Estimating Relative Error in Nematode Numbers from Single Soil Samples Composed of Multiple Cores

Spatial distributions of several species of plant-parasitic nematodes were determined in each of three fallow vegetable fields and in smaller subunits of those fields. Goodness of fit to each of several theoretical distributions was tested hy means of a X² test. Distributions for most species showed good agreement with a negative binomial model. An exception occurred with Crictmemella sp., which showed a better fit to the Neyman Type A distribution. For nematodes distributed according to the negative binomial model, the number of cores per composite sample needed to achieve specified relative errors was calculated. For a given nematode species, such as Quinisulcius actus (Allen) Siddiqi or Meloidogyne incognita (Kofoid &White) Chitwood, the k values for the negative binomial distribution increased as field size decreased, with the result that fewer cores were needed to achieve the same level of precision in a smaller field. Best results were achieved when the single sample was used to estimate populations in fields of 0.25-0.45 ha in size. When using only a single composite sample to estimate mixed populations of the nematodes studied here in a field of that size, approximately 22 cores per composite sample would be needed to estimate all population means within a standard error to mean ratio of 25%. Considerably, more cores were needed to maintain a given level of precision in fields of 1.0 ha or greater, and it may be necessary to subdivide larger unils (ca. 1.5 ha and up) for accurate sampling.     

Equal-frequency Tolerance Ellipses for Population Studies of Belonolaimus longicaudatus

A biometrical method, using x-y plots of measurements of normaUy-distributed bivariate characters to construct a 95% equal-frequency ellipse representing 95% o f the specimens within its boundary, is presented. Comparisons of ellipses of four populations of Belonolaimus longicaudatus Rau show mean stylet lengths are relatively stable compared to mean tail lengths and there is greater styler length variability in short stylet forms. The extent of variability and regression between the populations can be seen by superimposing the bivariate means and orienting the longitudinal axes o f the ellipses. To compare ellipses the 95% binomial distribution is used to determine whether a sample population is significantly different from the model. The method is useful for graphic representation of morphological relationships within a nematode population, its relationship to other populations or species and to estimate environmental, ecological and genetic effects upon population morphology.     

Accounting for variability in sample size estimation with applications to nonadherence and estimation of variance and effect size

We consider sample size calculations for testing differences in means between two samples and allowing for different variances in the two groups. Typically, the power functions depend on the sample size and a set of parameters assumed known, and the sample size needed to obtain a prespecified power is calculated. Here, we account for two sources of variability: we allow the sample size in the power function to be a stochastic variable, and we consider estimating the parameters from preliminary data. An example of the first source of variability is nonadherence (noncompliance). We assume that the proportion of subjects who will adhere to their treatment regimen is not known before the study, but that the proportion is a stochastic variable with a known distribution. Under this assumption, we develop simple closed form sample size calculations based on asymptotic normality. The second source of variability is in parameter estimates that are estimated from prior data. For example, we account for variability in estimating the variance of the normal response from existing data which are assumed to have the same variance as the study for which we are calculating the sample size. We show that we can account for the variability of the variance estimate by simply using a slightly larger nominal power in the usual sample size calculation, which we call the calibrated power. We show that the calculation of the calibrated power depends only on the sample size of the existing data, and we give a table of calibrated power by sample size. Further, we consider the calculation of the sample size in the rarer situation where we account for the variability in estimating the standardized effect size from some existing data. This latter situation, as well as several of the previous ones, is motivated by sample size calculations for a Phase II trial of a malaria vaccine candidate.     

Spatial variability in forest soil CO2 efflux assessed with a calibrated soda lime technique

Accurate estimates of soil CO₂ efflux are important in the current discussion on the carbon balance of forests, and can be used to validate models and remotely sensed data. Due to the typically large spatial variability, large sample numbers are required to estimate mean forest soil CO₂ efflux with reasonable confidence intervals. Most infrared gas analysis (IRGA) systems are not well suited to simultaneously produce daily means and handle this spatial variability problem. The soda lime technique gives daily means and allows the required large sample numbers, but is less accurate than the IRGA systems. Using an elaborate cross-calibration, we tried to combine the accuracy of an IRGA method with the spatial integration potential of the soda lime technique. This paper reports on the calibration technique used to improve the accuracy of the soda lime technique and confirms the spatial variability in soil CO₂ efflux in a heterogeneous forest.     

