Bloodmeal metabarcoding reveals host feeding patterns for Aedes aegypti and Culex quinquefasciatus in Jutiapa, Guatemala and Texas, USA

Bloodmeal metabarcoding reveals host feeding patterns for Aedes aegypti and Culex quinquefasciatus in Jutiapa, Guatemala and Texas, USA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Bloodmeal metabarcoding reveals host feeding patterns for Aedes aegypti and Culex quinquefasciatus in Jutiapa, Guatemala and Texas, USA Abdisalam A. Abdi, Sujata Balasubramanian, Jose Juarez, Nicole A. Scavo, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8855613/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Mosquito host contact determines arboviral transmission efficiency. Aedes aegypti and Culex quinquefasciatus are important vectors of dengue, Zika, chikungunya, West Nile virus, and other arboviruses, yet their feeding patterns remain poorly characterized in many tropical regions. We used bloodmeal metabarcoding to detect DNA from multiple vertebrate species within individual blood-fed mosquitoes collected from rural Guatemala and south Texas, USA. Mosquitoes were collected using aspiration in Guatemala and BG-Sentinel traps in south Texas. We calculated forage ratios (FR) to assess host utilization relative to availability. In Guatemala, Ae. aegypti exhibited strong anthropophilic behavior (human DNA: 90.2% of bloodmeals and FR = 3.62 (95% CI: 2.70–4.54), indicating significant over-utilization. In south Texas, Ae. aegypti strongly over-utilized dogs (88.2% of bloodmeals; FR = 4.65, 95% CI: 2.43–6.87) while under-utilizing humans (FR = 0.53, 95% CI: 0.25–0.81). In Guatemala, Cx. quinquefasciatus displayed high anthropophilic behavior (85.3% of bloodmeals; FR = 2.60, 95% CI: 2.24–2.97). Mixed bloodmeals were common in both species at both sites (19.5–85.3%), with up to four host species detected in single mosquitoes. These results demonstrate that mosquito host selection is variable and context-dependent and underscore the need for location-specific surveillance to inform vector control strategies. Health sciences/Diseases Biological sciences/Ecology Earth and environmental sciences/Ecology Biological sciences/Zoology bloodmeal analysis metabarcoding Aedes aegypti Culex quinquefasciatus forage ratio arbovirus transmission Figures Figure 1 Figure 2 Figure 3 Introduction Mosquito-borne pathogens cause over 700,000 deaths annually and impose billions of dollars in economic costs worldwide [ 1 ]. These pathogens include malaria ( Plasmodium spp .), dengue virus (DENV), Zika virus (ZIKV), chikungunya virus (CHIKV), yellow fever virus (YFV), West Nile virus (WNV), and Japanese encephalitis virus (JEV) [ 2 , 3 ]. These diverse pathogens exist in cycles between competent mosquito vector species and competent amplification vertebrate host species [ 4 , 5 ]. In some cases, humans function as amplification hosts and develop an infectious viremia capable of reinfecting a mosquito [ 6 , 7 ]. When humans function as amplification hosts and transmission is maintained primarily between mosquitoes and humans in built environments, this pattern is known as an “urban cycle” or a “human-amplified transmission cycle”. This transmission pattern characterizes several major pathogens transmitted by Aedes aegypti , including DENV, ZIKV, and CHIKV [ 8 – 10 ]. Alternatively, other pathogens such as WNV and eastern equine encephalitis virus (EEEV) utilize wild or domestic animals as amplification hosts which are fed on by diverse mosquito species and maintained in what are known as ‘sylvatic cycles’ [ 11 , 12 ]. As these mosquito-borne pathogens amplify in the mosquito and animal cycle, a bridge vector species is capable of spill-over transmission to humans or other domestic animals which can suffer disease, while not necessarily being capable of developing an infectious viremia (i.e., dead-end host for virus) [ 5 , 6 , 13 ]. The type of transmission cycle is fundamentally linked to the host-feeding behavior of vector mosquito species [ 14 – 16 ]. Anthropophilic species such as Ae. aegypti preferentially feed on humans [ 17 – 20 ], which makes this species a potent vector of human pathogens in tropical and subtropical regions. In contrast, ornithophilic (bird-feeding) species like members of the Culex pipiens complex maintain sylvatic and zoonotic cycles by feeding primarily on avian hosts [ 21 – 23 ]. However, some studies have documented atypical non-human feeding by Ae. aegypti in both South Texas [ 24 ], and Kenya [ 25 ], which may reduce mosquito–human contact rates and thereby lower transmission potential for human-amplified arboviruses. Therefore, variation in Ae. aegypti host use can shape the intensity and spatial pattern of Aedes -borne pathogen transmission [ 15 , 26 , 27 ]. An important mosquito vector of arthropod-borne viruses (arboviruses) globally are members of the Culex pipiens complex. This complex includes the northern house mosquito, Cx. pipiens pipiens , in more temperate latitudes globally, and the southern house mosquito, Culex. quinquefasciatus , found in more tropical and subtropical regions globally [ 28 ]. The feeding patterns of species belonging to the Cx. pipiens complex has been extensively studied [ 29 , 30 ], given that they are important in the transmission of many zoonotic pathogens. However, most of this research attention on Culex spp. mosquitoes has been in temperate regions while vectors in this genus have been neglected in many tropical and subtropical regions which are endemic for human-amplified mosquito-borne pathogens such as malaria and dengue [ 31 ]. Many studies report an ornithophilic feeding pattern of Cx. pipiens complex [ 21 – 23 , 32 – 34 ]. However, Cx. quinquefasciatus feeding patterns are more complex with some studies reporting high utilization of birds as hosts while others document high feeding rates on mammals, including humans [ 35 , 36 ]. This uncertainty in the feeding patterns of Cx. quinquefasciatus may also be driven by few bloodmeal analysis studies being conducted in tropical and subtropical regions. Recent meta-analyses reveal that Cx. quinquefasciatus feeding patterns vary substantially across biogeographic regions, with some tropical populations exhibiting predominantly mammalian or human feeding rather than avian feeding [ 37 ]. Beyond this geographic variability, accurately characterizing mosquito host use is further complicated by the feeding behavior of key vector species. Both Ae. aegypti and Culex spp. are known to take multiple bloodmeals from different host species within a single gonotrophic cycle, increasing the frequency of mixed-species bloodmeals [ 15 , 38 ]. Traditional PCR followed by Sanger sequencing has been the primary method for bloodmeal analysis, but it has critical limitations: it typically detects only the most abundant DNA in a sample, often missing hosts in mixed bloodmeals [ 39 , 40 ]. For example, our previous study in South Texas used PCR-Sanger sequencing documented Ae. aegypti feeding patterns [ 41 ], but this approach may have underestimated the frequency of human feeding in mixed bloodmeals where dog DNA was preferentially amplified in the PCR. These methodological constraints mean that our understanding of mosquito host-feeding patterns may be incomplete, particularly for species that frequently feed on multiple hosts. High-throughput sequencing methods can overcome these limitations by detecting most vertebrate DNA present in a bloodmeal, regardless of abundance. Specifically, bloodmeal metabarcoding employs deep sequencing of vertebrate-specific DNA loci, enabling detection of multiple host species within bloodmeals from individual arthropods [ 42 , 43 ]. This approach has been successfully applied to characterize host-feeding patterns in various hematophagous arthropods, including mosquitoes [ 44 , 45 ], soft ticks [ 43 ], and triatomines [ 46 ]. Given the uncertainty of Ae. aegypti and Cx. quinquefasciatus feeding patterns in tropical regions, where most previous bloodmeal studies employed Sanger sequencing with limited capacity to detect mixed-species meals, the objective of this study was to conduct bloodmeal metabarcoding on field-collected females of both Ae. aegypti and Cx. quinquefasciatus species from rural communities in Guatemala and south Texas, USA. The work in south Texas builds on our previous work using PCR-Sanger sequencing, which documented high use of non-human animals [ 24 ]. We will now verify these unexpected feeding patterns using new mosquito collections from different years, different study sites, and using the bloodmeal metabarcoding pipeline. The collections in Guatemala provide an opportunity to utilize this novel metabarcoding pipeline in a tropical setting. This study provides a unique opportunity to study multiple bloodfeeding behaviors within the same gonotropic cycle, which is well-documented for Ae. aegypti [ 15 , 17 , 47 – 50 ] but less understood for Culex spp. mosquitoes [ 51 , 52 ]. Results Guatemala A total of 243 blood-fed female mosquitoes were collected from 77 households during the 2022 rainy season in Guatemala. Of these, 228 (93.8%) were molecularly confirmed to species: 67 Ae. aegypti (29.4%), 2 Ae. albopictus (0.9%), 155 Cx. quinquefasciatus (68.0%), 2 Cx. nigripalpus (0.9%), and 2 Cx. corniger (0.9%). All molecular identifications matched morphological assignments. Of the 67 Ae. aegypti tested, 41 (61.2%) produced PCR product and sequencing reads that yielded host identifications. Among the specimens with results, 33 (80.5%) were single-host meals, including 29 human ( Homo sapiens ) and 4 chicken ( Gallus gallus ). The remaining 8 (19.5%) meals were mixed, consisting of 6 human-chicken combinations and 2 human-bird ( Turdus sp.) combinations. Humans were detected in 37/41 (90.2%) of the Ae. aegypti bloodmeals, chickens in 10/41 (24.4%), and Turdus sp. in 2/41 (4.9%) (Table 1 ). Forage-ratio inference indicated over-utilization of humans relative to availability (FR = 3.62, 95% CI 2.70–4.54), whereas feeding on chickens was not significantly different from availability (FR = 1.25, 95% CI 0.41–2.08) (Table 2 ). Both Ae. albopictus (n = 2) yielded identifiable meals: one single-host (human) and one mixed-host (human-chicken). Among blood-fed Cx. quinquefasciatus processed (n = 155), 136 (87.7%) yielded host identifications. Of these, 48 were single-host meals (35.3%), including 31 human, 15 chicken, 1 turkey ( Meleagris gallopavo ), and 1 passerine bird ( Passeriformes ). A further 70 (51.5%) were mixed two-host meals, composed of 61 human–chicken, 5 human–dog ( Canis lupus familiaris ), 1 human–turkey, 2 chicken–dog, and 1 chicken–turkey. The remaining 18 (13.2%) were mixed three-host meals, including 16 human–chicken–dog, 1 human–chicken–turkey, and 1 human–chicken–bird ( Turdus sp .) (Table 1 ). For the less common mosquito species, both Cx. nigripalpus (n = 2) contained single-host meals 1 human and 1 chicken, while the single Cx. corniger (n = 1) specimen yielded a mixed two-host meal (human–chicken). Across all Cx. quinquefasciatus bloodmeals, humans were detected in 116/136 (85.3%), chickens in 97/136 (71.3%), and dogs in 21/136 (15.4%) (Table 1 ). For the Cx. quinquefasciatus forage-ratio, humans were significantly over-utilized (FR = 2.60, 95% CI 2.24–2.97), chickens were marginally over-utilized (FR = 1.27, 95% CI 1.07–1.46), and utilization of dogs did not differ from availability (FR = 0.99, 95% CI 0.51–1.47) (Table 2 ). South Texas, USA Of 121 blood-fed Ae. aegypti examined in south Texas, 68 (56.2%) yielded at least one host identification. Of these, 10 (14.7%) were single-host meals, including 2 human ( Homo sapiens ), 3 dog ( Canis lupus familiaris ), 1 chicken ( Gallus gallus ), 3 house mouse ( Mus musculus ), and 1 brown rat ( Rattus norvegicus ). The remaining 58 (85.3%) bloodmeals were mixed, including 35 (51.5%) two-host meals, 19 (27.9%) three-host meals, and 4 (5.9%) four-host meals. These consisted of 25 human–dog, 7 dog–cat ( Felis catus ), 2 chicken–dog, 1 chicken–mouse, 17 human–dog–cat, 1 human–dog–rat, 1 chicken–dog–mouse, and 4 human–dog–cat–rat. Across all meals, dogs were detected in 60/68 (88.2%) of bloodmeals, humans in 49/68 (72.1%), cats in 28/68 (41.2%), brown rats in 6/68 (8.8%), house mice in 5/68 (7.4%), and chickens in 5/68 (7.4%) (Table 1 ). Forage-ratio inference indicated significant under-utilization of humans relative to their abundance (FR 0.53, 95% CI 0.25–0.81) and strong over-utilization of dogs (FR 4.65, 95% CI 2.43–6.87). Cats did not differ from availability (FR 1.55, 95% CI 0.52–2.57), and chicken FRs were not calculated because household availability was zero for most records (Table 2 ). Table 1 Bloodmeal host composition by mosquito species and collection site. Hosts detected in mosquito abdomens Ae. aegypti Texas (n = 68) Ae. aegypti Guatemala (n = 41) Cx quinquefasciatus Guatemala (n = 136) No. samples (%) No, samples (%) No. samples (%) Single host species detected: Human ( Homo sapiens) 2 (2.9%) 29(70.7%) 31(22.8%) Chicken (Gallus gallus) 1(1.5%) 4(9.8%) 15(11.0%) Dog (Canis lupus familiaris) 3(4.4%) — — Turkey (Meleagris gallopavo) — 1(0.7%) Bird ( Order Passeriformes) — — 1(0.7%) Brown rat (Rattus norvegicus) 1(1.5%) — — House mouse (Mus musculus) 3(4.4%) — — Total single host bloodmeal 10(14.7%) 33(80.5%) 48(35.3%) Multiple host species detected : Human + Chicken — 6(14.6%) 61(44.9%) Human + Dog 25(36.8) — 5(3.7%) Human + Turkey — — 1(0.7%) Human + Bird ( Turdus sp ) — 2(4.9%) Chicken + turkey — — 1(0.7%) Human + Chicken + Dog — — 16(11.8) Human + Chicken + Turkey — — 1(0.7%) Human + Chicken + Bird ( Turdus sp ) — — 1(0.7%) Human + Dog + Cat 17(25%) — — Human + Dog + Rat ( Rattus norvegicus) 1(1.5%) — — Human + Dog + Cat + Rat 4(5.9%) — — Chicken + Dog 2 (2.9%) — 2(1.5%) Dog + Cat ( Felis catus ) 7(10.3) — — Chicken + House mouse 1(1.5%) — — Chicken + Dog + house mouse 1(1.5%) — — Total multiple host bloodmeal 58(85.3%) 8 (19.5%) 88 (64.7%) Mixed feeding comparisons in Guatemala and Texas Mixed feeding patterns differed significantly between mosquito species within Guatemala; Cx. quinquefasciatus exhibited higher rates of mixed feeding than Ae. aegypti (64.7% vs. 19.5%; χ² = 24.13, df = 1, p < 0.001) (Table 1 ). South Texas Ae. aeg ypti showed the highest mixed feeding rate at 85.3%, significantly greater than Guatemala Ae. aegypti (χ² = 43.62, df = 1, p < 0.001) (Table 1 ). Table 2 Host Selection Patterns: Forage Ratios by Mosquito Species and Location. Texas Ae. aegypti FR (95% CI) Host Guatemala Ae. aegypti FR (95% CI) Guatemala Cx. quinquefasciatus FR (95% CI) Human 3.62 ± 0.45 (2.70–4.54) * 2.60 ± 0.19 (2.24–2.97) * 0.53 ± 0.14 (0.25–0.81) Chicken 1.25 ± 0.34 (0.41–2.08) 1.27 ± 0.10 (1.07–1.46) — Dog — 0.99 ± 0.22 (0.51–1.47) 4.65 ± 1.02 (2.43–6.87) * Cat — — 1.55 ± 0.47 (0.52–2.57) FR = forage ratio (mean ± SE), calculated as the ratio of observed feeding frequency to expected feeding based on host availability from household census data. 95% CI = 95% confidence interval. Asterisk (*) indicates significant over- or under-utilization (95% CI does not include 1.0). "—" indicates FR was not estimable due to zero household availability. Household Characteristics Household surveys documented substantial differences in housing infrastructure between study sites. In Guatemala, none of the 77 households had air conditioning and all lacked intact window or door screens. In South Texas, 46 of 48 surveyed households (95.8%) had air conditioning (19 central systems, 27 window units), and most had screened windows or doors. Discussion Our bloodmeal metabarcoding revealed substantial geographic heterogeneity in the host use of Ae. aegypti and Cx. quinquefasciatus across south Texas and rural Guatemala, including shifts between human and domestic animal feeding with implications for arboviral epidemiology. In Guatemala, Ae. aegypti exhibited anthropophilic feeding behavior, with human DNA being detected in 90.2% of the identified bloodmeals, and the human FR being 3.62 ± 0.45 (2.70–4.54) (Table 2 ), indicating significant over-utilization of humans relative to their abundance. This high anthropophilic behavior aligns with most studies documenting Ae. aegypti feeding primarily on humans in domestic environments where close human-mosquito contact is facilitated by lack of physical barriers such as window screens and air conditioning [ 53 ]. In contrast, Ae. aegypti from south Texas showed substantially lower human feeding (72.1% of identified meals) while dogs were detected in 88.2% of meals. The FR for the south Texas Ae. aegypti feeding on humans was 0.53 (95% CI: 0.25–0.81), indicating significant under-utilization of humans relative to their abundance. The south Texas Ae. aegypti FR for dog was 4.65 (95% CI: 2.43–6.87) (Table 2 ) indicating significant over-utilization of dogs. These findings of high utilization of dogs by south Texas Ae. aegypti corroborate our prior study from the same region which documented about 31% human bloodmeals, 50% dog, and 19% other vertebrates [ 24 ]. This current south Texas study sampled mosquitoes in different years, different south Texas communities, involved different project personnel, and utilized a different bloodmeal analysis technique. While the current study reports bloodmeal metabarcoding results, the prior study conducted PCR-Sanger sequencing methodology [ 24 ]. Note that our prior study, which documented 31% of human bloodmeals by PCR-Sanger sequencing, could have missed human meals within mixed bloodmeals while identifying other taxa; conventional PCR followed by direct Sanger sequencing typically yields a single sequence per sample and tends to under-detect minority hosts in mixed bloodmeals, whereas next-generation metabarcoding approaches recover multiple vertebrate hosts from individual mosquitoes and substantially improve the resolution of mixed-source bloodmeals [ 54 , 55 ]. Consequently, bloodmeals containing both human and dog DNA, where dog DNA was preferentially amplified in the PCR, would often have been classified as “dog only” by PCR Sanger sequencing. With metabarcoding, these meals would have been more accurately identified as mixed (human + dog) meals, helping to explain the higher percentage of human-positive bloodmeals (72%) observed in the current study. One limitation of the current study is that the mosquito collection method was different in Guatemala compared to south Texas; Ae. aegypti in Guatemala were collected by indoor and outdoor aspiration while in south Texas were collected using BG Sentinel 2 traps placed outdoors. The low-income communities in Guatemala often lacked doors, screens, and sometimes walls, and homeowners are experienced with local ministries of health personnel entering the indoor environment for vector surveillance and indoor residual spraying (IRS) of insecticides. Therefore, our field team, which was accompanied by a government employee from the ministries of health, was allowed access to the homes for indoor aspirating. The low-income communities in south Texas are very different Hispanic communities, and our past studies on community engagement have characterized these challenges [ 56 ]. Government employees do not routinely enter homes, IRS campaigns are not typically conducted in the US, and therefore our bloodfed mosquito sampling was limited to the outdoor environment. The different sampling collections would be expected to influence the availability of the different hosts, and thus could have influenced feeding patterns. Forage ratios also depend on household-based host availability estimates, which likely undercount free-ranging hosts such as wild birds that are difficult to enumerate. Because metabarcoding detected some wild-bird feeding, forage ratios, particularly those involving avian hosts should be interpreted cautiously and primarily as relative selection was based on the surveyed domestic/peridomestic host community and not wild host community. The lower utilization of humans by Ae. aegypti in south Texas could be due to outdoor mosquitoes not having access to the humans due to the presence of doors, windows, and screens. However, studies have documented variable utilization of humans by Ae. aegypti using outdoor collections with BG Sentinel traps; human feeding by Ae. aegypti collected by BG Sentinel traps in Mombasa, Kenya was about 35% while in Kisumu, Kenya was about 12% [ 25 ]. Future studies should interrogate Ae. aegypti feeding patterns with more standardized sampling designs to understand differences in the indoor and outdoor environments. Despite the differences in sampling methods and locations, our results likely reveal important differences in mosquito-human contact patterns that reflect differences in household infrastructure and human behavior. In Guatemala, homes lacked air conditioning and window screens, allowing mosquitoes constant access to indoor spaces where humans spend time. Additionally, many household activities (e.g. washing clothes, food preparation) occurred outdoors, or in kitchens not enclosed by walls on all sides, further facilitating human-mosquito contact. Indeed, high human-feeding rates have been documented in Ae. aegypti collected outdoors where humans are readily accessible, as shown in Kenya [ 57 , 58 ] and India [ 59 ], supporting the importance of host accessibility regardless of collection location. In contrast, south Texas households, despite being lower-income communities, typically had air conditioning and screened windows, creating physical barriers that limit mosquito access to humans indoors. Household activities in the south Texas settings are more likely to occur inside, further reducing human-mosquito contact opportunities. This difference in accessibility may explain the contrasting forage ratios observed: significant over-utilization of humans in Guatemala (FR = 3.62) versus significant under-utilization in South Texas (FR = 0.53). Importantly, the Texas pattern could reflect reduced mosquito access to humans rather than an intrinsic preference for non-human hosts. When barriers limit human accessibility, Ae. aegypti could exhibit opportunistic feeding on available hosts, particularly dogs. These findings challenge the traditional view of Ae. aegypti as obligately anthropophilic, demonstrating instead that this species exhibits opportunistic feeding behavior when preferred hosts are less accessible. These patterns have important implications for arbovirus transmission. In settings like Guatemala where human-mosquito contact is unrestricted, high anthropophilic feeding maintains intense dengue transmission [ 60 ]. In settings like south Texas where physical barriers reduce human accessibility, transmission potential may be lower despite the presence of competent vectors and susceptible human populations, as mosquitoes divert feeding effort to alternative hosts [ 41 ]. Understanding the mechanisms underlying this behavioral variation requires consideration of both genetic and ecological factors. The domestic subspecies Ae. aegypti aegypti (Aaa), which predominates in the Americas and urban settings globally, is widely considered strongly anthropophilic, whereas the sylvatic African subspecies Ae. aegypti formosus (Aef), found only in sub-Saharan Africa, exhibits more generalist feeding on various vertebrate hosts. McBride et al.[ 61 ] identified odorant receptor differences associated with human preference in Aaa. Recent population genetics studies in Kenya have revealed that some geographic variation in arbovirus transmission may reflect subspecies differences; Mulwa et al. [ 62 ] found coastal Kenyan Ae. aegypti populations (Mombasa) are strongly admixed between Aef and Aaa, with Aaa ancestry highest at the coast, while Anyango et al. [ 63 ] confirmed western Kenya populations (Kisumu, Busia) are dominated by Aef ancestry. This genetic structure may partly explain Kenya's pattern of dengue outbreaks occurring in coastal cities but not interior cities. Aaa-enriched coastal populations exhibit higher anthropophily that facilitates transmission, while Aef-dominated western populations remain more zoophilic despite comparable vector competence [ 25 ]. In North America, Ae. aegypti populations are overwhelmingly Ae. aegypti aegypti (Aaa), so the geographic variation in host feeding observed in the Americas is unlikely to be explained by Aaa–Aef subspecies composition alone. However, substantial geographic variation in host use is also evident among populations likely representing Aaa, underscoring a strong role for local ecology and host accessibility. Our findings from Guatemala and South Texas demonstrate pronounced differences: Guatemala showed 90% human-positive bloodmeals (FR = 3.62) versus South Texas with 72% human-positive but 88% dog-positive (dog FR = 4.65). Similarly, Agha et al. [ 25 ] documented feeding differences among Kenyan urban Aaa populations: coastal Mombasa showed 40% human feeding while inland Kisumu showed only 10% (p = 0.03), with Kisumu mosquitoes feeding heavily on dogs, goats, and cows despite human presence. Kamau et al. [ 58 ] likewise reported higher human blood indices in coastal compared with inland settings. Together, these findings indicate that while genetic background can shape host preference, local host availability and, especially host accessibility mediated by household infrastructure and human behavior can strongly modulate realized feeding patterns, with downstream consequences for arbovirus transmission risk. We observed similar patterns of context-dependent feeding behavior in Cx. quinquefasciatus . Our results for Cx. quinquefasciatus feeding patterns in rural Guatemala confirm high human feeding behavior. While members of the Culex pipiens complex, including Cx. quinquefasciatus , are widely considered strongly ornithophilic [ 29 ], we found substantial human feeding alongside chicken feeding, with humans significantly over-utilized relative to their availability. This contrasts with classical descriptions of members of the Culex pipiens complex as predominantly bird-feeding mosquitoes. Recent meta-analyses demonstrate that Culex feeding patterns are highly variable and context-dependent across biogeographic realms. Griep et al . [ 37 ] compiled data from 109 publications, representing 29,990 bloodmeals over 15 years, and found that Culex feeding patterns were not significantly explained by mosquito phylogeny alone, indicating that external factors play major roles in determining host utilization. Moreover, their analysis of Cx. quinquefasciatus across different biogeographical realms revealed significant regional variation in feeding patterns. Based on 10,969 bloodmeals across 40 publications, they found dramatic regional variation: when aggregated across all realms, only about one-third of bloodmeals were avian (34.3%), whereas nearly half were from non-human mammals (48.0%) and 17.4% were from humans, with reptiles accounting for < 1%. Afrotropical (sub-Saharan Africa) populations fed overwhelmingly on non-human mammals (93.4% non-human mammals, 6.2% humans, 0.4% avian), whereas Indomalayan (South and Southeast Asia) populations were predominantly human feeding (62.0% humans, 32.4% non-human mammals, 5.5% avian). In contrast, Australasian (Australia–Pacific) and Neotropical (Central and South America) populations were largely ornithophilic (90.5% and 66.4% avian, respectively), and Nearctic (North American) populations showed a more mixed pattern with roughly equal avian and mammalian feeding (50.1% avian, 18.7% humans, 30.7% non-human mammals, 0.6% reptiles). These realm-specific summaries indicate that the same nominal species can occupy very different feeding niches in different parts of the world. Consistent with this regional variation, field studies have documented high anthropophilic or mammalophilic feeding by Cx. quinquefasciatus in tropical settings: 79.8% human feeding in Mauritania [ 64 ], and predominantly bovine (57.6%) and human (24.2%) feeding with minimal chicken use (4.2%) in coastal Kenya [ 65 ], and mammalophilic populations in Mexico [ 66 ]. Together with our Guatemalan findings, these studies demonstrate that Cx. quinquefasciatus host use is highly context-dependent. Similar to patterns observed for Ae. aegypti , household features and host accessibility likely play important roles in determining Cx. quinquefasciatus feeding patterns, as mosquitoes feed opportunistically on available and accessible hosts within their local environment. Beyond species-specific feeding patterns, our metabarcoding approach revealed important insights into mixed-species feeding behavior. Metabarcoding revealed a high frequency of mixed blood meals. In south Texas, 85.3% of Ae . aegypti bloodmeals contained DNA from two or more host vertebrate species, while in Guatemala, 19.5% of Ae. aegypti had mixed feeding and 64.7% of Cx. quinquefasciatus had mixed feeding. While multiple feeding (including partial or interrupted feeding) has been documented in some Culex species (e.g., Cx. tarsalis, Cx. tritaeniorhynchus ) [ 51 , 52 ], the extent to which members of the Cx. pipiens complex routinely take multiple partial bloodmeals, particularly from different host species, remains less well characterized. Mixed-source bloodmeals have been reported in members of the Cx. pipiens complex, including