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Context Land use change and deforestation drive both biodiversity loss and zoonotic disease transmission in tropical countrysides. For mosquito communities that can include disease vectors, forest loss has been linked to reduced biodiversity and increased vector presence. The spatial scales at which land use and tree cover shape mosquito communities present a knowledge gap relevant to both biodiversity and public health. Objectives We investigated the responses of mosquito species richness and Aedes albopictus disease vector presence to land use and to tree cover surrounding survey sites at different spatial scales. We also investigated species compositional turnover across land uses and along environmental gradients. Methods We paired a field survey of mosquito communities in agricultural, residential, and forested lands in rural southern Costa Rica with remotely sensed tree cover data. We compared mosquito richness and vector presence responses to tree cover measured across scales from 30 to 1000 m, and across land uses. We analyzed mosquito community compositional turnover between land uses and along environmental gradients of tree cover, temperature, elevation, and geographic distance. Results Tree cover was both positively correlated with mosquito species richness and negatively correlated with the presence of the common invasive dengue vector Ae. albopictus at small spatial scales of 90–250 m. Land use predicted community composition and Ae. albopictus presence. Conclusions The results suggest that local tree cover preservation and expansion can support mosquito species richness and reduce disease vector presence. The identified spatial range at which tree cover shapes mosquito communities can inform the development of land management practices to protect both ecosystem and public health. 

The incidence ofAedes-borne pathogens has been increasing despite vector control efforts. Control strategies typically target households (HH), whereAedesmosquitoes breed in HH containers and bite indoors. However, our study in Kenyan cities of Kisumu and Ukunda (2019–2022) revealed highAedesabundance in public spaces, prompting the question: How important are non-household (NH) environments for dengue transmission and control? Using field data and human activity patterns, we developed an agent-based model simulating transmission across HH and five types of NH environments, which was then used to evaluate preventive (before an epidemic) and reactive (after an epidemic commences) vector control scenarios. Our findings estimate over half of infections occurring in NH settings, particularly workplaces, markets and recreational sites. Container removal was more effective in NH than in HH areas, contrasting with the global focus on HH-based management. Greater reductions in dengue cases occurred with early, high-coverage interventions, especially in NH locations. Additionally, local ecological factors, such as uneven water container distribution, influence control outcomes. This study underscores the importance of vector control in both HH and NH environments in endemic settings. It highlights a specific approach to inform evidence-based decision-making to target limited vector control resources for optimal control. 

Climate warming is expected to shift the distributions of mosquitoes and mosquito-borne diseases, promoting expansions at cool range edges and contractions at warm range edges. However, whether mosquito populations could maintain their warm edges through evolutionary adaptation remains unknown. Here, we investigate the potential for thermal adaptation inAedes sierrensis, a congener of the major disease vector species that experiences large thermal gradients in its native range, by assaying tolerance to prolonged and acute heat exposure, and its genetic basis in a diverse, field-derived population. We found pervasive evidence of heritable genetic variation in mosquito heat tolerance, and phenotypic trade-offs in tolerance to prolonged versus acute heat exposure. Further, we found genomic variation associated with prolonged heat tolerance was clustered in several regions of the genome, suggesting the presence of larger structural variants such as chromosomal inversions. A simple evolutionary model based on our data estimates that the maximum rate of evolutionary adaptation in mosquito heat tolerance will exceed the projected rate of climate warming, implying the potential for mosquitoes to track warming via genetic adaptation. 

