Xanthomonas citri pv. citri, a genetically monomorphic bacterium causing Asiatic citrus canker (ACC). Using a genotyping method that did not require cultivating the bacterium or purifying DNA, we deciphered the pathogen's spatial genetic structure at several microgeographic scales, down to single lesion, in a situation of ACC endemicity. In a grove where copper was recurrently applied for ACC management, copper‐susceptible and copper‐resistant X. citri pv. citri coexisted and the bacterial population structured as three genetic clusters, suggesting a polyclonal contamination. The range of spatial dependency, estimated for the two largest clusters, was four times greater for the cluster predominantly composed of copper‐resistant bacteria. Consistently, the evenness value calculated for this cluster was indicative of increased transmission. Linkage disequilibrium was high even at a tree scale, probably due to a combination of clonality and admixture. Approximately 1% of samples exhibited within‐lesion multilocus polymorphism, explained at least in part by polyclonal infections. Canker lesions, which are of major biological significance as an inoculum source, may also represent a preferred niche for horizontal gene transfer. This study points out the potential of genotyping data for estimating the range of spatial dependency of plant bacterial pathogens, an important parameter for guiding disease management strategies.Although some plant pathogenic bacteria represent a significant threat to agriculture, the determinants of their ecological success and evolutionary potential are still poorly understood. Refining our understanding of bacterial strain circulation at small spatial scales and the biological significance and evolutionary consequences of co‐infections are key questions. The study of bacterial population biology can be challenging, because it requires high‐resolution markers that can be genotyped with a high throughput. Here, we overcame this difficulty for  Understanding how clonal and partially clonal pathogens initially adapt to new environmental conditions and, subsequently, propagate is key when it comes to anticipating large‐scale epidemics in agro‐ecosystems.Many economically important plant diseases are caused by bacteria  is the causal agent of Asiatic citrus canker (ACC), a striking example of a plant bacterial pathogen that poses a major economic risk to agriculture on several continents. It affects crop profitability, both directly  and indirectly  has recently been demonstrated by the emergence of plasmid‐borne copper resistance. Copper resistance has been first identified in Argentina in the 1990s and two decades later in Reunion Island where the present study was conducted , which are associated with insect damage or grove maintenance operations  at local to regional scales  and/or does it reveal the existence of polyclonal infections, which involve genetically distant strains? Consequently, what is the spatial genetic structure of X. citri pv. citri populations and what does it reveal about the heterogeneity of inoculum and the pathogen's transmission capability in citrus groves?The aim of the present study was to better understand how epidemiological dynamics determine the potential for HGT in a situation of ACC endemicity. To achieve this, we investigated the distribution of genetic variability at several microgeographic scales , where bacterial cells interact, using a hierarchical sampling of ACC isolates in two citrus groves in Reunion Island. Recent molecular epidemiology studies on 22.1X. citri pv. citri isolated from Kaffir lime (Citrus hystrix) in Réunion Island in 2010  prepared in 0.01 M tris buffer pH 7.2  from 18‐hr‐old cultures on YPGA . Two strain mixes, (a) LH212/LH220 polymorphic at 10 TR loci and (b) LH318/LH339 polymorphic at four TR loci, were prepared from the suspensions with various relative concentrations (expressed as CFU/ml): strain 1 1 × 106 / strain 2 1 × 106, thereafter referred to as 1:1; 1 × 106/1 × 105, 10:1; 1 × 106/1 × 104, 100:1; 1 × 105/1 × 106, 1:10; 1 × 104/1 × 106, 1:100 . These mixes were infiltrated into the mesophyll of Kaffir lime leaves from the youngest vegetative flush (leaf size ranged from two‐thirds to three‐quarters of full expansion). For each strain combination, we inoculated eight sites (0.3–0.4 cm2) per leaf on duplicates, consisting of two leaves on different one‐year‐old potted plants. A suspension of each strain  containing 1 × 106 CFU/ml was inoculated as the genotyping control, as explained above. Leaves inoculated with tris buffer were used as the negative control. The experiment was replicated once. Kaffir lime plants were placed in growth chambers at 30 ± 1°C day and 26 ± 1°C night and 80 ± 5% relative humidity with a photoperiod of 12 hr for 22 days. Leaf disks centered on individual lesion were punctured with a 12‐mm sterile punch and individually soaked in 0.5 ml sterile tris buffer supplemented with 0.2% Tween‐20 in AB0661 microplates  for 1 hr at room temperature under gentle (50 rpm) orbital shaking.We used four pathotype A, genetic lineage 1 (DAPC 1) strains of 2.2X. citri pathovars ; 0.5× Q‐solution ; and HPLC water to yield a final volume of 15 µl per reaction. PCR amplifications were performed in a GeneAmp PCR System 9700 Thermocycler (Applied Biosystems) under the following conditions: 2 min at 98°C for hot‐start activation; 35 cycles of denaturation at 98°C for 10 s, annealing at temperatures ranging from 64°C for primer pool 3  and 4  to 68°C for pool 1  and 2  for 15 s, extension at 68°C for 1 min; and a final extension step at 68°C for 30 min. Aliquots (1 µl) of amplicons diluted 1/40 to 1/100 were mixed to 10.9 µl of Hi‐Di formamide and 0.1 µl of a GeneScan 500LIZ internal lane size standard (Applied Biosystems). Capillary electrophoresis was performed in an ABI PRISM 3130xl genetic analyzer (Applied Biosystems) using performance‐optimized polymer POP‐7 at 15,000 V for ≈ 20 min at 60°C, with an initial injection of 21 s. Electropherograms were analyzed using Genemapper software 4.0 (Applied Biosystems). In the proof‐of‐concept experiments involving inoculum relative concentrations of 1:10, 10:1, 1:100, and 100:1, densitograms were standardized so that the height of the peak corresponding to the most concentrated strain was adjusted to 7,000 relative fluorescent units (rfu). Once standardization was achieved, peaks corresponding to the least concentrated strain were scored positive when their height exceeded 100 rfu. Strain IAPAR 306 of X. citri pv. citri was used as a positive control in each experiment , referred to as grove 1 and grove 2, respectively. The groves were 12 and 5 years old at the time of sampling. They were managed according to a standard integrated pest management scheme .2.5X. citri pv. citri population structure.At least 75% of trees in each grove were randomly sampled for canker leaf lesions for assessing the diversity at grove scale. Up to five lesions per tree were assayed by direct MLVA‐14 genotyping (as described above), yielding a dataset in which MLVA‐14 data were obtained for 361 and 420 single canker lesions in groves 1 and 2, respectively. All symptoms were examined with a stereoscopic microscope to eliminate the ones that could represent two coalescing lesions. For each sample, unambiguous microsatellite data  were used to assess the An intensive sampling of six trees in grove 2 (distant one to another by 13–50 m) was performed in order to assess the diversity at the branch and tree scales. Twenty‐nine to thirty three lesions from each of the three main branches of each analyzed tree were sampled, yielding a total number of samples per tree ranging from 91 to 96, which were subsequently analyzed as described above.X. citri pv. citri was performed on KC semi‐selective medium using 10‐fold dilutions of macerates from nine lesions (collected in grove 1), where polymorphism at >4 TR was recorded by direct MLVA‐14 genotyping  and private allelic richness (Ap) were computed using the rarefaction procedure for unequal sample sizes with HP‐RARE version 1.0 , and clonal evenness were calculated with the poppr 2.2.1 package in R  and Euclidean distances in PHYLOVIZ v1.1  was performed using the FST and RST values were computed, and their significance was tested with 999 permutations using arlequin. RST may better represent differentiation when the mutational process follows a stepwise mutation model  to determine whether stepwise‐like mutations contributed to genetic differentiation . Population pairwise 2.7ijF or r (see below), is calculated between individuals for each possible pair that is a specified distance apart. These pairwise genetic coefficients are regressed on pairwise spatial distances. We computed mean values per distance interval. Spatial autocorrelation analyses were conducted on groves 1 and 2. Tree spacing was 4 m, both in and between rows. We defined different distance classes with a spatial lag ranging from 4 to 10 m. Analyses were performed using GenAIEx 6.5 , which provides a measure of the genetic similarity between pairs of individuals belonging to the same distance class. An autocorrelogram plots r as a function of distance class. The assumption of absence of SGS was tested by random permutation of all individuals from each grove. We then estimated the 95% confidence of the null hypothesis of no spatial genetic structure around r after 999 permutations . If F(d), the mean estimates of ijF over a given distance interval d, tends to decrease linearly with d or ln(d), the extent of SGS, referred to as the Sp statistic, can be quantified by the ratio −F/b(1 − F(1)), where Fb is the regression slope of ijF on ln(ijd), separating the pairs of isolates, and F(1) is the mean kinship coefficient of the first distance class  is approximately linearly proportional