Cx. quinquefasciatus , although reported frequencies are often low and likely method- and context-dependent [ 67 , 68 ]. The ability to detect multiple host species within a single bloodmeal represents a methodological advance that may challenge previous understanding of mosquito host selection. Earlier techniques for detecting mixed-species feeding include histological examination, serological methods, species-specific PCRs, or PCR cloning, but these are labor-intensive and offer limited resolution [ 69 – 75 ]. As a result, historical feeding studies may have substantially underestimated the frequency of multiple-host feeding, and these methodological limitations should be considered when interpreting earlier data. For example, Scott et al. [ 53 ] found that only about 7% of Ae. aegypti in rural Thailand had detectable multiple-host-type bloodmeals, all of which included a human host. Ponlawat & Harrington [ 18 ] reported that Ae. aegypti in Thailand fed almost exclusively on humans, with multiple-host-type bloodmeals being rare, though they documented that individual mosquitoes frequently took multiple meals from the same host species (humans) within a single gonotrophic cycle. Multiple bloodfeeding within the same gonotrophic cycle increases host-vector contact and pathogen transmission potential [ 76 , 77 ], whether from the same or different host species. Although detection of multiple host taxa is consistent with mixed feeding within a single gonotrophic cycle, we have not yet evaluated whether metabarcoding may detect residual vertebrate DNA from a prior gonotrophic cycle. While metabarcoding substantially improves detection of mixed-species feeding, it cannot distinguish multiple individuals of the same host species. Forensic tools such as microsatellites and short tandem repeats can detect unique individual profiles and have identified heterogeneous feeding patterns where Ae. aegypti bites concentrate on specific demographic groups or in specific geographic locations, both of which could have important epidemiological consequences [ 76 , 78 ]. Coupling forensic tools with metabarcoding could enable detection of multiple human individual profiles in human-containing bloodmeals, further refining transmission dynamics analysis [ 78 ]. Mosquito host selection is more variable and context-dependent than classical paradigms suggest. Aedes aegypti populations are not uniformly anthropophilic; some exhibit high rates of non-human feeding that may reduce arbovirus transmission potential. Similarly, Cx. quinquefasciatus feeding behavior varies with local conditions and can be highly anthropophilic in tropical regions. Methods Study Design We conducted field collections of Ae. aegypti and Cx. quinquefasciatus from rural Guatemala and south Texas, USA (Fig. 1 ) and identified blood-fed females to characterize natural host-feeding patterns using bloodmeal metabarcoding. Household surveys were conducted at participating homes in both sites to characterize housing features that may influence mosquito access to indoor environments (e.g., presence of air conditioning and window/door screens) and to support comparisons between Guatemala and south Texas. The household surveys in Guatemala were reviewed by the Research Ethics Committee of Centro de Estudios en Salud at Universidad del Valle de Guatemala (UVG) which classified it as “Research not involving human subjects” (Protocol No. 270-05-2022). The household surveys in south Texas received approval from the Institutional Review Board of Texas A&M University (IRB2021-0886D). We obtained individual written informed consent from each household owner for the questionnaire. All research was performed in accordance with relevant guidelines and regulations. Field collections and sample processing Field Sites – Guatemala Mosquitoes were collected from four communities in the Municipality of Comapa, Department of Jutiapa, Guatemala (Fig. 1 ). The neighborhoods are in a semi-rural area characterized by poor infrastructure. These communities have had extensive community engagement with investigators from Universidad del Valle de Guatemala during past research projects in partnership with the Ministry of Health [ 46 , 79 , 80 ]. Sampling was carried out during the rainy season (June - August 2022) in 77 households. Prokopack aspirators were used for mosquito collections [ 81 ]. All indoor and outdoor sampling with the Prokopack was done as follows. Indoor aspirating was conducted in all bedrooms, kitchens, and other living quarters, while outdoor aspirating was done around structures and near stored debris. Mosquito sampling occurred between 7:00 and 10:00 AM in the morning and 4:00 to 6:30 pm in the evening. Field Sites – Texas Mosquitoes were collected from eight low-income communities called ‘colonias’ in the Lower Rio Grande Valley, South Texas (Fig. 1 ), as previously described [ 82 ]. Between June, 2021 and March, 2022, mosquito sampling was done using BG Sentinel 2 traps (Biogents, Germany) baited with BG lures (Biogents, Germany) placed around homes. Mosquito processing, DNA extraction, molecular barcoding Mosquitoes were morphologically identified to species and sex using illustrations and dichotomous keys [ 83 – 85 ]. Blood-fed females were placed in individual nuclease-free 1.5 mL microcentrifuge tubes and stored at -20 or -80°C with labels indicating species, collection date, and house identification number. These blood-fed mosquitoes were later photographed and assigned a Sella score to record bloodmeal digestion stage and ovary development [ 86 ]. To minimize exogenous DNA contamination, each whole mosquito was washed in 10% bleach followed by two rinses with nuclease-free water. On a new microscope slide (VWR VistaVision microscope slides; VWR, Radnor, PA, USA), the abdomen was carefully separated from the rest of the mosquito (thorax and head) using forceps, and the abdominal contents were transferred into a new labeled, DNA-free 1.5 mL microcentrifuge tube. Forceps were sterilized between specimens. DNA was extracted from gut contents using the Thermo Scientific™ KingFisher™ Flex Purification System and MagMAX™ Core Nucleic Acid Purification Kit (Thermo Fisher Scientific, Waltham, MA, USA), following previously published protocols [ 24 , 87 , 88 ]. DNA from each sample was divided into two aliquots in microcentrifuge tubes to support: (i) molecular verification of mosquito species to confirm morphological identifications, and (ii) bloodmeal metabarcoding. Molecular verification of mosquito species Although field-collected mosquitoes were morphologically identified under a microscope, molecular confirmation was conducted for all blood-fed specimens processed for bloodmeal analysis using a modified cytochrome c oxidase subunit I (COI) barcoding assay using a modified version of the Folmer et al. [ 89 ] protocol. Briefly, we followed our adapted previously published PCR–Sanger workflow by Oslon et al. (24 ,63,64), the COI region (~ 658 bp) was amplified with primers LCO1490 and HCO2198. PCR reactions (25 µL) contained 12.5 µL FailSafe™ PCR 2X Premix E (Lucigen), 0.5 µL FailSafe™ PCR Enzyme Mix (Lucigen), 1 µL of each primer, 3 µL DNA template, and 7 µL nuclease-free water. Thermocycling included an initial denaturation step at 94°C for 3 min, followed by 44 cycles of 94°C for 30 s, 50°C for 30 s, and 72°C for 30 s, and a final extension at 72°C for 8 min. PCR products were purified using Exo-SAP-IT™ (Thermo Fisher Scientific) and sequenced bidirectionally at Eton Bioscience (San Diego, CA, USA). Consensus sequences were assembled in Geneious Prime v2024.0 and queried against the NCBI nucleotide database with BLASTn [ 92 ]. A match of ≥ 98% identity over ≥ 580 bp was accepted as confirmation. Molecular confirmation was successful for most specimens, and all successfully sequenced specimens matched their original morphological assignments. Metabarcoding PCR and sequence analysis Bloodmeal host identification was performed using a vertebrate 12S rRNA mitochondrial gene following previously published protocols developed and optimized in our laboratory [ 43 , 93 ]. Briefly, primers with dual identical barcode tags were used to amplify a ~ 145 bp fragment of the mitochondrial 12S rRNA gene [ 94 – 96 ]. Samples were amplified in duplicate. PCR products were pooled, purified using SPRI beads (Beckman Coulter, Indianapolis, IN and Sigma-Aldrich, St. Louis, MO) and submitted to the Texas A&M Institute for Genome Sciences and Society for library preparation (xGen™ ssDNA & Low Input DNA Library Prep Kit, Integrated DNA Technologies). Sequencing of samples from Guatemala were done on an Illumina Nextseq 2000 platform and sequencing of the samples from Texas was done on an Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA). Sequencing data were processed following our published workflow [ 43 , 93 ]. Samples were demultiplexed on barcodes using Cutadapt 5.0 [ 97 ], primers were trimmed, and reads were merged and quality-filtered using Qiime2 Amplicon 2025.4 [ 98 ]. Resulting amplicon sequence variants that had fewer than 100 reads across the study were not considered further. Taxonomic assignment of host sequences was performed with BLAST searches against the NCBI GenBank nucleotide database. Hosts that matched less than 1% of reads for individual samples were also rejected. Only hosts appearing in both PCR replicates were retained, unless one of the replicates did not yield data after processing steps, in which case the available information from one replicate was accepted. Sequences with identity ≥ 98% to database matches were binned to species level. Lower identity matches or multiple species showing identical match values were resolved to the genus or higher taxonomic level. Presence of a host within a geographic region was verified using GBIF ( https://www.gbif.org/ Global Biodiversity Information Facility). Host quantification and selection analysis Observed host use for all bloodmeals with single species and mixed (2 or more species) was quantified using relative read abundance (RRA) following Deagle et al [ 99 ]. For each mosquito j and host species i , let Nij be the observed read count for host i in mosquito j. The RRA for host i in mosquito j was calculated as: $$\:{RRA}_{ij}=\frac{{N}_{ij}}{{\sum\:}_{k}{N}_{kj}}$$ where k indexes all host species detected in mosquito j , and \(\:{N}_{kj}\) is the read count for host species \(\:k\) in mosquito \(\:j\) , so that host proportions sum to 1.0 per mosquito. Mosquitoes were classified as single-host when only one host species was detected; otherwise they were classified as mixed, with meals apportioned fractionally by RRA. Household-level host availability was obtained from the contemporaneous household census data collected using a questionnaire while visiting homes in Guatemala and Texas (humans, chickens, dogs, ducks, cats, other) [ 80 , 100 ]. For household \(\:h\) , the availability proportion for host i was $$\:{a}_{i,h}=\frac{{\text{count}}_{i,h}}{{\sum\:}_{k}{\text{count}}_{k,h}}$$ where \(\:coun{t}_{i,h}\) is the number of host \(\:i\) recorded in household \(\:h\) , and the denominator sums counts across all host categories \(\:k\) recorded for that household. Each mosquito was matched to the census of its collection household. Host selection was assessed using mosquito-specific, household-matched forage ratios (FR)[ 90 , 101 – 103 ]. $$\:F{R}_{ij}=\frac{{RRA}_{ij}}{{a}_{i,house\left(j\right)}}$$ where \(\:house\left(j\right)\) denotes the household in which mosquito \(\:j\) was collected (i.e., \(\:{a}_{i,house\left(j\right)}\) is the same availability proportion defined above for that household). \(\:F{R}_{ij}\) was computed only when \(\:{a}_{i,house\left(j\right)}>0\) ; if a host category was unrecorded or had zero availability in the household census, \(\:FR\) was not estimable for that host. Forage ratios were interpreted as \(\:FR>1\) indicating over-utilization relative to availability, \(\:FR<1\) indicating under-utilization, and \(\:FR\approx\:1\) indicating use proportional to availability. For each host, we calculated the mean FR across mosquitoes and reported 95% confidence intervals; evidence of over-/under-utilization was inferred when the 95% CI lay entirely above or below 1, respectively. If the 95% CI included 1, host use was interpreted as not distinguishable from proportional use relative to availability. For the Texas study site, some property owners allowed trapping but declined the household questionnaire; therefore, FRs were computed only for specimens with matched census, while we report the bloodmeal presence and percentages used for all individual mosquitoes. Maps were generated in R (v4.5.1) using freely available administrative boundaries from Natural Earth, U.S. Census TIGER/Line, and GADM [ 104 – 107 ]. Mixed feeding patterns were compared between mosquito species in Guatemala using Pearson’s chi-square tests with Yates’ continuity correction, specifically testing whether the frequency of mixed feeding differed between Ae. aegypti and Cx. quinquefasciatus in Guatemala. Statistical significance was assessed at α = 0.05. Analyses were performed in R (v4.5.1) Declarations Competing interests The authors declare no competing interests. Funding This research was funded by the Centers for Disease Control and Prevention, contract 200- 2017-93141, NIH R21AI166446–01, Texas A&M AgriLife Research, and the Fulbright US Scholars Program to SAH. We received additional support from Department of the Army, U.S. Army Contracting Command, Aberdeen Proving Ground, Natick Contracting Division, Ft Detrick MD through the Department of Defense Deployed Warfighter Protection research program contract W911SR2510002. Author Contribution GLH conceptualized the study; SAH, PP, NP, GLH contributed to resources and funding acquisition; JGJ, NAS, NAF-S supervised field work; AAA and SB performed laboratory analysis of samples; AAA, SB, JGJ, YT, analyzed data; AAA wrote the manuscript. All authors reviewed and approved the final manuscript. Acknowledgement We appreciate the support of the residents and city and country public health agencies in the Lower Rio Grande Valley, Texas who collaborated with us to conduct this study. We also thank the support and hospitality provided by members of the community of Comapa municipality during household visits in Guatemala. We thank Danya Garza, Salvador Solis, Odaliz Sauceda, Javier Elizondo, Chris Roundy, and Charlotte Rhodes for their assistance in the field in south Texas and Andrea Moller-Vasquez, Maria Granados-Presas, and Henry Esquivel for assistance in the field and lab in Guatemala. We thank Tereza Magalhaes for reviewing and improving an earlier draft of the manuscript. Data Availability Data from next generation sequencing have been deposited into the SRA database (NCBI) under the accession numbers (Guatemala data: BioProject PRJNA1398928, BioSamples SAMN54467175 to SAMN54467543; Reviewer Link: [https://dataview.ncbi.nlm.nih.gov/object/PRJNA1398928?reviewer=hmoapjnp7tdehaunl8m79t8sm4](https:/urldefense.com/v3/__https:/dataview.ncbi.nlm.nih.gov/object/PRJNA1398928?reviewer=hmoapjnp7tdehaunl8m79t8sm4__;!!KwNVnqRv!Bp12o7tBGXamHOHBaxDVK_VEAHxLx5zoJRlsg7_0y2uTdajBH5mTQ0_10ONsfPavqeROgYzUYVsF9jpEoZetmS4mgQmXirHIn-S0$) . South Texas data: BioProject PRJNA1417760, BioSamples SAMN55013641 to SAMN55013752; Reviewer Link: [https://dataview.ncbi.nlm.nih.gov/object/PRJNA1417760?reviewer=es3h605apd4dajl26oh7vvtggs](https:/urldefense.com/v3/__https:/dataview.ncbi.nlm.nih.gov/object/PRJNA1417760?reviewer=es3h605apd4dajl26oh7vvtggs__;!!KwNVnqRv!Bp12o7tBGXamHOHBaxDVK_VEAHxLx5zoJRlsg7_0y2uTdajBH5mTQ0_10ONsfPavqeROgYzUYVsF9jpEoZetmS4mgQmXioxTgudb$) . 