Trypanosoma cruzi infection reduces the population fitness of Mepraia spinolai, a Chagas disease vector.

The hematophagous insect Mepraia spinolai (Hemiptera: Reduviidae: Triatominae) is naturally infected with the protozoan parasite Trypanosoma cruzi, the agent of Chagas disease in humans. In this study, we compared the demographic parameters of M. spinolai with and without T. cruzi infection. We collected the immature life table data of 479 M. spinolai individuals of control cohort (reared on mice without T. cruzi infection) and 563 M. spinolai individuals of treatment cohort (reared on mice with T. cruzi infection). Nymphs were maintained in individual compartments inside a growth chamber (26°C; 65-75%) until adult emergence; moulting and survival were recorded daily. For the adult life table study of the control, we used 24 pairs of adults from the control cohort. For the adult life table study of T. cruzi-infected cohort, 25 infected females were paired with 25 males from the control cohort. Life table data were analysed using bootstrap-match technique based on the age-stage, two-sex life table. The preadult survival rate (0.5282) of the control cohort was significantly higher than that of the infected cohort (0.2913). However, the mean fecundity of reproductive females (Fr = 22.29 eggs/♀) and net reproductive rate of population (R0 = 5.07 offspring/individual) of the 0.5th percentile bootstrap-match control cohort were not significantly different from those of the infected cohort (Fr = 23.35 eggs/♀, R0 = 3.77 offspring/individual). Due to the shorter total preoviposition period and higher proportion of reproductive female, the intrinsic rate of increase (r = 0.0053 d-1 ) and finite rate of increase (λ = 1.0053 d-1 ) of control cohort of M. spinolai were significantly higher than those of the T. cruzi-infected cohort (r = 0.0035 d-1 , λ = 1.0035 d-1 ). These results suggest that T. cruzi infection reduces the population fitness of the Chagas disease vector M. spinolai.

Dos medidas de asimetría basadas en la moda: cálculo y normas interpretativas / Two measures of skewness based on mode: calculation and interpretative rules

Resumen Este estudio metodológico de simulación presenta de forma ejemplificada dos medidas de asimetría. Aunque pueden ser útiles cuando la distribución es unimodal, no se reportan en la investigación psicológica. Una es la distancia estandarizada de la media a la moda de Pearson. La otra es la medida robusta de asimetría de Bickel. Se muestra cómo calcular la estimación puntual y de intervalo con el programa R. Además, se calculan intervalos de confianza al 90 %, 95 % y 99 % con 10 000 extracciones con reemplazamiento de muestras-población con distribución normal y diferentes tamaños para disponer de directrices interpretativas de simetría. Se concluye que la regla ∓0.1 no aplica, la moda de Grenander proporciona los intervalos de confianza más eficientes, pero la asimetría de Bickel es la opción con variables ordinales.

Abstract This methodological study of simulation presents in exemplified form two measures of skewness. Although they may be useful when the distribution is unimodal, they are not reported in psychological research. One is Pearson's standardized distance from the mean to the mode. The other is the Bickel's robust measure of skewness. It is shown how to compute the point and interval estimate with the R program. Moreover, interval confidences at 90%, 95% and 99% are calculated with 10 000 draws with replacement from normally distributed samples-population with different sizes to have interpretative guidelines for symmetry. It is concluded that the ∓0.1 rule does not apply with these measures, Grenander's mode provides the most efficient confidence intervals, but Bickel's skewness is the option with ordinal variables.

Likelihood-Based Tests of Species Tree Hypotheses.

Likelihood-based tests of phylogenetic trees are a foundation of modern systematics. Over the past decade, an enormous wealth and diversity of model-based approaches have been developed for phylogenetic inference of both gene trees and species trees. However, while many techniques exist for conducting formal likelihood-based tests of gene trees, such frameworks are comparatively underdeveloped and underutilized for testing species tree hypotheses. To date, widely used tests of tree topology are designed to assess the fit of classical models of molecular sequence data and individual gene trees and thus are not readily applicable to the problem of species tree inference. To address this issue, we derive several analogous likelihood-based approaches for testing topologies using modern species tree models and heuristic algorithms that use gene tree topologies as input for maximum likelihood estimation under the multispecies coalescent. For the purpose of comparing support for species trees, these tests leverage the statistical procedures of their original gene tree-based counterparts that have an extended history for testing phylogenetic hypotheses at a single locus. We discuss and demonstrate a number of applications, limitations, and important considerations of these tests using simulated and empirical phylogenomic data sets that include both bifurcating topologies and reticulate network models of species relationships. Finally, we introduce the open-source R package SpeciesTopoTestR (SpeciesTopology Tests in R) that includes a suite of functions for conducting formal likelihood-based tests of species topologies given a set of input gene tree topologies.