to d or ln(d)  was estimated after 999 permutations. We tested the significance of the SGS by comparing the observed regression slope Fb with those obtained under the null hypothesis, by permuting individual locations (N = 999). The standard error of the regression slope Fb was obtained by jackknifing over loci (N = 999). It was used as an estimate of the variability of Sp  over the same range of 0–160 m (not enough pairs for comparisons over larger distances) using the whole datasets, and in the case of grove 2, independently for the two main genetic clusters identified .The spatial genetic structure (SGS) at the individual level  was investigated using a multivariate approach to spatial autocorrelation analysis for multiallelic co‐dominant markers  was estimated for the MLVA data using the poppr package in R . The nonrandom association of alleles was evaluated both on strains and on haplotypes  to minimize the effect of frequent haplotypes in some local populations.Multilocus linkage disequilibrium . Canker‐like lesions were produced by all strains and strain mixes, but not by the negative controls. All the samples with equi‐concentrated bacterial suspensions produced the expected amplicons. When differences in TR numbers among strains were ≤2 at a given locus, the peak height ratio of amplicons was around 1. This suggests that the PCRs were equally effective, irrespective of the allele. As expected for the TR differences >2, the PCR amplification was more efficient for the shortest allele (Table 3.2C. hystrix), some of which were contaminated in the nursery prior to grove establishment. Disease incidence in both orchards was high (>0.8), in agreement with the species' high susceptibility to X. citri pv. citri pathotype A . At this scale, permutation test analyses in SPAGeDI indicated that RST was significantly different from FST (p < 0.001). This suggests that allele size was informative for assessing population differentiation.The two groves under study were established with Kaffir lime plants . Evenness was 0.961, indicating that the frequencies of the different haplotypes were quite similar. Haplotypes structured as 44 clonal complexes (CC), that is, networks of single‐locus variants (SLV), and 213 singletons, that is, haplotypes with no identified SLV, the latter representing 59% of the samples. The largest CC in size consisted of 31 haplotypes . In grove 2, the structure was markedly different. The 409 MLMGs analyzed were separated as 273 distinct haplotypes, corresponding to a mean allelic richness of 7.28 . They structured as 22 CCs and 53 singletons, the latter representing only 13% of the samples. The largest CC in size consisted of 105 haplotypes, including 54% of the samples analyzed. Some haplotypes were strongly overrepresented in the dataset, which yielded an evenness of 0.417.n1 = 306), cluster 2 (n2 = 62), and cluster 3 (n3 = 41) , evenness was markedly different for cluster 1 (0.410), as compared to clusters 2 (0.944) and 3 (0.923). This suggests that the heterogeneous haplotype frequencies identified in grove 2 were actually restricted to cluster 1.Genetic clusters were delineated from the produced minimum spanning trees. The clusters were defined so that each cluster contained haplotype networks linking up to quadruple‐locus variants. When these cluster delineation rules were applied, data from grove 1 yielded a single cluster and eight singletons, each composed of a single sample , indicated the widespread presence of copper resistance in grove 2 . Copper resistance was not detected in grove 1 (n = 51). All strains collected from grove 2 were subject to microsatellite genotyping. Interestingly, all copper‐resistant strains grouped in cluster 1, while copper‐susceptible strains split into clusters 1 (n = 2), 2 (n = 4), and 3 (n = 2).Assays for the presence of 3.3ijF) decreased continuously with increasing spatial distances between pairs of samples for groves 1 and 2  . This suggests that cluster 1 has a stronger spatial genetic structure.The spatial autocorrelation analysis for two of the three genetic clusters identified in grove 2 produced similar highly positive kinship coefficients for the first distance class. These decreased steadily over distance  or secondary branch scale . All pairwise RST values observed among X. citri pv. citri populations sampled at a tree scale were significant (p < 0.05) (Supporting Information Table RST comparisons between branch populations originating from distinct trees that were strongly differentiated (p < 0.01) was 90%, but dropped to 44% for branch populations sampled from the same tree  was rejected for populations from grove 1  and grove 2 . It was also highly significant when estimated using clone‐corrected datasets . Using both types of datasets for populations sampled from individual trees, except PF315  were successfully genotyped at all TR loci. Most MLMGs from assayed lesions appeared monomorphic . The observed polymorphism most often consisted of single‐locus  and single‐repeat  variations. However, multiple‐locus variations were also observed within single canker lesions  were subject to MLVA‐14. The bacterial population was structured as a CC only in a single lesion  in Japan. The population size of strains differing in aggressiveness due to the presence/absence of a plasmid encoding for an important pathogenicity effector was measured in planta, and the differences between strains were consistently found lower than the two log limitation of our technique , was observed at an infrabranch scale.Our results clearly indicated an isolation‐by‐distance (IBD) pattern at a small scale within groves. In both groves, the highest positive 4.3X. citri pv. citri can occur if free water is available on plant surfaces. The presence of copper ions in free water on trees largely restricts secondary infections by copper‐susceptible strains. The present study is an example of anthropogenic activities driving changes in the population structure of a pathogen in an agro‐ecosystem  and the presence of several ACC‐infected sites in the vicinity of grove 2. Copper resistance, first reported in Reunion Island in 2014  coexist, lesions may also be privileged sites for DNA exchange between genetically distant strains, which share the same niche. Plasmids encoding the major TALE pathogenicity gene pthA were transferable between X. citri pv. citri and Xanthomonas citri pv. aurantifolii, two citrus pathogens  or negative way and are often associated with changes in pathogen load. Gaining an improved knowledge of the impact of such multiple infections is a promising area for future  that are likely to minimize gene or genotype flow and multiple infections  should be promoted to improve the durability of control. New management strategies creating an increased crop plant and environmental heterogeneity of agro‐ecosystems are desirable options for the future of agriculture (McDonald & Stukenbrock, None declared. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Background: The provision of genetic services, along with research in the fields of genomics and genetics, has evolved in recent years to meet the increasing demand of consumers interested in prediction of genetic diseases and various inherited traits. The aim of this study is to evaluate genetic services in order to identify and classify delivery models for the provision of genetic testing in European and in extra-European countries.Methods: A systematic review of the literature was conducted using five electronic resources. Inclusion criteria were that studies be published in English or Italian during the period 2000–2015 and carried out in European or extra-European countries .Results: 148 genetic programs were identified in 117 articles and were delivered mostly in the UK , USA  or Australia . The programs were available nationally , regionally  or in urban areas . Ninety-six (64%) of the programs were integrated into healthcare systems, 48 (32.21%) were pilot programs and five (3%) were direct-to-consumer genetic services. The genetic tests offered were mainly for BRCA1/2 , Lynch syndrome , and newborn screening . Healthcare professionals with different backgrounds are increasingly engaged in the provision of genetic services. Based on which healthcare professionals have prominent roles in the respective patient care pathways, genetic programs were classified into five models: (i) the geneticists model; (ii) the primary care model; (iii) the medical specialist model; (iv) the population screening programs model; and (v) the direct-to-consumer model.Conclusions: New models of genetic service delivery are currently under development worldwide to address the increasing demand for accessible and affordable services. These models require the integration of genetics into all medical specialties, collaboration among different healthcare professionals, and the redistribution of professional roles. An appropriate model for genetic service provision in a specific setting should ideally be defined according to the type of healthcare system, the genetic test provided within a genetic program, and the cost-effectiveness of the intervention. Only applications with proven efficacy and cost-effectiveness should be implemented in healthcare systems and made available to all citizens. In genomic medicine, the transfer of genetic tests from research to clinical practice can be defined as stage three (T3) translational research, which “attempts to move evidence-based guidelines into health practice, through delivery, dissemination, and diffusion research”  framework in which genetic services are offered to individuals and families with or at risk of genetic disorders. In other words, a genetic service delivery model is a combination of personal healthcare services provided by healthcare professionals to individuals and families , and public health services and functions   What are genetic service delivery models? (ii) Who delivers genetic services and where? (iii) Who pays for genetic services and how? (iv) How do providers get paid? (v) Where do providers and consumers get information? (vi) What is the government's role? (vii) What are the cultural, ethical and policy issues? (viii) What are alternative delivery models? (ix) What public policy changes do we need? (x) How is information about genetic services disseminated?  