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Jr A revised checklist of the mosquitoes of Guatemala including a new country record, Psorophora cyanescens. J. Am. Mosq. Control Assoc. -Mosq News . 10 , 511–514 (1994). Sella, M. The antimalaria campaign at Fiumicino (Rome), with epidemiological and biological notes. (1920). Greenstone, M. H., Weber, D. C., Coudron, T. A., Payton, M. E. & Hu, J. S. Removing external DNA contamination from arthropod predators destined for molecular gut-content analysis. Mol. Ecol. Resour. 12 , 464–469 (2012). Hamer, S. A. et al. Comparison of DNA and Carbon and Nitrogen Stable Isotope-based Techniques for Identification of Prior Vertebrate Hosts of Ticks. J. Med. Entomol. 52 , 1043–1049 (2015). Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3 , 294–299 (1994). Hamer, G. L. et al. Host selection by Culex pipiens mosquitoes and west nile virus amplification. Am. J. Trop. Med. Hyg. 80 , 268–278 (2009). Medeiros, M. C. I., Ricklefs, R. E., Brawn, J. D. & Hamer, G. L. Plasmodium prevalence across avian host species is positively associated with exposure to mosquito vectors. Parasitology 142 , 1612–1620 (2015). BLAST. Basic Local Alignment Search Tool. https://blast.ncbi.nlm.nih.gov/Blast.cgi Juarez, J. G. et al. Triatoma dimidiata, domestic animals and acute Chagas disease: a 10-year follow-up after an eco-bio-social intervention. Parasit. Vectors . 18 , 253 (2025). Schnell, I. B., Bohmann, K. & Gilbert, M. T. P. Tag jumps illuminated – reducing sequence-to-sample misidentifications in metabarcoding studies. Mol. Ecol. Resour. 15 , 1289–1303 (2015). Humair, P. F. et al. Molecular Identification of Bloodmeal Source in Ixodes ricinus Ticks Using 12S rDNA As a Genetic Marker. J. Med. Entomol. 44 , 869–880 (2007). Kieran, T. J. et al. Blood Meal Source Characterization Using Illumina Sequencing in the Chagas Disease Vector Rhodnius pallescens (Hemiptera: Reduviidae) in Panamá. J. Med. Entomol. 54 , 1786–1789 (2017). Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17 , 10 (2011). Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37 , 852–857 (2019). Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data? Mol. Ecol. 28 , 391–406 (2019). Scavo, N. A. et al. Little disease but lots of bites: social, urbanistic, and entomological risk factors of human exposure to Aedes aegypti in South Texas, U.S. PLoS Negl. Trop. Dis. 18 , e0011953 (2024). Komar, N., Panella, N. A., Golnar, A. J. & Hamer, G. L. Forage ratio analysis of the southern house mosquito in college station, texas. Vector-Borne Zoonotic Dis. 18 , 485–490 (2018). Hess, A. D., Haves, R. O. & Tempelis, C. H. The use of the forage ratio technique in mosquito host preference studies. (1968). Boreham, P. F. L. & Garrett-Jones, C. Prevalence of mixed blood meals and double feeding in a malaria vector (Anopheles sacharovi Favre). Bull. World Health Organ. 48 , 605–614 (1973). R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2025). Pebesma, E. Simple features for R: standardized support for spatial vector data. (2018). Wickham, H. Ggplot2: Elegant Graphics for Data Analysis (Springer Publishing Company, 2016). Massicotte, P. & South, A. Rnaturalearth: World Map Data from Natural Earth. R Package Version 1.0. 1.9000 . (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Apr, 2026 Reviews received at journal 03 Mar, 2026 Reviews received at journal 02 Mar, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers agreed at journal 20 Feb, 2026 Reviewers invited by journal 20 Feb, 2026 Editor assigned by journal 20 Feb, 2026 Editor invited by journal 20 Feb, 2026 Submission checks completed at journal 18 Feb, 2026 First submitted to journal 18 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Abdi","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Abdisalam","middleName":"A.","lastName":"Abdi","suffix":""},{"id":593392792,"identity":"0d982313-4ee5-40d6-a226-a1e2a04816df","order_by":1,"name":"Sujata Balasubramanian","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Sujata","middleName":"","lastName":"Balasubramanian","suffix":""},{"id":593392794,"identity":"dbbadd34-04b8-4076-a612-2cdf535c3fe2","order_by":2,"name":"Jose Juarez","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"","lastName":"Juarez","suffix":""},{"id":593392795,"identity":"2db2fcdf-dd74-471b-9fb5-50bdf7dffe2b","order_by":3,"name":"Nicole A. Scavo","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Nicole","middleName":"A.","lastName":"Scavo","suffix":""},{"id":593392796,"identity":"1d087354-8f74-43ca-a1d8-badcc22d99db","order_by":4,"name":"Nadia A. Fernandez-Santos","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Nadia","middleName":"A.","lastName":"Fernandez-Santos","suffix":""},{"id":593392797,"identity":"74c7ec27-ad02-452c-af98-c9958a73e7ff","order_by":5,"name":"Yuexun Tian","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Yuexun","middleName":"","lastName":"Tian","suffix":""},{"id":593392798,"identity":"d6db18f4-306c-4547-99d9-746b4f19de5e","order_by":6,"name":"Sarah A. Hamer","email":"","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":false,"prefix":"","firstName":"Sarah","middleName":"A.","lastName":"Hamer","suffix":""},{"id":593392800,"identity":"ee8c777b-69e6-4cf9-aad8-b1369603ce58","order_by":7,"name":"Pamela Pennington","email":"","orcid":"","institution":"Universidad del Valle de Guatemala","correspondingAuthor":false,"prefix":"","firstName":"Pamela","middleName":"","lastName":"Pennington","suffix":""},{"id":593392803,"identity":"8a2bd117-d1dc-46ce-bf03-65f1fad27db4","order_by":8,"name":"Norma Padilla","email":"","orcid":"","institution":"Universidad del Valle de Guatemala","correspondingAuthor":false,"prefix":"","firstName":"Norma","middleName":"","lastName":"Padilla","suffix":""},{"id":593392805,"identity":"e0b049cd-37a5-44f8-ac28-f2bbc98bacbe","order_by":9,"name":"Gabriel L. Hamer","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIiWNgGAWjYFACHgYGxgYQg/kAiiAxWtgSgIQBSVp4DIjTIu9+9uADxh120fzSZz5+/MLwR153RgLjg7dtuLUYnslLNmA8k5w7sy93s7QMg4HhthsJzIZz8WlpyDGTYGxjzt1whneDtASDASNQC5s0Lz4t/W/MfzC21efuP8Pz+DdQiz1QC/tvfFrkJXLMGBjbDudu4OFhk/zAYJAIsoUZnxYDiXfJEoltx3NnnGEzs2YwME7eduZhs+Scc3hs6c89+OFjW3Vufw/z45s/KuRstx1PPvjhTRkeWw4AiQQohxkcNdBowm0LsjTjD7xqR8EoGAWjYKQCANt0UgfC6+O0AAAAAElFTkSuQmCC","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Gabriel","middleName":"L.","lastName":"Hamer","suffix":""}],"badges":[],"createdAt":"2026-02-11 21:08:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8855613/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8855613/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102969689,"identity":"5c801218-1df2-457f-9da9-abc0b7f74eb8","added_by":"auto","created_at":"2026-02-19 05:40:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1010145,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy regions in South Texas, USA, and Comapa, Guatemala\u003c/strong\u003e. (A) Regional map showing the southern United States, Mexico, Guatemala, and Nicaragua, with Texas and Guatemala highlighted and points indicating the South Texas (Hidalgo County) and Comapa sampling areas. (B) Inset map of South Texas showing Hidalgo County and study site locations (red points). (C) Inset map of Jutiapa Department showing Comapa municipality and study site locations (red points). Maps were generated in R (sf, ggplot2, ggspatial) using boundaries from Natural Earth (via rnaturalearth), U.S. Census TIGER/Line (via tigris), and GADM (via geodata). Natural Earth data are public domain; TIGER/Line and GADM data were used under their respective terms of use.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8855613/v1/1675a5dfe39b19fc3746dad2.png"},{"id":102969753,"identity":"9308420b-86e2-4fec-82bf-3e5632f792d1","added_by":"auto","created_at":"2026-02-19 05:40:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":18222323,"visible":true,"origin":"","legend":"\u003cp\u003eHousehold and community environments in Comapa, Guatemala. Representative photographs of typical household structures, peridomestic spaces, and community settings in Comapa, Guatemala, where mosquito collections and household surveys were conducted. (A) Typical community/peridomestic setting with open-front home structures and free-ranging domestic animals (e.g., chickens and dogs). (B) Field team conducting household surveys with residents in outdoor living spaces, illustrating common domestic animal presence near human activity. (C) Interior of a typical household showing sleeping areas with minimal barriers to mosquito entry. (D) Indoor mosquito collection using aspiration and a flashlight in a household interior with limited screening.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8855613/v1/26f1a2732d1efb93dc4f29fc.png"},{"id":102969700,"identity":"d2748e5a-d4f0-43c6-8421-2ac883fc4e90","added_by":"auto","created_at":"2026-02-19 05:40:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":18812181,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHousehold and neighborhood environments in South Texas, USA\u003c/strong\u003e. Representative photographs of residential and peridomestic environments in South Texas, USA, including typical housing and surrounding yard vegetation in neighborhoods where mosquito collections were conducted. (A) Typical residential structure and surrounding yard/peridomestic space in a colonia setting. (B) Backyard environment showing a fenced yard with accumulated items/containers and vegetation that may serve as potential container habitat and mosquito resting sites; domestic dog present. (C) Typical peridomestic yard with a free-roaming domestic dog and surrounding vegetation. (D) BG-Sentinel 2 trap deployed outdoors adjacent to a residence.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8855613/v1/ec94214ef10e69377e4f7de3.png"},{"id":102969838,"identity":"ae1c06f1-3839-4eb6-b292-0bf3fe0a2899","added_by":"auto","created_at":"2026-02-19 05:40:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":52915456,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8855613/v1/6ff04420-579d-47e9-bb9a-91f8334b10ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bloodmeal metabarcoding reveals host feeding patterns for Aedes aegypti and Culex quinquefasciatus in Jutiapa, Guatemala and Texas, USA","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMosquito-borne pathogens cause over 700,000 deaths annually and impose billions of dollars in economic costs worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These pathogens include malaria (\u003cem\u003ePlasmodium spp\u003c/em\u003e.), dengue virus (DENV), Zika virus (ZIKV), chikungunya virus (CHIKV), yellow fever virus (YFV), West Nile virus (WNV), and Japanese encephalitis virus (JEV) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These diverse pathogens exist in cycles between competent mosquito vector species and competent amplification vertebrate host species [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In some cases, humans function as amplification hosts and develop an infectious viremia capable of reinfecting a mosquito [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. When humans function as amplification hosts and transmission is maintained primarily between mosquitoes and humans in built environments, this pattern is known as an \u0026ldquo;urban cycle\u0026rdquo; or a \u0026ldquo;human-amplified transmission cycle\u0026rdquo;. This transmission pattern characterizes several major pathogens transmitted by \u003cem\u003eAedes aegypti\u003c/em\u003e, including DENV, ZIKV, and CHIKV [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Alternatively, other pathogens such as WNV and eastern equine encephalitis virus (EEEV) utilize wild or domestic animals as amplification hosts which are fed on by diverse mosquito species and maintained in what are known as \u0026lsquo;sylvatic cycles\u0026rsquo; [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. As these mosquito-borne pathogens amplify in the mosquito and animal cycle, a bridge vector species is capable of spill-over transmission to humans or other domestic animals which can suffer disease, while not necessarily being capable of developing an infectious viremia (i.e., dead-end host for virus) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe type of transmission cycle is fundamentally linked to the host-feeding behavior of vector mosquito species [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Anthropophilic species such as \u003cem\u003eAe. aegypti\u003c/em\u003e preferentially feed on humans [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], which makes this species a potent vector of human pathogens in tropical and subtropical regions. In contrast, ornithophilic (bird-feeding) species like members of the \u003cem\u003eCulex pipiens\u003c/em\u003e complex maintain sylvatic and zoonotic cycles by feeding primarily on avian hosts [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, some studies have documented atypical non-human feeding by \u003cem\u003eAe. aegypti\u003c/em\u003e in both South Texas [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and Kenya [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which may reduce mosquito\u0026ndash;human contact rates and thereby lower transmission potential for human-amplified arboviruses. Therefore, variation in \u003cem\u003eAe. aegypti\u003c/em\u003e host use can shape the intensity and spatial pattern of \u003cem\u003eAedes\u003c/em\u003e-borne pathogen transmission [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn important mosquito vector of arthropod-borne viruses (arboviruses) globally are members of the \u003cem\u003eCulex pipiens\u003c/em\u003e complex. This complex includes the northern house mosquito, \u003cem\u003eCx. pipiens pipiens\u003c/em\u003e, in more temperate latitudes globally, and the southern house mosquito, \u003cem\u003eCulex. quinquefasciatus\u003c/em\u003e, found in more tropical and subtropical regions globally [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The feeding patterns of species belonging to the \u003cem\u003eCx. pipiens\u003c/em\u003e complex has been extensively studied [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], given that they are important in the transmission of many zoonotic pathogens. However, most of this research attention on \u003cem\u003eCulex\u003c/em\u003e spp. mosquitoes has been in temperate regions while vectors in this genus have been neglected in many tropical and subtropical regions which are endemic for human-amplified mosquito-borne pathogens such as malaria and dengue [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Many studies report an ornithophilic feeding pattern of \u003cem\u003eCx. pipiens\u003c/em\u003e complex [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. However, \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e feeding patterns are more complex with some studies reporting high utilization of birds as hosts while others document high feeding rates on mammals, including humans [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis uncertainty in the feeding patterns of \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e may also be driven by few bloodmeal analysis studies being conducted in tropical and subtropical regions. Recent meta-analyses reveal that \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e feeding patterns vary substantially across biogeographic regions, with some tropical populations exhibiting predominantly mammalian or human feeding rather than avian feeding [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Beyond this geographic variability, accurately characterizing mosquito host use is further complicated by the feeding behavior of key vector species. Both \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCulex\u003c/em\u003e spp. are known to take multiple bloodmeals from different host species within a single gonotrophic cycle, increasing the frequency of mixed-species bloodmeals [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Traditional PCR followed by Sanger sequencing has been the primary method for bloodmeal analysis, but it has critical limitations: it typically detects only the most abundant DNA in a sample, often missing hosts in mixed bloodmeals [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For example, our previous study in South Texas used PCR-Sanger sequencing documented \u003cem\u003eAe. aegypti\u003c/em\u003e feeding patterns [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], but this approach may have underestimated the frequency of human feeding in mixed bloodmeals where dog DNA was preferentially amplified in the PCR. These methodological constraints mean that our understanding of mosquito host-feeding patterns may be incomplete, particularly for species that frequently feed on multiple hosts. High-throughput sequencing methods can overcome these limitations by detecting most vertebrate DNA present in a bloodmeal, regardless of abundance. Specifically, bloodmeal metabarcoding employs deep sequencing of vertebrate-specific DNA loci, enabling detection of multiple host species within bloodmeals from individual arthropods [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This approach has been successfully applied to characterize host-feeding patterns in various hematophagous arthropods, including mosquitoes [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], soft ticks [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], and triatomines [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGiven the uncertainty of \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e feeding patterns in tropical regions, where most previous bloodmeal studies employed Sanger sequencing with limited capacity to detect mixed-species meals, the objective of this study was to conduct bloodmeal metabarcoding on field-collected females of both \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e species from rural communities in Guatemala and south Texas, USA. The work in south Texas builds on our previous work using PCR-Sanger sequencing, which documented high use of non-human animals [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We will now verify these unexpected feeding patterns using new mosquito collections from different years, different study sites, and using the bloodmeal metabarcoding pipeline. The collections in Guatemala provide an opportunity to utilize this novel metabarcoding pipeline in a tropical setting. This study provides a unique opportunity to study multiple bloodfeeding behaviors within the same gonotropic cycle, which is well-documented for \u003cem\u003eAe. aegypti\u003c/em\u003e [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] but less understood for \u003cem\u003eCulex\u003c/em\u003e spp. mosquitoes [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGuatemala\u003c/h2\u003e \u003cp\u003eA total of 243 blood-fed female mosquitoes were collected from 77 households during the 2022 rainy season in Guatemala. Of these, 228 (93.8%) were molecularly confirmed to species: 67 \u003cem\u003eAe. aegypti\u003c/em\u003e (29.4%), 2 \u003cem\u003eAe. albopictus\u003c/em\u003e (0.9%), 155 \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e (68.0%), 2 \u003cem\u003eCx. nigripalpus\u003c/em\u003e (0.9%), and 2 \u003cem\u003eCx. corniger\u003c/em\u003e (0.9%). All molecular identifications matched morphological assignments.\u003c/p\u003e \u003cp\u003eOf the 67 \u003cem\u003eAe. aegypti\u003c/em\u003e tested, 41 (61.2%) produced PCR product and sequencing reads that yielded host identifications. Among the specimens with results, 33 (80.5%) were single-host meals, including 29 human (\u003cem\u003eHomo sapiens\u003c/em\u003e) and 4 chicken (\u003cem\u003eGallus gallus\u003c/em\u003e). The remaining 8 (19.5%) meals were mixed, consisting of 6 human-chicken combinations and 2 human-bird (\u003cem\u003eTurdus\u003c/em\u003e sp.) combinations. Humans were detected in 37/41 (90.2%) of the \u003cem\u003eAe. aegypti\u003c/em\u003e bloodmeals, chickens in 10/41 (24.4%), and \u003cem\u003eTurdus\u003c/em\u003e sp. in 2/41 (4.9%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Forage-ratio inference indicated over-utilization of humans relative to availability (FR\u0026thinsp;=\u0026thinsp;3.62, 95% CI 2.70\u0026ndash;4.54), whereas feeding on chickens was not significantly different from availability (FR\u0026thinsp;=\u0026thinsp;1.25, 95% CI 0.41\u0026ndash;2.08) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both \u003cem\u003eAe. albopictus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2) yielded identifiable meals: one single-host (human) and one mixed-host (human-chicken).\u003c/p\u003e \u003cp\u003eAmong blood-fed \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e processed (n\u0026thinsp;=\u0026thinsp;155), 136 (87.7%) yielded host identifications. Of these, 48 were single-host meals (35.3%), including 31 human, 15 chicken, 1 turkey (\u003cem\u003eMeleagris gallopavo\u003c/em\u003e), and 1 passerine bird (\u003cem\u003ePasseriformes\u003c/em\u003e). A further 70 (51.5%) were mixed two-host meals, composed of 61 human\u0026ndash;chicken, 5 human\u0026ndash;dog (\u003cem\u003eCanis lupus familiaris\u003c/em\u003e), 1 human\u0026ndash;turkey, 2 chicken\u0026ndash;dog, and 1 chicken\u0026ndash;turkey. The remaining 18 (13.2%) were mixed three-host meals, including 16 human\u0026ndash;chicken\u0026ndash;dog, 1 human\u0026ndash;chicken\u0026ndash;turkey, and 1 human\u0026ndash;chicken\u0026ndash;bird (\u003cem\u003eTurdus sp\u003c/em\u003e.) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the less common mosquito species, both \u003cem\u003eCx. nigripalpus\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2) contained single-host meals 1 human and 1 chicken, while the single \u003cem\u003eCx. corniger\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;1) specimen yielded a mixed two-host meal (human\u0026ndash;chicken). Across all \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e bloodmeals, humans were detected in 116/136 (85.3%), chickens in 97/136 (71.3%), and dogs in 21/136 (15.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). For the \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e forage-ratio, humans were significantly over-utilized (FR\u0026thinsp;=\u0026thinsp;2.60, 95% CI 2.24\u0026ndash;2.97), chickens were marginally over-utilized (FR\u0026thinsp;=\u0026thinsp;1.27, 95% CI 1.07\u0026ndash;1.46), and utilization of dogs did not differ from availability (FR\u0026thinsp;=\u0026thinsp;0.99, 95% CI 0.51\u0026ndash;1.47) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSouth Texas, USA\u003c/h3\u003e\n\u003cp\u003eOf 121 blood-fed \u003cem\u003eAe. aegypti\u003c/em\u003e examined in south Texas, 68 (56.2%) yielded at least one host identification. Of these, 10 (14.7%) were single-host meals, including 2 human (\u003cem\u003eHomo sapiens\u003c/em\u003e), 3 dog (\u003cem\u003eCanis lupus familiaris\u003c/em\u003e), 1 chicken (\u003cem\u003eGallus gallus\u003c/em\u003e), 3 house mouse (\u003cem\u003eMus musculus\u003c/em\u003e), and 1 brown rat (\u003cem\u003eRattus norvegicus\u003c/em\u003e). The remaining 58 (85.3%) bloodmeals were mixed, including 35 (51.5%) two-host meals, 19 (27.9%) three-host meals, and 4 (5.9%) four-host meals. These consisted of 25 human\u0026ndash;dog, 7 dog\u0026ndash;cat (\u003cem\u003eFelis catus\u003c/em\u003e), 2 chicken\u0026ndash;dog, 1 chicken\u0026ndash;mouse, 17 human\u0026ndash;dog\u0026ndash;cat, 1 human\u0026ndash;dog\u0026ndash;rat, 1 chicken\u0026ndash;dog\u0026ndash;mouse, and 4 human\u0026ndash;dog\u0026ndash;cat\u0026ndash;rat.\u003c/p\u003e \u003cp\u003eAcross all meals, dogs were detected in 60/68 (88.2%) of bloodmeals, humans in 49/68 (72.1%), cats in 28/68 (41.2%), brown rats in 6/68 (8.8%), house mice in 5/68 (7.4%), and chickens in 5/68 (7.4%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eForage-ratio inference indicated significant under-utilization of humans relative to their abundance (FR 0.53, 95% CI 0.25\u0026ndash;0.81) and strong over-utilization of dogs (FR 4.65, 95% CI 2.43\u0026ndash;6.87). Cats did not differ from availability (FR 1.55, 95% CI 0.52\u0026ndash;2.57), and chicken FRs were not calculated because household availability was zero for most records (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBloodmeal host composition by mosquito species and collection site.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHosts detected in mosquito abdomens\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eAe. aegypti\u003c/em\u003e Texas (n\u0026thinsp;=\u0026thinsp;68)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eAe. aegypti\u003c/em\u003e Guatemala\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCx quinquefasciatus\u003c/em\u003e Guatemala\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. samples (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo, samples (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. samples (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle host species detected:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman (\u003cem\u003eHomo sapiens)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(70.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(22.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken \u003cem\u003e(Gallus gallus)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(9.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(11.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDog \u003cem\u003e(Canis lupus familiaris)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTurkey \u003cem\u003e(Meleagris gallopavo)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBird \u003cem\u003e(\u003c/em\u003eOrder \u003cem\u003ePasseriformes)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrown rat \u003cem\u003e(Rattus norvegicus)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouse mouse \u003cem\u003e(Mus musculus)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3(4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal single host bloodmeal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e10(14.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e33(80.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e48(35.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultiple host species detected\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Chicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(44.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Dog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25(36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(3.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Turkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Bird (\u003cem\u003eTurdus sp\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(4.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u0026thinsp;+\u0026thinsp;turkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Chicken\u0026thinsp;+\u0026thinsp;Dog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(11.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Chicken\u0026thinsp;+\u0026thinsp;Turkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Chicken\u0026thinsp;+\u0026thinsp;Bird (\u003cem\u003eTurdus sp\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(0.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Dog\u0026thinsp;+\u0026thinsp;Cat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(25%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Dog\u0026thinsp;+\u0026thinsp;Rat (\u003cem\u003eRattus norvegicus)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u0026thinsp;+\u0026thinsp;Dog\u0026thinsp;+\u0026thinsp;Cat\u0026thinsp;+\u0026thinsp;Rat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4(5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u0026thinsp;+\u0026thinsp;Dog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (2.