{"paper_id":"02da76f8-1c7e-4053-9dd2-b562308ac69c","body_text":"Ecological diversity of Anopheles gambiae s.l. and insecticide resistance 1 \nacross refugee camps in Kenya. 2 \n 3 \nMartin K . Rono1,2*, Caroline Wanjiku1, Brian Bartilol 1,2, Audrey Oronda 1,2, Adam Nduni 1, 4 \nGarama Kenga 2, Mercy Tuwei 1, Lynette I . Ochola-Oyier1, Robert W. Snow 3,4, Joseph 5 \nMwangangi1, Marta Maia1,4 6 \n 7 \nAffiliations 8 \n1. Centre for Geographic Medicine Research (Coast), Kenya Medical Research Institute -9 \nWellcome Trust Research Programme, Kilifi, Kenya 10 \n2. Pwani University Bioscience Research Centre, Pwani University, Kilifi, Kenya 11 \n3. Population & Health Impact Surveillance Group, KEMRI -Wellcome Trust Research 12 \nProgramme, Nairobi, Kenya 13 \n4. Centre for Global Health and Tropical Medicine, Nuffield Department of Medicine, 14 \nUniversity of Oxford, Oxford, UK 15 \n 16 \n*Correspondence 17 \nMKR:  mrono@kemri-wellcome.org 18 \nE-mail addresses 19 \nCW: CWanjiku@kemri-wellcome.org 20 \nBB: bbartilol@kemri-wellcome.org 21 \nAO: aoronda@gmail.com 22 \nAN: adamnduni@gmail.com 23 \nGK: garamakenga4@gmail.com 24 \nMT: mercytuweij@gmail.com 25 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nLIO: Lioyier@kemri-wellcome.org 26 \nRWS: rsnow@kemri-wellcome.org 27 \nJM: JMwangangi@kemri-wellcome.org 28 \nMM: MMaia@kemri-wellcome.org 29 \n 30 \nAbstract  31 \nMalaria remains a major threat during humanitarian crises, necessitating targeted vector control 32 \nstrategies informed by local vector dynamics. Between May and July 2023, we conducted 33 \nlarval surveys in refugee settlements across Dadaab, Kakuma, and Kalobeyei (Kenya), 34 \ncollecting  Anopheles larvae. Genotyping of 728 specimen s revealed spatial variations in 35 \nspecies composition. Overall, Anopheles arabiensis was the dominant species (59%, n=42 6), 36 \nfollowed by  Anopheles coluzzii  (35%, n=25 2), and  Anopheles rufipes  (1%, n=7).  In 37 \nDadaab, An. arabiensis  was overwhelmingly dominant (94%, n=352/37 4). In contrast, the 38 \nKakuma/Kalobeyei complex was characterized by the co -occurrence of  An. coluzzii  (72%, 39 \nn=252/350) and  An. arabiensis  (22%, n=74/3 50), with  An. rufipes  exclusively found in 40 \nKalobeyei (7%, n=6/89) (Figure 2B). Notably, no members of the  Anopheles funestus group 41 \nor Anopheles stephensi were detected. However, approximately 5% of the larvae across the  42 \nsites could not be resolved molecularly. High frequencies of the  L1014F kdr  mutation, a 43 \npyrethroid resistance marker, were detected  in An. coluzzii (Kakuma: 50%; Kalobeyei: 63%) 44 \nand An. arabiensis  (Kakuma: 10%; Kalobeyei : 30%) populations in Turkana County. 45 \nInterestingly, no kdr mutations were observed in the  An. arabiensis population from Dadaab. 46 \nThese findings highlight significant spatial diversity in vector species composition and 47 \nresistance profiles, with An. coluzzii emerging as a dominant, pyrethroid-resistant vector in the 48 \nKakuma/Kalobeyei complex. The results underscore the urgent need for targeted interventions, 49 \nincluding resistance monitoring and alternative insecticide-based strategies, to mitigate malaria 50 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\ntransmission risks in fragile, humanitarian settings. Further studies are warranted to address 51 \nunidentified larval species and seasonal transmission dynamics. 52 \n 53 \nKeywords: Malaria; Anopheles coluzzii; insecticide resistance; refugee camps; vector 54 \nsurveillance; Kenya 55 \n 56 \nIntroduction 57 \nConflict and the displacement of large populations are recognized as significant public health 58 \nrisks. The rapid influx of refugees, coupled with limited sanitation infrastructure, often leads 59 \nto disease outbreaks. Infectious diseases are a major cause of morbidity and mortality in refugee 60 \ncamps. Between 2009 and 2017, the United Nations High Commissioner for Refugees 61 \n(UNHCR) reported over 350 infectious disease outbreaks in refugee camps globally[1, 2], with 62 \nKenya, Chad, and Thailand among the countries bearing the highest disease burdens. Vector -63 \nborne diseases, including yellow fever, dengue, and malaria, contribute substantially to this 64 \nburden during humanitarian crises[3-6]. 