A comprehensive assessment of energy efficiency of wastewater treatment plants: An efficiency analysis tree approach.

Wastewater treatment plants (WWTPs) are energy intensive facilities. Controlling energy use in WWTPs could bring substantial benefits to people and environment. Understanding how energy efficient the wastewater treatment process is and what drives efficiency would allow treating wastewater in a more sustainable way. In this study, we employed the efficiency analysis trees approach, that combines machine learning and linear programming techniques, to estimate energy efficiency of wastewater treatment process. The findings indicated that considerable energy inefficiency among WWTPs in Chile existed. The mean energy efficiency was 0.287 suggesting that energy use should cut reduce by 71.3 % to treat the same volume of wastewater. This was equivalent to a reduction in energy use by 0.40 kWh/m3 on average. Moreover, only 4 out of 203 assessed WWTPs (1.97 %) were identified as energy efficient. It was also found that the age of treatment plant and type of secondary technology played an important role in explaining energy efficiency variations among WWTPs.

A general modeling framework for quantitative tracking, accurate prediction of ICU, and assessing vaccination for COVID-19 in Chile.

Background: One of the main lessons of the COVID-19 pandemic is that we must prepare to face another pandemic like it. Consequently, this article aims to develop a general framework consisting of epidemiological modeling and a practical identifiability approach to assess combined vaccination and non-pharmaceutical intervention (NPI) strategies for the dynamics of any transmissible disease. Materials and methods: Epidemiological modeling of the present work relies on delay differential equations describing time variation and transitions between suitable compartments. The practical identifiability approach relies on parameter optimization, a parametric bootstrap technique, and data processing. We implemented a careful parameter optimization algorithm by searching for suitable initialization according to each processed dataset. In addition, we implemented a parametric bootstrap technique to accurately predict the ICU curve trend in the medium term and assess vaccination. Results: We show the framework's calibration capabilities for several processed COVID-19 datasets of different regions of Chile. We found a unique range of parameters that works well for every dataset and provides overall numerical stability and convergence for parameter optimization. Consequently, the framework produces outstanding results concerning quantitative tracking of COVID-19 dynamics. In addition, it allows us to accurately predict the ICU curve trend in the medium term and assess vaccination. Finally, it is reproducible since we provide open-source codes that consider parameter initialization standardized for every dataset. Conclusion: This work attempts to implement a holistic and general modeling framework for quantitative tracking of the dynamics of any transmissible disease, focusing on accurately predicting the ICU curve trend in the medium term and assessing vaccination. The scientific community could adapt it to evaluate the impact of combined vaccination and NPIs strategies for COVID-19 or any transmissible disease in any country and help visualize the potential effects of implemented plans by policymakers. In future work, we want to improve the computational cost of the parametric bootstrap technique or use another more efficient technique. The aim would be to reconstruct epidemiological curves to predict the combined NPIs and vaccination policies' impact on the ICU curve trend in real-time, providing scientific evidence to help anticipate policymakers' decisions.

Factors influencing eco-efficiency of municipal solid waste management in Chile: A double-bootstrap approach.

Improving eco-efficiency in the provision of municipal solid waste plays an important role for a sustainable economy. Eco-efficiency of municipal solid waste service providers (MSWSPs) has been generally assessed using the conventional data envelopment analysis (DEA) method. However, this approach is sensitive to data noise and has no statistical properties. To overcome these limitations, in this paper, we adopt the double-bootstrap DEA model to derive robust eco-efficiency scores. This nonparametric method allows conducting statistical inference to explore environmental factors affecting the eco-efficiency of MSWSPs. The empirical approach focused on a sample of 298 MSWSPs in Chile, a middle-income country whose policies for promoting waste recycling are incipient. The results indicated that based on the bias-corrected eco-efficiency scores, the potential saving in costs and unsorted waste could be up to 37.8% on average to generate the same level of output (recycled waste). The findings showed that dealing with data noise and uncertainly is of great importance when conducting benchmarking analysis. The region where the municipality is located, tourism, population density and waste per capita are environmental variables that significantly influenced eco-efficiency of Chilean MSWSPs. Several policy implications are discussed based on the findings of this study.