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDog\u0026thinsp;+\u0026thinsp;Cat (\u003cem\u003eFelis catus\u003c/em\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7(10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u0026thinsp;+\u0026thinsp;House mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u0026thinsp;+\u0026thinsp;Dog\u0026thinsp;+\u0026thinsp;house mouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal multiple host bloodmeal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e58(85.3%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8 (19.5%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e88 (64.7%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eMixed feeding comparisons in Guatemala and Texas\u003c/h3\u003e\n\u003cp\u003eMixed feeding patterns differed significantly between mosquito species within Guatemala; \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e exhibited higher rates of mixed feeding than \u003cem\u003eAe. aegypti\u003c/em\u003e (64.7% vs. 19.5%; χ\u0026sup2; = 24.13, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). South Texas \u003cem\u003eAe. aeg\u003c/em\u003eypti showed the highest mixed feeding rate at 85.3%, significantly greater than Guatemala \u003cem\u003eAe. aegypti\u003c/em\u003e (χ\u0026sup2; = 43.62, df\u0026thinsp;=\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHost Selection Patterns: Forage Ratios by Mosquito Species and Location.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTexas \u003cem\u003eAe. aegypti\u003c/em\u003e\u003c/p\u003e \u003cp\u003eFR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHost\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eGuatemala\u003c/b\u003e \u003cb\u003eAe. aegypti\u003c/b\u003e \u003cb\u003eFR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eGuatemala\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eCx. quinquefasciatus\u003c/b\u003e \u003cb\u003eFR (95% CI)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45 (2.70\u0026ndash;4.54) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 (2.24\u0026ndash;2.97) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.53\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14 (0.25\u0026ndash;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChicken\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 (0.41\u0026ndash;2.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10 (1.07\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDog\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22 (0.51\u0026ndash;1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 (2.43\u0026ndash;6.87) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCat\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.55\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47 (0.52\u0026ndash;2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFR\u0026thinsp;=\u0026thinsp;forage ratio (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE), calculated as the ratio of observed feeding frequency to expected feeding based on host availability from household census data. 95% CI\u0026thinsp;=\u0026thinsp;95% confidence interval. Asterisk (*) indicates significant over- or under-utilization (95% CI does not include 1.0). \"\u0026mdash;\" indicates FR was not estimable due to zero household availability.\u003c/p\u003e\n\u003ch3\u003eHousehold Characteristics\u003c/h3\u003e\n\u003cp\u003eHousehold surveys documented substantial differences in housing infrastructure between study sites. In Guatemala, none of the 77 households had air conditioning and all lacked intact window or door screens. In South Texas, 46 of 48 surveyed households (95.8%) had air conditioning (19 central systems, 27 window units), and most had screened windows or doors.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur bloodmeal metabarcoding revealed substantial geographic heterogeneity in the host use of \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e across south Texas and rural Guatemala, including shifts between human and domestic animal feeding with implications for arboviral epidemiology. In Guatemala, \u003cem\u003eAe. aegypti\u003c/em\u003e exhibited anthropophilic feeding behavior, with human DNA being detected in 90.2% of the identified bloodmeals, and the human FR being 3.62 ± 0.45 (2.70–4.54) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e), indicating significant over-utilization of humans relative to their abundance. This high anthropophilic behavior aligns with most studies documenting \u003cem\u003eAe. aegypti\u003c/em\u003e feeding primarily on humans in domestic environments where close human-mosquito contact is facilitated by lack of physical barriers such as window screens and air conditioning [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e]. In contrast, \u003cem\u003eAe. aegypti\u003c/em\u003e from south Texas showed substantially lower human feeding (72.1% of identified meals) while dogs were detected in 88.2% of meals. The FR for the south Texas \u003cem\u003eAe. aegypti\u003c/em\u003e feeding on humans was 0.53 (95% CI: 0.25–0.81), indicating significant under-utilization of humans relative to their abundance. The south Texas \u003cem\u003eAe. aegypti\u003c/em\u003e FR for dog was 4.65 (95% CI: 2.43–6.87) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) indicating significant over-utilization of dogs. These findings of high utilization of dogs by south Texas \u003cem\u003eAe. aegypti\u003c/em\u003e corroborate our prior study from the same region which documented about 31% human bloodmeals, 50% dog, and 19% other vertebrates [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. This current south Texas study sampled mosquitoes in different years, different south Texas communities, involved different project personnel, and utilized a different bloodmeal analysis technique. While the current study reports bloodmeal metabarcoding results, the prior study conducted PCR-Sanger sequencing methodology [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Note that our prior study, which documented 31% of human bloodmeals by PCR-Sanger sequencing, could have missed human meals within mixed bloodmeals while identifying other taxa; conventional PCR followed by direct Sanger sequencing typically yields a single sequence per sample and tends to under-detect minority hosts in mixed bloodmeals, whereas next-generation metabarcoding approaches recover multiple vertebrate hosts from individual mosquitoes and substantially improve the resolution of mixed-source bloodmeals [\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e]. Consequently, bloodmeals containing both human and dog DNA, where dog DNA was preferentially amplified in the PCR, would often have been classified as “dog only” by PCR Sanger sequencing. With metabarcoding, these meals would have been more accurately identified as mixed (human + dog) meals, helping to explain the higher percentage of human-positive bloodmeals (72%) observed in the current study.\u003c/p\u003e \u003cp\u003eOne limitation of the current study is that the mosquito collection method was different in Guatemala compared to south Texas; \u003cem\u003eAe. aegypti\u003c/em\u003e in Guatemala were collected by indoor and outdoor aspiration while in south Texas were collected using BG Sentinel 2 traps placed outdoors. The low-income communities in Guatemala often lacked doors, screens, and sometimes walls, and homeowners are experienced with local ministries of health personnel entering the indoor environment for vector surveillance and indoor residual spraying (IRS) of insecticides. Therefore, our field team, which was accompanied by a government employee from the ministries of health, was allowed access to the homes for indoor aspirating. The low-income communities in south Texas are very different Hispanic communities, and our past studies on community engagement have characterized these challenges [\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e]. Government employees do not routinely enter homes, IRS campaigns are not typically conducted in the US, and therefore our bloodfed mosquito sampling was limited to the outdoor environment. The different sampling collections would be expected to influence the availability of the different hosts, and thus could have influenced feeding patterns. Forage ratios also depend on household-based host availability estimates, which likely undercount free-ranging hosts such as wild birds that are difficult to enumerate. Because metabarcoding detected some wild-bird feeding, forage ratios, particularly those involving avian hosts should be interpreted cautiously and primarily as relative selection was based on the surveyed domestic/peridomestic host community and not wild host community. The lower utilization of humans by \u003cem\u003eAe. aegypti\u003c/em\u003e in south Texas could be due to outdoor mosquitoes not having access to the humans due to the presence of doors, windows, and screens. However, studies have documented variable utilization of humans by \u003cem\u003eAe. aegypti\u003c/em\u003e using outdoor collections with BG Sentinel traps; human feeding by \u003cem\u003eAe. aegypti\u003c/em\u003e collected by BG Sentinel traps in Mombasa, Kenya was about 35% while in Kisumu, Kenya was about 12% [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Future studies should interrogate \u003cem\u003eAe. aegypti\u003c/em\u003e feeding patterns with more standardized sampling designs to understand differences in the indoor and outdoor environments.\u003c/p\u003e \u003cp\u003eDespite the differences in sampling methods and locations, our results likely reveal important differences in mosquito-human contact patterns that reflect differences in household infrastructure and human behavior. In Guatemala, homes lacked air conditioning and window screens, allowing mosquitoes constant access to indoor spaces where humans spend time. Additionally, many household activities (e.g. washing clothes, food preparation) occurred outdoors, or in kitchens not enclosed by walls on all sides, further facilitating human-mosquito contact. Indeed, high human-feeding rates have been documented in \u003cem\u003eAe. aegypti\u003c/em\u003e collected outdoors where humans are readily accessible, as shown in Kenya [\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e] and India [\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e], supporting the importance of host accessibility regardless of collection location. In contrast, south Texas households, despite being lower-income communities, typically had air conditioning and screened windows, creating physical barriers that limit mosquito access to humans indoors. Household activities in the south Texas settings are more likely to occur inside, further reducing human-mosquito contact opportunities.\u003c/p\u003e \u003cp\u003eThis difference in accessibility may explain the contrasting forage ratios observed: significant over-utilization of humans in Guatemala (FR = 3.62) versus significant under-utilization in South Texas (FR = 0.53). Importantly, the Texas pattern could reflect reduced mosquito access to humans rather than an intrinsic preference for non-human hosts. When barriers limit human accessibility, \u003cem\u003eAe. aegypti\u003c/em\u003e could exhibit opportunistic feeding on available hosts, particularly dogs. These findings challenge the traditional view of \u003cem\u003eAe. aegypti\u003c/em\u003e as obligately anthropophilic, demonstrating instead that this species exhibits opportunistic feeding behavior when preferred hosts are less accessible. These patterns have important implications for arbovirus transmission. In settings like Guatemala where human-mosquito contact is unrestricted, high anthropophilic feeding maintains intense dengue transmission [\u003cspan class=\"CitationRef\"\u003e60\u003c/span\u003e]. In settings like south Texas where physical barriers reduce human accessibility, transmission potential may be lower despite the presence of competent vectors and susceptible human populations, as mosquitoes divert feeding effort to alternative hosts [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnderstanding the mechanisms underlying this behavioral variation requires consideration of both genetic and ecological factors. The domestic subspecies \u003cem\u003eAe. aegypti aegypti\u003c/em\u003e (Aaa), which predominates in the Americas and urban settings globally, is widely considered strongly anthropophilic, whereas the sylvatic African subspecies \u003cem\u003eAe. aegypti formosus\u003c/em\u003e (Aef), found only in sub-Saharan Africa, exhibits more generalist feeding on various vertebrate hosts. McBride et al.[\u003cspan class=\"CitationRef\"\u003e61\u003c/span\u003e] identified odorant receptor differences associated with human preference in Aaa. Recent population genetics studies in Kenya have revealed that some geographic variation in arbovirus transmission may reflect subspecies differences; Mulwa et al. [\u003cspan class=\"CitationRef\"\u003e62\u003c/span\u003e] found coastal Kenyan \u003cem\u003eAe. aegypti\u003c/em\u003e populations (Mombasa) are strongly admixed between Aef and Aaa, with Aaa ancestry highest at the coast, while Anyango et al. [\u003cspan class=\"CitationRef\"\u003e63\u003c/span\u003e] confirmed western Kenya populations (Kisumu, Busia) are dominated by Aef ancestry. This genetic structure may partly explain Kenya's pattern of dengue outbreaks occurring in coastal cities but not interior cities. Aaa-enriched coastal populations exhibit higher anthropophily that facilitates transmission, while Aef-dominated western populations remain more zoophilic despite comparable vector competence [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. In North America, \u003cem\u003eAe. aegypti\u003c/em\u003e populations are overwhelmingly \u003cem\u003eAe. aegypti aegypti\u003c/em\u003e (Aaa), so the geographic variation in host feeding observed in the Americas is unlikely to be explained by Aaa–Aef subspecies composition alone.\u003c/p\u003e \u003cp\u003eHowever, substantial geographic variation in host use is also evident among populations likely representing Aaa, underscoring a strong role for local ecology and host accessibility. Our findings from Guatemala and South Texas demonstrate pronounced differences: Guatemala showed 90% human-positive bloodmeals (FR = 3.62) versus South Texas with 72% human-positive but 88% dog-positive (dog FR = 4.65). Similarly, Agha et al. [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e] documented feeding differences among Kenyan urban Aaa populations: coastal Mombasa showed 40% human feeding while inland Kisumu showed only 10% (p = 0.03), with Kisumu mosquitoes feeding heavily on dogs, goats, and cows despite human presence. Kamau \u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e] likewise reported higher human blood indices in coastal compared with inland settings. Together, these findings indicate that while genetic background can shape host preference, local host availability and, especially host accessibility mediated by household infrastructure and human behavior can strongly modulate realized feeding patterns, with downstream consequences for arbovirus transmission risk.