Species Identity, Life History, and Geographic Distance Influence Gut Bacterial Communities in Lab-Reared and European Field-Collected Culicoides Biting midges.

Bacteria are part of the insect gut system and influence many physiological traits of their host. Gut bacteria may even reduce or block the transmission of arboviruses in several species of arthropod vectors. Culicoides biting midges are important arboviral vectors of several livestock and wildlife diseases, yet limited information is available on their gut bacterial communities. Addressing this gap will help inform how these communities can be manipulated and ultimately used as novel tools to control pathogens. To assess how bacterial communities change during the life stages of lab-reared C. nubeculosus and C. sonorensis, endosymbiotic bacteria were identified using Illumina sequencing of 16S rRNA and taxonomically characterised. Analyses were conducted to determine how gut bacterial communities in adults are influenced by species identity and geographic distance among biting midge populations. Communities of the two lab-reared Culicoides species significantly changed after pupation and with maturation into 6-day-old adults. Pseudomonas, Burkholderiaceae and Leucobacter bacteria were part of a core community that was trans-stadially transmitted and found throughout their life cycle. Among field-collected biting midges, the bacterial communities were unique for almost each species. Cardinium, Rickettsia and Wolbachia were some of the most abundant bacteria in midges collected from wetlands. Only Pseudomonas was present in high relative abundance in all field-collected species. In this study, species identity, as well as geographic distance, influenced the gut bacterial communities and may partly explain known inter- and intra-species variability in vector competence. Additionally, stably associated bacterial species could be candidates for paratransgenic strategies to control vector-borne pathogens.

Impact of Gut Bacteria on the Infection and Transmission of Pathogenic Arboviruses by Biting Midges and Mosquitoes.

Tripartite interactions among insect vectors, midgut bacteria, and viruses may determine the ability of insects to transmit pathogenic arboviruses. Here, we investigated the impact of gut bacteria on the susceptibility of Culicoides nubeculosus and Culicoides sonorensis biting midges for Schmallenberg virus, and of Aedes aegypti mosquitoes for Zika and chikungunya viruses. Gut bacteria were manipulated by treating the adult insects with antibiotics. The gut bacterial communities were investigated using Illumina MiSeq sequencing of 16S rRNA, and susceptibility to arbovirus infection was tested by feeding insects with an infectious blood meal. Antibiotic treatment led to changes in gut bacteria for all insects. Interestingly, the gut bacterial composition of untreated Ae. aegypti and C. nubeculosus showed Asaia as the dominant genus, which was drastically reduced after antibiotic treatment. Furthermore, antibiotic treatment resulted in relatively more Delftia bacteria in both biting midge species, but not in mosquitoes. Antibiotic treatment and subsequent changes in gut bacterial communities were associated with a significant, 1.8-fold increased infection rate of C. nubeculosus with Schmallenberg virus, but not for C. sonorensis. We did not find any changes in infection rates for Ae. aegypti mosquitoes with Zika or chikungunya virus. We conclude that resident gut bacteria may dampen arbovirus transmission in biting midges, but not so in mosquitoes. Use of antimicrobial compounds at livestock farms might therefore have an unexpected contradictory effect on the health of animals, by increasing the transmission of viral pathogens by biting midges.

Integrating spatial and phylogenetic information in the fourth-corner analysis to test trait-environment relationships.

The fourth-corner analysis aims to quantify and test for relationships between species traits and site-specific environmental variables, mediated by site-specific species abundances. Since there is no common unit of observation, the significance of the relationships is tested using a double permutation procedure (site based and species based). This method implies that all species and sites are independent of each other. However, this fundamental hypothesis might be flawed because of phylogenetic relatedness between species and spatial autocorrelation in the environmental data. Here, using a simulation-based experiment, we demonstrate how the presence of spatial and phylogenetic autocorrelations can, in some circumstances, lead to inflated type I error rates, suggesting that significant associations can be misidentified. As an alternative, we propose a new randomization approach designed to avoid this issue, based on Moran's spectral randomization. In this approach, standard permutations are replaced by constrained randomizations so that the distribution of the statistic under the null hypothesis is built with additional constraints to preserve the phylogenetic and spatial structures of the observed data. The inclusion of this new randomization approach provides total control over type I error rates and should be used in real studies where spatial and phylogenetic autocorrelations often occur.