65 \nIn Kenya, Kakuma and Dadaab refugee camps were established in response to humanitarian 66 \ncrises stemming from conflict and famine in neighboring countries. Kakuma, located in 67 \nTurkana County in northwestern Kenya, was established in 1992 and is home to approximately 68 \n300,000 inhabitants, primarily refugees from South Sudan and Somalia [7, 8] . In 2016, the 69 \nUNHCR, in collaboration with the national and county governments of Turkana, established 70 \nthe Kalobeyei Integrated Settlement to alleviate congestion in Kakuma and promote socio -71 \neconomic integration with the host community. 72 \nDadaab, established in 1991, is the largest refugee camp in Kenya, located in Garissa County 73 \nnear the Kenyan-Somali border. With a population of approximately 400,000, predominantly 74 \nSomali refugees, Dadaab is administratively divided into three sub-camps: Ifo, Hagadera, and 75 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nDagahaley[9]. These camps face significant challenges in managing public health risks, 76 \nparticularly vector-borne diseases, due to overcrowding, limited resources, and the dynamic 77 \nnature of refugee populations. 78 \nTurkana County, Kenya, despite its semi -arid climate and low rainfall, experiences a 79 \ndisproportionately high malaria burden [10-12], sustained by local, year -round transmission 80 \nthat shows little correlation with rainfall[13]. This persistent threat is now compounded by the 81 \nrecent discovery of two novel malaria vectors uniquely adapted to arid ecologies: the 82 \ninvasive Anopheles stephensi  [14]and the West African  Anopheles coluzzii  [15]. The larval 83 \necology of An. stephensi,  in particular, is characterized by its specialization for breeding in a 84 \nwide range of human -made water containers  including storage tanks, cisterns, and discarded 85 \ntires which are abundant in urban settings and essential for water security in drought -prone 86 \nareas.. This adaptability is further enhanced by its independence from rainfall and its ecological 87 \nflexibility to utilize natural sites like riverbeds. Together, these traits underpin its role as a major 88 \nand emerging urban malaria vector in Africa  [16-18]. Similarly, An. coluzzii  is strongly 89 \nassociated with anthropogenic habitats in arid ecologies, with larvae typically found in 90 \npermanent or semi -permanent human -made aquatic habitats such as irrigated fields, urban 91 \ndrainage, and stored water containers[19]. These adaptations make refugee camps like Kakuma 92 \nparticularly vulnerable. Essential water harvesting and storage practices create abundant 93 \nbreeding sites, a factor strongly linked to increased mosquito-borne disease transmission[20-94 \n22].The coexistence of  An. coluzzii  and An. stephensi  with established vectors like  An. 95 \narabiensis suggests a complex and evolving transmission landscape, potentially exacerbating 96 \nmalaria burden in Turkana and neighboring regions.  97 \nUnderstanding the bionomics and insecticide resistance profiles of these vectors is critical for 98 \nrefining disease control strategies and especially within humanitarian contexts.  In this study, 99 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nwe examined the diversity and insecticide resistance profile of local malaria vector populations 100 \nof Kakuma and Dadaab refugee camps.   101 \n 102 \nMethods 103 \nLarval surveys 104 \nMosquito larvae were collected from randomly selected sites in Kakuma and Dadaab refugee 105 \ncamps between May 2023 and July 2023 (Supplementary Table 1). Habitats were sampled once 106 \nduring the study period. Larvae at the L1-L3 developmental stages were sampled from a variety 107 \nof habitats, including water containers, stagnant water, and roadside pools, using a standard 108 \n350 mL dipper. At each site, at least three dips were taken to quantify larval density. Anopheles 109 \nlarvae were separated and individually preserved in 1.5 mL microcentrifuge tubes containing 110 \n95% ethanol for transport to the KEMRI Wellcome Trust Research Programme in Kilifi, 111 \nKenya. 