\u003c/p\u003e \u003cp\u003eWe observed similar patterns of context-dependent feeding behavior in \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e. Our results for \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e feeding patterns in rural Guatemala confirm high human feeding behavior. While members of the \u003cem\u003eCulex\u003c/em\u003e pipiens complex, including \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e, are widely considered strongly ornithophilic [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e], we found substantial human feeding alongside chicken feeding, with humans significantly over-utilized relative to their availability. This contrasts with classical descriptions of members of the \u003cem\u003eCulex pipiens\u003c/em\u003e complex as predominantly bird-feeding mosquitoes. Recent meta-analyses demonstrate that \u003cem\u003eCulex\u003c/em\u003e feeding patterns are highly variable and context-dependent across biogeographic realms. Griep \u003cem\u003eet al\u003c/em\u003e. [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] compiled data from 109 publications, representing 29,990 bloodmeals over 15 years, and found that \u003cem\u003eCulex\u003c/em\u003e feeding patterns were not significantly explained by mosquito phylogeny alone, indicating that external factors play major roles in determining host utilization. Moreover, their analysis of \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e across different biogeographical realms revealed significant regional variation in feeding patterns. Based on 10,969 bloodmeals across 40 publications, they found dramatic regional variation: when aggregated across all realms, only about one-third of bloodmeals were avian (34.3%), whereas nearly half were from non-human mammals (48.0%) and 17.4% were from humans, with reptiles accounting for \u0026lt; 1%. Afrotropical (sub-Saharan Africa) populations fed overwhelmingly on non-human mammals (93.4% non-human mammals, 6.2% humans, 0.4% avian), whereas Indomalayan (South and Southeast Asia) populations were predominantly human feeding (62.0% humans, 32.4% non-human mammals, 5.5% avian). In contrast, Australasian (Australia–Pacific) and Neotropical (Central and South America) populations were largely ornithophilic (90.5% and 66.4% avian, respectively), and Nearctic (North American) populations showed a more mixed pattern with roughly equal avian and mammalian feeding (50.1% avian, 18.7% humans, 30.7% non-human mammals, 0.6% reptiles). These realm-specific summaries indicate that the same nominal species can occupy very different feeding niches in different parts of the world. Consistent with this regional variation, field studies have documented high anthropophilic or mammalophilic feeding by \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e in tropical settings: 79.8% human feeding in Mauritania [\u003cspan class=\"CitationRef\"\u003e64\u003c/span\u003e], and predominantly bovine (57.6%) and human (24.2%) feeding with minimal chicken use (4.2%) in coastal Kenya [\u003cspan class=\"CitationRef\"\u003e65\u003c/span\u003e], and mammalophilic populations in Mexico [\u003cspan class=\"CitationRef\"\u003e66\u003c/span\u003e]. Together with our Guatemalan findings, these studies demonstrate that \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e host use is highly context-dependent. Similar to patterns observed for \u003cem\u003eAe. aegypti\u003c/em\u003e, household features and host accessibility likely play important roles in determining \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e feeding patterns, as mosquitoes feed opportunistically on available and accessible hosts within their local environment.\u003c/p\u003e \u003cp\u003eBeyond species-specific feeding patterns, our metabarcoding approach revealed important insights into mixed-species feeding behavior. Metabarcoding revealed a high frequency of mixed blood meals. In south Texas, 85.3% of \u003cem\u003eAe\u003c/em\u003e. \u003cem\u003eaegypti\u003c/em\u003e bloodmeals contained DNA from two or more host vertebrate species, while in Guatemala, 19.5% of \u003cem\u003eAe. aegypti\u003c/em\u003e had mixed feeding and 64.7% of \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e had mixed feeding. While multiple feeding (including partial or interrupted feeding) has been documented in some \u003cem\u003eCulex\u003c/em\u003e species (e.g., \u003cem\u003eCx. tarsalis, Cx. tritaeniorhynchus\u003c/em\u003e) [\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e], the extent to which members of the \u003cem\u003eCx. pipiens\u003c/em\u003e complex routinely take multiple partial bloodmeals, particularly from different host species, remains less well characterized. Mixed-source bloodmeals have been reported in members of the \u003cem\u003eCx. pipiens\u003c/em\u003e complex, including \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e, although reported frequencies are often low and likely method- and context-dependent [\u003cspan class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e68\u003c/span\u003e]. The ability to detect multiple host species within a single bloodmeal represents a methodological advance that may challenge previous understanding of mosquito host selection. Earlier techniques for detecting mixed-species feeding include histological examination, serological methods, species-specific PCRs, or PCR cloning, but these are labor-intensive and offer limited resolution [\u003cspan class=\"CitationRef\"\u003e69\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e75\u003c/span\u003e]. As a result, historical feeding studies may have substantially underestimated the frequency of multiple-host feeding, and these methodological limitations should be considered when interpreting earlier data. For example, Scott et al. [\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e] found that only about 7% of \u003cem\u003eAe. aegypti\u003c/em\u003e in rural Thailand had detectable multiple-host-type bloodmeals, all of which included a human host. Ponlawat \u0026amp; Harrington [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] reported that \u003cem\u003eAe. aegypti\u003c/em\u003e in Thailand fed almost exclusively on humans, with multiple-host-type bloodmeals being rare, though they documented that individual mosquitoes frequently took multiple meals from the same host species (humans) within a single gonotrophic cycle. Multiple bloodfeeding within the same gonotrophic cycle increases host-vector contact and pathogen transmission potential [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e77\u003c/span\u003e], whether from the same or different host species. Although detection of multiple host taxa is consistent with mixed feeding within a single gonotrophic cycle, we have not yet evaluated whether metabarcoding may detect residual vertebrate DNA from a prior gonotrophic cycle.\u003c/p\u003e \u003cp\u003eWhile metabarcoding substantially improves detection of mixed-species feeding, it cannot distinguish multiple individuals of the same host species. Forensic tools such as microsatellites and short tandem repeats can detect unique individual profiles and have identified heterogeneous feeding patterns where \u003cem\u003eAe. aegypti\u003c/em\u003e bites concentrate on specific demographic groups or in specific geographic locations, both of which could have important epidemiological consequences [\u003cspan class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e]. Coupling forensic tools with metabarcoding could enable detection of multiple human individual profiles in human-containing bloodmeals, further refining transmission dynamics analysis [\u003cspan class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMosquito host selection is more variable and context-dependent than classical paradigms suggest. \u003cem\u003eAedes aegypti\u003c/em\u003e populations are not uniformly anthropophilic; some exhibit high rates of non-human feeding that may reduce arbovirus transmission potential. Similarly, \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e feeding behavior varies with local conditions and can be highly anthropophilic in tropical regions.\u003c/p\u003e \n\n "},{"header":"Methods","content":"\u003ch2\u003eStudy Design\u003c/h2\u003e\u003cp\u003eWe conducted field collections of \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e from rural Guatemala and south Texas, USA (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) and identified blood-fed females to characterize natural host-feeding patterns using bloodmeal metabarcoding. Household surveys were conducted at participating homes in both sites to characterize housing features that may influence mosquito access to indoor environments (e.g., presence of air conditioning and window/door screens) and to support comparisons between Guatemala and south Texas.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eThe household surveys in Guatemala were reviewed by the Research Ethics Committee of Centro de Estudios en Salud at Universidad del Valle de Guatemala (UVG) which classified it as “Research not involving human subjects” (Protocol No. 270-05-2022). The household surveys in south Texas received approval from the Institutional Review Board of Texas A\u0026amp;M University (IRB2021-0886D). We obtained individual written informed consent from each household owner for the questionnaire. All research was performed in accordance with relevant guidelines and regulations.\u003c/p\u003e\u003ch3\u003eField collections and sample processing\u003c/h3\u003e\u003ch2\u003eField Sites – Guatemala\u003c/h2\u003e\u003cp\u003eMosquitoes were collected from four communities in the Municipality of Comapa, Department of Jutiapa, Guatemala (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The neighborhoods are in a semi-rural area characterized by poor infrastructure. These communities have had extensive community engagement with investigators from Universidad del Valle de Guatemala during past research projects in partnership with the Ministry of Health [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSampling was carried out during the rainy season (June - August 2022) in 77 households. Prokopack aspirators were used for mosquito collections [\u003cspan class=\"CitationRef\"\u003e81\u003c/span\u003e]. All indoor and outdoor sampling with the Prokopack was done as follows. Indoor aspirating was conducted in all bedrooms, kitchens, and other living quarters, while outdoor aspirating was done around structures and near stored debris. Mosquito sampling occurred between 7:00 and 10:00 AM in the morning and 4:00 to 6:30 pm in the evening.\u003c/p\u003e\u003ch2\u003eField Sites – Texas\u003c/h2\u003e\u003cp\u003eMosquitoes were collected from eight low-income communities called ‘colonias’ in the Lower Rio Grande Valley, South Texas (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), as previously described [\u003cspan class=\"CitationRef\"\u003e82\u003c/span\u003e]. Between June, 2021 and March, 2022, mosquito sampling was done using BG Sentinel 2 traps (Biogents, Germany) baited with BG lures (Biogents, Germany) placed around homes.\u003c/p\u003e\u003ch2\u003eMosquito processing, DNA extraction, molecular barcoding\u003c/h2\u003e\u003cp\u003eMosquitoes were morphologically identified to species and sex using illustrations and dichotomous keys [\u003cspan class=\"CitationRef\"\u003e83\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e85\u003c/span\u003e]. Blood-fed females were placed in individual nuclease-free 1.5 mL microcentrifuge tubes and stored at -20 or -80°C with labels indicating species, collection date, and house identification number. These blood-fed mosquitoes were later photographed and assigned a Sella score to record bloodmeal digestion stage and ovary development [\u003cspan class=\"CitationRef\"\u003e86\u003c/span\u003e]. To minimize exogenous DNA contamination, each whole mosquito was washed in 10% bleach followed by two rinses with nuclease-free water.\u003c/p\u003e\u003cp\u003eOn a new microscope slide (VWR VistaVision microscope slides; VWR, Radnor, PA, USA), the abdomen was carefully separated from the rest of the mosquito (thorax and head) using forceps, and the abdominal contents were transferred into a new labeled, DNA-free 1.5 mL microcentrifuge tube. Forceps were sterilized between specimens. DNA was extracted from gut contents using the Thermo Scientific™ KingFisher™ Flex Purification System and MagMAX™ Core Nucleic Acid Purification Kit (Thermo Fisher Scientific, Waltham, MA, USA), following previously published protocols [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e88\u003c/span\u003e]. DNA from each sample was divided into two aliquots in microcentrifuge tubes to support: (i) molecular verification of mosquito species to confirm morphological identifications, and (ii) bloodmeal metabarcoding.\u003c/p\u003e\u003ch2\u003eMolecular verification of mosquito species\u003c/h2\u003e\u003cp\u003eAlthough field-collected mosquitoes were morphologically identified under a microscope, molecular confirmation was conducted for all blood-fed specimens processed for bloodmeal analysis using a modified cytochrome c oxidase subunit I (COI) barcoding assay using a modified version of the Folmer \u003cem\u003eet al.\u003c/em\u003e [\u003cspan class=\"CitationRef\"\u003e89\u003c/span\u003e] protocol. Briefly, we followed our adapted previously published PCR–Sanger workflow by Oslon et al. (24 ,63,64), the COI region (~ 658 bp) was amplified with primers LCO1490 and HCO2198. PCR reactions (25 µL) contained 12.5 µL FailSafe™ PCR 2X Premix E (Lucigen), 0.5 µL FailSafe™ PCR Enzyme Mix (Lucigen), 1 µL of each primer, 3 µL DNA template, and 7 µL nuclease-free water.\u003c/p\u003e\u003cp\u003eThermocycling included an initial denaturation step at 94°C for 3 min, followed by 44 cycles of 94°C for 30 s, 50°C for 30 s, and 72°C for 30 s, and a final extension at 72°C for 8 min. PCR products were purified using Exo-SAP-IT™ (Thermo Fisher Scientific) and sequenced bidirectionally at Eton Bioscience (San Diego, CA, USA). Consensus sequences were assembled in Geneious Prime v2024.0 and queried against the NCBI nucleotide database with BLASTn [\u003cspan class=\"CitationRef\"\u003e92\u003c/span\u003e]. A match of ≥ 98% identity over ≥ 580 bp was accepted as confirmation. Molecular confirmation was successful for most specimens, and all successfully sequenced specimens matched their original morphological assignments.