Analysing chemical-induced changes in macroinvertebrate communities in aquatic mesocosm experiments: a comparison of methods.

Mesocosm experiments that study the ecological impact of chemicals are often analysed using the multivariate method 'Principal Response Curves' (PRCs). Recently, the extension of generalised linear models (GLMs) to multivariate data was introduced as a tool to analyse community data in ecology. Moreover, data aggregation techniques that can be analysed with univariate statistics have been proposed. The aim of this study was to compare their performance. We compiled macroinvertebrate abundance datasets of mesocosm experiments designed for studying the effect of various organic chemicals, mainly pesticides, and re-analysed them. GLMs for multivariate data and selected aggregated endpoints were compared to PRCs regarding their performance and potential to identify affected taxa. In addition, we analysed the inter-replicate variability encountered in the studies. Mesocosm experiments characterised by a higher taxa richness of the community and/or lower taxonomic resolution showed a greater inter-replicate variability, whereas variability decreased the more zero counts were encountered in the samples. GLMs for multivariate data performed equally well as PRCs regarding the community response. However, compared to first axis PRCs, GLMs provided a better indication of individual taxa responding to treatments, as separate models are fitted to each taxon. Data aggregation methods performed considerably poorer compared to PRCs. Multivariate community data, which are generated during mesocosm experiments, should be analysed using multivariate methods to reveal treatment-related community-level responses. GLMs for multivariate data are an alternative to the widely used PRCs.

Combining the fourth-corner and the RLQ methods for assessing trait responses to environmental variation.

Assessing trait responses to environmental gradients requires the simultaneous analysis of the information contained in three tables: L (species distribution across samples), R (environmental characteristics of samples), and Q (species traits). Among the available methods, the so-called fourth-corner and RLQ methods are two appealing alternatives that provide a direct way to test and estimate trait-nvironment relationships. Both methods are based on the analysis of the fourth-corner matrix, which crosses traits and environmental variables weighted by species abundances. However, they differ greatly in their outputs: RLQ is a multivariate technique that provides ordination scores to summarize the joint structure among the three tables, whereas the fourth-corner method mainly tests for individual trait-environment relationships (i.e., one trait and one environmental variable at a time). Here, we illustrate how the complementarity between these two methods can be exploited to promote new ecological knowledge and to improve the study of trait-environment relationships. After a short description of each method, we apply them to real ecological data to present their different outputs and provide hints about the gain resulting from their combined use.

Genome-wide computational function prediction of Arabidopsis proteins by integration of multiple data sources.

Although Arabidopsis (Arabidopsis thaliana) is the best studied plant species, the biological role of one-third of its proteins is still unknown. We developed a probabilistic protein function prediction method that integrates information from sequences, protein-protein interactions, and gene expression. The method was applied to proteins from Arabidopsis. Evaluation of prediction performance showed that our method has improved performance compared with single source-based prediction approaches and two existing integration approaches. An innovative feature of our method is that it enables transfer of functional information between proteins that are not directly associated with each other. We provide novel function predictions for 5,807 proteins. Recent experimental studies confirmed several of the predictions. We highlight these in detail for proteins predicted to be involved in flowering and floral organ development.

Improved testing of species traits--environment relationships in the fourth-corner problem.

The fourth-corner problem entails estimation and statistical testing of the relationship between species traits and environmental variables from the analysis of three data tables. In a 2008 paper, S. Dray and P. Legendre proposed and evaluated five permutation methods for statistical significance testing, including a new two-step testing procedure. However, none of these attained the correct type I error in all cases of interest. We solve this problem by showing that a small modification of their two-step procedure controls the type I error in all cases. The modification consists of adjusting the significance level from mean square root of alpha to alpha or, equivalently, of reporting the maximum of the individual P. values as the final one. The test is also applicable to the three-table ordination method RLQ.

QTL linkage analysis of connected populations using ancestral marker and pedigree information.

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals' genotype probability without significantly increasing computational demand.

Correlated mutations via regularized multinomial regression.

