher abundance of fungal plant pathogens (Mitchell et al., 2002), (ii) the disease and infection risk of populations of wild pepper (chiltepin) with different viruses increased with the level of human management, which was associated with decreased species diversity and host genetic diversity, and increased host plant density (Pagán et al., 2012), and (iii) the prevalence of four generalist aphid-vectored pathogens (barley and cereal yellow dwarf viruses) increased in grass host plants as local host richness declined, indicating that virus incidence in the focal host species is affected by the composition of the entire host community rather than by dependence on species diversity (Mitchell et al., 2002; Pagán et al., 2012; Lacroix et al., 2014). This is in agreement with the strong observed association between the prevalence of a certain virus and the abundance of its main mosquito host species in our study.

We detected novel strains of two previously characterized viruses, PCLV and Anopheles flavivirus, in A. aegypti and Anopheles gambiae mosquitoes, respectively, corresponding to previous findings of these viruses in these mosquito species in Asia and Africa (Chandler et al., 2014; Fauver et al., 2016; Zhang et al., 2018). This further supports the specific associations between certain viruses and mosquitoes on a broader geographical scale.

Mosquitoes harbor a diverse natural virome that can influence subsequent virus infections (Hall et al., 2016; Junglen and Drosten, 2013). We observed four frequently co-occurring viruses in C. nebulosus mosquitoes (GOLV, HEBV, FERV, and CAVV) that also grew together in cell culture, while in contrast the two C. decens-associated viruses, Cimo rhabdovirus I and JONV, were rarely detected together. This could hint, on the one hand, at synergistic interactions and, on the other hand, at superinfection exclusion between different ISVs in mosquitoes. For several ISVs in the genera Flavivirus and Alphavirus, an interference with related arboviruses was observed (Bolling et al., 2012; Nasar et al., 2015; Hobson-Peters et al., 2013). A better knowledge of the virome of different mosquito species as well as studies on mutual interference might help to assess vector competence and possibilities of geographic spread.

Virus detection in mosquito homogenates independent of virus isolation extents potential virus findings but has the limitation that exogenous and integrated viruses can be discovered. The detection of likely integrated sequences derived from flavi- and rhabdoviruses in our mosquito sample is in agreement with frequent previous findings of NIRVS from these families in mosquito genomes (Fort et al., 2012; Katzourakis et al., 2010; Lequime and Lambrechts, 2017; Whitfield et al., 2017). NIRVS are more closely related to ISVs than to arboviruses, probably limiting their influence on vector competence. The transovarial transmission of ISVs might increase the chance of germline integrations (Palatini et al., 2017; Olson and Bonizzoni, 2017). NIRVS can be transcriptionally active and might play a role in immunity against related viruses by producing piRNAs (Fort et al., 2012; Lequime and Lambrechts, 2017; Whitfield et al., 2017; Ter Horst et al., 2019).

Collectively, our data show that only some viruses of a huge viral community benefited from ecosystem disturbance. This effect was found for five viruses (GOLV, HEBV, FERV, Spilikins virus, and CAVV) and was determined by abundance patterns of their mosquito hosts, which profited from habitat disturbance and strongly increased in numbers from pristine to disturbed habitats. In contrast, we also detected two viruses (Cimo phenuivirus II and Cimo flavivirus I) that were associated with mosquito hosts specific to primary habitat types. Detection rates of the associated viruses were also closely linked to host abundance, and these viruses were found with higher frequencies in the primary and secondary rainforest. However, our analyses have also shown that virus prevalence is not always a consequence of the abundance of host species in a certain area. The prevalence pattern of JONV did not follow the abundance pattern of C. decens mosquitoes and its abundance seems to be determined by unknown mechanisms, possibly by superinfection exclusion mediated by Cimo rhabdovirus I. The study of prevalence patterns of a broad genetic diversity of RNA viruses and their associated hosts allowed us to seek for general mechanisms influencing emergence and geographic spread of mosquito-associated viruses. No general dilution or amplification effect was observed. Instead, we identified changes in mosquito community composition causing an increase or decrease in the main host species as the most important factor determining virus prevalence patterns, an effect named abundance effect. Remarkably, host infection rates were not affected by higher host abundance in our study.

The viral sequence fragments and genomes as well as the potential non-retroviral integrated RNA virus sequences (NIRVS) were assigned the GenBank accession numbers MZ202249–MZ202305 and MZ399697–MZ399709, respectively.

1. 1. Abílio AP
   2. Kampango A
   3. Armando EJ
   4. Gudo ES
   5. das Neves LCB
   6. Parreira R
   7. Sidat M
   8. Fafetine JM
   9. de Almeida APG

   (2020) First confirmed occurrence of the yellow fever virus and dengue virus vector Aedes (Stegomyia) luteocephalus (Newstead, 1907) in Mozambique

   Parasites & Vectors 13:350.

   https://doi.org/10.1186/s13071-020-04217-9

   • PubMed
   • Google Scholar
2. 1. Abudurexiti A
   2. Adkins S
   3. Alioto D
   4. Alkhovsky SV
   5. Avšič-Županc T
   6. Ballinger MJ
   7. Bente DA
   8. Beer M
   9. Bergeron É
   10. Blair CD
   11. Briese T
   12. Buchmeier MJ
   13. Burt FJ
   14. Calisher CH
   15. Cháng C
   16. Charrel RN
   17. Choi IR
   18. Clegg JCS
   19. de la Torre JC
   20. de Lamballerie X
   21. Dèng F
   22. Di Serio F
   23. Digiaro M
   24. Drebot MA
   25. Duàn X
   26. Ebihara H
   27. Elbeaino T
   28. Ergünay K
   29. Fulhorst CF
   30. Garrison AR
   31. Gāo GF
   32. Gonzalez JPJ
   33. Groschup MH
   34. Günther S
   35. Haenni AL
   36. Hall RA
   37. Hepojoki J
   38. Hewson R
   39. Hú Z
   40. Hughes HR
   41. Jonson MG
   42. Junglen S
   43. Klempa B
   44. Klingström J
   45. Kòu C
   46. Laenen L
   47. Lambert AJ
   48. Langevin SA
   49. Liu D
   50. Lukashevich IS
   51. Luò T
   52. Lǚ C
   53. Maes P
   54. de Souza WM
   55. Marklewitz M
   56. Martelli GP
   57. Matsuno K
   58. Mielke-Ehret N
   59. Minutolo M
   60. Mirazimi A
   61. Moming A
   62. Mühlbach HP
   63. Naidu R
   64. Navarro B
   65. Nunes MRT
   66. Palacios G
   67. Papa A
   68. Pauvolid-Corrêa A
   69. Pawęska JT
   70. Qiáo J
   71. Radoshitzky SR
   72. Resende RO
   73. Romanowski V
   74. Sall AA
   75. Salvato MS
   76. Sasaya T
   77. Shěn S
   78. Shí X
   79. Shirako Y
   80. Simmonds P
   81. Sironi M
   82. Song JW
   83. Spengler JR
   84. Stenglein MD
   85. Sū Z
   86. Sūn S
   87. Táng S
   88. Turina M
   89. Wáng B
   90. Wáng C
   91. Wáng H
   92. Wáng J
   93. Wèi T
   94. Whitfield AE
   95. Zerbini FM
   96. Zhāng J
   97. Zhāng L
   98. Zhāng Y
   99. Zhang YZ
   100. Zhāng Y
   101. Zhou X
   102. Zhū L
   103. Kuhn JH