112 \nNucleic acid  extraction and molecular analysis for species identification 113 \nField-collected larvae were rinsed with nuclease-free water, and genomic DNA extracted using 114 \nthe Chelex method. Briefly, whole larvae were transferred into individual 1.5 mL 115 \nmicrocentrifuge tubes containing 50 µL of 20% Chelex  resin (Bio-Rad, USA) and 116 \nhomogenized using polypropylene pestles. The lysate was incubated at 100 °C while shaking 117 \nat 650 rpm on a ThermoMixer (Eppendorf, Hamburg, Germany). The solution was centrifuged 118 \nat 10,000 × g for 2 minutes, and the supernatant transferred to a new 1.5 mL microcentrifuge 119 \ntube. This process was repeated twice, and the extracted DNA stored at −80 °C until further 120 \nanalysis.  Anopheles gambiae s.l. sibling species were identified using a previously described 121 \nPCR method targeting the intergenic spacer (IGS) region of the ribosomal DNA [23]. Samples 122 \nthat did not amplify for An. gambiae s.l.  were screened for members of the An. funestus  123 \ncomplex using established protocols[24] and An. stephensi using established protocols [14].  124 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nNucleic acid extraction and molecular analysis for species identification 125 \nField-collected larvae were rinsed with nuclease-free water, and genomic DNA was extracted 126 \nusing the Chelex method. Briefly, whole larvae were transferred into individual 1.5 mL 127 \nmicrocentrifuge tubes containing 50 µL of 20% Chelex resin (Bio -Rad, USA) and 128 \nhomogenized using polypropylene pestles. The lysate was incubated at 100 °C while shaking 129 \nat 650 rpm on a ThermoMixer (Eppendorf, Hamburg, Germany). The solution was centrifuged 130 \nat 10,000 ×  *g* for 2 minutes, and the supernatant was transferred to a new 1.5 mL 131 \nmicrocentrifuge tube. This process was repeated twice, and the extracted DNA was stored at 132 \n−80 °C until further analysis. 133 \nSpecies identification was performed using a sequential molecular workflow. First, Anopheles 134 \ngambiae s.l. sibling species were identified using a PCR method targeting the intergenic spacer 135 \n(IGS) region of the ribosomal DNA [23]. Samples identified as An. gambiae s.s. were further 136 \ncharacterized to distinguish between the M (An. coluzzii) and S (An. gambiae) forms via a PCR 137 \nassay targeting the SINE200 insertion/deletion polymorphism on the X chromosome [25]. The 138 \nprimers used were: Forward: 5′ -GTGTGCACCTCGACGTAC-3′ and Reverse: 5′ -139 \nCGGAGTGACCAGGACACC-3′. PCR products were resolved on a 2% agarose gel; samples 140 \nshowing a band at ~315 bp were classified as An. coluzzii, those with a band at ~250 bp as An. 141 \ngambiae, and those with both bands as hybrid forms. Samples that did not amplify for  An. 142 \ngambiae s.l. were subsequently screened for members of the  An. funestus complex [24] and 143 \nfor An. stephensi [14]. This sequential identification workflow was chosen based on historical 144 \ndata and previous studies indicating the dominance of the  An. gambiae complex over the An. 145 \nfunestus group in these arid ecological zones [26]. 146 \n 147 \nMolecular Identification of PCR-Unresolved Mosquito Specimens  148 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nFor mosquitoes unresolved by initial PCR, the ITS2 region was amplified using primers from 149 \nBeebe & Saul (1995)[2]. The 10 µL PCR reaction contained 5 µL of 2X GoTaq® Master Mix 150 \n(Promega, USA), 0.5 µL of each primer, 1.5 µL of DNA, and 2.5 µL nuclease-free water. 151 \nCycling conditions were: 95°C for 5 min; 40 cycles of 95°C for 15 sec, 52°C for 20 sec, and 152 \n72°C for 1 min; with a final 72°C for 10 min. Amplicons were purified with the QIAquick PCR 153 \nPurification Kit (Qiagen, Germany). Sanger sequencing used BigDye Terminator v3.1 (Applied 154 \nBiosystems, UK) on an ABI 3730xl sequencer.  