Dynamics diagnosis of the COVID-19 deaths using the Pearson diagram.

The pandemic COVID-19 brings with it the need for studies and tools to help those in charge make decisions. Working with classical time series methods such as ARIMA and SARIMA has shown promising results in the first studies of COVID-19. We advance in this branch by proposing a risk factor map induced by the well-known Pearson diagram based on multivariate kurtosis and skewness measures to analyze the dynamics of deaths from COVID-19. In particular, we combine bootstrap for time series with SARIMA modeling in a new paradigm to construct a map on which one can analyze the dynamics of a set of time series. The proposed map allows a risk analysis of multiple countries in the four different periods of the pandemic COVID-19 in 55 countries. Our empirical evidence suggests a direct relationship between the multivariate skewness and kurtosis. We observe that the multivariate kurtosis increase leads to the rise of the multivariate skewness. Our findings reveal that the countries with high risk from the behavior of the number of deaths tend to have pronounced skewness and kurtosis values.

Time-varying coefficient model estimation through radial basis functions.

In this paper, we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.

Estimate of the rate of unreported COVID-19 cases during the first outbreak in Rio de Janeiro.

In this work we fit an epidemiological model SEIAQR (Susceptible - Exposed - Infectious - Asymptomatic - Quarantined - Removed) to the data of the first COVID-19 outbreak in Rio de Janeiro, Brazil. Particular emphasis is given to the unreported rate, that is, the proportion of infected individuals that is not detected by the health system. The evaluation of the parameters of the model is based on a combination of error-weighted least squares method and appropriate B-splines. The structural and practical identifiability is analyzed to support the feasibility and robustness of the parameters' estimation. We use the Bootstrap method to quantify the uncertainty of the estimates. For the outbreak of March-July 2020 in Rio de Janeiro, we estimate about 90% of unreported cases, with a 95% confidence interval (85%, 93%).

An equivalence test between features lists, based on the Sorensen-Dice index and the joint frequencies of GO term enrichment.

BACKGROUND: In integrative bioinformatic analyses, it is of great interest to stablish the equivalence between gene or (more in general) feature lists, up to a given level and in terms of their annotations in the Gene Ontology. The aim of this article is to present an equivalence test based on the proportion of GO terms which are declared as enriched in both lists simultaneously. RESULTS: On the basis of these data, the dissimilarity between gene lists is measured by means of the Sorensen-Dice index. We present two flavours of the same test: One of them based on the asymptotic normality of the test statistic and the other based on the bootstrap method. CONCLUSIONS: The accuracy of these tests is studied by means of simulation and their possible interest is illustrated by using them over two real datasets: A collection of gene lists related to cancer and a collection of gene lists related to kidney rejection after transplantation.

The efficiency of municipal solid waste collection in Mexico.

Since the 1980's, some Mexican municipalities have subcontracted waste collection services to private companies, with an eye on increasing the efficiency of this service. However, the impact of private management on the Mexican waste sector performance has not been evaluated. In this study, the efficiency of waste collection in Mexico was analyzed to test the hypothesis that private companies are more efficient at waste collection than municipal governments. A two stage double bootstrap Data Envelopment Analysis (DEA) was applied to a sample of 1,626 municipalities. In the first stage, unbiased efficiency scores were calculated, and in the second stage, these scores were regressed against a set of environmental covariates which were thought to affect efficiency, including a dummy variable to identify the municipalities where waste is collected by a private company. Results suggest that private waste collection companies are more efficient than municipal governments. Other environmental variables such as population density, average household income, and tourism were also found to affect waste collection efficiency. The analysis also indicates that curbside collection is associated with a higher efficiency, while separate collection of waste is negatively correlated to efficiency.

Population level comparisons in central Mexico using cranial nonmetric traits.