BACKGROUND: In addition to sequence conservation, protein multiple sequence alignments contain evolutionary signal in the form of correlated variation among amino acid positions. This signal indicates positions in the sequence that influence each other, and can be applied for the prediction of intra- or intermolecular contacts. Although various approaches exist for the detection of such correlated mutations, in general these methods utilize only pairwise correlations. Hence, they tend to conflate direct and indirect dependencies. RESULTS: We propose RMRCM, a method for Regularized Multinomial Regression in order to obtain Correlated Mutations from protein multiple sequence alignments. Importantly, our method is not restricted to pairwise (column-column) comparisons only, but takes into account the network nature of relationships between protein residues in order to predict residue-residue contacts. The use of regularization ensures that the number of predicted links between columns in the multiple sequence alignment remains limited, preventing overprediction. Using simulated datasets we analyzed the performance of our approach in predicting residue-residue contacts, and studied how it is influenced by various types of noise. For various biological datasets, validation with protein structure data indicates a good performance of the proposed algorithm for the prediction of residue-residue contacts, in comparison to previous results. RMRCM can also be applied to predict interactions (in addition to only predicting interaction sites or contact sites), as demonstrated by predicting PDZ-peptide interactions. CONCLUSIONS: A novel method is presented, which uses regularized multinomial regression in order to obtain correlated mutations from protein multiple sequence alignments. AVAILABILITY: R-code of our implementation is available via http://www.ab.wur.nl/rmrcm.

Double constrained ordination for assessing biological trait responses to multiple stressors: A case study with benthic macroinvertebrate communities.

Benthic macroinvertebrate communities are used as indicators for anthropogenic stress in freshwater ecosystems. To better understand the relationship between anthropogenic stress and changes in macroinvertebrate community composition, it is important to understand how different stressors and species traits are associated, and how these associations influence variation in species occurrence and abundances. Here, we show the capacity of the multivariate technique of double constrained correspondence analysis (dc-CA) to analyse trait-environment relationships, and we compare it with the redundancy analysis method on community weighted mean values of traits (CWM-RDA), which is frequently used for this type of analysis. The analyses were based on available biomonitoring data for macroinvertebrate communities from the Danube River. Results from forward selection of traits and environmental variables using dc-CA analyses showed that aquatic stages, reproduction techniques, dispersal tactics, locomotion and substrate relations, altitude, longitudinal and transversal distribution, and substrate preferendum were significantly related to habitat characteristics, hydromorphological alterations and water quality measurements such as physico-chemical parameters, heavy metals, pesticides and pharmaceuticals. Environmental variables significantly associated with traits using the CWM-RDA method were generally consistent with those found in dc-CA analysis. However, the CWM-RDA does neither test nor explicitly select traits, while dc-CA tests and selects both traits and environmental variables. Moreover, the dc-CA analysis revealed that the set of environmental variables was much better in explaining the community data than the available trait set, a kind of information that can neither be obtained from CWM-RDA nor from RLQ (Environment, Link and Trait data), which is a close cousin of dc-CA but not regression-based. Our results suggest that trait-based analysis based on dc-CA may be useful to assess mechanistic links between multiple anthropogenic stressors and ecosystem health, but more data sets should be analysed in the same manner.

Log-ratio analysis of microbiome data with many zeroes is library size dependent.

Microbiome composition data collected through amplicon sequencing are count data on taxa in which the total count per sample (the library size) is an artefact of the sequencing platform, and as a result, such data are compositional. To avoid library size dependency, one common way of analysing multivariate compositional data is to perform a principal component analysis (PCA) on data transformed with the centred log-ratio, hereafter called a log-ratio PCA. Two aspects typical of amplicon sequencing data are the large differences in library size and the large number of zeroes. In this study, we show on real data and by simulation that, applied to data that combine these two aspects, log-ratio PCA is nevertheless heavily dependent on the library size. This leads to a reduction in power when testing against any explanatory variable in log-ratio redundancy analysis. If there is additionally a correlation between the library size and the explanatory variable, then the type 1 error becomes inflated. We explore putative solutions to this problem.

Mixed model approaches for the identification of QTLs within a maize hybrid breeding program.

