Conventional genetic testing is a process in which clinicians take samples from patients as clinically needed, send them to a laboratory for genetic testing, and discuss the test results with patients. Direct-to-consumer (DTC) genetic testing is different from conventional genetic testing in that consumers learn about DTC genetic testing from TV commercials, the internet, or in-store advertising, and request the tests to be performed by DTC companies by their own choice. If conventional genetic testing is a clinician-centric framework, then DTC genetic testing is a consumer-centric one.Normally, saliva or mouth cells obtained by scratching the inside of one’s mouth with a cotton swab are sent to the DTC company. The company isolates DNA from the sample and conducts a genetic test. The test results are analyzed through proprietary in-house programs and a genetic profile report of various phenotypes is sent to the consumer. The test items or test report may vary depending on the needs of the consumer, as well as operational characteristics of the company.Conventional clinician-centric tests are aimed at diagnosing and treating patients’ diseases, mainly limited to genetic diseases or cancers. Consumer-centric DTC tests do also carry out genetic tests for diseases, but have an additional emphasis on biometric/life-related concerns, such as obesity, nutrition, skin, hair loss, ancestry, and life cycle. Therefore, the aim of DTC genetic testing is to determine consumers’ propensity for such phenotypes or genetic predisposition to the tested diseases for preventive purposes, rather than for the diagnosis and treatment of diseases. The cost of DTC genetic testing varies from company to company, but is roughly in the range of 100–1,000 US dollars, and DTC genetic testing is generally not covered by insurance.The advantages of DTC genetic testing can be described as follows ,2. FirstAPOE genetic test in relation to Alzheimer disease. The average likelihood of developing Alzheimer disease in carriers of the relevant allele is more than twice as high than in people who do not carry it. However, not everyone with the APOE e4 gene will develop Alzheimer disease, and having the APOE e2 gene, which confers resistance to Alzheimer disease, is likewise not a guarantee that one will never get Alzheimer disease. Consumers sometimes experience stress when they receive unexpected test results, especially if the results are related to serious diseases, such as cancer [BRCA1 and BRCA2 conducted by 23andMe with U.S. Food and Drug Administration approval. However, as many as 1,000 mutations of BRCA1 and BRCA2 are known to affect the risk of breast and ovarian cancer. Furthermore, family history is known to explain only about 5%–10% of cancer cases. A wide variety of factors, including age, sex, nutrition, exercise, race, disease history, hormonal factors, and reproductive factors, can affect the development of cancer. Consumers may make decisions on their own with inaccurate or non-deterministic DTC results, and take actions that can damage their health without appropriate consultation with clinicians. Lastly, there is often a lack of scientific evidence for the genetic tests carried out by DTC companies, and consumers’ genetic information may be used for other purposes without their approval or might even be stolen due to inappropriate security measures.Nonetheless, DTC genetic testing has important drawbacks ,2. DTC gs cancer . It is rThe discovery of genetic variations in diseases or phenotypes of interest  is quickly paying off. Clinical applications have focused on predictive models of disease. Previously, disease predictions were made using a few genetic variants that showed significance in genome-wide association studies (GWASs), on which basis a polygenic score (PGS) was calculated. Most predictions resulted in poor discrimination and imprecision. Predictive models are gradually starting to use more genetic variants, as GWASs are discovering more genetic variations as a consequence of using an increased number of samples, and recently developed predictive models take into account genome-wide variations, calculating a genome-wide polygenic score (GPS). In a study using a GPS for body mass index, the people with the highest score category had an obesity prevalence rate four times higher than that of the other categories . Thus, tThe most worrisome factor of all relates to the reporting of genetic test results. The results of DTC genotyping should be clearly communicated to the consumer; in particular, clear guidance should be given regarding what these tests indicate about their illness or health and what they cannot show, with special emphasis on the fact that these tests cannot be a diagnosis of disease. Consumers may make decisions on their own based on inaccurate or non-deterministic DTC results, and may even take actions that can damage their health without appropriate consultation with clinicians. DTC companies should therefore inform consumers of the limitations of the test results and encourage consumers to consult with clinicians before taking action.Many of the concerns mentioned above as shortcomings of DTC genetic testing remain pending. At this point, the guidelines provided by the American College of Medical Genetics and Genomics for DTC genotyping are as follows . First, DTC genetic testing provides consumers with the opportunity to learn about their genetic profiles related to phenotypes of interest in a convenient and less expensive manner. Furthermore, the list of test items will increase and the predictive accuracy will be improved as related research continues to progress rapidly. However, many of the concerns previously mentioned as shortcomings of DTC genetic testing remain pending. While DTC companies should try to compensate for these shortcomings themselves to the extent possible, regulation over DTC companies should be established by the appropriate agency to safeguard consumers from the abuse of DTC genetic testing.


Wildlife diseases are emerging at a higher rate than ever before meaning that understanding their potential impacts is essential, especially for those species and populations that may already be of conservation concern. The link between population genetic structure and the resistance of populations to disease is well understood: high genetic diversity allows populations to better cope with environmental changes, including the outbreak of novel diseases. Perhaps following this common wisdom, numerous empirical and theoretical studies have investigated the link between disease and disassortative mating patterns, which can increase genetic diversity. Few however have looked at the possible link between disease and the establishment of assortative mating patterns. Given that assortative mating can reduce genetic variation within a population thus reducing the adaptive potential and long-term viability of populations, we suggest that this link deserves greater attention, particularly in those species already threatened by a lack of genetic diversity. Here, we summarise the potential broad scale genetic implications of assortative mating patterns and outline how infection by pathogens or parasites might bring them about. We include a review of the empirical literature pertaining to disease-induced assortative mating. We also suggest future directions and methodological improvements that could advance our understanding of how the link between disease and mating patterns influences genetic variation and long-term population viability. One of the core tenets of conservation genetics is the need to maintain and manage genetic diversity at both local population and species wide levels. Often species of greatest conservation concern exist in small, isolated and fragmented populations which are directly associated with a loss of genetic diversity  revealed that to date, relatively few empirical studies have investigated the potentially important role that pathogens or parasites can have in establishing assortative mating patterns Table . In thisGenetic diversity is the term used to describe the variety in genetic composition at a given ecological level, and can be discussed in terms of communities, species, populations and individuals. Across all levels the literature generally indicates that the higher the genetic diversity present, the better the chances of survival in the face of an environmental change. For example, individuals with high genetic variation are heterozygous at a large number of genetic loci. Increased levels of within individual genetic variation are desirable as low heterozygosity has long been associated with a number of conditions that negatively impact both fitness and survival  mediated disassortative mating has been reviewed in detail more than once . Microsatellite genotyping of wild populations that were either under infection or free from a lethal viral pathogen (Ranavirus) revealed that infected populations undergo a loss of genetic variation but do not show signs of inbreeding. This is indicative of an assortative mating pattern , mate choice can be influenced by the strength of carotenoid pigmentation in male fish. Males that had been exposed to the parasite Gyrodactylus turnbulli had significantly reduced orange colour intensity and appeared duller. Females that were given a choice between uninfected and infected males in a laboratory trial were found to preferentially mate with uninfected males  infected with G.turnbulli. In laboratory mate choice trials both infected and naïve focal females were given a choice between an infected and a naïve male. Infected females spent significantly more time soliciting mating attempts from the infected males, which suggests a loss of choosiness due to their own infection status  and the broad spotted molly (P.latipunctata) are listed as critically endangered. The evaluation of potential mating pattern changes in these species could form a useful part of their conservation strategy. A similar loss of choice in infected individuals has also been observed during empirical studies on wild turkeys  infected with a coccidian species  infected with a Cryptocotyle trematode . Infected female shrikes arrived later to the wild breeding sites than uninfected females. Infected males displayed larger melanin spots on their tails, which act as an honest signal of parasite load and male quality, allowing females to choose mates of the highest possible quality. The authors found an assortative pattern of pair formation in regards to infection status. This was suggested to be the result of the infected females arriving late to the breeding sites when only the low quality infected males were still unpaired  during field studies. Males infected with Trypanosoma species arrived later to the breeding sites than uninfected males and were found to be in poorer general condition as judged by morphometric measures of quality such as wing feather length. It is again suggested that the late arriving, low quality males will compete for low quality females as those of higher quality will