practice setting and financial resources (public vs. private); (ii) service provider and patient access [geneticists vs. primary care physicians/other medical specialists ]; (iii) policy regulation ; (iv) laboratory practice standards ; and (v) information dissemination (methods of providing information about genetic services to patients and service providers). These factors, which are unique to each genetic service delivery model and depend on organizational aspects of the genetic service and on patient characteristics, will be considered in the present literature review. We aim to evaluate genetic services in order to identify and classify the existing genetic service delivery models for the provision of genetic testing in European and extra-European (Anglophone) countries .This systematic review of the literature was carried out within a European multicenter project “The Personalized pREvention of Chronic Diseases-PRECeDI (Marie Sklodowska-Curie Research and Innovation Staff Exchange 2014).”The PRECeDI project is conducted through a multidimensional approach, which includes (i) a preliminary (non-systematic) literature search to identify and define the terms “genetic service” and “genetic service delivery models;” (ii) a systematic review of published literature on existing genetic service delivery models and selected country websites for policy documents; (iii) structured interviews with health experts on genetic service delivery models, policies governing the use of genomics medicine, and evaluation of genetic testing and related services in their respective countries; and (iv) a survey of European Public Health Association (EUPHA) members' knowledge and attitudes regarding the use of genomic applications in clinical practice. This review focuses on genetic service delivery models; thus, results of the policy review, the cross-sectional studies addressing European health experts and EUPHA members are not reported. Moreover, details of the preliminary non-systematic research can be found in the research protocol  services OR genetic(s) service provision OR genetic(s) service delivery OR genomic service delivery OR genetic(s) delivery models. A preliminary non-systematic search and a manual review of references from relevant systematic reviews were also performed. The inclusion criteria were: (i) relevant articles and reports on pilot studies, best practices, and funded projects inherent to genetic service delivery; (ii) provision of all types of genetic tests by genetic specialist teams and healthcare professionals practicing in primary or secondary care; (iii) studies published in English and Italian between 2000 and 2015; and (iv) interventions carried out in European and extra-European (Anglophone) countries . The extra-European countries were used for comparison purposes only. The exclusion criteria were: (i) studies reporting only on genetic counseling services; (ii) descriptive studies where pathways to care were not well defined; and (iii) studies not specifying the type of genetic test considered.The research was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement , the genetic service and its programs ;Information on patients and pathways to care. This section investigates the characteristics of the target population of the genetic programs offered  and pathways to care, as well as cost-effectiveness and efficacy of the genetic program;Genetic service evaluation. This section investigates the strengths and weaknesses of the genetic service and its programs in regard to cost-effectiveness and feasibility of the genetic programs, the genetic service capacity in terms of population and geographic area served, staff qualification, and laboratory standards.Four members of the working group made an independent evaluation of each genetic service and the genetic programs offered using the data extraction form, followed by extensive group discussions. Any discrepancies in individual evaluations were resolved after discussion with the coordinators of the project.For the different types of genetic testing considered in the review , the definitions of the National Institutes of Health (NIH) were adopted [The National Institutes of Health (NIH), The preliminary literature search produced six records that were useful in defining and identifying genetic services and different models of genetic service delivery   genetic programs were integrated into healthcare systems, while 48 (32%) were pilot programs and five (3%) were DTC services . Two stuGenetic tests, most commonly BRCA1/2 , Lynch syndrome , and the newborn screening panel  , were ofThree programs, which were part of a project described in three distinct studies  were mainly informed about genetic services through professional boards; conferences, meetings and workshops; or scientific journals . Other sVarious communication channels were also used to inform patients about the availability of genetic services, largely comprising HCPs, service websites, and the media . Other means reported in some studies were distribution of educational materials  by patient support organizations or genetic services; public education courses ; and community events organized by local organizations. However, in 35 genetic programs, information dissemination to patients was not reported by any means .Information and communications technologies  and associated services were used either alone or in combination to organize medical records ; for vidGenetic services offered their employees training in genetic medicine consisting of continuing education programs; seminars, conferences and workshops; provision of educational materials; interactive computer programs; referral guidelines; and staff supervision by geneticists or genetic counselors . The traSeveral studies reported that physicians from various specialties had a specific background in medical genetics. Medical geneticists and other medical specialists  with genetics knowledge were more common than general practitioners (GPs). Among non-medical HCPs, genetic counselors (73/148 programs), laboratory staff (73/148 programs) and nurses (49/148 programs) also had a genetics background.In 26 programs, the quality of the laboratories involved corresponded to the standards set out in regional or national regulations . These pPatients of both genders across a wide age range (1–62 years) used the genetic programs identified. Females were the only users in 31 programs, mainly for BRCA1/2 testing . PregnanDirect access to genetic services was reported in 48 programs , predomiNon-medical HCPs involved in patient referrals included nurses  and Lynch syndrome (19/23 programs) . The posFamily history collection and risk assessment were provided prior to or during genetic counseling or medical examinations by medical geneticists , other mRisk assessment was performed largely through questionnaires, computer programs and face-to-face interviews. Other tools reported in a few studies were screening tests , medical records, death certificates, cancer registry, telephone counseling and physician referral letters. Family history collection and risk assessment were mostly performed in programs providing BRCA1/2 (52/59 programs), Lynch syndrome (21/23 programs) , and CF Among medical HCPs, genetic testing was initiated principally by medical geneticists ; other mA consent form prior to genetic testing was explicitly required and reported in 29% (43/148) of the genetic programs . Of thesCascade testing on relatives of index cases (probands) was performed in several genetic programs, mainly for BRCA1/2 Lynch syndrome; FH testing  and newbFollow-up services were provided in several programs and the period of surveillance ranged from 12 months  and patient care pathways (II) laid the groundwork for the identification and classification of genetic service delivery models. Delivery models for the provision of genetic testing can be classified into five categories according to which healthcare professional plays the most prominent role in patient pathways to care: (i) genetic services led by geneticists; (ii) the primary care model; (iii) the medical specialist model; (iv) genetic services integrated into population screening programs; and (v) the DTC model . The claIn this model the professional team may include medical geneticists, genetic counselors, and other healthcare professionals . The professional team is responsible for risk assessment, counseling and testing of individuals or families affected or at risk of genetic disorders. Depending on the case, the genetic team collaborates with other medical specialists  who could also be part of the genetic service . Classical examples of this model are genetic services for rare diseases. The access of patients to this model of genetic service may occur through two different pathways:Patient-GP or medical specialist-Counselor-LabPatient-Counselor-LabThe first pathway (Ia) occurs when a patient seeks medical assistance from a GP or any specialist doctor who then makes a referral to the genetic service where a genetic counselor or a medical geneticist can perform a risk assessment. If a genetic test is relevant and available, they may suggest genetic testing to the patient; then samples are collected, and tests are performed in the laboratory. Based on the results of