Chromatograms were edited in CLC Main 155 \nWorkbench 24. Consensus sequences were identified via BLASTn against the NCBI nt 156 \ndatabase under default parameters[27]. 157 \n 158 \nInsecticide resistance profiling 159 \nThe presence of point mutations at position 1014 of the voltage -gated sodium channel (vgsc) 160 \ngene (knock down resistance (kdr) mutations) were investigated using a TaqMan probe-based 161 \nquantitative real-time PCR (qPCR) assay. The assay utilized one set of primers: Forward: 5′ -162 \nCAT TTT TCT TGG CCA CTG TAG TGA T-3′, Reverse: 5′-CGA TCT TGG TCC ATG TTA 163 \nATT TGC A-3′ and three probes for detection of: wild -type allele: 5′ -CTT ACG ACT AAA 164 \nTTT C-3′ (labeled with HEX fluorophore), Vgsc -L1014F mutation: 5′-ACG ACA AAA TTT 165 \nC-3′ (labeled with FAM fluorophore) and Vgsc-L1014S mutation: 5′-ACG ACT GAA TTT C-166 \n3′ (labeled with FAM fluorophore). The qPCR cycling conditions consisted of an initial 167 \ndenaturation at 95 °C for 10 minutes, followed by 40 cycles of denaturation at 95 °C for 10 168 \nseconds and annealing and extension at 65 °C for 45 seconds. A sample is 169 \nconsidered positive for a specific allele (wild-type, L1014F, or L1014S) if its associated probe 170 \ngenerates a fluorescence signal above the threshold during qPCR. 171 \n 172 \nResults 173 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nVector abundance, distribution and taxonomic assignment  174 \nLarval surveillance conducted in May and July 2023 identified productive Anopheles breeding 175 \nhabitats in both the Dadaab (Garissa County) and Kakuma/Kalobeyei (Turkana County) 176 \nrefugee camps (Figure 1A). The survey of 13 habitats revealed an overall mean density of  6.6 177 \nlarvae/dip, though this varied significantly between camp environments (Table 1). 178 \nIn Dadaab, larval production was confined to anthropogenic habitats.  Vehicle washing 179 \nbays and water-filled vehicle ruts were the primary larval sources, yielding a sub -total of 457 180 \nlarvae. While vehicle ruts were the most prolific habitat type in this camp (mean density:  8.5 181 \nlarvae/dip), the overall mean density for Dadaab was 5.1 larvae/dip (Table 1). 182 \nIn contrast, breeding sites in Kakuma and Kalobeyei were more varied and productive, 183 \ncomprising natural and peri -domestic habitats (Figure 1B&C). These sites yielded a higher 184 \noverall mean density of  10.0 larvae/dip. As detailed in Table 1, the most productive habitat 185 \ncategories were streams/riverbeds (mean density: 32.5 larvae/dip) and roadside ponds (mean 186 \ndensity: 24.6 larvae/dip). The single most productive habitat identified in the entire study was 187 \na roadside pond in Kakuma, which reached a density of  57.5 larvae/dip. The highly variable 188 \nproductivity of roadside drainages, which ranged from zero to 16.8 larvae/dip, underscores the 189 \nheterogeneous distribution of breeding sites within the camp environment. 190 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\n 191 \nFigure1 : Malaria vector larval sampling in Kakuma and Dadaab refugee complex  192 \n A)Map of sites sampled for anopheline larvae; B and C) larval breeding sites  193 \n 194 \nFrom 877 larvae collected , DNA was extracted successfully from 728 and subjected to 195 \nmolecular genotyping for An. gambiae, An. funestus species complexes and An. stephensi and 196 \nITS-2 amplicon sequencing . Notably, no specimens of the  Anopheles funestus  group 197 \nor Anopheles stephensi  were identified in any of the camps . Overall, Anopheles arabiensis 198 \ndorminated larval collection in refugee camps(59%) followed by An. coluzzii (35%), An rufipes 199 \n(1%) and lastly a single specimen of An. gambiae s.s.  identified. Approximately 5% of 200 \nmosquitoes collected could not be identified by combination of PCR based methods nor ITS-2 201 \nsequencing using the sanger sequencing approach (Figure 2A, Table S1 and Table S2).  