Mosquito vectors of pathogens (e.g.,Aedes,Anopheles, andCulexspp. which transmit dengue, Zika, chikungunya, West Nile, malaria, and others) are of increasing concern for global public health. These vectors are geographically shifting under climate and other anthropogenic changes. As small-bodied ectotherms, mosquitoes are strongly affected by temperature, which causes unimodal responses in mosquito life history traits (e.g., biting rate, adult mortality rate, mosquito development rate, and probability of egg-to-adult survival) that exhibit upper and lower thermal limits and intermediate thermal optima in laboratory studies. However, it remains unknown how mosquito thermal responses measured in laboratory experiments relate to the realized thermal responses of mosquitoes in the field. To address this gap, we leverage thousands of global mosquito occurrences and geospatial satellite data at high spatial resolution to construct machine-learning based species distribution models, from which vector thermal responses are estimated. We apply methods to restrict models to the relevant mosquito activity season and to conduct ecologically plausible spatial background sampling centered around ecoregions for comparison to mosquito occurrence records. We found that thermal minima estimated from laboratory studies were highly correlated with those from the species distributions (r = 0.87). The thermal optima were less strongly correlated (r = 0.69). For most species, we did not detect thermal maxima from their observed distributions so were unable to compare to laboratory-based estimates. The results suggest that laboratory studies have the potential to be highly transportable to predicting lower thermal limits and thermal optima of mosquitoes in the field. At the same time, lab-based models likely capture physiological limits on mosquito persistence at high temperatures that are not apparent from field-based observational studies but may critically determine mosquito responses to climate warming. Our results indicate that lab-based and field-based studies are highly complementary; performing the analyses in concert can help to more comprehensively understand vector response to climate change. 

Abstract Despite the increasing burden of dengue in Kenya and Africa, the introduction and expansion of the virus in the region remain poorly understood. The objective of this study is to examine the genetic diversity and evolutionary histories of dengue virus (DENV) serotypes 1 and 3 in Kenya and contextualize their circulation within circulation dynamics in the broader African region. Viral RNA was extracted from samples collected from a cohort of febrile patients recruited at clinical sites in Kenya from 2013 to 2022. Samples were tested by polymerase chain reaction (PCR) for DENV presence. Five DENV-positive samples were serotyped, and complete viral genomes for phylogenetic inference were obtained via sequencing on Illumina platforms. Sequences generated in our study were combined with global datasets of sequences, and Bayesian and maximum likelihood methods were used to infer phylogenetic trees and geographic patterns of spread with a focus on Kenya and Africa as a whole. Four new DENV-1 and one new DENV-3 genomes were successfully sequenced and combined with 328 DENV-1 and 395 DENV-3 genomes from elsewhere for phylogenetic analyses. The DENV-1 sequences from our study formed a monophyletic cluster with an inferred common ancestor in 2019 (most recent common ancestor 2019 and 95% high posterior density 2018–19), which was closely related to sequences from Tanzania. The single DENV-3 sequence clustered with sequences from Tanzania and Kenya, was collected between 2017 and 2019 and was related to recent outbreaks in the region. Phylogenetic trees resolved multiple clades of DENV-1 and DENV-3 concurrently circulating in Africa, introduced in the early-to mid-2000s. Three DENV-1 and four DENV-3 clades are highlighted, introduced between 2000 and 2015. Phylogeographic models suggest frequent, independent importations of DENV lineages into Kenya and Africa from East and South-East Asia via distinct geographic pathways. DENV-1 and DENV-3 evolutionary dynamics in Africa are characterized by the cocirculation of multiple recently introduced lineages. Circulating lineages are introduced via distinct geographic pathways that may be centered around regional nexus locations. Increased surveillance is required to identify key regional locations that drive spread, and dengue interventions should focus on interrupting spread at these locations. 

Global risk maps are an important tool for assessing the global threat of mosquito and tick-transmitted arboviral diseases. Public health officials increasingly rely on risk maps to understand the drivers of transmission, forecast spread, identify gaps in surveillance, estimate disease burden, and target and evaluate the impact of interventions. Here, we describe how current approaches to mapping arboviral diseases have become unnecessarily siloed, ignoring the strengths and weaknesses of different data types and methods. This places limits on data and model output comparability, uncertainty estimation and generalisation that limit the answers they can provide to some of the most pressing questions in arbovirus control. We argue for a new generation of risk mapping models that jointly infer risk from multiple data types. We outline how this can be achieved conceptually and show how this new framework creates opportunities to better integrate epidemiological understanding and uncertainty quantification. We advocate for more co-development of risk maps among modellers and end-users to better enable risk maps to inform public health decisions. Prospective validation of risk maps for specific applications can inform further targeted data collection and subsequent model refinement in an iterative manner. If the expanding use of arbovirus risk maps for control is to continue, methods must develop and adapt to changing questions, interventions and data availability.