the test, genetic counselors or medical geneticists recommend surveillance and/or intervention. Clinical management of genetic conditions may involve various medical specialists, other than geneticists . The second pathway (Ib) occurs when a patient, without a medical referral, contacts a genetic service where a genetic counselor or a medical geneticist can perform a risk assessment. Pathway Ib corresponds to pathway Ia from this point onward.Model I was identified in 74 genetic programs  and pathIn this model, a prominent role is played by primary care units in which GPs have specific genetic skills and can undertake an initial risk assessment using standardized referral guidelines. In some cases, GPs refer patients who are categorized as “high risk” to genetic services, while in other cases they can deliver genetic counseling, request genetic testing, and interpret the results. Therefore, in this model, there are two possible patient pathways:Patient-GP-Counselor-LabPatient-GP-LabPathway IIa occurs when a patient contacts a GP who undertakes the initial risk assessment and then makes referrals to a genetic service, where a genetic counselor or a medical geneticist can perform counseling and suggest genetic testing to the patient. A variation of pathway IIa was found in the GSPP Report 2008  who may be able to manage patients with genetic disorders without consulting medical geneticists. Thus, a medical specialist may request genetic testing, communicate genetic test results to patients and families and set up treatment with or without consulting a medical geneticist. There are two main patient pathways in Model III:Patient-(GP)-Medical specialist-LabPatient-(GP)-Medical specialist-Counselor-LabPathway IIIa occurs when a patient contacts  a medical specialist who performs a risk assessment, undertakes genetic counseling, and suggests genetic testing. Two variations of pathway IIIa have been identified in the studies of Shepherd et al.  and SchoModel III was identified in 54 genetic programs . The assIn this model, genetic services are provided within organized population screening programs . There are three possible patient pathways in Model IV:Patient-GP/Medical specialist-Counselor-LabPatient-GP/Medical specialist-LabPatient-Counselor-LabPathway IVa occurs when a patient takes part in a population-based screening program; a physician (or another HCP) involved in the screening program can perform an initial risk assessment and refer the patient for genetic counseling. The genetic counselor or medical geneticist can undertake counseling, suggest genetic testing and, based on the results of the test, can recommend surveillance and/or intervention. A variation of the IVa pathway was found in one record Healthcare professionals are usually not involved in the process and medical referrals are not required for genetic testing through DTC companies; thus, patients are self-referred. Furthermore, the companies usually do not offer risk assessment and genetic counseling. In pathway Va, patients purchase the test, take their own sample at home, send it to the lab, and receive the results directly. In contrast, a web-based virtual genetics clinic  operating pathway Vb requires referrals from GPs or other medical specialists, offers risk assessment, pre- and post-test genetic counseling performed by genetic counselors, and genetic testing that can be requested by genetic counselors or medical specialists  were reported for numerous genetic programs . The cosThe genetic conditions and the related tests identified in the review are presented as a three-tier classification –8 accordAccording to the aforementioned criteria, most genetic programs identified in the review are included under Tier 1; specifically, these are genetic programs for HBOC, Lynch syndrome, FH, hypertrophic cardiomyopathy, and newborn screening . Thirty-Frontiers in Public Health 2017; volume 5, article 223 . Concerning quality indicators, the studies are assessed in terms of reported effectiveness, cost-effectiveness, feasibility data, or evidence-based guidelines.There are no competing interests and the funder had no role in the study design, data collection and analysis, or decision to publish.Although some genetic tests with insufficient evidence of clinical utility and validity are offered to the general population, most genetic tests identified in the review have considerable evidence of efficacy and cost-effectiveness and are ready for full implementation in clinical and public health practice. Leading examples of genetic tests with such specifications, and included in Tier 1, are BRCA1/2 genetic testing, genetic screening for Lynch syndrome, and FH. However, not all programs offering these three tests can be deemed equivalent or recommended considering the target population. Economic evaluations of genetic applications recognize three categories of BRCA1/2 genetic testing programs as cost-effective: (i) population-based screening among Ashkenazi Jews; (ii) family history-based screening, although methods on how to select high-risk women from the general population and the related cost are not detailed in literature studies; and (iii) cancer-based genetic screening, which includes tools for the identification of affected women at higher risk of inherited breast and ovarian cancers  provided mainly by geneticists; this is still the most common model of delivery. However, genetic applications are increasingly utilized by a wide range of healthcare professionals who are involved to various degrees in patient management . More recently developed professional roles  have also been identified in several settings where they are vital in supporting clinicians in multidisciplinary teams. This is particularly evident in genetic services led by medical specialists, which is the second most common model of delivery. Genetic services are also progressively integrated into population-based screening programs. The review by Battista et al.  reportedBRCA1/2 and Lynch syndrome testing were the most frequently offered genetic tests. The most cost-effective BRCA1/2 tests, as well as family history-based screening and cancer-based genetic screening, are all delivered predominantly via genetic services led by geneticists, followed by the medical specialist and the primary care models. Furthermore, the BRCA1/2 population-based screening among Ashkenazi Jews is provided by physicians involved in population screening programs and referrals are carried out by different medical specialists. Lynch syndrome testing, including the cost-effective strategies, is mostly delivered by the geneticists and the medical specialist models, and in a few cases by the primary care model. HBOC and Lynch syndrome are typical examples of genetic disorders still managed principally by geneticists, although there is a progressive shift toward the involvement of other medical specialists. However, the clinical conditions mostly require the collaboration of several different specialists in a multidisciplinary team. Among other cost-effective approaches, FH cascade testing of relatives of index cases is delivered mainly through the medical specialist and the primary care models. This indicates that FH is mostly managed by primary care physicians, endocrinologists, or lipid specialists, and not necessarily by geneticists. The newborn screening panel, alongside BRCA1/2 screening among Ashkenazi Jews, is another cost-effective genetic testing service delivered via population-based screening programs.Three critical findings stem from the review. First, some genetic programs, and the related delivery models that have been developed for the provision of the relevant genetic tests, lack sufficient evidence of clinical utility and validity and are currently not recommended for use in practice. The provision of these tests, classified as Tiers 2 and 3, could be related to faster genotyping technologies, the reduced cost of testing, commercial interests, and major public demand. It should be noted that these genetic programs comprise project proposals and demonstration projects  . To our knowledge this is the first study proposing a comprehensive classification of genetic service delivery models based on the role of healthcare professionals in service provision and patient care pathways. The present review may be useful in allowing clinical or public health practitioners and policy makers to identify current trends in the provision of personalized medicine.BU and TL identified the studies through a literature search on online databases. BU, EP, TL, and GA performed the data extraction from the studies. PV, BU, CD, EP, and CM critically discussed and interpreted the results of the study. BU and PV wrote the manuscript with input from all authors. All authors read and approved the final manuscript. All authors contributed to the conception and design of the study.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