   (2019) Taxonomy of the order Bunyavirales: update 2019

   Archives of Virology 164:1949–1965.

   https://doi.org/10.1007/s00705-019-04253-6

   • PubMed
   • Google Scholar
3. 1. Agboli E
   2. Leggewie M
   3. Altinli M
   4. Schnettler E

   (2019) Mosquito-Specific Viruses-Transmission and Interaction

   Viruses 11:873.

   https://doi.org/10.3390/v11090873

   • PubMed
   • Google Scholar
4. 1. Agwu EJ
   2. Igbinosa IB
   3. Isaac C

   (2016) Entomological assessment of yellow fever-epidemic risk indices in Benue State, Nigeria, 2010-2011

   Acta Tropica 161:18–25.

   https://doi.org/10.1016/j.actatropica.2016.05.005

   • PubMed
   • Google Scholar
5. 1. Aitken TH
   2. Woodall JP
   3. De Andrade AH
   4. Bensabath G
   5. Shope RE

   (1975) Pacui virus, phlebotomine flies, and small mammals in Brazil: an epidemiological study

   The American Journal of Tropical Medicine and Hygiene 24:358–368.

   https://doi.org/10.4269/ajtmh.1975.24.358

   • PubMed
   • Google Scholar
6. 1. Allan BF
   2. Langerhans RB
   3. Ryberg WA
   4. Landesman WJ
   5. Griffin NW
   6. Katz RS
   7. Oberle BJ
   8. Schutzenhofer MR
   9. Smyth KN
   10. de St Maurice A
   11. Clark L
   12. Crooks KR
   13. Hernandez DE
   14. McLean RG
   15. Ostfeld RS
   16. Chase JM

   (2009) Ecological correlates of risk and incidence of West Nile virus in the United States

   Oecologia 158:699–708.

   https://doi.org/10.1007/s00442-008-1169-9

   • PubMed
   • Google Scholar
7. Book

   1. Becnel

   (2012)

   Virus Taxonomy: Classification and Nomenclature of Viruses: Ninth Report of the International Committee on Taxonomy of Viruses

   Elsevier Inc.

   • Google Scholar
8. Software

   1. Biggerstaff

   (2009)

   Pooledinfrate, Microsoft office excel add-in to compute prevalence estimates from pooled samples, version 4.0

   Centers for Disease Control and Prevention.
9. 1. Birnberg L
   2. Temmam S
   3. Aranda C
   4. Correa-Fiz F
   5. Talavera S
   6. Bigot T
   7. Eloit M
   8. Busquets N

   (2020) Viromics on honey-baited FTA Cards as a New Tool for the detection of circulating viruses in Mosquitoes

   Viruses 12:274.

   https://doi.org/10.3390/v12030274

   • PubMed
   • Google Scholar
10. 1. Blitvich BJ
    2. Firth AE

    (2015) Insect-specific flaviviruses: a systematic review of their discovery, host range, mode of transmission, superinfection exclusion potential and genomic organization

    Viruses 7:1927–1959.

    https://doi.org/10.3390/v7041927

    • PubMed
    • Google Scholar
11. 1. Bolling BG
    2. Olea-Popelka FJ
    3. Eisen L
    4. Moore CG
    5. Blair CD

    (2012) Transmission dynamics of an insect-specific flavivirus in a naturally infected Culex pipiens laboratory colony and effects of co-infection on vector competence for West Nile virus

    Virology 427:90–97.

    https://doi.org/10.1016/j.virol.2012.02.016

    • PubMed
    • Google Scholar
12. 1. Burkett-Cadena ND
    2. Vittor AY

    (2018) Deforestation and vector-borne disease: Forest conversion favors important mosquito vectors of human pathogens

    Basic and Applied Ecology 26:101–110.

    https://doi.org/10.1016/j.baae.2017.09.012

    • PubMed
    • Google Scholar
13. 1. Cagnacci F
    2. Bolzoni L
    3. Rosà R
    4. Carpi G
    5. Hauffe HC
    6. Valent M
    7. Tagliapietra V
    8. Kazimirova M
    9. Koci J
    10. Stanko M
    11. Lukan M
    12. Henttonen H
    13. Rizzoli A

    (2012) Effects of deer density on tick infestation of rodents and the hazard of tick-borne encephalitis. I: Empirical assessment

    International Journal for Parasitology 42:365–372.

    https://doi.org/10.1016/j.ijpara.2012.02.012

    • PubMed
    • Google Scholar
14. 1. Chandler JA
    2. Thongsripong P
    3. Green A
    4. Kittayapong P
    5. Wilcox BA
    6. Schroth GP
    7. Kapan DD
    8. Bennett SN

    (2014) Metagenomic shotgun sequencing of a Bunyavirus in wild-caught Aedes aegypti from Thailand informs the evolutionary and genomic history of the Phleboviruses

    Virology 464–465:312–319.

    https://doi.org/10.1016/j.virol.2014.06.036

    • PubMed
    • Google Scholar
15. 1. Chao A
    2. Gotelli NJ
    3. Hsieh TC
    4. Sander EL
    5. Ma KH
    6. Colwell RK
    7. Ellison AM

    (2014) Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies

    Ecological Monographs 84:45–67.

    https://doi.org/10.1890/13-0133.1

    • Google Scholar
16. Software

    1. Chao
    2. Hsieh TCM

    (2020)

    iNEXT: iNterpolation and extrapolation for species diversity, version 2.0.20

    R Package.
17. 1. Chen R
    2. Mukhopadhyay S
    3. Merits A
    4. Bolling B
    5. Nasar F
    6. Coffey LL
    7. Powers A
    8. Weaver SC

    (2018) ICTV Virus Taxonomy Profile: Togaviridae

    The Journal of General Virology 99:761–762.

    https://doi.org/10.1099/jgv.0.001072

    • PubMed
    • Google Scholar
18. 1. Clay CA
    2. Lehmer EM
    3. Jeor SS
    4. Dearing MD

    (2009) Sin nombre virus and rodent species diversity: a test of the dilution and amplification hypotheses

    PLOS ONE 4:e6467.

    https://doi.org/10.1371/journal.pone.0006467

    • PubMed
    • Google Scholar
19. 1. Cook S
    2. Bennett SN
    3. Holmes EC
    4. De Chesse R
    5. Moureau G
    6. de Lamballerie X

    (2006) Isolation of a new strain of the flavivirus cell fusing agent virus in a natural mosquito population from Puerto Rico

    The Journal of General Virology 87:735–748.

    https://doi.org/10.1099/vir.0.81475-0

    • PubMed
    • Google Scholar
20. 1. Crochu S
    2. Cook S
    3. Attoui H
    4. Charrel RN
    5. De Chesse R
    6. Belhouchet M
    7. Lemasson JJ
    8. de Micco P
    9. de Lamballerie X