OBJECTIVES: We study the genetic diversity between Classic Teotihuacan and its neighboring towns trying to understand how far or close they are at the genetic level. MATERIALS AND METHODS: We use cranial nonmetric traits to study a sample of 280 adult skulls from archaeological sites running from the late Preclassic to the early Postclassic. Samples of Classic Teotihuacan were studied for La Ventilla and San Sebastián Xolalpan neighbors. For the Epiclassic period, samples from Xaltocan, Toluca valley, Mogotes and Xico were used. For the Preclassic and Postclassic samples from Xico were also used. We used a parametric bootstrap for the mean measure of divergence for the statistical analysis. RESULTS: Samples from Xico have small biodistance from Preclassic to Postclassic. Samples from Los Mogotes differ depending on the functional context of deposition, with individuals from household burials (funerary) differing from non-funerary, ceremonial interments and exhibiting affinities to Epiclassic samples from Toluca valley. Epiclassic populations from Xaltocan vary significantly from any samples analyzed. Samples from Classic period Teotihuacan vary considerably among them but form a separate genetic group from all the other populations under study. CONCLUSIONS: The great biodistance separation among Classic Teotihuacan and its neighbor villages of central Mexico let us conclude that, contrary from the classical idea that those villages were confirmed by the inhabitants of Teotihuacan's collapse: They indeed remain as separate populations by themselves.

Covariance-based sample selection for heterogeneous data: Applications to gene expression and autism risk gene detection.

Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, "guilt by association" methods such as DAWN have been developed to identify these autism risk genes. Previous research analyze the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue is known to affect the gene expression's covariance, previous research have focused only on a specific subset of samples to avoid the issue of heterogeneity. This analysis leads to a potential loss of power when detecting risk genes. In this article, we develop a new method called COBS (COvariance-Based sample Selection) to find a larger and more homogeneous subset of samples that share the same population covariance matrix for the downstream DAWN analysis. To demonstrate COBS's effectiveness, we use genetic risk scores from two sequential data freezes obtained in 2014 and 2020. We show COBS improves DAWN's ability to predict risk genes detected in the newer data freeze when using the risk scores of the older data freeze as input.

A non-parametric statistic for testing conditional heteroscedasticity for unobserved component models.

When prediction intervals are constructed using unobserved component models (UCM), problems can arise due to the possible existence of components that may or may not be conditionally heteroscedastic. Accurate coverage depends on correctly identifying the source of the heteroscedasticity. Different proposals for testing heteroscedasticity have been applied to UCM; however, in most cases, these procedures are unable to identify the heteroscedastic component correctly. The main issue is that test statistics are affected by the presence of serial correlation, causing the distribution of the statistic under conditional homoscedasticity to remain unknown. We propose a nonparametric statistic for testing heteroscedasticity based on the well-known Wilcoxon's rank statistic. We study the asymptotic validation of the statistic and examine bootstrap procedures for approximating its finite sample distribution. Simulation results show an improvement in the size of the homoscedasticity tests and a power that is clearly comparable with the best alternative in the literature. We also apply the test on real inflation data. Looking for the presence of a conditionally heteroscedastic effect on the error terms, we arrive at conclusions that almost all cases are different than those given by the alternative test statistics presented in the literature.

Twelve-Year Analysis of NO2 Concentration Measurements at Belisario Station (Quito, Ecuador) Using Statistical Inference Techniques.

In this paper, a robust analysis of nitrogen dioxide (NO2) concentration measurements taken at Belisario station (Quito, Ecuador) was performed. The data used for the analysis constitute a set of measurements taken from 1 January 2008 to 31 December 2019. Furthermore, the analysis was carried out in a robust way, defining variables that represent years, months, days and hours, and classifying these variables based on estimates of the central tendency and dispersion of the data. The estimators used here were classic, nonparametric, based on a bootstrap method, and robust. Additionally, confidence intervals based on these estimators were built, and these intervals were used to categorize the variables under study. The results of this research showed that the NO2 concentration at Belisario station is not harmful to humans. Moreover, it was shown that this concentration tends to be stable across the years, changes slightly during the days of the week, and varies greatly when analyzed by months and hours of the day. Here, the precision provided by both nonparametric and robust statistical methods served to comprehensively proof the aforementioned. Finally, it can be concluded that the city of Quito is progressing on the right path in terms of improving air quality, because it has been shown that there is a decreasing tendency in the NO2 concentration across the years. In addition, according to the Quito Air Quality Index, most of the observations are in either the desirable level or acceptable level of air pollution, and the number of observations that are in the desirable level of air pollution increases across the years.