have already formed pairs  that is often subject to heavy parasitism from the trematode Microphallus papillorobustus. Thomas et al. (Pathogens and parasites can also in some cases directly manipulate the behaviour of their host. Such behavioural manipulation can result in infected individuals no longer being able to mate with uninfected individuals within a population. This is especially true of parasites, which will often modify the behaviour of their intermediate host to make them more vulnerable to predation by their ultimate host, facilitating transfer. A good example of this is seen in the amphipod s et al.  demonstrUnder such assortative mating patterns, variation in heritable resistance to infection will be lost due to a rise in the number of individuals with homozygous genotypes for either resistance or susceptibility. If the infection is lethal then susceptible individuals could be lost entirely from the population. The decrease in susceptible individuals will negatively impact the spreading potential of the parasite or pathogen infecting the population thus increasing host viability in the short term (King and Lively As such, disease-induced assortative mating should be of particular interest to those studying the conservation of endangered species in the face of environmental change.As discussed above, the long-term implications for populations affected by assortative mating could be profoundly negative and as such we believe this to be an important area of study that warrants further and renewed empirical research effort. In this section we review gaps in existing research literature, some of which could mean that the frequency of occurrence of disease-induced assortative mating and the threat posed to vulnerable species could currently be underestimated.There are species in the literature that demonstrate how the apparent impact of an infection upon a mating system can vary based upon which sex is the focal animal of a study. Female Trinidadian guppies were explicitly shown by Houde and Torio  to prefeGammarus pulex. Poulton and Thompson (A further example is seen in literature pertaining to the shrimp Thompson  showed tThompson  that theThese two examples make it clear that in order to fully understand how a pathogen affects the mating ecology of a population it is imperative to study the impact that it has on both sexes. At present, few species have received investigation into how infection influences the mating behaviours of both sexes and therefore there is potential that assortative mating patterns may be more prevalent than the current literature suggests.In their review of MHC disassortative mating, Penn and Potts  suggest Further issue has been found with laboratory-based mating experiments in that they often utilise a dichotomous design, where focal animals are given a direct choice between just two stimulus animals Wagner . In manyDespite these shortcomings the majority of studies in the field of infection induced assortative mating rely only on data collected in the laboratory Table . ReasonsThe current field based literature comes from either avian or gammarid systems. Birds often pair up and remain on nests for an extensive breeding season, display parental care and can easily be identified using ringing techniques, meaning that reproductive fitness can be measured directly by brood size/fledgling success. Gammarid shrimp species, though aquatic, occur in shallow water and display prolonged breeding behaviours such as mate guarding, which are easily observed in the field to infer mate preference. The number of eggs laid can again be used as a measure of reproductive fitness. As such, these systems lend themselves more easily to field based research than do others. Advances in technology that are being used in other areas of behavioural research offer opportunities for improving experimental designs to allow them to more closely resemble wild conditions. For example, breeding populations of many species could be more accurately recorded by using technology (Sumner et al. In the wild, populations may be subject to infection by multiple different agents at any one time (Petney and Andrews The co-evolution of hosts and parasites plays a large role in how a population or species copes with an infection. For example, populations may be less resistant to non-locally occurring isolates of the same pathogen that normally infects them (Ridenhour and Storfer To date, there is a lack of empirical appraisal of this theory. Addressing this is potentially critical for fully understanding the conservation implications of disease induced assortative mating. Future work should make a concerted effort to evaluate the true impact of disease induced shift in population mating ecology by combining the disciplines of evolutionary biology, behavioural ecology and epidemiology - not only studying how infection can affect host mating biology, but also how the disease-induced changes to mating biology can impact on the spread of the causative pathogen. The inclusion of additional variables into experimental designs such as both a local and non-local pathogen strain, or host and pathogen samples taken at different time-points over a long-term study, would allow for the exploration of co-evolutionary dynamics in the establishment of assortative mating patterns. Experimental exploration of the effect of assortative mating patterns on the spread of disease would require more complex methodological approaches but could potentially be achieved using model systems with short generation times, existing within mesocosms. Such systems have been used to study the impact of genetic diversity on disease transmission in insects (Liersch and Schmid-Hempel All of the focal species of empirical case studies presented in this review are listed by the IUCN as “Least Concern” or are unevaluated. Given the growing number of cases where threatened species are subjected to infectious diseases (Lyles and Dobson Assortative mating, be it by active mate choice or passive changes to competitiveness, can benefit a population by allowing faster adaptation against pathogens and parasites that are particularly virulent and persistent within an environment. It can however result in a loss of genetic diversity from the population at large. Reduced genetic diversity is well known to increase the vulnerability of populations to environmental stochasticity (Willi et al.


Glossina) are the only vector for the parasitic trypanosomes responsible for sleeping sickness and nagana across sub‐Saharan Africa. In Uganda, the tsetse fly Glossina fuscipes fuscipes is responsible for transmission of the parasite in 90% of sleeping sickness cases, and co‐occurrence of both forms of human‐infective trypanosomes makes vector control a priority. We use population genetic data from 38 samples from northern Uganda in a novel methodological pipeline that integrates genetic data, remotely sensed environmental data, and hundreds of field‐survey observations. This methodological pipeline identifies isolated habitat by first identifying environmental parameters correlated with genetic differentiation, second, predicting spatial connectivity using field‐survey observations and the most predictive environmental parameter(s), and third, overlaying the connectivity surface onto a habitat suitability map. Results from this pipeline indicated that net photosynthesis was the strongest predictor of genetic differentiation in G. f. fuscipes in northern Uganda. The resulting connectivity surface identified a large area of well‐connected habitat in northwestern Uganda, and twenty‐four isolated patches on the northeastern margin of the G. f. fuscipes distribution. We tested this novel methodological pipeline by completing an ad hoc sample and genetic screen of G. f. fuscipes samples from a model‐predicted isolated patch, and evaluated whether the ad hoc sample was in fact as genetically isolated as predicted. Results indicated that genetic isolation of the ad hoc sample was as genetically isolated as predicted, with differentiation well above estimates made in samples from within well‐connected habitat separated by similar geographic distances. This work has important practical implications for the control of tsetse and other disease vectors, because it provides a way to identify isolated populations where it will be safer and easier to implement vector control and that should be prioritized as study sites during the development and improvement of vector control methods.Tsetse flies (genus  Together, the animal and human diseases pose health threats and economic burdens to vast regions of sub‐Saharan Africa where they are endemic  from >800 G. f. fuscipes flies from 38 sampling sites  and field‐survey data from > 300 locations . This pipeline is novel because it integrates a landscape genetics approach  with maximum entropy modeling using contemporary field‐survey observations . The first step  identifies the major environmental parameters correlated with the accumulation of genetic differentiation . In this first step, we anticipate detecting historical pathways of tsetse connectivity over hundreds to thousands of generations. The second step  identifies connectivity between GPS coordinates currently inhabited with tsetse flies . In this second step, we anticipate detecting contemporary pathways of tsetse connectivity over just several generations because predictions are dependent upon current observations of tsetse presence. The output is then analyzed  to identify habitat patches within ecologically suitable habitat with the least risk of long‐range recolonization over multiple generations  and thereby allows for the identification of areas with the least risk of long‐range colonization over both short and long time scales ranging from several to thousands of generations. We validate this pipeline by (1) conducting a field‐survey searching for tsetse flies in geographic areas predicted by the model to have suitable habitat for G. f. fuscipes and to be isolated from other similarly suitable areas; (2) testing the level of genetic isolation of tsetse from one of these patches, as they should be more genetically differentiated for other tsetse populations from connected areas. We discuss how the integration of landscape genetics approaches with maximum entropy modeling provides potential advantages by accounting for historical and current movement patterns. We also discuss the possible immediate applications of this pipeline to help control tsetse flies in northern Uganda and consider its general applicability to identify isolated habitat in management and conservation of wild populations.In this study, our main goal was to identify isolated geographic regions for consideration in targeted vector control of a Figure  and to da Figure : I1 from22.11–3). The population genetic data are used to estimate genetic differentiation in the software ARLEQUIN v.3.5 , which outputs a pairwise FST matrix among sampling sites . The environmental data are used to create a resistance‐based landscape model in the program