202 \nThe vectors identified were spatially distributed as follows. In Dadaab, An. arabiensis was the 203 \npredominant species, accounting for 9 4% (n = 3 52) of the collections, with only a single 204 \nspecimen of An. gambiae s.s. identified. In Kakuma, An. coluzzii. dominated (84%, n = 222), 205 \nwhile An. arabiensis  represented 10% (n = 2 7). In Kalobeyei,   An. arabiensis  (54%, n=47) 206 \nand An. coluzzii (34%, n=30) and An. rufipes (7%, n=6) were the primary species (Figure 2B). 207 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nIn addition, samples from western Kenya (Luyeshe and Maseno) were included for 208 \ncomparative purposes and only identified An. gambiae s.s. (Figure S1).  209 \n 210 \nFigure 2.  Species composition of  Anopheles mosquitoes in Kakuma, Kalobeyei, and 211 \nDadaab refugee camps, Kenya . (A) Overall relative abundance of Anopheles 212 \nspecies identified by PCR and ITS2 sequencing. (B) Spatial distribution and abundance of the 213 \npredominant species, An. coluzzii and An. arabiensis, across individual sampling sites. 214 \n 215 \nFrequency of kdr mutations in vgsc of An. gambiae s.l. 216 \nThe kdr-L1014F mutation was detected at high frequencies in  An. coluzzii from Kakuma and 217 \nKalobeyei, with allelic frequencies of 5 0% and 63 %, respectively. In contrast,  An. 218 \narabiensis exhibited lower frequencies of the L1014F mutation (10% and 30% in Kakuma and 219 \nKalobeyei, respectively). Neither the L1014S nor L1014F mutation was observed in specimens 220 \nfrom Dadaab. High levels of insecticide resistance mutations were thus limited to An. coluzzii. 221 \nin Turkana County and absent in Garissa (Table 2). 222 \n 223 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nDiscussion 224 \nMalaria outbreaks in Turkana County have increased in recent years, with year -round P . 225 \nfalciparum transmission that is not strongly correlated with rainfall patterns [28]. 226 \nAdditionally, P . vivax infections have been reported among semi -nomadic populations in 227 \nTurkana and northeastern Kenya[29] who have been implicated in facilitating parasite spread 228 \nthrough their migration routes. Shifts in mosquito vector populations are known to influence 229 \nmalaria transmission [30, 31]. Understanding bionomics of vector populations at the local scale 230 \nis a critical first step for elucidating transmission dynamics and informing targeted intervention. 231 \nThis study investigated the larval ecology of malaria vectors in Kakuma and Dadaab refugee 232 \ncamps, revealing significant changes in vector composition. In Kakuma this represents a 233 \nsignificant shift from the previously documented vector population from 2005 -2006, which 234 \nwas reported to be homogeneously An. arabiensis [26]. The population has now changed to a 235 \nmore heterogeneous population comprising  An. coluzzii,  An. arabiensis and An rufipes  two 236 \ndecades later. Notably, An. rufipes was identified through the ITS2 sequencing approach after 237 \nbeing missed by standard PCR, underscoring the value of complementary molecular methods 238 \nfor accurate species resolution. The role of this vector in the Kakuma -Kalobeyei complex 239 \ncannot be overlooked, as it may contribute to the local transmission dynamics.  While malaria 240 \nvector populations shifts have been documented in various settings, they have tended towards 241 \na decline in An. gambiae s.s and an increase in An. funestus as the dominant species[32]. The 242 \nabsence of  An. funestus  and An. stephensi  in our larval collections, despite their known 243 \npresence in other parts of Kenya, suggests that the specific breeding habitats in these camps—244 \ndominated by temporary sunlit pools and vehicle ruts  may not be suitable for these species, 245 \nwhich prefer more permanent, vegetated, or container -based habitats respectively . These 246 \necological shifts are largely attributed to widespread insecticide use[32]. Despite year-round 247 \nmalaria transmission, vector surveillance  In Kakuma has been limited , leaving the primary 248 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nmalaria vectors poorly characterized. Concurrently, the camp's human population has surged 249 \nover the past two decades, prompting establishment of the Kalobeyei settlement.  