    (2004) Sequences of flavivirus-related RNA viruses persist in DNA form integrated in the genome of Aedes spp. mosquitoes

    The Journal of General Virology 85:1971–1980.

    https://doi.org/10.1099/vir.0.79850-0

    • PubMed
    • Google Scholar
21. 1. Diagne MM
    2. Gaye A
    3. Ndione MHD
    4. Faye M
    5. Fall G
    6. Dieng I
    7. Widen SG
    8. Wood TG
    9. Popov V
    10. Guzman H
    11. Bâ Y
    12. Weaver SC
    13. Diallo M
    14. Tesh R
    15. Faye O
    16. Vasilakis N
    17. Sall AA

    (2020) Dianke virus: A new mesonivirus species isolated from mosquitoes in Eastern Senegal

    Virus Research 275:197802.

    https://doi.org/10.1016/j.virusres.2019.197802

    • PubMed
    • Google Scholar
22. 1. Dunn RR
    2. Davies TJ
    3. Harris NC
    4. Gavin MC

    (2010) Global drivers of human pathogen richness and prevalence

    Proceedings. Biological Sciences 277:2587–2595.

    https://doi.org/10.1098/rspb.2010.0340

    • PubMed
    • Google Scholar
23. 1. Ezenwa VO
    2. Godsey MS
    3. King RJ
    4. Guptill SC

    (2006) Avian diversity and West Nile virus: testing associations between biodiversity and infectious disease risk

    Proceedings. Biological Sciences 273:109–117.

    https://doi.org/10.1098/rspb.2005.3284

    • PubMed
    • Google Scholar
24. 1. Fauver JR
    2. Grubaugh ND
    3. Krajacich BJ
    4. Weger-Lucarelli J
    5. Lakin SM
    6. Fakoli LS
    7. Bolay FK
    8. Diclaro JW
    9. Dabiré KR
    10. Foy BD
    11. Brackney DE
    12. Ebel GD
    13. Stenglein MD

    (2016) West African Anopheles gambiae mosquitoes harbor a taxonomically diverse virome including new insect-specific flaviviruses, mononegaviruses, and totiviruses

    Virology 498:288–299.

    https://doi.org/10.1016/j.virol.2016.07.031

    • PubMed
    • Google Scholar
25. 1. Ferraguti M
    2. Martínez-de la Puente J
    3. Roiz D
    4. Ruiz S
    5. Soriguer R
    6. Figuerola J

    (2016) Effects of landscape anthropization on mosquito community composition and abundance

    Scientific Reports 6:29002.

    https://doi.org/10.1038/srep29002

    • PubMed
    • Google Scholar
26. 1. Ferraguti M
    2. Martínez-de la Puente J
    3. Figuerola J

    (2021) Ecological Effects on the Dynamics of West Nile Virus and Avian Plasmodium: the importance of Mosquito Communities and Landscape

    Viruses 13:1208.

    https://doi.org/10.3390/v13071208

    • PubMed
    • Google Scholar
27. 1. Folmer O
    2. Black M
    3. Hoeh W
    4. Lutz R
    5. Vrijenhoek R

    (1994)

    DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates

    Molecular Marine Biology and Biotechnology 3:294–299.

    • PubMed
    • Google Scholar
28. 1. Fort P
    2. Albertini A
    3. Van-Hua A
    4. Berthomieu A
    5. Roche S
    6. Delsuc F
    7. Pasteur N
    8. Capy P
    9. Gaudin Y
    10. Weill M

    (2012) Fossil rhabdoviral sequences integrated into arthropod genomes: ontogeny, evolution, and potential functionality

    Molecular Biology and Evolution 29:381–390.

    https://doi.org/10.1093/molbev/msr226

    • PubMed
    • Google Scholar
29. 1. Gardner AM
    2. Lampman RL
    3. Muturi EJ

    (2014) Land use patterns and the risk of West Nile virus transmission in central Illinois

    Vector Borne and Zoonotic Diseases 14:338–345.

    https://doi.org/10.1089/vbz.2013.1477

    • PubMed
    • Google Scholar
30. 1. Gaye A
    2. Diagne MM
    3. Ndiaye EH
    4. Dior Ndione MH
    5. Faye M
    6. Talla C
    7. Fall G
    8. Ba Y
    9. Diallo D
    10. Dia I
    11. Handschumacher P
    12. Faye O
    13. Sall AA
    14. Diallo M

    (2020) Vector competence of anthropophilic mosquitoes for a new mesonivirus in Senegal

    Emerging Microbes & Infections 9:496–504.

    https://doi.org/10.1080/22221751.2020.1730710

    • PubMed
    • Google Scholar
31. 1. Gibb R
    2. Redding DW
    3. Chin KQ
    4. Donnelly CA
    5. Blackburn TM
    6. Newbold T
    7. Jones KE

    (2020) Zoonotic host diversity increases in human-dominated ecosystems

    Nature 584:398–402.

    https://doi.org/10.1038/s41586-020-2562-8

    • PubMed
    • Google Scholar
32. 1. Gould E
    2. Pettersson J
    3. Higgs S
    4. Charrel R
    5. de Lamballerie X

    (2017) Emerging arboviruses: Why today?

    One Health 4:1–13.

    https://doi.org/10.1016/j.onehlt.2017.06.001

    • PubMed
    • Google Scholar
33. 1. Guindon S
    2. Dufayard JF
    3. Lefort V
    4. Anisimova M
    5. Hordijk W
    6. Gascuel O

    (2010) New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0

    Systematic Biology 59:307–321.

    https://doi.org/10.1093/sysbio/syq010

    • PubMed
    • Google Scholar
34. 1. Guo F
    2. Bonebrake TC
    3. Gibson L

    (2019) Land-use change alters host and vector communities and may elevate disease risk

    EcoHealth 16:647–658.

    https://doi.org/10.1007/s10393-018-1336-3

    • PubMed
    • Google Scholar
35. 1. Halbach R
    2. Junglen S
    3. van Rij RP

    (2017) Mosquito-specific and mosquito-borne viruses: evolution, infection, and host defense

    Current Opinion in Insect Science 22:16–27.

    https://doi.org/10.1016/j.cois.2017.05.004

    • PubMed
    • Google Scholar
36. 1. Hall RA
    2. Bielefeldt-Ohmann H
    3. McLean BJ
    4. O’Brien CA
    5. Colmant AMG
    6. Piyasena TBH
    7. Harrison JJ
    8. Newton ND
    9. Barnard RT
    10. Prow NA
    11. Deerain JM
    12. Mah MGKY
    13. Hobson-Peters J

    (2016) Commensal Viruses of Mosquitoes: Host Restriction, Transmission, and Interaction with Arboviral Pathogens

    Evolutionary Bioinformatics Online 12:35–44.

    https://doi.org/10.4137/EBO.S40740

    • PubMed
    • Google Scholar
37. 1. Halliday FW
    2. Heckman RW
    3. Wilfahrt PA
    4. Mitchell CE

    (2017) A multivariate test of disease risk reveals conditions leading to disease amplification

    Proceedings. Biological Sciences 284:20171340.