\u003c/p\u003e\u003ch2\u003eMetabarcoding PCR and sequence analysis\u003c/h2\u003e\u003cp\u003eBloodmeal host identification was performed using a vertebrate 12S rRNA mitochondrial gene following previously published protocols developed and optimized in our laboratory [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e]. Briefly, primers with dual identical barcode tags were used to amplify a ~ 145 bp fragment of the mitochondrial 12S rRNA gene [\u003cspan class=\"CitationRef\"\u003e94\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e96\u003c/span\u003e]. Samples were amplified in duplicate. PCR products were pooled, purified using SPRI beads (Beckman Coulter, Indianapolis, IN and Sigma-Aldrich, St. Louis, MO) and submitted to the Texas A\u0026amp;M Institute for Genome Sciences and Society for library preparation (xGen™ ssDNA \u0026amp; Low Input DNA Library Prep Kit, Integrated DNA Technologies). Sequencing of samples from Guatemala were done on an Illumina Nextseq 2000 platform and sequencing of the samples from Texas was done on an Illumina Novaseq 6000 platform (Illumina, San Diego, CA, USA).\u003c/p\u003e\u003cp\u003eSequencing data were processed following our published workflow [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e93\u003c/span\u003e]. Samples were demultiplexed on barcodes using Cutadapt 5.0 [\u003cspan class=\"CitationRef\"\u003e97\u003c/span\u003e], primers were trimmed, and reads were merged and quality-filtered using Qiime2 Amplicon 2025.4 [\u003cspan class=\"CitationRef\"\u003e98\u003c/span\u003e]. Resulting amplicon sequence variants that had fewer than 100 reads across the study were not considered further. Taxonomic assignment of host sequences was performed with BLAST searches against the NCBI GenBank nucleotide database. Hosts that matched less than 1% of reads for individual samples were also rejected. Only hosts appearing in both PCR replicates were retained, unless one of the replicates did not yield data after processing steps, in which case the available information from one replicate was accepted. Sequences with identity ≥ 98% to database matches were binned to species level. Lower identity matches or multiple species showing identical match values were resolved to the genus or higher taxonomic level. Presence of a host within a geographic region was verified using GBIF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gbif.org/\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Global Biodiversity Information Facility).\u003c/p\u003e\u003ch2\u003eHost quantification and selection analysis\u003c/h2\u003e\u003cp\u003eObserved host use for all bloodmeals with single species and mixed (2 or more species) was quantified using relative read abundance (RRA) following Deagle et al [\u003cspan class=\"CitationRef\"\u003e99\u003c/span\u003e]. For each mosquito \u003cem\u003ej\u003c/em\u003e and host species \u003cem\u003ei\u003c/em\u003e, let \u003cem\u003eNij\u003c/em\u003e be the observed read count for host \u003cem\u003ei\u003c/em\u003e in mosquito \u003cem\u003ej.\u003c/em\u003e The RRA for host \u003cem\u003ei\u003c/em\u003e in mosquito \u003cem\u003ej\u003c/em\u003e was calculated as:\u003c/p\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{RRA}_{ij}=\\frac{{N}_{ij}}{{\\sum\\:}_{k}{N}_{kj}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cem\u003ek\u003c/em\u003e indexes all host species detected in mosquito \u003cem\u003ej\u003c/em\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{kj}\\)\u003c/span\u003e\u003c/span\u003e is the read count for host species \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e in mosquito \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e, so that host proportions sum to 1.0 per mosquito. Mosquitoes were classified as single-host when only one host species was detected; otherwise they were classified as mixed, with meals apportioned fractionally by RRA.\u003c/p\u003e\u003cp\u003eHousehold-level host availability was obtained from the contemporaneous household census data collected using a questionnaire while visiting homes in Guatemala and Texas (humans, chickens, dogs, ducks, cats, other) [\u003cspan class=\"CitationRef\"\u003e80\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e100\u003c/span\u003e]. For household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e, the availability proportion for host \u003cem\u003ei\u003c/em\u003e was\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{a}_{i,h}=\\frac{{\\text{count}}_{i,h}}{{\\sum\\:}_{k}{\\text{count}}_{k,h}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:coun{t}_{i,h}\\)\u003c/span\u003e\u003c/span\u003e is the number of host \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e recorded in household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h\\)\u003c/span\u003e\u003c/span\u003e, and the denominator sums counts across all host categories \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e recorded for that household. Each mosquito was matched to the census of its collection household. Host selection was assessed using mosquito-specific, household-matched forage ratios (FR)[\u003cspan class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e101\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e103\u003c/span\u003e].\u003c/p\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:F{R}_{ij}=\\frac{{RRA}_{ij}}{{a}_{i,house\\left(j\\right)}}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:house\\left(j\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the household in which mosquito \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e was collected (i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{i,house\\left(j\\right)}\\)\u003c/span\u003e\u003c/span\u003e is the same availability proportion defined above for that household). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F{R}_{ij}\\)\u003c/span\u003e\u003c/span\u003e was computed only when \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{i,house\\left(j\\right)}\u0026gt;0\\)\u003c/span\u003e\u003c/span\u003e; if a host category was unrecorded or had zero availability in the household census, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FR\\)\u003c/span\u003e\u003c/span\u003e was not estimable for that host. Forage ratios were interpreted as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FR\u0026gt;1\\)\u003c/span\u003e\u003c/span\u003e indicating over-utilization relative to availability, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FR\u0026lt;1\\)\u003c/span\u003e\u003c/span\u003e indicating under-utilization, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:FR\\approx\\:1\\)\u003c/span\u003e\u003c/span\u003e indicating use proportional to availability. For each host, we calculated the mean FR across mosquitoes and reported 95% confidence intervals; evidence of over-/under-utilization was inferred when the 95% CI lay entirely above or below 1, respectively. If the 95% CI included 1, host use was interpreted as not distinguishable from proportional use relative to availability.\u003c/p\u003e\u003cp\u003eFor the Texas study site, some property owners allowed trapping but declined the household questionnaire; therefore, FRs were computed only for specimens with matched census, while we report the bloodmeal presence and percentages used for all individual mosquitoes. Maps were generated in R (v4.5.1) using freely available administrative boundaries from Natural Earth, U.S. Census TIGER/Line, and GADM [\u003cspan class=\"CitationRef\"\u003e104\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e107\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMixed feeding patterns were compared between mosquito species in Guatemala using Pearson’s chi-square tests with Yates’ continuity correction, specifically testing whether the frequency of mixed feeding differed between \u003cem\u003eAe. aegypti\u003c/em\u003e and \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e in Guatemala. Statistical significance was assessed at α = 0.05. Analyses were performed in R (v4.5.1)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u0026nbsp;\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was funded by the Centers for Disease Control and Prevention, contract 200- 2017-93141, NIH R21AI166446\u0026ndash;01, Texas A\u0026amp;M AgriLife Research, and the Fulbright US Scholars Program to SAH. We received additional support from Department of the Army, U.S. Army Contracting Command, Aberdeen Proving Ground, Natick Contracting Division, Ft Detrick MD through the Department of Defense Deployed Warfighter Protection research program contract W911SR2510002.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eGLH conceptualized the study; SAH, PP, NP, GLH contributed to resources and funding acquisition; JGJ, NAS, NAF-S supervised field work; AAA and SB performed laboratory analysis of samples; AAA, SB, JGJ, YT, analyzed data; AAA wrote the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe appreciate the support of the residents and city and country public health agencies in the Lower Rio Grande Valley, Texas who collaborated with us to conduct this study. We also thank the support and hospitality provided by members of the community of Comapa municipality during household visits in Guatemala. We thank Danya Garza, Salvador Solis, Odaliz Sauceda, Javier Elizondo, Chris Roundy, and Charlotte Rhodes for their assistance in the field in south Texas and Andrea Moller-Vasquez, Maria Granados-Presas, and Henry Esquivel for assistance in the field and lab in Guatemala. We thank Tereza Magalhaes for reviewing and improving an earlier draft of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData from next generation sequencing have been deposited into the SRA database (NCBI) under the accession numbers (Guatemala data: BioProject PRJNA1398928, BioSamples SAMN54467175 to SAMN54467543; Reviewer Link: [https://dataview.ncbi.nlm.nih.gov/object/PRJNA1398928?reviewer=hmoapjnp7tdehaunl8m79t8sm4](https:/urldefense.com/v3/__https:/dataview.ncbi.nlm.nih.gov/object/PRJNA1398928?reviewer=hmoapjnp7tdehaunl8m79t8sm4__;!!KwNVnqRv!Bp12o7tBGXamHOHBaxDVK_VEAHxLx5zoJRlsg7_0y2uTdajBH5mTQ0_10ONsfPavqeROgYzUYVsF9jpEoZetmS4mgQmXirHIn-S0$) . South Texas data: BioProject PRJNA1417760, BioSamples SAMN55013641 to SAMN55013752; Reviewer Link: [https://dataview.ncbi.nlm.nih.gov/object/PRJNA1417760?reviewer=es3h605apd4dajl26oh7vvtggs](https:/urldefense.com/v3/__https:/dataview.ncbi.nlm.nih.gov/object/PRJNA1417760?reviewer=es3h605apd4dajl26oh7vvtggs__;!!KwNVnqRv!Bp12o7tBGXamHOHBaxDVK_VEAHxLx5zoJRlsg7_0y2uTdajBH5mTQ0_10ONsfPavqeROgYzUYVsF9jpEoZetmS4mgQmXioxTgudb$) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. Vector-borne diseases. 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(2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"bloodmeal analysis, metabarcoding, Aedes aegypti, Culex quinquefasciatus, forage ratio, arbovirus transmission","lastPublishedDoi":"10.21203/rs.3.rs-8855613/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8855613/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMosquito host contact determines arboviral transmission efficiency. \u003cem\u003eAedes aegypti\u003c/em\u003e and \u003cem\u003eCulex quinquefasciatus\u003c/em\u003e are important vectors of dengue, Zika, chikungunya, West Nile virus, and other arboviruses, yet their feeding patterns remain poorly characterized in many tropical regions. We used bloodmeal metabarcoding to detect DNA from multiple vertebrate species within individual blood-fed mosquitoes collected from rural Guatemala and south Texas, USA. Mosquitoes were collected using aspiration in Guatemala and BG-Sentinel traps in south Texas. We calculated forage ratios (FR) to assess host utilization relative to availability. In Guatemala, \u003cem\u003eAe. aegypti\u003c/em\u003e exhibited strong anthropophilic behavior (human DNA: 90.2% of bloodmeals and FR\u0026thinsp;=\u0026thinsp;3.62 (95% CI: 2.70\u0026ndash;4.54), indicating significant over-utilization. In south Texas, \u003cem\u003eAe. aegypti\u003c/em\u003e strongly over-utilized dogs (88.2% of bloodmeals; FR\u0026thinsp;=\u0026thinsp;4.65, 95% CI: 2.43\u0026ndash;6.87) while under-utilizing humans (FR\u0026thinsp;=\u0026thinsp;0.53, 95% CI: 0.25\u0026ndash;0.81). In Guatemala, \u003cem\u003eCx. quinquefasciatus\u003c/em\u003e displayed high anthropophilic behavior (85.3% of bloodmeals; FR\u0026thinsp;=\u0026thinsp;2.60, 95% CI: 2.24\u0026ndash;2.97). Mixed bloodmeals were common in both species at both sites (19.5\u0026ndash;85.3%), with up to four host species detected in single mosquitoes. These results demonstrate that mosquito host selection is variable and context-dependent and underscore the need for location-specific surveillance to inform vector control strategies.\u003c/p\u003e","manuscriptTitle":"Bloodmeal metabarcoding reveals host feeding patterns for Aedes aegypti and Culex quinquefasciatus in Jutiapa, Guatemala and Texas, USA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-19 05:38:14","doi":"10.21203/rs.3.rs-8855613/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-08T15:25:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-03T20:13:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T20:44:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150851677984220486609108600382270631534","date":"2026-02-20T19:01:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73263511666587845543841219221582711514","date":"2026-02-20T18:41:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-20T18:28:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-20T18:22:06+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-20T07:19:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T13:35:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-18T13:29:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"09653d6b-70a7-436c-ba43-1ee6cbea4448","owner":[],"postedDate":"February 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63143968,"name":"Health sciences/Diseases"},{"id":63143969,"name":"Biological sciences/Ecology"},{"id":63143970,"name":"Earth and environmental sciences/Ecology"},{"id":63143971,"name":"Biological sciences/Zoology"}],"tags":[],"updatedAt":"2026-04-13T21:09:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-19 05:38:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8855613","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8855613","identity":"rs-8855613","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}