We provide preliminary insights into the global phylogeographic and evolutionary patterns across species of the hydrozoan superfamily Plumularioidea (Cnidaria: Hydrozoa). We analyzed 1,114 16S sequences of 198 putative species of Plumularioidea collected worldwide. We investigated genetic connections and divergence in relation to present‐day and ancient biogeographic barriers, climate changes and oceanic circulation. Geographical distributions of most species are generally more constrained than previously assumed. Some species able to raft are dispersed widely. Human‐mediated dispersal explains some wide geographical ranges. Trans‐Atlantic genetic connections are presently unlikely for most of the tropical‐temperate species, but were probably more frequent until the Miocene–Pliocene transition, before restriction of the Tethys Sea and the Central American Seaway. Trans‐Atlantic colonizations were predominantly directed westwards through (sub)tropical waters. The Azores were colonized multiple times and through different routes, mainly from the east Atlantic, at least since the Pliocene. Extant geminate clades separated by the Isthmus of Panama have predominantly Atlantic origin. Various ancient colonizations mainly directed from the Indian Ocean to the Atlantic occurred through the Tethys Sea and around South Africa in periods of lower intensity of the Benguela upwelling. Thermal tolerance, population sizes, dispersal strategies, oceanic currents, substrate preference, and land barriers are important factors for dispersal and speciation of marine hydroids. We use hundreds of DNA barcodes, with a calibrated “molecular clock” analysis of the hydroid superfamily Plumularioidea, to investigate phylogeographic patterns, and the processes likely underlying them, on a global scale. Our analysis uses ancestral state reconstructions for oceanic region to make inferences that help to explain both present and past biogeographic divergences and genetic interconnections, in relation to changes in climate and ocean circulation. In general, dispersal ability, temperature, productivity, oceanic circulation and habitat complexity and configuration are major determinants of population abundance and intra‐ and interspecific ecological interactions that promote diversification and control biogeographic ranges  and Bayesian inference (BI) tree searches for all the generated alignments. The general time‐reversible model of nucleotide evolution with gamma and invariant parameters (GTR + G+I) was used for all analyses, after assessment of the most suitable model using the “Akaike Information Criterion.” ML analyses were conducted in PhyML Pleistocene Figure , as they3.23.2.1Macrorhynchia philippina, Kirchenpaueria halecioides, Halopteris cf. alternata (“lineage 5”), and Antennella similis (“lineage 3”) . This taxon is thus likely to disperse via boats to remote locations. Remarkably, the eight other haplotypes of K. halecioides, also mainly collected from ports or marinas, are not so widely dispersed (each one collected once). This suggests that the haplotype found simultaneously at eight sampling stations may be the best adapted for adverse/diverse abiotic conditions associated with boat travel and to different destinations.Macrorhynchia philippina has a widespread haplotype occurring in W Africa, Brazil, the Caribbean, and the E Pacific, suggesting also dispersal mediated by boats. Two other haplotypes of that taxon are widely distributed across the Indo‐Pacific and Indian Ocean, respectively. Besides boat traffic, the wide distribution of M. philippina, may also be explained by the liberation of medusoids as part of its life cycle, which could aid dispersal via oceanic currents, and its large population's sizes in warm waters.Halopteris cf. alternata (“lineage 5”) has small sessile colonies and is frequently an epibiont on algae, sponges, and rocks. Although it seems to not release medusoids, the rafting abilities of this taxon  may explain the presence of two haplotypes with amphi‐Atlantic distribution, with one of these haplotypes also present in the central E Pacific. Although this taxon was not found in marinas or ports, we do not discard the hypothesis of its transport via boats as well.Antennella similis (“lineage 3”) forms small colonies collected from a wide variety of substrates, including rock, dead coral, sponges, and fishing lines. Therefore, rafting on natural of artificial substrates probably also explains its recent trans‐oceanic dispersal.That no other Plumularioidea were represented by shared haplotypes between the E and W Atlantic suggests that presently trans‐Atlantic genetic connections are unlikely for most of these benthic suspension feeders, not even through rafting (except on boats). This agrees with the general pattern of present‐time restricted trans‐Atlantic genetic connections observed in most studies of marine invertebrates , Plumularia cf. setacea (“lineage 11”), and Monotheca cf. margaretta (“lineage 2”). The latter two taxa are frequent epibionts  species contain genetically close haplotypes on both sides of the Atlantic, suggesting relatively recent trans‐Atlantic genetic connections, namely ra et al. is intrira et al.. Despite3.2.2All five detected cases of past trans‐Atlantic colonizations through tropical/subtropical shallow waters seem to have progressed in a westward direction Figure  and in cNemertesia lineages from the east to western Atlantic that likely took place around the end of the Pliocene  multiple colonization events of the Azores from the east, at distinct times and through different routes , the majority of the Azorean lineages cluster together, segregated from lineages of other localities  observed in the archipelago, especially in deep waters, is notable , exhibit the same 16S haplotypes in the Atlantic and E. Pacific, which suggests recent or ongoing genetic interconnections between these oceanic basins. These taxa reveal preference for warm/tropical waters and present amphi‐Atlantic distributions as well (cf. last section). We therefore suspect these two species crossed the Isthmus of Panama with the aid of boats and/or rafting on other floating or swimming substrates .Only two of the species represented in this study—Aglaophenia postdentata; A. trifida + A. prominens; Halopteris diaphana “lineage 1”; Halopteria alternata “lineage 3”; Monostaechas spp.; Plumularia setacea “lineage 12”; Plumularia floridana complex. These sister taxa were collected from shallow waters of the Pacific and Caribbean coasts of Panama and/or Costa Rica, and share close morphological and genetic similarities, suggesting that they diverged at a time close to the completion of the Isthmus ca. 3 Mya. The following two lineages probably migrated across the Central American Seaway but were excluded from “molecular clock” calibrations because their respective sister groups on the Caribbean side were not sampled or readily apparent in the phylogenetic results by phylogenetic analyses: Antennella “secundaria lineages 6 and 7” and Antennella “secundaria lineage 11” + Monostaechas sp. Our analyses suggest the majority of genetic colonizations across the Central American Seaway were from the Atlantic to the Pacific  sampled from W. USA, Chile, Argentina, and New Zealand. That transition occurred probably from the south Pacific to the south Atlantic possibly before the early‐middle Pleistocene . Furthermore, that clade is apparently absent from the warm waters of the E. Pacific, presenting an antitropical distribution along the cold shallow waters of the E. Pacific, which reflects the sensitivity of these invertebrates to seawater temperature.Our data only display one evident example of colonization across the seaway between South America and Antarctica, represented by a cold‐water clade of e Figure , when gl3.3.3Plumularia setacea from the Atlantic versus the cryptic cold‐water Plumularia present in the eastern Pacific; (b) Kirchenpaueria pinnata from the Pacific versus Atlantic; and (c) Aglaophenia latirostris plus A. struthionides from California versus cluster with A. lophocarpa and A. parvula. These interoceanic genetic interchanges likely took place around the Miocene–Pliocene transition  n Figure , in accon Figure , 2004. Tn Figure , which cn Figure . However3.43.4.1We did not find any Plumularioidea haplotype occurring simultaneously in the Atlantic and Indian Ocean Figure . This suNemertesia ramosa collected from the NE Atlantic and Mediterranean, in relation to Nemertesia sp. from Japan, seems to have occurred by the end of the Miocene –Pliocene  two independent times to the Atlantic, because lineages from the SW Indian Ocean cluster independently from lineages from Central West Africa and from the Caribbean, respectively  that derived clearly from the Indo‐Pacific and radiated extensively in the Atlantic into several species after the closure of the Tethys Sea  and G. sinuosum likely took to colonize the Caribbean Sea. However, an ancestor of Gymnangium montagui probably colonized the NE Atlantic from the Indian Ocean through the Tethys Sea; and subsequent colonizations occurred to the Caribbean side resulting in Gymnangium allmani (lineages 3 and 4) and Gymnangium speciosum (“lineage 2”)  and Halopteris spp.  seem to have crossed the EPB through cold waters of the South Pacific , possibly from east to west, prior to the Pliocene–Pleistocene tropical  are the other species represented with widely distributed haplotypes  dataset of DNA barcodes and “molecular clock” calibrations, we may discover many phylogeographic and evolutionary patterns. Nevertheless, the present study is exploratory and a generator of phylogeographic hypotheses, which apply to marine benthic invertebrates. Much of marine genetic diversity (including that of the Plumularioidea) remains little sampled/investigated; but with an increased number of DNA Barcoding and genomic studies, we will gradually increase our understanding of marine evolutionary history. To complement this particular study, it is desirable to sample more DNA barcodes from other oceanic settings, such as the southern and northwest Atlantic, and much of the cold waters of the Pacific and the deep sea. Furthermore, as other (combinations of) genes may suggest discordant phylogeographic patterns and thus more fine‐scale complex movements of genetic lineages, the usage of further genetic markers will also increase robustness of evolutionary inferences.The major constraints to dispersal and speciation we hypothesized were dispersal strategy , population sizes, water temperature, and consequent tolerance of the marine fauna, coupled with the effects of oceanic circulation and land barriers. For example, vicariance induced by