Circuitscape v.4.0.5 , which outputs a circuit map for each environmental variable that is then used to estimate pairwise landscape resistance matrices among sampling sites . We then used multiple matrix regression with randomization  in the R  for each environmental variable, while accounting for geographic distance and linear spatial autocorrelation. The environmental variable(s) identified as correlated with genetic differentiation  are then combined with field‐survey presence data  in a maximum entropy model using MaxEnt  to build a connectivity surface . This connectivity surface reflects the genetic differentiation among populations that exceeds isolation by distance  is then used as input to a spatial clustering algorithm in the R package “SDMTools”  to identify discrete isolated landscape patches . Finally, discrete isolated landscape patches  are overlaid onto a habitat suitability model to yield predictions of where populations are relatively isolated within ecologically suitable habitat .The inputs for the pipeline include population genetic data, environmental data, and field‐survey presence data Figure : I1–3. Ts Figure : O1. The2.22.2.1G. f. fuscipes from northern Uganda . We used a subset in order to exclude sampling sites with divergent genetic backgrounds that could introduce patterns of divergence unrelated to current environmental factors. Table HO), expected heterozygosity (HE), the individual fixation index relative to the sample (FIS) as estimated using GENALEX v6.501  and discriminant analysis of principal components (DAPC) with prior groupings based on sampling site and with the cross‐validation formula for optimizing number of principal components to retain, using the adegenet R library . We also performed clustering analysis with STRUCTURE v2.3.4  and that also explained the most variance in the PCA. This reduced the number of environmental variables used as input data from 13 to five: RNF, DST, NST, NDVI, and PSN .To exclude covarying variables, we estimated pairwise straight‐path distances between each of the 38 sampling sites for each of the 13 environmental variables and calculated the mean value encountered along these straight‐path distances, using the Zonal Statistics tool in ArcMap 10.3 ESRI . We then2.2.32. Coordinates from multiple traps (within <2 km distance) were averaged to provide a single geospatial coordinate per sample. Very scant information on G. f. fuscipes distribution was available for this region before these surveys, because of the area was difficult to access due to civil unrest that plagued this region from the 1980s through 2009. Table S2 .We used data from Opiro et al.  collecteAppendix  summariz2.32.3.1FST estimates  to quantify levels of genetic differentiation among the 38 sites .We used pairwise s Figure . This ma2.3.23). Then, to prepare the environmental data input as resistance surfaces, we followed an approach similar to that which is described in Wang, Savage, and Bradley Shaffer , calculated as the mean resistance value encountered along a straight path between each pair of sampling sites. We created resistance matrices from the current maps, so that we had one geographic‐distance‐only resistance matrix and 20 environment‐specific resistance matrices  of the same dimensions as the FST matrix  to use as input for the test for correlation of the two matrices .We used Circuitscape , the geographic‐distance‐only resistance matrix and the 20 environment‐specific resistance matrices . Prior to using MMRR, we used R to test for violation of the two main assumptions of linear regression, normality of residuals and homogeneity of variance. The MMRR was performed using the Wang  as candidates of high influence on genetic differentiation. These models were selected as inputs for modeling a G. f. fuscipes connectivity surface (see below).For both MMRR and the partial Mantel tests, we used 10,000 permutations to calculate empirically based 2.3.44a) to produce a connectivity surface, using the environmental variables significantly correlated with genetic differentiation from the previous step  and field‐survey presence data from 317 traps from northern Uganda . MaxEnt was chosen because of its widespread use in modeling movement corridors  for G. fuscipes tsetse flies in northern Uganda that accounts for genetics of the target species in a specific region of interest. We used the default program parameters, which included a logistic output that provides a 0‐1 scale probability of a presence. We gave a 0% predicted probability of presence to large continuous water bodies, such as Lake Albert, Lake Kyoga, Victoria Nile, and Albert Nile  as the input of a clustering analysis . For the clustering analysis, we used the R packages “raster”  in northern Uganda were as follows: a final cutoff score of >0.5, a minimum size of 4 pixels, and a clustering range of 5 km.To identify geographic regions with low connectivity to the main tsetse habitat (isolated landscape patches), we used the connectivity surface Figure : O4a as s Figure : M5. ForHijmans, , “rgdal”Hijmans,  and “disHijmans,  to find Hijmans, . We firs2.3.65) onto a habitat suitability model to identify and exclude the largest continuous patch of suitable habitat (referred to as the “main habitat belt” from here forward) for consideration as an isolated patch. For a habitat suitability model, we started with the existing FAO model , with the same default program parameters used to build the connectivity surface. We then merged this MaxEnt output with the existing FAO model  present and (2) more genetically isolated from neighboring areas than would be expected based on geographic distance alone, we conducted a field survey and collected samples along three transects that crossed five of the model‐defined isolated patches along the northern shore of Lake Kyoga. The survey was carried out over a period of 3 days for each transect in November of 2016. Presence data were collected by deploying 317 biconical tsetse traps as described in previous work , following manufacturer's protocols and stored at −20°C. Genotypic data from 16 microsatellite loci were collected using published protocols to be able to merge the new data with the existing genotypic database for this region , and then we compared the level of differentiation of the isolated patch to the closest sites that we sampled from within the main habitat (25–100 km away), to the level of differentiation found between pairs of sites within the main habitat belt that were separated by the same range of geographic distances (25–100 km), and tested the prediction that the ad hoc isolated patch would have higher differentiation than pairs from within the main habitat belt with a t‐test in JMP.Pairwise Rousset, , using t33.1p‐value <.00001).The results of the PCA, DAPC, and STRUCTURE analyses are presented in Figures S1, S2, and S3, respectively (Appendix 3.2R| > .80 and p < .001) and . Figure S6, Appendix R| was >.85. NDVI was selected to represent covarying fPAR, LAI, TC, EVI, LE, and ET based on the PCA, where NDVI explained the most variance on the first axis  produce 20 current maps in Circuitscape . Current density , indicating higher genetic differentiation at low PSN (high modeled connectivity at high PSN). The partial Mantel tests also detected a significant association of FST with low PSN . None of the other four environmental variables were significant in any tests  identified one large continuous suitable habitat patch and 1351 smaller ones. The main habitat patch, representative of major G. f. fuscipes dispersal, extends from Uganda's western border with the Democratic Republic of the Congo to the 33° latitude line and from the shores of Lake Kyoga north to the nation's border with South Sudan ] between samples within the main continuous habitat separated by 25–100 km averaged 0.04 (range: 0.00–0.11). In contrast, estimates from pairs of samples separated by 25–100 km that included the isolated patch averaged 0.14 (range: 0.12–0.16). This difference was statistically significant, according to a t test p‐value <.0001  only in one location north of Lake Kyoga at Bugondo, Serere district .Another important factor to consider in landscape genetics is the possible artifacts caused by demographic history. For instance, founder events can lead to genetic drift, which inflates genetic differentiation in isolated populations, and can confound landscape genetics interpretations by uncoupling patterns of differentiation from its environmental drivers. For this reason, we consider Ne estimates and bottleneck tests results  indicated that net photosynthesis, PSN, is the strongest predictor of tsetse fly genetic differentiation beyond that expected based on geographic distance alone (Table 4). The correlation of PSN with FST aligns with previous work on G. f. fuscipes in Uganda and our knowledge of its ecology , the PSN‐based circuit map  population structure or (i) bottlenecks, thereby making the correlation irrelevant to ongoing patterns of migration. First, genetic structure (beyond IBD) that is not accounted for can confound results because shared evolutionary history across biogeographic barriers can create a correlation that is unrelated to dispersal patterns. Although still possible, results presented here do not indicate a deep history of allopatry between subsets of the data , and second to build a model of habitat isolation based on current‐day conditions and tsetse presence . This is different than the approach used by Bouyer et al.  allows us to build an understanding of the patterns of movement along corridors of habitat over hundreds to thousands of generations. When we combine this output  with models of connectivity using field‐survey presence data, we integrate the evolutionary and ecological time scales to obtain a more complete understanding of patterns of migration over hundreds to thousands of generations and current regions of high ecological suitability. This is especially relevant in this context because long‐range migration events have been described in G. f. fuscipes populations, as evidenced by the finding of migrants from distances of up to ~60 km  over what could be found using a landscape friction model using GPS coordinates from a limited population genetics sample .The connectivity surface and final habitat suitability map are available in raster/ArcGIS format in the DRYAD database (doi: Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Trypanosoma cruzi, the parasitic agent of Chagas disease and a public health concern throughout Latin America, has for decades been presumed to exchange genetic material rarely and without classic meiotic sex. We present compelling evidence from 45 genomes sequenced from southern Ecuador that T. cruzi in fact maintains truly sexual, panmictic groups that can occur alongside others that remain highly clonal after past hybridization events. These groups with divergent reproductive strategies appear genetically isolated despite possible co-occurrence in vectors and hosts. We propose biological explanations for the fine-scale disconnectivity we observe and discuss the epidemiological consequences of flexible reproductive modes. Our study reinvigorates the hunt for the site of genetic exchange in the T. cruzi life cycle, provides tools to define the genetic determinants of parasite virulence, and reforms longstanding theory on clonality in trypanosomatid parasites.Genetic exchange enables parasites to rapidly transform disease phenotypes and exploit new host populations.  