Two outlines for mixed model based approaches to quantitative trait locus (QTL) mapping in existing maize hybrid selection programs are presented: a restricted maximum likelihood (REML) and a Bayesian Markov Chain Monte Carlo (MCMC) approach. The methods use the in-silico-mapping procedure developed by Parisseaux and Bernardo (2004) as a starting point. The original single-point approach is extended to a multi-point approach that facilitates interval mapping procedures. For computational and conceptual reasons, we partition the full set of relationships from founders to parents of hybrids into two types of relations by defining so-called intermediate founders. QTL effects are defined in terms of those intermediate founders. Marker based identity by descent relationships between intermediate founders define structuring matrices for the QTL effects that change along the genome. The dimension of the vector of QTL effects is reduced by the fact that there are fewer intermediate founders than parents. Furthermore, additional reduction in the number of QTL effects follows from the identification of founder groups by various algorithms. As a result, we obtain a powerful mixed model based statistical framework to identify QTLs in genetic backgrounds relevant to the elite germplasm of a commercial breeding program. The identification of such QTLs will provide the foundation for effective marker assisted and genome wide selection strategies. Analyses of an example data set show that QTLs are primarily identified in different heterotic groups and point to complementation of additive QTL effects as an important factor in hybrid performance.

An improved statistical approach for reconstructing past climates from biotic assemblages.

Quantitative reconstructions of past climates are an important resource for evaluating how well climate models reproduce climate changes. One widely used statistical approach for making such reconstructions from fossil biotic assemblages is weighted averaging partial least-squares regression (WA-PLS). There is however a known tendency for WA-PLS to yield reconstructions compressed towards the centre of the climate range used for calibration, potentially biasing the reconstructed past climates. We present an improvement of WA-PLS by assuming that: (i) the theoretical abundance of each taxon is unimodal with respect to the climate variable considered; (ii) observed taxon abundances follow a multinomial distribution in which the total abundance of a sample is climatically uninformative; and (iii) the estimate of the climate value at a given site and time makes the observation most probable, i.e. it maximizes the log-likelihood function. This climate estimate is approximated by weighting taxon abundances in WA-PLS by the inverse square of their climate tolerances. We further improve the approach by considering the frequency ( fx) of the climate variable in the training dataset. Tolerance-weighted WA-PLS with fx correction greatly reduces the compression bias, compared with WA-PLS, and improves model performance in reconstructions based on an extensive modern pollen dataset.

Investigating microbial associations from sequencing survey data with co-correspondence analysis.

Microbial communities, which drive major ecosystem functions, consist of a wide range of interacting species. Understanding how microbial communities are structured and the processes underlying this is crucial to interpreting ecosystem responses to global change but is challenging as microbial interactions cannot usually be directly observed. Multiple efforts are currently focused to combine next-generation sequencing (NGS) techniques with refined statistical analysis (e.g., network analysis, multivariate analysis) to characterize the structures of microbial communities. However, most of these approaches consider a single table of sequencing data measured for several samples. Technological advances now make it possible to collect NGS data on different taxonomic groups simultaneously for the same samples, allowing us to analyse a pair of tables. Here, an analytical framework based on co-correspondence analysis (CoCA) is proposed to study the distributions, assemblages and interactions between two microbial communities. We show the ability of this approach to highlight the relationships between two microbial communities, using two data sets exhibiting various types of interactions. CoCA identified strong association patterns between autotrophic and heterotrophic microbial eukaryote assemblages, on the one hand, and between microalgae and viruses, on the other. We demonstrate also how CoCA can be used, complementary to network analysis, to reorder co-occurrence networks and thus investigate the presence of patterns in ecological networks.

Benthic invertebrate and microbial biodiversity in sub-tropical urban rivers: Correlations with environmental variables and emerging chemicals.

Urban rivers often function as sinks for various contaminants potentially placing the benthic communities at risk of exposure. We performed a comprehensive biological survey of the benthic macroinvertebrate and bacterial community compositions in six rivers from the suburb to the central urban area of Guangzhou city (South China), and evaluated their correlations with emerging organic contaminants, heavy metals and nutrients. Overall, the benthic macroinvertebrate community shifted from molluscs to oligochaete from the suburban to the central urban rivers that receive treated and untreated sewage. An exception was the site in the Sha River where chironomids were most abundant. The differences in macroinvertebrate community assemblages were significantly associated with chromium, total phosphorus, galaxolide, triclosan and sand content in the sediment. There was no significant difference in benthic macroinvertebrate composition between the dry and wet season. As assessed by double constrained ordination, sexual reproduction was the only trait of benthic macroinvertebrates that showed a significant correlation with pollution variables, as it was significantly positively correlated with chromium and total phosphorus. This suggests that r-strategist occurs in polluted sampling sites. The benthic bacterial community composition showed a significant difference between seasons and among the Liuxi River, Zhujiang River and central urban rivers. The differences in community composition of the benthic bacteria were significantly correlated with galaxolide, total phosphorus, lead and triclosan. These results suggest that input of treated and untreated sewage significantly altered the benthic macroinvertebrate and bacterial community compositions in urban rivers.