the closures of the Tethys Sea and Central American Seaway, as well as by glacial periods and oceanic current modifications, seems to have promoted considerable diversification of hydroids. Additionally, the Indo‐West Pacific seems to have played a major role as a source of genetic diversity to the Atlantic. Indeed, the majority of Atlantic species of Plumularioidea seem to have derived evolutionarily from the Indian Ocean, and from species radiations inside the Atlantic. We found some lineages that colonized the Atlantic via the ancient Tethys Sea. The maritime passage rounding South Africa served predominantly for westward genetic connections between the Atlantic and Indian Ocean, in periods of less activity of the Benguela current.More recently, trans‐Atlantic migrations seem unlikely for most Plumularioidea hydroids of tropical to temperate waters. Haplotypes with amphi‐Atlantic distribution, suggestive of recent genetic interconnections across the Atlantic, were only detected in four hydroid species that likely disperse through rafting on natural and/or human‐made floating substrates. However, various tropical taxa migrated across the Atlantic in the past, mainly from east to west, taking advantage of equatorial currents. The main genetic break of trans‐Atlantic colonizations probably occurred around the Miocene–Pliocene transition, coinciding with the constriction of the Tethys Sea and the Central American Seaway, and consequent change of oceanic current velocities and other properties. Deep‐water clades of the N Atlantic seem to have mostly crossed the Atlantic from east to west, but with some exceptions.Presently, the Gulf Stream does not seem to contribute much to trans‐Atlantic dispersal of Plumularioidea hydroids. The Azores, in the middle of the North Atlantic and greatly influenced by the Gulf Stream, is instead more phylogeographically related with the Mediterranean, Lusitanian province and Macaronesian islands and seamounts. The archipelago has been colonized multiple times and through different routes from the eastern Atlantic side, since the Pliocene to (close to) the present, sometimes more than once for a single species. Stepping‐stone dispersal across Macaronesian seamounts and islands, as well as eddies and deep‐sea currents departing from the vicinity of the Mediterranean, may explain the occurrence of Plumularioidea hydroids in the Azores. Few species arrived at the archipelago with the aid of boats. We displayed evidence of one lineage that reached the mid‐Atlantic ridge from the West Atlantic, but via deep waters.Most (extant) geminate clades of Plumularioidea separated by the rise of the Isthmus of Panama have Atlantic origin. The few examples of colonizations across the ancient trans‐Artic route also suggest primarily colonizations from the Atlantic to the Pacific. To the south, however, Pacific fauna migrated to the SW Atlantic  using the passage between South America and Antarctica. The “Eastern Pacific Barrier” prevented higher phylogeographic influence of tropical West Pacific fauna, in the East Pacific and Atlantic. Nevertheless, a few lineages overcame that barrier, at different times and adopting different routes and directions, revealing the sporadic permeability of the barrier.The authors declare no competing financial and nonfinancial interests.The present work makes part of the postdoctoral research of C.J.M., supervised by R.S.S., A.G.C. and H.L. C.J.M. performed most of the analyses, under the supervision of H.L. C.J.M., H.L. and A.G.C. drafted the original manuscript. All authors read, corrected and approved the final version of the text. Click here for additional data file. Click here for additional data file.


Canis lupus) in the Iberian Peninsula (Western Europe) during the last two decades to present a particular case of low dispersal levels in a large carnivore population persisting in human-dominated landscapes. We found an exceptionally reticulated pattern of cryptic population structure emerging at two hierarchical levels, in which four or eleven meaningful genetic clusters can be recognized, respectively. These clusters were characterized by moderate-high levels of differentiation (average pairwise FST = 0.09–0.19), low levels of admixture and varying degrees of genetic diversity. The number of dispersers identified among the 11 clusters was very low (<4% out of 218 wolves). Spatial information of tracked wolves further confirmed the geographical genetic patterns (only 2 out of 85 collared wolves overlapped with more than one genetic cluster). The high levels of genetic structure in this population may be determined by the recent demographic history of this population, among other factors. The identification of meaningful genetic clusters has implications for the delineation of conservation units and, consequently, on the conservation and management actions for Iberian wolves.Highly mobile mammalian carnivores are expected to have the capability to maintain high levels of gene flow across large geographic scales. Nonetheless, surprising levels of genetic structure have been found in many such populations. We combined genetic and spatial behavioural information from wolves ( Many mammalian carnivores can disperse over very large distances and occupy a great variety of habitats. It is therefore expected that they have the potential for maintaining high rates of gene flow and consequently reduced genetic differentiation across large parts of their ranges6. However, recent studies have shown that continuous populations of these species can also exhibit notable levels of population genetic structure at different spatial scales, which cannot be explained by past or current barriers to dispersal alone10.The rates at which populations exchange genes  is one of the main driving forces determining the scale and magnitude of population genetic differentiation. It is expected that high rates of gene flow will lead to reduced spatial genetic structure at small scales, although isolation-by-distance patterns can emerge with increasing geographical distances, even in the absence of barriers to dispersal11), spatial constraints , or historical factors (such as past range restrictions14). Other, human-related factors can also influence patterns of genetic structure. For example, hunting pressure has been observed to influence population structure in mountain lions (Puma concolor)15 and grey wolves (Canis lupus)10. However, recent studies have brought attention to less well-understood behavioural mechanisms promoting genetic population division by limiting dispersal, such as dietary specialization, natal habitat-biased dispersal and territoriality21. Wolves are an illustrative example of a species that is territorial, widely distributed, and moves across large distances22. Wolf dispersal distance is thought to be affected by population density and the probability of finding a mate25. New wolf packs can also be established on the edges of the natal territory of one of the founding individuals, leading to familial ties between geographically close groups25. Consequently, genetic clusters consisting of related individuals can emerge in wolf populations, although the level of genetic differentiation will depend on the rates of inter-pack gene flow26. Despite their high dispersal capabilities, wolf populations can nonetheless present low levels of gene flow and short dispersal distances, leading to genetic structure at small spatial scales and genetically differentiated groups that behave like a meta-population29.Traditionally, population genetic structure has been considered the outcome of well-defined behavioural traits (such as in the establishment of colonies30 (cryptic population structure – CPS). The existence of these cryptic clusters is of great interest in terms of the behavioural and social processes they can reflect, their potential role in ecological and evolutionary processes, and ultimately their consequences for conservation and population management. Connectivity among clusters will have an effect on subpopulation growth rates and, consequently, may give rise to source-sink dynamics among cryptic clusters15. The combined use of behavioural data, such as spatial information from tagged animals, in addition to assignment procedures and estimates of differentiation from genetic data, has allowed the assessment of CPS patterns on a fine spatial scale and how individual clusters are spatially organized31.In many populations, discrete and geographically coherent groupings of genetically similar individuals (clusters) have been identified even in the absence of gaps in the local distribution and/or of physical barriers to movement, and often without evident phenotypic distinction33. This population suffered a severe decline since the beginning of the 20th century until the 1970s due to intense persecution34. In recent decades, the population has been expanding32: most recent estimates put the population at >2,000 individuals in >350 packs, distributed over ca. 140,000 km2 36. A large part of this population occurs in human-dominated landscapes39 and is remarkable for feeding mainly on anthropogenic sources of food in some areas42.The North-western Iberian wolf population, shared between Spain and Portugal, represents the largest wolf population in Western Europe, and is currently isolated from the remaining European wolf populationsTaking advantage of an extensive collection of genotype and collar-tracking data, collected over approximately 20 years, we examined the spatial genetic population structure and spatial behaviour in the Iberian wolf population. We used Bayesian clustering methods to identify geographically and genetically meaningful groups and combined this information with the location of sampled individuals, as well as information about the spatial