Trypanosoma cruzi, the causative agent of Chagas disease. These findings have implications for the epidemiology of the disease in endemic regions and challenge existing ideas that the parasites are strictly clonal.Here, Llewellyn and colleagues present evidence of meiotic sex in  Mucosal or abrasion contact with the infected feces of hematophagous triatomines constitutes the primary mode of T. cruzi transmission. Infection with T. cruzi results in chronic Chagas disease in 30–40% of cases, characterized by a spectrum of fatal cardiac and intestinal pathologies. Early-stage acute Chagas disease can also be fatal, especially among infants and in orally transmitted outbreaks of the disease2. T. cruzi transmission is a zoonosis maintained by numerous species of triatomine insects and hundreds of different species of mammals3.TT. cruzi belongs, is a monophyletic group of obligate parasites and includes several species of medical and veterinary importance – e.g., Trypanosoma brucei ssp., Leishmania spp., Trypanosoma vivax, and Trypanosoma congolense4. The Trypanosomatidae are early branching eukaryotes in evolutionary terms and share many biological characteristics, including the process of U-indel RNA editing in the kinetoplast5 and polycistronic transcription control6. Despite their basal status, the Trypanosomatidae possess much of the core meiotic machinery of higher eukaryotes7. However, the extent to which such machinery might actually support genetic exchange within trypanosomatid species has been slow to come to light8. Establishing the occurrence of regular meiotic recombination in T. b. brucei has taken decades of laboratory and field research; not until 2014 was haploid gamete production (coincident to peak meiosis-specific gene expression) confirmed by fluorescence microscopy as a normal phase of development in the vector’s salivary gland11. More recently, genome-scale signatures of meiosis have also been detected in T. congolense13. In contrast, robust genomic evidence now suggests that the human-infective T. b. gambiense subspecies is completely asexual14. Life histories in Leishmania seem no less complex. Despite a clear propensity for mitotic clonality, sporadic sexual hybrid formation appears to underlie important diversification events both within and between species16, and meiotic offspring are readily produced in laboratory crosses18. An alternation of clonal and sexual, endogamic reproduction has also been proposed to define population genetic structure in the Viannia complex19.The Trypanosomatidae, the family to which T. cruzi is the last of the Tritryps  for which the extent and mechanism of genetic exchange remains to be fully elucidated. Limited evidence for genetic recombination has been observed in the field21 although inappropriate study designs, genetic marker systems of insufficient resolution, and low genetic diversity in study populations have all hampered interpretation of the data8. Furthermore, the parasexual mechanism of genetic exchange proposed for T. cruzi based on a single experimental cross – one of whole-genome fusion followed by stochastic chromosomal decay and return to diploidy22 – has been irreconcilable with patterns of somy and genetic diversity observed in natural populations24. This lack of clarity has lead some to propose T. cruzi as a paradigm for Predominant Clonal Evolution (PCE)26 in parasitic protozoa – an idea which may not reflect biological reality.T. cruzi Discrete Typing Unit I clones, as well as several non-cloned T. cruzi strains, collected from triatomine vectors and mammalian hosts in an endemic transmission focus in Loja Province, southern Ecuador. After mapping sequences against a recent PacBio sequence assembly27, we explore patterns of population structure and genetic recombination. Our data reveal that T. cruzi does indeed reproduce sexually at high frequency via a mechanism consistent with classic meiosis. However, we demonstrate that parasite groups with radically distinct reproductive modes also co-occur at the same transmission focus. As the last medically important trypanosome for which meiosis has not yet been demonstrated in lab or field, our data on T. cruzi make a significant contribution towards the consolidation of current theories around genetic exchange in the Trypanosomatidae.To address this fundamental knowledge gap in the biology of trypanosomatids, in this study we generate whole-genome sequence data from 45 T. cruzi cultures aligned to the reference assembly (T. cruzi TcI X10/1 Sylvio) at a mean depth of 27× , ranging between 13 and 64× . Including only single-clone T. cruzi cultures founded from individual parasites in the laboratory, 130,996 SNP sites were identified that clearly separated our samples into two highly distinct phylogenetic clusters within the small study area  among the samples, although mild improvements to model fit continued through to k = 6  showed shared ancestry across Clusters 1 and 2. Clones derived from the same strains did not show shared ancestry. These data may indicate the presence of multiclonal infections in which parasites from these distinct groups co-occur in the same vectors and hosts , we reconstructed each phased genome as a mosaic of haplo-segments sharing ancestry with other samples of the datasetT. cruzi, we established key metrics of population genetic structure at different sites. Among the 15 Bella Maria clones of Cluster 1, allele frequencies at variable loci matched those predicted for random mating, with estimated inbreeding coefficients predominantly near zero  in the latter group  than Bella Maria Cluster 1)  are predicted to decay with map distance due to crossover that occurs between homologous chromosomes during meiosis. We plotted r2 against pairwise map distance for all diagnostic SNP loci identified at Bella Maria. Figure T. b. brucei and L. major)  in our study groups by comparing two different estimates of effective population size. The first estimate, Nρ, is based on recombinational diversity observed in the sample and represents the number of cells derived from mating. The second, Nθ, is based on mutational diversity and represents the total number of cells, irrespective of sexual or mitotic origin (see Methods). As in linkage decay analysis, we considered the best-mapping chromosomes 1, 5, 21, and 26. Values of ρ for Bella Maria suggested ca. 3 meioses per 1000 mitotic events in this group. In contrast, all approximations of ρ for El Huayco and Ardanza fell within confidence limits of the simulated, non-recombinant FSC_n control. These limits also contained ρ = 0  among clones Fig. . As indik = 5 in Supplementary Fig. Apart from disrupting sequence patterns within chromosomes, sexual reproduction breaks up associations between chromosomes within the genome. Given sufficient population diversity, therefore, incongruent phylogenies are expected depending on the chromosome used to construct them. As one might expect given estimated rates of meiotic sex in this group, we encountered many such incongruences among Bella Maria clones belonging to Cluster 1 Fig. . IntriguOf 80,052 SNP sites that differed from the TcI-Sylvio reference genome in El Huayco, 62,036 also differed in Ardanza, and >50% of this polymorphism occurred as fixed heterozygous loci across the two groups. These observations, supported by population genetic statistics . With the exception of TBM_2824 clones (trisomic for chromosomes 32 and 44), no Bella Maria clones showed increased somy despite similar levels of intra-chromosomal read-depth variation as clones from Cluster 2. Interestingly, most Bella Maria genomes showed severe reductions in sequencing coverage over chromosome 13. Such reductions did not occur in El Huayco or Ardanza Fig. . Somy plcytochrome b alignment in Supplementary Fig. Two samples in the dataset stand out as clear migrants with idiosyncratic genomic features that indicate the possibility of further genetic exchange events. TRT_3949 (sampled near El Huayco but associated to Cluster 1) and TCQ_3087 clones (sampled in Bella Maria but associated to Cluster 2) were the only samples for which geographic and nuclear phylogenetic neighbors did not match  that grossly disparate reproductive strategies and rates of genetic exchange occur simultaneously at a single disease focus.Our comparative genomic analysis of 45 biological clones from an area of endemic transmission supports the remarkable conclusion that (a) In a subsection of the region (Bella Maria), signs of regular meiotic sex are markedly clear. Genome-wide allele frequencies occur at Hardy–Weinberg equilibrium and ancestries among individuals fluctuate from chromosome to chromosome. Parasite genotypes on individual chromosomes appear equally mosaic: linkage between polymorphisms clearly correlates with map distance, disequilibrium plummeting within just a few hundreds of bp. We gauge that the meiosis driving these patterns of diversity occurs more than once every 1000 reproductive events in Bella Maria. Meanwhile, in nearby El Huayco, Ardanza and Gerinoma groups, meiosis appears essentially absent. Instead, these groups exhibit high levels of heterozygosity across the entire genome. We do detect discordant chromosomal phylogenies among these parasites, but recombination estimates within chromosomes match those for simulated, non-recombining controls and there are no signs of intra-chromosomal linkage decay. Alongside excess heterozygosity, several El Huayco and Ardanza clones also present extensive aneuploidy as well as long blocks of near-identical diplotypes.T. cruzi biology and, alongside data from T. b. brucei11 and Leishmania17, indicate that this mode of genetic exchange is ancestral among medically important trypanosomatids11. We previously advised caution to those applying generalized theories of clonal evolution  to parasitic protozoa8. Our revelations around T. cruzi population genomic structure in this study broadly support our case. Nonetheless, meiotic sex has never been observed in the laboratory and multiple aspects of meiosis in T. cruzi remain obscure22. The site of genetic exchange (vector or host) in T. cruzi is still not known, for example, nor is it understood from which parasite life cycle stage gametes might develop. In contrast, T. b. brucei gametes have been characterized in the salivary glands of tsetse flies and a mechanism for subsequent cytoplasmic fusion described11. Clearly much basic research remains to be done.The strong signatures of meiotic sex we report from Bella Maria redefine our understanding of T. b. gambiense were attributed to the Meselson Effect30, whereby mutations