    https://doi.org/10.1098/rspb.2017.1340

    • PubMed
    • Google Scholar
38. 1. Hermanns K
    2. Zirkel F
    3. Kurth A
    4. Drosten C
    5. Junglen S

    (2014) Cimodo virus belongs to a novel lineage of reoviruses isolated from African mosquitoes

    The Journal of General Virology 95:905–909.

    https://doi.org/10.1099/vir.0.062349-0

    • PubMed
    • Google Scholar
39. 1. Hermanns K
    2. Zirkel F
    3. Kopp A
    4. Marklewitz M
    5. Rwego IB
    6. Estrada A
    7. Gillespie TR
    8. Drosten C
    9. Junglen S

    (2017) Discovery of a novel alphavirus related to Eilat virus

    The Journal of General Virology 98:43–49.

    https://doi.org/10.1099/jgv.0.000694

    • PubMed
    • Google Scholar
40. 1. Hermanns K
    2. Marklewitz M
    3. Zirkel F
    4. Overheul GJ
    5. Page RA
    6. Loaiza JR
    7. Drosten C
    8. van Rij RP
    9. Junglen S

    (2020) Agua Salud alphavirus defines a novel lineage of insect-specific alphaviruses discovered in the New World

    The Journal of General Virology 101:96–104.

    https://doi.org/10.1099/jgv.0.001344

    • PubMed
    • Google Scholar
41. 1. Hobson-Peters J
    2. Yam AWY
    3. Lu JWF
    4. Setoh YX
    5. May FJ
    6. Kurucz N
    7. Walsh S
    8. Prow NA
    9. Davis SS
    10. Weir R
    11. Melville L
    12. Hunt N
    13. Webb RI
    14. Blitvich BJ
    15. Whelan P
    16. Hall RA

    (2013) A new insect-specific flavivirus from northern Australia suppresses replication of West Nile virus and Murray Valley encephalitis virus in co-infected mosquito cells

    PLOS ONE 8:e56534.

    https://doi.org/10.1371/journal.pone.0056534

    • PubMed
    • Google Scholar
42. 1. Houé V
    2. Bonizzoni M
    3. Failloux AB

    (2019) Endogenous non-retroviral elements in genomes of Aedes mosquitoes and vector competence

    Emerging Microbes & Infections 8:542–555.

    https://doi.org/10.1080/22221751.2019.1599302

    • PubMed
    • Google Scholar
43. 1. Hsieh TC
    2. Ma KH
    3. Chao A
    4. McInerny G

    (2016) iNEXT: an R package for rarefaction and extrapolation of species diversity ( H ill numbers)

    Methods in Ecology and Evolution 7:1451–1456.

    https://doi.org/10.1111/2041-210X.12613

    • Google Scholar
44. 1. Hughes HR
    2. Adkins S
    3. Alkhovskiy S
    4. Beer M
    5. Blair C
    6. Calisher CH
    7. Drebot M
    8. Lambert AJ
    9. de Souza WM
    10. Marklewitz M
    11. Nunes MRT
    12. Shí (石晓宏) X
    13. ICTV Report Consortium

    (2020) ICTV Virus Taxonomy Profile: Peribunyaviridae

    Journal of General Virology 101:1–2.

    https://doi.org/10.1099/jgv.0.001365

    • Google Scholar
45. 1. Johnson PTJ
    2. de Roode JC
    3. Fenton A

    (2015a) Why infectious disease research needs community ecology

    Science 349:1259504.

    https://doi.org/10.1126/science.1259504

    • PubMed
    • Google Scholar
46. 1. Johnson PTJ
    2. Ostfeld RS
    3. Keesing F

    (2015b) Frontiers in research on biodiversity and disease

    Ecology Letters 18:1119–1133.

    https://doi.org/10.1111/ele.12479

    • PubMed
    • Google Scholar
47. 1. Joseph MB
    2. Mihaljevic JR
    3. Orlofske SA
    4. Paull SH
    5. Ostfeld R

    (2013) Does life history mediate changing disease risk when communities disassemble?

    Ecology Letters 16:1405–1412.

    https://doi.org/10.1111/ele.12180

    • PubMed
    • Google Scholar
48. 1. Junglen S
    2. Kopp A
    3. Kurth A
    4. Pauli G
    5. Ellerbrok H
    6. Leendertz FH

    (2009a) A new flavivirus and A new vector: characterization of A novel flavivirus isolated from uranotaenia mosquitoes from A tropical rain forest

    Journal of Virology 83:4462–4468.

    https://doi.org/10.1128/JVI.00014-09

    • PubMed
    • Google Scholar
49. 1. Junglen S
    2. Kurth A
    3. Kuehl H
    4. Quan PL
    5. Ellerbrok H
    6. Pauli G
    7. Nitsche A
    8. Nunn C
    9. Rich SM
    10. Lipkin WI
    11. Briese T
    12. Leendertz FH

    (2009b) Examining landscape factors influencing relative distribution of mosquito genera and frequency of virus infection

    EcoHealth 6:239–249.

    https://doi.org/10.1007/s10393-009-0260-y

    • PubMed
    • Google Scholar
50. 1. Junglen S
    2. Drosten C

    (2013) Virus discovery and recent insights into virus diversity in arthropods

    Current Opinion in Microbiology 16:507–513.

    https://doi.org/10.1016/j.mib.2013.06.005

    • PubMed
    • Google Scholar
51. 1. Junglen S

    (2016) Evolutionary origin of pathogenic arthropod-borne viruses-a case study in the family Bunyaviridae

    Current Opinion in Insect Science 16:81–86.

    https://doi.org/10.1016/j.cois.2016.05.017

    • PubMed
    • Google Scholar
52. 1. Junglen S
    2. Korries M
    3. Grasse W
    4. Wieseler J
    5. Kopp A
    6. Hermanns K
    7. León-Juárez M
    8. Drosten C
    9. Kümmerer BM

    (2017) HOst range restriction of insect-specific flaviviruses occurs at several levels of the viral life cycle

    mSphere 2:e00375-16.

    https://doi.org/10.1128/mSphere.00375-16

    • PubMed
    • Google Scholar
53. 1. Kallies R
    2. Kopp A
    3. Zirkel F
    4. Estrada A
    5. Gillespie TR
    6. Drosten C
    7. Junglen S

    (2014) Genetic characterization of goutanap virus, a novel virus related to negeviruses, cileviruses and higreviruses

    Viruses 6:4346–4357.

    https://doi.org/10.3390/v6114346

    • PubMed
    • Google Scholar
54. 1. Katoh K
    2. Standley DM

    (2013) MAFFT multiple sequence alignment software version 7: improvements in performance and usability

    Molecular Biology and Evolution 30:772–780.

    https://doi.org/10.1093/molbev/mst010

    • PubMed
    • Google Scholar
55. 1. Katzourakis A
    2. Gifford RJ
    3. Malik HS

    (2010) Endogenous viral elements in animal genomes

    PLOS Genetics 6:e1001191.