Meta-analysis of the difference of medians

We consider the problem of meta-analyzing two-group studies that report the median of the outcome. Often, these studies are excluded from meta-analysis because there are no well-established statistical methods to pool the difference of medians. To include these studies in meta-analysis, several authors have recently proposed methods to estimate the sample mean and standard deviation from the median, sample size, and several commonly reported measures of spread. Researchers frequently apply these methods to estimate the difference of means and its variance for each primary study and pool the difference of means using inverse variance weighting. In this work, we develop several methods to directly meta-analyze the difference of medians. We conduct a simulation study evaluating the performance of the proposed median-based methods and the competing transformation-based methods. The simulation results show that the median-based methods outperform the transformation-based methods when meta-analyzing studies that report the median of the outcome, especially when the outcome is skewed. Moreover, we illustrate the various methods on a real-life data set.     

Statistical aspects of quantitative real-time PCR experiment design

Experiments using quantitative real-time PCR to test hypotheses are limited by technical and biological variability; we seek to minimise sources of confounding variability through optimum use of biological and technical replicates. The quality of an experiment design is commonly assessed by calculating its prospective power. Such calculations rely on knowledge of the expected variances of the measurements of each group of samples and the magnitude of the treatment effect; the estimation of which is often uninformed and unreliable. Here we introduce a method that exploits a small pilot study to estimate the biological and technical variances in order to improve the design of a subsequent large experiment. We measure the variance contributions at several ‘levels’ of the experiment design and provide a means of using this information to predict both the total variance and the prospective power of the assay. A validation of the method is provided through a variance analysis of representative genes in several bovine tissue-types. We also discuss the effect of normalisation to a reference gene in terms of the measured variance components of the gene of interest. Finally, we describe a software implementation of these methods, powerNest, that gives the user the opportunity to input data from a pilot study and interactively modify the design of the assay. The software automatically calculates expected variances, statistical power, and optimal design of the larger experiment. powerNest enables the researcher to minimise the total confounding variance and maximise prospective power for a specified maximum cost for the large study.     

Spatial heterogeneity and the design of immunization programs

Conventional epidemiological models usually assume homogeneous mixing, with susceptible and infected individuals mingling like the molecules in an ideal gas. It has recently been noted, however, that variability in transmission rates—arising, for example, from some hosts being in dense aggregates while others are in small or remote groups—can result in the intrinsic reproductive rate, R₀, of a microparasitic infection being greater than would be estimated under the usual assumption of homogeneous mixing; this implies the infection may be harder to eradicate under a homogeneously applied immunization programme (that is, a larger proportion of the population must be vaccinated) than simple estimates might suggest. In this paper we consider a spatially heterogeneous population arbitrarily subdivided into n groups, with one transmission rate among individuals within any one group, and another, lower transmission rate between groups. We define an optimum eradication program as that which—treating different groups differently—achieves its aim by immunizing the smallest overall number in each cohort of newborns, and we show this optimum program requires fewer immunizations than would be estimated under the (false) assumption that the population is homogeneously mixed. We prove this result in general form, and illustrate it for some special examples (in particular, for a population subdivided into one large ”city“ and several small ”villages“).     

Effect of An Educational Inpatient Diabetes Management Program on Medical Resident Knowledge and Measures of Glycemic Control: A Randomized Controlled Trial

ObjectiveTo investigate the effectiveness of an Inpatient Diabetes Management Program (IDMP) on physician knowledge and inpatient glycemic control.MethodsResidents assigned to General Internal Medicine inpatient services were randomized to receive the IDMP (IDMP group) or usual education only (nonIDMP group). Both groups received an overview of inpatient diabetes management in conjunction with reminders of existing order sets on the hospital Web site. The IDMP group received print copies of the program and access to an electronic version for a personal digital assistant (PDA). A Diabetes Knowledge Test (DKT) was administered at baseline and at the end of the 1-month rotation. The frequency of hyperglycemia among patients under surveillance by each group was compared by using capillary blood glucose values and a dispersion index of glycemic variability. IDMP users completed a questionnaire related to the program.ResultsTwenty-two residents participated (11 in the IDMP group and 11 in the non-IDMP group). Overall Diabetes Knowledge Test scores improved in both groups (IDMP: 69% ± 1.7% versus 83% ± 2.1%, P = .003; nonIDMP: 76% ± 1.2% versus 84% ± 1.4%, P = .02). The percentage of correct responses for management of corticosteroid-associated hyperglycemia (P = .004) and preoperative glycemic management (P = .006) improved in only the IDMP group. The frequency of hyperglycemia (blood glucose level > 180 mg/dL) and the dispersion index (5.3 ± 7.6 versus 3.7 ± 5.6; P = .2) were similar between the 2 groups.ConclusionAn IDMP was effective at improving physician knowledge for managing hyperglycemia in hospitalized patients treated with corticosteroids or in preparation for surgical procedures. Educational programs directed at improving overall health care provider knowledge for inpatient glycemic management may be beneficial; however, improvements in knowledge do not necessarily result in improved glycemic outcomes. (Endocr Pract. 2012;18:238-249)     

