Cluster Analysis Approach Research Articles

Psycho-Behavioral Profiles of Pediatric Inpatients with Past and Recent Onset of Nonsuicidal Self-Injury: A Cluster Analysis Approach.

Few studies have focused on the persistence of nonsuicidal self-injury (NSSI) over time in developmental age. This study aimed to define the psycho-behavioral profiles of young inpatients according to past or recent NSSI onset (i.e., NSSI for more or less than one year, respectively), and identify possible risk factors for maintaining NSSI over time. A total of 118 Italian NSSI inpatients aged 9–17 were involved. The Youth Self-Report (YSR) was administered. K-means cluster analyses were conducted using the YSR affective disorders, social competencies, and social problems scales as clustering variables. A binomial logistic regression was run to clarify which of these variables discriminate between the past and recent NSSI onset groups. Chi-square tests were performed to pinpoint the variables associated with long-standing NSSI. The final cluster solution displayed four psycho-behavioral profiles; a greater number of inpatients with recent NSSI onset was found in the clusters characterized by scarce social competencies. Affective disorders and social competencies were significant predictors, and higher scores on both scales were more likely in the past NSSI onset group. School problems and alcohol/substance use were related to long-standing NSSI. Therefore, a lack of social skills may be involved in recent NSSI onset, while affective disorders and other problem behaviors may dictate the continuation of NSSI over time.

Sensitisation to recombinant Aspergillus fumigatus allergens and clinical outcomes in COPD.

Variable clinical outcomes are reported with fungal sensitisation in chronic obstructive pulmonary disease (COPD), and it remains unclear which fungi and what allergens associate with the poorest outcomes. The use of recombinant as opposed to crude allergens for such assessment is unknown. A prospective multicentre assessment of stable COPD (n=614) was undertaken in five hospitals across three countries: Singapore, Malaysia and Hong Kong. Clinical and serological assessment was performed against a panel of 35 fungal allergens including crude and recombinant Aspergillus and non-Aspergillus allergens. Unsupervised clustering and topological data analysis (TDA) approaches were employed using the measured sensitisation responses to elucidate if sensitisation subgroups exist and their related clinical outcomes. Aspergillus fumigatus sensitisation was associated with increased exacerbations in COPD. Unsupervised cluster analyses revealed two "fungal sensitisation" groups. The first was characterised by Aspergillus sensitisation and increased exacerbations, poorer lung function and worse prognosis. Polysensitisation in this group conferred even poorer outcome. The second group, characterised by Cladosporium sensitisation, was more symptomatic. Significant numbers of individuals demonstrated sensitisation responses to only recombinant (as opposed to crude) A. fumigatus allergens f 1, 3, 5 and 6, and exhibited increased exacerbations, poorer lung function and an overall worse prognosis. TDA validated these findings and additionally identified a subgroup within Aspergillus-sensitised COPD of patients with frequent exacerbations. Aspergillus sensitisation is a treatable trait in COPD. Measuring sensitisation responses to recombinant Aspergillus allergens identifies an important patient subgroup with poor COPD outcomes that remains overlooked by assessment of only crude Aspergillus allergens.

Influence of Edaphic Properties in Determining Forest Community Patterns of the Zabarwan Mountain Range in the Kashmir Himalayas

The significance of edaphic factors in describing forest vegetation patterns is becoming more well acknowledged, with significant implications for the description of biogeographical regions and biome classification, as well as abundance and growth patterns at regional levels. The current study examines the vegetation association in the Zabarwan mountain range of the Western Himalayas and its association with edaphic factors. To collect data on forest types, we employed a systematic random sampling strategy in 60 plots (0.1 ha) across five forest types. We investigated data using ordination and cluster analysis approaches after calculating the important value index (henceforth IVI) for each plant species and edaphic data from forests. In total, 76 plant species from 39 different families were found in the area. The Rosaceae family was the most numerous, followed by Fabaceae and Asteraceae. Scrub forest types have lower diversity indices, while broad-leaved forest types have greater diversity indices. Two-way cluster analyses classified the forest vegetation of the Zabarwan mountain range into two plant communities on the basis of indicator plant species. The ordination analysis (canonical correspondence analysis) indicated that vegetation association tended to be influenced differently by distinct levels of soil parameters. The soil pH and calcium content were the main factors influencing the species distribution in the different forest types. The phytosociological features (basal area) were higher in coniferous forest type (74.49 m2ha−1) compared to broad-leaved (58.63 m2ha−1) and scrub forest type (15.4 m2ha−1). Overall, the goal of this research is to gain a better understanding of the impact of soil elements on forest composition and associations in order to develop scientifically based management options for forest ecosystem protection in the Himalayan region.