accumulate in the absence of recombination between homologous chromosomes during long-term clonality. The high levels of heterozygosity we observe in Cluster 2 differ in important ways from the T. b. gambiense dataset and from predictions of the Meselson Effect30. For example, discontinuities in genetic differentiation among individuals, instead of occurring as stretches of absolute homozygosity on disomic chromosomes as they did in T. gambiense14, occur in our dataset as shared patches of heterozygosity among geographically distinct groups . Furthermore, we see no evidence of accumulation of private heterozygous sites within individuals as one might expect during long term asexual propagation – rather, over 50% of heterozygous sites are shared among samples in Cluster 2. If long-term asexuality is a poor explanation for heterozygosity in our dataset, an ancestral outcrossing event could perhaps have played a role.The distribution of genetic diversity we describe in Cluster 2 suggests that meiosis is largely absent among these strains. Patterns of heterozygosity recently observed in T. cruzi to date22, parental genomes fused to tetraploid hybrids and then began erosion back toward the disomic state. This fusion-then-loss process resembles that in parasexual pathogenic fungi (Candida spp.) and allows for independent chromosomal ancestries without intra-chromosomal linkage decay31. Moreover, gene conversion in tetraploids can produce long tracts of increased identity on both homologs  without loss of heterozygosity upon reduction to the disomic state32. This is especially true when genome erosion is biased against the retention of similar homologs33, a condition that aligns with our results 42. The parasite’s life cycle thus likely represents a continuum of bottlenecks linked to frequent local extinction and recolonization events that increase levels of genetic drift and identity by descent (IBD). It may thus come to less surprise that observations of diffuse hybrid clonality around a restricted focus of sex in Bella Maria resemble spatio-temporal patterns of heterogony demonstrated in various other metapopulation systems46. Facultative sex often coincides with strong metapopulation structure, in which sexual variants are predicted to occupy core habitat (where population subdivision and inbreeding depression are minimized) while asexual variants disperse more freely without fitness costs from high IBD during frequent founding events47. Extensive asexual dispersal eventually brings divergent lineages into contact, creates potential to mate, form heterotic offspring, and reset clonal decay. Divergent homologs, however, may impair canonical sex when F1 hybrids mate50. We noted mass elevation of Tajima’s D in Cluster 2 of this study  demographic scenarios when mating becomes very scarce, whereupon the influence of demographic changes on the site-frequency spectrum becomes difficult to disentangle by current methods of inference53. Nevertheless, large patches of low differentiation observed in this study suggest a relatively recent contribution of hybridization on allelic divergence in El Huayco and Ardanza. Spatially correlated genetic substructures and low effective population sizes further substantiate the role of metapopulation dynamics in structuring genetic diversity in these groups.The ecological and evolutionary drivers of distinct but sympatric reproductive modes in L. major crosses formed in non-native vectors50 and of irregular, biparental mitochondrial inheritance in T. b. brucei54.Our genotype- and haplotype-based summaries of co-ancestry indicate that the meiotic parasite group in Bella Maria is genetically segregated from others with distinct reproductive histories in nearby El Huayco and Ardanza. Genetic discontinuity occurs consistently for samples collected within a few kilometers distance and despite evidence for vector/host co-infection and migration between divergent groups. Putative migrants, possibly the progeny of these divergent groups, exhibit extensive (nuclear) homozygosity and, in the case of TCQ_3087 clones, extreme maxicircle divergence and very high maxicircle read-depth. Such observations are reminiscent of 55. Sensitivity to cryptic biochemical cues is clearly high, but the molecular signals that incite recombination and control mating compatibilities within these species remain essentially unknown56. Our observations from the field do not identify such mechanisms but provide many relevant questions to explore. For instance, do ploidy barriers segregate transmission cycles in T. cruzi? Is certain monosomy  associated with mating locus activation and sex? Is high homozygosity a direct result of improper mating or a subsequent effect ? What are the adaptive processes that underpin switching between different reproductive modes?Unexpected and poorly repeatable hybrid genomes have arisen on a number of occasions in experimental Tritryps researchT. cruzi, as well as evidence for widespread clonal expansion, after episodic hybridization events. Recent evidence for sex obtained from Arequipa, Peru, in contrast, cannot be reliably distinguished from complex patterns of gene conversion in a fully clonal population21. Complex mating structures are of acute relevance to Chagas disease control. Recombination implies that important epidemiological traits are transferable, not locked into stable subdivisions in space and time . Recombination has driven major changes in T. cruzi transmission in the past, including adaptation to the domestic niche59. Our data suggest that recombination may continue to transform contemporary disease cycles, as suggested for Toxoplasma gondii60 and in Leishmania spp.61. The proven presence of a sexual cycle in T. cruzi should now reinvigorate the hunt for the site of genetic exchange within the host or vector, as well as its cytological mechanism. An in vitro model for meiotic genetic exchange in T. cruzi will dramatically improve our ability to distinguish the genetic bases of virulence, drug resistance and other epidemiologically relevant phenotypes. Determination of such traits may underpin future efforts to treat and control Chagas disease.Our work presents hard evidence for meiotic sex in Rhodnius ecuadoriensis, Panstrongylus chinai, P. rufotuberculatus, and Triatoma carrioni), rodents  and bats (Artibeus fraterculus) captured between 2011 and 2015 in eastern Loja Province, Ecuador. Capture coordinates, dates, and ecotypes  are provided in Supplementary Table 62. Individual parasite cells were cloned on solid medium to derive single-strain colonies following Yeo et al.63. Briefly, aliquots of 102–103 epimastigote cells were mixed with 36 °C (molten) low melting point agarose and distributed over supplemented blood agar for stationary colony formation on petri dishes humidified with 5% CO2 at 28 °C for ca. three months. Successful microcolonies were then expanded in biphasic Novy-MacNeal-Nicolle (NNN) and Liver Infusion Tryptose (LIT) media. Complementary to 19 non-cloned primary cultures, this process yielded 64 axenic monocultures for subsequent DNA extraction and sequencing.Trypanosomes were isolated from triatomines  by covalent binding prior to paired-end sequencing on the Illumina HiSeq 2500 platform. To guide variant discovery from resultant 125 nt sequence reads, we optimized reference-mapping and SNP-calling pipelines using paired-end Illumina reads  for T. cruzi TcI X10/1 (termed TcI-Sylvio elsewhere in the text) against the newly available PacBio sequence for the same reference strain27. Based on comparisons with TcI-Sylvio mapping results from various configurations in Smalt v.0.7.4 (we tested 12–14 kmer hash indexes and 2–8 base skip sizes), we chose to map samples using default settings  in BWA-mem v0.7.3. We then sorted alignments with Samtools v0.1.18, marked PCR-duplicates with Picard v1.85 and identified single-nucleotide polymorphisms (SNPs) by local re-assembly with Genome Analysis Toolkit (GATK) v3.7.064 . Individual records produced by the HaplotypeCaller algorithm were subsequently merged for population-based genotype and likelihood assignment (GATK GenotypeGVCFs). Next, we calibrated variant filters by incrementally tightening thresholds for genotype quality (Q), read-depth (D) and local polymorphism density (C) until non-reference homozygous SNP-calls for TcI-Sylvio reached asymptotic decay. We then applied a virtual mappability (V) mask to exclude variant-calls in unreliable mapping areas of the reference genome. Specifically, we generated synthetic, non-overlapping 125 nt sequence reads from the PacBio assembly and mapped these back to itself with the Genomic Multi-tool software suite65. Only variants from areas with perfect, i.e., singleton (V = 1), synthetic mapping coverage were kept for analysis. Regions without singleton synthetic mapping coverage represented areas of low sequence complexity and/or redundancy and made up large fractions of all reference chromosomes , samples retained tens of thousands of homozygous variant loci, whereas TcI-Sylvio Illumina vs. TcI-Sylvio PacBio showed just 58. Nevertheless, the guide-sample presented ca. 20,000 small insertions and ca. 1,000 small deletions relative to the reference. We placed an additional mask ± 3 nt around these positions to avoid potential faults in the published genome. Final masking thus disqualified a total of 24 Mb  from polymorphism analysis. This highly conservative, diagnostic variant-screening approach also led us to exclude 24 low-depth samples for which genotypes could not be assigned at more than 40% variant sites. The final set of SNPs (in 59 samples) were annotated with snpEff v4.3t66 using the TcI-Sylvio annotation file (http://tritrypdb.org/common/downloads/release-34/TcruziSylvioX10-1/gff/data).Genomic DNA was extracted from 83 67. The algorithm also imputes missing genotypes from identity-by-state segments found in the data. For haplotype co-ancestry and general comparative analysis, we restricted imputation to sites containing information for >60% samples. Later, in windowed phylogenetic comparison, however, we refrained from genotype imputation, i.e., used only sites with genotypes called in all individuals of the dataset.Heterozygous SNP sites were phased over 30 iterations in BEAGLE v4.168 to visualize genome-wide phylogenetic relationships among samples in split network representation. Neighbor-Net extends Satou and Nei’s neighbor-joining algorithm to accommodate evolutionary processes such as recombination and hybridization that lead to non-treelike patterns of inheritance. We also optimized a general time-reversible (GTR) substitution model with ascertainment bias correction (for accurate branch lengths in the absence of constant sites) to construct phylogenies from proportions of non-shared alleles, i.e., considering two haplotypes per variant site. Haplotype concatenations were also used to derive a minimum-spanning network, the set of edges that links nodes  by the shortest possible cumulative distance . We inferred genetic subdivisions in the sample-set by unsupervised k-means