behaviour of the tracked wolves to investigate the spatial organization of these groups. In particular, we explored (i) the distinct genetic groups that can be identified in the NW Iberian wolf population, (ii) the spatial organization and connectivity among these groups, and (iii) the partition of genetic diversity among groups. This integrated approach revealed a marked fine scale population structure within the Iberian wolf population, with a very low number of disperser individuals among groups and consequently, low levels of admixture. The genetic patterns are confirmed by the spatial behaviour information, providing further evidence for the extremely low dispersal in this wolf population.DAPC43 and the Bayesian clustering analyses of Structure44 and BAPS45.We based our genetic analyses on a dataset of 218 individual wolves genotyped at 46 microsatellite loci, spanning the entire distribution in the Iberian Peninsula  with increasing values of K , while ΔK values46 peaked at K = 2 and K = 4  serves only as indication of the true number of groups and biological plausibility has to be taken into account44 . The ΔK statistic was developed as a further aid to this task, by measuring the rate of change in the probability of data between successive values of K46. While K = 2 showed the highest ΔK value, this partitioning seems to reflect a West-East continuum of membership proportions  results in a similar pattern as K = 4 in the general analysis  and K = 8 in the spatial clustering. Compared to the Structure analysis at K = 11, some clusters merge but maintain their general pattern , and not from population clustering . Average cut-off values to differentiate between ‘non-admixed’ and ‘admixed’ individuals were very high, both for K = 4: 0.83 (0.77–0.87), and for K = 11: 0.88 (0.81–0.95) , K = 4: 0.94–0.96, K = 11: 0.90–0.97. In total, 166 (76%) and 137 (63%) out of 218 individuals were classified as ‘non-admixed’, for K = 4 and K = 11, respectively , N Portugal, the W Cantabrian Mountains (N Spain), and a large area extending from the E Cantabrian Mountains to the plateau of Castilla y León  and divides the W Galician in two  than for K = 11 (mean Qi over populations: 0.79). Surprisingly, in both cases the genetic contribution from external groups was extremely low at both levels. Each group received the genetic contribution of only one or two other groups (5% threshold), indicating low exchange of genes among groups, and not always from the closest ones  Table . Howevers Tables . Groups s Tables .non-admixed)  and 63% (22–100%) of Ptotal areas at K = 4 and K = 11, respectively  occurred within the area defined by the most external ‘genetically non-admixed’ individuals for each group (P) Tables ; Fig. 2.total of another group) was extremely low. No dispersers were integrated in any of the groups at K = 4   overlapped with more than one genetic group  Fig. . Second,e), varying between 0.57 and 0.62 (mean He = 0.59)  , the latter also presenting the lowest allelic richness. Furthermore, the S Douro group showed a higher private allelic richness (pAR = 0.15) compared to the other groups at K = 11 (pAR ≤ 0.11). On the contrary, E Trás-os-Montes and E Galicia showed the highest He and allelic richness values.Due to the low number of dispersers found in our dataset, genetic diversity values did not change substantially when considering either geographical groups or genetic clusters, both at K = 4 and K = 11  , the highest differentiation being observed between S Douro and E Asturias, and the lowest between E Trás-os-Montes and W Trás-os-Montes , while the neighbouring E Trás-os-Montes and W Trás-os-Montes showed the lowest differentiation (average FST: 0.09). Results implementing Jost’s D distance were similar  between the two Galician clusters and between the clusters in Asturias  and the generalist behaviour and mobility capabilities of wolves22. The size and genetic diversity of these fine-scale groups was very variable: four distinct genetic groups were consistently identified along the Cantabrian mountain range in all our analyses; two to three groups appear to be present in Portugal, north of the Douro river; and most of the regions of Galicia and Castilla y León seem to be occupied by wolves from a single genetic group, although some smaller groupings could be identified in W Galicia. It should be noted that our sampling is sparser in the central and eastern parts of the Iberian wolf distribution , where wolf pack density is also lower . The number of wolves identified as dispersers was extremely low, both in the genetic dataset  and in the collared wolves dataset . Furthermore, genetic and behaviour information were extremely consistent in the geographical configuration of genetic groups: information about the spatial behaviour of wolves was available for 9 of the 11 genetic groups, and in all of them except two individual cases, wolves spent the entire monitoring time within the area defined by the genetic samples  55; on the other hand, Iberian wolves are also remarkable for their reliance on livestock, which is relatively abundant and available41, possibly explaining the maintenance of high wolf population densities with reduced dispersal.Several environmental conditions have been found to constrain the dispersal of wolves and other mobile carnivores, including habitat-specific adaptations to prey or climate57. For example, wolf protection and population management practices in the Iberian Peninsula vary greatly depending on regional jurisdictions, leading to a heterogeneous human pressure: wolves north of the Douro river in Spain are listed in Annex V of the European Habitats Directive (92/43/EEC), being either game species with hunting quotas, game species without hunting quotas, or subject to a special regime, depending on the Spanish autonomous region; wolves south of the Douro river in Spain and in the entire Portuguese territory are fully protected, being listed in Annexes II and IV of the EU Habitats Directive. It is also worth mentioning that the genetic group of Castilla y León encompasses areas under both Annexes of the directive, with wolves being listed either as game species or being fully protected. Nonetheless, illegal persecution is also common throughout the entire wolf range, both in Spain and Portugal58.Other factors deserving further study are those resulting from anthropogenic pressure: infrastructure development, heterogeneous human persecution of wolves and management strategies, and other socio-economic trends can cause local extinctions and induce fragmentation or promote source-sink dynamics32, at least some parts of the Iberian wolf population are still declining, particularly the populations of Sierra Morena59 and South of the Douro river in Portugal60. While at K = 4 the identified groups showed similar levels of genetic diversity, a larger variability was found at K = 11, which might reflect distinct local population trajectories. Larger and more diverse groups might represent areas where wolf abundance has been consistently high even during the population minimum in the 1970s, such as the genetic groups detected in Galicia or the Cantabrian mountains62 . Over the past century, wolf populations North of the Douro river have maintained a larger degree of connectivity, while the populations South of this river became very fragmented, with an estimated maximum number of 16 population fragments in the 1960–70sth century, it is difficult to assess from extant genetic data how much of the observed genetic population structure results from the anthropogenic impacts of the last decades and the recovery of wolves in recent decades, or if it reflects a more long-term trait of the Iberian wolf population, determined by ecological or geographical features. Furthermore, information on how population genetic structures last over large time periods is still scarce60. Also, given that patterns of isolation by distance and genetic clustering are difficult to distinguish65, further work is needed to clarify the relative influence of these patterns on the structure of the Iberian wolf population.The genetic structure of a population results from a complex interaction between ecological traits, environmental features affecting gene flow and historical events. Given the lack of an extensive sampling before the population decline and fragmentation in mid-2066. Identifying conservation units below the population level67, particularly in relation to CPS, their connectivity and long-term viability are increasingly recognised as important goals for conservation  and population management (because the same population is often heterogeneously managed or because different populations are managed as a single unit). However, few attempts have been made to integrate this type of information into management68. Our results bring the opportunity to open a debate of this topic and integrate information regarding CPS into the NW Iberian wolf population management.The results of our work are also relevant for the management and conservation of the Iberian wolf population. The Wolf Specialist Group of the Species Survival Commission of the International Union for the Conservation of Nature (IUCN) recognized in point 3 of its Wolf Manifesto ‘that wolf populations have differentiated into entities which are genetically adapted to particular environments’ and that ‘it is of first importance that these local populations be maintained in viable populations in their natural environments in a wild state’. The Iberian wolf population is the largest remaining population in Western Europe, but is also currently isolated, potentially representing a unique population from the genetic, behavioural and ecological perspectives. Furthermore, genetic diversity is one of the three categories of biodiversity for