Genetic variability of sexual size dimorphism in a natural population of<Emphasis Type="Italic">Drosophila melanogaster</Emphasis>: An isofemale-line approach

Most animal species exhibit sexual size dimorphism (SSD). SSD is a trait difficult to quantify for genetical purposes since it must be simultaneously measured on two kinds of individuals, and it is generally expressed either as a difference or as a ratio between sexes. Here we ask two related questions: What is the best way to describe SSD, and is it possible to conveniently demonstrate its genetic variability in a natural population? We show that a simple experimental design, the isofemale-line technique (full-sib families), may provide an estimate of genetic variability, using the coefficient of intraclass correlation. We consider two SSD indices, the female-male difference and the female/male ratio. For two size-related traits, wing and thorax length, we found that both SSD indices were normally distributed. Within each family, the variability of SSD was estimated by considering individual values in one sex (the female) with respect to the mean value in the other sex (the male). In a homogeneous sample of 30 lines ofDrosophila melanogaster, both indices provided similar intraclass correlations, on average 0.21, significantly greater than zero but lower than those for the traits themselves: 0.50 and 0.36 for wing and thorax length respectively. Wing and thorax length were strongly positively correlated within each sex. SSD indices of wing and thorax length were also positively correlated, but to a lesser degree than for the traits themselves. For comparative evolutionary studies, the ratio between sexes seems a better index of SSD since it avoids scaling effects among populations or species, permits comparisons between different traits, and has an unambiguous biological significance. In the case ofD. melanogaster grown at 25?C, the average female/male ratios are very similar for the wing (1.16) and the thorax (1.15), and indicate that, on average, these size traits are 15–16% longer in females.     

Natural Diversity of Frankia Strains in Actinorhizal Root Nodules from Promiscuous Hosts in the Family Myricaceae

Actinorhizal plants invade nitrogen-poor soils because of their ability to form root nodule symbioses with N₂-fixing actinomycetes known as Frankia. Frankia strains are difficult to isolate, so the diversity of strains inhabiting nodules in nature is not known. To address this problem, we have used the variability in bacterial 16S rRNA gene sequences amplified from root nodules as a means to estimate molecular diversity. Nodules were collected from 96 sites primarily in northeastern North America; each site contained one of three species of the family Myricaceae. Plants in this family are considered to be promiscuous hosts because several species are effectively nodulated by most isolated strains of Frankia in the greenhouse. We found that strain evenness varies greatly between the plant species so that estimating total strain richness of Frankia within myricaceous nodules with the sample size used was problematical. Nevertheless, Myrica pensylvanica, the common bayberry, was found to have sufficient diversity to serve as a reservoir host for Frankia strains that infect plants from other actinorhizal families. Myrica gale, sweet gale, yielded a few dominant sequences, indicating either symbiont specialization or niche selection of particular ecotypes. Strains in Comptonia peregrina nodules had an intermediate level of diversity and were all from a single major group of Frankia.     