    https://doi.org/10.1371/journal.pgen.1001191

    • PubMed
    • Google Scholar
56. 1. Kearse M
    2. Moir R
    3. Wilson A
    4. Stones-Havas S
    5. Cheung M
    6. Sturrock S
    7. Buxton S
    8. Cooper A
    9. Markowitz S
    10. Duran C
    11. Thierer T
    12. Ashton B
    13. Meintjes P
    14. Drummond A

    (2012) Geneious Basic: an integrated and extendable desktop software platform for the organization and analysis of sequence data

    Bioinformatics 28:1647–1649.

    https://doi.org/10.1093/bioinformatics/bts199

    • PubMed
    • Google Scholar
57. 1. Keesing F
    2. Holt RD
    3. Ostfeld RS

    (2006) Effects of species diversity on disease risk

    Ecology Letters 9:485–498.

    https://doi.org/10.1111/j.1461-0248.2006.00885.x

    • PubMed
    • Google Scholar
58. 1. Keesing F
    2. Belden LK
    3. Daszak P
    4. Dobson A
    5. Harvell CD
    6. Holt RD
    7. Hudson P
    8. Jolles A
    9. Jones KE
    10. Mitchell CE
    11. Myers SS
    12. Bogich T
    13. Ostfeld RS

    (2010) Impacts of biodiversity on the emergence and transmission of infectious diseases

    Nature 468:647–652.

    https://doi.org/10.1038/nature09575

    • PubMed
    • Google Scholar
59. 1. Khalil H
    2. Ecke F
    3. Evander M
    4. Magnusson M
    5. Hörnfeldt B

    (2016) Declining ecosystem health and the dilution effect

    Scientific Reports 6:31314.

    https://doi.org/10.1038/srep31314

    • PubMed
    • Google Scholar
60. 1. Lacroix C
    2. Jolles A
    3. Seabloom EW
    4. Power AG
    5. Mitchell CE
    6. Borer ET

    (2014) Non-random biodiversity loss underlies predictable increases in viral disease prevalence

    Journal of the Royal Society, Interface 11:20130947.

    https://doi.org/10.1098/rsif.2013.0947

    • PubMed
    • Google Scholar
61. 1. Lee JM
    2. Wasserman RJ
    3. Gan JY
    4. Wilson RF
    5. Rahman S
    6. Yek SH

    (2020) Human activities attract harmful mosquitoes in a tropical urban landscape

    EcoHealth 17:52–63.

    https://doi.org/10.1007/s10393-019-01457-9

    • PubMed
    • Google Scholar
62. 1. Lefort V
    2. Longueville JE
    3. Gascuel O

    (2017) SMS: Smart Model Selection in PhyML

    Molecular Biology and Evolution 34:2422–2424.

    https://doi.org/10.1093/molbev/msx149

    • PubMed
    • Google Scholar
63. 1. Legendre P
    2. De Cáceres M

    (2013) Beta diversity as the variance of community data: dissimilarity coefficients and partitioning

    Ecology Letters 16:951–963.

    https://doi.org/10.1111/ele.12141

    • PubMed
    • Google Scholar
64. 1. Lequime S
    2. Lambrechts L

    (2017) Discovery of flavivirus-derived endogenous viral elements in Anopheles mosquito genomes supports the existence of Anopheles -associated insect-specific flaviviruses

    Virus Evolution 3:vew035.

    https://doi.org/10.1093/ve/vew035

    • Google Scholar
65. 1. Levine RS
    2. Hedeen DL
    3. Hedeen MW
    4. Hamer GL
    5. Mead DG
    6. Kitron UD

    (2017) Avian species diversity and transmission of West Nile virus in Atlanta, Georgia

    Parasites & Vectors 10:62.

    https://doi.org/10.1186/s13071-017-1999-6

    • PubMed
    • Google Scholar
66. 1. Lingenfelser A
    2. Rydzanicz K
    3. Kaiser A
    4. Becker N

    (2010)

    Mosquito fauna and perspectives for integrated control of urban vector-mosquito populations in Southern Benin (West Africa)

    Annals of Agricultural and Environmental Medicine 17:49–57.

    • PubMed
    • Google Scholar
67. 1. Loss SR
    2. Hamer GL
    3. Walker ED
    4. Ruiz MO
    5. Goldberg TL
    6. Kitron UD
    7. Brawn JD

    (2009) Avian host community structure and prevalence of West Nile virus in Chicago, Illinois

    Oecologia 159:415–424.

    https://doi.org/10.1007/s00442-008-1224-6

    • Google Scholar
68. 1. Lutomiah JJL
    2. Mwandawiro C
    3. Magambo J
    4. Sang RC

    (2007) Infection and vertical transmission of Kamiti river virus in laboratory bred Aedes aegypti mosquitoes

    Journal of Insect Science 7:1–7.

    https://doi.org/10.1673/031.007.5501

    • PubMed
    • Google Scholar
69. 1. Ma E
    2. Zhu Y
    3. Liu Z
    4. Wei T
    5. Wang P
    6. Cheng G

    (2021) Interaction of Viruses with the Insect Intestine

    Annual Review of Virology 8:115–131.

    https://doi.org/10.1146/annurev-virology-091919-100543

    • PubMed
    • Google Scholar
70. 1. Marklewitz M
    2. Handrick S
    3. Grasse W
    4. Kurth A
    5. Lukashev A
    6. Drosten C
    7. Ellerbrok H
    8. Leendertz FH
    9. Pauli G
    10. Junglen S

    (2011) Gouleako virus isolated from West African mosquitoes constitutes a proposed novel genus in the family Bunyaviridae

    Journal of Virology 85:9227–9234.

    https://doi.org/10.1128/JVI.00230-11

    • PubMed
    • Google Scholar
71. 1. Marklewitz M
    2. Zirkel F
    3. Rwego IB
    4. Heidemann H
    5. Trippner P
    6. Kurth A
    7. Kallies R
    8. Briese T
    9. Lipkin WI
    10. Drosten C
    11. Gillespie TR
    12. Junglen S

    (2013) Discovery of a unique novel clade of mosquito-associated bunyaviruses

    Journal of Virology 87:12850–12865.

    https://doi.org/10.1128/JVI.01862-13

    • PubMed
    • Google Scholar
72. 1. Marklewitz M
    2. Zirkel F
    3. Kurth A
    4. Drosten C
    5. Junglen S

    (2015) Evolutionary and phenotypic analysis of live virus isolates suggests arthropod origin of a pathogenic RNA virus family

    PNAS 112:7536–7541.

    https://doi.org/10.1073/pnas.1502036112

    • PubMed
    • Google Scholar
73. 1. Marklewitz M
    2. Dutari LC
    3. Paraskevopoulou S
    4. Page RA
    5. Loaiza JR
    6. Junglen S

    (2019) Diverse novel phleboviruses in sandflies from the Panama Canal area, Central Panama

    The Journal of General Virology 100:938–949.

    https://doi.org/10.1099/jgv.0.001260

    • PubMed
    • Google Scholar
74. 1. Martínez-de la Puente J
    2. Ferraguti M
    3. Ruiz S
    4. Roiz D
    5. Llorente F
    6. Pérez-Ramírez E
    7. Jiménez-Clavero MÁ
    8. Soriguer R
    9. Figuerola J