Principal response curves technique for the analysis of multivariate biomonitoring time series.

Although chemical and biological monitoring is often used to evaluate the quality of surface waters for regulatory purposes and/or to evaluate environmental status and trends, the resulting biological and chemical data sets are large and difficult to evaluate. Multivariate techniques have long been used to analyse complex data sets. This paper discusses the methods currently in use and introduces the principal response curves method, which overcomes the problem of cluttered graphical results representation that is a great drawback of most conventional methods. To illustrate this, two example data sets are analysed using two ordination techniques, principal component analysis and principal response curves. Whereas PCA results in a difficult-to-interpret diagram, principal response curves related methods are able to show changes in community composition in a diagram that is easy to read. The principal response curves method is used to show trends over time with an internal reference (overall mean or reference year) or external reference (e.g. preferred water quality or reference site). Advantages and disadvantages of both methods are discussed and illustrated.

Differently Pre-treated Alfalfa Silages Affect the in vitro Ruminal Microbiota Composition.

Alfalfa (Medicago sativa L.) silage (AS) is an important feedstuff in ruminant nutrition. However, its high non-protein nitrogen content often leads to poor ruminal nitrogen retention. Various pre-ensiling treatments differing with respect to dry matter concentrations, wilting intensities and sucrose addition have been previously shown to improve the quality and true protein preservation of AS, and have substantial effects on in vitro ruminal fermentation of the resulting silages. However, it is unknown how these pre-ensiling treatments affect the ruminal microbiota composition, and whether alterations in the microbiota explain previously observed differences in ruminal fermentation. Therefore, during AS incubation in a rumen simulation system, liquid and solid phases were sampled 2 and 7 days after first incubating AS, representing an early (ET) and late (LT) time point, respectively. Subsequently, DNA was extracted and qPCR (bacteria, archaea, and anaerobic fungi) and prokaryotic 16S rRNA gene amplicon sequence analyses were performed. At the ET, high dry matter concentration and sucrose addition increased concentrations of archaea in the liquid phase (P = 0.001) and anaerobic fungi in the solid phase (P < 0.001). At the LT, only sucrose addition increased archaeal concentration in the liquid phase (P = 0.014) and anaerobic fungal concentration in the solid phase (P < 0.001). Bacterial concentrations were not affected by pre-ensiling treatments. The prokaryotic phylogenetic diversity index decreased in the liquid phase from ET to LT (P = 0.034), whereas the solid phase was not affected (P = 0.060). This is suggestive of a general adaption of the microbiota to the soluble metabolites released from the incubated AS, particularly regarding the sucrose-treated AS. Redundancy analysis of the sequence data at the genus level indicated that sucrose addition (P = 0.001), time point (P = 0.001), and their interaction (P = 0.001) affected microbial community composition in both phases. In summary, of the pre-ensiling treatments tested sucrose addition had the largest effect on the microbiota, and together with sampling time point affected microbiota composition in both phases of the rumen simulation system. Thus, microbiota composition analysis helped to understand the ruminal fermentation patterns, but could not fully explain them.

Biomarker Research in ADHD: the Impact of Nutrition (BRAIN) - study protocol of an open-label trial to investigate the mechanisms underlying the effects of a few-foods diet on ADHD symptoms in children.