Spatial water quality analysis of the watercourses in Northwestern New Territories (Hong Kong)

Abstract River water is an important source for drinking water supply in Northwestern New Territories of Hong Kong. Thus, there is no denying the fact that monitor the quality of river water is a must for the locals. In this study, a mixed multivariate analysis method was used to lower monitoring costs by optimizing the layout of water quality monitoring stations. To this purpose, the data from a period of five years and over 36,000 observations was evaluated in this article. The cluster analysis approach was also used to categorize monitoring stations into three groups. What's more, three latent factors that predominantly influence the river water quality were assessed using factor analysis: anthropogenic pollution, seawater intrusion and geological processes, and the nitrification process. A spatial pattern using the three latent factor scores was plotted and six redundant monitoring stations were identified by this pattern. Finally, discriminant analysis was used to extract seven significant parameters. The results showed that the surface water-monitoring program of the watercourses in the Northwestern New Territories (Hong Kong) could be adjusted by reducing the monitoring stations to 18 and the measured chemical parameters to seven to ensure the detection of water quality and reduce the cost.

IgE epitopes are within the hydrophobic domain of sesame oleosin Ses i 4

Aim of the studyOleosins are allergens implicated in severe anaphylactic reactions after sesame, peanut or hazelnut consumption. However, identifying oleosin allergenic epitopes is hindered by their hydrophobic properties that do not allow analysis in an aqueous solution. Therefore, the present study focused on determining IgE-binding domains of sesame oleosin Ses i 4 using immunoblotting. Patients and methodsWe relied on sera from ten patients with clinically established allergy to sesame. Immunoblotting coupled to Hydrophobic Cluster Analysis (HCA) approach using a series of recombinant full-length and truncated Ses i 4 variants enabled detection of IgE reactivity domains. Results and conclusionThe data showed that IgE-binding sequences were located in the hydrophobic domain of the Ses i 4. The three IgE-binding segments were in proximity but on the flanks of the central-most hydrophobic segment. Further, their amino acid sequences are highly conserved in Ses i 4-homologs from different allergenic sources. Therefore, unlike most clinical immunoassays, Western blotting may be helpful for in vitro detection of oleosin IgE reactivity.

A machine learning approach using partitioning around medoids clustering and random forest classification to model groups of farms in regard to production parameters and bulk tank milk antibody status of two major internal parasites in dairy cows.

Fasciola hepatica and Ostertagia ostertagi are internal parasites of cattle compromising physiology, productivity, and well-being. Parasites are complex in their effect on hosts, sometimes making it difficult to identify clear directions of associations between infection and production parameters. Therefore, unsupervised approaches not assuming a structure reduce the risk of introducing bias to the analysis. They may provide insights which cannot be obtained with conventional, supervised methodology. An unsupervised, exploratory cluster analysis approach using the k-mode algorithm and partitioning around medoids detected two distinct clusters in a cross-sectional data set of milk yield, milk fat content, milk protein content as well as F. hepatica or O. ostertagi bulk tank milk antibody status from 606 dairy farms in three structurally different dairying regions in Germany. Parasite-positive farms grouped together with their respective production parameters to form separate clusters. A random forests algorithm characterised clusters with regard to external variables. Across all study regions, co-infections with F. hepatica or O. ostertagi, respectively, farming type, and pasture access appeared to be the most important factors discriminating clusters (i.e. farms). Furthermore, farm level lameness prevalence, herd size, BCS, stage of lactation, and somatic cell count were relevant criteria distinguishing clusters. This study is among the first to apply a cluster analysis approach in this context and potentially the first to implement a k-medoids algorithm and partitioning around medoids in the veterinary field. The results demonstrated that biologically relevant patterns of parasite status and milk parameters exist between farms positive for F. hepatica or O. ostertagi, respectively, and negative farms. Moreover, the machine learning approach confirmed results of previous work and shed further light on the complex setting of associations a between parasitic diseases, milk yield and milk constituents, and management practices.

The identification and classification of struggling readers based on the simple view of reading.