A Model To Estimate the Optimal Sample Size for Microbiological Surveys

Estimating optimal sample size for microbiological surveys is a challenge for laboratory managers. When insufficient sampling is conducted, biased inferences are likely; however, when excessive sampling is conducted valuable laboratory resources are wasted. This report presents a statistical model for the estimation of the sample size appropriate for the accurate identification of the bacterial subtypes of interest in a specimen. This applied model for microbiology laboratory use is based on a Bayesian mode of inference, which combines two inputs: (ii) a prespecified estimate, or prior distribution statement, based on available scientific knowledge and (ii) observed data. The specific inputs for the model are a prior distribution statement of the number of strains per specimen provided by an informed microbiologist and data from a microbiological survey indicating the number of strains per specimen. The model output is an updated probability distribution of strains per specimen, which can be used to estimate the probability of observing all strains present according to the number of colonies that are sampled. In this report two scenarios that illustrate the use of the model to estimate bacterial colony sample size requirements are presented. In the first scenario, bacterial colony sample size is estimated to correctly identify Campylobacter amplified restriction fragment length polymorphism types on broiler carcasses. The second scenario estimates bacterial colony sample size to correctly identify Salmonella enterica serotype Enteritidis phage types in fecal drag swabs from egg-laying poultry flocks. An advantage of the model is that as updated inputs from ongoing surveys are incorporated into the model, increasingly precise sample size estimates are likely to be made.     

Fertility effects of family planning programs: a methodological review.

This paper reviews and compares the methodologies of some 70 investigations of the effect of family planning programs on fertility levels. Differences among the studies include variations in questions asked, research methods used, program type investigated, and made of program action assumed. Programs can affect fertility by providing means of fertility control, education, legitimation, incentives/disincentives or any combination of these but the actual effect of these modes is unclear. Comparison is usually made between the effects on fertility of a program as a whole and a hypothetical estimate of what fertility rates would have been without the program. This hypothetical estimate is in fact not subject to empirical measurement, and a variety of methods have been developed largely to attack this methodological problem. The article compares the characteristics of several methods and provides a matrix comparing their strength, limitations and applications. The methods discussed include: 1) decomposition of change which identifies several factors affecting changes in crude birth rate; 2) correspondence between program activity and fertility trends over time to across areas or groups, which often takes close statistical association as evidence of causality; 3) matching studies, which try to remove the influence of nonprogram factors by controlling the characteristics of the subject; 4) experimental and control areas, comparing presumably similar groups with and without family planning programs; 5) multiple regression across areal units which provides some information on areal trends but requires extensive data for many statistical areas; 6) calculation of national effects of births averted among acceptors; and 7) simulation, which compares data to mathematical projection models, such as the TABRAP/CONVERSE and POPSIM models. No one method is best but certain methods are best to answer certain questions. All of the methods have difficulty establishing actual causality between the program studied and fertility trends and ruling out alternative explanations. Recent studies encouraged by the U.N. Population Division are seeking cross-method and cross-program evaluation.     

Development of an optimal monitoring program in cotton: Emphasis on spidermites and<Emphasis Type="Italic">Heliothis</Emphasis> spp.

To develop a monitoring program requires that not only the spatial pattern of distribution of a species be known, but also the cost of sampling the area(s) within the host plants occupied by a species. As sample unit size (plant, branch, leaf, etc.) decreases, cost to sample each unit decreases, while the number of units required to estimate a population parameter with a given reliability increases. The concept of costreliability allows for available sampling procedures to be compared and the best one to be chosen. The paper discusses several ways in which monitoring costs have been reduced forTetranychus spp.,Heliothis spp. and predators without reducing the reliability of the estimate. These include the use of subsampling plans, binomial sampling and sequential sampling.     

Genetic Influence on Slope Variability in a Childhood Reflexive Attention Task

Individuals are not perfectly consistent, and interindividual variability is a common feature in all varieties of human behavior. Some individuals respond more variably than others, however, and this difference may be important to understanding how the brain works. In this paper, we explore genetic contributions to response time (RT) slope variability on a reflexive attention task. We are interested in such variability because we believe it is an important part of the overall picture of attention that, if understood, has the potential to improve intervention for those with attentional deficits. Genetic association studies are valuable in discovering biological pathways of variability and several studies have found such associations with a sustained attention task. Here, we expand our knowledge to include a reflexive attention task. We ask whether specific candidate genes are associated with interindividual variability on a childhood reflexive attention task in 9–16 year olds. The genetic makers considered are on 11 genes: APOE, BDNF, CHRNA4, COMT, DRD4, HTR4, IGF2, MAOA, SLC5A7, SLC6A3, and SNAP25. We find significant associations with variability with markers on nine and we discuss the results in terms of neurotransmitters associated with each gene and the characteristics of the associated measures from the reflexive attention task.     