INTRODUCTION: Attention deficit hyperactivity disorder (ADHD) is the most common childhood behavioural disorder, causing significant impediment to a child's development. It is a complex disorder with numerous contributing (epi)genetic and environmental factors. Currently, treatment consists of behavioural and pharmacological therapy. However, ADHD medication is associated with several side effects, and concerns about long-term effects and efficacy exist. Therefore, there is considerable interest in the development of alternative treatment options. Double-blind research investigating the effects of a few-foods diet (FFD) has demonstrated a significant decrease in ADHD symptoms following an FFD. However, an FFD requires a considerable effort of both child and parents, limiting its applicability as a general ADHD treatment. To make FFD intervention less challenging or potentially obsolete, we need to understand how, and in which children, an FFD affects ADHD behaviour and, consequently, the child's well-being. We hypothesise that an FFD affects brain function, and that the nutritional impact on ADHD is effectuated by a complex interplay between the microbiota, gut and brain, that is, the microbiota-gut-brain axis. METHODS AND ANALYSIS: The Biomarker Research in ADHD: the Impact of Nutrition (BRAIN) study is an open-label trial with researchers blinded to changes in ADHD symptoms during sample processing and initial data analyses. ETHICS AND DISSEMINATION: The Medical Research and Ethics Committee of Wageningen University has approved this study (NL63851.081.17, application 17/24). Results will be disseminated through peer-reviewed journal publications, conference presentations, (social) media and the BRAIN study website. A summary of the findings will be provided to the participants. TRIAL REGISTRATION NUMBER: NCT03440346. STUDY DATES: Collection of primary outcome data started in March 2018 and will be ongoing until 100 children have participated in the study. Sample data analysis will start after all samples have been collected.

Arthropod assemblages are best predicted by plant species composition.

Insects and spiders comprise more than two-thirds of the Earth's total species diversity. There is wide concern, however, that the global diversity of arthropods may be declining even more rapidly than the diversity of vertebrates and plants. For adequate conservation planning, ecologists need to understand the driving factors for arthropod communities and devise methods, that provide reliable predictions when resources do not permit exhaustive ground surveys. Which factor most successfully predicts arthropod community structure is still a matter of debate, however. The purpose of this study was to identify the factor best predicting arthropod assemblage composition. We investigated the species composition of seven functionally different arthropod groups (epigeic spiders, grasshoppers, ground beetles, weevils, hoppers, hoverflies, and bees) at 47 sites in The Netherlands comprising a range of seminatural grassland types and one heathland type. We then compared the actual arthropod composition with predictions based on plant species composition, vegetation structure, environmental data, flower richness, and landscape composition. For this we used the recently published method of predictive co-correspondence analysis, and a predictive variant of canonical correspondence analysis, depending on the type of predictor data. Our results demonstrate that local plant species composition is the most effective predictor of arthropod assemblage composition, for all investigated groups. In predicting arthropod assemblages, plant community composition consistently outperforms both vegetation structure and environmental conditions (even when the two are combined), and also performs better than the surrounding landscape. These results run against a common expectation of vegetation structure as the decisive factor. Such expectations, however, have always been biased by the fact that until recently no methods existed that could use an entire (plant) species composition in the explanatory role. Although more recent experimental diversity work has reawakened interest in the role of plant species, these studies still have not used (or have not been able to use) entire species compositions. They only consider diversity measures, both for plant and insect assemblages, which may obscure relationships. The present study demonstrates that the species compositions of insect and plant communities are clearly linked.

Attractiveness of MM-X traps baited with human or synthetic odor to mosquitoes (Diptera: Culicidae) in The Gambia.

Chemical cues play an important role in the host-seeking behavior of blood-feeding mosquitoes (Diptera: Culicidae). A field study was carried out in The Gambia to investigate the effects of human odor or synthetic odor blends on the attraction of mosquitoes. MM-X traps baited with 16 odor blends to which carbon dioxide (CO2) was added were tested in four sets of experiments. In a second series of experiments, MM-X traps with 14 odor blends without CO2 were tested. A blend of ammonia and L-lactic acid with or without CO2 was used as control odor in series 1 and 2, respectively. Centers for Disease Control and Prevention (CDC) traps were placed in a traditional house and an experimental house to monitor mosquito densities during the experiments. The MM-X traps caught a total number of 196,756 mosquitoes, with the most abundant species belonging to the genera Mansonia (70.6%), Anopheles (17.5%), and Culex (11.5%). The most abundant mosquito species caught by the CDC traps (56,290 in total) belonged to the genera Mansonia (59.4%), Anopheles (16.0% An. gambiae s.l. Giles, and 11.3% An. ziemanni Grünberg), and Culex (11.6%). MM-X traps baited with synthetic blends were in many cases more attractive than MM-X traps baited with human odors. Addition of CO2 to synthetic odors substantially increased the catch of all mosquito species in the MM-X traps. A blend of ammonia + L-lactic acid + CO, + 3-methylbutanoic acid was the most attractive odor for most mosquito species. The candidate odor blend shows the potential to enhance trap collections so that traps will provide better surveillance and possible control.