The simple view of reading (SVR) predicts that reading difficulties can result from decoding difficulties, language comprehension difficulties, or a combination of these difficulties. However, classification studies have identified a fourth group of children whose reading difficulties are unexplained by the model. This may be due to the type of classification model used. The current research included 209 children in Grades 3–5 (8–10 years of age) from New Zealand. Children were classified using the traditional approach and a cluster analysis. In contrast to the traditional classification model, the cluster analysis approach eliminated the unexplained reading difficulties group, suggesting that poor readers can be accurately assigned to one of three groups, which are consistent with those predicted by the SVR. The second set of analyses compared the three poor reader groups across 14 measures of reading comprehension, decoding, language comprehension, phonological awareness, and rapid naming. All three groups demonstrated reading comprehension difficulties, but the dyslexia group showed particular weaknesses in word processing and phonological areas, the SCD group showed problems deriving meaning from oral language, and the mixed group showed general deficits in most measures. The findings suggest that the SVR does have the potential to determine reading profiles and differential intervention methods.

Application of Machine Learning for Lithofacies Prediction and Cluster Analysis Approach to Identify Rock Type

Nowadays, there are significant issues in the classification of lithofacies and the identification of rock types in particular. Zamzama gas field demonstrates the complex nature of lithofacies due to the heterogeneous nature of the reservoir formation, while it is quite challenging to identify the lithofacies. Using our machine learning approach and cluster analysis, we can not only resolve these difficulties, but also minimize their time-consuming aspects and provide an accurate result even when the user is inexperienced. To constrain accurate reservoir models, rock type identification is a critical step in reservoir characterization. Many empirical and statistical methodologies have been established based on the effect of rock type on reservoir performance. Only well-logged data are provided, and no cores are sampled. Given these circumstances, and the fact that traditional methods such as regression are intractable, we have chosen to apply three strategies: (1) using a self-organizing map (SOM) to arrange depth intervals with similar facies into clusters; (2) clustering to split various facies into specific zones; and (3) the cluster analysis technique is used to identify rock type. In the Zamzama gas field, SOM and cluster analysis techniques discovered four group of facies, each of which was internally comparable in petrophysical properties but distinct from the others. Gamma Ray (GR), Effective Porosity(eff), Permeability (Perm) and Water Saturation (Sw) are used to generate these results. The findings and behavior of four facies shows that facies-01 and facies-02 have good characteristics for acting as gas-bearing sediments, whereas facies-03 and facies-04 are non-reservoir sediments. The outcomes of this study stated that facies-01 is an excellent rock-type zone in the reservoir of the Zamzama gas field.

Targeting the very important buyers VIB: A cluster analysis approach

The Pareto Principle, known as the 80/20 rule, predicts that most sales generate from a minority of buyers. Further, consumer theory stipulates certain shoppers have a preference to consume experience versus tangible goods. Some people value the consumption of experience due to lifestyle and innate factors. Following a cluster analysis approach, this paper collected survey data from 700 consumers (adults that belonged to a consumer panel) to examine these propositions. The results support the propositions and show that the e-market comprises three segments: VIB of Experience, VIB of Material, and Normals. The discriminatory analysis shows that the affluent shoppers of experience diverge from the other segments on psychological and lifestyle factors. In addition, social class helps pinpoint these shoppers. Whereas VIB of Experience primarily belong to the Upper Uppers and the Upper Middles, VIB of Material belong to the Lower Uppers and the generic Middle Class. The results are of value to practitioners that aim to target affluent shoppers of experience versus affluent shoppers of material goods. The results suggest that the affluent e-shoppers are not homogeneous according to their shopping preference and highlight the relevance of the Pareto Principle to segment and target the e-shoppers.

Patterns of Parental Reactions to Their Children's Negative Emotions: A Cluster Analysis with a Clinical Sample.