Challenges for mapping cyanotoxin patterns from remote sensing of cyanobacteria

Using satellite imagery to quantify the spatial patterns of cyanobacterial toxins has several challenges. These challenges include the need for surrogate pigments – since cyanotoxins cannot be directly detected by remote sensing, the variability in the relationship between the pigments and cyanotoxins – especially microcystins (MC), and the lack of standardization of the various measurement methods. A dual-model strategy can provide an approach to address these challenges. One model uses either chlorophyll-a (Chl-a) or phycocyanin (PC) collected in situ as a surrogate to estimate the MC concentration. The other uses a remote sensing algorithm to estimate the concentration of the surrogate pigment. Where blooms are mixtures of cyanobacteria and eukaryotic algae, PC should be the preferred surrogate to Chl-a. Where cyanobacteria dominate, Chl-a is a better surrogate than PC for remote sensing. Phycocyanin is less sensitive to detection by optical remote sensing, it is less frequently measured, PC laboratory methods are still not standardized, and PC has greater intracellular variability. Either pigment should not be presumed to have a fixed relationship with MC for any water body. The MC-pigment relationship can be valid over weeks, but have considerable intra- and inter-annual variability due to changes in the amount of MC produced relative to cyanobacterial biomass. To detect pigments by satellite, three classes of algorithms (analytic, semi-analytic, and derivative) have been used. Analytical and semi-analytical algorithms are more sensitive but less robust than derivatives because they depend on accurate atmospheric correction; as a result derivatives are more commonly used. Derivatives can estimate Chl-a concentration, and research suggests they can detect and possibly quantify PC. Derivative algorithms, however, need to be standardized in order to evaluate the reproducibility of parameterizations between lakes. A strategy for producing useful estimates of microcystins from cyanobacterial biomass is described, provided cyanotoxin variability is addressed.     

Spatial Variation in Soil Properties among North American Ecosystems and Guidelines for Sampling Designs

Soils are highly variable at many spatial scales, which makes designing studies to accurately estimate the mean value of soil properties across space challenging. The spatial correlation structure is critical to develop robust sampling strategies (e.g., sample size and sample spacing). Current guidelines for designing studies recommend conducting preliminary investigation(s) to characterize this structure, but are rarely followed and sampling designs are often defined by logistics rather than quantitative considerations. The spatial variability of soils was assessed across ∼1 ha at 60 sites. Sites were chosen to represent key US ecosystems as part of a scaling strategy deployed by the National Ecological Observatory Network. We measured soil temperature (T_s) and water content (SWC) because these properties mediate biological/biogeochemical processes below- and above-ground, and quantified spatial variability using semivariograms to estimate spatial correlation. We developed quantitative guidelines to inform sample size and sample spacing for future soil studies, e.g., 20 samples were sufficient to measure T_s to within 10% of the mean with 90% confidence at every temperate and sub-tropical site during the growing season, whereas an order of magnitude more samples were needed to meet this accuracy at some high-latitude sites. SWC was significantly more variable than T_s at most sites, resulting in at least 10× more SWC samples needed to meet the same accuracy requirement. Previous studies investigated the relationship between the mean and variability (i.e., sill) of SWC across space at individual sites across time and have often (but not always) observed the variance or standard deviation peaking at intermediate values of SWC and decreasing at low and high SWC. Finally, we quantified how far apart samples must be spaced to be statistically independent. Semivariance structures from 10 of the 12-dominant soil orders across the US were estimated, advancing our continental-scale understanding of soil behavior.     

VARIABILITY IN ANTARCTIC FUR SEAL DIVE DATA: IMPLICATIONS FOR TDR STUDIES

We analyzed 19 dive records obtained from female antarctic fur seals ( Arctocephalas gazella ) during three austral summer breeding seasons on Seal Island, Antarctica, to assess the extent of individual variation and the potential for using time-depth recorder (TDR) statistics to detect annual changes in six measures of foraging behavior. We report the mean values and typical variability among individuals for dive duration, dive depth, proportion of time submerged, transit time, vertical distance dived, and diving intensity. Dive duration was the least variable and vertical distance dived was the most variable among individual seals. The results were used to estimate the sample sizes required to detect—with acceptable precision and power—differences in the six measures between sites, years, or species. Statistics that vary most among individuals require the largest sample sizes to reliably detect a given percentage difference between annual means. Interestingly, we also observed the most significant interannual differences in those same statistics. These results emphasize that specifying the magnitude of the (interannual, intersite or interspecific) difference that is biologically significant to the study population is an important, though sometimes difficult, component of TDR survey design.     