    (2018) Mosquito community influences West Nile virus seroprevalence in wild birds: implications for the risk of spillover into human populations

    Scientific Reports 8:2599.

    https://doi.org/10.1038/s41598-018-20825-z

    • PubMed
    • Google Scholar
75. 1. McLeish M
    2. Sacristán S
    3. Fraile A
    4. García-Arenal F

    (2017) Scale dependencies and generalism in host use shape virus prevalence

    Proceedings. Biological Sciences 284:20172066.

    https://doi.org/10.1098/rspb.2017.2066

    • PubMed
    • Google Scholar
76. 1. Meki IK
    2. Huditz HI
    3. Strunov A
    4. van der Vlugt RAA
    5. Kariithi HM
    6. Rezapanah M
    7. Miller WJ
    8. Vlak JM
    9. van Oers MM
    10. Abd-Alla AMM

    (2021) Characterization and Tissue Tropism of Newly Identified Iflavirus and Negeviruses in Glossina morsitans morsitans Tsetse Flies

    Viruses 13:2472.

    https://doi.org/10.3390/v13122472

    • PubMed
    • Google Scholar
77. 1. Mitchell CE
    2. Tilman D
    3. Groth JV

    (2002) Effects of grassland plant species diversity, abundance, and composition on foliar fungal disease

    Ecology 83:1713–1726.

    https://doi.org/10.1890/0012-9658(2002)083[1713:EOGPSD]2.0.CO;2

    • Google Scholar
78. 1. Moureau G
    2. Temmam S
    3. Gonzalez JP
    4. Charrel RN
    5. Grard G
    6. de Lamballerie X

    (2007) A real-time RT-PCR method for the universal detection and identification of flaviviruses

    Vector Borne and Zoonotic Diseases 7:467–477.

    https://doi.org/10.1089/vbz.2007.0206

    • PubMed
    • Google Scholar
79. 1. Nasar F
    2. Erasmus JH
    3. Haddow AD
    4. Tesh RB
    5. Weaver SC

    (2015) Eilat virus induces both homologous and heterologous interference

    Virology 484:51–58.

    https://doi.org/10.1016/j.virol.2015.05.009

    • PubMed
    • Google Scholar
80. 1. Nault LR
    2. Ammar ED

    (1989) Leafhopper and planthopper transmission of plant viruses

    Annual Review of Entomology 34:503–529.

    https://doi.org/10.1146/annurev.en.34.010189.002443

    • Google Scholar
81. 1. Newton ND
    2. Colmant AMG
    3. O’Brien CA
    4. Ledger E
    5. Paramitha D
    6. Bielefeldt-Ohmann H
    7. Watterson D
    8. McLean BJ
    9. Hall-Mendelin S
    10. Warrilow D
    11. van den Hurk AF
    12. Liu W
    13. Hoare C
    14. Kizu JR
    15. Gauci PJ
    16. Haniotis J
    17. Doggett SL
    18. Shaban B
    19. Johansen CA
    20. Hall RA
    21. Hobson-Peters J

    (2020) Genetic, morphological and antigenic relationships between mesonivirus isolates from australian mosquitoes and evidence for their horizontal transmission

    Viruses 12:1159.

    https://doi.org/10.3390/v12101159

    • PubMed
    • Google Scholar
82. Software

    1. Oksanen JB
    2. Friendly M
    3. Kindt R

    (2019) Vegan: community Ecology package, version 2.5-6

    R Package.

    https://cran.r-project.org/web/packages/vegan/vegan.pdf
83. 1. Olival KJ
    2. Hosseini PR
    3. Zambrana-Torrelio C
    4. Ross N
    5. Bogich TL
    6. Daszak P

    (2017) Host and viral traits predict zoonotic spillover from mammals

    Nature 548:646–650.

    https://doi.org/10.1038/nature22975

    • PubMed
    • Google Scholar
84. 1. Olson KE
    2. Bonizzoni M

    (2017) Nonretroviral integrated RNA viruses in arthropod vectors: an occasional event or something more?

    Current Opinion in Insect Science 22:45–53.

    https://doi.org/10.1016/j.cois.2017.05.010

    • PubMed
    • Google Scholar
85. 1. Ostfeld RS
    2. Keesing F

    (2000) Biodiversity and disease risk: The case of lyme disease

    Conservation Biology 14:722–728.

    https://doi.org/10.1046/j.1523-1739.2000.99014.x

    • Google Scholar
86. 1. Ostfeld RS
    2. Keesing F

    (2012) Effects of Host Diversity on Infectious Disease

    Annual Review of Ecology, Evolution, and Systematics 43:157–182.

    https://doi.org/10.1146/annurev-ecolsys-102710-145022

    • Google Scholar
87. 1. Ostfeld RS

    (2017) Biodiversity loss and the ecology of infectious disease

    The Lancet. Planetary Health 1:e2–e3.

    https://doi.org/10.1016/S2542-5196(17)30010-4

    • PubMed
    • Google Scholar
88. 1. Pagán I
    2. González-Jara P
    3. Moreno-Letelier A
    4. Rodelo-Urrego M
    5. Fraile A
    6. Piñero D
    7. García-Arenal F

    (2012) Effect of biodiversity changes in disease risk: exploring disease emergence in a plant-virus system

    PLOS Pathogens 8:e1002796.

    https://doi.org/10.1371/journal.ppat.1002796

    • PubMed
    • Google Scholar
89. 1. Palatini U
    2. Miesen P
    3. Carballar-Lejarazu R
    4. Ometto L
    5. Rizzo E
    6. Tu Z
    7. van Rij RP
    8. Bonizzoni M

    (2017) Comparative genomics shows that viral integrations are abundant and express piRNAs in the arboviral vectors Aedes aegypti and Aedes albopictus

    BMC Genomics 18:512.

    https://doi.org/10.1186/s12864-017-3903-3

    • PubMed
    • Google Scholar
90. 1. Patz JA
    2. Graczyk TK
    3. Geller N
    4. Vittor AY

    (2000) Effects of environmental change on emerging parasitic diseases

    International Journal for Parasitology 30:1395–1405.

    https://doi.org/10.1016/s0020-7519(00)00141-7

    • PubMed
    • Google Scholar
91. 1. Perrin A
    2. Glaizot O
    3. Christe P

    (2022) Worldwide impacts of landscape anthropization on mosquito abundance and diversity: A meta-analysis

    Global Change Biology 28:6857–6871.

    https://doi.org/10.1111/gcb.16406

    • PubMed
    • Google Scholar
92. 1. Perrin A
    2. Schaffner F
    3. Christe P
    4. Glaizot O

    (2023) Relative effects of urbanisation, deforestation, and agricultural development on mosquito communities

    Landscape Ecology 38:1527–1536.

    https://doi.org/10.1007/s10980-023-01634-w

    • PubMed
    • Google Scholar
93. 1. Quan PL
    2. Junglen S
    3. Tashmukhamedova A
    4. Conlan S
    5. Hutchison SK
    6. Kurth A
    7. Ellerbrok H
    8. Egholm M
    9. Briese T
    10. Leendertz FH
    11. Lipkin WI