Parents’ emotion socialization practices are an important source of influence in the development of children’s emotional competencies This study examined parental reactions to child negative emotions in a clinical sample using a cluster analysis approach and explored the associations between clusters of parents’ reactions and children’s and parents’ adjustment. The sample comprised 80 parents of Portuguese children (aged 3–13 years) attending a child and adolescent psychiatry unit. Measures to assess parental reactions to children’s negative emotions, parents’ psychopathological symptoms, parents’ emotion dysregulation, and children’s adjustment were administered to parents. Model-based cluster analysis resulted in three clusters: low unsupportive, high supportive, and inconsistent reactions clusters. These clusters differed significantly in terms of parents’ psychopathological symptoms, emotion dysregulation, and children’s adjustment. A pattern characterized by high supportive reactions to the child’s emotions was associated with higher levels of children’s adjustment. On the other hand, an inconsistent reactions pattern was associated with the worst indicators of children’s adjustment and parental emotion dysregulation. These results suggest the importance of supporting parents of children with emotional and behavioural problems so that they can be more responsive to their children’s emotional manifestations.

A grid-based approach to exploratory data analysis

A grid-based approach to exploratory data analysis is described wherein the size of a grid’s cells may be altered. The primary tool of exploratory data analysis described here is cluster analysis. Background information is provided regarding the different types of clustering algorithms and their effectiveness. A grid-based approach for exploratory and cluster analysis is then described. Both exploratory and cluster analyses are performed for a series of different sizes of grid cells. Two marketing data sets are utilized to illustrate research questions often faced by market researchers. The first is a cluster analysis of cities used to create strata for selecting a representative probability sample. The second example is a segmentation of customers into groups for the purpose of targeting advertising and product insight. These examples illustrate related issues such as the elimination of “background noise” observations, significance tests for departures from uniform density, and metrics for the shape of a cluster.

Characterization of Parkinson's Disease Subtypes and Related Attributes.

Parkinson's disease is a progressive neurodegenerative disease with complex, heterogeneous motor and non-motor symptoms. The current evidence shows that there is still a marked heterogeneity in the subtyping of Parkinson's disease using both clinical and data-driven approaches. Another challenge posed in PD subtyping is the reproducibility of previously identified PD subtypes. These issues require additional results to confirm previous findings and help reconcile discrepancies, as well as establish a standardized application of cluster analysis to facilitate comparison and reproducibility of identified PD subtypes. Our study aimed to address this gap by investigating subtypes of Parkinson's disease using comprehensive clinical (motor and non-motor features) data retrieved from 408 de novo Parkinson's disease patients with the complete clinical data in the Parkinson's Progressive Marker Initiative database. A standardized k-means cluster analysis approach was developed by taking into consideration of common practice and recommendations from previous studies. All data analysis codes were made available online to promote data comparison and validation of reproducibility across research groups. We identified two distinct PD subtypes, termed the severe motor-non-motor subtype (SMNS) and the mild motor- non-motor subtype (MMNS). SMNS experienced symptom onset at an older age and manifested more intense motor and non-motor symptoms than MMNS, who experienced symptom onset at a younger age and manifested milder forms of Parkinson's symptoms. The SPECT imaging makers supported clinical findings such that the severe motor-non-motor subtype showed lower binding values than the mild motor- non-motor subtype, indicating more significant neural damage at the nigral pathway. In addition, SMNS and MMNS show distinct motor (ANCOVA test: F = 47.35, p< 0.001) and cognitive functioning (F = 33.93, p< 0.001) progression trends. Such contrast between SMNS and MMNS in both motor and cognitive functioning can be consistently observed up to 3 years following the baseline visit, demonstrating the potential prognostic value of identified PD subtypes.

The relationship between traditional project management, agile project management and teamwork quality on project success

Purpose The aim of the study is to explore how the different combinations of traditional and agile project management (APM) enhance project success under different levels of teamwork quality. Design/methodology/approach The study used system approach, using cluster analysis, to examine the relationships between project success and traditional project management (TPM) and APM under different levels of teamwork quality. A cluster analysis approach provides a method to examine how the entire range of variables combine to improve project success. Findings The results of the study revealed three profiles of project managers’ differences in teamwork quality and type of project management used: pure agile, TPM leaning hybrid and APM (APM) leaning hybrid. The results found evidence that TPM leaning hybrid received the highest score in project success. Practical implications Organizations should develop teamwork skills to learn to use both TPM and APM so that specific risks presented by one approach use are compensated by the use of other. Finally, organizations should have good TPM practices before planning to implement agile practices. Originality/value The findings of this study suggest that TPM with high teamwork quality supported by APM can enhance project success. APM supported by TPM contributes to improved project success.

Cluster Analysis and Classification Model of Nutritional Anemia Associated Risk Factors Among Palestinian Schoolchildren, 2014.