conservation priority recommended by the IUCNet al.69, and physical development. Our sampling encompasses ca. 80% of the wolf estimated range in 2005 in the Iberian Peninsula70.Between 1995 and 2014, we collected 289 wolf tissue samples in Portugal and Spain  of wolf packs (centred on the estimated breeding sites in the correspondent period), we noted the date in which every sample was collected; (iii) considering the annual cycle of wolves (from births in May of a given year to the next May), we focused on those samples separated ≥5 yrs. This criteria ensured that more than 1 generation spanned between samples (mean generation time in wolves is estimated to be 3–4 yrs72); (iv) we selected a maximum of two samples for each 5-yr temporal window. To do this, we took into account preliminary information of missing data from microsatellite analyses (selecting those samples with the best performance), and the age of individuals, prioritizing adult individuals (>2 yrs.). Samples with >20% missing data were also excluded for subsequent analyses. Given the occurrence of wolf-dog hybridization in the Iberian Peninsula73, possible hybrids were eliminated based on genetic data, using the procedure and dog reference samples in Godinho et al.73. The final genetic dataset used for this study contained 218 individuals.Because several sampled individuals could belong to the same pack, we used the following criteria to avoid close familiar relationships in our dataset: (i) we plotted all samples against the wolf packs detected between 1993 and 2005 .Total genomic DNA was extracted using the QIAGEN DNeasy Blood & Tissue Kit  according to the manufacturer’s instructions. DNA quality and concentration were assessed using agarose gel electrophoresis and quantified in a Qubit fluorometer . Samples were amplified for a set of 46 microsatellite loci in four multiplex reactions , and a singleplex including the Dbar2 locus, following Godinho Structure 2.3.344 and BAPS 645 to group all samples into clusters using their multilocus genotypes.We employed the Bayesian clustering algorithms of Structure was run for 2 million iterations after 500 thousand burn-in steps, using the admixture and correlated allele frequencies models, assuming values of the K parameter (number of populations) between 1 and 20. For each K, runs were repeated 20 times to ensure the consistency of the results. To select the most likely number of groups we tabulated the posterior probabilities of the data for each K (ln Pr(X|K)) and employed the procedure proposed by Evanno et al.46, which involves calculating the quantity ΔK for each pair of successive Ks, using StructureHarvester75. Having chosen K, we classified each individual in each cluster i based on the distribution of the individual membership proportions to that cluster (qi). A cut-off value was set for each cluster at the point where a gap in the distribution of membership proportions was visible, and above which membership proportions no longer varied considerably between individuals  for K between 1 and 5, and used to calculate ΔK values; in the second iteration, samples were again divided according to the inferred clusters and the procedure was repeated.BAPS with both the regular  and spatial population mixture analysis, starting from different values of the maximum number of clusters (K), up to K = 20. For each K, 20 replicate runs were performed. Individual admixture proportions were then calculated based on each of the clustering analyses using the ‘admixture of individuals based on mixture clustering’ option.Clustering of individuals was also performed in DAPC43 was used to identify clusters of genetically related individuals, as implemented in the R package adegenet 2.077. DAPC is a model-free method, and therefore free of population genetics assumptions, in particular Hardy-Weinberg equilibrium. The function ‘find.clusters’ was used with 80 principal components  to determine the number of clusters maximizing the variation between clusters. The Bayesian Information Criterion (BIC) was used to identify the optimal number of clusters. The number of retained PCs was calculated using a cross validation methods implemented in the ‘xvalDapc’ function.Additionally, the multivariate method 78. We grouped samples according to similar posterior assignment probabilities to the different groups identified in our dataset in order to spatially identify geographical clusters. For a given genetic cluster, all adjacent individuals with a membership proportion to the same cluster higher than 0.50 were grouped. These individuals were used to define a minimum convex polygon for each genetic cluster . Individuals for which position and/or genetic makeup did not allow for a clear attribution were not included. In a second step, we defined another minimum convex polygon encompassing the area comprising only genetically ‘non-admixed’ individuals (Pnon-admixed). All ‘non-admixed’ individuals that were not located within the Ptotal area of their respective population were considered as dispersers.Samples were spatially projected using QGIS 2.8.2total and Pnon-admixed, computed the mean membership proportions, the percentage of ‘admixed’ individuals at the Ptotal level, and within the Pnon-admixed, the number of immigrants . Moreover, in order to gain insights into the connection between genetic clusters, we also calculated the number of contributing genetic clusters to the ancestry of each group, by considering only groups with >5% membership proportions, and the smallest distance to the closest group .For each identified geographical group, we calculated the area of the PGenepop 4.379. Given that Mantel tests and tests of spatial autocorrelation are not capable of distinguishing between patterns resulting from genetic clustering and IBD64, we did not attempt to perform further IBD tests at smaller scales.We tested for correlation between genetic and geographical distance (isolation by distance – IBD) using all samples in the dataset with a Mantel test implemented in Structure , we calculated standard population genetics parameters including: (i) the number of alleles, (ii) observed and expected heterozygosities, and (iii) the fixation coefficient (F), using GenAlEx 6.50180; (iv) allelic richness and private allelic richness, rarefied to the smallest sample size, using ADZE 1.081. We used Genepop 4.379 to test for significant deviation from Hardy-Weinberg equilibrium and association between genotypes at pairs of loci (linkage disequilibrium). Statistical significance levels were adjusted using Bonferroni corrections82. Additionally, we also estimated the average relatedness between pairs of individuals for each group/cluster using the estimator of Lynch & Ritland83 with GenAlEx. Differentiation between groups/clusters was measured by FST (ϕST) calculated from an AMOVA84 , using 1,000 permutations to test their significance, and Jost’s D distance86, both calculated using GenoDive 2.07b2787. A total of three and eight individuals were excluded at K = 4 and K = 11, respectively, for these analyses because they could not be attributed to a particular geographical group.For each geographical group, as well as for each genetic cluster identified by BayesAss 3.0.488. The program was run for 20 million MCMC iterations, with a burn-in of 2 million. The allele frequencies, inbreeding coefficients and migration rates parameters were set to 0.3, 0.7 and 0.6, respectively, to adjust acceptance rates. MCMC mixing was assessed visually by observing the trace file with Tracer89. Consistency of the results was confirmed by comparing 5 runs with different random number seeds.Additionally we estimated the migration rates  between all pairs of subpopulations and identified migrants using 98 (Supplementary Table 98 for respective capture and handling details), all experimental procedures that included the capture, handling and collaring of wolves were specifically approved by the national authorities in Portugal (Instituto da Conservação da Natureza e das Florestas – ICNF) under permits 326/2007/CAPT, 259/2008/CAPT, 261/2009/CAPT, 330/2010/CAPT, 332/2010/MANU, 20/2012/CAPT, 25/2012/MANU, 26/2012/MANU, 27/2012/MANU, 371/2012/CAPT, 229/2015/CAPT, 230/2015/CAPT; and Spain , with permits 19P72009, 481/2011, 1389/2013, CO709/0164/2013, 526/2015, 19/2006, 71/2009 and 86/2011. All field procedures and animal handling were carried out in accordance with animal welfare regulations of Portugal and Spain and followed the guidelines of the American Society of Mammalogists99.To investigate whether the identified genetic structure was reflected in the spatial behaviour of wolves, we analysed tracking information of 85 collared individuals. This information was collected from 1982 to 2015 in the context of several research projects on the ecology of this species in Portugal and SpainA total of 85 wolves were equipped with VHF collars  or GPS-GSM/Iridium collars . Our dataset was composed by 39 females and 46 males and contained information from 41 wolves with an estimated age <2 years and 44 wolves with an estimated age ≥2 years. Monitoring period (defined as the time elapsed between the first and last location obtained for each wolf) averaged 378 days ; and the mean number of locations per wolf was 2685   using all of the VHF/GPS locations during the entire monitoring time. Since in this study we were interested in detecting overlaps between individual wolf Ps and the spatial distribution of the detected genetic groups , we maximized potential overlaps by considering the entire monitoring period of wolves regardless of changes in social status over time. Overall, the mean full monitoring Ps for wolves was 408 km2   overlapping with every individual wolf Ps in space.For each wolf, we calculated a minimum convex polygon (PSupporting Information