    (2010) Moussa virus: a new member of the Rhabdoviridae family isolated from Culex decens mosquitoes in Côte d’Ivoire

    Virus Research 147:17–24.

    https://doi.org/10.1016/j.virusres.2009.09.013

    • PubMed
    • Google Scholar
94. 1. Ramírez AL
    2. Hall-Mendelin S
    3. Doggett SL
    4. Hewitson GR
    5. McMahon JL
    6. Ritchie SA
    7. van den Hurk AF

    (2018) Mosquito excreta: A sample type with many potential applications for the investigation of ross river virus and west nile virus ecology

    PLOS Neglected Tropical Diseases 12:e0006771.

    https://doi.org/10.1371/journal.pntd.0006771

    • PubMed
    • Google Scholar
95. 1. Randolph SE
    2. Dobson ADM

    (2012) Pangloss revisited: a critique of the dilution effect and the biodiversity-buffers-disease paradigm

    Parasitology 139:847–863.

    https://doi.org/10.1017/S0031182012000200

    • PubMed
    • Google Scholar
96. Software

    1. R Development core Team

    (2019) A language and environment for statistical Computingr core team

    R Foundation for Statistical Computing, Vienna, Austria.

    https://www.R-project.org
97. 1. Rodrigues DSG
    2. Medeiros DA
    3. Rodrigues SG
    4. Martins LC
    5. de Lima CPS
    6. de Oliveira LF
    7. de Vasconcelos JM
    8. Da Silva DE
    9. Cardoso JF
    10. da Silva SP
    11. Vianez-Júnior J da S
    12. Nunes MRT
    13. Vasconcelos PC

    (2014) Pacui virus, rio preto da eva virus, and tapirape virus, three distinct viruses within the family bunyaviridae

    Genome Announcements 2:e00923-14.

    https://doi.org/10.1128/genomeA.00923-14

    • PubMed
    • Google Scholar
98. 1. Roiz D
    2. Vázquez A
    3. Seco MPS
    4. Tenorio A
    5. Rizzoli A

    (2009) Detection of novel insect flavivirus sequences integrated in Aedes albopictus (Diptera: Culicidae) in Northern Italy

    Virology Journal 6:93.

    https://doi.org/10.1186/1743-422X-6-93

    • PubMed
    • Google Scholar
99. 1. Roiz D
    2. Ruiz S
    3. Soriguer R
    4. Figuerola J
    5. Schneider BS

    (2015) Landscape effects on the presence, abundance and diversity of mosquitoes in mediterranean wetlands

    PLOS ONE 10:e0128112.

    https://doi.org/10.1371/journal.pone.0128112

    • Google Scholar
100. 1. Roiz D
     2. Vázquez A
     3. Ruiz S
     4. Tenorio A
     5. Soriguer R
     6. Figuerola J

     (2019) Evidence that passerine birds act as amplifying hosts for usutu virus circulation

     EcoHealth 16:734–742.

     https://doi.org/10.1007/s10393-019-01441-3

     • PubMed
     • Google Scholar
101. 1. Ruyts SC
     2. Ampoorter E
     3. Coipan EC
     4. Baeten L
     5. Heylen D
     6. Sprong H
     7. Matthysen E
     8. Verheyen K

     (2016) Diversifying forest communities may change Lyme disease risk: extra dimension to the dilution effect in Europe

     Parasitology 143:1310–1319.

     https://doi.org/10.1017/S0031182016000688

     • PubMed
     • Google Scholar
102. 1. Sadeghi M
     2. Popov V
     3. Guzman H
     4. Phan TG
     5. Vasilakis N
     6. Tesh R
     7. Delwart E

     (2017) Genomes of viral isolates derived from different mosquitos species

     Virus Research 242:49–57.

     https://doi.org/10.1016/j.virusres.2017.08.012

     • PubMed
     • Google Scholar
103. 1. Salkeld DJ
     2. Padgett KA
     3. Jones JH

     (2013) A meta-analysis suggesting that the relationship between biodiversity and risk of zoonotic pathogen transmission is idiosyncratic

     Ecology Letters 16:679–686.

     https://doi.org/10.1111/ele.12101

     • PubMed
     • Google Scholar
104. 1. Schrama M
     2. Hunting ER
     3. Beechler BR
     4. Guarido MM
     5. Govender D
     6. Nijland W
     7. van ‘t Zelfde M
     8. Venter M
     9. van Bodegom PM
     10. Gorsich EE

     (2020) Human practices promote presence and abundance of disease-transmitting mosquito species

     Scientific Reports 10:13543.

     https://doi.org/10.1038/s41598-020-69858-3

     • Google Scholar
105. 1. Schuster S
     2. Zirkel F
     3. Kurth A
     4. van Cleef KWR
     5. Drosten C
     6. van Rij RP
     7. Junglen S

     (2014) A unique nodavirus with novel features: mosinovirus expresses two subgenomic RNAs, A capsid gene of unknown origin, and A suppressor of the antiviral RNA interference pathway

     Journal of Virology 88:13447–13459.

     https://doi.org/10.1128/JVI.02144-14

     • PubMed
     • Google Scholar
106. 1. Shi M
     2. Lin XD
     3. Tian JH
     4. Chen LJ
     5. Chen X
     6. Li CX
     7. Qin XC
     8. Li J
     9. Cao JP
     10. Eden JS
     11. Buchmann J
     12. Wang W
     13. Xu J
     14. Holmes EC
     15. Zhang YZ

     (2016) Redefining the invertebrate RNA virosphere

     Nature 540:539–543.

     https://doi.org/10.1038/nature20167

     • PubMed
     • Google Scholar
107. 1. Swaddle JP
     2. Calos SE

     (2008) Increased avian diversity is associated with lower incidence of human West Nile infection: observation of the dilution effect

     PLOS ONE 3:e2488.

     https://doi.org/10.1371/journal.pone.0002488

     • PubMed
     • Google Scholar
108. 1. Swei A
     2. Couper LI
     3. Coffey LL
     4. Kapan D
     5. Bennett S

     (2020) Patterns, drivers, and challenges of vector-borne disease emergence

     Vector Borne and Zoonotic Diseases 20:159–170.

     https://doi.org/10.1089/vbz.2018.2432

     • PubMed
     • Google Scholar
109. 1. Takken W
     2. Verhulst NO

     (2013) Host preferences of blood-feeding mosquitoes

     Annual Review of Entomology 58:433–453.

     https://doi.org/10.1146/annurev-ento-120811-153618

     • PubMed
     • Google Scholar
110. 1. Ter Horst AM
     2. Nigg JC
     3. Dekker FM
     4. Falk BW

     (2019) Endogenous Viral Elements Are Widespread in Arthropod Genomes and Commonly Give Rise to PIWI-Interacting RNAs

     Journal of Virology 93:e02124-18.

     https://doi.org/10.1128/JVI.02124-18

     • PubMed
     • Google Scholar
111. 1. Thongsripong P
     2. Green A
     3. Kittayapong P
     4. Kapan D
     5. Wilcox B
     6. Bennett S
     7. Barrera R