clustering and discriminant analysis of principle components (DAPC)69. These analyses applied genetic distances as the proportion of non-shared genotypes at all variant loci , as did Neighbor-Net and subsequent measurements of FST. After phasing heterozygous SNP sites (see above), we used fineSTRUCTURE v2.0.428 to recover traces of identity-by-descent in similar haplotypes. This program was recently used to expose hybridization events in congeneric T. congolense12, as well as to disentangle reticulate ancestries in the closely-related L. donovani complex39. Its Chromopainter algorithm constructs a semi-parametric summarization of co-ancestry among all pairs of individuals based on variable rates of haplotype-sharing and linkage disequilibrium across sample genomes. We applied fineSTRUCTURE over a uniform recombination map, running 6 × 105 Markov chain Monte Carlo (MCMC) iterations (1 × 105 iterations burn-in) and 4 × 105 maximization steps in the final tree-building step. Following indications of mosaic inheritance in these analyses, we assessed phylogenetic (dis)continuity by comparing genotype-trees built for individual chromosomes using neighbor-joining as implemented in ape v5.170 in R. We also built distance matrices based on haplotypes phased without imputation (see previous section) to quantify changes in genetic similarity between windows within chromosomes.We used the Neighbor-Net algorithm in SplitsTree v4π), Watterson’s theta (θ) and FIS using hierfstat v0.04-2271 in R. FIS values rate heterozygosity observed within and between individuals, varying between -1  and 1 . Values at 0 indicate Hardy–Weinberg equilibrium. We also measured rates of shared and private allele use , assessed variant neutrality based on Tajima’s D, quantified haplotype diversity by counting unique haplotypes per 10–100 kb, and scanned for long runs of homozygosity using VCFtools v0.1.1372. To determine linkage patterns within chromosomes 1, 5, 21, and 26  as the correlation between genotypic allele counts and then binned r2 into distance classes (from 0 to 100 kb in increments of 2 kb) to visualize relationships between map distance and linkage disequilibrium in R. These analyses were also run separately on core sequence areas, as defined by areas of synteny among TcI-Sylvio, T. b. brucei and L. major sequence assemblies annotated at http://tritrypdb.org. Intra-haplotypic recombination is unlikely to accompany meiotic crossover events in these areas of the genome27. Furthermore, we considered the extent to which our multiple-clone sampling strategy (chosen to avoid underrepresentation of SNP linkage (or diversity) within infections) might affect sample independence and variance-based statistical results. Linkage decay plots and other diversity metrics above were therefore also repeated using only one clone per infection source.To assess group-level genetic diversity, we calculated site-wise nucleotide diversity , where ρ denotes nucleotide covariation between sites, r denotes rate of recombination per bp per generation, and F represents Wright’s inbreeding coefficient. The second estimate, Nθ, is based on mutational diversity observed in the sample. Nθ represents the total population size, i.e., the number of cells irrespective of sexual or mitotic origin, and is calculated as θ (1 + F)/4μ, where θ denotes nucleotide variation at single sites and μ denotes the rate of mutation per bp per generation. Nρ/Nθ thus quantifies the frequency of meiotic reproduction in the population. To estimate this quotient from our sample, we derived θ from Watterson’s estimator at non-coding sites and derived ρ based on reversible-jump MCMC likelihood curves generated by the interval program in LDhat74. We used 1 × 107 MCMC iterations with 2000 updates between samples and block penalties set to five. We estimated r from the equation r = 0.043 × S−1.310 and μ from the equation μ = 2.5866 × 1010 × S0.584. These regression models were developed in Rogers et al.75 based on the observation that genome size (S) correlates strongly to rates of recombination and mutation in unicellular eukaryotes. We validated ρ estimates by simulating input for LDhat in two ways. First, we created sequence alignment maps for ten non-recombinant individuals based on observed genotypes using BAMSurgeon v1.0.076. Maps were set up for each individual by inserting fixed polymorphisms from the true sample set into TcI-Sylvio sequence reads, then spiking in random mutations at rates corresponding to the average number of pairwise differences in the observed data. Individual SNP records for the ten mutant alignment files were then compiled and merged in GATK as outlined above. In the second approach, we used fastsimcoal2 v5.2.877 to simulate ten non-recombinant and ten recombinant genotypes, applying r and μ from above equations to an effective population of 100,000 diploid individuals under a finite-sites model of evolution for chromosome 1. We also visualized linkage patterns by measuring taxon topology weightings in windowed analysis. Taxon topology weightings provide a means to clarify phylogenetic structure by summarizing the extent to which tree topologies for a subset of samples contribute to the topology of the full tree29. We applied this concept to neighbor-joining trees constructed for overlapping 50 kb sequence windows in PhyML v3.1. Topology weightings were calculated and plotted across chromosomes with loess smoothing (span = 0.125) using scripts provided at GitHub repository https://github.com/simonhmartin/twisst. These analyses prompted further sequence visualizations with Artemis v.16.0.078 genome browser tool.Following methods established to quantify complex microbial life cyclesm), i.e., a median-of-means (Mm), for each chromosome. After testing at various distribution points, we let the 30th percentile (p30) of (skewed) Mm values represent expectations for the disomic state, estimating copy number for each chromosome by dividing its Mm by the sample’s p30 value and multiplying by two. This procedure produced estimates of disomy for all chromosomes of the TcI–Sylvio guide-sample and outperformed techniques based on different window-sizes as well as those refined according to sequence annotation  or mapping quality (data not shown). We validated cases of chromosomal copy number variation by plotting kernel densities for m, as well as by assessing raw depth and alternate allele frequencies across variant sites. True, whole-chromosomal trisomy, for example, should translate to chromosome-wide elevations in read-depth and reductions in minor allele contributions to ca. 33%  at all heterozygous  sites. Intra-chromosomal amplification, in contrast, should create local shifts in read-depth and allelic composition within chromosomes. In follow-up assessment of temporal and sub-clonal ploidy variation, we re-sequenced three clones and derivative subclones on the Illumina NextSeq 500 platform  1640 medium, then divided into 200 µl aliquots across multiple 96-microwell plates. Wells presenting individual cells were incubated at 28 °C for ca. 6 weeks and further expanded in LIT.To estimate somy levels for each sample, we first measured mean read-depth for successive 1 kb windows spanning each chromosome. We then calculated the median of these windowed depth-means (Further information on research design is available in the Supplementary InformationPeer Review FileReporting Summary


Xanthoceras sorbifolium (yellowhorn). Long‐term over utilization along with climate change is affecting the viability of yellowhorn wild populations. To preserve the species known and unknown valuable gene pools, the identification of genetic diversity “hotspots” is a prerequisite for their consideration as in situ conservation high priority. Chloroplast DNA (cpDNA) diversity was high among 38 natural populations  and characterized by high genetic divergence (FST = 0.765) and relatively low gene flow (Nm = 0.03), indicating populations isolation reflecting the species’ habitat fragmentation and inbreeding depression. Six out of the studied 38 populations are defined as genetic diversity “hotspots.” The number and geographic direction of cpDNA mutation steps supported the species southwest to northeast migration history. Climatic factors such as extreme minimum temperature over 30 years indicated that the identified genetic “hotspots” are expected to experience 5°C temperature increase in next following 50 years. The results identified vulnerable genetic diversity “hotspots” and provided fundamental information for the species’ future conservation and breeding activities under the anticipated climate change. More specifically, the role of breeding as a component of a gene resource management strategy aimed at fulfilling both utilization and conservation goals.Genetic structure and major climate factors may contribute to the distribution of genetic diversity of a highly valued oil tree species  The amplification for all the chloroplast regions consisted of 4 min at 94°C, followed by 3 cycles of , 3 cycles of , 3 cycles of , 3 cycles of , 23 cycles of , extending with 10 min at 72°C, finishing with 4°C. PCR products detected using polyacrylamide gel electrophoresis (PAGE) at 120 V for 5 hr (agarose gel. (0.8 g) for run PCR products in 1X TAE along with 1 kb ladder as molecular size marker; 0.5 µl 4S Red Plus Acid Stain for visualization). Sequencing was performed at Sangon Biotech . All the sequences were manually checked and edited using MEGA 6.0  regions with identified 24 haplotypes.Total genomic DNA was extracted from the leaf material following the cetyltrimethylammonium bromide (CTAB) procedure Clarke, . For PCR2.32.3.1Hd;  were ca Tajima,  for sele2.3.2Aesculus assanuca, Acer coriaceifolium, and Koelreuteria paniculata as out‐group. PAUP and RAxML‐HPC BlackBox Toolbox in CIPRES Portal v.3.3 .Phylogenetic relationships based on cpDNA sequence data were conducted using the Bayesian phylogenetic analysis following the online version of the program MrBayes at CIPRES Portal v.3.3  ordination to identify the climate variables that are correlated with the distribution of haplotype diversity. CCA was calculated by the “cca” function in package “vegan” in R 3.3.1  = 0.760; average number of nucleotide differences (K) = 4.616, with six populations  harboring high haplotype diversity (Hd: ranged between 0.600 and 0.705) and could be recognized as genetic “hotspots” (Table FST (population subdivision) and Nm (gene flow) were 0.893 and 0.03, respectively, indicating that the observed genetic difference mainly occurred among rather than within populations. Tajima's D neutrality test varied among the 38 populations but did not differ significantly from zero  is mainly distributed in the northern part of Luliang (Shanxi Province) and Taihang  Mountains, B (H14 and H15) is representative of the Taiyuan Basin (Taiyuan Province), C (H7) located in northeastern Qinling Mountain (Shaanxi Province) and east of the Yellow River, D  representing the western part of Luliang Mountain (Shanxi Province), and E  distributed in the western part