Estimating the Counterfactual Impact of Conservation Programs on Land Cover Outcomes: The Role of Matching and Panel Regression Techniques

Deforestation and conversion of native habitats continues to be the leading driver of biodiversity and ecosystem service loss. A number of conservation policies and programs are implemented—from protected areas to payments for ecosystem services (PES)—to deter these losses. Currently, empirical evidence on whether these approaches stop or slow land cover change is lacking, but there is increasing interest in conducting rigorous, counterfactual impact evaluations, especially for many new conservation approaches, such as PES and REDD, which emphasize additionality. In addition, several new, globally available and free high-resolution remote sensing datasets have increased the ease of carrying out an impact evaluation on land cover change outcomes. While the number of conservation evaluations utilizing ‘matching’ to construct a valid control group is increasing, the majority of these studies use simple differences in means or linear cross-sectional regression to estimate the impact of the conservation program using this matched sample, with relatively few utilizing fixed effects panel methods—an alternative estimation method that relies on temporal variation in the data. In this paper we compare the advantages and limitations of (1) matching to construct the control group combined with differences in means and cross-sectional regression, which control for observable forms of bias in program evaluation, to (2) fixed effects panel methods, which control for observable and time-invariant unobservable forms of bias, with and without matching to create the control group. We then use these four approaches to estimate forest cover outcomes for two conservation programs: a PES program in Northeastern Ecuador and strict protected areas in European Russia. In the Russia case we find statistically significant differences across estimators—due to the presence of unobservable bias—that lead to differences in conclusions about effectiveness. The Ecuador case illustrates that if time-invariant unobservables are not present, matching combined with differences in means or cross-sectional regression leads to similar estimates of program effectiveness as matching combined with fixed effects panel regression. These results highlight the importance of considering observable and unobservable forms of bias and the methodological assumptions across estimators when designing an impact evaluation of conservation programs.     

Hypervariable genes—experimental error or hidden dynamics

In a homogeneous group of samples, not all genes of high variability stem from experimental errors in microarray experiments. These expression variations can be attributed to many factors including natural biological oscillations or metabolic processes. The behavior of these genes can tease out important clues about naturally occurring dynamic processes in the organism or experimental system under study. We developed a statistical procedure for the selection of genes with high variability denoted hypervariable (HV) genes. After the exclusion of low expressed genes and a stabilizing log-transformation, the majority of genes have comparable residual variability. Based on an F-test, HV genes are selected as having a statistically significant difference from the majority of variability stabilized genes measured by the ‘reference group’. A novel F-test clustering technique, further noted as ‘F-means clustering’, groups HV genes with similar variability patterns, presumably from their participation in a common dynamic biological process. F-means clustering establishes, for the first time, groups of co-expressed HV genes and is illustrated with microarray data from patients with juvenile rheumatoid arthritis and healthy controls.     

Molecular phylogeny of Phalaenopsis Blume (Orchidaceae) on the basis of plastid and nuclear DNA

Representatives of the genus Ancistrocladus, woody lianas of tropical Africa and Asia, contain pharmaceutically interesting alkaloids and have been the subjects of intensive phytochemical investigations. In Southeast Asia, Ancistrocladus tectorius, previously regarded as the only species of the genus from this region, is extremely polymorphic with respect to naphthylisoquinoline alkaloids, indicating that this taxon might be comprised of several morphologically similar species. We performed a comparative study of the ITS region of nuclear rDNA and of the trnK intron of cpDNA as well as an ISSR fingerprint analysis. Using 75 samples of A. tectorius from 21 locations in comparison to samples of other Ancistrocladus species from Asia and from West and Central Africa, we investigated patterns of species differentiation within this taxon. We found the high variability of chemical compounds described for A. tectorius to be paralleled by a high genetic variability of the units that have been assigned to this taxon. Samples assigned to A. tectorius were paraphyletic with respect to species from the Indian subcontinent, and intraspecific variability was comparable to interspecific variability among the African taxa. In addition, groups of individuals occurring in sympatry were found to be more similar to those from other locations, suggesting low levels of gene flow between those sympatric groups. This indicates either a considerable number of hybridization events during the evolution of A. tectorius or the existence of several distinguishable species not yet recognized. Our results are a first step in the development of species or population-specific markers for the prediction of the alkaloid spectrum of samples. This will help to improve the reproducibility of phytochemical research on Ancistrocladus.