     (2013) Mosquito vector diversity across habitats in central Thailand endemic for dengue and other arthropod-borne diseases

     PLOS Neglected Tropical Diseases 7:e2507.

     https://doi.org/10.1371/journal.pntd.0002507

     • PubMed
     • Google Scholar
112. 1. Thongsripong P
     2. Chandler JA
     3. Kittayapong P
     4. Wilcox BA
     5. Kapan DD
     6. Bennett SN

     (2021) Metagenomic shotgun sequencing reveals host species as an important driver of virome composition in mosquitoes

     Scientific Reports 11:8448.

     https://doi.org/10.1038/s41598-021-87122-0

     • PubMed
     • Google Scholar
113. 1. Tomazatos A
     2. Marschang RE
     3. Maranda I
     4. Baum H
     5. Bialonski A
     6. Spînu M
     7. Lühken R
     8. Schmidt-Chanasit J
     9. Cadar D

     (2020) Letea Virus: Comparative Genomics and Phylogenetic Analysis of a Novel Reassortant Orbivirus Discovered in Grass Snakes (Natrix natrix)

     Viruses 12:243.

     https://doi.org/10.3390/v12020243

     • PubMed
     • Google Scholar
114. 1. Valles SM
     2. Chen Y
     3. Firth AE
     4. Guérin DMA
     5. Hashimoto Y
     6. Herrero S
     7. de Miranda JR
     8. Ryabov E
     9. ICTV Report Consortium

     (2017) ICTV Virus Taxonomy Profile: Iflaviridae

     Journal of General Virology 98:527–528.

     https://doi.org/10.1099/jgv.0.000757

     • Google Scholar
115. 1. Walker PJ
     2. Blasdell KR
     3. Calisher CH
     4. Dietzgen RG
     5. Kondo H
     6. Kurath G
     7. Longdon B
     8. Stone DM
     9. Tesh RB
     10. Tordo N
     11. Vasilakis N
     12. Whitfield AE

     (2018) ICTV Virus Taxonomy Profile: Rhabdoviridae

     The Journal of General Virology 99:447–448.

     https://doi.org/10.1099/jgv.0.001020

     • PubMed
     • Google Scholar
116. 1. Weaver SC
     2. Charlier C
     3. Vasilakis N
     4. Lecuit M

     (2018a) Zika, chikungunya, and other emerging vector-borne viral diseases

     Annual Review of Medicine 69:395–408.

     https://doi.org/10.1146/annurev-med-050715-105122

     • PubMed
     • Google Scholar
117. 1. Weaver S
     2. Shank SD
     3. Spielman SJ
     4. Li M
     5. Muse SV
     6. Kosakovsky Pond SL

     (2018b) Datamonkey 2.0: A modern web application for characterizing selective and other evolutionary processes

     Molecular Biology and Evolution 35:773–777.

     https://doi.org/10.1093/molbev/msx335

     • PubMed
     • Google Scholar
118. 1. Whitfield ZJ
     2. Dolan PT
     3. Kunitomi M
     4. Tassetto M
     5. Seetin MG
     6. Oh S
     7. Heiner C
     8. Paxinos E
     9. Andino R

     (2017) The Diversity, Structure, and Function of Heritable Adaptive Immunity Sequences in the Aedes aegypti Genome

     Current Biology 27:3511–3519.

     https://doi.org/10.1016/j.cub.2017.09.067

     • PubMed
     • Google Scholar
119. 1. Young KI
     2. Mundis S
     3. Widen SG
     4. Wood TG
     5. Tesh RB
     6. Cardosa J
     7. Vasilakis N
     8. Perera D
     9. Hanley KA

     (2017) Abundance and distribution of sylvatic dengue virus vectors in three different land cover types in Sarawak, Malaysian Borneo

     Parasites & Vectors 10:406.

     https://doi.org/10.1186/s13071-017-2341-z

     • PubMed
     • Google Scholar
120. 1. Zell R
     2. Delwart E
     3. Gorbalenya AE
     4. Hovi T
     5. King AMQ
     6. Knowles NJ
     7. Lindberg AM
     8. Pallansch MA
     9. Palmenberg AC
     10. Reuter G
     11. Simmonds P
     12. Skern T
     13. Stanway G
     14. Yamashita T

     (2017) ICTV Virus Taxonomy Profile: Picornaviridae

     The Journal of General Virology 98:2421–2422.

     https://doi.org/10.1099/jgv.0.000911

     • PubMed
     • Google Scholar
121. 1. Zhang X
     2. Huang S
     3. Jin T
     4. Lin P
     5. Huang Y
     6. Wu C
     7. Peng B
     8. Wei L
     9. Chu H
     10. Wang M
     11. Jia Z
     12. Zhang S
     13. Xie J
     14. Cheng J
     15. Wan C
     16. Zhang R

     (2018) Discovery and high prevalence of Phasi Charoen-like virus in field-captured Aedes aegypti in South China

     Virology 523:35–40.

     https://doi.org/10.1016/j.virol.2018.07.021

     • Google Scholar
122. 1. Zirkel F
     2. Kurth A
     3. Quan PL
     4. Briese T
     5. Ellerbrok H
     6. Pauli G
     7. Leendertz FH
     8. Lipkin WI
     9. Ziebuhr J
     10. Drosten C
     11. Junglen S

     (2011) An insect nidovirus emerging from a primary tropical rainforest

     mBio 2:e00077-11.

     https://doi.org/10.1128/mBio.00077-11

     • PubMed
     • Google Scholar
123. 1. Zirkel F
     2. Roth H
     3. Kurth A
     4. Drosten C
     5. Ziebuhr J
     6. Junglen S

     (2013) Identification and characterization of genetically divergent members of the newly established family Mesoniviridae

     Journal of Virology 87:6346–6358.

     https://doi.org/10.1128/JVI.00416-13

     • PubMed
     • Google Scholar

Author details

1. Kyra Hermanns

   Institute of Virology, Charité - Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt-Universtiy Berlin, and Berlin Institute of Health, Berlin, Germany

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Visualization, Writing - original draft, Writing – review and editing

   Competing interests

   No competing interests declared

2. Marco Marklewitz

   Institute of Virology, Charité - Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt-Universtiy Berlin, and Berlin Institute of Health, Berlin, Germany

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

3. Florian Zirkel

   Institute of Virology, University of Bonn Medical Centre, Berlin, Germany

   Present address

   Biotest AG, Dreieich, Germany

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Anne Kopp

   Institute of Virology, Charité - Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt-Universtiy Berlin, and Berlin Institute of Health, Berlin, Germany

   Contribution

   Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

5. Stephanie Kramer-Schadt

   1. Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
   2. Institute of Ecology, Technische Universität Berlin, Berlin, Germany

   Contribution

   Formal analysis, Visualization, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9269-4446

6. Sandra Junglen

   Institute of Virology, Charité - Universitätsmedizin Berlin, corporate member of Free University Berlin, Humboldt-Universtiy Berlin, and Berlin Institute of Health, Berlin, Germany

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Writing - original draft, Writing – review and editing

   For correspondence

   sandra.junglen@charite.de

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3799-6011

• 1,866

  views

• 363

  downloads

• 5

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.