of Ziwuling Mountain (Shaanxi and Gansu Provinces). The 24 cpDNA microsatellite haplotypes network analysis indicated that there are 6 mutation steps between H7 and H14, 5 steps between H7 and H9, and 3 steps between each of H7 and H23, H8 and H23, and H1 and H19 , maximum parsimony (MP), and maximum likelihood (ML) , the phylogenetic relationships among the 24 yellowhorn cpDNA microsatellite haplotypes showed similar results  ordination results, 12 , mean warmest month temperature (MWMT), mean coldest month temperature (MCMT), temperature difference between MWMT and MCMT (TD), degree‐days below 0°C (DD < 0), degree‐days above 5°C (DD5), degree‐days above 18°C (DD < 18), number of frost‐free days (NFFD), precipitation as snow (PAS), extreme minimum temperature over 30 years (EMT), extreme maximum temperature over 30 years (EXT), and Hargreaves reference evaporation (Eref)) out of the 16 studied climate factors significantly associated with haplotype distribution Figure .44.1Hd = 0.760, K = 4.616) compared to other species , but very low gene flow (Nm = 0.03) among the studied 38 populations, while no individual population value of Tajima's D was significantly different from random expectations. Similar results also found in other long‐lived tree species , the capsule shells become hard and woody. These fruits contain on average 15–25 seeds (with hard seed coats)  that was frequently observed in the studied populations could have acted as a vector aiding the spreading of this haplotype. The presence of the Ziwuling Mountain that occurred in 1.67–1.45 Ma BP  with other species in Sapindaceae  that is coupled with very low gene flow indicates that yellowhorn populations have been isolated for a very long time and may undergo further habitat fragmentation. Furthermore, while the mode of inheritance of yellowhorn's chloroplast is unknown, it is expect to be material as most angiosperms . This habitat distribution type is more sensitive to edge effects and habitat fragmentation which may be exasperated by global warming , the Chinese National Natural Conservation Areas program is providing the most restrictive laws to protect wild plant and animal species , the natural distribution areas of rare or endangered species are considered as locations for the establishment of new protect area . Our yellowhorn previous research showed that the total distribution area of wild yellowhorn is expected to decline by 10%–17%  in the next 30–50 years by the impact of global warming and identified the species as endangered in its wild populations  in Convention on Biological Diversity  could be implemented  and conservation initiative is proposed.None declared.Y.A.E. and J.C. conceived and designed the experiments. R.Z., Y.M, B.T., and J.C. performed the experiment. R.Z., Y.M, B.T., and Q.W. contributed to data analysis. R.Z., Q.W., W.G, and Y.A.E. wrote, edited, and reviewed the MS.


In North America and around the globe, there has been emerging recognition of the size and scope of urban shrinkage, yet little is understood about how decline impacts commercial centers and downtowns. In order to facilitate geographically targeted policymaking, this paper examines the physical patterns of downtown decline in three distinct regions. We seek to test the hypothesis that differences in the process of urban decline in downtown districts vary due to national or historic context. Using statistical analysis and direct observations, we found that while the scale of population decline was greatest in New England, downtowns in both Ontario and Québec have seen substantial decline and have appeared to have better weathered the change with respect to physical signs of decline. This haDecline or shrinkage in the urban planning literature has often been used to describe population loss, employment loss, or decrease in neighborhood quality . The very physical fabric of neighborhoods—these artifacts—is expected to “erode away” in a period of decline. For many of the cases Rust studied, once the decline began, the effects are “expected to be felt most strongly a generation after the cessation of growth and to persist for up to 50 years” (p. 187). For the booming cities of today, these results present a clarion call for a new way to do planning; a new model is needed to think about neighborhood change in the face of ongoing population and housing loss .Regarding depopulation, research has established parallel patterns of depopulation in both the USA and Canada . An examination of retail patterns in shrinking Canadian cities by the Center for the Study of Commercial Activity (CSCA) at Ryerson University found that “Growing cities have more stores in downtowns…” than declining cities Simmons , p. 6. TJust as research in the USA has shown the impact of depopulation, Canadian research has confirmed that “the loss of population by a city may signal more than a simple decline in market size. The loss of population becomes a signal that both leads to disinvestment and discourages new investment and enterprises.” (p. 6). The cascade effect is the loss of even further commercial activity in downtowns, the focus of the next section.Prior to discussing our research on downtowns, a brief introduction to the concept is necessary. Traditionally, downtowns have been the “regional hubs of commerce, culture, and shopping” What is missing in this literature is any substantial examination of ways that shrinking downtowns can adjust to a smaller business footprint, to fewer jobs, fewer businesses. A comparative view is especially useful here, because US downtowns have had a mixed history of efficacy in managing vacancy, while Canadian downtowns have been viewed as more successful:For Canadians, downtowns have traditionally been regarded as safe, healthy, and vibrant places, especially when compared to their U.S. counterparts. However, multiple pressures from the suburbanization of population and commercial structure (facilitated by car-based transport infrastructure expansion), the general aging of the downtown, and the demise of the downtown department stores have resulted in the signs of decline in many of Canada’s downtowns , a novel database only recently made available to interested researchers . Though changes in housing occupancy can happen for a variety of reasons, these data correlated strongly with segments of the US Sunbelt that experienced widespread vacancy and abandonment during the Great Recession in the USA. Building on this system, the Canadian Census has recently made occupancy information available at census tract level, extrapolated based on broad-scope decennial surveys. Both of these approaches allow for the systematic collection and analysis of occupancy data at fine temporal resolutionsA bigger challenge is understanding abandonment in commercial centers and downtowns. USPS data are also available for commercial properties, but businesses do not constitute themselves in the same way that residential households do: there are minimum square foot requirements for domiciles, where a business can occupy an address with only a mailbox. Beyond government sources, real estate scholars have developed their own proprietary databases for measuring and studying commercial vacancy. The Centre for the Study of Commercial Activity at Ryerson University in Toronto regularly produces reports on the state of the commercial real estate industry in Canada. Hernandez et al.  concludeThe aforementioned literature on shrinking cities and downtowns helps us consider a broader conceptual framework that can be applied in testing the following hypothesis: Differences in the process of urban decline in downtown districts vary due to national or historic context. While certainly the myriad of forces at work in cities and regions play a role in shaping what happens in a downtown, the scholarly literature cited above tells a story which we present in Fig. What follows such decline will most always be a concomitant decrease in demand for downtown real estate. Given that a downtown functions as a center for a city, in the face of larger decline, the number of firms, organizations, investors, and individuals seeking real estate in the downtown is expected to fall. The consequence of such a drop in demand for downtown real estate is a decrease in the occupancy rates of downtown buildings. With lower rents (and lower profits), building conditions are also expected to degrade due to decreased investment in maintenance, protection, and care.Unfortunately, the story does not end there due to a well-documented trend of how vacancy tends to spread (explored in the literature above). Figure The empirical analyses run for this study focus on data sets that indicate change in downtown building stock. Population change is frequently used as a proxy to measure urban decline, though we find it to be an imperfect measure—and one that could in fact confound our analysis. Rather, this study looks to identify prevalence of increasing vacant commercial and residential spaces and buildings.This study offers a methodology for cross-national comparison of downtown real estate trends over the last decade, adding to the gaps in the literature around the need to study, empirically, the processes of urban decline through a comparative lens. The choice of New England, Québec, and Ontario was made because they act as sister regions across an international border, with many common and many unique economic, political, and social frameworks and institutions, and their histories of managing vacancy and depopulation in their downtowns are regarded as quite different.For New England, housing occupancy data aggregated and disseminated at the postal code level by USPS was utilized in conjunction with US Census demographic, housing, and employment data. For Québec and Ontario, all of the data are obtained from Canadian quinquennial Census collections from 2006 to 2011 at the census tract scale. Older data have been widely seen as incongruent with the 2006 data and no more recent data are available publicly. Likewise, only population data were publicly available at the municipality scale, making comparisons with the New England cities difficult.To begin, we examined some basic demographic and housing characteristics of all New England, Ontario and Québec cities. Next, in order to more clearly understand the phenomenon of downtown shrinkage, we sought to develop a method to identify CBDs. From our list of all New England cities with a population over 20,000, we began to search online for zoning maps or Chamber of Commerce diagrams to indicate the boundaries of CBDs. We found 41 cities with delineated CBDs, and then were able to roughly map out the corresponding census tracts in each city . To identify CBDs in Ontario and Québec, we used a combination of Google maps and Streetview and interviews with local officials to delineate boundaries.Based on the theory that CBDs tend to be predominantly business oriented, we returned to the full list of New England cities and selected all census tracts within those cities. Next, we calculated the ratio