250 \nThese anthropogenic changes, along with environmental and ecological factors may have 251 \ncreated conditions conducive for  proliferation of An. coluzzii  [33]. It is theori zed that the 252 \nspeciation of An. gambiae and An. coluzzii is driven not only by genetics but ecological factors 253 \nincluding quality of aquatic habitats[19].  The later emergence of An. coluzzii is associated with 254 \nthe diversification of aquatic habitats including those of marginal quality, for which it appears 255 \npredisposed[19]. This is to some extent reflected in the diversity and unusual habitats within 256 \nwhich Anophelines were found breeding in Kakuma. With regards to infection, susceptibility 257 \nto Plasmodium falciparum of all three vector species varies depending on local environment, 258 \ngenetic factors and parasite strain. However, high P . falciparum infection rates have been 259 \nreported in An. coluzzii and in one study P . vivax infection was documented [34]. In Kakuma, 260 \nthe current vector composition represents a notable change from the previously documented 261 \npopulation from 2005-2006, which was reported to be homogeneously An. arabiensis [26]. The 262 \npopulation now comprises a more heterogeneous mixture of  An. coluzzii , An. arabiensis , 263 \nand An. rufipes. Given the lack of continuous longitudinal data, this change could represent 264 \neither a local population shift or the recent introduction and subsequent establishment of  An. 265 \ncoluzzii in the region . The presence and establishment or expansion of  An. coluzzii  in the 266 \nregion appears to be a relatively recent phenomenon, as it was not detected during vector 267 \nsurveillance in Kakuma fifteen years ago [26]. Therefore, its role in past and current malaria 268 \noutbreaks in Turkana County is still poorly understood  warranting  further investigation, 269 \nparticularly given conflicting evidence on its vectorial capacity and susceptibility 270 \nto Plasmodium infections[35, 36]. 271 \nIn contrast, An. arabiensis remained the dominant vector in Dadaab. Unlike Kakuma, Dadaab 272 \nexperiences little to no malaria transmission.  The underlying mechanisms for this low 273 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\ntransmission, despite the presence of a competent vector, are not fully understood.  Potential 274 \ncontributing factors could include the zoophilic tendencies of local An. arabiensis populations, 275 \nthe influence of endosymbiotic microorganisms, or other genetic, environmental, and 276 \nanthropogenic factors that may reduce transmission efficiency [37, 38]. However, the specific 277 \ndrivers in this context remain unclear, underscoring the need for dedicated studies to elucidate 278 \nthe malaria transmission dynamics and the relative contribution of different factors in Dadaab 279 \nOverall, the identity of approximately 5% of the samples could not be resolved using standard 280 \nAn. gambiae s.l. and An. funestus s.l. species complex, An stephensi species specific PCR or 281 \nITS2 amplicon sequencing identification protocols. This includes definitively ruling out  An. 282 \nstephensi, for which we employed a s pecific PCR assay [14], confirming it was not present 283 \namong these unresolved specimens.  The fact that  An. rufipes  was only detected through 284 \nsequencing highlights a key limitation of standard PCR assays and highlights the critical need 285 \nto employ complementary sequencing and genomic tools to fully characterize anopheline 286 \ndiversity. This approach is essential to uncover potentially overlooked secondary vectors whose 287 \nrole in transmission may currently be underestimated. 