Nutritional inadequacy has been a major health problem worldwide. One of the many health problems that result from it is anemia. Anemia is considered a health concern among all ages, particularly children, as it has been associated with cognitive and developmental delays. Researchers have investigated the association between nutritional deficiencies and anemia through various methods. As novel analytical methods are needed to ascertain the association and reveal indirect ones, we aimed to classify nutritional anemia using the cluster analysis approach. In this study, we included 4,762 students aged between 10 and 17 years attending public and UNRWA schools in the West Bank. Students' 24-h food recall and blood sample data were collected for nutrient intake and hemoglobin analysis. The K-means cluster analysis was used to cluster the hemoglobin levels into two groups. Vitamin B12, folate, and iron intakes were used as the indicators of nutrient intake associated with anemia and were classified as per the Recommended Dietary Allowance (RDA) values. We applied the Classification and Regression Tree (CRT) model for studying the association between hemoglobin clusters and vitamin B12, folate, and iron intakes, sociodemographic variables, and health-related risk factors, accounting for grade and age. Results indicated that 46.4% of the students were classified into the low hemoglobin cluster, and 60.7, 72.5, and 30.3% of vitamin B12, folate, and iron intakes, respectively, were below RDA. The CRT analysis indicated that vitamin B12, iron, and folate intakes are important factors related to anemia in girls associated with age, locality, food consumption patterns, and physical activity levels, while iron and folate intakes were significant factors related to anemia in boys associated with the place of residence and the educational level of their mothers. The deployment of clustering and classification techniques for identifying the association between anemia and nutritional factors might facilitate the development of nutritional anemia prevention and intervention programs that will improve the health and wellbeing of schoolchildren.

Difficulties experienced by managers in the coordination of teams working from home: an exploratory study considering the COVID-19 pandemic

PurposeThis research aims to analyse difficulties experienced by Brazilian managers in coordinating teams working from home during the coronavirus disease (2019) COVID-19 pandemic.Design/methodology/approachThe methodological strategy used was a survey with 39 managers who led teams working from home during the COVID-19 pandemic. Seven difficulties indicated by current literature were analysed using a fuzzy scale. First, a hierarchical cluster analysis (HCA) approach was used to group managers according to managers' similarities and capacity to infer the difficulties. Responses of each group identified were weighted considering the capacity to assess the theme. In the sequence, data were analysed via frequencies and the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) approach and difficulties were ordered.FindingsComparatively, the main difficulties evidenced are (1) to reconcile personal and professional life tasks in the same place; (2) to motivate collaborators in a period when social isolation affect employee's mental health and (3) to keep team members integrated and working within the activities scope in a virtual environment.Originality/valueThe findings present in this paper contribute to theory and practice. For theory, this article contributes to the knowledge on WFH and leadership, evidencing in a comparatively way the difficulties that are experienced by managers during the COVID-19 pandemic. Researchers in future studies can better analyse these difficulties. For practice, managers who conduct managers' teams remotely can use the information to analyse teams' practices and improve performance critically. This was an atypical moment of humanity, and different aspects need to be considered by managers compared with previous periods.

EMG analysis across different tasks improves prevention screenings in diabetes: a cluster analysis approach

The aim of this work was twofold: on one side to determine the most suitable parameters of surface electromyography (sEMG) to classify diabetic subjects with and without neuropathy and discriminate them from healthy controls and second to assess the role of the task acquired in the classification process. For this purpose 30 subjects were examined (10 controls, 10 diabetics with and 10 without neuropathy) whilst walking and stair ascending and descending. The electrical activity of six muscles was recorded bilaterally through a 16-channel sEMG system synchronised with a stereophotogrammetric system: Rectus Femoris, Gluteus Medius, Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis and Extensor Digitorum. Spatiotemporal parameters of gait and stair climbing and the following sEMG parameters were extracted: signal envelope, activity duration, timing of activation and deactivation. A hierarchical clustering algorithm was applied to the whole set of parameters with different distances and linkage methods. Results showed that only by applying the Ward agglomerative hierarchical clustering (Hamming distance) to the all set of parameters extracted from both tasks, 5 well-separated clusters were obtained: cluster 3 included only DS subjects, cluster 2 and 4 only controls and cluster 1 and 5 only DNS subjects. This method could be used for planning rehabilitation treatments.Graphical abstract

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach.