of total businesses to total residences for the 16,000 census tracts that fulfill this designation. Among those 41 New England cities where we acquired precise CBD boundaries, the CBD census tracts ranked in the top 20% of the business-to-residence ratio, thus confirming the validity of this approach.Case study cities were chosen from the results of the above analysis using a two-step process. First, cities with clear signs of population decline and downtown vacancy were identified. Of the remaining cities, selections were made with the aim of reaching a broad range of urban places, including differences in density, size, population levels, and economic conditions. Nine cities in total, three from New England, three from Ontario, and three from Québec, were sampled for the study.New England is a diverse region, comprising six US states, Maine, New Hampshire, Vermont, Massachusetts, Rhode Island, and Connecticut. Its largest city is Boston and today boasts a mix of industrial, technological, service, and extractive industries. Ontario’s largest city is Toronto, which includes a substantial metropolitan area that dwarfs all other economic activity in the Ontario region, making Ontario that biggest contributor of all Canadian provinces toward GDP. Linguistically and culturally, Quebec is distinct from the rest of Canada and from New England, with its capital city of Montreal, and the region is a diverse mix of extractive, agricultural, high-tech, and industrial Haddow .We conducted direct observation of the conditions of all buildings within the CBDs of the nine case study cities  to help minimize disparities in the data. However, the categorization of direct observations by multiple individuals inherently introduces some subjectivity and potential inconsistencies to the data collection process. To further limit direct observation variance, if any team member was unsure of how to score a given address during a visit, other members of the research team were solicited for their opinions either based upon photographs taken or based on Google maps street view. Likewise, if there were buildings that did not have clear addresses during the visit, we sought to determine this with online mapping sites (or just by typing a business name into a search engine). This not only avoided incorrect categorizations, the discussion and analysis helped build understanding and cohesion within the research team. Due to limitations in our travel budget, we did not physically visit Baie-Comeau, instead relying on interviews with local officials and conducting a Google Streetview tour of the CBD there.In order to analyze the direct observation data, we first mapped the spatial patterns of occupancy and condition. The data were geocoded using tabular data and Google’s geocoding engine. The resulting KML file was imported to ArcGIS and joined with the complete qualitative findings. Inverse distance weighted interpolation was used with a fixed search radius of 0.001 to complete the dataset. Lastly, we adjusted the symbology of the results from the inverse distance weighted interpolation to create the heat map visualizations.p value is returned, the null hypothesis is rejected meaning that either: (1) the spatial distribution of high and/or low points are more spatially clustered than would be expected (indicated by a positive z-score), or (2) the spatial distribution of high and low points are more spatially dispersed than would be expected (indicated by a negative z-score). Although Moran’s I can tell us whether spatial autocorrelation occurs, it does not provide detailed evidence of where statistically significant clusters occur. LISA provides a more localized analysis by assessing the similarity of each observation with its surroundings. LISA analysis detects five potential scenarios: (1) hot spots , (2) cold spots , (3) spatial outliers  spatial outliers , and (5) locations without statistically significant autocorrelations.We also conducted spatial analysis of the direct observation data using GeoDa software to compliment the visual analysis of the mapped data. In order to capture whether any of the cities exhibited statistically significant clustered patterns of occupancy or condition, the spatial autocorrelation of the direct observation data was tested by calculating global Moran’s I as an overall measure of the phenomenon and Local Indicator of Spatial Association (LISA) to gauge the local effects of spatial autocorrelation. Moran’s I Moran  tests thThe concept of spatial autocorrelation relies on notion that contiguous spatial elements can influence one another. Generally, a dichotomic contiguity matrix is defined based on whether one spatial area touches the boundary of another. However, with point data, a threshold distance is defined to gauge whether two points are contiguous. As this analysis concentrates on downtown CBDs, we defined the threshold distance to be 243 m (800 feet)—roughly two average North American city blocks. And since the observation data was categorical, dummy variables were created to test the spatial autocorrelation of each of the occupancy and condition variables (0 through 4) individually, as well as select combinations.The US Census, combined with other US real estate and housing databases, allowed for a rich exploration of both historic trends and contemporary conditions in New England at a fairly fine-grained level. In contrast, the Canadian Census underwent a major transformation recently, making historical comparisons difficult and the limitations of publicly available data make the results quite coarse geographically.For New England, we began with a broad picture of all 182 cities and towns with a population of 20,000 or higher in the 2010 US Decennial Census. Among those cities, their populations grew on average 17% between 1980 and 2010, and their number of occupied housing units grew by 31%. But growth was not a hallmark of all New England cities . Following the Canadian Census definitions, dwellings occupied by usual residents was used as a proxy for occupied housing units. The results show in Table Based on the results from our quantitative analysis, nine cities from the three regions were selected as individual case studies. Cities were selected in each region where sustained population, housing, and economic decline were present and they were, statistically speaking, representative of shrinking cities in their region across density, size, population levels, and economic conditions. Additionally, in our selection, we sought some degree of geographic diversity. Bridgeport (CT), Springfield (MA), and Lewiston (ME) were selected from New England. Leamington, Windsor, and Chatham-Kent were selected from Ontario. And Shawinigan, Baie-Comeau, and Dolbeau-Mistassini were selected from Québec. Table For each CBD we collected between 62 (Baie-Comeau) and 270 (Shawinigan) observations  where the other two cities had none rated as poor.To better understand these results and their spatial distribution, we mapped and analyzed these trends for all nine cities. The maps show where concentrations of vacancy Fig.  and poorExamining the condition of the CBDs Fig. , the NewThe spatial autocorrelation of the direct observation data was first measured using global Moran’s I. Results from the spatial analysis of the direct observation data echo the trends identified visually using the heat maps Figs. , 3. ConcThe stronger scope and scale of vacancy in New England and Ontario inferred from the heat map analysis was confirmed as Bridgeport, Lewiston, Leamington, and Windsor all had statistically significant clustering patterns. In Lewiston, partially occupied buildings with for rent signs (occupancy variable 2) were clustered, whereas the other three cities had clusters of unoccupied buildings (occupancy variable 3). As global Moran’s I only provides insight to the study area as a whole, the significant results in Bridgeport, Lewiston, Leamington, and Windsor encourage further local analysis.Results from LISA Fig.  depict tWith measurable population decline and increases in vacancy across Ontario, Québec, and New England, what we sought to do in this research was to understand how that change manifested spatially in downtown areas. Drawing on a conceptual framework presented in Fig. The Québec cities show less vacancy and less concentration of poor condition buildings. Baie-Comeau and Dolbeau-Mistassini, in particular, appeared to have managed depopulation and adjusted to a smaller population better than the New England cities. In Ontario, Windsor looked most similar to the New England cities, with high levels of vacancy and relatively poor building conditions, where the smaller Ontario cities looked more like their Québec counterparts. In general, the smaller cities faired better than large ones.  This could be the result of less relative activity and dependence on the downtown area due to a more rural economic base. The downtowns of these smaller cities simply may not play the same role as their bigger city equivalents and, therefore, have not experienced the same physical decline.The research also raises key questions about the differences between residential and commercial land use in city centers. We focused here on commercial uses, but residential patterns may also play a role in occupancy and conditions of buildings. Future research should seek to further untangle the relationships between residential and commercial land uses in these shrinking downtowns. The spatial analysis revealed clusters of low occupancy in several of the cities. The direct observation data and resulting heat and LISA maps Figs. , and 4 sAll three of the Québec cities examined had no statistically significant clusters of low occupancy. Cities, such as Shawinigan, with no identified vacancy hot spots demonstrate an opportunity to rethink the core of the CBD. Here, a new thinking around future uses can be valuable. While much of the shrinking cities literature has examined the programming of shrinking residential areas for non-residential uses, the shrinking downtown is harder to envision that way. In this research, we have found that many of the shrinking cities studied are surrounded by rich and economically vital agricultural regions. While urban agriculture has been widely seen as a prospective future use in residential areas of shrinking cities, CBDs would hardly seem to be compatible with agriculture. But with proper siting and considerations for preserving existing streetscapes, it might not be such a crazy idea.We recommend downtown planners consider the emerging urban practice of hydroponic, indoor,  farming. Using containers retrofitted to support hydroponic farms, planners can work with property owners to preserve the historic facades and exteriors of CBD buildings and then “drop” in these pre-built, pre-configured, and module hydroponic containers. Efforts to do this in relatively open areas of Boston have been met with much success, and we recommend exploration of this idea for the shrinking downtowns which have been the subject of this study Abraham .