288 \nRegarding insecticide resistance, high frequencies of the kdr-L1014F mutation were observed 289 \nin An. coluzzii from Kakuma and Kalobeyei  suggesting a move towards fixation , with most 290 \nmosquitoes exhibiting either homozygous or heterozygous genotypes. Lower frequencies of 291 \nthe mutation were detected in An. arabiensis from the same sites, while no kdr mutations were 292 \nfound in specimens from Dadaab. These findings suggest selecti on pressure favoring 293 \ninsecticide resistance genotypes in Turkana County, potentially driven by local insecticide use 294 \nor the introduction of resistant mosquitoes from neighboring regions, such as eastern Uganda, 295 \nwhere high levels of resistance have been documented[39]. However, this study focused solely 296 \non genotypic resistance; phenotypic assays are needed to confirm the susceptibility of these 297 \npopulations to insecticides used in vector control programs. 298 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nLong-lasting insecticidal nets (LLINs) and indoor residual spraying (IRS) remain critical for 299 \nmalaria control in refugee camps, but their efficacy depends on mosquito susceptibility to 300 \ninsecticides. The emergence of pyrethroid resistance in Turkana County , evidenced by 301 \nhigh kdr-L1014F frequencies, demands integrated vector management strategies 302 \nand continuous monitoring of kdr allele frequencies to mitigate this threat. 303 \nWhile our study provides key insights into larval ecology across three refugee settlements, we 304 \nacknowledge limitations inherent to larval-based surveillance. Larval collections are subject to 305 \nsampling bias, as cryptic breeding sites, particularly those of Anopheles funestus and secondary 306 \nvectors, often evade detection despite confirmed adult presence. Consequently, our data reflect 307 \nlarval diversity within  accessible habitats rather than exhaustive vector composition. 308 \nNevertheless, in Turkana (Kakuma/Kalobeyei) and Dadaab, where anthropogenic sites (water 309 \nstorage containers, drainage ditches) sustain dominant  An. coluzzii and An. 310 \narabiensis populations, larval source management (LSM) emerges as a high -priority 311 \nintervention. Targeted LSM (e.g., container covering, habitat drainage, site -specific 312 \nlarviciding) could effectively suppress these container -breeding gambiae-complex vectors. In 313 \ncontrast, settings with confirmed  funestus-group dominance would require alternative 314 \napproaches due to their association with cryptic, vegetated aquatic habitats. Thus, our findings 315 \nstrongly support LSM in  semi-arid humanitarian contexts where  gambiae-complex vectors 316 \ndominate identifiable artificial habitats, while underscoring the need for complementary adult 317 \nsurveillance to resolve cryptic vector dynamics. 318 \n 319 \nConclusion 320 \nThis study documents significant ecological shifts in malaria vectors across Kenya's arid-zone 321 \nrefugee settlements, revealing the emergence of Anopheles coluzzii alongside An. arabiensis in 322 \nTurkana County , linked to anthropogenic habitat modifications that enable year -round 323 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\ntransmission—while Dadaab exhibits persistent  An. arabiensis  dominance without 324 \nproportional malaria burden, suggesting unexplained local refractoriness. Critically, high kdr-325 \nL1014F frequencies in Turkana's  An. coluzzii  (upto 63%) pose an immediate threat to 326 \npyrethroid-based interventions (LLINs/IRS), necessitating urgent resistance management 327 \nthrough next-generation tools and phenotypic validation. Furthermore, the unresolved identity 328 \nof 5% of specimens underscores the need for enhanced surveillance using whole -genome 329 \nsequencing or        330 \nMatrix-Assisted Laser Desorption/Ionization Time -of-Flight (MALDI-TOF). These findings 331 \ncollectively highlight the urgency for habitat-focused vector control in semi-arid humanitarian 332 \ncontexts: where gambiae-complex vectors dominate identifiable artificial breeding sites (water 333 \nstorage containers, drainage ditches), targeted larval source management (container covering, 334 \nsite-specific larviciding) offers a critical strategy to disrupt transmission at its source, 335 \ncomplementing insecticide resistance countermeasures to protect crisis-affected populations. 