Revisiting the Relationship Between Suicide and Unemployment in Mexico: Evidence From Linear and Non-linear Co-integration.

This study attempts to investigate if suicide is interlinked with unemployment in Mexico by making use of a recently developed Bootstrap ARDL bound test over the years of 1981-2016. To avoid omitting variable bias, we use economic growth rate as a control variable. The empirical results indicate that no co-integration among these three variables and there is a positively bidirectional causality between suicide rate and the unemployment rate. This study will showcase that the economic growth rate negatively affects unemployment rate and unidirectional Granger causality running from economic growth rate to the unemployment rate in Mexico. The findings presented in this study could provide with valuable information for society and health policy makers to formulate the policies on suicide prevention in Mexico.

A Parametric Bootstrap for the Mean Measure of Divergence.

For more than 50 years the Mean Measure of Divergence (MMD) has been one of the most prominent tools used in anthropology for the study of non-metric traits. However, one of the problems, in anthropology including palaeoanthropology (more often there), is the lack of big enough samples or the existence of samples without sufficiently measured traits. Since 1969, with the advent of bootstrapping techniques, this issue has been tackled successfully in many different ways. Here, we present a parametric bootstrap technique based on the fact that the transformed θ, obtained from the Anscombe transformation to stabilize the variance, nearly follows a normal distribution with standard deviation $\sigma = 1 / \sqrt{N + 1/2}$σ=1/N+1/2, where N is the size of the measured trait. When the probabilistic distribution is known, parametric procedures offer more powerful results than non-parametric ones. We profit from knowing the probabilistic distribution of θ to develop a parametric bootstrapping method. We explain it carefully with mathematical support. We give examples, both with artificial data and with real ones. Our results show that this parametric bootstrap procedure is a powerful tool to study samples with scarcity of data.

Impact of environmental variables on the efficiency of water companies in England and Wales: a double-bootstrap approach.

An important aspect of the regulatory process is the performance comparison of regulated firms. This exists in regulated industries where tariffs are determined through a benchmarking process such as the English and Welsh water industry. A double-bootstrap data envelopment analysis (DEA) approach was applied to overcome the uncertainty in efficiency scores and to reveal the influence of environmental variables on 18 water companies in England and Wales during the 2001-2016 period. The results showed that bias and bias-corrected efficiency scores lead to changes in the water companies' rankings. This reveals the importance of using reliable methodologies to support the decision-making process. Higher levels of average pumping head, leakage, and abstraction of water from reservoirs lead to lower efficiency. In contrast, increased population density leads to larger efficiency. We also link the results from the efficiency of water companies with the regulatory cycle. Our findings can be useful to policy makers for them to better understand water utilities' performance and to aid them in reshaping their current policies and practices to improve efficiency and provide better service to customers.

Determination of physicochemical properties of petroleum derivatives and biodiesel using GC/MS and chemometric methods with uncertainty estimation.

The physicochemical properties of a substance, such as a fuel, can vary significantly with composition. Determining these properties with ASTM standard methods is both expensive and time-consuming, which has led to a desire to use chemometric modeling as an alternative. In this study, we compare the accuracy and robustness of two chemometric models, partial least squares (PLS) regression and support vector machine (SVM) with uncertainty estimation to determine how the physicochemical properties depend on the composition. A set of hydrocarbon mixtures, including crude oil, oil, gasoline, and biofuel/biodiesel, were collected. GC-MS data were taken, and physicochemical properties were measured for these mixtures using ASTM standard methods. PLS and SVM were used to develop predictive models of the physicochemical properties. Uncertainty in the estimated property values was estimated using a bootstrapping technique. With this uncertainty estimate, it is possible to assess the trustworthiness of any prediction, which ensures that the chemometric models can be applied for general purposes. SVM was found to be generally better for predicting the physicochemical properties, although we expect that with a more comprehensive data set the performance of the PLS models can be improved. We show in this work that PLS and SVM can be used to generate a predictive model of physicochemical properties based on GC-MS data. Combined with uncertainty analysis, these models provide robust predictions that can be used for regulatory, economic, and safety purposes.