BackgroundAlthough the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed.ObjectiveThis study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data.MethodsWe adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters.ResultsBased on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients.ConclusionsOur study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.

Feasibility Study of Free Route Airspace over the North Pacific

A fast-time simulation study was conducted to examine the expansion of flexible track operations into the area occupied by the five North Pacific (NOPAC) air traffic service routes, an area of high traffic demand. In this study, user-preferred routes were created in a proposed NOPAC free route airspace (FRA) area, in which airspace users may design routes with few constraints and compared with current fixed NOPAC route structure by fast-time simulation. To reflect the effect of daily and seasonal wind variations, a clustering analysis approach was applied to select representative wind conditions for flight route generation. Fuel burn was used to evaluate the effect of the NOPAC FRA on the efficiency of flight operations, and potential loss of separation (PLOS) was examined for three minimum lateral separations to evaluate the effects of communication, navigation, and surveillance performance on airspace capacity and air traffic control workload. The simulation results show a trend of increased efficiency of individual NOPAC FRA flight routes, with potential mean fuel consumption savings of 849.2 kg for eastbound Alaska flights and 532.4 kg for westbound North America flights compared with current route configurations. Results of PLOS also show reduced overall PLOS time. This indicates that a NOPAC FRA could improve capacity and efficiency while maintaining or increasing safety.

Psychological Profiles of Treatment-Seeking Adults with Overweight and Obesity: A Cluster Analysis Approach.

Background: Overweight and obesity are associated with depression and well-being. Some psychological characteristics play a role in explaining well-being and depression in obesity and in identifying specific patient profiles. However, subtyping individuals with overweight/obesity based on variables like self-esteem or stress has not often been done. Therefore, our objective was to explore the psychological profiles of treatment-seeking individuals overweight or with obesity and to compare their depression and well-being. Methods: Data regarding eating self-efficacy, well-being, depression, physical hunger, self-esteem, body satisfaction and perceived stress in individuals with overweight/obesity were collected from the ESTEAM cohort. Hierarchical cluster analysis and mean comparisons were performed on female (n = 1427) and male samples (n = 310). Results: Three psychological profiles were identified in both samples. The “High psychological concerns” profile and the “Low psychological concerns” profile were identical in both samples. The third profile, “Bodily concerns”, differed by sex and was characterized by appearance dissatisfaction for women and by appearance and eating concerns for men. The ”Low psychological concerns” profile presented the highest well-being and the lowest depression scores in both samples. Discussion: The findings support the hypothesis of the heterogeneity of individuals with overweight and obesity and suggest sex-related therapeutic approaches.

Multiple colonizations and genetic differentiation in goldenrod populations on recently formed nearshore islands

AbstractAimAlthough the evolution of island endemic plants has long been investigated, the majority of such studies have focused on species with remarkable levels of morphological variation and on islands substantially far from the mainland. Endemic plants on nearshore oceanic islands have received less attention. We investigate the population genetic structure and dynamics in plants endemic to nearshore and recently formed oceanic islands and examined the possibility of multiple colonizations onto the islands.LocationJapanese mainland Honshu and the adjacent Izu Islands.TaxonSolidago virgaurea (Asteraceae).MethodsSixteen and nine populations of S. virgaurea complex were sampled from the mainland and islands respectively; phylogeographical and population genetics analyses, including Bayesian Phylogeographic and Ecological Clustering (BPEC) analysis and Discrete Phylogeographic Approach (DPA) to trace the history of colonization events onto the islands, were performed using plastid DNA and nuclear microsatellite DNA variations.ResultsPhylogenetically close plastid DNA haplotypes were shared between the mainland and islands, although the populations of S. virgaurea from different islands tended to exhibit phylogenetically distinct haplotypes. Admixture analyses based on nuclear DNA variations revealed distinct genetic structures between the mainland and island populations. Gene flow among islands is restricted but may partially offset genetic drift on each island.Main conclusionsThe genetic structure observed in this study may not have originated from a single dispersal event and successive expansion but rather from at least three colonization events and subsequent gene flow among island populations. Based on the nuclear DNA variations, the Izu Island populations of S. virgaurea are genetically distinct from the mainland ones. Repeated colonization events may have provided sufficient genetic diversity, which would generally be susceptible to founder effects and exert a driving force for evolutionary adaptation, to these oceanic island populations.