336 \n 337 \nAcknowledgement 338 \nWe thank the technical and field staff: Festus Yaa, Gabriel Nzai, and Julius Tineja, who helped 339 \nwith mosquito sample collection in the field. 340 \n 341 \nAuthor contributions 342 \nM.K.R. conceptualisation, methodology, formal analysis, funding acquisition, writing —343 \noriginal draft. M.M. conceptuali sation, methodology, reviewing and editing. C.W. 344 \nmethodology, reviewing and editing. L.I.O. funding acquisition and reviewing and editing. J.M. 345 \nreviewing and editing. R.W.S. reviewing and editing. B.B., K.G., A.D., M.T. and A.O.  sample 346 \ncollection and processing, laboratory analysis and reviewing and editing. All authors read and 347 \napproved the manuscript. 348 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\n 349 \nFunding 350 \nThis work is supported by The Royal Society FLAIR fellowship grant: FLR \\R1\\190497, 351 \nFCG\\R1\\211043 and KEMRI IRG grant: KEMRI \\IRG\\NN02 (awarded to M.K.R.). and 352 \nmolecular reagent support from a pathogen genomics sub -Award (to L.I.O) from Africa CDC 353 \nPGI and ASLM.  RWS is supported by the Wellcome Trust Principal Fellowship (#212176) . 354 \nAll authors are grateful for the support of the Wellcome Trust to the Kenya Major Overseas 355 \nProgramme (#203077).  356 \n 357 \nEthics approval and consent to participate  358 \nThe study was approved by the KEMRI Scientific and Ethics Review Unit (SERU) with the 359 \nprotocol number: 337 KEMRI/SERU/CGMR-C/024/3148.  360 \n 361 \nConsent for publication  362 \nThis manuscript is published with the permission of the Director-General of the Kenya Medical 363 \nResearch Institute.The funding bodies had no role in the design, data collection, and drafting 364 \nof the manuscript. 365 \n 366 \nCompeting interests  367 \nAll authors have declared there are no competing interests. 368 \n 369 \nAvailability of data and materials 370 \nMost of the dataset used for analysis is available in the manuscript. We withheld  the geo-data 371 \nthat may predispose individual homesteads to a high risk  of identifiability. However, they are 372 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint \n\nunder the custodianship of the KEMRI Wellcome Trust Data Governance Committee and are 373 \naccessible upon request addressed to that committee. 374 \n 375 \nTables  376 \nTable 1. Summary of Anopheles larval habitat type in refugee camps in Kenya. 377 \n 378 \nCamp & Habitat  No. of sites Total Dips Total \nLarvae \nMean Larvae/Dip \n(Range) \nDadaab \nVehicle Washing Bay 3 50 117 2.3 (0.0 - 3.8) \nVehicle Ruts 2 40 340 8.5 (2.0 - 15.0) \nSubtotal 5 90 457 5.1 \n     \nKakuma/Kalobeyei \nStream/River 2 4 130 32.5 (23.5 - 41.5) \nRoadside Pond 2 5 123 24.6 (2.7 - 57.5) \nRoadside Drainage 4 33 167 5.1 (0.0 - 16.8) \nSubtotal 8 42 420 10.0 \n     \nOverall Total 13 132 877 6.6 \n 379 \nTable 2. kdr-West (L1014F) genotype distribution and allelic frequencies in Anopheles 380 \npopulations across study sites 381 \nSite kdr-West(L1014F) \nSpecies Genotype count Allelic frequency \n# RR RS SS R S \nDadaab An. arabiensis 322 0 0 322 0 1 \nkakuma An. coluzzii 58 3 52 3 0.5 0.5 \nKakuma An. arabiensis 26 5 4 17 0.27 0.73 \nKalobeyei An. coluzzii 27 9 16 2 0.63 0.37 \nKalobeyei An. arabiensis 40 0 8 32 0.1 0.9 \n 382 \nReference 383 \n.CC-BY 4.0 International licenseperpetuity. 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PLoS One 2022, 17:e0271347. 517 \n 518 \n.CC-BY 4.0 International licenseperpetuity. It is made available under a \npreprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in \nThe copyright holder for thisthis version posted October 15, 2025. ; https://doi.org/10.1101/2025.10.13.682237doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}