Nuevos marcadores mitocondriales mejoran la filogenia de la tortuga carey Eretmochelys imbricata (Testudines: Cheloniidae) / New mitochondrial markers improve the phylogeny of the hawksbill turtle Eretmochelys imbricata (Testudines: Cheloniidae)

Las tortugas marinas (Cheloniidae) son un grupo de siete especies originadas en el cretaceo. Analisis de secuencias parciales de DNA mitocondrial han revelado inconsistencias filogeneticas dentro de este grupo de quelonios. Sin embargo, estos marcadores mitocondriales han permitido entender y dilucidar la composicion de las poblaciones en areas de forrajeo, habitos reproductivos, inferencias de patrones de migracion y tambien definir las unidades de manejo en el mundo, con el fin de proponer planes de manejo y conservacion. El objetivo de este estudio fue evaluar la posicion de la tortuga carey E. imbricata dentro de la familia Cheloniidae y la filogenia de las tortugas marinas utilizando genes mitocondriales codificantes de proteinas, genes ribosomicos y el genoma mitocondrial completo de la tortuga carey anidante del Caribe colombiano, al compararlo con las otras seis especies de tortugas marinas disponibles en GenBank. Se utilizaron cuatro metodos de inferencias filogeneticas: Neighbor-Joining (NJ), Maxima Verosimilitud (ML), Maxima Parsimonia (MP) e Inferencia Bayesiana (IB). Los arboles NJ, ML, MP e IB mostraron que ND2, COX1, 16S ARNr, ND5, 12S ARNr, ND4, COX3 y ND1 son los marcadores que presentan una mejor resolucion filogenetica con sustentos bootstrap entre 89,0% y 99,98%. Los genes ATP6, ATP8, COX2, ND3, ND4L y ND5 presentaron politomias y establecieron relaciones filogeneticas equivocadas. El analisis con el mitogenoma completo presento arboles altamente sustentados (bootstrap de 98,0%) en comparacion con el analisis con marcadores individuales. Los arboles obtenidos con el gen ND2 e IB resolvieron con buen sustento las relaciones evolutivas entre las especies comparadas, consolidandose la posicion de E. imbricata dentro de la tribu Carettini con probabilidad posterior de 0,98-1,0. Los marcadores ND2, ND5, ND4, COX3 y ND1 no han sido utilizados en trabajos previos y representan una nueva alternativa para explicar la filogenia en este grupo de reptiles marinos. En el presente caso utilizando mitogenomas completos se obtuvieron arboles robustos y altamente sustentados.

The sea turtles (Cheloniidae) are a group of seven species of cretaceous origin. Analyses of partial mitochondrial sequences have revealed phylogenetic inconsistences within this group. Nevertheless, these mitochondrial markers have allowed us to understand, explain and clarify population composition in areas of foraging, reproductive habits, inferences of migration patterns and, also, to define management units in the world, in order to trace conservation and monitoring plans. In this study, four methods were evaluated and compared for phylogenetic inference (Neighbor-Joining-NJ, Maximum Likelihood-ML, Maximum Parsimony-MP and Bayesian inference-BI) by using coding genes, ribosomal genes and full mitogenomes of the hawksbill, E. imbricata, and other six species of sea turtles obtained from GenBank. The sequences were analyzed independently and jointly to identify the method and marker that better explain the phylogenetic relationships among this group of reptiles. The NJ, ML, MP and BI trees showed that ND2, COX1, 16S rRNA, ND5, 12S rRNA, ND4 and COX3 are the markers that give phylogenetic trees with better resolution and support, with bootstrap values ranging from 89.0% to 99.98%. ATP6, ATP8, COX2, ND1, ND3, ND5 and ND4L genes presented polytomies. The analysis with full mitogenome often provides highly supported trees (bootstrap 98.0%) compared with single marker analysis. Trees obtained with the BI method and the ND2 gene is the one that better resolved the evolutionary relationships among the species, consolidating the position of E. imbricata within the Carettini tribe with a value of posterior probability of 0.98-1.0. The markers ND2, ND4, ND5 and COIII, not used in previous works, represent a new alternative to explain the phylogeny in this group of marine reptiles. In the present study, a complete mitogenome analysis produced robust and highly supported trees.